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Sample records for featuring embedded multi

  1. A Depth Video-based Human Detection and Activity Recognition using Multi-features and Embedded Hidden Markov Models for Health Care Monitoring Systems

    Ahmad Jalal

    2017-08-01

    Full Text Available Increase in number of elderly people who are living independently needs especial care in the form of healthcare monitoring systems. Recent advancements in depth video technologies have made human activity recognition (HAR realizable for elderly healthcare applications. In this paper, a depth video-based novel method for HAR is presented using robust multi-features and embedded Hidden Markov Models (HMMs to recognize daily life activities of elderly people living alone in indoor environment such as smart homes. In the proposed HAR framework, initially, depth maps are analyzed by temporal motion identification method to segment human silhouettes from noisy background and compute depth silhouette area for each activity to track human movements in a scene. Several representative features, including invariant, multi-view differentiation and spatiotemporal body joints features were fused together to explore gradient orientation change, intensity differentiation, temporal variation and local motion of specific body parts. Then, these features are processed by the dynamics of their respective class and learned, modeled, trained and recognized with specific embedded HMM having active feature values. Furthermore, we construct a new online human activity dataset by a depth sensor to evaluate the proposed features. Our experiments on three depth datasets demonstrated that the proposed multi-features are efficient and robust over the state of the art features for human action and activity recognition.

  2. Biometric feature embedding using robust steganography technique

    Rashid, Rasber D.; Sellahewa, Harin; Jassim, Sabah A.

    2013-05-01

    This paper is concerned with robust steganographic techniques to hide and communicate biometric data in mobile media objects like images, over open networks. More specifically, the aim is to embed binarised features extracted using discrete wavelet transforms and local binary patterns of face images as a secret message in an image. The need for such techniques can arise in law enforcement, forensics, counter terrorism, internet/mobile banking and border control. What differentiates this problem from normal information hiding techniques is the added requirement that there should be minimal effect on face recognition accuracy. We propose an LSB-Witness embedding technique in which the secret message is already present in the LSB plane but instead of changing the cover image LSB values, the second LSB plane will be changed to stand as a witness/informer to the receiver during message recovery. Although this approach may affect the stego quality, it is eliminating the weakness of traditional LSB schemes that is exploited by steganalysis techniques for LSB, such as PoV and RS steganalysis, to detect the existence of secrete message. Experimental results show that the proposed method is robust against PoV and RS attacks compared to other variants of LSB. We also discussed variants of this approach and determine capacity requirements for embedding face biometric feature vectors while maintain accuracy of face recognition.

  3. Feature-based component model for design of embedded systems

    Zha, Xuan Fang; Sriram, Ram D.

    2004-11-01

    An embedded system is a hybrid of hardware and software, which combines software's flexibility and hardware real-time performance. Embedded systems can be considered as assemblies of hardware and software components. An Open Embedded System Model (OESM) is currently being developed at NIST to provide a standard representation and exchange protocol for embedded systems and system-level design, simulation, and testing information. This paper proposes an approach to representing an embedded system feature-based model in OESM, i.e., Open Embedded System Feature Model (OESFM), addressing models of embedded system artifacts, embedded system components, embedded system features, and embedded system configuration/assembly. The approach provides an object-oriented UML (Unified Modeling Language) representation for the embedded system feature model and defines an extension to the NIST Core Product Model. The model provides a feature-based component framework allowing the designer to develop a virtual embedded system prototype through assembling virtual components. The framework not only provides a formal precise model of the embedded system prototype but also offers the possibility of designing variation of prototypes whose members are derived by changing certain virtual components with different features. A case study example is discussed to illustrate the embedded system model.

  4. Embedded Incremental Feature Selection for Reinforcement Learning

    2012-05-01

    Prior to this work, feature selection for reinforce- ment learning has focused on linear value function ap- proximation ( Kolter and Ng, 2009; Parr et al...InProceed- ings of the the 23rd International Conference on Ma- chine Learning, pages 449–456. Kolter , J. Z. and Ng, A. Y. (2009). Regularization and feature

  5. Multi-purpose passive debugging for embedded wireless

    Hansen, Morten Tranberg

    Debugging embedded wireless systems can be cumbersome and hard due to low visibility. To ease the task of debugging we propose a multi-purpose passive debugging framework, called TinyDebug, for developing embedded wireless systems. TinyDebug is designed to be used throughout the entire system...

  6. Topological Embedding Feature Based Resource Allocation in Network Virtualization

    Hongyan Cui

    2014-01-01

    Full Text Available Virtualization provides a powerful way to run multiple virtual networks on a shared substrate network, which needs accurate and efficient mathematical models. Virtual network embedding is a challenge in network virtualization. In this paper, considering the degree of convergence when mapping a virtual network onto substrate network, we propose a new embedding algorithm based on topology mapping convergence-degree. Convergence-degree means the adjacent degree of virtual network’s nodes when they are mapped onto a substrate network. The contributions of our method are as below. Firstly, we map virtual nodes onto the substrate nodes with the maximum convergence-degree. The simulation results show that our proposed algorithm largely enhances the network utilization efficiency and decreases the complexity of the embedding problem. Secondly, we define the load balance rate to reflect the load balance of substrate links. The simulation results show our proposed algorithm achieves better load balance. Finally, based on the feature of star topology, we further improve our embedding algorithm and make it suitable for application in the star topology. The test result shows it gets better performance than previous works.

  7. Hardware Synchronization for Embedded Multi-Core Processors

    Stoif, Christian; Schoeberl, Martin; Liccardi, Benito

    2011-01-01

    Multi-core processors are about to conquer embedded systems — it is not the question of whether they are coming but how the architectures of the microcontrollers should look with respect to the strict requirements in the field. We present the step from one to multiple cores in this paper, establi......Multi-core processors are about to conquer embedded systems — it is not the question of whether they are coming but how the architectures of the microcontrollers should look with respect to the strict requirements in the field. We present the step from one to multiple cores in this paper...

  8. Deep PDF parsing to extract features for detecting embedded malware.

    Munson, Miles Arthur; Cross, Jesse S. (Missouri University of Science and Technology, Rolla, MO)

    2011-09-01

    The number of PDF files with embedded malicious code has risen significantly in the past few years. This is due to the portability of the file format, the ways Adobe Reader recovers from corrupt PDF files, the addition of many multimedia and scripting extensions to the file format, and many format properties the malware author may use to disguise the presence of malware. Current research focuses on executable, MS Office, and HTML formats. In this paper, several features and properties of PDF Files are identified. Features are extracted using an instrumented open source PDF viewer. The feature descriptions of benign and malicious PDFs can be used to construct a machine learning model for detecting possible malware in future PDF files. The detection rate of PDF malware by current antivirus software is very low. A PDF file is easy to edit and manipulate because it is a text format, providing a low barrier to malware authors. Analyzing PDF files for malware is nonetheless difficult because of (a) the complexity of the formatting language, (b) the parsing idiosyncrasies in Adobe Reader, and (c) undocumented correction techniques employed in Adobe Reader. In May 2011, Esparza demonstrated that PDF malware could be hidden from 42 of 43 antivirus packages by combining multiple obfuscation techniques [4]. One reason current antivirus software fails is the ease of varying byte sequences in PDF malware, thereby rendering conventional signature-based virus detection useless. The compression and encryption functions produce sequences of bytes that are each functions of multiple input bytes. As a result, padding the malware payload with some whitespace before compression/encryption can change many of the bytes in the final payload. In this study we analyzed a corpus of 2591 benign and 87 malicious PDF files. While this corpus is admittedly small, it allowed us to test a system for collecting indicators of embedded PDF malware. We will call these indicators features throughout

  9. The research and application of multi-biometric acquisition embedded system

    Deng, Shichao; Liu, Tiegen; Guo, Jingjing; Li, Xiuyan

    2009-11-01

    The identification technology based on multi-biometric can greatly improve the applicability, reliability and antifalsification. This paper presents a multi-biometric system bases on embedded system, which includes: three capture daughter boards are applied to obtain different biometric: one each for fingerprint, iris and vein of the back of hand; FPGA (Field Programmable Gate Array) is designed as coprocessor, which uses to configure three daughter boards on request and provides data path between DSP (digital signal processor) and daughter boards; DSP is the master processor and its functions include: control the biometric information acquisition, extracts feature as required and responsible for compare the results with the local database or data server through network communication. The advantages of this system were it can acquire three different biometric in real time, extracts complexity feature flexibly in different biometrics' raw data according to different purposes and arithmetic and network interface on the core-board will be the solution of big data scale. Because this embedded system has high stability, reliability, flexibility and fit for different data scale, it can satisfy the demand of multi-biometric recognition.

  10. Re-weighted Discriminatively Embedded K-Means for Multi-view Clustering.

    Xu, Jinglin; Han, Junwei; Nie, Feiping; Li, Xuelong

    2017-02-08

    Recent years, more and more multi-view data are widely used in many real world applications. This kind of data (such as image data) are high dimensional and obtained from different feature extractors, which represents distinct perspectives of the data. How to cluster such data efficiently is a challenge. In this paper, we propose a novel multi-view clustering framework, called Re-weighted Discriminatively Embedded KMeans (RDEKM), for this task. The proposed method is a multiview least-absolute residual model which induces robustness to efficiently mitigates the influence of outliers and realizes dimension reduction during multi-view clustering. Specifically, the proposed model is an unsupervised optimization scheme which utilizes Iterative Re-weighted Least Squares to solve leastabsolute residual and adaptively controls the distribution of multiple weights in a re-weighted manner only based on its own low-dimensional subspaces and a common clustering indicator matrix. Furthermore, theoretical analysis (including optimality and convergence analysis) and the optimization algorithm are also presented. Compared to several state-of-the-art multi-view clustering methods, the proposed method substantially improves the accuracy of the clustering results on widely used benchmark datasets, which demonstrates the superiority of the proposed work.

  11. Fluidic origami with embedded pressure dependent multi-stability: a plant inspired innovation.

    Li, Suyi; Wang, K W

    2015-10-06

    Inspired by the impulsive movements in plants, this research investigates the physics of a novel fluidic origami concept for its pressure-dependent multi-stability. In this innovation, fluid-filled tubular cells are synthesized by integrating different Miura-Ori sheets into a three-dimensional topological system, where the internal pressures are strategically controlled similar to the motor cells in plants. Fluidic origami incorporates two crucial physiological features observed in nature: one is distributed, pressurized cellular organization, and the other is embedded multi-stability. For a single fluidic origami cell, two stable folding configurations can coexist due to the nonlinear relationships among folding, crease material deformation and internal volume change. When multiple origami cells are integrated, additional multi-stability characteristics could occur via the interactions between pressurized cells. Changes in the fluid pressure can tailor the existence and shapes of these stable folding configurations. As a result, fluidic origami can switch between being mono-stable, bistable and multi-stable with pressure control, and provide a rapid 'snap-through' type of shape change based on the similar principles as in plants. The outcomes of this research could lead to the development of new adaptive materials or structures, and provide insights for future plant physiology studies at the cellular level. © 2015 The Author(s).

  12. Feature Genes Selection Using Supervised Locally Linear Embedding and Correlation Coefficient for Microarray Classification.

    Xu, Jiucheng; Mu, Huiyu; Wang, Yun; Huang, Fangzhou

    2018-01-01

    The selection of feature genes with high recognition ability from the gene expression profiles has gained great significance in biology. However, most of the existing methods have a high time complexity and poor classification performance. Motivated by this, an effective feature selection method, called supervised locally linear embedding and Spearman's rank correlation coefficient (SLLE-SC 2 ), is proposed which is based on the concept of locally linear embedding and correlation coefficient algorithms. Supervised locally linear embedding takes into account class label information and improves the classification performance. Furthermore, Spearman's rank correlation coefficient is used to remove the coexpression genes. The experiment results obtained on four public tumor microarray datasets illustrate that our method is valid and feasible.

  13. Further Thoughts on "How Task Features Impact Evidence from Assessments Embedded in Simulations and Games"

    Oliveri, María Elena; Khan, Saad

    2014-01-01

    María Oliveri, and Saad Khan write that the article: "How Task Features Impact Evidence from Assessments Embedded in Simulations and Games" provided helpful illustrations regarding the implementation of evidence-centered assessment design (Mislevy & Haertel, 2006; Mislevy, Steinberg, & Almond, 1999) with games and simulations.…

  14. Multi-scale salient feature extraction on mesh models

    Yang, Yongliang; Shen, ChaoHui

    2012-01-01

    We present a new method of extracting multi-scale salient features on meshes. It is based on robust estimation of curvature on multiple scales. The coincidence between salient feature and the scale of interest can be established straightforwardly, where detailed feature appears on small scale and feature with more global shape information shows up on large scale. We demonstrate this multi-scale description of features accords with human perception and can be further used for several applications as feature classification and viewpoint selection. Experiments exhibit that our method as a multi-scale analysis tool is very helpful for studying 3D shapes. © 2012 Springer-Verlag.

  15. Multi-domain transformational design flow for embedded systems

    Rovers, K.C.; van de Burgwal, M.D.; Kuper, Jan; Kokkeler, Andre B.J.; Smit, Gerardus Johannes Maria

    2011-01-01

    Current tools for embedded system design have limited support for modelling the interaction of the system with its physical environment. Furthermore, the natural representation of (streaming, real-time) applications with dataflow models is not supported by most tools. However, integrating multiple

  16. Co Modeling and Co Synthesis of Safety Critical Multi threaded Embedded Software for Multi Core Embedded Platforms

    2017-03-20

    Kaiserslautern Kaiserslautern, Germany Sandeep Shukla FERMAT Lab Electrical and Computer Engineering Department Virginia Tech 900 North Glebe Road...Software Engineering , Software Producibility, Component-based software design, behavioral types, behavioral type inference, Polychronous model of...near future, many embedded applications including safety critical ones as used in avionics, automotive , mission control systems will run on

  17. Design concepts for a virtualizable embedded MPSoC architecture enabling virtualization in embedded multi-processor systems

    Biedermann, Alexander

    2014-01-01

    Alexander Biedermann presents a generic hardware-based virtualization approach, which may transform an array of any off-the-shelf embedded processors into a multi-processor system with high execution dynamism. Based on this approach, he highlights concepts for the design of energy aware systems, self-healing systems as well as parallelized systems. For the latter, the novel so-called Agile Processing scheme is introduced by the author, which enables a seamless transition between sequential and parallel execution schemes. The design of such virtualizable systems is further aided by introduction

  18. Real time polarization sensor image processing on an embedded FPGA/multi-core DSP system

    Bednara, Marcus; Chuchacz-Kowalczyk, Katarzyna

    2015-05-01

    Most embedded image processing SoCs available on the market are highly optimized for typical consumer applications like video encoding/decoding, motion estimation or several image enhancement processes as used in DSLR or digital video cameras. For non-consumer applications, on the other hand, optimized embedded hardware is rarely available, so often PC based image processing systems are used. We show how a real time capable image processing system for a non-consumer application - namely polarization image data processing - can be efficiently implemented on an FPGA and multi-core DSP based embedded hardware platform.

  19. STFTP: Secure TFTP Protocol for Embedded Multi-Agent Systems Communication

    ZAGAR, D.

    2013-05-01

    Full Text Available Today's embedded systems have evolved into multipurpose devices moving towards an embedded multi-agent system (MAS infrastructure. With the involvement of MAS in embedded systems, one remaining issues is establishing communication between agents in low computational power and low memory embedded systems without present Embedded Operating System (EOS. One solution is the extension of an outdated Trivial File Transfer Protocol (TFTP. The main advantage of using TFTP in embedded systems is the easy implementation. However, the problem at hand is the overall lack of security mechanisms in TFTP. This paper proposes an extension to the existing TFTP in a form of added security mechanisms: STFTP. The authentication is proposed using Digest Access Authentication process whereas the data encryption can be performed by various cryptographic algorithms. The proposal is experimentally tested using two embedded systems based on micro-controller architecture. Communication is analyzed for authentication, data rate and transfer time versus various data encryption ciphers and files sizes. STFTP results in an expected drop in performance, which is in the range of similar encryption algorithms. The system could be improved by using embedded systems of higher computational power or by the use of hardware encryption modules.

  20. Design of massively parallel hardware multi-processors for highly-demanding embedded applications

    Jozwiak, L.; Jan, Y.

    2013-01-01

    Many new embedded applications require complex computations to be performed to tight schedules, while at the same time demanding low energy consumption and low cost. For implementation of these highly-demanding applications, highly-optimized application-specific multi-processor system-on-a-chip

  1. Commentary on "How Task Features Impact Evidence from Assessments Embedded in Simulations and Games" by Almond et al.

    Timms, Mike

    2014-01-01

    In his commentary on "How Task Features Impact Evidence from Assessments Embedded in Simulations and Games" by Almond et al., Mike Timms writes that his own research has involved the use of embedded assessments using simulations in interactive learning environments, and the Evidence Centered Design (ECD) approach has provided a solid…

  2. Application of evolutionary algorithms for multi-objective optimization in VLSI and embedded systems

    2015-01-01

    This book describes how evolutionary algorithms (EA), including genetic algorithms (GA) and particle swarm optimization (PSO) can be utilized for solving multi-objective optimization problems in the area of embedded and VLSI system design. Many complex engineering optimization problems can be modelled as multi-objective formulations. This book provides an introduction to multi-objective optimization using meta-heuristic algorithms, GA and PSO, and how they can be applied to problems like hardware/software partitioning in embedded systems, circuit partitioning in VLSI, design of operational amplifiers in analog VLSI, design space exploration in high-level synthesis, delay fault testing in VLSI testing, and scheduling in heterogeneous distributed systems. It is shown how, in each case, the various aspects of the EA, namely its representation, and operators like crossover, mutation, etc. can be separately formulated to solve these problems. This book is intended for design engineers and researchers in the field ...

  3. Embedded memory design for multi-core and systems on chip

    Mohammad, Baker

    2014-01-01

    This book describes the various tradeoffs systems designers face when designing embedded memory.  Readers designing multi-core systems and systems on chip will benefit from the discussion of different topics from memory architecture, array organization, circuit design techniques and design for test.  The presentation enables a multi-disciplinary approach to chip design, which bridges the gap between the architecture level and circuit level, in order to address yield, reliability and power-related issues for embedded memory.  ·         Provides a comprehensive overview of embedded memory design and associated challenges and choices; ·         Explains tradeoffs and dependencies across different disciplines involved with multi-core and system on chip memory design; ·         Includes detailed discussion of memory hierarchy and its impact on energy and performance; ·         Uses real product examples to demonstrate embedded memory design flow from architecture, to circuit ...

  4. Multi-dimension feature fusion for action recognition

    Dong, Pei; Li, Jie; Dong, Junyu; Qi, Lin

    2018-04-01

    Typical human actions last several seconds and exhibit characteristic spatio-temporal structure. The challenge for action recognition is to capture and fuse the multi-dimension information in video data. In order to take into account these characteristics simultaneously, we present a novel method that fuses multiple dimensional features, such as chromatic images, depth and optical flow fields. We built our model based on the multi-stream deep convolutional networks with the help of temporal segment networks and extract discriminative spatial and temporal features by fusing ConvNets towers multi-dimension, in which different feature weights are assigned in order to take full advantage of this multi-dimension information. Our architecture is trained and evaluated on the currently largest and most challenging benchmark NTU RGB-D dataset. The experiments demonstrate that the performance of our method outperforms the state-of-the-art methods.

  5. The sound of music: Differentiating musicians using a fast, musical multi-feature mismatch negativity paradigm

    Vuust, Peter; Brattico, Elvira; Seppänen, Miia

    2012-01-01

    to the other deviants in jazz musicians and left lateralization of the MMN to timbre in classical musicians. These findings indicate that the characteristics of the style/genre of music played by musicians influence their perceptual skills and the brain processing of sound features embedded in a musical......Musicians' skills in auditory processing depend highly on instrument, performance practice, and on level of expertise. Yet, it is not known though whether the style/genre of music might shape auditory processing in the brains of musicians. Here, we aimed at tackling the role of musical style....../genre on modulating neural and behavioral responses to changes in musical features. Using a novel, fast and musical sounding multi-feature paradigm, we measured the mismatch negativity (MMN), a pre-attentive brain response, to six types of musical feature change in musicians playing three distinct styles of music...

  6. Design Optimization of Multi-Cluster Embedded Systems for Real-Time Applications

    Pop, Paul; Eles, Petru; Peng, Zebo

    2004-01-01

    We present an approach to design optimization of multi-cluster embedded systems consisting of time-triggered and event-triggered clusters, interconnected via gateways. In this paper, we address design problems which are characteristic to multi-clusters: partitioning of the system functionality...... into time-triggered and event-triggered domains, process mapping, and the optimization of parameters corresponding to the communication protocol. We present several heuristics for solving these problems. Our heuristics are able to find schedulable implementations under limited resources, achieving...... an efficient utilization of the system. The developed algorithms are evaluated using extensive experiments and a real-life example....

  7. Design Optimization of Multi-Cluster Embedded Systems for Real-Time Applications

    Pop, Paul; Eles, Petru; Peng, Zebo

    2006-01-01

    We present an approach to design optimization of multi-cluster embedded systems consisting of time-triggered and event-triggered clusters, interconnected via gateways. In this paper, we address design problems which are characteristic to multi-clusters: partitioning of the system functionality...... into time-triggered and event-triggered domains, process mapping, and the optimization of parameters corresponding to the communication protocol. We present several heuristics for solving these problems. Our heuristics are able to find schedulable implementations under limited resources, achieving...... an efficient utilization of the system. The developed algorithms are evaluated using extensive experiments and a real-life example....

  8. Reliability assessment of embedded digital system using multi-state function

    Choi, Jong Gyun; Seong, Poong Hyun

    2006-01-01

    This work describes a combinatorial model for estimating the reliability of the embedded digital system by means of multi-state function. This model includes a coverage model for fault-handling techniques implemented in digital systems. The fault-handling techniques make it difficult for many types of components in digital system to be treated as binary state, good or bad. The multi-state function provides a complete analysis of multi-state systems as which the digital systems can be regarded. Through adaptation of software operational profile flow to multi-state function, the HW/SW interaction is also considered for estimation of the reliability of digital system. Using this model, we evaluate the reliability of one board controller in a digital system, Interposing Logic System (ILS), which is installed in YGN nuclear power units 3 and 4. Since the proposed model is a generalized combinatorial model, the simplification of this model becomes the conventional model that treats the system as binary state. This modeling method is particularly attractive for embedded systems in which small sized application software is implemented since it will require very laborious work for this method to be applied to systems with large software

  9. Feature-based Alignment of Volumetric Multi-modal Images

    Toews, Matthew; Zöllei, Lilla; Wells, William M.

    2014-01-01

    This paper proposes a method for aligning image volumes acquired from different imaging modalities (e.g. MR, CT) based on 3D scale-invariant image features. A novel method for encoding invariant feature geometry and appearance is developed, based on the assumption of locally linear intensity relationships, providing a solution to poor repeatability of feature detection in different image modalities. The encoding method is incorporated into a probabilistic feature-based model for multi-modal image alignment. The model parameters are estimated via a group-wise alignment algorithm, that iteratively alternates between estimating a feature-based model from feature data, then realigning feature data to the model, converging to a stable alignment solution with few pre-processing or pre-alignment requirements. The resulting model can be used to align multi-modal image data with the benefits of invariant feature correspondence: globally optimal solutions, high efficiency and low memory usage. The method is tested on the difficult RIRE data set of CT, T1, T2, PD and MP-RAGE brain images of subjects exhibiting significant inter-subject variability due to pathology. PMID:24683955

  10. Multi-axial strain transfer from laminated CFRP composites to embedded Bragg sensor: I. Parametric study

    Luyckx, G; Voet, E; De Waele, W; Degrieck, J

    2010-01-01

    Embedded optical fibre sensors are considered in numerous applications for structural health monitoring purposes. However, since the optical fibre and the host material in which it is embedded, will have different material properties, strain in both materials will not be equal when load is applied. Therefore, the multi-axial strain transfer from the host material to the embedded sensor (optical fibre) has to be considered in detail. In the first part of this paper the strain transfer will be determined using finite element modelling of a circular isotropic glass fibre embedded first in an isotropic host and second in an anisotropic composite material. The strain transfer or relation depends on the mechanical properties of the host material and the sensor (Young's modulus and Poisson's ratio), on the lay-up of the composite material (uni-directional lay-up/cross-ply lay-up) and the position of the sensor in a certain layer. In the second part of the paper the developed strain transfer model will be evaluated for one specific lay-up and sensor type

  11. An Embedded Multi-Agent Systems Based Industrial Wireless Sensor Network.

    Taboun, Mohammed S; Brennan, Robert W

    2017-09-14

    With the emergence of cyber-physical systems, there has been a growing interest in network-connected devices. One of the key requirements of a cyber-physical device is the ability to sense its environment. Wireless sensor networks are a widely-accepted solution for this requirement. In this study, an embedded multi-agent systems-managed wireless sensor network is presented. A novel architecture is proposed, along with a novel wireless sensor network architecture. Active and passive wireless sensor node types are defined, along with their communication protocols, and two application-specific examples are presented. A series of three experiments is conducted to evaluate the performance of the agent-embedded wireless sensor network.

  12. An Embedded Multi-Agent Systems Based Industrial Wireless Sensor Network

    Brennan, Robert W.

    2017-01-01

    With the emergence of cyber-physical systems, there has been a growing interest in network-connected devices. One of the key requirements of a cyber-physical device is the ability to sense its environment. Wireless sensor networks are a widely-accepted solution for this requirement. In this study, an embedded multi-agent systems-managed wireless sensor network is presented. A novel architecture is proposed, along with a novel wireless sensor network architecture. Active and passive wireless sensor node types are defined, along with their communication protocols, and two application-specific examples are presented. A series of three experiments is conducted to evaluate the performance of the agent-embedded wireless sensor network. PMID:28906452

  13. Multi-task feature learning by using trace norm regularization

    Jiangmei Zhang

    2017-11-01

    Full Text Available Multi-task learning can extract the correlation of multiple related machine learning problems to improve performance. This paper considers applying the multi-task learning method to learn a single task. We propose a new learning approach, which employs the mixture of expert model to divide a learning task into several related sub-tasks, and then uses the trace norm regularization to extract common feature representation of these sub-tasks. A nonlinear extension of this approach by using kernel is also provided. Experiments conducted on both simulated and real data sets demonstrate the advantage of the proposed approach.

  14. Design of multi-channel analyzer's monitoring system based on embedded system

    Yang Tao; Wei Yixiang

    2007-01-01

    A new Multi-Channel Analyzer's Monitoring system based on ARM9 Embedded system is introduced in this paper. Some solutions to problem are also discussed during the procedure of design, installation and debugging on Linux system. The Monitoring system is developed by using MiniGUI and Linux software system API, with the functions of collecting, displaying and I/O data controlling 1024 channels datum. They are all realized in real time, with the merits of low cost, small size and portability. All these lay the foundation of developing homemade Digital and Portable nuclear spectrometers. (authors)

  15. Quality-Driven Model-Based Design of MultiProcessor Embedded Systems for Highlydemanding Applications

    Jozwiak, Lech; Madsen, Jan

    2013-01-01

    The recent spectacular progress in modern nano-dimension semiconductor technology enabled implementation of a complete complex multi-processor system on a single chip (MPSoC), global networking and mobile wire-less communication, and facilitated a fast progress in these areas. New important...... accessible or distant) objects, installations, machines or devices, or even implanted in human or animal body can serve as examples. However, many of the modern embedded application impose very stringent functional and parametric demands. Moreover, the spectacular advances in microelectronics introduced...

  16. Schedulability Analysis and Optimization for the Synthesis of Multi-Cluster Distributed Embedded Systems

    Pop, Paul; Eles, Petru; Peng, Zebo

    2003-01-01

    We present an approach to schedulability analysis for the synthesis of multi-cluster distributed embedded systems consisting of time-triggered and event-triggered clusters, interconnected via gateways. We have also proposed a buffer size and worst case queuing delay analysis for the gateways......, responsible for routing inter-cluster traffic. Optimization heuristics for the priority assignment and synthesis of bus access parameters aimed at producing a schedulable system with minimal buffer needs have been proposed. Extensive experiments and a real-life example show the efficiency of our approaches....

  17. Schedulability Analysis and Optimization for the Synthesis of Multi-Cluster Distributed Embedded Systems

    Pop, Paul; Eles, Petru; Peng, Zebo

    2003-01-01

    An approach to schedulability analysis for the synthesis of multi-cluster distributed embedded systems consisting of time-triggered and event-triggered clusters, interconnected via gateways, is presented. A buffer size and worst case queuing delay analysis for the gateways, responsible for routing...... inter-cluster traffic, is also proposed. Optimisation heuristics for the priority assignment and synthesis of bus access parameters aimed at producing a schedulable system with minimal buffer needs have been proposed. Extensive experiments and a real-life example show the efficiency of the approaches....

  18. Rolling Bearing Fault Diagnosis Using Modified Neighborhood Preserving Embedding and Maximal Overlap Discrete Wavelet Packet Transform with Sensitive Features Selection

    Fei Dong

    2018-01-01

    Full Text Available In order to enhance the performance of bearing fault diagnosis and classification, features extraction and features dimensionality reduction have become more important. The original statistical feature set was calculated from single branch reconstruction vibration signals obtained by using maximal overlap discrete wavelet packet transform (MODWPT. In order to reduce redundancy information of original statistical feature set, features selection by adjusted rand index and sum of within-class mean deviations (FSASD was proposed to select fault sensitive features. Furthermore, a modified features dimensionality reduction method, supervised neighborhood preserving embedding with label information (SNPEL, was proposed to realize low-dimensional representations for high-dimensional feature space. Finally, vibration signals collected from two experimental test rigs were employed to evaluate the performance of the proposed procedure. The results show that the effectiveness, adaptability, and superiority of the proposed procedure can serve as an intelligent bearing fault diagnosis system.

  19. Safe and Efficient Support for Embeded Multi-Processors in ADA

    Ruiz, Jose F.

    2010-08-01

    New software demands increasing processing power, and multi-processor platforms are spreading as the answer to achieve the required performance. Embedded real-time systems are also subject to this trend, but in the case of real-time mission-critical systems, the properties of reliability, predictability and analyzability are also paramount. The Ada 2005 language defined a subset of its tasking model, the Ravenscar profile, that provides the basis for the implementation of deterministic and time analyzable applications on top of a streamlined run-time system. This Ravenscar tasking profile, originally designed for single processors, has proven remarkably useful for modelling verifiable real-time single-processor systems. This paper proposes a simple extension to the Ravenscar profile to support multi-processor systems using a fully partitioned approach. The implementation of this scheme is simple, and it can be used to develop applications amenable to schedulability analysis.

  20. Contextual Multi-armed Bandits under Feature Uncertainty

    Yun, Seyoung [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Nam, Jun Hyun [Korea Advanced Inst. Science and Technology (KAIST), Daejeon (Korea, Republic of); Mo, Sangwoo [Korea Advanced Inst. Science and Technology (KAIST), Daejeon (Korea, Republic of); Shin, Jinwoo [Korea Advanced Inst. Science and Technology (KAIST), Daejeon (Korea, Republic of)

    2017-03-03

    We study contextual multi-armed bandit problems under linear realizability on rewards and uncertainty (or noise) on features. For the case of identical noise on features across actions, we propose an algorithm, coined NLinRel, having O(T⁷/₈(log(dT)+K√d)) regret bound for T rounds, K actions, and d-dimensional feature vectors. Next, for the case of non-identical noise, we observe that popular linear hypotheses including NLinRel are impossible to achieve such sub-linear regret. Instead, under assumption of Gaussian feature vectors, we prove that a greedy algorithm has O(T²/₃√log d)regret bound with respect to the optimal linear hypothesis. Utilizing our theoretical understanding on the Gaussian case, we also design a practical variant of NLinRel, coined Universal-NLinRel, for arbitrary feature distributions. It first runs NLinRel for finding the ‘true’ coefficient vector using feature uncertainties and then adjust it to minimize its regret using the statistical feature information. We justify the performance of Universal-NLinRel on both synthetic and real-world datasets.

  1. Cloud Detection by Fusing Multi-Scale Convolutional Features

    Li, Zhiwei; Shen, Huanfeng; Wei, Yancong; Cheng, Qing; Yuan, Qiangqiang

    2018-04-01

    Clouds detection is an important pre-processing step for accurate application of optical satellite imagery. Recent studies indicate that deep learning achieves best performance in image segmentation tasks. Aiming at boosting the accuracy of cloud detection for multispectral imagery, especially for those that contain only visible and near infrared bands, in this paper, we proposed a deep learning based cloud detection method termed MSCN (multi-scale cloud net), which segments cloud by fusing multi-scale convolutional features. MSCN was trained on a global cloud cover validation collection, and was tested in more than ten types of optical images with different resolution. Experiment results show that MSCN has obvious advantages over the traditional multi-feature combined cloud detection method in accuracy, especially when in snow and other areas covered by bright non-cloud objects. Besides, MSCN produced more detailed cloud masks than the compared deep cloud detection convolution network. The effectiveness of MSCN make it promising for practical application in multiple kinds of optical imagery.

  2. Feature selection and multi-kernel learning for adaptive graph regularized nonnegative matrix factorization

    Wang, Jim Jing-Yan

    2014-09-20

    Nonnegative matrix factorization (NMF), a popular part-based representation technique, does not capture the intrinsic local geometric structure of the data space. Graph regularized NMF (GNMF) was recently proposed to avoid this limitation by regularizing NMF with a nearest neighbor graph constructed from the input data set. However, GNMF has two main bottlenecks. First, using the original feature space directly to construct the graph is not necessarily optimal because of the noisy and irrelevant features and nonlinear distributions of data samples. Second, one possible way to handle the nonlinear distribution of data samples is by kernel embedding. However, it is often difficult to choose the most suitable kernel. To solve these bottlenecks, we propose two novel graph-regularized NMF methods, AGNMFFS and AGNMFMK, by introducing feature selection and multiple-kernel learning to the graph regularized NMF, respectively. Instead of using a fixed graph as in GNMF, the two proposed methods learn the nearest neighbor graph that is adaptive to the selected features and learned multiple kernels, respectively. For each method, we propose a unified objective function to conduct feature selection/multi-kernel learning, NMF and adaptive graph regularization simultaneously. We further develop two iterative algorithms to solve the two optimization problems. Experimental results on two challenging pattern classification tasks demonstrate that the proposed methods significantly outperform state-of-the-art data representation methods.

  3. Aura: A Multi-Featured Programming Framework in Python

    2010-09-01

    Full Text Available This paper puts forward the design, programming and application of innovative educational software, ‘Aura’ made using Python and PyQt Python bindings. The research paper presents a new concept of using a single tool to relate between syntaxes of various programming languages and algorithms. It radically increases their understanding and retaining capacity, since they can correlate between many programming languages. The software is a totally unorthodox attempt towards helping students who have their first tryst with programming languages. The application is designed to help students understand how algorithms work and thus, help them in learning multiple programming languages on a single platform using an interactive graphical user interface. This paper elucidates how using Python and PyQt bindings, a comprehensive feature rich application, that implements an interactive algorithm building technique, a web browser, multiple programming language framework, a code generator and a real time code sharing hub be embedded into a single interface. And also explains, that using Python as building tool, it requires much less coding than conventional feature rich applications coded in other programming languages, and at the same time does not compromise on stability, inter-operability and robustness of the application.

  4. Effect of foundation embedment on the response of a multi-story building to earthquake excitation

    Hamood Mohammed

    2018-01-01

    Full Text Available During an earthquake, the behaviour of any structure is affected not only by the superstructure response, but also by the response of the soil beneath. Recent structural failure patterns have indicated the significance of soil-structure interaction (SSI effects. The present study focuses on SSI analysis considering the embedment depth of the foundation of a symmetric six stories reinforced concrete (RCspace bare frame building resting on stiff soil and subjected to seismic loading. The finite element analysis software ANSYS v17.2 is used. Time history (TH analysis has been adopted. The Response in terms of lateral displacements, base shear forces, base moments and variation in natural time periods are calculated from the analysis of the soil foundation structure interaction (SFSI model. Results are compared with that obtained from conventional method assuming rigid support at the base (fixed base of the structure. The results show that the SFSI considering different embedment depths are significant in altering the seismic response of the multi-story building (MSB.

  5. Schedulability-Driven Frame Packing for Multi-Cluster Distributed Embedded Systems

    Pop, Paul; Eles, Petru; Peng, Zebo

    2003-01-01

    We present an approach to frame packing for multi-cluster distributed embedded systems consisting of time-triggered and event-triggered clusters, interconnected via gateways. In our approach, the application messages are packed into frames such that the application is schedulable. Thus, we have...... also proposed a schedulability analysis for applications consisting of mixed event-triggered and time-triggered processes and messages, and a worst case queuing delay analysis for the gateways, responsible for routing inter-cluster traffic. Optimization heuristics for frame packing aiming at producing...... a schedulable system have been proposed. Extensive experiments and a real-life example show the efficiency of our frame-packing approach....

  6. Powering embedded electronics for wind turbine monitoring using multi-source energy harvesting techniques

    Anton, S. R.; Taylor, S. G.; Raby, E. Y.; Farinholt, K. M.

    2013-03-01

    With a global interest in the development of clean, renewable energy, wind energy has seen steady growth over the past several years. Advances in wind turbine technology bring larger, more complex turbines and wind farms. An important issue in the development of these complex systems is the ability to monitor the state of each turbine in an effort to improve the efficiency and power generation. Wireless sensor nodes can be used to interrogate the current state and health of wind turbine structures; however, a drawback of most current wireless sensor technology is their reliance on batteries for power. Energy harvesting solutions present the ability to create autonomous power sources for small, low-power electronics through the scavenging of ambient energy; however, most conventional energy harvesting systems employ a single mode of energy conversion, and thus are highly susceptible to variations in the ambient energy. In this work, a multi-source energy harvesting system is developed to power embedded electronics for wind turbine applications in which energy can be scavenged simultaneously from several ambient energy sources. Field testing is performed on a full-size, residential scale wind turbine where both vibration and solar energy harvesting systems are utilized to power wireless sensing systems. Two wireless sensors are investigated, including the wireless impedance device (WID) sensor node, developed at Los Alamos National Laboratory (LANL), and an ultra-low power RF system-on-chip board that is the basis for an embedded wireless accelerometer node currently under development at LANL. Results indicate the ability of the multi-source harvester to successfully power both sensors.

  7. Manifold regularized multi-task feature selection for multi-modality classification in Alzheimer's disease.

    Jie, Biao; Zhang, Daoqiang; Cheng, Bo; Shen, Dinggang

    2013-01-01

    Accurate diagnosis of Alzheimer's disease (AD), as well as its prodromal stage (i.e., mild cognitive impairment, MCI), is very important for possible delay and early treatment of the disease. Recently, multi-modality methods have been used for fusing information from multiple different and complementary imaging and non-imaging modalities. Although there are a number of existing multi-modality methods, few of them have addressed the problem of joint identification of disease-related brain regions from multi-modality data for classification. In this paper, we proposed a manifold regularized multi-task learning framework to jointly select features from multi-modality data. Specifically, we formulate the multi-modality classification as a multi-task learning framework, where each task focuses on the classification based on each modality. In order to capture the intrinsic relatedness among multiple tasks (i.e., modalities), we adopted a group sparsity regularizer, which ensures only a small number of features to be selected jointly. In addition, we introduced a new manifold based Laplacian regularization term to preserve the geometric distribution of original data from each task, which can lead to the selection of more discriminative features. Furthermore, we extend our method to the semi-supervised setting, which is very important since the acquisition of a large set of labeled data (i.e., diagnosis of disease) is usually expensive and time-consuming, while the collection of unlabeled data is relatively much easier. To validate our method, we have performed extensive evaluations on the baseline Magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) data of Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our experimental results demonstrate the effectiveness of the proposed method.

  8. 3D Printing Multi-Functionality: Embedded RF Antennas and Components

    Shemelya, C. M.; Zemba, M.; Liang, M.; Espalin, D.; Kief, C.; Xin, H.; Wicker, R. B.; MacDonald, E. W.

    2015-01-01

    Significant research and press has recently focused on the fabrication freedom of Additive Manufacturing (AM) to create both conceptual models and final end-use products. This flexibility allows design modifications to be immediately reflected in 3D printed structures, creating new paradigms within the manufacturing process. 3D printed products will inevitably be fabricated locally, with unit-level customization, optimized to unique mission requirements. However, for the technology to be universally adopted, the processes must be enhanced to incorporate additional technologies; such as electronics, actuation, and electromagnetics. Recently, a novel 3D printing platform, Multi3D manufacturing, was funded by the presidential initiative for revitalizing manufacturing in the USA using 3D printing (America Makes - also known as the National Additive Manufacturing Innovation Institute). The Multi3D system specifically targets 3D printed electronics in arbitrary form; and building upon the potential of this system, this paper describes RF antennas and components fabricated through the integration of material extrusion 3D printing with embedded wire, mesh, and RF elements.

  9. Behavior-aware cache hierarchy optimization for low-power multi-core embedded systems

    Zhao, Huatao; Luo, Xiao; Zhu, Chen; Watanabe, Takahiro; Zhu, Tianbo

    2017-07-01

    In modern embedded systems, the increasing number of cores requires efficient cache hierarchies to ensure data throughput, but such cache hierarchies are restricted by their tumid size and interference accesses which leads to both performance degradation and wasted energy. In this paper, we firstly propose a behavior-aware cache hierarchy (BACH) which can optimally allocate the multi-level cache resources to many cores and highly improved the efficiency of cache hierarchy, resulting in low energy consumption. The BACH takes full advantage of the explored application behaviors and runtime cache resource demands as the cache allocation bases, so that we can optimally configure the cache hierarchy to meet the runtime demand. The BACH was implemented on the GEM5 simulator. The experimental results show that energy consumption of a three-level cache hierarchy can be saved from 5.29% up to 27.94% compared with other key approaches while the performance of the multi-core system even has a slight improvement counting in hardware overhead.

  10. Embedded multi-channel data acquisition system on FPGA for Aditya Tokamak

    Rajpal, Rachana, E-mail: rachana@ipr.res.in [Institute for Plasma Research, Gandhinagar, Gujarat (India); Mandaliya, Hitesh, E-mail: hitesh@ipr.res.in [ITER, Cadarache (France); Patel, Jignesh, E-mail: jjp@ipr.res.in [ITER, Cadarache (France); Kumari, Praveena, E-mail: praveena@ipr.res.in [Institute for Plasma Research, Gandhinagar, Gujarat (India); Gautam, Pramila, E-mail: pramila@ipr.res.in [Institute for Plasma Research, Gandhinagar, Gujarat (India); Raulji, Vismaysinh, E-mail: vismay@ipr.res.in [Institute for Plasma Research, Gandhinagar, Gujarat (India); Edappala, Praveenlal, E-mail: praveen@ipr.res.in [Institute for Plasma Research, Gandhinagar, Gujarat (India); Pujara, H.D, E-mail: pujara@ipr.res [Institute for Plasma Research, Gandhinagar, Gujarat (India); Jha, R., E-mail: jha@ipr.res.in [Institute for Plasma Research, Gandhinagar, Gujarat (India)

    2016-11-15

    Highlights: • 64 channel data acquisition, interface to PC/104 bus, using single board computer. • Integration of all components in single hardware to make it standalone and portable. • Development of application software in Qt on Linux platform for better performance and low cost compared to Windows. • Explored and utilized FPGA resources for hardware interfacing. - Abstract: The 64 channel data acquisition board is designed to meet the future demand of acquisition channels for plasma diagnostics. The inherent features of the board are 16 bit resolution, programmable sampling rate upto 200 kS/s/ch and simultaneous acquisition. To make system embedded and compact, 8 Analog Inputs ADC chip, 4M × 16 bit RAM memory, Field Programmable Gate Arrays, PC/104 platform and single board computer are used. High speed timing control signals for all ADCs and RAMs are generated by FPGA. The system is standalone, portable and interface through Ethernet. The acquisition application is developed in Qt. on Linux platform, in SBC. Due to ethernet connectivity and onboard processing, system can be integrated into Aditya and SST-1 data acquisition system. The performance of hardware is tested on Linux and Windows Embedded OS. The paper describes design, hardware and software architecture, implementation and results of 64 channel DAQ system.

  11. Embedded multi-channel data acquisition system on FPGA for Aditya Tokamak

    Rajpal, Rachana; Mandaliya, Hitesh; Patel, Jignesh; Kumari, Praveena; Gautam, Pramila; Raulji, Vismaysinh; Edappala, Praveenlal; Pujara, H.D; Jha, R.

    2016-01-01

    Highlights: • 64 channel data acquisition, interface to PC/104 bus, using single board computer. • Integration of all components in single hardware to make it standalone and portable. • Development of application software in Qt on Linux platform for better performance and low cost compared to Windows. • Explored and utilized FPGA resources for hardware interfacing. - Abstract: The 64 channel data acquisition board is designed to meet the future demand of acquisition channels for plasma diagnostics. The inherent features of the board are 16 bit resolution, programmable sampling rate upto 200 kS/s/ch and simultaneous acquisition. To make system embedded and compact, 8 Analog Inputs ADC chip, 4M × 16 bit RAM memory, Field Programmable Gate Arrays, PC/104 platform and single board computer are used. High speed timing control signals for all ADCs and RAMs are generated by FPGA. The system is standalone, portable and interface through Ethernet. The acquisition application is developed in Qt. on Linux platform, in SBC. Due to ethernet connectivity and onboard processing, system can be integrated into Aditya and SST-1 data acquisition system. The performance of hardware is tested on Linux and Windows Embedded OS. The paper describes design, hardware and software architecture, implementation and results of 64 channel DAQ system.

  12. Integrating Multi-omic features exploiting Chromosome Conformation Capture data

    Ivan eMerelli

    2015-02-01

    Full Text Available The representation, integration and interpretation of omic data is a complex task, in particular considering the huge amount of information that is daily produced in molecular biology laboratories all around the world. The reason is that sequencing data regarding expression profiles, methylation patterns, and chromatin domains is difficult to harmonize in a systems biology view, since genome browsers only allow coordinate-based representations, discarding functional clusters created by the spatial conformation of the DNA in the nucleus. In this context, recent progresses in high throughput molecular biology techniques and bioinformatics have provided insights into chromatin interactions on a larger scale and offer a formidable support for the interpretation of multi-omic data. In particular, a novel sequencing technique called Chromosome Conformation Capture (3C allows the analysis of the chromosome organization in the cell’s natural state. While performed genome wide, this technique is usually called Hi-C. Inspired by service applications such as Google Maps, we developed NuChart, an R package that integrates Hi-C data to describe the chromosomal neighbourhood starting from the information about gene positions, with the possibility of mapping on the achieved graphs genomic features such as methylation patterns and histone modifications, along with expression profiles. In this paper we show the importance of the NuChart application for the integration of multi-omic data in a systems biology fashion, with particular interest in cytogenetic applications of these techniques. Moreover, we demonstrate how the integration of multi-omic data can provide useful information in understanding why genes are in certain specific positions inside the nucleus and how epigenetic patterns correlate with their expression.

  13. The sound of music: differentiating musicians using a fast, musical multi-feature mismatch negativity paradigm.

    Vuust, Peter; Brattico, Elvira; Seppänen, Miia; Näätänen, Risto; Tervaniemi, Mari

    2012-06-01

    Musicians' skills in auditory processing depend highly on instrument, performance practice, and on level of expertise. Yet, it is not known though whether the style/genre of music might shape auditory processing in the brains of musicians. Here, we aimed at tackling the role of musical style/genre on modulating neural and behavioral responses to changes in musical features. Using a novel, fast and musical sounding multi-feature paradigm, we measured the mismatch negativity (MMN), a pre-attentive brain response, to six types of musical feature change in musicians playing three distinct styles of music (classical, jazz, rock/pop) and in non-musicians. Jazz and classical musicians scored higher in the musical aptitude test than band musicians and non-musicians, especially with regards to tonal abilities. These results were extended by the MMN findings: jazz musicians had larger MMN-amplitude than all other experimental groups across the six different sound features, indicating a greater overall sensitivity to auditory outliers. In particular, we found enhanced processing of pith and sliding up to pitches in jazz musicians only. Furthermore, we observed a more frontal MMN to pitch and location compared to the other deviants in jazz musicians and left lateralization of the MMN to timbre in classical musicians. These findings indicate that the characteristics of the style/genre of music played by musicians influence their perceptual skills and the brain processing of sound features embedded in a musical context. Musicians' brain is hence shaped by the type of training, musical style/genre, and listening experiences. Copyright © 2012 Elsevier Ltd. All rights reserved.

  14. Development and Characterization of Embedded Sensory Particles Using Multi-Scale 3D Digital Image Correlation

    Cornell, Stephen R.; Leser, William P.; Hochhalter, Jacob D.; Newman, John A.; Hartl, Darren J.

    2014-01-01

    A method for detecting fatigue cracks has been explored at NASA Langley Research Center. Microscopic NiTi shape memory alloy (sensory) particles were embedded in a 7050 aluminum alloy matrix to detect the presence of fatigue cracks. Cracks exhibit an elevated stress field near their tip inducing a martensitic phase transformation in nearby sensory particles. Detectable levels of acoustic energy are emitted upon particle phase transformation such that the existence and location of fatigue cracks can be detected. To test this concept, a fatigue crack was grown in a mode-I single-edge notch fatigue crack growth specimen containing sensory particles. As the crack approached the sensory particles, measurements of particle strain, matrix-particle debonding, and phase transformation behavior of the sensory particles were performed. Full-field deformation measurements were performed using a novel multi-scale optical 3D digital image correlation (DIC) system. This information will be used in a finite element-based study to determine optimal sensory material behavior and density.

  15. Multi-elemental imaging of paraffin-embedded human samples by laser-induced breakdown spectroscopy

    Moncayo, S.; Trichard, F.; Busser, B.; Sabatier-Vincent, M.; Pelascini, F.; Pinel, N.; Templier, I.; Charles, J.; Sancey, L.; Motto-Ros, V.

    2017-07-01

    Chemical elements play central roles for physiological homeostasis in human cells, and their dysregulation might lead to a certain number of pathologies. Novel imaging techniques that improve the work of pathologists for tissue analysis and diagnostics are continuously sought. We report the use of Laser-Induced Breakdown Spectroscopy (LIBS) to perform multi-elemental images of human paraffin-embedded skin samples on the entire biopsy scale in a complementary and compatible way with microscope histopathological examination. A specific instrumental configuration is proposed in order to detect most of the elements of medical interest (i.e. P, Al, Mg, Na, Zn, Si, Fe, and Cu). As an example of medical application, we selected and analysed skin biopsies, including healthy skin tissue, cutaneous metastasis of melanoma, Merkel-cell carcinoma and squamous cell carcinoma. Clear distinctions in the distribution of chemical elements are observed from the different samples investigated. This study demonstrates the high complementarity of LIBS elemental imaging with conventional histopathology, opening new opportunities for any medical application involving metals.

  16. How Task Features Impact Evidence from Assessments Embedded in Simulations and Games

    Almond, Russell G.; Kim, Yoon Jeon; Velasquez, Gertrudes; Shute, Valerie J.

    2014-01-01

    One of the key ideas of evidence-centered assessment design (ECD) is that task features can be deliberately manipulated to change the psychometric properties of items. ECD identifies a number of roles that task-feature variables can play, including determining the focus of evidence, guiding form creation, determining item difficulty and…

  17. A Pareto-based Ensemble with Feature and Instance Selection for Learning from Multi-Class Imbalanced Datasets.

    Fernández, Alberto; Carmona, Cristobal José; José Del Jesus, María; Herrera, Francisco

    2017-09-01

    Imbalanced classification is related to those problems that have an uneven distribution among classes. In addition to the former, when instances are located into the overlapped areas, the correct modeling of the problem becomes harder. Current solutions for both issues are often focused on the binary case study, as multi-class datasets require an additional effort to be addressed. In this research, we overcome these problems by carrying out a combination between feature and instance selections. Feature selection will allow simplifying the overlapping areas easing the generation of rules to distinguish among the classes. Selection of instances from all classes will address the imbalance itself by finding the most appropriate class distribution for the learning task, as well as possibly removing noise and difficult borderline examples. For the sake of obtaining an optimal joint set of features and instances, we embedded the searching for both parameters in a Multi-Objective Evolutionary Algorithm, using the C4.5 decision tree as baseline classifier in this wrapper approach. The multi-objective scheme allows taking a double advantage: the search space becomes broader, and we may provide a set of different solutions in order to build an ensemble of classifiers. This proposal has been contrasted versus several state-of-the-art solutions on imbalanced classification showing excellent results in both binary and multi-class problems.

  18. Efficient Multi-Label Feature Selection Using Entropy-Based Label Selection

    Jaesung Lee

    2016-11-01

    Full Text Available Multi-label feature selection is designed to select a subset of features according to their importance to multiple labels. This task can be achieved by ranking the dependencies of features and selecting the features with the highest rankings. In a multi-label feature selection problem, the algorithm may be faced with a dataset containing a large number of labels. Because the computational cost of multi-label feature selection increases according to the number of labels, the algorithm may suffer from a degradation in performance when processing very large datasets. In this study, we propose an efficient multi-label feature selection method based on an information-theoretic label selection strategy. By identifying a subset of labels that significantly influence the importance of features, the proposed method efficiently outputs a feature subset. Experimental results demonstrate that the proposed method can identify a feature subset much faster than conventional multi-label feature selection methods for large multi-label datasets.

  19. Human Amygdala Tracks a Feature-Based Valence Signal Embedded within the Facial Expression of Surprise.

    Kim, M Justin; Mattek, Alison M; Bennett, Randi H; Solomon, Kimberly M; Shin, Jin; Whalen, Paul J

    2017-09-27

    Human amygdala function has been traditionally associated with processing the affective valence (negative vs positive) of an emotionally charged event, especially those that signal fear or threat. However, this account of human amygdala function can be explained by alternative views, which posit that the amygdala might be tuned to either (1) general emotional arousal (activation vs deactivation) or (2) specific emotion categories (fear vs happy). Delineating the pure effects of valence independent of arousal or emotion category is a challenging task, given that these variables naturally covary under many circumstances. To circumvent this issue and test the sensitivity of the human amygdala to valence values specifically, we measured the dimension of valence within the single facial expression category of surprise. Given the inherent valence ambiguity of this category, we show that surprised expression exemplars are attributed valence and arousal values that are uniquely and naturally uncorrelated. We then present fMRI data from both sexes, showing that the amygdala tracks these consensus valence values. Finally, we provide evidence that these valence values are linked to specific visual features of the mouth region, isolating the signal by which the amygdala detects this valence information. SIGNIFICANCE STATEMENT There is an open question as to whether human amygdala function tracks the valence value of cues in the environment, as opposed to either a more general emotional arousal value or a more specific emotion category distinction. Here, we demonstrate the utility of surprised facial expressions because exemplars within this emotion category take on valence values spanning the dimension of bipolar valence (positive to negative) at a consistent level of emotional arousal. Functional neuroimaging data showed that amygdala responses tracked the valence of surprised facial expressions, unconfounded by arousal. Furthermore, a machine learning classifier identified

  20. Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features.

    Nikfarjam, Azadeh; Sarker, Abeed; O'Connor, Karen; Ginn, Rachel; Gonzalez, Graciela

    2015-05-01

    Social media is becoming increasingly popular as a platform for sharing personal health-related information. This information can be utilized for public health monitoring tasks, particularly for pharmacovigilance, via the use of natural language processing (NLP) techniques. However, the language in social media is highly informal, and user-expressed medical concepts are often nontechnical, descriptive, and challenging to extract. There has been limited progress in addressing these challenges, and thus far, advanced machine learning-based NLP techniques have been underutilized. Our objective is to design a machine learning-based approach to extract mentions of adverse drug reactions (ADRs) from highly informal text in social media. We introduce ADRMine, a machine learning-based concept extraction system that uses conditional random fields (CRFs). ADRMine utilizes a variety of features, including a novel feature for modeling words' semantic similarities. The similarities are modeled by clustering words based on unsupervised, pretrained word representation vectors (embeddings) generated from unlabeled user posts in social media using a deep learning technique. ADRMine outperforms several strong baseline systems in the ADR extraction task by achieving an F-measure of 0.82. Feature analysis demonstrates that the proposed word cluster features significantly improve extraction performance. It is possible to extract complex medical concepts, with relatively high performance, from informal, user-generated content. Our approach is particularly scalable, suitable for social media mining, as it relies on large volumes of unlabeled data, thus diminishing the need for large, annotated training data sets. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

  1. Oriented Edge-Based Feature Descriptor for Multi-Sensor Image Alignment and Enhancement

    Myung-Ho Ju

    2013-10-01

    Full Text Available In this paper, we present an efficient image alignment and enhancement method for multi-sensor images. The shape of the object captured in a multi-sensor images can be determined by comparing variability of contrast using corresponding edges across multi-sensor image. Using this cue, we construct a robust feature descriptor based on the magnitudes of the oriented edges. Our proposed method enables fast image alignment by identifying matching features in multi-sensor images. We enhance the aligned multi-sensor images through the fusion of the salient regions from each image. The results of stitching the multi-sensor images and their enhancement demonstrate that our proposed method can align and enhance multi-sensor images more efficiently than previous methods.

  2. Middle latency response correlates of single and double deviant stimuli in a multi-feature paradigm.

    Althen, H; Huotilainen, M; Grimm, S; Escera, C

    2016-01-01

    This study aimed to test single and double deviance-related modulations of the middle latency response (MLR) and the applicability of the optimum-2 multi-feature paradigm. The MLR and the MMN to frequency, intensity and double-feature deviants of an optimum-2 multi-feature paradigm and the MMN to double-feature deviants of an oddball paradigm were recorded in young adults. Double deviants elicited significant enhancements of the Nb and Pb MLR waves compared with the waves elicited by standard stimuli. These enhancements equalled approximately the sum of the numerical amplitude differences elicited by the single deviants. In contrast, the MMN to double deviants did not show such additivity. MMNs elicited by double deviants of the multi-feature and the oddball paradigm showed no significant difference in amplitude or latency. The optimum-2 multi-feature paradigm is suitable for recording double deviance-related modulations of the MLR. Interspersed intensity and frequency deviants in the standard trace of the optimum-2 condition multi-feature paradigm did not weaken the double MMN. The optimum-2 multi-feature paradigm could be especially beneficial for clinical studies on early deviance-related modulations in the MLR, due to its optimized utilization of the recording time. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  3. A feature dictionary supporting a multi-domain medical knowledge base.

    Naeymi-Rad, F

    1989-01-01

    Because different terminology is used by physicians of different specialties in different locations to refer to the same feature (signs, symptoms, test results), it is essential that our knowledge development tools provide a means to access a common pool of terms. This paper discusses the design of an online medical dictionary that provides a solution to this problem for developers of multi-domain knowledge bases for MEDAS (Medical Emergency Decision Assistance System). Our Feature Dictionary supports phrase equivalents for features, feature interactions, feature classifications, and translations to the binary features generated by the expert during knowledge creation. It is also used in the conversion of a domain knowledge to the database used by the MEDAS inference diagnostic sessions. The Feature Dictionary also provides capabilities for complex queries across multiple domains using the supported relations. The Feature Dictionary supports three methods for feature representation: (1) for binary features, (2) for continuous valued features, and (3) for derived features.

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

    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. Embedded security system for multi-modal surveillance in a railway carriage

    Zouaoui, Rhalem; Audigier, Romaric; Ambellouis, Sébastien; Capman, François; Benhadda, Hamid; Joudrier, Stéphanie; Sodoyer, David; Lamarque, Thierry

    2015-10-01

    Public transport security is one of the main priorities of the public authorities when fighting against crime and terrorism. In this context, there is a great demand for autonomous systems able to detect abnormal events such as violent acts aboard passenger cars and intrusions when the train is parked at the depot. To this end, we present an innovative approach which aims at providing efficient automatic event detection by fusing video and audio analytics and reducing the false alarm rate compared to classical stand-alone video detection. The multi-modal system is composed of two microphones and one camera and integrates onboard video and audio analytics and fusion capabilities. On the one hand, for detecting intrusion, the system relies on the fusion of "unusual" audio events detection with intrusion detections from video processing. The audio analysis consists in modeling the normal ambience and detecting deviation from the trained models during testing. This unsupervised approach is based on clustering of automatically extracted segments of acoustic features and statistical Gaussian Mixture Model (GMM) modeling of each cluster. The intrusion detection is based on the three-dimensional (3D) detection and tracking of individuals in the videos. On the other hand, for violent events detection, the system fuses unsupervised and supervised audio algorithms with video event detection. The supervised audio technique detects specific events such as shouts. A GMM is used to catch the formant structure of a shout signal. Video analytics use an original approach for detecting aggressive motion by focusing on erratic motion patterns specific to violent events. As data with violent events is not easily available, a normality model with structured motions from non-violent videos is learned for one-class classification. A fusion algorithm based on Dempster-Shafer's theory analyses the asynchronous detection outputs and computes the degree of belief of each probable event.

  6. MixDroid: A multi-features and multi-classifiers bagging system for Android malware detection

    Huang, Weiqing; Hou, Erhang; Zheng, Liang; Feng, Weimiao

    2018-05-01

    In the past decade, Android platform has rapidly taken over the mobile market for its superior convenience and open source characteristics. However, with the popularity of Android, malwares targeting on Android devices are increasing rapidly, while the conventional rule-based and expert-experienced approaches are no longer able to handle such explosive growth. In this paper, combining with the theory of natural language processing and machine learning, we not only implement the basic feature extraction of permission application features, but also propose two innovative schemes of feature extraction: Dalvik opcode features and malicious code image, and implement an automatic Android malware detection system MixDroid which is based on multi-features and multi-classifiers. According to our experiment results on 20,000 Android applications, detection accuracy of MixDroid is 98.1%, which proves our schemes' effectiveness in Android malware detection.

  7. Multi-Stage Recognition of Speech Emotion Using Sequential Forward Feature Selection

    Liogienė Tatjana

    2016-07-01

    Full Text Available The intensive research of speech emotion recognition introduced a huge collection of speech emotion features. Large feature sets complicate the speech emotion recognition task. Among various feature selection and transformation techniques for one-stage classification, multiple classifier systems were proposed. The main idea of multiple classifiers is to arrange the emotion classification process in stages. Besides parallel and serial cases, the hierarchical arrangement of multi-stage classification is most widely used for speech emotion recognition. In this paper, we present a sequential-forward-feature-selection-based multi-stage classification scheme. The Sequential Forward Selection (SFS and Sequential Floating Forward Selection (SFFS techniques were employed for every stage of the multi-stage classification scheme. Experimental testing of the proposed scheme was performed using the German and Lithuanian emotional speech datasets. Sequential-feature-selection-based multi-stage classification outperformed the single-stage scheme by 12–42 % for different emotion sets. The multi-stage scheme has shown higher robustness to the growth of emotion set. The decrease in recognition rate with the increase in emotion set for multi-stage scheme was lower by 10–20 % in comparison with the single-stage case. Differences in SFS and SFFS employment for feature selection were negligible.

  8. The evaluation of multi-structure, multi-atlas pelvic anatomy features in a prostate MR lymphography CAD system

    Meijs, M.; Debats, O.; Huisman, H.

    2015-03-01

    In prostate cancer, the detection of metastatic lymph nodes indicates progression from localized disease to metastasized cancer. The detection of positive lymph nodes is, however, a complex and time consuming task for experienced radiologists. Assistance of a two-stage Computer-Aided Detection (CAD) system in MR Lymphography (MRL) is not yet feasible due to the large number of false positives in the first stage of the system. By introducing a multi-structure, multi-atlas segmentation, using an affine transformation followed by a B-spline transformation for registration, the organ location is given by a mean density probability map. The atlas segmentation is semi-automatically drawn with ITK-SNAP, using Active Contour Segmentation. Each anatomic structure is identified by a label number. Registration is performed using Elastix, using Mutual Information and an Adaptive Stochastic Gradient optimization. The dataset consists of the MRL scans of ten patients, with lymph nodes manually annotated in consensus by two expert readers. The feature map of the CAD system consists of the Multi-Atlas and various other features (e.g. Normalized Intensity and multi-scale Blobness). The voxel-based Gentleboost classifier is evaluated using ROC analysis with cross validation. We show in a set of 10 studies that adding multi-structure, multi-atlas anatomical structure likelihood features improves the quality of the lymph node voxel likelihood map. Multiple structure anatomy maps may thus make MRL CAD more feasible.

  9. Multi-Level and Multi-Scale Feature Aggregation Using Pretrained Convolutional Neural Networks for Music Auto-Tagging

    Lee, Jongpil; Nam, Juhan

    2017-08-01

    Music auto-tagging is often handled in a similar manner to image classification by regarding the 2D audio spectrogram as image data. However, music auto-tagging is distinguished from image classification in that the tags are highly diverse and have different levels of abstractions. Considering this issue, we propose a convolutional neural networks (CNN)-based architecture that embraces multi-level and multi-scaled features. The architecture is trained in three steps. First, we conduct supervised feature learning to capture local audio features using a set of CNNs with different input sizes. Second, we extract audio features from each layer of the pre-trained convolutional networks separately and aggregate them altogether given a long audio clip. Finally, we put them into fully-connected networks and make final predictions of the tags. Our experiments show that using the combination of multi-level and multi-scale features is highly effective in music auto-tagging and the proposed method outperforms previous state-of-the-arts on the MagnaTagATune dataset and the Million Song Dataset. We further show that the proposed architecture is useful in transfer learning.

  10. A multi-approach feature extractions for iris recognition

    Sanpachai, H.; Settapong, M.

    2014-04-01

    Biometrics is a promising technique that is used to identify individual traits and characteristics. Iris recognition is one of the most reliable biometric methods. As iris texture and color is fully developed within a year of birth, it remains unchanged throughout a person's life. Contrary to fingerprint, which can be altered due to several aspects including accidental damage, dry or oily skin and dust. Although iris recognition has been studied for more than a decade, there are limited commercial products available due to its arduous requirement such as camera resolution, hardware size, expensive equipment and computational complexity. However, at the present time, technology has overcome these obstacles. Iris recognition can be done through several sequential steps which include pre-processing, features extractions, post-processing, and matching stage. In this paper, we adopted the directional high-low pass filter for feature extraction. A box-counting fractal dimension and Iris code have been proposed as feature representations. Our approach has been tested on CASIA Iris Image database and the results are considered successful.

  11. Extended feature-fusion guidelines to improve image-based multi-modal biometrics

    Brown, Dane

    2016-09-01

    Full Text Available The feature-level, unlike the match score-level, lacks multi-modal fusion guidelines. This work demonstrates a practical approach for improved image-based biometric feature-fusion. The approach extracts and combines the face, fingerprint...

  12. Multi-task feature selection in microarray data by binary integer programming.

    Lan, Liang; Vucetic, Slobodan

    2013-12-20

    A major challenge in microarray classification is that the number of features is typically orders of magnitude larger than the number of examples. In this paper, we propose a novel feature filter algorithm to select the feature subset with maximal discriminative power and minimal redundancy by solving a quadratic objective function with binary integer constraints. To improve the computational efficiency, the binary integer constraints are relaxed and a low-rank approximation to the quadratic term is applied. The proposed feature selection algorithm was extended to solve multi-task microarray classification problems. We compared the single-task version of the proposed feature selection algorithm with 9 existing feature selection methods on 4 benchmark microarray data sets. The empirical results show that the proposed method achieved the most accurate predictions overall. We also evaluated the multi-task version of the proposed algorithm on 8 multi-task microarray datasets. The multi-task feature selection algorithm resulted in significantly higher accuracy than when using the single-task feature selection methods.

  13. Single and Multiple Object Tracking Using a Multi-Feature Joint Sparse Representation.

    Hu, Weiming; Li, Wei; Zhang, Xiaoqin; Maybank, Stephen

    2015-04-01

    In this paper, we propose a tracking algorithm based on a multi-feature joint sparse representation. The templates for the sparse representation can include pixel values, textures, and edges. In the multi-feature joint optimization, noise or occlusion is dealt with using a set of trivial templates. A sparse weight constraint is introduced to dynamically select the relevant templates from the full set of templates. A variance ratio measure is adopted to adaptively adjust the weights of different features. The multi-feature template set is updated adaptively. We further propose an algorithm for tracking multi-objects with occlusion handling based on the multi-feature joint sparse reconstruction. The observation model based on sparse reconstruction automatically focuses on the visible parts of an occluded object by using the information in the trivial templates. The multi-object tracking is simplified into a joint Bayesian inference. The experimental results show the superiority of our algorithm over several state-of-the-art tracking algorithms.

  14. Multi-Objective Particle Swarm Optimization Approach for Cost-Based Feature Selection in Classification.

    Zhang, Yong; Gong, Dun-Wei; Cheng, Jian

    2017-01-01

    Feature selection is an important data-preprocessing technique in classification problems such as bioinformatics and signal processing. Generally, there are some situations where a user is interested in not only maximizing the classification performance but also minimizing the cost that may be associated with features. This kind of problem is called cost-based feature selection. However, most existing feature selection approaches treat this task as a single-objective optimization problem. This paper presents the first study of multi-objective particle swarm optimization (PSO) for cost-based feature selection problems. The task of this paper is to generate a Pareto front of nondominated solutions, that is, feature subsets, to meet different requirements of decision-makers in real-world applications. In order to enhance the search capability of the proposed algorithm, a probability-based encoding technology and an effective hybrid operator, together with the ideas of the crowding distance, the external archive, and the Pareto domination relationship, are applied to PSO. The proposed PSO-based multi-objective feature selection algorithm is compared with several multi-objective feature selection algorithms on five benchmark datasets. Experimental results show that the proposed algorithm can automatically evolve a set of nondominated solutions, and it is a highly competitive feature selection method for solving cost-based feature selection problems.

  15. Feature Selection using Multi-objective Genetic Algorith m: A Hybrid Approach

    Ahuja, Jyoti; GJUST - Guru Jambheshwar University of Sciecne and Technology; Ratnoo, Saroj Dahiya; GJUST - Guru Jambheshwar University of Sciecne and Technology

    2015-01-01

    Feature selection is an important pre-processing task for building accurate and comprehensible classification models. Several researchers have applied filter, wrapper or hybrid approaches using genetic algorithms which are good candidates for optimization problems that involve large search spaces like in the case of feature selection. Moreover, feature selection is an inherently multi-objective problem with many competing objectives involving size, predictive power and redundancy of the featu...

  16. Knee Kinematics Estimation Using Multi-Body Optimisation Embedding a Knee Joint Stiffness Matrix: A Feasibility Study.

    Vincent Richard

    Full Text Available The use of multi-body optimisation (MBO to estimate joint kinematics from stereophotogrammetric data while compensating for soft tissue artefact is still open to debate. Presently used joint models embedded in MBO, such as mechanical linkages, constitute a considerable simplification of joint function, preventing a detailed understanding of it. The present study proposes a knee joint model where femur and tibia are represented as rigid bodies connected through an elastic element the behaviour of which is described by a single stiffness matrix. The deformation energy, computed from the stiffness matrix and joint angles and displacements, is minimised within the MBO. Implemented as a "soft" constraint using a penalty-based method, this elastic joint description challenges the strictness of "hard" constraints. In this study, estimates of knee kinematics obtained using MBO embedding four different knee joint models (i.e., no constraints, spherical joint, parallel mechanism, and elastic joint were compared against reference kinematics measured using bi-planar fluoroscopy on two healthy subjects ascending stairs. Bland-Altman analysis and sensitivity analysis investigating the influence of variations in the stiffness matrix terms on the estimated kinematics substantiate the conclusions. The difference between the reference knee joint angles and displacements and the corresponding estimates obtained using MBO embedding the stiffness matrix showed an average bias and standard deviation for kinematics of 0.9±3.2° and 1.6±2.3 mm. These values were lower than when no joint constraints (1.1±3.8°, 2.4±4.1 mm or a parallel mechanism (7.7±3.6°, 1.6±1.7 mm were used and were comparable to the values obtained with a spherical joint (1.0±3.2°, 1.3±1.9 mm. The study demonstrated the feasibility of substituting an elastic joint for more classic joint constraints in MBO.

  17. Knee Kinematics Estimation Using Multi-Body Optimisation Embedding a Knee Joint Stiffness Matrix: A Feasibility Study.

    Richard, Vincent; Lamberto, Giuliano; Lu, Tung-Wu; Cappozzo, Aurelio; Dumas, Raphaël

    2016-01-01

    The use of multi-body optimisation (MBO) to estimate joint kinematics from stereophotogrammetric data while compensating for soft tissue artefact is still open to debate. Presently used joint models embedded in MBO, such as mechanical linkages, constitute a considerable simplification of joint function, preventing a detailed understanding of it. The present study proposes a knee joint model where femur and tibia are represented as rigid bodies connected through an elastic element the behaviour of which is described by a single stiffness matrix. The deformation energy, computed from the stiffness matrix and joint angles and displacements, is minimised within the MBO. Implemented as a "soft" constraint using a penalty-based method, this elastic joint description challenges the strictness of "hard" constraints. In this study, estimates of knee kinematics obtained using MBO embedding four different knee joint models (i.e., no constraints, spherical joint, parallel mechanism, and elastic joint) were compared against reference kinematics measured using bi-planar fluoroscopy on two healthy subjects ascending stairs. Bland-Altman analysis and sensitivity analysis investigating the influence of variations in the stiffness matrix terms on the estimated kinematics substantiate the conclusions. The difference between the reference knee joint angles and displacements and the corresponding estimates obtained using MBO embedding the stiffness matrix showed an average bias and standard deviation for kinematics of 0.9±3.2° and 1.6±2.3 mm. These values were lower than when no joint constraints (1.1±3.8°, 2.4±4.1 mm) or a parallel mechanism (7.7±3.6°, 1.6±1.7 mm) were used and were comparable to the values obtained with a spherical joint (1.0±3.2°, 1.3±1.9 mm). The study demonstrated the feasibility of substituting an elastic joint for more classic joint constraints in MBO.

  18. Reconfiguration of Computation and Communication Resources in Multi-Core Real-Time Embedded Systems

    Pezzarossa, Luca

    -core platform. Our approach is to associate reconfiguration with operational mode changes where the system, during normal operation, changes a subset of the executing tasks to adapt its behaviour to new conditions. Reconfiguration is therefore used during a mode change to modify the real-time guaranteed services...... of the communication channels between the tasks that are affected by the reconfiguration. This thesis investigates the use of reconfiguration in the context of multicore realtime systems targeting embedded applications. We address the reconfiguration of both the computation and the communication resources of a multi...... by the communication fabric between the cores of the platform. To support this, we present a new network on chip architecture, named Argo 2, that allows instantaneous and time-predictable reconfiguration of the communication channels. Our reconfiguration-capable architecture is prototyped using the existing time...

  19. Manifold Regularized Multi-Task Feature Selection for Multi-Modality Classification in Alzheimer’s Disease

    Jie, Biao; Cheng, Bo

    2014-01-01

    Accurate diagnosis of Alzheimer’s disease (AD), as well as its pro-dromal stage (i.e., mild cognitive impairment, MCI), is very important for possible delay and early treatment of the disease. Recently, multi-modality methods have been used for fusing information from multiple different and complementary imaging and non-imaging modalities. Although there are a number of existing multi-modality methods, few of them have addressed the problem of joint identification of disease-related brain regions from multi-modality data for classification. In this paper, we proposed a manifold regularized multi-task learning framework to jointly select features from multi-modality data. Specifically, we formulate the multi-modality classification as a multi-task learning framework, where each task focuses on the classification based on each modality. In order to capture the intrinsic relatedness among multiple tasks (i.e., modalities), we adopted a group sparsity regularizer, which ensures only a small number of features to be selected jointly. In addition, we introduced a new manifold based Laplacian regularization term to preserve the geometric distribution of original data from each task, which can lead to the selection of more discriminative features. Furthermore, we extend our method to the semi-supervised setting, which is very important since the acquisition of a large set of labeled data (i.e., diagnosis of disease) is usually expensive and time-consuming, while the collection of unlabeled data is relatively much easier. To validate our method, we have performed extensive evaluations on the baseline Magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) data of Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Our experimental results demonstrate the effectiveness of the proposed method. PMID:24505676

  20. Using Multi-Viewpoint Contracts for Negotiation of Embedded Software Updates

    Sönke Holthusen

    2016-05-01

    Full Text Available In this paper we address the issue of change after deployment in safety-critical embedded system applications. Our goal is to substitute lab-based verification with in-field formal analysis to determine whether an update may be safely applied. This is challenging because it requires an automated process able to handle multiple viewpoints such as functional correctness, timing, etc. For this purpose, we propose an original methodology for contract-based negotiation of software updates. The use of contracts allows us to cleanly split the verification effort between the lab and the field. In addition, we show how to rely on existing viewpoint-specific methods for update negotiation. We illustrate our approach on a concrete example inspired by the automotive domain.

  1. Alexnet Feature Extraction and Multi-Kernel Learning for Objectoriented Classification

    Ding, L.; Li, H.; Hu, C.; Zhang, W.; Wang, S.

    2018-04-01

    In view of the fact that the deep convolutional neural network has stronger ability of feature learning and feature expression, an exploratory research is done on feature extraction and classification for high resolution remote sensing images. Taking the Google image with 0.3 meter spatial resolution in Ludian area of Yunnan Province as an example, the image segmentation object was taken as the basic unit, and the pre-trained AlexNet deep convolution neural network model was used for feature extraction. And the spectral features, AlexNet features and GLCM texture features are combined with multi-kernel learning and SVM classifier, finally the classification results were compared and analyzed. The results show that the deep convolution neural network can extract more accurate remote sensing image features, and significantly improve the overall accuracy of classification, and provide a reference value for earthquake disaster investigation and remote sensing disaster evaluation.

  2. ALEXNET FEATURE EXTRACTION AND MULTI-KERNEL LEARNING FOR OBJECTORIENTED CLASSIFICATION

    L. Ding

    2018-04-01

    Full Text Available In view of the fact that the deep convolutional neural network has stronger ability of feature learning and feature expression, an exploratory research is done on feature extraction and classification for high resolution remote sensing images. Taking the Google image with 0.3 meter spatial resolution in Ludian area of Yunnan Province as an example, the image segmentation object was taken as the basic unit, and the pre-trained AlexNet deep convolution neural network model was used for feature extraction. And the spectral features, AlexNet features and GLCM texture features are combined with multi-kernel learning and SVM classifier, finally the classification results were compared and analyzed. The results show that the deep convolution neural network can extract more accurate remote sensing image features, and significantly improve the overall accuracy of classification, and provide a reference value for earthquake disaster investigation and remote sensing disaster evaluation.

  3. Carrier dynamics in InAs quantum dots embedded in InGaAs/GaAs multi quantum well structures

    Espinola, J L Casas; Dybic, M; Ostapenko, S; Torchynska, T V; Polupan, G

    2007-01-01

    Ground and multi excited state photoluminescence, as well as its temperature dependence, in InAs quantum dots embedded in symmetric In x Ga 1-x As/GaAs (x = 0.15) quantum wells (DWELL) have been investigated. The solution of the set of rate equations for exciton dynamics (relaxation into QWs or QDs and thermal escape) solved by us earlier is used for analysis the variety of thermal activation energies of photoluminescence thermal quenching for ground and multi excited states of InAs QDs. The obtained solutions were used at the discussion of the variety of activation energies of PL thermal quenching in InAs QDs. It is revealed three different regimes of thermally activated quenching of the QD PL intensity. These three regimes were attributed to thermal escape of excitons: i) from the high energy excited states of InAs QDs into the WL with follows exciton re-localization; ii) from the In x Ga 1-x As QWs into the GaAs barrier and iii) from the WL into the GaAs barrier with their subsequent nonradiative recombination in GaAs barrier

  4. Performance evaluation of multi-channel wireless mesh networks with embedded systems.

    Lam, Jun Huy; Lee, Sang-Gon; Tan, Whye Kit

    2012-01-01

    Many commercial wireless mesh network (WMN) products are available in the marketplace with their own proprietary standards, but interoperability among the different vendors is not possible. Open source communities have their own WMN implementation in accordance with the IEEE 802.11s draft standard, Linux open80211s project and FreeBSD WMN implementation. While some studies have focused on the test bed of WMNs based on the open80211s project, none are based on the FreeBSD. In this paper, we built an embedded system using the FreeBSD WMN implementation that utilizes two channels and evaluated its performance. This implementation allows the legacy system to connect to the WMN independent of the type of platform and distributes the load between the two non-overlapping channels. One channel is used for the backhaul connection and the other one is used to connect to the stations to wireless mesh network. By using the power efficient 802.11 technology, this device can also be used as a gateway for the wireless sensor network (WSN).

  5. [The relationship of attachment features and multi-impulsive symptoms in eating disorders].

    Szalai, Tamás Dömötör

    2017-07-01

    Attachment dysfunctions determine borderline personality disorder, which is a frequent background factor of multi-impulsivity; however, the relationship between attachment and multi-impulsive eating disorders is almost unexplored. To compare attachment features of multi-impulsive and classical eating disorder patients with individuals without eating disorders, and to test attachment as a predictor of multi-impulsivity. A cross-sectional survey (148 females, mean age: 30.9 years) investigated maternal, paternal and adult attachment, depression, anxiety, eating disorder and multi-impulsive symptoms in these groups. Altogether 41.3% of the individuals without eating disorders, 17.6% of classical and 11.8% of multi-impulsive eating disorder patients had secure attachment. Multi-impulsive patients had the most severe eating disorder symptoms (F (2) = 17.733) and the lowest paternal care (F (2) = 3.443). Preoccupied and fearful attachment explained 14.5% of multi-impulsive symptoms; however, with adjustment for depression only latter one remained the predictor of multi-impulsivity (t = 5.166, peating disorder patients from the aspects of both symptoms and attachment. Handling their negative moods may hold therapeutic potentials. Longitudinal studies are required to investigate the therapeutic value of paternal care, attachment preoccupation and fearfulness. Orv Hetil. 2017; 158(27): 1058-1066.

  6. Tensor-based Multi-view Feature Selection with Applications to Brain Diseases

    Cao, Bokai; He, Lifang; Kong, Xiangnan; Yu, Philip S.; Hao, Zhifeng; Ragin, Ann B.

    2015-01-01

    In the era of big data, we can easily access information from multiple views which may be obtained from different sources or feature subsets. Generally, different views provide complementary information for learning tasks. Thus, multi-view learning can facilitate the learning process and is prevalent in a wide range of application domains. For example, in medical science, measurements from a series of medical examinations are documented for each subject, including clinical, imaging, immunologic, serologic and cognitive measures which are obtained from multiple sources. Specifically, for brain diagnosis, we can have different quantitative analysis which can be seen as different feature subsets of a subject. It is desirable to combine all these features in an effective way for disease diagnosis. However, some measurements from less relevant medical examinations can introduce irrelevant information which can even be exaggerated after view combinations. Feature selection should therefore be incorporated in the process of multi-view learning. In this paper, we explore tensor product to bring different views together in a joint space, and present a dual method of tensor-based multi-view feature selection (dual-Tmfs) based on the idea of support vector machine recursive feature elimination. Experiments conducted on datasets derived from neurological disorder demonstrate the features selected by our proposed method yield better classification performance and are relevant to disease diagnosis. PMID:25937823

  7. Simulation Research Framework with Embedded Intelligent Algorithms for Analysis of Multi-Target, Multi-Sensor, High-Cluttered Environments

    Hanlon, Nicholas P.

    nearly identical performance metrics at orders of magnitude faster in execution. Second, a fuzzy inference system is presented that alleviates air traffic controllers from information overload by utilizing flight plan data and radar/GPS correlation values to highlight aircraft that deviate from their intended routes. Third, a genetic algorithm optimizes sensor placement that is robust and capable of handling unexpected routes in the environment. Fourth, a fuzzy CUSUM algorithm more accurately detects and corrects aircraft mode changes. Finally, all the work is packaged in a holistic simulation research framework that provides evaluation and analysis of various multi-sensor, multi-target scenarios.

  8. An improved feature extraction algorithm based on KAZE for multi-spectral image

    Yang, Jianping; Li, Jun

    2018-02-01

    Multi-spectral image contains abundant spectral information, which is widely used in all fields like resource exploration, meteorological observation and modern military. Image preprocessing, such as image feature extraction and matching, is indispensable while dealing with multi-spectral remote sensing image. Although the feature matching algorithm based on linear scale such as SIFT and SURF performs strong on robustness, the local accuracy cannot be guaranteed. Therefore, this paper proposes an improved KAZE algorithm, which is based on nonlinear scale, to raise the number of feature and to enhance the matching rate by using the adjusted-cosine vector. The experiment result shows that the number of feature and the matching rate of the improved KAZE are remarkably than the original KAZE algorithm.

  9. SAR Target Recognition Based on Multi-feature Multiple Representation Classifier Fusion

    Zhang Xinzheng

    2017-10-01

    Full Text Available In this paper, we present a Synthetic Aperture Radar (SAR image target recognition algorithm based on multi-feature multiple representation learning classifier fusion. First, it extracts three features from the SAR images, namely principal component analysis, wavelet transform, and Two-Dimensional Slice Zernike Moments (2DSZM features. Second, we harness the sparse representation classifier and the cooperative representation classifier with the above-mentioned features to get six predictive labels. Finally, we adopt classifier fusion to obtain the final recognition decision. We researched three different classifier fusion algorithms in our experiments, and the results demonstrate thatusing Bayesian decision fusion gives thebest recognition performance. The method based on multi-feature multiple representation learning classifier fusion integrates the discrimination of multi-features and combines the sparse and cooperative representation classification performance to gain complementary advantages and to improve recognition accuracy. The experiments are based on the Moving and Stationary Target Acquisition and Recognition (MSTAR database,and they demonstrate the effectiveness of the proposed approach.

  10. Vehicle license plate recognition based on geometry restraints and multi-feature decision

    Wu, Jianwei; Wang, Zongyue

    2005-10-01

    Vehicle license plate (VLP) recognition is of great importance to many traffic applications. Though researchers have paid much attention to VLP recognition there has not been a fully operational VLP recognition system yet for many reasons. This paper discusses a valid and practical method for vehicle license plate recognition based on geometry restraints and multi-feature decision including statistical and structural features. In general, the VLP recognition includes the following steps: the location of VLP, character segmentation, and character recognition. This paper discusses the three steps in detail. The characters of VLP are always declining caused by many factors, which makes it more difficult to recognize the characters of VLP, therefore geometry restraints such as the general ratio of length and width, the adjacent edges being perpendicular are used for incline correction. Image Moment has been proved to be invariant to translation, rotation and scaling therefore image moment is used as one feature for character recognition. Stroke is the basic element for writing and hence taking it as a feature is helpful to character recognition. Finally we take the image moment, the strokes and the numbers of each stroke for each character image and some other structural features and statistical features as the multi-feature to match each character image with sample character images so that each character image can be recognized by BP neural net. The proposed method combines statistical and structural features for VLP recognition, and the result shows its validity and efficiency.

  11. Geomorphological change detection using object-based feature extraction from multi-temporal LIDAR data

    Seijmonsbergen, A.C.; Anders, N.S.; Bouten, W.; Feitosa, R.Q.; da Costa, G.A.O.P.; de Almeida, C.M.; Fonseca, L.M.G.; Kux, H.J.H.

    2012-01-01

    Multi-temporal LiDAR DTMs are used for the development and testing of a method for geomorphological change analysis in western Austria. Our test area is located on a mountain slope in the Gargellen Valley in western Austria. Six geomorphological features were mapped by using stratified Object-Based

  12. The Multi-Feature Hypothesis: Connectionist Guidelines for L2 Task Design

    Moonen, Machteld; de Graaff, Rick; Westhoff, Gerard; Brekelmans, Mieke

    2014-01-01

    This study focuses on the effects of task type on the retention and ease of activation of second language (L2) vocabulary, based on the multi-feature hypothesis (Moonen, De Graaff, & Westhoff, 2006). Two tasks were compared: a writing task and a list-learning task. It was hypothesized that performing the writing task would yield higher…

  13. An Improved Rotation Forest for Multi-Feature Remote-Sensing Imagery Classification

    Yingchang Xiu

    2017-11-01

    Full Text Available Multi-feature, especially multi-temporal, remote-sensing data have the potential to improve land cover classification accuracy. However, sometimes it is difficult to utilize all the features efficiently. To enhance classification performance based on multi-feature imagery, an improved rotation forest, combining Principal Component Analysis (PCA and a boosting naïve Bayesian tree (NBTree, is proposed. First, feature extraction was carried out with PCA. The feature set was randomly split into several disjoint subsets; then, PCA was applied to each subset, and new training data for linear extracted features based on original training data were obtained. These steps were repeated several times. Second, based on the new training data, a boosting naïve Bayesian tree was constructed as the base classifier, which aims to achieve lower prediction error than a decision tree in the original rotation forest. At the classification phase, the improved rotation forest has two-layer voting. It first obtains several predictions through weighted voting in a boosting naïve Bayesian tree; then, the first-layer vote predicts by majority to obtain the final result. To examine the classification performance, the improved rotation forest was applied to multi-feature remote-sensing images, including MODIS Enhanced Vegetation Index (EVI imagery time series, MODIS Surface Reflectance products and ancillary data in Shandong Province for 2013. The EVI imagery time series was preprocessed using harmonic analysis of time series (HANTS to reduce the noise effects. The overall accuracy of the final classification result was 89.17%, and the Kappa coefficient was 0.71, which outperforms the original rotation forest and other classifier ensemble results, as well as the NASA land cover product. However, this new algorithm requires more computational time, meaning the efficiency needs to be further improved. Generally, the improved rotation forest has a potential advantage in

  14. Design of an Embedded Multi-Camera Vision System—A Case Study in Mobile Robotics

    Valter Costa

    2018-02-01

    Full Text Available The purpose of this work is to explore the design principles for a Real-Time Robotic Multi Camera Vision System, in a case study involving a real world competition of autonomous driving. Design practices from vision and real-time research areas are applied into a Real-Time Robotic Vision application, thus exemplifying good algorithm design practices, the advantages of employing the “zero copy one pass” methodology and associated trade-offs leading to the selection of a controller platform. The vision tasks under study are: (i recognition of a “flat” signal; and (ii track following, requiring 3D reconstruction. This research firstly improves the used algorithms for the mentioned tasks and finally selects the controller hardware. Optimization for the shown algorithms yielded from 1.5 times to 190 times improvements, always with acceptable quality for the target application, with algorithm optimization being more important on lower computing power platforms. Results also include a 3-cm and five-degree accuracy for lane tracking and 100% accuracy for signalling panel recognition, which are better than most results found in the literature for this application. Clear results comparing different PC platforms for the mentioned Robotic Vision tasks are also shown, demonstrating trade-offs between accuracy and computing power, leading to the proper choice of control platform. The presented design principles are portable to other applications, where Real-Time constraints exist.

  15. Quadrilateral mesh fitting that preserves sharp features based on multi-normals for Laplacian energy

    Yusuke Imai

    2014-04-01

    Full Text Available Because the cost of performance testing using actual products is expensive, manufacturers use lower-cost computer-aided design simulations for this function. In this paper, we propose using hexahedral meshes, which are more accurate than tetrahedral meshes, for finite element analysis. We propose automatic hexahedral mesh generation with sharp features to precisely represent the corresponding features of a target shape. Our hexahedral mesh is generated using a voxel-based algorithm. In our previous works, we fit the surface of the voxels to the target surface using Laplacian energy minimization. We used normal vectors in the fitting to preserve sharp features. However, this method could not represent concave sharp features precisely. In this proposal, we improve our previous Laplacian energy minimization by adding a term that depends on multi-normal vectors instead of using normal vectors. Furthermore, we accentuate a convex/concave surface subset to represent concave sharp features.

  16. Prostate cancer multi-feature analysis using trans-rectal ultrasound images

    Mohamed, S S; Salama, M M A; Kamel, M; El-Saadany, E F; Rizkalla, K; Chin, J

    2005-01-01

    This note focuses on extracting and analysing prostate texture features from trans-rectal ultrasound (TRUS) images for tissue characterization. One of the principal contributions of this investigation is the use of the information of the images' frequency domain features and spatial domain features to attain a more accurate diagnosis. Each image is divided into regions of interest (ROIs) by the Gabor multi-resolution analysis, a crucial stage, in which segmentation is achieved according to the frequency response of the image pixels. The pixels with a similar response to the same filter are grouped to form one ROI. Next, from each ROI two different statistical feature sets are constructed; the first set includes four grey level dependence matrix (GLDM) features and the second set consists of five grey level difference vector (GLDV) features. These constructed feature sets are then ranked by the mutual information feature selection (MIFS) algorithm. Here, the features that provide the maximum mutual information of each feature and class (cancerous and non-cancerous) and the minimum mutual information of the selected features are chosen, yeilding a reduced feature subset. The two constructed feature sets, GLDM and GLDV, as well as the reduced feature subset, are examined in terms of three different classifiers: the condensed k-nearest neighbour (CNN), the decision tree (DT) and the support vector machine (SVM). The accuracy classification results range from 87.5% to 93.75%, where the performance of the SVM and that of the DT are significantly better than the performance of the CNN. (note)

  17. A computational environment for long-term multi-feature and multi-algorithm seizure prediction.

    Teixeira, C A; Direito, B; Costa, R P; Valderrama, M; Feldwisch-Drentrup, H; Nikolopoulos, S; Le Van Quyen, M; Schelter, B; Dourado, A

    2010-01-01

    The daily life of epilepsy patients is constrained by the possibility of occurrence of seizures. Until now, seizures cannot be predicted with sufficient sensitivity and specificity. Most of the seizure prediction studies have been focused on a small number of patients, and frequently assuming unrealistic hypothesis. This paper adopts the view that for an appropriate development of reliable predictors one should consider long-term recordings and several features and algorithms integrated in one software tool. A computational environment, based on Matlab (®), is presented, aiming to be an innovative tool for seizure prediction. It results from the need of a powerful and flexible tool for long-term EEG/ECG analysis by multiple features and algorithms. After being extracted, features can be subjected to several reduction and selection methods, and then used for prediction. The predictions can be conducted based on optimized thresholds or by applying computational intelligence methods. One important aspect is the integrated evaluation of the seizure prediction characteristic of the developed predictors.

  18. Feature-Fusion Guidelines for Image-Based Multi-Modal Biometric Fusion

    Dane Brown

    2017-07-01

    Full Text Available The feature level, unlike the match score level, lacks multi-modal fusion guidelines. This work demonstrates a new approach for improved image-based biometric feature-fusion. The approach extracts and combines the face, fingerprint and palmprint at the feature level for improved human identification accuracy. Feature-fusion guidelines, proposed in our recent work, are extended by adding a new face segmentation method and the support vector machine classifier. The new face segmentation method improves the face identification equal error rate (EER by 10%. The support vector machine classifier combined with the new feature selection approach, proposed in our recent work, outperforms other classifiers when using a single training sample. Feature-fusion guidelines take the form of strengths and weaknesses as observed in the applied feature processing modules during preliminary experiments. The guidelines are used to implement an effective biometric fusion system at the feature level, using a novel feature-fusion methodology, reducing the EER of two groups of three datasets namely: SDUMLA face, SDUMLA fingerprint and IITD palmprint; MUCT Face, MCYT Fingerprint and CASIA Palmprint.

  19. An integrated multi-sensor fusion-based deep feature learning approach for rotating machinery diagnosis

    Liu, Jie; Hu, Youmin; Wang, Yan; Wu, Bo; Fan, Jikai; Hu, Zhongxu

    2018-05-01

    The diagnosis of complicated fault severity problems in rotating machinery systems is an important issue that affects the productivity and quality of manufacturing processes and industrial applications. However, it usually suffers from several deficiencies. (1) A considerable degree of prior knowledge and expertise is required to not only extract and select specific features from raw sensor signals, and but also choose a suitable fusion for sensor information. (2) Traditional artificial neural networks with shallow architectures are usually adopted and they have a limited ability to learn the complex and variable operating conditions. In multi-sensor-based diagnosis applications in particular, massive high-dimensional and high-volume raw sensor signals need to be processed. In this paper, an integrated multi-sensor fusion-based deep feature learning (IMSFDFL) approach is developed to identify the fault severity in rotating machinery processes. First, traditional statistics and energy spectrum features are extracted from multiple sensors with multiple channels and combined. Then, a fused feature vector is constructed from all of the acquisition channels. Further, deep feature learning with stacked auto-encoders is used to obtain the deep features. Finally, the traditional softmax model is applied to identify the fault severity. The effectiveness of the proposed IMSFDFL approach is primarily verified by a one-stage gearbox experimental platform that uses several accelerometers under different operating conditions. This approach can identify fault severity more effectively than the traditional approaches.

  20. Cancer microarray data feature selection using multi-objective binary particle swarm optimization algorithm

    Annavarapu, Chandra Sekhara Rao; Dara, Suresh; Banka, Haider

    2016-01-01

    Cancer investigations in microarray data play a major role in cancer analysis and the treatment. Cancer microarray data consists of complex gene expressed patterns of cancer. In this article, a Multi-Objective Binary Particle Swarm Optimization (MOBPSO) algorithm is proposed for analyzing cancer gene expression data. Due to its high dimensionality, a fast heuristic based pre-processing technique is employed to reduce some of the crude domain features from the initial feature set. Since these pre-processed and reduced features are still high dimensional, the proposed MOBPSO algorithm is used for finding further feature subsets. The objective functions are suitably modeled by optimizing two conflicting objectives i.e., cardinality of feature subsets and distinctive capability of those selected subsets. As these two objective functions are conflicting in nature, they are more suitable for multi-objective modeling. The experiments are carried out on benchmark gene expression datasets, i.e., Colon, Lymphoma and Leukaemia available in literature. The performance of the selected feature subsets with their classification accuracy and validated using 10 fold cross validation techniques. A detailed comparative study is also made to show the betterment or competitiveness of the proposed algorithm. PMID:27822174

  1. Study on the construction of multi-dimensional Remote Sensing feature space for hydrological drought

    Xiang, Daxiang; Tan, Debao; Wen, Xiongfei; Shen, Shaohong; Li, Zhe; Cui, Yuanlai

    2014-01-01

    Hydrological drought refers to an abnormal water shortage caused by precipitation and surface water shortages or a groundwater imbalance. Hydrological drought is reflected in a drop of surface water, decrease of vegetation productivity, increase of temperature difference between day and night and so on. Remote sensing permits the observation of surface water, vegetation, temperature and other information from a macro perspective. This paper analyzes the correlation relationship and differentiation of both remote sensing and surface measured indicators, after the selection and extraction a series of representative remote sensing characteristic parameters according to the spectral characterization of surface features in remote sensing imagery, such as vegetation index, surface temperature and surface water from HJ-1A/B CCD/IRS data. Finally, multi-dimensional remote sensing features such as hydrological drought are built on a intelligent collaborative model. Further, for the Dong-ting lake area, two drought events are analyzed for verification of multi-dimensional features using remote sensing data with different phases and field observation data. The experiments results proved that multi-dimensional features are a good method for hydrological drought

  2. Exploiting Higher Order and Multi-modal Features for 3D Object Detection

    Kiforenko, Lilita

    that describe object visual appearance such as shape, colour, texture etc. This thesis focuses on robust object detection and pose estimation of rigid objects using 3D information. The thesis main contributions are novel feature descriptors together with object detection and pose estimation algorithms....... The initial work introduces a feature descriptor that uses edge categorisation in combination with a local multi-modal histogram descriptor in order to detect objects with little or no texture or surface variation. The comparison is performed with a state-of-the-art method, which is outperformed...... of the methods work well for one type of objects in a specific scenario, in another scenario or with different objects they might fail, therefore more robust solutions are required. The typical problem solution is the design of robust feature descriptors, where feature descriptors contain information...

  3. Generating description with multi-feature fusion and saliency maps of image

    Liu, Lisha; Ding, Yuxuan; Tian, Chunna; Yuan, Bo

    2018-04-01

    Generating description for an image can be regard as visual understanding. It is across artificial intelligence, machine learning, natural language processing and many other areas. In this paper, we present a model that generates description for images based on RNN (recurrent neural network) with object attention and multi-feature of images. The deep recurrent neural networks have excellent performance in machine translation, so we use it to generate natural sentence description for images. The proposed method uses single CNN (convolution neural network) that is trained on ImageNet to extract image features. But we think it can not adequately contain the content in images, it may only focus on the object area of image. So we add scene information to image feature using CNN which is trained on Places205. Experiments show that model with multi-feature extracted by two CNNs perform better than which with a single feature. In addition, we make saliency weights on images to emphasize the salient objects in images. We evaluate our model on MSCOCO based on public metrics, and the results show that our model performs better than several state-of-the-art methods.

  4. A novel framework for feature extraction in multi-sensor action potential sorting.

    Wu, Shun-Chi; Swindlehurst, A Lee; Nenadic, Zoran

    2015-09-30

    Extracellular recordings of multi-unit neural activity have become indispensable in neuroscience research. The analysis of the recordings begins with the detection of the action potentials (APs), followed by a classification step where each AP is associated with a given neural source. A feature extraction step is required prior to classification in order to reduce the dimensionality of the data and the impact of noise, allowing source clustering algorithms to work more efficiently. In this paper, we propose a novel framework for multi-sensor AP feature extraction based on the so-called Matched Subspace Detector (MSD), which is shown to be a natural generalization of standard single-sensor algorithms. Clustering using both simulated data and real AP recordings taken in the locust antennal lobe demonstrates that the proposed approach yields features that are discriminatory and lead to promising results. Unlike existing methods, the proposed algorithm finds joint spatio-temporal feature vectors that match the dominant subspace observed in the two-dimensional data without needs for a forward propagation model and AP templates. The proposed MSD approach provides more discriminatory features for unsupervised AP sorting applications. Copyright © 2015 Elsevier B.V. All rights reserved.

  5. Solving multi-objective job shop problem using nature-based algorithms: new Pareto approximation features

    Jarosław Rudy

    2015-01-01

    Full Text Available In this paper the job shop scheduling problem (JSP with minimizing two criteria simultaneously is considered. JSP is frequently used model in real world applications of combinatorial optimization. Multi-objective job shop problems (MOJSP were rarely studied. We implement and compare two multi-agent nature-based methods, namely ant colony optimization (ACO and genetic algorithm (GA for MOJSP. Both of those methods employ certain technique, taken from the multi-criteria decision analysis in order to establish ranking of solutions. ACO and GA differ in a method of keeping information about previously found solutions and their quality, which affects the course of the search. In result, new features of Pareto approximations provided by said algorithms are observed: aside from the slight superiority of the ACO method the Pareto frontier approximations provided by both methods are disjoint sets. Thus, both methods can be used to search mutually exclusive areas of the Pareto frontier.

  6. Electronic nose with a new feature reduction method and a multi-linear classifier for Chinese liquor classification

    Jing, Yaqi; Meng, Qinghao, E-mail: qh-meng@tju.edu.cn; Qi, Peifeng; Zeng, Ming; Li, Wei; Ma, Shugen [Tianjin Key Laboratory of Process Measurement and Control, Institute of Robotics and Autonomous Systems, School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072 (China)

    2014-05-15

    An electronic nose (e-nose) was designed to classify Chinese liquors of the same aroma style. A new method of feature reduction which combined feature selection with feature extraction was proposed. Feature selection method used 8 feature-selection algorithms based on information theory and reduced the dimension of the feature space to 41. Kernel entropy component analysis was introduced into the e-nose system as a feature extraction method and the dimension of feature space was reduced to 12. Classification of Chinese liquors was performed by using back propagation artificial neural network (BP-ANN), linear discrimination analysis (LDA), and a multi-linear classifier. The classification rate of the multi-linear classifier was 97.22%, which was higher than LDA and BP-ANN. Finally the classification of Chinese liquors according to their raw materials and geographical origins was performed using the proposed multi-linear classifier and classification rate was 98.75% and 100%, respectively.

  7. Electronic nose with a new feature reduction method and a multi-linear classifier for Chinese liquor classification

    Jing, Yaqi; Meng, Qinghao; Qi, Peifeng; Zeng, Ming; Li, Wei; Ma, Shugen

    2014-01-01

    An electronic nose (e-nose) was designed to classify Chinese liquors of the same aroma style. A new method of feature reduction which combined feature selection with feature extraction was proposed. Feature selection method used 8 feature-selection algorithms based on information theory and reduced the dimension of the feature space to 41. Kernel entropy component analysis was introduced into the e-nose system as a feature extraction method and the dimension of feature space was reduced to 12. Classification of Chinese liquors was performed by using back propagation artificial neural network (BP-ANN), linear discrimination analysis (LDA), and a multi-linear classifier. The classification rate of the multi-linear classifier was 97.22%, which was higher than LDA and BP-ANN. Finally the classification of Chinese liquors according to their raw materials and geographical origins was performed using the proposed multi-linear classifier and classification rate was 98.75% and 100%, respectively

  8. Electrical memory features of ferromagnetic CoFeAlSi nano-particles embedded in metal-oxide-semiconductor matrix

    Lee, Ja Bin; Kim, Ki Woong; Lee, Jun Seok; An, Gwang Guk; Hong, Jin Pyo

    2011-01-01

    Half-metallic Heusler material Co 2 FeAl 0.5 Si 0.5 (CFAS) nano-particles (NPs) embedded in metal-oxide-semiconductor (MOS) structures with thin HfO 2 tunneling and MgO control oxides were investigated. The CFAS NPs were prepared by rapid thermal annealing. The formation of well-controlled CFAS NPs on thin HfO 2 tunneling oxide was confirmed by atomic force microscopy (AFM). Memory characteristics of CFAS NPs in MOS devices exhibited a large memory window of 4.65 V, as well as good retention and endurance times of 10 5 cycles and 10 9 s, respectively, demonstrating the potential of CFAS NPs as promising candidates for use in charge storage.

  9. Arc-welding quality assurance by means of embedded fiber sensor and spectral processing combining feature selection and neural networks

    Mirapeix, J.; García-Allende, P. B.; Cobo, A.; Conde, O.; López-Higuera, J. M.

    2007-07-01

    A new spectral processing technique designed for its application in the on-line detection and classification of arc-welding defects is presented in this paper. A non-invasive fiber sensor embedded within a TIG torch collects the plasma radiation originated during the welding process. The spectral information is then processed by means of two consecutive stages. A compression algorithm is first applied to the data allowing real-time analysis. The selected spectral bands are then used to feed a classification algorithm, which will be demonstrated to provide an efficient weld defect detection and classification. The results obtained with the proposed technique are compared to a similar processing scheme presented in a previous paper, giving rise to an improvement in the performance of the monitoring system.

  10. Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models

    Khalvati, Farzad; Wong, Alexander; Haider, Masoom A.

    2015-01-01

    Prostate cancer is the most common form of cancer and the second leading cause of cancer death in North America. Auto-detection of prostate cancer can play a major role in early detection of prostate cancer, which has a significant impact on patient survival rates. While multi-parametric magnetic resonance imaging (MP-MRI) has shown promise in diagnosis of prostate cancer, the existing auto-detection algorithms do not take advantage of abundance of data available in MP-MRI to improve detection accuracy. The goal of this research was to design a radiomics-based auto-detection method for prostate cancer via utilizing MP-MRI data. In this work, we present new MP-MRI texture feature models for radiomics-driven detection of prostate cancer. In addition to commonly used non-invasive imaging sequences in conventional MP-MRI, namely T2-weighted MRI (T2w) and diffusion-weighted imaging (DWI), our proposed MP-MRI texture feature models incorporate computed high-b DWI (CHB-DWI) and a new diffusion imaging modality called correlated diffusion imaging (CDI). Moreover, the proposed texture feature models incorporate features from individual b-value images. A comprehensive set of texture features was calculated for both the conventional MP-MRI and new MP-MRI texture feature models. We performed feature selection analysis for each individual modality and then combined best features from each modality to construct the optimized texture feature models. The performance of the proposed MP-MRI texture feature models was evaluated via leave-one-patient-out cross-validation using a support vector machine (SVM) classifier trained on 40,975 cancerous and healthy tissue samples obtained from real clinical MP-MRI datasets. The proposed MP-MRI texture feature models outperformed the conventional model (i.e., T2w+DWI) with regard to cancer detection accuracy. Comprehensive texture feature models were developed for improved radiomics-driven detection of prostate cancer using MP-MRI. Using a

  11. A Hierarchical Feature Extraction Model for Multi-Label Mechanical Patent Classification

    Jie Hu

    2018-01-01

    Full Text Available Various studies have focused on feature extraction methods for automatic patent classification in recent years. However, most of these approaches are based on the knowledge from experts in related domains. Here we propose a hierarchical feature extraction model (HFEM for multi-label mechanical patent classification, which is able to capture both local features of phrases as well as global and temporal semantics. First, a n-gram feature extractor based on convolutional neural networks (CNNs is designed to extract salient local lexical-level features. Next, a long dependency feature extraction model based on the bidirectional long–short-term memory (BiLSTM neural network model is proposed to capture sequential correlations from higher-level sequence representations. Then the HFEM algorithm and its hierarchical feature extraction architecture are detailed. We establish the training, validation and test datasets, containing 72,532, 18,133, and 2679 mechanical patent documents, respectively, and then check the performance of HFEMs. Finally, we compared the results of the proposed HFEM and three other single neural network models, namely CNN, long–short-term memory (LSTM, and BiLSTM. The experimental results indicate that our proposed HFEM outperforms the other compared models in both precision and recall.

  12. Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System

    Hongqiang Li

    2016-10-01

    Full Text Available Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias.

  13. Classification of Focal and Non Focal Epileptic Seizures Using Multi-Features and SVM Classifier.

    Sriraam, N; Raghu, S

    2017-09-02

    Identifying epileptogenic zones prior to surgery is an essential and crucial step in treating patients having pharmacoresistant focal epilepsy. Electroencephalogram (EEG) is a significant measurement benchmark to assess patients suffering from epilepsy. This paper investigates the application of multi-features derived from different domains to recognize the focal and non focal epileptic seizures obtained from pharmacoresistant focal epilepsy patients from Bern Barcelona database. From the dataset, five different classification tasks were formed. Total 26 features were extracted from focal and non focal EEG. Significant features were selected using Wilcoxon rank sum test by setting p-value (p z > 1.96) at 95% significance interval. Hypothesis was made that the effect of removing outliers improves the classification accuracy. Turkey's range test was adopted for pruning outliers from feature set. Finally, 21 features were classified using optimized support vector machine (SVM) classifier with 10-fold cross validation. Bayesian optimization technique was adopted to minimize the cross-validation loss. From the simulation results, it was inferred that the highest sensitivity, specificity, and classification accuracy of 94.56%, 89.74%, and 92.15% achieved respectively and found to be better than the state-of-the-art approaches. Further, it was observed that the classification accuracy improved from 80.2% with outliers to 92.15% without outliers. The classifier performance metrics ensures the suitability of the proposed multi-features with optimized SVM classifier. It can be concluded that the proposed approach can be applied for recognition of focal EEG signals to localize epileptogenic zones.

  14. Multi-level gene/MiRNA feature selection using deep belief nets and active learning.

    Ibrahim, Rania; Yousri, Noha A; Ismail, Mohamed A; El-Makky, Nagwa M

    2014-01-01

    Selecting the most discriminative genes/miRNAs has been raised as an important task in bioinformatics to enhance disease classifiers and to mitigate the dimensionality curse problem. Original feature selection methods choose genes/miRNAs based on their individual features regardless of how they perform together. Considering group features instead of individual ones provides a better view for selecting the most informative genes/miRNAs. Recently, deep learning has proven its ability in representing the data in multiple levels of abstraction, allowing for better discrimination between different classes. However, the idea of using deep learning for feature selection is not widely used in the bioinformatics field yet. In this paper, a novel multi-level feature selection approach named MLFS is proposed for selecting genes/miRNAs based on expression profiles. The approach is based on both deep and active learning. Moreover, an extension to use the technique for miRNAs is presented by considering the biological relation between miRNAs and genes. Experimental results show that the approach was able to outperform classical feature selection methods in hepatocellular carcinoma (HCC) by 9%, lung cancer by 6% and breast cancer by around 10% in F1-measure. Results also show the enhancement in F1-measure of our approach over recently related work in [1] and [2].

  15. MULTI-SCALE SEGMENTATION OF HIGH RESOLUTION REMOTE SENSING IMAGES BY INTEGRATING MULTIPLE FEATURES

    Y. Di

    2017-05-01

    Full Text Available Most of multi-scale segmentation algorithms are not aiming at high resolution remote sensing images and have difficulty to communicate and use layers’ information. In view of them, we proposes a method of multi-scale segmentation of high resolution remote sensing images by integrating multiple features. First, Canny operator is used to extract edge information, and then band weighted distance function is built to obtain the edge weight. According to the criterion, the initial segmentation objects of color images can be gained by Kruskal minimum spanning tree algorithm. Finally segmentation images are got by the adaptive rule of Mumford–Shah region merging combination with spectral and texture information. The proposed method is evaluated precisely using analog images and ZY-3 satellite images through quantitative and qualitative analysis. The experimental results show that the multi-scale segmentation of high resolution remote sensing images by integrating multiple features outperformed the software eCognition fractal network evolution algorithm (highest-resolution network evolution that FNEA on the accuracy and slightly inferior to FNEA on the efficiency.

  16. Processing of word stress related acoustic information: A multi-feature MMN study.

    Honbolygó, Ferenc; Kolozsvári, Orsolya; Csépe, Valéria

    2017-08-01

    In the present study, we investigated the processing of word stress related acoustic features in a word context. In a passive oddball multi-feature MMN experiment, we presented a disyllabic pseudo-word with two acoustically similar syllables as standard stimulus, and five contrasting deviants that differed from the standard in that they were either stressed on the first syllable or contained a vowel change. Stress was realized by an increase of f0, intensity, vowel duration or consonant duration. The vowel change was used to investigate if phonemic and prosodic changes elicit different MMN components. As a control condition, we presented non-speech counterparts of the speech stimuli. Results showed all but one feature (non-speech intensity deviant) eliciting the MMN component, which was larger for speech compared to non-speech stimuli. Two other components showed stimulus related effects: the N350 and the LDN (Late Discriminative Negativity). The N350 appeared to the vowel duration and consonant duration deviants, specifically to features related to the temporal characteristics of stimuli, while the LDN was present for all features, and it was larger for speech than for non-speech stimuli. We also found that the f0 and consonant duration features elicited a larger MMN than other features. These results suggest that stress as a phonological feature is processed based on long-term representations, and listeners show a specific sensitivity to segmental and suprasegmental cues signaling the prosodic boundaries of words. These findings support a two-stage model in the perception of stress and phoneme related acoustical information. Copyright © 2017 Elsevier B.V. All rights reserved.

  17. The comparison of MSCT multi-phase scan features between benign prostatic hyperplasia and prostate cancer

    Liu Jingang; Wang Xizhen; Niu Qingliang; Lu Hongkai; Wang Bin

    2009-01-01

    Objective: To investigate the multi-phase contrast-enhanced features of multi-slice computed tomography (MSCT) of benign prostatic hyperplasia (BPH) and prostate cancer (PCa). Methods: Thirty-five BPH and twenty- seven PCa were examined with multi-phase contrast-enhanced MSCT scan. The peak time, maximum attenuation value (MAV) and time density curve (TDC) were recorded, and the slope of the contrast media uptake curve was calculated. Result: Significant differences between BPH and PCa in the type of the curves and the peak time were observed (P<0.01). The slopes of BPH and PCa were 0.45+0.25 and 0.7 6+0.34 respectively, the slope of PCa was higher than that of BPH (P<0.05). MAVs of BPH and PCa were (44.057±10.261) HU and (46.778±11.140) HU respectively, and there was no significant difference between them (P>0.05). Conclusion: The multi-phase MSCT scan can reflect the blood supply and enhancement characters of BPH and PCa, which are important in detection and differential diagnosis of the prostate diseases. (authors)

  18. Using medical history embedded in biometrics medical card for user identity authentication: privacy preserving authentication model by features matching.

    Fong, Simon; Zhuang, Yan

    2012-01-01

    Many forms of biometrics have been proposed and studied for biometrics authentication. Recently researchers are looking into longitudinal pattern matching that based on more than just a singular biometrics; data from user's activities are used to characterise the identity of a user. In this paper we advocate a novel type of authentication by using a user's medical history which can be electronically stored in a biometric security card. This is a sequel paper from our previous work about defining abstract format of medical data to be queried and tested upon authentication. The challenge to overcome is preserving the user's privacy by choosing only the useful features from the medical data for use in authentication. The features should contain less sensitive elements and they are implicitly related to the target illness. Therefore exchanging questions and answers about a few carefully chosen features in an open channel would not easily or directly expose the illness, but yet it can verify by inference whether the user has a record of it stored in his smart card. The design of a privacy preserving model by backward inference is introduced in this paper. Some live medical data are used in experiments for validation and demonstration.

  19. Using Medical History Embedded in Biometrics Medical Card for User Identity Authentication: Privacy Preserving Authentication Model by Features Matching

    Simon Fong

    2012-01-01

    Full Text Available Many forms of biometrics have been proposed and studied for biometrics authentication. Recently researchers are looking into longitudinal pattern matching that based on more than just a singular biometrics; data from user’s activities are used to characterise the identity of a user. In this paper we advocate a novel type of authentication by using a user’s medical history which can be electronically stored in a biometric security card. This is a sequel paper from our previous work about defining abstract format of medical data to be queried and tested upon authentication. The challenge to overcome is preserving the user’s privacy by choosing only the useful features from the medical data for use in authentication. The features should contain less sensitive elements and they are implicitly related to the target illness. Therefore exchanging questions and answers about a few carefully chosen features in an open channel would not easily or directly expose the illness, but yet it can verify by inference whether the user has a record of it stored in his smart card. The design of a privacy preserving model by backward inference is introduced in this paper. Some live medical data are used in experiments for validation and demonstration.

  20. An effective method for cirrhosis recognition based on multi-feature fusion

    Chen, Yameng; Sun, Gengxin; Lei, Yiming; Zhang, Jinpeng

    2018-04-01

    Liver disease is one of the main causes of human healthy problem. Cirrhosis, of course, is the critical phase during the development of liver lesion, especially the hepatoma. Many clinical cases are still influenced by the subjectivity of physicians in some degree, and some objective factors such as illumination, scale, edge blurring will affect the judgment of clinicians. Then the subjectivity will affect the accuracy of diagnosis and the treatment of patients. In order to solve the difficulty above and improve the recognition rate of liver cirrhosis, we propose a method of multi-feature fusion to obtain more robust representations of texture in ultrasound liver images, the texture features we extract include local binary pattern(LBP), gray level co-occurrence matrix(GLCM) and histogram of oriented gradient(HOG). In this paper, we firstly make a fusion of multi-feature to recognize cirrhosis and normal liver based on parallel combination concept, and the experimental results shows that the classifier is effective for cirrhosis recognition which is evaluated by the satisfying classification rate, sensitivity and specificity of receiver operating characteristic(ROC), and cost time. Through the method we proposed, it will be helpful to improve the accuracy of diagnosis of cirrhosis and prevent the development of liver lesion towards hepatoma.

  1. Modern system architectures in embedded systems

    Korhonen, T.

    2012-01-01

    Several new technologies are making their way also in embedded systems. In addition to the FPGA technology which has become commonplace, multi-core CPUs and I/O virtualization (the implementation of the tasks of a software hyper-visor in hardware to improve the efficiency) are being introduced to the embedded systems. In this paper we review the trends and discuss how to take advantage of these features in control systems. Some potential application examples like parallelization, data streaming, high-speed data acquisition and virtualization are discussed

  2. A Generic multi-dimensional feature extraction method using multiobjective genetic programming.

    Zhang, Yang; Rockett, Peter I

    2009-01-01

    In this paper, we present a generic feature extraction method for pattern classification using multiobjective genetic programming. This not only evolves the (near-)optimal set of mappings from a pattern space to a multi-dimensional decision space, but also simultaneously optimizes the dimensionality of that decision space. The presented framework evolves vector-to-vector feature extractors that maximize class separability. We demonstrate the efficacy of our approach by making statistically-founded comparisons with a wide variety of established classifier paradigms over a range of datasets and find that for most of the pairwise comparisons, our evolutionary method delivers statistically smaller misclassification errors. At very worst, our method displays no statistical difference in a few pairwise comparisons with established classifier/dataset combinations; crucially, none of the misclassification results produced by our method is worse than any comparator classifier. Although principally focused on feature extraction, feature selection is also performed as an implicit side effect; we show that both feature extraction and selection are important to the success of our technique. The presented method has the practical consequence of obviating the need to exhaustively evaluate a large family of conventional classifiers when faced with a new pattern recognition problem in order to attain a good classification accuracy.

  3. The Speech multi features fusion perceptual hash algorithm based on tensor decomposition

    Huang, Y. B.; Fan, M. H.; Zhang, Q. Y.

    2018-03-01

    With constant progress in modern speech communication technologies, the speech data is prone to be attacked by the noise or maliciously tampered. In order to make the speech perception hash algorithm has strong robustness and high efficiency, this paper put forward a speech perception hash algorithm based on the tensor decomposition and multi features is proposed. This algorithm analyses the speech perception feature acquires each speech component wavelet packet decomposition. LPCC, LSP and ISP feature of each speech component are extracted to constitute the speech feature tensor. Speech authentication is done by generating the hash values through feature matrix quantification which use mid-value. Experimental results showing that the proposed algorithm is robust for content to maintain operations compared with similar algorithms. It is able to resist the attack of the common background noise. Also, the algorithm is highly efficiency in terms of arithmetic, and is able to meet the real-time requirements of speech communication and complete the speech authentication quickly.

  4. Multi-scale Analysis of High Resolution Topography: Feature Extraction and Identification of Landscape Characteristic Scales

    Passalacqua, P.; Sangireddy, H.; Stark, C. P.

    2015-12-01

    With the advent of digital terrain data, detailed information on terrain characteristics and on scale and location of geomorphic features is available over extended areas. Our ability to observe landscapes and quantify topographic patterns has greatly improved, including the estimation of fluxes of mass and energy across landscapes. Challenges still remain in the analysis of high resolution topography data; the presence of features such as roads, for example, challenges classic methods for feature extraction and large data volumes require computationally efficient extraction and analysis methods. Moreover, opportunities exist to define new robust metrics of landscape characterization for landscape comparison and model validation. In this presentation we cover recent research in multi-scale and objective analysis of high resolution topography data. We show how the analysis of the probability density function of topographic attributes such as slope, curvature, and topographic index contains useful information for feature localization and extraction. The analysis of how the distributions change across scales, quantified by the behavior of modal values and interquartile range, allows the identification of landscape characteristic scales, such as terrain roughness. The methods are introduced on synthetic signals in one and two dimensions and then applied to a variety of landscapes of different characteristics. Validation of the methods includes the analysis of modeled landscapes where the noise distribution is known and features of interest easily measured.

  5. Embedded Systems

    Embedded system, micro-con- troller ... Embedded systems differ from general purpose computers in many ... Low cost: As embedded systems are extensively used in con- .... operating systems for the desktop computers where scheduling.

  6. An Indoor Slam Method Based on Kinect and Multi-Feature Extended Information Filter

    Chang, M.; Kang, Z.

    2017-09-01

    Based on the frame of ORB-SLAM in this paper the transformation parameters between adjacent Kinect image frames are computed using ORB keypoints, from which priori information matrix and information vector are calculated. The motion update of multi-feature extended information filter is then realized. According to the point cloud data formed by depth image, ICP algorithm was used to extract the point features of the point cloud data in the scene and built an observation model while calculating a-posteriori information matrix and information vector, and weakening the influences caused by the error accumulation in the positioning process. Furthermore, this paper applied ORB-SLAM frame to realize autonomous positioning in real time in interior unknown environment. In the end, Lidar was used to get data in the scene in order to estimate positioning accuracy put forward in this paper.

  7. AN INDOOR SLAM METHOD BASED ON KINECT AND MULTI-FEATURE EXTENDED INFORMATION FILTER

    M. Chang

    2017-09-01

    Full Text Available Based on the frame of ORB-SLAM in this paper the transformation parameters between adjacent Kinect image frames are computed using ORB keypoints, from which priori information matrix and information vector are calculated. The motion update of multi-feature extended information filter is then realized. According to the point cloud data formed by depth image, ICP algorithm was used to extract the point features of the point cloud data in the scene and built an observation model while calculating a-posteriori information matrix and information vector, and weakening the influences caused by the error accumulation in the positioning process. Furthermore, this paper applied ORB-SLAM frame to realize autonomous positioning in real time in interior unknown environment. In the end, Lidar was used to get data in the scene in order to estimate positioning accuracy put forward in this paper.

  8. Detection of relationships among multi-modal brain imaging meta-features via information flow.

    Miller, Robyn L; Vergara, Victor M; Calhoun, Vince D

    2018-01-15

    Neuroscientists and clinical researchers are awash in data from an ever-growing number of imaging and other bio-behavioral modalities. This flow of brain imaging data, taken under resting and various task conditions, combines with available cognitive measures, behavioral information, genetic data plus other potentially salient biomedical and environmental information to create a rich but diffuse data landscape. The conditions being studied with brain imaging data are often extremely complex and it is common for researchers to employ more than one imaging, behavioral or biological data modality (e.g., genetics) in their investigations. While the field has advanced significantly in its approach to multimodal data, the vast majority of studies still ignore joint information among two or more features or modalities. We propose an intuitive framework based on conditional probabilities for understanding information exchange between features in what we are calling a feature meta-space; that is, a space consisting of many individual featurae spaces. Features can have any dimension and can be drawn from any data source or modality. No a priori assumptions are made about the functional form (e.g., linear, polynomial, exponential) of captured inter-feature relationships. We demonstrate the framework's ability to identify relationships between disparate features of varying dimensionality by applying it to a large multi-site, multi-modal clinical dataset, balance between schizophrenia patients and controls. In our application it exposes both expected (previously observed) relationships, and novel relationships rarely considered investigated by clinical researchers. To the best of our knowledge there is not presently a comparably efficient way to capture relationships of indeterminate functional form between features of arbitrary dimension and type. We are introducing this method as an initial foray into a space that remains relatively underpopulated. The framework we propose is

  9. Joint Facial Action Unit Detection and Feature Fusion: A Multi-conditional Learning Approach.

    Eleftheriadis, Stefanos; Rudovic, Ognjen; Pantic, Maja

    2016-10-05

    Automated analysis of facial expressions can benefit many domains, from marketing to clinical diagnosis of neurodevelopmental disorders. Facial expressions are typically encoded as a combination of facial muscle activations, i.e., action units. Depending on context, these action units co-occur in specific patterns, and rarely in isolation. Yet, most existing methods for automatic action unit detection fail to exploit dependencies among them, and the corresponding facial features. To address this, we propose a novel multi-conditional latent variable model for simultaneous fusion of facial features and joint action unit detection. Specifically, the proposed model performs feature fusion in a generative fashion via a low-dimensional shared subspace, while simultaneously performing action unit detection using a discriminative classification approach. We show that by combining the merits of both approaches, the proposed methodology outperforms existing purely discriminative/generative methods for the target task. To reduce the number of parameters, and avoid overfitting, a novel Bayesian learning approach based on Monte Carlo sampling is proposed, to integrate out the shared subspace. We validate the proposed method on posed and spontaneous data from three publicly available datasets (CK+, DISFA and Shoulder-pain), and show that both feature fusion and joint learning of action units leads to improved performance compared to the state-of-the-art methods for the task.

  10. Utilizing Multi-Field Text Features for Efficient Email Spam Filtering

    Wuying Liu

    2012-06-01

    Full Text Available Large-scale spam emails cause a serious waste of time and resources. This paper investigates the text features of email documents and the feature noises among multi-field texts, resulting in an observation of a power law distribution of feature strings within each text field. According to the observation, we propose an efficient filtering approach including a compound weight method and a lightweight field text classification algorithm. The compound weight method considers both the historical classifying ability of each field classifier and the classifying contribution of each text field in the current classified email. The lightweight field text classification algorithm straightforwardly calculates the arithmetical average of multiple conditional probabilities predicted from feature strings according to a string-frequency index for labeled emails storing. The string-frequency index structure has a random-sampling-based compressible property owing to the power law distribution and can largely reduce the storage space. The experimental results in the TREC spam track show that the proposed approach can complete the filtering task in low space cost and high speed, whose overall performance 1-ROCA exceeds the best one among the participators at the trec07p evaluation.

  11. Predicting human splicing branchpoints by combining sequence-derived features and multi-label learning methods.

    Zhang, Wen; Zhu, Xiaopeng; Fu, Yu; Tsuji, Junko; Weng, Zhiping

    2017-12-01

    Alternative splicing is the critical process in a single gene coding, which removes introns and joins exons, and splicing branchpoints are indicators for the alternative splicing. Wet experiments have identified a great number of human splicing branchpoints, but many branchpoints are still unknown. In order to guide wet experiments, we develop computational methods to predict human splicing branchpoints. Considering the fact that an intron may have multiple branchpoints, we transform the branchpoint prediction as the multi-label learning problem, and attempt to predict branchpoint sites from intron sequences. First, we investigate a variety of intron sequence-derived features, such as sparse profile, dinucleotide profile, position weight matrix profile, Markov motif profile and polypyrimidine tract profile. Second, we consider several multi-label learning methods: partial least squares regression, canonical correlation analysis and regularized canonical correlation analysis, and use them as the basic classification engines. Third, we propose two ensemble learning schemes which integrate different features and different classifiers to build ensemble learning systems for the branchpoint prediction. One is the genetic algorithm-based weighted average ensemble method; the other is the logistic regression-based ensemble method. In the computational experiments, two ensemble learning methods outperform benchmark branchpoint prediction methods, and can produce high-accuracy results on the benchmark dataset.

  12. Flexible feature-space-construction architecture and its VLSI implementation for multi-scale object detection

    Luo, Aiwen; An, Fengwei; Zhang, Xiangyu; Chen, Lei; Huang, Zunkai; Jürgen Mattausch, Hans

    2018-04-01

    Feature extraction techniques are a cornerstone of object detection in computer-vision-based applications. The detection performance of vison-based detection systems is often degraded by, e.g., changes in the illumination intensity of the light source, foreground-background contrast variations or automatic gain control from the camera. In order to avoid such degradation effects, we present a block-based L1-norm-circuit architecture which is configurable for different image-cell sizes, cell-based feature descriptors and image resolutions according to customization parameters from the circuit input. The incorporated flexibility in both the image resolution and the cell size for multi-scale image pyramids leads to lower computational complexity and power consumption. Additionally, an object-detection prototype for performance evaluation in 65 nm CMOS implements the proposed L1-norm circuit together with a histogram of oriented gradients (HOG) descriptor and a support vector machine (SVM) classifier. The proposed parallel architecture with high hardware efficiency enables real-time processing, high detection robustness, small chip-core area as well as low power consumption for multi-scale object detection.

  13. Embedding EfS in Teacher Education through a Multi-Level Systems Approach: Lessons from Queensland

    Evans, Neus; Ferreira, Jo-Anne; Davis, Julie; Stevenson, Robert B.

    2016-01-01

    This article reports on the fourth stage of an evolving study to develop a systems model for embedding education for sustainability (EfS) into preservice teacher education. The fourth stage trialled the extension of the model to a comprehensive state-wide systems approach involving representatives from all eight Queensland teacher education…

  14. Identification of Subtype-Specific Prognostic Genes for Early-Stage Lung Adenocarcinoma and Squamous Cell Carcinoma Patients Using an Embedded Feature Selection Algorithm.

    Suyan Tian

    Full Text Available The existence of fundamental differences between lung adenocarcinoma (AC and squamous cell carcinoma (SCC in their underlying mechanisms motivated us to postulate that specific genes might exist relevant to prognosis of each histology subtype. To test on this research hypothesis, we previously proposed a simple Cox-regression model based feature selection algorithm and identified successfully some subtype-specific prognostic genes when applying this method to real-world data. In this article, we continue our effort on identification of subtype-specific prognostic genes for AC and SCC, and propose a novel embedded feature selection method by extending Threshold Gradient Descent Regularization (TGDR algorithm and minimizing on a corresponding negative partial likelihood function. Using real-world datasets and simulated ones, we show these two proposed methods have comparable performance whereas the new proposal is superior in terms of model parsimony. Our analysis provides some evidence on the existence of such subtype-specific prognostic genes, more investigation is warranted.

  15. Embedded Leverage

    Frazzini, Andrea; Heje Pedersen, Lasse

    find that asset classes with embedded leverage offer low risk-adjusted returns and, in the cross-section, higher embedded leverage is associated with lower returns. A portfolio which is long low-embedded-leverage securities and short high-embedded-leverage securities earns large abnormal returns...

  16. a Framework of Change Detection Based on Combined Morphologica Features and Multi-Index Classification

    Li, S.; Zhang, S.; Yang, D.

    2017-09-01

    Remote sensing images are particularly well suited for analysis of land cover change. In this paper, we present a new framework for detection of changing land cover using satellite imagery. Morphological features and a multi-index are used to extract typical objects from the imagery, including vegetation, water, bare land, buildings, and roads. Our method, based on connected domains, is different from traditional methods; it uses image segmentation to extract morphological features, while the enhanced vegetation index (EVI), the differential water index (NDWI) are used to extract vegetation and water, and a fragmentation index is used to the correct extraction results of water. HSV transformation and threshold segmentation extract and remove the effects of shadows on extraction results. Change detection is performed on these results. One of the advantages of the proposed framework is that semantic information is extracted automatically using low-level morphological features and indexes. Another advantage is that the proposed method detects specific types of change without any training samples. A test on ZY-3 images demonstrates that our framework has a promising capability to detect change.

  17. A FRAMEWORK OF CHANGE DETECTION BASED ON COMBINED MORPHOLOGICA FEATURES AND MULTI-INDEX CLASSIFICATION

    S. Li

    2017-09-01

    Full Text Available Remote sensing images are particularly well suited for analysis of land cover change. In this paper, we present a new framework for detection of changing land cover using satellite imagery. Morphological features and a multi-index are used to extract typical objects from the imagery, including vegetation, water, bare land, buildings, and roads. Our method, based on connected domains, is different from traditional methods; it uses image segmentation to extract morphological features, while the enhanced vegetation index (EVI, the differential water index (NDWI are used to extract vegetation and water, and a fragmentation index is used to the correct extraction results of water. HSV transformation and threshold segmentation extract and remove the effects of shadows on extraction results. Change detection is performed on these results. One of the advantages of the proposed framework is that semantic information is extracted automatically using low-level morphological features and indexes. Another advantage is that the proposed method detects specific types of change without any training samples. A test on ZY-3 images demonstrates that our framework has a promising capability to detect change.

  18. Automatic Sleep Staging using Multi-dimensional Feature Extraction and Multi-kernel Fuzzy Support Vector Machine

    Yanjun Zhang

    2014-01-01

    Full Text Available This paper employed the clinical Polysomnographic (PSG data, mainly including all-night Electroencephalogram (EEG, Electrooculogram (EOG and Electromyogram (EMG signals of subjects, and adopted the American Academy of Sleep Medicine (AASM clinical staging manual as standards to realize automatic sleep staging. Authors extracted eighteen different features of EEG, EOG and EMG in time domains and frequency domains to construct the vectors according to the existing literatures as well as clinical experience. By adopting sleep samples self-learning, the linear combination of weights and parameters of multiple kernels of the fuzzy support vector machine (FSVM were learned and the multi-kernel FSVM (MK-FSVM was constructed. The overall agreement between the experts' scores and the results presented was 82.53%. Compared with previous results, the accuracy of N1 was improved to some extent while the accuracies of other stages were approximate, which well reflected the sleep structure. The staging algorithm proposed in this paper is transparent, and worth further investigation.

  19. A comparative study of different feature sets for recognition of handwritten Arabic numerals using a Multi Layer Perceptron

    Das, Nibaran; Mollah, Ayatullah Faruk; Sarkar, Ram; Basu, Subhadip

    2010-01-01

    The work presents a comparative assessment of seven different feature sets for recognition of handwritten Arabic numerals using a Multi Layer Perceptron (MLP) based classifier. The seven feature sets employed here consist of shadow features, octant centroids, longest runs, angular distances, effective spans, dynamic centers of gravity, and some of their combinations. On experimentation with a database of 3000 samples, the maximum recognition rate of 95.80% is observed with both of two separat...

  20. A multi-feature integration method for fatigue crack detection and crack length estimation in riveted lap joints using Lamb waves

    He, Jingjing; Guan, Xuefei; Peng, Tishun; Liu, Yongming; Saxena, Abhinav; Celaya, Jose; Goebel, Kai

    2013-10-01

    This paper presents an experimental study of damage detection and quantification in riveted lap joints. Embedded lead zirconate titanate piezoelectric (PZT) ceramic wafer-type sensors are employed to perform in situ non-destructive evaluation (NDE) during fatigue cyclical loading. PZT wafers are used to monitor the wave reflection from the boundaries of the fatigue crack at the edge of bolt joints. The group velocity of the guided wave is calculated to select a proper time window in which the received signal contains the damage information. It is found that the fatigue crack lengths are correlated with three main features of the signal, i.e., correlation coefficient, amplitude change, and phase change. It was also observed that a single feature cannot be used to quantify the damage among different specimens since a considerable variability was observed in the response from different specimens. A multi-feature integration method based on a second-order multivariate regression analysis is proposed for the prediction of fatigue crack lengths using sensor measurements. The model parameters are obtained using training datasets from five specimens. The effectiveness of the proposed methodology is demonstrated using several lap joint specimens from different manufactures and under different loading conditions.

  1. A multi-feature integration method for fatigue crack detection and crack length estimation in riveted lap joints using Lamb waves

    He, Jingjing; Guan, Xuefei; Peng, Tishun; Liu, Yongming; Saxena, Abhinav; Celaya, Jose; Goebel, Kai

    2013-01-01

    This paper presents an experimental study of damage detection and quantification in riveted lap joints. Embedded lead zirconate titanate piezoelectric (PZT) ceramic wafer-type sensors are employed to perform in situ non-destructive evaluation (NDE) during fatigue cyclical loading. PZT wafers are used to monitor the wave reflection from the boundaries of the fatigue crack at the edge of bolt joints. The group velocity of the guided wave is calculated to select a proper time window in which the received signal contains the damage information. It is found that the fatigue crack lengths are correlated with three main features of the signal, i.e., correlation coefficient, amplitude change, and phase change. It was also observed that a single feature cannot be used to quantify the damage among different specimens since a considerable variability was observed in the response from different specimens. A multi-feature integration method based on a second-order multivariate regression analysis is proposed for the prediction of fatigue crack lengths using sensor measurements. The model parameters are obtained using training datasets from five specimens. The effectiveness of the proposed methodology is demonstrated using several lap joint specimens from different manufactures and under different loading conditions. (paper)

  2. The Interaction Features of the Multi-Level Retaining Walls with Soil Mass

    Boyko Igor

    2017-09-01

    Full Text Available The interaction features of multi-level retaining walls with soil base were researched by changing their geometric parameters and locality at the plan. During excavation of deep foundation pits it is important to choose the type of constructions which influences on the horizontal displacements. The distance between the levels of retaining walls should be based on the results of numerical modelling. The objective of this paper is to present a comparison between the data of numerical simulations and the results of the in-situ lateral tests of couple piles. The problems have been solved by using the following soil models: Coulomb-Mohr model; model, which is based on the dilatation theory; elastic-plastic model with variable stiffness parameters.

  3. The Interaction Features of the Multi-Level Retaining Walls with Soil Mass

    Boyko, Igor; Skochko, Liudmyla; Zhuk, Veronica

    2017-09-01

    The interaction features of multi-level retaining walls with soil base were researched by changing their geometric parameters and locality at the plan. During excavation of deep foundation pits it is important to choose the type of constructions which influences on the horizontal displacements. The distance between the levels of retaining walls should be based on the results of numerical modelling. The objective of this paper is to present a comparison between the data of numerical simulations and the results of the in-situ lateral tests of couple piles. The problems have been solved by using the following soil models: Coulomb-Mohr model; model, which is based on the dilatation theory; elastic-plastic model with variable stiffness parameters.

  4. AUTOMATIC GENERALIZABILITY METHOD OF URBAN DRAINAGE PIPE NETWORK CONSIDERING MULTI-FEATURES

    S. Zhu

    2018-05-01

    Full Text Available Urban drainage systems are indispensable dataset for storm-flooding simulation. Given data availability and current computing power, the structure and complexity of urban drainage systems require to be simplify. However, till data, the simplify procedure mainly depend on manual operation that always leads to mistakes and lower work efficiency. This work referenced the classification methodology of road system, and proposed a conception of pipeline stroke. Further, length of pipeline, angle between two pipelines, the pipeline belonged road level and diameter of pipeline were chosen as the similarity criterion to generate the pipeline stroke. Finally, designed the automatic method to generalize drainage systems with the concern of multi-features. This technique can improve the efficiency and accuracy of the generalization of drainage systems. In addition, it is beneficial to the study of urban storm-floods.

  5. Automatic Generalizability Method of Urban Drainage Pipe Network Considering Multi-Features

    Zhu, S.; Yang, Q.; Shao, J.

    2018-05-01

    Urban drainage systems are indispensable dataset for storm-flooding simulation. Given data availability and current computing power, the structure and complexity of urban drainage systems require to be simplify. However, till data, the simplify procedure mainly depend on manual operation that always leads to mistakes and lower work efficiency. This work referenced the classification methodology of road system, and proposed a conception of pipeline stroke. Further, length of pipeline, angle between two pipelines, the pipeline belonged road level and diameter of pipeline were chosen as the similarity criterion to generate the pipeline stroke. Finally, designed the automatic method to generalize drainage systems with the concern of multi-features. This technique can improve the efficiency and accuracy of the generalization of drainage systems. In addition, it is beneficial to the study of urban storm-floods.

  6. Full Wafer Redistribution and Wafer Embedding as Key Technologies for a Multi-Scale Neuromorphic Hardware Cluster

    Zoschke, Kai; Güttler, Maurice; Böttcher, Lars; Grübl, Andreas; Husmann, Dan; Schemmel, Johannes; Meier, Karlheinz; Ehrmann, Oswin

    2018-01-01

    Together with the Kirchhoff-Institute for Physics(KIP) the Fraunhofer IZM has developed a full wafer redistribution and embedding technology as base for a large-scale neuromorphic hardware system. The paper will give an overview of the neuromorphic computing platform at the KIP and the associated hardware requirements which drove the described technological developments. In the first phase of the project standard redistribution technologies from wafer level packaging were adapted to enable a ...

  7. Intrusion detection model using fusion of chi-square feature selection and multi class SVM

    Ikram Sumaiya Thaseen

    2017-10-01

    Full Text Available Intrusion detection is a promising area of research in the domain of security with the rapid development of internet in everyday life. Many intrusion detection systems (IDS employ a sole classifier algorithm for classifying network traffic as normal or abnormal. Due to the large amount of data, these sole classifier models fail to achieve a high attack detection rate with reduced false alarm rate. However by applying dimensionality reduction, data can be efficiently reduced to an optimal set of attributes without loss of information and then classified accurately using a multi class modeling technique for identifying the different network attacks. In this paper, we propose an intrusion detection model using chi-square feature selection and multi class support vector machine (SVM. A parameter tuning technique is adopted for optimization of Radial Basis Function kernel parameter namely gamma represented by ‘ϒ’ and over fitting constant ‘C’. These are the two important parameters required for the SVM model. The main idea behind this model is to construct a multi class SVM which has not been adopted for IDS so far to decrease the training and testing time and increase the individual classification accuracy of the network attacks. The investigational results on NSL-KDD dataset which is an enhanced version of KDDCup 1999 dataset shows that our proposed approach results in a better detection rate and reduced false alarm rate. An experimentation on the computational time required for training and testing is also carried out for usage in time critical applications.

  8. Vibration and acoustic frequency spectra for industrial process modeling using selective fusion multi-condition samples and multi-source features

    Tang, Jian; Qiao, Junfei; Wu, ZhiWei; Chai, Tianyou; Zhang, Jian; Yu, Wen

    2018-01-01

    Frequency spectral data of mechanical vibration and acoustic signals relate to difficult-to-measure production quality and quantity parameters of complex industrial processes. A selective ensemble (SEN) algorithm can be used to build a soft sensor model of these process parameters by fusing valued information selectively from different perspectives. However, a combination of several optimized ensemble sub-models with SEN cannot guarantee the best prediction model. In this study, we use several techniques to construct mechanical vibration and acoustic frequency spectra of a data-driven industrial process parameter model based on selective fusion multi-condition samples and multi-source features. Multi-layer SEN (MLSEN) strategy is used to simulate the domain expert cognitive process. Genetic algorithm and kernel partial least squares are used to construct the inside-layer SEN sub-model based on each mechanical vibration and acoustic frequency spectral feature subset. Branch-and-bound and adaptive weighted fusion algorithms are integrated to select and combine outputs of the inside-layer SEN sub-models. Then, the outside-layer SEN is constructed. Thus, "sub-sampling training examples"-based and "manipulating input features"-based ensemble construction methods are integrated, thereby realizing the selective information fusion process based on multi-condition history samples and multi-source input features. This novel approach is applied to a laboratory-scale ball mill grinding process. A comparison with other methods indicates that the proposed MLSEN approach effectively models mechanical vibration and acoustic signals.

  9. Multi-sparse dictionary colorization algorithm based on the feature classification and detail enhancement

    Yan, Dan; Bai, Lianfa; Zhang, Yi; Han, Jing

    2018-02-01

    For the problems of missing details and performance of the colorization based on sparse representation, we propose a conceptual model framework for colorizing gray-scale images, and then a multi-sparse dictionary colorization algorithm based on the feature classification and detail enhancement (CEMDC) is proposed based on this framework. The algorithm can achieve a natural colorized effect for a gray-scale image, and it is consistent with the human vision. First, the algorithm establishes a multi-sparse dictionary classification colorization model. Then, to improve the accuracy rate of the classification, the corresponding local constraint algorithm is proposed. Finally, we propose a detail enhancement based on Laplacian Pyramid, which is effective in solving the problem of missing details and improving the speed of image colorization. In addition, the algorithm not only realizes the colorization of the visual gray-scale image, but also can be applied to the other areas, such as color transfer between color images, colorizing gray fusion images, and infrared images.

  10. Multi-Input Converter with MPPT Feature for Wind-PV Power Generation System

    Chih-Lung Shen

    2013-01-01

    Full Text Available A multi-input converter (MIC to process wind-PV power is proposed, designed, analyzed, simulated, and implemented. The MIC cannot only process solar energy but deal with wind power, of which structure is derived from forward-type DC/DC converter to step-down/up voltage for charger systems, DC distribution applications, or grid connection. The MIC comprises an upper modified double-ended forward, a lower modified double-ended forward, a common output inductor, and a DSP-based system controller. The two modified double-ended forwards can operate individually or simultaneously so as to accommodate the variation of the hybrid renewable energy under different atmospheric conditions. While the MIC operates at interleaving mode, better performance can be achieved and volume also is reduced. The proposed MIC is capable of recycling the energy stored in the leakage inductance and obtaining high step-up output voltage. In order to draw maximum power from wind turbine and PV panel, perturb-and-observe method is adopted to achieve maximum power point tracking (MPPT feature. The MIC is constructed, analyzed, simulated, and tested. Simulations and hardware measurements have demonstrated the feasibility and functionality of the proposed multi-input converter.

  11. Ablation mass features in multi-pulses femtosecond laser ablate molybdenum target

    Zhao, Dongye; Gierse, Niels; Wegner, Julian; Pretzler, Georg; Oelmann, Jannis; Brezinsek, Sebastijan; Liang, Yunfeng; Neubauer, Olaf; Rasinski, Marcin; Linsmeier, Christian; Ding, Hongbin

    2018-03-01

    In this study, the ablation mass features related to reflectivity of bulk Molybdenum (Mo) were investigated by a Ti: Sa 6 fs laser pulse at central wavelength 790 nm. The ablated mass removal was determined using Confocal Microscopy (CM) technique. The surface reflectivity was calibrated and measured by a Lambda 950 spectrophotometer as well as a CCD camera during laser ablation. The ablation mass loss per pulse increase with the increasing of laser shots, meanwhile the surface reflectivity decrease. The multi-pulses (100 shots) ablation threshold of Mo was determined to be 0.15 J/cm2. The incubation coefficient was estimated as 0.835. The reflectivity change of the Mo target surface following multi-pulses laser ablation were studied as a function of laser ablation shots at various laser fluences from 1.07 J/cm2 to 36.23 J/cm2. The results of measured reflectivity indicate that surface reflectivity of Mo target has a significant decline in the first 3-laser pulses at the various fluences. These results are important for developing a quantitative analysis model for laser induced ablation and laser induced breakdown spectroscopy for the first wall diagnosis of EAST tokamak.

  12. Multi-example feature-constrained back-projection method for image super-resolution

    Junlei Zhang; Dianguang Gai; Xin Zhang; Xuemei Li

    2017-01-01

    Example-based super-resolution algorithms,which predict unknown high-resolution image information using a relationship model learnt from known high- and low-resolution image pairs, have attracted considerable interest in the field of image processing. In this paper, we propose a multi-example feature-constrained back-projection method for image super-resolution. Firstly, we take advantage of a feature-constrained polynomial interpolation method to enlarge the low-resolution image. Next, we consider low-frequency images of different resolutions to provide an example pair. Then, we use adaptive k NN search to find similar patches in the low-resolution image for every image patch in the high-resolution low-frequency image, leading to a regression model between similar patches to be learnt. The learnt model is applied to the low-resolution high-frequency image to produce high-resolution high-frequency information. An iterative back-projection algorithm is used as the final step to determine the final high-resolution image.Experimental results demonstrate that our method improves the visual quality of the high-resolution image.

  13. Exterior orientation of CBERS-2B imagery using multi-feature control and orbital data

    Marcato Junior, J.; Tommaselli, A. M. G.

    2013-05-01

    The major contribution of this paper relates to the practical advantages of combining Ground Control Points (GCPs), Ground Control Lines (GCLs) and orbital data to estimate the exterior orientation parameters of images collected by CBERS-2B (China-Brazil Earth Resources Satellite) HRC (High-resolution Camera) and CCD (High-resolution CCD Camera) sensors. Although the CBERS-2B is no longer operational, its images are still being used in Brazil, and the next generations of the CBERS satellite will have sensors with similar technical features, which motivates the study presented in this paper. The mathematical models that relate the object and image spaces are based on collinearity (for points) and coplanarity (for lines) conditions. These models were created in an in-house developed software package called TMS (Triangulation with Multiple Sensors) with multi-feature control (GCPs and GCLs). Experiments on a block of four CBERS-2B HRC images and on one CBERS-2B CCD image were performed using both models. It was observed that the combination of GCPs and GCLs provided better bundle block adjustment results than conventional bundle adjustment using only GCPs. The results also demonstrate the advantages of using primarily orbital data when the number of control entities is reduced.

  14. Visual Localization across Seasons Using Sequence Matching Based on Multi-Feature Combination.

    Qiao, Yongliang

    2017-10-25

    Visual localization is widely used in autonomous navigation system and Advanced Driver Assistance Systems (ADAS). However, visual-based localization in seasonal changing situations is one of the most challenging topics in computer vision and the intelligent vehicle community. The difficulty of this task is related to the strong appearance changes that occur in scenes due to weather or season changes. In this paper, a place recognition based visual localization method is proposed, which realizes the localization by identifying previously visited places using the sequence matching method. It operates by matching query image sequences to an image database acquired previously (video acquired during traveling period). In this method, in order to improve matching accuracy, multi-feature is constructed by combining a global GIST descriptor and local binary feature CSLBP (Center-symmetric local binary patterns) to represent image sequence. Then, similarity measurement according to Chi-square distance is used for effective sequences matching. For experimental evaluation, the relationship between image sequence length and sequences matching performance is studied. To show its effectiveness, the proposed method is tested and evaluated in four seasons outdoor environments. The results have shown improved precision-recall performance against the state-of-the-art SeqSLAM algorithm.

  15. Visual Localization across Seasons Using Sequence Matching Based on Multi-Feature Combination

    Yongliang Qiao

    2017-10-01

    Full Text Available Visual localization is widely used in autonomous navigation system and Advanced Driver Assistance Systems (ADAS. However, visual-based localization in seasonal changing situations is one of the most challenging topics in computer vision and the intelligent vehicle community. The difficulty of this task is related to the strong appearance changes that occur in scenes due to weather or season changes. In this paper, a place recognition based visual localization method is proposed, which realizes the localization by identifying previously visited places using the sequence matching method. It operates by matching query image sequences to an image database acquired previously (video acquired during traveling period. In this method, in order to improve matching accuracy, multi-feature is constructed by combining a global GIST descriptor and local binary feature CSLBP (Center-symmetric local binary patterns to represent image sequence. Then, similarity measurement according to Chi-square distance is used for effective sequences matching. For experimental evaluation, the relationship between image sequence length and sequences matching performance is studied. To show its effectiveness, the proposed method is tested and evaluated in four seasons outdoor environments. The results have shown improved precision–recall performance against the state-of-the-art SeqSLAM algorithm.

  16. Urban Area Extent Extraction in Spaceborne HR and VHR Data Using Multi-Resolution Features

    Gianni Cristian Iannelli

    2014-09-01

    Full Text Available Detection of urban area extents by means of remotely sensed data is a difficult task, especially because of the multiple, diverse definitions of what an “urban area” is. The models of urban areas listed in technical literature are based on the combination of spectral information with spatial patterns, possibly at different spatial resolutions. Starting from the same data set, “urban area” extraction may thus lead to multiple outputs. If this is done in a well-structured framework, however, this may be considered as an advantage rather than an issue. This paper proposes a novel framework for urban area extent extraction from multispectral Earth Observation (EO data. The key is to compute and combine spectral and multi-scale spatial features. By selecting the most adequate features, and combining them with proper logical rules, the approach allows matching multiple urban area models. Experimental results for different locations in Brazil and Kenya using High-Resolution (HR data prove the usefulness and flexibility of the framework.

  17. Angular difference feature extraction for urban scene classification using ZY-3 multi-angle high-resolution satellite imagery

    Huang, Xin; Chen, Huijun; Gong, Jianya

    2018-01-01

    Spaceborne multi-angle images with a high-resolution are capable of simultaneously providing spatial details and three-dimensional (3D) information to support detailed and accurate classification of complex urban scenes. In recent years, satellite-derived digital surface models (DSMs) have been increasingly utilized to provide height information to complement spectral properties for urban classification. However, in such a way, the multi-angle information is not effectively exploited, which is mainly due to the errors and difficulties of the multi-view image matching and the inaccuracy of the generated DSM over complex and dense urban scenes. Therefore, it is still a challenging task to effectively exploit the available angular information from high-resolution multi-angle images. In this paper, we investigate the potential for classifying urban scenes based on local angular properties characterized from high-resolution ZY-3 multi-view images. Specifically, three categories of angular difference features (ADFs) are proposed to describe the angular information at three levels (i.e., pixel, feature, and label levels): (1) ADF-pixel: the angular information is directly extrapolated by pixel comparison between the multi-angle images; (2) ADF-feature: the angular differences are described in the feature domains by comparing the differences between the multi-angle spatial features (e.g., morphological attribute profiles (APs)). (3) ADF-label: label-level angular features are proposed based on a group of urban primitives (e.g., buildings and shadows), in order to describe the specific angular information related to the types of primitive classes. In addition, we utilize spatial-contextual information to refine the multi-level ADF features using superpixel segmentation, for the purpose of alleviating the effects of salt-and-pepper noise and representing the main angular characteristics within a local area. The experiments on ZY-3 multi-angle images confirm that the proposed

  18. Web Server Embedded System

    Adharul Muttaqin

    2014-07-01

    Full Text Available Abstrak Embedded sistem saat ini menjadi perhatian khusus pada teknologi komputer, beberapa sistem operasi linux dan web server yang beraneka ragam juga sudah dipersiapkan untuk mendukung sistem embedded, salah satu aplikasi yang dapat digunakan dalam operasi pada sistem embedded adalah web server. Pemilihan web server pada lingkungan embedded saat ini masih jarang dilakukan, oleh karena itu penelitian ini dilakukan dengan menitik beratkan pada dua buah aplikasi web server yang tergolong memiliki fitur utama yang menawarkan “keringanan” pada konsumsi CPU maupun memori seperti Light HTTPD dan Tiny HTTPD. Dengan menggunakan parameter thread (users, ramp-up periods, dan loop count pada stress test embedded system, penelitian ini menawarkan solusi web server manakah diantara Light HTTPD dan Tiny HTTPD yang memiliki kecocokan fitur dalam penggunaan embedded sistem menggunakan beagleboard ditinjau dari konsumsi CPU dan memori. Hasil penelitian menunjukkan bahwa dalam hal konsumsi CPU pada beagleboard embedded system lebih disarankan penggunaan Light HTTPD dibandingkan dengan tiny HTTPD dikarenakan terdapat perbedaan CPU load yang sangat signifikan antar kedua layanan web tersebut Kata kunci: embedded system, web server Abstract Embedded systems are currently of particular concern in computer technology, some of the linux operating system and web server variegated also prepared to support the embedded system, one of the applications that can be used in embedded systems are operating on the web server. Selection of embedded web server on the environment is still rarely done, therefore this study was conducted with a focus on two web application servers belonging to the main features that offer a "lightness" to the CPU and memory consumption as Light HTTPD and Tiny HTTPD. By using the parameters of the thread (users, ramp-up periods, and loop count on a stress test embedded systems, this study offers a solution of web server which between the Light

  19. Multi-stage classification method oriented to aerial image based on low-rank recovery and multi-feature fusion sparse representation.

    Ma, Xu; Cheng, Yongmei; Hao, Shuai

    2016-12-10

    Automatic classification of terrain surfaces from an aerial image is essential for an autonomous unmanned aerial vehicle (UAV) landing at an unprepared site by using vision. Diverse terrain surfaces may show similar spectral properties due to the illumination and noise that easily cause poor classification performance. To address this issue, a multi-stage classification algorithm based on low-rank recovery and multi-feature fusion sparse representation is proposed. First, color moments and Gabor texture feature are extracted from training data and stacked as column vectors of a dictionary. Then we perform low-rank matrix recovery for the dictionary by using augmented Lagrange multipliers and construct a multi-stage terrain classifier. Experimental results on an aerial map database that we prepared verify the classification accuracy and robustness of the proposed method.

  20. Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform.

    Xu, Huile; Liu, Jinyi; Hu, Haibo; Zhang, Yi

    2016-12-02

    Wearable sensors-based human activity recognition introduces many useful applications and services in health care, rehabilitation training, elderly monitoring and many other areas of human interaction. Existing works in this field mainly focus on recognizing activities by using traditional features extracted from Fourier transform (FT) or wavelet transform (WT). However, these signal processing approaches are suitable for a linear signal but not for a nonlinear signal. In this paper, we investigate the characteristics of the Hilbert-Huang transform (HHT) for dealing with activity data with properties such as nonlinearity and non-stationarity. A multi-features extraction method based on HHT is then proposed to improve the effect of activity recognition. The extracted multi-features include instantaneous amplitude (IA) and instantaneous frequency (IF) by means of empirical mode decomposition (EMD), as well as instantaneous energy density (IE) and marginal spectrum (MS) derived from Hilbert spectral analysis. Experimental studies are performed to verify the proposed approach by using the PAMAP2 dataset from the University of California, Irvine for wearable sensors-based activity recognition. Moreover, the effect of combining multi-features vs. a single-feature are investigated and discussed in the scenario of a dependent subject. The experimental results show that multi-features combination can further improve the performance measures. Finally, we test the effect of multi-features combination in the scenario of an independent subject. Our experimental results show that we achieve four performance indexes: recall, precision, F-measure, and accuracy to 0.9337, 0.9417, 0.9353, and 0.9377 respectively, which are all better than the achievements of related works.

  1. Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform

    Huile Xu

    2016-12-01

    Full Text Available Wearable sensors-based human activity recognition introduces many useful applications and services in health care, rehabilitation training, elderly monitoring and many other areas of human interaction. Existing works in this field mainly focus on recognizing activities by using traditional features extracted from Fourier transform (FT or wavelet transform (WT. However, these signal processing approaches are suitable for a linear signal but not for a nonlinear signal. In this paper, we investigate the characteristics of the Hilbert-Huang transform (HHT for dealing with activity data with properties such as nonlinearity and non-stationarity. A multi-features extraction method based on HHT is then proposed to improve the effect of activity recognition. The extracted multi-features include instantaneous amplitude (IA and instantaneous frequency (IF by means of empirical mode decomposition (EMD, as well as instantaneous energy density (IE and marginal spectrum (MS derived from Hilbert spectral analysis. Experimental studies are performed to verify the proposed approach by using the PAMAP2 dataset from the University of California, Irvine for wearable sensors-based activity recognition. Moreover, the effect of combining multi-features vs. a single-feature are investigated and discussed in the scenario of a dependent subject. The experimental results show that multi-features combination can further improve the performance measures. Finally, we test the effect of multi-features combination in the scenario of an independent subject. Our experimental results show that we achieve four performance indexes: recall, precision, F-measure, and accuracy to 0.9337, 0.9417, 0.9353, and 0.9377 respectively, which are all better than the achievements of related works.

  2. Facile synthesis of enzyme-embedded magnetic metal-organic frameworks as a reusable mimic multi-enzyme system: mimetic peroxidase properties and colorimetric sensor.

    Hou, Chen; Wang, Yang; Ding, Qinghua; Jiang, Long; Li, Ming; Zhu, Weiwei; Pan, Duo; Zhu, Hao; Liu, Mingzhu

    2015-11-28

    This work reports a facile and easily-achieved approach for enzyme immobilization by embedding glucose oxidase (GOx) in magnetic zeolitic imidazolate framework 8 (mZIF-8) via a de novo approach. As a demonstration of the power of such materials, the resulting GOx embedded mZIF-8 (mZIF-8@GOx) was utilized as a colorimetric sensor for rapid detection of glucose. This method was constructed on the basis of metal-organic frameworks (MOFs), which possessed very fascinating peroxidase-like properties, and the cascade reaction for the visual detection of glucose was combined into one step through the mZIF-8@GOx based mimic multi-enzyme system. After characterization by electron microscopy, X-ray diffraction, nitrogen sorption, fourier transform infrared spectroscopy and vibrating sample magnetometry, the as-prepared mZIF-8@GOx was confirmed with the robust core-shell structure, the monodisperse nanoparticle had an average diameter of about 200 nm and displayed superparamagnetism with a saturation magnetization value of 40.5 emu g(-1), it also exhibited a large surface area of 396.10 m(2) g(-1). As a peroxidase mimic, mZIF-8 was verified to be highly stable and of low cost, and showed a strong affinity towards H2O2. Meanwhile, the mZIF-8 embedded GOx also exhibited improved activity, stability and greatly enhanced selectivity in glucose detection. Moreover, the mZIF-8@GOx had excellent recyclability with high activity (88.7% residual activity after 12 times reuse).

  3. Joint kinematics estimation using a multi-body kinematics optimisation and an extended Kalman filter, and embedding a soft tissue artefact model.

    Bonnet, Vincent; Richard, Vincent; Camomilla, Valentina; Venture, Gentiane; Cappozzo, Aurelio; Dumas, Raphaël

    2017-09-06

    To reduce the impact of the soft tissue artefact (STA) on the estimate of skeletal movement using stereophotogrammetric and skin-marker data, multi-body kinematics optimisation (MKO) and extended Kalman filters (EKF) have been proposed. This paper assessed the feasibility and efficiency of these methods when they embed a mathematical model of the STA and simultaneously estimate the ankle, knee and hip joint kinematics and the model parameters. A STA model was used that provides an estimate of the STA affecting the marker-cluster located on a body segment as a function of the kinematics of the adjacent joints. The MKO and the EKF were implemented with and without the STA model. To assess these methods, intra-cortical pin and skin markers located on the thigh, shank, and foot of three subjects and tracked during the stance phase of running were used. Embedding the STA model in MKO and EKF reduced the average RMS of marker tracking from 12.6 to 1.6mm and from 4.3 to 1.9mm, respectively, showing that a STA model trial-specific calibration is feasible. Nevertheless, with the STA model embedded in MKO, the RMS difference between the estimated and the reference joint kinematics determined from the pin markers slightly increased (from 2.0 to 2.1deg) On the contrary, when the STA model was embedded in the EKF, this RMS difference was slightly reduced (from 2.0 to 1.7deg) thus showing a better potentiality of this method to attenuate STA effects and improve the accuracy of joint kinematics estimate. Copyright © 2017 Elsevier Ltd. All rights reserved.

  4. Preparation and application of a carbon paste electrode modified with multi-walled carbon nanotubes and boron-embedded molecularly imprinted composite membranes.

    Wang, Hongjuan; Qian, Duo; Xiao, Xilin; Deng, Chunyan; Liao, Lifu; Deng, Jian; Lin, Ying-Wu

    2018-06-01

    An innovative electrochemical sensor was fabricated for the sensitive and selective determination of tinidazole (TNZ), based on a carbon paste electrode (CPE) modified with multi-walled carbon nanotubes (MWCNTs) and boron-embedded molecularly imprinted composite membranes (B-MICMs). Density functional theory (DFT) calculations were carried out to investigate the utility of template-monomer interactions to screen appropriate monomers for the rational design of B-MICMs. The distinct synergic effect of MWCNTs and B-MICMs was evidenced by the positive shift of the reduction peak potential of TNZ at B-MICMs/MWCNTs modified CPE (B-MICMs/MWCNTs/CPE) by about 200 mV, and the 12-fold amplification of the peak current, compared with a bare carbon paste electrode (CPE). Moreover, the coordinate interactions between trisubstituted boron atoms embedded in B-MICMs matrix and nitrogen atoms of TNZ endow the sensor with advanced affinity and specific directionality. Thereafter, a highly sensitive electrochemical analytical method for TNZ was established by different pulse voltammetry (DPV) at B-MICMs/MWCNTs/CPE with a lower detection limit (1.25 × 10 -12  mol L -1 ) (S/N = 3). The practical application of the sensor was demonstrated by determining TNZ in pharmaceutical and biological samples with good precision (RSD 1.36% to 3.85%) and acceptable recoveries (82.40%-104.0%). Copyright © 2018 Elsevier B.V. All rights reserved.

  5. Silver-embedded screens in the intensive care unit. A new tool to control multi-drug resistant bacterial cross-transmission.

    Ruiz, J; Ramirez, P; Villarreal, E; Gordon, M; Cuesta, S; Piñol, M; Frasquet, J; Castellanos, Á

    2017-08-01

    The purpose of this study was to assess the effectiveness of silver-embedded surfaces (BactiBlock®) to prevent surface colonization by multi-resistant bacteria (MRB) and to reduce the incidence of MRB colonization and infection in patients admitted to an intensive care unit (ICU). A 6-month prospective observational study in a 24-bed mixed ICU divided into two identical subunits (12 beds each) was designed. Seven solid mobile screens were placed in one of the subunits while in the other cloth screens remained. Solid screens were constructed with high-density polyethylene embedded in Bactiblock®. To evaluate the effectiveness of screens coated with Bactiblock®, number of MRB isolates on screens were compared for 6 months. Likewise, numbers of new patients and ICU-stays with MRB colonization in the two subunits were compared. One hundred forty screen samples were collected in 10-point prevalent days. MRB were detected on 28 (20.0%) samples. Over the 70 samples taken on cloth folding screens, MRB were detected in 25 (35.7%), while only 3 (4.3%) of the 70 samples taken on Bactiblock® screens were positive for MRB (p unit with Bactiblock® screens presented fewer number of ICU stays with MRB colonization (27.8% vs 47.1%; p units, proving to be an useful tool in the control of MRB.

  6. An optimal set of features for predicting type IV secretion system effector proteins for a subset of species based on a multi-level feature selection approach.

    Zhila Esna Ashari

    Full Text Available Type IV secretion systems (T4SS are multi-protein complexes in a number of bacterial pathogens that can translocate proteins and DNA to the host. Most T4SSs function in conjugation and translocate DNA; however, approximately 13% function to secrete proteins, delivering effector proteins into the cytosol of eukaryotic host cells. Upon entry, these effectors manipulate the host cell's machinery for their own benefit, which can result in serious illness or death of the host. For this reason recognition of T4SS effectors has become an important subject. Much previous work has focused on verifying effectors experimentally, a costly endeavor in terms of money, time, and effort. Having good predictions for effectors will help to focus experimental validations and decrease testing costs. In recent years, several scoring and machine learning-based methods have been suggested for the purpose of predicting T4SS effector proteins. These methods have used different sets of features for prediction, and their predictions have been inconsistent. In this paper, an optimal set of features is presented for predicting T4SS effector proteins using a statistical approach. A thorough literature search was performed to find features that have been proposed. Feature values were calculated for datasets of known effectors and non-effectors for T4SS-containing pathogens for four genera with a sufficient number of known effectors, Legionella pneumophila, Coxiella burnetii, Brucella spp, and Bartonella spp. The features were ranked, and less important features were filtered out. Correlations between remaining features were removed, and dimensional reduction was accomplished using principal component analysis and factor analysis. Finally, the optimal features for each pathogen were chosen by building logistic regression models and evaluating each model. The results based on evaluation of our logistic regression models confirm the effectiveness of our four optimal sets of

  7. Implementing eco friendly highly reliable upload feature using multi 3G service

    Tanutama, Lukas; Wijaya, Rico

    2017-12-01

    The current trend of eco friendly Internet access is preferred. In this research the understanding of eco friendly is minimum power consumption. The devices that are selected have operationally low power consumption and normally have no power consumption as they are hibernating during idle state. To have the reliability a router of a router that has internal load balancing feature will provide the improvement of previous research on multi 3G services for broadband lines. Previous studies emphasized on accessing and downloading information files from Public Cloud residing Web Servers. The demand is not only for speed but high reliability of access as well. High reliability will mean mitigating both direct and indirect high cost due to repeated attempts of uploading and downloading the large files. Nomadic and mobile computer users need viable solution. Following solution for downloading information has been proposed and tested. The solution is promising. The result is now extended to providing reliable access line by means of redundancy and automatic reconfiguration for uploading and downloading large information files to a Web Server in the Cloud. The technique is taking advantage of internal load balancing feature to provision a redundant line acting as a backup line. A router that has the ability to provide load balancing to several WAN lines is chosen. The WAN lines are constructed using multiple 3G lines. The router supports the accessing Internet with more than one 3G access line which increases the reliability and availability of the Internet access as the second line immediately takes over if the first line is disturbed.

  8. Multi-feature classifiers for burst detection in single EEG channels from preterm infants

    Navarro, X.; Porée, F.; Kuchenbuch, M.; Chavez, M.; Beuchée, Alain; Carrault, G.

    2017-08-01

    Objective. The study of electroencephalographic (EEG) bursts in preterm infants provides valuable information about maturation or prognostication after perinatal asphyxia. Over the last two decades, a number of works proposed algorithms to automatically detect EEG bursts in preterm infants, but they were designed for populations under 35 weeks of post menstrual age (PMA). However, as the brain activity evolves rapidly during postnatal life, these solutions might be under-performing with increasing PMA. In this work we focused on preterm infants reaching term ages (PMA  ⩾36 weeks) using multi-feature classification on a single EEG channel. Approach. Five EEG burst detectors relying on different machine learning approaches were compared: logistic regression (LR), linear discriminant analysis (LDA), k-nearest neighbors (kNN), support vector machines (SVM) and thresholding (Th). Classifiers were trained by visually labeled EEG recordings from 14 very preterm infants (born after 28 weeks of gestation) with 36-41 weeks PMA. Main results. The most performing classifiers reached about 95% accuracy (kNN, SVM and LR) whereas Th obtained 84%. Compared to human-automatic agreements, LR provided the highest scores (Cohen’s kappa  =  0.71) using only three EEG features. Applying this classifier in an unlabeled database of 21 infants  ⩾36 weeks PMA, we found that long EEG bursts and short inter-burst periods are characteristic of infants with the highest PMA and weights. Significance. In view of these results, LR-based burst detection could be a suitable tool to study maturation in monitoring or portable devices using a single EEG channel.

  9. TargetCrys: protein crystallization prediction by fusing multi-view features with two-layered SVM.

    Hu, Jun; Han, Ke; Li, Yang; Yang, Jing-Yu; Shen, Hong-Bin; Yu, Dong-Jun

    2016-11-01

    The accurate prediction of whether a protein will crystallize plays a crucial role in improving the success rate of protein crystallization projects. A common critical problem in the development of machine-learning-based protein crystallization predictors is how to effectively utilize protein features extracted from different views. In this study, we aimed to improve the efficiency of fusing multi-view protein features by proposing a new two-layered SVM (2L-SVM) which switches the feature-level fusion problem to a decision-level fusion problem: the SVMs in the 1st layer of the 2L-SVM are trained on each of the multi-view feature sets; then, the outputs of the 1st layer SVMs, which are the "intermediate" decisions made based on the respective feature sets, are further ensembled by a 2nd layer SVM. Based on the proposed 2L-SVM, we implemented a sequence-based protein crystallization predictor called TargetCrys. Experimental results on several benchmark datasets demonstrated the efficacy of the proposed 2L-SVM for fusing multi-view features. We also compared TargetCrys with existing sequence-based protein crystallization predictors and demonstrated that the proposed TargetCrys outperformed most of the existing predictors and is competitive with the state-of-the-art predictors. The TargetCrys webserver and datasets used in this study are freely available for academic use at: http://csbio.njust.edu.cn/bioinf/TargetCrys .

  10. Prognosis Essay Scoring and Article Relevancy Using Multi-Text Features and Machine Learning

    Arif Mehmood

    2017-01-01

    Full Text Available This study develops a model for essay scoring and article relevancy. Essay scoring is a costly process when we consider the time spent by an evaluator. It may lead to inequalities of the effort by various evaluators to apply the same evaluation criteria. Bibliometric research uses the evaluation criteria to find relevancy of articles instead. Researchers mostly face relevancy issues while searching articles. Therefore, they classify the articles manually. However, manual classification is burdensome due to time needed for evaluation. The proposed model performs automatic essay evaluation using multi-text features and ensemble machine learning. The proposed method is implemented in two data sets: a Kaggle short answer data set for essay scoring that includes four ranges of disciplines (Science, Biology, English, and English language Arts, and a bibliometric data set having IoT (Internet of Things and non-IoT classes. The efficacy of the model is measured against the Tandalla and AutoP approach using Cohen’s kappa. The model achieves kappa values of 0.80 and 0.83 for the first and second data sets, respectively. Kappa values show that the proposed model has better performance than those of earlier approaches.

  11. A hybrid model for dissolved oxygen prediction in aquaculture based on multi-scale features

    Chen Li

    2018-03-01

    Full Text Available To increase prediction accuracy of dissolved oxygen (DO in aquaculture, a hybrid model based on multi-scale features using ensemble empirical mode decomposition (EEMD is proposed. Firstly, original DO datasets are decomposed by EEMD and we get several components. Secondly, these components are used to reconstruct four terms including high frequency term, intermediate frequency term, low frequency term and trend term. Thirdly, according to the characteristics of high and intermediate frequency terms, which fluctuate violently, the least squares support vector machine (LSSVR is used to predict the two terms. The fluctuation of low frequency term is gentle and periodic, so it can be modeled by BP neural network with an optimal mind evolutionary computation (MEC-BP. Then, the trend term is predicted using grey model (GM because it is nearly linear. Finally, the prediction values of DO datasets are calculated by the sum of the forecasting values of all terms. The experimental results demonstrate that our hybrid model outperforms EEMD-ELM (extreme learning machine based on EEMD, EEMD-BP and MEC-BP models based on the mean absolute error (MAE, mean absolute percentage error (MAPE, mean square error (MSE and root mean square error (RMSE. Our hybrid model is proven to be an effective approach to predict aquaculture DO.

  12. Sea-land segmentation for infrared remote sensing images based on superpixels and multi-scale features

    Lei, Sen; Zou, Zhengxia; Liu, Dunge; Xia, Zhenghuan; Shi, Zhenwei

    2018-06-01

    Sea-land segmentation is a key step for the information processing of ocean remote sensing images. Traditional sea-land segmentation algorithms ignore the local similarity prior of sea and land, and thus fail in complex scenarios. In this paper, we propose a new sea-land segmentation method for infrared remote sensing images to tackle the problem based on superpixels and multi-scale features. Considering the connectivity and local similarity of sea or land, we interpret the sea-land segmentation task in view of superpixels rather than pixels, where similar pixels are clustered and the local similarity are explored. Moreover, the multi-scale features are elaborately designed, comprising of gray histogram and multi-scale total variation. Experimental results on infrared bands of Landsat-8 satellite images demonstrate that the proposed method can obtain more accurate and more robust sea-land segmentation results than the traditional algorithms.

  13. A sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image

    Li, Jing; Xie, Weixin; Pei, Jihong

    2018-03-01

    Sea-land segmentation is one of the key technologies of sea target detection in remote sensing images. At present, the existing algorithms have the problems of low accuracy, low universality and poor automatic performance. This paper puts forward a sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image removing island. Firstly, the coastline data is extracted and all of land area is labeled by using the geographic information in large-field remote sensing image. Secondly, three features (local entropy, local texture and local gradient mean) is extracted in the sea-land border area, and the three features combine a 3D feature vector. And then the MultiGaussian model is adopted to describe 3D feature vectors of sea background in the edge of the coastline. Based on this multi-gaussian sea background model, the sea pixels and land pixels near coastline are classified more precise. Finally, the coarse segmentation result and the fine segmentation result are fused to obtain the accurate sea-land segmentation. Comparing and analyzing the experimental results by subjective vision, it shows that the proposed method has high segmentation accuracy, wide applicability and strong anti-disturbance ability.

  14. Effect of Load Model Using Ranking Identification Technique for Multi Type DG Incorporating Embedded Meta EP-Firefly Algorithm

    Abdul Rahim Siti Rafidah

    2018-01-01

    Full Text Available This paper presents the effect of load model prior to the distributed generation (DG planning in distribution system. In achieving optimal allocation and placement of DG, a ranking identification technique was proposed in order to study the DG planning using pre-developed Embedded Meta Evolutionary Programming–Firefly Algorithm. The aim of this study is to analyze the effect of different type of DG in order to reduce the total losses considering load factor. To realize the effectiveness of the proposed technique, the IEEE 33 bus test systems was utilized as the test specimen. In this study, the proposed techniques were used to determine the DG sizing and the suitable location for DG planning. The results produced are utilized for the optimization process of DG for the benefit of power system operators and planners in the utility. The power system planner can choose the suitable size and location from the result obtained in this study with the appropriate company’s budget. The modeling of voltage dependent loads has been presented and the results show the voltage dependent load models have a significant effect on total losses of a distribution system for different DG type.

  15. Self-Organization in Embedded Real-Time Systems

    Brinkschulte, Uwe; Rettberg, Achim

    2013-01-01

    This book describes the emerging field of self-organizing, multicore, distributed and real-time embedded systems.  Self-organization of both hardware and software can be a key technique to handle the growing complexity of modern computing systems. Distributed systems running hundreds of tasks on dozens of processors, each equipped with multiple cores, requires self-organization principles to ensure efficient and reliable operation. This book addresses various, so-called Self-X features such as self-configuration, self-optimization, self-adaptation, self-healing and self-protection. Presents open components for embedded real-time adaptive and self-organizing applications; Describes innovative techniques in: scheduling, memory management, quality of service, communications supporting organic real-time applications; Covers multi-/many-core embedded systems supporting real-time adaptive systems and power-aware, adaptive hardware and software systems; Includes case studies of open embedded real-time self-organizi...

  16. Research on oral test modeling based on multi-feature fusion

    Shi, Yuliang; Tao, Yiyue; Lei, Jun

    2018-04-01

    In this paper, the spectrum of speech signal is taken as an input of feature extraction. The advantage of PCNN in image segmentation and other processing is used to process the speech spectrum and extract features. And a new method combining speech signal processing and image processing is explored. At the same time of using the features of the speech map, adding the MFCC to establish the spectral features and integrating them with the features of the spectrogram to further improve the accuracy of the spoken language recognition. Considering that the input features are more complicated and distinguishable, we use Support Vector Machine (SVM) to construct the classifier, and then compare the extracted test voice features with the standard voice features to achieve the spoken standard detection. Experiments show that the method of extracting features from spectrograms using PCNN is feasible, and the fusion of image features and spectral features can improve the detection accuracy.

  17. Multi-view 3D echocardiography compounding based on feature consistency

    Yao, Cheng; Simpson, John M.; Schaeffter, Tobias; Penney, Graeme P.

    2011-09-01

    Echocardiography (echo) is a widely available method to obtain images of the heart; however, echo can suffer due to the presence of artefacts, high noise and a restricted field of view. One method to overcome these limitations is to use multiple images, using the 'best' parts from each image to produce a higher quality 'compounded' image. This paper describes our compounding algorithm which specifically aims to reduce the effect of echo artefacts as well as improving the signal-to-noise ratio, contrast and extending the field of view. Our method weights image information based on a local feature coherence/consistency between all the overlapping images. Validation has been carried out using phantom, volunteer and patient datasets consisting of up to ten multi-view 3D images. Multiple sets of phantom images were acquired, some directly from the phantom surface, and others by imaging through hard and soft tissue mimicking material to degrade the image quality. Our compounding method is compared to the original, uncompounded echocardiography images, and to two basic statistical compounding methods (mean and maximum). Results show that our method is able to take a set of ten images, degraded by soft and hard tissue artefacts, and produce a compounded image of equivalent quality to images acquired directly from the phantom. Our method on phantom, volunteer and patient data achieves almost the same signal-to-noise improvement as the mean method, while simultaneously almost achieving the same contrast improvement as the maximum method. We show a statistically significant improvement in image quality by using an increased number of images (ten compared to five), and visual inspection studies by three clinicians showed very strong preference for our compounded volumes in terms of overall high image quality, large field of view, high endocardial border definition and low cavity noise.

  18. Multi-view 3D echocardiography compounding based on feature consistency

    Yao Cheng; Schaeffter, Tobias; Penney, Graeme P; Simpson, John M

    2011-01-01

    Echocardiography (echo) is a widely available method to obtain images of the heart; however, echo can suffer due to the presence of artefacts, high noise and a restricted field of view. One method to overcome these limitations is to use multiple images, using the 'best' parts from each image to produce a higher quality 'compounded' image. This paper describes our compounding algorithm which specifically aims to reduce the effect of echo artefacts as well as improving the signal-to-noise ratio, contrast and extending the field of view. Our method weights image information based on a local feature coherence/consistency between all the overlapping images. Validation has been carried out using phantom, volunteer and patient datasets consisting of up to ten multi-view 3D images. Multiple sets of phantom images were acquired, some directly from the phantom surface, and others by imaging through hard and soft tissue mimicking material to degrade the image quality. Our compounding method is compared to the original, uncompounded echocardiography images, and to two basic statistical compounding methods (mean and maximum). Results show that our method is able to take a set of ten images, degraded by soft and hard tissue artefacts, and produce a compounded image of equivalent quality to images acquired directly from the phantom. Our method on phantom, volunteer and patient data achieves almost the same signal-to-noise improvement as the mean method, while simultaneously almost achieving the same contrast improvement as the maximum method. We show a statistically significant improvement in image quality by using an increased number of images (ten compared to five), and visual inspection studies by three clinicians showed very strong preference for our compounded volumes in terms of overall high image quality, large field of view, high endocardial border definition and low cavity noise.

  19. Association between pathology and texture features of multi parametric MRI of the prostate

    Kuess, Peter; Andrzejewski, Piotr; Nilsson, David; Georg, Petra; Knoth, Johannes; Susani, Martin; Trygg, Johan; Helbich, Thomas H.; Polanec, Stephan H.; Georg, Dietmar; Nyholm, Tufve

    2017-10-01

    The role of multi-parametric (mp)MRI in the diagnosis and treatment of prostate cancer has increased considerably. An alternative to visual inspection of mpMRI is the evaluation using histogram-based (first order statistics) parameters and textural features (second order statistics). The aims of the present work were to investigate the relationship between benign and malignant sub-volumes of the prostate and textures obtained from mpMR images. The performance of tumor prediction was investigated based on the combination of histogram-based and textural parameters. Subsequently, the relative importance of mpMR images was assessed and the benefit of additional imaging analyzed. Finally, sub-structures based on the PI-RADS classification were investigated as potential regions to automatically detect maligned lesions. Twenty-five patients who received mpMRI prior to radical prostatectomy were included in the study. The imaging protocol included T2, DWI, and DCE. Delineation of tumor regions was performed based on pathological information. First and second order statistics were derived from each structure and for all image modalities. The resulting data were processed with multivariate analysis, using PCA (principal component analysis) and OPLS-DA (orthogonal partial least squares discriminant analysis) for separation of malignant and healthy tissue. PCA showed a clear difference between tumor and healthy regions in the peripheral zone for all investigated images. The predictive ability of the OPLS-DA models increased for all image modalities when first and second order statistics were combined. The predictive value reached a plateau after adding ADC and T2, and did not increase further with the addition of other image information. The present study indicates a distinct difference in the signatures between malign and benign prostate tissue. This is an absolute prerequisite for automatic tumor segmentation, but only the first step in that direction. For the specific

  20. A scale-entropy diffusion equation to describe the multi-scale features of turbulent flames near a wall

    Queiros-Conde, D.; Foucher, F.; Mounaïm-Rousselle, C.; Kassem, H.; Feidt, M.

    2008-12-01

    Multi-scale features of turbulent flames near a wall display two kinds of scale-dependent fractal features. In scale-space, an unique fractal dimension cannot be defined and the fractal dimension of the front is scale-dependent. Moreover, when the front approaches the wall, this dependency changes: fractal dimension also depends on the wall-distance. Our aim here is to propose a general geometrical framework that provides the possibility to integrate these two cases, in order to describe the multi-scale structure of turbulent flames interacting with a wall. Based on the scale-entropy quantity, which is simply linked to the roughness of the front, we thus introduce a general scale-entropy diffusion equation. We define the notion of “scale-evolutivity” which characterises the deviation of a multi-scale system from the pure fractal behaviour. The specific case of a constant “scale-evolutivity” over the scale-range is studied. In this case, called “parabolic scaling”, the fractal dimension is a linear function of the logarithm of scale. The case of a constant scale-evolutivity in the wall-distance space implies that the fractal dimension depends linearly on the logarithm of the wall-distance. We then verified experimentally, that parabolic scaling represents a good approximation of the real multi-scale features of turbulent flames near a wall.

  1. Multi-class parkinsonian disorders classification with quantitative MR markers and graph-based features using support vector machines.

    Morisi, Rita; Manners, David Neil; Gnecco, Giorgio; Lanconelli, Nico; Testa, Claudia; Evangelisti, Stefania; Talozzi, Lia; Gramegna, Laura Ludovica; Bianchini, Claudio; Calandra-Buonaura, Giovanna; Sambati, Luisa; Giannini, Giulia; Cortelli, Pietro; Tonon, Caterina; Lodi, Raffaele

    2018-02-01

    In this study we attempt to automatically classify individual patients with different parkinsonian disorders, making use of pattern recognition techniques to distinguish among several forms of parkinsonisms (multi-class classification), based on a set of binary classifiers that discriminate each disorder from all others. We combine diffusion tensor imaging, proton spectroscopy and morphometric-volumetric data to obtain MR quantitative markers, which are provided to support vector machines with the aim of recognizing the different parkinsonian disorders. Feature selection is used to find the most important features for classification. We also exploit a graph-based technique on the set of quantitative markers to extract additional features from the dataset, and increase classification accuracy. When graph-based features are not used, the MR markers that are most frequently automatically extracted by the feature selection procedure reflect alterations in brain regions that are also usually considered to discriminate parkinsonisms in routine clinical practice. Graph-derived features typically increase the diagnostic accuracy, and reduce the number of features required. The results obtained in the work demonstrate that support vector machines applied to multimodal brain MR imaging and using graph-based features represent a novel and highly accurate approach to discriminate parkinsonisms, and a useful tool to assist the diagnosis. Copyright © 2017 Elsevier Ltd. All rights reserved.

  2. Generating Long Scale-Length Plasma Jets Embedded in a Uniform, Multi-Tesla Magnetic-Field

    Manuel, Mario; Kuranz, Carolyn; Rasmus, Alex; Klein, Sallee; Fein, Jeff; Belancourt, Patrick; Drake, R. P.; Pollock, Brad; Hazi, Andrew; Park, Jaebum; Williams, Jackson; Chen, Hui

    2013-10-01

    Collimated plasma jets emerge in many classes of astrophysical objects and are of great interest to explore in the laboratory. In many cases, these astrophysical jets exist within a background magnetic field where the magnetic pressure approaches the plasma pressure. Recent experiments performed at the Jupiter Laser Facility utilized a custom-designed solenoid to generate the multi-tesla fields necessary to achieve proper magnetization of the plasma. Time-gated interferometry, Schlieren imaging, and proton radiography were used to characterize jet evolution and collimation under varying degrees of magnetization. Experimental results will be presented and discussed. This work is funded by the NNSA-DS and SC-OFES Joint Program in High-Energy-Density Laboratory Plasmas, grant number DE-NA0001840, by the National Laser User Facility Program, grant number DE-NA0000850, by the Predictive Sciences Academic Alliances Program in NNSA-ASC, grant number DEFC52-08NA28616, and by NASA through Einstein Postdoctoral Fellowship grant number PF3-140111 awarded by the Chandra X-ray Center, which is operated by the Smithsonian Astrophysical Observatory for NASA under contract NAS8-03060.

  3. Representation of Block-Based Image Features in a Multi-Scale Framework for Built-Up Area Detection

    Zhongwen Hu

    2016-02-01

    Full Text Available The accurate extraction and mapping of built-up areas play an important role in many social, economic, and environmental studies. In this paper, we propose a novel approach for built-up area detection from high spatial resolution remote sensing images, using a block-based multi-scale feature representation framework. First, an image is divided into small blocks, in which the spectral, textural, and structural features are extracted and represented using a multi-scale framework; a set of refined Harris corner points is then used to select blocks as training samples; finally, a built-up index image is obtained by minimizing the normalized spectral, textural, and structural distances to the training samples, and a built-up area map is obtained by thresholding the index image. Experiments confirm that the proposed approach is effective for high-resolution optical and synthetic aperture radar images, with different scenes and different spatial resolutions.

  4. An Approach for Patient-Specific Multi-domain Vascular Mesh Generation Featuring Spatially Varying Wall Thickness Modeling

    Raut, Samarth S.; Liu, Peng; Finol, Ender A.

    2015-01-01

    In this work, we present a computationally efficient image-derived volume mesh generation approach for vasculatures that implements spatially varying patient-specific wall thickness with a novel inward extrusion of the wall surface mesh. Multi-domain vascular meshes with arbitrary numbers, locations, and patterns of both iliac bifurcations and thrombi can be obtained without the need to specify features or landmark points as input. In addition, the mesh output is coordinate-frame independent ...

  5. Multi-modal assessment of on-road demand of voice and manual phone calling and voice navigation entry across two embedded vehicle systems

    Mehler, Bruce; Kidd, David; Reimer, Bryan; Reagan, Ian; Dobres, Jonathan; McCartt, Anne

    2016-01-01

    Abstract One purpose of integrating voice interfaces into embedded vehicle systems is to reduce drivers’ visual and manual distractions with ‘infotainment’ technologies. However, there is scant research on actual benefits in production vehicles or how different interface designs affect attentional demands. Driving performance, visual engagement, and indices of workload (heart rate, skin conductance, subjective ratings) were assessed in 80 drivers randomly assigned to drive a 2013 Chevrolet Equinox or Volvo XC60. The Chevrolet MyLink system allowed completing tasks with one voice command, while the Volvo Sensus required multiple commands to navigate the menu structure. When calling a phone contact, both voice systems reduced visual demand relative to the visual–manual interfaces, with reductions for drivers in the Equinox being greater. The Equinox ‘one-shot’ voice command showed advantages during contact calling but had significantly higher error rates than Sensus during destination address entry. For both secondary tasks, neither voice interface entirely eliminated visual demand. Practitioner Summary: The findings reinforce the observation that most, if not all, automotive auditory–vocal interfaces are multi-modal interfaces in which the full range of potential demands (auditory, vocal, visual, manipulative, cognitive, tactile, etc.) need to be considered in developing optimal implementations and evaluating drivers’ interaction with the systems. Social Media: In-vehicle voice-interfaces can reduce visual demand but do not eliminate it and all types of demand need to be taken into account in a comprehensive evaluation. PMID:26269281

  6. Rapid Detection of Ascorbic Acid Based on a Dual-Electrode Sensor System Using a Powder Microelectrode Embedded with Carboxyl Multi-Walled Carbon Nanotubes.

    He, Bao-Shan; Zhang, Jun-Xia

    2017-07-02

    In this paper, carboxyl groups were introduced by liquid oxidation methods onto multi-walled carbon nanotubes (MWCNTs) to improve the MWCNTs' electrocatalytic properties. A platinum wire microelectrode (ME) was corroded using aqua regia and subsequently embedded with MWCNTs to achieve more active sites, producing a so-called powder microelectrode (PME). Compared with conventional MEs, the PME has a larger specific surface area and more active sites. When PME was used to detect ascorbic acid (AA), the AA oxidation potential shifted negatively and current peak was visibly increased. The calibration curve obtained for AA was in a range of 5.00 × 10 -6 ~9.50 × 10 -4 mol·L -1 : I pa (μA) = 3.259 × 10 -2 + 1.801 × 10² C (mol·L -1 ) under the optimum testing conditions. Moreover, the detection and quantitation limits were confirmed at 4.89 × 10 -7 mol·L -1 and 1.63 × 10 -7 mol·L -1 , respectively. When the fabricated PME was practically applied to detect AA, it was shown a recovery rate of 94~107% with relative standard deviation (RSD) <5%. The proposed strategy thus offers a promising, rapid, selective and low-cost approach to effective analysis of AA.

  7. Multi-modal assessment of on-road demand of voice and manual phone calling and voice navigation entry across two embedded vehicle systems.

    Mehler, Bruce; Kidd, David; Reimer, Bryan; Reagan, Ian; Dobres, Jonathan; McCartt, Anne

    2016-03-01

    One purpose of integrating voice interfaces into embedded vehicle systems is to reduce drivers' visual and manual distractions with 'infotainment' technologies. However, there is scant research on actual benefits in production vehicles or how different interface designs affect attentional demands. Driving performance, visual engagement, and indices of workload (heart rate, skin conductance, subjective ratings) were assessed in 80 drivers randomly assigned to drive a 2013 Chevrolet Equinox or Volvo XC60. The Chevrolet MyLink system allowed completing tasks with one voice command, while the Volvo Sensus required multiple commands to navigate the menu structure. When calling a phone contact, both voice systems reduced visual demand relative to the visual-manual interfaces, with reductions for drivers in the Equinox being greater. The Equinox 'one-shot' voice command showed advantages during contact calling but had significantly higher error rates than Sensus during destination address entry. For both secondary tasks, neither voice interface entirely eliminated visual demand. Practitioner Summary: The findings reinforce the observation that most, if not all, automotive auditory-vocal interfaces are multi-modal interfaces in which the full range of potential demands (auditory, vocal, visual, manipulative, cognitive, tactile, etc.) need to be considered in developing optimal implementations and evaluating drivers' interaction with the systems. Social Media: In-vehicle voice-interfaces can reduce visual demand but do not eliminate it and all types of demand need to be taken into account in a comprehensive evaluation.

  8. CT and MR imaging features of mucinous tubular and spindle cell carcinoma of the kidneys. A multi-institutional review

    Cornelis, F.; Grenier, N. [Pellegrin Hospital, Department of Radiology, Bordeaux (France); Ambrosetti, D. [Pasteur Hospital, Department of Pathology, Nice (France); Rocher, L. [Kremlin-Bicetre Hospital, Department of Radiology, Paris (France); Derchi, L.E. [University of Genoa, IRCCS AOU Ospedale, San Martino IST, Department of Health Sciences (DISSAL), Genoa (Italy); Renard, B.; Puech, P. [Claude Huriez Hospital, Department of Radiology, Lille (France); Claudon, M. [Brabois Hospital, Department of Radiology, Vandoeuvre-les-Nancy (France); Rouviere, O. [E. Herriot Hospital, Department of Radiology, Lyon (France); Ferlicot, S. [Kremlin-Bicetre Hospital, Department of Pathology, Paris (France); Roy, C. [Civil Hospital, Department of Radiology, Strasbourg (France); Yacoub, M. [Pellegrin Hospital, Department of Pathology, Bordeaux (France); Bernhard, J.C. [Pellegrin Hospital, Department of Urologic Surgery, Bordeaux (France)

    2017-03-15

    Mucinous tubular and spindle cell carcinoma (MTSCC) of the kidney is a recently identified renal malignancy. Diagnosis of this rare subtype of renal tumour can be challenging for pathologists, and as such, any additional data would be helpful to improve diagnostic reliability. As imaging features of this new and rare sub-type have not yet been clearly described, the purpose of this study was to describe the main radiologic features on computed tomography (CT) and magnetic resonance imaging (MRI), based jointly on the literature and findings from a multi-institutional retrospective review of pathology and imaging databases. Using a combination of CT/MRI features, diagnosis of MTSCC could be suggested in many cases. A combination of slow enhancement with plateau on dynamic contrast-enhanced CT/MRI, intermediate to high T2 signal intensity contrasting with low apparent diffusion coefficient values on MRI appeared evocative of this diagnosis. (orig.)

  9. Extraction of multi-scale landslide morphological features based on local Gi* using airborne LiDAR-derived DEM

    Shi, Wenzhong; Deng, Susu; Xu, Wenbing

    2018-02-01

    For automatic landslide detection, landslide morphological features should be quantitatively expressed and extracted. High-resolution Digital Elevation Models (DEMs) derived from airborne Light Detection and Ranging (LiDAR) data allow fine-scale morphological features to be extracted, but noise in DEMs influences morphological feature extraction, and the multi-scale nature of landslide features should be considered. This paper proposes a method to extract landslide morphological features characterized by homogeneous spatial patterns. Both profile and tangential curvature are utilized to quantify land surface morphology, and a local Gi* statistic is calculated for each cell to identify significant patterns of clustering of similar morphometric values. The method was tested on both synthetic surfaces simulating natural terrain and airborne LiDAR data acquired over an area dominated by shallow debris slides and flows. The test results of the synthetic data indicate that the concave and convex morphologies of the simulated terrain features at different scales and distinctness could be recognized using the proposed method, even when random noise was added to the synthetic data. In the test area, cells with large local Gi* values were extracted at a specified significance level from the profile and the tangential curvature image generated from the LiDAR-derived 1-m DEM. The morphologies of landslide main scarps, source areas and trails were clearly indicated, and the morphological features were represented by clusters of extracted cells. A comparison with the morphological feature extraction method based on curvature thresholds proved the proposed method's robustness to DEM noise. When verified against a landslide inventory, the morphological features of almost all recent (historical (> 10 years) landslides were extracted. This finding indicates that the proposed method can facilitate landslide detection, although the cell clusters extracted from curvature images should

  10. A Hybrid FPGA/Coarse Parallel Processing Architecture for Multi-modal Visual Feature Descriptors

    Jensen, Lars Baunegaard With; Kjær-Nielsen, Anders; Alonso, Javier Díaz

    2008-01-01

    This paper describes the hybrid architecture developed for speeding up the processing of so-called multi-modal visual primitives which are sparse image descriptors extracted along contours. In the system, the first stages of visual processing are implemented on FPGAs due to their highly parallel...

  11. Feature selection and multi-kernel learning for sparse representation on a manifold

    Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin

    2014-01-01

    combination of some basic items in a dictionary. Gao etal. (2013) recently proposed Laplacian sparse coding by regularizing the sparse codes with an affinity graph. However, due to the noisy features and nonlinear distribution of the data samples, the affinity

  12. Sensor-Based Auto-Focusing System Using Multi-Scale Feature Extraction and Phase Correlation Matching

    Jinbeum Jang

    2015-03-01

    Full Text Available This paper presents a novel auto-focusing system based on a CMOS sensor containing pixels with different phases. Robust extraction of features in a severely defocused image is the fundamental problem of a phase-difference auto-focusing system. In order to solve this problem, a multi-resolution feature extraction algorithm is proposed. Given the extracted features, the proposed auto-focusing system can provide the ideal focusing position using phase correlation matching. The proposed auto-focusing (AF algorithm consists of four steps: (i acquisition of left and right images using AF points in the region-of-interest; (ii feature extraction in the left image under low illumination and out-of-focus blur; (iii the generation of two feature images using the phase difference between the left and right images; and (iv estimation of the phase shifting vector using phase correlation matching. Since the proposed system accurately estimates the phase difference in the out-of-focus blurred image under low illumination, it can provide faster, more robust auto focusing than existing systems.

  13. Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal

    Mariela Cerrada

    2015-09-01

    Full Text Available There are growing demands for condition-based monitoring of gearboxes, and techniques to improve the reliability, effectiveness and accuracy for fault diagnosis are considered valuable contributions. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance in the diagnosis system. The main aim of this research is to propose a multi-stage feature selection mechanism for selecting the best set of condition parameters on the time, frequency and time-frequency domains, which are extracted from vibration signals for fault diagnosis purposes in gearboxes. The selection is based on genetic algorithms, proposing in each stage a new subset of the best features regarding the classifier performance in a supervised environment. The selected features are augmented at each stage and used as input for a neural network classifier in the next step, while a new subset of feature candidates is treated by the selection process. As a result, the inherent exploration and exploitation of the genetic algorithms for finding the best solutions of the selection problem are locally focused. The Sensors 2015, 15 23904 approach is tested on a dataset from a real test bed with several fault classes under different running conditions of load and velocity. The model performance for diagnosis is over 98%.

  14. Feature extraction based on extended multi-attribute profiles and sparse autoencoder for remote sensing image classification

    Teffahi, Hanane; Yao, Hongxun; Belabid, Nasreddine; Chaib, Souleyman

    2018-02-01

    The satellite images with very high spatial resolution have been recently widely used in image classification topic as it has become challenging task in remote sensing field. Due to a number of limitations such as the redundancy of features and the high dimensionality of the data, different classification methods have been proposed for remote sensing images classification particularly the methods using feature extraction techniques. This paper propose a simple efficient method exploiting the capability of extended multi-attribute profiles (EMAP) with sparse autoencoder (SAE) for remote sensing image classification. The proposed method is used to classify various remote sensing datasets including hyperspectral and multispectral images by extracting spatial and spectral features based on the combination of EMAP and SAE by linking them to kernel support vector machine (SVM) for classification. Experiments on new hyperspectral image "Huston data" and multispectral image "Washington DC data" shows that this new scheme can achieve better performance of feature learning than the primitive features, traditional classifiers and ordinary autoencoder and has huge potential to achieve higher accuracy for classification in short running time.

  15. Enhancement in visible light-responsive photocatalytic activity by embedding Cu-doped ZnO nanoparticles on multi-walled carbon nanotubes

    Ahmad, M., E-mail: mzkhm73@gmail.com [Department of Physics, Bahauddin Zakariya University, Multan 60800 (Pakistan); State Key Laboratory of Silicon Materials, Department of Materials Science and Engineering, Zhejiang University, Hangzhou 310027 (China); Ahmed, E., E-mail: profejaz@gmail.com [Department of Physics, Bahauddin Zakariya University, Multan 60800 (Pakistan); Hong, Z.L.; Jiao, X.L. [State Key Laboratory of Silicon Materials, Department of Materials Science and Engineering, Zhejiang University, Hangzhou 310027 (China); Abbas, T. [Institute of Industrial Control System, Rawalpindi (Pakistan); Khalid, N.R. [Department of Physics, Bahauddin Zakariya University, Multan 60800 (Pakistan); State Key Laboratory of Silicon Materials, Department of Materials Science and Engineering, Zhejiang University, Hangzhou 310027 (China)

    2013-11-15

    Copper doped ZnO nanoparticles embedded on multi-walled carbon nanotubes (CNTs) were successfully synthesized using a facile, nontoxic sol method. The resulting visible light-responsive Cu-doped ZnO/CNTs composites were characterized using powder X-ray diffraction (XRD), high resolution transmission electron microscope (HR-TEM), transmission electron microscope (TEM), scanning electron microscope (SEM) with energy dispersive X-ray analysis (EDX), X-ray photoelectron spectroscopy (XPS) and Brunauer Emmett Teller (BET) surface area analyzer. Optical properties of Cu-doped ZnO/CNTs nanocomposites, studied using UV–vis diffuse reflectance spectroscopy and photoluminescence spectroscopy (PL), which exhibited extended light absorption in visible light region and possessed better charge separation capability, respectively as compared to Cu-doped ZnO, pure ZnO and ZnO/CNTs composite. The photocatalytic activity was tested by degradation of methyl orange (MO) dye under visible light irradiation. The results demonstrated that Cu-doped ZnO/CNTs nanocomposites effectively bleached out MO, showing an impressive photocatalytic enhancement over ZnO, commercial ZnO, Cu-doped ZnO nanoparticles and ZnO/CNTs nanocomposites. Chemical oxygen demand (COD) of textile wastewater was also measured before and after the photocatalysis experiment under sunlight to evaluate the mineralization of wastewater. The significant decrease in COD of the treated effluent revealed a complete destruction of the organic molecules along with color removal. This dramatically enhanced photoactivity of nanocomposite photocatalysts was attributed to greater adsorptivity of dyes, extended light absorption and increased charge separation efficiency due to excellent electrical properties of carbon nanotubes and the large surface area.

  16. 5G Terminals with Multi-Streaming Features for Real-Time Mobile Broadband Applications

    T. Shuminoski

    2017-06-01

    Full Text Available In this paper we present a novel QoS framework on the network layer for 5G terminals with vertical multi-homing and multi-streaming capabilities by using radio networks aggregation. The proposed framework is leading to high performance utility networks with QoS provisioning for real-time multimedia services by achieving low packet delays, stochastic queuing network stability and highest mobile broadband capabilities i.e. bitrates. The proposed QoS algorithm is implemented within the mobile terminals on one side, and in dedicated proxy servers on mobile core network side. It is based on Lyapunov optimization techniques and it is targeted to handle simultaneously multiple multimedia service flows via multiple radio network interfaces in parallel.

  17. Automated Analysis and Classification of Histological Tissue Features by Multi-Dimensional Microscopic Molecular Profiling.

    Daniel P Riordan

    Full Text Available Characterization of the molecular attributes and spatial arrangements of cells and features within complex human tissues provides a critical basis for understanding processes involved in development and disease. Moreover, the ability to automate steps in the analysis and interpretation of histological images that currently require manual inspection by pathologists could revolutionize medical diagnostics. Toward this end, we developed a new imaging approach called multidimensional microscopic molecular profiling (MMMP that can measure several independent molecular properties in situ at subcellular resolution for the same tissue specimen. MMMP involves repeated cycles of antibody or histochemical staining, imaging, and signal removal, which ultimately can generate information analogous to a multidimensional flow cytometry analysis on intact tissue sections. We performed a MMMP analysis on a tissue microarray containing a diverse set of 102 human tissues using a panel of 15 informative antibody and 5 histochemical stains plus DAPI. Large-scale unsupervised analysis of MMMP data, and visualization of the resulting classifications, identified molecular profiles that were associated with functional tissue features. We then directly annotated H&E images from this MMMP series such that canonical histological features of interest (e.g. blood vessels, epithelium, red blood cells were individually labeled. By integrating image annotation data, we identified molecular signatures that were associated with specific histological annotations and we developed statistical models for automatically classifying these features. The classification accuracy for automated histology labeling was objectively evaluated using a cross-validation strategy, and significant accuracy (with a median per-pixel rate of 77% per feature from 15 annotated samples for de novo feature prediction was obtained. These results suggest that high-dimensional profiling may advance the

  18. A data-driven multi-model methodology with deep feature selection for short-term wind forecasting

    Feng, Cong; Cui, Mingjian; Hodge, Bri-Mathias; Zhang, Jie

    2017-01-01

    Highlights: • An ensemble model is developed to produce both deterministic and probabilistic wind forecasts. • A deep feature selection framework is developed to optimally determine the inputs to the forecasting methodology. • The developed ensemble methodology has improved the forecasting accuracy by up to 30%. - Abstract: With the growing wind penetration into the power system worldwide, improving wind power forecasting accuracy is becoming increasingly important to ensure continued economic and reliable power system operations. In this paper, a data-driven multi-model wind forecasting methodology is developed with a two-layer ensemble machine learning technique. The first layer is composed of multiple machine learning models that generate individual forecasts. A deep feature selection framework is developed to determine the most suitable inputs to the first layer machine learning models. Then, a blending algorithm is applied in the second layer to create an ensemble of the forecasts produced by first layer models and generate both deterministic and probabilistic forecasts. This two-layer model seeks to utilize the statistically different characteristics of each machine learning algorithm. A number of machine learning algorithms are selected and compared in both layers. This developed multi-model wind forecasting methodology is compared to several benchmarks. The effectiveness of the proposed methodology is evaluated to provide 1-hour-ahead wind speed forecasting at seven locations of the Surface Radiation network. Numerical results show that comparing to the single-algorithm models, the developed multi-model framework with deep feature selection procedure has improved the forecasting accuracy by up to 30%.

  19. HybridGO-Loc: mining hybrid features on gene ontology for predicting subcellular localization of multi-location proteins.

    Wan, Shibiao; Mak, Man-Wai; Kung, Sun-Yuan

    2014-01-01

    Protein subcellular localization prediction, as an essential step to elucidate the functions in vivo of proteins and identify drugs targets, has been extensively studied in previous decades. Instead of only determining subcellular localization of single-label proteins, recent studies have focused on predicting both single- and multi-location proteins. Computational methods based on Gene Ontology (GO) have been demonstrated to be superior to methods based on other features. However, existing GO-based methods focus on the occurrences of GO terms and disregard their relationships. This paper proposes a multi-label subcellular-localization predictor, namely HybridGO-Loc, that leverages not only the GO term occurrences but also the inter-term relationships. This is achieved by hybridizing the GO frequencies of occurrences and the semantic similarity between GO terms. Given a protein, a set of GO terms are retrieved by searching against the gene ontology database, using the accession numbers of homologous proteins obtained via BLAST search as the keys. The frequency of GO occurrences and semantic similarity (SS) between GO terms are used to formulate frequency vectors and semantic similarity vectors, respectively, which are subsequently hybridized to construct fusion vectors. An adaptive-decision based multi-label support vector machine (SVM) classifier is proposed to classify the fusion vectors. Experimental results based on recent benchmark datasets and a new dataset containing novel proteins show that the proposed hybrid-feature predictor significantly outperforms predictors based on individual GO features as well as other state-of-the-art predictors. For readers' convenience, the HybridGO-Loc server, which is for predicting virus or plant proteins, is available online at http://bioinfo.eie.polyu.edu.hk/HybridGoServer/.

  20. Multi-level governance of forest resources (Editorial to the special feature

    Esther Mwangi

    2012-08-01

    Full Text Available A major challenge for many researchers and practitioners relates to how to recognize and address cross-scale dynamics in space and over time in order to design and implement effective governance arrangements. This editorial provides an overview of the concept of multi-level governance (MLG. In particular we highlight definitional issues, why the concept matters as well as more practical concerns related to the processes and structure of multi-level governance. It is increasingly clear that multi-level governance of forest resources involves complex interactions of state, private and civil society actors at various levels, and institutions linking higher levels of social and political organization. Local communities are increasingly connected to global networks and influences. This creates new opportunities to learn and address problems but may also introduce new pressures and risks. We conclude by stressing the need for a much complex approach to the varieties of MLG to better understand how policies work as instruments of governance and to organize communities within systems of power and authority.

  1. Smart multicore embedded systems

    Bertels, Koen; Karlsson, Sven; Pacull, François

    2014-01-01

    This book provides a single-source reference to the state-of-the-art of high-level programming models and compilation tool-chains for embedded system platforms. The authors address challenges faced by programmers developing software to implement parallel applications in embedded systems, where very often they are forced to rewrite sequential programs into parallel software, taking into account all the low level features and peculiarities of the underlying platforms. Readers will benefit from these authors’ approach, which takes into account both the application requirements and the platform specificities of various embedded systems from different industries. Parallel programming tool-chains are described that take as input parameters both the application and the platform model, then determine relevant transformations and mapping decisions on the concrete platform, minimizing user intervention and hiding the difficulties related to the correct and efficient use of memory hierarchy and low level code generati...

  2. Deep features for efficient multi-biometric recognition with face and ear images

    Omara, Ibrahim; Xiao, Gang; Amrani, Moussa; Yan, Zifei; Zuo, Wangmeng

    2017-07-01

    Recently, multimodal biometric systems have received considerable research interest in many applications especially in the fields of security. Multimodal systems can increase the resistance to spoof attacks, provide more details and flexibility, and lead to better performance and lower error rate. In this paper, we present a multimodal biometric system based on face and ear, and propose how to exploit the extracted deep features from Convolutional Neural Networks (CNNs) on the face and ear images to introduce more powerful discriminative features and robust representation ability for them. First, the deep features for face and ear images are extracted based on VGG-M Net. Second, the extracted deep features are fused by using a traditional concatenation and a Discriminant Correlation Analysis (DCA) algorithm. Third, multiclass support vector machine is adopted for matching and classification. The experimental results show that the proposed multimodal system based on deep features is efficient and achieves a promising recognition rate up to 100 % by using face and ear. In addition, the results indicate that the fusion based on DCA is superior to traditional fusion.

  3. Feature selection and multi-kernel learning for sparse representation on a manifold

    Wang, Jim Jing-Yan

    2014-03-01

    Sparse representation has been widely studied as a part-based data representation method and applied in many scientific and engineering fields, such as bioinformatics and medical imaging. It seeks to represent a data sample as a sparse linear combination of some basic items in a dictionary. Gao etal. (2013) recently proposed Laplacian sparse coding by regularizing the sparse codes with an affinity graph. However, due to the noisy features and nonlinear distribution of the data samples, the affinity graph constructed directly from the original feature space is not necessarily a reliable reflection of the intrinsic manifold of the data samples. To overcome this problem, we integrate feature selection and multiple kernel learning into the sparse coding on the manifold. To this end, unified objectives are defined for feature selection, multiple kernel learning, sparse coding, and graph regularization. By optimizing the objective functions iteratively, we develop novel data representation algorithms with feature selection and multiple kernel learning respectively. Experimental results on two challenging tasks, N-linked glycosylation prediction and mammogram retrieval, demonstrate that the proposed algorithms outperform the traditional sparse coding methods. © 2013 Elsevier Ltd.

  4. Feature selection and multi-kernel learning for sparse representation on a manifold.

    Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin

    2014-03-01

    Sparse representation has been widely studied as a part-based data representation method and applied in many scientific and engineering fields, such as bioinformatics and medical imaging. It seeks to represent a data sample as a sparse linear combination of some basic items in a dictionary. Gao et al. (2013) recently proposed Laplacian sparse coding by regularizing the sparse codes with an affinity graph. However, due to the noisy features and nonlinear distribution of the data samples, the affinity graph constructed directly from the original feature space is not necessarily a reliable reflection of the intrinsic manifold of the data samples. To overcome this problem, we integrate feature selection and multiple kernel learning into the sparse coding on the manifold. To this end, unified objectives are defined for feature selection, multiple kernel learning, sparse coding, and graph regularization. By optimizing the objective functions iteratively, we develop novel data representation algorithms with feature selection and multiple kernel learning respectively. Experimental results on two challenging tasks, N-linked glycosylation prediction and mammogram retrieval, demonstrate that the proposed algorithms outperform the traditional sparse coding methods. Copyright © 2013 Elsevier Ltd. All rights reserved.

  5. Classification of Urban Feature from Unmanned Aerial Vehicle Images Using Gasvm Integration and Multi-Scale Segmentation

    Modiri, M.; Salehabadi, A.; Mohebbi, M.; Hashemi, A. M.; Masumi, M.

    2015-12-01

    The use of UAV in the application of photogrammetry to obtain cover images and achieve the main objectives of the photogrammetric mapping has been a boom in the region. The images taken from REGGIOLO region in the province of, Italy Reggio -Emilia by UAV with non-metric camera Canon Ixus and with an average height of 139.42 meters were used to classify urban feature. Using the software provided SURE and cover images of the study area, to produce dense point cloud, DSM and Artvqvtv spatial resolution of 10 cm was prepared. DTM area using Adaptive TIN filtering algorithm was developed. NDSM area was prepared with using the difference between DSM and DTM and a separate features in the image stack. In order to extract features, using simultaneous occurrence matrix features mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation for each of the RGB band image was used Orthophoto area. Classes used to classify urban problems, including buildings, trees and tall vegetation, grass and vegetation short, paved road and is impervious surfaces. Class consists of impervious surfaces such as pavement conditions, the cement, the car, the roof is stored. In order to pixel-based classification and selection of optimal features of classification was GASVM pixel basis. In order to achieve the classification results with higher accuracy and spectral composition informations, texture, and shape conceptual image featureOrthophoto area was fencing. The segmentation of multi-scale segmentation method was used.it belonged class. Search results using the proposed classification of urban feature, suggests the suitability of this method of classification complications UAV is a city using images. The overall accuracy and kappa coefficient method proposed in this study, respectively, 47/93% and 84/91% was.

  6. Revisiting the Robustness of PET-Based Textural Features in the Context of Multi-Centric Trials.

    Clément Bailly

    Full Text Available This study aimed to investigate the variability of textural features (TF as a function of acquisition and reconstruction parameters within the context of multi-centric trials.The robustness of 15 selected TFs were studied as a function of the number of iterations, the post-filtering level, input data noise, the reconstruction algorithm and the matrix size. A combination of several reconstruction and acquisition settings was devised to mimic multi-centric conditions. We retrospectively studied data from 26 patients enrolled in a diagnostic study that aimed to evaluate the performance of PET/CT 68Ga-DOTANOC in gastro-entero-pancreatic neuroendocrine tumors. Forty-one tumors were extracted and served as the database. The coefficient of variation (COV or the absolute deviation (for the noise study was derived and compared statistically with SUVmax and SUVmean results.The majority of investigated TFs can be used in a multi-centric context when each parameter is considered individually. The impact of voxel size and noise in the input data were predominant as only 4 TFs presented a high/intermediate robustness against SUV-based metrics (Entropy, Homogeneity, RP and ZP. When combining several reconstruction settings to mimic multi-centric conditions, most of the investigated TFs were robust enough against SUVmax except Correlation, Contrast, LGRE, LGZE and LZLGE.Considering previously published results on either reproducibility or sensitivity against delineation approach and our findings, it is feasible to consider Homogeneity, Entropy, Dissimilarity, HGRE, HGZE and ZP as relevant for being used in multi-centric trials.

  7. Revisiting the Robustness of PET-Based Textural Features in the Context of Multi-Centric Trials.

    Bailly, Clément; Bodet-Milin, Caroline; Couespel, Solène; Necib, Hatem; Kraeber-Bodéré, Françoise; Ansquer, Catherine; Carlier, Thomas

    2016-01-01

    This study aimed to investigate the variability of textural features (TF) as a function of acquisition and reconstruction parameters within the context of multi-centric trials. The robustness of 15 selected TFs were studied as a function of the number of iterations, the post-filtering level, input data noise, the reconstruction algorithm and the matrix size. A combination of several reconstruction and acquisition settings was devised to mimic multi-centric conditions. We retrospectively studied data from 26 patients enrolled in a diagnostic study that aimed to evaluate the performance of PET/CT 68Ga-DOTANOC in gastro-entero-pancreatic neuroendocrine tumors. Forty-one tumors were extracted and served as the database. The coefficient of variation (COV) or the absolute deviation (for the noise study) was derived and compared statistically with SUVmax and SUVmean results. The majority of investigated TFs can be used in a multi-centric context when each parameter is considered individually. The impact of voxel size and noise in the input data were predominant as only 4 TFs presented a high/intermediate robustness against SUV-based metrics (Entropy, Homogeneity, RP and ZP). When combining several reconstruction settings to mimic multi-centric conditions, most of the investigated TFs were robust enough against SUVmax except Correlation, Contrast, LGRE, LGZE and LZLGE. Considering previously published results on either reproducibility or sensitivity against delineation approach and our findings, it is feasible to consider Homogeneity, Entropy, Dissimilarity, HGRE, HGZE and ZP as relevant for being used in multi-centric trials.

  8. The algorithm of fast image stitching based on multi-feature extraction

    Yang, Chunde; Wu, Ge; Shi, Jing

    2018-05-01

    This paper proposed an improved image registration method combining Hu-based invariant moment contour information and feature points detection, aiming to solve the problems in traditional image stitching algorithm, such as time-consuming feature points extraction process, redundant invalid information overload and inefficiency. First, use the neighborhood of pixels to extract the contour information, employing the Hu invariant moment as similarity measure to extract SIFT feature points in those similar regions. Then replace the Euclidean distance with Hellinger kernel function to improve the initial matching efficiency and get less mismatching points, further, estimate affine transformation matrix between the images. Finally, local color mapping method is adopted to solve uneven exposure, using the improved multiresolution fusion algorithm to fuse the mosaic images and realize seamless stitching. Experimental results confirm high accuracy and efficiency of method proposed in this paper.

  9. Embedded defects

    Barriola, M.; Vachaspati, T.; Bucher, M.

    1994-01-01

    We give a prescription for embedding classical solutions and, in particular, topological defects in field theories which are invariant under symmetry groups that are not necessarily simple. After providing examples of embedded defects in field theories based on simple groups, we consider the electroweak model and show that it contains the Z string and a one-parameter family of strings called the W(α) string. It is argued that although the members of this family are gauge equivalent when considered in isolation, each member becomes physically distinct when multistring configurations are considered. We then turn to the issue of stability of embedded defects and demonstrate the instability of a large class of such solutions in the absence of bound states or condensates. The Z string is shown to be unstable for all values of the Higgs boson mass when θ W =π/4. W strings are also shown to be unstable for a large range of parameters. Embedded monopoles suffer from the Brandt-Neri-Coleman instability. Finally, we connect the electroweak string solutions to the sphaleron

  10. Designing a Web Spam Classifier Based on Feature Fusion in the Layered Multi-Population Genetic Programming Framework

    Amir Hosein KEYHANIPOUR

    2013-11-01

    Full Text Available Nowadays, Web spam pages are a critical challenge for Web retrieval systems which have drastic influence on the performance of such systems. Although these systems try to combat the impact of spam pages on their final results list, spammers increasingly use more sophisticated techniques to increase the number of views for their intended pages in order to have more commercial success. This paper employs the recently proposed Layered Multi-population Genetic Programming model for Web spam detection task as well application of correlation coefficient analysis for feature space reduction. Based on our tentative results, the designed classifier, which is based on a combination of easy to compute features, has a very reasonable performance in comparison with similar methods.

  11. A New Feature Extraction Method Based on EEMD and Multi-Scale Fuzzy Entropy for Motor Bearing

    Huimin Zhao

    2016-12-01

    Full Text Available Feature extraction is one of the most important, pivotal, and difficult problems in mechanical fault diagnosis, which directly relates to the accuracy of fault diagnosis and the reliability of early fault prediction. Therefore, a new fault feature extraction method, called the EDOMFE method based on integrating ensemble empirical mode decomposition (EEMD, mode selection, and multi-scale fuzzy entropy is proposed to accurately diagnose fault in this paper. The EEMD method is used to decompose the vibration signal into a series of intrinsic mode functions (IMFs with a different physical significance. The correlation coefficient analysis method is used to calculate and determine three improved IMFs, which are close to the original signal. The multi-scale fuzzy entropy with the ability of effective distinguishing the complexity of different signals is used to calculate the entropy values of the selected three IMFs in order to form a feature vector with the complexity measure, which is regarded as the inputs of the support vector machine (SVM model for training and constructing a SVM classifier (EOMSMFD based on EDOMFE and SVM for fulfilling fault pattern recognition. Finally, the effectiveness of the proposed method is validated by real bearing vibration signals of the motor with different loads and fault severities. The experiment results show that the proposed EDOMFE method can effectively extract fault features from the vibration signal and that the proposed EOMSMFD method can accurately diagnose the fault types and fault severities for the inner race fault, the outer race fault, and rolling element fault of the motor bearing. Therefore, the proposed method provides a new fault diagnosis technology for rotating machinery.

  12. Computer-Aided Diagnosis of Solid Breast Lesions Using an Ultrasonic Multi-Feature Analysis Procedure

    2011-01-01

    ultrasound. 1. BACKGROUND AND INTRODUCTION Breast cancer affects one of every eight women, it kills one of 29 women in the United States, and is the leading...feature analysis procedure for computer-aided diagnosis of solid breast lesions,” Ultrason Imag, 2010 (In Press). 22. C. B. Shakespeare , personal

  13. Feature selection and multi-kernel learning for adaptive graph regularized nonnegative matrix factorization

    Wang, Jim Jing-Yan; Huang, Jianhua Z.; Sun, Yijun; Gao, Xin

    2014-01-01

    by regularizing NMF with a nearest neighbor graph constructed from the input data set. However, GNMF has two main bottlenecks. First, using the original feature space directly to construct the graph is not necessarily optimal because of the noisy and irrelevant

  14. A kernel-based multi-feature image representation for histopathology image classification

    Moreno J; Caicedo J Gonzalez F

    2010-01-01

    This paper presents a novel strategy for building a high-dimensional feature space to represent histopathology image contents. Histogram features, related to colors, textures and edges, are combined together in a unique image representation space using kernel functions. This feature space is further enhanced by the application of latent semantic analysis, to model hidden relationships among visual patterns. All that information is included in the new image representation space. Then, support vector machine classifiers are used to assign semantic labels to images. Processing and classification algorithms operate on top of kernel functions, so that; the structure of the feature space is completely controlled using similarity measures and a dual representation. The proposed approach has shown a successful performance in a classification task using a dataset with 1,502 real histopathology images in 18 different classes. The results show that our approach for histological image classification obtains an improved average performance of 20.6% when compared to a conventional classification approach based on SVM directly applied to the original kernel.

  15. Disguised face identification using multi-modal features in a quaternionic form

    Apostolopoulos, George; Tzitzilonis, Vasileios; Kappatos, Vassilios

    2017-01-01

    Disguised face recognition is considered as very challenging and important problem in the face recognition field. A disguised face recognition algorithm is proposed using quaternionic representation. The feature extraction module is accomplished with a new method, decomposing each face image...... that the proposed algorithm can achieve high recognition results under disguised conditions....

  16. A KERNEL-BASED MULTI-FEATURE IMAGE REPRESENTATION FOR HISTOPATHOLOGY IMAGE CLASSIFICATION

    J Carlos Moreno

    2010-09-01

    Full Text Available This paper presents a novel strategy for building a high-dimensional feature space to represent histopathology image contents. Histogram features, related to colors, textures and edges, are combined together in a unique image representation space using kernel functions. This feature space is further enhanced by the application of Latent Semantic Analysis, to model hidden relationships among visual patterns. All that information is included in the new image representation space. Then, Support Vector Machine classifiers are used to assign semantic labels to images. Processing and classification algorithms operate on top of kernel functions, so that, the structure of the feature space is completely controlled using similarity measures and a dual representation. The proposed approach has shown a successful performance in a classification task using a dataset with 1,502 real histopathology images in 18 different classes. The results show that our approach for histological image classification obtains an improved average performance of 20.6% when compared to a conventional classification approach based on SVM directly applied to the original kernel.

  17. Tensor Train Neighborhood Preserving Embedding

    Wang, Wenqi; Aggarwal, Vaneet; Aeron, Shuchin

    2018-05-01

    In this paper, we propose a Tensor Train Neighborhood Preserving Embedding (TTNPE) to embed multi-dimensional tensor data into low dimensional tensor subspace. Novel approaches to solve the optimization problem in TTNPE are proposed. For this embedding, we evaluate novel trade-off gain among classification, computation, and dimensionality reduction (storage) for supervised learning. It is shown that compared to the state-of-the-arts tensor embedding methods, TTNPE achieves superior trade-off in classification, computation, and dimensionality reduction in MNIST handwritten digits and Weizmann face datasets.

  18. Multi-detector CT features of acute intestinal ischemia and their prognostic correlations.

    Moschetta, Marco; Telegrafo, Michele; Rella, Leonarda; Stabile Ianora, Amato Antonio; Angelelli, Giuseppe

    2014-05-28

    Acute intestinal ischemia is an abdominal emergency occurring in nearly 1% of patients presenting with acute abdomen. The causes can be occlusive or non occlusive. Early diagnosis is important to improve survival rates. In most cases of late or missed diagnosis, the mortality rate from intestinal infarction is very high, with a reported value ranging from 60% to 90%. Multi-detector computed tomography (MDCT) is a fundamental imaging technique that must be promptly performed in all patients with suspected bowel ischemia. Thanks to the new dedicated reconstruction program, its diagnostic potential is much improved compared to the past and currently it is superior to that of any other noninvasive technique. The increased spatial and temporal resolution, high-quality multi-planar reconstructions, maximum intensity projections, vessel probe, surface-shaded volume rending and tissue transition projections make MDCT the gold standard for the diagnosis of intestinal ischemia, with reported sensitivity, specificity, positive and negative predictive values of 64%-93%, 92%-100%, 90%-100% and 94%-98%, respectively. MDCT contributes to appropriate treatment planning and provides important prognostic information thanks to its ability to define the nature and extent of the disease. The purpose of this review is to examine the diagnostic and prognostic role of MDCT in bowel ischemia with special regard to the state of art new reconstruction software.

  19. User oriented design features of Korea Multi-purpose Research Reactor and its utilization plan

    Kim, Byungkoo; Jun, Byungjin

    1994-01-01

    Construction of a 30 MW class Korea Multi-purpose Research Reactor (KMRR) is near its completion and expected to reach initial criticality by the end of 1994 at KAERI Taejon site. As Korea will become one of developed countries during the lifetime of this reactor and many worldwide high performance research reactors of the first generation are reaching end of life, it is believed that KMRR will meet the increasing domestic needs to utilize high performance research reactor and its worldwide role will be important as well. In reactor design, effort has been focused on optimization which can satisfy various future utilization demands as much as possible with enhanced safety as a basic requirement. Light water cooled and heavy water reflected compact core using low enriched and high uranium loaded fuel, computer controlled operation, etc. are expected to provide truly multi-purpose user environments with stable high quality neutron flux. High level experimental facilities and equipment for reactor fuel and material test, various studies using neutron beam, radioisotope production, semiconductor doping, neutron activation analysis, etc., will be completed in parallel with the reactor or gradually depending on users' needs. When KMRR becomes fully operational, it will not only serve the domestic users but also be a valuable tool for a worldwide research community using a research reactor

  20. Multi-Feature Based Multiple Landmine Detection Using Ground Penetration Radar

    S. Park

    2014-06-01

    Full Text Available This paper presents a novel method for detection of multiple landmines using a ground penetrating radar (GPR. Conventional algorithms mainly focus on detection of a single landmine, which cannot linearly extend to the multiple landmine case. The proposed algorithm is composed of four steps; estimation of the number of multiple objects buried in the ground, isolation of each object, feature extraction and detection of landmines. The number of objects in the GPR signal is estimated by using the energy projection method. Then signals for the objects are extracted by using the symmetry filtering method. Each signal is then processed for features, which are given as input to the support vector machine (SVM for landmine detection. Three landmines buried in various ground conditions are considered for the test of the proposed method. They demonstrate that the proposed method can successfully detect multiple landmines.

  1. Network features of sector indexes spillover effects in China: A multi-scale view

    Feng, Sida; Huang, Shupei; Qi, Yabin; Liu, Xueyong; Sun, Qingru; Wen, Shaobo

    2018-04-01

    The spillover effects among sectors are of concern for distinct market participants, who are in distinct investment horizons and concerned with the information in different time scales. In order to uncover the hidden spillover information in multi-time scales in the rapidly changing stock market and thereby offer guidance to different investors concerning distinct time scales from a system perspective, this paper constructed directional spillover effect networks for the economic sectors in distinct time scales. The results are as follows: (1) The "2-4 days" scale is the most risky scale, and the "8-16 days" scale is the least risky one. (2) The most influential and sensitive sectors are distinct in different time scales. (3) Although two sectors in the same community may not have direct spillover relations, the volatility of one sector will have a relatively strong influence on the other through indirect relations.

  2. A multi-port 10GbE PCIe NIC featuring UDP offload and GPUDirect capabilities

    Ammendola, Roberto; Frezza, Ottorino; Lamanna, Gianluca; Cicero, Francesca Lo; Lonardo, Alessandro; Martinelli, Michele; Paolucci, Pier Stanislao; Pastorelli, Elena; Pontisso, Luca; Rossetti, Davide; Simula, Francesco; Sozzi, Marco; Tosoratto, Laura; Vicini, Piero

    2015-01-01

    NaNet-10 is a four-ports 10GbE PCIe Network Interface Card designed for low-latency real-time operations with GPU systems. To this purpose the design includes an UDP offload module, for fast and clock-cycle deterministic handling of the transport layer protocol, plus a GPUDirect P2P/RDMA engine for low-latency communication with NVIDIA Tesla GPU devices. A dedicated module (Multi-Stream) can optionally process input UDP streams before data is delivered through PCIe DMA to their destination devices, re-organizing data from different streams guaranteeing computational optimization. NaNet-10 is going to be integrated in the NA62 CERN experiment in order to assess the suitability of GPGPU systems as real-time triggers, results and lessons learned while performing this activity will be reported herein.

  3. Ontology-aided feature correlation for multi-modal urban sensing

    Misra, Archan; Lantra, Zaman; Jayarajah, Kasthuri

    2016-05-01

    The paper explores the use of correlation across features extracted from different sensing channels to help in urban situational understanding. We use real-world datasets to show how such correlation can improve the accuracy of detection of city-wide events by combining metadata analysis with image analysis of Instagram content. We demonstrate this through a case study on the Singapore Haze. We show that simple ontological relationships and reasoning can significantly help in automating such correlation-based understanding of transient urban events.

  4. Multi-modal demands of a smartphone used to place calls and enter addresses during highway driving relative to two embedded systems

    Reimer, Bryan; Mehler, Bruce; Reagan, Ian; Kidd, David; Dobres, Jonathan

    2016-01-01

    Abstract There is limited research on trade-offs in demand between manual and voice interfaces of embedded and portable technologies. Mehler et al. identified differences in driving performance, visual engagement and workload between two contrasting embedded vehicle system designs (Chevrolet MyLink and Volvo Sensus). The current study extends this work by comparing these embedded systems with a smartphone (Samsung Galaxy S4). None of the voice interfaces eliminated visual demand. Relative to placing calls manually, both embedded voice interfaces resulted in less eyes-off-road time than the smartphone. Errors were most frequent when calling contacts using the smartphone. The smartphone and MyLink allowed addresses to be entered using compound voice commands resulting in shorter eyes-off-road time compared with the menu-based Sensus but with many more errors. Driving performance and physiological measures indicated increased demand when performing secondary tasks relative to ‘just driving’, but were not significantly different between the smartphone and embedded systems. Practitioner Summary: The findings show that embedded system and portable device voice interfaces place fewer visual demands on the driver than manual interfaces, but they also underscore how differences in system designs can significantly affect not only the demands placed on drivers, but also the successful completion of tasks. PMID:27110964

  5. Multi-modal demands of a smartphone used to place calls and enter addresses during highway driving relative to two embedded systems.

    Reimer, Bryan; Mehler, Bruce; Reagan, Ian; Kidd, David; Dobres, Jonathan

    2016-12-01

    There is limited research on trade-offs in demand between manual and voice interfaces of embedded and portable technologies. Mehler et al. identified differences in driving performance, visual engagement and workload between two contrasting embedded vehicle system designs (Chevrolet MyLink and Volvo Sensus). The current study extends this work by comparing these embedded systems with a smartphone (Samsung Galaxy S4). None of the voice interfaces eliminated visual demand. Relative to placing calls manually, both embedded voice interfaces resulted in less eyes-off-road time than the smartphone. Errors were most frequent when calling contacts using the smartphone. The smartphone and MyLink allowed addresses to be entered using compound voice commands resulting in shorter eyes-off-road time compared with the menu-based Sensus but with many more errors. Driving performance and physiological measures indicated increased demand when performing secondary tasks relative to 'just driving', but were not significantly different between the smartphone and embedded systems. Practitioner Summary: The findings show that embedded system and portable device voice interfaces place fewer visual demands on the driver than manual interfaces, but they also underscore how differences in system designs can significantly affect not only the demands placed on drivers, but also the successful completion of tasks.

  6. Appreciating the broad clinical features of SMAD4 mutation carriers: a multi-center chart review

    Wain, K.E.; Ellingson, M.S.; McDonald, J.; Gammon, A.; Roberts, M.; Pichurin, P.; Winship, I.; Riegert-Johnson, D.; Weitzel, J. N.; Lindor, N.M.

    2014-01-01

    Heterozygous loss-of-function (LOF) SMAD4 mutations are associated with juvenile polyposis syndrome (JP) and hereditary hemorrhagic telangiectasia (HHT). Some carriers exhibit symptoms of both conditions, leading to the name JP-HHT syndrome. Three families have been reported with connective tissue abnormalities. In order to better understand the spectrum and extent of clinical findings in SMAD4 carriers, medical records of 34 patients (20 families) from five clinical practices were reviewed. Twenty-one percent (7/34) had features suggesting a connective tissue defect: enlarged aortic root (n=3), aortic and mitral insufficiency (n=2), aortic dissection (n=1), retinal detachment (n=1), brain aneurysms (n=1), lax skin and joints (n=1). JP-specific findings were almost uniformly present but variable. Ninety-seven percent had colon polyps that were generally pan-colonic and of variable histology and number. Forty-eight percent (15/31) had extensive gastric polyposis. HHT features were documented in 76% including epistaxis (19/31, 61%), mucocutaneous telangiectases (15/31, 48%), liver arteriovenous malformation (AVM) (6/16, 38%), brain AVM (1/26, 4%), pulmonary AVM (9/17, 53%), and intrapulmonary shunting (14/23, 61%). SMAD4 carriers should be managed for JP and HHT, since symptoms of both are likely yet unpredictable. Connective tissue abnormalities are an emerging component of JP-HHT syndrome, and larger studies are needed to understand these manifestations. PMID:24525918

  7. Multi-source feature extraction and target recognition in wireless sensor networks based on adaptive distributed wavelet compression algorithms

    Hortos, William S.

    2008-04-01

    Proposed distributed wavelet-based algorithms are a means to compress sensor data received at the nodes forming a wireless sensor network (WSN) by exchanging information between neighboring sensor nodes. Local collaboration among nodes compacts the measurements, yielding a reduced fused set with equivalent information at far fewer nodes. Nodes may be equipped with multiple sensor types, each capable of sensing distinct phenomena: thermal, humidity, chemical, voltage, or image signals with low or no frequency content as well as audio, seismic or video signals within defined frequency ranges. Compression of the multi-source data through wavelet-based methods, distributed at active nodes, reduces downstream processing and storage requirements along the paths to sink nodes; it also enables noise suppression and more energy-efficient query routing within the WSN. Targets are first detected by the multiple sensors; then wavelet compression and data fusion are applied to the target returns, followed by feature extraction from the reduced data; feature data are input to target recognition/classification routines; targets are tracked during their sojourns through the area monitored by the WSN. Algorithms to perform these tasks are implemented in a distributed manner, based on a partition of the WSN into clusters of nodes. In this work, a scheme of collaborative processing is applied for hierarchical data aggregation and decorrelation, based on the sensor data itself and any redundant information, enabled by a distributed, in-cluster wavelet transform with lifting that allows multiple levels of resolution. The wavelet-based compression algorithm significantly decreases RF bandwidth and other resource use in target processing tasks. Following wavelet compression, features are extracted. The objective of feature extraction is to maximize the probabilities of correct target classification based on multi-source sensor measurements, while minimizing the resource expenditures at

  8. Multi-center MRI carotid plaque component segmentation using feature normalization and transfer learning

    van Engelen, Arna; van Dijk, Anouk C; Truijman, Martine T.B.

    2015-01-01

    implementation of supervised methods. In this paper we segment carotid plaque components of clinical interest (fibrous tissue, lipid tissue, calcification and intraplaque hemorrhage) in a multicenter MRI study. We perform voxelwise tissue classification by traditional same-center training, and compare results...... not yield significant differences from that reference. We conclude that both extensive feature normalization and transfer learning can be valuable for the development of supervised methods that perform well on different types of datasets.......Automated segmentation of plaque components in carotid artery MRI is important to enable large studies on plaque vulnerability, and for incorporating plaque composition as an imaging biomarker in clinical practice. Especially supervised classification techniques, which learn from labeled examples...

  9. Embedded Hardware

    Ganssle, Jack G; Eady, Fred; Edwards, Lewin; Katz, David J; Gentile, Rick

    2007-01-01

    The Newnes Know It All Series takes the best of what our authors have written to create hard-working desk references that will be an engineer's first port of call for key information, design techniques and rules of thumb. Guaranteed not to gather dust on a shelf!. Circuit design using microcontrollers is both a science and an art. This book covers it all. It details all of the essential theory and facts to help an engineer design a robust embedded system. Processors, memory, and the hot topic of interconnects (I/O) are completely covered. Our authors bring a wealth of experience and ideas; thi

  10. A Novel Approach for Multi Class Fault Diagnosis in Induction Machine Based on Statistical Time Features and Random Forest Classifier

    Sonje, M. Deepak; Kundu, P.; Chowdhury, A.

    2017-08-01

    Fault diagnosis and detection is the important area in health monitoring of electrical machines. This paper proposes the recently developed machine learning classifier for multi class fault diagnosis in induction machine. The classification is based on random forest (RF) algorithm. Initially, stator currents are acquired from the induction machine under various conditions. After preprocessing the currents, fourteen statistical time features are estimated for each phase of the current. These parameters are considered as inputs to the classifier. The main scope of the paper is to evaluate effectiveness of RF classifier for individual and mixed fault diagnosis in induction machine. The stator, rotor and mixed faults (stator and rotor faults) are classified using the proposed classifier. The obtained performance measures are compared with the multilayer perceptron neural network (MLPNN) classifier. The results show the much better performance measures and more accurate than MLPNN classifier. For demonstration of planned fault diagnosis algorithm, experimentally obtained results are considered to build the classifier more practical.

  11. Investigating the multi-causal and complex nature of the accident causal influence of construction project features.

    Manu, Patrick A; Ankrah, Nii A; Proverbs, David G; Suresh, Subashini

    2012-09-01

    Construction project features (CPFs) are organisational, physical and operational attributes that characterise construction projects. Although previous studies have examined the accident causal influence of CPFs, the multi-causal attribute of this causal phenomenon still remain elusive and thus requires further investigation. Aiming to shed light on this facet of the accident causal phenomenon of CPFs, this study examines relevant literature and crystallises the attained insight of the multi-causal attribute by a graphical model which is subsequently operationalised by a derived mathematical risk expression that offers a systematic approach for evaluating the potential of CPFs to cause harm and consequently their health and safety (H&S) risk implications. The graphical model and the risk expression put forth by the study thus advance current understanding of the accident causal phenomenon of CPFs and they present an opportunity for project participants to manage the H&S risk associated with CPFs from the early stages of project procurement. Copyright © 2011 Elsevier Ltd. All rights reserved.

  12. A generic miniature multi-feature programmable wireless powering headstage ASIC for implantable biomedical systems.

    Kubendran, Rajkumar; Krishnan, Harish; Manola, Bhupendra; John, Simon W M; Chappell, William J; Irazoqui, Pedro P

    2011-01-01

    Wireless powering holds immense promise to enable a variety of implantable biomedical measurement systems with different power supply and current budget requirements. Effective power management demands more functionality in the headstage design like power level detection for range estimation and power save modes for sleep-wake operation. This paper proposes a single chip ASIC solution that addresses these problems by incorporating digitally programmable features and thus has the potential to enable wireless powering for many implantable systems. The ASIC includes an RF rectifier which has a peak efficiency of 17.9% at 900 MHz and 11.0% at 2.4 GHz, a robust 1 V bandgap reference and LDO voltage regulator whose output can be programmed in the range of 1 V-1.5 V, and can drive upto 4 mA of load current. The input RF power level detector has a threshold of 1.6 V and the power management block can be programmed to give a 6%, 12.5% or 25% duty cycle power line to the transmitter resulting in upto 60% reduction in average power. The ASIC was fabricated using the TSMC 65 nm process, occupies 1mm(2) die area and the headstage consumes ~300 μA at 1.2V regulated supply.

  13. Multi-feature snore sound analysis in obstructive sleep apnea–hypopnea syndrome

    Karunajeewa, Asela S; Abeyratne, Udantha R; Hukins, Craig

    2011-01-01

    Snoring is the most common symptom of obstructive sleep apnea hypopnea syndrome (OSAHS), which is a serious disease with high community prevalence. The standard method of OSAHS diagnosis, known as polysomnography (PSG), is expensive and time consuming. There is evidence suggesting that snore-related sounds (SRS) carry sufficient information to diagnose OSAHS. In this paper we present a technique for diagnosing OSAHS based solely on snore sound analysis. The method comprises a logistic regression model fed with snore parameters derived from its features such as the pitch and total airway response (TAR) estimated using a higher order statistics (HOS)-based algorithm. Pitch represents a time domain characteristic of the airway vibrations and the TAR represents the acoustical changes brought about by the collapsing upper airways. The performance of the proposed method was evaluated using the technique of K-fold cross validation, on a clinical database consisting of overnight snoring sounds of 41 subjects. The method achieved 89.3% sensitivity with 92.3% specificity (the area under the ROC curve was 0.96). These results establish the feasibility of developing a snore-based OSAHS community-screening device, which does not require any contact measurements

  14. Rotation-invariant features for multi-oriented text detection in natural images.

    Cong Yao

    Full Text Available Texts in natural scenes carry rich semantic information, which can be used to assist a wide range of applications, such as object recognition, image/video retrieval, mapping/navigation, and human computer interaction. However, most existing systems are designed to detect and recognize horizontal (or near-horizontal texts. Due to the increasing popularity of mobile-computing devices and applications, detecting texts of varying orientations from natural images under less controlled conditions has become an important but challenging task. In this paper, we propose a new algorithm to detect texts of varying orientations. Our algorithm is based on a two-level classification scheme and two sets of features specially designed for capturing the intrinsic characteristics of texts. To better evaluate the proposed method and compare it with the competing algorithms, we generate a comprehensive dataset with various types of texts in diverse real-world scenes. We also propose a new evaluation protocol, which is more suitable for benchmarking algorithms for detecting texts in varying orientations. Experiments on benchmark datasets demonstrate that our system compares favorably with the state-of-the-art algorithms when handling horizontal texts and achieves significantly enhanced performance on variant texts in complex natural scenes.

  15. Multi-objective optimization of generalized reliability design problems using feature models-A concept for early design stages

    Limbourg, Philipp; Kochs, Hans-Dieter

    2008-01-01

    Reliability optimization problems such as the redundancy allocation problem (RAP) have been of considerable interest in the past. However, due to the restrictions of the design space formulation, they may not be applicable in all practical design problems. A method with high modelling freedom for rapid design screening is desirable, especially in early design stages. This work presents a novel approach to reliability optimization. Feature modelling, a specification method originating from software engineering, is applied for the fast specification and enumeration of complex design spaces. It is shown how feature models can not only describe arbitrary RAPs but also much more complex design problems. The design screening is accomplished by a multi-objective evolutionary algorithm for probabilistic objectives. Comparing averages or medians may hide the true characteristics of this distributions. Therefore the algorithm uses solely the probability of a system dominating another to achieve the Pareto optimal set. We illustrate the approach by specifying a RAP and a more complex design space and screening them with the evolutionary algorithm

  16. Predicting protein-ATP binding sites from primary sequence through fusing bi-profile sampling of multi-view features

    Zhang Ya-Nan

    2012-05-01

    Full Text Available Abstract Background Adenosine-5′-triphosphate (ATP is one of multifunctional nucleotides and plays an important role in cell biology as a coenzyme interacting with proteins. Revealing the binding sites between protein and ATP is significantly important to understand the functionality of the proteins and the mechanisms of protein-ATP complex. Results In this paper, we propose a novel framework for predicting the proteins’ functional residues, through which they can bind with ATP molecules. The new prediction protocol is achieved by combination of sequence evolutional information and bi-profile sampling of multi-view sequential features and the sequence derived structural features. The hypothesis for this strategy is single-view feature can only represent partial target’s knowledge and multiple sources of descriptors can be complementary. Conclusions Prediction performances evaluated by both 5-fold and leave-one-out jackknife cross-validation tests on two benchmark datasets consisting of 168 and 227 non-homologous ATP binding proteins respectively demonstrate the efficacy of the proposed protocol. Our experimental results also reveal that the residue structural characteristics of real protein-ATP binding sites are significant different from those normal ones, for example the binding residues do not show high solvent accessibility propensities, and the bindings prefer to occur at the conjoint points between different secondary structure segments. Furthermore, results also show that performance is affected by the imbalanced training datasets by testing multiple ratios between positive and negative samples in the experiments. Increasing the dataset scale is also demonstrated useful for improving the prediction performances.

  17. Morphing the feature-based multi-blocks of normative/healthy vertebral geometries to scoliosis vertebral geometries: development of personalized finite element models.

    Hadagali, Prasannaah; Peters, James R; Balasubramanian, Sriram

    2018-03-12

    Personalized Finite Element (FE) models and hexahedral elements are preferred for biomechanical investigations. Feature-based multi-block methods are used to develop anatomically accurate personalized FE models with hexahedral mesh. It is tedious to manually construct multi-blocks for large number of geometries on an individual basis to develop personalized FE models. Mesh-morphing method mitigates the aforementioned tediousness in meshing personalized geometries every time, but leads to element warping and loss of geometrical data. Such issues increase in magnitude when normative spine FE model is morphed to scoliosis-affected spinal geometry. The only way to bypass the issue of hex-mesh distortion or loss of geometry as a result of morphing is to rely on manually constructing the multi-blocks for scoliosis-affected spine geometry of each individual, which is time intensive. A method to semi-automate the construction of multi-blocks on the geometry of scoliosis vertebrae from the existing multi-blocks of normative vertebrae is demonstrated in this paper. High-quality hexahedral elements were generated on the scoliosis vertebrae from the morphed multi-blocks of normative vertebrae. Time taken was 3 months to construct the multi-blocks for normative spine and less than a day for scoliosis. Efforts taken to construct multi-blocks on personalized scoliosis spinal geometries are significantly reduced by morphing existing multi-blocks.

  18. Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features.

    Zhang, Xin; Yan, Lin-Feng; Hu, Yu-Chuan; Li, Gang; Yang, Yang; Han, Yu; Sun, Ying-Zhi; Liu, Zhi-Cheng; Tian, Qiang; Han, Zi-Yang; Liu, Le-De; Hu, Bin-Quan; Qiu, Zi-Yu; Wang, Wen; Cui, Guang-Bin

    2017-07-18

    Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance imaging (MRI) data. However, the efficacies of different machine learning methods in glioma grading have not been investigated.A comprehensive comparison of varied machine learning methods in differentiating low-grade gliomas (LGGs) and high-grade gliomas (HGGs) as well as WHO grade II, III and IV gliomas based on multi-parametric MRI images was proposed in the current study. The parametric histogram and image texture attributes of 120 glioma patients were extracted from the perfusion, diffusion and permeability parametric maps of preoperative MRI. Then, 25 commonly used machine learning classifiers combined with 8 independent attribute selection methods were applied and evaluated using leave-one-out cross validation (LOOCV) strategy. Besides, the influences of parameter selection on the classifying performances were investigated. We found that support vector machine (SVM) exhibited superior performance to other classifiers. By combining all tumor attributes with synthetic minority over-sampling technique (SMOTE), the highest classifying accuracy of 0.945 or 0.961 for LGG and HGG or grade II, III and IV gliomas was achieved. Application of Recursive Feature Elimination (RFE) attribute selection strategy further improved the classifying accuracies. Besides, the performances of LibSVM, SMO, IBk classifiers were influenced by some key parameters such as kernel type, c, gama, K, etc. SVM is a promising tool in developing automated preoperative glioma grading system, especially when being combined with RFE strategy. Model parameters should be considered in glioma grading model optimization.

  19. Design of signal reception and processing system of embedded ultrasonic endoscope

    Li, Ming; Yu, Feng; Zhang, Ruiqiang; Li, Yan; Chen, Xiaodong; Yu, Daoyin

    2009-11-01

    Embedded Ultrasonic Endoscope, based on embedded microprocessor and embedded real-time operating system, sends a micro ultrasonic probe into coelom through the biopsy channel of the Electronic Endoscope to get the fault histology features of digestive organs by rotary scanning, and acquires the pictures of the alimentary canal mucosal surface. At the same time, ultrasonic signals are processed by signal reception and processing system, forming images of the full histology of the digestive organs. Signal Reception and Processing System is an important component of Embedded Ultrasonic Endoscope. However, the traditional design, using multi-level amplifiers and special digital processing circuits to implement signal reception and processing, is no longer satisfying the standards of high-performance, miniaturization and low power requirements that embedded system requires, and as a result of the high noise that multi-level amplifier brought, the extraction of small signal becomes hard. Therefore, this paper presents a method of signal reception and processing based on double variable gain amplifier and FPGA, increasing the flexibility and dynamic range of the Signal Reception and Processing System, improving system noise level, and reducing power consumption. Finally, we set up the embedded experiment system, using a transducer with the center frequency of 8MHz to scan membrane samples, and display the image of ultrasonic echo reflected by each layer of membrane, with a frame rate of 5Hz, verifying the correctness of the system.

  20. Embedded data acquisition system with MDSPlus

    Rajpal, Rachana; Patel, Jigneshkumar; Kumari, Praveena; Panchal, Vipul; Chattopadhyay, P.K.; Pujara, Harshad; Saxena, Y.C.

    2012-01-01

    This data acquisition system (DAS) is designed and developed to cater the increasing demand of Plasma Diagnostics for Aditya Tokamak as well as to support the basic physics research going on at Institute for Plasma Research. The main design criteria were to design a system with minimum resources and flexible to cater the needs of slow and fast diagnostic channels and can be easily integrated with the existing data acquisition system of Aditya Tokamak. The DAS is designed on embedded PC/104 platform. This is a multi channel system which supports standard features of commercially available DAS. The control and bus interface logic are implemented using Very High Speed Hardware Description Language (VHDL) on Complex Programmable Logic Device (CPLD). For Aditya Tokamak pulse experiment, the software application is designed such that the data is directly integrated to the MDSplus tree of Aditya DAS. The detailed hardware and software design, development and testing results will be discussed in the paper.

  1. Multi-scale textural feature extraction and particle swarm optimization based model selection for false positive reduction in mammography.

    Zyout, Imad; Czajkowska, Joanna; Grzegorzek, Marcin

    2015-12-01

    The high number of false positives and the resulting number of avoidable breast biopsies are the major problems faced by current mammography Computer Aided Detection (CAD) systems. False positive reduction is not only a requirement for mass but also for calcification CAD systems which are currently deployed for clinical use. This paper tackles two problems related to reducing the number of false positives in the detection of all lesions and masses, respectively. Firstly, textural patterns of breast tissue have been analyzed using several multi-scale textural descriptors based on wavelet and gray level co-occurrence matrix. The second problem addressed in this paper is the parameter selection and performance optimization. For this, we adopt a model selection procedure based on Particle Swarm Optimization (PSO) for selecting the most discriminative textural features and for strengthening the generalization capacity of the supervised learning stage based on a Support Vector Machine (SVM) classifier. For evaluating the proposed methods, two sets of suspicious mammogram regions have been used. The first one, obtained from Digital Database for Screening Mammography (DDSM), contains 1494 regions (1000 normal and 494 abnormal samples). The second set of suspicious regions was obtained from database of Mammographic Image Analysis Society (mini-MIAS) and contains 315 (207 normal and 108 abnormal) samples. Results from both datasets demonstrate the efficiency of using PSO based model selection for optimizing both classifier hyper-parameters and parameters, respectively. Furthermore, the obtained results indicate the promising performance of the proposed textural features and more specifically, those based on co-occurrence matrix of wavelet image representation technique. Copyright © 2015 Elsevier Ltd. All rights reserved.

  2. Nested-PCR for the detection of Mycoplasma hyopneumoniae in bronchial alveolar swabs, frozen tissues and formalin-fixed paraffin-embedded swine lung samples: comparative evaluation with immunohistochemical findings and histological features

    Paula R. Almeida

    2012-08-01

    Full Text Available The diagnosis of Mycoplasma hyopneumoniae infection is often performed through histopathology, immunohistochemistry (IHC and polymerase chain reaction (PCR or a combination of these techniques. PCR can be performed on samples using several conservation methods, including swabs, frozen tissue or formalin-fixed and paraffin-embedded (FFPE tissue. However, the formalin fixation process often inhibits DNA amplification. To evaluate whether M. hyopneumoniae DNA could be recovered from FFPE tissues, 15 lungs with cranioventral consolidation lesions were collected in a slaughterhouse from swine bred in herds with respiratory disease. Bronchial swabs and fresh lung tissue were collected, and a fragment of the corresponding lung section was placed in neutral buffered formalin for 48 hours. A PCR assay was performed to compare FFPE tissue samples with samples that were only refrigerated (bronchial swabs or frozen (tissue pieces. M. hyopneumoniae was detected by PCR in all 15 samples of the swab and frozen tissue, while it was detected in only 11 of the 15 FFPE samples. Histological features of M. hyopneumoniae infection were presented in 11 cases and 7 of these samples stained positive in IHC. Concordance between the histological features and detection results was observed in 13 of the FFPE tissue samples. PCR was the most sensitive technique. Comparison of different sample conservation methods indicated that it is possible to detect M. hyopneumoniae from FFPE tissue. It is important to conduct further research using archived material because the efficiency of PCR could be compromised under these conditions.

  3. MO-FG-303-01: FEATURED PRESENTATION and BEST IN PHYSICS (THERAPY): Automating LINAC QA: Design and Testing of An Image Acquisition and Processing System Utilizing a Combination of Radioluminescent Phosphors, Embedded X-Ray Markers and Optical Measurements

    Jenkins, C; Naczynski, D; Yu, S; Xing, L

    2015-01-01

    Purpose: The recent development of phosphors to visualize radiation beams from linear accelerators (LINAC) offers a unique opportunity for evaluating radiation fields within the context of the treatment space. The purpose of this study was to establish an automated, self-calibrating prototype system for performing quality assurance (QA) measurements. Methods: A thin layer of Gd 2 O 2 S:Tb phosphor and fiducial markers were embedded on several planar faces of a custom-designed phantom. The phantom was arbitrarily placed near iso-center on the couch of a LINAC equipped with on-board megavoltage (MV) and kilovoltage (kV) imagers. A plan consisting of several beams and integrated image acquisitions was delivered. Images of the phantom were collected throughout the delivery. Salient features, such as fiducials, crosshairs and beam edges were then extracted from these images used to calibrate the system, adjust for variations in phantom placement, and perform measurements. Beam edges were visualized by imaging the light generated by the phosphor on the phantom enabling direct comparison with the light field and laser locations. Registration of MV, kV and optical image data was performed using the embedded fiducial markers, enabling comparison of imaging center locations. Measurements specified by TG-142 were calculated and compared with those obtained from a commercially available QA system. Results: The system was able to automatically extract the location of the fiducials, lasers, light field and radiation field from the acquired images regardless of phantom positioning. It was also able to automatically identify the locations of fiducial markers on kV and MV images. All collected measurements were within TG-142 guidelines. The difference between the prototype and commercially available system were less than 0.2 mm. Conclusion: The prototype system demonstrated the capability of accurately and autonomously evaluating various TG-142 parameters independent of operator

  4. Automatic Craniomaxillofacial Landmark Digitization via Segmentation-guided Partially-joint Regression Forest Model and Multi-scale Statistical Features

    Zhang, Jun; Gao, Yaozong; Wang, Li; Tang, Zhen; Xia, James J.; Shen, Dinggang

    2016-01-01

    Objective The goal of this paper is to automatically digitize craniomaxillofacial (CMF) landmarks efficiently and accurately from cone-beam computed tomography (CBCT) images, by addressing the challenge caused by large morphological variations across patients and image artifacts of CBCT images. Methods We propose a Segmentation-guided Partially-joint Regression Forest (S-PRF) model to automatically digitize CMF landmarks. In this model, a regression voting strategy is first adopted to localize each landmark by aggregating evidences from context locations, thus potentially relieving the problem caused by image artifacts near the landmark. Second, CBCT image segmentation is utilized to remove uninformative voxels caused by morphological variations across patients. Third, a partially-joint model is further proposed to separately localize landmarks based on the coherence of landmark positions to improve the digitization reliability. In addition, we propose a fast vector quantization (VQ) method to extract high-level multi-scale statistical features to describe a voxel's appearance, which has low dimensionality, high efficiency, and is also invariant to the local inhomogeneity caused by artifacts. Results Mean digitization errors for 15 landmarks, in comparison to the ground truth, are all less than 2mm. Conclusion Our model has addressed challenges of both inter-patient morphological variations and imaging artifacts. Experiments on a CBCT dataset show that our approach achieves clinically acceptable accuracy for landmark digitalization. Significance Our automatic landmark digitization method can be used clinically to reduce the labor cost and also improve digitalization consistency. PMID:26625402

  5. Multi-feature machine learning model for automatic segmentation of green fractional vegetation cover for high-throughput field phenotyping.

    Sadeghi-Tehran, Pouria; Virlet, Nicolas; Sabermanesh, Kasra; Hawkesford, Malcolm J

    2017-01-01

    Accurately segmenting vegetation from the background within digital images is both a fundamental and a challenging task in phenotyping. The performance of traditional methods is satisfactory in homogeneous environments, however, performance decreases when applied to images acquired in dynamic field environments. In this paper, a multi-feature learning method is proposed to quantify vegetation growth in outdoor field conditions. The introduced technique is compared with the state-of the-art and other learning methods on digital images. All methods are compared and evaluated with different environmental conditions and the following criteria: (1) comparison with ground-truth images, (2) variation along a day with changes in ambient illumination, (3) comparison with manual measurements and (4) an estimation of performance along the full life cycle of a wheat canopy. The method described is capable of coping with the environmental challenges faced in field conditions, with high levels of adaptiveness and without the need for adjusting a threshold for each digital image. The proposed method is also an ideal candidate to process a time series of phenotypic information throughout the crop growth acquired in the field. Moreover, the introduced method has an advantage that it is not limited to growth measurements only but can be applied on other applications such as identifying weeds, diseases, stress, etc.

  6. Multi-feature machine learning model for automatic segmentation of green fractional vegetation cover for high-throughput field phenotyping

    Pouria Sadeghi-Tehran

    2017-11-01

    Full Text Available Abstract Background Accurately segmenting vegetation from the background within digital images is both a fundamental and a challenging task in phenotyping. The performance of traditional methods is satisfactory in homogeneous environments, however, performance decreases when applied to images acquired in dynamic field environments. Results In this paper, a multi-feature learning method is proposed to quantify vegetation growth in outdoor field conditions. The introduced technique is compared with the state-of the-art and other learning methods on digital images. All methods are compared and evaluated with different environmental conditions and the following criteria: (1 comparison with ground-truth images, (2 variation along a day with changes in ambient illumination, (3 comparison with manual measurements and (4 an estimation of performance along the full life cycle of a wheat canopy. Conclusion The method described is capable of coping with the environmental challenges faced in field conditions, with high levels of adaptiveness and without the need for adjusting a threshold for each digital image. The proposed method is also an ideal candidate to process a time series of phenotypic information throughout the crop growth acquired in the field. Moreover, the introduced method has an advantage that it is not limited to growth measurements only but can be applied on other applications such as identifying weeds, diseases, stress, etc.

  7. Time-Frequency Feature Representation Using Multi-Resolution Texture Analysis and Acoustic Activity Detector for Real-Life Speech Emotion Recognition

    Kun-Ching Wang

    2015-01-01

    Full Text Available The classification of emotional speech is mostly considered in speech-related research on human-computer interaction (HCI. In this paper, the purpose is to present a novel feature extraction based on multi-resolutions texture image information (MRTII. The MRTII feature set is derived from multi-resolution texture analysis for characterization and classification of different emotions in a speech signal. The motivation is that we have to consider emotions have different intensity values in different frequency bands. In terms of human visual perceptual, the texture property on multi-resolution of emotional speech spectrogram should be a good feature set for emotion classification in speech. Furthermore, the multi-resolution analysis on texture can give a clearer discrimination between each emotion than uniform-resolution analysis on texture. In order to provide high accuracy of emotional discrimination especially in real-life, an acoustic activity detection (AAD algorithm must be applied into the MRTII-based feature extraction. Considering the presence of many blended emotions in real life, in this paper make use of two corpora of naturally-occurring dialogs recorded in real-life call centers. Compared with the traditional Mel-scale Frequency Cepstral Coefficients (MFCC and the state-of-the-art features, the MRTII features also can improve the correct classification rates of proposed systems among different language databases. Experimental results show that the proposed MRTII-based feature information inspired by human visual perception of the spectrogram image can provide significant classification for real-life emotional recognition in speech.

  8. Embedded Processor Laboratory

    Federal Laboratory Consortium — The Embedded Processor Laboratory provides the means to design, develop, fabricate, and test embedded computers for missile guidance electronics systems in support...

  9. Computer vision camera with embedded FPGA processing

    Lecerf, Antoine; Ouellet, Denis; Arias-Estrada, Miguel

    2000-03-01

    Traditional computer vision is based on a camera-computer system in which the image understanding algorithms are embedded in the computer. To circumvent the computational load of vision algorithms, low-level processing and imaging hardware can be integrated in a single compact module where a dedicated architecture is implemented. This paper presents a Computer Vision Camera based on an open architecture implemented in an FPGA. The system is targeted to real-time computer vision tasks where low level processing and feature extraction tasks can be implemented in the FPGA device. The camera integrates a CMOS image sensor, an FPGA device, two memory banks, and an embedded PC for communication and control tasks. The FPGA device is a medium size one equivalent to 25,000 logic gates. The device is connected to two high speed memory banks, an IS interface, and an imager interface. The camera can be accessed for architecture programming, data transfer, and control through an Ethernet link from a remote computer. A hardware architecture can be defined in a Hardware Description Language (like VHDL), simulated and synthesized into digital structures that can be programmed into the FPGA and tested on the camera. The architecture of a classical multi-scale edge detection algorithm based on a Laplacian of Gaussian convolution has been developed to show the capabilities of the system.

  10. MUC1 Expression by Immunohistochemistry Is Associated with Adverse Pathologic Features in Prostate Cancer: A Multi-Institutional Study.

    Okyaz Eminaga

    Full Text Available The uncertainties inherent in clinical measures of prostate cancer (CaP aggressiveness endorse the investigation of clinically validated tissue biomarkers. MUC1 expression has been previously reported to independently predict aggressive localized prostate cancer. We used a large cohort to validate whether MUC1 protein levels measured by immunohistochemistry (IHC predict aggressive cancer, recurrence and survival outcomes after radical prostatectomy independent of clinical and pathological parameters.MUC1 IHC was performed on a multi-institutional tissue microarray (TMA resource including 1,326 men with a median follow-up of 5 years. Associations with clinical and pathological parameters were tested by the Chi-square test and the Wilcoxon rank sum test. Relationships with outcome were assessed with univariable and multivariable Cox proportional hazard models and the Log-rank test.The presence of MUC1 expression was significantly associated with extracapsular extension and higher Gleason score, but not with seminal vesicle invasion, age, positive surgical margins or pre-operative serum PSA levels. In univariable analyses, positive MUC1 staining was significantly associated with a worse recurrence free survival (RFS (HR: 1.24, CI 1.03-1.49, P = 0.02, although not with disease specific survival (DSS, P>0.5. On multivariable analyses, the presence of positive surgical margins, extracapsular extension, seminal vesicle invasion, as well as higher pre-operative PSA and increasing Gleason score were independently associated with RFS, while MUC1 expression was not. Positive MUC1 expression was not independently associated with disease specific survival (DSS, but was weakly associated with overall survival (OS.In our large, rigorously designed validation cohort, MUC1 protein expression was associated with adverse pathological features, although it was not an independent predictor of outcome after radical prostatectomy.

  11. The impact of image reconstruction settings on 18F-FDG PET radiomic features. Multi-scanner phantom and patient studies

    Shiri, Isaac; Abdollahi, Hamid; Rahmim, Arman; Ghaffarian, Pardis; Geramifar, Parham; Bitarafan-Rajabi, Ahmad

    2017-01-01

    The purpose of this study was to investigate the robustness of different PET/CT image radiomic features over a wide range of different reconstruction settings. Phantom and patient studies were conducted, including two PET/CT scanners. Different reconstruction algorithms and parameters including number of sub-iterations, number of subsets, full width at half maximum (FWHM) of Gaussian filter, scan time per bed position and matrix size were studied. Lesions were delineated and one hundred radiomic features were extracted. All radiomics features were categorized based on coefficient of variation (COV). Forty seven percent features showed COV ≤ 5% and 10% of which showed COV > 20%. All geometry based, 44% and 41% of intensity based and texture based features were found as robust respectively. In regard to matrix size, 56% and 6% of all features were found non-robust (COV > 20%) and robust (COV ≤ 5%) respectively. Variability and robustness of PET/CT image radiomics in advanced reconstruction settings is feature-dependent, and different settings have different effects on different features. Radiomic features with low COV can be considered as good candidates for reproducible tumour quantification in multi-center studies. (orig.)

  12. The impact of image reconstruction settings on 18F-FDG PET radiomic features. Multi-scanner phantom and patient studies

    Shiri, Isaac; Abdollahi, Hamid [Iran University of Medical Sciences, Department of Medical Physics, School of Medicine, Tehran (Iran, Islamic Republic of); Rahmim, Arman [Johns Hopkins University, Department of Radiology, Baltimore, MD (United States); Johns Hopkins University, Department of Electrical and Computer Engineering, Baltimore, MD (United States); Ghaffarian, Pardis [Shahid Beheshti University of Medical Sciences, Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Tehran (Iran, Islamic Republic of); Shahid Beheshti University of Medical Sciences, PET/CT and Cyclotron Center, Masih Daneshvari Hospital, Tehran (Iran, Islamic Republic of); Geramifar, Parham [Tehran University of Medical Sciences, Research Center for Nuclear Medicine, Shariati Hospital, Tehran (Iran, Islamic Republic of); Bitarafan-Rajabi, Ahmad [Iran University of Medical Sciences, Department of Medical Physics, School of Medicine, Tehran (Iran, Islamic Republic of); Iran University of Medical Sciences, Department of Nuclear Medicine, Rajaei Cardiovascular, Medical and Research Center, Tehran (Iran, Islamic Republic of)

    2017-11-15

    The purpose of this study was to investigate the robustness of different PET/CT image radiomic features over a wide range of different reconstruction settings. Phantom and patient studies were conducted, including two PET/CT scanners. Different reconstruction algorithms and parameters including number of sub-iterations, number of subsets, full width at half maximum (FWHM) of Gaussian filter, scan time per bed position and matrix size were studied. Lesions were delineated and one hundred radiomic features were extracted. All radiomics features were categorized based on coefficient of variation (COV). Forty seven percent features showed COV ≤ 5% and 10% of which showed COV > 20%. All geometry based, 44% and 41% of intensity based and texture based features were found as robust respectively. In regard to matrix size, 56% and 6% of all features were found non-robust (COV > 20%) and robust (COV ≤ 5%) respectively. Variability and robustness of PET/CT image radiomics in advanced reconstruction settings is feature-dependent, and different settings have different effects on different features. Radiomic features with low COV can be considered as good candidates for reproducible tumour quantification in multi-center studies. (orig.)

  13. The Feasibility of Embedding Data Collection into the Routine Service Delivery of a Multi-Component Program for High-Risk Young People.

    Knight, Alice; Havard, Alys; Shakeshaft, Anthony; Maple, Myfanwy; Snijder, Mieke; Shakeshaft, Bernie

    2017-02-20

    There is little evidence about how to improve outcomes for high-risk young people, of whom Indigenous young people are disproportionately represented, due to few evaluation studies of interventions. One way to increase the evidence is to have researchers and service providers collaborate to embed evaluation into the routine delivery of services, so program delivery and evaluation occur simultaneously. This study aims to demonstrate the feasibility of integrating best-evidence measures into the routine data collection processes of a service for high-risk young people, and identify the number and nature of risk factors experienced by participants. The youth service is a rural based NGO comprised of multiple program components: (i) engagement activities; (ii) case management; (iii) diversionary activities; (iv) personal development; and (v) learning and skills. A best-evidence assessment tool was developed by staff and researchers and embedded into the service's existing intake procedure. Assessment items were organised into demographic characteristics and four domains of risk: education and employment; health and wellbeing; substance use; and crime. Descriptive data are presented and summary risk variables were created for each domain of risk. A count of these summary variables represented the number of co-occurring risks experienced by each participant. The feasibility of this process was determined by the proportion of participants who completed the intake assessment and provided research consent. This study shows 85% of participants completed the assessment tool demonstrating that data on participant risk factors can feasibly be collected by embedding a best-evidence assessment tool into the routine data collection processes of a service. The most prevalent risk factors were school absence, unemployment, suicide ideation, mental distress, substance use, low levels of physical activity, low health service utilisation, and involvement in crime or with the juvenile

  14. The Feasibility of Embedding Data Collection into the Routine Service Delivery of a Multi-Component Program for High-Risk Young People

    Alice Knight

    2017-02-01

    Full Text Available Background: There is little evidence about how to improve outcomes for high-risk young people, of whom Indigenous young people are disproportionately represented, due to few evaluation studies of interventions. One way to increase the evidence is to have researchers and service providers collaborate to embed evaluation into the routine delivery of services, so program delivery and evaluation occur simultaneously. This study aims to demonstrate the feasibility of integrating best-evidence measures into the routine data collection processes of a service for high-risk young people, and identify the number and nature of risk factors experienced by participants. Methods: The youth service is a rural based NGO comprised of multiple program components: (i engagement activities; (ii case management; (iii diversionary activities; (iv personal development; and (v learning and skills. A best-evidence assessment tool was developed by staff and researchers and embedded into the service’s existing intake procedure. Assessment items were organised into demographic characteristics and four domains of risk: education and employment; health and wellbeing; substance use; and crime. Descriptive data are presented and summary risk variables were created for each domain of risk. A count of these summary variables represented the number of co-occurring risks experienced by each participant. The feasibility of this process was determined by the proportion of participants who completed the intake assessment and provided research consent. Results: This study shows 85% of participants completed the assessment tool demonstrating that data on participant risk factors can feasibly be collected by embedding a best-evidence assessment tool into the routine data collection processes of a service. The most prevalent risk factors were school absence, unemployment, suicide ideation, mental distress, substance use, low levels of physical activity, low health service utilisation

  15. Embedded multiprocessors scheduling and synchronization

    Sriram, Sundararajan

    2009-01-01

    Techniques for Optimizing Multiprocessor Implementations of Signal Processing ApplicationsAn indispensable component of the information age, signal processing is embedded in a variety of consumer devices, including cell phones and digital television, as well as in communication infrastructure, such as media servers and cellular base stations. Multiple programmable processors, along with custom hardware running in parallel, are needed to achieve the computation throughput required of such applications. Reviews important research in key areas related to the multiprocessor implementation of multi

  16. Development of an Erlang System Adaopted to Embedded Devices

    Andersson, Fredrik; Bergström, Fabian

    2011-01-01

    Erlang is a powerful and robust language for writing massively parallel and distributed applications. With the introduction of multi-core ARM processors, the embedded market will be looking for ways of taking advantage of the newfound opportunities for parallelism. To support the development of embedded applications using Erlang we want to provide Erlang and Embedded developers with a run-time system suited for embedded devices. We have managed to shrink the disk size of the Erlang runtime sy...

  17. Conceptualizing Embedded Configuration

    Oddsson, Gudmundur Valur; Hvam, Lars; Lysgaard, Ole

    2006-01-01

    and services. The general idea can be named embedded configuration. In this article we intend to conceptualize embedded configuration, what it is and is not. The difference between embedded configuration, sales configuration and embedded software is explained. We will look at what is needed to make embedded...... configuration systems. That will include requirements to product modelling techniques. An example with consumer electronics will illuminate the elements of embedded configuration in settings that most can relate to. The question of where embedded configuration would be relevant is discussed, and the current...

  18. An Empirical Study of Wrappers for Feature Subset Selection based on a Parallel Genetic Algorithm: The Multi-Wrapper Model

    Soufan, Othman

    2012-01-01

    proper criterion seeks to find the best subset of features describing data (relevance) and achieving better performance (optimality). Wrapper approaches are feature selection methods which are wrapped around a classification algorithm and use a

  19. An Empirical Study of Wrappers for Feature Subset Selection based on a Parallel Genetic Algorithm: The Multi-Wrapper Model

    Soufan, Othman

    2012-09-01

    Feature selection is the first task of any learning approach that is applied in major fields of biomedical, bioinformatics, robotics, natural language processing and social networking. In feature subset selection problem, a search methodology with a proper criterion seeks to find the best subset of features describing data (relevance) and achieving better performance (optimality). Wrapper approaches are feature selection methods which are wrapped around a classification algorithm and use a performance measure to select the best subset of features. We analyze the proper design of the objective function for the wrapper approach and highlight an objective based on several classification algorithms. We compare the wrapper approaches to different feature selection methods based on distance and information based criteria. Significant improvement in performance, computational time, and selection of minimally sized feature subsets is achieved by combining different objectives for the wrapper model. In addition, considering various classification methods in the feature selection process could lead to a global solution of desirable characteristics.

  20. Using a multi-feature paradigm to measure mismatch responses to minimal sound contrasts in children with cochlear implants and hearing aids.

    Uhlén, Inger; Engström, Elisabet; Kallioinen, Petter; Nakeva von Mentzer, Cecilia; Lyxell, Björn; Sahlén, Birgitta; Lindgren, Magnus; Ors, Marianne

    2017-10-01

    Our aim was to explore whether a multi-feature paradigm (Optimum-1) for eliciting mismatch negativity (MMN) would objectively capture difficulties in perceiving small sound contrasts in children with hearing impairment (HI) listening through their hearing aids (HAs) and/or cochlear implants (CIs). Children aged 5-7 years with HAs, CIs and children with normal hearing (NH) were tested in a free-field setting using a multi-feature paradigm with deviations in pitch, intensity, gap, duration, and location. There were significant mismatch responses across all subjects that were positive (p-MMR) for the gap and pitch deviants (F(1,43) = 5.17, p = 0.028 and F(1,43) = 6.56, p = 0.014, respectively) and negative (MMN) for the duration deviant (F(1,43) = 4.74, p = 0.035). Only the intensity deviant showed a significant group interaction with MMN in the HA group and p-MMR in the CI group (F(2,43) = 3.40, p = 0.043). The p-MMR correlated negatively with age, with the strongest correlation in the NH subjects. In the CI group, the late discriminative negativity (LDN) was replaced by a late positivity with a significant group interaction for the location deviant. Children with severe HI can be assessed through their hearing device with a fast multi-feature paradigm. For further studies a multi-feature paradigm including more complex speech sounds may better capture variation in auditory processing in these children. © 2017 Scandinavian Psychological Associations and John Wiley & Sons Ltd.

  1. Healthy full-term infants' brain responses to emotionally and linguistically relevant sounds using a multi-feature mismatch negativity (MMN) paradigm.

    Kostilainen, Kaisamari; Wikström, Valtteri; Pakarinen, Satu; Videman, Mari; Karlsson, Linnea; Keskinen, Maria; Scheinin, Noora M; Karlsson, Hasse; Huotilainen, Minna

    2018-03-23

    We evaluated the feasibility of a multi-feature mismatch negativity (MMN) paradigm in studying auditory processing of healthy newborns. The aim was to examine the automatic change-detection and processing of semantic and emotional information in speech in newborns. Brain responses of 202 healthy newborns were recorded with a multi-feature paradigm including a Finnish bi-syllabic pseudo-word/ta-ta/as a standard stimulus, six linguistically relevant deviant stimuli and three emotionally relevant stimuli (happy, sad, angry). Clear responses to emotional sounds were found already at the early latency window 100-200 ms, whereas responses to linguistically relevant minor changes and emotional stimuli at the later latency window 300-500 ms did not reach significance. Moreover, significant interaction between gender and emotional stimuli was found in the early latency window. Further studies on using multi-feature paradigms with linguistic and emotional stimuli in newborns are needed, especially those containing of follow-ups, enabling the assessment of the predictive value of early variations between subjects. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

  2. Raft cultivation area extraction from high resolution remote sensing imagery by fusing multi-scale region-line primitive association features

    Wang, Min; Cui, Qi; Wang, Jie; Ming, Dongping; Lv, Guonian

    2017-01-01

    In this paper, we first propose several novel concepts for object-based image analysis, which include line-based shape regularity, line density, and scale-based best feature value (SBV), based on the region-line primitive association framework (RLPAF). We then propose a raft cultivation area (RCA) extraction method for high spatial resolution (HSR) remote sensing imagery based on multi-scale feature fusion and spatial rule induction. The proposed method includes the following steps: (1) Multi-scale region primitives (segments) are obtained by image segmentation method HBC-SEG, and line primitives (straight lines) are obtained by phase-based line detection method. (2) Association relationships between regions and lines are built based on RLPAF, and then multi-scale RLPAF features are extracted and SBVs are selected. (3) Several spatial rules are designed to extract RCAs within sea waters after land and water separation. Experiments show that the proposed method can successfully extract different-shaped RCAs from HR images with good performance.

  3. Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: Preliminary findings from a multi-institutional study.

    Ginsburg, Shoshana B; Algohary, Ahmad; Pahwa, Shivani; Gulani, Vikas; Ponsky, Lee; Aronen, Hannu J; Boström, Peter J; Böhm, Maret; Haynes, Anne-Maree; Brenner, Phillip; Delprado, Warick; Thompson, James; Pulbrock, Marley; Taimen, Pekka; Villani, Robert; Stricker, Phillip; Rastinehad, Ardeshir R; Jambor, Ivan; Madabhushi, Anant

    2017-07-01

    To evaluate in a multi-institutional study whether radiomic features useful for prostate cancer (PCa) detection from 3 Tesla (T) multi-parametric MRI (mpMRI) in the transition zone (TZ) differ from those in the peripheral zone (PZ). 3T mpMRI, including T2-weighted (T2w), apparent diffusion coefficient (ADC) maps, and dynamic contrast-enhanced MRI (DCE-MRI), were retrospectively obtained from 80 patients at three institutions. This study was approved by the institutional review board of each participating institution. First-order statistical, co-occurrence, and wavelet features were extracted from T2w MRI and ADC maps, and contrast kinetic features were extracted from DCE-MRI. Feature selection was performed to identify 10 features for PCa detection in the TZ and PZ, respectively. Two logistic regression classifiers used these features to detect PCa and were evaluated by area under the receiver-operating characteristic curve (AUC). Classifier performance was compared with a zone-ignorant classifier. Radiomic features that were identified as useful for PCa detection differed between TZ and PZ. When classification was performed on a per-voxel basis, a PZ-specific classifier detected PZ tumors on an independent test set with significantly higher accuracy (AUC = 0.61-0.71) than a zone-ignorant classifier trained to detect cancer throughout the entire prostate (P  0.14) were obtained for all institutions. A zone-aware classifier significantly improves the accuracy of cancer detection in the PZ. 3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:184-193. © 2016 International Society for Magnetic Resonance in Medicine.

  4. Trusted computing for embedded systems

    Soudris, Dimitrios; Anagnostopoulos, Iraklis

    2015-01-01

    This book describes the state-of-the-art in trusted computing for embedded systems. It shows how a variety of security and trusted computing problems are addressed currently and what solutions are expected to emerge in the coming years. The discussion focuses on attacks aimed at hardware and software for embedded systems, and the authors describe specific solutions to create security features. Case studies are used to present new techniques designed as industrial security solutions. Coverage includes development of tamper resistant hardware and firmware mechanisms for lightweight embedded devices, as well as those serving as security anchors for embedded platforms required by applications such as smart power grids, smart networked and home appliances, environmental and infrastructure sensor networks, etc. ·         Enables readers to address a variety of security threats to embedded hardware and software; ·         Describes design of secure wireless sensor networks, to address secure authen...

  5. Operating system concepts for embedded multicores

    Horst, Oliver; Schmidt, Adriaan

    2014-01-01

    Currently we can see an increasing adoption of multi-core platforms in the area of embedded systems. While these new hardware platforms offer the potential to satisfy the ever increasing demand for computational power, they pose considerable challenges with regard to software development. This affects the application software itself, but also the system design and architecture. Here, we address the consequences for operating system architecture in embedded systems. After dis-cussing current a...

  6. Evaluation of biomethane technologies in Europe – Technical concepts under the scope of a Delphi-Survey embedded in a multi-criteria analysis

    Billig, Eric; Thrän, Daniela

    2016-01-01

    Methane from biomass is a well suited renewable energy carrier with a wide range of applications. The main technologies for its production out of biomass are biochemical conversion from the upgrading of biogas and thermochemical conversion by gasification and methanation. Presently there exists no methodology to compare the process alternatives for methane production from biomass. This paper investigates a comprehensive evaluation method based on a multi-criteria analysis. Due to the comparable well developed biomethane market in Europe, compared to other regions in the world, the study area was restricted to Europe. The weighting of the different criteria is carried out in two rounds as a pair-to-pair comparison of the criteria by experts from different technology fields in a Delphi-Survey. As a result, the prioritisation can be used to classify the biomass conversion technologies to convert biomass to biomethane. According to the weightings given by experts, the two criteria energy efficiency and production costs are of great importance compared to the other criteria. - Highlights: • Overview of technologies for renewable methane (biochemical and thermochemical). • Overview of multi-criteria analysis. • Novel methodology for comparison of biochemical and thermochemical conversion. • Delphi-Survey (approach and evaluation) in the field of biomethane resp. bio-SNG.

  7. Far-Infrared Based Pedestrian Detection for Driver-Assistance Systems Based on Candidate Filters, Gradient-Based Feature and Multi-Frame Approval Matching.

    Wang, Guohua; Liu, Qiong

    2015-12-21

    Far-infrared pedestrian detection approaches for advanced driver-assistance systems based on high-dimensional features fail to simultaneously achieve robust and real-time detection. We propose a robust and real-time pedestrian detection system characterized by novel candidate filters, novel pedestrian features and multi-frame approval matching in a coarse-to-fine fashion. Firstly, we design two filters based on the pedestrians' head and the road to select the candidates after applying a pedestrian segmentation algorithm to reduce false alarms. Secondly, we propose a novel feature encapsulating both the relationship of oriented gradient distribution and the code of oriented gradient to deal with the enormous variance in pedestrians' size and appearance. Thirdly, we introduce a multi-frame approval matching approach utilizing the spatiotemporal continuity of pedestrians to increase the detection rate. Large-scale experiments indicate that the system works in real time and the accuracy has improved about 9% compared with approaches based on high-dimensional features only.

  8. Far-Infrared Based Pedestrian Detection for Driver-Assistance Systems Based on Candidate Filters, Gradient-Based Feature and Multi-Frame Approval Matching

    Guohua Wang

    2015-12-01

    Full Text Available Far-infrared pedestrian detection approaches for advanced driver-assistance systems based on high-dimensional features fail to simultaneously achieve robust and real-time detection. We propose a robust and real-time pedestrian detection system characterized by novel candidate filters, novel pedestrian features and multi-frame approval matching in a coarse-to-fine fashion. Firstly, we design two filters based on the pedestrians’ head and the road to select the candidates after applying a pedestrian segmentation algorithm to reduce false alarms. Secondly, we propose a novel feature encapsulating both the relationship of oriented gradient distribution and the code of oriented gradient to deal with the enormous variance in pedestrians’ size and appearance. Thirdly, we introduce a multi-frame approval matching approach utilizing the spatiotemporal continuity of pedestrians to increase the detection rate. Large-scale experiments indicate that the system works in real time and the accuracy has improved about 9% compared with approaches based on high-dimensional features only.

  9. Sparse representation of multi parametric DCE-MRI features using K-SVD for classifying gene expression based breast cancer recurrence risk

    Mahrooghy, Majid; Ashraf, Ahmed B.; Daye, Dania; Mies, Carolyn; Rosen, Mark; Feldman, Michael; Kontos, Despina

    2014-03-01

    We evaluate the prognostic value of sparse representation-based features by applying the K-SVD algorithm on multiparametric kinetic, textural, and morphologic features in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). K-SVD is an iterative dimensionality reduction method that optimally reduces the initial feature space by updating the dictionary columns jointly with the sparse representation coefficients. Therefore, by using K-SVD, we not only provide sparse representation of the features and condense the information in a few coefficients but also we reduce the dimensionality. The extracted K-SVD features are evaluated by a machine learning algorithm including a logistic regression classifier for the task of classifying high versus low breast cancer recurrence risk as determined by a validated gene expression assay. The features are evaluated using ROC curve analysis and leave one-out cross validation for different sparse representation and dimensionality reduction numbers. Optimal sparse representation is obtained when the number of dictionary elements is 4 (K=4) and maximum non-zero coefficients is 2 (L=2). We compare K-SVD with ANOVA based feature selection for the same prognostic features. The ROC results show that the AUC of the K-SVD based (K=4, L=2), the ANOVA based, and the original features (i.e., no dimensionality reduction) are 0.78, 0.71. and 0.68, respectively. From the results, it can be inferred that by using sparse representation of the originally extracted multi-parametric, high-dimensional data, we can condense the information on a few coefficients with the highest predictive value. In addition, the dimensionality reduction introduced by K-SVD can prevent models from over-fitting.

  10. CASPER: Embedding Power Estimation and Hardware-Controlled Power Management in a Cycle-Accurate Micro-Architecture Simulation Platform for Many-Core Multi-Threading Heterogeneous Processors

    Arun Ravindran

    2012-02-01

    Full Text Available Despite the promising performance improvement observed in emerging many-core architectures in high performance processors, high power consumption prohibitively affects their use and marketability in the low-energy sectors, such as embedded processors, network processors and application specific instruction processors (ASIPs. While most chip architects design power-efficient processors by finding an optimal power-performance balance in their design, some use sophisticated on-chip autonomous power management units, which dynamically reduce the voltage or frequencies of idle cores and hence extend battery life and reduce operating costs. For large scale designs of many-core processors, a holistic approach integrating both these techniques at different levels of abstraction can potentially achieve maximal power savings. In this paper we present CASPER, a robust instruction trace driven cycle-accurate many-core multi-threading micro-architecture simulation platform where we have incorporated power estimation models of a wide variety of tunable many-core micro-architectural design parameters, thus enabling processor architects to explore a sufficiently large design space and achieve power-efficient designs. Additionally CASPER is designed to accommodate cycle-accurate models of hardware controlled power management units, enabling architects to experiment with and evaluate different autonomous power-saving mechanisms to study the run-time power-performance trade-offs in embedded many-core processors. We have implemented two such techniques in CASPER–Chipwide Dynamic Voltage and Frequency Scaling, and Performance Aware Core-Specific Frequency Scaling, which show average power savings of 35.9% and 26.2% on a baseline 4-core SPARC based architecture respectively. This power saving data accounts for the power consumption of the power management units themselves. The CASPER simulation platform also provides users with complete support of SPARCV9

  11. Extraction of auditory features and elicitation of attributes for the assessment of multi-channel reproduced sound

    Choisel, Sylvain; Wickelmaier, Florian Maria

    2005-01-01

    ), subjects were asked to directly assign verbal labels to the features when encountering them and to subsequently rate the sounds on the scales thus obtained. The second method requires the subjects to consistently identify the perceptually relevant features before assigning them a verbal label. Under...

  12. Extraction of auditory features and elicitation of attributes for the assessment of multi-channel reproduced sound

    Choisel, Sylvain; Wickelmaier, Florian

    2005-01-01

    ), subjects were asked to directly assign verbal labels to the features when encountering them, and to subsequently rate the sounds on the scales thus obtained. The second method requires the subjects to consistently identify the perceptually relevant features before assigning them a verbal label. Under...

  13. An embedded longitudinal multi-faceted qualitative evaluation of a complex cluster randomized controlled trial aiming to reduce clinically important errors in medicines management in general practice

    Cresswell Kathrin M

    2012-06-01

    . However, important concerns were identified about the likely sustainability of this new model of delivering care, in the absence of an appropriate support network for pharmacists and career development pathways. Conclusions This embedded qualitative inquiry has helped to understand the complex organizational and social environment in which the trial was undertaken and the PINCER intervention was delivered. The longitudinal element has given insight into the dynamic changes and developments over time. Medication errors and ways to address these are high on stakeholders’ agendas. Our results further indicate that pharmacists were, because of their professional standing and skill-set, able to engage with the complex general practice environment and able to identify and manage many clinically important errors in medicines management. The transferability of the PINCER intervention approach, both in relation to other prescribing errors and to other practices, is likely to be high.

  14. Polymorphic Embedding of DSLs

    Hofer, Christian; Ostermann, Klaus; Rendel, Tillmann

    2008-01-01

    propose polymorphic embedding of DSLs, where many different interpretations of a DSL can be provided as reusable components, and show how polymorphic embedding can be realized in the programming language Scala. With polymorphic embedding, the static type-safety, modularity, composability and rapid...

  15. Tensor decomposition-based unsupervised feature extraction applied to matrix products for multi-view data processing

    2017-01-01

    In the current era of big data, the amount of data available is continuously increasing. Both the number and types of samples, or features, are on the rise. The mixing of distinct features often makes interpretation more difficult. However, separate analysis of individual types requires subsequent integration. A tensor is a useful framework to deal with distinct types of features in an integrated manner without mixing them. On the other hand, tensor data is not easy to obtain since it requires the measurements of huge numbers of combinations of distinct features; if there are m kinds of features, each of which has N dimensions, the number of measurements needed are as many as Nm, which is often too large to measure. In this paper, I propose a new method where a tensor is generated from individual features without combinatorial measurements, and the generated tensor was decomposed back to matrices, by which unsupervised feature extraction was performed. In order to demonstrate the usefulness of the proposed strategy, it was applied to synthetic data, as well as three omics datasets. It outperformed other matrix-based methodologies. PMID:28841719

  16. Segmentation of myocardial perfusion MR sequences with multi-band Active Appearance Models driven by spatial and temporal features

    Baka, N.; Milles, J.; Hendriks, E.A.; Suinesiaputra, A.; Jerosh Herold, M.; Reiber, J.H.C.; Lelieveldt, B.P.F.

    2008-01-01

    This work investigates knowledge driven segmentation of cardiac MR perfusion sequences. We build upon previous work on multi-band AAMs to integrate into the segmentation both spatial priors about myocardial shape as well as temporal priors about characteristic perfusion patterns. Different temporal

  17. Computational intelligence in multi-feature visual pattern recognition hand posture and face recognition using biologically inspired approaches

    Pisharady, Pramod Kumar; Poh, Loh Ai

    2014-01-01

    This book presents a collection of computational intelligence algorithms that addresses issues in visual pattern recognition such as high computational complexity, abundance of pattern features, sensitivity to size and shape variations and poor performance against complex backgrounds. The book has 3 parts. Part 1 describes various research issues in the field with a survey of the related literature. Part 2 presents computational intelligence based algorithms for feature selection and classification. The algorithms are discriminative and fast. The main application area considered is hand posture recognition. The book also discusses utility of these algorithms in other visual as well as non-visual pattern recognition tasks including face recognition, general object recognition and cancer / tumor classification. Part 3 presents biologically inspired algorithms for feature extraction. The visual cortex model based features discussed have invariance with respect to appearance and size of the hand, and provide good...

  18. A fuzzy feature fusion method for auto-segmentation of gliomas with multi-modality diffusion and perfusion magnetic resonance images in radiotherapy.

    Guo, Lu; Wang, Ping; Sun, Ranran; Yang, Chengwen; Zhang, Ning; Guo, Yu; Feng, Yuanming

    2018-02-19

    The diffusion and perfusion magnetic resonance (MR) images can provide functional information about tumour and enable more sensitive detection of the tumour extent. We aimed to develop a fuzzy feature fusion method for auto-segmentation of gliomas in radiotherapy planning using multi-parametric functional MR images including apparent diffusion coefficient (ADC), fractional anisotropy (FA) and relative cerebral blood volume (rCBV). For each functional modality, one histogram-based fuzzy model was created to transform image volume into a fuzzy feature space. Based on the fuzzy fusion result of the three fuzzy feature spaces, regions with high possibility belonging to tumour were generated automatically. The auto-segmentations of tumour in structural MR images were added in final auto-segmented gross tumour volume (GTV). For evaluation, one radiation oncologist delineated GTVs for nine patients with all modalities. Comparisons between manually delineated and auto-segmented GTVs showed that, the mean volume difference was 8.69% (±5.62%); the mean Dice's similarity coefficient (DSC) was 0.88 (±0.02); the mean sensitivity and specificity of auto-segmentation was 0.87 (±0.04) and 0.98 (±0.01) respectively. High accuracy and efficiency can be achieved with the new method, which shows potential of utilizing functional multi-parametric MR images for target definition in precision radiation treatment planning for patients with gliomas.

  19. Development of feature extraction analysis for a multi-functional optical profiling device applied to field engineering applications

    Han, Xu; Xie, Guangping; Laflen, Brandon; Jia, Ming; Song, Guiju; Harding, Kevin G.

    2015-05-01

    In the real application environment of field engineering, a large variety of metrology tools are required by the technician to inspect part profile features. However, some of these tools are burdensome and only address a sole application or measurement. In other cases, standard tools lack the capability of accessing irregular profile features. Customers of field engineering want the next generation metrology devices to have the ability to replace the many current tools with one single device. This paper will describe a method based on the ring optical gage concept to the measurement of numerous kinds of profile features useful for the field technician. The ring optical system is composed of a collimated laser, a conical mirror and a CCD camera. To be useful for a wide range of applications, the ring optical system requires profile feature extraction algorithms and data manipulation directed toward real world applications in field operation. The paper will discuss such practical applications as measuring the non-ideal round hole with both off-centered and oblique axes. The algorithms needed to analyze other features such as measuring the width of gaps, radius of transition fillets, fall of step surfaces, and surface parallelism will also be discussed in this paper. With the assistance of image processing and geometric algorithms, these features can be extracted with a reasonable performance. Tailoring the feature extraction analysis to this specific gage offers the potential for a wider application base beyond simple inner diameter measurements. The paper will present experimental results that are compared with standard gages to prove the performance and feasibility of the analysis in real world field engineering. Potential accuracy improvement methods, a new dual ring design and future work will be discussed at the end of this paper.

  20. Analytical and numerical analyses for a penny-shaped crack embedded in an infinite transversely isotropic multi-ferroic composite medium: semi-permeable electro-magnetic boundary condition

    Zheng, R.-F.; Wu, T.-H.; Li, X.-Y.; Chen, W.-Q.

    2018-06-01

    The problem of a penny-shaped crack embedded in an infinite space of transversely isotropic multi-ferroic composite medium is investigated. The crack is assumed to be subjected to uniformly distributed mechanical, electric and magnetic loads applied symmetrically on the upper and lower crack surfaces. The semi-permeable (limited-permeable) electro-magnetic boundary condition is adopted. By virtue of the generalized method of potential theory and the general solutions, the boundary integro-differential equations governing the mode I crack problem, which are of nonlinear nature, are established and solved analytically. Exact and complete coupling magneto-electro-elastic field is obtained in terms of elementary functions. Important parameters in fracture mechanics on the crack plane, e.g., the generalized crack surface displacements, the distributions of generalized stresses at the crack tip, the generalized stress intensity factors and the energy release rate, are explicitly presented. To validate the present solutions, a numerical code by virtue of finite element method is established for 3D crack problems in the framework of magneto-electro-elasticity. To evaluate conveniently the effect of the medium inside the crack, several empirical formulae are developed, based on the numerical results.

  1. Multi-feature-based plaque characterization in ex vivo MRI trained by registration to 3D histology

    Van Engelen, Arna; Niessen, Wiro J; Klein, Stefan; De Bruijne, Marleen; Groen, Harald C; Wentzel, Jolanda J; Verhagen, Hence JM; Lugt, Aad van der

    2012-01-01

    We present a new method for automated characterization of atherosclerotic plaque composition in ex vivo MRI. It uses MRI intensities as well as four other types of features: smoothed, gradient magnitude and Laplacian images at several scales, and the distances to the lumen and outer vessel wall. The ground truth for fibrous, necrotic and calcified tissue was provided by histology and μCT in 12 carotid plaque specimens. Semi-automatic registration of a 3D stack of histological slices and μCT images to MRI allowed for 3D rotations and in-plane deformations of histology. By basing voxelwise classification on different combinations of features, we evaluated their relative importance. To establish whether training by 3D registration yields different results than training by 2D registration, we determined plaque composition using (1) a 2D slice-based registration approach for three manually selected MRI and histology slices per specimen, and (2) an approach that uses only the three corresponding MRI slices from the 3D-registered volumes. Voxelwise classification accuracy was best when all features were used (73.3 ± 6.3%) and was significantly better than when only original intensities and distance features were used (Friedman, p < 0.05). Although 2D registration or selection of three slices from the 3D set slightly decreased accuracy, these differences were non-significant. (paper)

  2. Views on Evolvability of Embedded Systems

    Laar, P. van de; Punter, T.

    2011-01-01

    Evolvability, the ability to respond effectively to change, represents a major challenge to today's high-end embedded systems, such as those developed in the medical domain by Philips Healthcare. These systems are typically developed by multi-disciplinary teams, located around the world, and are in

  3. Views on evolvability of embedded systems

    Laar, van de P.J.L.J.; Punter, H.T.

    2011-01-01

    Evolvability, the ability to respond effectively to change, represents a major challenge to today's high-end embedded systems, such as those developed in the medical domain by Philips Healthcare. These systems are typically developed by multi-disciplinary teams, located around the world, and are in

  4. Embedding beyond electrostatics

    Nåbo, Lina J.; Olsen, Jógvan Magnus Haugaard; Holmgaard List, Nanna

    2016-01-01

    We study excited states of cholesterol in solution and show that, in this specific case, solute wave-function confinement is the main effect of the solvent. This is rationalized on the basis of the polarizable density embedding scheme, which in addition to polarizable embedding includes non-electrostatic...... repulsion that effectively confines the solute wave function to its cavity. We illustrate how the inclusion of non-electrostatic repulsion results in a successful identification of the intense π → π∗ transition, which was not possible using an embedding method that only includes electrostatics....... This underlines the importance of non-electrostatic repulsion in quantum-mechanical embedding-based methods....

  5. Embedded systems handbook

    Zurawski, Richard

    2005-01-01

    Embedded systems are nearly ubiquitous, and books on individual topics or components of embedded systems are equally abundant. Unfortunately, for those designers who thirst for knowledge of the big picture of embedded systems there is not a drop to drink. Until now. The Embedded Systems Handbook is an oasis of information, offering a mix of basic and advanced topics, new solutions and technologies arising from the most recent research efforts, and emerging trends to help you stay current in this ever-changing field.With preeminent contributors from leading industrial and academic institutions

  6. Embedded systems handbook networked embedded systems

    Zurawski, Richard

    2009-01-01

    Considered a standard industry resource, the Embedded Systems Handbook provided researchers and technicians with the authoritative information needed to launch a wealth of diverse applications, including those in automotive electronics, industrial automated systems, and building automation and control. Now a new resource is required to report on current developments and provide a technical reference for those looking to move the field forward yet again. Divided into two volumes to accommodate this growth, the Embedded Systems Handbook, Second Edition presents a comprehensive view on this area

  7. Composable Virtual Platforms for Mixed-Criticality Embedded Systems

    Beyranvand Nejad, A.

    2014-01-01

    Recent trends show a steady increase towards concurrently executing more and more applications on a single embedded system. Multi-Processor System-on-Chip (MPSoC) architectures are proposed to allow complex design of embedded systems. This is achieved by integrating as many processing resources as

  8. Composable virtual platforms for mixed-criticality embedded systems

    Nejad, A.B.

    2014-01-01

    Recent trends show a steady increase towards concurrently executing more and more applications on a single embedded system. Multi-Processor System-on-Chip (MPSoC) architectures are proposed to allow complex design of embedded systems. This is achieved by integrating as many processing resources as

  9. The data embedding method

    Sandford, M.T. II; Bradley, J.N.; Handel, T.G.

    1996-06-01

    Data embedding is a new steganographic method for combining digital information sets. This paper describes the data embedding method and gives examples of its application using software written in the C-programming language. Sandford and Handel produced a computer program (BMPEMBED, Ver. 1.51 written for IBM PC/AT or compatible, MS/DOS Ver. 3.3 or later) that implements data embedding in an application for digital imagery. Information is embedded into, and extracted from, Truecolor or color-pallet images in Microsoft{reg_sign} bitmap (.BMP) format. Hiding data in the noise component of a host, by means of an algorithm that modifies or replaces the noise bits, is termed {open_quote}steganography.{close_quote} Data embedding differs markedly from conventional steganography, because it uses the noise component of the host to insert information with few or no modifications to the host data values or their statistical properties. Consequently, the entropy of the host data is affected little by using data embedding to add information. The data embedding method applies to host data compressed with transform, or {open_quote}lossy{close_quote} compression algorithms, as for example ones based on discrete cosine transform and wavelet functions. Analysis of the host noise generates a key required for embedding and extracting the auxiliary data from the combined data. The key is stored easily in the combined data. Images without the key cannot be processed to extract the embedded information. To provide security for the embedded data, one can remove the key from the combined data and manage it separately. The image key can be encrypted and stored in the combined data or transmitted separately as a ciphertext much smaller in size than the embedded data. The key size is typically ten to one-hundred bytes, and it is in data an analysis algorithm.

  10. Hybridization between multi-objective genetic algorithm and support vector machine for feature selection in walker-assisted gait.

    Martins, Maria; Costa, Lino; Frizera, Anselmo; Ceres, Ramón; Santos, Cristina

    2014-03-01

    Walker devices are often prescribed incorrectly to patients, leading to the increase of dissatisfaction and occurrence of several problems, such as, discomfort and pain. Thus, it is necessary to objectively evaluate the effects that assisted gait can have on the gait patterns of walker users, comparatively to a non-assisted gait. A gait analysis, focusing on spatiotemporal and kinematics parameters, will be issued for this purpose. However, gait analysis yields redundant information that often is difficult to interpret. This study addresses the problem of selecting the most relevant gait features required to differentiate between assisted and non-assisted gait. For that purpose, it is presented an efficient approach that combines evolutionary techniques, based on genetic algorithms, and support vector machine algorithms, to discriminate differences between assisted and non-assisted gait with a walker with forearm supports. For comparison purposes, other classification algorithms are verified. Results with healthy subjects show that the main differences are characterized by balance and joints excursion in the sagittal plane. These results, confirmed by clinical evidence, allow concluding that this technique is an efficient feature selection approach. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  11. ENLIST 1: An International Multi-centre Cross-sectional Study of the Clinical Features of Erythema Nodosum Leprosum.

    Stephen L Walker

    Full Text Available Erythema nodosum leprosum (ENL is a severe multisystem immune mediated complication of borderline lepromatous leprosy and lepromatous leprosy. ENL is associated with skin lesions, neuritis, arthritis, dactylitis, eye inflammation, osteitis, orchitis, lymphadenitis and nephritis. The treatment of ENL requires immunosuppression, which is often required for prolonged periods of time and may lead to serious adverse effects. ENL and its treatment is associated with increased mortality and economic hardship. Improved, evidence-based treatments for ENL are needed; however, defining the severity of ENL and outcome measures for treatment studies is difficult because of the multiple organ systems involved. A cross-sectional study was performed, by the members of the Erythema Nodosum Leprosum International STudy (ENLIST Group, of patients with ENL attending seven leprosy referral centres in Brazil, Ethiopia, India, Nepal, the Philippines and the United Kingdom. We systematically documented the clinical features and type of ENL, its severity and the drugs used to treat it. Patients with chronic ENL were more likely to be assessed as having severe ENL. Pain, the most frequent symptom, assessed using a semi-quantitative scale was significantly worse in individuals with "severe" ENL. Our findings will determine the items to be included in a severity scale of ENL which we are developing and validating. The study also provides data on the clinical features of ENL, which can be incorporated into a definition of ENL and used for outcome measures in treatment studies.

  12. A multi-feature integration method for fatigue crack detection and crack length estimation in riveted lap joints using Lamb waves

    National Aeronautics and Space Administration — This paper presents an experimental study of damage detection and quantification in riveted lap joints. Embedded lead zirconate titanate piezoelectric (PZT) ceramic...

  13. Smart Multicore Embedded Systems

    This book provides a single-source reference to the state-of-the-art of high-level programming models and compilation tool-chains for embedded system platforms. The authors address challenges faced by programmers developing software to implement parallel applications in embedded systems, where ve...

  14. Embedded engineering education

    Kaštelan, Ivan; Temerinac, Miodrag; Barak, Moshe; Sruk, Vlado

    2016-01-01

    This book focuses on the outcome of the European research project “FP7-ICT-2011-8 / 317882: Embedded Engineering Learning Platform” E2LP. Additionally, some experiences and researches outside this project have been included. This book provides information about the achieved results of the E2LP project as well as some broader views about the embedded engineering education. It captures project results and applications, methodologies, and evaluations. It leads to the history of computer architectures, brings a touch of the future in education tools and provides a valuable resource for anyone interested in embedded engineering education concepts, experiences and material. The book contents 12 original contributions and will open a broader discussion about the necessary knowledge and appropriate learning methods for the new profile of embedded engineers. As a result, the proposed Embedded Computer Engineering Learning Platform will help to educate a sufficient number of future engineers in Europe, capable of d...

  15. Multi-sourced, 3D geometric characterization of volcanogenic karst features: Integrating lidar, sonar, and geophysical datasets (Invited)

    Sharp, J. M.; Gary, M. O.; Reyes, R.; Halihan, T.; Fairfield, N.; Stone, W. C.

    2009-12-01

    Karstic aquifers can form very complex hydrogeological systems and 3-D mapping has been difficult, but Lidar, phased array sonar, and improved earth resistivity techniques show promise in this and in linking metadata to models. Zacatón, perhaps the Earth’s deepest cenote, has a sub-aquatic void space exceeding 7.5 x 106 cubic m3. It is the focus of this study which has created detailed 3D maps of the system. These maps include data from above and beneath the the water table and within the rock matrix to document the extent of the immense karst features and to interpret the geologic processes that formed them. Phase 1 used high resolution (20 mm) Lidar scanning of surficial features of four large cenotes. Scan locations, selected to achieve full feature coverage once registered, were established atop surface benchmarks with UTM coordinates established using GPS and Total Stations. The combined datasets form a geo-registered mesh of surface features down to water level in the cenotes. Phase 2 conducted subsurface imaging using Earth Resistivity Imaging (ERI) geophysics. ERI identified void spaces isolated from open flow conduits. A unique travertine morphology exists in which some cenotes are dry or contain shallow lakes with flat travertine floors; some water-filled cenotes have flat floors without the cone of collapse material; and some have collapse cones. We hypothesize that the floors may have large water-filled voids beneath them. Three separate flat travertine caps were imaged: 1) La Pilita, which is partially open, exposing cap structure over a deep water-filled shaft; 2) Poza Seca, which is dry and vegetated; and 3) Tule, which contains a shallow (<1 m) lake. A fourth line was run adjacent to cenote Verde. La Pilita ERI, verified by SCUBA, documented the existence of large water-filled void zones ERI at Poza Seca showed a thin cap overlying a conductive zone extending to at least 25 m depth beneath the cap with no lower boundary of this zone evident

  16. Embedded design based virtual instrument program for positron beam automation

    Jayapandian, J.; Gururaj, K.; Abhaya, S.; Parimala, J.; Amarendra, G.

    2008-01-01

    Automation of positron beam experiment with a single chip embedded design using a programmable system on chip (PSoC) which provides easy interfacing of the high-voltage DC power supply is reported. Virtual Instrument (VI) control program written in Visual Basic 6.0 ensures the following functions (i) adjusting of sample high voltage by interacting with the programmed PSoC hardware, (ii) control of personal computer (PC) based multi channel analyzer (MCA) card for energy spectroscopy, (iii) analysis of the obtained spectrum to extract the relevant line shape parameters, (iv) plotting of relevant parameters and (v) saving the file in the appropriate format. The present study highlights the hardware features of the PSoC hardware module as well as the control of MCA and other units through programming in Visual Basic

  17. 3D Embedded Reconfigurable Riometer for Heliospheric Space Missions

    Dekoulis, George

    2016-07-01

    This paper describes the development of a new three-dimensional embedded reconfigurable Riometer for performing remote sensing of planetary magnetospheres. The system couples the in situ measurements of probe or orbiter magnetospheric space missions. The new prototype features a multi-frequency mode that allows measurements at frequencies, where heliospheric physics events' signatures are distinct on the ionized planetary plasma. For our planet similar measurements are meaningful for frequencies below 55 MHz. Observation frequencies above 55 MHz yield to direct measurements of the Cosmic Microwave Background intensity. The system acts as a prototyping platform for subsequent space exploration phased-array imaging experiments, due to its high-intensity scientific processing capabilities. The performance improvement over existing systems in operation is in the range of 80%, due to the state-of-the-art hardware and scientific processing used.

  18. Embedded Linux in het onderwijs

    Dr Ruud Ermers

    2008-01-01

    Embedded Linux wordt bij steeds meer grote bedrijven ingevoerd als embedded operating system. Binnen de opleiding Technische Informatica van Fontys Hogeschool ICT is Embedded Linux geïntroduceerd in samenwerking met het lectoraat Architectuur van Embedded Systemen. Embedded Linux is als vakgebied

  19. Multi-omic network-based interrogation of rat liver metabolism following gastric bypass surgery featuring SWATH proteomics.

    Sridharan, Gautham Vivek; D'Alessandro, Matthew; Bale, Shyam Sundhar; Bhagat, Vicky; Gagnon, Hugo; Asara, John M; Uygun, Korkut; Yarmush, Martin L; Saeidi, Nima

    2017-09-01

    Morbidly obese patients often elect for Roux-en-Y gastric bypass (RYGB), a form of bariatric surgery that triggers a remarkable 30% reduction in excess body weight and reversal of insulin resistance for those who are type II diabetic. A more complete understanding of the underlying molecular mechanisms that drive the complex metabolic reprogramming post-RYGB could lead to innovative non-invasive therapeutics that mimic the beneficial effects of the surgery, namely weight loss, achievement of glycemic control, or reversal of non-alcoholic steatohepatitis (NASH). To facilitate these discoveries, we hereby demonstrate the first multi-omic interrogation of a rodent RYGB model to reveal tissue-specific pathway modules implicated in the control of body weight regulation and energy homeostasis. In this study, we focus on and evaluate liver metabolism three months following RYGB in rats using both SWATH proteomics, a burgeoning label free approach using high resolution mass spectrometry to quantify protein levels in biological samples, as well as MRM metabolomics. The SWATH analysis enabled the quantification of 1378 proteins in liver tissue extracts, of which we report the significant down-regulation of Thrsp and Acot13 in RYGB as putative targets of lipid metabolism for weight loss. Furthermore, we develop a computational graph-based metabolic network module detection algorithm for the discovery of non-canonical pathways, or sub-networks, enriched with significantly elevated or depleted metabolites and proteins in RYGB-treated rat livers. The analysis revealed a network connection between the depleted protein Baat and the depleted metabolite taurine, corroborating the clinical observation that taurine-conjugated bile acid levels are perturbed post-RYGB.

  20. Coupled binary embedding for large-scale image retrieval.

    Zheng, Liang; Wang, Shengjin; Tian, Qi

    2014-08-01

    Visual matching is a crucial step in image retrieval based on the bag-of-words (BoW) model. In the baseline method, two keypoints are considered as a matching pair if their SIFT descriptors are quantized to the same visual word. However, the SIFT visual word has two limitations. First, it loses most of its discriminative power during quantization. Second, SIFT only describes the local texture feature. Both drawbacks impair the discriminative power of the BoW model and lead to false positive matches. To tackle this problem, this paper proposes to embed multiple binary features at indexing level. To model correlation between features, a multi-IDF scheme is introduced, through which different binary features are coupled into the inverted file. We show that matching verification methods based on binary features, such as Hamming embedding, can be effectively incorporated in our framework. As an extension, we explore the fusion of binary color feature into image retrieval. The joint integration of the SIFT visual word and binary features greatly enhances the precision of visual matching, reducing the impact of false positive matches. Our method is evaluated through extensive experiments on four benchmark datasets (Ukbench, Holidays, DupImage, and MIR Flickr 1M). We show that our method significantly improves the baseline approach. In addition, large-scale experiments indicate that the proposed method requires acceptable memory usage and query time compared with other approaches. Further, when global color feature is integrated, our method yields competitive performance with the state-of-the-arts.

  1. PredPPCrys: accurate prediction of sequence cloning, protein production, purification and crystallization propensity from protein sequences using multi-step heterogeneous feature fusion and selection.

    Huilin Wang

    Full Text Available X-ray crystallography is the primary approach to solve the three-dimensional structure of a protein. However, a major bottleneck of this method is the failure of multi-step experimental procedures to yield diffraction-quality crystals, including sequence cloning, protein material production, purification, crystallization and ultimately, structural determination. Accordingly, prediction of the propensity of a protein to successfully undergo these experimental procedures based on the protein sequence may help narrow down laborious experimental efforts and facilitate target selection. A number of bioinformatics methods based on protein sequence information have been developed for this purpose. However, our knowledge on the important determinants of propensity for a protein sequence to produce high diffraction-quality crystals remains largely incomplete. In practice, most of the existing methods display poorer performance when evaluated on larger and updated datasets. To address this problem, we constructed an up-to-date dataset as the benchmark, and subsequently developed a new approach termed 'PredPPCrys' using the support vector machine (SVM. Using a comprehensive set of multifaceted sequence-derived features in combination with a novel multi-step feature selection strategy, we identified and characterized the relative importance and contribution of each feature type to the prediction performance of five individual experimental steps required for successful crystallization. The resulting optimal candidate features were used as inputs to build the first-level SVM predictor (PredPPCrys I. Next, prediction outputs of PredPPCrys I were used as the input to build second-level SVM classifiers (PredPPCrys II, which led to significantly enhanced prediction performance. Benchmarking experiments indicated that our PredPPCrys method outperforms most existing procedures on both up-to-date and previous datasets. In addition, the predicted crystallization

  2. Integral Images: Efficient Algorithms for Their Computation and Storage in Resource-Constrained Embedded Vision Systems.

    Ehsan, Shoaib; Clark, Adrian F; Naveed ur Rehman; McDonald-Maier, Klaus D

    2015-07-10

    The integral image, an intermediate image representation, has found extensive use in multi-scale local feature detection algorithms, such as Speeded-Up Robust Features (SURF), allowing fast computation of rectangular features at constant speed, independent of filter size. For resource-constrained real-time embedded vision systems, computation and storage of integral image presents several design challenges due to strict timing and hardware limitations. Although calculation of the integral image only consists of simple addition operations, the total number of operations is large owing to the generally large size of image data. Recursive equations allow substantial decrease in the number of operations but require calculation in a serial fashion. This paper presents two new hardware algorithms that are based on the decomposition of these recursive equations, allowing calculation of up to four integral image values in a row-parallel way without significantly increasing the number of operations. An efficient design strategy is also proposed for a parallel integral image computation unit to reduce the size of the required internal memory (nearly 35% for common HD video). Addressing the storage problem of integral image in embedded vision systems, the paper presents two algorithms which allow substantial decrease (at least 44.44%) in the memory requirements. Finally, the paper provides a case study that highlights the utility of the proposed architectures in embedded vision systems.

  3. Integral Images: Efficient Algorithms for Their Computation and Storage in Resource-Constrained Embedded Vision Systems

    Shoaib Ehsan

    2015-07-01

    Full Text Available The integral image, an intermediate image representation, has found extensive use in multi-scale local feature detection algorithms, such as Speeded-Up Robust Features (SURF, allowing fast computation of rectangular features at constant speed, independent of filter size. For resource-constrained real-time embedded vision systems, computation and storage of integral image presents several design challenges due to strict timing and hardware limitations. Although calculation of the integral image only consists of simple addition operations, the total number of operations is large owing to the generally large size of image data. Recursive equations allow substantial decrease in the number of operations but require calculation in a serial fashion. This paper presents two new hardware algorithms that are based on the decomposition of these recursive equations, allowing calculation of up to four integral image values in a row-parallel way without significantly increasing the number of operations. An efficient design strategy is also proposed for a parallel integral image computation unit to reduce the size of the required internal memory (nearly 35% for common HD video. Addressing the storage problem of integral image in embedded vision systems, the paper presents two algorithms which allow substantial decrease (at least 44.44% in the memory requirements. Finally, the paper provides a case study that highlights the utility of the proposed architectures in embedded vision systems.

  4. Novel images extraction model using improved delay vector variance feature extraction and multi-kernel neural network for EEG detection and prediction.

    Ge, Jing; Zhang, Guoping

    2015-01-01

    Advanced intelligent methodologies could help detect and predict diseases from the EEG signals in cases the manual analysis is inefficient available, for instance, the epileptic seizures detection and prediction. This is because the diversity and the evolution of the epileptic seizures make it very difficult in detecting and identifying the undergoing disease. Fortunately, the determinism and nonlinearity in a time series could characterize the state changes. Literature review indicates that the Delay Vector Variance (DVV) could examine the nonlinearity to gain insight into the EEG signals but very limited work has been done to address the quantitative DVV approach. Hence, the outcomes of the quantitative DVV should be evaluated to detect the epileptic seizures. To develop a new epileptic seizure detection method based on quantitative DVV. This new epileptic seizure detection method employed an improved delay vector variance (IDVV) to extract the nonlinearity value as a distinct feature. Then a multi-kernel functions strategy was proposed in the extreme learning machine (ELM) network to provide precise disease detection and prediction. The nonlinearity is more sensitive than the energy and entropy. 87.5% overall accuracy of recognition and 75.0% overall accuracy of forecasting were achieved. The proposed IDVV and multi-kernel ELM based method was feasible and effective for epileptic EEG detection. Hence, the newly proposed method has importance for practical applications.

  5. A Multi-Functional Microelectrode Array Featuring 59760 Electrodes, 2048 Electrophysiology Channels, Stimulation, Impedance Measurement and Neurotransmitter Detection Channels.

    Dragas, Jelena; Viswam, Vijay; Shadmani, Amir; Chen, Yihui; Bounik, Raziyeh; Stettler, Alexander; Radivojevic, Milos; Geissler, Sydney; Obien, Marie; Müller, Jan; Hierlemann, Andreas

    2017-06-01

    Biological cells are characterized by highly complex phenomena and processes that are, to a great extent, interdependent. To gain detailed insights, devices designed to study cellular phenomena need to enable tracking and manipulation of multiple cell parameters in parallel; they have to provide high signal quality and high spatiotemporal resolution. To this end, we have developed a CMOS-based microelectrode array system that integrates six measurement and stimulation functions, the largest number to date. Moreover, the system features the largest active electrode array area to date (4.48×2.43 mm 2 ) to accommodate 59,760 electrodes, while its power consumption, noise characteristics, and spatial resolution (13.5 μm electrode pitch) are comparable to the best state-of-the-art devices. The system includes: 2,048 action-potential (AP, bandwidth: 300 Hz to 10 kHz) recording units, 32 local-field-potential (LFP, bandwidth: 1 Hz to 300 Hz) recording units, 32 current recording units, 32 impedance measurement units, and 28 neurotransmitter detection units, in addition to the 16 dual-mode voltage-only or current/voltage-controlled stimulation units. The electrode array architecture is based on a switch matrix, which allows for connecting any measurement/stimulation unit to any electrode in the array and for performing different measurement/stimulation functions in parallel.

  6. Brauer type embedding problems

    Ledet, Arne

    2005-01-01

    This monograph is concerned with Galois theoretical embedding problems of so-called Brauer type with a focus on 2-groups and on finding explicit criteria for solvability and explicit constructions of the solutions. The advantage of considering Brauer type embedding problems is their comparatively simple condition for solvability in the form of an obstruction in the Brauer group of the ground field. This book presupposes knowledge of classical Galois theory and the attendant algebra. Before considering questions of reducing the embedding problems and reformulating the solvability criteria, the

  7. Time-dependent embedding

    Inglesfield, J. E.

    2007-01-01

    A method of solving the time-dependent Schr\\"odinger equation is presented, in which a finite region of space is treated explicitly, with the boundary conditions for matching the wave-functions on to the rest of the system replaced by an embedding term added on to the Hamiltonian. This time-dependent embedding term is derived from the Fourier transform of the energy-dependent embedding potential, which embeds the time-independent Schr\\"odinger equation. Results are presented for a one-dimensi...

  8. Frozen density embedding with non-integer subsystems' particle numbers.

    Fabiano, Eduardo; Laricchia, Savio; Della Sala, Fabio

    2014-03-21

    We extend the frozen density embedding theory to non-integer subsystems' particles numbers. Different features of this formulation are discussed, with special concern for approximate embedding calculations. In particular, we highlight the relation between the non-integer particle-number partition scheme and the resulting embedding errors. Finally, we provide a discussion of the implications of the present theory for the derivative discontinuity issue and the calculation of chemical reactivity descriptors.

  9. Computers as components principles of embedded computing system design

    Wolf, Marilyn

    2012-01-01

    Computers as Components: Principles of Embedded Computing System Design, 3e, presents essential knowledge on embedded systems technology and techniques. Updated for today's embedded systems design methods, this edition features new examples including digital signal processing, multimedia, and cyber-physical systems. Author Marilyn Wolf covers the latest processors from Texas Instruments, ARM, and Microchip Technology plus software, operating systems, networks, consumer devices, and more. Like the previous editions, this textbook: Uses real processors to demonstrate both technology and tec

  10. Clinicopathological features and prognosis of mucin-producing bile duct tumor and mucinous cystic tumor of the liver: a multi-institutional study by the Japan Biliary Association.

    Kubota, Keiichi; Nakanuma, Yasuni; Kondo, Fukuo; Hachiya, Hiroyuki; Miyazaki, Masaru; Nagino, Masato; Yamamoto, Masakazu; Isayama, Hiroyuki; Tabata, Masami; Kinoshita, Hisafumi; Kamisawa, Terumi; Inui, Kazuo

    2014-03-01

    The aim of this study was to determine the clinicopathological features and surgical outcomes of mucinous cystic neoplasm of the liver (MCN) and mucin-producing intraductal papillary neoplasm of the intrahepatic bile duct (M-IPNB). We performed a multi-institutional, retrospective study of patients with MCN or M-IPNB pathologically defined by the presence or absence of an ovarian-like stroma. The M-IPNB and MCN were diagnosed in 119 and nine patients, respectively. MCN was observed in female patients, while M-IPNB produced symptoms of cholangitis. M-IPNBs were classed as low or intermediate grade in 53 cases, high grade in 23 and invasive carcinoma in 43. Fifty-one of the M-IPNBs were the pancreatobiliary type (PT), 33 were the intestinal type (IT), 23 were the oncocytic type (OT), and 12 were the gastric type (GT). The 1-, 5- and 10-year survival rates for the 105 patients with M-IPNB were 96%, 84% and 81%, respectively, while the 5-year survival rate for patients with MCN was 100%. OT and GT M-IPNB had better 10-year survival rates than PT and IT M-IPNB. Although MCN has different features from M-IPNB, both diseases have a good prognosis after resection. The cellular type of M-IPNB appears to predict outcome. © 2013 Japanese Society of Hepato-Biliary-Pancreatic Surgery.

  11. Online Capacity Estimation of Lithium-Ion Batteries Based on Novel Feature Extraction and Adaptive Multi-Kernel Relevance Vector Machine

    Yang Zhang

    2015-11-01

    Full Text Available Prognostics is necessary to ensure the reliability and safety of lithium-ion batteries for hybrid electric vehicles or satellites. This process can be achieved by capacity estimation, which is a direct fading indicator for assessing the state of health of a battery. However, the capacity of a lithium-ion battery onboard is difficult to monitor. This paper presents a data-driven approach for online capacity estimation. First, six novel features are extracted from cyclic charge/discharge cycles and used as indirect health indicators. An adaptive multi-kernel relevance machine (MKRVM based on accelerated particle swarm optimization algorithm is used to determine the optimal parameters of MKRVM and characterize the relationship between extracted features and battery capacity. The overall estimation process comprises offline and online stages. A supervised learning step in the offline stage is established for model verification to ensure the generalizability of MKRVM for online application. Cross-validation is further conducted to validate the performance of the proposed model. Experiment and comparison results show the effectiveness, accuracy, efficiency, and robustness of the proposed approach for online capacity estimation of lithium-ion batteries.

  12. Electronics for embedded systems

    Bindal, Ahmet

    2017-01-01

    This book provides semester-length coverage of electronics for embedded systems, covering most common analog and digital circuit-related issues encountered while designing embedded system hardware. It is written for students and young professionals who have basic circuit theory background and want to learn more about passive circuits, diode and bipolar transistor circuits, the state-of-the-art CMOS logic family and its interface with older logic families such as TTL, sensors and sensor physics, operational amplifier circuits to condition sensor signals, data converters and various circuits used in electro-mechanical device control in embedded systems. The book also provides numerous hardware design examples by integrating the topics learned in earlier chapters. The last chapter extensively reviews the combinational and sequential logic design principles to be able to design the digital part of embedded system hardware.

  13. Embedded Fragments Registry (EFR)

    Department of Veterans Affairs — In 2009, the Department of Defense estimated that approximately 40,000 service members who served in OEF/OIF may have embedded fragment wounds as the result of small...

  14. Smart Multicore Embedded Systems

    This book provides a single-source reference to the state-of-the-art of high-level programming models and compilation tool-chains for embedded system platforms. The authors address challenges faced by programmers developing software to implement parallel applications in embedded systems, where very...... specificities of various embedded systems from different industries. Parallel programming tool-chains are described that take as input parameters both the application and the platform model, then determine relevant transformations and mapping decisions on the concrete platform, minimizing user intervention...... and hiding the difficulties related to the correct and efficient use of memory hierarchy and low level code generation. Describes tools and programming models for multicore embedded systems Emphasizes throughout performance per watt scalability Discusses realistic limits of software parallelization Enables...

  15. Dynamic memory management for embedded systems

    Atienza Alonso, David; Poucet, Christophe; Peón-Quirós, Miguel; Bartzas, Alexandros; Catthoor, Francky; Soudris, Dimitrios

    2015-01-01

    This book provides a systematic and unified methodology, including basic principles and reusable processes, for dynamic memory management (DMM) in embedded systems.  The authors describe in detail how to design and optimize the use of dynamic memory in modern, multimedia and network applications, targeting the latest generation of portable embedded systems, such as smartphones. Coverage includes a variety of design and optimization topics in electronic design automation of DMM, from high-level software optimization to microarchitecture-level hardware support. The authors describe the design of multi-layer dynamic data structures for the final memory hierarchy layers of the target portable embedded systems and how to create a low-fragmentation, cost-efficient, dynamic memory management subsystem out of configurable components for the particular memory allocation and de-allocation patterns for each type of application.  The design methodology described in this book is based on propagating constraints among de...

  16. An Integrated “Multi-Omics” Comparison of Embryo and Endosperm Tissue-Specific Features and Their Impact on Rice Seed Quality

    Marc Galland

    2017-11-01

    Full Text Available Although rice is a key crop species, few studies have addressed both rice seed physiological and nutritional quality, especially at the tissue level. In this study, an exhaustive “multi-omics” dataset on the mature rice seed was obtained by combining transcriptomics, label-free shotgun proteomics and metabolomics from embryo and endosperm, independently. These high-throughput analyses provide a new insight on the tissue-specificity related to rice seed quality. Foremost, we pinpointed that extensive post-transcriptional regulations occur at the end of rice seed development such that the embryo proteome becomes much more diversified than the endosperm proteome. Secondly, we observed that survival in the dry state in each seed compartment depends on contrasted metabolic and enzymatic apparatus in the embryo and the endosperm, respectively. Thirdly, it was remarkable to identify two different sets of starch biosynthesis enzymes as well as seed storage proteins (glutelins in both embryo and endosperm consistently with the supernumerary embryo hypothesis origin of the endosperm. The presence of a putative new glutelin with a possible embryonic favored abundance is described here for the first time. Finally, we quantified the rate of mRNA translation into proteins. Consistently, the embryonic panel of protein translation initiation factors is much more diverse than that of the endosperm. This work emphasizes the value of tissue-specificity-centered “multi-omics” study in the seed to highlight new features even from well-characterized pathways. It paves the way for future studies of critical genetic determinants of rice seed physiological and nutritional quality.

  17. Aging linear viscoelasticity of matrix-inclusion composite materials featuring ellipsoidal inclusions

    LAVERGNE, Francis; SAB, Karam; SANAHUJA, Julien; BORNERT, Michel; TOULEMONDE, Charles

    2016-01-01

    A multi-scale homogenization scheme is proposed to estimate the time-dependent strains of fiber-reinforced concrete. This material is modeled as an aging linear viscoelastic composite material featuring ellipsoidal inclusions embedded in a viscoelastic cementitious matrix characterized by a time-dependent Poisson's ratio. To this end, the homogenization scheme proposed in Lavergne et al. [1] is adapted to the case of a time-dependent Poisson's ratio and it is successfully validated on a non-a...

  18. Embedded Systems Design: Optimization Challenges

    Pop, Paul

    2005-01-01

    Summary form only given. Embedded systems are everywhere: from alarm clocks to PDAs, from mobile phones to cars, almost all the devices we use are controlled by embedded systems. Over 99% of the microprocessors produced today are used in embedded systems, and recently the number of embedded systems...

  19. The art of programming embedded systems

    Ganssle, Jack

    1992-01-01

    Embedded systems are products such as microwave ovens, cars, and toys that rely on an internal microprocessor. This book is oriented toward the design engineer or programmer who writes the computer code for such a system. There are a number of problems specific to the embedded systems designer, and this book addresses them and offers practical solutions.Key Features* Offers cookbook routines, algorithms, and design techniques* Includes tips for handling debugging management and testing* Explores the philosophy of tightly coupling software and hardware in programming and dev

  20. Polarizable Density Embedding

    Olsen, Jógvan Magnus Haugaard; Steinmann, Casper; Ruud, Kenneth

    2015-01-01

    We present a new QM/QM/MM-based model for calculating molecular properties and excited states of solute-solvent systems. We denote this new approach the polarizable density embedding (PDE) model and it represents an extension of our previously developed polarizable embedding (PE) strategy. The PDE...... model is a focused computational approach in which a core region of the system studied is represented by a quantum-chemical method, whereas the environment is divided into two other regions: an inner and an outer region. Molecules belonging to the inner region are described by their exact densities...

  1. Nanofluidic Device with Embedded Nanopore

    Zhang, Yuning; Reisner, Walter

    2014-03-01

    Nanofluidic based devices are robust methods for biomolecular sensing and single DNA manipulation. Nanopore-based DNA sensing has attractive features that make it a leading candidate as a single-molecule DNA sequencing technology. Nanochannel based extension of DNA, combined with enzymatic or denaturation-based barcoding schemes, is already a powerful approach for genome analysis. We believe that there is revolutionary potential in devices that combine nanochannels with nanpore detectors. In particular, due to the fast translocation of a DNA molecule through a standard nanopore configuration, there is an unfavorable trade-off between signal and sequence resolution. With a combined nanochannel-nanopore device, based on embedding a nanopore inside a nanochannel, we can in principle gain independent control over both DNA translocation speed and sensing signal, solving the key draw-back of the standard nanopore configuration. We demonstrate that we can detect - using fluorescent microscopy - successful translocation of DNA from the nanochannel out through the nanopore, a possible method to 'select' a given barcode for further analysis. We also show that in equilibrium DNA will not escape through an embedded sub-persistence length nanopore until a certain voltage bias is added.

  2. Embedding JIT into MRP

    Flapper, S.D.P.; Miltenburg, G.J.; Wijngaard, J.

    1991-01-01

    Today many companies who are using MRP production control systems are investigating how they can produce some or all of their products using just-in time (JIT) principles. They wonder to what extent MRP can provide support for JIT production. This paper describes how JIT can be embedded into MRP. A

  3. Embedded Multimaterial Extrusion Bioprinting

    Rocca, Marco; Fragasso, Alessio; Liu, Wanjun; Heinrich, Marcel A.; Zhang, Yu Shrike

    Embedded extrusion bioprinting allows for the generation of complex structures that otherwise cannot be achieved with conventional layer-by-layer deposition from the bottom, by overcoming the limits imposed by gravitational force. By taking advantage of a hydrogel bath, serving as a sacrificial

  4. Embedded data representations

    Willett, Wesley; Jansen, Yvonne; Dragicevic, Pierre

    2017-01-01

    We introduce embedded data representations, the use of visual and physical representations of data that are deeply integrated with the physical spaces, objects, and entities to which the data refers. Technologies like lightweight wireless displays, mixed reality hardware, and autonomous vehicles...

  5. Polarizable Density Embedding

    Reinholdt, Peter; Kongsted, Jacob; Olsen, Jógvan Magnus Haugaard

    2017-01-01

    We analyze the performance of the polarizable density embedding (PDE) model-a new multiscale computational approach designed for prediction and rationalization of general molecular properties of large and complex systems. We showcase how the PDE model very effectively handles the use of large...

  6. Embedded enzymes catalyse capture

    Kentish, Sandra

    2018-05-01

    Membrane technologies for carbon capture can offer economic and environmental advantages over conventional amine-based absorption, but can suffer from limited gas flux and selectivity to CO2. Now, a membrane based on enzymes embedded in hydrophilic pores is shown to exhibit combined flux and selectivity that challenges the state of the art.

  7. Diverse Power Iteration Embeddings and Its Applications

    Huang H.; Yoo S.; Yu, D.; Qin, H.

    2014-12-14

    Abstract—Spectral Embedding is one of the most effective dimension reduction algorithms in data mining. However, its computation complexity has to be mitigated in order to apply it for real-world large scale data analysis. Many researches have been focusing on developing approximate spectral embeddings which are more efficient, but meanwhile far less effective. This paper proposes Diverse Power Iteration Embeddings (DPIE), which not only retains the similar efficiency of power iteration methods but also produces a series of diverse and more effective embedding vectors. We test this novel method by applying it to various data mining applications (e.g. clustering, anomaly detection and feature selection) and evaluating their performance improvements. The experimental results show our proposed DPIE is more effective than popular spectral approximation methods, and obtains the similar quality of classic spectral embedding derived from eigen-decompositions. Moreover it is extremely fast on big data applications. For example in terms of clustering result, DPIE achieves as good as 95% of classic spectral clustering on the complex datasets but 4000+ times faster in limited memory environment.

  8. Comparison of Pilot Symbol Embedded Channel Estimation Algorithms

    P. Kadlec

    2009-12-01

    Full Text Available In the paper, algorithms of the pilot symbol embedded channel estimation are compared. Attention is turned to the Least Square (LS channel estimation and the Sliding Correlator (SC algorithm. Both algorithms are implemented in Matlab to estimate the Channel Impulse Response (CIR of a channel exhibiting multi-path propagation. Algorithms are compared from the viewpoint of computational demands, influence of the Additive White Gaussian Noise (AWGN, an embedded pilot symbol and a computed CIR over the estimation error.

  9. Multi-Center Evaluation of the Fully Automated PCR-Based Idylla™ KRAS Mutation Assay for Rapid KRAS Mutation Status Determination on Formalin-Fixed Paraffin-Embedded Tissue of Human Colorectal Cancer

    Solassol, Jérôme; Vendrell, Julie; Märkl, Bruno

    2016-01-01

    , was assessed on archived formalin-fixed paraffin-embedded (FFPE) tissue sections by comparing its results with the results previously obtained by routine reference approaches for KRAS genotyping. In case of discordance, samples were assessed further by additional methods. Among the 374 colorectal cancer FFPE...

  10. Feasibility study on embedded transport core calculations

    Ivanov, B.; Zikatanov, L.; Ivanov, K.

    2007-01-01

    The main objective of this study is to develop an advanced core calculation methodology based on embedded diffusion and transport calculations. The scheme proposed in this work is based on embedded diffusion or SP 3 pin-by-pin local fuel assembly calculation within the framework of the Nodal Expansion Method (NEM) diffusion core calculation. The SP 3 method has gained popularity in the last 10 years as an advanced method for neutronics calculation. NEM is a multi-group nodal diffusion code developed, maintained and continuously improved at the Pennsylvania State University. The developed calculation scheme is a non-linear iteration process, which involves cross-section homogenization, on-line discontinuity factors generation, and boundary conditions evaluation by the global solution passed to the local calculation. In order to accomplish the local calculation, a new code has been developed based on the Finite Elements Method (FEM), which is capable of performing both diffusion and SP 3 calculations. The new code will be used in the framework of the NEM code in order to perform embedded pin-by-pin diffusion and SP 3 calculations on fuel assembly basis. The development of the diffusion and SP 3 FEM code is presented first following by its application to several problems. Description of the proposed embedded scheme is provided next as well as the obtained preliminary results of the C3 MOX benchmark. The results from the embedded calculations are compared with direct pin-by-pin whole core calculations in terms of accuracy and efficiency followed by conclusions made about the feasibility of the proposed embedded approach. (authors)

  11. Intrinsic Embedded Sensors for Polymeric Mechatronics: Flexure and Force Sensing

    Leif P. Jentoft

    2014-02-01

    Full Text Available While polymeric fabrication processes, including recent advances in additive manufacturing, have revolutionized manufacturing, little work has been done on effective sensing elements compatible with and embedded within polymeric structures. In this paper, we describe the development and evaluation of two important sensing modalities for embedding in polymeric mechatronic and robotic mechanisms: multi-axis flexure joint angle sensing utilizing IR phototransistors, and a small (12 mm, three-axis force sensing via embedded silicon strain gages with similar performance characteristics as an equally sized metal element based sensor.

  12. Intrinsic embedded sensors for polymeric mechatronics: flexure and force sensing.

    Jentoft, Leif P; Dollar, Aaron M; Wagner, Christopher R; Howe, Robert D

    2014-02-25

    While polymeric fabrication processes, including recent advances in additive manufacturing, have revolutionized manufacturing, little work has been done on effective sensing elements compatible with and embedded within polymeric structures. In this paper, we describe the development and evaluation of two important sensing modalities for embedding in polymeric mechatronic and robotic mechanisms: multi-axis flexure joint angle sensing utilizing IR phototransistors, and a small (12 mm), three-axis force sensing via embedded silicon strain gages with similar performance characteristics as an equally sized metal element based sensor.

  13. Multi-sensor in situ observations to resolve the sub-mesoscale features in the stratified Gulf of Finland, Baltic Sea

    Lips, Urmas; Kikas, Villu; Liblik, Taavi; Lips, Inga

    2016-05-01

    High-resolution numerical modeling, remote sensing, and in situ data have revealed significant role of sub-mesoscale features in shaping the distribution pattern of tracers in the ocean's upper layer. However, in situ measurements are difficult to conduct with the required resolution and coverage in time and space to resolve the sub-mesoscale, especially in such relatively shallow basins as the Gulf of Finland, where the typical baroclinic Rossby radius is 2-5 km. To map the multi-scale spatiotemporal variability in the gulf, we initiated continuous measurements with autonomous devices, including a moored profiler and Ferrybox system, which were complemented by dedicated research-vessel-based surveys. The analysis of collected high-resolution data in the summers of 2009-2012 revealed pronounced variability at the sub-mesoscale in the presence of mesoscale upwelling/downwelling, fronts, and eddies. The horizontal wavenumber spectra of temperature variance in the surface layer had slopes close to -2 between the lateral scales from 10 to 0.5 km. Similar tendency towards the -2 slopes of horizontal wavenumber spectra of temperature variance was found in the seasonal thermocline between the lateral scales from 10 to 1 km. It suggests that the ageostrophic sub-mesoscale processes could contribute considerably to the energy cascade in such a stratified sea basin. We showed that the intrusions of water with different salinity, which indicate the occurrence of a layered flow structure, could appear in the process of upwelling/downwelling development and relaxation in response to variable wind forcing. We suggest that the sub-mesoscale processes play a major role in feeding surface blooms in the conditions of coupled coastal upwelling and downwelling events in the Gulf of Finland.

  14. Embedding in thermosetting resins

    Buzonniere, A. de

    1985-01-01

    Medium activity waste coming either from nuclear power plants in operation such as evaporator concentrates, spent resins, filter cartridges or the dismantling of installations are embedded in order to obtain a product suitable for long term disposal. Embedding in thermosetting resins (polyester or epoxy) is one among currently used techniques; it is being developed by the CEA (Commissariat a l'Energie Atomique) and Technicatome (subsidiary of CEA and EDF). The process is easy to operate and yields excellent results particularly as far as volume reduction and radioelement containment (cesium particularly) are concerned. The process has already been in operation in four stationary plants for several years. Extension of the process to mobile units has been completed by Technicatome in collaboration with the CEA [fr

  15. Physical Activity Recognition from Smartphone Embedded Sensors

    Prudêncio, João; Aguiar, Ana; Roetter, Daniel Enrique Lucani

    2013-01-01

    The ubiquity of smartphones has motivated efforts to use the embedded sensors to detect various aspects of user context to transparently provide personalized and contextualized services to the user. One relevant piece of context is the physical activity of the smartphone user. In this paper, we...... propose a novel set of features for distinguishing five physical activities using only sensors embedded in the smartphone. Specifically, we introduce features that are normalized using the orientation sensor such that horizontal and vertical movements are explicitly computed. We evaluate a neural network...... classifier in experiments in the wild with multiple users and hardware, we achieve accuracies above 90% for a single user and phone, and above 65% for multiple users, which is higher that similar works on the same set of activities, demonstrating the potential of our approach....

  16. Embedded Ultrasonics for SHM of Space Applications

    2012-07-30

    information on material properties and other forms of damage such as cracks, structural fatigue and/or impact events. This synergistic aspect of the embedded...larger the phase shift. However, high excitation levels could contribute to sensor fatigue and levels in a range 15 to 20 (110 to 130 volts) are...joints each featuring three bolts. Piezoelectric wafers ( PZT ) with UNF electrodes were bonded to the isogrid panels using 3M 2216 epoxy

  17. Modelling and Analyses of Embedded Systems Design

    Brekling, Aske Wiid

    We present the MoVES languages: a language with which embedded systems can be specified at a stage in the development process where an application is identified and should be mapped to an execution platform (potentially multi- core). We give a formal model for MoVES that captures and gives......-based verification is a promising approach for assisting developers of embedded systems. We provide examples of system verifications that, in size and complexity, point in the direction of industrially-interesting systems....... semantics to the elements of specifications in the MoVES language. We show that even for seem- ingly simple systems, the complexity of verifying real-time constraints can be overwhelming - but we give an upper limit to the size of the search-space that needs examining. Furthermore, the formal model exposes...

  18. Bubbling and on-off intermittency in bailout embeddings.

    Cartwright, Julyan H E; Magnasco, Marcelo O; Piro, Oreste; Tuval, Idan

    2003-07-01

    We establish and investigate the conceptual connection between the dynamics of the bailout embedding of a Hamiltonian system and the dynamical regimes associated with the occurrence of bubbling and blowout bifurcations. The roles of the invariant manifold and the dynamics restricted to it, required in bubbling and blowout bifurcating systems, are played in the bailout embedding by the embedded Hamiltonian dynamical system. The Hamiltonian nature of the dynamics is precisely the distinctive feature of this instance of a bubbling or blowout bifurcation. The detachment of the embedding trajectories from the original ones can thus be thought of as transient on-off intermittency, and noise-induced avoidance of some regions of the embedded phase space can be recognized as Hamiltonian bubbling.

  19. Embedded software verification and debugging

    Winterholer, Markus

    2017-01-01

    This book provides comprehensive coverage of verification and debugging techniques for embedded software, which is frequently used in safety critical applications (e.g., automotive), where failures are unacceptable. Since the verification of complex systems needs to encompass the verification of both hardware and embedded software modules, this book focuses on verification and debugging approaches for embedded software with hardware dependencies. Coverage includes the entire flow of design, verification and debugging of embedded software and all key approaches to debugging, dynamic, static, and hybrid verification. This book discusses the current, industrial embedded software verification flow, as well as emerging trends with focus on formal and hybrid verification and debugging approaches. Includes in a single source the entire flow of design, verification and debugging of embedded software; Addresses the main techniques that are currently being used in the industry for assuring the quality of embedded softw...

  20. Embedded control system for high power RF amplifiers

    Sharma, Deepak Kumar; Gupta, Alok Kumar; Jain, Akhilesh; Hannurkar, P.R.

    2011-01-01

    RF power devices are usually very sensitive to overheat and reflected RF power; hence a protective interlock system is required to be embedded with high power solid state RF amplifiers. The solid state RF amplifiers have salient features of graceful degradation and very low mean time to repair (MTTR). In order to exploit these features in favour of lowest system downtime, a real-time control system is embedded with high power RF amplifiers. The control system is developed with the features of monitoring, measurement and network publishing of various parameters, historical data logging, alarm generation, displaying data to the operator and tripping the system in case of any interlock failure. This paper discusses the design philosophy, features, functions and implementation details of the embedded control system. (author)

  1. Unsteady Flame Embedding

    El-Asrag, Hossam A.

    2011-01-01

    Direct simulation of all the length and time scales relevant to practical combustion processes is computationally prohibitive. When combustion processes are driven by reaction and transport phenomena occurring at the unresolved scales of a numerical simulation, one must introduce a dynamic subgrid model that accounts for the multiscale nature of the problem using information available on a resolvable grid. Here, we discuss a model that captures unsteady flow-flame interactions- including extinction, re-ignition, and history effects-via embedded simulations at the subgrid level. The model efficiently accounts for subgrid flame structure and incorporates detailed chemistry and transport, allowing more accurate prediction of the stretch effect and the heat release. In this chapter we first review the work done in the past thirty years to develop the flame embedding concept. Next we present a formulation for the same concept that is compatible with Large Eddy Simulation in the flamelet regimes. The unsteady flame embedding approach (UFE) treats the flame as an ensemble of locally one-dimensional flames, similar to the flamelet approach. However, a set of elemental one-dimensional flames is used to describe the turbulent flame structure directly at the subgrid level. The calculations employ a one-dimensional unsteady flame model that incorporates unsteady strain rate, curvature, and mixture boundary conditions imposed by the resolved scales. The model is used for closure of the subgrid terms in the context of large eddy simulation. Direct numerical simulation (DNS) data from a flame-vortex interaction problem is used for comparison. © Springer Science+Business Media B.V. 2011.

  2. Embedded microcontroller interfacing

    Gupta, Gourab Sen

    2010-01-01

    Mixed-Signal Embedded Microcontrollers are commonly used in integrating analog components needed to control non-digital electronic systems. They are used in automatically controlled devices and products, such as automobile engine control systems, wireless remote controllers, office machines, home appliances, power tools, and toys. Microcontrollers make it economical to digitally control even more devices and processes by reducing the size and cost, compared to a design that uses a separate microprocessor, memory, and input/output devices. In many undergraduate and post-graduate courses, teachi

  3. Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm

    Heidari, Morteza; Zargari Khuzani, Abolfazl; Hollingsworth, Alan B.; Danala, Gopichandh; Mirniaharikandehei, Seyedehnafiseh; Qiu, Yuchen; Liu, Hong; Zheng, Bin

    2018-02-01

    In order to automatically identify a set of effective mammographic image features and build an optimal breast cancer risk stratification model, this study aims to investigate advantages of applying a machine learning approach embedded with a locally preserving projection (LPP) based feature combination and regeneration algorithm to predict short-term breast cancer risk. A dataset involving negative mammograms acquired from 500 women was assembled. This dataset was divided into two age-matched classes of 250 high risk cases in which cancer was detected in the next subsequent mammography screening and 250 low risk cases, which remained negative. First, a computer-aided image processing scheme was applied to segment fibro-glandular tissue depicted on mammograms and initially compute 44 features related to the bilateral asymmetry of mammographic tissue density distribution between left and right breasts. Next, a multi-feature fusion based machine learning classifier was built to predict the risk of cancer detection in the next mammography screening. A leave-one-case-out (LOCO) cross-validation method was applied to train and test the machine learning classifier embedded with a LLP algorithm, which generated a new operational vector with 4 features using a maximal variance approach in each LOCO process. Results showed a 9.7% increase in risk prediction accuracy when using this LPP-embedded machine learning approach. An increased trend of adjusted odds ratios was also detected in which odds ratios increased from 1.0 to 11.2. This study demonstrated that applying the LPP algorithm effectively reduced feature dimensionality, and yielded higher and potentially more robust performance in predicting short-term breast cancer risk.

  4. Features of the non-collinear one-phonon anomalous light scattering controlled by elastic waves with elevated linear losses: potentials for multi-frequency parallel spectrum analysis of radio-wave signals.

    Shcherbakov, Alexandre S; Arellanes, Adan Omar

    2017-12-01

    During subsequent development of the recently proposed multi-frequency parallel spectrometer for precise spectrum analysis of wideband radio-wave signals, we study potentials of new acousto-optical cells exploiting selected crystalline materials at the limits of their capabilities. Characterizing these wide-aperture cells is non-trivial due to new features inherent in the chosen regime of an advanced non-collinear one-phonon anomalous light scattering by elastic waves with significantly elevated acoustic losses. These features can be observed simpler in uniaxial, tetragonal, and trigonal crystals possessing linear acoustic attenuation. We demonstrate that formerly studied additional degree of freedom, revealed initially for multi-phonon regimes of acousto-optical interaction, can be identified within the one-phonon geometry as well and exploited for designing new cells. We clarify the role of varying the central acoustic frequency and acoustic attenuation using the identified degree of freedom. Therewith, we are strongly restricted by a linear regime of acousto-optical interaction to avoid the origin of multi-phonon processes within carrying out a multi-frequency parallel spectrum analysis of radio-wave signals. Proof-of-principle experiments confirm the developed approaches and illustrate their applicability to innovative technique for an advanced spectrum analysis of wideband radio-wave signals with the improved resolution in an extended frequency range.

  5. EMBEDDED CONTROL SYSTEM FOR MOBILE ROBOTS WITH DIFFERENTIAL DRIVE

    Michal KOPČÍK

    2017-09-01

    Full Text Available This article deals with design and implementation of control system for mobile robots with differential drive using embedded system. This designed embedded system consists of single control board featuring ARM based microcontroller which control the peripherals in real time and perform all low-level motion control. Designed embedded system can be easily expanded with additional sensors, actuators or control units to enhance applicability of mobile robot. Designed embedded system also features build-in communication module, which can be used for data for data acquisition and control of the mobile robot. Control board was implemented on two different types of mobile robots with differential drive, one of which was wheeled and other was tracked. These mobile robots serve as testing platform for Fault Detection and Isolation using hardware and analytical redundancy using Multisensor Data Fusion based on Kalman filters.

  6. Theoretical analysis of enhanced light output from a GaN light emitting diode with an embedded photonic crystal

    Wen Feng; Liu Deming; Huang Lirong

    2010-01-01

    The enhancement of the light output of an embedded photonic crystal light emitting diode is investigated based on the finite-difference time-domain modeling. The embedded photonic crystal (PC) lattice type, multi-layer embedded PC, distance between the multiple quantum well and the embedded PC are studied. It is found that the embedded one dimensional PC can act as well as embedded two dimensional PCs. The emitted light flux in the up direction can be increased by a new kind of multi-layer embedded PC. Also, we show that the light output in the up direction for the LED with both surfaces and embedded PC could be as high as five times that of a conventional LED. (semiconductor devices)

  7. Theoretical analysis of enhanced light output from a GaN light emitting diode with an embedded photonic crystal

    Wen Feng; Liu Deming; Huang Lirong, E-mail: hlr5649@163.co [Wuhan National Laboratory for Optoelectronics, College of Opto-Electronics Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074 (China)

    2010-10-15

    The enhancement of the light output of an embedded photonic crystal light emitting diode is investigated based on the finite-difference time-domain modeling. The embedded photonic crystal (PC) lattice type, multi-layer embedded PC, distance between the multiple quantum well and the embedded PC are studied. It is found that the embedded one dimensional PC can act as well as embedded two dimensional PCs. The emitted light flux in the up direction can be increased by a new kind of multi-layer embedded PC. Also, we show that the light output in the up direction for the LED with both surfaces and embedded PC could be as high as five times that of a conventional LED. (semiconductor devices)

  8. A Versatile Embedded Platform for EMG Acquisition and Gesture Recognition.

    Benatti, Simone; Casamassima, Filippo; Milosevic, Bojan; Farella, Elisabetta; Schönle, Philipp; Fateh, Schekeb; Burger, Thomas; Huang, Qiuting; Benini, Luca

    2015-10-01

    Wearable devices offer interesting features, such as low cost and user friendliness, but their use for medical applications is an open research topic, given the limited hardware resources they provide. In this paper, we present an embedded solution for real-time EMG-based hand gesture recognition. The work focuses on the multi-level design of the system, integrating the hardware and software components to develop a wearable device capable of acquiring and processing EMG signals for real-time gesture recognition. The system combines the accuracy of a custom analog front end with the flexibility of a low power and high performance microcontroller for on-board processing. Our system achieves the same accuracy of high-end and more expensive active EMG sensors used in applications with strict requirements on signal quality. At the same time, due to its flexible configuration, it can be compared to the few wearable platforms designed for EMG gesture recognition available on market. We demonstrate that we reach similar or better performance while embedding the gesture recognition on board, with the benefit of cost reduction. To validate this approach, we collected a dataset of 7 gestures from 4 users, which were used to evaluate the impact of the number of EMG channels, the number of recognized gestures and the data rate on the recognition accuracy and on the computational demand of the classifier. As a result, we implemented a SVM recognition algorithm capable of real-time performance on the proposed wearable platform, achieving a classification rate of 90%, which is aligned with the state-of-the-art off-line results and a 29.7 mW power consumption, guaranteeing 44 hours of continuous operation with a 400 mAh battery.

  9. Embedded Multimaterial Extrusion Bioprinting.

    Rocca, Marco; Fragasso, Alessio; Liu, Wanjun; Heinrich, Marcel A; Zhang, Yu Shrike

    2018-04-01

    Embedded extrusion bioprinting allows for the generation of complex structures that otherwise cannot be achieved with conventional layer-by-layer deposition from the bottom, by overcoming the limits imposed by gravitational force. By taking advantage of a hydrogel bath, serving as a sacrificial printing environment, it is feasible to extrude a bioink in freeform until the entire structure is deposited and crosslinked. The bioprinted structure can be subsequently released from the supporting hydrogel and used for further applications. Combining this advanced three-dimensional (3D) bioprinting technique with a multimaterial extrusion printhead setup enables the fabrication of complex volumetric structures built from multiple bioinks. The work described in this paper focuses on the optimization of the experimental setup and proposes a workflow to automate the bioprinting process, resulting in a fast and efficient conversion of a virtual 3D model into a physical, extruded structure in freeform using the multimaterial embedded bioprinting system. It is anticipated that further development of this technology will likely lead to widespread applications in areas such as tissue engineering, pharmaceutical testing, and organs-on-chips.

  10. Learning optimal embedded cascades.

    Saberian, Mohammad Javad; Vasconcelos, Nuno

    2012-10-01

    The problem of automatic and optimal design of embedded object detector cascades is considered. Two main challenges are identified: optimization of the cascade configuration and optimization of individual cascade stages, so as to achieve the best tradeoff between classification accuracy and speed, under a detection rate constraint. Two novel boosting algorithms are proposed to address these problems. The first, RCBoost, formulates boosting as a constrained optimization problem which is solved with a barrier penalty method. The constraint is the target detection rate, which is met at all iterations of the boosting process. This enables the design of embedded cascades of known configuration without extensive cross validation or heuristics. The second, ECBoost, searches over cascade configurations to achieve the optimal tradeoff between classification risk and speed. The two algorithms are combined into an overall boosting procedure, RCECBoost, which optimizes both the cascade configuration and its stages under a detection rate constraint, in a fully automated manner. Extensive experiments in face, car, pedestrian, and panda detection show that the resulting detectors achieve an accuracy versus speed tradeoff superior to those of previous methods.

  11. Six transformer based asymmetrical embedded Z-source inverters

    Wei, Mo; Poh Chiang, Loh; Chi, Jin

    2013-01-01

    Embedded/Asymmetrical embedded Z-source inverters were proposed to maintain smooth input current/voltage across the dc source and within the impedance network, remain the shoot-through feature used to boost up the dc-link voltage without adding bulky filter at input side. This paper introduces a ...... a class of transformer based asymmetrical embedded Z-source inverters which keep the smooth input current and voltage while achieving enhanced voltage boost capability. The presented inverters are verified by laboratory prototypes experimentally....

  12. VideoStory Embeddings Recognize Events when Examples are Scarce

    Habibian, Amirhossein; Mensink, Thomas; Snoek, Cees G. M.

    2015-01-01

    This paper aims for event recognition when video examples are scarce or even completely absent. The key in such a challenging setting is a semantic video representation. Rather than building the representation from individual attribute detectors and their annotations, we propose to learn the entire representation from freely available web videos and their descriptions using an embedding between video features and term vectors. In our proposed embedding, which we call VideoStory, the correlati...

  13. Video2vec Embeddings Recognize Events when Examples are Scarce

    Habibian, A.; Mensink, T.; Snoek, C.G.M.

    2017-01-01

    This paper aims for event recognition when video examples are scarce or even completely absent. The key in such a challenging setting is a semantic video representation. Rather than building the representation from individual attribute detectors and their annotations, we propose to learn the entire representation from freely available web videos and their descriptions using an embedding between video features and term vectors. In our proposed embedding, which we call Video2vec, the correlatio...

  14. Model Checking Feature Interactions

    Le Guilly, Thibaut; Olsen, Petur; Pedersen, Thomas

    2015-01-01

    This paper presents an offline approach to analyzing feature interactions in embedded systems. The approach consists of a systematic process to gather the necessary information about system components and their models. The model is first specified in terms of predicates, before being refined to t...... to timed automata. The consistency of the model is verified at different development stages, and the correct linkage between the predicates and their semantic model is checked. The approach is illustrated on a use case from home automation....

  15. Spatially Embedded Inequality

    Holck, Lotte

    2016-01-01

    /methodology/approach: – The (re)production of inequality is explored by linking research on organizational space with HRM diversity management. Data from an ethnographic study undertaken in a Danish municipal center illustrates how a substructure of inequality is spatially upheld alongside a formal diversity policy. Archer...... and ethnification of job categories. However, the same spatial structures allows for a variety of opposition and conciliation strategies among minority employees, even though the latter tend to prevail in a reproduction rather than a transformation of the organizational opportunity structures. Research limitations...... the more subtle, spatially embedded forms of inequality. Originality/value: – Theoretical and empirical connections between research on organizational space and HRM diversity management have thus far not been systematically studied. This combination might advance knowledge on the persistence of micro...

  16. Embedded sensor systems

    Agrawal, Dharma Prakash

    2017-01-01

    This inspiring textbook provides an introduction to wireless technologies for sensors, explores potential use of sensors for numerous applications, and utilizes probability theory and mathematical methods as a means of embedding sensors in system design. It discusses the need for synchronization and underlying limitations, inter-relation between given coverage and connectivity to number of sensors needed, and the use of geometrical distance to determine location of the base station for data collection and explore use of anchor nodes for relative position determination of sensors. The book explores energy conservation, communication using TCP, the need for clustering and data aggregation, and residual energy determination and energy harvesting. It covers key topics of sensor communication like mobile base stations and relay nodes, delay-tolerant sensor networks, and remote sensing and possible applications. The book defines routing methods and do performance evaluation for random and regular sensor topology an...

  17. Communicating embedded systems networks applications

    Krief, Francine

    2013-01-01

    Embedded systems become more and more complex and require having some knowledge in various disciplines such as electronics, data processing, telecommunications and networks. Without detailing all the aspects related to the design of embedded systems, this book, which was written by specialists in electronics, data processing and telecommunications and networks, gives an interesting point of view of communication techniques and problems in embedded systems. This choice is easily justified by the fact that embedded systems are today massively communicating and that telecommunications and network

  18. Advances in embedded computer vision

    Kisacanin, Branislav

    2014-01-01

    This illuminating collection offers a fresh look at the very latest advances in the field of embedded computer vision. Emerging areas covered by this comprehensive text/reference include the embedded realization of 3D vision technologies for a variety of applications, such as stereo cameras on mobile devices. Recent trends towards the development of small unmanned aerial vehicles (UAVs) with embedded image and video processing algorithms are also examined. The authoritative insights range from historical perspectives to future developments, reviewing embedded implementation, tools, technolog

  19. Embedded Systems Design with FPGAs

    Pnevmatikatos, Dionisios; Sklavos, Nicolas

    2013-01-01

    This book presents methodologies for modern applications of embedded systems design, using field programmable gate array (FPGA) devices.  Coverage includes state-of-the-art research from academia and industry on a wide range of topics, including advanced electronic design automation (EDA), novel system architectures, embedded processors, arithmetic, dynamic reconfiguration and applications. Describes a variety of methodologies for modern embedded systems design;  Implements methodologies presented on FPGAs; Covers a wide variety of applications for reconfigurable embedded systems, including Bioinformatics, Communications and networking, Application acceleration, Medical solutions, Experiments for high energy physics, Astronomy, Aerospace, Biologically inspired systems and Computational fluid dynamics (CFD).

  20. Embedding Complementarity in HCI Methods

    Nielsen, Janni; Yssing, Carsten; Tweddell Levinsen, Karin

    2007-01-01

    Differences in cultural contexts constitute differences in cognition, and research has shown that different cultures may use different cognitive tools for perception and reasoning. The cultural embeddings are significant in relation to HCI, because the cultural context is also embedded in the tec......Differences in cultural contexts constitute differences in cognition, and research has shown that different cultures may use different cognitive tools for perception and reasoning. The cultural embeddings are significant in relation to HCI, because the cultural context is also embedded...

  1. Embedding potentials for excited states of embedded species

    Wesolowski, Tomasz A.

    2014-01-01

    Frozen-Density-Embedding Theory (FDET) is a formalism to obtain the upper bound of the ground-state energy of the total system and the corresponding embedded wavefunction by means of Euler-Lagrange equations [T. A. Wesolowski, Phys. Rev. A 77(1), 012504 (2008)]. FDET provides the expression for the embedding potential as a functional of the electron density of the embedded species, electron density of the environment, and the field generated by other charges in the environment. Under certain conditions, FDET leads to the exact ground-state energy and density of the whole system. Following Perdew-Levy theorem on stationary states of the ground-state energy functional, the other-than-ground-state stationary states of the FDET energy functional correspond to excited states. In the present work, we analyze such use of other-than-ground-state embedded wavefunctions obtained in practical calculations, i.e., when the FDET embedding potential is approximated. Three computational approaches based on FDET, that assure self-consistent excitation energy and embedded wavefunction dealing with the issue of orthogonality of embedded wavefunctions for different states in a different manner, are proposed and discussed

  2. Research on Face Recognition Based on Embedded System

    Hong Zhao

    2013-01-01

    Full Text Available Because a number of image feature data to store, complex calculation to execute during the face recognition, therefore the face recognition process was realized only by PCs with high performance. In this paper, the OpenCV facial Haar-like features were used to identify face region; the Principal Component Analysis (PCA was employed in quick extraction of face features and the Euclidean Distance was also adopted in face recognition; as thus, data amount and computational complexity would be reduced effectively in face recognition, and the face recognition could be carried out on embedded platform. Finally, based on Tiny6410 embedded platform, a set of embedded face recognition systems was constructed. The test results showed that the system has stable operation and high recognition rate can be used in portable and mobile identification and authentication.

  3. Anisotropic Transport of Electrons in a Novel FET Channel with Chains of InGaAs Nano-Islands Embedded along Quasi-Periodic Multi-Atomic Steps on Vicinal (111)B GaAs

    Akiyama, Y.; Kawazu, T.; Noda, T.; Sakaki, H.

    2010-01-01

    We have studied electron transport in n-AlGaAs/GaAs heterojunction FET channels, in which chains of InGaAs nano-islands are embedded along quasi-periodic steps. By using two samples, conductance G para (V g ) parallel to the steps and G perp (V g ) perpendicular to them were measured at 80 K as functions of gate voltage V g . At sufficiently high V g , G para at 80 K is several times as high as G perp , which manifests the anisotropic two-dimensional transport of electrons. When V g is reduced to -0.7 V, G perp almost vanishes, while Gpara stays sizable unless V g is set below -0.8 V. These results indicate that 'inter-chain' barriers play stronger roles than 'intra-chain' barriers.

  4. Data-Wave-Based Features Extraction and Its Application in Symbol Identifier Recognition and Positioning Suitable for Multi-Robot Systems

    Xilong Liu

    2012-12-01

    Full Text Available In this paper, feature extraction based on data-wave is proposed. The concept of data-wave is introduced to describe the rising and falling trends of the data over the long-term which are detected based on ripple and wave filters. Supported by data-wave, a novel symbol identifier with significant structure features is designed and these features are extracted by constructing pixel chains. On this basis, the corresponding recognition and positioning approach is presented. The effectiveness of the proposed approach is verified by experiments.

  5. Evaluation of radiographic features of embedded primary molar ...

    2014-05-20

    May 20, 2014 ... Key words: Ankylosis, panoramic radiography, primary molar root ... coverage of the jaws is needed for diagnosis. ... and direct continuity of the bone, called as bony ankylosis. .... Floating retained root lesion mimicking.

  6. Modeling of Embedded Human Systems

    2013-07-01

    ISAT study [7] for DARPA in 20051 concretized the notion of an embedded human, who is a necessary component of the system. The proposed work integrates...Technology, IEEE Transactions on, vol. 16, no. 2, pp. 229–244, March 2008. [7] C. J. Tomlin and S. S. Sastry, “Embedded humans,” tech. rep., DARPA ISAT

  7. Design of Embedded Wireless Sensor and its Soft Encapsulation for Embedded Monitoring of Helicopter Planetary Gear Set

    Qin Guojun; Hu Niaoqing

    2012-01-01

    Planetary gear set, as an important part of helicopter, is with the characteristics of multi-point and time-varying position engagement. For the revolution of planetary gears round sun gear, directions of vibration and pulse created by tooth damage change continuously. If an accelerometer fixed on the surface of gearbox, the angle between the directions of pulse force and accelerometer sensitivity will change continuously, which will causes that the components of pulse force on the sensitivity direction vary with time and the features of damage are very difficult to extract from the signals. Aiming at this problem, a type of embedded wireless sensor node was designed firstly, which can be fixed on the carrier of planetary gear, and acquires the damage-related vibration signals in a fixed direction of pulse force. Then, to avoid the corrosion of electronic components by the lubrication oil in gearbox, the protect restrictions of the sensor node was investigated and a kind of soft encapsulation method is applied. Finally, real vibration signal is measured and transmitted by the designed and/or encapsulated sensor node. The experiments show that the sensor can measure vibration effectively.

  8. Radiomic features from the peritumoral brain parenchyma on treatment-naive multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings

    Prasanna, Prateek; Patel, Jay; Madabhushi, Anant; Tiwari, Pallavi [Case Western Reserve University, Department of Biomedical Engineering, Cleveland, OH (United States); Partovi, Sasan [University Hospitals Case Medical Center, Case Western Reserve School of Medicine, Cleveland, OH (United States)

    2017-10-15

    Despite 90 % of glioblastoma (GBM) recurrences occurring in the peritumoral brain zone (PBZ), its contribution in patient survival is poorly understood. The current study leverages computerized texture (i.e. radiomic) analysis to evaluate the efficacy of PBZ features from pre-operative MRI in predicting long- (>18 months) versus short-term (<7 months) survival in GBM. Sixty-five patient examinations (29 short-term, 36 long-term) with gadolinium-contrast T{sub 1w}, FLAIR and T{sub 2w} sequences from the Cancer Imaging Archive were employed. An expert manually segmented each study as: enhancing lesion, PBZ and tumour necrosis. 402 radiomic features (capturing co-occurrence, grey-level dependence and directional gradients) were obtained for each region. Evaluation was performed using threefold cross-validation, such that a subset of studies was used to select the most predictive features, and the remaining subset was used to evaluate their efficacy in predicting survival. A subset of ten radiomic 'peritumoral' MRI features, suggestive of intensity heterogeneity and textural patterns, was found to be predictive of survival (p = 1.47 x 10{sup -5}) as compared to features from enhancing tumour, necrotic regions and known clinical factors. Our preliminary analysis suggests that radiomic features from the PBZ on routine pre-operative MRI may be predictive of long- versus short-term survival in GBM. (orig.)

  9. Design of hand held RID's monitoring system based on embedded system

    Wang Hongwei; Wei Yixiang

    2008-01-01

    In this paper we introduce the design of monitoring system for the hand held radionuclide identification device (RID), constructed under the embedded operating system of WinCE. At first, we introduce the design of hardware and software platform, and following is the major part of technical view of the software system, including the driver development, P/Invoke mechanism to call the C/C++ subroutines, multi-thread technology. In the experimental hardware platform, we have developed a front-end monitoring system for portable device targeted nuclide identification and orientation. It's a full-featured and flexible system, with the functions of data acquisition, radioactivity locating, data import and export, etc. (authors)

  10. Embedding of radioactive wastes by thermosetting resins

    Baer, A.; Traxler, A.; Limongi, A.; Thiery, D.

    The process for embedding radioactive wastes in thermosetting resins perfected and applied at the Grenoble Nuclear Research Center and its application to the treatment of radioactive wastes from Light-Water Nuclear Power Plants (PWR and BWR) are presented. The various types of wastes are enumerated and their activities and quantities are estimated: evaporator concentrates, ion exchange resins, filtration sludges, filters, various solid wastes, etc. The authors review the orientations of the research performed and indicate, for each type of waste considered, the cycle of treatment operations from rendering the radioelements insoluble to drying the concentrates to final embedding. The operational safety of the process and the safety of transport and storage of the embedded wastes are investigated. The essential technical features concerning the safety of the installation and of the final product obtained are presented. In particular, results are presented from tests of resistance to fire, irradiation, leaching, etc., these being characteristics which represent safety criteria. The economic aspects of the process are considered by presenting the influences of the reduction of volume and weight of wastes to be stored, simplicity of installations and cost of primary materials

  11. Phylogenetic trees and Euclidean embeddings.

    Layer, Mark; Rhodes, John A

    2017-01-01

    It was recently observed by de Vienne et al. (Syst Biol 60(6):826-832, 2011) that a simple square root transformation of distances between taxa on a phylogenetic tree allowed for an embedding of the taxa into Euclidean space. While the justification for this was based on a diffusion model of continuous character evolution along the tree, here we give a direct and elementary explanation for it that provides substantial additional insight. We use this embedding to reinterpret the differences between the NJ and BIONJ tree building algorithms, providing one illustration of how this embedding reflects tree structures in data.

  12. A embedded Linux system based on PowerPC

    Ye Mei; Zhao Jingwei; Chu Yuanping

    2006-01-01

    The authors will introduce a Embedded Linux System based on PowerPC as well as the base method on how to establish the system. The goal of the system is to build a test system of VMEbus device. It also can be used to setup the small data acquisition and control system. Two types of compiler are provided by the developer system according to the features of the system and the Power PC. At the top of the article some typical embedded Operation system will be introduced and the features of different system will be provided. And then the method on how to build a embedded Linux system as well as the key technique will be discussed in detail. Finally a successful read-write example will be given based on the test system. (authors)

  13. Embedded Face Detection and Recognition

    Göksel Günlü

    2012-10-01

    Full Text Available The need to increase security in open or public spaces has in turn given rise to the requirement to monitor these spaces and analyse those images on-site and on-time. At this point, the use of smart cameras – of which the popularity has been increasing – is one step ahead. With sensors and Digital Signal Processors (DSPs, smart cameras generate ad hoc results by analysing the numeric images transmitted from the sensor by means of a variety of image-processing algorithms. Since the images are not transmitted to a distance processing unit but rather are processed inside the camera, it does not necessitate high-bandwidth networks or high processor powered systems; it can instantaneously decide on the required access. Nonetheless, on account of restricted memory, processing power and overall power, image processing algorithms need to be developed and optimized for embedded processors. Among these algorithms, one of the most important is for face detection and recognition. A number of face detection and recognition methods have been proposed recently and many of these methods have been tested on general-purpose processors. In smart cameras – which are real-life applications of such methods – the widest use is on DSPs. In the present study, the Viola-Jones face detection method – which was reported to run faster on PCs – was optimized for DSPs; the face recognition method was combined with the developed sub-region and mask-based DCT (Discrete Cosine Transform. As the employed DSP is a fixed-point processor, the processes were performed with integers insofar as it was possible. To enable face recognition, the image was divided into sub-regions and from each sub-region the robust coefficients against disruptive elements – like face expression, illumination, etc. – were selected as the features. The discrimination of the selected features was enhanced via LDA (Linear Discriminant Analysis and then employed for recognition. Thanks to its

  14. Fault Diagnosis Method of Rolling Bearings Based on Multi-dimensional Vibration Features%基于多维振动特征的滚动轴承故障诊断方法

    付云骁; 贾利民; 季常煦; 姚德臣; 李文球

    2014-01-01

    单独提取滚动轴承振动信号的时域或频域特征进行故障诊断,是目前常用的轴承诊断方法,诊断精度有待提高。以时域和频域的多维振动特征参量为指标,以历史诊断正确率作为特征参量权值,分别对滚动轴承的无故障和经常出现的滚珠故障、内环故障和外环故障工况进行特征提取和故障识别。多维时频域振动特征是单维特征依据诊断精度权重的集合。运用BP神经网络分别对信号的时域特征(TDF)、IMF能量矩(IEM)、小波包能量矩(WPEM),以及多维时频域特征进行智能故障判别。实验验证用多维时频域振动特征参量综合诊断的方法进行滚动轴承故障诊断,比单维特征的诊断结果精确且效率较高,该方法可以在滚动轴承故障诊断领域展开应用。%Extracting the time-domain or the frequency-domain features of vibration signals for analysis is a conventional method for rolling bearings fault diagnosis. But the effects of this diagnosis method need to be improved. In this paper, taking the multi-dimensional vibration characteristic parameters in time-domain and frequency-domain as the indexes and the correctness rate of historical diagnosis as the parametric weight, the features of fault-free rolling bearings and the features of rolling bearings with ball fault, inner and outer race faults are extracted and the faults are identified. It shows that the multi-dimensional vibration characteristic in time-frequency domains is the assemblage of single features. BP neural network is used for intelligent fault classification of signals according to the time-domain feature (TDF) parameters, IMF energy moment (IEM), wavelet package energy moment (WPEM) and multi-dimensional features respectively. Results of the diagnoses are compared one another. The experiment results verify that using the multi-dimensional feature in time and frequency domains to evaluate the rolling bearing faults is

  15. Parametric embedding for class visualization.

    Iwata, Tomoharu; Saito, Kazumi; Ueda, Naonori; Stromsten, Sean; Griffiths, Thomas L; Tenenbaum, Joshua B

    2007-09-01

    We propose a new method, parametric embedding (PE), that embeds objects with the class structure into a low-dimensional visualization space. PE takes as input a set of class conditional probabilities for given data points and tries to preserve the structure in an embedding space by minimizing a sum of Kullback-Leibler divergences, under the assumption that samples are generated by a gaussian mixture with equal covariances in the embedding space. PE has many potential uses depending on the source of the input data, providing insight into the classifier's behavior in supervised, semisupervised, and unsupervised settings. The PE algorithm has a computational advantage over conventional embedding methods based on pairwise object relations since its complexity scales with the product of the number of objects and the number of classes. We demonstrate PE by visualizing supervised categorization of Web pages, semisupervised categorization of digits, and the relations of words and latent topics found by an unsupervised algorithm, latent Dirichlet allocation.

  16. Embedded System for Biometric Identification

    Rosli, Ahmad Nasir Che

    2010-01-01

    This chapter describes the design and implementation of an Embedded System for Biometric Identification from hardware and software perspectives. The first part of the chapter describes the idea of biometric identification. This includes the definition of

  17. Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule

    Liu, Hui; Song, Yongduan; Xue, Fangzheng; Li, Xiumin, E-mail: xmli@cqu.edu.cn [Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education, Chongqing University, Chongqing 400044 (China); College of Automation, Chongqing University, Chongqing 400044 (China)

    2015-11-15

    In this paper, the generation of multi-clustered structure of self-organized neural network with different neuronal firing patterns, i.e., bursting or spiking, has been investigated. The initially all-to-all-connected spiking neural network or bursting neural network can be self-organized into clustered structure through the symmetric spike-timing-dependent plasticity learning for both bursting and spiking neurons. However, the time consumption of this clustering procedure of the burst-based self-organized neural network (BSON) is much shorter than the spike-based self-organized neural network (SSON). Our results show that the BSON network has more obvious small-world properties, i.e., higher clustering coefficient and smaller shortest path length than the SSON network. Also, the results of larger structure entropy and activity entropy of the BSON network demonstrate that this network has higher topological complexity and dynamical diversity, which benefits for enhancing information transmission of neural circuits. Hence, we conclude that the burst firing can significantly enhance the efficiency of clustering procedure and the emergent clustered structure renders the whole network more synchronous and therefore more sensitive to weak input. This result is further confirmed from its improved performance on stochastic resonance. Therefore, we believe that the multi-clustered neural network which self-organized from the bursting dynamics has high efficiency in information processing.

  18. Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule

    Liu, Hui; Song, Yongduan; Xue, Fangzheng; Li, Xiumin

    2015-01-01

    In this paper, the generation of multi-clustered structure of self-organized neural network with different neuronal firing patterns, i.e., bursting or spiking, has been investigated. The initially all-to-all-connected spiking neural network or bursting neural network can be self-organized into clustered structure through the symmetric spike-timing-dependent plasticity learning for both bursting and spiking neurons. However, the time consumption of this clustering procedure of the burst-based self-organized neural network (BSON) is much shorter than the spike-based self-organized neural network (SSON). Our results show that the BSON network has more obvious small-world properties, i.e., higher clustering coefficient and smaller shortest path length than the SSON network. Also, the results of larger structure entropy and activity entropy of the BSON network demonstrate that this network has higher topological complexity and dynamical diversity, which benefits for enhancing information transmission of neural circuits. Hence, we conclude that the burst firing can significantly enhance the efficiency of clustering procedure and the emergent clustered structure renders the whole network more synchronous and therefore more sensitive to weak input. This result is further confirmed from its improved performance on stochastic resonance. Therefore, we believe that the multi-clustered neural network which self-organized from the bursting dynamics has high efficiency in information processing

  19. Embedded random matrix ensembles in quantum physics

    Kota, V K B

    2014-01-01

    Although used with increasing frequency in many branches of physics, random matrix ensembles are not always sufficiently specific to account for important features of the physical system at hand. One refinement which retains the basic stochastic approach but allows for such features consists in the use of embedded ensembles.  The present text is an exhaustive introduction to and survey of this important field. Starting with an easy-to-read introduction to general random matrix theory, the text then develops the necessary concepts from the beginning, accompanying the reader to the frontiers of present-day research. With some notable exceptions, to date these ensembles have primarily been applied in nuclear spectroscopy. A characteristic example is the use of a random two-body interaction in the framework of the nuclear shell model. Yet, topics in atomic physics, mesoscopic physics, quantum information science and statistical mechanics of isolated finite quantum systems can also be addressed using these ensemb...

  20. Hardware Support for Embedded Java

    Schoeberl, Martin

    2012-01-01

    The general Java runtime environment is resource hungry and unfriendly for real-time systems. To reduce the resource consumption of Java in embedded systems, direct hardware support of the language is a valuable option. Furthermore, an implementation of the Java virtual machine in hardware enables...... worst-case execution time analysis of Java programs. This chapter gives an overview of current approaches to hardware support for embedded and real-time Java....

  1. Molecular Properties through Polarizable Embedding

    Olsen, Jógvan Magnus Haugaard; Kongsted, Jacob

    2011-01-01

    We review the theory related to the calculation of electric and magnetic molecular properties through polarizable embedding. In particular, we derive the expressions for the response functions up to the level of cubic response within the density functional theory-based polarizable embedding (PE......-DFT) formalism. In addition, we discuss some illustrative applications related to the calculation of nuclear magnetic resonance parameters, nonlinear optical properties, and electronic excited states in solution....

  2. A Foundation for Embedded Languages

    Rhiger, Morten

    2003-01-01

    Recent work on embedding object languages into Haskell use "phantom types" (i.e., parameterized types whose parameter does not occur on the right-hand side of the type definition) to ensure that the embedded object-language terms are simply typed. But is it a safe assumption that only simply...... be answered affirmatively for an idealized Haskell-like language and discuss to which extent Haskell can be used as a meta-language....

  3. Unsupervised Document Embedding With CNNs

    Liu, Chundi; Zhao, Shunan; Volkovs, Maksims

    2017-01-01

    We propose a new model for unsupervised document embedding. Leading existing approaches either require complex inference or use recurrent neural networks (RNN) that are difficult to parallelize. We take a different route and develop a convolutional neural network (CNN) embedding model. Our CNN architecture is fully parallelizable resulting in over 10x speedup in inference time over RNN models. Parallelizable architecture enables to train deeper models where each successive layer has increasin...

  4. Gait recognition using kinect and locally linear embedding ...

    This paper presents the use of locally linear embedding (LLE) as feature extraction technique for classifying a person's identity based on their walking gait patterns. Skeleton data acquired from Microsoft Kinect camera were used as an input for (1). Multilayer Perceptron (MLP) and (2). LLE with MLP. The MLP classification ...

  5. Mapping embedded applications on MPSoCs : the MNEMEE approach

    Baloukas, C.; Papadopoulos, L.; Soudris, D.; Stuijk, S.; Jovanovic, O.; Schmoll, F.; Cordes, D.; Pyka, A.; Mallik, A.; Mamagkakis, S.; Capman, F.; Collet, S.; Mitas, N.; Kritharidis, D.

    2010-01-01

    As embedded systems are becoming the center of our digital life, system design becomes progressively harder. The integration of multiple features on devices with limited resources requires careful and exhaustive exploration of the design search space in order to efficiently map modern applications

  6. Trends in languages for embedded systems

    Boasson, M.

    1986-01-01

    Characteristics of embedded systems are discussed. In particular, the role of the computer in such systems is highlighted. Special emphasis is placed on the different requirements different kinds of systems may place on program execution. From such requirements necessary programming constructs are derived and in an overview of currently used languages it is shown how evolution led to modern languages like Ada and CHILL. With the advent of cheap, fast and small processing units, exploitation of parallelism for enhancing system performance is becoming increasingly tempting. However, few languages support the design of such multi-processor systems. Some methods for dealing with this problem are discussed. Finally, systems architectures and associated languages for the use of techniques originally developed for AI research are adumbrated. (Auth.)

  7. Graph embedding with rich information through heterogeneous graph

    Sun, Guolei

    2017-11-12

    Graph embedding, aiming to learn low-dimensional representations for nodes in graphs, has attracted increasing attention due to its critical application including node classification, link prediction and clustering in social network analysis. Most existing algorithms for graph embedding only rely on the topology information and fail to use the copious information in nodes as well as edges. As a result, their performance for many tasks may not be satisfactory. In this thesis, we proposed a novel and general framework for graph embedding with rich text information (GERI) through constructing a heterogeneous network, in which we integrate node and edge content information with graph topology. Specially, we designed a novel biased random walk to explore the constructed heterogeneous network with the notion of flexible neighborhood. Our sampling strategy can compromise between BFS and DFS local search on heterogeneous graph. To further improve our algorithm, we proposed semi-supervised GERI (SGERI), which learns graph embedding in an discriminative manner through heterogeneous network with label information. The efficacy of our method is demonstrated by extensive comparison experiments with 9 baselines over multi-label and multi-class classification on various datasets including Citeseer, Cora, DBLP and Wiki. It shows that GERI improves the Micro-F1 and Macro-F1 of node classification up to 10%, and SGERI improves GERI by 5% in Wiki.

  8. Dependency Parsing with Transformed Feature

    Fuxiang Wu

    2017-01-01

    Full Text Available Dependency parsing is an important subtask of natural language processing. In this paper, we propose an embedding feature transforming method for graph-based parsing, transform-based parsing, which directly utilizes the inner similarity of the features to extract information from all feature strings including the un-indexed strings and alleviate the feature sparse problem. The model transforms the extracted features to transformed features via applying a feature weight matrix, which consists of similarities between the feature strings. Since the matrix is usually rank-deficient because of similar feature strings, it would influence the strength of constraints. However, it is proven that the duplicate transformed features do not degrade the optimization algorithm: the margin infused relaxed algorithm. Moreover, this problem can be alleviated by reducing the number of the nearest transformed features of a feature. In addition, to further improve the parsing accuracy, a fusion parser is introduced to integrate transformed and original features. Our experiments verify that both transform-based and fusion parser improve the parsing accuracy compared to the corresponding feature-based parser.

  9. Understanding Legacy Features with Featureous

    Olszak, Andrzej; Jørgensen, Bo Nørregaard

    2011-01-01

    Java programs called Featureous that addresses this issue. Featureous allows a programmer to easily establish feature-code traceability links and to analyze their characteristics using a number of visualizations. Featureous is an extension to the NetBeans IDE, and can itself be extended by third...

  10. Influence of Ga3+ ions on spectroscopic and dielectric features of multi component lithium lead boro bismuth silicate glasses doped with manganese ions

    Ramesh Babu, P.; Vijay, R.; Nageswara Rao, P.; Veeraiah, N.; Krishna Rao, D.

    2013-01-01

    Graphical abstract: The plots between ε″(ω)ω vs. ε′(ω) and ε″(ω)/ω vs. ε′(ω) yield straight lines with slope 1/τ and τ, respectively. Considerable deviation from the straight line is observed in the high frequency region. Such deviation suggests spreading of relaxation times and this is attributed to the presence of multiple type of dipoles in the glass matrix. Variation of the parameters ωε″(ω) and ε″(ω)/ω with ε′(ω) of glass Li 2 O–PbO–B 2 O 3 –SiO 2 –Bi 2 O 3 –MnO multi-component glasses mixed with 2.0 mol% of Ga 2 O 3 measured at 373 K. - Highlights: • A series of Li 2 O–PbO–B 2 O 3 –SiO 2 –Bi 2 O 3 –MnO:Ga 2 O 3 glasses have been synthesized. • A variety of spectroscopic and dielectric properties have been investigated. • Analysis of the results indicated that glasses with below 3.0 mol% Ga 2 O 3 are good conducting materials. - Abstract: Multi-component glasses of the chemical composition 19.5Li 2 O–20PbO–20B 2 O 3 –30SiO–(10 − x)Bi 2 O 3 –0.5MnO:xGa 2 O 3 with 0 ≤ x ≤ 5.0 have been synthesized. Spectroscopic (optical absorption, IR, Raman and ESR) and dielectric properties were investigated. Optical absorption and ESR spectral studies have indicated that managanese ions do exist in Mn 3+ state in addition to Mn 2+ state in the samples containing low concentration of Ga 2 O 3 . The IR and Raman studies indicated increasing degree of disorder in the glass network with the concentration of Ga 2 O 3 up to 3.0 mol%. The dielectric constant, loss and ac conductivity are observed to increase with the concentration of Ga 2 O 3 up to 3.0 mol%. The quantitative analysis of the results of dielectric properties has indicated an increase in the insulating strength of the glasses as the concentration of Ga 2 O 3 is raised beyond 3.0 mol%. This has been attributed to adaption of gallium ions from octahedral to tetrahedral coordination

  11. HIV is Now a Manageable Long-Term Condition, But What Makes it Unique? A Qualitative Study Exploring Views About Distinguishing Features from Multi-Professional HIV Specialists in North West England.

    Jelliman, Pauline; Porcellato, Lorna

    HIV is evolving from a life-threatening infection to a long-term, manageable condition because of medical advances, radical changes in health and social care policy, and the impact of an aging population. However, HIV remains complex, presenting unique characteristics distinguishing it from other long-term conditions (LTCs). Our aim in this qualitative descriptive study was to identify and explore these features in the context of LTCs. A focus group (FG) method was used to gather the views and experiences of multi-professional HIV specialists who worked in North West England. Twenty-four staff participated in FGs (n = 3), which were audio recorded, manually transcribed, and thematically analyzed. We found four main themes: (a) stigma, (b) challenges faced by HIV specialists, (c) lack HIV-related knowledge, and (d) unique features, termed "stand alone." We concluded that these distinguishing features hindered full recognition and acceptance of HIV as an LTC. Crown Copyright © 2016. Published by Elsevier Inc. All rights reserved.

  12. An integrated compact airborne multispectral imaging system using embedded computer

    Zhang, Yuedong; Wang, Li; Zhang, Xuguo

    2015-08-01

    An integrated compact airborne multispectral imaging system using embedded computer based control system was developed for small aircraft multispectral imaging application. The multispectral imaging system integrates CMOS camera, filter wheel with eight filters, two-axis stabilized platform, miniature POS (position and orientation system) and embedded computer. The embedded computer has excellent universality and expansibility, and has advantages in volume and weight for airborne platform, so it can meet the requirements of control system of the integrated airborne multispectral imaging system. The embedded computer controls the camera parameters setting, filter wheel and stabilized platform working, image and POS data acquisition, and stores the image and data. The airborne multispectral imaging system can connect peripheral device use the ports of the embedded computer, so the system operation and the stored image data management are easy. This airborne multispectral imaging system has advantages of small volume, multi-function, and good expansibility. The imaging experiment results show that this system has potential for multispectral remote sensing in applications such as resource investigation and environmental monitoring.

  13. Embedded Linux projects using Yocto project cookbook

    González, Alex

    2015-01-01

    If you are an embedded developer learning about embedded Linux with some experience with the Yocto project, this book is the ideal way to become proficient and broaden your knowledge with examples that are immediately applicable to your embedded developments. Experienced embedded Yocto developers will find new insight into working methodologies and ARM specific development competence.

  14. Analysis and optimisation of heterogeneous real-time embedded systems

    Pop, Paul; Eles, Petru; Peng, Zebo

    2005-01-01

    . The success of such new design methods depends on the availability of analysis and optimisation techniques. Analysis and optimisation techniques for heterogeneous real-time embedded systems are presented in the paper. The authors address in more detail a particular class of such systems called multi...... of application messages to frames. Optimisation heuristics for frame packing aimed at producing a schedulable system are presented. Extensive experiments and a real-life example show the efficiency of the frame-packing approach....

  15. Analysis and optimisation of heterogeneous real-time embedded systems

    Pop, Paul; Eles, Petru; Peng, Zebo

    2006-01-01

    . The success of such new design methods depends on the availability of analysis and optimisation techniques. Analysis and optimisation techniques for heterogeneous real-time embedded systems are presented in the paper. The authors address in more detail a particular class of such systems called multi...... of application messages to frames. Optimisation heuristics for frame packing aimed at producing a schedulable system are presented. Extensive experiments and a real-life example show the efficiency of the frame-packing approach....

  16. Analysis and Optimization of Heterogeneous Real-Time Embedded Systems

    Pop, Paul; Eles, Petru; Peng, Zebo

    2005-01-01

    . The success of such new design methods depends on the availability of analysis and optimization techniques. In this paper, we present analysis and optimization techniques for heterogeneous real-time embedded systems. We address in more detail a particular class of such systems called multi-clusters, composed...... to frames. Optimization heuristics for frame packing aiming at producing a schedulable system are presented. Extensive experiments and a real-life example show the efficiency of the frame-packing approach....

  17. Secure wireless embedded systems via component-based design

    Hjorth, T.; Torbensen, R.

    2010-01-01

    This paper introduces the method secure-by-design as a way of constructing wireless embedded systems using component-based modeling frameworks. This facilitates design of secure applications through verified, reusable software. Following this method we propose a security framework with a secure c......, with full support for confidentiality, authentication, and integrity using keypairs. The approach has been demonstrated in a multi-platform home automation prototype that can remotely unlock a door using a PDA over the Internet....

  18. Incorporating Data Link Features into a Multi-Function Display to Support Self-Separation and Spacing Tasks for General Aviation Pilots

    Adams, Catherine A.; Murdoch, Jennifer L.; Consiglio, Maria C.; WIlliams, Daniel M.

    2005-01-01

    One objective of the Small Aircraft Transportation System (SATS) Higher Volume Operations (HVO) project is to increase the capacity and utilization of small non-towered, non-radar equipped airports by transferring traffic management activities to an automated Airport Management Module (AMM) and separation responsibilities to general aviation (GA) pilots. Implementation of this concept required the development of a research Multi-Function Display (MFD) to support the interactive communications between pilots and the AMM. The interface also had to accommodate traffic awareness, self-separation, and spacing tasks through dynamic messaging and symbology for flight path conformance and conflict detection and alerting (CDA). The display served as the mechanism to support the examination of the viability of executing instrument operations designed for SATS designated airports. Results of simulation and flight experiments conducted at the National Aeronautics and Space Administration's (NASA) Langley Research Center indicate that the concept, as facilitated by the research MFD, did not increase pilots subjective workload levels or reduce their situation awareness (SA). Post-test usability assessments revealed that pilots preferred using the enhanced MFD to execute flight procedures, reporting improved SA over conventional instrument flight rules (IFR) procedures.

  19. Safety-critical Java for low-end embedded platforms

    Søndergaard, Hans; Korsholm, Stephan E.; Ravn, Anders Peter

    2012-01-01

    We present an implementation of the Safety-Critical Java profile (SCJ), targeted for low-end embedded platforms with as little as 16 kB RAM and 256 kB flash. The distinctive features of the implementation are a combination of a lean Java virtual machine (HVM), with a bare metal kernel implementing...... hardware objects, first level interrupt handlers, and native variables, and an infrastructure written in Java which is minimized through program specialization. The HVM allows the implementation to be easily ported to embedded platforms which have a C compiler as part of the development environment...

  20. FASTBUS Standard Routines implementation for Fermilab embedded processor boards

    Pangburn, J.; Patrick, J.; Kent, S.; Oleynik, G.; Pordes, R.; Votava, M.; Heyes, G.; Watson, W.A. III

    1992-10-01

    In collaboration with CEBAF, Fermilab's Online Support Department and the CDF experiment have produced a new implementation of the IEEE FASTBUS Standard Routines for two embedded processor FASTBUS boards: the Fermilab Smart Crate Controller (FSCC) and the FASTBUS Readout Controller (FRC). Features of this implementation include: portability (to other embedded processor boards), remote source-level debugging, high speed, optional generation of very high-speed code for readout applications, and built-in Sun RPC support for execution of FASTBUS transactions and lists over the network

  1. Atomic dynamics of tin nanoparticles embedded into porous glass

    Parshin, P. P.; Zemlyanov, M. G., E-mail: zeml@isssph.kiae.ru; Panova, G. Kh.; Shikov, A. A. [Russian Research Centre Kurchatov Institute (Russian Federation); Kumzerov, Yu. A.; Naberezhnov, A. A. [Russian Academy of Sciences, Ioffe Physicotechnical Institute (Russian Federation); Sergueev, I.; Crichton, W. [European Synchrotron Radiation Facility (France); Chumakov, A. I. [Russian Research Centre Kurchatov Institute (Russian Federation); Rueffer, R. [European Synchrotron Radiation Facility (France)

    2012-03-15

    The method of resonant nuclear inelastic absorption of synchrotron radiation has been used to study the phonon spectrum for tin nanoparticles (with a natural isotope mixture) embedded into a porous glassy (silica) matrix with an average pore diameter of 7 nm in comparison to the analogous spectrum of bulk tin enriched with {sup 119}Sn isotope. Differences between the spectra have been observed, which are related to both the dimensional effects and specific structural features of the porous glass-tin nanocomposite. Peculiarities in the dynamics of tin atoms embedded into nanopores of glass are interpreted in terms of a qualitative model of the nanocomposite structure.

  2. Atomic dynamics of tin nanoparticles embedded into porous glass

    Parshin, P. P.; Zemlyanov, M. G.; Panova, G. Kh.; Shikov, A. A.; Kumzerov, Yu. A.; Naberezhnov, A. A.; Sergueev, I.; Crichton, W.; Chumakov, A. I.; Rüffer, R.

    2012-01-01

    The method of resonant nuclear inelastic absorption of synchrotron radiation has been used to study the phonon spectrum for tin nanoparticles (with a natural isotope mixture) embedded into a porous glassy (silica) matrix with an average pore diameter of 7 nm in comparison to the analogous spectrum of bulk tin enriched with 119 Sn isotope. Differences between the spectra have been observed, which are related to both the dimensional effects and specific structural features of the porous glass-tin nanocomposite. Peculiarities in the dynamics of tin atoms embedded into nanopores of glass are interpreted in terms of a qualitative model of the nanocomposite structure.

  3. Luminescent features of sol–gel derived rare-earth multi-doped oxyfluoride nano-structured phosphors for white LED application

    Gouveia-Neto, A.S.; Silva, A.F. da; Bueno, L.A.; Costa, E.B. da

    2012-01-01

    Rare-earth doped oxyfluoride 75SiO 2 :25PbF 2 nano-structured phosphors for white-light-emitting diodes were synthesized by thermal treatment of precursor sol–gel derived glasses. Room temperature luminescence features of Eu 3+ , Sm 3+ , Tb 3+ , Eu 3+ /Tb 3+ , and Sm 3+ /Tb 3+ ions incorporated into low-phonon-energy PbF 2 nanocrystals dispersed in the aluminosilicate glass matrix and excited with UV light emitting diode were investigated. The luminescence spectra exhibited strong emission signals in the red (600, 610, 625, and 646 nm), green (548 and 560 nm), and blue (485 nm) wavelength regions. White-light emission was observed in Sm/Tb and Eu/Tb double-doped activated phosphors employing UV-LED excitation at 395 nm. The dependence of the luminescence emission intensities upon annealing temperature and rare-earth concentration was also examined. The results indicated that there exist optimum annealing temperature and activator ion concentration in order to obtain intense visible emission light with high color rendering index. The study suggests that the nanocomposite phosphor based upon 75SiO 2 :25PbF 2 host herein reported is a promising contender for white-light LED applications. - Highlights: ► White-light emission in double-doped activated phosphors employing UV-LED excitation. ► Luminescent features of europium, samarium, and terbium in nanocrystals dispersed in aluminosilicate glass. ► New nanocomposite phosphor host for white-light LED applications.

  4. Design Methodologies for Secure Embedded Systems

    Biedermann, Alexander

    2011-01-01

    Embedded systems have been almost invisibly pervading our daily lives for several decades. They facilitate smooth operations in avionics, automotive electronics, or telecommunication. New problems arise by the increasing employment, interconnection, and communication of embedded systems in heterogeneous environments: How secure are these embedded systems against attacks or breakdowns? Therefore, how can embedded systems be designed to be more secure? And how can embedded systems autonomically react to threats? Facing these questions, Sorin A. Huss is significantly involved in the exploration o

  5. Relationship between the cortisol awakening response and other features of the diurnal cortisol rhythm: the Multi-Ethnic Study of Atherosclerosis.

    Golden, Sherita Hill; Sánchez, Brisa N; Wu, Meihua; Champaneri, Shivam; Diez Roux, Ana V; Seeman, Teresa; Wand, Gary S

    2013-11-01

    Cumulative cortisol burden is known to influence neuropsychiatric and metabolic disorders. To better understand the relationship between daily cortisol exposure and measures of the diurnal circadian cortisol rhythm, we examined the cross-sectional association of the cortisol awakening response (CAR) with wake-up cortisol, bedtime cortisol, diurnal slope, and total cortisol area under the curve (AUC). Up to 18 salivary cortisol samples were collected over 3 days from 935 White, Hispanic, and Black individuals (mean age 65 ± 9.8 years) in the Multi-Ethnic Study of Atherosclerosis. Outcome measures included awakening cortisol, CAR (awakening to 30 min post-awakening), early decline (30 min to 2h post-awakening), late decline (2h post-awakening to bedtime), and the corresponding AUCs. Total cortisol AUC was a summary measure of cumulative cortisol exposure. Higher CAR was associated with significantly lower wake-up cortisol (β=-0.56; 95% CI: -0.59 to -0.53) and a higher early decline AUC (β=0.38; 95% CI: 0.34-0.42) but was not associated with total cortisol AUC (β=0.04; 95% CI: -0.01 to 0.09), or other diurnal cortisol curve components following multivariable adjustment. Total cortisol AUC was significantly and positively associated with wake-up cortisol (β=0.36; 95% CI: 0.32-0.40), bedtime cortisol (β=0.61; 95% CI: 0.58-0.64), and other AUC measures, following multivariable adjustment. Associations were similar by sex, race/ethnicity, and age categories. We conclude that bedtime cortisol showed the strongest correlation with total cortisol AUC, suggesting it may be a marker of daily cortisol exposure. Copyright © 2013 Elsevier Ltd. All rights reserved.

  6. Feature Article

    Home; Journals; Resonance – Journal of Science Education. Feature Article. Articles in Resonance – Journal of Science Education. Volume 1 Issue 1 January 1996 pp 80-85 Feature Article. What's New in Computers Windows 95 · Vijnan Shastri · More Details Fulltext PDF. Volume 1 Issue 1 January 1996 pp 86-89 Feature ...

  7. Carbon nanotube embedded PVDF membranes: Effect of solvent composition on the structural morphology for membrane distillation

    Mapunda, Edgar C.; Mamba, Bhekie B.; Msagati, Titus A. M.

    2017-08-01

    Rapid population increase, growth in industrial and agricultural sectors and global climate change have added significant pressure on conventional freshwater resources. Tapping freshwater from non-conventional water sources such as desalination and wastewater recycling is considered as sustainable alternative to the fundamental challenges of water scarcity. However, affordable and sustainable technologies need to be applied for the communities to benefit from the treatment of non-conventional water source. Membrane distillation is a potential desalination technology which can be used sustainably for this purpose. In this work multi-walled carbon nanotube embedded polyvinylidene fluoride membranes for application in membrane distillation desalination were prepared via non-solvent induced phase separation method. The casting solution was prepared using mixed solvents (N, N-dimethylacetamide and triethyl phosphate) at varying ratios to study the effect of solvent composition on membrane morphological structures. Membrane morphological features were studied using a number of techniques including scanning electron microscope, atomic force microscope, SAXSpace tensile strength analysis, membrane thickness, porosity and contact angle measurements. It was revealed that membrane hydrophobicity, thickness, tensile strength and surface roughness were increasing as the composition of N, N-dimethylacetamide in the solvent was increasing with maximum values obtained between 40 and 60% N, N-dimethylacetamide. Internal morphological structures were changing from cellular structures to short finger-like and sponge-like pores and finally to large macro void type of pores when the amount of N, N-dimethylacetamide in the solvent was changed from low to high respectively. Multi-walled carbon nanotube embedded polyvinylidene fluoride membranes of desired morphological structures and physical properties can be synthesized by regulating the composition of solvents used to prepare the

  8. Certifiable Java for Embedded Systems

    Schoeberl, Martin; Dalsgaard, Andreas Engelbredt; Hansen, Rene Rydhof

    2014-01-01

    The Certifiable Java for Embedded Systems (CJ4ES) project aimed to develop a prototype development environment and platform for safety-critical software for embedded applications. There are three core constituents: A profile of the Java programming language that is tailored for safety......-critical applications, a predictable Java processor built with FPGA technology, and an Eclipse based application development environment that binds the profile and the platform together and provides analyses that help to provide evidence that can be used as part of a safety case. This paper summarizes key contributions...

  9. Morphware - Fremtidens Embedded System Platform

    Madsen, Jan

    2006-01-01

    FPGA'er bliver i stigende grad brugt som komponenter i embedded systemer. Faldende priser, større kapacitet og en større felksibilitet har gjort FPGA'en til en attraktiv og konkurrencedygtig teknologi der tillader en stadig stigende grad af system integration, hvor traditionel hardware og software...... kombineres og rekonfigureres. Muligheden for at rekonfigurere systemet, og specielt rekonfigurerer det medens det kører, giver nogle helt nye muligheder for at designe og programmere embedded systemer. Dette foredrag vil give et indblik i disse nye og fremtidige muligheder....

  10. Implementation of an embedded computer

    Pikl, Bojan

    2011-01-01

    The goal of this thesis is to describe a production of an embedded computer. The thesis describes development and production of an embedded computer for the medical diode laser DL30 that is being developed in Robomed d.o.o.. The first part of the thesis describes the choice of hardware devices. I mostly describe the technologies that one can buy on the market. Moreover for every part of the computer installed and developed there is an argument why we selected that exact part. The second part ...

  11. Homogeneous Spaces and Equivariant Embeddings

    Timashev, DA

    2011-01-01

    Homogeneous spaces of linear algebraic groups lie at the crossroads of algebraic geometry, theory of algebraic groups, classical projective and enumerative geometry, harmonic analysis, and representation theory. By standard reasons of algebraic geometry, in order to solve various problems on a homogeneous space it is natural and helpful to compactify it keeping track of the group action, i.e. to consider equivariant completions or, more generally, open embeddings of a given homogeneous space. Such equivariant embeddings are the subject of this book. We focus on classification of equivariant em

  12. A Foundation for Embedded Languages

    Rhiger, Morten

    2003-01-01

    Recent work on embedding object languages into Haskell use "phantom types" (i.e., parameterized types whose parameter does not occur on the right-hand side of the type definition) to ensure that the embedded object-language terms are simply typed. But is it a safe assumption that only simply......-typed terms can be represented in Haskell using phantom types? And conversely, can all simply-typed terms be represented in Haskell under the restrictions imposed by phantom types? In this article we investigate the conditions under which these assumptions are true: We show that these questions can...

  13. A Foundation for Embedded Languages

    Rhiger, Morten

    2002-01-01

    Recent work on embedding object languages into Haskell use "phantom types" (i.e., parameterized types whose parameter does not occur on the right-hand side of the type definition) to ensure that the embedded object-language terms are simply typed. But is it a safe assumption that only simply......-typed terms can be represented in Haskell using phantom types? And conversely, can all simply-typed terms be represented in Haskell under the restrictions imposed by phantom types? In this article we investigate the conditions under which these assumptions are true: We show that these questions can...

  14. Multi-Composite Bioactive Osteogenic Sponges Featuring Mesenchymal Stem Cells, Platelet-Rich Plasma, Nanoporous Silicon Enclosures, and Peptide Amphiphiles for Rapid Bone Regeneration

    Dongmei Fan

    2011-06-01

    Full Text Available A novel bioactive sponge was created with a composite of type I collagen sponges or porous poly(e-caprolactone (PCL scaffolds, platelet-rich plasma (PRP, BMP2-loaded nanoporous silicon enclosure (NSE microparticles, mineralizing peptide amphiphiles (PA, and mesenchymal stem cells (MSC. Primary MSC from cortical bone (CB  tissue proved to form more and larger colony units, as well as produce more mineral matrix under osteogenic differentiation, than MSC from bone marrow (BM. Coating pre-treatments were optimized for maximum cell adhesion and mineralization, while a PRP-based gel carrier was created to efficiently deliver and retain MSC and  microparticles within a porous scaffold while simultaneously promoting cell recruitment, proliferation, and angiogenesis. Components and composite sponges were evaluated for osteogenic differentiation in vitro. Osteogenic sponges were loaded with MSC, PRP, PA, and NSE and implanted subcutaneously in rats to evaluate the formation of bone tissue and angiogenesis in vivo. It was found that the combination of a collagen sponge with CB MSC, PRP, PA, and the BMP2-releasing NSE formed the most bone and was most vascularized by four weeks compared to analogous composites featuring BM MSC or PCL or lacking PRP, PA, and NSE. This study indicates that CB MSC should be considered as an alternative to marrow as a source of stem cells, while the PRP-PA cell and microparticle delivery system may be utilized for diverse tissue engineering applications.

  15. An enhanced data visualization method for diesel engine malfunction classification using multi-sensor signals.

    Li, Yiqing; Wang, Yu; Zi, Yanyang; Zhang, Mingquan

    2015-10-21

    The various multi-sensor signal features from a diesel engine constitute a complex high-dimensional dataset. The non-linear dimensionality reduction method, t-distributed stochastic neighbor embedding (t-SNE), provides an effective way to implement data visualization for complex high-dimensional data. However, irrelevant features can deteriorate the performance of data visualization, and thus, should be eliminated a priori. This paper proposes a feature subset score based t-SNE (FSS-t-SNE) data visualization method to deal with the high-dimensional data that are collected from multi-sensor signals. In this method, the optimal feature subset is constructed by a feature subset score criterion. Then the high-dimensional data are visualized in 2-dimension space. According to the UCI dataset test, FSS-t-SNE can effectively improve the classification accuracy. An experiment was performed with a large power marine diesel engine to validate the proposed method for diesel engine malfunction classification. Multi-sensor signals were collected by a cylinder vibration sensor and a cylinder pressure sensor. Compared with other conventional data visualization methods, the proposed method shows good visualization performance and high classification accuracy in multi-malfunction classification of a diesel engine.

  16. The Clinical Features and Predictive Risk Factors for Reoperation in Patients With Perianal Crohn Diseases; A Multi-Center Study of a Korean Inflammatory Bowel Disease Study Group

    Lee, Jae Bum; Yoon, Seo-Gue; Park, Kyu Joo; Lee, Kang Young; Kim, Dae Dong; Yoon, Sang Nam

    2015-01-01

    Purpose Perianal lesions are common in Crohn disease, but their clinical course is unpredictable. Nevertheless, predicting the clinical course after surgery for perianal Crohn disease (PCD) is important because repeated operations may decrease patient's quality of life. The aim of this study was to predict the risk of reoperation in patients with PCD. Methods From September 1994 to February 2010, 377 patients with PCD were recruited in twelve major tertiary university-affiliated hospitals and two specialized colorectal hospitals in Korea. Data on the patient's demographics, clinical features, and surgical outcomes were analyzed. Results Among 377 patients, 227 patients were ultimately included in the study. Among the 227 patients, 64 patients underwent at least one reoperation. The median period of reoperation following the first perianal surgery was 94 months. Overall 3-year, 5-year, and 10-year cumulative rates of reoperation-free individuals were 68.8%, 61.2%, and 50.5%, respectively. In multivariate analysis (Cox-regression hazard model), reoperation was significantly correlated with an age of onset less than 20 years (hazard ratio [HR], 1.93; 95% confidence interval [CI], 1.07-3.48; P = 0.03), history of abdominal surgery (HR, 1.99; 95% CI, 1.08-3.64; P = 0.03), and the type of surgery. Among types of surgery, fistulotomy or fistulectomy was associated with a decreased incidence of reoperation in comparison with incision and drainage (HR, 0.19; 95% CI, 0.09-0.42; P < 0.001). Conclusion Young age of onset and a history of abdominal surgery were associated with a high risk of reoperation for PCD, and the risk of reoperation were relatively low in fistulotomy or fistulectomy procedures. PMID:26576395

  17. Embedding Sensors During Additive Manufacturing

    Sbriglia, Lexey Raylene [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2015-08-10

    This PowerPoint presentation had the following headings: Fused deposition modeling (FDM); Open source 3D printing; Objectives; Vibration analysis; Equipment; Design; Material choices; Failure causes, such as tension, bubbling; Potential solutions; Simulations; Embedding the sensors; LabView programming; Alternate data acquisition; Problem and proposed solution; and, Conclusions

  18. Embedded EZ-Source Inverters

    Blaabjerg, Frede; Loh, Poh Chiang; Gao, F.

    2008-01-01

    -voltage oscillations to the system. Therefore, Z-source inverters are in effect safer and less complex, and can be implemented using only passive elements with no additional active semiconductor needed. Believing in the prospects of Z-source inverters, this paper contributes by introducing a new family of embedded EZ...

  19. Software for Embedded Control Systems

    Broenink, Johannes F.; Hilderink, G.H.; Jovanovic, D.S.

    2001-01-01

    The research of our team deals with the realization of control schemes on digital computers. As such the emphasis is on embedded control software implementation. Applications are in the field of mechatronic devices, using a mechatronic design approach (the integrated and optimal design of a

  20. Noncommutativity and Duality through the Symplectic Embedding Formalism

    Everton M.C. Abreu

    2010-07-01

    Full Text Available This work is devoted to review the gauge embedding of either commutative and noncommutative (NC theories using the symplectic formalism framework. To sum up the main features of the method, during the process of embedding, the infinitesimal gauge generators of the gauge embedded theory are easily and directly chosen. Among other advantages, this enables a greater control over the final Lagrangian and brings some light on the so-called ''arbitrariness problem''. This alternative embedding formalism also presents a way to obtain a set of dynamically dual equivalent embedded Lagrangian densities which is obtained after a finite number of steps in the iterative symplectic process, oppositely to the result proposed using the BFFT formalism. On the other hand, we will see precisely that the symplectic embedding formalism can be seen as an alternative and an efficient procedure to the standard introduction of the Moyal product in order to produce in a natural way a NC theory. In order to construct a pedagogical explanation of the method to the nonspecialist we exemplify the formalism showing that the massive NC U(1 theory is embedded in a gauge theory using this alternative systematic path based on the symplectic framework. Further, as other applications of the method, we describe exactly how to obtain a Lagrangian description for the NC version of some systems reproducing well known theories. Naming some of them, we use the procedure in the Proca model, the irrotational fluid model and the noncommutative self-dual model in order to obtain dual equivalent actions for these theories. To illustrate the process of noncommutativity introduction we use the chiral oscillator and the nondegenerate mechanics.

  1. FAILSAFE Health Management for Embedded Systems

    Horvath, Gregory A.; Wagner, David A.; Wen, Hui Ying; Barry, Matthew

    2010-01-01

    The FAILSAFE project is developing concepts and prototype implementations for software health management in mission- critical, real-time embedded systems. The project unites features of the industry-standard ARINC 653 Avionics Application Software Standard Interface and JPL s Mission Data System (MDS) technology (see figure). The ARINC 653 standard establishes requirements for the services provided by partitioned, real-time operating systems. The MDS technology provides a state analysis method, canonical architecture, and software framework that facilitates the design and implementation of software-intensive complex systems. The MDS technology has been used to provide the health management function for an ARINC 653 application implementation. In particular, the focus is on showing how this combination enables reasoning about, and recovering from, application software problems.

  2. Integrated Optical Interconnect Architectures for Embedded Systems

    Nicolescu, Gabriela

    2013-01-01

    This book provides a broad overview of current research in optical interconnect technologies and architectures. Introductory chapters on high-performance computing and the associated issues in conventional interconnect architectures, and on the fundamental building blocks for integrated optical interconnect, provide the foundations for the bulk of the book which brings together leading experts in the field of optical interconnect architectures for data communication. Particular emphasis is given to the ways in which the photonic components are assembled into architectures to address the needs of data-intensive on-chip communication, and to the performance evaluation of such architectures for specific applications.   Provides state-of-the-art research on the use of optical interconnects in Embedded Systems; Begins with coverage of the basics for high-performance computing and optical interconnect; Includes a variety of on-chip optical communication topologies; Features coverage of system integration and opti...

  3. Embedded, everywhere: a research agenda for networked systems of embedded computers

    Committee on Networked Systems of Embedded Computers; National Research Council Staff; Division on Engineering and Physical Sciences; Computer Science and Telecommunications Board; National Academy of Sciences

    2001-01-01

    .... Embedded, Everywhere explores the potential of networked systems of embedded computers and the research challenges arising from embedding computation and communications technology into a wide variety of applicationsâ...

  4. Laser Cladding of Embedded Sensors for Thermal Barrier Coating Applications

    Yanli Zhang

    2018-05-01

    Full Text Available The accurate real-time monitoring of surface or internal temperatures of thermal barrier coatings (TBCs in hostile environments presents significant benefits to the efficient and safe operation of gas turbines. A new method for fabricating high-temperature K-type thermocouple sensors on gas turbine engines using coaxial laser cladding technology has been developed. The deposition of the thermocouple sensors was optimized to provide minimal intrusive features to the TBC, which is beneficial for the operational reliability of the protective coatings. Notably, this avoids a melt pool on the TBC surface. Sensors were deposited onto standard yttria-stabilized zirconia (7–8 wt % YSZ coated substrates; subsequently, they were embedded with second YSZ layers by the Atmospheric Plasma Spray (APS process. Morphology of cladded thermocouples before and after embedding was optimized in terms of topography and internal homogeneity, respectively. The dimensions of the cladded thermocouple were in the order of 200 microns in thickness and width. The thermal and electrical response of the cladded thermocouple was tested before and after embedding in temperatures ranging from ambient to approximately 450 °C in a furnace. Seebeck coefficients of bared and embedded thermocouples were also calculated correspondingly, and the results were compared to that of a commercial standard K-type thermocouple, which demonstrates that laser cladding is a prospective technology for manufacturing microsensors on the surface of or even embedded into functional coatings.

  5. Modeling activity recognition of multi resident using label combination of multi label classification in smart home

    Mohamed, Raihani; Perumal, Thinagaran; Sulaiman, Md Nasir; Mustapha, Norwati; Zainudin, M. N. Shah

    2017-10-01

    Pertaining to the human centric concern and non-obtrusive way, the ambient sensor type technology has been selected, accepted and embedded in the environment in resilient style. Human activities, everyday are gradually becoming complex and thus complicate the inferences of activities when it involving the multi resident in the same smart environment. Current works solutions focus on separate model between the resident, activities and interactions. Some study use data association and extra auxiliary of graphical nodes to model human tracking information in an environment and some produce separate framework to incorporate the auxiliary for interaction feature model. Thus, recognizing the activities and which resident perform the activity at the same time in the smart home are vital for the smart home development and future applications. This paper will cater the above issue by considering the simplification and efficient method using the multi label classification framework. This effort eliminates time consuming and simplifies a lot of pre-processing tasks comparing with previous approach. Applications to the multi resident multi label learning in smart home problems shows the LC (Label Combination) using Decision Tree (DT) as base classifier can tackle the above problems.

  6. Review of battery powered embedded systems design for mission-critical low-power applications

    Malewski, Matthew; Cowell, David M. J.; Freear, Steven

    2018-06-01

    The applications and uses of embedded systems is increasingly pervasive. Mission and safety critical systems relying on embedded systems pose specific challenges. Embedded systems is a multi-disciplinary domain, involving both hardware and software. Systems need to be designed in a holistic manner so that they are able to provide the desired reliability and minimise unnecessary complexity. The large problem landscape means that there is no one solution that fits all applications of embedded systems. With the primary focus of these mission and safety critical systems being functionality and reliability, there can be conflicts with business needs, and this can introduce pressures to reduce cost at the expense of reliability and functionality. This paper examines the challenges faced by battery powered systems, and then explores at more general problems, and several real-world embedded systems.

  7. Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding.

    Wang, Xiang; Zheng, Yuan; Zhao, Zhenzhou; Wang, Jinping

    2015-07-06

    Fault diagnosis is essentially a kind of pattern recognition. The measured signal samples usually distribute on nonlinear low-dimensional manifolds embedded in the high-dimensional signal space, so how to implement feature extraction, dimensionality reduction and improve recognition performance is a crucial task. In this paper a novel machinery fault diagnosis approach based on a statistical locally linear embedding (S-LLE) algorithm which is an extension of LLE by exploiting the fault class label information is proposed. The fault diagnosis approach first extracts the intrinsic manifold features from the high-dimensional feature vectors which are obtained from vibration signals that feature extraction by time-domain, frequency-domain and empirical mode decomposition (EMD), and then translates the complex mode space into a salient low-dimensional feature space by the manifold learning algorithm S-LLE, which outperforms other feature reduction methods such as PCA, LDA and LLE. Finally in the feature reduction space pattern classification and fault diagnosis by classifier are carried out easily and rapidly. Rolling bearing fault signals are used to validate the proposed fault diagnosis approach. The results indicate that the proposed approach obviously improves the classification performance of fault pattern recognition and outperforms the other traditional approaches.

  8. Video2vec Embeddings Recognize Events When Examples Are Scarce.

    Habibian, Amirhossein; Mensink, Thomas; Snoek, Cees G M

    2017-10-01

    This paper aims for event recognition when video examples are scarce or even completely absent. The key in such a challenging setting is a semantic video representation. Rather than building the representation from individual attribute detectors and their annotations, we propose to learn the entire representation from freely available web videos and their descriptions using an embedding between video features and term vectors. In our proposed embedding, which we call Video2vec, the correlations between the words are utilized to learn a more effective representation by optimizing a joint objective balancing descriptiveness and predictability. We show how learning the Video2vec embedding using a multimodal predictability loss, including appearance, motion and audio features, results in a better predictable representation. We also propose an event specific variant of Video2vec to learn a more accurate representation for the words, which are indicative of the event, by introducing a term sensitive descriptiveness loss. Our experiments on three challenging collections of web videos from the NIST TRECVID Multimedia Event Detection and Columbia Consumer Videos datasets demonstrate: i) the advantages of Video2vec over representations using attributes or alternative embeddings, ii) the benefit of fusing video modalities by an embedding over common strategies, iii) the complementarity of term sensitive descriptiveness and multimodal predictability for event recognition. By its ability to improve predictability of present day audio-visual video features, while at the same time maximizing their semantic descriptiveness, Video2vec leads to state-of-the-art accuracy for both few- and zero-example recognition of events in video.

  9. Damaged region segmentation of Thangka based on domain knowledge and multi-feature%结合领域知识和多特征表示的唐卡破损区域分割算法

    胡文瑾; 王维兰; 刘仲民

    2016-01-01

    A method was proposed to segment the consistent broken area of Buddha Thangka. Firstly, the head light area was projected and the symmetry axis was obtained by one-dimensional function symmetry detection method, and then the initial segmentation result was received based on the symmetric axis block segmentation. Secondly, the texture feature was extracted by Gabor transform, and the multi-scale features set was constructed combined with the Lab color space feature. The consistent broken area template was ultimately achieved by K−Nearest Neighbor (KNN) classification. The results show that this algorithm has an effective segmentation on the consistent broken area of the Buddha Thangka.%针对佛像类唐卡中出现的一致性破损区域的分割进行研究。首先对头光区域投影,利用一维函数对称性检测方法得到图像的对称轴,提出基于对称轴的分块分割方法得到初始分割结果;然后利用Gabor变换提取纹理特征,结合Lab空间颜色特征,构造多尺度多特征集合,最后采用K最近邻分类算法(K−Nearest Neighbor, KNN)得到一致性破损区域的模板。研究结果表明:该方法对于具有对称性的佛像类破损唐卡图像中出现的一致性破损区域的分割效果良好。

  10. Isometric embeddings in cosmology and astrophysics

    embedding theory, a given spacetime (or 'brane') is embedded in a higher- ..... If one recalls that the motivation (at least in part) for non-compact extra ... to successfully embed (apparently perfect fluid) astrophysical models, we typically need to.

  11. Poincare ball embeddings of the optical geometry

    Abramowicz, M A; Bengtsson, I; Karas, V; Rosquist, K

    2002-01-01

    It is shown that the optical geometry of the Reissner-Nordstroem exterior metric can be embedded in a hyperbolic space all the way down to its outer horizon. The adopted embedding procedure removes a breakdown of flat-space embeddings which occurs outside the horizon, at and below the Buchdahl-Bondi limit (R/M=9/4 in the Schwarzschild case). In particular, the horizon can be captured in the optical geometry embedding diagram. Moreover, by using the compact Poincare ball representation of the hyperbolic space, the embedding diagram can cover the whole extent of radius from spatial infinity down to the horizon. Attention is drawn to the advantages of such embeddings in an appropriately curved space: this approach gives compact embeddings and it clearly distinguishes the case of an extremal black hole from a non-extremal one in terms of the topology of the embedded horizon

  12. Model-based design of adaptive embedded systems

    Hamberg, Roelof; Reckers, Frans; Verriet, Jacques

    2013-01-01

    Today’s embedded systems have to operate in a wide variety of dynamically changing environmental circumstances. Adaptivity, the ability of a system to autonomously adapt itself, is a means to optimise a system’s behaviour to accommodate changes in its environment. It involves making in-product trade-offs between system qualities at system level. The main challenge in the development of adaptive systems is keeping control of the intrinsic complexity of such systems while working with multi-disciplinary teams to create different parts of the system. Model-Based Development of Adaptive Embedded Systems focuses on the development of adaptive embedded systems both from an architectural and methodological point of view. It describes architectural solution patterns for adaptive systems and state-of-the-art model-based methods and techniques to support adaptive system development. In particular, the book describes the outcome of the Octopus project, a cooperation of a multi-disciplinary team of academic and indus...

  13. The embedded operating system project

    Campbell, R. H.

    1984-01-01

    This progress report describes research towards the design and construction of embedded operating systems for real-time advanced aerospace applications. The applications concerned require reliable operating system support that must accommodate networks of computers. The report addresses the problems of constructing such operating systems, the communications media, reconfiguration, consistency and recovery in a distributed system, and the issues of realtime processing. A discussion is included on suitable theoretical foundations for the use of atomic actions to support fault tolerance and data consistency in real-time object-based systems. In particular, this report addresses: atomic actions, fault tolerance, operating system structure, program development, reliability and availability, and networking issues. This document reports the status of various experiments designed and conducted to investigate embedded operating system design issues.

  14. Perturbation Theory of Embedded Eigenvalues

    Engelmann, Matthias

    project gives a general and systematic approach to analytic perturbation theory of embedded eigenvalues. The spectral deformation technique originally developed in the theory of dilation analytic potentials in the context of Schrödinger operators is systematized by the use of Mourre theory. The group...... of dilations is thereby replaced by the unitary group generated y the conjugate operator. This then allows to treat the perturbation problem with the usual Kato theory.......We study problems connected to perturbation theory of embedded eigenvalues in two different setups. The first part deals with second order perturbation theory of mass shells in massive translation invariant Nelson type models. To this end an expansion of the eigenvalues w.r.t. fiber parameter up...

  15. An Embedded Reconfigurable Logic Module

    Tucker, Jerry H.; Klenke, Robert H.; Shams, Qamar A. (Technical Monitor)

    2002-01-01

    A Miniature Embedded Reconfigurable Computer and Logic (MERCAL) module has been developed and verified. MERCAL was designed to be a general-purpose, universal module that that can provide significant hardware and software resources to meet the requirements of many of today's complex embedded applications. This is accomplished in the MERCAL module by combining a sub credit card size PC in a DIMM form factor with a XILINX Spartan I1 FPGA. The PC has the ability to download program files to the FPGA to configure it for different hardware functions and to transfer data to and from the FPGA via the PC's ISA bus during run time. The MERCAL module combines, in a compact package, the computational power of a 133 MHz PC with up to 150,000 gate equivalents of digital logic that can be reconfigured by software. The general architecture and functionality of the MERCAL hardware and system software are described.

  16. The Modified Embedded Atom Method

    Baskes, M.I.

    1994-08-01

    Recent modifications have been made to generalize the Embedded Atom Method (EAM) to describe bonding in diverse materials. By including angular dependence of the electron density in an empirical way, the Modified Embedded Atom Method (MEAM) has been able to reproduce the basic energetic and structural properties of 45 elements. This method is ideal for examining interfacial behavior of dissimilar materials. This paper explains in detail the derivation of the method, shows how parameters of MEAM are determined directly from experiment or first principles calculations, and examine the quality of the reproduction of the database. Materials with fcc, bcc, hcp, and diamond cubic crystal structure are discussed. A few simple examples of the application of the MEAM to surfaces and interfaces are presented. Calculations of pullout of a SiC fiber in a diamond matrix as a function of applied stress show nonuniform deformation of the fiber.

  17. Embedding

    Høyrup, Jens

    2016-01-01

    systems, in particular place-value and quasi place-value systems. 2. The development of algebraic symbolisms. 3. The discussion whether “scientific revolutions” ever take place in mathematics, or new conceptualizations always include what preceded them. A final section investigates the relation between...

  18. Sustainable embedded software lifecycle planning

    Lee, Dong-Hyun; In, Hoh Peter; Lee, Keun; Park, Sooyong; Hinchey, Mike

    2012-01-01

    peer-reviewed Time-to-market is a crucial factor in increasing market share in the consumer electronics (CE) market. Furthermore, fierce competition in the market tends to sharply lower the prices of brand-new CE products as soon as they are released. Software-intensive embedded system design methods such as hardware/software co-design have been studied with the goal of reducing development lead-time by designing hardware and software simultaneously. Many researchers, however, concentra...

  19. Bilipschitz embedding of homogeneous fractals

    Lü, Fan; Lou, Man-Li; Wen, Zhi-Ying; Xi, Li-Feng

    2014-01-01

    In this paper, we introduce a class of fractals named homogeneous sets based on some measure versions of homogeneity, uniform perfectness and doubling. This fractal class includes all Ahlfors-David regular sets, but most of them are irregular in the sense that they may have different Hausdorff dimensions and packing dimensions. Using Moran sets as main tool, we study the dimensions, bilipschitz embedding and quasi-Lipschitz equivalence of homogeneous fractals.

  20. Corrosion Monitors for Embedded Evaluation

    Robinson, Alex L. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Pfeifer, Kent B. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Casias, Adrian L. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Howell, Stephen W. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sorensen, Neil R. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Missert, Nancy A. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-05-01

    We have developed and characterized novel in-situ corrosion sensors to monitor and quantify the corrosive potential and history of localized environments. Embedded corrosion sensors can provide information to aid health assessments of internal electrical components including connectors, microelectronics, wires, and other susceptible parts. When combined with other data (e.g. temperature and humidity), theory, and computational simulation, the reliability of monitored systems can be predicted with higher fidelity.

  1. Characterization of Embedded BPM Collimators

    VALENTINO, Gianluca

    2015-01-01

    During LS1, 16 tertiary collimators (TCTs) and 2 secondary collimators (TCSGs) in IR6 were replaced by new embedded BPM collimators. The BPM functionality allows the possibility to align the collimators more quickly and therefore be able to respond faster to machine configuration changes, as well as a direct monitoring of the beam orbit at the collimators. Following an initial commissioning phase, an MD was carried out to test the new collimators and acquisition electronics with beam in the LHC.

  2. Liquid-Embedded Elastomer Electronics

    Kramer, Rebecca; Majidi, Carmel; Park, Yong-Lae; Paik, Jamie; Wood, Robert

    2012-02-01

    Hyperelastic sensors are fabricated by embedding a silicone rubber film with microchannels of conductive liquid. In the case of soft tactile sensors, pressing the surface of the elastomer will deform the cross-section of underlying channels and change their electrical resistance. Soft pressure sensors may be employed in a variety of applications. For example, a network of pressure sensors can serve as artificial skin by yielding detailed information about contact pressures. This concept was demonstrated in a hyperelastic keypad, where perpendicular conductive channels form a quasi-planar network within an elastomeric matrix that registers the location, intensity and duration of applied pressure. In a second demonstration, soft curvature sensors were used for joint angle proprioception. Because the sensors are soft and stretchable, they conform to the host without interfering with the natural mechanics of motion. This marked the first use of liquid-embedded elastomer electronics to monitor human or robotic motion. Finally, liquid-embedded elastomers may be implemented as conductors in applications that call for flexible or stretchable circuitry, such as robotic origami.

  3. Embedding of the radiation cosmos

    Wang, J.Z.

    1986-01-01

    The embedding of the Friedmann manifold into a higher dimensional Minkowski space is investigated. As solutions of the Friedmann equation with vanishing cosmological term, Friedmann models describe a first expanding, then contracting universe and predict a big bang singularity. For cosmic time t → 0, R(t) → 0, there is an infinite scalar, curvature in the matter cosmos, and an infinite eigenvalue corresponding to the unique timelike eigenvector of the energy-momentum tensor in the radiation cosmos. The big bang, therefore, is an intrinsic singularity of the space time. To investigate the singularity one resorts to the embedding of the Friedmann manifold into a higher dimensional Minkowski space. For the matter cosmos such an investigation has already been done (Lauro and Schucking, 1984). However, the matter cosmos is not a suitable model to discuss the very early universe where the radiation dominates. Geometric properties, such as the Riemann tensor, the Guassian curvature and the global behavior of the geodesics of the embedded manifold, are discussed in detail

  4. CVXPY: A Python-Embedded Modeling Language for Convex Optimization

    Diamond, Steven; Boyd, Stephen

    2016-01-01

    CVXPY is a domain-specific language for convex optimization embedded in Python. It allows the user to express convex optimization problems in a natural syntax that follows the math, rather than in the restrictive standard form required by solvers. CVXPY makes it easy to combine convex optimization with high-level features of Python such as parallelism and object-oriented design. CVXPY is available at http://www.cvxpy.org/ under the GPL license, along with documentation and examples.

  5. CVXPY: A Python-Embedded Modeling Language for Convex Optimization.

    Diamond, Steven; Boyd, Stephen

    2016-04-01

    CVXPY is a domain-specific language for convex optimization embedded in Python. It allows the user to express convex optimization problems in a natural syntax that follows the math, rather than in the restrictive standard form required by solvers. CVXPY makes it easy to combine convex optimization with high-level features of Python such as parallelism and object-oriented design. CVXPY is available at http://www.cvxpy.org/ under the GPL license, along with documentation and examples.

  6. Embedding initial data for black hole collisions

    Romano, Joseph D.; Price, Richard H.

    1994-01-01

    We discuss isometric embedding diagrams for the visualization of initial data for the problem of the head-on collision of two black holes. The problem of constructing the embedding diagrams is explicitly presented for the best studied initial data, the Misner geometry. We present a partial solution of the embedding diagrams and discuss issues related to completing the solution.

  7. Multi-Objective Design Space Exploration of Embedded System Platforms

    Madsen, Jan; Stidsen, Thomas K.; Kjærulff, Peter

    2006-01-01

    on local memory sizes and interface buffer sizes. Our approach allows for mapping onto a fixed platform or onto a flexible platform where architectural changes are explored during the mapping. We demonstrate our approach through an exploration of a smart phone, where five task graphs with a total of 530...

  8. Multi-Objective Design Space Exploration of Embedded System Platfoms

    Madsen, Jan; Stidsen, Thomas K.; Kjærulff, Peter

    on local memory sizes and interface buffer sizes. Our approach allows for mapping onto a fixed platform or onto a flexible platform where architectural changes are explored during the mapping. We demonstrate our approach through an exploration of a smart phone, where five task graphs with a total of 530...

  9. Multi-Class Classification for Identifying JPEG Steganography Embedding Methods

    2008-09-01

    digital pictures on Web sites or sending them through email (Astrowsky, 2000). Steganography may also be used to allow communication between affiliates...B.H. (2000). STEGANOGRAPHY: Hidden Images, A New Challenge in the Fight Against Child Porn . UPDATE, Volume 13, Number 2, pp. 1-4, Retrieved June 3

  10. Feature Extraction

    CERN. Geneva

    2015-01-01

    Feature selection and reduction are key to robust multivariate analyses. In this talk I will focus on pros and cons of various variable selection methods and focus on those that are most relevant in the context of HEP.

  11. Solar Features

    National Oceanic and Atmospheric Administration, Department of Commerce — Collection includes a variety of solar feature datasets contributed by a number of national and private solar observatories located worldwide.

  12. Site Features

    U.S. Environmental Protection Agency — This dataset consists of various site features from multiple Superfund sites in U.S. EPA Region 8. These data were acquired from multiple sources at different times...

  13. Permutation entropy with vector embedding delays

    Little, Douglas J.; Kane, Deb M.

    2017-12-01

    Permutation entropy (PE) is a statistic used widely for the detection of structure within a time series. Embedding delay times at which the PE is reduced are characteristic timescales for which such structure exists. Here, a generalized scheme is investigated where embedding delays are represented by vectors rather than scalars, permitting PE to be calculated over a (D -1 ) -dimensional space, where D is the embedding dimension. This scheme is applied to numerically generated noise, sine wave and logistic map series, and experimental data sets taken from a vertical-cavity surface emitting laser exhibiting temporally localized pulse structures within the round-trip time of the laser cavity. Results are visualized as PE maps as a function of embedding delay, with low PE values indicating combinations of embedding delays where correlation structure is present. It is demonstrated that vector embedding delays enable identification of structure that is ambiguous or masked, when the embedding delay is constrained to scalar form.

  14. Habitual Tastes and Embedded Taste

    Hedegaard, Liselotte

    2016-01-01

    The interest of this paper is to position taste within the framework of time. This might seem peculiar given that taste, in its physical sense, is referred to as an ephemeral experience taking place in the mouth. Taste, however, is more than that. It is the transient experience that infiltrates...... may be bridged by story-telling or other ways of handing over historically embedded practices, but this leaves a more fundamental question unanswered. Namely, that given that all remembrance has individual recollection as the point of departure, then how does individual recollection of tastes...

  15. Professional Windows Embedded Compact 7

    Phung, Samuel; Joubert, Thierry; Hall, Mike

    2011-01-01

    Learn to program an array of customized devices and solutions As a compact, highly efficient, scalable operating system, Windows Embedded Compact 7 (WEC7) is one of the best options for developing a new generation of network-enabled, media-rich, and service-oriented devices. This in-depth resource takes you through the benefits and capabilities of WEC7 so that you can start using this performance development platform today. Divided into several major sections, the book begins with an introduction and then moves on to coverage of OS design, application development, advanced application developm

  16. Simulation and Embedded Smart Control

    Conrad, Finn; Fan, Zhun; Sørensen, Torben

    2006-01-01

    The paper presents results obtained from a Danish mechatronic research program focusing on intelligent motion control, simulation and embedded smart controllers for hydraulic actuators and robots as well as results from the EU projects. A mechatronic test facility with digital controllers...... for a hydraulic robot was implemented. The controllers apply digital signal processors (DSPs), and Field Programmable Gate Array, short named as FPGA, respectively. The DSP controller utilizes the dSPACE System that is suitable for real-time experimentation, evaluation and validation of control laws...... and algorithms. Furthermore, a developed IT-tool concept for controller and system design utilizing the ISO 10303 STEP Standard is proposed....

  17. A Heterogeneous Multi-core Architecture with a Hardware Kernel for Control Systems

    Li, Gang; Guan, Wei; Sierszecki, Krzysztof

    2012-01-01

    Rapid industrialisation has resulted in a demand for improved embedded control systems with features such as predictability, high processing performance and low power consumption. Software kernel implementation on a single processor is becoming more difficult to satisfy those constraints....... This paper presents a multi-core architecture incorporating a hardware kernel on FPGAs, intended for high performance applications in control engineering domain. First, the hardware kernel is investigated on the basis of a component-based real-time kernel HARTEX (Hard Real-Time Executive for Control Systems...

  18. Java for Cost Effective Embedded Real-Time Software

    Korsholm, Stephan Erbs

    2012-01-01

    This thesis presents the analysis, design and implementation of the Hardware near Virtual Machine (HVM) - a Java virtual machine for embedded devices. The HVM supports the execution of Java programs on low-end embedded hard- ware environments with as little as a few kB of RAM and 32 kB of ROM....... The HVM is based on a Java-to-C translation mechanism and it produces selfcontained, strict ANSI-C code that has been specially crafted to allow it to be embedded into existing C based build and execution environments; environ- ments which may be based on non standard C compilers and libraries. The HVM...... does not require a POSIX-like OS, nor does it require a C runtime library to be present for the target. The main distinguishing feature of the HVM is to support the stepwise addition of Java into an existing C based build and execution environment for low-end embedded systems. This will allow...

  19. Java for Cost Effective Embedded Real-Time Software

    Korsholm, Stephan

    This thesis presents the analysis, design and implementation of the Hardware near Virtual Machine (HVM) - a Java virtual machine for embedded devices. The HVM supports the execution of Java programs on low-end embedded hard- ware environments with as little as a few kB of RAM and 32 kB of ROM....... The HVM is based on a Java-to-C translation mechanism and it produces self- contained, strict ANSI-C code that has been specially crafted to allow it to be embedded into existing C based build and execution environments; environ- ments which may be based on non standard C compilers and libraries. The HVM...... does not require a POSIX-like OS, nor does it require a C runtime library to be present for the target. The main distinguishing feature of the HVM is to support the stepwise addition of Java into an existing C based build and execution environment for low-end embedded systems. This will allow...

  20. Java Source Code Analysis for API Migration to Embedded Systems

    Winter, Victor [Univ. of Nebraska, Omaha, NE (United States); McCoy, James A. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Guerrero, Jonathan [Univ. of Nebraska, Omaha, NE (United States); Reinke, Carl Werner [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Perry, James Thomas [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2015-02-01

    Embedded systems form an integral part of our technological infrastructure and oftentimes play a complex and critical role within larger systems. From the perspective of reliability, security, and safety, strong arguments can be made favoring the use of Java over C in such systems. In part, this argument is based on the assumption that suitable subsets of Java’s APIs and extension libraries are available to embedded software developers. In practice, a number of Java-based embedded processors do not support the full features of the JVM. For such processors, source code migration is a mechanism by which key abstractions offered by APIs and extension libraries can made available to embedded software developers. The analysis required for Java source code-level library migration is based on the ability to correctly resolve element references to their corresponding element declarations. A key challenge in this setting is how to perform analysis for incomplete source-code bases (e.g., subsets of libraries) from which types and packages have been omitted. This article formalizes an approach that can be used to extend code bases targeted for migration in such a manner that the threats associated the analysis of incomplete code bases are eliminated.

  1. ETHERNET BASED EMBEDDED SYSTEM FOR FEL DIAGNOSTICS AND CONTROLS

    Jianxun Yan; Daniel Sexton; Steven Moore; Albert Grippo; Kevin Jordan

    2006-01-01

    An Ethernet based embedded system has been developed to upgrade the Beam Viewer and Beam Position Monitor (BPM) systems within the free-electron laser (FEL) project at Jefferson Lab. The embedded microcontroller was mounted on the front-end I/O cards with software packages such as Experimental Physics and Industrial Control System (EPICS) and Real Time Executive for Multiprocessor System (RTEMS) running as an Input/Output Controller (IOC). By cross compiling with the EPICS, the RTEMS kernel, IOC device supports, and databases all of these can be downloaded into the microcontroller. The first version of the BPM electronics based on the embedded controller was built and is currently running in our FEL system. The new version of BPM that will use a Single Board IOC (SBIOC), which integrates with an Field Programming Gate Array (FPGA) and a ColdFire embedded microcontroller, is presently under development. The new system has the features of a low cost IOC, an open source real-time operating system, plug and play-like ease of installation and flexibility, and provides a much more localized solution

  2. Nanomechanical Optical Fiber with Embedded Electrodes Actuated by Joule Heating.

    Lian, Zhenggang; Segura, Martha; Podoliak, Nina; Feng, Xian; White, Nicholas; Horak, Peter

    2014-07-31

    Nanomechanical optical fibers with metal electrodes embedded in the jacket were fabricated by a multi-material co-draw technique. At the center of the fibers, two glass cores suspended by thin membranes and surrounded by air form a directional coupler that is highly temperature-dependent. We demonstrate optical switching between the two fiber cores by Joule heating of the electrodes with as little as 0.4 W electrical power, thereby demonstrating an electrically actuated all-fiber microelectromechanical system (MEMS). Simulations show that the main mechanism for optical switching is the transverse thermal expansion of the fiber structure.

  3. Nanomechanical Optical Fiber with Embedded Electrodes Actuated by Joule Heating

    Lian, Zhenggang; Segura, Martha; Podoliak, Nina; Feng, Xian; White, Nicholas; Horak, Peter

    2014-01-01

    Nanomechanical optical fibers with metal electrodes embedded in the jacket were fabricated by a multi-material co-draw technique. At the center of the fibers, two glass cores suspended by thin membranes and surrounded by air form a directional coupler that is highly temperature-dependent. We demonstrate optical switching between the two fiber cores by Joule heating of the electrodes with as little as 0.4 W electrical power, thereby demonstrating an electrically actuated all-fiber microelectromechanical system (MEMS). Simulations show that the main mechanism for optical switching is the transverse thermal expansion of the fiber structure. PMID:28788148

  4. Testing framework for embedded languages

    Leskó, Dániel; Tejfel, Máté

    2012-09-01

    Embedding a new programming language into an existing one is a widely used technique, because it fastens the development process and gives a part of a language infrastructure for free (e.g. lexical, syntactical analyzers). In this paper we are presenting a new advantage of this development approach regarding to adding testing support for these new languages. Tool support for testing is a crucial point for a newly designed programming language. It could be done in the hard way by creating a testing tool from scratch, or we could try to reuse existing testing tools by extending them with an interface to our new language. The second approach requires less work, and also it fits very well for the embedded approach. The problem is that the creation of such interfaces is not straightforward at all, because the existing testing tools were mostly not designed to be extendable and to be able to deal with new languages. This paper presents an extendable and modular model of a testing framework, in which the most basic design decision was to keep the - previously mentioned - interface creation simple and straightforward. Other important aspects of our model are the test data generation, the oracle problem and the customizability of the whole testing phase.

  5. Drilling azimuth gamma embedded design

    Zhou Yi Ren

    2016-01-01

    Full Text Available Embedded drilling azimuth gamma design, the use of radioactive measuring principle embedded gamma measurement while drilling a short section analysis. Monte Carlo method, in response to the density of horizontal well logging numerical simulation of 16 orientation, the orientation of horizontal well analysed, calliper, bed boundary location, space, different formation density, formation thickness, and other factors inclined strata dip the impact by simulating 137Cs sources under different formation conditions of the gamma distribution, to determine the orientation of drilling density tool can detect window size and space, draw depth of the logging methods. The data 360° azimuth imaging, image processing method to obtain graph, display density of the formation, dip and strata thickness and other parameters, the logging methods obtain real-time geo-steering. To establish a theoretical basis for the orientation density logging while drilling method implementation and application of numerical simulation in-depth study of the MWD azimuth and density log response factors of horizontal wells.

  6. Introduction to Embedded Systems Using ANSI C and the Arduino Development Environment

    Russell, David

    2010-01-01

    Many electrical and computer engineering projects involve some kind of embedded system in which a microcontroller sits at the center as the primary source of control. The recently-developed Arduino development platform includes an inexpensive hardware development board hosting an eight-bit ATMEL ATmega-family processor and a Java-based software-development environment. These features allow an embedded systems beginner the ability to focus their attention on learning how to write embedded software instead of wasting time overcoming the engineering CAD tools learning curve. The goal of this text

  7. Sensor Selection method for IoT systems – focusing on embedded system requirements

    Hirayama Masayuki

    2016-01-01

    Full Text Available Recently, various types of sensors have been developed. Using these sensors, IoT systems have become hot topics in embedded system domain. However, sensor selections for embedded systems are not well discussed up to now. This paper focuses on embedded system’s features and architecture, and proposes a sensor selection method which is composed seven steps. In addition, we applied the proposed method to a simple example – a sensor selection for computer scored answer sheet reader unit. From this case study, an idea to use FTA in sensor selection is also discussed.

  8. Visual processing in adolescents with autism spectrum disorders: Evidence from embedded figures and configural superiority tests

    Dillen, Claudia; Steyaert, Jean; Op de Beeck, Hans; Boets, Bart

    2015-01-01

    The embedded figures test has often been used to reveal weak central coherence in individuals with autism spectrum disorder (ASD). Here, we administered a more standardized automated version of the embedded figures test in combination with the configural superiority task, to investigate the effect of contextual modulation on local feature detection in 23 adolescents with ASD and 26 matched typically developing controls. On both tasks both groups performed l...

  9. Virtual network embedding in cross-domain network based on topology and resource attributes

    Zhu, Lei; Zhang, Zhizhong; Feng, Linlin; Liu, Lilan

    2018-03-01

    Aiming at the network architecture ossification and the diversity of access technologies issues, this paper researches the cross-domain virtual network embedding algorithm. By analysing the topological attribute from the local and global perspective of nodes in the virtual network and the physical network, combined with the local network resource property, we rank the embedding priority of the nodes with PCA and TOPSIS methods. Besides, the link load distribution is considered. Above all, We proposed an cross-domain virtual network embedding algorithm based on topology and resource attributes. The simulation results depicts that our algorithm increases the acceptance rate of multi-domain virtual network requests, compared with the existing virtual network embedding algorithm.

  10. French processes for waste embedding. The use of epoxy resin for waste containment

    Augustin, X.; Gauthey, J.C.

    1993-01-01

    The low- and medium-level wastes generated by nuclear facilities when operating as well as during their decommissioning (dismantling, decontamination, etc.) are embedded for the purpose of obtaining a product suitable for disposal. Due to the varieties of waste produced, it was necessary to resort to multi-purpose techniques to solve problems relating to their embedding. The process for waste embedding in thermosetting polymer (polyester, epoxy) developed by the French Atomic Energy Commission (CEA) and its subsidiary TECHNICATOME is easy to operate and yields excellent results having regard to volume reduction and containment of radioisotopes (particularly caesium). The industrial development of this process has led to the design of small, flexible, fixed or mobile, embedding stations. Examples illustrating the increasing use of this process during facility dismantling are described

  11. Very wide register : an asymmetric register file organization for low power embedded processors.

    Raghavan, P.; Lambrechts, A.; Jayapala, M.; Catthoor, F.; Verkest, D.T.M.L.; Corporaal, H.

    2007-01-01

    In current embedded systems processors, multi-ported register files are one of the most power hungry parts of the processor, even when they are clustered. This paper presents a novel register file architecture, which has single ported cells and asymmetric interfaces to the memory and to the

  12. Impact of Video Self-Monitoring with Graduated Training on Implementation of Embedded Instructional Learning Trials

    Bishop, Crystal D.; Snyder, Patricia A.; Crow, Robert E.

    2015-01-01

    We used a multi-component single-subject experimental design across three preschool teachers to examine the effects of video self-monitoring with graduated training and feedback on the accuracy with which teachers monitored their implementation of embedded instructional learning trials. We also examined changes in teachers' implementation of…

  13. Composable local memory organisation for streaming applications on embedded MPSoCs

    Ambrose, J.; Molnos, A.; Nelson, A.; Cotofana, S.; Goossens, K.G.W.; Juurlink, B.

    2011-01-01

    Multi-Processor Systems on a Chip (MPSoCs) are suitable platforms for the implementation of complex embedded applications. An MPSoC is composable if the functional and temporal behaviour of each application is independent of the absence or presence of other applications. Composability is required

  14. Onychomatricoma with misleading features.

    Fayol, J; Baran, R; Perrin, C; Labrousse, F

    2000-01-01

    Onychomatricoma is a rare tumour of the nail matrix with peculiar clinical and histological features and electron microscopic findings. We report on 5 cases with appearances which were misleading. Three presented as longitudinal melanonychia, a previously unreported observation. One case had the appearance of a cutaneous horn. In 3 of the 5 cases the tumour was associated with an onychomycosis and this may thus have been a predisposing factor in the secondary fungal infestation. Onychomatricoma appears as a multi-faceted tumour which can be mimicked by longitudinal melanonychia and/or onychomycosis.

  15. Embedded 100 Gbps Photonic Components:

    Kuznia, Charlie

    2018-04-26

    This innovation to fiber optic component technology increases the performance, reduces the size and reduces the power consumption of optical communications within dense network systems, such as advanced distributed computing systems and data centers. VCSEL technology is enabling short-reach (< 100 m) and >100 Gbps optical interconnections over multi-mode fiber in commercial applications.

  16. Cross functional organisational embedded system development

    Lennon, Sophie

    2015-01-01

    peer-reviewed Embedded system development is continuing to grow. Medical, automotive and Internet of Things are just some of the market segments. There is a tight coupling between hardware and software when developing an embedded system, often needing to meet strict performance targets, standards requirements and aggressive schedules. Embedded software developers need to consider hardware requirements in far greater detail as they can have a significant impact on the quality and value of t...

  17. Homomorphic embeddings in n-groups

    Mona Cristescu

    2013-06-01

    Full Text Available We prove that an cancellative n-groupoid A can be homotopic embedded in an n-group if and only if in A are satisfied all n-ary Malcev conditions. Now we shall prove that in the presence of associative law we obtain homomorphic embeddings. Furthermore, if A has a lateral identity a such embeddings is assured by a subset of n-ary Malcev conditions - unary Malcev conditions.

  18. Slice&Dice: Recognizing Food Preparation Activities Using Embedded Accelerometers

    Pham, Cuong; Olivier, Patrick

    Within the context of an endeavor to provide situated support for people with cognitive impairments in the kitchen, we developed and evaluated classifiers for recognizing 11 actions involved in food preparation. Data was collected from 20 lay subjects using four specially designed kitchen utensils incorporating embedded 3-axis accelerometers. Subjects were asked to prepare a mixed salad in our laboratory-based instrumented kitchen environment. Video of each subject's food preparation activities were independently annotated by three different coders. Several classifiers were trained and tested using these features. With an overall accuracy of 82.9% our investigation demonstrated that a broad set of food preparation actions can be reliably recognized using sensors embedded in kitchen utensils.

  19. Knowledge Engineering for Embedded Configuration

    Oddsson, Gudmundur Valur

    2008-01-01

    into the system the knowledge needed to achieve them. In order to understand the system, one draws simplified functional streams and identifies archetypes from the product assortment, and then one maps the two together into a system breakdown model. The system model indicates how many encapsulation models (EMs......This thesis presents a way to simplify setup of complex product systems with the help of embedded configuration. To achieve this, one has to focus on what subsystems need to communicate between themselves. The required internal knowledge is then structured at three abstraction levels......, and predefined relation types are suggested. The models are stringent and thought out so they can be implemented in software. They should allow both import and export of product knowledge from the knowledge-based system. The purpose of this work is to simplify the installation process of product systems...

  20. The embedded operating system project

    Campbell, R. H.

    1985-01-01

    The design and construction of embedded operating systems for real-time advanced aerospace applications was investigated. The applications require reliable operating system support that must accommodate computer networks. Problems that arise in the construction of such operating systems, reconfiguration, consistency and recovery in a distributed system, and the issues of real-time processing are reported. A thesis that provides theoretical foundations for the use of atomic actions to support fault tolerance and data consistency in real-time object-based system is included. The following items are addressed: (1) atomic actions and fault-tolerance issues; (2) operating system structure; (3) program development; (4) a reliable compiler for path Pascal; and (5) mediators, a mechanism for scheduling distributed system processes.

  1. Embedding knowledge in a workstation

    Barber, G

    1982-01-01

    This paper describes an approach to supporting work in the office. Using and extending ideas from the field of artificial intelligence (AI) it describes office work as a problem solving activity. A knowledge embedding language called OMEGA is used to embed knowledge of the organization into an office worker's workstation in order to support the office worker in his or her problem solving. A particular approach to reasoning about change and contradiction is discussed. This approach uses OMEGA's viewpoint mechanism. OMEGA's viewpoint mechanism is a general contradiction handling facility. Unlike other knowledge representation systems, when a contradiction is reached the reasons for the contradiction can be analyzed by the reduction mechanism without having to resort to a backtracking mechanism. The viewpoint mechanism is the heart of the problem solving support paradigm. This paradigm is an alternative to the classical view of problem solving in AI. Office workers are supported using the problem solving support paradigm. 16 references.

  2. Embedding Complementarity in HCI Methods and Techniques

    Nielsen, Janni; Yssing, Carsten; Tweddell Levinsen, Karin

    Differences in cultural contexts constitute differences in cognition, and research has shown that different cultures may use different cognitive tools for perception and reasoning. The cultural embeddings are significant in relation to HCI, because the cultural context is also embedded in the tec......Differences in cultural contexts constitute differences in cognition, and research has shown that different cultures may use different cognitive tools for perception and reasoning. The cultural embeddings are significant in relation to HCI, because the cultural context is also embedded...... the HCI paradigm in system development....

  3. Embedded Fiber Optic Sensors for Integral Armor

    Fink, Bruce

    2000-01-01

    This report describes the work performed with Production Products Manufacturing & Sales (PPMS), Inc., under the "Liquid Molded Composite Armor Smart Structures Using Embedded Sensors" Small Business Innovative Research...

  4. Graphical Model Debugger Framework for Embedded Systems

    Zeng, Kebin

    2010-01-01

    Model Driven Software Development has offered a faster way to design and implement embedded real-time software by moving the design to a model level, and by transforming models to code. However, the testing of embedded systems has remained at the code level. This paper presents a Graphical Model...... Debugger Framework, providing an auxiliary avenue of analysis of system models at runtime by executing generated code and updating models synchronously, which allows embedded developers to focus on the model level. With the model debugger, embedded developers can graphically test their design model...

  5. Multichannel analyzer embedded in FPGA

    Garcia D, A.; Hernandez D, V. M.; Vega C, H. R.; Ordaz G, O. O.; Bravo M, I.

    2017-10-01

    Ionizing radiation has different applications, so it is a very significant and useful tool, which in turn can be dangerous for living beings if they are exposed to uncontrolled doses. However, due to its characteristics, it cannot be perceived by any of the senses of the human being, so that in order to know the presence of it, radiation detectors and additional devices are required to quantify and classify it. A multichannel analyzer is responsible for separating the different pulse heights that are generated in the detectors, in a certain number of channels; according to the number of bits of the analog to digital converter. The objective of the work was to design and implement a multichannel analyzer and its associated virtual instrument, for nuclear spectrometry. The components of the multichannel analyzer were created in VHDL hardware description language and packaged in the Xilinx Vivado design suite, making use of resources such as the ARM processing core that the System on Chip Zynq contains and the virtual instrument was developed on the LabView programming graphics platform. The first phase was to design the hardware architecture to be embedded in the FPGA and for the internal control of the multichannel analyzer the application was generated for the ARM processor in C language. For the second phase, the virtual instrument was developed for the management, control and visualization of the results. The data obtained as a result of the development of the system were observed graphically in a histogram showing the spectrum measured. The design of the multichannel analyzer embedded in FPGA was tested with two different radiation detection systems (hyper-pure germanium and scintillation) which allowed determining that the spectra obtained are similar in comparison with the commercial multichannel analyzers. (Author)

  6. Embedded palmprint recognition system using OMAP 3530.

    Shen, Linlin; Wu, Shipei; Zheng, Songhao; Ji, Zhen

    2012-01-01

    We have proposed in this paper an embedded palmprint recognition system using the dual-core OMAP 3530 platform. An improved algorithm based on palm code was proposed first. In this method, a Gabor wavelet is first convolved with the palmprint image to produce a response image, where local binary patterns are then applied to code the relation among the magnitude of wavelet response at the central pixel with that of its neighbors. The method is fully tested using the public PolyU palmprint database. While palm code achieves only about 89% accuracy, over 96% accuracy is achieved by the proposed G-LBP approach. The proposed algorithm was then deployed to the DSP processor of OMAP 3530 and work together with the ARM processor for feature extraction. When complicated algorithms run on the DSP processor, the ARM processor can focus on image capture, user interface and peripheral control. Integrated with an image sensing module and central processing board, the designed device can achieve accurate and real time performance.

  7. An object-oriented feature-based design system face-based detection of feature interactions

    Ariffin Abdul Razak

    1999-01-01

    This paper presents an object-oriented, feature-based design system which supports the integration of design and manufacture by ensuring that part descriptions fully account for any feature interactions. Manufacturing information is extracted from the feature descriptions in the form of volumes and Tool Access Directions, TADs. When features interact, both volumes and TADs are updated. This methodology has been demonstrated by developing a prototype system in which ACIS attributes are used to record feature information within the data structure of the solid model. The system implemented in the C++ programming language and embedded in a menu-driven X-windows user interface to the ACIS 3D Toolkit. (author)

  8. Design optimization of embedded ultrasonic transducers for concrete structures assessment.

    Dumoulin, Cédric; Deraemaeker, Arnaud

    2017-08-01

    In the last decades, the field of structural health monitoring and damage detection has been intensively explored. Active vibration techniques allow to excite structures at high frequency vibrations which are sensitive to small damage. Piezoelectric PZT transducers are perfect candidates for such testing due to their small size, low cost and large bandwidth. Current ultrasonic systems are based on external piezoelectric transducers which need to be placed on two faces of the concrete specimen. The limited accessibility of in-service structures makes such an arrangement often impractical. An alternative is to embed permanently low-cost transducers inside the structure. Such types of transducers have been applied successfully for the in-situ estimation of the P-wave velocity in fresh concrete, and for crack monitoring. Up to now, the design of such transducers was essentially based on trial and error, or in a few cases, on the limitation of the acoustic impedance mismatch between the PZT and concrete. In the present study, we explore the working principles of embedded piezoelectric transducers which are found to be significantly different from external transducers. One of the major challenges concerning embedded transducers is to produce very low cost transducers. We show that a practical way to achieve this imperative is to consider the radial mode of actuation of bulk PZT elements. This is done by developing a simple finite element model of a piezoelectric transducer embedded in an infinite medium. The model is coupled with a multi-objective genetic algorithm which is used to design specific ultrasonic embedded transducers both for hard and fresh concrete monitoring. The results show the efficiency of the approach and a few designs are proposed which are optimal for hard concrete, fresh concrete, or both, in a given frequency band of interest. Copyright © 2017 Elsevier B.V. All rights reserved.

  9. Micro-precise spatiotemporal delivery system embedded in 3D printing for complex tissue regeneration.

    Tarafder, Solaiman; Koch, Alia; Jun, Yena; Chou, Conrad; Awadallah, Mary R; Lee, Chang H

    2016-04-25

    Three dimensional (3D) printing has emerged as an efficient tool for tissue engineering and regenerative medicine, given its advantages for constructing custom-designed scaffolds with tunable microstructure/physical properties. Here we developed a micro-precise spatiotemporal delivery system embedded in 3D printed scaffolds. PLGA microspheres (μS) were encapsulated with growth factors (GFs) and then embedded inside PCL microfibers that constitute custom-designed 3D scaffolds. Given the substantial difference in the melting points between PLGA and PCL and their low heat conductivity, μS were able to maintain its original structure while protecting GF's bioactivities. Micro-precise spatial control of multiple GFs was achieved by interchanging dispensing cartridges during a single printing process. Spatially controlled delivery of GFs, with a prolonged release, guided formation of multi-tissue interfaces from bone marrow derived mesenchymal stem/progenitor cells (MSCs). To investigate efficacy of the micro-precise delivery system embedded in 3D printed scaffold, temporomandibular joint (TMJ) disc scaffolds were fabricated with micro-precise spatiotemporal delivery of CTGF and TGFβ3, mimicking native-like multiphase fibrocartilage. In vitro, TMJ disc scaffolds spatially embedded with CTGF/TGFβ3-μS resulted in formation of multiphase fibrocartilaginous tissues from MSCs. In vivo, TMJ disc perforation was performed in rabbits, followed by implantation of CTGF/TGFβ3-μS-embedded scaffolds. After 4 wks, CTGF/TGFβ3-μS embedded scaffolds significantly improved healing of the perforated TMJ disc as compared to the degenerated TMJ disc in the control group with scaffold embedded with empty μS. In addition, CTGF/TGFβ3-μS embedded scaffolds significantly prevented arthritic changes on TMJ condyles. In conclusion, our micro-precise spatiotemporal delivery system embedded in 3D printing may serve as an efficient tool to regenerate complex and inhomogeneous tissues.

  10. Controllable edge feature sharpening for dental applications.

    Fan, Ran; Jin, Xiaogang

    2014-01-01

    This paper presents a new approach to sharpen blurred edge features in scanned tooth preparation surfaces generated by structured-light scanners. It aims to efficiently enhance the edge features so that the embedded feature lines can be easily identified in dental CAD systems, and to avoid unnatural oversharpening geometry. We first separate the feature regions using graph-cut segmentation, which does not require a user-defined threshold. Then, we filter the face normal vectors to propagate the geometry from the smooth region to the feature region. In order to control the degree of the sharpness, we propose a feature distance measure which is based on normal tensor voting. Finally, the vertex positions are updated according to the modified face normal vectors. We have applied the approach to scanned tooth preparation models. The results show that the blurred edge features are enhanced without unnatural oversharpening geometry.

  11. Controllable Edge Feature Sharpening for Dental Applications

    Ran Fan

    2014-01-01

    Full Text Available This paper presents a new approach to sharpen blurred edge features in scanned tooth preparation surfaces generated by structured-light scanners. It aims to efficiently enhance the edge features so that the embedded feature lines can be easily identified in dental CAD systems, and to avoid unnatural oversharpening geometry. We first separate the feature regions using graph-cut segmentation, which does not require a user-defined threshold. Then, we filter the face normal vectors to propagate the geometry from the smooth region to the feature region. In order to control the degree of the sharpness, we propose a feature distance measure which is based on normal tensor voting. Finally, the vertex positions are updated according to the modified face normal vectors. We have applied the approach to scanned tooth preparation models. The results show that the blurred edge features are enhanced without unnatural oversharpening geometry.

  12. Multithreading for Embedded Reconfigurable Multicore Systems

    Zaykov, P.G.

    2014-01-01

    In this dissertation, we address the problem of performance efficient multithreading execution on heterogeneous multicore embedded systems. By heterogeneous multicore embedded systems we refer to those, which have real-time requirements and consist of processor tiles with General Purpose Processor

  13. Multithreading for embedded reconfigurable multicore systems

    Zaykov, P.G.

    2014-01-01

    In this dissertation, we address the problem of performance efficient multithreading execution on heterogeneous multicore embedded systems. By heterogeneous multicore embedded systems we refer to those, which have real-time requirements and consist of processor tiles with General Purpose Processor

  14. TTCN-3 for distributed testing embedded systems

    Blom, S.C.C.; Deiß, T.; Ioustinova, N.; Kontio, A.; Pol, van de J.C.; Rennoch, A.; Sidorova, N.; Virbitskaite, I.; Voronkov, A.

    2007-01-01

    Abstract. TTCN-3 is a standardized language for specifying and executing test suites that is particularly popular for testing embedded systems. Prior to testing embedded software in a target environment, the software is usually tested in the host environment. Executing in the host environment often

  15. Verification and Performance Analysis for Embedded Systems

    Larsen, Kim Guldstrand

    2009-01-01

    This talk provides a thorough tutorial of the UPPAAL tool suite for, modeling, simulation, verification, optimal scheduling, synthesis, testing and performance analysis of embedded and real-time systems.......This talk provides a thorough tutorial of the UPPAAL tool suite for, modeling, simulation, verification, optimal scheduling, synthesis, testing and performance analysis of embedded and real-time systems....

  16. Teaching Embedded System Concepts for Technological Literacy

    Winzker, M.; Schwandt, A.

    2011-01-01

    A basic understanding of technology is recognized as important knowledge even for students not connected with engineering and computer science. This paper shows that embedded system concepts can be taught in a technological literacy course. An embedded system teaching block that has been used in an electronics module for non-engineers is…

  17. Embedding methods for phi4-interaction

    Hanckowiak, J.

    1985-01-01

    The idea of embedding a given theory in a class of similar theories is applied to quantum field theory in the case of phi 4 -interaction to derive different equations for the generating functional. The number of possible embeddings has been restricted by demanding that for the defined projections of the generating functional a closed system of equations be obtained

  18. Embedded Java security security for mobile devices

    Debbabi, Mourad; Talhi, Chamseddine

    2007-01-01

    Java brings more functionality and versatility to the world of mobile devices, but it also introduces new security threats. This book contains a presentation of embedded Java security and presents the main components of embedded Java. It gives an idea of the platform architecture and is useful for researchers and practitioners.

  19. Heterogeneous Embedded Real-Time Systems Environment

    2003-12-01

    AFRL-IF-RS-TR-2003-290 Final Technical Report December 2003 HETEROGENEOUS EMBEDDED REAL - TIME SYSTEMS ENVIRONMENT Integrated...HETEROGENEOUS EMBEDDED REAL - TIME SYSTEMS ENVIRONMENT 6. AUTHOR(S) Cosmo Castellano and James Graham 5. FUNDING NUMBERS C - F30602-97-C-0259

  20. Embedded Systems Design with 8051 Microcontrollers

    Karakahayov, Zdravko; Winther, Ole; Christensen, Knud Smed

    Textbook on embedded microcontrollers. Example microcontroller family: Intel 8051 with special emphasis on Philips 80C552. Structure, design examples and programming in C and assembler. Hardware - software codesign. EProm emulator.......Textbook on embedded microcontrollers. Example microcontroller family: Intel 8051 with special emphasis on Philips 80C552. Structure, design examples and programming in C and assembler. Hardware - software codesign. EProm emulator....