WorldWideScience

Sample records for adaptive sensor optimization

  1. Multi-Sensor Optimal Data Fusion Based on the Adaptive Fading Unscented Kalman Filter.

    Science.gov (United States)

    Gao, Bingbing; Hu, Gaoge; Gao, Shesheng; Zhong, Yongmin; Gu, Chengfan

    2018-02-06

    This paper presents a new optimal data fusion methodology based on the adaptive fading unscented Kalman filter for multi-sensor nonlinear stochastic systems. This methodology has a two-level fusion structure: at the bottom level, an adaptive fading unscented Kalman filter based on the Mahalanobis distance is developed and serves as local filters to improve the adaptability and robustness of local state estimations against process-modeling error; at the top level, an unscented transformation-based multi-sensor optimal data fusion for the case of N local filters is established according to the principle of linear minimum variance to calculate globally optimal state estimation by fusion of local estimations. The proposed methodology effectively refrains from the influence of process-modeling error on the fusion solution, leading to improved adaptability and robustness of data fusion for multi-sensor nonlinear stochastic systems. It also achieves globally optimal fusion results based on the principle of linear minimum variance. Simulation and experimental results demonstrate the efficacy of the proposed methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integrated navigation.

  2. Adaptive Sensor Optimization and Cognitive Image Processing Using Autonomous Optical Neuroprocessors; TOPICAL

    International Nuclear Information System (INIS)

    CAMERON, STEWART M.

    2001-01-01

    Measurement and signal intelligence demands has created new requirements for information management and interoperability as they affect surveillance and situational awareness. Integration of on-board autonomous learning and adaptive control structures within a remote sensing platform architecture would substantially improve the utility of intelligence collection by facilitating real-time optimization of measurement parameters for variable field conditions. A problem faced by conventional digital implementations of intelligent systems is the conflict between a distributed parallel structure on a sequential serial interface functionally degrading bandwidth and response time. In contrast, optically designed networks exhibit the massive parallelism and interconnect density needed to perform complex cognitive functions within a dynamic asynchronous environment. Recently, all-optical self-organizing neural networks exhibiting emergent collective behavior which mimic perception, recognition, association, and contemplative learning have been realized using photorefractive holography in combination with sensory systems for feature maps, threshold decomposition, image enhancement, and nonlinear matched filters. Such hybrid information processors depart from the classical computational paradigm based on analytic rules-based algorithms and instead utilize unsupervised generalization and perceptron-like exploratory or improvisational behaviors to evolve toward optimized solutions. These systems are robust to instrumental systematics or corrupting noise and can enrich knowledge structures by allowing competition between multiple hypotheses. This property enables them to rapidly adapt or self-compensate for dynamic or imprecise conditions which would be unstable using conventional linear control models. By incorporating an intelligent optical neuroprocessor in the back plane of an imaging sensor, a broad class of high-level cognitive image analysis problems including geometric

  3. Sensor selection and chemo-sensory optimization: toward an adaptable chemo-sensory system

    Directory of Open Access Journals (Sweden)

    Alexander eVergara

    2012-01-01

    Full Text Available Over the past two decades, despite the tremendous research effort performed on chemical sensors and machine olfaction to develop micro-sensory systems that will accomplish the growing existent needs in personal health (implantable sensors, environment monitoring (widely distributed sensor networks, and security/threat detection (chemo/bio warfare agents, simple, low-cost molecular sensing platforms capable of long-term autonomous operation remain beyond the current state-of-the-art of chemical sensing. A fundamental issue within this context is that most of the chemical sensors depend on interactions between the targeted species and the surfaces functionalized with receptors that bind the target species selectively, and that these binding events are coupled with transduction processes that begin to change when they are exposed to the messy world of real samples. With the advent of fundamental breakthroughs at the intersection of materials science, micro/nano-technology, and signal processing, hybrid chemo-sensory systems have incorporated tunable, optimizable operating parameters, through which changes in the response characteristics can be modeled and compensated as the environmental conditions or application needs change.The objective of this article, in this context, is to bring together the key advances at the device, data processing, and system levels that enable chemo-sensory systems to adapt in response to their environments. Accordingly, in this review we will feature the research effort made by selected experts on chemical sensing and information theory, whose work has been devoted to develop strategies that provide tunability and adaptability to single sensor devices or sensory array systems. Particularly, we consider sensor-array selection, modulation of internal sensing parameters, and active sensing. The article ends with some conclusions drawn from the results presented and a visionary look toward the future in terms of how the

  4. Adaptive sensor fusion using genetic algorithms

    International Nuclear Information System (INIS)

    Fitzgerald, D.S.; Adams, D.G.

    1994-01-01

    Past attempts at sensor fusion have used some form of Boolean logic to combine the sensor information. As an alteniative, an adaptive ''fuzzy'' sensor fusion technique is described in this paper. This technique exploits the robust capabilities of fuzzy logic in the decision process as well as the optimization features of the genetic algorithm. This paper presents a brief background on fuzzy logic and genetic algorithms and how they are used in an online implementation of adaptive sensor fusion

  5. Energy and Delay Optimization of Heterogeneous Multicore Wireless Multimedia Sensor Nodes by Adaptive Genetic-Simulated Annealing Algorithm

    Directory of Open Access Journals (Sweden)

    Xing Liu

    2018-01-01

    Full Text Available Energy efficiency and delay optimization are significant for the proliferation of wireless multimedia sensor network (WMSN. In this article, an energy-efficient, delay-efficient, hardware and software cooptimization platform is researched to minimize the energy cost while guaranteeing the deadline of the real-time WMSN tasks. First, a multicore reconfigurable WMSN hardware platform is designed and implemented. This platform uses both the heterogeneous multicore architecture and the dynamic voltage and frequency scaling (DVFS technique. By this means, the nodes can adjust the hardware characteristics dynamically in terms of the software run-time contexts. Consequently, the software can be executed more efficiently with less energy cost and shorter execution time. Then, based on this hardware platform, an energy and delay multiobjective optimization algorithm and a DVFS adaption algorithm are investigated. These algorithms aim to search out the global energy optimization solution within the acceptable calculation time and strip the time redundancy in the task executing process. Thus, the energy efficiency of the WMSN node can be improved significantly even under strict constraint of the execution time. Simulation and real-world experiments proved that the proposed approaches can decrease the energy cost by more than 29% compared to the traditional single-core WMSN node. Moreover, the node can react quickly to the time-sensitive events.

  6. HEAT Sensor: Harsh Environment Adaptable Thermionic Sensor

    Energy Technology Data Exchange (ETDEWEB)

    Limb, Scott J. [Palo Alto Research Center, Palo Alto, CA (United States)

    2016-05-31

    This document is the final report for the “HARSH ENVIRONMENT ADAPTABLE THERMIONIC SENSOR” project under NETL’s Crosscutting contract DE-FE0013062. This report addresses sensors that can be made with thermionic thin films along with the required high temperature hermetic packaging process. These sensors can be placed in harsh high temperature environments and potentially be wireless and self-powered.

  7. Adaptive Bacterial Foraging Optimization

    Directory of Open Access Journals (Sweden)

    Hanning Chen

    2011-01-01

    Full Text Available Bacterial Foraging Optimization (BFO is a recently developed nature-inspired optimization algorithm, which is based on the foraging behavior of E. coli bacteria. Up to now, BFO has been applied successfully to some engineering problems due to its simplicity and ease of implementation. However, BFO possesses a poor convergence behavior over complex optimization problems as compared to other nature-inspired optimization techniques. This paper first analyzes how the run-length unit parameter of BFO controls the exploration of the whole search space and the exploitation of the promising areas. Then it presents a variation on the original BFO, called the adaptive bacterial foraging optimization (ABFO, employing the adaptive foraging strategies to improve the performance of the original BFO. This improvement is achieved by enabling the bacterial foraging algorithm to adjust the run-length unit parameter dynamically during algorithm execution in order to balance the exploration/exploitation tradeoff. The experiments compare the performance of two versions of ABFO with the original BFO, the standard particle swarm optimization (PSO and a real-coded genetic algorithm (GA on four widely-used benchmark functions. The proposed ABFO shows a marked improvement in performance over the original BFO and appears to be comparable with the PSO and GA.

  8. Adaptively optimizing stochastic resonance in visual system

    Science.gov (United States)

    Yang, Tao

    1998-08-01

    Recent psychophysics experiment has showed that the noise strength could affect the perceived image quality. This work gives an adaptive process for achieving the optimal perceived image quality in a simple image perception array, which is a simple model of an image sensor. A reference image from memory is used for constructing a cost function and defining the optimal noise strength where the cost function gets its minimum point. The reference image is a binary image, which is used to define the background and the object. Finally, an adaptive algorithm is proposed for searching the optimal noise strength. Computer experimental results show that if the reference image is a thresholded version of the sub-threshold input image then the output of the sensor array gives an optimal output, in which the background and the object have the biggest contrast. If the reference image is different from a thresholded version of the sub-threshold input image then the output usually gives a sub-optimal contrast between the object and the background.

  9. ENERGY OPTIMIZATION IN CLUSTER BASED WIRELESS SENSOR NETWORKS

    Directory of Open Access Journals (Sweden)

    T. SHANKAR

    2014-04-01

    Full Text Available Wireless sensor networks (WSN are made up of sensor nodes which are usually battery-operated devices, and hence energy saving of sensor nodes is a major design issue. To prolong the networks lifetime, minimization of energy consumption should be implemented at all layers of the network protocol stack starting from the physical to the application layer including cross-layer optimization. Optimizing energy consumption is the main concern for designing and planning the operation of the WSN. Clustering technique is one of the methods utilized to extend lifetime of the network by applying data aggregation and balancing energy consumption among sensor nodes of the network. This paper proposed new version of Low Energy Adaptive Clustering Hierarchy (LEACH, protocols called Advanced Optimized Low Energy Adaptive Clustering Hierarchy (AOLEACH, Optimal Deterministic Low Energy Adaptive Clustering Hierarchy (ODLEACH, and Varying Probability Distance Low Energy Adaptive Clustering Hierarchy (VPDL combination with Shuffled Frog Leap Algorithm (SFLA that enables selecting best optimal adaptive cluster heads using improved threshold energy distribution compared to LEACH protocol and rotating cluster head position for uniform energy dissipation based on energy levels. The proposed algorithm optimizing the life time of the network by increasing the first node death (FND time and number of alive nodes, thereby increasing the life time of the network.

  10. Soft Thermal Sensor with Mechanical Adaptability.

    Science.gov (United States)

    Yang, Hui; Qi, Dianpeng; Liu, Zhiyuan; Chandran, Bevita K; Wang, Ting; Yu, Jiancan; Chen, Xiaodong

    2016-11-01

    A soft thermal sensor with mechanical adaptability is fabricated by the combination of single-wall carbon nanotubes with carboxyl groups and self-healing polymers. This study demonstrates that this soft sensor has excellent thermal response and mechanical adaptability. It shows tremendous promise for improving the service life of soft artificial-intelligence robots and protecting thermally sensitive electronics from the risk of damage by high temperature. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  11. Optimal sensor configuration for complex systems

    DEFF Research Database (Denmark)

    Sadegh, Payman; Spall, J. C.

    1998-01-01

    configuration is based on maximizing the overall sensor response while minimizing the correlation among the sensor outputs. The procedure for sensor configuration is based on simultaneous perturbation stochastic approximation (SPSA). SPSA avoids the need for detailed modeling of the sensor response by simply......Considers the problem of sensor configuration for complex systems. Our approach involves definition of an appropriate optimality criterion or performance measure, and description of an efficient and practical algorithm for achieving the optimality objective. The criterion for optimal sensor...... relying on observed responses as obtained by limited experimentation with test sensor configurations. We illustrate the approach with the optimal placement of acoustic sensors for signal detection in structures. This includes both a computer simulation study for an aluminum plate, and real...

  12. Optimal sensor configuration for complex systems

    DEFF Research Database (Denmark)

    Sadegh, Payman; Spall, J. C.

    1998-01-01

    . The procedure for sensor configuration is based on the simultaneous perturbation stochastic approximation (SPSA) algorithm. SPSA avoids the need for detailed modeling of the sensor response by simply relying on the observed responses obtained by limited experimentation with test sensor configurations. We......The paper considers the problem of sensor configuration for complex systems with the aim of maximizing the useful information about certain quantities of interest. Our approach involves: 1) definition of an appropriate optimality criterion or performance measure; and 2) description of an efficient...... and practical algorithm for achieving the optimality objective. The criterion for optimal sensor configuration is based on maximizing the overall sensor response while minimizing the correlation among the sensor outputs, so as to minimize the redundant information being provided by the multiple sensors...

  13. Adaptive scalarization methods in multiobjective optimization

    CERN Document Server

    Eichfelder, Gabriele

    2008-01-01

    This book presents adaptive solution methods for multiobjective optimization problems based on parameter dependent scalarization approaches. Readers will benefit from the new adaptive methods and ideas for solving multiobjective optimization.

  14. Wireless sensor network adaptive cooling

    Energy Technology Data Exchange (ETDEWEB)

    Mitchell, T. [SynapSense Corp., Folsom, CA (United States)

    2009-07-01

    Options for reducing data centre cooling energy requirements and their cost savings were discussed with particular reference to a wireless control solution developed by SynapSense Corporation. The wireless sensor network reduces cooling energy use at data centres by providing improved air flow management through the installation of cold aisle containment. The use of this low cost, non-invasive wireless sensor network has reduced the cooling energy use in a data center at BC Hydro by 30 per cent. The system also reduced the server and storage fan energy by 3 per cent by maintaining inlet air temperature below ASHRAE recommended operating range. The distribution of low power, low cost wireless sensors has enabled visualization tools that are changing the way that data centres are managed. The annual savings have been estimated at 4,560,000 kWh and the annual carbon dioxide abatement is approximately 1,400 metric tons. tabs., figs.

  15. Efficient Sensor Placement Optimization Using Gradient Descent and Probabilistic Coverage

    Directory of Open Access Journals (Sweden)

    Vahab Akbarzadeh

    2014-08-01

    Full Text Available We are proposing an adaptation of the gradient descent method to optimize the position and orientation of sensors for the sensor placement problem. The novelty of the proposed method lies in the combination of gradient descent optimization with a realistic model, which considers both the topography of the environment and a set of sensors with directional probabilistic sensing. The performance of this approach is compared with two other black box optimization methods over area coverage and processing time. Results show that our proposed method produces competitive results on smaller maps and superior results on larger maps, while requiring much less computation than the other optimization methods to which it has been compared.

  16. Biomimetic micromechanical adaptive flow-sensor arrays

    Science.gov (United States)

    Krijnen, Gijs; Floris, Arjan; Dijkstra, Marcel; Lammerink, Theo; Wiegerink, Remco

    2007-05-01

    We report current developments in biomimetic flow-sensors based on flow sensitive mechano-sensors of crickets. Crickets have one form of acoustic sensing evolved in the form of mechanoreceptive sensory hairs. These filiform hairs are highly perceptive to low-frequency sound with energy sensitivities close to thermal threshold. In this work we describe hair-sensors fabricated by a combination of sacrificial poly-silicon technology, to form silicon-nitride suspended membranes, and SU8 polymer processing for fabrication of hairs with diameters of about 50 μm and up to 1 mm length. The membranes have thin chromium electrodes on top forming variable capacitors with the substrate that allow for capacitive read-out. Previously these sensors have been shown to exhibit acoustic sensitivity. Like for the crickets, the MEMS hair-sensors are positioned on elongated structures, resembling the cercus of crickets. In this work we present optical measurements on acoustically and electrostatically excited hair-sensors. We present adaptive control of flow-sensitivity and resonance frequency by electrostatic spring stiffness softening. Experimental data and simple analytical models derived from transduction theory are shown to exhibit good correspondence, both confirming theory and the applicability of the presented approach towards adaptation.

  17. Robust Optimal Adaptive Control Method with Large Adaptive Gain

    Science.gov (United States)

    Nguyen, Nhan T.

    2009-01-01

    In the presence of large uncertainties, a control system needs to be able to adapt rapidly to regain performance. Fast adaptation is referred to the implementation of adaptive control with a large adaptive gain to reduce the tracking error rapidly. However, a large adaptive gain can lead to high-frequency oscillations which can adversely affect robustness of an adaptive control law. A new adaptive control modification is presented that can achieve robust adaptation with a large adaptive gain without incurring high-frequency oscillations as with the standard model-reference adaptive control. The modification is based on the minimization of the Y2 norm of the tracking error, which is formulated as an optimal control problem. The optimality condition is used to derive the modification using the gradient method. The optimal control modification results in a stable adaptation and allows a large adaptive gain to be used for better tracking while providing sufficient stability robustness. Simulations were conducted for a damaged generic transport aircraft with both standard adaptive control and the adaptive optimal control modification technique. The results demonstrate the effectiveness of the proposed modification in tracking a reference model while maintaining a sufficient time delay margin.

  18. A New, Adaptable, Optical High-Resolution 3-Axis Sensor

    Directory of Open Access Journals (Sweden)

    Niels Buchhold

    2017-01-01

    Full Text Available This article presents a new optical, multi-functional, high-resolution 3-axis sensor which serves to navigate and can, for example, replace standard joysticks in medical devices such as electric wheelchairs, surgical robots or medical diagnosis devices. A light source, e.g., a laser diode, is affixed to a movable axis and projects a random geometric shape on an image sensor (CMOS or CCD. The downstream microcontroller’s software identifies the geometric shape’s center, distortion and size, and then calculates x, y, and z coordinates, which can be processed in attached devices. Depending on the image sensor in use (e.g., 6.41 megapixels, the 3-axis sensor features a resolution of 1544 digits from right to left and 1038 digits up and down. Through interpolation, these values rise by a factor of 100. A unique feature is the exact reproducibility (deflection to coordinates and its precise ability to return to its neutral position. Moreover, optical signal processing provides a high level of protection against electromagnetic and radio frequency interference. The sensor is adaptive and adjustable to fit a user’s range of motion (stroke and force. This recommendation aims to optimize sensor systems such as joysticks in medical devices in terms of safety, ease of use, and adaptability.

  19. Optimizing Systems of Threshold Detection Sensors

    National Research Council Canada - National Science Library

    Banschbach, David C

    2008-01-01

    .... Below the threshold all signals are ignored. We develop a mathematical model for setting individual sensor thresholds to obtain optimal probability of detecting a significant event, given a limit on the total number of false positives allowed...

  20. Optimization of metal-clad waveguide sensors

    DEFF Research Database (Denmark)

    Skivesen, N.; Horvath, R.; Pedersen, H.C.

    2005-01-01

    The present paper deals with the optimization of metal-clad waveguides for sensor applications to achieve high sensitivity for adlayer and refractive index measurements. By using the Fresnel reflection coefficients both the angular shift and the width of the resonances in the sensorgrams are taken...... into account. Our optimization shows that it is possible for metal-clad waveguides to achieve a sensitivity improvement of 600% compared to surface-plasmon-resonance sensors....

  1. Sensor Placement Optimization using Chama

    Energy Technology Data Exchange (ETDEWEB)

    Klise, Katherine A. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Geotechnology and Engineering Dept.; Nicholson, Bethany L. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Discrete Math and Optimization Dept.; Laird, Carl Damon [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Discrete Math and Optimization Dept.

    2017-10-01

    Continuous or regularly scheduled monitoring has the potential to quickly identify changes in the environment. However, even with low - cost sensors, only a limited number of sensors can be deployed. The physical placement of these sensors, along with the sensor technology and operating conditions, can have a large impact on the performance of a monitoring strategy. Chama is an open source Python package which includes mixed - integer, stochastic programming formulations to determine sensor locations and technology that maximize monitoring effectiveness. The methods in Chama are general and can be applied to a wide range of applications. Chama is currently being used to design sensor networks to monitor airborne pollutants and to monitor water quality in water distribution systems. The following documentation includes installation instructions and examples, description of software features, and software license. The software is intended to be used by regulatory agencies, industry, and the research community. It is assumed that the reader is familiar with the Python Programming Language. References are included for addit ional background on software components. Online documentation, hosted at http://chama.readthedocs.io/, will be updated as new features are added. The online version includes API documentation .

  2. Energy optimization in mobile sensor networks

    Science.gov (United States)

    Yu, Shengwei

    Mobile sensor networks are considered to consist of a network of mobile robots, each of which has computation, communication and sensing capabilities. Energy efficiency is a critical issue in mobile sensor networks, especially when mobility (i.e., locomotion control), routing (i.e., communications) and sensing are unique characteristics of mobile robots for energy optimization. This thesis focuses on the problem of energy optimization of mobile robotic sensor networks, and the research results can be extended to energy optimization of a network of mobile robots that monitors the environment, or a team of mobile robots that transports materials from stations to stations in a manufacturing environment. On the energy optimization of mobile robotic sensor networks, our research focuses on the investigation and development of distributed optimization algorithms to exploit the mobility of robotic sensor nodes for network lifetime maximization. In particular, the thesis studies these five problems: 1. Network-lifetime maximization by controlling positions of networked mobile sensor robots based on local information with distributed optimization algorithms; 2. Lifetime maximization of mobile sensor networks with energy harvesting modules; 3. Lifetime maximization using joint design of mobility and routing; 4. Optimal control for network energy minimization; 5. Network lifetime maximization in mobile visual sensor networks. In addressing the first problem, we consider only the mobility strategies of the robotic relay nodes in a mobile sensor network in order to maximize its network lifetime. By using variable substitutions, the original problem is converted into a convex problem, and a variant of the sub-gradient method for saddle-point computation is developed for solving this problem. An optimal solution is obtained by the method. Computer simulations show that mobility of robotic sensors can significantly prolong the lifetime of the whole robotic sensor network while

  3. Optimization of the coplanar interdigital capacitive sensor

    Science.gov (United States)

    Huang, Yunzhi; Zhan, Zheng; Bowler, Nicola

    2017-02-01

    Interdigital capacitive sensors are applied in nondestructive testing and material property characterization of low-conductivity materials. The sensor performance is typically described based on the penetration depth of the electric field into the sample material, the sensor signal strength and its sensitivity. These factors all depend on the geometry and material properties of the sensor and sample. In this paper, a detailed analysis is provided, through finite element simulations, of the ways in which the sensor's geometrical parameters affect its performance. The geometrical parameters include the number of digits forming the interdigital electrodes and the ratio of digit width to their separation. In addition, the influence of the presence or absence of a metal backplane on the sample is analyzed. Further, the effects of sensor substrate thickness and material on signal strength are studied. The results of the analysis show that it is necessary to take into account a trade-off between the desired sensitivity and penetration depth when designing the sensor. Parametric equations are presented to assist the sensor designer or nondestructive evaluation specialist in optimizing the design of a capacitive sensor.

  4. Model wavefront sensor for adaptive confocal microscopy

    Science.gov (United States)

    Booth, Martin J.; Neil, Mark A. A.; Wilson, Tony

    2000-05-01

    A confocal microscope permits 3D imaging of volume objects by the inclusion of a pinhole in the detector path which eliminates out of focus light. This configuration is however very sensitive to aberrations induced by the specimen or the optical system and would therefore benefit from an adaptive optics approach. We present a wavefront sensor capable of measuring directly the Zernike components of an aberrated wavefront and show that it is particularly applicable to the confocal microscope since only those wavefronts originating in the focal region contribute to the measured aberration.

  5. Lifetime Maximizing Adaptive Power Control in Wireless Sensor Networks

    National Research Council Canada - National Science Library

    Sun, Fangting; Shayman, Mark

    2006-01-01

    ...: adaptive power control. They focus on the sensor networks that consist of a sink and a set of homogeneous wireless sensor nodes, which are randomly deployed according to a uniform distribution...

  6. Russian Loanword Adaptation in Persian; Optimal Approach

    Science.gov (United States)

    Kambuziya, Aliye Kord Zafaranlu; Hashemi, Eftekhar Sadat

    2011-01-01

    In this paper we analyzed some of the phonological rules of Russian loanword adaptation in Persian, on the view of Optimal Theory (OT) (Prince & Smolensky, 1993/2004). It is the first study of phonological process on Russian loanwords adaptation in Persian. By gathering about 50 current Russian loanwords, we selected some of them to analyze. We…

  7. Optimal Sensor Selection for Health Monitoring Systems

    Science.gov (United States)

    Santi, L. Michael; Sowers, T. Shane; Aguilar, Robert B.

    2005-01-01

    Sensor data are the basis for performance and health assessment of most complex systems. Careful selection and implementation of sensors is critical to enable high fidelity system health assessment. A model-based procedure that systematically selects an optimal sensor suite for overall health assessment of a designated host system is described. This procedure, termed the Systematic Sensor Selection Strategy (S4), was developed at NASA John H. Glenn Research Center in order to enhance design phase planning and preparations for in-space propulsion health management systems (HMS). Information and capabilities required to utilize the S4 approach in support of design phase development of robust health diagnostics are outlined. A merit metric that quantifies diagnostic performance and overall risk reduction potential of individual sensor suites is introduced. The conceptual foundation for this merit metric is presented and the algorithmic organization of the S4 optimization process is described. Representative results from S4 analyses of a boost stage rocket engine previously under development as part of NASA's Next Generation Launch Technology (NGLT) program are presented.

  8. Dynamic optimization and adaptive controller design

    Science.gov (United States)

    Inamdar, S. R.

    2010-10-01

    In this work I present a new type of controller which is an adaptive tracking controller which employs dynamic optimization for optimizing current value of controller action for the temperature control of nonisothermal continuously stirred tank reactor (CSTR). We begin with a two-state model of nonisothermal CSTR which are mass and heat balance equations and then add cooling system dynamics to eliminate input multiplicity. The initial design value is obtained using local stability of steady states where approach temperature for cooling action is specified as a steady state and a design specification. Later we make a correction in the dynamics where material balance is manipulated to use feed concentration as a system parameter as an adaptive control measure in order to avoid actuator saturation for the main control loop. The analysis leading to design of dynamic optimization based parameter adaptive controller is presented. The important component of this mathematical framework is reference trajectory generation to form an adaptive control measure.

  9. Modeling for deformable mirrors and the adaptive optics optimization program

    International Nuclear Information System (INIS)

    Henesian, M.A.; Haney, S.W.; Trenholme, J.B.; Thomas, M.

    1997-01-01

    We discuss aspects of adaptive optics optimization for large fusion laser systems such as the 192-arm National Ignition Facility (NIF) at LLNL. By way of example, we considered the discrete actuator deformable mirror and Hartmann sensor system used on the Beamlet laser. Beamlet is a single-aperture prototype of the 11-0-5 slab amplifier design for NIF, and so we expect similar optical distortion levels and deformable mirror correction requirements. We are now in the process of developing a numerically efficient object oriented C++ language implementation of our adaptive optics and wavefront sensor code, but this code is not yet operational. Results are based instead on the prototype algorithms, coded-up in an interpreted array processing computer language

  10. Adaptive stimulus optimization for sensory systems neuroscience

    OpenAIRE

    DiMattina, Christopher; Zhang, Kechen

    2013-01-01

    In this paper, we review several lines of recent work aimed at developing practical methods for adaptive on-line stimulus generation for sensory neurophysiology. We consider various experimental paradigms where on-line stimulus optimization is utilized, including the classical optimal stimulus paradigm where the goal of experiments is to identify a stimulus which maximizes neural responses, the iso-response paradigm which finds sets of stimuli giving rise to constant responses, and the system...

  11. Adaptive Sensing Based on Profiles for Sensor Systems

    Directory of Open Access Journals (Sweden)

    Yoshiteru Ishida

    2009-10-01

    Full Text Available This paper proposes a profile-based sensing framework for adaptive sensor systems based on models that relate possibly heterogeneous sensor data and profiles generated by the models to detect events. With these concepts, three phases for building the sensor systems are extracted from two examples: a combustion control sensor system for an automobile engine, and a sensor system for home security. The three phases are: modeling, profiling, and managing trade-offs. Designing and building a sensor system involves mapping the signals to a model to achieve a given mission.

  12. Optimization of wireless Bluetooth sensor systems.

    Science.gov (United States)

    Lonnblad, J; Castano, J; Ekstrom, M; Linden, M; Backlund, Y

    2004-01-01

    Within this study, three different Bluetooth sensor systems, replacing cables for transmission of biomedical sensor data, have been designed and evaluated. The three sensor architectures are built on 1-, 2- and 3-chip solutions and depending on the monitoring situation and signal character, different solutions are optimal. Essential parameters for all systems have been low physical weight and small size, resistance to interference and interoperability with other technologies as global- or local networks, PC's and mobile phones. Two different biomedical input signals, ECG and PPG (photoplethysmography), have been used to evaluate the three solutions. The study shows that it is possibly to continuously transmit an analogue signal. At low sampling rates and slowly varying parameters, as monitoring the heart rate with PPG, the 1-chip solution is the most suitable, offering low power consumption and thus a longer battery lifetime or a smaller battery, minimizing the weight of the sensor system. On the other hand, when a higher sampling rate is required, as an ECG, the 3-chip architecture, with a FPGA or micro-controller, offers the best solution and performance. Our conclusion is that Bluetooth might be useful in replacing cables of medical monitoring systems.

  13. Adaptive finite element method for shape optimization

    KAUST Repository

    Morin, Pedro; Nochetto, Ricardo H.; Pauletti, Miguel S.; Verani, Marco

    2012-01-01

    We examine shape optimization problems in the context of inexact sequential quadratic programming. Inexactness is a consequence of using adaptive finite element methods (AFEM) to approximate the state and adjoint equations (via the dual weighted residual method), update the boundary, and compute the geometric functional. We present a novel algorithm that equidistributes the errors due to shape optimization and discretization, thereby leading to coarse resolution in the early stages and fine resolution upon convergence, and thus optimizing the computational effort. We discuss the ability of the algorithm to detect whether or not geometric singularities such as corners are genuine to the problem or simply due to lack of resolution - a new paradigm in adaptivity. © EDP Sciences, SMAI, 2012.

  14. Adaptive finite element method for shape optimization

    KAUST Repository

    Morin, Pedro

    2012-01-16

    We examine shape optimization problems in the context of inexact sequential quadratic programming. Inexactness is a consequence of using adaptive finite element methods (AFEM) to approximate the state and adjoint equations (via the dual weighted residual method), update the boundary, and compute the geometric functional. We present a novel algorithm that equidistributes the errors due to shape optimization and discretization, thereby leading to coarse resolution in the early stages and fine resolution upon convergence, and thus optimizing the computational effort. We discuss the ability of the algorithm to detect whether or not geometric singularities such as corners are genuine to the problem or simply due to lack of resolution - a new paradigm in adaptivity. © EDP Sciences, SMAI, 2012.

  15. Adaptive Multichannel Radiation Sensors for Plant Parameter Monitoring

    Science.gov (United States)

    Mollenhauer, Hannes; Remmler, Paul; Schuhmann, Gudrun; Lausch, Angela; Merbach, Ines; Assing, Martin; Mollenhauer, Olaf; Dietrich, Peter; Bumberger, Jan

    2016-04-01

    Nutrients such as nitrogen are playing a key role in the plant life cycle. They are much needed for chlorophyll production and other plant cell components. Therefore, the crop yield is strongly affected by plant nutrient status. Due to the spatial and temporal variability of soil characteristics or swaying agricultural inputs the plant development varies within a field. Thus, the determination of these fluctuations in the plant development is valuable for a detection of stress conditions and optimization of fertilisation due to its high environmental and economic impact. Plant parameters play crucial roles in plant growth estimation and prediction since they are used as indicators of plant performance. Especially indices derived out of remote sensing techniques provide quantitative information about agricultural crops instantaneously, and above all, non-destructively. Due to the specific absorption of certain plant pigments, a characteristic spectral signature can be seen in the visible and IR part of the electromagnetic spectrum, known as narrow-band peaks. In an analogous manner, the presence and concentration of different nutrients cause a characteristic spectral signature. To this end, an adequate remote sensing monitoring concept is needed, considering heterogeneity and dynamic of the plant population and economical aspects. This work will present the development and field investigations of an inexpensive multichannel radiation sensor to observe the incoming and reflected specific parts or rather distinct wavelengths of the solar light spectrum on the crop and facilitate the determination of different plant indices. Based on the selected sensor wavelengths, the sensing device allows the detection of specific parameters, e.g. plant vitality, chlorophyll content or nitrogen content. Besides the improvement of the sensor characteristic, the simple wavelength adaption, and the price-performance ratio, the achievement of appropriate energy efficiency as well as a

  16. Adaptive stimulus optimization for sensory systems neuroscience.

    Science.gov (United States)

    DiMattina, Christopher; Zhang, Kechen

    2013-01-01

    In this paper, we review several lines of recent work aimed at developing practical methods for adaptive on-line stimulus generation for sensory neurophysiology. We consider various experimental paradigms where on-line stimulus optimization is utilized, including the classical optimal stimulus paradigm where the goal of experiments is to identify a stimulus which maximizes neural responses, the iso-response paradigm which finds sets of stimuli giving rise to constant responses, and the system identification paradigm where the experimental goal is to estimate and possibly compare sensory processing models. We discuss various theoretical and practical aspects of adaptive firing rate optimization, including optimization with stimulus space constraints, firing rate adaptation, and possible network constraints on the optimal stimulus. We consider the problem of system identification, and show how accurate estimation of non-linear models can be highly dependent on the stimulus set used to probe the network. We suggest that optimizing stimuli for accurate model estimation may make it possible to successfully identify non-linear models which are otherwise intractable, and summarize several recent studies of this type. Finally, we present a two-stage stimulus design procedure which combines the dual goals of model estimation and model comparison and may be especially useful for system identification experiments where the appropriate model is unknown beforehand. We propose that fast, on-line stimulus optimization enabled by increasing computer power can make it practical to move sensory neuroscience away from a descriptive paradigm and toward a new paradigm of real-time model estimation and comparison.

  17. Adaptive Gradient Multiobjective Particle Swarm Optimization.

    Science.gov (United States)

    Han, Honggui; Lu, Wei; Zhang, Lu; Qiao, Junfei

    2017-10-09

    An adaptive gradient multiobjective particle swarm optimization (AGMOPSO) algorithm, based on a multiobjective gradient (stocktickerMOG) method and a self-adaptive flight parameters mechanism, is developed to improve the computation performance in this paper. In this AGMOPSO algorithm, the stocktickerMOG method is devised to update the archive to improve the convergence speed and the local exploitation in the evolutionary process. Meanwhile, the self-adaptive flight parameters mechanism, according to the diversity information of the particles, is then established to balance the convergence and diversity of AGMOPSO. Attributed to the stocktickerMOG method and the self-adaptive flight parameters mechanism, this AGMOPSO algorithm not only has faster convergence speed and higher accuracy, but also its solutions have better diversity. Additionally, the convergence is discussed to confirm the prerequisite of any successful application of AGMOPSO. Finally, with regard to the computation performance, the proposed AGMOPSO algorithm is compared with some other multiobjective particle swarm optimization algorithms and two state-of-the-art multiobjective algorithms. The results demonstrate that the proposed AGMOPSO algorithm can find better spread of solutions and have faster convergence to the true Pareto-optimal front.

  18. SPOT-A SENSOR PLACEMENT OPTIMIZATION TOOL FOR ...

    Science.gov (United States)

    journal article This paper presents SPOT, a Sensor Placement Optimization Tool. SPOT provides a toolkit that facilitates research in sensor placement optimization and enables the practical application of sensor placement solvers to real-world CWS design applications. This paper provides an overview of SPOT’s key features, and then illustrates how this tool can be flexibly applied to solve a variety of different types of sensor placement problems.

  19. Organization of an optimal adaptive immune system

    Science.gov (United States)

    Walczak, Aleksandra; Mayer, Andreas; Balasubramanian, Vijay; Mora, Thierry

    The repertoire of lymphocyte receptors in the adaptive immune system protects organisms from a diverse set of pathogens. A well-adapted repertoire should be tuned to the pathogenic environment to reduce the cost of infections. I will discuss a general framework for predicting the optimal repertoire that minimizes the cost of infections contracted from a given distribution of pathogens. The theory predicts that the immune system will have more receptors for rare antigens than expected from the frequency of encounters and individuals exposed to the same infections will have sparse repertoires that are largely different, but nevertheless exploit cross-reactivity to provide the same coverage of antigens. I will show that the optimal repertoires can be reached by dynamics that describes the competitive binding of antigens by receptors, and selective amplification of stimulated receptors.

  20. Speech Adaptation to Kinematic Recording Sensors: Perceptual and Acoustic Findings

    Science.gov (United States)

    Dromey, Christopher; Hunter, Elise; Nissen, Shawn L.

    2018-01-01

    Purpose: This study used perceptual and acoustic measures to examine the time course of speech adaptation after the attachment of electromagnetic sensor coils to the tongue, lips, and jaw. Method: Twenty native English speakers read aloud stimulus sentences before the attachment of the sensors, immediately after attachment, and again 5, 10, 15,…

  1. Interference mitigation through adaptive power control in wireless sensor networks

    NARCIS (Netherlands)

    Chincoli, M.; Bacchiani, C.; Syed, Aly; Exarchakos, G.; Liotta, A.

    2016-01-01

    Adaptive transmission power control schemes have been introduced in wireless sensor networks to adjust energy consumption under different network conditions. This is a crucial goal, given the constraints under which sensor communications operate. Power reduction may however have counter-productive

  2. Biomimetic micromechanical adaptive flow-sensor arrays

    NARCIS (Netherlands)

    Krijnen, Gijsbertus J.M.; Floris, J.; Dijkstra, Marcel; Lammerink, Theodorus S.J.; Wiegerink, Remco J.

    2007-01-01

    We report current developments in biomimetic flow-sensors based on flow sensitive mechano-sensors of crickets. Crickets have one form of acoustic sensing evolved in the form of mechanoreceptive sensory hairs. These filiform hairs are highly perceptive to low-frequency sound with energy sensitivities

  3. Adaptive Sampling in Autonomous Marine Sensor Networks

    National Research Council Canada - National Science Library

    Eickstedt, Donald P

    2006-01-01

    ... oceanographic network scenario. This architecture has three major components, an intelligent, logical sensor that provides high-level environmental state information to a behavior-based autonomous vehicle control system, a new...

  4. Sensor Calibration Design Based on D-Optimality Criterion

    Directory of Open Access Journals (Sweden)

    Hajiyev Chingiz

    2016-09-01

    Full Text Available In this study, a procedure for optimal selection of measurement points using the D-optimality criterion to find the best calibration curves of measurement sensors is proposed. The coefficients of calibration curve are evaluated by applying the classical Least Squares Method (LSM. As an example, the problem of optimal selection for standard pressure setters when calibrating a differential pressure sensor is solved. The values obtained from the D-optimum measurement points for calibration of the differential pressure sensor are compared with those from actual experiments. Comparison of the calibration errors corresponding to the D-optimal, A-optimal and Equidistant calibration curves is done.

  5. Communal Sensor Network for Adaptive Noise Reduction in Aircraft Engine Nacelles

    Science.gov (United States)

    Jones, Kennie H.; Nark, Douglas M.; Jones, Michael G.

    2011-01-01

    Emergent behavior, a subject of much research in biology, sociology, and economics, is a foundational element of Complex Systems Science and is apropos in the design of sensor network systems. To demonstrate engineering for emergent behavior, a novel approach in the design of a sensor/actuator network is presented maintaining optimal noise attenuation as an adaptation to changing acoustic conditions. Rather than use the conventional approach where sensors are managed by a central controller, this new paradigm uses a biomimetic model where sensor/actuators cooperate as a community of autonomous organisms, sharing with neighbors to control impedance based on local information. From the combination of all individual actions, an optimal attenuation emerges for the global system.

  6. Nonlinearity Analysis and Parameters Optimization for an Inductive Angle Sensor

    Directory of Open Access Journals (Sweden)

    Lin Ye

    2014-02-01

    Full Text Available Using the finite element method (FEM and particle swarm optimization (PSO, a nonlinearity analysis based on parameter optimization is proposed to design an inductive angle sensor. Due to the structure complexity of the sensor, understanding the influences of structure parameters on the nonlinearity errors is a critical step in designing an effective sensor. Key parameters are selected for the design based on the parameters’ effects on the nonlinearity errors. The finite element method and particle swarm optimization are combined for the sensor design to get the minimal nonlinearity error. In the simulation, the nonlinearity error of the optimized sensor is 0.053% in the angle range from −60° to 60°. A prototype sensor is manufactured and measured experimentally, and the experimental nonlinearity error is 0.081% in the angle range from −60° to 60°.

  7. Context-Aware Local Optimization of Sensor Network Deployment

    Directory of Open Access Journals (Sweden)

    Meysam Argany

    2015-07-01

    Full Text Available Wireless sensor networks are increasingly used for tracking and monitoring dynamic phenomena in urban and natural areas. Spatial coverage is an important issue in sensor networks in order to fulfill the needs of sensing applications. Optimization methods are widely used to efficiently distribute sensor nodes in the network to achieve a desired level of coverage. Most of the existing algorithms do not consider the characteristics of the real environment in the optimization process. In this paper, we propose the integration of contextual information in optimization algorithms to improve sensor network coverage. First, we investigate the implication of contextual information in sensor networks. Then, a conceptual framework for local context-aware sensor network deployment optimization method is introduced and related algorithms are presented in detail. Finally, several experiments are carried out to evaluate the validity of the proposed method. The results obtained from these experiments show the effectiveness of our approach in different contextual situations.

  8. Dynamic Hierarchical Energy-Efficient Method Based on Combinatorial Optimization for Wireless Sensor Networks.

    Science.gov (United States)

    Chang, Yuchao; Tang, Hongying; Cheng, Yongbo; Zhao, Qin; Yuan, Baoqing Li andXiaobing

    2017-07-19

    Routing protocols based on topology control are significantly important for improving network longevity in wireless sensor networks (WSNs). Traditionally, some WSN routing protocols distribute uneven network traffic load to sensor nodes, which is not optimal for improving network longevity. Differently to conventional WSN routing protocols, we propose a dynamic hierarchical protocol based on combinatorial optimization (DHCO) to balance energy consumption of sensor nodes and to improve WSN longevity. For each sensor node, the DHCO algorithm obtains the optimal route by establishing a feasible routing set instead of selecting the cluster head or the next hop node. The process of obtaining the optimal route can be formulated as a combinatorial optimization problem. Specifically, the DHCO algorithm is carried out by the following procedures. It employs a hierarchy-based connection mechanism to construct a hierarchical network structure in which each sensor node is assigned to a special hierarchical subset; it utilizes the combinatorial optimization theory to establish the feasible routing set for each sensor node, and takes advantage of the maximum-minimum criterion to obtain their optimal routes to the base station. Various results of simulation experiments show effectiveness and superiority of the DHCO algorithm in comparison with state-of-the-art WSN routing algorithms, including low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed clustering (HEED), genetic protocol-based self-organizing network clustering (GASONeC), and double cost function-based routing (DCFR) algorithms.

  9. Sample Adaptive Offset Optimization in HEVC

    Directory of Open Access Journals (Sweden)

    Yang Zhang

    2014-11-01

    Full Text Available As the next generation of video coding standard, High Efficiency Video Coding (HEVC adopted many useful tools to improve coding efficiency. Sample Adaptive Offset (SAO, is a technique to reduce sample distortion by providing offsets to pixels in in-loop filter. In SAO, pixels in LCU are classified into several categories, then categories and offsets are given based on Rate-Distortion Optimization (RDO of reconstructed pixels in a Largest Coding Unit (LCU. Pixels in a LCU are operated by the same SAO process, however, transform and inverse transform makes the distortion of pixels in Transform Unit (TU edge larger than the distortion inside TU even after deblocking filtering (DF and SAO. And the categories of SAO can also be refined, since it is not proper for many cases. This paper proposed a TU edge offset mode and a category refinement for SAO in HEVC. Experimental results shows that those two kinds of optimization gets -0.13 and -0.2 gain respectively compared with the SAO in HEVC. The proposed algorithm which using the two kinds of optimization gets -0.23 gain on BD-rate compared with the SAO in HEVC which is a 47 % increase with nearly no increase on coding time.

  10. An Adaptive Sensor Mining Framework for Pervasive Computing Applications

    Science.gov (United States)

    Rashidi, Parisa; Cook, Diane J.

    Analyzing sensor data in pervasive computing applications brings unique challenges to the KDD community. The challenge is heightened when the underlying data source is dynamic and the patterns change. We introduce a new adaptive mining framework that detects patterns in sensor data, and more importantly, adapts to the changes in the underlying model. In our framework, the frequent and periodic patterns of data are first discovered by the Frequent and Periodic Pattern Miner (FPPM) algorithm; and then any changes in the discovered patterns over the lifetime of the system are discovered by the Pattern Adaptation Miner (PAM) algorithm, in order to adapt to the changing environment. This framework also captures vital context information present in pervasive computing applications, such as the startup triggers and temporal information. In this paper, we present a description of our mining framework and validate the approach using data collected in the CASAS smart home testbed.

  11. Adaptive inferential sensors based on evolving fuzzy models.

    Science.gov (United States)

    Angelov, Plamen; Kordon, Arthur

    2010-04-01

    A new technique to the design and use of inferential sensors in the process industry is proposed in this paper, which is based on the recently introduced concept of evolving fuzzy models (EFMs). They address the challenge that the modern process industry faces today, namely, to develop such adaptive and self-calibrating online inferential sensors that reduce the maintenance costs while keeping the high precision and interpretability/transparency. The proposed new methodology makes possible inferential sensors to recalibrate automatically, which reduces significantly the life-cycle efforts for their maintenance. This is achieved by the adaptive and flexible open-structure EFM used. The novelty of this paper lies in the following: (1) the overall concept of inferential sensors with evolving and self-developing structure from the data streams; (2) the new methodology for online automatic selection of input variables that are most relevant for the prediction; (3) the technique to detect automatically a shift in the data pattern using the age of the clusters (and fuzzy rules); (4) the online standardization technique used by the learning procedure of the evolving model; and (5) the application of this innovative approach to several real-life industrial processes from the chemical industry (evolving inferential sensors, namely, eSensors, were used for predicting the chemical properties of different products in The Dow Chemical Company, Freeport, TX). It should be noted, however, that the methodology and conclusions of this paper are valid for the broader area of chemical and process industries in general. The results demonstrate that well-interpretable and with-simple-structure inferential sensors can automatically be designed from the data stream in real time, which predict various process variables of interest. The proposed approach can be used as a basis for the development of a new generation of adaptive and evolving inferential sensors that can address the

  12. LinkMind: Link Optimization in Swarming Mobile Sensor Networks

    DEFF Research Database (Denmark)

    Ngo, Trung Dung

    2012-01-01

    of the most advantageous properties of the swarming wireless sensor network is that mobile nodes can work cooperatively to organize an ad-hoc network and optimize the network link capacity to maximize the transmission of gathered data from a source to a target. This paper describes a new method of link...... optimization of swarming mobile sensor networks. The new method is based on combination of the artificial potential force guaranteeing connectivities of the mobile sensor nodes and the max-flow min-cut theorem of graph theory ensuring optimization of the network link capacity. The developed algorithm...

  13. Genetic Algorithm and its Application in Optimal Sensor Layout

    Directory of Open Access Journals (Sweden)

    Xiang-Yang Chen

    2015-05-01

    Full Text Available This paper aims at the problem of multi sensor station distribution, based on multi- sensor systems of different types as the research object, in the analysis of various types of sensors with different application background, different indicators of demand, based on the different constraints, for all kinds of multi sensor station is studied, the application of genetic algorithms as a tool for the objective function of the models optimization, then the optimal various types of multi sensor station distribution plan, improve the performance of the system, and achieved good military effect. In the field of application of sensor radar, track measuring instrument, the satellite, passive positioning equipment of various types, specific problem, use care indicators and station arrangement between the mathematical model of geometry, using genetic algorithm to get the optimization results station distribution, to solve a variety of practical problems provides useful help, but also reflects the improved genetic algorithm in electronic weapon system based on multi sensor station distribution on the applicability and effectiveness of the optimization; finally the genetic algorithm for integrated optimization of multi sensor station distribution using the good to the training exercise tasks based on actual in, and have achieved good military effect.

  14. Adaptive Synchronization of Robotic Sensor Networks

    OpenAIRE

    Yıldırım, Kasım Sinan; Gürcan, Önder

    2014-01-01

    The main focus of recent time synchronization research is developing power-efficient synchronization methods that meet pre-defined accuracy requirements. However, an aspect that has been often overlooked is the high dynamics of the network topology due to the mobility of the nodes. Employing existing flooding-based and peer-to-peer synchronization methods, are networked robots still be able to adapt themselves and self-adjust their logical clocks under mobile network dynamics? In this paper, ...

  15. A Game Theoretic Optimization Method for Energy Efficient Global Connectivity in Hybrid Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    JongHyup Lee

    2016-08-01

    Full Text Available For practical deployment of wireless sensor networks (WSN, WSNs construct clusters, where a sensor node communicates with other nodes in its cluster, and a cluster head support connectivity between the sensor nodes and a sink node. In hybrid WSNs, cluster heads have cellular network interfaces for global connectivity. However, when WSNs are active and the load of cellular networks is high, the optimal assignment of cluster heads to base stations becomes critical. Therefore, in this paper, we propose a game theoretic model to find the optimal assignment of base stations for hybrid WSNs. Since the communication and energy cost is different according to cellular systems, we devise two game models for TDMA/FDMA and CDMA systems employing power prices to adapt to the varying efficiency of recent wireless technologies. The proposed model is defined on the assumptions of the ideal sensing field, but our evaluation shows that the proposed model is more adaptive and energy efficient than local selections.

  16. A Game Theoretic Optimization Method for Energy Efficient Global Connectivity in Hybrid Wireless Sensor Networks

    Science.gov (United States)

    Lee, JongHyup; Pak, Dohyun

    2016-01-01

    For practical deployment of wireless sensor networks (WSN), WSNs construct clusters, where a sensor node communicates with other nodes in its cluster, and a cluster head support connectivity between the sensor nodes and a sink node. In hybrid WSNs, cluster heads have cellular network interfaces for global connectivity. However, when WSNs are active and the load of cellular networks is high, the optimal assignment of cluster heads to base stations becomes critical. Therefore, in this paper, we propose a game theoretic model to find the optimal assignment of base stations for hybrid WSNs. Since the communication and energy cost is different according to cellular systems, we devise two game models for TDMA/FDMA and CDMA systems employing power prices to adapt to the varying efficiency of recent wireless technologies. The proposed model is defined on the assumptions of the ideal sensing field, but our evaluation shows that the proposed model is more adaptive and energy efficient than local selections. PMID:27589743

  17. Reference-shaping adaptive control by using gradient descent optimizers.

    Directory of Open Access Journals (Sweden)

    Baris Baykant Alagoz

    Full Text Available This study presents a model reference adaptive control scheme based on reference-shaping approach. The proposed adaptive control structure includes two optimizer processes that perform gradient descent optimization. The first process is the control optimizer that generates appropriate control signal for tracking of the controlled system output to a reference model output. The second process is the adaptation optimizer that performs for estimation of a time-varying adaptation gain, and it contributes to improvement of control signal generation. Numerical update equations derived for adaptation gain and control signal perform gradient descent optimization in order to decrease the model mismatch errors. To reduce noise sensitivity of the system, a dead zone rule is applied to the adaptation process. Simulation examples show the performance of the proposed Reference-Shaping Adaptive Control (RSAC method for several test scenarios. An experimental study demonstrates application of method for rotor control.

  18. Distributed adaptive diagnosis of sensor faults using structural response data

    Science.gov (United States)

    Dragos, Kosmas; Smarsly, Kay

    2016-10-01

    The reliability and consistency of wireless structural health monitoring (SHM) systems can be compromised by sensor faults, leading to miscalibrations, corrupted data, or even data loss. Several research approaches towards fault diagnosis, referred to as ‘analytical redundancy’, have been proposed that analyze the correlations between different sensor outputs. In wireless SHM, most analytical redundancy approaches require centralized data storage on a server for data analysis, while other approaches exploit the on-board computing capabilities of wireless sensor nodes, analyzing the raw sensor data directly on board. However, using raw sensor data poses an operational constraint due to the limited power resources of wireless sensor nodes. In this paper, a new distributed autonomous approach towards sensor fault diagnosis based on processed structural response data is presented. The inherent correlations among Fourier amplitudes of acceleration response data, at peaks corresponding to the eigenfrequencies of the structure, are used for diagnosis of abnormal sensor outputs at a given structural condition. Representing an entirely data-driven analytical redundancy approach that does not require any a priori knowledge of the monitored structure or of the SHM system, artificial neural networks (ANN) are embedded into the sensor nodes enabling cooperative fault diagnosis in a fully decentralized manner. The distributed analytical redundancy approach is implemented into a wireless SHM system and validated in laboratory experiments, demonstrating the ability of wireless sensor nodes to self-diagnose sensor faults accurately and efficiently with minimal data traffic. Besides enabling distributed autonomous fault diagnosis, the embedded ANNs are able to adapt to the actual condition of the structure, thus ensuring accurate and efficient fault diagnosis even in case of structural changes.

  19. Wireless Sensor Network Optimization: Multi-Objective Paradigm.

    Science.gov (United States)

    Iqbal, Muhammad; Naeem, Muhammad; Anpalagan, Alagan; Ahmed, Ashfaq; Azam, Muhammad

    2015-07-20

    Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks.

  20. Wireless Sensor Network Optimization: Multi-Objective Paradigm

    Science.gov (United States)

    Iqbal, Muhammad; Naeem, Muhammad; Anpalagan, Alagan; Ahmed, Ashfaq; Azam, Muhammad

    2015-01-01

    Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks. PMID:26205271

  1. Adaptive Media Access Control for Energy Harvesting - Wireless Sensor Networks

    DEFF Research Database (Denmark)

    Fafoutis, Xenofon; Dragoni, Nicola

    2012-01-01

    ODMAC (On-Demand Media Access Control) is a recently proposed MAC protocol designed to support individual duty cycles for Energy Harvesting — Wireless Sensor Networks (EH-WSNs). Individual duty cycles are vital for EH-WSNs, because they allow nodes to adapt their energy consumption to the ever-ch...

  2. LinkMind: link optimization in swarming mobile sensor networks.

    Science.gov (United States)

    Ngo, Trung Dung

    2011-01-01

    A swarming mobile sensor network is comprised of a swarm of wirelessly connected mobile robots equipped with various sensors. Such a network can be applied in an uncertain environment for services such as cooperative navigation and exploration, object identification and information gathering. One of the most advantageous properties of the swarming wireless sensor network is that mobile nodes can work cooperatively to organize an ad-hoc network and optimize the network link capacity to maximize the transmission of gathered data from a source to a target. This paper describes a new method of link optimization of swarming mobile sensor networks. The new method is based on combination of the artificial potential force guaranteeing connectivities of the mobile sensor nodes and the max-flow min-cut theorem of graph theory ensuring optimization of the network link capacity. The developed algorithm is demonstrated and evaluated in simulation.

  3. LinkMind: Link Optimization in Swarming Mobile Sensor Networks

    Directory of Open Access Journals (Sweden)

    Trung Dung Ngo

    2011-08-01

    Full Text Available A swarming mobile sensor network is comprised of a swarm of wirelessly connected mobile robots equipped with various sensors. Such a network can be applied in an uncertain environment for services such as cooperative navigation and exploration, object identification and information gathering. One of the most advantageous properties of the swarming wireless sensor network is that mobile nodes can work cooperatively to organize an ad-hoc network and optimize the network link capacity to maximize the transmission of gathered data from a source to a target. This paper describes a new method of link optimization of swarming mobile sensor networks. The new method is based on combination of the artificial potential force guaranteeing connectivities of the mobile sensor nodes and the max-flow min-cut theorem of graph theory ensuring optimization of the network link capacity. The developed algorithm is demonstrated and evaluated in simulation.

  4. Extending the Lifetime of Sensor Networks through Adaptive Reclustering

    Directory of Open Access Journals (Sweden)

    Gianluigi Ferrari

    2007-06-01

    Full Text Available We analyze the lifetime of clustered sensor networks with decentralized binary detection under a physical layer quality-of-service (QoS constraint, given by the maximum tolerable probability of decision error at the access point (AP. In order to properly model the network behavior, we consider four different distributions (exponential, uniform, Rayleigh, and lognormal for the lifetime of a single sensor. We show the benefits, in terms of longer network lifetime, of adaptive reclustering. We also derive an analytical framework for the computation of the network lifetime and the penalty, in terms of time delay and energy consumption, brought by adaptive reclustering. On the other hand, absence of reclustering leads to a shorter network lifetime, and we show the impact of various clustering configurations under different QoS conditions. Our results show that the organization of sensors in a few big clusters is the winning strategy to maximize the network lifetime. Moreover, the observation of the phenomenon should be frequent in order to limit the penalties associated with the reclustering procedure. We also apply the developed framework to analyze the energy consumption associated with the proposed reclustering protocol, obtaining results in good agreement with the performance of realistic wireless sensor networks. Finally, we present simulation results on the lifetime of IEEE 802.15.4 wireless sensor networks, which enrich the proposed analytical framework and show that typical networking performance metrics (such as throughput and delay are influenced by the sensor network lifetime.

  5. Extending the Lifetime of Sensor Networks through Adaptive Reclustering

    Directory of Open Access Journals (Sweden)

    Ferrari Gianluigi

    2007-01-01

    Full Text Available We analyze the lifetime of clustered sensor networks with decentralized binary detection under a physical layer quality-of-service (QoS constraint, given by the maximum tolerable probability of decision error at the access point (AP. In order to properly model the network behavior, we consider four different distributions (exponential, uniform, Rayleigh, and lognormal for the lifetime of a single sensor. We show the benefits, in terms of longer network lifetime, of adaptive reclustering. We also derive an analytical framework for the computation of the network lifetime and the penalty, in terms of time delay and energy consumption, brought by adaptive reclustering. On the other hand, absence of reclustering leads to a shorter network lifetime, and we show the impact of various clustering configurations under different QoS conditions. Our results show that the organization of sensors in a few big clusters is the winning strategy to maximize the network lifetime. Moreover, the observation of the phenomenon should be frequent in order to limit the penalties associated with the reclustering procedure. We also apply the developed framework to analyze the energy consumption associated with the proposed reclustering protocol, obtaining results in good agreement with the performance of realistic wireless sensor networks. Finally, we present simulation results on the lifetime of IEEE 802.15.4 wireless sensor networks, which enrich the proposed analytical framework and show that typical networking performance metrics (such as throughput and delay are influenced by the sensor network lifetime.

  6. Optimize Etching Based Single Mode Fiber Optic Temperature Sensor

    OpenAIRE

    Ajay Kumar; Dr. Pramod Kumar

    2014-01-01

    This paper presents a description of etching process for fabrication single mode optical fiber sensors. The process of fabrication demonstrates an optimized etching based method to fabricate single mode fiber (SMF) optic sensors in specified constant time and temperature. We propose a single mode optical fiber based temperature sensor, where the temperature sensing region is obtained by etching its cladding diameter over small length to a critical value. It is observed that th...

  7. New Low Cost Structure for Dual Axis Mount Solar Tracking System Using Adaptive Solar Sensor

    DEFF Research Database (Denmark)

    Argeseanu, Alin; Ritchie, Ewen; Leban, Krisztina Monika

    2010-01-01

    A solar tracking system is designed to optimize the operation of solar energy receivers. The objective of this paper is proposing a new tracking system structure with two axis. The success strategy of this new project focuses on the economical analysis of solar energy. Therefore it is important...... to determine the most cost effective design, to consider the costs of production and maintenance, and operating. The proposed tracking system uses a new solar sensor position with an adaptive feature....

  8. Bond graph to digraph conversion: A sensor placement optimization ...

    Indian Academy of Sciences (India)

    In this paper, we consider the optimal sensors placement problem for ... is due to the fact that the construction is generally done from the state equations, ... The Bond Graph (BG) tool defined in Paynter (1961) formal- ... Sensor placement and structural problem formulation .... Thus the obtained four matrices are as follows:.

  9. Energy-efficient Organization of Wireless Sensor Networks with Adaptive Forecasting

    Directory of Open Access Journals (Sweden)

    Dao-Wei Bi

    2008-04-01

    Full Text Available Due to the wide potential applications of wireless sensor networks, this topic has attracted great attention. The strict energy constraints of sensor nodes result in great challenges for energy efficiency. This paper proposes an energy-efficient organization method. The organization of wireless sensor networks is formulated for target tracking. Target localization is achieved by collaborative sensing with multi-sensor fusion. The historical localization results are utilized for adaptive target trajectory forecasting. Combining autoregressive moving average (ARMA model and radial basis function networks (RBFNs, robust target position forecasting is performed. Moreover, an energyefficient organization method is presented to enhance the energy efficiency of wireless sensor networks. The sensor nodes implement sensing tasks are awakened in a distributed manner. When the sensor nodes transfer their observations to achieve data fusion, the routing scheme is obtained by ant colony optimization. Thus, both the operation and communication energy consumption can be minimized. Experimental results verify that the combination of ARMA model and RBFN can estimate the target position efficiently and energy saving is achieved by the proposed organization method in wireless sensor networks.

  10. Network inference via adaptive optimal design

    Directory of Open Access Journals (Sweden)

    Stigter Johannes D

    2012-09-01

    Full Text Available Abstract Background Current research in network reverse engineering for genetic or metabolic networks very often does not include a proper experimental and/or input design. In this paper we address this issue in more detail and suggest a method that includes an iterative design of experiments based, on the most recent data that become available. The presented approach allows a reliable reconstruction of the network and addresses an important issue, i.e., the analysis and the propagation of uncertainties as they exist in both the data and in our own knowledge. These two types of uncertainties have their immediate ramifications for the uncertainties in the parameter estimates and, hence, are taken into account from the very beginning of our experimental design. Findings The method is demonstrated for two small networks that include a genetic network for mRNA synthesis and degradation and an oscillatory network describing a molecular network underlying adenosine 3’-5’ cyclic monophosphate (cAMP as observed in populations of Dyctyostelium cells. In both cases a substantial reduction in parameter uncertainty was observed. Extension to larger scale networks is possible but needs a more rigorous parameter estimation algorithm that includes sparsity as a constraint in the optimization procedure. Conclusion We conclude that a careful experiment design very often (but not always pays off in terms of reliability in the inferred network topology. For large scale networks a better parameter estimation algorithm is required that includes sparsity as an additional constraint. These algorithms are available in the literature and can also be used in an adaptive optimal design setting as demonstrated in this paper.

  11. Geometrical optimization of a local ballistic magnetic sensor

    Energy Technology Data Exchange (ETDEWEB)

    Kanda, Yuhsuke; Hara, Masahiro [Graduate School of Science and Technology, Kumamoto University, 2-39-1 Kurokami, Kumamoto 860-8555 (Japan); Nomura, Tatsuya [Advanced Electronics Research Division, INAMORI Frontier Research Center, Kyushu University, 744 Motooka, Fukuoka 819-0395 (Japan); Kimura, Takashi [Advanced Electronics Research Division, INAMORI Frontier Research Center, Kyushu University, 744 Motooka, Fukuoka 819-0395 (Japan); Department of Physics, Kyushu University, 6-10-1 Hakozaki, Fukuoka 812-8581 (Japan)

    2014-04-07

    We have developed a highly sensitive local magnetic sensor by using a ballistic transport property in a two-dimensional conductor. A semiclassical simulation reveals that the sensitivity increases when the geometry of the sensor and the spatial distribution of the local field are optimized. We have also experimentally demonstrated a clear observation of a magnetization process in a permalloy dot whose size is much smaller than the size of an optimized ballistic magnetic sensor fabricated from a GaAs/AlGaAs two-dimensional electron gas.

  12. Adaptive hybrid optimal quantum control for imprecisely characterized systems.

    Science.gov (United States)

    Egger, D J; Wilhelm, F K

    2014-06-20

    Optimal quantum control theory carries a huge promise for quantum technology. Its experimental application, however, is often hindered by imprecise knowledge of the input variables, the quantum system's parameters. We show how to overcome this by adaptive hybrid optimal control, using a protocol named Ad-HOC. This protocol combines open- and closed-loop optimal control by first performing a gradient search towards a near-optimal control pulse and then an experimental fidelity estimation with a gradient-free method. For typical settings in solid-state quantum information processing, adaptive hybrid optimal control enhances gate fidelities by an order of magnitude, making optimal control theory applicable and useful.

  13. Noise-exploitation and adaptation in neuromorphic sensors

    Science.gov (United States)

    Hindo, Thamira; Chakrabartty, Shantanu

    2012-04-01

    Even though current micro-nano fabrication technology has reached integration levels where ultra-sensitive sensors can be fabricated, the sensing performance (resolution per joule) of synthetic systems are still orders of magnitude inferior to those observed in neurobiology. For example, the filiform hairs in crickets operate at fundamental limits of noise; auditory sensors in a parasitoid fly can overcome fundamental limitations to precisely localize ultra-faint acoustic signatures. Even though many of these biological marvels have served as inspiration for different types of neuromorphic sensors, the main focus these designs have been to faithfully replicate the biological functionalities, without considering the constructive role of "noise". In man-made sensors device and sensor noise are typically considered as a nuisance, where as in neurobiology "noise" has been shown to be a computational aid that enables biology to sense and operate at fundamental limits of energy efficiency and performance. In this paper, we describe some of the important noise-exploitation and adaptation principles observed in neurobiology and how they can be systematically used for designing neuromorphic sensors. Our focus will be on two types of noise-exploitation principles, namely, (a) stochastic resonance; and (b) noise-shaping, which are unified within our previously reported framework called Σ▵ learning. As a case-study, we describe the application of Σ▵ learning for the design of a miniature acoustic source localizer whose performance matches that of its biological counterpart(Ormia Ochracea).

  14. Adaptive Opportunistic Cooperative Control Mechanism Based on Combination Forecasting and Multilevel Sensing Technology of Sensors for Mobile Internet of Things

    Directory of Open Access Journals (Sweden)

    Yong Jin

    2014-01-01

    Full Text Available In mobile Internet of Things, there are many challenges, including sensing technology of sensors, how and when to join cooperative transmission, and how to select the cooperative sensors. To address these problems, we studied the combination forecasting based on the multilevel sensing technology of sensors, building upon which we proposed the adaptive opportunistic cooperative control mechanism based on the threshold values such as activity probability, distance, transmitting power, and number of relay sensors, in consideration of signal to noise ratio and outage probability. More importantly, the relay sensors would do self-test real time in order to judge whether to join the cooperative transmission, for maintaining the optimal cooperative transmission state with high performance. The mathematical analyses results show that the proposed adaptive opportunistic cooperative control approach could perform better in terms of throughput ratio, packet error rate and delay, and energy efficiency, compared with the direct transmission and opportunistic cooperative approaches.

  15. Optimizing Key Updates in Sensor Networks

    DEFF Research Database (Denmark)

    Yuksel, Ender; Nielson, Hanne Riis; Nielson, Flemming

    2011-01-01

    Sensor networks offer the advantages of simple and low–resource communication. Nevertheless, security is of particular importance in many cases such as when sensitive data is communicated or tamper-resistance is required. Updating the security keys is one of the key points in security, which...

  16. An Energy Aware Adaptive Sampling Algorithm for Energy Harvesting WSN with Energy Hungry Sensors

    Science.gov (United States)

    Srbinovski, Bruno; Magno, Michele; Edwards-Murphy, Fiona; Pakrashi, Vikram; Popovici, Emanuel

    2016-01-01

    Wireless sensor nodes have a limited power budget, though they are often expected to be functional in the field once deployed for extended periods of time. Therefore, minimization of energy consumption and energy harvesting technology in Wireless Sensor Networks (WSN) are key tools for maximizing network lifetime, and achieving self-sustainability. This paper proposes an energy aware Adaptive Sampling Algorithm (ASA) for WSN with power hungry sensors and harvesting capabilities, an energy management technique that can be implemented on any WSN platform with enough processing power to execute the proposed algorithm. An existing state-of-the-art ASA developed for wireless sensor networks with power hungry sensors is optimized and enhanced to adapt the sampling frequency according to the available energy of the node. The proposed algorithm is evaluated using two in-field testbeds that are supplied by two different energy harvesting sources (solar and wind). Simulation and comparison between the state-of-the-art ASA and the proposed energy aware ASA (EASA) in terms of energy durability are carried out using in-field measured harvested energy (using both wind and solar sources) and power hungry sensors (ultrasonic wind sensor and gas sensors). The simulation results demonstrate that using ASA in combination with an energy aware function on the nodes can drastically increase the lifetime of a WSN node and enable self-sustainability. In fact, the proposed EASA in conjunction with energy harvesting capability can lead towards perpetual WSN operation and significantly outperform the state-of-the-art ASA. PMID:27043559

  17. An Energy Aware Adaptive Sampling Algorithm for Energy Harvesting WSN with Energy Hungry Sensors

    Directory of Open Access Journals (Sweden)

    Bruno Srbinovski

    2016-03-01

    Full Text Available Wireless sensor nodes have a limited power budget, though they are often expected to be functional in the field once deployed for extended periods of time. Therefore, minimization of energy consumption and energy harvesting technology in Wireless Sensor Networks (WSN are key tools for maximizing network lifetime, and achieving self-sustainability. This paper proposes an energy aware Adaptive Sampling Algorithm (ASA for WSN with power hungry sensors and harvesting capabilities, an energy management technique that can be implemented on any WSN platform with enough processing power to execute the proposed algorithm. An existing state-of-the-art ASA developed for wireless sensor networks with power hungry sensors is optimized and enhanced to adapt the sampling frequency according to the available energy of the node. The proposed algorithm is evaluated using two in-field testbeds that are supplied by two different energy harvesting sources (solar and wind. Simulation and comparison between the state-of-the-art ASA and the proposed energy aware ASA (EASA in terms of energy durability are carried out using in-field measured harvested energy (using both wind and solar sources and power hungry sensors (ultrasonic wind sensor and gas sensors. The simulation results demonstrate that using ASA in combination with an energy aware function on the nodes can drastically increase the lifetime of a WSN node and enable self-sustainability. In fact, the proposed EASA in conjunction with energy harvesting capability can lead towards perpetual WSN operation and significantly outperform the state-of-the-art ASA.

  18. Optimized quantum sensing with a single electron spin using real-time adaptive measurements

    Science.gov (United States)

    Bonato, C.; Blok, M. S.; Dinani, H. T.; Berry, D. W.; Markham, M. L.; Twitchen, D. J.; Hanson, R.

    2016-03-01

    Quantum sensors based on single solid-state spins promise a unique combination of sensitivity and spatial resolution. The key challenge in sensing is to achieve minimum estimation uncertainty within a given time and with high dynamic range. Adaptive strategies have been proposed to achieve optimal performance, but their implementation in solid-state systems has been hindered by the demanding experimental requirements. Here, we realize adaptive d.c. sensing by combining single-shot readout of an electron spin in diamond with fast feedback. By adapting the spin readout basis in real time based on previous outcomes, we demonstrate a sensitivity in Ramsey interferometry surpassing the standard measurement limit. Furthermore, we find by simulations and experiments that adaptive protocols offer a distinctive advantage over the best known non-adaptive protocols when overhead and limited estimation time are taken into account. Using an optimized adaptive protocol we achieve a magnetic field sensitivity of 6.1 ± 1.7 nT Hz-1/2 over a wide range of 1.78 mT. These results open up a new class of experiments for solid-state sensors in which real-time knowledge of the measurement history is exploited to obtain optimal performance.

  19. Optimization of Northeastern Region First-Graders’ Adaptation to School

    Directory of Open Access Journals (Sweden)

    S A Yakimchuk

    2013-12-01

    Full Text Available The work is devoted to the adaptation of the children in the northeastern region of Russia to school. In the article the influence of the extreme environmental conditions on the health of the children living in the Magadan region, which is an additional factor influencing the success of the child’s adaptation to the changing conditions of life, is proven; the recommendations to optimize the process of adaptation that enhance the adaptive opportunities of first-graders are provided.

  20. Robust Adaptive Beamforming with Sensor Position Errors Using Weighted Subspace Fitting-Based Covariance Matrix Reconstruction.

    Science.gov (United States)

    Chen, Peng; Yang, Yixin; Wang, Yong; Ma, Yuanliang

    2018-05-08

    When sensor position errors exist, the performance of recently proposed interference-plus-noise covariance matrix (INCM)-based adaptive beamformers may be severely degraded. In this paper, we propose a weighted subspace fitting-based INCM reconstruction algorithm to overcome sensor displacement for linear arrays. By estimating the rough signal directions, we construct a novel possible mismatched steering vector (SV) set. We analyze the proximity of the signal subspace from the sample covariance matrix (SCM) and the space spanned by the possible mismatched SV set. After solving an iterative optimization problem, we reconstruct the INCM using the estimated sensor position errors. Then we estimate the SV of the desired signal by solving an optimization problem with the reconstructed INCM. The main advantage of the proposed algorithm is its robustness against SV mismatches dominated by unknown sensor position errors. Numerical examples show that even if the position errors are up to half of the assumed sensor spacing, the output signal-to-interference-plus-noise ratio is only reduced by 4 dB. Beam patterns plotted using experiment data show that the interference suppression capability of the proposed beamformer outperforms other tested beamformers.

  1. Evolving RBF neural networks for adaptive soft-sensor design.

    Science.gov (United States)

    Alexandridis, Alex

    2013-12-01

    This work presents an adaptive framework for building soft-sensors based on radial basis function (RBF) neural network models. The adaptive fuzzy means algorithm is utilized in order to evolve an RBF network, which approximates the unknown system based on input-output data from it. The methodology gradually builds the RBF network model, based on two separate levels of adaptation: On the first level, the structure of the hidden layer is modified by adding or deleting RBF centers, while on the second level, the synaptic weights are adjusted with the recursive least squares with exponential forgetting algorithm. The proposed approach is tested on two different systems, namely a simulated nonlinear DC Motor and a real industrial reactor. The results show that the produced soft-sensors can be successfully applied to model the two nonlinear systems. A comparison with two different adaptive modeling techniques, namely a dynamic evolving neural-fuzzy inference system (DENFIS) and neural networks trained with online backpropagation, highlights the advantages of the proposed methodology.

  2. Condition Monitoring of Sensors in a NPP Using Optimized PCA

    Directory of Open Access Journals (Sweden)

    Wei Li

    2018-01-01

    Full Text Available An optimized principal component analysis (PCA framework is proposed to implement condition monitoring for sensors in a nuclear power plant (NPP in this paper. Compared with the common PCA method in previous research, the PCA method in this paper is optimized at different modeling procedures, including data preprocessing stage, modeling parameter selection stage, and fault detection and isolation stage. Then, the model’s performance is greatly improved through these optimizations. Finally, sensor measurements from a real NPP are used to train the optimized PCA model in order to guarantee the credibility and reliability of the simulation results. Meanwhile, artificial faults are sequentially imposed to sensor measurements to estimate the fault detection and isolation ability of the proposed PCA model. Simulation results show that the optimized PCA model is capable of detecting and isolating the sensors regardless of whether they exhibit major or small failures. Meanwhile, the quantitative evaluation results also indicate that better performance can be obtained in the optimized PCA method compared with the common PCA method.

  3. Optimal Sensor Networks Scheduling in Identification of Distributed Parameter Systems

    CERN Document Server

    Patan, Maciej

    2012-01-01

    Sensor networks have recently come into prominence because they hold the potential to revolutionize a wide spectrum of both civilian and military applications. An ingenious characteristic of sensor networks is the distributed nature of data acquisition. Therefore they seem to be ideally prepared for the task of monitoring processes with spatio-temporal dynamics which constitute one of most general and important classes of systems in modelling of the real-world phenomena. It is clear that careful deployment and activation of sensor nodes are critical for collecting the most valuable information from the observed environment. Optimal Sensor Network Scheduling in Identification of Distributed Parameter Systems discusses the characteristic features of the sensor scheduling problem, analyzes classical and recent approaches, and proposes a wide range of original solutions, especially dedicated for networks with mobile and scanning nodes. Both researchers and practitioners will find the case studies, the proposed al...

  4. Embedded pitch adapters: A high-yield interconnection solution for strip sensors

    Energy Technology Data Exchange (ETDEWEB)

    Ullán, M., E-mail: miguel.ullan@imb-cnm.csic.es [Centro Nacional de Microelectronica (IMB-CNM, CSIC), Campus UAB-Bellaterra, 08193 Barcelona (Spain); Allport, P.P.; Baca, M.; Broughton, J.; Chisholm, A.; Nikolopoulos, K.; Pyatt, S.; Thomas, J.P.; Wilson, J.A. [School of Physics and Astronomy, University of Birmingham, Birmingham B15 2TT (United Kingdom); Kierstead, J.; Kuczewski, P.; Lynn, D. [Brookhaven National Laboratory, Physics Department and Instrumentation Division, Upton, NY 11973-5000 (United States); Hommels, L.B.A. [Cavendish Laboratory, University of Cambridge, JJ Thomson Avenue, Cambridge CB3 0HE (United Kingdom); Fleta, C.; Fernandez-Tejero, J.; Quirion, D. [Centro Nacional de Microelectronica (IMB-CNM, CSIC), Campus UAB-Bellaterra, 08193 Barcelona (Spain); Bloch, I.; Díez, S.; Gregor, I.M.; Lohwasser, K. [DESY, Notkestrasse 85, 22607 Hamburg (Germany); and others

    2016-09-21

    A proposal to fabricate large area strip sensors with integrated, or embedded, pitch adapters is presented for the End-cap part of the Inner Tracker in the ATLAS experiment. To implement the embedded pitch adapters, a second metal layer is used in the sensor fabrication, for signal routing to the ASICs. Sensors with different embedded pitch adapters have been fabricated in order to optimize the design and technology. Inter-strip capacitance, noise, pick-up, cross-talk, signal efficiency, and fabrication yield have been taken into account in their design and fabrication. Inter-strip capacitance tests taking into account all channel neighbors reveal the important differences between the various designs considered. These tests have been correlated with noise figures obtained in full assembled modules, showing that the tests performed on the bare sensors are a valid tool to estimate the final noise in the full module. The full modules have been subjected to test beam experiments in order to evaluate the incidence of cross-talk, pick-up, and signal loss. The detailed analysis shows no indication of cross-talk or pick-up as no additional hits can be observed in any channel not being hit by the beam above 170 mV threshold, and the signal in those channels is always below 1% of the signal recorded in the channel being hit, above 100 mV threshold. First results on irradiated mini-sensors with embedded pitch adapters do not show any change in the interstrip capacitance measurements with only the first neighbors connected.

  5. Steam distribution and energy delivery optimization using wireless sensors

    Science.gov (United States)

    Olama, Mohammed M.; Allgood, Glenn O.; Kuruganti, Teja P.; Sukumar, Sreenivas R.; Djouadi, Seddik M.; Lake, Joe E.

    2011-05-01

    The Extreme Measurement Communications Center at Oak Ridge National Laboratory (ORNL) explores the deployment of a wireless sensor system with a real-time measurement-based energy efficiency optimization framework in the ORNL campus. With particular focus on the 12-mile long steam distribution network in our campus, we propose an integrated system-level approach to optimize the energy delivery within the steam distribution system. We address the goal of achieving significant energy-saving in steam lines by monitoring and acting on leaking steam valves/traps. Our approach leverages an integrated wireless sensor and real-time monitoring capabilities. We make assessments on the real-time status of the distribution system by mounting acoustic sensors on the steam pipes/traps/valves and observe the state measurements of these sensors. Our assessments are based on analysis of the wireless sensor measurements. We describe Fourier-spectrum based algorithms that interpret acoustic vibration sensor data to characterize flows and classify the steam system status. We are able to present the sensor readings, steam flow, steam trap status and the assessed alerts as an interactive overlay within a web-based Google Earth geographic platform that enables decision makers to take remedial action. We believe our demonstration serves as an instantiation of a platform that extends implementation to include newer modalities to manage water flow, sewage and energy consumption.

  6. Adaptive extremal optimization by detrended fluctuation analysis

    International Nuclear Information System (INIS)

    Hamacher, K.

    2007-01-01

    Global optimization is one of the key challenges in computational physics as several problems, e.g. protein structure prediction, the low-energy landscape of atomic clusters, detection of community structures in networks, or model-parameter fitting can be formulated as global optimization problems. Extremal optimization (EO) has become in recent years one particular, successful approach to the global optimization problem. As with almost all other global optimization approaches, EO is driven by an internal dynamics that depends crucially on one or more parameters. Recently, the existence of an optimal scheme for this internal parameter of EO was proven, so as to maximize the performance of the algorithm. However, this proof was not constructive, that is, one cannot use it to deduce the optimal parameter itself a priori. In this study we analyze the dynamics of EO for a test problem (spin glasses). Based on the results we propose an online measure of the performance of EO and a way to use this insight to reformulate the EO algorithm in order to construct optimal values of the internal parameter online without any input by the user. This approach will ultimately allow us to make EO parameter free and thus its application in general global optimization problems much more efficient

  7. Adapting Mobile Beacon-Assisted Localization in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Wei Dong

    2009-04-01

    Full Text Available The ability to automatically locate sensor nodes is essential in many Wireless Sensor Network (WSN applications. To reduce the number of beacons, many mobile-assisted approaches have been proposed. Current mobile-assisted approaches for localization require special hardware or belong to centralized localization algorithms involving some deterministic approaches due to the fact that they explicitly consider the impreciseness of location estimates. In this paper, we first propose a range-free, distributed and probabilistic Mobile Beacon-assisted Localization (MBL approach for static WSNs. Then, we propose another approach based on MBL, called Adapting MBL (A-MBL, to increase the efficiency and accuracy of MBL by adapting the size of sample sets and the parameter of the dynamic model during the estimation process. Evaluation results show that the accuracy of MBL and A-MBL outperform both Mobile and Static sensor network Localization (MSL and Arrival and Departure Overlap (ADO when both of them use only a single mobile beacon for localization in static WSNs.

  8. Adapting mobile beacon-assisted localization in wireless sensor networks.

    Science.gov (United States)

    Teng, Guodong; Zheng, Kougen; Dong, Wei

    2009-01-01

    The ability to automatically locate sensor nodes is essential in many Wireless Sensor Network (WSN) applications. To reduce the number of beacons, many mobile-assisted approaches have been proposed. Current mobile-assisted approaches for localization require special hardware or belong to centralized localization algorithms involving some deterministic approaches due to the fact that they explicitly consider the impreciseness of location estimates. In this paper, we first propose a range-free, distributed and probabilistic Mobile Beacon-assisted Localization (MBL) approach for static WSNs. Then, we propose another approach based on MBL, called Adapting MBL (A-MBL), to increase the efficiency and accuracy of MBL by adapting the size of sample sets and the parameter of the dynamic model during the estimation process. Evaluation results show that the accuracy of MBL and A-MBL outperform both Mobile and Static sensor network Localization (MSL) and Arrival and Departure Overlap (ADO) when both of them use only a single mobile beacon for localization in static WSNs.

  9. Optimize pollutant emissions through adaptive highway management.

    Science.gov (United States)

    2011-09-01

    In this project, we investigated the possibility to reduce green house emission : (mainly CO2) from urban highways by adaptive ramp meter control. QUADSTONE : PARAMICS software was used to build a microscopic traffic model for a 4-lane highway : sect...

  10. AN OPTIMALIZATION OF NATURAL LIGHTING BY APPLYING AUTOMATIC LIGHTING USING MOTION SENSOR AND LUX SENSOR FOR HISTORICAL OLD BUILDINGS

    Directory of Open Access Journals (Sweden)

    Saeful Bahri

    2016-07-01

    Full Text Available ABSTRACT One of the problems that occurs within city centres, particularly within capital cities, is the existence of many historical old buildings. Historical old buildings within city centres, that have abandoned for years because of their condition, suffer from a lack of utilities, infrastructure and facilities [2][3]. These conditions occur because of low levels of maintenance arising as a consequence of a lack of finance of the owner of a building, be they government or private sector. To solve the problem of abandoned historical old buildings, the concept of adaptive reuse can be adopted and applied. This concept of adaptive reuse may continously cover the cost of building maintenance. The adaptive reuse concept usually covers the interior of a building and its utilities, though the need for utilities depends on the function of a building [4]. By adopting a concept of adaptive reuse, new building functions will be designed as the needs and demand of the market dictate, and which is appropriate for feasibility study. One utility element that has to be designed for historical old buildings is the provision of lighting within a building. To minimize the cost of building maintenance, one of the solutions is to optimize natural lighting and to minimize the use of artificial lighting such as lamps. This paper will discuss the extent to which artificial lighting can be minimized by using automatic lighting; the automatic lighting types discussed in this paper are lighting controlled by motion sensor and lux sensor. Keywords: Natural lighting, automatic lighting, motion sensor, lux sensor, historical old buildings ABSTRAK Salah satu permasalahan yang muncul dalam sebuah kota metropolitan, khususnya sebuah ibukota adalah keberadaan dari banyaknya bangunan-bangunan tua bersejarah. Bangunan-bangunan tua bersejarah dalam sebuah kota besar terutama yang diabaikan selama bertahun-tahun biasanya disebabkan karena kondisinya yang menua, minimnya utilitas

  11. Optimization of modal filters based on arrays of piezoelectric sensors

    International Nuclear Information System (INIS)

    Pagani, Carlos C Jr; Trindade, Marcelo A

    2009-01-01

    Modal filters may be obtained by a properly designed weighted sum of the output signals of an array of sensors distributed on the host structure. Although several research groups have been interested in techniques for designing and implementing modal filters based on a given array of sensors, the effect of the array topology on the effectiveness of the modal filter has received much less attention. In particular, it is known that some parameters, such as size, shape and location of a sensor, are very important in determining the observability of a vibration mode. Hence, this paper presents a methodology for the topological optimization of an array of sensors in order to maximize the effectiveness of a set of selected modal filters. This is done using a genetic algorithm optimization technique for the selection of 12 piezoceramic sensors from an array of 36 piezoceramic sensors regularly distributed on an aluminum plate, which maximize the filtering performance, over a given frequency range, of a set of modal filters, each one aiming to isolate one of the first vibration modes. The vectors of the weighting coefficients for each modal filter are evaluated using QR decomposition of the complex frequency response function matrix. Results show that the array topology is not very important for lower frequencies but it greatly affects the filter effectiveness for higher frequencies. Therefore, it is possible to improve the effectiveness and frequency range of a set of modal filters by optimizing the topology of an array of sensors. Indeed, using 12 properly located piezoceramic sensors bonded on an aluminum plate it is shown that the frequency range of a set of modal filters may be enlarged by 25–50%

  12. A Stochastic Multiobjective Optimization Framework for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Shibo He

    2010-01-01

    Full Text Available In wireless sensor networks (WSNs, there generally exist many different objective functions to be optimized. In this paper, we propose a stochastic multiobjective optimization approach to solve such kind of problem. We first formulate a general multiobjective optimization problem. We then decompose the optimization formulation through Lagrange dual decomposition and adopt the stochastic quasigradient algorithm to solve the primal-dual problem in a distributed way. We show theoretically that our algorithm converges to the optimal solution of the primal problem by using the knowledge of stochastic programming. Furthermore, the formulation provides a general stochastic multiobjective optimization framework for WSNs. We illustrate how the general framework works by considering an example of the optimal rate allocation problem in multipath WSNs with time-varying channel. Extensive simulation results are given to demonstrate the effectiveness of our algorithm.

  13. Cooperative aquatic sensing using the telesupervised adaptive ocean sensor fleet

    Science.gov (United States)

    Dolan, John M.; Podnar, Gregg W.; Stancliff, Stephen; Low, Kian Hsiang; Elfes, Alberto; Higinbotham, John; Hosler, Jeffrey; Moisan, Tiffany; Moisan, John

    2009-09-01

    Earth science research must bridge the gap between the atmosphere and the ocean to foster understanding of Earth's climate and ecology. Typical ocean sensing is done with satellites or in situ buoys and research ships which are slow to reposition. Cloud cover inhibits study of localized transient phenomena such as Harmful Algal Blooms (HAB). A fleet of extended-deployment surface autonomous vehicles will enable in situ study of characteristics of HAB, coastal pollutants, and related phenomena. We have developed a multiplatform telesupervision architecture that supports adaptive reconfiguration based on environmental sensor inputs. Our system allows the autonomous repositioning of smart sensors for HAB study by networking a fleet of NOAA OASIS (Ocean Atmosphere Sensor Integration System) surface autonomous vehicles. In situ measurements intelligently modify the search for areas of high concentration. Inference Grid and complementary information-theoretic techniques support sensor fusion and analysis. Telesupervision supports sliding autonomy from high-level mission tasking, through vehicle and data monitoring, to teleoperation when direct human interaction is appropriate. This paper reports on experimental results from multi-platform tests conducted in the Chesapeake Bay and in Pittsburgh, Pennsylvania waters using OASIS platforms, autonomous kayaks, and multiple simulated platforms to conduct cooperative sensing of chlorophyll-a and water quality.

  14. Optimism and Adaptation to Multiple Sclerosis: What Does Optimism Mean?

    NARCIS (Netherlands)

    Fournier, M.; Ridder, D.T.D. de; Bensing, J.

    1999-01-01

    The aim of the present study was to determine the meaning of optimism by explicating the dimensions underlying the notion and their links to adjusting to MS. Seventy-three patients responded to optimism questionnaire s (i.e., the LOT, Generalized Self-Efficacy Scale) and outcome questionnaires.

  15. Optimism and adaptation to multiple sclerosis: what does optimism mean?

    NARCIS (Netherlands)

    Fournier, M.; Ridder, D. de; Bensing, J.

    1999-01-01

    The aim of the present study was to determine the meaning of optimism by explicating the dimensions underlying the notion and their links to adjusting to MS. Seventy-three patients responded to optimism questionnaires (i.e., the LOT, Generalized Self-Efficacy Scale) and outcome questionnaires. In

  16. Collaborative Object Framework for Adaptive System Optimization, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — TeamVision proposes that we research the feasibility of incorporating an adaptive object based optimization system into an existing multi-user object oriented...

  17. A two-hop based adaptive routing protocol for real-time wireless sensor networks.

    Science.gov (United States)

    Rachamalla, Sandhya; Kancherla, Anitha Sheela

    2016-01-01

    One of the most important and challenging issues in wireless sensor networks (WSNs) is to optimally manage the limited energy of nodes without degrading the routing efficiency. In this paper, we propose an energy-efficient adaptive routing mechanism for WSNs, which saves energy of nodes by removing the much delayed packets without degrading the real-time performance of the used routing protocol. It uses the adaptive transmission power algorithm which is based on the attenuation of the wireless link to improve the energy efficiency. The proposed routing mechanism can be associated with any geographic routing protocol and its performance is evaluated by integrating with the well known two-hop based real-time routing protocol, PATH and the resulting protocol is energy-efficient adaptive routing protocol (EE-ARP). The EE-ARP performs well in terms of energy consumption, deadline miss ratio, packet drop and end-to-end delay.

  18. Improved Differential Evolution Algorithm for Wireless Sensor Network Coverage Optimization

    Directory of Open Access Journals (Sweden)

    Xing Xu

    2014-04-01

    Full Text Available In order to serve for the ecological monitoring efficiency of Poyang Lake, an improved hybrid algorithm, mixed with differential evolution and particle swarm optimization, is proposed and applied to optimize the coverage problem of wireless sensor network. And then, the affect of the population size and the number of iterations on the coverage performance are both discussed and analyzed. The four kinds of statistical results about the coverage rate are obtained through lots of simulation experiments.

  19. Influence of model errors in optimal sensor placement

    Science.gov (United States)

    Vincenzi, Loris; Simonini, Laura

    2017-02-01

    The paper investigates the role of model errors and parametric uncertainties in optimal or near optimal sensor placements for structural health monitoring (SHM) and modal testing. The near optimal set of measurement locations is obtained by the Information Entropy theory; the results of placement process considerably depend on the so-called covariance matrix of prediction error as well as on the definition of the correlation function. A constant and an exponential correlation function depending on the distance between sensors are firstly assumed; then a proposal depending on both distance and modal vectors is presented. With reference to a simple case-study, the effect of model uncertainties on results is described and the reliability and the robustness of the proposed correlation function in the case of model errors are tested with reference to 2D and 3D benchmark case studies. A measure of the quality of the obtained sensor configuration is considered through the use of independent assessment criteria. In conclusion, the results obtained by applying the proposed procedure on a real 5-spans steel footbridge are described. The proposed method also allows to better estimate higher modes when the number of sensors is greater than the number of modes of interest. In addition, the results show a smaller variation in the sensor position when uncertainties occur.

  20. Multi-Sensor Information Fusion for Optimizing Electric Bicycle Routes Using a Swarm Intelligence Algorithm

    Science.gov (United States)

    Villarubia, Gabriel; De Paz, Juan F.; Bajo, Javier

    2017-01-01

    The use of electric bikes (e-bikes) has grown in popularity, especially in large cities where overcrowding and traffic congestion are common. This paper proposes an intelligent engine management system for e-bikes which uses the information collected from sensors to optimize battery energy and time. The intelligent engine management system consists of a built-in network of sensors in the e-bike, which is used for multi-sensor data fusion; the collected data is analysed and fused and on the basis of this information the system can provide the user with optimal and personalized assistance. The user is given recommendations related to battery consumption, sensors, and other parameters associated with the route travelled, such as duration, speed, or variation in altitude. To provide a user with these recommendations, artificial neural networks are used to estimate speed and consumption for each of the segments of a route. These estimates are incorporated into evolutionary algorithms in order to make the optimizations. A comparative analysis of the results obtained has been conducted for when routes were travelled with and without the optimization system. From the experiments, it is evident that the use of an engine management system results in significant energy and time savings. Moreover, user satisfaction increases as the level of assistance adapts to user behavior and the characteristics of the route. PMID:29088087

  1. Multi-Sensor Information Fusion for Optimizing Electric Bicycle Routes Using a Swarm Intelligence Algorithm

    Directory of Open Access Journals (Sweden)

    Daniel H. De La Iglesia

    2017-10-01

    Full Text Available The use of electric bikes (e-bikes has grown in popularity, especially in large cities where overcrowding and traffic congestion are common. This paper proposes an intelligent engine management system for e-bikes which uses the information collected from sensors to optimize battery energy and time. The intelligent engine management system consists of a built-in network of sensors in the e-bike, which is used for multi-sensor data fusion; the collected data is analysed and fused and on the basis of this information the system can provide the user with optimal and personalized assistance. The user is given recommendations related to battery consumption, sensors, and other parameters associated with the route travelled, such as duration, speed, or variation in altitude. To provide a user with these recommendations, artificial neural networks are used to estimate speed and consumption for each of the segments of a route. These estimates are incorporated into evolutionary algorithms in order to make the optimizations. A comparative analysis of the results obtained has been conducted for when routes were travelled with and without the optimization system. From the experiments, it is evident that the use of an engine management system results in significant energy and time savings. Moreover, user satisfaction increases as the level of assistance adapts to user behavior and the characteristics of the route.

  2. Multi-Sensor Information Fusion for Optimizing Electric Bicycle Routes Using a Swarm Intelligence Algorithm.

    Science.gov (United States)

    De La Iglesia, Daniel H; Villarrubia, Gabriel; De Paz, Juan F; Bajo, Javier

    2017-10-31

    The use of electric bikes (e-bikes) has grown in popularity, especially in large cities where overcrowding and traffic congestion are common. This paper proposes an intelligent engine management system for e-bikes which uses the information collected from sensors to optimize battery energy and time. The intelligent engine management system consists of a built-in network of sensors in the e-bike, which is used for multi-sensor data fusion; the collected data is analysed and fused and on the basis of this information the system can provide the user with optimal and personalized assistance. The user is given recommendations related to battery consumption, sensors, and other parameters associated with the route travelled, such as duration, speed, or variation in altitude. To provide a user with these recommendations, artificial neural networks are used to estimate speed and consumption for each of the segments of a route. These estimates are incorporated into evolutionary algorithms in order to make the optimizations. A comparative analysis of the results obtained has been conducted for when routes were travelled with and without the optimization system. From the experiments, it is evident that the use of an engine management system results in significant energy and time savings. Moreover, user satisfaction increases as the level of assistance adapts to user behavior and the characteristics of the route.

  3. Adaptive optimization and control using neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Mead, W.C.; Brown, S.K.; Jones, R.D.; Bowling, P.S.; Barnes, C.W.

    1993-10-22

    Recent work has demonstrated the ability of neural-network-based controllers to optimize and control machines with complex, non-linear, relatively unknown control spaces. We present a brief overview of neural networks via a taxonomy illustrating some capabilities of different kinds of neural networks. We present some successful control examples, particularly the optimization and control of a small-angle negative ion source.

  4. A model for optimal constrained adaptive testing

    NARCIS (Netherlands)

    van der Linden, Willem J.; Reese, Lynda M.

    2001-01-01

    A model for constrained computerized adaptive testing is proposed in which the information on the test at the ability estimate is maximized subject to a large variety of possible constraints on the contents of the test. At each item-selection step, a full test is first assembled to have maximum

  5. A model for optimal constrained adaptive testing

    NARCIS (Netherlands)

    van der Linden, Willem J.; Reese, Lynda M.

    1997-01-01

    A model for constrained computerized adaptive testing is proposed in which the information in the test at the ability estimate is maximized subject to a large variety of possible constraints on the contents of the test. At each item-selection step, a full test is first assembled to have maximum

  6. Adaptive LINE-P: An Adaptive Linear Energy Prediction Model for Wireless Sensor Network Nodes.

    Science.gov (United States)

    Ahmed, Faisal; Tamberg, Gert; Le Moullec, Yannick; Annus, Paul

    2018-04-05

    In the context of wireless sensor networks, energy prediction models are increasingly useful tools that can facilitate the power management of the wireless sensor network (WSN) nodes. However, most of the existing models suffer from the so-called fixed weighting parameter, which limits their applicability when it comes to, e.g., solar energy harvesters with varying characteristics. Thus, in this article we propose the Adaptive LINE-P (all cases) model that calculates adaptive weighting parameters based on the stored energy profiles. Furthermore, we also present a profile compression method to reduce the memory requirements. To determine the performance of our proposed model, we have used real data for the solar and wind energy profiles. The simulation results show that our model achieves 90-94% accuracy and that the compressed method reduces memory overheads by 50% as compared to state-of-the-art models.

  7. Optimization of autonomous magnetic field sensor consisting of giant magnetoimpedance sensor and surface acoustic wave transducer

    KAUST Repository

    Li, Bodong; Morsy, Ahmed Mohamed Aly; Kosel, Jü rgen

    2012-01-01

    This paper presents a novel autonomous thin film magnetic field sensor consisting of a tri-layer giant magnetoimpedance sensor and a surface acoustic wave transponder. Double and single electrode interdigital transducer (IDT) designs are employed and compared. The integrated sensor is fabricated using standard microfabrication technology. The results show the double electrode IDT has an advantage in terms of the sensitivity. In order to optimize the matching component, a simulation based on P-matrix is carried out. A maximum change of 2.4 dB of the reflection amplitude and a sensitivity of 0.34 dB/Oe are obtained experimentally. © 2012 IEEE.

  8. Optimization of autonomous magnetic field sensor consisting of giant magnetoimpedance sensor and surface acoustic wave transducer

    KAUST Repository

    Li, Bodong

    2012-11-01

    This paper presents a novel autonomous thin film magnetic field sensor consisting of a tri-layer giant magnetoimpedance sensor and a surface acoustic wave transponder. Double and single electrode interdigital transducer (IDT) designs are employed and compared. The integrated sensor is fabricated using standard microfabrication technology. The results show the double electrode IDT has an advantage in terms of the sensitivity. In order to optimize the matching component, a simulation based on P-matrix is carried out. A maximum change of 2.4 dB of the reflection amplitude and a sensitivity of 0.34 dB/Oe are obtained experimentally. © 2012 IEEE.

  9. Adaptive optimization for active queue management supporting TCP flows

    NARCIS (Netherlands)

    Baldi, S.; Kosmatopoulos, Elias B.; Pitsillides, Andreas; Lestas, Marios; Ioannou, Petros A.; Wan, Y.; Chiu, George; Johnson, Katie; Abramovitch, Danny

    2016-01-01

    An adaptive decentralized strategy for active queue management of TCP flows over communication networks is presented. The proposed strategy solves locally, at each link, an optimal control problem, minimizing a cost composed of residual capacity and buffer queue size. The solution of the optimal

  10. Adaptive RD Optimized Hybrid Sound Coding

    NARCIS (Netherlands)

    Schijndel, N.H. van; Bensa, J.; Christensen, M.G.; Colomes, C.; Edler, B.; Heusdens, R.; Jensen, J.; Jensen, S.H.; Kleijn, W.B.; Kot, V.; Kövesi, B.; Lindblom, J.; Massaloux, D.; Niamut, O.A.; Nordén, F.; Plasberg, J.H.; Vafin, R.; Virette, D.; Wübbolt, O.

    2008-01-01

    Traditionally, sound codecs have been developed with a particular application in mind, their performance being optimized for specific types of input signals, such as speech or audio (music), and application constraints, such as low bit rate, high quality, or low delay. There is, however, an

  11. Optimal Sensor Placement for Latticed Shell Structure Based on an Improved Particle Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Xun Zhang

    2014-01-01

    Full Text Available Optimal sensor placement is a key issue in the structural health monitoring of large-scale structures. However, some aspects in existing approaches require improvement, such as the empirical and unreliable selection of mode and sensor numbers and time-consuming computation. A novel improved particle swarm optimization (IPSO algorithm is proposed to address these problems. The approach firstly employs the cumulative effective modal mass participation ratio to select mode number. Three strategies are then adopted to improve the PSO algorithm. Finally, the IPSO algorithm is utilized to determine the optimal sensors number and configurations. A case study of a latticed shell model is implemented to verify the feasibility of the proposed algorithm and four different PSO algorithms. The effective independence method is also taken as a contrast experiment. The comparison results show that the optimal placement schemes obtained by the PSO algorithms are valid, and the proposed IPSO algorithm has better enhancement in convergence speed and precision.

  12. Global optimization in the adaptive assay of subterranean uranium nodules

    International Nuclear Information System (INIS)

    Vulkan, U.; Ben-Haim, Y.

    1989-01-01

    An adaptive assay is one in which the design of the assay system is modified during operation in response to measurements obtained on-line. The present work has two aims: to design an adaptive system for borehole assay of isolated subterranean uranium nodules, and to investigate globality of optimal design in adaptive assay. It is shown experimentally that reasonably accurate estimates of uranium mass are obtained for a wide range of nodule shapes, on the basis of an adaptive assay system based on a simple geomorphological model. Furthermore, two concepts are identified which underlie the optimal design of the assay system. The adaptive assay approach shows promise for successful measurement of spatially random material in many geophysical applications. (author)

  13. A Modified Adaptive Stochastic Resonance for Detecting Faint Signal in Sensors

    Directory of Open Access Journals (Sweden)

    Hengwei Li

    2007-02-01

    Full Text Available In this paper, an approach is presented to detect faint signals with strong noises in sensors by stochastic resonance (SR. We adopt the power spectrum as the evaluation tool of SR, which can be obtained by the fast Fourier transform (FFT. Furthermore, we introduce the adaptive filtering scheme to realize signal processing automatically. The key of the scheme is how to adjust the barrier height to satisfy the optimal condition of SR in the presence of any input. For the given input signal, we present an operable procedure to execute the adjustment scheme. An example utilizing one audio sensor to detect the fault information from the power supply is given. Simulation results show that th

  14. Optimal Sensor and Actuator Location for Unstable Systems

    DEFF Research Database (Denmark)

    Shaker, Hamid Reza; Tahavori, Maryamsadat

    2013-01-01

    on the processes. Dually the problem of placing actuators on the processes is equally important. In this paper, the problem of determining optimal sensor and actuator locations for the linear systems is addressed. The problem of the sensor locations is viewed as the problem of maximizing the output energy...... generated by a given state and for the actuator locations is viewed as the problem of minimizing the input energy required to reach a given state. Such design problems occur in many applications, and therefore have been studied extensively. Unfortunately, the results in this context, which have been...

  15. Using RGB-D sensors and evolutionary algorithms for the optimization of workstation layouts.

    Science.gov (United States)

    Diego-Mas, Jose Antonio; Poveda-Bautista, Rocio; Garzon-Leal, Diana

    2017-11-01

    RGB-D sensors can collect postural data in an automatized way. However, the application of these devices in real work environments requires overcoming problems such as lack of accuracy or body parts' occlusion. This work presents the use of RGB-D sensors and genetic algorithms for the optimization of workstation layouts. RGB-D sensors are used to capture workers' movements when they reach objects on workbenches. Collected data are then used to optimize workstation layout by means of genetic algorithms considering multiple ergonomic criteria. Results show that typical drawbacks of using RGB-D sensors for body tracking are not a problem for this application, and that the combination with intelligent algorithms can automatize the layout design process. The procedure described can be used to automatically suggest new layouts when workers or processes of production change, to adapt layouts to specific workers based on their ways to do the tasks, or to obtain layouts simultaneously optimized for several production processes. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. Intelligent fault recognition strategy based on adaptive optimized multiple centers

    Science.gov (United States)

    Zheng, Bo; Li, Yan-Feng; Huang, Hong-Zhong

    2018-06-01

    For the recognition principle based optimized single center, one important issue is that the data with nonlinear separatrix cannot be recognized accurately. In order to solve this problem, a novel recognition strategy based on adaptive optimized multiple centers is proposed in this paper. This strategy recognizes the data sets with nonlinear separatrix by the multiple centers. Meanwhile, the priority levels are introduced into the multi-objective optimization, including recognition accuracy, the quantity of optimized centers, and distance relationship. According to the characteristics of various data, the priority levels are adjusted to ensure the quantity of optimized centers adaptively and to keep the original accuracy. The proposed method is compared with other methods, including support vector machine (SVM), neural network, and Bayesian classifier. The results demonstrate that the proposed strategy has the same or even better recognition ability on different distribution characteristics of data.

  17. Optimal adaptation to extreme rainfalls under climate change

    Science.gov (United States)

    Rosbjerg, Dan

    2017-04-01

    More intense and frequent rainfalls have increased the number of urban flooding events in recent years, prompting adaptation efforts. Economic optimization is considered an efficient tool to decide on the design level for adaptation. The costs associated with a flooding to the T-year level and the annual capital and operational costs of adapting to this level are described with log-linear relations. The total flooding costs are developed as the expected annual damage of flooding above the T-year level plus the annual capital and operational costs for ensuring no flooding below the T-year level. The value of the return period T that corresponds to the minimum of the sum of these costs will then be the optimal adaptation level. The change in climate, however, is expected to continue in the next century, which calls for expansion of the above model. The change can be expressed in terms of a climate factor (the ratio between the future and the current design level) which is assumed to increase in time. This implies increasing costs of flooding in the future for many places in the world. The optimal adaptation level is found for immediate as well as for delayed adaptation. In these cases the optimum is determined by considering the net present value of the incurred costs during a sufficiently long time span. Immediate as well as delayed adaptation is considered.

  18. Optimal adaptation to extreme rainfalls in current and future climate

    Science.gov (United States)

    Rosbjerg, Dan

    2017-01-01

    More intense and frequent rainfalls have increased the number of urban flooding events in recent years, prompting adaptation efforts. Economic optimization is considered an efficient tool to decide on the design level for adaptation. The costs associated with a flooding to the T-year level and the annual capital and operational costs of adapting to this level are described with log-linear relations. The total flooding costs are developed as the expected annual damage of flooding above the T-year level plus the annual capital and operational costs for ensuring no flooding below the T-year level. The value of the return period T that corresponds to the minimum of the sum of these costs will then be the optimal adaptation level. The change in climate, however, is expected to continue in the next century, which calls for expansion of the above model. The change can be expressed in terms of a climate factor (the ratio between the future and the current design level) which is assumed to increase in time. This implies increasing costs of flooding in the future for many places in the world. The optimal adaptation level is found for immediate as well as for delayed adaptation. In these cases, the optimum is determined by considering the net present value of the incurred costs during a sufficiently long time-span. Immediate as well as delayed adaptation is considered.

  19. A Cluster-Based Dual-Adaptive Topology Control Approach in Wireless Sensor Networks

    Science.gov (United States)

    Gui, Jinsong; Zhou, Kai; Xiong, Naixue

    2016-01-01

    Multi-Input Multi-Output (MIMO) can improve wireless network performance. Sensors are usually single-antenna devices due to the high hardware complexity and cost, so several sensors are used to form virtual MIMO array, which is a desirable approach to efficiently take advantage of MIMO gains. Also, in large Wireless Sensor Networks (WSNs), clustering can improve the network scalability, which is an effective topology control approach. The existing virtual MIMO-based clustering schemes do not either fully explore the benefits of MIMO or adaptively determine the clustering ranges. Also, clustering mechanism needs to be further improved to enhance the cluster structure life. In this paper, we propose an improved clustering scheme for virtual MIMO-based topology construction (ICV-MIMO), which can determine adaptively not only the inter-cluster transmission modes but also the clustering ranges. Through the rational division of cluster head function and the optimization of cluster head selection criteria and information exchange process, the ICV-MIMO scheme effectively reduces the network energy consumption and improves the lifetime of the cluster structure when compared with the existing typical virtual MIMO-based scheme. Moreover, the message overhead and time complexity are still in the same order of magnitude. PMID:27681731

  20. A Cluster-Based Dual-Adaptive Topology Control Approach in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Jinsong Gui

    2016-09-01

    Full Text Available Multi-Input Multi-Output (MIMO can improve wireless network performance. Sensors are usually single-antenna devices due to the high hardware complexity and cost, so several sensors are used to form virtual MIMO array, which is a desirable approach to efficiently take advantage of MIMO gains. Also, in large Wireless Sensor Networks (WSNs, clustering can improve the network scalability, which is an effective topology control approach. The existing virtual MIMO-based clustering schemes do not either fully explore the benefits of MIMO or adaptively determine the clustering ranges. Also, clustering mechanism needs to be further improved to enhance the cluster structure life. In this paper, we propose an improved clustering scheme for virtual MIMO-based topology construction (ICV-MIMO, which can determine adaptively not only the inter-cluster transmission modes but also the clustering ranges. Through the rational division of cluster head function and the optimization of cluster head selection criteria and information exchange process, the ICV-MIMO scheme effectively reduces the network energy consumption and improves the lifetime of the cluster structure when compared with the existing typical virtual MIMO-based scheme. Moreover, the message overhead and time complexity are still in the same order of magnitude.

  1. A Cluster-Based Dual-Adaptive Topology Control Approach in Wireless Sensor Networks.

    Science.gov (United States)

    Gui, Jinsong; Zhou, Kai; Xiong, Naixue

    2016-09-25

    Multi-Input Multi-Output (MIMO) can improve wireless network performance. Sensors are usually single-antenna devices due to the high hardware complexity and cost, so several sensors are used to form virtual MIMO array, which is a desirable approach to efficiently take advantage of MIMO gains. Also, in large Wireless Sensor Networks (WSNs), clustering can improve the network scalability, which is an effective topology control approach. The existing virtual MIMO-based clustering schemes do not either fully explore the benefits of MIMO or adaptively determine the clustering ranges. Also, clustering mechanism needs to be further improved to enhance the cluster structure life. In this paper, we propose an improved clustering scheme for virtual MIMO-based topology construction (ICV-MIMO), which can determine adaptively not only the inter-cluster transmission modes but also the clustering ranges. Through the rational division of cluster head function and the optimization of cluster head selection criteria and information exchange process, the ICV-MIMO scheme effectively reduces the network energy consumption and improves the lifetime of the cluster structure when compared with the existing typical virtual MIMO-based scheme. Moreover, the message overhead and time complexity are still in the same order of magnitude.

  2. A self-optimizing scheme for energy balanced routing in Wireless Sensor Networks using SensorAnt.

    Science.gov (United States)

    Shamsan Saleh, Ahmed M; Ali, Borhanuddin Mohd; Rasid, Mohd Fadlee A; Ismail, Alyani

    2012-01-01

    Planning of energy-efficient protocols is critical for Wireless Sensor Networks (WSNs) because of the constraints on the sensor nodes' energy. The routing protocol should be able to provide uniform power dissipation during transmission to the sink node. In this paper, we present a self-optimization scheme for WSNs which is able to utilize and optimize the sensor nodes' resources, especially the batteries, to achieve balanced energy consumption across all sensor nodes. This method is based on the Ant Colony Optimization (ACO) metaheuristic which is adopted to enhance the paths with the best quality function. The assessment of this function depends on multi-criteria metrics such as the minimum residual battery power, hop count and average energy of both route and network. This method also distributes the traffic load of sensor nodes throughout the WSN leading to reduced energy usage, extended network life time and reduced packet loss. Simulation results show that our scheme performs much better than the Energy Efficient Ant-Based Routing (EEABR) in terms of energy consumption, balancing and efficiency.

  3. Adaptive treatment-length optimization in spatiobiologically integrated radiotherapy

    Science.gov (United States)

    Ajdari, Ali; Ghate, Archis; Kim, Minsun

    2018-04-01

    Recent theoretical research on spatiobiologically integrated radiotherapy has focused on optimization models that adapt fluence-maps to the evolution of tumor state, for example, cell densities, as observed in quantitative functional images acquired over the treatment course. We propose an optimization model that adapts the length of the treatment course as well as the fluence-maps to such imaged tumor state. Specifically, after observing the tumor cell densities at the beginning of a session, the treatment planner solves a group of convex optimization problems to determine an optimal number of remaining treatment sessions, and a corresponding optimal fluence-map for each of these sessions. The objective is to minimize the total number of tumor cells remaining (TNTCR) at the end of this proposed treatment course, subject to upper limits on the biologically effective dose delivered to the organs-at-risk. This fluence-map is administered in future sessions until the next image is available, and then the number of sessions and the fluence-map are re-optimized based on the latest cell density information. We demonstrate via computer simulations on five head-and-neck test cases that such adaptive treatment-length and fluence-map planning reduces the TNTCR and increases the biological effect on the tumor while employing shorter treatment courses, as compared to only adapting fluence-maps and using a pre-determined treatment course length based on one-size-fits-all guidelines.

  4. Direct aperture optimization for online adaptive radiation therapy

    International Nuclear Information System (INIS)

    Mestrovic, Ante; Milette, Marie-Pierre; Nichol, Alan; Clark, Brenda G.; Otto, Karl

    2007-01-01

    This paper is the first investigation of using direct aperture optimization (DAO) for online adaptive radiation therapy (ART). A geometrical model representing the anatomy of a typical prostate case was created. To simulate interfractional deformations, four different anatomical deformations were created by systematically deforming the original anatomy by various amounts (0.25, 0.50, 0.75, and 1.00 cm). We describe a series of techniques where the original treatment plan was adapted in order to correct for the deterioration of dose distribution quality caused by the anatomical deformations. We found that the average time needed to adapt the original plan to arrive at a clinically acceptable plan is roughly half of the time needed for a complete plan regeneration, for all four anatomical deformations. Furthermore, through modification of the DAO algorithm the optimization search space was reduced and the plan adaptation was significantly accelerated. For the first anatomical deformation (0.25 cm), the plan adaptation was six times more efficient than the complete plan regeneration. For the 0.50 and 0.75 cm deformations, the optimization efficiency was increased by a factor of roughly 3 compared to the complete plan regeneration. However, for the anatomical deformation of 1.00 cm, the reduction of the optimization search space during plan adaptation did not result in any efficiency improvement over the original (nonmodified) plan adaptation. The anatomical deformation of 1.00 cm demonstrates the limit of this approach. We propose an innovative approach to online ART in which the plan adaptation and radiation delivery are merged together and performed concurrently--adaptive radiation delivery (ARD). A fundamental advantage of ARD is the fact that radiation delivery can start almost immediately after image acquisition and evaluation. Most of the original plan adaptation is done during the radiation delivery, so the time spent adapting the original plan does not

  5. An Optimal Control Modification to Model-Reference Adaptive Control for Fast Adaptation

    Science.gov (United States)

    Nguyen, Nhan T.; Krishnakumar, Kalmanje; Boskovic, Jovan

    2008-01-01

    This paper presents a method that can achieve fast adaptation for a class of model-reference adaptive control. It is well-known that standard model-reference adaptive control exhibits high-gain control behaviors when a large adaptive gain is used to achieve fast adaptation in order to reduce tracking error rapidly. High gain control creates high-frequency oscillations that can excite unmodeled dynamics and can lead to instability. The fast adaptation approach is based on the minimization of the squares of the tracking error, which is formulated as an optimal control problem. The necessary condition of optimality is used to derive an adaptive law using the gradient method. This adaptive law is shown to result in uniform boundedness of the tracking error by means of the Lyapunov s direct method. Furthermore, this adaptive law allows a large adaptive gain to be used without causing undesired high-gain control effects. The method is shown to be more robust than standard model-reference adaptive control. Simulations demonstrate the effectiveness of the proposed method.

  6. Semantic Edge Based Disparity Estimation Using Adaptive Dynamic Programming for Binocular Sensors.

    Science.gov (United States)

    Zhu, Dongchen; Li, Jiamao; Wang, Xianshun; Peng, Jingquan; Shi, Wenjun; Zhang, Xiaolin

    2018-04-03

    Disparity calculation is crucial for binocular sensor ranging. The disparity estimation based on edges is an important branch in the research of sparse stereo matching and plays an important role in visual navigation. In this paper, we propose a robust sparse stereo matching method based on the semantic edges. Some simple matching costs are used first, and then a novel adaptive dynamic programming algorithm is proposed to obtain optimal solutions. This algorithm makes use of the disparity or semantic consistency constraint between the stereo images to adaptively search parameters, which can improve the robustness of our method. The proposed method is compared quantitatively and qualitatively with the traditional dynamic programming method, some dense stereo matching methods, and the advanced edge-based method respectively. Experiments show that our method can provide superior performance on the above comparison.

  7. Optimizing Input/Output Using Adaptive File System Policies

    Science.gov (United States)

    Madhyastha, Tara M.; Elford, Christopher L.; Reed, Daniel A.

    1996-01-01

    Parallel input/output characterization studies and experiments with flexible resource management algorithms indicate that adaptivity is crucial to file system performance. In this paper we propose an automatic technique for selecting and refining file system policies based on application access patterns and execution environment. An automatic classification framework allows the file system to select appropriate caching and pre-fetching policies, while performance sensors provide feedback used to tune policy parameters for specific system environments. To illustrate the potential performance improvements possible using adaptive file system policies, we present results from experiments involving classification-based and performance-based steering.

  8. Optimal adaptation to extreme rainfalls in current and future climate

    DEFF Research Database (Denmark)

    Rosbjerg, Dan

    2017-01-01

    . The value of the return period T that corresponds to the minimum of the sum of these costs will then be the optimal adaptation level. The change in climate, however, is expected to continue in the next century, which calls for expansion of the above model. The change can be expressed in terms of a climate......More intense and frequent rainfalls have increased the number of urban flooding events in recent years, prompting adaptation efforts. Economic optimization is considered an efficient tool to decide on the design level for adaptation. The costs associated with a flooding to the T-year level...... and the annual capital and operational costs of adapting to this level are described with log-linear relations. The total flooding costs are developed as the expected annual damage of flooding above the T-year level plus the annual capital and operational costs for ensuring no flooding below the T-year level...

  9. Optimized Feature Extraction for Temperature-Modulated Gas Sensors

    Directory of Open Access Journals (Sweden)

    Alexander Vergara

    2009-01-01

    Full Text Available One of the most serious limitations to the practical utilization of solid-state gas sensors is the drift of their signal. Even if drift is rooted in the chemical and physical processes occurring in the sensor, improved signal processing is generally considered as a methodology to increase sensors stability. Several studies evidenced the augmented stability of time variable signals elicited by the modulation of either the gas concentration or the operating temperature. Furthermore, when time-variable signals are used, the extraction of features can be accomplished in shorter time with respect to the time necessary to calculate the usual features defined in steady-state conditions. In this paper, we discuss the stability properties of distinct dynamic features using an array of metal oxide semiconductors gas sensors whose working temperature is modulated with optimized multisinusoidal signals. Experiments were aimed at measuring the dispersion of sensors features in repeated sequences of a limited number of experimental conditions. Results evidenced that the features extracted during the temperature modulation reduce the multidimensional data dispersion among repeated measurements. In particular, the Energy Signal Vector provided an almost constant classification rate along the time with respect to the temperature modulation.

  10. Self-adaptive calibration for staring infrared sensors

    Science.gov (United States)

    Kendall, William B.; Stocker, Alan D.

    1993-10-01

    This paper presents a new, self-adaptive technique for the correlation of non-uniformities (fixed-pattern noise) in high-density infrared focal-plane detector arrays. We have developed a new approach to non-uniformity correction in which we use multiple image frames of the scene itself, and take advantage of the aim-point wander caused by jitter, residual tracking errors, or deliberately induced motion. Such wander causes each detector in the array to view multiple scene elements, and each scene element to be viewed by multiple detectors. It is therefore possible to formulate (and solve) a set of simultaneous equations from which correction parameters can be computed for the detectors. We have tested our approach with actual images collected by the ARPA-sponsored MUSIC infrared sensor. For these tests we employed a 60-frame (0.75-second) sequence of terrain images for which an out-of-date calibration was deliberately used. The sensor was aimed at a point on the ground via an operator-assisted tracking system having a maximum aim point wander on the order of ten pixels. With these data, we were able to improve the calibration accuracy by a factor of approximately 100.

  11. Multiplatform Mission Planning and Operations Simulation Environment for Adaptive Remote Sensors

    Science.gov (United States)

    Smith, G.; Ball, C.; O'Brien, A.; Johnson, J. T.

    2017-12-01

    We report on the design and development of mission simulator libraries to support the emerging field of adaptive remote sensors. We will outline the current state of the art in adaptive sensing, provide analysis of how the current approach to performing observing system simulation experiments (OSSEs) must be changed to enable adaptive sensors for remote sensing, and present an architecture to enable their inclusion in future OSSEs.The growing potential of sensors capable of real-time adaptation of their operational parameters calls for a new class of mission planning and simulation tools. Existing simulation tools used in OSSEs assume a fixed set of sensor parameters in terms of observation geometry, frequencies used, resolution, or observation time, which allows simplifications to be made in the simulation and allows sensor observation errors to be characterized a priori. Adaptive sensors may vary these parameters depending on the details of the scene observed, so that sensor performance is not simple to model without conducting OSSE simulations that include sensor adaptation in response to varying observational environment. Adaptive sensors are of significance to resource-constrained, small satellite platforms because they enable the management of power and data volumes while providing methods for multiple sensors to collaborate.The new class of OSSEs required to utilize adaptive sensors located on multiple platforms must answer the question: If the physical act of sensing has a cost, how does the system determine if the science value of a measurement is worth the cost and how should that cost be shared among the collaborating sensors?Here we propose to answer this question using an architecture structured around three modules: ADAPT, MANAGE and COLLABORATE. The ADAPT module is a set of routines to facilitate modeling of adaptive sensors, the MANAGE module will implement a set of routines to facilitate simulations of sensor resource management when power and data

  12. Adaptive Naive Bayes classification for wireless sensor networks

    NARCIS (Netherlands)

    Zwartjes, G.J.

    2017-01-01

    Wireless Sensor Networks are tiny devices equipped with sensors and wireless communication. These devices observe environments and communicatie about these observations. Machine Learning techniques are of interest for Wireless Sensor Network applications since they can reduce the amount of needed

  13. Optimization of Emissions Sensor Networks Incorporating Tradeoffs Between Different Sensor Technologies

    Science.gov (United States)

    Nicholson, B.; Klise, K. A.; Laird, C. D.; Ravikumar, A. P.; Brandt, A. R.

    2017-12-01

    In order to comply with current and future methane emissions regulations, natural gas producers must develop emissions monitoring strategies for their facilities. In addition, regulators must develop air monitoring strategies over wide areas incorporating multiple facilities. However, in both of these cases, only a limited number of sensors can be deployed. With a wide variety of sensors to choose from in terms of cost, precision, accuracy, spatial coverage, location, orientation, and sampling frequency, it is difficult to design robust monitoring strategies for different scenarios while systematically considering the tradeoffs between different sensor technologies. In addition, the geography, weather, and other site specific conditions can have a large impact on the performance of a sensor network. In this work, we demonstrate methods for calculating optimal sensor networks. Our approach can incorporate tradeoffs between vastly different sensor technologies, optimize over typical wind conditions for a particular area, and consider different objectives such as time to detection or geographic coverage. We do this by pre-computing site specific scenarios and using them as input to a mixed-integer, stochastic programming problem that solves for a sensor network that maximizes the effectiveness of the detection program. Our methods and approach have been incorporated within an open source Python package called Chama with the goal of providing facility operators and regulators with tools for designing more effective and efficient monitoring systems. Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energys National Nuclear Security Administration under contract DE-NA0003525.

  14. A triaxial accelerometer monkey algorithm for optimal sensor placement in structural health monitoring

    Science.gov (United States)

    Jia, Jingqing; Feng, Shuo; Liu, Wei

    2015-06-01

    Optimal sensor placement (OSP) technique is a vital part of the field of structural health monitoring (SHM). Triaxial accelerometers have been widely used in the SHM of large-scale structures in recent years. Triaxial accelerometers must be placed in such a way that all of the important dynamic information is obtained. At the same time, the sensor configuration must be optimal, so that the test resources are conserved. The recommended practice is to select proper degrees of freedom (DOF) based upon several criteria and the triaxial accelerometers are placed at the nodes corresponding to these DOFs. This results in non-optimal placement of many accelerometers. A ‘triaxial accelerometer monkey algorithm’ (TAMA) is presented in this paper to solve OSP problems of triaxial accelerometers. The EFI3 measurement theory is modified and involved in the objective function to make it more adaptable in the OSP technique of triaxial accelerometers. A method of calculating the threshold value based on probability theory is proposed to improve the healthy rate of monkeys in a troop generation process. Meanwhile, the processes of harmony ladder climb and scanning watch jump are proposed and given in detail. Finally, Xinghai NO.1 Bridge in Dalian is implemented to demonstrate the effectiveness of TAMA. The final results obtained by TAMA are compared with those of the original monkey algorithm and EFI3 measurement, which show that TAMA can improve computational efficiency and get a better sensor configuration.

  15. Space Mapping With Adaptive Response Correction for Microwave Design Optimization

    DEFF Research Database (Denmark)

    Koziel, S.; Bandler, J.W.; Madsen, Kaj

    2009-01-01

    at which the term was calculated, as in the surrogate model optimization process. In this paper, an adaptive response correction scheme is presented to work in conjunction with space-mapping optimization algorithms. This technique is designed to alleviate the difficulties of the standard output space......Output space mapping is a technique introduced to enhance the robustness of the space-mapping optimization process in case the space-mapped coarse model cannot provide sufficient matching with the fine model. The technique often works very well; however, in some cases it fails. Especially...

  16. Optimization of DNA Sensor Model Based Nanostructured Graphene Using Particle Swarm Optimization Technique

    Directory of Open Access Journals (Sweden)

    Hediyeh Karimi

    2013-01-01

    Full Text Available It has been predicted that the nanomaterials of graphene will be among the candidate materials for postsilicon electronics due to their astonishing properties such as high carrier mobility, thermal conductivity, and biocompatibility. Graphene is a semimetal zero gap nanomaterial with demonstrated ability to be employed as an excellent candidate for DNA sensing. Graphene-based DNA sensors have been used to detect the DNA adsorption to examine a DNA concentration in an analyte solution. In particular, there is an essential need for developing the cost-effective DNA sensors holding the fact that it is suitable for the diagnosis of genetic or pathogenic diseases. In this paper, particle swarm optimization technique is employed to optimize the analytical model of a graphene-based DNA sensor which is used for electrical detection of DNA molecules. The results are reported for 5 different concentrations, covering a range from 0.01 nM to 500 nM. The comparison of the optimized model with the experimental data shows an accuracy of more than 95% which verifies that the optimized model is reliable for being used in any application of the graphene-based DNA sensor.

  17. ADAPTIVE ANT COLONY OPTIMIZATION BASED GRADIENT FOR EDGE DETECTION

    Directory of Open Access Journals (Sweden)

    Febri Liantoni

    2014-08-01

    Full Text Available Ant Colony Optimization (ACO is a nature-inspired optimization algorithm which is motivated by ants foraging behavior. Due to its favorable advantages, ACO has been widely used to solve several NP-hard problems, including edge detection. Since ACO initially distributes ants at random, it may cause imbalance ant distribution which later affects path discovery process. In this paper an adaptive ACO is proposed to optimize edge detection by adaptively distributing ant according to gradient analysis. Ants are adaptively distributed according to gradient ratio of each image regions. Region which has bigger gradient ratio, will have bigger number of ant distribution. Experiments are conducted using images from various datasets. Precision and recall are used to quantitatively evaluate performance of the proposed algorithm. Precision and recall of adaptive ACO reaches 76.98 % and 96.8 %. Whereas highest precision and recall for standard ACO are 69.74 % and 74.85 %. Experimental results show that the adaptive ACO outperforms standard ACO which randomly distributes ants.

  18. Correlations in state space can cause sub-optimal adaptation of optimal feedback control models.

    Science.gov (United States)

    Aprasoff, Jonathan; Donchin, Opher

    2012-04-01

    Control of our movements is apparently facilitated by an adaptive internal model in the cerebellum. It was long thought that this internal model implemented an adaptive inverse model and generated motor commands, but recently many reject that idea in favor of a forward model hypothesis. In theory, the forward model predicts upcoming state during reaching movements so the motor cortex can generate appropriate motor commands. Recent computational models of this process rely on the optimal feedback control (OFC) framework of control theory. OFC is a powerful tool for describing motor control, it does not describe adaptation. Some assume that adaptation of the forward model alone could explain motor adaptation, but this is widely understood to be overly simplistic. However, an adaptive optimal controller is difficult to implement. A reasonable alternative is to allow forward model adaptation to 're-tune' the controller. Our simulations show that, as expected, forward model adaptation alone does not produce optimal trajectories during reaching movements perturbed by force fields. However, they also show that re-optimizing the controller from the forward model can be sub-optimal. This is because, in a system with state correlations or redundancies, accurate prediction requires different information than optimal control. We find that adding noise to the movements that matches noise found in human data is enough to overcome this problem. However, since the state space for control of real movements is far more complex than in our simple simulations, the effects of correlations on re-adaptation of the controller from the forward model cannot be overlooked.

  19. An optimization-based framework for anisotropic simplex mesh adaptation

    Science.gov (United States)

    Yano, Masayuki; Darmofal, David L.

    2012-09-01

    We present a general framework for anisotropic h-adaptation of simplex meshes. Given a discretization and any element-wise, localizable error estimate, our adaptive method iterates toward a mesh that minimizes error for a given degrees of freedom. Utilizing mesh-metric duality, we consider a continuous optimization problem of the Riemannian metric tensor field that provides an anisotropic description of element sizes. First, our method performs a series of local solves to survey the behavior of the local error function. This information is then synthesized using an affine-invariant tensor manipulation framework to reconstruct an approximate gradient of the error function with respect to the metric tensor field. Finally, we perform gradient descent in the metric space to drive the mesh toward optimality. The method is first demonstrated to produce optimal anisotropic meshes minimizing the L2 projection error for a pair of canonical problems containing a singularity and a singular perturbation. The effectiveness of the framework is then demonstrated in the context of output-based adaptation for the advection-diffusion equation using a high-order discontinuous Galerkin discretization and the dual-weighted residual (DWR) error estimate. The method presented provides a unified framework for optimizing both the element size and anisotropy distribution using an a posteriori error estimate and enables efficient adaptation of anisotropic simplex meshes for high-order discretizations.

  20. Optimal Pid Controller Design Using Adaptive Vurpso Algorithm

    Science.gov (United States)

    Zirkohi, Majid Moradi

    2015-04-01

    The purpose of this paper is to improve theVelocity Update Relaxation Particle Swarm Optimization algorithm (VURPSO). The improved algorithm is called Adaptive VURPSO (AVURPSO) algorithm. Then, an optimal design of a Proportional-Integral-Derivative (PID) controller is obtained using the AVURPSO algorithm. An adaptive momentum factor is used to regulate a trade-off between the global and the local exploration abilities in the proposed algorithm. This operation helps the system to reach the optimal solution quickly and saves the computation time. Comparisons on the optimal PID controller design confirm the superiority of AVURPSO algorithm to the optimization algorithms mentioned in this paper namely the VURPSO algorithm, the Ant Colony algorithm, and the conventional approach. Comparisons on the speed of convergence confirm that the proposed algorithm has a faster convergence in a less computation time to yield a global optimum value. The proposed AVURPSO can be used in the diverse areas of optimization problems such as industrial planning, resource allocation, scheduling, decision making, pattern recognition and machine learning. The proposed AVURPSO algorithm is efficiently used to design an optimal PID controller.

  1. An Adaptive Unified Differential Evolution Algorithm for Global Optimization

    Energy Technology Data Exchange (ETDEWEB)

    Qiang, Ji; Mitchell, Chad

    2014-11-03

    In this paper, we propose a new adaptive unified differential evolution algorithm for single-objective global optimization. Instead of the multiple mutation strate- gies proposed in conventional differential evolution algorithms, this algorithm employs a single equation unifying multiple strategies into one expression. It has the virtue of mathematical simplicity and also provides users the flexibility for broader exploration of the space of mutation operators. By making all control parameters in the proposed algorithm self-adaptively evolve during the process of optimization, it frees the application users from the burden of choosing appro- priate control parameters and also improves the performance of the algorithm. In numerical tests using thirteen basic unimodal and multimodal functions, the proposed adaptive unified algorithm shows promising performance in compari- son to several conventional differential evolution algorithms.

  2. Time Optimized Algorithm for Web Document Presentation Adaptation

    DEFF Research Database (Denmark)

    Pan, Rong; Dolog, Peter

    2010-01-01

    Currently information on the web is accessed through different devices. Each device has its own properties such as resolution, size, and capabilities to display information in different format and so on. This calls for adaptation of information presentation for such platforms. This paper proposes...... content-optimized and time-optimized algorithms for information presentation adaptation for different devices based on its hierarchical model. The model is formalized in order to experiment with different algorithms.......Currently information on the web is accessed through different devices. Each device has its own properties such as resolution, size, and capabilities to display information in different format and so on. This calls for adaptation of information presentation for such platforms. This paper proposes...

  3. Optical Communication System for Remote Monitoring and Adaptive Control of Distributed Ground Sensors Exhibiting Collective Intelligence

    Energy Technology Data Exchange (ETDEWEB)

    Cameron, S.M.; Stantz, K.M.; Trahan, M.W.; Wagner, J.S.

    1998-11-01

    Comprehensive management of the battle-space has created new requirements in information management, communication, and interoperability as they effect surveillance and situational awareness. The objective of this proposal is to expand intelligent controls theory to produce a uniquely powerful implementation of distributed ground-based measurement incorporating both local collective behavior, and interoperative global optimization for sensor fusion and mission oversight. By using a layered hierarchal control architecture to orchestrate adaptive reconfiguration of autonomous robotic agents, we can improve overall robustness and functionality in dynamic tactical environments without information bottlenecks. In this concept, each sensor is equipped with a miniaturized optical reflectance modulator which is interactively monitored as a remote transponder using a covert laser communication protocol from a remote mothership or operative. Robot data-sharing at the ground level can be leveraged with global evaluation criteria, including terrain overlays and remote imaging data. Information sharing and distributed intelli- gence opens up a new class of remote-sensing applications in which small single-function autono- mous observers at the local level can collectively optimize and measure large scale ground-level signals. AS the need for coverage and the number of agents grows to improve spatial resolution, cooperative behavior orchestrated by a global situational awareness umbrella will be an essential ingredient to offset increasing bandwidth requirements within the net. A system of the type described in this proposal will be capable of sensitively detecting, tracking, and mapping spatial distributions of measurement signatures which are non-stationary or obscured by clutter and inter- fering obstacles by virtue of adaptive reconfiguration. This methodology could be used, for example, to field an adaptive ground-penetrating radar for detection of underground structures in

  4. Illumination adaptation with rapid-response color sensors

    Science.gov (United States)

    Zhang, Xinchi; Wang, Quan; Boyer, Kim L.

    2014-09-01

    Smart lighting solutions based on imaging sensors such as webcams or time-of-flight sensors suffer from rising privacy concerns. In this work, we use low-cost non-imaging color sensors to measure local luminous flux of different colors in an indoor space. These sensors have much higher data acquisition rate and are much cheaper than many o_-the-shelf commercial products. We have developed several applications with these sensors, including illumination feedback control and occupancy-driven lighting.

  5. Robust Optimal Adaptive Trajectory Tracking Control of Quadrotor Helicopter

    Directory of Open Access Journals (Sweden)

    M. Navabi

    Full Text Available Abstract This paper focuses on robust optimal adaptive control strategy to deal with tracking problem of a quadrotor unmanned aerial vehicle (UAV in presence of parametric uncertainties, actuator amplitude constraints, and unknown time-varying external disturbances. First, Lyapunov-based indirect adaptive controller optimized by particle swarm optimization (PSO is developed for multi-input multi-output (MIMO nonlinear quadrotor to prevent input constraints violation, and then disturbance observer-based control (DOBC technique is aggregated with the control system to attenuate the effects of disturbance generated by an exogenous system. The performance of synthesis control method is evaluated by a new performance index function in time-domain, and the stability analysis is carried out using Lyapunov theory. Finally, illustrative numerical simulations are conducted to demonstrate the effectiveness of the presented approach in altitude and attitude tracking under several conditions, including large time-varying uncertainty, exogenous disturbance, and control input constraints.

  6. Nonlinear adaptive optimization of biomass productivity in continuous bioreactors

    Energy Technology Data Exchange (ETDEWEB)

    Sauvaire, P; Mellichamp, D A; Agrawal, P [California Univ., Santa Barbara, CA (United States). Dept. of Chemical and Nuclear Engineering

    1991-11-01

    A novel on-line adaptive optimization algorithm is developed and applied to continuous biological reactors. The algorithm makes use of a simple nonlinear estimation model that relates either the cell-mass productivity or the cell-mass concentration to the dilution rate. On-line estimation is used to recursively identify the parameters in the nonlinear process model and to periodically calculate and steer the bioreactor to the dilution rate that yields optimum cell-mass productivity. Thus, the algorithm does not require an accurate process model, locates the optimum dilution rate online, and maintains the bioreactors at this optimum condition at all times. The features of the proposed new algorithm are compared with those of other adaptive optimization techniques presented in the literature. A detailed simulation study using three different microbial system models was conducted to illustrate the performance of the optimization algorithms. (orig.).

  7. Optimal power allocation of a sensor node under different rate constraints

    KAUST Repository

    Ayala Solares, Jose Roberto; Rezki, Zouheir; Alouini, Mohamed-Slim

    2012-01-01

    The optimal transmit power of a sensor node while satisfying different rate constraints is derived. First, an optimization problem with an instantaneous transmission rate constraint is addressed. Next, the optimal power is analyzed, but now

  8. Path Planning Algorithms for the Adaptive Sensor Fleet

    Science.gov (United States)

    Stoneking, Eric; Hosler, Jeff

    2005-01-01

    The Adaptive Sensor Fleet (ASF) is a general purpose fleet management and planning system being developed by NASA in coordination with NOAA. The current mission of ASF is to provide the capability for autonomous cooperative survey and sampling of dynamic oceanographic phenomena such as current systems and algae blooms. Each ASF vessel is a software model that represents a real world platform that carries a variety of sensors. The OASIS platform will provide the first physical vessel, outfitted with the systems and payloads necessary to execute the oceanographic observations described in this paper. The ASF architecture is being designed for extensibility to accommodate heterogenous fleet elements, and is not limited to using the OASIS platform to acquire data. This paper describes the path planning algorithms developed for the acquisition phase of a typical ASF task. Given a polygonal target region to be surveyed, the region is subdivided according to the number of vessels in the fleet. The subdivision algorithm seeks a solution in which all subregions have equal area and minimum mean radius. Once the subregions are defined, a dynamic programming method is used to find a minimum-time path for each vessel from its initial position to its assigned region. This path plan includes the effects of water currents as well as avoidance of known obstacles. A fleet-level planning algorithm then shuffles the individual vessel assignments to find the overall solution which puts all vessels in their assigned regions in the minimum time. This shuffle algorithm may be described as a process of elimination on the sorted list of permutations of a cost matrix. All these path planning algorithms are facilitated by discretizing the region of interest onto a hexagonal tiling.

  9. Optimal Node Placement in Underwater Wireless Sensor Networks

    KAUST Repository

    Felamban, M.

    2013-03-25

    Wireless Sensor Networks (WSN) are expected to play a vital role in the exploration and monitoring of underwater areas which are not easily reachable by humans. However, underwater communication via acoustic waves is subject to several performance limitations that are very different from those used for terresstrial networks. In this paper, we investigate node placement for building an initial underwater WSN infrastructure. We formulate this problem as a nonlinear mathematical program with the objective of minimizing the total transmission loss under a given number of sensor nodes and targeted coverage volume. The obtained solution is the location of each node represented via a truncated octahedron to fill out the 3D space. Experiments are conducted to verify the proposed formulation, which is solved using Matlab optimization tool. Simulation is also conducted using an ns-3 simulator, and the simulation results are consistent with the obtained results from mathematical model with less than 10% error.

  10. Big Data Reduction and Optimization in Sensor Monitoring Network

    Directory of Open Access Journals (Sweden)

    Bin He

    2014-01-01

    Full Text Available Wireless sensor networks (WSNs are increasingly being utilized to monitor the structural health of the underground subway tunnels, showing many promising advantages over traditional monitoring schemes. Meanwhile, with the increase of the network size, the system is incapable of dealing with big data to ensure efficient data communication, transmission, and storage. Being considered as a feasible solution to these issues, data compression can reduce the volume of data travelling between sensor nodes. In this paper, an optimization algorithm based on the spatial and temporal data compression is proposed to cope with these issues appearing in WSNs in the underground tunnel environment. The spatial and temporal correlation functions are introduced for the data compression and data recovery. It is verified that the proposed algorithm is applicable to WSNs in the underground tunnel.

  11. Hybrid Swarm Intelligence Optimization Approach for Optimal Data Storage Position Identification in Wireless Sensor Networks

    Science.gov (United States)

    Mohanasundaram, Ranganathan; Periasamy, Pappampalayam Sanmugam

    2015-01-01

    The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes) to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks. The works that have been carried out earlier did not utilize swarm intelligence based optimization approaches to find the optimal data storage positions. To achieve this goal, an efficient swam intelligence approach is used to choose suitable positions for a storage node. Thus, hybrid particle swarm optimization algorithm has been used to find the suitable positions for storage nodes while the total energy cost of data transmission is minimized. Clustering-based distributed data storage is utilized to solve clustering problem using fuzzy-C-means algorithm. This research work also considers the data rates and locations of multiple producers and consumers to find optimal data storage positions. The algorithm is implemented in a network simulator and the experimental results show that the proposed clustering and swarm intelligence based ODS strategy is more effective than the earlier approaches. PMID:25734182

  12. Weighted Optimization-Based Distributed Kalman Filter for Nonlinear Target Tracking in Collaborative Sensor Networks.

    Science.gov (United States)

    Chen, Jie; Li, Jiahong; Yang, Shuanghua; Deng, Fang

    2017-11-01

    The identification of the nonlinearity and coupling is crucial in nonlinear target tracking problem in collaborative sensor networks. According to the adaptive Kalman filtering (KF) method, the nonlinearity and coupling can be regarded as the model noise covariance, and estimated by minimizing the innovation or residual errors of the states. However, the method requires large time window of data to achieve reliable covariance measurement, making it impractical for nonlinear systems which are rapidly changing. To deal with the problem, a weighted optimization-based distributed KF algorithm (WODKF) is proposed in this paper. The algorithm enlarges the data size of each sensor by the received measurements and state estimates from its connected sensors instead of the time window. A new cost function is set as the weighted sum of the bias and oscillation of the state to estimate the "best" estimate of the model noise covariance. The bias and oscillation of the state of each sensor are estimated by polynomial fitting a time window of state estimates and measurements of the sensor and its neighbors weighted by the measurement noise covariance. The best estimate of the model noise covariance is computed by minimizing the weighted cost function using the exhaustive method. The sensor selection method is in addition to the algorithm to decrease the computation load of the filter and increase the scalability of the sensor network. The existence, suboptimality and stability analysis of the algorithm are given. The local probability data association method is used in the proposed algorithm for the multitarget tracking case. The algorithm is demonstrated in simulations on tracking examples for a random signal, one nonlinear target, and four nonlinear targets. Results show the feasibility and superiority of WODKF against other filtering algorithms for a large class of systems.

  13. Individual optimization of pacing sensors improves exercise capacity without influencing quality of life

    NARCIS (Netherlands)

    Erol-Yilmaz, Ayten; Schrama, Tim A.; Tanka, Jutta Schroeder; Tijssen, Jan G.; Wilde, Arthur A.; Tukkie, Raymond

    2005-01-01

    Introduction: Programmable pacemaker sensor features are frequently used in default setting. Limited data are available about the effect of sensor optimization on exercise capacity and quality of life (QOL), Influence of individual optimization of sensors on QOL and exercise tolerance was

  14. Adaptive surrogate model based multiobjective optimization for coastal aquifer management

    Science.gov (United States)

    Song, Jian; Yang, Yun; Wu, Jianfeng; Wu, Jichun; Sun, Xiaomin; Lin, Jin

    2018-06-01

    In this study, a novel surrogate model assisted multiobjective memetic algorithm (SMOMA) is developed for optimal pumping strategies of large-scale coastal groundwater problems. The proposed SMOMA integrates an efficient data-driven surrogate model with an improved non-dominated sorted genetic algorithm-II (NSGAII) that employs a local search operator to accelerate its convergence in optimization. The surrogate model based on Kernel Extreme Learning Machine (KELM) is developed and evaluated as an approximate simulator to generate the patterns of regional groundwater flow and salinity levels in coastal aquifers for reducing huge computational burden. The KELM model is adaptively trained during evolutionary search to satisfy desired fidelity level of surrogate so that it inhibits error accumulation of forecasting and results in correctly converging to true Pareto-optimal front. The proposed methodology is then applied to a large-scale coastal aquifer management in Baldwin County, Alabama. Objectives of minimizing the saltwater mass increase and maximizing the total pumping rate in the coastal aquifers are considered. The optimal solutions achieved by the proposed adaptive surrogate model are compared against those solutions obtained from one-shot surrogate model and original simulation model. The adaptive surrogate model does not only improve the prediction accuracy of Pareto-optimal solutions compared with those by the one-shot surrogate model, but also maintains the equivalent quality of Pareto-optimal solutions compared with those by NSGAII coupled with original simulation model, while retaining the advantage of surrogate models in reducing computational burden up to 94% of time-saving. This study shows that the proposed methodology is a computationally efficient and promising tool for multiobjective optimizations of coastal aquifer managements.

  15. Optimal time interval for induction of immunologic adaptive response

    International Nuclear Information System (INIS)

    Ju Guizhi; Song Chunhua; Liu Shuzheng

    1994-01-01

    The optimal time interval between prior dose (D1) and challenge dose (D2) for the induction of immunologic adaptive response was investigated. Kunming mice were exposed to 75 mGy X-rays at a dose rate of 12.5 mGy/min. 3, 6, 12, 24 or 60 h after the prior irradiation the mice were challenged with a dose of 1.5 Gy at a dose rate of 0.33 Gy/min. 18h after D2, the mice were sacrificed for examination of immunological parameters. The results showed that with an interval of 6 h between D1 and D2, the adaptive response of the reaction of splenocytes to LPS was induced, and with an interval of 12 h the adaptive responses of spontaneous incorporation of 3 H-TdR into thymocytes and the reaction of splenocytes to Con A and LPS were induced with 75 mGy prior irradiation. The data suggested that the optimal time intervals between D1 and D2 for the induction of immunologic adaptive response were 6 h and 12 h with a D1 of 75 mGy and a D2 of 1.5 Gy. The mechanism of immunologic adaptation following low dose radiation is discussed

  16. Topology optimization of pressure adaptive honeycomb for a morphing flap

    Science.gov (United States)

    Vos, Roelof; Scheepstra, Jan; Barrett, Ron

    2011-03-01

    The paper begins with a brief historical overview of pressure adaptive materials and structures. By examining avian anatomy, it is seen that pressure-adaptive structures have been used successfully in the Natural world to hold structural positions for extended periods of time and yet allow for dynamic shape changes from one flight state to the next. More modern pneumatic actuators, including FAA certified autopilot servoactuators are frequently used by aircraft around the world. Pneumatic artificial muscles (PAM) show good promise as aircraft actuators, but follow the traditional model of load concentration and distribution commonly found in aircraft. A new system is proposed which leaves distributed loads distributed and manipulates structures through a distributed actuator. By using Pressure Adaptive Honeycomb (PAH), it is shown that large structural deformations in excess of 50% strains can be achieved while maintaining full structural integrity and enabling secondary flight control mechanisms like flaps. The successful implementation of pressure-adaptive honeycomb in the trailing edge of a wing section sparked the motivation for subsequent research into the optimal topology of the pressure adaptive honeycomb within the trailing edge of a morphing flap. As an input for the optimization two known shapes are required: a desired shape in cruise configuration and a desired shape in landing configuration. In addition, the boundary conditions and load cases (including aerodynamic loads and internal pressure loads) should be specified for each condition. Finally, a set of six design variables is specified relating to the honeycomb and upper skin topology of the morphing flap. A finite-element model of the pressure-adaptive honeycomb structure is developed specifically tailored to generate fast but reliable results for a given combination of external loading, input variables, and boundary conditions. Based on two bench tests it is shown that this model correlates well

  17. A fully adaptive hybrid optimization of aircraft engine blades

    Science.gov (United States)

    Dumas, L.; Druez, B.; Lecerf, N.

    2009-10-01

    A new fully adaptive hybrid optimization method (AHM) has been developed and applied to an industrial problem in the field of the aircraft engine industry. The adaptivity of the coupling between a global search by a population-based method (Genetic Algorithms or Evolution Strategies) and the local search by a descent method has been particularly emphasized. On various analytical test cases, the AHM method overperforms the original global search method in terms of computational time and accuracy. The results obtained on the industrial case have also confirmed the interest of AHM for the design of new and original solutions in an affordable time.

  18. Nuclear fuel management optimization using adaptive evolutionary algorithms with heuristics

    International Nuclear Information System (INIS)

    Axmann, J.K.; Van de Velde, A.

    1996-01-01

    Adaptive Evolutionary Algorithms in combination with expert knowledge encoded in heuristics have proved to be a robust and powerful optimization method for the design of optimized PWR fuel loading pattern. Simple parallel algorithmic structures coupled with a low amount of communications between computer processor units in use makes it possible for workstation clusters to be employed efficiently. The extension of classic evolution strategies not only by new and alternative methods but also by the inclusion of heuristics with effects on the exchange probabilities of the fuel assemblies at specific core positions leads to the RELOPAT optimization code of the Technical University of Braunschweig. In combination with the new, neutron-physical 3D nodal core simulator PRISM developed by SIEMENS the PRIMO loading pattern optimization system has been designed. Highly promising results in the recalculation of known reload plans for German PWR's new lead to a commercially usable program. (author)

  19. Optimization by GRASP greedy randomized adaptive search procedures

    CERN Document Server

    Resende, Mauricio G C

    2016-01-01

    This is the first book to cover GRASP (Greedy Randomized Adaptive Search Procedures), a metaheuristic that has enjoyed wide success in practice with a broad range of applications to real-world combinatorial optimization problems. The state-of-the-art coverage and carefully crafted pedagogical style lends this book highly accessible as an introductory text not only to GRASP, but also to combinatorial optimization, greedy algorithms, local search, and path-relinking, as well as to heuristics and metaheuristics, in general. The focus is on algorithmic and computational aspects of applied optimization with GRASP with emphasis given to the end-user, providing sufficient information on the broad spectrum of advances in applied optimization with GRASP. For the more advanced reader, chapters on hybridization with path-relinking and parallel and continuous GRASP present these topics in a clear and concise fashion. Additionally, the book offers a very complete annotated bibliography of GRASP and combinatorial optimizat...

  20. Optimized Reputable Sensing Participants Extraction for Participatory Sensor Networks

    Directory of Open Access Journals (Sweden)

    Weiwei Yuan

    2014-01-01

    Full Text Available By collecting data via sensors embedded personal smart devices, sensing participants play a key role in participatory sensor networks. Using information provided by reputable sensing participants ensures the reliability of participatory sensing data. Setting a threshold for the reputation, and those whose reputations are bigger than this value are regarded as reputable. The bigger the threshold value is, the more reliable the extracted reputable sensing participant is. However, if the threshold value is too big, only very limited participatory sensing data can be involved. This may cause unexpected bias in information collection. Existing works did not consider the relationship between the reliability of extracted reputable sensing participants and the ratio of usable participatory sensing data. In this work, we propose a criterion for optimized reputable sensing participant extraction in participatory sensor networks. This is achieved based on the mathematical analysis on the ratio of available participatory sensing data and the reliability of extracted reputable sensing participants. Our suggested threshold value for reputable sensing participant extraction is only related to the power of sensing participant’s reputation distribution. It is easy to be applied in real applications. Simulation results tested on real application data further verified the effectiveness of our proposed method.

  1. Optimal Node Placement in Underwater Acoustic Sensor Network

    KAUST Repository

    Felemban, Muhamad

    2011-10-01

    Almost 70% of planet Earth is covered by water. A large percentage of underwater environment is unexplored. In the past two decades, there has been an increase in the interest of exploring and monitoring underwater life among scientists and in industry. Underwater operations are extremely difficult due to the lack of cheap and efficient means. Recently, Wireless Sensor Networks have been introduced in underwater environment applications. However, underwater communication via acoustic waves is subject to several performance limitations, which makes the relevant research issues very different from those on land. In this thesis, we investigate node placement for building an initial Underwater Wireless Sensor Network infrastructure. Firstly, we formulated the problem into a nonlinear mathematic program with objectives of minimizing the total transmission loss under a given number of sensor nodes and targeted volume. We conducted experiments to verify the proposed formulation, which is solved using Matlab optimization tool. We represented each node with a truncated octahedron to fill out the 3D space. The truncated octahedrons are tiled in the 3D space with each node in the center where locations of the nodes are given using 3D coordinates. Results are supported using ns-3 simulator. Results from simulation are consistent with the obtained results from mathematical model with less than 10% error.

  2. Optimal sensor placement for modal testing on wind turbines

    Science.gov (United States)

    Schulze, Andreas; Zierath, János; Rosenow, Sven-Erik; Bockhahn, Reik; Rachholz, Roman; Woernle, Christoph

    2016-09-01

    The mechanical design of wind turbines requires a profound understanding of the dynamic behaviour. Even though highly detailed simulation models are already in use to support wind turbine design, modal testing on a real prototype is irreplaceable to identify site-specific conditions such as the stiffness of the tower foundation. Correct identification of the mode shapes of a complex mechanical structure much depends on the placement of the sensors. For operational modal analysis of a 3 MW wind turbine with a 120 m rotor on a 100 m tower developed by W2E Wind to Energy, algorithms for optimal placement of acceleration sensors are applied. The mode shapes used for the optimisation are calculated by means of a detailed flexible multibody model of the wind turbine. Among the three algorithms in this study, the genetic algorithm with weighted off-diagonal criterion yields the sensor configuration with the highest quality. The ongoing measurements on the prototype will be the basis for the development of optimised wind turbine designs.

  3. Multihopping Multilevel Clustering Heterogeneity-Sensitive Optimized Routing Protocol for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Muhammad Aslam

    2017-01-01

    Full Text Available Effective utilization of energy resources in Wireless Sensor Networks (WSNs has become challenging under uncertain distributed cluster-formation and single-hop intercluster communication capabilities. So, sensor nodes are forced to operate at expensive full rate transmission power level continuously during whole network operation. These challenging network environments experience unwanted phenomena of drastic energy consumption and packet drop. In this paper, we propose an adaptive immune Multihopping Multilevel Clustering (MHMLC protocol that executes a Hybrid Clustering Algorithm (HCA to perform optimal centralized selection of Cluster-Heads (CHs within radius of centrally located Base Station (BS and distributed CHs selection in the rest of network area. HCA of MHMLC also produces optimal intermediate CHs for intercluster multihop communications that develop heterogeneity-aware economical links. This hybrid cluster-formation facilitates the sensors to function at short range transmission power level that enhances link quality and avoids packet drop. The simulation environments produce fair comparison among proposed MHMLC and existing state-of-the-art routing protocols. Experimental results give significant evidence of better performance of the proposed model in terms of network lifetime, stability period, and data delivery ratio.

  4. Optimal and adaptive methods of processing hydroacoustic signals (review)

    Science.gov (United States)

    Malyshkin, G. S.; Sidel'nikov, G. B.

    2014-09-01

    Different methods of optimal and adaptive processing of hydroacoustic signals for multipath propagation and scattering are considered. Advantages and drawbacks of the classical adaptive (Capon, MUSIC, and Johnson) algorithms and "fast" projection algorithms are analyzed for the case of multipath propagation and scattering of strong signals. The classical optimal approaches to detecting multipath signals are presented. A mechanism of controlled normalization of strong signals is proposed to automatically detect weak signals. The results of simulating the operation of different detection algorithms for a linear equidistant array under multipath propagation and scattering are presented. An automatic detector is analyzed, which is based on classical or fast projection algorithms, which estimates the background proceeding from median filtering or the method of bilateral spatial contrast.

  5. Adaptive dynamic programming with applications in optimal control

    CERN Document Server

    Liu, Derong; Wang, Ding; Yang, Xiong; Li, Hongliang

    2017-01-01

    This book covers the most recent developments in adaptive dynamic programming (ADP). The text begins with a thorough background review of ADP making sure that readers are sufficiently familiar with the fundamentals. In the core of the book, the authors address first discrete- and then continuous-time systems. Coverage of discrete-time systems starts with a more general form of value iteration to demonstrate its convergence, optimality, and stability with complete and thorough theoretical analysis. A more realistic form of value iteration is studied where value function approximations are assumed to have finite errors. Adaptive Dynamic Programming also details another avenue of the ADP approach: policy iteration. Both basic and generalized forms of policy-iteration-based ADP are studied with complete and thorough theoretical analysis in terms of convergence, optimality, stability, and error bounds. Among continuous-time systems, the control of affine and nonaffine nonlinear systems is studied using the ADP app...

  6. Optimal Sparse Upstream Sensor Placement for Hydrokinetic Turbines

    Science.gov (United States)

    Cavagnaro, Robert; Strom, Benjamin; Ross, Hannah; Hill, Craig; Polagye, Brian

    2016-11-01

    Accurate measurement of the flow field incident upon a hydrokinetic turbine is critical for performance evaluation during testing and setting boundary conditions in simulation. Additionally, turbine controllers may leverage real-time flow measurements. Particle image velocimetry (PIV) is capable of rendering a flow field over a wide spatial domain in a controlled, laboratory environment. However, PIV's lack of suitability for natural marine environments, high cost, and intensive post-processing diminish its potential for control applications. Conversely, sensors such as acoustic Doppler velocimeters (ADVs), are designed for field deployment and real-time measurement, but over a small spatial domain. Sparsity-promoting regression analysis such as LASSO is utilized to improve the efficacy of point measurements for real-time applications by determining optimal spatial placement for a small number of ADVs using a training set of PIV velocity fields and turbine data. The study is conducted in a flume (0.8 m2 cross-sectional area, 1 m/s flow) with laboratory-scale axial and cross-flow turbines. Predicted turbine performance utilizing the optimal sparse sensor network and associated regression model is compared to actual performance with corresponding PIV measurements.

  7. Adaptive electric potential sensors for smart signal acquisition and processing

    International Nuclear Information System (INIS)

    Prance, R J; Beardsmore-Rust, S; Prance, H; Harland, C J; Stiffell, P B

    2007-01-01

    Current applications of the Electric Potential Sensor operate in a strongly (capacitively) coupled limit, with the sensor physically close to or touching the source. This mode of operation screens the sensor effectively from the majority of external noise. To date however the full capability of these sensors operating in a remote mode has not been realised outside of a screened environment (Faraday cage). This paper describes the results of preliminary work in tailoring the response of the sensors to particular signals and so reject background noise, thereby enhancing both the dynamic range and signal to noise ratio significantly

  8. Adaptive electric potential sensors for smart signal acquisition and processing

    Science.gov (United States)

    Prance, R. J.; Beardsmore-Rust, S.; Prance, H.; Harland, C. J.; Stiffell, P. B.

    2007-07-01

    Current applications of the Electric Potential Sensor operate in a strongly (capacitively) coupled limit, with the sensor physically close to or touching the source. This mode of operation screens the sensor effectively from the majority of external noise. To date however the full capability of these sensors operating in a remote mode has not been realised outside of a screened environment (Faraday cage). This paper describes the results of preliminary work in tailoring the response of the sensors to particular signals and so reject background noise, thereby enhancing both the dynamic range and signal to noise ratio significantly.

  9. Correlations in state space can cause sub-optimal adaptation of optimal feedback control models

    OpenAIRE

    Aprasoff, Jonathan; Donchin, Opher

    2011-01-01

    Control of our movements is apparently facilitated by an adaptive internal model in the cerebellum. It was long thought that this internal model implemented an adaptive inverse model and generated motor commands, but recently many reject that idea in favor of a forward model hypothesis. In theory, the forward model predicts upcoming state during reaching movements so the motor cortex can generate appropriate motor commands. Recent computational models of this process rely on the optimal feedb...

  10. A new adaptive hybrid electromagnetic damper: modelling, optimization, and experiment

    International Nuclear Information System (INIS)

    Asadi, Ehsan; Ribeiro, Roberto; Behrad Khamesee, Mir; Khajepour, Amir

    2015-01-01

    This paper presents the development of a new electromagnetic hybrid damper which provides regenerative adaptive damping force for various applications. Recently, the introduction of electromagnetic technologies to the damping systems has provided researchers with new opportunities for the realization of adaptive semi-active damping systems with the added benefit of energy recovery. In this research, a hybrid electromagnetic damper is proposed. The hybrid damper is configured to operate with viscous and electromagnetic subsystems. The viscous medium provides a bias and fail-safe damping force while the electromagnetic component adds adaptability and the capacity for regeneration to the hybrid design. The electromagnetic component is modeled and analyzed using analytical (lumped equivalent magnetic circuit) and electromagnetic finite element method (FEM) (COMSOL ® software package) approaches. By implementing both modeling approaches, an optimization for the geometric aspects of the electromagnetic subsystem is obtained. Based on the proposed electromagnetic hybrid damping concept and the preliminary optimization solution, a prototype is designed and fabricated. A good agreement is observed between the experimental and FEM results for the magnetic field distribution and electromagnetic damping forces. These results validate the accuracy of the modeling approach and the preliminary optimization solution. An analytical model is also presented for viscous damping force, and is compared with experimental results The results show that the damper is able to produce damping coefficients of 1300 and 0–238 N s m −1 through the viscous and electromagnetic components, respectively. (paper)

  11. Adaptive multi-node multiple input and multiple output (MIMO) transmission for mobile wireless multimedia sensor networks.

    Science.gov (United States)

    Cho, Sunghyun; Choi, Ji-Woong; You, Cheolwoo

    2013-10-02

    Mobile wireless multimedia sensor networks (WMSNs), which consist of mobile sink or sensor nodes and use rich sensing information, require much faster and more reliable wireless links than static wireless sensor networks (WSNs). This paper proposes an adaptive multi-node (MN) multiple input and multiple output (MIMO) transmission to improve the transmission reliability and capacity of mobile sink nodes when they experience spatial correlation. Unlike conventional single-node (SN) MIMO transmission, the proposed scheme considers the use of transmission antennas from more than two sensor nodes. To find an optimal antenna set and a MIMO transmission scheme, a MN MIMO channel model is introduced first, followed by derivation of closed-form ergodic capacity expressions with different MIMO transmission schemes, such as space-time transmit diversity coding and spatial multiplexing. The capacity varies according to the antenna correlation and the path gain from multiple sensor nodes. Based on these statistical results, we propose an adaptive MIMO mode and antenna set switching algorithm that maximizes the ergodic capacity of mobile sink nodes. The ergodic capacity of the proposed scheme is compared with conventional SN MIMO schemes, where the gain increases as the antenna correlation and path gain ratio increase.

  12. Adaptive Multi-Agent Systems for Constrained Optimization

    Science.gov (United States)

    Macready, William; Bieniawski, Stefan; Wolpert, David H.

    2004-01-01

    Product Distribution (PD) theory is a new framework for analyzing and controlling distributed systems. Here we demonstrate its use for distributed stochastic optimization. First we review one motivation of PD theory, as the information-theoretic extension of conventional full-rationality game theory to the case of bounded rational agents. In this extension the equilibrium of the game is the optimizer of a Lagrangian of the (probability distribution of) the joint state of the agents. When the game in question is a team game with constraints, that equilibrium optimizes the expected value of the team game utility, subject to those constraints. The updating of the Lagrange parameters in the Lagrangian can be viewed as a form of automated annealing, that focuses the MAS more and more on the optimal pure strategy. This provides a simple way to map the solution of any constrained optimization problem onto the equilibrium of a Multi-Agent System (MAS). We present computer experiments involving both the Queen s problem and K-SAT validating the predictions of PD theory and its use for off-the-shelf distributed adaptive optimization.

  13. Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization

    Science.gov (United States)

    Abdulameer, Mohammed Hasan; Othman, Zulaiha Ali

    2014-01-01

    Existing face recognition methods utilize particle swarm optimizer (PSO) and opposition based particle swarm optimizer (OPSO) to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO) technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM). In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented. PMID:24790584

  14. Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Mohammed Hasan Abdulameer

    2014-01-01

    Full Text Available Existing face recognition methods utilize particle swarm optimizer (PSO and opposition based particle swarm optimizer (OPSO to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM. In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented.

  15. Optimal updating magnitude in adaptive flat-distribution sampling.

    Science.gov (United States)

    Zhang, Cheng; Drake, Justin A; Ma, Jianpeng; Pettitt, B Montgomery

    2017-11-07

    We present a study on the optimization of the updating magnitude for a class of free energy methods based on flat-distribution sampling, including the Wang-Landau (WL) algorithm and metadynamics. These methods rely on adaptive construction of a bias potential that offsets the potential of mean force by histogram-based updates. The convergence of the bias potential can be improved by decreasing the updating magnitude with an optimal schedule. We show that while the asymptotically optimal schedule for the single-bin updating scheme (commonly used in the WL algorithm) is given by the known inverse-time formula, that for the Gaussian updating scheme (commonly used in metadynamics) is often more complex. We further show that the single-bin updating scheme is optimal for very long simulations, and it can be generalized to a class of bandpass updating schemes that are similarly optimal. These bandpass updating schemes target only a few long-range distribution modes and their optimal schedule is also given by the inverse-time formula. Constructed from orthogonal polynomials, the bandpass updating schemes generalize the WL and Langfeld-Lucini-Rago algorithms as an automatic parameter tuning scheme for umbrella sampling.

  16. arXiv Signal coupling to embedded pitch adapters in silicon sensors

    CERN Document Server

    Artuso, M.; Bezshyiko, I.; Blusk, S.; Bruendler, R.; Bugiel, S.; Dasgupta, R.; Dendek, A.; Dey, B.; Ely, S.; Lionetto, F.; Petruzzo, M.; Polyakov, I.; Rudolph, M.; Schindler, H.; Steinkamp, O.; Stone, S.

    2018-01-01

    We have examined the effects of embedded pitch adapters on signal formation in n-substrate silicon microstrip sensors with data from beam tests and simulation. According to simulation, the presence of the pitch adapter metal layer changes the electric field inside the sensor, resulting in slowed signal formation on the nearby strips and a pick-up effect on the pitch adapter. This can result in an inefficiency to detect particles passing through the pitch adapter region. All these effects have been observed in the beam test data.

  17. A Long-Distance RF-Powered Sensor Node with Adaptive Power Management for IoT Applications.

    Science.gov (United States)

    Pizzotti, Matteo; Perilli, Luca; Del Prete, Massimo; Fabbri, Davide; Canegallo, Roberto; Dini, Michele; Masotti, Diego; Costanzo, Alessandra; Franchi Scarselli, Eleonora; Romani, Aldo

    2017-07-28

    We present a self-sustained battery-less multi-sensor platform with RF harvesting capability down to -17 dBm and implementing a standard DASH7 wireless communication interface. The node operates at distances up to 17 m from a 2 W UHF carrier. RF power transfer allows operation when common energy scavenging sources (e.g., sun, heat, etc.) are not available, while the DASH7 communication protocol makes it fully compatible with a standard IoT infrastructure. An optimized energy-harvesting module has been designed, including a rectifying antenna (rectenna) and an integrated nano-power DC/DC converter performing maximum-power-point-tracking (MPPT). A nonlinear/electromagnetic co-design procedure is adopted to design the rectenna, which is optimized to operate at ultra-low power levels. An ultra-low power microcontroller controls on-board sensors and wireless protocol, to adapt the power consumption to the available detected power by changing wake-up policies. As a result, adaptive behavior can be observed in the designed platform, to the extent that the transmission data rate is dynamically determined by RF power. Among the novel features of the system, we highlight the use of nano-power energy harvesting, the implementation of specific hardware/software wake-up policies, optimized algorithms for best sampling rate implementation, and adaptive behavior by the node based on the power received.

  18. A Long-Distance RF-Powered Sensor Node with Adaptive Power Management for IoT Applications

    Directory of Open Access Journals (Sweden)

    Matteo Pizzotti

    2017-07-01

    Full Text Available We present a self-sustained battery-less multi-sensor platform with RF harvesting capability down to −17 dBm and implementing a standard DASH7 wireless communication interface. The node operates at distances up to 17 m from a 2 W UHF carrier. RF power transfer allows operation when common energy scavenging sources (e.g., sun, heat, etc. are not available, while the DASH7 communication protocol makes it fully compatible with a standard IoT infrastructure. An optimized energy-harvesting module has been designed, including a rectifying antenna (rectenna and an integrated nano-power DC/DC converter performing maximum-power-point-tracking (MPPT. A nonlinear/electromagnetic co-design procedure is adopted to design the rectenna, which is optimized to operate at ultra-low power levels. An ultra-low power microcontroller controls on-board sensors and wireless protocol, to adapt the power consumption to the available detected power by changing wake-up policies. As a result, adaptive behavior can be observed in the designed platform, to the extent that the transmission data rate is dynamically determined by RF power. Among the novel features of the system, we highlight the use of nano-power energy harvesting, the implementation of specific hardware/software wake-up policies, optimized algorithms for best sampling rate implementation, and adaptive behavior by the node based on the power received.

  19. An Adaptive Multiobjective Particle Swarm Optimization Based on Multiple Adaptive Methods.

    Science.gov (United States)

    Han, Honggui; Lu, Wei; Qiao, Junfei

    2017-09-01

    Multiobjective particle swarm optimization (MOPSO) algorithms have attracted much attention for their promising performance in solving multiobjective optimization problems (MOPs). In this paper, an adaptive MOPSO (AMOPSO) algorithm, based on a hybrid framework of the solution distribution entropy and population spacing (SP) information, is developed to improve the search performance in terms of convergent speed and precision. First, an adaptive global best (gBest) selection mechanism, based on the solution distribution entropy, is introduced to analyze the evolutionary tendency and balance the diversity and convergence of nondominated solutions in the archive. Second, an adaptive flight parameter adjustment mechanism, using the population SP information, is proposed to obtain the distribution of particles with suitable diversity and convergence, which can balance the global exploration and local exploitation abilities of the particles. Third, based on the gBest selection mechanism and the adaptive flight parameter mechanism, this proposed AMOPSO algorithm not only has high accuracy, but also attain a set of optimal solutions with better diversity. Finally, the performance of the proposed AMOPSO algorithm is validated and compared with other five state-of-the-art algorithms on a number of benchmark problems and water distribution system. The experimental results validate the effectiveness of the proposed AMOPSO algorithm, as well as demonstrate that AMOPSO outperforms other MOPSO algorithms in solving MOPs.

  20. A probabilistic approach for optimal sensor allocation in structural health monitoring

    International Nuclear Information System (INIS)

    Azarbayejani, M; Reda Taha, M M; El-Osery, A I; Choi, K K

    2008-01-01

    Recent advances in sensor technology promote using large sensor networks to efficiently and economically monitor, identify and quantify damage in structures. In structural health monitoring (SHM) systems, the effectiveness and reliability of the sensor network are crucial to determine the optimal number and locations of sensors in SHM systems. Here, we suggest a probabilistic approach for identifying the optimal number and locations of sensors for SHM. We demonstrate a methodology to establish the probability distribution function that identifies the optimal sensor locations such that damage detection is enhanced. The approach is based on using the weights of a neural network trained from simulations using a priori knowledge about damage locations and damage severities to generate a normalized probability distribution function for optimal sensor allocation. We also demonstrate that the optimal sensor network can be related to the highest probability of detection (POD). The redundancy of the proposed sensor network is examined using a 'leave one sensor out' analysis. A prestressed concrete bridge is selected as a case study to demonstrate the effectiveness of the proposed method. The results show that the proposed approach can provide a robust design for sensor networks that are more efficient than a uniform distribution of sensors on a structure

  1. Multimode Adaptable Microwave Radar Sensor Based on Leaky-Wave Antennas

    Czech Academy of Sciences Publication Activity Database

    Hudec, P.; Pánek, Petr; Jeník, V.

    2017-01-01

    Roč. 65, č. 9 (2017), s. 3464-3473 ISSN 0018-9480 Institutional support: RVO:67985882 Keywords : adaptable sensor * low-range radar * multimode sensor Subject RIV: JA - Electronics ; Optoelectronics, Electrical Engineering OBOR OECD: Electrical and electronic engineering Impact factor: 2.897, year: 2016

  2. Discrete Particle Swarm Optimization Routing Protocol for Wireless Sensor Networks with Multiple Mobile Sinks.

    Science.gov (United States)

    Yang, Jin; Liu, Fagui; Cao, Jianneng; Wang, Liangming

    2016-07-14

    Mobile sinks can achieve load-balancing and energy-consumption balancing across the wireless sensor networks (WSNs). However, the frequent change of the paths between source nodes and the sinks caused by sink mobility introduces significant overhead in terms of energy and packet delays. To enhance network performance of WSNs with mobile sinks (MWSNs), we present an efficient routing strategy, which is formulated as an optimization problem and employs the particle swarm optimization algorithm (PSO) to build the optimal routing paths. However, the conventional PSO is insufficient to solve discrete routing optimization problems. Therefore, a novel greedy discrete particle swarm optimization with memory (GMDPSO) is put forward to address this problem. In the GMDPSO, particle's position and velocity of traditional PSO are redefined under discrete MWSNs scenario. Particle updating rule is also reconsidered based on the subnetwork topology of MWSNs. Besides, by improving the greedy forwarding routing, a greedy search strategy is designed to drive particles to find a better position quickly. Furthermore, searching history is memorized to accelerate convergence. Simulation results demonstrate that our new protocol significantly improves the robustness and adapts to rapid topological changes with multiple mobile sinks, while efficiently reducing the communication overhead and the energy consumption.

  3. Improved laser-based triangulation sensor with enhanced range and resolution through adaptive optics-based active beam control.

    Science.gov (United States)

    Reza, Syed Azer; Khwaja, Tariq Shamim; Mazhar, Mohsin Ali; Niazi, Haris Khan; Nawab, Rahma

    2017-07-20

    Various existing target ranging techniques are limited in terms of the dynamic range of operation and measurement resolution. These limitations arise as a result of a particular measurement methodology, the finite processing capability of the hardware components deployed within the sensor module, and the medium through which the target is viewed. Generally, improving the sensor range adversely affects its resolution and vice versa. Often, a distance sensor is designed for an optimal range/resolution setting depending on its intended application. Optical triangulation is broadly classified as a spatial-signal-processing-based ranging technique and measures target distance from the location of the reflected spot on a position sensitive detector (PSD). In most triangulation sensors that use lasers as a light source, beam divergence-which severely affects sensor measurement range-is often ignored in calculations. In this paper, we first discuss in detail the limitations to ranging imposed by beam divergence, which, in effect, sets the sensor dynamic range. Next, we show how the resolution of laser-based triangulation sensors is limited by the interpixel pitch of a finite-sized PSD. In this paper, through the use of tunable focus lenses (TFLs), we propose a novel design of a triangulation-based optical rangefinder that improves both the sensor resolution and its dynamic range through adaptive electronic control of beam propagation parameters. We present the theory and operation of the proposed sensor and clearly demonstrate a range and resolution improvement with the use of TFLs. Experimental results in support of our claims are shown to be in strong agreement with theory.

  4. An Architectural Style for Optimizing System Qualities in Adaptive Embedded Systems using Multi-Objective Optimization

    NARCIS (Netherlands)

    de Roo, Arjan; Sözer, Hasan; Aksit, Mehmet

    Customers of today's complex embedded systems demand the optimization of multiple system qualities under varying operational conditions. To be able to influence the system qualities, the system must have parameters that can be adapted. Constraints may be defined on the value of these parameters.

  5. Adaptive symbiotic organisms search (SOS algorithm for structural design optimization

    Directory of Open Access Journals (Sweden)

    Ghanshyam G. Tejani

    2016-07-01

    Full Text Available The symbiotic organisms search (SOS algorithm is an effective metaheuristic developed in 2014, which mimics the symbiotic relationship among the living beings, such as mutualism, commensalism, and parasitism, to survive in the ecosystem. In this study, three modified versions of the SOS algorithm are proposed by introducing adaptive benefit factors in the basic SOS algorithm to improve its efficiency. The basic SOS algorithm only considers benefit factors, whereas the proposed variants of the SOS algorithm, consider effective combinations of adaptive benefit factors and benefit factors to study their competence to lay down a good balance between exploration and exploitation of the search space. The proposed algorithms are tested to suit its applications to the engineering structures subjected to dynamic excitation, which may lead to undesirable vibrations. Structure optimization problems become more challenging if the shape and size variables are taken into account along with the frequency. To check the feasibility and effectiveness of the proposed algorithms, six different planar and space trusses are subjected to experimental analysis. The results obtained using the proposed methods are compared with those obtained using other optimization methods well established in the literature. The results reveal that the adaptive SOS algorithm is more reliable and efficient than the basic SOS algorithm and other state-of-the-art algorithms.

  6. Sensor Virtual Adaptable de Concentración de Etanol para Fermentadores Industriales

    Directory of Open Access Journals (Sweden)

    Boris Martínez

    2009-07-01

    Full Text Available Resumen: Los sistemas de control emplean sensores para observar el estado del proceso y tomar decisiones. En ocasiones, se necesita estimar las variables del proceso pues el sensor adecuado no existe, es prohibitivamente costoso o las mediciones son difíciles de realizar. Una solución consiste en inferir las variables no medidas a partir de otras variables mediante sensores virtuales o sensores por software (soft-sensors. En los procesos de fermentación alcohólica, la medición de la concentración del etanol es esencial. Sin embargo, no existen sensores baratos y confiables para medirla en línea ni existe una solución aceptada por todos del modelado de dicha variable. Además, las fermentaciones nunca son iguales pues los microorganismos son muy sensibles a pequeñas desviaciones en las variables involucradas. Por tanto, estos procesos requieren un sistema de estimación adaptable y altamente robusto. En este trabajo se presenta un sensor virtual adaptable para un proceso fermentativo de bioetanol empleando un modelo borroso evolutivo a partir de datos del proceso. Además, el modelo obtenido es compacto y presenta una estructura adecuada para su aplicación futura en estrategias de control, en aras de optimizar la productividad del proceso y disminuir los costos de producción. Palabras clave: bioetanol, procesos fermentativos, sensores virtuales o sensores software, sistemas adaptables, sistemas borrosos

  7. Adaptive Information Access on Multiple Applications Support Wireless Sensor Networks

    DEFF Research Database (Denmark)

    Tobgay, Sonam; Olsen, Rasmus Løvenstein; Prasad, Ramjee

    2014-01-01

    information is challenged by dynamic nature of information elements. These challenges are more prominent in case of wireless sensor network (WSN) applications, as the information that the sensor node collects are mostly dynamic in nature (say, temperature). Therefore, it is likely that there can be a mismatch...

  8. Adaptive Information Access in Multiple Applications Support Wireless Sensor Network

    DEFF Research Database (Denmark)

    Tobgay, Sonam; Olsen, Rasmus Løvenstein; Prasad, Ramjee

    2012-01-01

    Nowadays, due to wide applicability of Wireless Sensor Network (WSN) added by the low cost sensor devices, its popularity among the researchers and industrialists are very much visible. A substantial amount of works can be seen in the literature on WSN which are mainly focused on application...

  9. An Adaptive Directed Query Dissemination Scheme for Wireless Sensor Networks

    NARCIS (Netherlands)

    Chatterjea, Supriyo; De Luigi, Simone; Havinga, Paul J.M.; Sun, M.T.

    This paper describes a directed query dissemination scheme, DirQ that routes queries to the appropriate source nodes based on both constant and dynamicvalued attributes such as sensor types and sensor values. Unlike certain other query dissemination schemes, location information is not essential for

  10. Fast optimal wavefront reconstruction for multi-conjugate adaptive optics using the Fourier domain preconditioned conjugate gradient algorithm.

    Science.gov (United States)

    Vogel, Curtis R; Yang, Qiang

    2006-08-21

    We present two different implementations of the Fourier domain preconditioned conjugate gradient algorithm (FD-PCG) to efficiently solve the large structured linear systems that arise in optimal volume turbulence estimation, or tomography, for multi-conjugate adaptive optics (MCAO). We describe how to deal with several critical technical issues, including the cone coordinate transformation problem and sensor subaperture grid spacing. We also extend the FD-PCG approach to handle the deformable mirror fitting problem for MCAO.

  11. Power optimization in body sensor networks: the case of an autonomous wireless EMG sensor powered by PV-cells.

    Science.gov (United States)

    Penders, J; Pop, V; Caballero, L; van de Molengraft, J; van Schaijk, R; Vullers, R; Van Hoof, C

    2010-01-01

    Recent advances in ultra-low-power circuits and energy harvesters are making self-powered body sensor nodes a reality. Power optimization at the system and application level is crucial in achieving ultra-low-power consumption for the entire system. This paper reviews system-level power optimization techniques, and illustrates their impact on the case of autonomous wireless EMG monitoring. The resulting prototype, an Autonomous wireless EMG sensor power by PV-cells, is presented.

  12. Analysis and optimization of Love wave liquid sensors.

    Science.gov (United States)

    Jakoby, B; Vellekoop, M J

    1998-01-01

    Love wave sensors are highly sensitive microacoustic devices, which are well suited for liquid sensing applications thanks to the shear polarization of the wave. The sensing mechanism thereby relies on the mechanical (or acoustic) interaction of the device with the liquid. The successful utilization of Love wave devices for this purpose requires proper shielding to avoid unwanted electric interaction of the liquid with the wave and the transducers. In this work we describe the effects of this electric interaction and the proper design of a shield to prevent it. We present analysis methods, which illustrate the impact of the interaction and which help to obtain an optimized design of the proposed shield. We also present experimental results for devices that have been fabricated according to these design rules.

  13. Developing Fast Fluorescent Protein Voltage Sensors by Optimizing FRET Interactions.

    Directory of Open Access Journals (Sweden)

    Uhna Sung

    Full Text Available FRET (Förster Resonance Energy Transfer-based protein voltage sensors can be useful for monitoring neuronal activity in vivo because the ratio of signals between the donor and acceptor pair reduces common sources of noise such as heart beat artifacts. We improved the performance of FRET based genetically encoded Fluorescent Protein (FP voltage sensors by optimizing the location of donor and acceptor FPs flanking the voltage sensitive domain of the Ciona intestinalis voltage sensitive phosphatase. First, we created 39 different "Nabi1" constructs by positioning the donor FP, UKG, at 8 different locations downstream of the voltage-sensing domain and the acceptor FP, mKO, at 6 positions upstream. Several of these combinations resulted in large voltage dependent signals and relatively fast kinetics. Nabi1 probes responded with signal size up to 11% ΔF/F for a 100 mV depolarization and fast response time constants both for signal activation (~2 ms and signal decay (~3 ms. We improved expression in neuronal cells by replacing the mKO and UKG FRET pair with Clover (donor FP and mRuby2 (acceptor FP to create Nabi2 probes. Nabi2 probes also had large signals and relatively fast time constants in HEK293 cells. In primary neuronal culture, a Nabi2 probe was able to differentiate individual action potentials at 45 Hz.

  14. Optimization of an Autonomous Car Controller Using a Self-Adaptive Evolutionary Strategy

    Directory of Open Access Journals (Sweden)

    Tae Seong Kim

    2012-09-01

    Full Text Available Autonomous cars control the steering wheel, acceleration and the brake pedal, the gears and the clutch using sensory information from multiple sources. Like a human driver, it understands the current situation on the roads from the live streaming of sensory values. The decision-making module often suffers from the limited range of sensors and complexity due to the large number of sensors and actuators. Because it is tedious and difficult to design the controller manually from trial-and-error, it is desirable to use intelligent optimization algorithms. In this work, we propose optimizing the parameters of an autonomous car controller using self-adaptive evolutionary strategies (SAESs which co-evolve solutions and mutation steps for each parameter. We also describe how the most generalized parameter set can be retrieved from the process of optimization. Open-source car racing simulation software (TORCS is used to test the goodness of the proposed methods on 6 different tracks. Experimental results show that the SAES is competitive with the manual design of authors and a simple ES.

  15. A Novel Adaptive Particle Swarm Optimization Algorithm with Foraging Behavior in Optimization Design

    Directory of Open Access Journals (Sweden)

    Liu Yan

    2018-01-01

    Full Text Available The method of repeated trial and proofreading is generally used to the convention reducer design, but these methods is low efficiency and the size of the reducer is often large. Aiming the problems, this paper presents an adaptive particle swarm optimization algorithm with foraging behavior, in this method, the bacterial foraging process is introduced into the adaptive particle swarm optimization algorithm, which can provide the function of particle chemotaxis, swarming, reproduction, elimination and dispersal, to improve the ability of local search and avoid premature behavior. By test verification through typical function and the application of the optimization design in the structure of the reducer with discrete and continuous variables, the results are shown that the new algorithm has the advantages of good reliability, strong searching ability and high accuracy. It can be used in engineering design, and has a strong applicability.

  16. Joint sensor location/power rating optimization for temporally-correlated source estimation

    KAUST Repository

    Bushnaq, Osama M.; Chaaban, Anas; Al-Naffouri, Tareq Y.

    2017-01-01

    via wireless AWGN channel. In addition to selecting the optimal sensing location, the sensor type to be placed in these locations is selected from a pool of T sensor types such that different sensor types have different power ratings and costs

  17. Optimal model-based sensorless adaptive optics for epifluorescence microscopy.

    Science.gov (United States)

    Pozzi, Paolo; Soloviev, Oleg; Wilding, Dean; Vdovin, Gleb; Verhaegen, Michel

    2018-01-01

    We report on a universal sample-independent sensorless adaptive optics method, based on modal optimization of the second moment of the fluorescence emission from a point-like excitation. Our method employs a sample-independent precalibration, performed only once for the particular system, to establish the direct relation between the image quality and the aberration. The method is potentially applicable to any form of microscopy with epifluorescence detection, including the practically important case of incoherent fluorescence emission from a three dimensional object, through minor hardware modifications. We have applied the technique successfully to a widefield epifluorescence microscope and to a multiaperture confocal microscope.

  18. Swarm intelligence algorithms for integrated optimization of piezoelectric actuator and sensor placement and feedback gains

    International Nuclear Information System (INIS)

    Dutta, Rajdeep; Ganguli, Ranjan; Mani, V

    2011-01-01

    Swarm intelligence algorithms are applied for optimal control of flexible smart structures bonded with piezoelectric actuators and sensors. The optimal locations of actuators/sensors and feedback gain are obtained by maximizing the energy dissipated by the feedback control system. We provide a mathematical proof that this system is uncontrollable if the actuators and sensors are placed at the nodal points of the mode shapes. The optimal locations of actuators/sensors and feedback gain represent a constrained non-linear optimization problem. This problem is converted to an unconstrained optimization problem by using penalty functions. Two swarm intelligence algorithms, namely, Artificial bee colony (ABC) and glowworm swarm optimization (GSO) algorithms, are considered to obtain the optimal solution. In earlier published research, a cantilever beam with one and two collocated actuator(s)/sensor(s) was considered and the numerical results were obtained by using genetic algorithm and gradient based optimization methods. We consider the same problem and present the results obtained by using the swarm intelligence algorithms ABC and GSO. An extension of this cantilever beam problem with five collocated actuators/sensors is considered and the numerical results obtained by using the ABC and GSO algorithms are presented. The effect of increasing the number of design variables (locations of actuators and sensors and gain) on the optimization process is investigated. It is shown that the ABC and GSO algorithms are robust and are good choices for the optimization of smart structures

  19. Analytical approach to cross-layer protocol optimization in wireless sensor networks

    Science.gov (United States)

    Hortos, William S.

    2008-04-01

    terms of the concatenated protocol parameters. New source-to-destination routes are sought that optimize cross-layer interdependencies to achieve the "best available" performance in the WSN. The protocol design, modified from a known reactive protocol, adapts the achievable performance to the transient network conditions and resource levels. Control of network behavior is realized through the conditional rates of the MVPPs. Optimal cross-layer protocol parameters are determined by stochastic dynamic programming conditions derived from models of transient packetized sensor data flows. Moreover, the defining conditions for WSN configurations, grouping sensor nodes into clusters and establishing data aggregation at processing nodes within those clusters, lead to computationally tractable solutions to the stochastic differential equations that describe network dynamics. Closed-form solution characteristics provide an alternative to the "directed diffusion" methods for resource-efficient WSN protocols published previously by other researchers. Performance verification of the resulting cross-layer designs is found by embedding the optimality conditions for the protocols in actual WSN scenarios replicated in a wireless network simulation environment. Performance tradeoffs among protocol parameters remain for a sequel to the paper.

  20. Adaptive and Reactive Security for Wireless Sensor Networks

    National Research Council Canada - National Science Library

    Stankovic, John A

    2007-01-01

    .... WSNs are also susceptible to malicious, non-random security attacks. For example, a wireless sensor network deployed in remote regions to detect and classify targets could be rendered inoperative by various security attacks...

  1. An Adaptive Channel Access Method for Dynamic Super Dense Wireless Sensor Networks.

    Science.gov (United States)

    Lei, Chunyang; Bie, Hongxia; Fang, Gengfa; Zhang, Xuekun

    2015-12-03

    Super dense and distributed wireless sensor networks have become very popular with the development of small cell technology, Internet of Things (IoT), Machine-to-Machine (M2M) communications, Vehicular-to-Vehicular (V2V) communications and public safety networks. While densely deployed wireless networks provide one of the most important and sustainable solutions to improve the accuracy of sensing and spectral efficiency, a new channel access scheme needs to be designed to solve the channel congestion problem introduced by the high dynamics of competing nodes accessing the channel simultaneously. In this paper, we firstly analyzed the channel contention problem using a novel normalized channel contention analysis model which provides information on how to tune the contention window according to the state of channel contention. We then proposed an adaptive channel contention window tuning algorithm in which the contention window tuning rate is set dynamically based on the estimated channel contention level. Simulation results show that our proposed adaptive channel access algorithm based on fast contention window tuning can achieve more than 95 % of the theoretical optimal throughput and 0 . 97 of fairness index especially in dynamic and dense networks.

  2. An Adaptive Channel Access Method for Dynamic Super Dense Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Chunyang Lei

    2015-12-01

    Full Text Available Super dense and distributed wireless sensor networks have become very popular with the development of small cell technology, Internet of Things (IoT, Machine-to-Machine (M2M communications, Vehicular-to-Vehicular (V2V communications and public safety networks. While densely deployed wireless networks provide one of the most important and sustainable solutions to improve the accuracy of sensing and spectral efficiency, a new channel access scheme needs to be designed to solve the channel congestion problem introduced by the high dynamics of competing nodes accessing the channel simultaneously. In this paper, we firstly analyzed the channel contention problem using a novel normalized channel contention analysis model which provides information on how to tune the contention window according to the state of channel contention. We then proposed an adaptive channel contention window tuning algorithm in which the contention window tuning rate is set dynamically based on the estimated channel contention level. Simulation results show that our proposed adaptive channel access algorithm based on fast contention window tuning can achieve more than 95 % of the theoretical optimal throughput and 0 . 97 of fairness index especially in dynamic and dense networks.

  3. Optimizing Reservoir Operation to Adapt to the Climate Change

    Science.gov (United States)

    Madadgar, S.; Jung, I.; Moradkhani, H.

    2010-12-01

    Climate change and upcoming variation in flood timing necessitates the adaptation of current rule curves developed for operation of water reservoirs as to reduce the potential damage from either flood or draught events. This study attempts to optimize the current rule curves of Cougar Dam on McKenzie River in Oregon addressing some possible climate conditions in 21th century. The objective is to minimize the failure of operation to meet either designated demands or flood limit at a downstream checkpoint. A simulation/optimization model including the standard operation policy and a global optimization method, tunes the current rule curve upon 8 GCMs and 2 greenhouse gases emission scenarios. The Precipitation Runoff Modeling System (PRMS) is used as the hydrology model to project the streamflow for the period of 2000-2100 using downscaled precipitation and temperature forcing from 8 GCMs and two emission scenarios. An ensemble of rule curves, each associated with an individual scenario, is obtained by optimizing the reservoir operation. The simulation of reservoir operation, for all the scenarios and the expected value of the ensemble, is conducted and performance assessment using statistical indices including reliability, resilience, vulnerability and sustainability is made.

  4. An energy-efficient adaptive sampling scheme for wireless sensor networks

    NARCIS (Netherlands)

    Masoum, Alireza; Meratnia, Nirvana; Havinga, Paul J.M.

    2013-01-01

    Wireless sensor networks are new monitoring platforms. To cope with their resource constraints, in terms of energy and bandwidth, spatial and temporal correlation in sensor data can be exploited to find an optimal sampling strategy to reduce number of sampling nodes and/or sampling frequencies while

  5. An ADC-free adaptive interface circuit of resistive sensor for electronic nose system.

    Science.gov (United States)

    Chang, Chia-Lin; Chiu, Shih-Wen; Tang, Kea-Tiong

    2013-01-01

    The initial resistance of chemiresistive gas sensors could be affected by temperature, humidity, and background odors. In a sensing system, the traditional interface circuit always requires an ADC to convert analog signal to digital signal. In this paper, we propose an ADC-free adaptive interface circuit for a resistive gas sensor to read sensor signal and cancel the baseline drift. Furthermore, methanol was used to test the proposed interface circuit, which was connected with a FIGARO® gas sensor. This circuit was fabricated by TSMC 0.18 µm CMOS process, and consumed 86.41 µW under 1 V supply voltage.

  6. Joint sensor location/power rating optimization for temporally-correlated source estimation

    KAUST Repository

    Bushnaq, Osama M.

    2017-12-22

    The optimal sensor selection for scalar state parameter estimation in wireless sensor networks is studied in the paper. A subset of N candidate sensing locations is selected to measure a state parameter and send the observation to a fusion center via wireless AWGN channel. In addition to selecting the optimal sensing location, the sensor type to be placed in these locations is selected from a pool of T sensor types such that different sensor types have different power ratings and costs. The sensor transmission power is limited based on the amount of energy harvested at the sensing location and the type of the sensor. The Kalman filter is used to efficiently obtain the MMSE estimator at the fusion center. Sensors are selected such that the MMSE estimator error is minimized subject to a prescribed system budget. This goal is achieved using convex relaxation and greedy algorithm approaches.

  7. Sensor placement optimization for structural modal identification of flexible structures using genetic algorithm

    International Nuclear Information System (INIS)

    Jung, B. K.; Cho, J. R.; Jeong, W. B.

    2015-01-01

    The position of vibration sensors influences the modal identification quality of flexible structures for a given number of sensors, and the quality of modal identification is usually estimated in terms of correlation between the natural modes using the modal assurance criterion (MAC). The sensor placement optimization is characterized by the fact that the design variables are not continuous but discrete, implying that the conventional sensitivity-driven optimization methods are not applicable. In this context, this paper presents the application of genetic algorithm to the sensor placement optimization for improving the modal identification quality of flexible structures. A discrete-type optimization problem using genetic algorithm is formulated by defining the sensor positions and the MAC as the design variables and the objective function, respectively. The proposed GA-based evolutionary optimization method is validated through the numerical experiment with a rectangular plate, and its excellence is verified from the comparison with the cases using different modal correlation measures.

  8. Transitions in optimal adaptive strategies for populations in fluctuating environments

    Science.gov (United States)

    Mayer, Andreas; Mora, Thierry; Rivoire, Olivier; Walczak, Aleksandra M.

    2017-09-01

    Biological populations are subject to fluctuating environmental conditions. Different adaptive strategies can allow them to cope with these fluctuations: specialization to one particular environmental condition, adoption of a generalist phenotype that compromises between conditions, or population-wise diversification (bet hedging). Which strategy provides the largest selective advantage in the long run depends on the range of accessible phenotypes and the statistics of the environmental fluctuations. Here, we analyze this problem in a simple mathematical model of population growth. First, we review and extend a graphical method to identify the nature of the optimal strategy when the environmental fluctuations are uncorrelated. Temporal correlations in environmental fluctuations open up new strategies that rely on memory but are mathematically challenging to study: We present analytical results to address this challenge. We illustrate our general approach by analyzing optimal adaptive strategies in the presence of trade-offs that constrain the range of accessible phenotypes. Our results extend several previous studies and have applications to a variety of biological phenomena, from antibiotic resistance in bacteria to immune responses in vertebrates.

  9. Optimal spectral tracking--adapting to dynamic regime change.

    Science.gov (United States)

    Brittain, John-Stuart; Halliday, David M

    2011-01-30

    Real world data do not always obey the statistical restraints imposed upon them by sophisticated analysis techniques. In spectral analysis for instance, an ergodic process--the interchangeability of temporal for spatial averaging--is assumed for a repeat-trial design. Many evolutionary scenarios, such as learning and motor consolidation, do not conform to such linear behaviour and should be approached from a more flexible perspective. To this end we previously introduced the method of optimal spectral tracking (OST) in the study of trial-varying parameters. In this extension to our work we modify the OST routines to provide an adaptive implementation capable of reacting to dynamic transitions in the underlying system state. In so doing, we generalise our approach to characterise both slow-varying and rapid fluctuations in time-series, simultaneously providing a metric of system stability. The approach is first applied to a surrogate dataset and compared to both our original non-adaptive solution and spectrogram approaches. The adaptive OST is seen to display fast convergence and desirable statistical properties. All three approaches are then applied to a neurophysiological recording obtained during a study on anaesthetic monitoring. Local field potentials acquired from the posterior hypothalamic region of a deep brain stimulation patient undergoing anaesthesia were analysed. The characterisation of features such as response delay, time-to-peak and modulation brevity are considered. Copyright © 2010 Elsevier B.V. All rights reserved.

  10. A new hybrid optimization method inspired from swarm intelligence: Fuzzy adaptive swallow swarm optimization algorithm (FASSO

    Directory of Open Access Journals (Sweden)

    Mehdi Neshat

    2015-11-01

    Full Text Available In this article, the objective was to present effective and optimal strategies aimed at improving the Swallow Swarm Optimization (SSO method. The SSO is one of the best optimization methods based on swarm intelligence which is inspired by the intelligent behaviors of swallows. It has been able to offer a relatively strong method for solving optimization problems. However, despite its many advantages, the SSO suffers from two shortcomings. Firstly, particles movement speed is not controlled satisfactorily during the search due to the lack of an inertia weight. Secondly, the variables of the acceleration coefficient are not able to strike a balance between the local and the global searches because they are not sufficiently flexible in complex environments. Therefore, the SSO algorithm does not provide adequate results when it searches in functions such as the Step or Quadric function. Hence, the fuzzy adaptive Swallow Swarm Optimization (FASSO method was introduced to deal with these problems. Meanwhile, results enjoy high accuracy which are obtained by using an adaptive inertia weight and through combining two fuzzy logic systems to accurately calculate the acceleration coefficients. High speed of convergence, avoidance from falling into local extremum, and high level of error tolerance are the advantages of proposed method. The FASSO was compared with eleven of the best PSO methods and SSO in 18 benchmark functions. Finally, significant results were obtained.

  11. Fuzzy Adaptive Particle Swarm Optimization for Power Loss Minimisation in Distribution Systems Using Optimal Load Response

    DEFF Research Database (Denmark)

    Hu, Weihao; Chen, Zhe; Bak-Jensen, Birgitte

    2014-01-01

    Consumers may decide to modify the profile of their demand from high price periods to low price periods in order to reduce their electricity costs. This optimal load response to electricity prices for demand side management generates different load profiles and provides an opportunity to achieve...... power loss minimization in distribution systems. In this paper, a new method to achieve power loss minimization in distribution systems by using a price signal to guide the demand side management is proposed. A fuzzy adaptive particle swarm optimization (FAPSO) is used as a tool for the power loss...

  12. Optimizing Electrodynamics Sensor Properties for High-AO Fluence Environment

    Data.gov (United States)

    National Aeronautics and Space Administration — To investigate the oxidation of sensor coatings in order to develop more robust sensors that can be used not only for Atmosphere-Space Transition Region Explorer...

  13. Testbeam Studies on Pick-Up in Sensors with Embedded Pitch Adapters

    CERN Document Server

    Rehnisch, Laura; The ATLAS collaboration

    2017-01-01

    For silicon strip sensors, the tracking information specifications can lead to challenging requirements for wire bonding. A common strategy is to use external pitch adapters to facilitate this step in the production of detector modules. A novel approach previously discussed in [1], is to implement the pitch adapters in the sensor, by embedding a second layer of metal tracks. The use of these embedded pitch adapters (EPAs) decouples the bond pad layout of the sensor from its implant layout by moving the adaption to the sensor production step. This solution, however, can yield the risk of performance losses due to the increase of inter-strip capacitance, or unwanted capacitive coupling between the metal layers (cross-talk) or the silicon bulk and the second metal layer (pick-up). In the prototyping stage of the ATLAS tracker end-cap upgrade, where different bond-pad layouts on sensor and readout chip lead to extremely challenging wire-bonding conditions, sensors with different geometries of EPA implementations ...

  14. An adaptive Kalman filter approach for cardiorespiratory signal extraction and fusion of non-contacting sensors.

    Science.gov (United States)

    Foussier, Jerome; Teichmann, Daniel; Jia, Jing; Misgeld, Berno; Leonhardt, Steffen

    2014-05-09

    Extracting cardiorespiratory signals from non-invasive and non-contacting sensor arrangements, i.e. magnetic induction sensors, is a challenging task. The respiratory and cardiac signals are mixed on top of a large and time-varying offset and are likely to be disturbed by measurement noise. Basic filtering techniques fail to extract relevant information for monitoring purposes. We present a real-time filtering system based on an adaptive Kalman filter approach that separates signal offsets, respiratory and heart signals from three different sensor channels. It continuously estimates respiration and heart rates, which are fed back into the system model to enhance performance. Sensor and system noise covariance matrices are automatically adapted to the aimed application, thus improving the signal separation capabilities. We apply the filtering to two different subjects with different heart rates and sensor properties and compare the results to the non-adaptive version of the same Kalman filter. Also, the performance, depending on the initialization of the filters, is analyzed using three different configurations ranging from best to worst case. Extracted data are compared with reference heart rates derived from a standard pulse-photoplethysmographic sensor and respiration rates from a flowmeter. In the worst case for one of the subjects the adaptive filter obtains mean errors (standard deviations) of -0.2 min(-1) (0.3 min(-1)) and -0.7 bpm (1.7 bpm) (compared to -0.2 min(-1) (0.4 min(-1)) and 42.0 bpm (6.1 bpm) for the non-adaptive filter) for respiration and heart rate, respectively. In bad conditions the heart rate is only correctly measurable when the Kalman matrices are adapted to the target sensor signals. Also, the reduced mean error between the extracted offset and the raw sensor signal shows that adapting the Kalman filter continuously improves the ability to separate the desired signals from the raw sensor data. The average total computational time needed

  15. Structural damage detection-oriented multi-type sensor placement with multi-objective optimization

    Science.gov (United States)

    Lin, Jian-Fu; Xu, You-Lin; Law, Siu-Seong

    2018-05-01

    A structural damage detection-oriented multi-type sensor placement method with multi-objective optimization is developed in this study. The multi-type response covariance sensitivity-based damage detection method is first introduced. Two objective functions for optimal sensor placement are then introduced in terms of the response covariance sensitivity and the response independence. The multi-objective optimization problem is formed by using the two objective functions, and the non-dominated sorting genetic algorithm (NSGA)-II is adopted to find the solution for the optimal multi-type sensor placement to achieve the best structural damage detection. The proposed method is finally applied to a nine-bay three-dimensional frame structure. Numerical results show that the optimal multi-type sensor placement determined by the proposed method can avoid redundant sensors and provide satisfactory results for structural damage detection. The restriction on the number of each type of sensors in the optimization can reduce the searching space in the optimization to make the proposed method more effective. Moreover, how to select a most optimal sensor placement from the Pareto solutions via the utility function and the knee point method is demonstrated in the case study.

  16. Multi-Sensor Detection with Particle Swarm Optimization for Time-Frequency Coded Cooperative WSNs Based on MC-CDMA for Underground Coal Mines

    Directory of Open Access Journals (Sweden)

    Jingjing Xu

    2015-08-01

    Full Text Available In this paper, a wireless sensor network (WSN technology adapted to underground channel conditions is developed, which has important theoretical and practical value for safety monitoring in underground coal mines. According to the characteristics that the space, time and frequency resources of underground tunnel are open, it is proposed to constitute wireless sensor nodes based on multicarrier code division multiple access (MC-CDMA to make full use of these resources. To improve the wireless transmission performance of source sensor nodes, it is also proposed to utilize cooperative sensors with good channel conditions from the sink node to assist source sensors with poor channel conditions. Moreover, the total power of the source sensor and its cooperative sensors is allocated on the basis of their channel conditions to increase the energy efficiency of the WSN. To solve the problem that multiple access interference (MAI arises when multiple source sensors transmit monitoring information simultaneously, a kind of multi-sensor detection (MSD algorithm with particle swarm optimization (PSO, namely D-PSO, is proposed for the time-frequency coded cooperative MC-CDMA WSN. Simulation results show that the average bit error rate (BER performance of the proposed WSN in an underground coal mine is improved significantly by using wireless sensor nodes based on MC-CDMA, adopting time-frequency coded cooperative transmission and D-PSO algorithm with particle swarm optimization.

  17. Multi-Sensor Detection with Particle Swarm Optimization for Time-Frequency Coded Cooperative WSNs Based on MC-CDMA for Underground Coal Mines.

    Science.gov (United States)

    Xu, Jingjing; Yang, Wei; Zhang, Linyuan; Han, Ruisong; Shao, Xiaotao

    2015-08-27

    In this paper, a wireless sensor network (WSN) technology adapted to underground channel conditions is developed, which has important theoretical and practical value for safety monitoring in underground coal mines. According to the characteristics that the space, time and frequency resources of underground tunnel are open, it is proposed to constitute wireless sensor nodes based on multicarrier code division multiple access (MC-CDMA) to make full use of these resources. To improve the wireless transmission performance of source sensor nodes, it is also proposed to utilize cooperative sensors with good channel conditions from the sink node to assist source sensors with poor channel conditions. Moreover, the total power of the source sensor and its cooperative sensors is allocated on the basis of their channel conditions to increase the energy efficiency of the WSN. To solve the problem that multiple access interference (MAI) arises when multiple source sensors transmit monitoring information simultaneously, a kind of multi-sensor detection (MSD) algorithm with particle swarm optimization (PSO), namely D-PSO, is proposed for the time-frequency coded cooperative MC-CDMA WSN. Simulation results show that the average bit error rate (BER) performance of the proposed WSN in an underground coal mine is improved significantly by using wireless sensor nodes based on MC-CDMA, adopting time-frequency coded cooperative transmission and D-PSO algorithm with particle swarm optimization.

  18. Adaptive Multipath Key Reinforcement for Energy Harvesting Wireless Sensor Networks

    DEFF Research Database (Denmark)

    Di Mauro, Alessio; Dragoni, Nicola

    2015-01-01

    Energy Harvesting - Wireless Sensor Networks (EH-WSNs) constitute systems of networked sensing nodes that are capable of extracting energy from the environment and that use the harvested energy to operate in a sustainable state. Sustainability, seen as design goal, has a significant impact...

  19. PARAMETERS OPTIMIZATION OF METAL-DIELECTRIC NANOSTRUCTURES FOR SENSOR APPLICATIONS

    Directory of Open Access Journals (Sweden)

    V. I. Egorov

    2014-07-01

    Full Text Available We present calculation results of optical properties of silver nanoparticles with dielectric shell in relation to their applications in chemical and biosensors. Absorption cross-section calculation for spherical silver nanoparticles was performed by quasi static dipole approximation. It is shown that dielectric shell thickness equal to 2-3 nm and its refraction index equal to 1,5-1,75 are optimal. Calculation results were compared to experimental data. Experimental investigation of metal-dielectric nanostructures sensitivity to external refraction index was performed. Synthesis of silver nanoparticles with dielectric shell on glass surface was performed by nanosecond laser ablation method in near-surface glass layer at 1,06 μm wavelength (Solar LQ129. Synthesis of silver nanoparticles without a shell on the glass surface with silver ions was performed using thermal treatment in wet atmosphere. Spectrophotometer Cary 500 (Varyan was used for spectral measurements. In case of laser ablation method application, external refraction index changes from 1 (the air to 1,33 (water and plasmon resonance band shift for 6 nm occurs. In case of another method application at the same conditions the registered shift was equal to 13 nm. However, in the latter case the particles can be easily removed from the substrate surface. Obtained results will be useful for developing chemical and biological sensors based on plasmon resonance band shift.

  20. On the Optimal Location of Sensors for Parametric Identification of Linear Structural Systems

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Brincker, Rune

    A survey of the field of optimal location of sensors for parametric identification of linear structural systems is presented. The survey shows that few papers are devoted to the case of optimal location sensors in which the measurements are modelled by a random field with non-trivial covariance...... function. Most often it is assumed that the results of the measurements are statistically independent variables. In an example the importance of considering the measurements as statistically dependent random variables is shown. The example is concerned with optimal location of sensors for parametric...... identification of modal parameters for a vibrating beam under random loading. The covariance of the modal parameters expected to be obtained is investigated to variations of number and location of sensors. Further, the influence of the noise on the optimal location of the sensors is investigated....

  1. The Study of Cross-layer Optimization for Wireless Rechargeable Sensor Networks Implemented in Coal Mines

    Science.gov (United States)

    Ding, Xu; Shi, Lei; Han, Jianghong; Lu, Jingting

    2016-01-01

    Wireless sensor networks deployed in coal mines could help companies provide workers working in coal mines with more qualified working conditions. With the underground information collected by sensor nodes at hand, the underground working conditions could be evaluated more precisely. However, sensor nodes may tend to malfunction due to their limited energy supply. In this paper, we study the cross-layer optimization problem for wireless rechargeable sensor networks implemented in coal mines, of which the energy could be replenished through the newly-brewed wireless energy transfer technique. The main results of this article are two-fold: firstly, we obtain the optimal relay nodes’ placement according to the minimum overall energy consumption criterion through the Lagrange dual problem and KKT conditions; secondly, the optimal strategies for recharging locomotives and wireless sensor networks are acquired by solving a cross-layer optimization problem. The cyclic nature of these strategies is also manifested through simulations in this paper. PMID:26828500

  2. ADEX optimized adaptive controllers and systems from research to industrial practice

    CERN Document Server

    Martín-Sánchez, Juan M

    2015-01-01

    This book is a didactic explanation of the developments of predictive, adaptive predictive and optimized adaptive control, including the latest methodology of adaptive predictive expert (ADEX) control, and their practical applications. It is focused on the stability perspective, used in the introduction of these methodologies, and is divided into six parts, with exercises and real-time simulations provided for the reader as appropriate. ADEX Optimized Adaptive Controllers and Systems begins with the conceptual and intuitive knowledge of the technology and derives the stability conditions to be verified by the driver block and the adaptive mechanism of the optimized adaptive controller to guarantee achievement of desired control performance. The second and third parts are centered on the design of the driver block and adaptive mechanism, which verify these stability conditions. The authors then proceed to detail the stability theory that supports predictive, adaptive predictive and optimized adaptive control m...

  3. Cooperative control of multi-agent systems optimal and adaptive design approaches

    CERN Document Server

    Lewis, Frank L; Hengster-Movric, Kristian; Das, Abhijit

    2014-01-01

    Task complexity, communication constraints, flexibility and energy-saving concerns are all factors that may require a group of autonomous agents to work together in a cooperative manner. Applications involving such complications include mobile robots, wireless sensor networks, unmanned aerial vehicles (UAVs), spacecraft, and so on. In such networked multi-agent scenarios, the restrictions imposed by the communication graph topology can pose severe problems in the design of cooperative feedback control systems.  Cooperative control of multi-agent systems is a challenging topic for both control theorists and practitioners and has been the subject of significant recent research. Cooperative Control of Multi-Agent Systems extends optimal control and adaptive control design methods to multi-agent systems on communication graphs.  It develops Riccati design techniques for general linear dynamics for cooperative state feedback design, cooperative observer design, and cooperative dynamic output feedback design.  B...

  4. Adaptation of sensor morphology: an integrative view of perception from biologically inspired robotics perspective

    Science.gov (United States)

    Nurzaman, Surya G.

    2016-01-01

    Sensor morphology, the morphology of a sensing mechanism which plays a role of shaping the desired response from physical stimuli from surroundings to generate signals usable as sensory information, is one of the key common aspects of sensing processes. This paper presents a structured review of researches on bioinspired sensor morphology implemented in robotic systems, and discusses the fundamental design principles. Based on literature review, we propose two key arguments: first, owing to its synthetic nature, biologically inspired robotics approach is a unique and powerful methodology to understand the role of sensor morphology and how it can evolve and adapt to its task and environment. Second, a consideration of an integrative view of perception by looking into multidisciplinary and overarching mechanisms of sensor morphology adaptation across biology and engineering enables us to extract relevant design principles that are important to extend our understanding of the unfinished concepts in sensing and perception. PMID:27499843

  5. Error sensitivity to refinement: a criterion for optimal grid adaptation

    Science.gov (United States)

    Luchini, Paolo; Giannetti, Flavio; Citro, Vincenzo

    2017-12-01

    Most indicators used for automatic grid refinement are suboptimal, in the sense that they do not really minimize the global solution error. This paper concerns with a new indicator, related to the sensitivity map of global stability problems, suitable for an optimal grid refinement that minimizes the global solution error. The new criterion is derived from the properties of the adjoint operator and provides a map of the sensitivity of the global error (or its estimate) to a local mesh refinement. Examples are presented for both a scalar partial differential equation and for the system of Navier-Stokes equations. In the last case, we also present a grid-adaptation algorithm based on the new estimator and on the FreeFem++ software that improves the accuracy of the solution of almost two order of magnitude by redistributing the nodes of the initial computational mesh.

  6. Discriminating Among Probability Weighting Functions Using Adaptive Design Optimization

    Science.gov (United States)

    Cavagnaro, Daniel R.; Pitt, Mark A.; Gonzalez, Richard; Myung, Jay I.

    2014-01-01

    Probability weighting functions relate objective probabilities and their subjective weights, and play a central role in modeling choices under risk within cumulative prospect theory. While several different parametric forms have been proposed, their qualitative similarities make it challenging to discriminate among them empirically. In this paper, we use both simulation and choice experiments to investigate the extent to which different parametric forms of the probability weighting function can be discriminated using adaptive design optimization, a computer-based methodology that identifies and exploits model differences for the purpose of model discrimination. The simulation experiments show that the correct (data-generating) form can be conclusively discriminated from its competitors. The results of an empirical experiment reveal heterogeneity between participants in terms of the functional form, with two models (Prelec-2, Linear in Log Odds) emerging as the most common best-fitting models. The findings shed light on assumptions underlying these models. PMID:24453406

  7. The Optimization by Using the Learning Styles in the Adaptive Hypermedia Applications

    Science.gov (United States)

    Hamza, Lamia; Tlili, Guiassa Yamina

    2018-01-01

    This article addresses the learning style as a criterion for optimization of adaptive content in hypermedia applications. First, the authors present the different optimization approaches proposed in the area of adaptive hypermedia systems whose goal is to define the optimization problem in this type of system. Then, they present the architecture…

  8. Theoretical model and optimization of a novel temperature sensor based on quartz tuning fork resonators

    International Nuclear Information System (INIS)

    Xu Jun; You Bo; Li Xin; Cui Juan

    2007-01-01

    To accurately measure temperatures, a novel temperature sensor based on a quartz tuning fork resonator has been designed. The principle of the quartz tuning fork temperature sensor is that the resonant frequency of the quartz resonator changes with the variation in temperature. This type of tuning fork resonator has been designed with a new doubly rotated cut work at flexural vibration mode as temperature sensor. The characteristics of the temperature sensor were evaluated and the results sufficiently met the target of development for temperature sensor. The theoretical model for temperature sensing has been developed and built. The sensor structure was analysed by finite element method (FEM) and optimized, including tuning fork geometry, tine electrode pattern and the sensor's elements size. The performance curve of output versus measured temperature is given. The results from theoretical analysis and experiments indicate that the sensor's sensitivity can reach 60 ppm 0 C -1 with the measured temperature range varying from 0 to 100 0 C

  9. Planar Hall effect sensor bridge geometries optimized for magnetic bead detection

    DEFF Research Database (Denmark)

    Østerberg, Frederik Westergaard; Rizzi, Giovanni; Henriksen, Anders Dahl

    2014-01-01

    Novel designs of planar Hall effect bridge sensors optimized for magnetic bead detection are presented and characterized. By constructing the sensor geometries appropriately, the sensors can be tailored to be sensitive to an external magnetic field, the magnetic field due to beads being magnetized...... by the sensor self-field or a combination thereof. The sensors can be made nominally insensitive to small external magnetic fields, while being maximally sensitive to magnetic beads, magnetized by the sensor self-field. Thus, the sensor designs can be tailored towards specific applications with minimal...... of the dynamic magnetic response of suspensions of magnetic beads with a nominal diameter of 80 nm are performed. Furthermore, a method to amplify the signal by appropriate combinations of multiple sensor segments is demonstrated....

  10. Design and Optimization of Dual Optical Fiber MEMS Pressure Sensor For Biomedical Applications

    International Nuclear Information System (INIS)

    Dagang, Guo; Po, Samuel Ng Choon; Hock, Francis Tay Eng; Rongming, Lin

    2006-01-01

    A novel Single Deeply Corrugated Diaphragm (SDCD) based dual optical fiber Fabry-Perot pressure sensor for blood pressure measurement is proposed. Both mechanical and optical simulations are performed to demonstrate the feasibility and superior performance of the proposed sensor. Result shows that less than 2% nonlinearity can be achieved for the proposed sensor using optimal Fabry-Perot microcavity. Also, the fabrication process of the proposed sensor is given, instead of complicated fusion bonding process, only bulk and surface micromachining techniques are required which facilitate the mass production of such biocompatible and disposable pressure sensors

  11. Robust sawtooth period control based on adaptive online optimization

    International Nuclear Information System (INIS)

    Bolder, J.J.; Witvoet, G.; De Baar, M.R.; Steinbuch, M.; Van de Wouw, N.; Haring, M.A.M.; Westerhof, E.; Doelman, N.J.

    2012-01-01

    The systematic design of a robust adaptive control strategy for the sawtooth period using electron cyclotron current drive (ECCD) is presented. Recent developments in extremum seeking control (ESC) are employed to derive an optimized controller structure and offer practical tuning guidelines for its parameters. In this technique a cost function in terms of the desired sawtooth period is optimized online by changing the ECCD deposition location based on online estimations of the gradient of the cost function. The controller design does not require a detailed model of the sawtooth instability. Therefore, the proposed ESC is widely applicable to any sawtoothing plasma or plasma simulation and is inherently robust against uncertainties or plasma variations. Moreover, it can handle a broad class of disturbances. This is demonstrated by time-domain simulations, which show successful tracking of time-varying sawtooth period references throughout the whole operating space, even in the presence of variations in plasma parameters, disturbances and slow launcher mirror dynamics. Due to its simplicity and robustness the proposed ESC is a valuable sawtooth control candidate for any experimental tokamak plasma, and may even be applicable to other fusion-related control problems. (paper)

  12. An adaptive N-body algorithm of optimal order

    International Nuclear Information System (INIS)

    Pruett, C. David.; Rudmin, Joseph W.; Lacy, Justin M.

    2003-01-01

    Picard iteration is normally considered a theoretical tool whose primary utility is to establish the existence and uniqueness of solutions to first-order systems of ordinary differential equations (ODEs). However, in 1996, Parker and Sochacki [Neural, Parallel, Sci. Comput. 4 (1996)] published a practical numerical method for a certain class of ODEs, based upon modified Picard iteration, that generates the Maclaurin series of the solution to arbitrarily high order. The applicable class of ODEs consists of first-order, autonomous systems whose right-hand side functions (generators) are projectively polynomial; that is, they can be written as polynomials in the unknowns. The class is wider than might be expected. The method is ideally suited to the classical N-body problem, which is projectively polynomial. Here, we recast the N-body problem in polynomial form and develop a Picard-based algorithm for its solution. The algorithm is highly accurate, parameter-free, and simultaneously adaptive in time and order. Test cases for both benign and chaotic N-body systems reveal that optimal order is dynamic. That is, in addition to dependency upon N and the desired accuracy, optimal order depends upon the configuration of the bodies at any instant

  13. Characterization and optimization of an ultrasonic piezo-optical ring sensor

    International Nuclear Information System (INIS)

    Frankforter, Erik; Lin, Bin; Giurgiutiu, Victor

    2016-01-01

    A resonant piezo-optical ring sensor with both piezoelectric and fiber Bragg grating (FBG) sensing elements was assessed for ultrasonic wave detection. The ring sensor is an existing device that has been shown experimentally to exhibit a number of sensing features: omnidirectionality, mode selectivity, and frequency tunability. The present study uses finite element modeling to understand these features as a means to characterize and optimize the sensor. A combined vibration-wave propagation modeling approach was used, where the vibrational modeling provided a basis for understanding sensing features, and the wave propagation modeling provided predictive power for sensor performance. The sensor features corresponded to the fundamental vibrational mode of the sensor, particularly to the base motion of this mode. The vibrational modeling was also used to guide sensor optimization, with an emphasis on the FBG and piezoelectric sensing elements. It was found that sensor symmetry and nodes of extraneous resonance modes could be exploited to provide a single-resonance response. A series of pitch-catch guided wave experiments were performed on a thin aluminum plate to assess the optimized sensor configuration. Tuning curves showed a single-frequency response to a Lamb wave and mechanical filtering away from the dominant frequency; the sensor capability for mechanical amplification of a Lamb wave and mechanical amplification of a pencil-lead-break acoustic emission event were also demonstrated. (paper)

  14. Secure Adaptive Topology Control for Wireless Ad-Hoc Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yen-Chieh Ouyang

    2010-02-01

    Full Text Available This paper presents a secure decentralized clustering algorithm for wireless ad-hoc sensor networks. The algorithm operates without a centralized controller, operates asynchronously, and does not require that the location of the sensors be known a priori. Based on the cluster-based topology, secure hierarchical communication protocols and dynamic quarantine strategies are introduced to defend against spam attacks, since this type of attacks can exhaust the energy of sensor nodes and will shorten the lifetime of a sensor network drastically. By adjusting the threshold of infected percentage of the cluster coverage, our scheme can dynamically coordinate the proportion of the quarantine region and adaptively achieve the cluster control and the neighborhood control of attacks. Simulation results show that the proposed approach is feasible and cost effective for wireless sensor networks.

  15. An adaptive immune optimization algorithm with dynamic lattice searching operation for fast optimization of atomic clusters

    International Nuclear Information System (INIS)

    Wu, Xia; Wu, Genhua

    2014-01-01

    Highlights: • A high efficient method for optimization of atomic clusters is developed. • Its performance is studied by optimizing Lennard-Jones clusters and Ag clusters. • The method is proved to be quite efficient. • A new Ag 61 cluster with stacking-fault face-centered cubic motif is found. - Abstract: Geometrical optimization of atomic clusters is performed by a development of adaptive immune optimization algorithm (AIOA) with dynamic lattice searching (DLS) operation (AIOA-DLS method). By a cycle of construction and searching of the dynamic lattice (DL), DLS algorithm rapidly makes the clusters more regular and greatly reduces the potential energy. DLS can thus be used as an operation acting on the new individuals after mutation operation in AIOA to improve the performance of the AIOA. The AIOA-DLS method combines the merit of evolutionary algorithm and idea of dynamic lattice. The performance of the proposed method is investigated in the optimization of Lennard-Jones clusters within 250 atoms and silver clusters described by many-body Gupta potential within 150 atoms. Results reported in the literature are reproduced, and the motif of Ag 61 cluster is found to be stacking-fault face-centered cubic, whose energy is lower than that of previously obtained icosahedron

  16. An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox

    Science.gov (United States)

    Jing, Luyang; Wang, Taiyong; Zhao, Ming; Wang, Peng

    2017-01-01

    A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment. PMID:28230767

  17. Expected treatment dose construction and adaptive inverse planning optimization: Implementation for offline head and neck cancer adaptive radiotherapy

    Energy Technology Data Exchange (ETDEWEB)

    Yan Di; Liang Jian [Department of Radiation Oncology, Beaumont Health System, Royal Oak, Michigan 48073 (United States)

    2013-02-15

    Purpose: To construct expected treatment dose for adaptive inverse planning optimization, and evaluate it on head and neck (h and n) cancer adaptive treatment modification. Methods: Adaptive inverse planning engine was developed and integrated in our in-house adaptive treatment control system. The adaptive inverse planning engine includes an expected treatment dose constructed using the daily cone beam (CB) CT images in its objective and constrains. Feasibility of the adaptive inverse planning optimization was evaluated retrospectively using daily CBCT images obtained from the image guided IMRT treatment of 19 h and n cancer patients. Adaptive treatment modification strategies with respect to the time and the number of adaptive inverse planning optimization during the treatment course were evaluated using the cumulative treatment dose in organs of interest constructed using all daily CBCT images. Results: Expected treatment dose was constructed to include both the delivered dose, to date, and the estimated dose for the remaining treatment during the adaptive treatment course. It was used in treatment evaluation, as well as in constructing the objective and constraints for adaptive inverse planning optimization. The optimization engine is feasible to perform planning optimization based on preassigned treatment modification schedule. Compared to the conventional IMRT, the adaptive treatment for h and n cancer illustrated clear dose-volume improvement for all critical normal organs. The dose-volume reductions of right and left parotid glands, spine cord, brain stem and mandible were (17 {+-} 6)%, (14 {+-} 6)%, (11 {+-} 6)%, (12 {+-} 8)%, and (5 {+-} 3)% respectively with the single adaptive modification performed after the second treatment week; (24 {+-} 6)%, (22 {+-} 8)%, (21 {+-} 5)%, (19 {+-} 8)%, and (10 {+-} 6)% with three weekly modifications; and (28 {+-} 5)%, (25 {+-} 9)%, (26 {+-} 5)%, (24 {+-} 8)%, and (15 {+-} 9)% with five weekly modifications. Conclusions

  18. Increasing the Lifetime of Mobile WSNs via Dynamic Optimization of Sensor Node Communication Activity.

    Science.gov (United States)

    Guimarães, Dayan Adionel; Sakai, Lucas Jun; Alberti, Antonio Marcos; de Souza, Rausley Adriano Amaral

    2016-09-20

    In this paper, a simple and flexible method for increasing the lifetime of fixed or mobile wireless sensor networks is proposed. Based on past residual energy information reported by the sensor nodes, the sink node or another central node dynamically optimizes the communication activity levels of the sensor nodes to save energy without sacrificing the data throughput. The activity levels are defined to represent portions of time or time-frequency slots in a frame, during which the sensor nodes are scheduled to communicate with the sink node to report sensory measurements. Besides node mobility, it is considered that sensors' batteries may be recharged via a wireless power transmission or equivalent energy harvesting scheme, bringing to the optimization problem an even more dynamic character. We report large increased lifetimes over the non-optimized network and comparable or even larger lifetime improvements with respect to an idealized greedy algorithm that uses both the real-time channel state and the residual energy information.

  19. Optimal sensor configuration for complex systems with application to signal detection in structures

    DEFF Research Database (Denmark)

    Sadegh, Payman; Spall, J. C.

    2000-01-01

    sensor outputs. Secondly, we describe an efficient and practical algorithm to achieve the optimization goals, based on simultaneous perturbation stochastic approximation (SPSA). SPSA avoids the need for detailed modeling of the sensor response by simply relying on observed responses as obtained......The paper considers the problem of sensor configuration for complex systems. The contribution of the paper is twofold. Firstly, we define an appropriate criterion that is based on maximizing overall sensor responses while minimizing redundant information as measured by correlations between multiple...... by limited experimentation with test sensor configurations. We illustrate the application of the approach to optimal placement of acoustic sensors for signal detection in structures. This includes both a computer simulation study for an aluminum plate, and real experimentations on a steel I-beam....

  20. Adaptive Home System Using Wireless Sensor Network And Multi Agent System

    OpenAIRE

    Jayarani Kamble; Prof.Nandini Dhole

    2014-01-01

    Smart Home is an emerging technology growing continuously which includes number of new technologies which helps to improve human’s quality of living. This paper proposes an adaptive home system for optimum utilization of power, through Artificial Intelligence and Wireless Sensor network. Artificial Intelligence is a technology for developing adaptive system that can perceive the enviornmrnt, learn from the environment and can make decision using Rule based system.Zigbee is a w...

  1. The optimal location of piezoelectric actuators and sensors for vibration control of plates

    Science.gov (United States)

    Kumar, K. Ramesh; Narayanan, S.

    2007-12-01

    This paper considers the optimal placement of collocated piezoelectric actuator-sensor pairs on a thin plate using a model-based linear quadratic regulator (LQR) controller. LQR performance is taken as objective for finding the optimal location of sensor-actuator pairs. The problem is formulated using the finite element method (FEM) as multi-input-multi-output (MIMO) model control. The discrete optimal sensor and actuator location problem is formulated in the framework of a zero-one optimization problem. A genetic algorithm (GA) is used to solve the zero-one optimization problem. Different classical control strategies like direct proportional feedback, constant-gain negative velocity feedback and the LQR optimal control scheme are applied to study the control effectiveness.

  2. Optimization of sensor introduction into laminated composite materials

    Science.gov (United States)

    Schaaf, Kristin; Nemat-Nasser, Sia

    2008-03-01

    This work seeks to extend the functionality of the composite material beyond that of simply load-bearing and to enable in situ sensing, without compromising the structural integrity of the host composite material. Essential to the application of smart composites is the issue of the mechanical coupling of the sensor to the host material. Here we present various methods of embedding sensors within the host composite material. In this study, quasi-static three-point bending (short beam) and fatigue three-point bending (short beam) tests are conducted in order to characterize the effects of introducing the sensors into the host composite material. The sensors that are examined include three types of polyvinylidene fluoride (PVDF) thin film sensors: silver ink with a protective coating of urethane, silver ink without a protective coating, and nickel-copper alloy without a protective coating. The methods of sensor integration include placement at the midplane between the layers of prepreg material as well as a sandwich configuration in which a PVDF thin film sensor is placed between the first and second and nineteenth and twentieth layers of prepreg. Each PVDF sensor is continuous and occupies the entire layer, lying in the plane normal to the thickness direction in laminated composites. The work described here is part of an ongoing effort to understand the structural effects of integrating microsensor networks into a host composite material.

  3. Hardware Abstraction and Protocol Optimization for Coded Sensor Networks

    DEFF Research Database (Denmark)

    Nistor, Maricica; Roetter, Daniel Enrique Lucani; Barros, João

    2015-01-01

    The design of the communication protocols in wireless sensor networks (WSNs) often neglects several key characteristics of the sensor's hardware, while assuming that the number of transmitted bits is the dominating factor behind the system's energy consumption. A closer look at the hardware speci...

  4. Optimal task scheduling policy in energy harvesting wireless sensor networks

    NARCIS (Netherlands)

    Rao, Vijay S.; Prasad, R. Venkatesha; Niemegeers, Ignas G M M

    2015-01-01

    Ambient energy harvesting for Wireless Sensor Networks (WSNs) is being pitched as a promising solution for long-lasting deployments in various WSN applications. However, the sensor nodes most often do not have enough energy to handle application, network and house-keeping tasks because amount of

  5. Optimal UAS Assignments and Trajectories for Persistent Surveillance and Data Collection from a Wireless Sensor Network

    Science.gov (United States)

    2015-12-24

    Optimal UAS Assignments and Trajectories for Persistent Surveillance and Data Collection from a Wireless Sensor Network DISSERTATION Nidal M. Jodeh...ASSIGNMENTS AND TRAJECTORIES FOR PERSISTENT SURVEILLANCE AND DATA COLLECTION FROM A WIRELESS SENSOR NETWORK DISSERTATION Presented to the Faculty...COLLECTION FROM A WIRELESS SENSOR NETWORK Nidal M. Jodeh, B.S., M.A.S., M.S. Lieutenant Colonel, USAF Committee Membership: Richard G. Cobb, PhD Chairman

  6. CPAC: Energy-Efficient Data Collection through Adaptive Selection of Compression Algorithms for Sensor Networks

    Science.gov (United States)

    Lee, HyungJune; Kim, HyunSeok; Chang, Ik Joon

    2014-01-01

    We propose a technique to optimize the energy efficiency of data collection in sensor networks by exploiting a selective data compression. To achieve such an aim, we need to make optimal decisions regarding two aspects: (1) which sensor nodes should execute compression; and (2) which compression algorithm should be used by the selected sensor nodes. We formulate this problem into binary integer programs, which provide an energy-optimal solution under the given latency constraint. Our simulation results show that the optimization algorithm significantly reduces the overall network-wide energy consumption for data collection. In the environment having a stationary sink from stationary sensor nodes, the optimized data collection shows 47% energy savings compared to the state-of-the-art collection protocol (CTP). More importantly, we demonstrate that our optimized data collection provides the best performance in an intermittent network under high interference. In such networks, we found that the selective compression for frequent packet retransmissions saves up to 55% energy compared to the best known protocol. PMID:24721763

  7. Optimal power allocation of a single transmitter-multiple receivers channel in a cognitive sensor network

    KAUST Repository

    Ayala Solares, Jose Roberto; Rezki, Zouheir; Alouini, Mohamed-Slim

    2012-01-01

    The optimal transmit power of a wireless sensor network with one transmitter and multiple receivers in a cognitive radio environment while satisfying independent peak, independent average, sum of peak and sum of average transmission rate constraints

  8. Optimal allocation of sensors for state estimation of distributed parameter systems

    International Nuclear Information System (INIS)

    Sunahara, Yoshifumi; Ohsumi, Akira; Mogami, Yoshio.

    1978-01-01

    The purpose of this paper is to present a method for finding the optimal allocation of sensors for state estimation of linear distributed parameter systems. This method is based on the criterion that the error covariance associated with the state estimate becomes minimal with respect to the allocation of the sensors. A theorem is established, giving the sufficient condition for optimizing the allocation of sensors to make minimal the error covariance approximated by a modal expansion. The remainder of this paper is devoted to illustrate important phases of the general theory of the optimal measurement allocation problem. To do this, several examples are demonstrated, including extensive discussions on the mutual relation between the optimal allocation and the dynamics of sensors. (author)

  9. Adaptive multi-objective Optimization scheme for cognitive radio resource management

    KAUST Repository

    Alqerm, Ismail; Shihada, Basem

    2014-01-01

    configuration by exploiting optimization and machine learning techniques. In this paper, we propose an Adaptive Multi-objective Optimization Scheme (AMOS) for cognitive radio resource management to improve spectrum operation and network performance

  10. TRUSTWORTHY OPTIMIZED CLUSTERING BASED TARGET DETECTION AND TRACKING FOR WIRELESS SENSOR NETWORK

    Directory of Open Access Journals (Sweden)

    C. Jehan

    2016-06-01

    Full Text Available In this paper, an efficient approach is proposed to address the problem of target tracking in wireless sensor network (WSN. The problem being tackled here uses adaptive dynamic clustering scheme for tracking the target. It is a specific problem in object tracking. The proposed adaptive dynamic clustering target tracking scheme uses three steps for target tracking. The first step deals with the identification of clusters and cluster heads using OGSAFCM. Here, kernel fuzzy c-means (KFCM and gravitational search algorithm (GSA are combined to create clusters. At first, oppositional gravitational search algorithm (OGSA is used to optimize the initial clustering center and then the KFCM algorithm is availed to guide the classification and the cluster formation process. In the OGSA, the concept of the opposition based population initialization in the basic GSA to improve the convergence profile. The identified clusters are changed dynamically. The second step deals with the data transmission to the cluster heads. The third step deals with the transmission of aggregated data to the base station as well as the detection of target. From the experimental results, the proposed scheme efficiently and efficiently identifies the target. As a result the tracking error is minimized.

  11. Optimal placement of multiple types of communicating sensors with availability and coverage redundancy constraints

    Science.gov (United States)

    Vecherin, Sergey N.; Wilson, D. Keith; Pettit, Chris L.

    2010-04-01

    Determination of an optimal configuration (numbers, types, and locations) of a sensor network is an important practical problem. In most applications, complex signal propagation effects and inhomogeneous coverage preferences lead to an optimal solution that is highly irregular and nonintuitive. The general optimization problem can be strictly formulated as a binary linear programming problem. Due to the combinatorial nature of this problem, however, its strict solution requires significant computational resources (NP-complete class of complexity) and is unobtainable for large spatial grids of candidate sensor locations. For this reason, a greedy algorithm for approximate solution was recently introduced [S. N. Vecherin, D. K. Wilson, and C. L. Pettit, "Optimal sensor placement with terrain-based constraints and signal propagation effects," Unattended Ground, Sea, and Air Sensor Technologies and Applications XI, SPIE Proc. Vol. 7333, paper 73330S (2009)]. Here further extensions to the developed algorithm are presented to include such practical needs and constraints as sensor availability, coverage by multiple sensors, and wireless communication of the sensor information. Both communication and detection are considered in a probabilistic framework. Communication signal and signature propagation effects are taken into account when calculating probabilities of communication and detection. Comparison of approximate and strict solutions on reduced-size problems suggests that the approximate algorithm yields quick and good solutions, which thus justifies using that algorithm for full-size problems. Examples of three-dimensional outdoor sensor placement are provided using a terrain-based software analysis tool.

  12. Self-adapted and tunable graphene strain sensors for detecting both subtle and large human motions.

    Science.gov (United States)

    Tao, Lu-Qi; Wang, Dan-Yang; Tian, He; Ju, Zhen-Yi; Liu, Ying; Pang, Yu; Chen, Yuan-Quan; Yang, Yi; Ren, Tian-Ling

    2017-06-22

    Conventional strain sensors rarely have both a high gauge factor and a large strain range simultaneously, so they can only be used in specific situations where only a high sensitivity or a large strain range is required. However, for detecting human motions that include both subtle and large motions, these strain sensors can't meet the diverse demands simultaneously. Here, we come up with laser patterned graphene strain sensors with self-adapted and tunable performance for the first time. A series of strain sensors with either an ultrahigh gauge factor or a preferable strain range can be fabricated simultaneously via one-step laser patterning, and are suitable for detecting all human motions. The strain sensors have a GF of up to 457 with a strain range of 35%, or have a strain range of up to 100% with a GF of 268. Most importantly, the performance of the strain sensors can be easily tuned by adjusting the patterns of the graphene, so that the sensors can meet diverse demands in both subtle and large motion situations. The graphene strain sensors show significant potential in applications such as wearable electronics, health monitoring and intelligent robots. Furthermore, the facile, fast and low-cost fabrication method will make them possible and practical to be used for commercial applications in the future.

  13. Active Hearing Mechanisms Inspire Adaptive Amplification in an Acoustic Sensor System.

    Science.gov (United States)

    Guerreiro, Jose; Reid, Andrew; Jackson, Joseph C; Windmill, James F C

    2018-06-01

    Over many millions of years of evolution, nature has developed some of the most adaptable sensors and sensory systems possible, capable of sensing, conditioning and processing signals in a very power- and size-effective manner. By looking into biological sensors and systems as a source of inspiration, this paper presents the study of a bioinspired concept of signal processing at the sensor level. By exploiting a feedback control mechanism between a front-end acoustic receiver and back-end neuronal based computation, a nonlinear amplification with hysteretic behavior is created. Moreover, the transient response of the front-end acoustic receiver can also be controlled and enhanced. A theoretical model is proposed and the concept is prototyped experimentally through an embedded system setup that can provide dynamic adaptations of a sensory system comprising a MEMS microphone placed in a closed-loop feedback system. It faithfully mimics the mosquito's active hearing response as a function of the input sound intensity. This is an adaptive acoustic sensor system concept that can be exploited by sensor and system designers within acoustics and ultrasonic engineering fields.

  14. Design and Optimization of a Low Power Pressure Sensor for Wireless Biomedical Applications

    Directory of Open Access Journals (Sweden)

    J. Sosa

    2015-01-01

    (ADC are designed, optimized, and integrated in the same substrate using a commercial 1 μm CMOS technology. As result of the optimization, we obtained a digital sensor with high sensitivity, low noise (0.002 μV/Hz, and low power consumption (358 μW. Finally, the piezoresistance noise does not affect the pressure sensor application since its value is lower than half least significant bit (LSB of the ADC.

  15. Towards adaptive security for convergent wireless sensor networks in beyond 3G environments

    DEFF Research Database (Denmark)

    Mitseva, Anelia; Aivaloglou, Efthimia; Marchitti, Maria-Antonietta

    2010-01-01

    The integration of wireless sensor networks with different network systems gives rise to many research challenges to ensure security, privacy and trust in the overall architecture. The main contribution of this paper is a generic security, privacy and trust framework providing context-aware adapt...

  16. Fault Diagnosis for Satellite Sensors and Actuators using Nonlinear Geometric Approach and Adaptive Observers

    DEFF Research Database (Denmark)

    Baldi, P.; Blanke, Mogens; Castaldi, P.

    2018-01-01

    This paper presents a novel scheme for diagnosis of faults affecting sensors that measure the satellite attitude, body angular velocity, flywheel spin rates, and defects in control torques from reaction wheel motors. The proposed methodology uses adaptive observers to provide fault estimates that...

  17. Strain sensors optimal placement for vibration-based structural health monitoring. The effect of damage on the initially optimal configuration

    Science.gov (United States)

    Loutas, T. H.; Bourikas, A.

    2017-12-01

    We revisit the optimal sensor placement of engineering structures problem with an emphasis on in-plane dynamic strain measurements and to the direction of modal identification as well as vibration-based damage detection for structural health monitoring purposes. The approach utilized is based on the maximization of a norm of the Fisher Information Matrix built with numerically obtained mode shapes of the structure and at the same time prohibit the sensorization of neighbor degrees of freedom as well as those carrying similar information, in order to obtain a satisfactory coverage. A new convergence criterion of the Fisher Information Matrix (FIM) norm is proposed in order to deal with the issue of choosing an appropriate sensor redundancy threshold, a concept recently introduced but not further investigated concerning its choice. The sensor configurations obtained via a forward sequential placement algorithm are sub-optimal in terms of FIM norm values but the selected sensors are not allowed to be placed in neighbor degrees of freedom providing thus a better coverage of the structure and a subsequent better identification of the experimental mode shapes. The issue of how service induced damage affects the initially nominated as optimal sensor configuration is also investigated and reported. The numerical model of a composite sandwich panel serves as a representative aerospace structure upon which our investigations are based.

  18. An Adaptive Fault-Tolerant Communication Scheme for Body Sensor Networks

    Directory of Open Access Journals (Sweden)

    Zichuan Xu

    2010-10-01

    Full Text Available A high degree of reliability for critical data transmission is required in body sensor networks (BSNs. However, BSNs are usually vulnerable to channel impairments due to body fading effect and RF interference, which may potentially cause data transmission to be unreliable. In this paper, an adaptive and flexible fault-tolerant communication scheme for BSNs, namely AFTCS, is proposed. AFTCS adopts a channel bandwidth reservation strategy to provide reliable data transmission when channel impairments occur. In order to fulfill the reliability requirements of critical sensors, fault-tolerant priority and queue are employed to adaptively adjust the channel bandwidth allocation. Simulation results show that AFTCS can alleviate the effect of channel impairments, while yielding lower packet loss rate and latency for critical sensors at runtime.

  19. A Real-Time Capable Software-Defined Receiver Using GPU for Adaptive Anti-Jam GPS Sensors

    Science.gov (United States)

    Seo, Jiwon; Chen, Yu-Hsuan; De Lorenzo, David S.; Lo, Sherman; Enge, Per; Akos, Dennis; Lee, Jiyun

    2011-01-01

    Due to their weak received signal power, Global Positioning System (GPS) signals are vulnerable to radio frequency interference. Adaptive beam and null steering of the gain pattern of a GPS antenna array can significantly increase the resistance of GPS sensors to signal interference and jamming. Since adaptive array processing requires intensive computational power, beamsteering GPS receivers were usually implemented using hardware such as field-programmable gate arrays (FPGAs). However, a software implementation using general-purpose processors is much more desirable because of its flexibility and cost effectiveness. This paper presents a GPS software-defined radio (SDR) with adaptive beamsteering capability for anti-jam applications. The GPS SDR design is based on an optimized desktop parallel processing architecture using a quad-core Central Processing Unit (CPU) coupled with a new generation Graphics Processing Unit (GPU) having massively parallel processors. This GPS SDR demonstrates sufficient computational capability to support a four-element antenna array and future GPS L5 signal processing in real time. After providing the details of our design and optimization schemes for future GPU-based GPS SDR developments, the jamming resistance of our GPS SDR under synthetic wideband jamming is presented. Since the GPS SDR uses commercial-off-the-shelf hardware and processors, it can be easily adopted in civil GPS applications requiring anti-jam capabilities. PMID:22164116

  20. A Real-Time Capable Software-Defined Receiver Using GPU for Adaptive Anti-Jam GPS Sensors

    Directory of Open Access Journals (Sweden)

    Dennis Akos

    2011-09-01

    Full Text Available Due to their weak received signal power, Global Positioning System (GPS signals are vulnerable to radio frequency interference. Adaptive beam and null steering of the gain pattern of a GPS antenna array can significantly increase the resistance of GPS sensors to signal interference and jamming. Since adaptive array processing requires intensive computational power, beamsteering GPS receivers were usually implemented using hardware such as field-programmable gate arrays (FPGAs. However, a software implementation using general-purpose processors is much more desirable because of its flexibility and cost effectiveness. This paper presents a GPS software-defined radio (SDR with adaptive beamsteering capability for anti-jam applications. The GPS SDR design is based on an optimized desktop parallel processing architecture using a quad-core Central Processing Unit (CPU coupled with a new generation Graphics Processing Unit (GPU having massively parallel processors. This GPS SDR demonstrates sufficient computational capability to support a four-element antenna array and future GPS L5 signal processing in real time. After providing the details of our design and optimization schemes for future GPU-based GPS SDR developments, the jamming resistance of our GPS SDR under synthetic wideband jamming is presented. Since the GPS SDR uses commercial-off-the-shelf hardware and processors, it can be easily adopted in civil GPS applications requiring anti-jam capabilities.

  1. Optimal Node Placement in Underwater Wireless Sensor Networks

    KAUST Repository

    Felamban, M.; Shihada, Basem; Jamshaid, K.

    2013-01-01

    Wireless Sensor Networks (WSN) are expected to play a vital role in the exploration and monitoring of underwater areas which are not easily reachable by humans. However, underwater communication via acoustic waves is subject to several performance

  2. Optimal adaptive normalized matched filter for large antenna arrays

    KAUST Repository

    Kammoun, Abla

    2016-09-13

    This paper focuses on the problem of detecting a target in the presence of a compound Gaussian clutter with unknown statistics. To this end, we focus on the design of the adaptive normalized matched filter (ANMF) detector which uses the regularized Tyler estimator (RTE) built from N-dimensional observations x, · · ·, x in order to estimate the clutter covariance matrix. The choice for the RTE is motivated by its possessing two major attributes: first its resilience to the presence of outliers, and second its regularization parameter that makes it more suitable to handle the scarcity in observations. In order to facilitate the design of the ANMF detector, we consider the regime in which n and N are both large. This allows us to derive closed-form expressions for the asymptotic false alarm and detection probabilities. Based on these expressions, we propose an asymptotically optimal setting for the regularization parameter of the RTE that maximizes the asymptotic detection probability while keeping the asymptotic false alarm probability below a certain threshold. Numerical results are provided in order to illustrate the gain of the proposed detector over a recently proposed setting of the regularization parameter.

  3. Optimal adaptive normalized matched filter for large antenna arrays

    KAUST Repository

    Kammoun, Abla; Couillet, Romain; Pascal, Fré dé ric; Alouini, Mohamed-Slim

    2016-01-01

    This paper focuses on the problem of detecting a target in the presence of a compound Gaussian clutter with unknown statistics. To this end, we focus on the design of the adaptive normalized matched filter (ANMF) detector which uses the regularized Tyler estimator (RTE) built from N-dimensional observations x, · · ·, x in order to estimate the clutter covariance matrix. The choice for the RTE is motivated by its possessing two major attributes: first its resilience to the presence of outliers, and second its regularization parameter that makes it more suitable to handle the scarcity in observations. In order to facilitate the design of the ANMF detector, we consider the regime in which n and N are both large. This allows us to derive closed-form expressions for the asymptotic false alarm and detection probabilities. Based on these expressions, we propose an asymptotically optimal setting for the regularization parameter of the RTE that maximizes the asymptotic detection probability while keeping the asymptotic false alarm probability below a certain threshold. Numerical results are provided in order to illustrate the gain of the proposed detector over a recently proposed setting of the regularization parameter.

  4. Optimizing surface acoustic wave sensors for trace chemical detection

    Energy Technology Data Exchange (ETDEWEB)

    Frye, G.C.; Kottenstette, R.J.; Heller, E.J. [and others

    1997-06-01

    This paper describes several recent advances for fabricating coated surface acoustic wave (SAW) sensors for applications requiring trace chemical detection. Specifically, we have demonstrated that high surface area microporous oxides can provide 100-fold improvements in SAW sensor responses compared with more typical polymeric coatings. In addition, we fabricated GaAs SAW devices with frequencies up to 500 MHz to provide greater sensitivity and an ideal substrate for integration with high-frequency electronics.

  5. Optimizing the data acquisition rate for a remotely controllable structural monitoring system with parallel operation and self-adaptive sampling

    International Nuclear Information System (INIS)

    Sheng, Wenjuan; Guo, Aihuang; Liu, Yang; Azmi, Asrul Izam; Peng, Gang-Ding

    2011-01-01

    We present a novel technique that optimizes the real-time remote monitoring and control of dispersed civil infrastructures. The monitoring system is based on fiber Bragg gating (FBG) sensors, and transfers data via Ethernet. This technique combines parallel operation and self-adaptive sampling to increase the data acquisition rate in remote controllable structural monitoring systems. The compact parallel operation mode is highly efficient at achieving the highest possible data acquisition rate for the FBG sensor based local data acquisition system. Self-adaptive sampling is introduced to continuously coordinate local acquisition and remote control for data acquisition rate optimization. Key issues which impact the operation of the whole system, such as the real-time data acquisition rate, data processing capability, and buffer usage, are investigated. The results show that, by introducing parallel operation and self-adaptive sampling, the data acquisition rate can be increased by several times without affecting the system operating performance on both local data acquisition and remote process control

  6. Location-Based Self-Adaptive Routing Algorithm for Wireless Sensor Networks in Home Automation

    Directory of Open Access Journals (Sweden)

    Hong SeungHo

    2011-01-01

    Full Text Available The use of wireless sensor networks in home automation (WSNHA is attractive due to their characteristics of self-organization, high sensing fidelity, low cost, and potential for rapid deployment. Although the AODVjr routing algorithm in IEEE 802.15.4/ZigBee and other routing algorithms have been designed for wireless sensor networks, not all are suitable for WSNHA. In this paper, we propose a location-based self-adaptive routing algorithm for WSNHA called WSNHA-LBAR. It confines route discovery flooding to a cylindrical request zone, which reduces the routing overhead and decreases broadcast storm problems in the MAC layer. It also automatically adjusts the size of the request zone using a self-adaptive algorithm based on Bayes' theorem. This makes WSNHA-LBAR more adaptable to the changes of the network state and easier to implement. Simulation results show improved network reliability as well as reduced routing overhead.

  7. An Emergency-Adaptive Routing Scheme for Wireless Sensor Networks for Building Fire Hazard Monitoring

    Directory of Open Access Journals (Sweden)

    Guilin Zheng

    2011-03-01

    Full Text Available Fire hazard monitoring and evacuation for building environments is a novel application area for the deployment of wireless sensor networks. In this context, adaptive routing is essential in order to ensure safe and timely data delivery in building evacuation and fire fighting resource applications. Existing routing mechanisms for wireless sensor networks are not well suited for building fires, especially as they do not consider critical and dynamic network scenarios. In this paper, an emergency-adaptive, real-time and robust routing protocol is presented for emergency situations such as building fire hazard applications. The protocol adapts to handle dynamic emergency scenarios and works well with the routing hole problem. Theoretical analysis and simulation results indicate that our protocol provides a real-time routing mechanism that is well suited for dynamic emergency scenarios in building fires when compared with other related work.

  8. Adaptive Flight Control Design with Optimal Control Modification on an F-18 Aircraft Model

    Science.gov (United States)

    Burken, John J.; Nguyen, Nhan T.; Griffin, Brian J.

    2010-01-01

    In the presence of large uncertainties, a control system needs to be able to adapt rapidly to regain performance. Fast adaptation is referred to as the implementation of adaptive control with a large adaptive gain to reduce the tracking error rapidly; however, a large adaptive gain can lead to high-frequency oscillations which can adversely affect the robustness of an adaptive control law. A new adaptive control modification is presented that can achieve robust adaptation with a large adaptive gain without incurring high-frequency oscillations as with the standard model-reference adaptive control. The modification is based on the minimization of the Y2 norm of the tracking error, which is formulated as an optimal control problem. The optimality condition is used to derive the modification using the gradient method. The optimal control modification results in a stable adaptation and allows a large adaptive gain to be used for better tracking while providing sufficient robustness. A damping term (v) is added in the modification to increase damping as needed. Simulations were conducted on a damaged F-18 aircraft (McDonnell Douglas, now The Boeing Company, Chicago, Illinois) with both the standard baseline dynamic inversion controller and the adaptive optimal control modification technique. The results demonstrate the effectiveness of the proposed modification in tracking a reference model.

  9. An Enhanced PSO-Based Clustering Energy Optimization Algorithm for Wireless Sensor Network.

    Science.gov (United States)

    Vimalarani, C; Subramanian, R; Sivanandam, S N

    2016-01-01

    Wireless Sensor Network (WSN) is a network which formed with a maximum number of sensor nodes which are positioned in an application environment to monitor the physical entities in a target area, for example, temperature monitoring environment, water level, monitoring pressure, and health care, and various military applications. Mostly sensor nodes are equipped with self-supported battery power through which they can perform adequate operations and communication among neighboring nodes. Maximizing the lifetime of the Wireless Sensor networks, energy conservation measures are essential for improving the performance of WSNs. This paper proposes an Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO) algorithm for Wireless Sensor Network in which clustering and clustering head selection are done by using Particle Swarm Optimization (PSO) algorithm with respect to minimizing the power consumption in WSN. The performance metrics are evaluated and results are compared with competitive clustering algorithm to validate the reduction in energy consumption.

  10. An Enhanced PSO-Based Clustering Energy Optimization Algorithm for Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    C. Vimalarani

    2016-01-01

    Full Text Available Wireless Sensor Network (WSN is a network which formed with a maximum number of sensor nodes which are positioned in an application environment to monitor the physical entities in a target area, for example, temperature monitoring environment, water level, monitoring pressure, and health care, and various military applications. Mostly sensor nodes are equipped with self-supported battery power through which they can perform adequate operations and communication among neighboring nodes. Maximizing the lifetime of the Wireless Sensor networks, energy conservation measures are essential for improving the performance of WSNs. This paper proposes an Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO algorithm for Wireless Sensor Network in which clustering and clustering head selection are done by using Particle Swarm Optimization (PSO algorithm with respect to minimizing the power consumption in WSN. The performance metrics are evaluated and results are compared with competitive clustering algorithm to validate the reduction in energy consumption.

  11. Optimized Charging Scheduling with Single Mobile Charger for Wireless Rechargeable Sensor Networks

    Directory of Open Access Journals (Sweden)

    Qihua Wang

    2017-11-01

    Full Text Available Due to the rapid development of wireless charging technology, the recharging issue in wireless rechargeable sensor network (WRSN has been a popular research problem in the past few years. The weakness of previous work is that charging route planning is not reasonable. In this work, a dynamic optimal scheduling scheme aiming to maximize the vacation time ratio of a single mobile changer for WRSN is proposed. In the proposed scheme, the wireless sensor network is divided into several sub-networks according to the initial topology of deployed sensor networks. After comprehensive analysis of energy states, working state and constraints for different sensor nodes in WRSN, we transform the optimized charging path problem of the whole network into the local optimization problem of the sub networks. The optimized charging path with respect to dynamic network topology in each sub-network is obtained by solving an optimization problem, and the lifetime of the deployed wireless sensor network can be prolonged. Simulation results show that the proposed scheme has good and reliable performance for a small wireless rechargeable sensor network.

  12. Optimization of PZT ceramic IDT sensors for health monitoring of structures.

    Science.gov (United States)

    Takpara, Rafatou; Duquennoy, Marc; Ouaftouh, Mohammadi; Courtois, Christian; Jenot, Frédéric; Rguiti, Mohamed

    2017-08-01

    Surface acoustic waves (SAW) are particularly suited to effectively monitoring and characterizing structural surfaces (condition of the surface, coating, thin layer, micro-cracks…) as their energy is localized on the surface, within approximately one wavelength. Conventionally, in non-destructive testing, wedge sensors are used to the generation guided waves but they are especially suited to flat surfaces and sized for a given type material (angle of refraction). Additionally, these sensors are quite expensive so it is quite difficult to leave the sensors permanently on the structure for its health monitoring. Therefore we are considering in this study, another type of ultrasonic sensors, able to generate SAW. These sensors are interdigital sensors or IDT sensors for InterDigital Transducer. This paper focuses on optimization of IDT sensors for non-destructive structural testing by using PZT ceramics. The challenge was to optimize the dimensional parameters of the IDT sensors in order to efficiently generate surface waves. Acoustic tests then confirmed these parameters. Copyright © 2017 Elsevier B.V. All rights reserved.

  13. Proceedings of the Adaptive Sensor Array Processing Workshop (12th) Held in Lexington, MA on 16-18 March 2004 (CD-ROM)

    National Research Council Canada - National Science Library

    James, F

    2004-01-01

    ...: The twelfth annual workshop on Adaptive Sensor Array Processing presented a diverse agenda featuring new work on adaptive methods for communications, radar and sonar, algorithmic challenges posed...

  14. Bi-Objective Optimal Control Modification Adaptive Control for Systems with Input Uncertainty

    Science.gov (United States)

    Nguyen, Nhan T.

    2012-01-01

    This paper presents a new model-reference adaptive control method based on a bi-objective optimal control formulation for systems with input uncertainty. A parallel predictor model is constructed to relate the predictor error to the estimation error of the control effectiveness matrix. In this work, we develop an optimal control modification adaptive control approach that seeks to minimize a bi-objective linear quadratic cost function of both the tracking error norm and predictor error norm simultaneously. The resulting adaptive laws for the parametric uncertainty and control effectiveness uncertainty are dependent on both the tracking error and predictor error, while the adaptive laws for the feedback gain and command feedforward gain are only dependent on the tracking error. The optimal control modification term provides robustness to the adaptive laws naturally from the optimal control framework. Simulations demonstrate the effectiveness of the proposed adaptive control approach.

  15. Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization.

    Science.gov (United States)

    Cui, Huanqing; Shu, Minglei; Song, Min; Wang, Yinglong

    2017-03-01

    Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors' memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm.

  16. Adaptive and Optimized RDF Query Interface for Distributed WFS Data

    Directory of Open Access Journals (Sweden)

    Tian Zhao

    2017-04-01

    Full Text Available Web Feature Service (WFS is a protocol for accessing geospatial data stores such as databases and Shapefiles over the Web. However, WFS does not provide direct access to data distributed in multiple servers. In addition, WFS features extracted from their original sources are not convenient for user access due to the lack of connection to high-level concepts. Users are facing the choices of either querying each WFS server first and then integrating the results, or converting the data from all WFS servers to a more expressive format such as RDF (Resource Description Framework and then querying the integrated data. The first choice requires additional programming while the second choice is not practical for large or frequently updated datasets. The new contribution of this paper is that we propose a novel adaptive and optimized RDF query interface to overcome the aforementioned limitation. Specifically, in this paper, we propose a novel algorithm to query and synthesize distributed WFS data through an RDF query interface, where users can specify data requests to multiple WFS servers using a single RDF query. Users can also define a simple configuration to associate WFS feature types, attributes, and values with RDF classes, properties, and values so that user queries can be written using a more uniform and informative vocabulary. The algorithm translates each RDF query written in SPARQL-like syntax to multiple WFS GetFeature requests, and then converts and integrates the multiple WFS results to get the answers to the original query. The generated GetFeature requests are sent asynchronously and simultaneously to WFS servers to take advantage of the server parallelism. The results of each GetFeature request are cached to improve query response time for subsequent queries that involve one or more of the cached requests. A JavaScript-based prototype is implemented and experimental results show that the query response time can be greatly reduced through

  17. Testbeam Studies on Pick-Up in Sensors with Embedded Pitch Adapters

    CERN Document Server

    Rehnisch, Laura; The ATLAS collaboration

    2018-01-01

    Embedded pitch adapters are an alternative solution to external pitch adapters widely used to facilitate the wire-bonding step when connecting silicon strip sensors and readout electronics of different pitch. The pad-pitch adaption can be moved into the sensor fabrication step by implementing a second layer of metal tracks, connected by vias to the primary metal layer of sensor strips. Such a solution, however, might bear the risk of performance losses introduced by various phenomena. One of these effects, the undesired capacitive coupling between the silicon bulk and this second metal layer (pick-up) has been investigated in photon testbeam measurements. For a worst-case embedded pitch adapter design, expected to be maximally susceptible to pick-up, a qualitative analysis has visualized the effect as a function of the location on the second metal layer structure. It was further found that the unwanted effect decreases towards expected values for operating thresholds of the binary readout used. Suggestions fo...

  18. Adaptive Square-Shaped Trajectory-Based Service Location Protocol in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Hwa-Jung Lim

    2010-04-01

    Full Text Available In this paper we propose an adaptive square-shaped trajectory (ASST-based service location method to ensure load scalability in wireless sensor networks. This first establishes a square-shaped trajectory over the nodes that surround a target point computed by the hash function and any user can access it, using the hash. Both the width and the size of the trajectory are dynamically adjustable, depending on the number of queries made to the service information on the trajectory. The number of sensor nodes on the trajectory varies in proportion to the changing trajectory shape, allowing high loads to be distributed around the hot spot area.

  19. Bio-Mimic Optimization Strategies in Wireless Sensor Networks: A Survey

    Science.gov (United States)

    Adnan, Md. Akhtaruzzaman; Razzaque, Mohammd Abdur; Ahmed, Ishtiaque; Isnin, Ismail Fauzi

    2014-01-01

    For the past 20 years, many authors have focused their investigations on wireless sensor networks. Various issues related to wireless sensor networks such as energy minimization (optimization), compression schemes, self-organizing network algorithms, routing protocols, quality of service management, security, energy harvesting, etc., have been extensively explored. The three most important issues among these are energy efficiency, quality of service and security management. To get the best possible results in one or more of these issues in wireless sensor networks optimization is necessary. Furthermore, in number of applications (e.g., body area sensor networks, vehicular ad hoc networks) these issues might conflict and require a trade-off amongst them. Due to the high energy consumption and data processing requirements, the use of classical algorithms has historically been disregarded. In this context contemporary researchers started using bio-mimetic strategy-based optimization techniques in the field of wireless sensor networks. These techniques are diverse and involve many different optimization algorithms. As far as we know, most existing works tend to focus only on optimization of one specific issue of the three mentioned above. It is high time that these individual efforts are put into perspective and a more holistic view is taken. In this paper we take a step in that direction by presenting a survey of the literature in the area of wireless sensor network optimization concentrating especially on the three most widely used bio-mimetic algorithms, namely, particle swarm optimization, ant colony optimization and genetic algorithm. In addition, to stimulate new research and development interests in this field, open research issues, challenges and future research directions are highlighted. PMID:24368702

  20. Bio-mimic optimization strategies in wireless sensor networks: a survey.

    Science.gov (United States)

    Adnan, Md Akhtaruzzaman; Abdur Razzaque, Mohammd; Ahmed, Ishtiaque; Isnin, Ismail Fauzi

    2013-12-24

    For the past 20 years, many authors have focused their investigations on wireless sensor networks. Various issues related to wireless sensor networks such as energy minimization (optimization), compression schemes, self-organizing network algorithms, routing protocols, quality of service management, security, energy harvesting, etc., have been extensively explored. The three most important issues among these are energy efficiency, quality of service and security management. To get the best possible results in one or more of these issues in wireless sensor networks optimization is necessary. Furthermore, in number of applications (e.g., body area sensor networks, vehicular ad hoc networks) these issues might conflict and require a trade-off amongst them. Due to the high energy consumption and data processing requirements, the use of classical algorithms has historically been disregarded. In this context contemporary researchers started using bio-mimetic strategy-based optimization techniques in the field of wireless sensor networks. These techniques are diverse and involve many different optimization algorithms. As far as we know, most existing works tend to focus only on optimization of one specific issue of the three mentioned above. It is high time that these individual efforts are put into perspective and a more holistic view is taken. In this paper we take a step in that direction by presenting a survey of the literature in the area of wireless sensor network optimization concentrating especially on the three most widely used bio-mimetic algorithms, namely, particle swarm optimization, ant colony optimization and genetic algorithm. In addition, to stimulate new research and development interests in this field, open research issues, challenges and future research directions are highlighted.

  1. An Architecture for Performance Optimization in a Collaborative Knowledge-Based Approach for  Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Juan Ramon Velasco

    2011-09-01

    Full Text Available Over the past few years, Intelligent Spaces (ISs have received the attention of many Wireless Sensor Network researchers. Recently, several studies have been devoted to identify their common capacities and to set up ISs over these networks. However, little attention has been paid to integrating Fuzzy Rule-Based Systems into collaborative Wireless Sensor Networks for the purpose of implementing ISs. This work presents a distributed architecture proposal for collaborative Fuzzy Rule-Based Systems embedded in Wireless Sensor Networks, which has been designed to optimize the implementation of ISs. This architecture includes the following: (a an optimized design for the inference engine; (b a visual interface; (c a module to reduce the redundancy and complexity of the knowledge bases; (d a module to evaluate the accuracy of the new knowledge base; (e a module to adapt the format of the rules to the structure used by the inference engine; and (f a communications protocol. As a real-world application of this architecture and the proposed methodologies, we show an application to the problem of modeling two plagues of the olive tree: prays (olive moth, Prays oleae Bern. and repilo (caused by the fungus Spilocaea oleagina. The results show that the architecture presented in this paper significantly decreases the consumption of resources (memory, CPU and battery without a substantial decrease in the accuracy of the inferred values.

  2. Optimized 425MHz passive wireless magnetic field sensor

    KAUST Repository

    Li, Bodong; Kosel, Jü rgen

    2014-01-01

    -X cut LiNbO3 LiNbO3 substrate. The integrated sensor is characterized with a network analyzer through an S-parameter measurement. Upon the application of a magnetic field, a maximum magnitude change and phase shift of 7.8 dB and 27 degree, respectively

  3. Parametric Optimization of Lateral NIPIN Phototransistors for Flexible Image Sensors

    Directory of Open Access Journals (Sweden)

    Min Seok Kim

    2017-08-01

    Full Text Available Curved image sensors, which are a key component in bio-inspired imaging systems, have been widely studied because they can improve an imaging system in various aspects such as low optical aberrations, small-form, and simple optics configuration. Many methods and materials to realize a curvilinear imager have been proposed to address the drawbacks of conventional imaging/optical systems. However, there have been few theoretical studies in terms of electronics on the use of a lateral photodetector as a flexible image sensor. In this paper, we demonstrate the applicability of a Si-based lateral phototransistor as the pixel of a high-efficiency curved photodetector by conducting various electrical simulations with technology computer aided design (TCAD. The single phototransistor is analyzed with different device parameters: the thickness of the active cell, doping concentration, and structure geometry. This work presents a method to improve the external quantum efficiency (EQE, linear dynamic range (LDR, and mechanical stability of the phototransistor. We also evaluated the dark current in a matrix form of phototransistors to estimate the feasibility of the device as a flexible image sensor. Moreover, we fabricated and demonstrated an array of phototransistors based on our study. The theoretical study and design guidelines of a lateral phototransistor create new opportunities in flexible image sensors.

  4. On the Optimization of a Probabilistic Data Aggregation Framework for Energy Efficiency in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Stella Kafetzoglou

    2015-08-01

    Full Text Available Among the key aspects of the Internet of Things (IoT is the integration of heterogeneous sensors in a distributed system that performs actions on the physical world based on environmental information gathered by sensors and application-related constraints and requirements. Numerous applications of Wireless Sensor Networks (WSNs have appeared in various fields, from environmental monitoring, to tactical fields, and healthcare at home, promising to change our quality of life and facilitating the vision of sensor network enabled smart cities. Given the enormous requirements that emerge in such a setting—both in terms of data and energy—data aggregation appears as a key element in reducing the amount of traffic in wireless sensor networks and achieving energy conservation. Probabilistic frameworks have been introduced as operational efficient and performance effective solutions for data aggregation in distributed sensor networks. In this work, we introduce an overall optimization approach that improves and complements such frameworks towards identifying the optimal probability for a node to aggregate packets as well as the optimal aggregation period that a node should wait for performing aggregation, so as to minimize the overall energy consumption, while satisfying certain imposed delay constraints. Primal dual decomposition is employed to solve the corresponding optimization problem while simulation results demonstrate the operational efficiency of the proposed approach under different traffic and topology scenarios.

  5. On the Optimization of a Probabilistic Data Aggregation Framework for Energy Efficiency in Wireless Sensor Networks.

    Science.gov (United States)

    Kafetzoglou, Stella; Aristomenopoulos, Giorgos; Papavassiliou, Symeon

    2015-08-11

    Among the key aspects of the Internet of Things (IoT) is the integration of heterogeneous sensors in a distributed system that performs actions on the physical world based on environmental information gathered by sensors and application-related constraints and requirements. Numerous applications of Wireless Sensor Networks (WSNs) have appeared in various fields, from environmental monitoring, to tactical fields, and healthcare at home, promising to change our quality of life and facilitating the vision of sensor network enabled smart cities. Given the enormous requirements that emerge in such a setting-both in terms of data and energy-data aggregation appears as a key element in reducing the amount of traffic in wireless sensor networks and achieving energy conservation. Probabilistic frameworks have been introduced as operational efficient and performance effective solutions for data aggregation in distributed sensor networks. In this work, we introduce an overall optimization approach that improves and complements such frameworks towards identifying the optimal probability for a node to aggregate packets as well as the optimal aggregation period that a node should wait for performing aggregation, so as to minimize the overall energy consumption, while satisfying certain imposed delay constraints. Primal dual decomposition is employed to solve the corresponding optimization problem while simulation results demonstrate the operational efficiency of the proposed approach under different traffic and topology scenarios.

  6. A market-based optimization approach to sensor and resource management

    Science.gov (United States)

    Schrage, Dan; Farnham, Christopher; Gonsalves, Paul G.

    2006-05-01

    Dynamic resource allocation for sensor management is a problem that demands solutions beyond traditional approaches to optimization. Market-based optimization applies solutions from economic theory, particularly game theory, to the resource allocation problem by creating an artificial market for sensor information and computational resources. Intelligent agents are the buyers and sellers in this market, and they represent all the elements of the sensor network, from sensors to sensor platforms to computational resources. These agents interact based on a negotiation mechanism that determines their bidding strategies. This negotiation mechanism and the agents' bidding strategies are based on game theory, and they are designed so that the aggregate result of the multi-agent negotiation process is a market in competitive equilibrium, which guarantees an optimal allocation of resources throughout the sensor network. This paper makes two contributions to the field of market-based optimization: First, we develop a market protocol to handle heterogeneous goods in a dynamic setting. Second, we develop arbitrage agents to improve the efficiency in the market in light of its dynamic nature.

  7. Optimal Piezoelectric Actuators and Sensors Configuration for Vibration Suppression of Aircraft Framework Using Particle Swarm Algorithm

    Directory of Open Access Journals (Sweden)

    Quanzhen Huang

    2017-01-01

    Full Text Available Numbers and locations of sensors and actuators play an important role in cost and control performance for active vibration control system of piezoelectric smart structure. This may lead to a diverse control system if sensors and actuators were not configured properly. An optimal location method of piezoelectric actuators and sensors is proposed in this paper based on particle swarm algorithm (PSA. Due to the complexity of the frame structure, it can be taken as a combination of many piezoelectric intelligent beams and L-type structures. Firstly, an optimal criterion of sensors and actuators is proposed with an optimal objective function. Secondly, each order natural frequency and modal strain are calculated and substituted into the optimal objective function. Preliminary optimal allocation is done using the particle swarm algorithm, based on the similar optimization method and the combination of the vibration stress and strain distribution at the lower modal frequency. Finally, the optimal location is given. An experimental platform was established and the experimental results indirectly verified the feasibility and effectiveness of the proposed method.

  8. An Adaption Broadcast Radius-Based Code Dissemination Scheme for Low Energy Wireless Sensor Networks.

    Science.gov (United States)

    Yu, Shidi; Liu, Xiao; Liu, Anfeng; Xiong, Naixue; Cai, Zhiping; Wang, Tian

    2018-05-10

    Due to the Software Defined Network (SDN) technology, Wireless Sensor Networks (WSNs) are getting wider application prospects for sensor nodes that can get new functions after updating program codes. The issue of disseminating program codes to every node in the network with minimum delay and energy consumption have been formulated and investigated in the literature. The minimum-transmission broadcast (MTB) problem, which aims to reduce broadcast redundancy, has been well studied in WSNs where the broadcast radius is assumed to be fixed in the whole network. In this paper, an Adaption Broadcast Radius-based Code Dissemination (ABRCD) scheme is proposed to reduce delay and improve energy efficiency in duty cycle-based WSNs. In the ABCRD scheme, a larger broadcast radius is set in areas with more energy left, generating more optimized performance than previous schemes. Thus: (1) with a larger broadcast radius, program codes can reach the edge of network from the source in fewer hops, decreasing the number of broadcasts and at the same time, delay. (2) As the ABRCD scheme adopts a larger broadcast radius for some nodes, program codes can be transmitted to more nodes in one broadcast transmission, diminishing the number of broadcasts. (3) The larger radius in the ABRCD scheme causes more energy consumption of some transmitting nodes, but radius enlarging is only conducted in areas with an energy surplus, and energy consumption in the hot-spots can be reduced instead due to some nodes transmitting data directly to sink without forwarding by nodes in the original hot-spot, thus energy consumption can almost reach a balance and network lifetime can be prolonged. The proposed ABRCD scheme first assigns a broadcast radius, which doesn’t affect the network lifetime, to nodes having different distance to the code source, then provides an algorithm to construct a broadcast backbone. In the end, a comprehensive performance analysis and simulation result shows that the proposed

  9. Adaptive Sensor Tuning for Seismic Event Detection in Environment with Electromagnetic Noise

    Science.gov (United States)

    Ziegler, Abra E.

    The goal of this research is to detect possible microseismic events at a carbon sequestration site. Data recorded on a continuous downhole microseismic array in the Farnsworth Field, an oil field in Northern Texas that hosts an ongoing carbon capture, utilization, and storage project, were evaluated using machine learning and reinforcement learning techniques to determine their effectiveness at seismic event detection on a dataset with electromagnetic noise. The data were recorded from a passive vertical monitoring array consisting of 16 levels of 3-component 15 Hz geophones installed in the field and continuously recording since January 2014. Electromagnetic and other noise recorded on the array has significantly impacted the utility of the data and it was necessary to characterize and filter the noise in order to attempt event detection. Traditional detection methods using short-term average/long-term average (STA/LTA) algorithms were evaluated and determined to be ineffective because of changing noise levels. To improve the performance of event detection and automatically and dynamically detect seismic events using effective data processing parameters, an adaptive sensor tuning (AST) algorithm developed by Sandia National Laboratories was utilized. AST exploits neuro-dynamic programming (reinforcement learning) trained with historic event data to automatically self-tune and determine optimal detection parameter settings. The key metric that guides the AST algorithm is consistency of each sensor with its nearest neighbors: parameters are automatically adjusted on a per station basis to be more or less sensitive to produce consistent agreement of detections in its neighborhood. The effects that changes in neighborhood configuration have on signal detection were explored, as it was determined that neighborhood-based detections significantly reduce the number of both missed and false detections in ground-truthed data. The performance of the AST algorithm was

  10. An Adaption Broadcast Radius-Based Code Dissemination Scheme for Low Energy Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Shidi Yu

    2018-05-01

    Full Text Available Due to the Software Defined Network (SDN technology, Wireless Sensor Networks (WSNs are getting wider application prospects for sensor nodes that can get new functions after updating program codes. The issue of disseminating program codes to every node in the network with minimum delay and energy consumption have been formulated and investigated in the literature. The minimum-transmission broadcast (MTB problem, which aims to reduce broadcast redundancy, has been well studied in WSNs where the broadcast radius is assumed to be fixed in the whole network. In this paper, an Adaption Broadcast Radius-based Code Dissemination (ABRCD scheme is proposed to reduce delay and improve energy efficiency in duty cycle-based WSNs. In the ABCRD scheme, a larger broadcast radius is set in areas with more energy left, generating more optimized performance than previous schemes. Thus: (1 with a larger broadcast radius, program codes can reach the edge of network from the source in fewer hops, decreasing the number of broadcasts and at the same time, delay. (2 As the ABRCD scheme adopts a larger broadcast radius for some nodes, program codes can be transmitted to more nodes in one broadcast transmission, diminishing the number of broadcasts. (3 The larger radius in the ABRCD scheme causes more energy consumption of some transmitting nodes, but radius enlarging is only conducted in areas with an energy surplus, and energy consumption in the hot-spots can be reduced instead due to some nodes transmitting data directly to sink without forwarding by nodes in the original hot-spot, thus energy consumption can almost reach a balance and network lifetime can be prolonged. The proposed ABRCD scheme first assigns a broadcast radius, which doesn’t affect the network lifetime, to nodes having different distance to the code source, then provides an algorithm to construct a broadcast backbone. In the end, a comprehensive performance analysis and simulation result shows that

  11. A Draw-In Sensor for Process Control and Optimization

    International Nuclear Information System (INIS)

    Mahayotsanun, Numpon; Cao, Jian; Peshkin, Michael

    2005-01-01

    Sheet metal forming is one of the major processes in manufacturing and is broadly used due to its high degree of design flexibility and low cost. In the sheet metal forming process, draw-in (planar movement of a sheet periphery) frequently occurs and is one of the most dominated indicators on the success of a forming process. Currently, monitoring and controlling draw-in during each stamping operation requires either time-consuming setup or a significant die modification. Most devices have been used only in laboratory settings. Our goal is to design a draw-in sensor providing high sensitivity in monitoring; ease of setup, measurement and controlling; and eventually be implemented in industry. Our design is based on the mutual inductance principle, which we considered physical factors affecting the characteristics of the draw-in sensor. Two different configurations, single-transducer and double-transducer of our draw-in sensors have been designed and tested. The results showed good linearity, especially for the double-transducer case. The output of the draw-in sensor was affected by the type of sheet metal, dimension of the transducer, and the distance between the transducer and the testing sheet metal. It was found that the result was insensitive to the waviness of the sheet metal if sheet thickness was thin. The invention, implementation, and integration of the draw-in sensor will have an enormous impact on revolutionizing the control of stamping process, will provide solid ground for process variation and uncertainty studies, and ultimately will affect the design decision process

  12. The economics of optimal adaptation to climate change

    Energy Technology Data Exchange (ETDEWEB)

    Callaway, J.M.; Ringius, L.

    2002-07-01

    This paper has set out to provide a consistent theoretical framework for understanding how consumers, producers and factor agents respond to the impacts of climate change, with a primary focus on the production and consumption of, and investment in, market goods and services under competitive conditions. However, we have also pointed out that this theory can be extended to economies where individuals and groups pursue other well-defined objectives, and we have provided some examples that show the consistency in adaptation behaviour between economic terms and those who maximise the objective of household nutrition. We have defined adaptation as the changes that economic agents make in the allocation of resources to consumption, production and investment to offset the effects of weather variability or climate change on their welfare. This definition is broad enough to encompass almost every conceivable kind of adaptation behaviour. Further, we have followed the distinction between adaptation that is autonomous and adaptation actions that are undertaken by governments in the form of adaptation strategies. Autonomous adaptation is adaptation that economic agents will undertake to change, without the assistance of government, to improve their welfare due to incentives that are built into the political economy of a country. Adaptation strategies involve conscious decisions by governments to undertake actions and implement projects to avoid (or benefit from) weather variability and climate change. We show how the extent to which economic activities are adapted to existing climate variability will affect how much autonomous adaptation will need to occur once the pure effect of climate change is taken into account. In this paper we argue that the ability of economic activities to adapt once the pure effect of climate change can be accounted for by the following factors: Presence of well-developed markets for inputs and outputs; Ability and competitiveness to produce

  13. Hardware implementation of adaptive filtering using charge-coupled devices. [For perimeter security sensors

    Energy Technology Data Exchange (ETDEWEB)

    Donohoe, G.W.

    1977-01-01

    Sandia Laboratories' Digital Systems Division/1734, as part of its work on the Base and Installation Security Systems (BISS) program has been making use of adaptive digital filters to improve the signal-to-noise ratio of perimeter sensor signals. In particular, the Widrow-Hoff least-mean-squares algorithm has been used extensively. This non-recursive linear predictor has been successful in extracting aperiodic signals from periodic noise. The adaptive filter generates a predictor signal which is subtracted from the input signal to produce an error signal. The value of this error is fed back to the filter to improve the quality of the next prediction. Implementation of the Widrow adaptive filter using a Charge-Coupled Device tapped analog delay line, analog voltage multipliers and operational amplifiers is described. The resulting filter adapts to signals with frequency components as high as several megahertz.

  14. Active vibration reduction by optimally placed sensors and actuators with application to stiffened plates by beams

    International Nuclear Information System (INIS)

    Daraji, A H; Hale, J M

    2014-01-01

    This study concerns new investigation of active vibration reduction of a stiffened plate bonded with discrete sensor/actuator pairs located optimally using genetic algorithms based on a developed finite element modeling. An isotropic plate element stiffened by a number of beam elements on its edges and having a piezoelectric sensor and actuator pair bonded to its surfaces is modeled using the finite element method and Hamilton’s principle, taking into account the effects of piezoelectric mass, stiffness and electromechanical coupling. The modeling is based on the first order shear deformation theory taking into account the effects of bending, membrane and shear deformation for the plate, the stiffening beam and the piezoelectric patches. A Matlab finite element program has been built for the stiffened plate model and verified with ANSYS and also experimentally. Optimal placement of ten piezoelectric sensor/actuator pairs and optimal feedback gain for active vibration reduction are investigated for a plate stiffened by two beams arranged in the form of a cross. The genetic algorithm was set up for optimization of sensor/actuator placement and feedback gain based on the minimization of the optimal linear quadratic index as an objective function to suppress the first six modes of vibration. Comparison study is presented for active vibration reduction of a square cantilever plate stiffened by crossed beams with two sensor/actuator configurations: firstly, ten piezoelectric sensor/actuator pairs are located in optimal positions; secondly, a piezoelectric layer of single sensor/actuator pair covering the whole of the stiffened plate as a SISO system. (paper)

  15. Optimal Power Allocation of a Wireless Sensor Node under Different Rate Constraints

    KAUST Repository

    Solares, Jose

    2011-07-01

    Wireless sensor networks consist of the placement of sensors over a broad area in order to acquire data. Depending on the application, different design criteria should be considered in the construction of the sensors but among all of them, the battery life-cycle is of crucial interest. Power minimization is a problem that has been addressed from different approaches which include an analysis from an architectural perspective and with bit error rate and/or discrete instantaneous transmission rate constraints, among others. In this work, the optimal transmit power of a sensor node while satisfying different rate constraints is derived. First, an optimization problem with an instantaneous transmission rate constraint is addressed. Next, the optimal power is analyzed, but now with an average transmission rate constraint. The optimal solution for a class of fading channels, in terms of system parameters, is presented and a suboptimal solution is also proposed for an easier, yet efficient, implementation. Insightful asymptotical analysis for both schemes, considering a Rayleigh fading channel, are shown. Furthermore, the optimal power allocation for a sensor node in a cognitive radio environment is analyzed where an optimum solution for a class of fading channels is again derived. In all cases, numerical results are provided for either Rayleigh or Nakagami-m fading channels. The results obtained are extended to scenarios where we have either one transmitter-multiple receivers or multiple transmitters-one receiver.

  16. Combination Adaptive Traffic Algorithm and Coordinated Sleeping in Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    M. Udin Harun Al Rasyid

    2014-12-01

    Full Text Available Wireless sensor network (WSN uses a battery as its primary power source, so that WSN will be limited to battery power for long operations. The WSN should be able to save the energy consumption in order to operate in a long time.WSN has the potential to be the future of wireless communications solutions. WSN are small but has a variety of functions that can help human life. WSN has the wide variety of sensors and can communicate quickly making it easier for people to obtain information accurately and quickly. In this study, we combine adaptive traffic algorithms and coordinated sleeping as power‐efficient WSN solution. We compared the performance of our proposed ideas combination adaptive traffic and coordinated sleeping algorithm with non‐adaptive scheme. From the simulation results, our proposed idea has good‐quality data transmission and more efficient in energy consumption, but it has higher delay than that of non‐adaptive scheme. Keywords:WSN,adaptive traffic,coordinated sleeping,beacon order,superframe order.

  17. An Adaptive Technique for a Redundant-Sensor Navigation System. Ph.D. Thesis

    Science.gov (United States)

    Chien, T. T.

    1972-01-01

    An on-line adaptive technique is developed to provide a self-contained redundant-sensor navigation system with a capability to utilize its full potentiality in reliability and performance. The gyro navigation system is modeled as a Gauss-Markov process, with degradation modes defined as changes in characteristics specified by parameters associated with the model. The adaptive system is formulated as a multistage stochastic process: (1) a detection system, (2) an identification system and (3) a compensation system. It is shown that the sufficient statistics for the partially observable process in the detection and identification system is the posterior measure of the state of degradation, conditioned on the measurement history.

  18. Learning-Based Adaptive Optimal Tracking Control of Strict-Feedback Nonlinear Systems.

    Science.gov (United States)

    Gao, Weinan; Jiang, Zhong-Ping; Weinan Gao; Zhong-Ping Jiang; Gao, Weinan; Jiang, Zhong-Ping

    2018-06-01

    This paper proposes a novel data-driven control approach to address the problem of adaptive optimal tracking for a class of nonlinear systems taking the strict-feedback form. Adaptive dynamic programming (ADP) and nonlinear output regulation theories are integrated for the first time to compute an adaptive near-optimal tracker without any a priori knowledge of the system dynamics. Fundamentally different from adaptive optimal stabilization problems, the solution to a Hamilton-Jacobi-Bellman (HJB) equation, not necessarily a positive definite function, cannot be approximated through the existing iterative methods. This paper proposes a novel policy iteration technique for solving positive semidefinite HJB equations with rigorous convergence analysis. A two-phase data-driven learning method is developed and implemented online by ADP. The efficacy of the proposed adaptive optimal tracking control methodology is demonstrated via a Van der Pol oscillator with time-varying exogenous signals.

  19. Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine

    Science.gov (United States)

    Luo, Guangchun; Qin, Ke; Wang, Nan; Niu, Weina

    2018-01-01

    Sensor drift is a common issue in E-Nose systems and various drift compensation methods have received fruitful results in recent years. Although the accuracy for recognizing diverse gases under drift conditions has been largely enhanced, few of these methods considered online processing scenarios. In this paper, we focus on building online drift compensation model by transforming two domain adaptation based methods into their online learning versions, which allow the recognition models to adapt to the changes of sensor responses in a time-efficient manner without losing the high accuracy. Experimental results using three different settings confirm that the proposed methods save large processing time when compared with their offline versions, and outperform other drift compensation methods in recognition accuracy. PMID:29494543

  20. Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Zhiyuan Ma

    2018-03-01

    Full Text Available Sensor drift is a common issue in E-Nose systems and various drift compensation methods have received fruitful results in recent years. Although the accuracy for recognizing diverse gases under drift conditions has been largely enhanced, few of these methods considered online processing scenarios. In this paper, we focus on building online drift compensation model by transforming two domain adaptation based methods into their online learning versions, which allow the recognition models to adapt to the changes of sensor responses in a time-efficient manner without losing the high accuracy. Experimental results using three different settings confirm that the proposed methods save large processing time when compared with their offline versions, and outperform other drift compensation methods in recognition accuracy.

  1. Topology optimization of pressure adaptive honeycomb for a morphing flap

    NARCIS (Netherlands)

    Vos, R.; Scheepstra, J.; Barrett, R.

    2011-01-01

    The paper begins with a brief historical overview of pressure adaptive materials and structures. By examining avian anatomy, it is seen that pressure-adaptive structures have been used successfully in the Natural world to hold structural positions for extended periods of time and yet allow for

  2. Adaptive coupling optimized spiking coherence and synchronization in Newman-Watts neuronal networks.

    Science.gov (United States)

    Gong, Yubing; Xu, Bo; Wu, Ya'nan

    2013-09-01

    In this paper, we have numerically studied the effect of adaptive coupling on the temporal coherence and synchronization of spiking activity in Newman-Watts Hodgkin-Huxley neuronal networks. It is found that random shortcuts can enhance the spiking synchronization more rapidly when the increment speed of adaptive coupling is increased and can optimize the temporal coherence of spikes only when the increment speed of adaptive coupling is appropriate. It is also found that adaptive coupling strength can enhance the synchronization of spikes and can optimize the temporal coherence of spikes when random shortcuts are appropriate. These results show that adaptive coupling has a big influence on random shortcuts related spiking activity and can enhance and optimize the temporal coherence and synchronization of spiking activity of the network. These findings can help better understand the roles of adaptive coupling for improving the information processing and transmission in neural systems.

  3. Multipoint dynamically reconfigure adaptive distributed fiber optic acoustic emission sensor (FAESense) system for condition based maintenance

    Science.gov (United States)

    Mendoza, Edgar; Prohaska, John; Kempen, Connie; Esterkin, Yan; Sun, Sunjian; Krishnaswamy, Sridhar

    2010-09-01

    This paper describes preliminary results obtained under a Navy SBIR contract by Redondo Optics Inc. (ROI), in collaboration with Northwestern University towards the development and demonstration of a next generation, stand-alone and fully integrated, dynamically reconfigurable, adaptive fiber optic acoustic emission sensor (FAESense™) system for the in-situ unattended detection and localization of shock events, impact damage, cracks, voids, and delaminations in new and aging critical infrastructures found in ships, submarines, aircraft, and in next generation weapon systems. ROI's FAESense™ system is based on the integration of proven state-of-the-art technologies: 1) distributed array of in-line fiber Bragg gratings (FBGs) sensors sensitive to strain, vibration, and acoustic emissions, 2) adaptive spectral demodulation of FBG sensor dynamic signals using two-wave mixing interferometry on photorefractive semiconductors, and 3) integration of all the sensor system passive and active optoelectronic components within a 0.5-cm x 1-cm photonic integrated circuit microchip. The adaptive TWM demodulation methodology allows the measurement of dynamic high frequnency acoustic emission events, while compensating for passive quasi-static strain and temperature drifts. It features a compact, low power, environmentally robust 1-inch x 1-inch x 4-inch small form factor (SFF) package with no moving parts. The FAESense™ interrogation system is microprocessor-controlled using high data rate signal processing electronics for the FBG sensors calibration, temperature compensation and the detection and analysis of acoustic emission signals. Its miniaturized package, low power operation, state-of-the-art data communications, and low cost makes it a very attractive solution for a large number of applications in naval and maritime industries, aerospace, civil structures, the oil and chemical industry, and for homeland security applications.

  4. Adaptive Reliable Routing Based on Cluster Hierarchy for Wireless Multimedia Sensor Networks

    Directory of Open Access Journals (Sweden)

    Kai Lin

    2010-01-01

    Full Text Available As a multimedia information acquisition and processing method, wireless multimedia sensor network(WMSN has great application potential in military and civilian areas. Compared with traditional wireless sensor network, the routing design of WMSN should obtain more attention on the quality of transmission. This paper proposes an adaptive reliable routing based on clustering hierarchy named ARCH, which includes energy prediction and power allocation mechanism. To obtain a better performance, the cluster structure is formed based on cellular topology. The introduced prediction mechanism makes the sensor nodes predict the remaining energy of other nodes, which dramatically reduces the overall information needed for energy balancing. ARCH can dynamically balance the energy consumption of nodes based on the predicted results provided by power allocation. The simulation results prove the efficiency of the proposed ARCH routing.

  5. An adaptive secret key-directed cryptographic scheme for secure transmission in wireless sensor networks

    International Nuclear Information System (INIS)

    Muhammad, K.; Jan, Z.; Khan, Z

    2015-01-01

    Wireless Sensor Networks (WSNs) are memory and bandwidth limited networks whose main goals are to maximize the network lifetime and minimize the energy consumption and transmission cost. To achieve these goals, different techniques of compression and clustering have been used. However, security is an open and major issue in WSNs for which different approaches are used, both in centralized and distributed WSNs' environments. This paper presents an adaptive cryptographic scheme for secure transmission of various sensitive parameters, sensed by wireless sensors to the fusion center for further processing in WSNs such as military networks. The proposed method encrypts the sensitive captured data of sensor nodes using various encryption procedures (bitxor operation, bits shuffling, and secret key based encryption) and then sends it to the fusion center. At the fusion center, the received encrypted data is decrypted for taking further necessary actions. The experimental results with complexity analysis, validate the effectiveness and feasibility of the proposed method in terms of security in WSNs. (author)

  6. Adaptive Pulsed Laser Line Extraction for Terrain Reconstruction using a Dynamic Vision Sensor

    Directory of Open Access Journals (Sweden)

    Christian eBrandli

    2014-01-01

    Full Text Available Mobile robots need to know the terrain in which they are moving for path planning and obstacle avoidance. This paper proposes the combination of a bio-inspired, redundancy-suppressing dynamic vision sensor with a pulsed line laser to allow fast terrain reconstruction. A stable laser stripe extraction is achieved by exploiting the sensor’s ability to capture the temporal dynamics in a scene. An adaptive temporal filter for the sensor output allows a reliable reconstruction of 3D terrain surfaces. Laser stripe extractions up to pulsing frequencies of 500Hz were achieved using a line laser of 3mW at a distance of 45cm using an event-based algorithm that exploits the sparseness of the sensor output. As a proof of concept, unstructured rapid prototype terrain samples have been successfully reconstructed with an accuracy of 2mm.

  7. Predictive simulations and optimization of nanowire field-effect PSA sensors including screening

    KAUST Repository

    Baumgartner, Stefan; Heitzinger, Clemens; Vacic, Aleksandar; Reed, Mark A

    2013-01-01

    We apply our self-consistent PDE model for the electrical response of field-effect sensors to the 3D simulation of nanowire PSA (prostate-specific antigen) sensors. The charge concentration in the biofunctionalized boundary layer at the semiconductor-electrolyte interface is calculated using the propka algorithm, and the screening of the biomolecules by the free ions in the liquid is modeled by a sensitivity factor. This comprehensive approach yields excellent agreement with experimental current-voltage characteristics without any fitting parameters. Having verified the numerical model in this manner, we study the sensitivity of nanowire PSA sensors by changing device parameters, making it possible to optimize the devices and revealing the attributes of the optimal field-effect sensor. © 2013 IOP Publishing Ltd.

  8. A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors.

    Science.gov (United States)

    Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

    2017-09-21

    In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors.

  9. A neuro-fuzzy inference system tuned by particle swarm optimization algorithm for sensor monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Oliveira, Mauro Vitor de [Instituto de Engenharia Nuclear (IEN), Rio de Janeiro, RJ (Brazil). Div. de Instrumentacao e Confiabilidade Humana]. E-mail: mvitor@ien.gov.br; Schirru, Roberto [Universidade Federal, Rio de Janeiro, RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia. Lab. de Monitoracao de Processos

    2005-07-01

    A neuro-fuzzy inference system (ANFIS) tuned by particle swarm optimization (PSO) algorithm has been developed for monitor the relevant sensor in a nuclear plant using the information of other sensors. The antecedent parameters of the ANFIS that estimates the relevant sensor signal are optimized by a PSO algorithm and consequent parameters use a least-squares algorithm. The proposed sensor-monitoring algorithm was demonstrated through the estimation of the nuclear power value in a pressurized water reactor using as input to the ANFIS six other correlated signals. The obtained results are compared to two similar ANFIS using one gradient descendent (GD) and other genetic algorithm (GA), as antecedent parameters training algorithm. (author)

  10. Predictive simulations and optimization of nanowire field-effect PSA sensors including screening

    KAUST Repository

    Baumgartner, Stefan

    2013-05-03

    We apply our self-consistent PDE model for the electrical response of field-effect sensors to the 3D simulation of nanowire PSA (prostate-specific antigen) sensors. The charge concentration in the biofunctionalized boundary layer at the semiconductor-electrolyte interface is calculated using the propka algorithm, and the screening of the biomolecules by the free ions in the liquid is modeled by a sensitivity factor. This comprehensive approach yields excellent agreement with experimental current-voltage characteristics without any fitting parameters. Having verified the numerical model in this manner, we study the sensitivity of nanowire PSA sensors by changing device parameters, making it possible to optimize the devices and revealing the attributes of the optimal field-effect sensor. © 2013 IOP Publishing Ltd.

  11. A neuro-fuzzy inference system tuned by particle swarm optimization algorithm for sensor monitoring

    International Nuclear Information System (INIS)

    Oliveira, Mauro Vitor de; Schirru, Roberto

    2005-01-01

    A neuro-fuzzy inference system (ANFIS) tuned by particle swarm optimization (PSO) algorithm has been developed for monitor the relevant sensor in a nuclear plant using the information of other sensors. The antecedent parameters of the ANFIS that estimates the relevant sensor signal are optimized by a PSO algorithm and consequent parameters use a least-squares algorithm. The proposed sensor-monitoring algorithm was demonstrated through the estimation of the nuclear power value in a pressurized water reactor using as input to the ANFIS six other correlated signals. The obtained results are compared to two similar ANFIS using one gradient descendent (GD) and other genetic algorithm (GA), as antecedent parameters training algorithm. (author)

  12. Optimized 425MHz passive wireless magnetic field sensor

    KAUST Repository

    Li, Bodong

    2014-06-01

    A passive, magnetic field sensor consisting of a 425 MHz surface acoustic wave device loaded with a giant magnetoimpedance element is developed. The GMI element with a multilayer structure composed of Ni80Fe 20/Cu/Ni80Fe20, is fabricated on a 128° Y-X cut LiNbO3 LiNbO3 substrate. The integrated sensor is characterized with a network analyzer through an S-parameter measurement. Upon the application of a magnetic field, a maximum magnitude change and phase shift of 7.8 dB and 27 degree, respectively, are observed. Within the linear region, the magnetic sensitivity is 1.6 dB/Oe and 5 deg/Oe. © 2014 IEEE.

  13. Optimization of Graphene Sensors to Detect Biological Warfare Agents

    Science.gov (United States)

    2014-03-27

    variations that use detection elements such as glucose, cholesterol, NADH, hydrogen peroxide, nitrites , nitrous oxide and aptamers (such as ssDNA...electrical current [34]. The sensor materials and detection limits listed in Table 1 illustrate the types of processed graphene that can be used to...and a 1% mortality rate for those treated[28]. Gastrointestinal anthrax results when B. anthracis enters the body by eating infected meat and has

  14. Optimal power allocation of a sensor node under different rate constraints

    KAUST Repository

    Ayala Solares, Jose Roberto

    2012-06-01

    The optimal transmit power of a sensor node while satisfying different rate constraints is derived. First, an optimization problem with an instantaneous transmission rate constraint is addressed. Next, the optimal power is analyzed, but now with an average transmission rate constraint. The optimal solution for a class of fading channels, in terms of system parameters, is presented and a suboptimal solution is also proposed for an easier, yet efficient, implementation. Insightful asymptotical analysis for both schemes, considering a Rayleigh fading channel, are shown. Finally, the optimal power allocation for a sensor node in a cognitive radio environment is analyzed where an optimum solution for a class of fading channels is again derived. In all cases, numerical results are provided for either Rayleigh or Nakagami-m fading channels. © 2012 IEEE.

  15. An Umeclidinium membrane sensor; Two-step optimization strategy for improved responses.

    Science.gov (United States)

    Yehia, Ali M; Monir, Hany H

    2017-09-01

    In the scientific context of membrane sensors and improved experimentation, we devised an experimentally designed protocol for sensor optimization. Two-step strategy was implemented for Umeclidinium bromide (UMEC) analysis which is a novel quinuclidine-based muscarinic antagonist used for maintenance treatment of symptoms accompanied with chronic obstructive pulmonary disease. In the first place, membrane components were screened for ideal ion exchanger, ionophore and plasticizer using three categorical factors at three levels in Taguchi design. Secondly, experimentally designed optimization was followed in order to tune the sensor up for finest responses. Twelve experiments were randomly carried out in a continuous factor design. Nernstian response, detection limit and selectivity were assigned as responses in these designs. The optimized membrane sensor contained tetrakis-[3,5-bis(trifluoro- methyl)phenyl] borate (0.44wt%) and calix[6]arene (0.43wt%) in 50.00% PVC plasticized with 49.13wt% 2-ni-tro-phenyl octylether. This sensor, along with an optimum concentration of inner filling solution (2×10 -4 molL -1 UMEC) and 2h of soaking time, attained the design objectives. Nernstian response approached 59.7mV/decade and detection limit decreased by about two order of magnitude (8×10 -8 mol L -1 ) through this optimization protocol. The proposed sensor was validated for UMEC determination in its linear range (3.16×10 -7 -1×10 -3 mol L -1 ) and challenged for selective discrimination of other congeners and inorganic cations. Results of INCRUSE ELLIPTA ® inhalation powder analyses obtained from the proposed sensor and manufacturer's UPLC were statistically compared. Moreover the proposed sensor was successfully used for the determination of UMEC in plasma samples. Copyright © 2017 Elsevier B.V. All rights reserved.

  16. Optimal sensor placement for leakage detection and isolation in water distribution networks

    OpenAIRE

    Rosich Oliva, Albert; Sarrate Estruch, Ramon; Nejjari Akhi-Elarab, Fatiha

    2012-01-01

    In this paper, the problem of leakage detection and isolation in water distribution networks is addressed applying an optimal sensor placement methodology. The chosen technique is based on structural models and thus it is suitable to handle non-linear and large scale systems. A drawback of this technique arises when costs are assigned uniformly. A main contribution of this paper is the proposal of an iterative methodology that focuses on identifying essential sensors which ultimately leads to...

  17. Adaptive Energy-Efficient Target Detection Based on Mobile Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Tengyue Zou

    2017-05-01

    Full Text Available Target detection is a widely used application for area surveillance, elder care, and fire alarms; its purpose is to find a particular object or event in a region of interest. Usually, fixed observing stations or static sensor nodes are arranged uniformly in the field. However, each part of the field has a different probability of being intruded upon; if an object suddenly enters an area with few guardian devices, a loss of detection will occur, and the stations in the safe areas will waste their energy for a long time without any discovery. Thus, mobile wireless sensor networks may benefit from adaptation and pertinence in detection. Sensor nodes equipped with wheels are able to move towards the risk area via an adaptive learning procedure based on Bayesian networks. Furthermore, a clustering algorithm based on k-means++ and an energy control mechanism is used to reduce the energy consumption of nodes. The extended Kalman filter and a voting data fusion method are employed to raise the localization accuracy of the target. The simulation and experimental results indicate that this new system with adaptive energy-efficient methods is able to achieve better performance than the traditional ones.

  18. An Autonomous Self-Aware and Adaptive Fault Tolerant Routing Technique for Wireless Sensor Networks.

    Science.gov (United States)

    Abba, Sani; Lee, Jeong-A

    2015-08-18

    We propose an autonomous self-aware and adaptive fault-tolerant routing technique (ASAART) for wireless sensor networks. We address the limitations of self-healing routing (SHR) and self-selective routing (SSR) techniques for routing sensor data. We also examine the integration of autonomic self-aware and adaptive fault detection and resiliency techniques for route formation and route repair to provide resilience to errors and failures. We achieved this by using a combined continuous and slotted prioritized transmission back-off delay to obtain local and global network state information, as well as multiple random functions for attaining faster routing convergence and reliable route repair despite transient and permanent node failure rates and efficient adaptation to instantaneous network topology changes. The results of simulations based on a comparison of the ASAART with the SHR and SSR protocols for five different simulated scenarios in the presence of transient and permanent node failure rates exhibit a greater resiliency to errors and failure and better routing performance in terms of the number of successfully delivered network packets, end-to-end delay, delivered MAC layer packets, packet error rate, as well as efficient energy conservation in a highly congested, faulty, and scalable sensor network.

  19. An Autonomous Self-Aware and Adaptive Fault Tolerant Routing Technique for Wireless Sensor Networks

    Science.gov (United States)

    Abba, Sani; Lee, Jeong-A

    2015-01-01

    We propose an autonomous self-aware and adaptive fault-tolerant routing technique (ASAART) for wireless sensor networks. We address the limitations of self-healing routing (SHR) and self-selective routing (SSR) techniques for routing sensor data. We also examine the integration of autonomic self-aware and adaptive fault detection and resiliency techniques for route formation and route repair to provide resilience to errors and failures. We achieved this by using a combined continuous and slotted prioritized transmission back-off delay to obtain local and global network state information, as well as multiple random functions for attaining faster routing convergence and reliable route repair despite transient and permanent node failure rates and efficient adaptation to instantaneous network topology changes. The results of simulations based on a comparison of the ASAART with the SHR and SSR protocols for five different simulated scenarios in the presence of transient and permanent node failure rates exhibit a greater resiliency to errors and failure and better routing performance in terms of the number of successfully delivered network packets, end-to-end delay, delivered MAC layer packets, packet error rate, as well as efficient energy conservation in a highly congested, faulty, and scalable sensor network. PMID:26295236

  20. Adaptive Energy-Efficient Target Detection Based on Mobile Wireless Sensor Networks.

    Science.gov (United States)

    Zou, Tengyue; Li, Zhenjia; Li, Shuyuan; Lin, Shouying

    2017-05-04

    Target detection is a widely used application for area surveillance, elder care, and fire alarms; its purpose is to find a particular object or event in a region of interest. Usually, fixed observing stations or static sensor nodes are arranged uniformly in the field. However, each part of the field has a different probability of being intruded upon; if an object suddenly enters an area with few guardian devices, a loss of detection will occur, and the stations in the safe areas will waste their energy for a long time without any discovery. Thus, mobile wireless sensor networks may benefit from adaptation and pertinence in detection. Sensor nodes equipped with wheels are able to move towards the risk area via an adaptive learning procedure based on Bayesian networks. Furthermore, a clustering algorithm based on k -means++ and an energy control mechanism is used to reduce the energy consumption of nodes. The extended Kalman filter and a voting data fusion method are employed to raise the localization accuracy of the target. The simulation and experimental results indicate that this new system with adaptive energy-efficient methods is able to achieve better performance than the traditional ones.

  1. Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization

    Directory of Open Access Journals (Sweden)

    Huanqing Cui

    2017-03-01

    Full Text Available Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm.

  2. Q-Learning Multi-Objective Sequential Optimal Sensor Parameter Weights

    Directory of Open Access Journals (Sweden)

    Raquel Cohen

    2016-04-01

    Full Text Available The goal of our solution is to deliver trustworthy decision making analysis tools which evaluate situations and potential impacts of such decisions through acquired information and add efficiency for continuing mission operations and analyst information.We discuss the use of cooperation in modeling and simulation and show quantitative results for design choices to resource allocation. The key contribution of our paper is to combine remote sensing decision making with Nash Equilibrium for sensor parameter weighting optimization. By calculating all Nash Equilibrium possibilities per period, optimization of sensor allocation is achieved for overall higher system efficiency. Our tool provides insight into what are the most important or optimal weights for sensor parameters and can be used to efficiently tune those weights.

  3. Front end optimization for the monolithic active pixel sensor of the ALICE Inner Tracking System upgrade

    International Nuclear Information System (INIS)

    Kim, D.; Rinella, G. Aglieri; Cavicchioli, C.; Hillemanns, H.; Hristozkov, S.; Junique, A.; Keil, M.; Kofarago, M.; Kugathasan, T.; Mager, M.; Chanlek, N.; Collu, A.; Degerli, Y.; Flouzat, C.; Guilloux, F.; Dorokhov, A.; Gajanana, D.; Gao, C.; Kwon, Y.; Lattuca, A.

    2016-01-01

    ALICE plans to replace its Inner Tracking System during the second long shut down of the LHC in 2019 with a new 10 m 2 tracker constructed entirely with monolithic active pixel sensors. The TowerJazz 180 nm CMOS imaging Sensor process has been selected to produce the sensor as it offers a deep pwell allowing full CMOS in-pixel circuitry and different starting materials. First full-scale prototypes have been fabricated and tested. Radiation tolerance has also been verified. In this paper the development of the charge sensitive front end and in particular its optimization for uniformity of charge threshold and time response will be presented

  4. Front end optimization for the monolithic active pixel sensor of the ALICE Inner Tracking System upgrade

    Science.gov (United States)

    Kim, D.; Aglieri Rinella, G.; Cavicchioli, C.; Chanlek, N.; Collu, A.; Degerli, Y.; Dorokhov, A.; Flouzat, C.; Gajanana, D.; Gao, C.; Guilloux, F.; Hillemanns, H.; Hristozkov, S.; Junique, A.; Keil, M.; Kofarago, M.; Kugathasan, T.; Kwon, Y.; Lattuca, A.; Mager, M.; Sielewicz, K. M.; Marin Tobon, C. A.; Marras, D.; Martinengo, P.; Mazza, G.; Mugnier, H.; Musa, L.; Pham, T. H.; Puggioni, C.; Reidt, F.; Riedler, P.; Rousset, J.; Siddhanta, S.; Snoeys, W.; Song, M.; Usai, G.; Van Hoorne, J. W.; Yang, P.

    2016-02-01

    ALICE plans to replace its Inner Tracking System during the second long shut down of the LHC in 2019 with a new 10 m2 tracker constructed entirely with monolithic active pixel sensors. The TowerJazz 180 nm CMOS imaging Sensor process has been selected to produce the sensor as it offers a deep pwell allowing full CMOS in-pixel circuitry and different starting materials. First full-scale prototypes have been fabricated and tested. Radiation tolerance has also been verified. In this paper the development of the charge sensitive front end and in particular its optimization for uniformity of charge threshold and time response will be presented.

  5. Optimizing Floating Guard Ring Designs for FASPAX N-in-P Silicon Sensors

    Energy Technology Data Exchange (ETDEWEB)

    Shin, Kyung-Wook [Argonne; Bradford, Robert [Argonne; Lipton, Ronald [Fermilab; Deptuch, Gregory [Fermilab; Fahim, Farah [Fermilab; Madden, Tim [Argonne; Zimmerman, Tom [Fermilab

    2016-10-06

    FASPAX (Fermi-Argonne Semiconducting Pixel Array X-ray detector) is being developed as a fast integrating area detector with wide dynamic range for time resolved applications at the upgraded Advanced Photon Source (APS.) A burst mode detector with intended $\\mbox{13 $MHz$}$ image rate, FASPAX will also incorporate a novel integration circuit to achieve wide dynamic range, from single photon sensitivity to $10^{\\text{5}}$ x-rays/pixel/pulse. To achieve these ambitious goals, a novel silicon sensor design is required. This paper will detail early design of the FASPAX sensor. Results from TCAD optimization studies, and characterization of prototype sensors will be presented.

  6. Collaborative Object Framework for Adaptive System Optimization, Phase II

    Data.gov (United States)

    National Aeronautics and Space Administration — The proposed innovation is to combine traditional and cutting edge optimization techniques into an existing powerful object based organic enterprise decision network...

  7. Piezoresistive Composite Silicon Dioxide Nanocantilever Surface Stress Sensor: Design and Optimization.

    Science.gov (United States)

    Mathew, Ribu; Sankar, A Ravi

    2018-05-01

    In this paper, we present the design and optimization of a rectangular piezoresistive composite silicon dioxide nanocantilever sensor. Unlike the conventional design approach, we perform the sensor optimization by not only considering its electro-mechanical response but also incorporating the impact of self-heating induced thermal drift in its terminal characteristics. Through extensive simulations first we comprehend and quantify the inaccuracies due to self-heating effect induced by the geometrical and intrinsic parameters of the piezoresistor. Then, by optimizing the ratio of electrical sensitivity to thermal sensitivity defined as the sensitivity ratio (υ) we improve the sensor performance and measurement reliability. Results show that to ensure υ ≥ 1, shorter and wider piezoresistors are better. In addition, it is observed that unlike the general belief that high doping concentration of piezoresistor reduces thermal sensitivity in piezoresistive sensors, to ensure υ ≥ 1 doping concentration (p) should be in the range: 1E18 cm-3 ≤ p ≤ 1E19 cm-3. Finally, we provide a set of design guidelines that will help NEMS engineers to optimize the performance of such sensors for chemical and biological sensing applications.

  8. Mesh Adaptation and Shape Optimization on Unstructured Meshes, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — In this SBIR CRM proposes to implement the entropy adjoint method for solution adaptive mesh refinement into the Loci/CHEM unstructured flow solver. The scheme will...

  9. Risk-adaptive optimization: Selective boosting of high-risk tumor subvolumes

    International Nuclear Information System (INIS)

    Kim, Yusung; Tome, Wolfgang A.

    2006-01-01

    Background and Purpose: A tumor subvolume-based, risk-adaptive optimization strategy is presented. Methods and Materials: Risk-adaptive optimization employs a biologic objective function instead of an objective function based on physical dose constraints. Using this biologic objective function, tumor control probability (TCP) is maximized for different tumor risk regions while at the same time minimizing normal tissue complication probability (NTCP) for organs at risk. The feasibility of risk-adaptive optimization was investigated for a variety of tumor subvolume geometries, risk-levels, and slopes of the TCP curve. Furthermore, the impact of a correlation parameter, δ, between TCP and NTCP on risk-adaptive optimization was investigated. Results: Employing risk-adaptive optimization, it is possible in a prostate cancer model to increase the equivalent uniform dose (EUD) by up to 35.4 Gy in tumor subvolumes having the highest risk classification without increasing predicted normal tissue complications in organs at risk. For all tumor subvolume geometries investigated, we found that the EUD to high-risk tumor subvolumes could be increased significantly without increasing normal tissue complications above those expected from a treatment plan aiming for uniform dose coverage of the planning target volume. We furthermore found that the tumor subvolume with the highest risk classification had the largest influence on the design of the risk-adaptive dose distribution. The parameter δ had little effect on risk-adaptive optimization. However, the clinical parameters D 5 and γ 5 that represent the risk classification of tumor subvolumes had the largest impact on risk-adaptive optimization. Conclusions: On the whole, risk-adaptive optimization yields heterogeneous dose distributions that match the risk level distribution of different subvolumes within the tumor volume

  10. On the Optimal Location of Sensors for Parametric Identification of Linear Systems

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Brincker, Rune

    1994-01-01

    . It is assumed most often that the results of the measurements are statistically independent random variables. In an example the importance of considering the measurements as statistically dependent random variables is shown. The covariance of the model parameters expected to be obtained is investigated......An outline of the field of optimal location of sensors for parametric identification of linear structural systems is presented. There are few papers devoted to the case of optimal location of sensors in which the measurements are modeled by a random field with non-trivial covariance function...

  11. Analysis and Optimization of Thermodiffusion of an FBG Sensor in the Gas Nitriding Process

    Directory of Open Access Journals (Sweden)

    Tso-Sheng Hsieh

    2016-12-01

    Full Text Available In this paper, we report the numerical calculations for a thermo-optical model and the temperature sensitivity of a fiber Bragg grating (FBG sensor. The thermally-induced behaviors of a FBG sensor in the gas nitriding process were analyzed for temperatures ranging from 100–650 °C. The FBG consisted of properly chosen photosensitive fiber materials with an optimized thermo-optic coefficient. The experimental and optimized thermo-optic coefficient results were consistent in terms of temperature sensitivity. In these experiments, the temperature sensitivity of the FBG was found to be 11.9 pm/°C.

  12. A Survey on Optimal Signal Processing Techniques Applied to Improve the Performance of Mechanical Sensors in Automotive Applications

    Science.gov (United States)

    Hernandez, Wilmar

    2007-01-01

    In this paper a survey on recent applications of optimal signal processing techniques to improve the performance of mechanical sensors is made. Here, a comparison between classical filters and optimal filters for automotive sensors is made, and the current state of the art of the application of robust and optimal control and signal processing techniques to the design of the intelligent (or smart) sensors that today's cars need is presented through several experimental results that show that the fusion of intelligent sensors and optimal signal processing techniques is the clear way to go. However, the switch between the traditional methods of designing automotive sensors and the new ones cannot be done overnight because there are some open research issues that have to be solved. This paper draws attention to one of the open research issues and tries to arouse researcher's interest in the fusion of intelligent sensors and optimal signal processing techniques.

  13. Optimal sensor configuration for flexible structures with multi-dimensional mode shapes

    International Nuclear Information System (INIS)

    Chang, Minwoo; Pakzad, Shamim N

    2015-01-01

    A framework for deciding the optimal sensor configuration is implemented for civil structures with multi-dimensional mode shapes, which enhances the applicability of structural health monitoring for existing structures. Optimal sensor placement (OSP) algorithms are used to determine the best sensor configuration for structures with a priori knowledge of modal information. The signal strength at each node is evaluated by effective independence and modified variance methods. Euclidean norm of signal strength indices associated with each node is used to expand OSP applicability into flexible structures. The number of sensors for each method is determined using the threshold for modal assurance criterion (MAC) between estimated (from a set of observations) and target mode shapes. Kriging is utilized to infer the modal estimates for unobserved locations with a weighted sum of known neighbors. A Kriging model can be expressed as a sum of linear regression and random error which is assumed as the realization of a stochastic process. This study presents the effects of Kriging parameters for the accurate estimation of mode shapes and the minimum number of sensors. The feasible ranges to satisfy MAC criteria are investigated and used to suggest the adequate searching bounds for associated parameters. The finite element model of a tall building is used to demonstrate the application of optimal sensor configuration. The dynamic modes of flexible structure at centroid are appropriately interpreted into the outermost sensor locations when OSP methods are implemented. Kriging is successfully used to interpolate the mode shapes from a set of sensors and to monitor structures associated with multi-dimensional mode shapes. (paper)

  14. Multiple-Optimizing Dynamic Sensor Networks with MIMO Technology (PREPRINT)

    Science.gov (United States)

    2010-06-01

    cluster edge is always d and never changes. A backbone is a rooted tree formed by cluster heads. The transmission distance for a backbone-edge...optimization considerations and algorithms,” IEEE Transactions on Mobile Computing, 2004. 15. T. Tang, M. Park, R. W. Heath, Jr. and Scott M. Nettles , “A

  15. Model Based Optimal Sensor Network Design for Condition Monitoring in an IGCC Plant

    Energy Technology Data Exchange (ETDEWEB)

    Kumar, Rajeeva; Kumar, Aditya; Dai, Dan; Seenumani, Gayathri; Down, John; Lopez, Rodrigo

    2012-12-31

    This report summarizes the achievements and final results of this program. The objective of this program is to develop a general model-based sensor network design methodology and tools to address key issues in the design of an optimal sensor network configuration: the type, location and number of sensors used in a network, for online condition monitoring. In particular, the focus in this work is to develop software tools for optimal sensor placement (OSP) and use these tools to design optimal sensor network configuration for online condition monitoring of gasifier refractory wear and radiant syngas cooler (RSC) fouling. The methodology developed will be applicable to sensing system design for online condition monitoring for broad range of applications. The overall approach consists of (i) defining condition monitoring requirement in terms of OSP and mapping these requirements in mathematical terms for OSP algorithm, (ii) analyzing trade-off of alternate OSP algorithms, down selecting the most relevant ones and developing them for IGCC applications (iii) enhancing the gasifier and RSC models as required by OSP algorithms, (iv) applying the developed OSP algorithm to design the optimal sensor network required for the condition monitoring of an IGCC gasifier refractory and RSC fouling. Two key requirements for OSP for condition monitoring are desired precision for the monitoring variables (e.g. refractory wear) and reliability of the proposed sensor network in the presence of expected sensor failures. The OSP problem is naturally posed within a Kalman filtering approach as an integer programming problem where the key requirements of precision and reliability are imposed as constraints. The optimization is performed over the overall network cost. Based on extensive literature survey two formulations were identified as being relevant to OSP for condition monitoring; one based on LMI formulation and the other being standard INLP formulation. Various algorithms to solve

  16. Real-Time Game Adaptation for Optimizing Player Satisfaction

    DEFF Research Database (Denmark)

    Yannakakis, Georgios; Hallam, John

    2009-01-01

    A methodology for optimizing player satisfaction in games on the "playware" physical interactive platform is demonstrated in this paper. Previously constructed artificial neural network user models, reported in the literature, map individual playing characteristics to reported entertainment...

  17. Design optimization and analysis of selected thermal devices using self-adaptive Jaya algorithm

    International Nuclear Information System (INIS)

    Rao, R.V.; More, K.C.

    2017-01-01

    Highlights: • Self-adaptive Jaya algorithm is proposed for optimal design of thermal devices. • Optimization of heat pipe, cooling tower, heat sink and thermo-acoustic prime mover is presented. • Results of the proposed algorithm are better than the other optimization techniques. • The proposed algorithm may be conveniently used for the optimization of other devices. - Abstract: The present study explores the use of an improved Jaya algorithm called self-adaptive Jaya algorithm for optimal design of selected thermal devices viz; heat pipe, cooling tower, honeycomb heat sink and thermo-acoustic prime mover. Four different optimization case studies of the selected thermal devices are presented. The researchers had attempted the same design problems in the past using niched pareto genetic algorithm (NPGA), response surface method (RSM), leap-frog optimization program with constraints (LFOPC) algorithm, teaching-learning based optimization (TLBO) algorithm, grenade explosion method (GEM) and multi-objective genetic algorithm (MOGA). The results achieved by using self-adaptive Jaya algorithm are compared with those achieved by using the NPGA, RSM, LFOPC, TLBO, GEM and MOGA algorithms. The self-adaptive Jaya algorithm is proved superior as compared to the other optimization methods in terms of the results, computational effort and function evalutions.

  18. Optimal Semi-Adaptive Search With False Targets

    Science.gov (United States)

    2017-12-01

    Kress, K. Y. Lin, and R. Szechtman, “Optimal discrete search with imperfect specificity,” Math Meth Oper Res, vol. 68, pp. 539–549, 2008. [16] L. D...constraints on employment of physical search assets will involve discrete approximations to the continuous solutions given by these techniques. These...model assumes. We optimize in the continuous case, to be able then to make the best possible discrete approximations if needed, given the constraints of a

  19. Nonlinear optimal filter technique for analyzing energy depositions in TES sensors driven into saturation

    Directory of Open Access Journals (Sweden)

    B. Shank

    2014-11-01

    Full Text Available We present a detailed thermal and electrical model of superconducting transition edge sensors (TESs connected to quasiparticle (qp traps, such as the W TESs connected to Al qp traps used for CDMS (Cryogenic Dark Matter Search Ge and Si detectors. We show that this improved model, together with a straightforward time-domain optimal filter, can be used to analyze pulses well into the nonlinear saturation region and reconstruct absorbed energies with optimal energy resolution.

  20. Intelligent Flood Adaptive Context-aware System: How Wireless Sensors Adapt their Configuration based on Environmental Phenomenon Events

    Directory of Open Access Journals (Sweden)

    Jie SUN

    2016-11-01

    Full Text Available Henceforth, new generations of Wireless Sensor Networks (WSN have to be able to adapt their behavior to collect, from the study phenomenon, quality data for long periods of time. We have thus proposed a new formalization for the design and the implementation of context-aware systems relying on a WSN for the data collection. To illustrate this proposal, we also present an environmental use case: the study of flood events in a watershed. In this paper, we detail the simulation tool that we have developed in order to implement our model. We simulate several scenarios of context-aware systems to monitor a watershed. The data used for the simulation are the observation data of the French Orgeval watershed.

  1. Topology Optimization of Nano-Mechanical Cantilever Sensors Using a C0 Discontinuous Galerkin-Type Approach

    DEFF Research Database (Denmark)

    Marhadi, Kun Saptohartyadi; Evgrafov, Anton; Sørensen, Mads Peter

    2011-01-01

    We demonstrate the use of a C0 discontinuous Galerkin method for topology optimization of nano-mechanical sensors, namely temperature, surface stress, and mass sensors. The sensors are modeled using classical thin plate theory, which requires C1 basis functions in the standard finite element method...

  2. A CMOS Image Sensor With In-Pixel Buried-Channel Source Follower and Optimized Row Selector

    NARCIS (Netherlands)

    Chen, Y.; Wang, X.; Mierop, A.J.; Theuwissen, A.J.P.

    2009-01-01

    This paper presents a CMOS imager sensor with pinned-photodiode 4T active pixels which use in-pixel buried-channel source followers (SFs) and optimized row selectors. The test sensor has been fabricated in a 0.18-mum CMOS process. The sensor characterization was carried out successfully, and the

  3. Optimization of adaptive radiation therapy in cervical cancer: Solutions for photon and proton therapy

    NARCIS (Netherlands)

    van de Schoot, A.J.A.J.

    2016-01-01

    In cervical cancer radiation therapy, an adaptive strategy is required to compensate for interfraction anatomical variations in order to achieve adequate dose delivery. In this thesis, we have aimed at optimizing adaptive radiation therapy in cervical cancer to improve treatment efficiency and

  4. Deployment-based lifetime optimization model for homogeneous Wireless Sensor Network under retransmission.

    Science.gov (United States)

    Li, Ruiying; Liu, Xiaoxi; Xie, Wei; Huang, Ning

    2014-12-10

    Sensor-deployment-based lifetime optimization is one of the most effective methods used to prolong the lifetime of Wireless Sensor Network (WSN) by reducing the distance-sensitive energy consumption. In this paper, data retransmission, a major consumption factor that is usually neglected in the previous work, is considered. For a homogeneous WSN, monitoring a circular target area with a centered base station, a sensor deployment model based on regular hexagonal grids is analyzed. To maximize the WSN lifetime, optimization models for both uniform and non-uniform deployment schemes are proposed by constraining on coverage, connectivity and success transmission rate. Based on the data transmission analysis in a data gathering cycle, the WSN lifetime in the model can be obtained through quantifying the energy consumption at each sensor location. The results of case studies show that it is meaningful to consider data retransmission in the lifetime optimization. In particular, our investigations indicate that, with the same lifetime requirement, the number of sensors needed in a non-uniform topology is much less than that in a uniform one. Finally, compared with a random scheme, simulation results further verify the advantage of our deployment model.

  5. Statistical-QoS Guaranteed Energy Efficiency Optimization for Energy Harvesting Wireless Sensor Networks.

    Science.gov (United States)

    Gao, Ya; Cheng, Wenchi; Zhang, Hailin

    2017-08-23

    Energy harvesting, which offers a never-ending energy supply, has emerged as a prominent technology to prolong the lifetime and reduce costs for the battery-powered wireless sensor networks. However, how to improve the energy efficiency while guaranteeing the quality of service (QoS) for energy harvesting based wireless sensor networks is still an open problem. In this paper, we develop statistical delay-bounded QoS-driven power control policies to maximize the effective energy efficiency (EEE), which is defined as the spectrum efficiency under given specified QoS constraints per unit harvested energy, for energy harvesting based wireless sensor networks. For the battery-infinite wireless sensor networks, our developed QoS-driven power control policy converges to the Energy harvesting Water Filling (E-WF) scheme and the Energy harvesting Channel Inversion (E-CI) scheme under the very loose and stringent QoS constraints, respectively. For the battery-finite wireless sensor networks, our developed QoS-driven power control policy becomes the Truncated energy harvesting Water Filling (T-WF) scheme and the Truncated energy harvesting Channel Inversion (T-CI) scheme under the very loose and stringent QoS constraints, respectively. Furthermore, we evaluate the outage probabilities to theoretically analyze the performance of our developed QoS-driven power control policies. The obtained numerical results validate our analysis and show that our developed optimal power control policies can optimize the EEE over energy harvesting based wireless sensor networks.

  6. Optimizing the Configuration of Sensor Networks to Detect Intruders.

    Energy Technology Data Exchange (ETDEWEB)

    Brown, Nathanael J. K. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Jones, Katherine A. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Nozick, Linda Karen [Cornell Univ., Ithaca, NY (United States); Xu, Ningxiong [Cornell Univ., Ithaca, NY (United States)

    2015-03-01

    This paper focuses on optimizing the selection and configuration of detection technologies to protect a target of interest. The ability of an intruder to simply reach the target is assumed to be sufficient to consider the security system a failure. To address this problem, we develop a game theoretic model of the strategic interactions between the system owner and a knowledgeable intruder. A decomposition-based exact method is used to solve the resultant model.

  7. A systematic review of gait analysis methods based on inertial sensors and adaptive algorithms.

    Science.gov (United States)

    Caldas, Rafael; Mundt, Marion; Potthast, Wolfgang; Buarque de Lima Neto, Fernando; Markert, Bernd

    2017-09-01

    The conventional methods to assess human gait are either expensive or complex to be applied regularly in clinical practice. To reduce the cost and simplify the evaluation, inertial sensors and adaptive algorithms have been utilized, respectively. This paper aims to summarize studies that applied adaptive also called artificial intelligence (AI) algorithms to gait analysis based on inertial sensor data, verifying if they can support the clinical evaluation. Articles were identified through searches of the main databases, which were encompassed from 1968 to October 2016. We have identified 22 studies that met the inclusion criteria. The included papers were analyzed due to their data acquisition and processing methods with specific questionnaires. Concerning the data acquisition, the mean score is 6.1±1.62, what implies that 13 of 22 papers failed to report relevant outcomes. The quality assessment of AI algorithms presents an above-average rating (8.2±1.84). Therefore, AI algorithms seem to be able to support gait analysis based on inertial sensor data. Further research, however, is necessary to enhance and standardize the application in patients, since most of the studies used distinct methods to evaluate healthy subjects. Copyright © 2017 Elsevier B.V. All rights reserved.

  8. Geometry Optimization Approaches of Inductively Coupled Printed Spiral Coils for Remote Powering of Implantable Biomedical Sensors

    Directory of Open Access Journals (Sweden)

    Sondos Mehri

    2016-01-01

    Full Text Available Electronic biomedical implantable sensors need power to perform. Among the main reported approaches, inductive link is the most commonly used method for remote powering of such devices. Power efficiency is the most important characteristic to be considered when designing inductive links to transfer energy to implantable biomedical sensors. The maximum power efficiency is obtained for maximum coupling and quality factors of the coils and is generally limited as the coupling between the inductors is usually very small. This paper is dealing with geometry optimization of inductively coupled printed spiral coils for powering a given implantable sensor system. For this aim, Iterative Procedure (IP and Genetic Algorithm (GA analytic based optimization approaches are proposed. Both of these approaches implement simple mathematical models that approximate the coil parameters and the link efficiency values. Using numerical simulations based on Finite Element Method (FEM and with experimental validation, the proposed analytic approaches are shown to have improved accurate performance results in comparison with the obtained performance of a reference design case. The analytical GA and IP optimization methods are also compared to a purely Finite Element Method based on numerical optimization approach (GA-FEM. Numerical and experimental validations confirmed the accuracy and the effectiveness of the analytical optimization approaches to design the optimal coil geometries for the best values of efficiency.

  9. Optimal adaptive control for a class of stochastic systems

    NARCIS (Netherlands)

    Bagchi, Arunabha; Chen, Han-Fu

    1995-01-01

    We study linear-quadratic adaptive tracking problems for a special class of stochastic systems expressed in the state-space form. This is a long-standing problem in the control of aircraft flying through atmospheric turbulence. Using an ELS-based algorithm and introducing dither in the control law

  10. On the Design of Smart Parking Networks in the Smart Cities: An Optimal Sensor Placement Model

    Science.gov (United States)

    Bagula, Antoine; Castelli, Lorenzo; Zennaro, Marco

    2015-01-01

    Smart parking is a typical IoT application that can benefit from advances in sensor, actuator and RFID technologies to provide many services to its users and parking owners of a smart city. This paper considers a smart parking infrastructure where sensors are laid down on the parking spots to detect car presence and RFID readers are embedded into parking gates to identify cars and help in the billing of the smart parking. Both types of devices are endowed with wired and wireless communication capabilities for reporting to a gateway where the situation recognition is performed. The sensor devices are tasked to play one of the three roles: (1) slave sensor nodes located on the parking spot to detect car presence/absence; (2) master nodes located at one of the edges of a parking lot to detect presence and collect the sensor readings from the slave nodes; and (3) repeater sensor nodes, also called “anchor” nodes, located strategically at specific locations in the parking lot to increase the coverage and connectivity of the wireless sensor network. While slave and master nodes are placed based on geographic constraints, the optimal placement of the relay/anchor sensor nodes in smart parking is an important parameter upon which the cost and efficiency of the parking system depends. We formulate the optimal placement of sensors in smart parking as an integer linear programming multi-objective problem optimizing the sensor network engineering efficiency in terms of coverage and lifetime maximization, as well as its economic gain in terms of the number of sensors deployed for a specific coverage and lifetime. We propose an exact solution to the node placement problem using single-step and two-step solutions implemented in the Mosel language based on the Xpress-MPsuite of libraries. Experimental results reveal the relative efficiency of the single-step compared to the two-step model on different performance parameters. These results are consolidated by simulation results

  11. On the Design of Smart Parking Networks in the Smart Cities: An Optimal Sensor Placement Model

    Directory of Open Access Journals (Sweden)

    Antoine Bagula

    2015-06-01

    Full Text Available Smart parking is a typical IoT application that can benefit from advances in sensor, actuator and RFID technologies to provide many services to its users and parking owners of a smart city. This paper considers a smart parking infrastructure where sensors are laid down on the parking spots to detect car presence and RFID readers are embedded into parking gates to identify cars and help in the billing of the smart parking. Both types of devices are endowed with wired and wireless communication capabilities for reporting to a gateway where the situation recognition is performed. The sensor devices are tasked to play one of the three roles: (1 slave sensor nodes located on the parking spot to detect car presence/absence; (2 master nodes located at one of the edges of a parking lot to detect presence and collect the sensor readings from the slave nodes; and (3 repeater sensor nodes, also called “anchor” nodes, located strategically at specific locations in the parking lot to increase the coverage and connectivity of the wireless sensor network. While slave and master nodes are placed based on geographic constraints, the optimal placement of the relay/anchor sensor nodes in smart parking is an important parameter upon which the cost and efficiency of the parking system depends. We formulate the optimal placement of sensors in smart parking as an integer linear programming multi-objective problem optimizing the sensor network engineering efficiency in terms of coverage and lifetime maximization, as well as its economic gain in terms of the number of sensors deployed for a specific coverage and lifetime. We propose an exact solution to the node placement problem using single-step and two-step solutions implemented in the Mosel language based on the Xpress-MPsuite of libraries. Experimental results reveal the relative efficiency of the single-step compared to the two-step model on different performance parameters. These results are consolidated by

  12. On the Design of Smart Parking Networks in the Smart Cities: An Optimal Sensor Placement Model.

    Science.gov (United States)

    Bagula, Antoine; Castelli, Lorenzo; Zennaro, Marco

    2015-06-30

    Smart parking is a typical IoT application that can benefit from advances in sensor, actuator and RFID technologies to provide many services to its users and parking owners of a smart city. This paper considers a smart parking infrastructure where sensors are laid down on the parking spots to detect car presence and RFID readers are embedded into parking gates to identify cars and help in the billing of the smart parking. Both types of devices are endowed with wired and wireless communication capabilities for reporting to a gateway where the situation recognition is performed. The sensor devices are tasked to play one of the three roles: (1) slave sensor nodes located on the parking spot to detect car presence/absence; (2) master nodes located at one of the edges of a parking lot to detect presence and collect the sensor readings from the slave nodes; and (3) repeater sensor nodes, also called "anchor" nodes, located strategically at specific locations in the parking lot to increase the coverage and connectivity of the wireless sensor network. While slave and master nodes are placed based on geographic constraints, the optimal placement of the relay/anchor sensor nodes in smart parking is an important parameter upon which the cost and efficiency of the parking system depends. We formulate the optimal placement of sensors in smart parking as an integer linear programming multi-objective problem optimizing the sensor network engineering efficiency in terms of coverage and lifetime maximization, as well as its economic gain in terms of the number of sensors deployed for a specific coverage and lifetime. We propose an exact solution to the node placement problem using single-step and two-step solutions implemented in the Mosel language based on the Xpress-MPsuite of libraries. Experimental results reveal the relative efficiency of the single-step compared to the two-step model on different performance parameters. These results are consolidated by simulation results

  13. Addressing practical challenges in utility optimization of mobile wireless sensor networks

    Science.gov (United States)

    Eswaran, Sharanya; Misra, Archan; La Porta, Thomas; Leung, Kin

    2008-04-01

    This paper examines the practical challenges in the application of the distributed network utility maximization (NUM) framework to the problem of resource allocation and sensor device adaptation in a mission-centric wireless sensor network (WSN) environment. By providing rich (multi-modal), real-time information about a variety of (often inaccessible or hostile) operating environments, sensors such as video, acoustic and short-aperture radar enhance the situational awareness of many battlefield missions. Prior work on the applicability of the NUM framework to mission-centric WSNs has focused on tackling the challenges introduced by i) the definition of an individual mission's utility as a collective function of multiple sensor flows and ii) the dissemination of an individual sensor's data via a multicast tree to multiple consuming missions. However, the practical application and performance of this framework is influenced by several parameters internal to the framework and also by implementation-specific decisions. This is made further complex due to mobile nodes. In this paper, we use discrete-event simulations to study the effects of these parameters on the performance of the protocol in terms of speed of convergence, packet loss, and signaling overhead thereby addressing the challenges posed by wireless interference and node mobility in ad-hoc battlefield scenarios. This study provides better understanding of the issues involved in the practical adaptation of the NUM framework. It also helps identify potential avenues of improvement within the framework and protocol.

  14. Optimization and validation of highly selective microfluidic integrated silicon nanowire chemical sensor

    Science.gov (United States)

    Ehfaed, Nuri. A. K. H.; Bathmanathan, Shillan A. L.; Dhahi, Th S.; Adam, Tijjani; Hashim, Uda; Noriman, N. Z.

    2017-09-01

    The study proposed characterization and optimization of silicon nanosensor for specific detection of heavy metal. The sensor was fabricated in-house and conventional photolithography coupled with size reduction via dry etching process in an oxidation furnace. Prior to heavy metal heavy metal detection, the capability to aqueous sample was determined utilizing serial DI water at various. The sensor surface was surface modified with Organofunctional alkoxysilanes (3-aminopropyl) triethoxysilane (APTES) to create molecular binding chemistry. This has allowed interaction between heavy metals being measured and the sensor component resulting in increasing the current being measured. Due to its, excellent detection capabilities, this sensor was able to identify different group heavy metal species. The device was further integrated with sub-50 µm for chemical delivery.

  15. Efficient and Adaptive Node Selection for Target Tracking in Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Juan Feng

    2016-01-01

    Full Text Available In target tracking wireless sensor network, choosing the proper working nodes can not only minimize the number of active nodes, but also satisfy the tracking reliability requirement. However, most existing works focus on selecting sensor nodes which are the nearest to the target for tracking missions and they did not consider the correlation of the location of the sensor nodes so that these approaches can not meet all the goals of the network. This work proposes an efficient and adaptive node selection approach for tracking a target in a distributed wireless sensor network. The proposed approach combines the distance-based node selection strategy and particle filter prediction considering the spatial correlation of the different sensing nodes. Moreover, a joint distance weighted measurement is proposed to estimate the information utility of sensing nodes. Experimental results show that EANS outperformed the state-of-the-art approaches by reducing the energy cost and computational complexity as well as guaranteeing the tracking accuracy.

  16. Field-Based Optimal Placement of Antennas for Body-Worn Wireless Sensors

    Directory of Open Access Journals (Sweden)

    Łukasz Januszkiewicz

    2016-05-01

    Full Text Available We investigate a case of automated energy-budget-aware optimization of the physical position of nodes (sensors in a Wireless Body Area Network (WBAN. This problem has not been presented in the literature yet, as opposed to antenna and routing optimization, which are relatively well-addressed. In our research, which was inspired by a safety-critical application for firefighters, the sensor network consists of three nodes located on the human body. The nodes communicate over a radio link operating in the 2.4 GHz or 5.8 GHz ISM frequency band. Two sensors have a fixed location: one on the head (earlobe pulse oximetry and one on the arm (with accelerometers, temperature and humidity sensors, and a GPS receiver, while the position of the third sensor can be adjusted within a predefined region on the wearer’s chest. The path loss between each node pair strongly depends on the location of the nodes and is difficult to predict without performing a full-wave electromagnetic simulation. Our optimization scheme employs evolutionary computing. The novelty of our approach lies not only in the formulation of the problem but also in linking a fully automated optimization procedure with an electromagnetic simulator and a simplified human body model. This combination turns out to be a computationally effective solution, which, depending on the initial placement, has a potential to improve performance of our example sensor network setup by up to about 20 dB with respect to the path loss between selected nodes.

  17. Cross-layer optimized rate adaptation and scheduling for multiple-user wireless video streaming

    NARCIS (Netherlands)

    Ozcelebi, T.; Sunay, M.O.; Tekalp, A.M.; Civanlar, M.R.

    2007-01-01

    We present a cross-layer optimized video rate adaptation and user scheduling scheme for multi-user wireless video streaming aiming for maximum quality of service (QoS) for each user,, maximum system video throughput, and QoS fairness among users. These objectives are jointly optimized using a

  18. Cross-layer protocol design for QoS optimization in real-time wireless sensor networks

    Science.gov (United States)

    Hortos, William S.

    2010-04-01

    The metrics of quality of service (QoS) for each sensor type in a wireless sensor network can be associated with metrics for multimedia that describe the quality of fused information, e.g., throughput, delay, jitter, packet error rate, information correlation, etc. These QoS metrics are typically set at the highest, or application, layer of the protocol stack to ensure that performance requirements for each type of sensor data are satisfied. Application-layer metrics, in turn, depend on the support of the lower protocol layers: session, transport, network, data link (MAC), and physical. The dependencies of the QoS metrics on the performance of the higher layers of the Open System Interconnection (OSI) reference model of the WSN protocol, together with that of the lower three layers, are the basis for a comprehensive approach to QoS optimization for multiple sensor types in a general WSN model. The cross-layer design accounts for the distributed power consumption along energy-constrained routes and their constituent nodes. Following the author's previous work, the cross-layer interactions in the WSN protocol are represented by a set of concatenated protocol parameters and enabling resource levels. The "best" cross-layer designs to achieve optimal QoS are established by applying the general theory of martingale representations to the parameterized multivariate point processes (MVPPs) for discrete random events occurring in the WSN. Adaptive control of network behavior through the cross-layer design is realized through the parametric factorization of the stochastic conditional rates of the MVPPs. The cross-layer protocol parameters for optimal QoS are determined in terms of solutions to stochastic dynamic programming conditions derived from models of transient flows for heterogeneous sensor data and aggregate information over a finite time horizon. Markov state processes, embedded within the complex combinatorial history of WSN events, are more computationally

  19. Optimized Autonomous Space In-situ Sensor-Web for volcano monitoring

    Science.gov (United States)

    Song, W.-Z.; Shirazi, B.; Kedar, S.; Chien, S.; Webb, F.; Tran, D.; Davis, A.; Pieri, D.; LaHusen, R.; Pallister, J.; Dzurisin, D.; Moran, S.; Lisowski, M.

    2008-01-01

    In response to NASA's announced requirement for Earth hazard monitoring sensor-web technology, a multidisciplinary team involving sensor-network experts (Washington State University), space scientists (JPL), and Earth scientists (USGS Cascade Volcano Observatory (CVO)), is developing a prototype dynamic and scaleable hazard monitoring sensor-web and applying it to volcano monitoring. The combined Optimized Autonomous Space -In-situ Sensor-web (OASIS) will have two-way communication capability between ground and space assets, use both space and ground data for optimal allocation of limited power and bandwidth resources on the ground, and use smart management of competing demands for limited space assets. It will also enable scalability and seamless infusion of future space and in-situ assets into the sensor-web. The prototype will be focused on volcano hazard monitoring at Mount St. Helens, which has been active since October 2004. The system is designed to be flexible and easily configurable for many other applications as well. The primary goals of the project are: 1) integrating complementary space (i.e., Earth Observing One (EO-1) satellite) and in-situ (ground-based) elements into an interactive, autonomous sensor-web; 2) advancing sensor-web power and communication resource management technology; and 3) enabling scalability for seamless infusion of future space and in-situ assets into the sensor-web. To meet these goals, we are developing: 1) a test-bed in-situ array with smart sensor nodes capable of making autonomous data acquisition decisions; 2) efficient self-organization algorithm of sensor-web topology to support efficient data communication and command control; 3) smart bandwidth allocation algorithms in which sensor nodes autonomously determine packet priorities based on mission needs and local bandwidth information in real-time; and 4) remote network management and reprogramming tools. The space and in-situ control components of the system will be

  20. QoS-Aware Error Recovery in Wireless Body Sensor Networks Using Adaptive Network Coding

    Science.gov (United States)

    Razzaque, Mohammad Abdur; Javadi, Saeideh S.; Coulibaly, Yahaya; Hira, Muta Tah

    2015-01-01

    Wireless body sensor networks (WBSNs) for healthcare and medical applications are real-time and life-critical infrastructures, which require a strict guarantee of quality of service (QoS), in terms of latency, error rate and reliability. Considering the criticality of healthcare and medical applications, WBSNs need to fulfill users/applications and the corresponding network's QoS requirements. For instance, for a real-time application to support on-time data delivery, a WBSN needs to guarantee a constrained delay at the network level. A network coding-based error recovery mechanism is an emerging mechanism that can be used in these systems to support QoS at very low energy, memory and hardware cost. However, in dynamic network environments and user requirements, the original non-adaptive version of network coding fails to support some of the network and user QoS requirements. This work explores the QoS requirements of WBSNs in both perspectives of QoS. Based on these requirements, this paper proposes an adaptive network coding-based, QoS-aware error recovery mechanism for WBSNs. It utilizes network-level and user-/application-level information to make it adaptive in both contexts. Thus, it provides improved QoS support adaptively in terms of reliability, energy efficiency and delay. Simulation results show the potential of the proposed mechanism in terms of adaptability, reliability, real-time data delivery and network lifetime compared to its counterparts. PMID:25551485

  1. QoS-Aware Error Recovery in Wireless Body Sensor Networks Using Adaptive Network Coding

    Directory of Open Access Journals (Sweden)

    Mohammad Abdur Razzaque

    2014-12-01

    Full Text Available Wireless body sensor networks (WBSNs for healthcare and medical applications are real-time and life-critical infrastructures, which require a strict guarantee of quality of service (QoS, in terms of latency, error rate and reliability. Considering the criticality of healthcare and medical applications, WBSNs need to fulfill users/applications and the corresponding network’s QoS requirements. For instance, for a real-time application to support on-time data delivery, a WBSN needs to guarantee a constrained delay at the network level. A network coding-based error recovery mechanism is an emerging mechanism that can be used in these systems to support QoS at very low energy, memory and hardware cost. However, in dynamic network environments and user requirements, the original non-adaptive version of network coding fails to support some of the network and user QoS requirements. This work explores the QoS requirements of WBSNs in both perspectives of QoS. Based on these requirements, this paper proposes an adaptive network coding-based, QoS-aware error recovery mechanism for WBSNs. It utilizes network-level and user-/application-level information to make it adaptive in both contexts. Thus, it provides improved QoS support adaptively in terms of reliability, energy efficiency and delay. Simulation results show the potential of the proposed mechanism in terms of adaptability, reliability, real-time data delivery and network lifetime compared to its counterparts.

  2. QOS-aware error recovery in wireless body sensor networks using adaptive network coding.

    Science.gov (United States)

    Razzaque, Mohammad Abdur; Javadi, Saeideh S; Coulibaly, Yahaya; Hira, Muta Tah

    2014-12-29

    Wireless body sensor networks (WBSNs) for healthcare and medical applications are real-time and life-critical infrastructures, which require a strict guarantee of quality of service (QoS), in terms of latency, error rate and reliability. Considering the criticality of healthcare and medical applications, WBSNs need to fulfill users/applications and the corresponding network's QoS requirements. For instance, for a real-time application to support on-time data delivery, a WBSN needs to guarantee a constrained delay at the network level. A network coding-based error recovery mechanism is an emerging mechanism that can be used in these systems to support QoS at very low energy, memory and hardware cost. However, in dynamic network environments and user requirements, the original non-adaptive version of network coding fails to support some of the network and user QoS requirements. This work explores the QoS requirements of WBSNs in both perspectives of QoS. Based on these requirements, this paper proposes an adaptive network coding-based, QoS-aware error recovery mechanism for WBSNs. It utilizes network-level and user-/application-level information to make it adaptive in both contexts. Thus, it provides improved QoS support adaptively in terms of reliability, energy efficiency and delay. Simulation results show the potential of the proposed mechanism in terms of adaptability, reliability, real-time data delivery and network lifetime compared to its counterparts.

  3. Image sensor system with bio-inspired efficient coding and adaptation.

    Science.gov (United States)

    Okuno, Hirotsugu; Yagi, Tetsuya

    2012-08-01

    We designed and implemented an image sensor system equipped with three bio-inspired coding and adaptation strategies: logarithmic transform, local average subtraction, and feedback gain control. The system comprises a field-programmable gate array (FPGA), a resistive network, and active pixel sensors (APS), whose light intensity-voltage characteristics are controllable. The system employs multiple time-varying reset voltage signals for APS in order to realize multiple logarithmic intensity-voltage characteristics, which are controlled so that the entropy of the output image is maximized. The system also employs local average subtraction and gain control in order to obtain images with an appropriate contrast. The local average is calculated by the resistive network instantaneously. The designed system was successfully used to obtain appropriate images of objects that were subjected to large changes in illumination.

  4. A Self Adaptive Differential Evolution Algorithm for Global Optimization

    Science.gov (United States)

    Kumar, Pravesh; Pant, Millie

    This paper presents a new Differential Evolution algorithm based on hybridization of adaptive control parameters and trigonometric mutation. First we propose a self adaptive DE named ADE where choice of control parameter F and Cr is not fixed at some constant value but is taken iteratively. The proposed algorithm is further modified by applying trigonometric mutation in it and the corresponding algorithm is named as ATDE. The performance of ATDE is evaluated on the set of 8 benchmark functions and the results are compared with the classical DE algorithm in terms of average fitness function value, number of function evaluations, convergence time and success rate. The numerical result shows the competence of the proposed algorithm.

  5. Design of application specific long period waveguide grating filters using adaptive particle swarm optimization algorithms

    International Nuclear Information System (INIS)

    Semwal, Girish; Rastogi, Vipul

    2014-01-01

    We present design optimization of wavelength filters based on long period waveguide gratings (LPWGs) using the adaptive particle swarm optimization (APSO) technique. We demonstrate optimization of the LPWG parameters for single-band, wide-band and dual-band rejection filters for testing the convergence of APSO algorithms. After convergence tests on the algorithms, the optimization technique has been implemented to design more complicated application specific filters such as erbium doped fiber amplifier (EDFA) amplified spontaneous emission (ASE) flattening, erbium doped waveguide amplifier (EDWA) gain flattening and pre-defined broadband rejection filters. The technique is useful for designing and optimizing the parameters of LPWGs to achieve complicated application specific spectra. (paper)

  6. Optimal Design of Large Dimensional Adaptive Subspace Detectors

    KAUST Repository

    Ben Atitallah, Ismail

    2016-05-27

    This paper addresses the design of Adaptive Subspace Matched Filter (ASMF) detectors in the presence of a mismatch in the steering vector. These detectors are coined as adaptive in reference to the step of utilizing an estimate of the clutter covariance matrix using training data of signalfree observations. To estimate the clutter covariance matrix, we employ regularized covariance estimators that, by construction, force the eigenvalues of the covariance estimates to be greater than a positive scalar . While this feature is likely to increase the bias of the covariance estimate, it presents the advantage of improving its conditioning, thus making the regularization suitable for handling high dimensional regimes. In this paper, we consider the setting of the regularization parameter and the threshold for ASMF detectors in both Gaussian and Compound Gaussian clutters. In order to allow for a proper selection of these parameters, it is essential to analyze the false alarm and detection probabilities. For tractability, such a task is carried out under the asymptotic regime in which the number of observations and their dimensions grow simultaneously large, thereby allowing us to leverage existing results from random matrix theory. Simulation results are provided in order to illustrate the relevance of the proposed design strategy and to compare the performances of the proposed ASMF detectors versus Adaptive normalized Matched Filter (ANMF) detectors under mismatch scenarios.

  7. Optimal Design of Large Dimensional Adaptive Subspace Detectors

    KAUST Repository

    Ben Atitallah, Ismail; Kammoun, Abla; Alouini, Mohamed-Slim; Alnaffouri, Tareq Y.

    2016-01-01

    This paper addresses the design of Adaptive Subspace Matched Filter (ASMF) detectors in the presence of a mismatch in the steering vector. These detectors are coined as adaptive in reference to the step of utilizing an estimate of the clutter covariance matrix using training data of signalfree observations. To estimate the clutter covariance matrix, we employ regularized covariance estimators that, by construction, force the eigenvalues of the covariance estimates to be greater than a positive scalar . While this feature is likely to increase the bias of the covariance estimate, it presents the advantage of improving its conditioning, thus making the regularization suitable for handling high dimensional regimes. In this paper, we consider the setting of the regularization parameter and the threshold for ASMF detectors in both Gaussian and Compound Gaussian clutters. In order to allow for a proper selection of these parameters, it is essential to analyze the false alarm and detection probabilities. For tractability, such a task is carried out under the asymptotic regime in which the number of observations and their dimensions grow simultaneously large, thereby allowing us to leverage existing results from random matrix theory. Simulation results are provided in order to illustrate the relevance of the proposed design strategy and to compare the performances of the proposed ASMF detectors versus Adaptive normalized Matched Filter (ANMF) detectors under mismatch scenarios.

  8. An adaptive clustering approach to dynamic load balancing and energy efficiency in wireless sensor networks

    International Nuclear Information System (INIS)

    Gherbi, Chirihane; Aliouat, Zibouda; Benmohammed, Mohamed

    2016-01-01

    Clustering is a well known approach to cope with large nodes density and efficiently conserving energy in Wireless Sensor Networks (WSN). Load balancing is an effective approach for optimizing resources like channel bandwidth, the main objective of this paper is to combine these two valuable approaches in order to significantly improve the main WSN service such as information routing. So, our proposal is a routing protocol in which load traffic is shared among cluster members in order to reduce the dropping probability due to queue overflow at some nodes. To this end, a novel hierarchical approach, called Hierarchical Energy-Balancing Multipath routing protocol for Wireless Sensor Networks (HEBM) is proposed. The HEBM approach aims to fulfill the following purposes: decreasing the overall network energy consumption, balancing the energy dissipation among the sensor nodes and as direct consequence: extending the lifetime of the network. In fact, the cluster-heads are optimally determined and suitably distributed over the area of interest allowing the member nodes reaching them with adequate energy dissipation and appropriate load balancing utilization. In addition, nodes radio are turned off for fixed time duration according to sleeping control rules optimizing so their energy consumption. The performance evaluation of the proposed protocol is carried out through the well-known NS2 simulator and the exhibited results are convincing. Like this, the residual energy of sensor nodes was measured every 20 s throughout the duration of simulation, in order to calculate the total number of alive nodes. Based on the simulation results, we concluded that our proposed HEBM protocol increases the profit of energy, and prolongs the network lifetime duration from 32% to 40% compared to DEEAC reference protocol and from 25% to 28% compared to FEMCHRP protocol. The authors also note that the proposed protocol is 41.7% better than DEEAC with respect to FND (Fist node die), and 25

  9. Simple Adaptive Single Differential Coherence Detection of BPSK Signals in IEEE 802.15.4 Wireless Sensor Networks.

    Science.gov (United States)

    Zhang, Gaoyuan; Wen, Hong; Wang, Longye; Xie, Ping; Song, Liang; Tang, Jie; Liao, Runfa

    2017-12-26

    In this paper, we propose an adaptive single differential coherent detection (SDCD) scheme for the binary phase shift keying (BPSK) signals in IEEE 802.15.4 Wireless Sensor Networks (WSNs). In particular, the residual carrier frequency offset effect (CFOE) for differential detection is adaptively estimated, with only linear operation, according to the changing channel conditions. It was found that the carrier frequency offset (CFO) and chip signal-to-noise ratio (SNR) conditions do not need a priori knowledge. This partly benefits from that the combination of the trigonometric approximation sin - 1 ( x ) ≈ x and a useful assumption, namely, the asymptotic or high chip SNR, is considered for simplification of the full estimation scheme. Simulation results demonstrate that the proposed algorithm can achieve an accurate estimation and the detection performance can completely meet the requirement of the IEEE 802.15.4 standard, although with a little loss of reliability and robustness as compared with the conventional optimal single-symbol detector.

  10. Policy Gradient Adaptive Dynamic Programming for Data-Based Optimal Control.

    Science.gov (United States)

    Luo, Biao; Liu, Derong; Wu, Huai-Ning; Wang, Ding; Lewis, Frank L

    2017-10-01

    The model-free optimal control problem of general discrete-time nonlinear systems is considered in this paper, and a data-based policy gradient adaptive dynamic programming (PGADP) algorithm is developed to design an adaptive optimal controller method. By using offline and online data rather than the mathematical system model, the PGADP algorithm improves control policy with a gradient descent scheme. The convergence of the PGADP algorithm is proved by demonstrating that the constructed Q -function sequence converges to the optimal Q -function. Based on the PGADP algorithm, the adaptive control method is developed with an actor-critic structure and the method of weighted residuals. Its convergence properties are analyzed, where the approximate Q -function converges to its optimum. Computer simulation results demonstrate the effectiveness of the PGADP-based adaptive control method.

  11. AN ADAPTIVE OPTIMAL KALMAN FILTER FOR STOCHASTIC VIBRATION CONTROL SYSTEM WITH UNKNOWN NOISE VARIANCES

    Institute of Scientific and Technical Information of China (English)

    Li Shu; Zhuo Jiashou; Ren Qingwen

    2000-01-01

    In this paper, an optimal criterion is presented for adaptive Kalman filter in a control sys tem with unknown variances of stochastic vibration by constructing a function of noise variances and minimizing the function. We solve the model and measure variances by using DFP optimal method to guarantee the results of Kalman filter to be optimized. Finally, the control of vibration can be implemented by LQG method.

  12. An Adaptive Genetic Algorithm with Dynamic Population Size for Optimizing Join Queries

    OpenAIRE

    Vellev, Stoyan

    2008-01-01

    The problem of finding the optimal join ordering executing a query to a relational database management system is a combinatorial optimization problem, which makes deterministic exhaustive solution search unacceptable for queries with a great number of joined relations. In this work an adaptive genetic algorithm with dynamic population size is proposed for optimizing large join queries. The performance of the algorithm is compared with that of several classical non-determinis...

  13. Multilevel Optimization Framework for Hierarchical Stiffened Shells Accelerated by Adaptive Equivalent Strategy

    Science.gov (United States)

    Wang, Bo; Tian, Kuo; Zhao, Haixin; Hao, Peng; Zhu, Tianyu; Zhang, Ke; Ma, Yunlong

    2017-06-01

    In order to improve the post-buckling optimization efficiency of hierarchical stiffened shells, a multilevel optimization framework accelerated by adaptive equivalent strategy is presented in this paper. Firstly, the Numerical-based Smeared Stiffener Method (NSSM) for hierarchical stiffened shells is derived by means of the numerical implementation of asymptotic homogenization (NIAH) method. Based on the NSSM, a reasonable adaptive equivalent strategy for hierarchical stiffened shells is developed from the concept of hierarchy reduction. Its core idea is to self-adaptively decide which hierarchy of the structure should be equivalent according to the critical buckling mode rapidly predicted by NSSM. Compared with the detailed model, the high prediction accuracy and efficiency of the proposed model is highlighted. On the basis of this adaptive equivalent model, a multilevel optimization framework is then established by decomposing the complex entire optimization process into major-stiffener-level and minor-stiffener-level sub-optimizations, during which Fixed Point Iteration (FPI) is employed to accelerate convergence. Finally, the illustrative examples of the multilevel framework is carried out to demonstrate its efficiency and effectiveness to search for the global optimum result by contrast with the single-level optimization method. Remarkably, the high efficiency and flexibility of the adaptive equivalent strategy is indicated by compared with the single equivalent strategy.

  14. Apodization Optimization of FBG Strain Sensor for Quasi-Distributed Sensing Measurement Applications

    Directory of Open Access Journals (Sweden)

    Fahd Chaoui

    2016-01-01

    Full Text Available A novel optimized apodization of Fiber Bragg Grating Sensor (FBGS for quasi-distributed strain sensing applications is developed and introduced in this paper. The main objective of the proposed optimization is to obtain a reflectivity level higher than 90% and a side lobe level around −40 dB, which is suitable for use in quasi-distributed strain sensing application. For this purpose, different design parameters as apodization profile, grating length, and refractive index have been investigated to enhance and optimize the FBGS design. The performance of the proposed apodization has then been compared in terms of reflectivity, side lobe level (SLL, and full width at half maximum (FWHM with apodization profiles proposed by other authors. The optimized sensor is integrated on quasi-distributed sensing system of 8 sensors demonstrating high reliability. Wide strain sensitivity range for each channel has also been achieved in the quasi-distributed system. Results prove the efficiency of the proposed optimization which can be further implemented for any quasi-distributed sensing application.

  15. Optimal and secure measurement protocols for quantum sensor networks

    Science.gov (United States)

    Eldredge, Zachary; Foss-Feig, Michael; Gross, Jonathan A.; Rolston, S. L.; Gorshkov, Alexey V.

    2018-04-01

    Studies of quantum metrology have shown that the use of many-body entangled states can lead to an enhancement in sensitivity when compared with unentangled states. In this paper, we quantify the metrological advantage of entanglement in a setting where the measured quantity is a linear function of parameters individually coupled to each qubit. We first generalize the Heisenberg limit to the measurement of nonlocal observables in a quantum network, deriving a bound based on the multiparameter quantum Fisher information. We then propose measurement protocols that can make use of Greenberger-Horne-Zeilinger (GHZ) states or spin-squeezed states and show that in the case of GHZ states the protocol is optimal, i.e., it saturates our bound. We also identify nanoscale magnetic resonance imaging as a promising setting for this technology.

  16. Adaptive Security in ODMAC for Multihop Energy Harvesting Wireless Sensor Networks

    DEFF Research Database (Denmark)

    Di Mauro, Alessio; Fafoutis, Xenofon; Dragoni, Nicola

    2015-01-01

    Energy Harvesting Wireless Sensor Networks (EH-WSNs) represent an interesting new paradigm where individual nodes forming a network are powered by energy sources scavenged from the surrounding environment. This technique provides numerous advantages, but also new design challenges. Securing...... the communications under energy constraints represents one of these key challenges. The amount of energy available is theoretically infinite in the long run but highly variable over short periods of time, and managing it is a crucial aspect. In this paper we present an adaptive approach for security in multihop EH...

  17. Distributed Multi-Commodity Network Flow Algorithm for Energy Optimal Routing in Wireless Sensor Networks.

    Directory of Open Access Journals (Sweden)

    J. Trdlicka

    2010-12-01

    Full Text Available This work proposes a distributed algorithm for energy optimal routing in a wireless sensor network. The routing problem is described as a mathematical problem by the minimum-cost multi-commodity network flow problem. Due to the separability of the problem, we use the duality theorem to derive the distributed algorithm. The algorithm computes the energy optimal routing in the network without any central node or knowledge of the whole network structure. Each node only needs to know the flow which is supposed to send or receive and the costs and capacities of the neighboring links. An evaluation of the presented algorithm on benchmarks for the energy optimal data flow routing in sensor networks with up to 100 nodes is presented.

  18. Coupled sensor/platform control design for low-level chemical detection with position-adaptive micro-UAVs

    Science.gov (United States)

    Goodwin, Thomas; Carr, Ryan; Mitra, Atindra K.; Selmic, Rastko R.

    2009-05-01

    We discuss the development of Position-Adaptive Sensors [1] for purposes for detecting embedded chemical substances in challenging environments. This concept is a generalization of patented Position-Adaptive Radar Concepts developed at AFRL for challenging conditions such as urban environments. For purposes of investigating the detection of chemical substances using multiple MAV (Micro-UAV) platforms, we have designed and implemented an experimental testbed with sample structures such as wooden carts that contain controlled leakage points. Under this general concept, some of the members of a MAV swarm can serve as external position-adaptive "transmitters" by blowing air over the cart and some of the members of a MAV swarm can serve as external position-adaptive "receivers" that are equipped with chemical or biological (chem/bio) sensors that function as "electronic noses". The objective can be defined as improving the particle count of chem/bio concentrations that impinge on a MAV-based position-adaptive sensor that surrounds a chemical repository, such as a cart, via the development of intelligent position-adaptive control algorithms. The overall effect is to improve the detection and false-alarm statistics of the overall system. Within the major sections of this paper, we discuss a number of different aspects of developing our initial MAV-Based Sensor Testbed. This testbed includes blowers to simulate position-adaptive excitations and a MAV from Draganfly Innovations Inc. with stable design modifications to accommodate our chem/bio sensor boom design. We include details with respect to several critical phases of the development effort including development of the wireless sensor network and experimental apparatus, development of the stable sensor boom for the MAV, integration of chem/bio sensors and sensor node onto the MAV and boom, development of position-adaptive control algorithms and initial tests at IDCAST (Institute for the Development and

  19. LPTA: Location Predictive and Time Adaptive Data Gathering Scheme with Mobile Sink for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Chuan Zhu

    2014-01-01

    Full Text Available This paper exploits sink mobility to prolong the lifetime of sensor networks while maintaining the data transmission delay relatively low. A location predictive and time adaptive data gathering scheme is proposed. In this paper, we introduce a sink location prediction principle based on loose time synchronization and deduce the time-location formulas of the mobile sink. According to local clocks and the time-location formulas of the mobile sink, nodes in the network are able to calculate the current location of the mobile sink accurately and route data packets timely toward the mobile sink by multihop relay. Considering that data packets generating from different areas may be different greatly, an adaptive dwelling time adjustment method is also proposed to balance energy consumption among nodes in the network. Simulation results show that our data gathering scheme enables data routing with less data transmission time delay and balance energy consumption among nodes.

  20. A Least Square-Based Self-Adaptive Localization Method for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Baoguo Yu

    2016-01-01

    Full Text Available In the wireless sensor network (WSN localization methods based on Received Signal Strength Indicator (RSSI, it is usually required to determine the parameters of the radio signal propagation model before estimating the distance between the anchor node and an unknown node with reference to their communication RSSI value. And finally we use a localization algorithm to estimate the location of the unknown node. However, this localization method, though high in localization accuracy, has weaknesses such as complex working procedure and poor system versatility. Concerning these defects, a self-adaptive WSN localization method based on least square is proposed, which uses the least square criterion to estimate the parameters of radio signal propagation model, which positively reduces the computation amount in the estimation process. The experimental results show that the proposed self-adaptive localization method outputs a high processing efficiency while satisfying the high localization accuracy requirement. Conclusively, the proposed method is of definite practical value.

  1. A Hybrid Adaptive Routing Algorithm for Event-Driven Wireless Sensor Networks

    Science.gov (United States)

    Figueiredo, Carlos M. S.; Nakamura, Eduardo F.; Loureiro, Antonio A. F.

    2009-01-01

    Routing is a basic function in wireless sensor networks (WSNs). For these networks, routing algorithms depend on the characteristics of the applications and, consequently, there is no self-contained algorithm suitable for every case. In some scenarios, the network behavior (traffic load) may vary a lot, such as an event-driven application, favoring different algorithms at different instants. This work presents a hybrid and adaptive algorithm for routing in WSNs, called Multi-MAF, that adapts its behavior autonomously in response to the variation of network conditions. In particular, the proposed algorithm applies both reactive and proactive strategies for routing infrastructure creation, and uses an event-detection estimation model to change between the strategies and save energy. To show the advantages of the proposed approach, it is evaluated through simulations. Comparisons with independent reactive and proactive algorithms show improvements on energy consumption. PMID:22423207

  2. LPTA: location predictive and time adaptive data gathering scheme with mobile sink for wireless sensor networks.

    Science.gov (United States)

    Zhu, Chuan; Wang, Yao; Han, Guangjie; Rodrigues, Joel J P C; Lloret, Jaime

    2014-01-01

    This paper exploits sink mobility to prolong the lifetime of sensor networks while maintaining the data transmission delay relatively low. A location predictive and time adaptive data gathering scheme is proposed. In this paper, we introduce a sink location prediction principle based on loose time synchronization and deduce the time-location formulas of the mobile sink. According to local clocks and the time-location formulas of the mobile sink, nodes in the network are able to calculate the current location of the mobile sink accurately and route data packets timely toward the mobile sink by multihop relay. Considering that data packets generating from different areas may be different greatly, an adaptive dwelling time adjustment method is also proposed to balance energy consumption among nodes in the network. Simulation results show that our data gathering scheme enables data routing with less data transmission time delay and balance energy consumption among nodes.

  3. A Hybrid Optimized Weighted Minimum Spanning Tree for the Shortest Intrapath Selection in Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Matheswaran Saravanan

    2014-01-01

    Full Text Available Wireless sensor network (WSN consists of sensor nodes that need energy efficient routing techniques as they have limited battery power, computing, and storage resources. WSN routing protocols should enable reliable multihop communication with energy constraints. Clustering is an effective way to reduce overheads and when this is aided by effective resource allocation, it results in reduced energy consumption. In this work, a novel hybrid evolutionary algorithm called Bee Algorithm-Simulated Annealing Weighted Minimal Spanning Tree (BASA-WMST routing is proposed in which randomly deployed sensor nodes are split into the best possible number of independent clusters with cluster head and optimal route. The former gathers data from sensors belonging to the cluster, forwarding them to the sink. The shortest intrapath selection for the cluster is selected using Weighted Minimum Spanning Tree (WMST. The proposed algorithm computes the distance-based Minimum Spanning Tree (MST of the weighted graph for the multihop network. The weights are dynamically changed based on the energy level of each sensor during route selection and optimized using the proposed bee algorithm simulated annealing algorithm.

  4. Joint Resource Optimization for Cognitive Sensor Networks with SWIPT-Enabled Relay.

    Science.gov (United States)

    Lu, Weidang; Lin, Yuanrong; Peng, Hong; Nan, Tian; Liu, Xin

    2017-09-13

    Energy-constrained wireless networks, such as wireless sensor networks (WSNs), are usually powered by fixed energy supplies (e.g., batteries), which limits the operation time of networks. Simultaneous wireless information and power transfer (SWIPT) is a promising technique to prolong the lifetime of energy-constrained wireless networks. This paper investigates the performance of an underlay cognitive sensor network (CSN) with SWIPT-enabled relay node. In the CSN, the amplify-and-forward (AF) relay sensor node harvests energy from the ambient radio-frequency (RF) signals using power splitting-based relaying (PSR) protocol. Then, it helps forward the signal of source sensor node (SSN) to the destination sensor node (DSN) by using the harvested energy. We study the joint resource optimization including the transmit power and power splitting ratio to maximize CSN's achievable rate with the constraint that the interference caused by the CSN to the primary users (PUs) is within the permissible threshold. Simulation results show that the performance of our proposed joint resource optimization can be significantly improved.

  5. Optimized autonomous space in-situ sensor web for volcano monitoring

    Science.gov (United States)

    Song, W.-Z.; Shirazi, B.; Huang, R.; Xu, M.; Peterson, N.; LaHusen, R.; Pallister, J.; Dzurisin, D.; Moran, S.; Lisowski, M.; Kedar, S.; Chien, S.; Webb, F.; Kiely, A.; Doubleday, J.; Davies, A.; Pieri, D.

    2010-01-01

    In response to NASA's announced requirement for Earth hazard monitoring sensor-web technology, a multidisciplinary team involving sensor-network experts (Washington State University), space scientists (JPL), and Earth scientists (USGS Cascade Volcano Observatory (CVO)), have developed a prototype of dynamic and scalable hazard monitoring sensor-web and applied it to volcano monitoring. The combined Optimized Autonomous Space In-situ Sensor-web (OASIS) has two-way communication capability between ground and space assets, uses both space and ground data for optimal allocation of limited bandwidth resources on the ground, and uses smart management of competing demands for limited space assets. It also enables scalability and seamless infusion of future space and in-situ assets into the sensor-web. The space and in-situ control components of the system are integrated such that each element is capable of autonomously tasking the other. The ground in-situ was deployed into the craters and around the flanks of Mount St. Helens in July 2009, and linked to the command and control of the Earth Observing One (EO-1) satellite. ?? 2010 IEEE.

  6. Genetic algorithms for optimal design and control of adaptive structures

    CERN Document Server

    Ribeiro, R; Dias-Rodrigues, J; Vaz, M

    2000-01-01

    Future High Energy Physics experiments require the use of light and stable structures to support their most precise radiation detection elements. These large structures must be light, highly stable, stiff and radiation tolerant in an environment where external vibrations, high radiation levels, material aging, temperature and humidity gradients are not negligible. Unforeseen factors and the unknown result of the coupling of environmental conditions, together with external vibrations, may affect the position stability of the detectors and their support structures compromising their physics performance. Careful optimization of static and dynamic behavior must be an essential part of the engineering design. Genetic Algorithms ( GA) belong to the group of probabilistic algorithms, combining elements of direct and stochastic search. They are more robust than existing directed search methods with the advantage of maintaining a population of potential solutions. There is a class of optimization problems for which Ge...

  7. Adaptive Decision Making Using Probabilistic Programming and Stochastic Optimization

    Science.gov (United States)

    2018-01-01

    world optimization problems (and hence 16 Approved for Public Release (PA); Distribution Unlimited Pred. demand (uncertain; discrete ...simplify the setting, we further assume that the demands are discrete , taking on values d1, . . . , dk with probabilities (conditional on x) (pθ)i ≡ p...Tyrrell Rockafellar. Implicit functions and solution mappings. Springer Monogr. Math ., 2009. Anthony V Fiacco and Yo Ishizuka. Sensitivity and stability

  8. Increasing the Lifetime of Mobile WSNs via Dynamic Optimization of Sensor Node Communication Activity

    Directory of Open Access Journals (Sweden)

    Dayan Adionel Guimarães

    2016-09-01

    Full Text Available In this paper, a simple and flexible method for increasing the lifetime of fixed or mobile wireless sensor networks is proposed. Based on past residual energy information reported by the sensor nodes, the sink node or another central node dynamically optimizes the communication activity levels of the sensor nodes to save energy without sacrificing the data throughput. The activity levels are defined to represent portions of time or time-frequency slots in a frame, during which the sensor nodes are scheduled to communicate with the sink node to report sensory measurements. Besides node mobility, it is considered that sensors’ batteries may be recharged via a wireless power transmission or equivalent energy harvesting scheme, bringing to the optimization problem an even more dynamic character. We report large increased lifetimes over the non-optimized network and comparable or even larger lifetime improvements with respect to an idealized greedy algorithm that uses both the real-time channel state and the residual energy information.

  9. A Survey on Multimedia-Based Cross-Layer Optimization in Visual Sensor Networks

    Science.gov (United States)

    Costa, Daniel G.; Guedes, Luiz Affonso

    2011-01-01

    Visual sensor networks (VSNs) comprised of battery-operated electronic devices endowed with low-resolution cameras have expanded the applicability of a series of monitoring applications. Those types of sensors are interconnected by ad hoc error-prone wireless links, imposing stringent restrictions on available bandwidth, end-to-end delay and packet error rates. In such context, multimedia coding is required for data compression and error-resilience, also ensuring energy preservation over the path(s) toward the sink and improving the end-to-end perceptual quality of the received media. Cross-layer optimization may enhance the expected efficiency of VSNs applications, disrupting the conventional information flow of the protocol layers. When the inner characteristics of the multimedia coding techniques are exploited by cross-layer protocols and architectures, higher efficiency may be obtained in visual sensor networks. This paper surveys recent research on multimedia-based cross-layer optimization, presenting the proposed strategies and mechanisms for transmission rate adjustment, congestion control, multipath selection, energy preservation and error recovery. We note that many multimedia-based cross-layer optimization solutions have been proposed in recent years, each one bringing a wealth of contributions to visual sensor networks. PMID:22163908

  10. An Efficient and Self-Adapting Localization in Static Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Wei Dong

    2009-08-01

    Full Text Available Localization is one of the most important subjects in Wireless Sensor Networks (WSNs. To reduce the number of beacons and adopt probabilistic methods, some particle filter-based mobile beacon-assisted localization approaches have been proposed, such as Mobile Beacon-assisted Localization (MBL, Adapting MBL (A-MBL, and the method proposed by Hang et al. Some new significant problems arise in these approaches, however. The first question is which probability distribution should be selected as the dynamic model in the prediction stage. The second is whether the unknown node adopts neighbors’ observation in the update stage. The third is how to find a self-adapting mechanism to achieve more flexibility in the adapting stage. In this paper, we give the theoretical analysis and experimental evaluations to suggest which probability distribution in the dynamic model should be adopted to improve the efficiency in the prediction stage. We also give the condition for whether the unknown node should use the observations from its neighbors to improve the accuracy. Finally, we propose a Self-Adapting Mobile Beacon-assisted Localization (SA-MBL approach to achieve more flexibility and achieve almost the same performance with A-MBL.

  11. Dynamic optimization of the complex adaptive controlling by the structure of enterprise’s product range

    Directory of Open Access Journals (Sweden)

    Andrey Fyodorovich Shorikov

    2013-06-01

    Full Text Available This paper reviews a methodical approach to solve multi-step dynamic problem of optimal integrated adaptive management of a product portfolio structure of the enterprise. For the organization of optimal adaptive terminal control of the system the recurrent algorithm, which reduces an initial multistage problem to the realization of the final sequence of problems of optimal program terminal control is offered. In turn, the decision of each problem of optimal program terminal control is reduced to the realization of the final sequence only single-step operations in the form of the problems solving of linear and convex mathematical programming. Thus, the offered approach allows to develop management solutions at current information support, which consider feedback, and which create the optimal structure of an enterprise’s product lines, contributing to optimising of profits, as well as maintenance of the desired level of profit for a long period of time

  12. A Novel adaptative Discrete Cuckoo Search Algorithm for parameter optimization in computer vision

    Directory of Open Access Journals (Sweden)

    loubna benchikhi

    2017-10-01

    Full Text Available Computer vision applications require choosing operators and their parameters, in order to provide the best outcomes. Often, the users quarry on expert knowledge and must experiment many combinations to find manually the best one. As performance, time and accuracy are important, it is necessary to automate parameter optimization at least for crucial operators. In this paper, a novel approach based on an adaptive discrete cuckoo search algorithm (ADCS is proposed. It automates the process of algorithms’ setting and provides optimal parameters for vision applications. This work reconsiders a discretization problem to adapt the cuckoo search algorithm and presents the procedure of parameter optimization. Some experiments on real examples and comparisons to other metaheuristic-based approaches: particle swarm optimization (PSO, reinforcement learning (RL and ant colony optimization (ACO show the efficiency of this novel method.

  13. A hybrid reliability algorithm using PSO-optimized Kriging model and adaptive importance sampling

    Science.gov (United States)

    Tong, Cao; Gong, Haili

    2018-03-01

    This paper aims to reduce the computational cost of reliability analysis. A new hybrid algorithm is proposed based on PSO-optimized Kriging model and adaptive importance sampling method. Firstly, the particle swarm optimization algorithm (PSO) is used to optimize the parameters of Kriging model. A typical function is fitted to validate improvement by comparing results of PSO-optimized Kriging model with those of the original Kriging model. Secondly, a hybrid algorithm for reliability analysis combined optimized Kriging model and adaptive importance sampling is proposed. Two cases from literatures are given to validate the efficiency and correctness. The proposed method is proved to be more efficient due to its application of small number of sample points according to comparison results.

  14. A Gradient Optimization Approach to Adaptive Multi-Robot Control

    Science.gov (United States)

    2009-09-01

    t) - v, (t) = 0 Vi from the first term in the sum, so the network converges to a near-optimal coverage configuration. Furthermore, from Ci(7) Td (t...they are not a function of 7) to get ry&(t)TW[j (TF)ICjIC[ d-l &di(t) = nYdcti( td Since 1V - 0, if limt,, Ai (t) is positive definite (we know the limit...property clearly, examine the magnitude of force exerted by one neighbor (m - 1 = 1) given by IIl f = 0ij - 02i/ llj - P I, and shown in the left of

  15. Optimal sensor placement for leak location in water distribution networks using genetic algorithms.

    Science.gov (United States)

    Casillas, Myrna V; Puig, Vicenç; Garza-Castañón, Luis E; Rosich, Albert

    2013-11-04

    This paper proposes a new sensor placement approach for leak location in water distribution networks (WDNs). The sensor placement problem is formulated as an integer optimization problem. The optimization criterion consists in minimizing the number of non-isolable leaks according to the isolability criteria introduced. Because of the large size and non-linear integer nature of the resulting optimization problem, genetic algorithms (GAs) are used as the solution approach. The obtained results are compared with a semi-exhaustive search method with higher computational effort, proving that GA allows one to find near-optimal solutions with less computational load. Moreover, three ways of increasing the robustness of the GA-based sensor placement method have been proposed using a time horizon analysis, a distance-based scoring and considering different leaks sizes. A great advantage of the proposed methodology is that it does not depend on the isolation method chosen by the user, as long as it is based on leak sensitivity analysis. Experiments in two networks allow us to evaluate the performance of the proposed approach.

  16. Optimal Sensor Placement for Leak Location in Water Distribution Networks Using Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    Luis E. Garza-Castañón

    2013-11-01

    Full Text Available This paper proposes a new sensor placement approach for leak location in water distribution networks (WDNs. The sensor placement problem is formulated as an integer optimization problem. The optimization criterion consists in minimizing the number of non-isolable leaks according to the isolability criteria introduced. Because of the large size and non-linear integer nature of the resulting optimization problem, genetic algorithms (GAs are used as the solution approach. The obtained results are compared with a semi-exhaustive search method with higher computational effort, proving that GA allows one to find near-optimal solutions with less computational load. Moreover, three ways of increasing the robustness of the GA-based sensor placement method have been proposed using a time horizon analysis, a distance-based scoring and considering different leaks sizes. A great advantage of the proposed methodology is that it does not depend on the isolation method chosen by the user, as long as it is based on leak sensitivity analysis. Experiments in two networks allow us to evaluate the performance of the proposed approach.

  17. Optimal Sensor Placement for Leak Location in Water Distribution Networks Using Genetic Algorithms

    Science.gov (United States)

    Casillas, Myrna V.; Puig, Vicenç; Garza-Castañón, Luis E.; Rosich, Albert

    2013-01-01

    This paper proposes a new sensor placement approach for leak location in water distribution networks (WDNs). The sensor placement problem is formulated as an integer optimization problem. The optimization criterion consists in minimizing the number of non-isolable leaks according to the isolability criteria introduced. Because of the large size and non-linear integer nature of the resulting optimization problem, genetic algorithms (GAs) are used as the solution approach. The obtained results are compared with a semi-exhaustive search method with higher computational effort, proving that GA allows one to find near-optimal solutions with less computational load. Moreover, three ways of increasing the robustness of the GA-based sensor placement method have been proposed using a time horizon analysis, a distance-based scoring and considering different leaks sizes. A great advantage of the proposed methodology is that it does not depend on the isolation method chosen by the user, as long as it is based on leak sensitivity analysis. Experiments in two networks allow us to evaluate the performance of the proposed approach. PMID:24193099

  18. Soft sensor modelling by time difference, recursive partial least squares and adaptive model updating

    International Nuclear Information System (INIS)

    Fu, Y; Xu, O; Yang, W; Zhou, L; Wang, J

    2017-01-01

    To investigate time-variant and nonlinear characteristics in industrial processes, a soft sensor modelling method based on time difference, moving-window recursive partial least square (PLS) and adaptive model updating is proposed. In this method, time difference values of input and output variables are used as training samples to construct the model, which can reduce the effects of the nonlinear characteristic on modelling accuracy and retain the advantages of recursive PLS algorithm. To solve the high updating frequency of the model, a confidence value is introduced, which can be updated adaptively according to the results of the model performance assessment. Once the confidence value is updated, the model can be updated. The proposed method has been used to predict the 4-carboxy-benz-aldehyde (CBA) content in the purified terephthalic acid (PTA) oxidation reaction process. The results show that the proposed soft sensor modelling method can reduce computation effectively, improve prediction accuracy by making use of process information and reflect the process characteristics accurately. (paper)

  19. A distance-aware replica adaptive data gathering protocol for Delay Tolerant Mobile Sensor Networks.

    Science.gov (United States)

    Feng, Yong; Gong, Haigang; Fan, Mingyu; Liu, Ming; Wang, Xiaomin

    2011-01-01

    In Delay Tolerant Mobile Sensor Networks (DTMSNs) that have the inherent features of intermitted connectivity and frequently changing network topology it is reasonable to utilize multi-replica schemes to improve the data gathering performance. However, most existing multi-replica approaches inject a large amount of message copies into the network to increase the probability of message delivery, which may drain each mobile node's limited battery supply faster and result in too much contention for the restricted resources of the DTMSN, so a proper data gathering scheme needs a trade off between the number of replica messages and network performance. In this paper, we propose a new data gathering protocol called DRADG (for Distance-aware Replica Adaptive Data Gathering protocol), which economizes network resource consumption through making use of a self-adapting algorithm to cut down the number of redundant replicas of messages, and achieves a good network performance by leveraging the delivery probabilities of the mobile sensors as main routing metrics. Simulation results have shown that the proposed DRADG protocol achieves comparable or higher message delivery ratios at the cost of the much lower transmission overhead than several current DTMSN data gathering schemes.

  20. Adaptive Portfolio Optimization for Multiple Electricity Markets Participation.

    Science.gov (United States)

    Pinto, Tiago; Morais, Hugo; Sousa, Tiago M; Sousa, Tiago; Vale, Zita; Praca, Isabel; Faia, Ricardo; Pires, Eduardo Jose Solteiro

    2016-08-01

    The increase of distributed energy resources, mainly based on renewable sources, requires new solutions that are able to deal with this type of resources' particular characteristics (namely, the renewable energy sources intermittent nature). The smart grid concept is increasing its consensus as the most suitable solution to facilitate the small players' participation in electric power negotiations while improving energy efficiency. The opportunity for players' participation in multiple energy negotiation environments (smart grid negotiation in addition to the already implemented market types, such as day-ahead spot markets, balancing markets, intraday negotiations, bilateral contracts, forward and futures negotiations, and among other) requires players to take suitable decisions on whether to, and how to participate in each market type. This paper proposes a portfolio optimization methodology, which provides the best investment profile for a market player, considering different market opportunities. The amount of power that each supported player should negotiate in each available market type in order to maximize its profits, considers the prices that are expected to be achieved in each market, in different contexts. The price forecasts are performed using artificial neural networks, providing a specific database with the expected prices in the different market types, at each time. This database is then used as input by an evolutionary particle swarm optimization process, which originates the most advantage participation portfolio for the market player. The proposed approach is tested and validated with simulations performed in multiagent simulator of competitive electricity markets, using real electricity markets data from the Iberian operator-MIBEL.

  1. An Adaptive Time-Spread Multiple-Access Policy for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Konstantinos Oikonomou

    2007-05-01

    Full Text Available Sensor networks require a simple and efficient medium access control policy achieving high system throughput with no or limited control overhead in order to increase the network lifetime by minimizing the energy consumed during transmission attempts. Time-spread multiple-access (TSMA policies that have been proposed for ad hoc network environments, can also be employed in sensor networks, since no control overhead is introduced. However, they do not take advantage of any cross-layer information in order to exploit the idiosyncrasies of the particular sensor network environment such as the presence of typically static nodes and a common destination for the forwarded data. An adaptive probabilistic TSMA-based policy, that is proposed and analyzed in this paper, exploits these idiosyncrasies and achieves higher system throughput than the existing TSMA-based policies without any need for extra control overhead. As it is analytically shown in this paper, the proposed policy always outperforms the existing TSMA-based policies, if certain parameter values are properly set; the analysis also provides for these proper values. It is also shown that the proposed policy is characterized by a certain convergence period and that high system throughput is achieved for long convergence periods. The claims and expectations of the provided analysis are supported by simulation results presented in this paper.

  2. PCA-based spatially adaptive denoising of CFA images for single-sensor digital cameras.

    Science.gov (United States)

    Zheng, Lei; Lukac, Rastislav; Wu, Xiaolin; Zhang, David

    2009-04-01

    Single-sensor digital color cameras use a process called color demosiacking to produce full color images from the data captured by a color filter array (CAF). The quality of demosiacked images is degraded due to the sensor noise introduced during the image acquisition process. The conventional solution to combating CFA sensor noise is demosiacking first, followed by a separate denoising processing. This strategy will generate many noise-caused color artifacts in the demosiacking process, which are hard to remove in the denoising process. Few denoising schemes that work directly on the CFA images have been presented because of the difficulties arisen from the red, green and blue interlaced mosaic pattern, yet a well-designed "denoising first and demosiacking later" scheme can have advantages such as less noise-caused color artifacts and cost-effective implementation. This paper presents a principle component analysis (PCA)-based spatially-adaptive denoising algorithm, which works directly on the CFA data using a supporting window to analyze the local image statistics. By exploiting the spatial and spectral correlations existing in the CFA image, the proposed method can effectively suppress noise while preserving color edges and details. Experiments using both simulated and real CFA images indicate that the proposed scheme outperforms many existing approaches, including those sophisticated demosiacking and denoising schemes, in terms of both objective measurement and visual evaluation.

  3. An Adaptive Orientation Estimation Method for Magnetic and Inertial Sensors in the Presence of Magnetic Disturbances

    Directory of Open Access Journals (Sweden)

    Bingfei Fan

    2017-05-01

    Full Text Available Magnetic and inertial sensors have been widely used to estimate the orientation of human segments due to their low cost, compact size and light weight. However, the accuracy of the estimated orientation is easily affected by external factors, especially when the sensor is used in an environment with magnetic disturbances. In this paper, we propose an adaptive method to improve the accuracy of orientation estimations in the presence of magnetic disturbances. The method is based on existing gradient descent algorithms, and it is performed prior to sensor fusion algorithms. The proposed method includes stationary state detection and magnetic disturbance severity determination. The stationary state detection makes this method immune to magnetic disturbances in stationary state, while the magnetic disturbance severity determination helps to determine the credibility of magnetometer data under dynamic conditions, so as to mitigate the negative effect of the magnetic disturbances. The proposed method was validated through experiments performed on a customized three-axis instrumented gimbal with known orientations. The error of the proposed method and the original gradient descent algorithms were calculated and compared. Experimental results demonstrate that in stationary state, the proposed method is completely immune to magnetic disturbances, and in dynamic conditions, the error caused by magnetic disturbance is reduced by 51.2% compared with original MIMU gradient descent algorithm.

  4. Rapid and highly integrated FPGA-based Shack-Hartmann wavefront sensor for adaptive optics system

    Science.gov (United States)

    Chen, Yi-Pin; Chang, Chia-Yuan; Chen, Shean-Jen

    2018-02-01

    In this study, a field programmable gate array (FPGA)-based Shack-Hartmann wavefront sensor (SHWS) programmed on LabVIEW can be highly integrated into customized applications such as adaptive optics system (AOS) for performing real-time wavefront measurement. Further, a Camera Link frame grabber embedded with FPGA is adopted to enhance the sensor speed reacting to variation considering its advantage of the highest data transmission bandwidth. Instead of waiting for a frame image to be captured by the FPGA, the Shack-Hartmann algorithm are implemented in parallel processing blocks design and let the image data transmission synchronize with the wavefront reconstruction. On the other hand, we design a mechanism to control the deformable mirror in the same FPGA and verify the Shack-Hartmann sensor speed by controlling the frequency of the deformable mirror dynamic surface deformation. Currently, this FPGAbead SHWS design can achieve a 266 Hz cyclic speed limited by the camera frame rate as well as leaves 40% logic slices for additionally flexible design.

  5. Adaptive Portfolio Optimization for Multiple Electricity Markets Participation

    DEFF Research Database (Denmark)

    Pinto, Tiago; Morais, Hugo; Sousa, Tiago M.

    2016-01-01

    as the most suitable solution to facilitate the small players' participation in electric power negotiations while improving energy efficiency. The opportunity for players' participation in multiple energy negotiation environments (smart grid negotiation in addition to the already implemented market types......, such as day-ahead spot markets, balancing markets, intraday negotiations, bilateral contracts, forward and futures negotiations, and among other) requires players to take suitable decisions on whether to, and how to participate in each market type. This paper proposes a portfolio optimization methodology......, which provides the best investment profile for a market player, considering different market opportunities. The amount of power that each supported player should negotiate in each available market type in order to maximize its profits, considers the prices that are expected to be achieved in each market...

  6. Data Analytics Based Dual-Optimized Adaptive Model Predictive Control for the Power Plant Boiler

    Directory of Open Access Journals (Sweden)

    Zhenhao Tang

    2017-01-01

    Full Text Available To control the furnace temperature of a power plant boiler precisely, a dual-optimized adaptive model predictive control (DoAMPC method is designed based on the data analytics. In the proposed DoAMPC, an accurate predictive model is constructed adaptively by the hybrid algorithm of the least squares support vector machine and differential evolution method. Then, an optimization problem is constructed based on the predictive model and many constraint conditions. To control the boiler furnace temperature, the differential evolution method is utilized to decide the control variables by solving the optimization problem. The proposed method can adapt to the time-varying situation by updating the sample data. The experimental results based on practical data illustrate that the DoAMPC can control the boiler furnace temperature with errors of less than 1.5% which can meet the requirements of the real production process.

  7. Online Adaptive Optimal Control of Vehicle Active Suspension Systems Using Single-Network Approximate Dynamic Programming

    Directory of Open Access Journals (Sweden)

    Zhi-Jun Fu

    2017-01-01

    Full Text Available In view of the performance requirements (e.g., ride comfort, road holding, and suspension space limitation for vehicle suspension systems, this paper proposes an adaptive optimal control method for quarter-car active suspension system by using the approximate dynamic programming approach (ADP. Online optimal control law is obtained by using a single adaptive critic NN to approximate the solution of the Hamilton-Jacobi-Bellman (HJB equation. Stability of the closed-loop system is proved by Lyapunov theory. Compared with the classic linear quadratic regulator (LQR approach, the proposed ADP-based adaptive optimal control method demonstrates improved performance in the presence of parametric uncertainties (e.g., sprung mass and unknown road displacement. Numerical simulation results of a sedan suspension system are presented to verify the effectiveness of the proposed control strategy.

  8. Cluster Optimization and Parallelization of Simulations with Dynamically Adaptive Grids

    KAUST Repository

    Schreiber, Martin; Weinzierl, Tobias; Bungartz, Hans-Joachim

    2013-01-01

    The present paper studies solvers for partial differential equations that work on dynamically adaptive grids stemming from spacetrees. Due to the underlying tree formalism, such grids efficiently can be decomposed into connected grid regions (clusters) on-the-fly. A graph on those clusters classified according to their grid invariancy, workload, multi-core affinity, and further meta data represents the inter-cluster communication. While stationary clusters already can be handled more efficiently than their dynamic counterparts, we propose to treat them as atomic grid entities and introduce a skip mechanism that allows the grid traversal to omit those regions completely. The communication graph ensures that the cluster data nevertheless are kept consistent, and several shared memory parallelization strategies are feasible. A hyperbolic benchmark that has to remesh selected mesh regions iteratively to preserve conforming tessellations acts as benchmark for the present work. We discuss runtime improvements resulting from the skip mechanism and the implications on shared memory performance and load balancing. © 2013 Springer-Verlag.

  9. An adaptive N-body algorithm of optimal order

    CERN Document Server

    Pruett, C D; Lacy, J M

    2003-01-01

    Picard iteration is normally considered a theoretical tool whose primary utility is to establish the existence and uniqueness of solutions to first-order systems of ordinary differential equations (ODEs). However, in 1996, Parker and Sochacki [Neural, Parallel, Sci. Comput. 4 (1996)] published a practical numerical method for a certain class of ODEs, based upon modified Picard iteration, that generates the Maclaurin series of the solution to arbitrarily high order. The applicable class of ODEs consists of first-order, autonomous systems whose right-hand side functions (generators) are projectively polynomial; that is, they can be written as polynomials in the unknowns. The class is wider than might be expected. The method is ideally suited to the classical N-body problem, which is projectively polynomial. Here, we recast the N-body problem in polynomial form and develop a Picard-based algorithm for its solution. The algorithm is highly accurate, parameter-free, and simultaneously adaptive in time and order. T...

  10. Adaptive multi-objective Optimization scheme for cognitive radio resource management

    KAUST Repository

    Alqerm, Ismail

    2014-12-01

    Cognitive Radio is an intelligent Software Defined Radio that is capable to alter its transmission parameters according to predefined objectives and wireless environment conditions. Cognitive engine is the actuator that performs radio parameters configuration by exploiting optimization and machine learning techniques. In this paper, we propose an Adaptive Multi-objective Optimization Scheme (AMOS) for cognitive radio resource management to improve spectrum operation and network performance. The optimization relies on adapting radio transmission parameters to environment conditions using constrained optimization modeling called fitness functions in an iterative manner. These functions include minimizing power consumption, Bit Error Rate, delay and interference. On the other hand, maximizing throughput and spectral efficiency. Cross-layer optimization is exploited to access environmental parameters from all TCP/IP stack layers. AMOS uses adaptive Genetic Algorithm in terms of its parameters and objective weights as the vehicle of optimization. The proposed scheme has demonstrated quick response and efficiency in three different scenarios compared to other schemes. In addition, it shows its capability to optimize the performance of TCP/IP layers as whole not only the physical layer.

  11. Wireless Sensor Network Congestion Control Based on Standard Particle Swarm Optimization and Single Neuron PID.

    Science.gov (United States)

    Yang, Xiaoping; Chen, Xueying; Xia, Riting; Qian, Zhihong

    2018-04-19

    Aiming at the problem of network congestion caused by the large number of data transmissions in wireless routing nodes of wireless sensor network (WSN), this paper puts forward an algorithm based on standard particle swarm⁻neural PID congestion control (PNPID). Firstly, PID control theory was applied to the queue management of wireless sensor nodes. Then, the self-learning and self-organizing ability of neurons was used to achieve online adjustment of weights to adjust the proportion, integral and differential parameters of the PID controller. Finally, the standard particle swarm optimization to neural PID (NPID) algorithm of initial values of proportion, integral and differential parameters and neuron learning rates were used for online optimization. This paper describes experiments and simulations which show that the PNPID algorithm effectively stabilized queue length near the expected value. At the same time, network performance, such as throughput and packet loss rate, was greatly improved, which alleviated network congestion and improved network QoS.

  12. Energy aware swarm optimization with intercluster search for wireless sensor network.

    Science.gov (United States)

    Thilagavathi, Shanmugasundaram; Geetha, Bhavani Gnanasambandan

    2015-01-01

    Wireless sensor networks (WSNs) are emerging as a low cost popular solution for many real-world challenges. The low cost ensures deployment of large sensor arrays to perform military and civilian tasks. Generally, WSNs are power constrained due to their unique deployment method which makes replacement of battery source difficult. Challenges in WSN include a well-organized communication platform for the network with negligible power utilization. In this work, an improved binary particle swarm optimization (PSO) algorithm with modified connected dominating set (CDS) based on residual energy is proposed for discovery of optimal number of clusters and cluster head (CH). Simulations show that the proposed BPSO-T and BPSO-EADS perform better than LEACH- and PSO-based system in terms of energy savings and QOS.

  13. Energy Aware Swarm Optimization with Intercluster Search for Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Shanmugasundaram Thilagavathi

    2015-01-01

    Full Text Available Wireless sensor networks (WSNs are emerging as a low cost popular solution for many real-world challenges. The low cost ensures deployment of large sensor arrays to perform military and civilian tasks. Generally, WSNs are power constrained due to their unique deployment method which makes replacement of battery source difficult. Challenges in WSN include a well-organized communication platform for the network with negligible power utilization. In this work, an improved binary particle swarm optimization (PSO algorithm with modified connected dominating set (CDS based on residual energy is proposed for discovery of optimal number of clusters and cluster head (CH. Simulations show that the proposed BPSO-T and BPSO-EADS perform better than LEACH- and PSO-based system in terms of energy savings and QOS.

  14. Lifetime Optimization of a Multiple Sink Wireless Sensor Network through Energy Balancing

    Directory of Open Access Journals (Sweden)

    Tapan Kumar Jain

    2015-01-01

    Full Text Available The wireless sensor network consists of small limited energy sensors which are connected to one or more sinks. The maximum energy consumption takes place in communicating the data from the nodes to the sink. Multiple sink WSN has an edge over the single sink WSN where very less energy is utilized in sending the data to the sink, as the number of hops is reduced. If the energy consumed by a node is balanced between the other nodes, the lifetime of the network is considerably increased. The network lifetime optimization is achieved by restructuring the network by modifying the neighbor nodes of a sink. Only those nodes are connected to a sink which makes the total energy of the sink less than the threshold. This energy balancing through network restructuring optimizes the network lifetime. This paper depicts this fact through simulations done in MATLAB.

  15. An adaptive robust optimization scheme for water-flooding optimization in oil reservoirs using residual analysis

    NARCIS (Netherlands)

    Siraj, M.M.; Van den Hof, P.M.J.; Jansen, J.D.

    2017-01-01

    Model-based dynamic optimization of the water-flooding process in oil reservoirs is a computationally complex problem and suffers from high levels of uncertainty. A traditional way of quantifying uncertainty in robust water-flooding optimization is by considering an ensemble of uncertain model

  16. Process Optimization for Monolithic Integration of Piezoresistive Pressure Sensor and MOSFET Amplifier with SOI Approach

    International Nuclear Information System (INIS)

    Kumar, V Vinoth; Dasgupta, A; Bhat, K N; KNatarajan

    2006-01-01

    In this paper we present the design and process optimization for fabricating piezoresitive pressure sensor and MOSFET Differential Amplifier simultaneously on the same chip. Silicon On Insulator approach has been used for realizing the membrane as well as the electronics on the same chip. The amplifier circuit has been configured in the common source connection and it has been designed with PSPICE simulation to achieve a voltage gain of about 5. In the initial set of experiments the Pressure sensor and the amplifier were fabricated on separate chips to optimize the process steps and tested in the hybrid mode. In the next set of experiments, SOI wafer having the SOI layer thickness of about 11 microns was used for realizing the membrane by anisotropic etching from the backside. The piezo-resistive pressure sensor was realized on this membrane by connecting the polysilicon resistors in the form of a Wheatstone bridge. The MOSFET source follower amplifier was also fabricated on the same SOI wafer by tailoring the process steps to suit the requirement of simultaneous fabrication of piezoresistors and the amplifier for achieving MOSFET Integrated Pressure Sensor. Reproducible results have been achieved on the SOI wafers, with the process steps developed in the laboratory. Sensitivity of 270 mV /Bar/10V, with the on chip amplifier gain of 4.5, has been achieved with this process

  17. Parallel multi-join query optimization algorithm for distributed sensor network in the internet of things

    Science.gov (United States)

    Zheng, Yan

    2015-03-01

    Internet of things (IoT), focusing on providing users with information exchange and intelligent control, attracts a lot of attention of researchers from all over the world since the beginning of this century. IoT is consisted of large scale of sensor nodes and data processing units, and the most important features of IoT can be illustrated as energy confinement, efficient communication and high redundancy. With the sensor nodes increment, the communication efficiency and the available communication band width become bottle necks. Many research work is based on the instance which the number of joins is less. However, it is not proper to the increasing multi-join query in whole internet of things. To improve the communication efficiency between parallel units in the distributed sensor network, this paper proposed parallel query optimization algorithm based on distribution attributes cost graph. The storage information relations and the network communication cost are considered in this algorithm, and an optimized information changing rule is established. The experimental result shows that the algorithm has good performance, and it would effectively use the resource of each node in the distributed sensor network. Therefore, executive efficiency of multi-join query between different nodes could be improved.

  18. Optimal placement of excitations and sensors for verification of large dynamical systems

    Science.gov (United States)

    Salama, M.; Rose, T.; Garba, J.

    1987-01-01

    The computationally difficult problem of the optimal placement of excitations and sensors to maximize the observed measurements is studied within the framework of combinatorial optimization, and is solved numerically using a variation of the simulated annealing heuristic algorithm. Results of numerical experiments including a square plate and a 960 degrees-of-freedom Control of Flexible Structure (COFS) truss structure, are presented. Though the algorithm produces suboptimal solutions, its generality and simplicity allow the treatment of complex dynamical systems which would otherwise be difficult to handle.

  19. Optimal power allocation of a single transmitter-multiple receivers channel in a cognitive sensor network

    KAUST Repository

    Ayala Solares, Jose Roberto

    2012-08-01

    The optimal transmit power of a wireless sensor network with one transmitter and multiple receivers in a cognitive radio environment while satisfying independent peak, independent average, sum of peak and sum of average transmission rate constraints is derived. A suboptimal scheme is proposed to overcome the frequency of outages for the independent peak transmission rate constraint. In all cases, numerical results are provided for Rayleigh fading channels. © 2012 IEEE.

  20. High-Dimensional Adaptive Particle Swarm Optimization on Heterogeneous Systems

    International Nuclear Information System (INIS)

    Wachowiak, M P; Sarlo, B B; Foster, A E Lambe

    2014-01-01

    Much work has recently been reported in parallel GPU-based particle swarm optimization (PSO). Motivated by the encouraging results of these investigations, while also recognizing the limitations of GPU-based methods for big problems using a large amount of data, this paper explores the efficacy of employing other types of parallel hardware for PSO. Most commodity systems feature a variety of architectures whose high-performance capabilities can be exploited. In this paper, high-dimensional problems and those that employ a large amount of external data are explored within the context of heterogeneous systems. Large problems are decomposed into constituent components, and analyses are undertaken of which components would benefit from multi-core or GPU parallelism. The current study therefore provides another demonstration that ''supercomputing on a budget'' is possible when subtasks of large problems are run on hardware most suited to these tasks. Experimental results show that large speedups can be achieved on high dimensional, data-intensive problems. Cost functions must first be analysed for parallelization opportunities, and assigned hardware based on the particular task

  1. Trajectory Planning and Optimized Adaptive Control for a Class of Wheeled Inverted Pendulum Vehicle Models.

    Science.gov (United States)

    Yang, Chenguang; Li, Zhijun; Li, Jing

    2013-02-01

    In this paper, we investigate optimized adaptive control and trajectory generation for a class of wheeled inverted pendulum (WIP) models of vehicle systems. Aiming at shaping the controlled vehicle dynamics to be of minimized motion tracking errors as well as angular accelerations, we employ the linear quadratic regulation optimization technique to obtain an optimal reference model. Adaptive control has then been developed using variable structure method to ensure the reference model to be exactly matched in a finite-time horizon, even in the presence of various internal and external uncertainties. The minimized yaw and tilt angular accelerations help to enhance the vehicle rider's comfort. In addition, due to the underactuated mechanism of WIP, the vehicle forward velocity dynamics cannot be controlled separately from the pendulum tilt angle dynamics. Inspired by the control strategy of human drivers, who usually manipulate the tilt angle to control the forward velocity, we design a neural-network-based adaptive generator of implicit control trajectory (AGICT) of the tilt angle which indirectly "controls" the forward velocity such that it tracks the desired velocity asymptotically. The stability and optimal tracking performance have been rigorously established by theoretic analysis. In addition, simulation studies have been carried out to demonstrate the efficiency of the developed AGICT and optimized adaptive controller.

  2. An Energy Efficient Adaptive Sampling Algorithm in a Sensor Network for Automated Water Quality Monitoring.

    Science.gov (United States)

    Shu, Tongxin; Xia, Min; Chen, Jiahong; Silva, Clarence de

    2017-11-05

    Power management is crucial in the monitoring of a remote environment, especially when long-term monitoring is needed. Renewable energy sources such as solar and wind may be harvested to sustain a monitoring system. However, without proper power management, equipment within the monitoring system may become nonfunctional and, as a consequence, the data or events captured during the monitoring process will become inaccurate as well. This paper develops and applies a novel adaptive sampling algorithm for power management in the automated monitoring of the quality of water in an extensive and remote aquatic environment. Based on the data collected on line using sensor nodes, a data-driven adaptive sampling algorithm (DDASA) is developed for improving the power efficiency while ensuring the accuracy of sampled data. The developed algorithm is evaluated using two distinct key parameters, which are dissolved oxygen (DO) and turbidity. It is found that by dynamically changing the sampling frequency, the battery lifetime can be effectively prolonged while maintaining a required level of sampling accuracy. According to the simulation results, compared to a fixed sampling rate, approximately 30.66% of the battery energy can be saved for three months of continuous water quality monitoring. Using the same dataset to compare with a traditional adaptive sampling algorithm (ASA), while achieving around the same Normalized Mean Error (NME), DDASA is superior in saving 5.31% more battery energy.

  3. An Energy Efficient Adaptive Sampling Algorithm in a Sensor Network for Automated Water Quality Monitoring

    Directory of Open Access Journals (Sweden)

    Tongxin Shu

    2017-11-01

    Full Text Available Power management is crucial in the monitoring of a remote environment, especially when long-term monitoring is needed. Renewable energy sources such as solar and wind may be harvested to sustain a monitoring system. However, without proper power management, equipment within the monitoring system may become nonfunctional and, as a consequence, the data or events captured during the monitoring process will become inaccurate as well. This paper develops and applies a novel adaptive sampling algorithm for power management in the automated monitoring of the quality of water in an extensive and remote aquatic environment. Based on the data collected on line using sensor nodes, a data-driven adaptive sampling algorithm (DDASA is developed for improving the power efficiency while ensuring the accuracy of sampled data. The developed algorithm is evaluated using two distinct key parameters, which are dissolved oxygen (DO and turbidity. It is found that by dynamically changing the sampling frequency, the battery lifetime can be effectively prolonged while maintaining a required level of sampling accuracy. According to the simulation results, compared to a fixed sampling rate, approximately 30.66% of the battery energy can be saved for three months of continuous water quality monitoring. Using the same dataset to compare with a traditional adaptive sampling algorithm (ASA, while achieving around the same Normalized Mean Error (NME, DDASA is superior in saving 5.31% more battery energy.

  4. Traffic Congestion Evaluation and Signal Control Optimization Based on Wireless Sensor Networks: Model and Algorithms

    Directory of Open Access Journals (Sweden)

    Wei Zhang

    2012-01-01

    Full Text Available This paper presents the model and algorithms for traffic flow data monitoring and optimal traffic light control based on wireless sensor networks. Given the scenario that sensor nodes are sparsely deployed along the segments between signalized intersections, an analytical model is built using continuum traffic equation and develops the method to estimate traffic parameter with the scattered sensor data. Based on the traffic data and principle of traffic congestion formation, we introduce the congestion factor which can be used to evaluate the real-time traffic congestion status along the segment and to predict the subcritical state of traffic jams. The result is expected to support the timing phase optimization of traffic light control for the purpose of avoiding traffic congestion before its formation. We simulate the traffic monitoring based on the Mobile Century dataset and analyze the performance of traffic light control on VISSIM platform when congestion factor is introduced into the signal timing optimization model. The simulation result shows that this method can improve the spatial-temporal resolution of traffic data monitoring and evaluate traffic congestion status with high precision. It is helpful to remarkably alleviate urban traffic congestion and decrease the average traffic delays and maximum queue length.

  5. Design, optimization and numerical modelling of a novel floating pendulum wave energy converter with tide adaptation

    Science.gov (United States)

    Yang, Jing; Zhang, Da-hai; Chen, Ying; Liang, Hui; Tan, Ming; Li, Wei; Ma, Xian-dong

    2017-10-01

    A novel floating pendulum wave energy converter (WEC) with the ability of tide adaptation is designed and presented in this paper. Aiming to a high efficiency, the buoy's hydrodynamic shape is optimized by enumeration and comparison. Furthermore, in order to keep the buoy's well-designed leading edge always facing the incoming wave straightly, a novel transmission mechanism is then adopted, which is called the tidal adaptation mechanism in this paper. Time domain numerical models of a floating pendulum WEC with or without tide adaptation mechanism are built to compare their performance on various water levels. When comparing these two WECs in terms of their average output based on the linear passive control strategy, the output power of WEC with the tide adaptation mechanism is much steadier with the change of the water level and always larger than that without the tide adaptation mechanism.

  6. Tap-length optimization of adaptive filters used in stereophonic acoustic echo cancellation

    DEFF Research Database (Denmark)

    Kar, Asutosh; Swamy, M.N.S.

    2017-01-01

    An adaptive filter with a large number of weights or taps is necessary for stereophonic acoustic echo cancellation (SAEC), depending on the room impulse response and acoustic path where the cancellation is performed. However, a large tap-length results in slow convergence and increases...... the complexity of the tapped delay line structure for FIR adaptive filters. To overcome this problem, there is a need for an optimum tap-length-estimation algorithm that provides better convergence for the adaptive filters used in SAEC. This paper presents a solution to the problem of balancing convergence...... and steady-state performance of long length adaptive filters used for SAEC by proposing a new tap-length-optimization algorithm. The optimum tap length and step size of the adaptive filter are derived considering an impulse response with an exponentially-decaying envelope, which models a wide range...

  7. Output Feedback Adaptive Control of Non-Minimum Phase Systems Using Optimal Control Modification

    Science.gov (United States)

    Nguyen, Nhan; Hashemi, Kelley E.; Yucelen, Tansel; Arabi, Ehsan

    2018-01-01

    This paper describes output feedback adaptive control approaches for non-minimum phase SISO systems with relative degree 1 and non-strictly positive real (SPR) MIMO systems with uniform relative degree 1 using the optimal control modification method. It is well-known that the standard model-reference adaptive control (MRAC) cannot be used to control non-SPR plants to track an ideal SPR reference model. Due to the ideal property of asymptotic tracking, MRAC attempts an unstable pole-zero cancellation which results in unbounded signals for non-minimum phase SISO systems. The optimal control modification can be used to prevent the unstable pole-zero cancellation which results in a stable adaptation of non-minimum phase SISO systems. However, the tracking performance using this approach could suffer if the unstable zero is located far away from the imaginary axis. The tracking performance can be recovered by using an observer-based output feedback adaptive control approach which uses a Luenberger observer design to estimate the state information of the plant. Instead of explicitly specifying an ideal SPR reference model, the reference model is established from the linear quadratic optimal control to account for the non-minimum phase behavior of the plant. With this non-minimum phase reference model, the observer-based output feedback adaptive control can maintain stability as well as tracking performance. However, in the presence of the mismatch between the SPR reference model and the non-minimum phase plant, the standard MRAC results in unbounded signals, whereas a stable adaptation can be achieved with the optimal control modification. An application of output feedback adaptive control for a flexible wing aircraft illustrates the approaches.

  8. A near-optimal low complexity sensor fusion technique for accurate indoor localization based on ultrasound time of arrival measurements from low-quality sensors

    Science.gov (United States)

    Mitilineos, Stelios A.; Argyreas, Nick D.; Thomopoulos, Stelios C. A.

    2009-05-01

    A fusion-based localization technique for location-based services in indoor environments is introduced herein, based on ultrasound time-of-arrival measurements from multiple off-the-shelf range estimating sensors which are used in a market-available localization system. In-situ field measurements results indicated that the respective off-the-shelf system was unable to estimate position in most of the cases, while the underlying sensors are of low-quality and yield highly inaccurate range and position estimates. An extensive analysis is performed and a model of the sensor-performance characteristics is established. A low-complexity but accurate sensor fusion and localization technique is then developed, which consists inof evaluating multiple sensor measurements and selecting the one that is considered most-accurate based on the underlying sensor model. Optimality, in the sense of a genie selecting the optimum sensor, is subsequently evaluated and compared to the proposed technique. The experimental results indicate that the proposed fusion method exhibits near-optimal performance and, albeit being theoretically suboptimal, it largely overcomes most flaws of the underlying single-sensor system resulting in a localization system of increased accuracy, robustness and availability.

  9. An Energy-Efficient Adaptive Clustering Protocol for Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Lü Tao

    2013-05-01

    Full Text Available An energy-efficient adaptive clustering hierarchy EEACH in wireless sensor networks based on LEACH and LEACH-C is proposed in this paper. The main consideration is the LEACH cluster structure, each cluster is not uniform energy consumption; LEACH-C using a centralized algorithm can achieve better clustering, but do not contribute to the implementation of distributed. In EEACH, we analyzed the effects of different numbers of cluster member node on the network energy consumption; and re-planning time slice to balance the energy consumption of each cluster; and avoid the energy hole problem by reasonable cluster head selection algorithm. Its objective is to balance the energy consumption and maximize the network lifetime. Analysis and simulation results show that EEACH provides more uniform energy consumption among nodes and can prolong network lifetime compared to LEACH and LEACH-C.

  10. Multimode delta-E effect magnetic field sensors with adapted electrodes

    Energy Technology Data Exchange (ETDEWEB)

    Zabel, Sebastian; Fichtner, Simon; Kirchhof, Christine; Quandt, Eckhard; Faupel, Franz, E-mail: ff@tf.uni-kiel.de [Faculty of Engineering, Institute for Materials Science, Kiel University, Kaiserstraße 2, 24143 Kiel (Germany); Reermann, Jens; Schmidt, Gerhard [Faculty of Engineering, Institute for Electrical Engineering, Kiel University, Kaiserstraße 2, 24143 Kiel (Germany); Wagner, Bernhard [Fraunhofer Institute for Silicon Technology ISIT, Fraunhoferstraße 1, 25524 Itzehoe (Germany)

    2016-05-30

    We present an analytical and experimental study on low-noise piezoelectric thin film resonators that utilize the delta-E effect of a magnetostrictive layer to measure magnetic fields at low frequencies. Calculations from a physical model of the electromechanical resonator enable electrode designs to efficiently operate in the first and second transversal bending modes. As predicted by our calculations, the adapted electrode design improves the sensitivity by a factor of 6 and reduces the dynamic range of the sensor output by 16 dB, which significantly eases the requirements on readout electronics. Magnetic measurements show a bandwidth of 100 Hz at a noise level of about 100 pTHz{sup −0.5}.

  11. Optimal distance of multi-plane sensor in three-dimensional electrical impedance tomography.

    Science.gov (United States)

    Hao, Zhenhua; Yue, Shihong; Sun, Benyuan; Wang, Huaxiang

    2017-12-01

    Electrical impedance tomography (EIT) is a visual imaging technique for obtaining the conductivity and permittivity distributions in the domain of interest. As an advanced technique, EIT has the potential to be a valuable tool for continuously bedside monitoring of pulmonary function. The EIT applications in any three-dimensional (3 D) field are very limited to the 3 D effects, i.e. the distribution of electric field spreads far beyond the electrode plane. The 3 D effects can result in measurement errors and image distortion. An important way to overcome the 3 D effect is to use the multiple groups of sensors. The aim of this paper is to find the best space resolution of EIT image over various electrode planes and select an optimal plane spacing in a 3 D EIT sensor, and provide guidance for 3 D EIT electrodes placement in monitoring lung function. In simulation and experiment, several typical conductivity distribution models, such as one rod (central, midway and edge), two rods and three rods, are set at different plane spacings between the two electrode planes. A Tikhonov regularization algorithm is utilized for reconstructing the images; the relative error and the correlation coefficient are utilized for evaluating the image quality. Based on numerical simulation and experimental results, the image performance at different spacing conditions is evaluated. The results demonstrate that there exists an optimal plane spacing between the two electrode planes for 3 D EIT sensor. And then the selection of the optimal plane spacing between the electrode planes is suggested for the electrodes placement of multi-plane EIT sensor.

  12. Self-adaptive multimethod optimization applied to a tailored heating forging process

    Science.gov (United States)

    Baldan, M.; Steinberg, T.; Baake, E.

    2018-05-01

    The presented paper describes an innovative self-adaptive multi-objective optimization code. Investigation goals concern proving the superiority of this code compared to NGSA-II and applying it to an inductor’s design case study addressed to a “tailored” heating forging application. The choice of the frequency and the heating time are followed by the determination of the turns number and their positions. Finally, a straightforward optimization is performed in order to minimize energy consumption using “optimal control”.

  13. Applying computer adaptive testing to optimize online assessment of suicidal behavior: a simulation study.

    NARCIS (Netherlands)

    de Beurs, D.P.; de Vries, A.L.M.; de Groot, M.H.; de Keijser, J.; Kerkhof, A.J.F.M.

    2014-01-01

    Background: The Internet is used increasingly for both suicide research and prevention. To optimize online assessment of suicidal patients, there is a need for short, good-quality tools to assess elevated risk of future suicidal behavior. Computer adaptive testing (CAT) can be used to reduce

  14. Joint optimization of phase diversity and adaptive optics : Demonstration of potential

    NARCIS (Netherlands)

    Korkiakoski, V.; Keller, C.U.; Doelman, N.; Fraanje, P.R.; Verhaegen, M.H.G.

    2011-01-01

    We study different possibilities to use adaptive optics (AO) and phase diversity (PD) together in a jointly optimized system. The potential of the joint system is demonstrated through numerical simulations. We find that the most significant benefits are obtained from the improved deconvolution of

  15. Career Adaptability, Hope, Optimism, and Life Satisfaction in Italian and Swiss Adolescents

    Science.gov (United States)

    Santilli, Sara; Marcionetti, Jenny; Rochat, Shékina; Rossier, Jérôme; Nota, Laura

    2017-01-01

    The consequences of economic crisis are different from one European context to the other. Based on life design (LD) approach, the present study focused on two variables--career adaptability and a positive orientation toward future (hope and optimism)--relevant to coping with the current work context and their role in affecting life satisfaction. A…

  16. Robust Nearfield Wideband Beamforming Design Based on Adaptive-Weighted Convex Optimization

    Directory of Open Access Journals (Sweden)

    Guo Ye-Cai

    2017-01-01

    Full Text Available Nearfield wideband beamformers for microphone arrays have wide applications in multichannel speech enhancement. The nearfield wideband beamformer design based on convex optimization is one of the typical representatives of robust approaches. However, in this approach, the coefficient of convex optimization is a constant, which has not used all the freedom provided by the weighting coefficient efficiently. Therefore, it is still necessary to further improve the performance. To solve this problem, we developed a robust nearfield wideband beamformer design approach based on adaptive-weighted convex optimization. The proposed approach defines an adaptive-weighted function by the adaptive array signal processing theory and adjusts its value flexibly, which has improved the beamforming performance. During each process of the adaptive updating of the weighting function, the convex optimization problem can be formulated as a SOCP (Second-Order Cone Program problem, which could be solved efficiently using the well-established interior-point methods. This method is suitable for the case where the sound source is in the nearfield range, can work well in the presence of microphone mismatches, and is applicable to arbitrary array geometries. Several design examples are presented to verify the effectiveness of the proposed approach and the correctness of the theoretical analysis.

  17. Online adaptive optimal control for continuous-time nonlinear systems with completely unknown dynamics

    Science.gov (United States)

    Lv, Yongfeng; Na, Jing; Yang, Qinmin; Wu, Xing; Guo, Yu

    2016-01-01

    An online adaptive optimal control is proposed for continuous-time nonlinear systems with completely unknown dynamics, which is achieved by developing a novel identifier-critic-based approximate dynamic programming algorithm with a dual neural network (NN) approximation structure. First, an adaptive NN identifier is designed to obviate the requirement of complete knowledge of system dynamics, and a critic NN is employed to approximate the optimal value function. Then, the optimal control law is computed based on the information from the identifier NN and the critic NN, so that the actor NN is not needed. In particular, a novel adaptive law design method with the parameter estimation error is proposed to online update the weights of both identifier NN and critic NN simultaneously, which converge to small neighbourhoods around their ideal values. The closed-loop system stability and the convergence to small vicinity around the optimal solution are all proved by means of the Lyapunov theory. The proposed adaptation algorithm is also improved to achieve finite-time convergence of the NN weights. Finally, simulation results are provided to exemplify the efficacy of the proposed methods.

  18. An efficient and adaptive mutual authentication framework for heterogeneous wireless sensor network-based applications.

    Science.gov (United States)

    Kumar, Pardeep; Ylianttila, Mika; Gurtov, Andrei; Lee, Sang-Gon; Lee, Hoon-Jae

    2014-02-11

    Robust security is highly coveted in real wireless sensor network (WSN) applications since wireless sensors' sense critical data from the application environment. This article presents an efficient and adaptive mutual authentication framework that suits real heterogeneous WSN-based applications (such as smart homes, industrial environments, smart grids, and healthcare monitoring). The proposed framework offers: (i) key initialization; (ii) secure network (cluster) formation (i.e., mutual authentication and dynamic key establishment); (iii) key revocation; and (iv) new node addition into the network. The correctness of the proposed scheme is formally verified. An extensive analysis shows the proposed scheme coupled with message confidentiality, mutual authentication and dynamic session key establishment, node privacy, and message freshness. Moreover, the preliminary study also reveals the proposed framework is secure against popular types of attacks, such as impersonation attacks, man-in-the-middle attacks, replay attacks, and information-leakage attacks. As a result, we believe the proposed framework achieves efficiency at reasonable computation and communication costs and it can be a safeguard to real heterogeneous WSN applications.

  19. Adaptive Data Gathering in Mobile Sensor Networks Using Speedy Mobile Elements

    Science.gov (United States)

    Lai, Yongxuan; Xie, Jinshan; Lin, Ziyu; Wang, Tian; Liao, Minghong

    2015-01-01

    Data gathering is a key operator for applications in wireless sensor networks; yet it is also a challenging problem in mobile sensor networks when considering that all nodes are mobile and the communications among them are opportunistic. This paper proposes an efficient data gathering scheme called ADG that adopts speedy mobile elements as the mobile data collector and takes advantage of the movement patterns of the network. ADG first extracts the network meta-data at initial epochs, and calculates a set of proxy nodes based on the meta-data. Data gathering is then mapped into the Proxy node Time Slot Allocation (PTSA) problem that schedules the time slots and orders, according to which the data collector could gather the maximal amount of data within a limited period. Finally, the collector follows the schedule and picks up the sensed data from the proxy nodes through one hop of message transmissions. ADG learns the period when nodes are relatively stationary, so that the collector is able to pick up the data from them during the limited data gathering period. Moreover, proxy nodes and data gathering points could also be timely updated so that the collector could adapt to the change of node movements. Extensive experimental results show that the proposed scheme outperforms other data gathering schemes on the cost of message transmissions and the data gathering rate, especially under the constraint of limited data gathering period. PMID:26389903

  20. Vibration suppression in cutting tools using collocated piezoelectric sensors/actuators with an adaptive control algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Radecki, Peter P [Los Alamos National Laboratory; Farinholt, Kevin M [Los Alamos National Laboratory; Park, Gyuhae [Los Alamos National Laboratory; Bement, Matthew T [Los Alamos National Laboratory

    2008-01-01

    The machining process is very important in many engineering applications. In high precision machining, surface finish is strongly correlated with vibrations and the dynamic interactions between the part and the cutting tool. Parameters affecting these vibrations and dynamic interactions, such as spindle speed, cut depth, feed rate, and the part's material properties can vary in real-time, resulting in unexpected or undesirable effects on the surface finish of the machining product. The focus of this research is the development of an improved machining process through the use of active vibration damping. The tool holder employs a high bandwidth piezoelectric actuator with an adaptive positive position feedback control algorithm for vibration and chatter suppression. In addition, instead of using external sensors, the proposed approach investigates the use of a collocated piezoelectric sensor for measuring the dynamic responses from machining processes. The performance of this method is evaluated by comparing the surface finishes obtained with active vibration control versus baseline uncontrolled cuts. Considerable improvement in surface finish (up to 50%) was observed for applications in modern day machining.

  1. RheoStim: Development of an Adaptive Multi-Sensor to Prevent Venous Stasis

    Directory of Open Access Journals (Sweden)

    Sören Weyer

    2016-03-01

    Full Text Available Chronic venous insufficiency of the lower limbs is often underestimated and, in the absence of therapy, results in increasingly severe complications, including therapy-resistant tissue defects. Therefore, early diagnosis and adequate therapy is of particular importance. External counter pulsation (ECP therapy is a method used to assist the venous system. The main principle of ECP is to squeeze the inner leg vessels by muscle contractions, which are evoked by functional electrical stimulation. A new adaptive trigger method is proposed, which improves and supplements the current therapeutic options by means of pulse synchronous electro-stimulation of the muscle pump. For this purpose, blood flow is determined by multi-sensor plethysmography. The hardware design and signal processing of this novel multi-sensor plethysmography device are introduced. The merged signal is used to determine the phase of the cardiac cycle, to ensure stimulation of the muscle pump during the filling phase of the heart. The pulse detection of the system is validated against a gold standard and provides a sensitivity of 98% and a false-negative rate of 2% after physical exertion. Furthermore, flow enhancement of the system has been validated by duplex ultrasonography. The results show a highly increased blood flow in the popliteal vein at the knee.

  2. RheoStim: Development of an Adaptive Multi-Sensor to Prevent Venous Stasis

    Science.gov (United States)

    Weyer, Sören; Weishaupt, Fabio; Kleeberg, Christian; Leonhardt, Steffen; Teichmann, Daniel

    2016-01-01

    Chronic venous insufficiency of the lower limbs is often underestimated and, in the absence of therapy, results in increasingly severe complications, including therapy-resistant tissue defects. Therefore, early diagnosis and adequate therapy is of particular importance. External counter pulsation (ECP) therapy is a method used to assist the venous system. The main principle of ECP is to squeeze the inner leg vessels by muscle contractions, which are evoked by functional electrical stimulation. A new adaptive trigger method is proposed, which improves and supplements the current therapeutic options by means of pulse synchronous electro-stimulation of the muscle pump. For this purpose, blood flow is determined by multi-sensor plethysmography. The hardware design and signal processing of this novel multi-sensor plethysmography device are introduced. The merged signal is used to determine the phase of the cardiac cycle, to ensure stimulation of the muscle pump during the filling phase of the heart. The pulse detection of the system is validated against a gold standard and provides a sensitivity of 98% and a false-negative rate of 2% after physical exertion. Furthermore, flow enhancement of the system has been validated by duplex ultrasonography. The results show a highly increased blood flow in the popliteal vein at the knee. PMID:27023544

  3. Adaptive Multi-Sensor Perception for Driving Automation in Outdoor Contexts

    Directory of Open Access Journals (Sweden)

    Annalisa Milella

    2014-08-01

    Full Text Available In this research, adaptive perception for driving automation is discussed so as to enable a vehicle to automatically detect driveable areas and obstacles in the scene. It is especially designed for outdoor contexts where conventional perception systems that rely on a priori knowledge of the terrain's geometric properties, appearance properties, or both, is prone to fail, due to the variability in the terrain properties and environmental conditions. In contrast, the proposed framework uses a self-learning approach to build a model of the ground class that is continuously adjusted online to reflect the latest ground appearance. The system also features high flexibility, as it can work using a single sensor modality or a multi-sensor combination. In the context of this research, different embodiments have been demonstrated using range data coming from either a radar or a stereo camera, and adopting self-supervised strategies where monocular vision is automatically trained by radar or stereo vision. A comprehensive set of experimental results, obtained with different ground vehicles operating in the field, are presented to validate and assess the performance of the system.

  4. An Efficient and Adaptive Mutual Authentication Framework for Heterogeneous Wireless Sensor Network-Based Applications

    Science.gov (United States)

    Kumar, Pardeep; Ylianttila, Mika; Gurtov, Andrei; Lee, Sang-Gon; Lee, Hoon-Jae

    2014-01-01

    Robust security is highly coveted in real wireless sensor network (WSN) applications since wireless sensors' sense critical data from the application environment. This article presents an efficient and adaptive mutual authentication framework that suits real heterogeneous WSN-based applications (such as smart homes, industrial environments, smart grids, and healthcare monitoring). The proposed framework offers: (i) key initialization; (ii) secure network (cluster) formation (i.e., mutual authentication and dynamic key establishment); (iii) key revocation; and (iv) new node addition into the network. The correctness of the proposed scheme is formally verified. An extensive analysis shows the proposed scheme coupled with message confidentiality, mutual authentication and dynamic session key establishment, node privacy, and message freshness. Moreover, the preliminary study also reveals the proposed framework is secure against popular types of attacks, such as impersonation attacks, man-in-the-middle attacks, replay attacks, and information-leakage attacks. As a result, we believe the proposed framework achieves efficiency at reasonable computation and communication costs and it can be a safeguard to real heterogeneous WSN applications. PMID:24521942

  5. Adaptive Data Gathering in Mobile Sensor Networks Using Speedy Mobile Elements.

    Science.gov (United States)

    Lai, Yongxuan; Xie, Jinshan; Lin, Ziyu; Wang, Tian; Liao, Minghong

    2015-09-15

    Data gathering is a key operator for applications in wireless sensor networks; yet it is also a challenging problem in mobile sensor networks when considering that all nodes are mobile and the communications among them are opportunistic. This paper proposes an efficient data gathering scheme called ADG that adopts speedy mobile elements as the mobile data collector and takes advantage of the movement patterns of the network. ADG first extracts the network meta-data at initial epochs, and calculates a set of proxy nodes based on the meta-data. Data gathering is then mapped into the Proxy node Time Slot Allocation (PTSA) problem that schedules the time slots and orders, according to which the data collector could gather the maximal amount of data within a limited period. Finally, the collector follows the schedule and picks up the sensed data from the proxy nodes through one hop of message transmissions. ADG learns the period when nodes are relatively stationary, so that the collector is able to pick up the data from them during the limited data gathering period. Moreover, proxy nodes and data gathering points could also be timely updated so that the collector could adapt to the change of node movements. Extensive experimental results show that the proposed scheme outperforms other data gathering schemes on the cost of message transmissions and the data gathering rate, especially under the constraint of limited data gathering period.

  6. Optimization of magnetoresistive sensor current for on-chip magnetic bead detection using the sensor self-field

    DEFF Research Database (Denmark)

    Henriksen, Anders Dahl; Rizzi, Giovanni; Østerberg, Frederik Westergaard

    2015-01-01

    We investigate the self-heating of magnetoresistive sensors used for measurements on magnetic beads in magnetic biosensors. The signal from magnetic beads magnetized by the field due to the sensor bias current is proportional to the bias current squared. Therefore, we aim to maximize the bias...... current while limiting the sensor self-heating. We systematically characterize and model the Joule heating of magnetoresistive sensors with different sensor geometries and stack compositions. The sensor heating is determined using the increase of the sensor resistance as function of the bias current......, thus the heat conductance is proportional to the sensor area and inversely proportional to the oxide thickness. This simple heat conductance determines the relationship between bias current and sensor temperature, and we show that View the MathML source25μm wide sensor on a View the MathML source1μm...

  7. METHODOLOGY FOR DETERMINING OPTIMAL EXPOSURE PARAMETERS OF A HYPERSPECTRAL SCANNING SENSOR

    Directory of Open Access Journals (Sweden)

    P. Walczykowski

    2016-06-01

    Full Text Available The purpose of the presented research was to establish a methodology that would allow the registration of hyperspectral images with a defined spatial resolution on a horizontal plane. The results obtained within this research could then be used to establish the optimum sensor and flight parameters for collecting aerial imagery data using an UAV or other aerial system. The methodology is based on an user-selected optimal camera exposure parameters (i.e. time, gain value and flight parameters (i.e. altitude, velocity. A push-broom hyperspectral imager- the Headwall MicroHyperspec A-series VNIR was used to conduct this research. The measurement station consisted of the following equipment: a hyperspectral camera MicroHyperspec A-series VNIR, a personal computer with HyperSpec III software, a slider system which guaranteed the stable motion of the sensor system, a white reference panel and a Siemens star, which was used to evaluate the spatial resolution. Hyperspectral images were recorded at different distances between the sensor and the target- from 5m to 100m. During the registration process of each acquired image, many exposure parameters were changed, such as: the aperture value, exposure time and speed of the camera’s movement on the slider. Based on all of the registered hyperspectral images, some dependencies between chosen parameters had been developed: - the Ground Sampling Distance – GSD and the distance between the sensor and the target, - the speed of the camera and the distance between the sensor and the target, - the exposure time and the gain value, - the Density Number and the gain value. The developed methodology allowed us to determine the speed and the altitude of an unmanned aerial vehicle on which the sensor would be mounted, ensuring that the registered hyperspectral images have the required spatial resolution.

  8. A chaos wolf optimization algorithm with self-adaptive variable step-size

    Directory of Open Access Journals (Sweden)

    Yong Zhu

    2017-10-01

    Full Text Available To explore the problem of parameter optimization for complex nonlinear function, a chaos wolf optimization algorithm (CWOA with self-adaptive variable step-size was proposed. The algorithm was based on the swarm intelligence of wolf pack, which fully simulated the predation behavior and prey distribution way of wolves. It possessed three intelligent behaviors such as migration, summons and siege. And the competition rule as “winner-take-all” and the update mechanism as “survival of the fittest” were also the characteristics of the algorithm. Moreover, it combined the strategies of self-adaptive variable step-size search and chaos optimization. The CWOA was utilized in parameter optimization of twelve typical and complex nonlinear functions. And the obtained results were compared with many existing algorithms, including the classical genetic algorithm, the particle swarm optimization algorithm and the leader wolf pack search algorithm. The investigation results indicate that CWOA possess preferable optimization ability. There are advantages in optimization accuracy and convergence rate. Furthermore, it demonstrates high robustness and global searching ability.

  9. A chaos wolf optimization algorithm with self-adaptive variable step-size

    Science.gov (United States)

    Zhu, Yong; Jiang, Wanlu; Kong, Xiangdong; Quan, Lingxiao; Zhang, Yongshun

    2017-10-01

    To explore the problem of parameter optimization for complex nonlinear function, a chaos wolf optimization algorithm (CWOA) with self-adaptive variable step-size was proposed. The algorithm was based on the swarm intelligence of wolf pack, which fully simulated the predation behavior and prey distribution way of wolves. It possessed three intelligent behaviors such as migration, summons and siege. And the competition rule as "winner-take-all" and the update mechanism as "survival of the fittest" were also the characteristics of the algorithm. Moreover, it combined the strategies of self-adaptive variable step-size search and chaos optimization. The CWOA was utilized in parameter optimization of twelve typical and complex nonlinear functions. And the obtained results were compared with many existing algorithms, including the classical genetic algorithm, the particle swarm optimization algorithm and the leader wolf pack search algorithm. The investigation results indicate that CWOA possess preferable optimization ability. There are advantages in optimization accuracy and convergence rate. Furthermore, it demonstrates high robustness and global searching ability.

  10. Achieving Optimal Self-Adaptivity for Dynamic Tuning of Organic Semiconductors through Resonance Engineering.

    Science.gov (United States)

    Tao, Ye; Xu, Lijia; Zhang, Zhen; Chen, Runfeng; Li, Huanhuan; Xu, Hui; Zheng, Chao; Huang, Wei

    2016-08-03

    Current static-state explorations of organic semiconductors for optimal material properties and device performance are hindered by limited insights into the dynamically changed molecular states and charge transport and energy transfer processes upon device operation. Here, we propose a simple yet successful strategy, resonance variation-based dynamic adaptation (RVDA), to realize optimized self-adaptive properties in donor-resonance-acceptor molecules by engineering the resonance variation for dynamic tuning of organic semiconductors. Organic light-emitting diodes hosted by these RVDA materials exhibit remarkably high performance, with external quantum efficiencies up to 21.7% and favorable device stability. Our approach, which supports simultaneous realization of dynamically adapted and selectively enhanced properties via resonance engineering, illustrates a feasible design map for the preparation of smart organic semiconductors capable of dynamic structure and property modulations, promoting the studies of organic electronics from static to dynamic.

  11. Adaptive Mesh Iteration Method for Trajectory Optimization Based on Hermite-Pseudospectral Direct Transcription

    Directory of Open Access Journals (Sweden)

    Humin Lei

    2017-01-01

    Full Text Available An adaptive mesh iteration method based on Hermite-Pseudospectral is described for trajectory optimization. The method uses the Legendre-Gauss-Lobatto points as interpolation points; then the state equations are approximated by Hermite interpolating polynomials. The method allows for changes in both number of mesh points and the number of mesh intervals and produces significantly smaller mesh sizes with a higher accuracy tolerance solution. The derived relative error estimate is then used to trade the number of mesh points with the number of mesh intervals. The adaptive mesh iteration method is applied successfully to the examples of trajectory optimization of Maneuverable Reentry Research Vehicle, and the simulation experiment results show that the adaptive mesh iteration method has many advantages.

  12. An optimized knife-edge method for on-orbit MTF estimation of optical sensors using powell parameter fitting

    Science.gov (United States)

    Han, Lu; Gao, Kun; Gong, Chen; Zhu, Zhenyu; Guo, Yue

    2017-08-01

    On-orbit Modulation Transfer Function (MTF) is an important indicator to evaluate the performance of the optical remote sensors in a satellite. There are many methods to estimate MTF, such as pinhole method, slit method and so on. Among them, knife-edge method is quite efficient, easy-to-use and recommended in ISO12233 standard for the wholefrequency MTF curve acquisition. However, the accuracy of the algorithm is affected by Edge Spread Function (ESF) fitting accuracy significantly, which limits the range of application. So in this paper, an optimized knife-edge method using Powell algorithm is proposed to improve the ESF fitting precision. Fermi function model is the most popular ESF fitting model, yet it is vulnerable to the initial values of the parameters. Considering the characteristics of simple and fast convergence, Powell algorithm is applied to fit the accurate parameters adaptively with the insensitivity to the initial parameters. Numerical simulation results reveal the accuracy and robustness of the optimized algorithm under different SNR, edge direction and leaning angles conditions. Experimental results using images of the camera in ZY-3 satellite show that this method is more accurate than the standard knife-edge method of ISO12233 in MTF estimation.

  13. Data-adaptive Robust Optimization Method for the Economic Dispatch of Active Distribution Networks

    DEFF Research Database (Denmark)

    Zhang, Yipu; Ai, Xiaomeng; Fang, Jiakun

    2018-01-01

    Due to the restricted mathematical description of the uncertainty set, the current two-stage robust optimization is usually over-conservative which has drawn concerns from the power system operators. This paper proposes a novel data-adaptive robust optimization method for the economic dispatch...... of active distribution network with renewables. The scenario-generation method and the two-stage robust optimization are combined in the proposed method. To reduce the conservativeness, a few extreme scenarios selected from the historical data are used to replace the conventional uncertainty set....... The proposed extreme-scenario selection algorithm takes advantage of considering the correlations and can be adaptive to different historical data sets. A theoretical proof is given that the constraints will be satisfied under all the possible scenarios if they hold in the selected extreme scenarios, which...

  14. Adaptive optimal stochastic state feedback control of resistive wall modes in tokamaks

    International Nuclear Information System (INIS)

    Sun, Z.; Sen, A.K.; Longman, R.W.

    2006-01-01

    An adaptive optimal stochastic state feedback control is developed to stabilize the resistive wall mode (RWM) instability in tokamaks. The extended least-square method with exponential forgetting factor and covariance resetting is used to identify (experimentally determine) the time-varying stochastic system model. A Kalman filter is used to estimate the system states. The estimated system states are passed on to an optimal state feedback controller to construct control inputs. The Kalman filter and the optimal state feedback controller are periodically redesigned online based on the identified system model. This adaptive controller can stabilize the time-dependent RWM in a slowly evolving tokamak discharge. This is accomplished within a time delay of roughly four times the inverse of the growth rate for the time-invariant model used

  15. An Adaptive Connectivity-based Centroid Algorithm for Node Positioning in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Aries Pratiarso

    2015-06-01

    Full Text Available In wireless sensor network applications, the position of nodes is randomly distributed following the contour of the observation area. A simple solution without any measurement tools is provided by range-free method. However, this method yields the coarse estimating position of the nodes. In this paper, we propose Adaptive Connectivity-based (ACC algorithm. This algorithm is a combination of Centroid as range-free based algorithm, and hop-based connectivity algorithm. Nodes have a possibility to estimate their own position based on the connectivity level between them and their reference nodes. Each node divides its communication range into several regions where each of them has a certain weight depends on the received signal strength. The weighted value is used to obtain the estimated position of nodes. Simulation result shows that the proposed algorithm has up to 3 meter error of estimated position on 100x100 square meter observation area, and up to 3 hop counts for 80 meters' communication range. The proposed algorithm performs an average error positioning up to 10 meters better than Weighted Centroid algorithm. Keywords: adaptive, connectivity, centroid, range-free.

  16. An Adaptive and Autonomous Sensor Sampling Frequency Control Scheme for Energy-Efficient Data Acquisition in Wireless Sensor Networks

    NARCIS (Netherlands)

    Chatterjea, Supriyo; Havinga, Paul J.M.; Nikoletseas, S.E.; Chlebus, B.S.; Johnson, D.; Krishnamachari, B.

    2008-01-01

    Wireless sensor networks are increasingly being used in environmental monitoring applications. Collecting raw data from these networks can lead to excessive energy consumption. This is especially true when the application requires specialized sensors that have very high energy consumption, e.g.

  17. Optimization of Indoor Thermal Comfort Parameters with the Adaptive Network-Based Fuzzy Inference System and Particle Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Jing Li

    2017-01-01

    Full Text Available The goal of this study is to improve thermal comfort and indoor air quality with the adaptive network-based fuzzy inference system (ANFIS model and improved particle swarm optimization (PSO algorithm. A method to optimize air conditioning parameters and installation distance is proposed. The methodology is demonstrated through a prototype case, which corresponds to a typical laboratory in colleges and universities. A laboratory model is established, and simulated flow field information is obtained with the CFD software. Subsequently, the ANFIS model is employed instead of the CFD model to predict indoor flow parameters, and the CFD database is utilized to train ANN input-output “metamodels” for the subsequent optimization. With the improved PSO algorithm and the stratified sequence method, the objective functions are optimized. The functions comprise PMV, PPD, and mean age of air. The optimal installation distance is determined with the hemisphere model. Results show that most of the staff obtain a satisfactory degree of thermal comfort and that the proposed method can significantly reduce the cost of building an experimental device. The proposed methodology can be used to determine appropriate air supply parameters and air conditioner installation position for a pleasant and healthy indoor environment.

  18. Virtual sensors for active noise control in acoustic-structural coupled enclosures using structural sensing: part II--Optimization of structural sensor placement.

    Science.gov (United States)

    Halim, Dunant; Cheng, Li; Su, Zhongqing

    2011-04-01

    The work proposed an optimization approach for structural sensor placement to improve the performance of vibro-acoustic virtual sensor for active noise control applications. The vibro-acoustic virtual sensor was designed to estimate the interior sound pressure of an acoustic-structural coupled enclosure using structural sensors. A spectral-spatial performance metric was proposed, which was used to quantify the averaged structural sensor output energy of a vibro-acoustic system excited by a spatially varying point source. It was shown that (i) the overall virtual sensing error energy was contributed additively by the modal virtual sensing error and the measurement noise energy; (ii) each of the modal virtual sensing error system was contributed by both the modal observability levels for the structural sensing and the target acoustic virtual sensing; and further (iii) the strength of each modal observability level was influenced by the modal coupling and resonance frequencies of the associated uncoupled structural/cavity modes. An optimal design of structural sensor placement was proposed to achieve sufficiently high modal observability levels for certain important panel- and cavity-controlled modes. Numerical analysis on a panel-cavity system demonstrated the importance of structural sensor placement on virtual sensing and active noise control performance, particularly for cavity-controlled modes.

  19. Advanced load alleviation for wind turbines using adaptive trailing edge flaps: Sensoring and control

    Energy Technology Data Exchange (ETDEWEB)

    Andersen, Peter Bjoern

    2010-02-15

    The purpose of wind turbines and their predecessors the windmill, is to convert the energy in the wind to usable energy forms. Whereas windmills of the past focused on the conversion of wind power to torque for grinding, pumping and winching, modern wind turbines convert the wind energy into electric power. They do so through incorporation of generators, which convert mechanical torque into electricity. Wind turbines are designed to keep the overall cost per produced Kilo Watt hour as low as possible. One way of improving the performance and lifetime of the wind turbine is through active flow control. Active control is often considered costly but if the lifespan of the components can be increased it could be justifiable. This thesis covers various aspects of 'smart control' such as control theory, sensoring, optimization, experiments and numerical modeling. (author)

  20. The optimal configuration of photovoltaic module arrays based on adaptive switching controls

    International Nuclear Information System (INIS)

    Chao, Kuei-Hsiang; Lai, Pei-Lun; Liao, Bo-Jyun

    2015-01-01

    Highlights: • We propose a strategy for determining the optimal configuration of a PV array. • The proposed strategy was based on particle swarm optimization (PSO) method. • It can identify the optimal module array connection scheme in the event of shading. • It can also find the optimal connection of a PV array even in module malfunctions. - Abstract: This study proposes a strategy for determining the optimal configuration of photovoltaic (PV) module arrays in shading or malfunction conditions. This strategy was based on particle swarm optimization (PSO). If shading or malfunctions of the photovoltaic module array occur, the module array immediately undergoes adaptive reconfiguration to increase the power output of the PV power generation system. First, the maximal power generated at various irradiation levels and temperatures was recorded during normal array operation. Subsequently, the irradiation level and module temperature, regardless of operating conditions, were used to recall the maximal power previously recorded. This previous maximum was compared with the maximal power value obtained using the maximum power point tracker to assess whether the PV module array was experiencing shading or malfunctions. After determining that the array was experiencing shading or malfunctions, PSO was used to identify the optimal module array connection scheme in abnormal conditions, and connection switches were used to implement optimal array reconfiguration. Finally, experiments were conducted to assess the strategy for identifying the optimal reconfiguration of a PV module array in the event of shading or malfunctions

  1. A Convergent Differential Evolution Algorithm with Hidden Adaptation Selection for Engineering Optimization

    Directory of Open Access Journals (Sweden)

    Zhongbo Hu

    2014-01-01

    Full Text Available Many improved differential Evolution (DE algorithms have emerged as a very competitive class of evolutionary computation more than a decade ago. However, few improved DE algorithms guarantee global convergence in theory. This paper developed a convergent DE algorithm in theory, which employs a self-adaptation scheme for the parameters and two operators, that is, uniform mutation and hidden adaptation selection (haS operators. The parameter self-adaptation and uniform mutation operator enhance the diversity of populations and guarantee ergodicity. The haS can automatically remove some inferior individuals in the process of the enhancing population diversity. The haS controls the proposed algorithm to break the loop of current generation with a small probability. The breaking probability is a hidden adaptation and proportional to the changes of the number of inferior individuals. The proposed algorithm is tested on ten engineering optimization problems taken from IEEE CEC2011.

  2. Optimal region of latching activity in an adaptive Potts model for networks of neurons

    International Nuclear Information System (INIS)

    Abdollah-nia, Mohammad-Farshad; Saeedghalati, Mohammadkarim; Abbassian, Abdolhossein

    2012-01-01

    In statistical mechanics, the Potts model is a model for interacting spins with more than two discrete states. Neural networks which exhibit features of learning and associative memory can also be modeled by a system of Potts spins. A spontaneous behavior of hopping from one discrete attractor state to another (referred to as latching) has been proposed to be associated with higher cognitive functions. Here we propose a model in which both the stochastic dynamics of Potts models and an adaptive potential function are present. A latching dynamics is observed in a limited region of the noise(temperature)–adaptation parameter space. We hence suggest noise as a fundamental factor in such alternations alongside adaptation. From a dynamical systems point of view, the noise–adaptation alternations may be the underlying mechanism for multi-stability in attractor-based models. An optimality criterion for realistic models is finally inferred

  3. Accurate human limb angle measurement: sensor fusion through Kalman, least mean squares and recursive least-squares adaptive filtering

    Science.gov (United States)

    Olivares, A.; Górriz, J. M.; Ramírez, J.; Olivares, G.

    2011-02-01

    Inertial sensors are widely used in human body motion monitoring systems since they permit us to determine the position of the subject's limbs. Limb angle measurement is carried out through the integration of the angular velocity measured by a rate sensor and the decomposition of the components of static gravity acceleration measured by an accelerometer. Different factors derived from the sensors' nature, such as the angle random walk and dynamic bias, lead to erroneous measurements. Dynamic bias effects can be reduced through the use of adaptive filtering based on sensor fusion concepts. Most existing published works use a Kalman filtering sensor fusion approach. Our aim is to perform a comparative study among different adaptive filters. Several least mean squares (LMS), recursive least squares (RLS) and Kalman filtering variations are tested for the purpose of finding the best method leading to a more accurate and robust limb angle measurement. A new angle wander compensation sensor fusion approach based on LMS and RLS filters has been developed.

  4. Accurate human limb angle measurement: sensor fusion through Kalman, least mean squares and recursive least-squares adaptive filtering

    International Nuclear Information System (INIS)

    Olivares, A; Olivares, G; Górriz, J M; Ramírez, J

    2011-01-01

    Inertial sensors are widely used in human body motion monitoring systems since they permit us to determine the position of the subject's limbs. Limb angle measurement is carried out through the integration of the angular velocity measured by a rate sensor and the decomposition of the components of static gravity acceleration measured by an accelerometer. Different factors derived from the sensors' nature, such as the angle random walk and dynamic bias, lead to erroneous measurements. Dynamic bias effects can be reduced through the use of adaptive filtering based on sensor fusion concepts. Most existing published works use a Kalman filtering sensor fusion approach. Our aim is to perform a comparative study among different adaptive filters. Several least mean squares (LMS), recursive least squares (RLS) and Kalman filtering variations are tested for the purpose of finding the best method leading to a more accurate and robust limb angle measurement. A new angle wander compensation sensor fusion approach based on LMS and RLS filters has been developed

  5. High-Power DFB Diode Laser-Based CO-QEPAS Sensor: Optimization and Performance

    Directory of Open Access Journals (Sweden)

    Yufei Ma

    2018-01-01

    Full Text Available A highly sensitive carbon monoxide (CO trace gas sensor based on quartz-enhanced photoacoustic spectroscopy (QEPAS was demonstrated. A high-power distributed feedback (DFB, continuous wave (CW 2.33 μm diode laser with an 8.8 mW output power was used as the QEPAS excitation source. By optimizing the modulation depth and adding an optimum micro-resonator, compared to a bare quartz tuning fork (QTF, a 10-fold enhancement of the CO-QEPAS signal amplitude was achieved. When water vapor acting as a vibrational transfer catalyst was added to the target gas, the signal was further increased by a factor of ~7. A minimum detection limit (MDL of 11.2 ppm and a calculated normalized noise equivalent absorption (NNEA coefficient of 1.8 × 10−5 cm−1W/√Hz were obtained for the reported CO-QEPAS sensor.

  6. High-Power DFB Diode Laser-Based CO-QEPAS Sensor: Optimization and Performance.

    Science.gov (United States)

    Ma, Yufei; Tong, Yao; He, Ying; Yu, Xin; Tittel, Frank K

    2018-01-04

    A highly sensitive carbon monoxide (CO) trace gas sensor based on quartz-enhanced photoacoustic spectroscopy (QEPAS) was demonstrated. A high-power distributed feedback (DFB), continuous wave (CW) 2.33 μm diode laser with an 8.8 mW output power was used as the QEPAS excitation source. By optimizing the modulation depth and adding an optimum micro-resonator, compared to a bare quartz tuning fork (QTF), a 10-fold enhancement of the CO-QEPAS signal amplitude was achieved. When water vapor acting as a vibrational transfer catalyst was added to the target gas, the signal was further increased by a factor of ~7. A minimum detection limit (MDL) of 11.2 ppm and a calculated normalized noise equivalent absorption (NNEA) coefficient of 1.8 × 10 -5 cm -1 W/√Hz were obtained for the reported CO-QEPAS sensor.

  7. Sensor Placement via Optimal Experiment Design in EMI Sensing of Metallic Objects

    Directory of Open Access Journals (Sweden)

    Lin-Ping Song

    2016-01-01

    Full Text Available This work, under the optimal experimental design framework, investigates the sensor placement problem that aims to guide electromagnetic induction (EMI sensing of multiple objects. We use the linearized model covariance matrix as a measure of estimation error to present a sequential experimental design (SED technique. The technique recursively minimizes data misfit to update model parameters and maximizes an information gain function for a future survey relative to previous surveys. The fundamental process of the SED seeks to increase weighted sensitivities to targets when placing sensors. The synthetic and field experiments demonstrate that SED can be used to guide the sensing process for an effective interrogation. It also can serve as a theoretic basis to improve empirical survey operation. We further study the sensitivity of the SED to the number of objects within the sensing range. The tests suggest that an appropriately overrepresented model about expected anomalies might be a feasible choice.

  8. An Immune Cooperative Particle Swarm Optimization Algorithm for Fault-Tolerant Routing Optimization in Heterogeneous Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yifan Hu

    2012-01-01

    Full Text Available The fault-tolerant routing problem is important consideration in the design of heterogeneous wireless sensor networks (H-WSNs applications, and has recently been attracting growing research interests. In order to maintain k disjoint communication paths from source sensors to the macronodes, we present a hybrid routing scheme and model, in which multiple paths are calculated and maintained in advance, and alternate paths are created once the previous routing is broken. Then, we propose an immune cooperative particle swarm optimization algorithm (ICPSOA in the model to provide the fast routing recovery and reconstruct the network topology for path failure in H-WSNs. In the ICPSOA, mutation direction of the particle is determined by multi-swarm evolution equation, and its diversity is improved by immune mechanism, which can enhance the capacity of global search and improve the converging rate of the algorithm. Then we validate this theoretical model with simulation results. The results indicate that the ICPSOA-based fault-tolerant routing protocol outperforms several other protocols due to its capability of fast routing recovery mechanism, reliable communications, and prolonging the lifetime of WSNs.

  9. Optimizing the passenger air bag of an adaptive restraint system for multiple size occupants.

    Science.gov (United States)

    Bai, Zhonghao; Jiang, Binhui; Zhu, Feng; Cao, Libo

    2014-01-01

    The development of the adaptive occupant restraint system (AORS) has led to an innovative way to optimize such systems for multiple size occupants. An AORS consists of multiple units such as adaptive air bags, seat belts, etc. During a collision, as a supplemental protective device, air bags can provide constraint force and play a role in dissipating the crash energy of the occupants' head and thorax. This article presents an investigation into an adaptive passenger air bag (PAB). The purpose of this study is to develop a base shape of a PAB for different size occupants using an optimization method. Four typical base shapes of a PAB were designed based on geometric data on the passenger side. Then 4 PAB finite element (FE) models and a validated sled with different size dummy models were developed in MADYMO (TNO, Rijswijk, The Netherlands) to conduct the optimization to obtain the best baseline PAB that would be used in the AORS. The objective functions-that is, the minimum total probability of injuries (∑Pcomb) of the 5th percentile female and 50th and 95th percentile male dummies-were adopted to evaluate the optimal configurations. The injury probability (Pcomb) for each dummy was adopted from the U.S. New Car Assessment Program (US-NCAP). The parameters of the AORS were first optimized for different types of PAB base shapes in a frontal impact. Then, contact time duration and force between the PAB and dummy head/chest were optimized by adjusting the parameters of the PAB, such as the number and position of tethers, lower the Pcomb of the 95th percentile male dummy. According to the optimization results, 4 typical PABs could provide effective protection to 5th and 50th percentile dummies. However, due to the heavy and large torsos of the 95th percentile occupants, the current occupant restraint system does not demonstrate satisfactory protective function, particularly for the thorax.

  10. Adaptive stimulus optimization and model-based experiments for sensory systems neuroscience

    Directory of Open Access Journals (Sweden)

    Christopher eDiMattina

    2013-06-01

    Full Text Available In this paper we review several lines of recent work aimed at developing practical methods for adaptive on-line stimulus generation for sensory neurophysiology. We consider various experimental paradigms where on-line stimulus optimization is utilized, including the classical textit{optimal stimulus} paradigm where the goal of experiments is to identify a stimulus which maximizes neural responses, the textit{iso-response} paradigm which finds sets of stimuli giving rise to constant responses, and the textit{system identification} paradigm where the experimental goal is to estimate and possibly compare sensory processing models. We discuss various theoretical and practical aspects of adaptive firing rate optimization, including optimization with stimulus space constraints, firing rate adaptation, and possible network constraints on the optimal stimulus. We consider the problem of system identification, and show how accurate estimation of nonlinear models can be highly dependent on the stimulus set used to probe the network. We suggest that optimizing stimuli for accurate model estimation may make it possible to successfully identify nonlinear models which are otherwise intractable, and summarize several recent studies of this type. Finally, we present a two-stage stimulus design procedure which combines the dual goals of model estimation and model comparison and may be especially useful for system identification experiments where the appropriate model is unknown beforehand. We propose that fast, on-line stimulus optimization enabled by increasing computer power can make it practical to move sensory neuroscience away from a descriptive paradigm and towards a new paradigm of real-time model estimation and comparison.

  11. An Optimal Medium Access Control with Partial Observations for Sensor Networks

    Directory of Open Access Journals (Sweden)

    Servetto Sergio D

    2005-01-01

    Full Text Available We consider medium access control (MAC in multihop sensor networks, where only partial information about the shared medium is available to the transmitter. We model our setting as a queuing problem in which the service rate of a queue is a function of a partially observed Markov chain representing the available bandwidth, and in which the arrivals are controlled based on the partial observations so as to keep the system in a desirable mildly unstable regime. The optimal controller for this problem satisfies a separation property: we first compute a probability measure on the state space of the chain, namely the information state, then use this measure as the new state on which the control decisions are based. We give a formal description of the system considered and of its dynamics, we formalize and solve an optimal control problem, and we show numerical simulations to illustrate with concrete examples properties of the optimal control law. We show how the ergodic behavior of our queuing model is characterized by an invariant measure over all possible information states, and we construct that measure. Our results can be specifically applied for designing efficient and stable algorithms for medium access control in multiple-accessed systems, in particular for sensor networks.

  12. Self-adaptive global best harmony search algorithm applied to reactor core fuel management optimization

    International Nuclear Information System (INIS)

    Poursalehi, N.; Zolfaghari, A.; Minuchehr, A.; Valavi, K.

    2013-01-01

    Highlights: • SGHS enhanced the convergence rate of LPO using some improvements in comparison to basic HS and GHS. • SGHS optimization algorithm obtained averagely better fitness relative to basic HS and GHS algorithms. • Upshot of the SGHS implementation in the LPO reveals its flexibility, efficiency and reliability. - Abstract: The aim of this work is to apply the new developed optimization algorithm, Self-adaptive Global best Harmony Search (SGHS), for PWRs fuel management optimization. SGHS algorithm has some modifications in comparison with basic Harmony Search (HS) and Global-best Harmony Search (GHS) algorithms such as dynamically change of parameters. For the demonstration of SGHS ability to find an optimal configuration of fuel assemblies, basic Harmony Search (HS) and Global-best Harmony Search (GHS) algorithms also have been developed and investigated. For this purpose, Self-adaptive Global best Harmony Search Nodal Expansion package (SGHSNE) has been developed implementing HS, GHS and SGHS optimization algorithms for the fuel management operation of nuclear reactor cores. This package uses developed average current nodal expansion code which solves the multi group diffusion equation by employment of first and second orders of Nodal Expansion Method (NEM) for two dimensional, hexagonal and rectangular geometries, respectively, by one node per a FA. Loading pattern optimization was performed using SGHSNE package for some test cases to present the SGHS algorithm capability in converging to near optimal loading pattern. Results indicate that the convergence rate and reliability of the SGHS method are quite promising and practically, SGHS improves the quality of loading pattern optimization results relative to HS and GHS algorithms. As a result, it has the potential to be used in the other nuclear engineering optimization problems

  13. Topology optimization of compliant adaptive wing leading edge with composite materials

    Directory of Open Access Journals (Sweden)

    Tong Xinxing

    2014-12-01

    Full Text Available An approach for designing the compliant adaptive wing leading edge with composite material is proposed based on the topology optimization. Firstly, an equivalent constitutive relationship of laminated glass fiber reinforced epoxy composite plates has been built based on the symmetric laminated plate theory. Then, an optimization objective function of compliant adaptive wing leading edge was used to minimize the least square error (LSE between deformed curve and desired aerodynamics shape. After that, the topology structures of wing leading edge of different glass fiber ply-orientations were obtained by using the solid isotropic material with penalization (SIMP model and sensitivity filtering technique. The desired aerodynamics shape of compliant adaptive wing leading edge was obtained based on the proposed approach. The topology structures of wing leading edge depend on the glass fiber ply-orientation. Finally, the corresponding morphing experiment of compliant wing leading edge with composite materials was implemented, which verified the morphing capability of topology structure and illustrated the feasibility for designing compliant wing leading edge. The present paper lays the basis of ply-orientation optimization for compliant adaptive wing leading edge in unmanned aerial vehicle (UAV field.

  14. A Survey on Optimal Signal Processing Techniques Applied to Improve the Performance of Mechanical Sensors in Automotive Applications

    Directory of Open Access Journals (Sweden)

    Wilmar Hernandez

    2007-01-01

    Full Text Available In this paper a survey on recent applications of optimal signal processing techniques to improve the performance of mechanical sensors is made. Here, a comparison between classical filters and optimal filters for automotive sensors is made, and the current state of the art of the application of robust and optimal control and signal processing techniques to the design of the intelligent (or smart sensors that today’s cars need is presented through several experimental results that show that the fusion of intelligent sensors and optimal signal processing techniques is the clear way to go. However, the switch between the traditional methods of designing automotive sensors and the new ones cannot be done overnight because there are some open research issues that have to be solved. This paper draws attention to one of the open research issues and tries to arouse researcher’s interest in the fusion of intelligent sensors and optimal signal processing techniques.

  15. Sensor-Based Optimized Control of the Full Load Instability in Large Hydraulic Turbines

    Directory of Open Access Journals (Sweden)

    Alexandre Presas

    2018-03-01

    Full Text Available Hydropower plants are of paramount importance for the integration of intermittent renewable energy sources in the power grid. In order to match the energy generated and consumed, Large hydraulic turbines have to work under off-design conditions, which may lead to dangerous unstable operating points involving the hydraulic, mechanical and electrical system. Under these conditions, the stability of the grid and the safety of the power plant itself can be compromised. For many Francis Turbines one of these critical points, that usually limits the maximum output power, is the full load instability. Therefore, these machines usually work far away from this unstable point, reducing the effective operating range of the unit. In order to extend the operating range of the machine, working closer to this point with a reasonable safety margin, it is of paramount importance to monitor and to control relevant parameters of the unit, which have to be obtained with an accurate sensor acquisition strategy. Within the framework of a large EU project, field tests in a large Francis Turbine located in Canada (rated power of 444 MW have been performed. Many different sensors were used to monitor several working parameters of the unit for all its operating range. Particularly for these tests, more than 80 signals, including ten type of different sensors and several operating signals that define the operating point of the unit, were simultaneously acquired. The present study, focuses on the optimization of the acquisition strategy, which includes type, number, location, acquisition frequency of the sensors and corresponding signal analysis to detect the full load instability and to prevent the unit from reaching this point. A systematic approach to determine this strategy has been followed. It has been found that some indicators obtained with different types of sensors are linearly correlated with the oscillating power. The optimized strategy has been determined

  16. Sensor-Based Optimized Control of the Full Load Instability in Large Hydraulic Turbines.

    Science.gov (United States)

    Presas, Alexandre; Valentin, David; Egusquiza, Mònica; Valero, Carme; Egusquiza, Eduard

    2018-03-30

    Hydropower plants are of paramount importance for the integration of intermittent renewable energy sources in the power grid. In order to match the energy generated and consumed, Large hydraulic turbines have to work under off-design conditions, which may lead to dangerous unstable operating points involving the hydraulic, mechanical and electrical system. Under these conditions, the stability of the grid and the safety of the power plant itself can be compromised. For many Francis Turbines one of these critical points, that usually limits the maximum output power, is the full load instability. Therefore, these machines usually work far away from this unstable point, reducing the effective operating range of the unit. In order to extend the operating range of the machine, working closer to this point with a reasonable safety margin, it is of paramount importance to monitor and to control relevant parameters of the unit, which have to be obtained with an accurate sensor acquisition strategy. Within the framework of a large EU project, field tests in a large Francis Turbine located in Canada (rated power of 444 MW) have been performed. Many different sensors were used to monitor several working parameters of the unit for all its operating range. Particularly for these tests, more than 80 signals, including ten type of different sensors and several operating signals that define the operating point of the unit, were simultaneously acquired. The present study, focuses on the optimization of the acquisition strategy, which includes type, number, location, acquisition frequency of the sensors and corresponding signal analysis to detect the full load instability and to prevent the unit from reaching this point. A systematic approach to determine this strategy has been followed. It has been found that some indicators obtained with different types of sensors are linearly correlated with the oscillating power. The optimized strategy has been determined based on the

  17. STDP in adaptive neurons gives close-to-optimal information transmission

    Directory of Open Access Journals (Sweden)

    Guillaume Hennequin

    2010-12-01

    Full Text Available Spike-frequency adaptation is known to enhance the transmission of information in sensory spiking neurons, by rescaling the dynamic range for input processing, matching it to the temporal statistics of the sensory stimulus. Achieving maximal information transmission has also been recently postulated as a role for Spike-Timing Dependent Plasticity (STDP. However, the link between optimal plasticity and STDP in cortex remains loose, and so does the relationship between STDP and adaptation processes. We investigate how STDP, as described by recent minimal models derived from experimental data, influences the quality of information transmission in an adapting neuron. We show that a phenomenological model based on triplets of spikes yields almost the same information rate as an optimal model specially designed to this end. In contrast, the standard pair-based model of STDP does not improve information transmission as much. This result holds not only for additive STDP with hard weight bounds, known to produce bimodal distributions of synaptic weights, but also for weight-dependent STDP in the context of unimodal but skewed weight distributions. We analyze the similarities between the triplet model and the optimal learning rule, and find that the triplet effect is an important feature of the optimal model when the neuron is adaptive. If STDP is optimized for information transmission, it must take into account the dynamical properties of the postsynaptic cell, which might explain the target-cell specificity of STDP. In particular, it accounts for the differences found in vitro between STDP at excitatory synapses onto principal cells and those onto fast-spiking interneurons.

  18. Resource Optimization Techniques and Security Levels for Wireless Sensor Networks Based on the ARSy Framework

    Science.gov (United States)

    Kitagawa, Akio

    2018-01-01

    Wireless Sensor Networks (WSNs) with limited battery, central processing units (CPUs), and memory resources are a widely implemented technology for early warning detection systems. The main advantage of WSNs is their ability to be deployed in areas that are difficult to access by humans. In such areas, regular maintenance may be impossible; therefore, WSN devices must utilize their limited resources to operate for as long as possible, but longer operations require maintenance. One method of maintenance is to apply a resource adaptation policy when a system reaches a critical threshold. This study discusses the application of a security level adaptation model, such as an ARSy Framework, for using resources more efficiently. A single node comprising a Raspberry Pi 3 Model B and a DS18B20 temperature sensor were tested in a laboratory under normal and stressful conditions. The result shows that under normal conditions, the system operates approximately three times longer than under stressful conditions. Maintaining the stability of the resources also enables the security level of a network’s data output to stay at a high or medium level. PMID:29772773

  19. [Method for optimal sensor placement in water distribution systems with nodal demand uncertainties].

    Science.gov (United States)

    Liu, Shu-Ming; Wu, Xue; Ouyang, Le-Yan

    2013-08-01

    The notion of identification fitness was proposed for optimizing sensor placement in water distribution systems. Nondominated Sorting Genetic Algorithm II was used to find the Pareto front between minimum overlap of possible detection times of two events and the best probability of detection, taking nodal demand uncertainties into account. This methodology was applied to an example network. The solutions show that the probability of detection and the number of possible locations are not remarkably affected by nodal demand uncertainties, but the sources identification accuracy declines with nodal demand uncertainties.

  20. Control Systems with Normalized and Covariance Adaptation by Optimal Control Modification

    Science.gov (United States)

    Nguyen, Nhan T. (Inventor); Burken, John J. (Inventor); Hanson, Curtis E. (Inventor)

    2016-01-01

    Disclosed is a novel adaptive control method and system called optimal control modification with normalization and covariance adjustment. The invention addresses specifically to current challenges with adaptive control in these areas: 1) persistent excitation, 2) complex nonlinear input-output mapping, 3) large inputs and persistent learning, and 4) the lack of stability analysis tools for certification. The invention has been subject to many simulations and flight testing. The results substantiate the effectiveness of the invention and demonstrate the technical feasibility for use in modern aircraft flight control systems.

  1. On Maximizing the Lifetime of Wireless Sensor Networks by Optimally Assigning Energy Supplies

    Science.gov (United States)

    Asorey-Cacheda, Rafael; García-Sánchez, Antonio Javier; García-Sánchez, Felipe; García-Haro, Joan; Gonzalez-Castaño, Francisco Javier

    2013-01-01

    The extension of the network lifetime of Wireless Sensor Networks (WSN) is an important issue that has not been appropriately solved yet. This paper addresses this concern and proposes some techniques to plan an arbitrary WSN. To this end, we suggest a hierarchical network architecture, similar to realistic scenarios, where nodes with renewable energy sources (denoted as primary nodes) carry out most message delivery tasks, and nodes equipped with conventional chemical batteries (denoted as secondary nodes) are those with less communication demands. The key design issue of this network architecture is the development of a new optimization framework to calculate the optimal assignment of renewable energy supplies (primary node assignment) to maximize network lifetime, obtaining the minimum number of energy supplies and their node assignment. We also conduct a second optimization step to additionally minimize the number of packet hops between the source and the sink. In this work, we present an algorithm that approaches the results of the optimization framework, but with much faster execution speed, which is a good alternative for large-scale WSN networks. Finally, the network model, the optimization process and the designed algorithm are further evaluated and validated by means of computer simulation under realistic conditions. The results obtained are discussed comparatively. PMID:23939582

  2. On Maximizing the Lifetime of Wireless Sensor Networks by Optimally Assigning Energy Supplies

    Directory of Open Access Journals (Sweden)

    Francisco Javier González-Castano

    2013-08-01

    Full Text Available The extension of the network lifetime of Wireless Sensor Networks (WSN is an important issue that has not been appropriately solved yet. This paper addresses this concern and proposes some techniques to plan an arbitrary WSN. To this end, we suggest a hierarchical network architecture, similar to realistic scenarios, where nodes with renewable energy sources (denoted as primary nodes carry out most message delivery tasks, and nodes equipped with conventional chemical batteries (denoted as secondary nodes are those with less communication demands. The key design issue of this network architecture is the development of a new optimization framework to calculate the optimal assignment of renewable energy supplies (primary node assignment to maximize network lifetime, obtaining the minimum number of energy supplies and their node assignment. We also conduct a second optimization step to additionally minimize the number of packet hops between the source and the sink. In this work, we present an algorithm that approaches the results of the optimization framework, but with much faster execution speed, which is a good alternative for large-scale WSN networks. Finally, the network model, the optimization process and the designed algorithm are further evaluated and validated by means of computer simulation under realistic conditions. The results obtained are discussed comparatively.

  3. Weather and Climate Manipulation as an Optimal Control for Adaptive Dynamical Systems

    Directory of Open Access Journals (Sweden)

    Sergei A. Soldatenko

    2017-01-01

    Full Text Available The weather and climate manipulation is examined as an optimal control problem for the earth climate system, which is considered as a complex adaptive dynamical system. Weather and climate manipulations are actually amorphous operations. Since their objectives are usually formulated vaguely, the expected results are fairly unpredictable and uncertain. However, weather and climate modification is a purposeful process and, therefore, we can formulate operations to manipulate weather and climate as the optimization problem within the framework of the optimal control theory. The complexity of the earth’s climate system is discussed and illustrated using the simplified low-order coupled chaotic dynamical system. The necessary conditions of optimality are derived for the large-scale atmospheric dynamics. This confirms that even a relatively simplified control problem for the atmospheric dynamics requires significant efforts to obtain the solution.

  4. Optimization of PWR fuel assembly radial enrichment and burnable poison location based on adaptive simulated annealing

    International Nuclear Information System (INIS)

    Rogers, Timothy; Ragusa, Jean; Schultz, Stephen; St Clair, Robert

    2009-01-01

    The focus of this paper is to present a concurrent optimization scheme for the radial pin enrichment and burnable poison location in PWR fuel assemblies. The methodology is based on the Adaptive Simulated Annealing (ASA) technique, coupled with a neutron lattice physics code to update the cost function values. In this work, the variations in the pin U-235 enrichment are variables to be optimized radially, i.e., pin by pin. We consider the optimization of two categories of fuel assemblies, with and without Gadolinium burnable poison pins. When burnable poisons are present, both the radial distribution of enrichment and the poison locations are variables in the optimization process. Results for 15 x 15 PWR fuel assembly designs are provided.

  5. What is the Optimal Strategy for Adaptive Servo-Ventilation Therapy?

    Science.gov (United States)

    Imamura, Teruhiko; Kinugawa, Koichiro

    2018-05-23

    Clinical advantages in the adaptive servo-ventilation (ASV) therapy have been reported in selected heart failure patients with/without sleep-disorder breathing, whereas multicenter randomized control trials could not demonstrate such advantages. Considering this discrepancy, optimal patient selection and device setting may be a key for the successful ASV therapy. Hemodynamic and echocardiographic parameters indicating pulmonary congestion such as elevated pulmonary capillary wedge pressure were reported as predictors of good response to ASV therapy. Recently, parameters indicating right ventricular dysfunction also have been reported as good predictors. Optimal device setting with appropriate pressure setting during appropriate time may also be a key. Large-scale prospective trial with optimal patient selection and optimal device setting is warranted.

  6. Optimal Placement and Sizing of PV-STATCOM in Power Systems Using Empirical Data and Adaptive Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Reza Sirjani

    2018-03-01

    Full Text Available Solar energy is a source of free, clean energy which avoids the destructive effects on the environment that have long been caused by power generation. Solar energy technology rivals fossil fuels, and its development has increased recently. Photovoltaic (PV solar farms can only produce active power during the day, while at night, they are completely idle. At the same time, though, active power should be supported by reactive power. Reactive power compensation in power systems improves power quality and stability. The use during the night of a PV solar farm inverter as a static synchronous compensator (or PV-STATCOM device has recently been proposed which can improve system performance and increase the utility of a PV solar farm. In this paper, a method for optimal PV-STATCOM placement and sizing is proposed using empirical data. Considering the objectives of power loss and cost minimization as well as voltage improvement, two sub-problems of placement and sizing, respectively, are solved by a power loss index and adaptive particle swarm optimization (APSO. Test results show that APSO not only performs better in finding optimal solutions but also converges faster compared with bee colony optimization (BCO and lightening search algorithm (LSA. Installation of a PV solar farm, STATCOM, and PV-STATCOM in a system are each evaluated in terms of efficiency and cost.

  7. Simulated annealing method for electronic circuits design: adaptation and comparison with other optimization methods

    International Nuclear Information System (INIS)

    Berthiau, G.

    1995-10-01

    The circuit design problem consists in determining acceptable parameter values (resistors, capacitors, transistors geometries ...) which allow the circuit to meet various user given operational criteria (DC consumption, AC bandwidth, transient times ...). This task is equivalent to a multidimensional and/or multi objective optimization problem: n-variables functions have to be minimized in an hyper-rectangular domain ; equality constraints can be eventually specified. A similar problem consists in fitting component models. In this way, the optimization variables are the model parameters and one aims at minimizing a cost function built on the error between the model response and the data measured on the component. The chosen optimization method for this kind of problem is the simulated annealing method. This method, provided by the combinatorial optimization domain, has been adapted and compared with other global optimization methods for the continuous variables problems. An efficient strategy of variables discretization and a set of complementary stopping criteria have been proposed. The different parameters of the method have been adjusted with analytical functions of which minima are known, classically used in the literature. Our simulated annealing algorithm has been coupled with an open electrical simulator SPICE-PAC of which the modular structure allows the chaining of simulations required by the circuit optimization process. We proposed, for high-dimensional problems, a partitioning technique which ensures proportionality between CPU-time and variables number. To compare our method with others, we have adapted three other methods coming from combinatorial optimization domain - the threshold method, a genetic algorithm and the Tabu search method - The tests have been performed on the same set of test functions and the results allow a first comparison between these methods applied to continuous optimization variables. Finally, our simulated annealing program

  8. Beam orientation optimization for intensity modulated radiation therapy using adaptive l2,1-minimization

    International Nuclear Information System (INIS)

    Jia Xun; Men Chunhua; Jiang, Steve B; Lou Yifei

    2011-01-01

    Beam orientation optimization (BOO) is a key component in the process of intensity modulated radiation therapy treatment planning. It determines to what degree one can achieve a good treatment plan in the subsequent plan optimization process. In this paper, we have developed a BOO algorithm via adaptive l 2,1 -minimization. Specifically, we introduce a sparsity objective function term into our model which contains weighting factors for each beam angle adaptively adjusted during the optimization process. Such an objective function favors a small number of beam angles. By optimizing a total objective function consisting of a dosimetric term and the sparsity term, we are able to identify unimportant beam angles and gradually remove them without largely sacrificing the dosimetric objective. In one typical prostate case, the convergence property of our algorithm, as well as how beam angles are selected during the optimization process, is demonstrated. Fluence map optimization (FMO) is then performed based on the optimized beam angles. The resulting plan quality is presented and is found to be better than that of equiangular beam orientations. We have further systematically validated our algorithm in the contexts of 5-9 coplanar beams for five prostate cases and one head and neck case. For each case, the final FMO objective function value is used to compare the optimized beam orientations with the equiangular ones. It is found that, in the majority of cases tested, our BOO algorithm leads to beam configurations which attain lower FMO objective function values than those of corresponding equiangular cases, indicating the effectiveness of our BOO algorithm. Superior plan qualities are also demonstrated by comparing DVH curves between BOO plans and equiangular plans.

  9. Cognitive radio adaptation for power consumption minimization using biogeography-based optimization

    International Nuclear Information System (INIS)

    Qi Pei-Han; Zheng Shi-Lian; Yang Xiao-Niu; Zhao Zhi-Jin

    2016-01-01

    Adaptation is one of the key capabilities of cognitive radio, which focuses on how to adjust the radio parameters to optimize the system performance based on the knowledge of the radio environment and its capability and characteristics. In this paper, we consider the cognitive radio adaptation problem for power consumption minimization. The problem is formulated as a constrained power consumption minimization problem, and the biogeography-based optimization (BBO) is introduced to solve this optimization problem. A novel habitat suitability index (HSI) evaluation mechanism is proposed, in which both the power consumption minimization objective and the quality of services (QoS) constraints are taken into account. The results show that under different QoS requirement settings corresponding to different types of services, the algorithm can minimize power consumption while still maintaining the QoS requirements. Comparison with particle swarm optimization (PSO) and cat swarm optimization (CSO) reveals that BBO works better, especially at the early stage of the search, which means that the BBO is a better choice for real-time applications. (paper)

  10. Adaptive feature selection using v-shaped binary particle swarm optimization.

    Science.gov (United States)

    Teng, Xuyang; Dong, Hongbin; Zhou, Xiurong

    2017-01-01

    Feature selection is an important preprocessing method in machine learning and data mining. This process can be used not only to reduce the amount of data to be analyzed but also to build models with stronger interpretability based on fewer features. Traditional feature selection methods evaluate the dependency and redundancy of features separately, which leads to a lack of measurement of their combined effect. Moreover, a greedy search considers only the optimization of the current round and thus cannot be a global search. To evaluate the combined effect of different subsets in the entire feature space, an adaptive feature selection method based on V-shaped binary particle swarm optimization is proposed. In this method, the fitness function is constructed using the correlation information entropy. Feature subsets are regarded as individuals in a population, and the feature space is searched using V-shaped binary particle swarm optimization. The above procedure overcomes the hard constraint on the number of features, enables the combined evaluation of each subset as a whole, and improves the search ability of conventional binary particle swarm optimization. The proposed algorithm is an adaptive method with respect to the number of feature subsets. The experimental results show the advantages of optimizing the feature subsets using the V-shaped transfer function and confirm the effectiveness and efficiency of the feature subsets obtained under different classifiers.

  11. A Self-Adaptive Fuzzy c-Means Algorithm for Determining the Optimal Number of Clusters

    Science.gov (United States)

    Wang, Zhihao; Yi, Jing

    2016-01-01

    For the shortcoming of fuzzy c-means algorithm (FCM) needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters. Firstly, a density-based algorithm was put forward. The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the empirical rule n and obtained the optimal initial cluster centroids, improving the limitation of FCM that randomly selected cluster centroids lead the convergence result to the local minimum. Secondly, this paper, by introducing a penalty function, proposed a new fuzzy clustering validity index based on fuzzy compactness and separation, which ensured that when the number of clusters verged on that of objects in the dataset, the value of clustering validity index did not monotonically decrease and was close to zero, so that the optimal number of clusters lost robustness and decision function. Then, based on these studies, a self-adaptive FCM algorithm was put forward to estimate the optimal number of clusters by the iterative trial-and-error process. At last, experiments were done on the UCI, KDD Cup 1999, and synthetic datasets, which showed that the method not only effectively determined the optimal number of clusters, but also reduced the iteration of FCM with the stable clustering result. PMID:28042291

  12. Optimization of Pockels electric field in transverse modulated optical voltage sensor

    Science.gov (United States)

    Huang, Yifan; Xu, Qifeng; Chen, Kun-Long; Zhou, Jie

    2018-05-01

    This paper investigates the possibilities of optimizing the Pockels electric field in a transverse modulated optical voltage sensor with a spherical electrode structure. The simulations show that due to the edge effect and the electric field concentrations and distortions, the electric field distributions in the crystal are non-uniform. In this case, a tiny variation in the light path leads to an integral error of more than 0.5%. Moreover, a 2D model cannot effectively represent the edge effect, so a 3D model is employed to optimize the electric field distributions. Furthermore, a new method to attach a quartz crystal to the electro-optic crystal along the electric field direction is proposed to improve the non-uniformity of the electric field. The integral error is reduced therefore from 0.5% to 0.015% and less. The proposed method is simple, practical and effective, and it has been validated by numerical simulations and experimental tests.

  13. Optimization of Dimensions of Cylindrical Piezoceramics as Radio-Clean Low Frequency Acoustic Sensors

    Directory of Open Access Journals (Sweden)

    M. Ardid

    2017-01-01

    Full Text Available Circular piezoelectric transducers with axial polarization are proposed as low frequency acoustic sensors for dark matter bubble chamber detectors. The axial vibration behaviour of the transducer is studied by three different methods: analytical models, FEM simulation, and experimental setup. To optimize disk geometry for this application, the dependence of the vibrational modes in function of the diameter-to-thickness ratio from 0.5 (a tall cylinder to 20.0 (a thin disk has been studied. Resonant and antiresonant frequencies for each of the lowest modes are determined and electromechanical coupling coefficients are calculated. From this analysis, due to the requirements of radiopurity and little volume, optimal diameter-to-thickness ratios for good transducer performance are discussed.

  14. Particle Swarm Optimization and harmony search based clustering and routing in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Veena Anand

    2017-01-01

    Full Text Available Wireless Sensor Networks (WSN has the disadvantage of limited and non-rechargeable energy resource in WSN creates a challenge and led to development of various clustering and routing algorithms. The paper proposes an approach for improving network lifetime by using Particle swarm optimization based clustering and Harmony Search based routing in WSN. So in this paper, global optimal cluster head are selected and Gateway nodes are introduced to decrease the energy consumption of the CH while sending aggregated data to the Base station (BS. Next, the harmony search algorithm based Local Search strategy finds best routing path for gateway nodes to the Base Station. Finally, the proposed algorithm is presented.

  15. Bayesian prediction and adaptive sampling algorithms for mobile sensor networks online environmental field reconstruction in space and time

    CERN Document Server

    Xu, Yunfei; Dass, Sarat; Maiti, Tapabrata

    2016-01-01

    This brief introduces a class of problems and models for the prediction of the scalar field of interest from noisy observations collected by mobile sensor networks. It also introduces the problem of optimal coordination of robotic sensors to maximize the prediction quality subject to communication and mobility constraints either in a centralized or distributed manner. To solve such problems, fully Bayesian approaches are adopted, allowing various sources of uncertainties to be integrated into an inferential framework effectively capturing all aspects of variability involved. The fully Bayesian approach also allows the most appropriate values for additional model parameters to be selected automatically by data, and the optimal inference and prediction for the underlying scalar field to be achieved. In particular, spatio-temporal Gaussian process regression is formulated for robotic sensors to fuse multifactorial effects of observations, measurement noise, and prior distributions for obtaining the predictive di...

  16. Time-optimal control of nuclear reactor power with adaptive proportional- integral-feedforward gains

    International Nuclear Information System (INIS)

    Park, Moon Ghu; Cho, Nam Zin

    1993-01-01

    A time-optimal control method which consists of coarse and fine control stages is described here. During the coarse control stage, the maximum control effort (time-optimal) is used to direct the system toward the switching boundary which is set near the desired power level. At this boundary, the controller is switched to the fine control stage in which an adaptive proportional-integral-feedforward (PIF) controller is used to compensate for any unmodeled reactivity feedback effects. This fine control is also introduced to obtain a constructive method for determining the (adaptive) feedback gains against the sampling effect. The feedforward control term is included to suppress the over-or undershoot. The estimation and feedback of the temperature-induced reactivity is also discussed

  17. Crop Classification by Forward Neural Network with Adaptive Chaotic Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Yudong Zhang

    2011-05-01

    Full Text Available This paper proposes a hybrid crop classifier for polarimetric synthetic aperture radar (SAR images. The feature sets consisted of span image, the H/A/α decomposition, and the gray-level co-occurrence matrix (GLCM based texture features. Then, the features were reduced by principle component analysis (PCA. Finally, a two-hidden-layer forward neural network (NN was constructed and trained by adaptive chaotic particle swarm optimization (ACPSO. K-fold cross validation was employed to enhance generation. The experimental results on Flevoland sites demonstrate the superiority of ACPSO to back-propagation (BP, adaptive BP (ABP, momentum BP (MBP, Particle Swarm Optimization (PSO, and Resilient back-propagation (RPROP methods. Moreover, the computation time for each pixel is only 1.08 × 10−7 s.

  18. An Image Enhancement Method Using the Quantum-Behaved Particle Swarm Optimization with an Adaptive Strategy

    Directory of Open Access Journals (Sweden)

    Xiaoping Su

    2013-01-01

    Full Text Available Image enhancement techniques are very important to image processing, which are used to improve image quality or extract the fine details in degraded images. In this paper, two novel objective functions based on the normalized incomplete Beta transform function are proposed to evaluate the effectiveness of grayscale image enhancement and color image enhancement, respectively. Using these objective functions, the parameters of transform functions are estimated by the quantum-behaved particle swarm optimization (QPSO. We also propose an improved QPSO with an adaptive parameter control strategy. The QPSO and the AQPSO algorithms, along with genetic algorithm (GA and particle swarm optimization (PSO, are tested on several benchmark grayscale and color images. The results show that the QPSO and AQPSO perform better than GA and PSO for the enhancement of these images, and the AQPSO has some advantages over QPSO due to its adaptive parameter control strategy.

  19. FDTD-based optical simulations methodology for CMOS image sensors pixels architecture and process optimization

    Science.gov (United States)

    Hirigoyen, Flavien; Crocherie, Axel; Vaillant, Jérôme M.; Cazaux, Yvon

    2008-02-01

    This paper presents a new FDTD-based optical simulation model dedicated to describe the optical performances of CMOS image sensors taking into account diffraction effects. Following market trend and industrialization constraints, CMOS image sensors must be easily embedded into even smaller packages, which are now equipped with auto-focus and short-term coming zoom system. Due to miniaturization, the ray-tracing models used to evaluate pixels optical performances are not accurate anymore to describe the light propagation inside the sensor, because of diffraction effects. Thus we adopt a more fundamental description to take into account these diffraction effects: we chose to use Maxwell-Boltzmann based modeling to compute the propagation of light, and to use a software with an FDTD-based (Finite Difference Time Domain) engine to solve this propagation. We present in this article the complete methodology of this modeling: on one hand incoherent plane waves are propagated to approximate a product-use diffuse-like source, on the other hand we use periodic conditions to limit the size of the simulated model and both memory and computation time. After having presented the correlation of the model with measurements we will illustrate its use in the case of the optimization of a 1.75μm pixel.

  20. An optimized electronic device for solar power harvesting dedicated to wireless sensor networks

    Energy Technology Data Exchange (ETDEWEB)

    Le Cam, Vincent; Le Maulf, Regis; Lemarchand, Laurent; Martin, William; Le Pen, Mathieu [LUNAM Univ., Bouguenais (France). IFSTTAR, MACS Dept.

    2012-07-01

    For economics as for practical reasons, this last decade, the use and dissemination of wireless sensor networks (WSN) became obvious; particularly in structural heath monitoring (SHM) use-cases where distances between sensors could be long and access to the structure quite difficult. Even if efforts are leaded to design small components and RF modules that ask for low-power, the need of an external source is often necessary. After have acquired knowledge in solar cells as in batteries technologies and methods to control charge/discharge phases as in optimizing algorithms, IFSTTAR laboratory has designed an electronic device that integrates those progress. This electronic device has a quite generic mission: for a panel of batteries chemistry (Lithium, NiMh) and a panel of solar cells sources (frome mW to some W), the system acts as an improved battery charger whatever the load ask for power. The system applies control algorithms based on battery capacity and chemistry profile. It also applies the MPPT (Maximum Power Point Tracking) algorithm. At any time, battery State Of Charge (SOC) can be requested via I2C bus as well as a warning signal is output when SOC becomes critical. Through standard pin connectors and a simple I2C interface, the system can be used by many wireless devices (sensors) that have to run autonomously. After the presentation of this system, a focus on its application on a real use-case will be given. (orig.)