Bio-inspired passive actuator simulating an abalone shell mechanism for structural control

International Nuclear Information System (INIS)

Yang, Henry T Y; Lin, Chun-Hung; Bridges, Daniel; Randall, Connor J; Hansma, Paul K

2010-01-01

An energy dispersion mechanism called 'sacrificial bonds and hidden length', which is found in some biological systems, such as abalone shells and bones, is the inspiration for new strategies for structural control. Sacrificial bonds and hidden length can substantially increase the stiffness and enhance energy dissipation in the constituent molecules of abalone shells and bone. Having been inspired by the usefulness and effectiveness of such a mechanism, which has evolved over millions of years and countless cycles of evolutions, the authors employ the conceptual underpinnings of this mechanism to develop a bio-inspired passive actuator. This paper presents a fundamental method for optimally designing such bio-inspired passive actuators for structural control. To optimize the bio-inspired passive actuator, a simple method utilizing the force–displacement–velocity (FDV) plots based on LQR control is proposed. A linear regression approach is adopted in this research to find the initial values of the desired parameters for the bio-inspired passive actuator. The illustrative examples, conducted by numerical simulation with experimental validation, suggest that the bio-inspired passive actuator based on sacrificial bonds and hidden length may be comparable in performance to state-of-the-art semi-active actuators

Bio-inspired passive actuator simulating an abalone shell mechanism for structural control

Science.gov (United States)

Yang, Henry T. Y.; Lin, Chun-Hung; Bridges, Daniel; Randall, Connor J.; Hansma, Paul K.

2010-10-01

An energy dispersion mechanism called 'sacrificial bonds and hidden length', which is found in some biological systems, such as abalone shells and bones, is the inspiration for new strategies for structural control. Sacrificial bonds and hidden length can substantially increase the stiffness and enhance energy dissipation in the constituent molecules of abalone shells and bone. Having been inspired by the usefulness and effectiveness of such a mechanism, which has evolved over millions of years and countless cycles of evolutions, the authors employ the conceptual underpinnings of this mechanism to develop a bio-inspired passive actuator. This paper presents a fundamental method for optimally designing such bio-inspired passive actuators for structural control. To optimize the bio-inspired passive actuator, a simple method utilizing the force-displacement-velocity (FDV) plots based on LQR control is proposed. A linear regression approach is adopted in this research to find the initial values of the desired parameters for the bio-inspired passive actuator. The illustrative examples, conducted by numerical simulation with experimental validation, suggest that the bio-inspired passive actuator based on sacrificial bonds and hidden length may be comparable in performance to state-of-the-art semi-active actuators.

Material requirements for bio-inspired sensing systems

Science.gov (United States)

Biggins, Peter; Lloyd, Peter; Salmond, David; Kusterbeck, Anne

2008-10-01

The aim of developing bio-inspired sensing systems is to try and emulate the amazing sensitivity and specificity observed in the natural world. These capabilities have evolved, often for specific tasks, which provide the organism with an advantage in its fight to survive and prosper. Capabilities cover a wide range of sensing functions including vision, temperature, hearing, touch, taste and smell. For some functions, the capabilities of natural systems are still greater than that achieved by traditional engineering solutions; a good example being a dog's sense of smell. Furthermore, attempting to emulate aspects of biological optics, processing and guidance may lead to more simple and effective devices. A bio-inspired sensing system is much more than the sensory mechanism. A system will need to collect samples, especially if pathogens or chemicals are of interest. Other functions could include the provision of power, surfaces and receptors, structure, locomotion and control. In fact it is possible to conceive of a complete bio-inspired system concept which is likely to be radically different from more conventional approaches. This concept will be described and individual component technologies considered.

Operant Conditioning: A Minimal Components Requirement in Artificial Spiking Neurons Designed for Bio-Inspired Robot’s Controller

Directory of Open Access Journals (Sweden)

André eCyr

2014-07-01

Full Text Available We demonstrate the operant conditioning (OC learning process within a basic bio-inspired robot controller paradigm, using an artificial spiking neural network (ASNN with minimal component count as artificial brain. In biological agents, OC results in behavioral changes that are learned from the consequences of previous actions, using progressive prediction adjustment triggered by reinforcers. In a robotics context, virtual and physical robots may benefit from a similar learning skill when facing unknown environments with no supervision. In this work, we demonstrate that a simple ASNN can efficiently realise many OC scenarios. The elementary learning kernel that we describe relies on a few critical neurons, synaptic links and the integration of habituation and spike-timing dependent plasticity (STDP as learning rules. Using four tasks of incremental complexity, our experimental results show that such minimal neural component set may be sufficient to implement many OC procedures. Hence, with the described bio-inspired module, OC can be implemented in a wide range of robot controllers, including those with limited computational resources.

A biologically inspired neural net for trajectory formation and obstacle avoidance.

Science.gov (United States)

Glasius, R; Komoda, A; Gielen, S C

1996-06-01

In this paper we present a biologically inspired two-layered neural network for trajectory formation and obstacle avoidance. The two topographically ordered neural maps consist of analog neurons having continuous dynamics. The first layer, the sensory map, receives sensory information and builds up an activity pattern which contains the optimal solution (i.e. shortest path without collisions) for any given set of current position, target positions and obstacle positions. Targets and obstacles are allowed to move, in which case the activity pattern in the sensory map will change accordingly. The time evolution of the neural activity in the second layer, the motor map, results in a moving cluster of activity, which can be interpreted as a population vector. Through the feedforward connections between the two layers, input of the sensory map directs the movement of the cluster along the optimal path from the current position of the cluster to the target position. The smooth trajectory is the result of the intrinsic dynamics of the network only. No supervisor is required. The output of the motor map can be used for direct control of an autonomous system in a cluttered environment or for control of the actuators of a biological limb or robot manipulator. The system is able to reach a target even in the presence of an external perturbation. Computer simulations of a point robot and a multi-joint manipulator illustrate the theory.

Creating a Bio-Inspired Solution to Prevent Erosion

Science.gov (United States)

Reher, R.; Martinez, A.; Cola, J.; Frost, D.

2016-12-01

Through the study of geophysical sciences, lessons can be developed which allow for the introduction of bio-inspired design and art concepts to K-5 elementary students. Students are placed into an engineering mindset in which they must apply the concepts of bio-geotechnics to observe how we can use nature to prevent and abate erosion. Problems are staged for students using realistic engineering scenarios such as erosion prevention through biomimicry and the study of anchorage characteristics of root structures in regard to stability of soil. Specifically, a lesson is introduced where students research, learn, and present information about bio-inspired designs to understand these concepts. They lean how plant roots differ in size and shape to stabilize soil. In addition, students perform a series of hands-on experiments which demonstrate how bio-cements and roots can slow erosion.

4th International Conference on Innovations in Bio-Inspired Computing and Applications

CERN Document Server

Krömer, Pavel; Snášel, Václav

2014-01-01

This volume of Advances in Intelligent Systems and Computing contains accepted papers presented at IBICA2013, the 4th International Conference on Innovations in Bio-inspired Computing and Applications. The aim of IBICA 2013 was to provide a platform for world research leaders and practitioners, to discuss the full spectrum of current theoretical developments, emerging technologies, and innovative applications of Bio-inspired Computing. Bio-inspired Computing is currently one of the most exciting research areas, and it is continuously demonstrating exceptional strength in solving complex real life problems. The main driving force of the conference is to further explore the intriguing potential of Bio-inspired Computing. IBICA 2013 was held in Ostrava, Czech Republic and hosted by the VSB - Technical University of Ostrava.

Optic flow estimation on trajectories generated by bio-inspired closed-loop flight.

Science.gov (United States)

Shoemaker, Patrick A; Hyslop, Andrew M; Humbert, J Sean

2011-05-01

We generated panoramic imagery by simulating a fly-like robot carrying an imaging sensor, moving in free flight through a virtual arena bounded by walls, and containing obstructions. Flight was conducted under closed-loop control by a bio-inspired algorithm for visual guidance with feedback signals corresponding to the true optic flow that would be induced on an imager (computed by known kinematics and position of the robot relative to the environment). The robot had dynamics representative of a housefly-sized organism, although simplified to two-degree-of-freedom flight to generate uniaxial (azimuthal) optic flow on the retina in the plane of travel. Surfaces in the environment contained images of natural and man-made scenes that were captured by the moving sensor. Two bio-inspired motion detection algorithms and two computational optic flow estimation algorithms were applied to sequences of image data, and their performance as optic flow estimators was evaluated by estimating the mutual information between outputs and true optic flow in an equatorial section of the visual field. Mutual information for individual estimators at particular locations within the visual field was surprisingly low (less than 1 bit in all cases) and considerably poorer for the bio-inspired algorithms that the man-made computational algorithms. However, mutual information between weighted sums of these signals and comparable sums of the true optic flow showed significant increases for the bio-inspired algorithms, whereas such improvement did not occur for the computational algorithms. Such summation is representative of the spatial integration performed by wide-field motion-sensitive neurons in the third optic ganglia of flies.

Bio-inspired hybrid microelectrodes. A hybrid solution to improve long-term performance of chronic intracortical implants.

Directory of Open Access Journals (Sweden)

Sara eDe Faveri

2014-04-01

Full Text Available The use of implants that allow chronic electrical stimulation and recording in the brain of human patients is currently limited by a series of events that cause the deterioration over time of both the electrode surface and the surrounding tissue. The main reason of failure is the tissue inflammatory reaction that eventually causes neuronal loss and glial encapsulation, resulting in a progressive increase of the electrode-electrolyte impedance. Here, we describe a new method to create bio-inspired electrodes to mimic the mechanical properties and biological composition of the host tissue. This combination has a great potential to increase the implant lifetime by reducing tissue reaction and improving electrical coupling. Our method implies coating the electrode with reprogrammed neural or glial cells encapsulated within a hydrogel layer. We chose fibrin as a hydrogel and primary hippocampal neurons or astrocytes from rat brain as cellular layer. We demonstrate that fibrin coating is highly biocompatible, forms uniform coatings of controllable thickness, does not alter the electrochemical properties of the microelectrode and allows good quality recordings. Moreover, it reduces the amount of host reactive astrocytes - over time - compared to a bare wire and is fully reabsorbed by the surrounding tissue within 7 days after implantation, avoiding the common problem of hydrogels swelling. Both astrocytes and neurons could be successfully grown onto the electrode surface within the fibrin hydrogel without altering the electrochemical properties of the microelectrode. This bio-hybrid device has therefore a good potential to improve the electrical integration at the neuron-electrode interface and support the long-term success of neural prostheses.

International Conference on Artificial Neural Networks (ICANN)

CERN Document Server

Mladenov, Valeri; Kasabov, Nikola; Artificial Neural Networks : Methods and Applications in Bio-/Neuroinformatics

2015-01-01

The book reports on the latest theories on artificial neural networks, with a special emphasis on bio-neuroinformatics methods. It includes twenty-three papers selected from among the best contributions on bio-neuroinformatics-related issues, which were presented at the International Conference on Artificial Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013 (ICANN 2013). The book covers a broad range of topics concerning the theory and applications of artificial neural networks, including recurrent neural networks, super-Turing computation and reservoir computing, double-layer vector perceptrons, nonnegative matrix factorization, bio-inspired models of cell communities, Gestalt laws, embodied theory of language understanding, saccadic gaze shifts and memory formation, and new training algorithms for Deep Boltzmann Machines, as well as dynamic neural networks and kernel machines. It also reports on new approaches to reinforcement learning, optimal control of discrete time-delay systems, new al...

Bio-Inspired Sustainability Assessment for Building Product Development—Concept and Case Study

Directory of Open Access Journals (Sweden)

Rafael Horn

2018-01-01

Full Text Available Technological advancement culminating in a globalized economy has brought tremendous improvements for mankind in manifold respects but comes at the cost of alienation from nature. Human activities nowadays are unsustainable and cause severe damage especially in terms of global depletion and destabilization of natural systems but also harm its own social resources. In this paper, a sustainability assessment method is developed based on a bio-inspired sustainability framework that has been developed in the project TRR 141-C01 “The biomimetic promise.” It is aims at regaining the advantages of societal embeddedness in its environment through biological inspiration. The method is developed using a structured approach including requirement specification, description of the inventory models on bio-inspiration and sustainability assessment, creation of a bio-inspired sustainability assessment model and its validation. It is defined as an accompanying assessment for decision support, using a six-fold two-dimensional structure of social, economic and environmental functions and burdens. The method is applied and validated in 6 projects of TRR 141 and its applicability is exemplarily shown by the assessment of “Bio-flexi”, a biobased and biodegradable natural fiber reinforced plastic composite for indoor cladding applications. Based on the findings of the application the assessment method itself is proposed to be advanced towards an adaptive structure and a consequent outlook is provided.

Quality-of-service sensitivity to bio-inspired/evolutionary computational methods for intrusion detection in wireless ad hoc multimedia sensor networks

Science.gov (United States)

Hortos, William S.

2012-06-01

In the author's previous work, a cross-layer protocol approach to wireless sensor network (WSN) intrusion detection an identification is created with multiple bio-inspired/evolutionary computational methods applied to the functions of the protocol layers, a single method to each layer, to improve the intrusion-detection performance of the protocol over that of one method applied to only a single layer's functions. The WSN cross-layer protocol design embeds GAs, anti-phase synchronization, ACO, and a trust model based on quantized data reputation at the physical, MAC, network, and application layer, respectively. The construct neglects to assess the net effect of the combined bioinspired methods on the quality-of-service (QoS) performance for "normal" data streams, that is, streams without intrusions. Analytic expressions of throughput, delay, and jitter, coupled with simulation results for WSNs free of intrusion attacks, are the basis for sensitivity analyses of QoS metrics for normal traffic to the bio-inspired methods.

Bio-inspired vision

International Nuclear Information System (INIS)

Posch, C

2012-01-01

Nature still outperforms the most powerful computers in routine functions involving perception, sensing and actuation like vision, audition, and motion control, and is, most strikingly, orders of magnitude more energy-efficient than its artificial competitors. The reasons for the superior performance of biological systems are subject to diverse investigations, but it is clear that the form of hardware and the style of computation in nervous systems are fundamentally different from what is used in artificial synchronous information processing systems. Very generally speaking, biological neural systems rely on a large number of relatively simple, slow and unreliable processing elements and obtain performance and robustness from a massively parallel principle of operation and a high level of redundancy where the failure of single elements usually does not induce any observable system performance degradation. In the late 1980's, Carver Mead demonstrated that silicon VLSI technology can be employed in implementing ''neuromorphic'' circuits that mimic neural functions and fabricating building blocks that work like their biological role models. Neuromorphic systems, as the biological systems they model, are adaptive, fault-tolerant and scalable, and process information using energy-efficient, asynchronous, event-driven methods. In this paper, some basics of neuromorphic electronic engineering and its impact on recent developments in optical sensing and artificial vision are presented. It is demonstrated that bio-inspired vision systems have the potential to outperform conventional, frame-based vision acquisition and processing systems in many application fields and to establish new benchmarks in terms of redundancy suppression/data compression, dynamic range, temporal resolution and power efficiency to realize advanced functionality like 3D vision, object tracking, motor control, visual feedback loops, etc. in real-time. It is argued that future artificial vision systems

Exploring Creativity in the Bio-Inspired Design Process

DEFF Research Database (Denmark)

Anggakara, K.; Aksdal, T.; Onarheim, Balder

2015-01-01

The growing interest in the of field bio-inspired design has been driven by the acknowledgement that inspiration from nature can serve as a valuable source of innovation. As an emerging approach, there has been a focus on building a principled methodology to address the challenges that arise...

Introducing Students to Bio-Inspiration and Biomimetic Design: A Workshop Experience

Science.gov (United States)

Santulli, Carlo; Langella, Carla

2011-01-01

In recent years, bio-inspired approach to design has gained considerable interest between designers, engineers and end-users. However, there are difficulties in introducing bio-inspiration concepts in the university curriculum in that they involve multi-disciplinary work, which can only possibly be successfully delivered by a team with integrated…

Separation prediction in two dimensional boundary layer flows using artificial neural networks

International Nuclear Information System (INIS)

Sabetghadam, F.; Ghomi, H.A.

2003-01-01

In this article, the ability of artificial neural networks in prediction of separation in steady two dimensional boundary layer flows is studied. Data for network training is extracted from numerical solution of an ODE obtained from Von Karman integral equation with approximate one parameter Pohlhousen velocity profile. As an appropriate neural network, a two layer radial basis generalized regression artificial neural network is used. The results shows good agreements between the overall behavior of the flow fields predicted by the artificial neural network and the actual flow fields for some cases. The method easily can be extended to unsteady separation and turbulent as well as compressible boundary layer flows. (author)

Erosion resistance of pipe bends with bio-inspired internal surfaces

Science.gov (United States)

Zhang, Chengchun; Matar, Omar

2013-11-01

Guided by the structure of a shell surface, a bio-inspired surface is proposed to enhance the erosion resistance of pipe bends carrying crude-oil and sand in the turbulent flow regime. A comparison of the erosion rate between a smooth bend and the bio-inspired one is carried out using numerical simulations: large eddy simulations are used to simulate turbulence, and these are coupled to a discrete element method for the solid particles. The results indicate that the bio-inspired surface can control effectively the liquid-solid flow near the wall, and decrease the particle-wall force. This, then, leads to a reduction in the erosion rate brought about by the sand transported by the crude-oil in the pipe bend. The China Scholarship Council is gratefully acknowledged.

Electrochemical construction of a bio-inspired micro/nano-textured structure with cell-sized microhole arrays on biomedical titanium to enhance bioactivity

International Nuclear Information System (INIS)

Liang, Jianhe; Song, Ran; Huang, Qiaoling; Yang, Yun; Lin, Longxiang; Zhang, Yanmei; Jiang, Pinliang; Duan, Hongping; Dong, Xiang; Lin, Changjian

2015-01-01

Highlights: • The bio-inspired structure mimicked mulit-level structures of natural bone. • Ordered cell-sized microhole arrays were employed as microscale structure. • High surface roughness and superhydrophilicity were achieved on the titanium surface. • The bio-inspired titanium surface showed superior ability of biomineralization. • Cell responses were enhanced on the bio-inspired micro/nano-texutred surface. - Abstract: Biomimetic surface design of medical implants is vitally crucial to improve cellular responses and the integration of tissue onto materials. In this study, a novel hierarchical cell-sized microhole array combined with a nano-network structure was fabricated on a medical titanium surface to mimic multi-level bone structure. A three-step procedure was developed as follows: 1) electrochemical self-organization of etching on titanium substrate to create highly ordered cell-sized microhole arrays, 2) suitable dual acid etching to increase the roughness of the microholes, and then 3) electrochemical anodization in a NaOH electrolyte to construct a nano-network porous titania layer on the above micro-roughened surface. The bio-inspired micro/nano-textured structure presented the enhanced wettability and superhydrophilicity. The ability of in vitro biomineralization and corrosion resistance of the bio-inspired micro/nano-textured structure were enhanced after annealing treatment. More importantly, the bio-inspired micro/nano-textured structure on the titanium surface possessed a favourable interfacial environment to enhance attachment and proliferation of human osteoblast-like MG63 cells. All of the results demonstrated that such a bio-inspired surface of micro/nano-textured porous TiO 2 is a most promising candidate for the next generation of titanium implants

Bio-inspired polymeric patterns with enhanced wear durability for microsystem applications

International Nuclear Information System (INIS)

Singh, R. Arvind; Siyuan, L.; Satyanarayana, N.; Kustandi, T.S.; Sinha, Sujeet K.

2011-01-01

At micro/nano-scale, friction force dominates at the interface between bodies moving in relative motion and severely affects their smooth operation. This effect limits the performance of microsystem devices such as micro-electro-mechanical systems (MEMS). In addition, friction force also leads to material removal or wear and thereby reduces the durability i.e. the useful operating life of the devices. In this work, we fabricated bio-inspired polymeric patterns for tribological applications. Inspired by the surface features on lotus leaves namely, the protuberances and wax, SU-8 polymeric films spin-coated on silicon wafers were topographically and chemically modified. For topographical modification, micro-scale patterns were fabricated using nanoimprint lithography and for chemical modification, the micro-patterns were coated with perfluoropolyether nanolubricant. Tribological investigation of the bio-inspired patterns revealed that the friction coefficients reduced significantly and the wear durability increased by several orders. In order to enhance the wear durability much further, the micro-patterns were exposed to argon/oxygen plasma and were subsequently coated with the perfluoropolyether nanolubricant. Bio-inspired patterns with enhanced wear durability, such as the ones investigated in the current work, have potential tribological applications in MEMS/Bio-MEMS actuator-based devices. Highlights: →Bio-inspired polymeric patterns for tribological applications in microsystems. →Novel surface modification for the patterns to enhance tribological properties. →Patterns show low friction properties and extremely high wear durability.

Flow around an autonomous underwater vehicle with bio-inspired coating

Science.gov (United States)

Watkins, Scott; Montoya-Segnini, Jose; Bocanegra Evans, Humberto; Curet, Oscar; Gorumlu, Serdar; Aksak, Burak; Kazemi, Amirkhosro; Chamorro, Leonardo; Castillo, Luciano

2017-11-01

Flow separation plays a major factor in the form drag of a moving object. In particular, suppressing or reducing flow separation is critical in the energy expenditure of autonomous underwater vehicles. Previous research suggests that bio-inspired micro-fibrillar structures are capable of reducing the boundary layer separation in a turbulent flow. Here, we present laboratory measurements using PIV near the wall and in the wake of two submersible vessel models; one had a coating composed of ordered fibers, and the other had smooth walls. Flow characterization with planar PIV included the presence or absence of a tail fin at multiple angles of attack of the vessels. Preliminary results reveal changes of the flow in the wake of the vessel with coating resulting in lower or similar velocity deficit in the wake compared to the smooth vessel.

Crickets as bio-inspiration for MEMS-based flow-sensing

NARCIS (Netherlands)

Krijnen, Gijsbertus J.M.; Droogendijk, H.; Dagamseh, A.M.K.; Jaganatharaja, R.K.; Casas, Jerome

2014-01-01

MEMS offers exciting possibilities for the fabrication of bio-inspired mechanosensors. Over the last few years, we have been working on cricket- inspired hair-sensor arrays for spatio-temporal flow-field observations (i.e. flow camera) and source localisation. Whereas making flow-sensors as energy

Bio-Inspired Autonomous Communications Systems with Anomaly Detection Monitoring, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — We propose to develop and demonstrate BioComm, a bio-inspired autonomous communications system (ACS) aimed at dynamically reconfiguring and redeploying autonomous...

7th International Conference on Bio-Inspired Computing : Theories and Applications

CERN Document Server

Singh, Pramod; Deep, Kusum; Pant, Millie; Nagar, Atulya

2013-01-01

The book is a collection of high quality peer reviewed research papers presented in Seventh International Conference on Bio-Inspired Computing (BIC-TA 2012) held at ABV-IIITM Gwalior, India. These research papers provide the latest developments in the broad area of "Computational Intelligence". The book discusses wide variety of industrial, engineering and scientific applications of nature/bio-inspired computing and presents invited papers from the inventors/originators of novel computational techniques.

Autonomic networking-on-chip bio-inspired specification, development, and verification

CERN Document Server

Cong-Vinh, Phan

2011-01-01

Despite the growing mainstream importance and unique advantages of autonomic networking-on-chip (ANoC) technology, Autonomic Networking-On-Chip: Bio-Inspired Specification, Development, and Verification is among the first books to evaluate research results on formalizing this emerging NoC paradigm, which was inspired by the human nervous system. The FIRST Book to Assess Research Results, Opportunities, & Trends in ""BioChipNets"" The third book in the Embedded Multi-Core Systems series from CRC Press, this is an advanced technical guide and reference composed of contributions from prominent re

Bio-inspired spiking neural network for nonlinear systems control.

Science.gov (United States)

Pérez, Javier; Cabrera, Juan A; Castillo, Juan J; Velasco, Juan M

2018-08-01

Spiking neural networks (SNN) are the third generation of artificial neural networks. SNN are the closest approximation to biological neural networks. SNNs make use of temporal spike trains to command inputs and outputs, allowing a faster and more complex computation. As demonstrated by biological organisms, they are a potentially good approach to designing controllers for highly nonlinear dynamic systems in which the performance of controllers developed by conventional techniques is not satisfactory or difficult to implement. SNN-based controllers exploit their ability for online learning and self-adaptation to evolve when transferred from simulations to the real world. SNN's inherent binary and temporary way of information codification facilitates their hardware implementation compared to analog neurons. Biological neural networks often require a lower number of neurons compared to other controllers based on artificial neural networks. In this work, these neuronal systems are imitated to perform the control of non-linear dynamic systems. For this purpose, a control structure based on spiking neural networks has been designed. Particular attention has been paid to optimizing the structure and size of the neural network. The proposed structure is able to control dynamic systems with a reduced number of neurons and connections. A supervised learning process using evolutionary algorithms has been carried out to perform controller training. The efficiency of the proposed network has been verified in two examples of dynamic systems control. Simulations show that the proposed control based on SNN exhibits superior performance compared to other approaches based on Neural Networks and SNNs. Copyright © 2018 Elsevier Ltd. All rights reserved.

Methodology for Designing and Developing a New Ultra-Wideband Antenna Based on Bio-Inspired Optimization Techniques

Science.gov (United States)

2017-11-01

on Bio -Inspired Optimization Techniques by Canh Ly, Nghia Tran, and Ozlem Kilic Approved for public release; distribution is...Research Laboratory Methodology for Designing and Developing a New Ultra-Wideband Antenna Based on Bio -Inspired Optimization Techniques by...SUBTITLE Methodology for Designing and Developing a New Ultra-Wideband Antenna Based on Bio -Inspired Optimization Techniques 5a. CONTRACT NUMBER

3D Printing of Bio-inspired surfaces

DEFF Research Database (Denmark)

Méndez Ribó, Macarena; Islam, Aminul

The ability of the gecko to scurry across smooth or rough surfaces, regardless of inclination (vertical or even upside down), has been traced to the multiscale hierarchical structures of the gecko toe [1 - 3]. Considering all the strategies to manufacture bio-inspired surfaces, the most common is...

A highly efficient sharp-interface immersed boundary method with adaptive mesh refinement for bio-inspired flow simulations

Science.gov (United States)

Deng, Xiaolong; Dong, Haibo

2017-11-01

Developing a high-fidelity, high-efficiency numerical method for bio-inspired flow problems with flow-structure interaction is important for understanding related physics and developing many bio-inspired technologies. To simulate a fast-swimming big fish with multiple finlets or fish schooling, we need fine grids and/or a big computational domain, which are big challenges for 3-D simulations. In current work, based on the 3-D finite-difference sharp-interface immersed boundary method for incompressible flows (Mittal et al., JCP 2008), we developed an octree-like Adaptive Mesh Refinement (AMR) technique to enhance the computational ability and increase the computational efficiency. The AMR is coupled with a multigrid acceleration technique and a MPI +OpenMP hybrid parallelization. In this work, different AMR layers are treated separately and the synchronization is performed in the buffer regions and iterations are performed for the convergence of solution. Each big region is calculated by a MPI process which then uses multiple OpenMP threads for further acceleration, so that the communication cost is reduced. With these acceleration techniques, various canonical and bio-inspired flow problems with complex boundaries can be simulated accurately and efficiently. This work is supported by the MURI Grant Number N00014-14-1-0533 and NSF Grant CBET-1605434.

Bio-Inspired Interaction Control of Robotic Machines for Motor Therapy

OpenAIRE

Zollo, Loredana; Formica, Domenico; Guglielmelli, Eugenio

2007-01-01

In this chapter basic criteria for the design and implementation of interaction control of robotic machines for motor therapy have been briefly introduced and two bio-inspired compliance control laws developed by the authors to address requirements coming from this specific application field have been presented. The two control laws are named the coactivation-based compliance control in the joint space and the torque-dependent compliance control in the joint space, respectively. They try to o...

Learning text representation using recurrent convolutional neural network with highway layers

OpenAIRE

Wen, Ying; Zhang, Weinan; Luo, Rui; Wang, Jun

2016-01-01

Recently, the rapid development of word embedding and neural networks has brought new inspiration to various NLP and IR tasks. In this paper, we describe a staged hybrid model combining Recurrent Convolutional Neural Networks (RCNN) with highway layers. The highway network module is incorporated in the middle takes the output of the bi-directional Recurrent Neural Network (Bi-RNN) module in the first stage and provides the Convolutional Neural Network (CNN) module in the last stage with the i...

Microneedle, bio-microneedle and bio-inspired microneedle: A review.

Science.gov (United States)

Ma, Guojun; Wu, Chengwei

2017-04-10

Microneedles (MNs) are micro-scale needles used for drug delivery and other targets. Micro-scale size endows them with many advantages over hypodermic needles, including painlessness, minimal invasiveness and convenient operation, but it may also lead to risk of mechanical failures, which should be prevented in the clinical applications of MNs. The objective of this review is mainly to introduce studies on the mechanics problems with respect to MNs. Firstly, the basic knowledge of MNs is introduced in brief, so that readers can understand the basic characteristics of MNs. Secondly, researches on inserting behavior and mechanical performances of MNs are discussed. Thirdly, literatures on the drug delivery and the pain resulted from the insertion of MNs are overviewed. Finally, some bio-microneedles and bio-inspired MNs are introduced. Copyright © 2017 Elsevier B.V. All rights reserved.

Bio-Inspired Asynchronous Pixel Event Tricolor Vision Sensor.

Science.gov (United States)

Lenero-Bardallo, Juan Antonio; Bryn, D H; Hafliger, Philipp

2014-06-01

This article investigates the potential of the first ever prototype of a vision sensor that combines tricolor stacked photo diodes with the bio-inspired asynchronous pixel event communication protocol known as Address Event Representation (AER). The stacked photo diodes are implemented in a 22 × 22 pixel array in a standard STM 90 nm CMOS process. Dynamic range is larger than 60 dB and pixels fill factor is 28%. The pixels employ either simple pulse frequency modulation (PFM) or a Time-to-First-Spike (TFS) mode. A heuristic linear combination of the chip's inherent pseudo colors serves to approximate RGB color representation. Furthermore, the sensor outputs can be processed to represent the radiation in the near infrared (NIR) band without employing external filters, and to color-encode direction of motion due to an asymmetry in the update rates of the different diode layers.

AER synthetic generation in hardware for bio-inspired spiking systems

Science.gov (United States)

Linares-Barranco, Alejandro; Linares-Barranco, Bernabe; Jimenez-Moreno, Gabriel; Civit-Balcells, Anton

2005-06-01

Address Event Representation (AER) is an emergent neuromorphic interchip communication protocol that allows for real-time virtual massive connectivity between huge number neurons located on different chips. By exploiting high speed digital communication circuits (with nano-seconds timings), synaptic neural connections can be time multiplexed, while neural activity signals (with mili-seconds timings) are sampled at low frequencies. Also, neurons generate 'events' according to their activity levels. More active neurons generate more events per unit time, and access the interchip communication channel more frequently, while neurons with low activity consume less communication bandwidth. When building multi-chip muti-layered AER systems it is absolutely necessary to have a computer interface that allows (a) to read AER interchip traffic into the computer and visualize it on screen, and (b) convert conventional frame-based video stream in the computer into AER and inject it at some point of the AER structure. This is necessary for test and debugging of complex AER systems. This paper addresses the problem of converting, in a computer, a conventional frame-based video stream into the spike event based representation AER. There exist several proposed software methods for synthetic generation of AER for bio-inspired systems. This paper presents a hardware implementation for one method, which is based on Linear-Feedback-Shift-Register (LFSR) pseudo-random number generation. The sequence of events generated by this hardware, which follows a Poisson distribution like a biological neuron, has been reconstructed using two AER integrator cells. The error of reconstruction for a set of images that produces different traffic loads of event in the AER bus is used as evaluation criteria. A VHDL description of the method, that includes the Xilinx PCI Core, has been implemented and tested using a general purpose PCI-AER board. This PCI-AER board has been developed by authors, and uses

Design and Analysis of a Bio-Inspired Wire-Driven Multi-Section Flexible Robot

OpenAIRE

Li, Zheng; Du, Ruxu

2013-01-01

This paper presents a bio-inspired wire-driven multi-section flexible robot. It is inspired by the snake skeleton and octopus arm muscle arrangements. The robot consists of three sections and each section is made up of several identical vertebras, which are articulated by both spherical joints and a flexible backbone. Each section is driven by two groups of wires, controlling the bending motion in X and Y directions. This design integrates the serpentine robots' structure and the continuum ro...

A Bio-Inspired QoS-Oriented Handover Model in Heterogeneous Wireless Networks

Directory of Open Access Journals (Sweden)

Daxin Tian

2014-01-01

Full Text Available We propose a bio-inspired model for making handover decision in heterogeneous wireless networks. It is based on an extended attractor selection model, which is biologically inspired by the self-adaptability and robustness of cellular response to the changes in dynamic environments. The goal of the proposed model is to guarantee multiple terminals’ satisfaction by meeting the QoS requirements of those terminals’ applications, and this model also attempts to ensure the fairness of network resources allocation, in the meanwhile, to enable the QoS-oriented handover decision adaptive to dynamic wireless environments. Some numerical simulations are preformed to validate our proposed bio-inspired model in terms of adaptive attractor selection in different noisy environments. And the results of some other simulations prove that the proposed handover scheme can adapt terminals’ network selection to the varying wireless environment and benefits the QoS of multiple terminal applications simultaneously and automatically. Furthermore, the comparative analysis also shows that the bio-inspired model outperforms the utility function based handover decision scheme in terms of ensuring a better QoS satisfaction and a better fairness of network resources allocation in dynamic heterogeneous wireless networks.

Spontaneous water filtration of bio-inspired membrane

Science.gov (United States)

Kim, Kiwoong; Kim, Hyejeong; Lee, Sang Joon

2016-11-01

Water is one of the most important elements for plants, because it is essential for various metabolic activities. Thus, water management systems of vascular plants, such as water collection and water filtration have been optimized through a long history. In this view point, bio-inspired technologies can be developed by mimicking the nature's strategies for the survival of the fittest. However, most of the underlying biophysical features of the optimized water management systems remain unsolved In this study, the biophysical characteristics of water filtration phenomena in the roots of mangrove are experimentally investigated. To understand water-filtration features of the mangrove, the morphological structures of its roots are analyzed. The electrokinetic properties of the root surface are also examined. Based on the quantitatively analyzed information, filtration of sodium ions in the roots are visualized. Motivated by this mechanism, spontaneous desalination mechanism in the root of mangrove is proposed by combining the electrokinetics and hydrodynamic transportation of ions. This study would be helpful for understanding the water-filtration mechanism of the roots of mangrove and developing a new bio-inspired desalination technology. This research was financially supported by the National Research Foundation (NRF) of Korea (Contract Grant Number: 2008-0061991).

Compressive Sensing Based Bio-Inspired Shape Feature Detection CMOS Imager

Science.gov (United States)

Duong, Tuan A. (Inventor)

2015-01-01

A CMOS imager integrated circuit using compressive sensing and bio-inspired detection is presented which integrates novel functions and algorithms within a novel hardware architecture enabling efficient on-chip implementation.

Touchable Computing: Computing-Inspired Bio-Detection.

Science.gov (United States)

Chen, Yifan; Shi, Shaolong; Yao, Xin; Nakano, Tadashi

2017-12-01

We propose a new computing-inspired bio-detection framework called touchable computing (TouchComp). Under the rubric of TouchComp, the best solution is the cancer to be detected, the parameter space is the tissue region at high risk of malignancy, and the agents are the nanorobots loaded with contrast medium molecules for tracking purpose. Subsequently, the cancer detection procedure (CDP) can be interpreted from the computational optimization perspective: a population of externally steerable agents (i.e., nanorobots) locate the optimal solution (i.e., cancer) by moving through the parameter space (i.e., tissue under screening), whose landscape (i.e., a prescribed feature of tissue environment) may be altered by these agents but the location of the best solution remains unchanged. One can then infer the landscape by observing the movement of agents by applying the "seeing-is-sensing" principle. The term "touchable" emphasizes the framework's similarity to controlling by touching the screen with a finger, where the external field for controlling and tracking acts as the finger. Given this analogy, we aim to answer the following profound question: can we look to the fertile field of computational optimization algorithms for solutions to achieve effective cancer detection that are fast, accurate, and robust? Along this line of thought, we consider the classical particle swarm optimization (PSO) as an example and propose the PSO-inspired CDP, which differs from the standard PSO by taking into account realistic in vivo propagation and controlling of nanorobots. Finally, we present comprehensive numerical examples to demonstrate the effectiveness of the PSO-inspired CDP for different blood flow velocity profiles caused by tumor-induced angiogenesis. The proposed TouchComp bio-detection framework may be regarded as one form of natural computing that employs natural materials to compute.

Learning behavior and temporary minima of two-layer neural networks

NARCIS (Netherlands)

Annema, Anne J.; Hoen, Klaas; Hoen, Klaas; Wallinga, Hans

1994-01-01

This paper presents a mathematical analysis of the occurrence of temporary minima during training of a single-output, two-layer neural network, with learning according to the back-propagation algorithm. A new vector decomposition method is introduced, which simplifies the mathematical analysis of

Bio Inspired Algorithms in Single and Multiobjective Reliability Optimization

DEFF Research Database (Denmark)

Madsen, Henrik; Albeanu, Grigore; Burtschy, Bernard

2014-01-01

Non-traditional search and optimization methods based on natural phenomena have been proposed recently in order to avoid local or unstable behavior when run towards an optimum state. This paper describes the principles of bio inspired algorithms and reports on Migration Algorithms and Bees...

Predicting Student Academic Performance: A Comparison of Two Meta-Heuristic Algorithms Inspired by Cuckoo Birds for Training Neural Networks

Directory of Open Access Journals (Sweden)

Jeng-Fung Chen

2014-10-01

Full Text Available Predicting student academic performance with a high accuracy facilitates admission decisions and enhances educational services at educational institutions. This raises the need to propose a model that predicts student performance, based on the results of standardized exams, including university entrance exams, high school graduation exams, and other influential factors. In this study, an approach to the problem based on the artificial neural network (ANN with the two meta-heuristic algorithms inspired by cuckoo birds and their lifestyle, namely, Cuckoo Search (CS and Cuckoo Optimization Algorithm (COA is proposed. In particular, we used previous exam results and other factors, such as the location of the student’s high school and the student’s gender as input variables, and predicted the student academic performance. The standard CS and standard COA were separately utilized to train the feed-forward network for prediction. The algorithms optimized the weights between layers and biases of the neuron network. The simulation results were then discussed and analyzed to investigate the prediction ability of the neural network trained by these two algorithms. The findings demonstrated that both CS and COA have potential in training ANN and ANN-COA obtained slightly better results for predicting student academic performance in this case. It is expected that this work may be used to support student admission procedures and strengthen the service system in educational institutions.

Bio-Inspired Design Approach Analysis: A Case Study of Antoni Gaudi and Santiago Calatrava

OpenAIRE

Marzieh Imani

2017-01-01

Antoni Gaudi and Santiago Calatrava have reputation for designing bio-inspired creative and technical buildings. Even though they have followed different independent approaches towards design, the source of bio-inspiration seems to be common. Taking a closer look at their projects reveals that Calatrava has been influenced by Gaudi in terms of interpreting nature and applying natural principles into the design process. This research firstly discusses the dialogue between Biomimicry and archit...

Locomotion Dynamics for Bio-inspired Robots with Soft Appendages: Application to Flapping Flight and Passive Swimming

Science.gov (United States)

Boyer, Frédéric; Porez, Mathieu; Morsli, Ferhat; Morel, Yannick

2017-08-01

In animal locomotion, either in fish or flying insects, the use of flexible terminal organs or appendages greatly improves the performance of locomotion (thrust and lift). In this article, we propose a general unified framework for modeling and simulating the (bio-inspired) locomotion of robots using soft organs. The proposed approach is based on the model of Mobile Multibody Systems (MMS). The distributed flexibilities are modeled according to two major approaches: the Floating Frame Approach (FFA) and the Geometrically Exact Approach (GEA). Encompassing these two approaches in the Newton-Euler modeling formalism of robotics, this article proposes a unique modeling framework suited to the fast numerical integration of the dynamics of a MMS in both the FFA and the GEA. This general framework is applied on two illustrative examples drawn from bio-inspired locomotion: the passive swimming in von Karman Vortex Street, and the hovering flight with flexible flapping wings.

Robust, Self-Contained and Bio-Inspired Shear Sensor Array, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — The proposed innovation is a robust, bio-inspired, and self-contained sensor array for the measurement of shear stress. The proposed system uses commercially...

Two-Layer Feedback Neural Networks with Associative Memories

International Nuclear Information System (INIS)

Gui-Kun, Wu; Hong, Zhao

2008-01-01

We construct a two-layer feedback neural network by a Monte Carlo based algorithm to store memories as fixed-point attractors or as limit-cycle attractors. Special attention is focused on comparing the dynamics of the network with limit-cycle attractors and with fixed-point attractors. It is found that the former has better retrieval property than the latter. Particularly, spurious memories may be suppressed completely when the memories are stored as a long-limit cycle. Potential application of limit-cycle-attractor networks is discussed briefly. (general)

S-Layer Based Bio-Imprinting - Synthetic S-Layer Polymers

Science.gov (United States)

2015-07-09

AFRL-OSR-VA-TR-2015-0161 S-Layer Based Bio- Imprinting - Synthetic S-Layer Polymers Dietmar Pum ZENTRUM FUER NANOBIOTECHNOLOGIE Final Report 07/09...COVERED (From - To)      01-06-2012 to 31-05-2015 4.  TITLE AND SUBTITLE S-Layer Based Bio- Imprinting - Synthetic S-Layer Polymers 5a.  CONTRACT...technology for the fabrication of nano patterned thin film imprints by using functional S-layer protein arrays as templates. The unique feature of

A bio-inspired apposition compound eye machine vision sensor system

International Nuclear Information System (INIS)

Davis, J D; Barrett, S F; Wright, C H G; Wilcox, M

2009-01-01

The Wyoming Information, Signal Processing, and Robotics Laboratory is developing a wide variety of bio-inspired vision sensors. We are interested in exploring the vision system of various insects and adapting some of their features toward the development of specialized vision sensors. We do not attempt to supplant traditional digital imaging techniques but rather develop sensor systems tailor made for the application at hand. We envision that many applications may require a hybrid approach using conventional digital imaging techniques enhanced with bio-inspired analogue sensors. In this specific project, we investigated the apposition compound eye and its characteristics commonly found in diurnal insects and certain species of arthropods. We developed and characterized an array of apposition compound eye-type sensors and tested them on an autonomous robotic vehicle. The robot exhibits the ability to follow a pre-defined target and avoid specified obstacles using a simple control algorithm.

A two-layer recurrent neural network for nonsmooth convex optimization problems.

Science.gov (United States)

Qin, Sitian; Xue, Xiaoping

2015-06-01

In this paper, a two-layer recurrent neural network is proposed to solve the nonsmooth convex optimization problem subject to convex inequality and linear equality constraints. Compared with existing neural network models, the proposed neural network has a low model complexity and avoids penalty parameters. It is proved that from any initial point, the state of the proposed neural network reaches the equality feasible region in finite time and stays there thereafter. Moreover, the state is unique if the initial point lies in the equality feasible region. The equilibrium point set of the proposed neural network is proved to be equivalent to the Karush-Kuhn-Tucker optimality set of the original optimization problem. It is further proved that the equilibrium point of the proposed neural network is stable in the sense of Lyapunov. Moreover, from any initial point, the state is proved to be convergent to an equilibrium point of the proposed neural network. Finally, as applications, the proposed neural network is used to solve nonlinear convex programming with linear constraints and L1 -norm minimization problems.

6th International Conference on Innovations in Bio-Inspired Computing and Applications

CERN Document Server

Abraham, Ajith; Krömer, Pavel; Pant, Millie; Muda, Azah

2016-01-01

This Volume contains the papers presented during the 6th International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2015 which was held in Kochi, India during December 16-18, 2015. The 51 papers presented in this Volume were carefully reviewed and selected. The 6th International Conference IBICA 2015 has been organized to discuss the state-of-the-art as well as to address various issues in the growing research field of Bio-inspired Computing which is currently one of the most exciting research areas, and is continuously demonstrating exceptional strength in solving complex real life problems. The Volume will be a valuable reference to researchers, students and practitioners in the computational intelligence field.

Bio-inspired fuel cells for miniaturized body-area-networks applications

NARCIS (Netherlands)

Xu, Wei; Gao, Lu; Danilov, Dmitri; Pop, V.; Notten, Peter

2010-01-01

The improvement in quality of modern health-care is closely related to the need for medical autonomous systems that enable people to ‘carry’ their personal wireless Body-Area-Network (BAN). Bio-inspired fuel cells (BFC) are a promising approach of energy harvesting to achieve autonomy and

Atomistic simulation on the plastic deformation and fracture of bio-inspired graphene/Ni nanocomposites

Science.gov (United States)

Yang, Zhenyu; Wang, Dandan; Lu, Zixing; Hu, Wenjun

2016-11-01

Molecular dynamics simulations were performed to investigate the plastic deformation and fracture behaviors of bio-inspired graphene/metal nanocomposites, which have a "brick-and-mortar" nanostructure, consisting of hard graphene single-layers embedded in a soft Ni matrix. The plastic deformation mechanisms of the nanocomposites were analyzed as well as their effects on the mechanical properties with various geometrical variations. It was found that the strength and ductility of the metal matrix can be highly enhanced with the addition of the staggered graphene layers, and the plastic deformation can be attributed to the interfacial sliding, dislocation nucleation, and cracks' combination. The strength of the nanocomposites strongly depends on the length scale of the nanostructure and the interlayer distance as well. In addition, slip at the interface releases the stress in graphene layers, leading to the stress distribution on the graphene more uniform. The present results are expected to contribute to the design of the nanolayered graphene/metal composites with high performance.

Application of quercetin and its bio-inspired nanoparticles as anti-adhesive agents against Bacillus subtilis attachment to surface

Energy Technology Data Exchange (ETDEWEB)

Raie, Diana S., E-mail: raiediana@yahoo.com [Process Design and Development Department, Egyptian Petroleum Research Institute (EPRI), Nasr City, Cairo (Egypt); Mhatre, Eisha [Terrestrial Biofilms Group, Institute of Microbiology, Friedrich Schiller University Jena (FSU), Jena (Germany); Thiele, Matthias [Nanobiophotonic Department, Leibniz Institute of Photonic Technology Jena (IPHT), Jena (Germany); Labena, A. [Process Design and Development Department, Egyptian Petroleum Research Institute (EPRI), Nasr City, Cairo (Egypt); El-Ghannam, Gamal [National Institute of Laser Enhanced Sciences (NILES), Cairo University, Giza (Egypt); Farahat, Laila A. [Process Design and Development Department, Egyptian Petroleum Research Institute (EPRI), Nasr City, Cairo (Egypt); Youssef, Tareq [National Institute of Laser Enhanced Sciences (NILES), Cairo University, Giza (Egypt); Fritzsche, Wolfgang [Nanobiophotonic Department, Leibniz Institute of Photonic Technology Jena (IPHT), Jena (Germany); Kovács, Ákos T., E-mail: akos-tibor.kovacs@uni-jena.de [Terrestrial Biofilms Group, Institute of Microbiology, Friedrich Schiller University Jena (FSU), Jena (Germany)

2017-01-01

The aim of this study was directed to reveal the repulsive effect of coated glass slides by quercetin and its bio-inspired titanium oxide and tungsten oxide nanoparticles on physical surface attachment of Bacillus subtilis as an ab-initio step of biofilm formation. Nanoparticles were successfully synthesized using sol–gel and acid precipitation methods for titanium oxide and tungsten oxide, respectively (in the absence or presence of quercetin). The anti-adhesive impact of the coated-slides was tested through the physical attachment of B. subtilis after 24 h using Confocal Laser Scanning Microscopy (CLSM). Here, quercetin was presented as a bio-route for the synthesis of tungsten mixed oxides nano-plates at room temperature. In addition, quercetin had an impact on zeta potential and adsorption capacity of both bio-inspired amorphous titanium oxide and tungsten oxide nano-plates. Interestingly, our experiments indicated a contrary effect of quercetin as an anti-adhesive agent than previously reported. However, its bio-inspired metal oxide proved their repulsive efficiency. In addition, quercetin-mediated nano-tungsten and quercetin-mediated amorphous titanium showed anti-adhesive activity against B. subtilis biofilm. - Highlights: • Novel quercetin-mediated nanoparticles were tested for anti-adhesion against attachment of cells forming biofilms. • Quercetin showed a low-grade of protection level against bacterial attachment. • Bio-inspired nano-anatase showed a lower efficiency than amorphous titanium. • Thermally treated bio-inspired nano-tungsten gets an improved anti-adhesive activity.

Theoretical properties of the global optimizer of two layer neural network

OpenAIRE

Boob, Digvijay; Lan, Guanghui

2017-01-01

In this paper, we study the problem of optimizing a two-layer artificial neural network that best fits a training dataset. We look at this problem in the setting where the number of parameters is greater than the number of sampled points. We show that for a wide class of differentiable activation functions (this class involves "almost" all functions which are not piecewise linear), we have that first-order optimal solutions satisfy global optimality provided the hidden layer is non-singular. ...

Efficient computation in adaptive artificial spiking neural networks

NARCIS (Netherlands)

D. Zambrano (Davide); R.B.P. Nusselder (Roeland); H.S. Scholte; S.M. Bohte (Sander)

2017-01-01

textabstractArtificial Neural Networks (ANNs) are bio-inspired models of neural computation that have proven highly effective. Still, ANNs lack a natural notion of time, and neural units in ANNs exchange analog values in a frame-based manner, a computationally and energetically inefficient form of

Bio-inspired approach for intelligent unattended ground sensors

Science.gov (United States)

Hueber, Nicolas; Raymond, Pierre; Hennequin, Christophe; Pichler, Alexander; Perrot, Maxime; Voisin, Philippe; Moeglin, Jean-Pierre

2015-05-01

Improving the surveillance capacity over wide zones requires a set of smart battery-powered Unattended Ground Sensors capable of issuing an alarm to a decision-making center. Only high-level information has to be sent when a relevant suspicious situation occurs. In this paper we propose an innovative bio-inspired approach that mimics the human bi-modal vision mechanism and the parallel processing ability of the human brain. The designed prototype exploits two levels of analysis: a low-level panoramic motion analysis, the peripheral vision, and a high-level event-focused analysis, the foveal vision. By tracking moving objects and fusing multiple criteria (size, speed, trajectory, etc.), the peripheral vision module acts as a fast relevant event detector. The foveal vision module focuses on the detected events to extract more detailed features (texture, color, shape, etc.) in order to improve the recognition efficiency. The implemented recognition core is able to acquire human knowledge and to classify in real-time a huge amount of heterogeneous data thanks to its natively parallel hardware structure. This UGS prototype validates our system approach under laboratory tests. The peripheral analysis module demonstrates a low false alarm rate whereas the foveal vision correctly focuses on the detected events. A parallel FPGA implementation of the recognition core succeeds in fulfilling the embedded application requirements. These results are paving the way of future reconfigurable virtual field agents. By locally processing the data and sending only high-level information, their energy requirements and electromagnetic signature are optimized. Moreover, the embedded Artificial Intelligence core enables these bio-inspired systems to recognize and learn new significant events. By duplicating human expertise in potentially hazardous places, our miniature visual event detector will allow early warning and contribute to better human decision making.

Bio-inspired algorithms applied to molecular docking simulations.

Science.gov (United States)

Heberlé, G; de Azevedo, W F

2011-01-01

Nature as a source of inspiration has been shown to have a great beneficial impact on the development of new computational methodologies. In this scenario, analyses of the interactions between a protein target and a ligand can be simulated by biologically inspired algorithms (BIAs). These algorithms mimic biological systems to create new paradigms for computation, such as neural networks, evolutionary computing, and swarm intelligence. This review provides a description of the main concepts behind BIAs applied to molecular docking simulations. Special attention is devoted to evolutionary algorithms, guided-directed evolutionary algorithms, and Lamarckian genetic algorithms. Recent applications of these methodologies to protein targets identified in the Mycobacterium tuberculosis genome are described.

Bio-inspired heterogeneous composites for broadband vibration mitigation.

Science.gov (United States)

Chen, Yanyu; Wang, Lifeng

2015-12-08

Structural biological materials have developed heterogeneous and hierarchical architectures that are responsible for the outstanding performance to provide protection against environmental threats including static and dynamic loading. Inspired by this observation, this research aims to develop new material and structural concepts for broadband vibration mitigation. The proposed composite materials possess a two-layered heterogeneous architecture where both layers consist of high-volume platelet-shape reinforcements and low-volume matrix, similar to the well-known "brick and mortar" microstructure of biological composites. Using finite element method, we numerically demonstrated that broadband wave attenuation zones can be achieved by tailoring the geometric features of the heterogeneous architecture. We reveal that the resulting broadband attenuation zones are gained by directly superimposing the attenuation zones in each constituent layer. This mechanism is further confirmed by the investigation into the phonon dispersion relation of each layer. Importantly, the broadband wave attenuation capability will be maintained when the mineral platelet orientation is locally manipulated, yet a contrast between the mineral platelet concentrations of the two constituent layers is essential. The findings of this work will provide new opportunities to design heterogeneous composites for broadband vibration mitigation and impact resistance under mechanically challenging environmental conditions.

Semiconductor Devices Inspired By and Integrated With Biology

Energy Technology Data Exchange (ETDEWEB)

Rogers, John [University of Illinois

2012-04-25

Biology is curved, soft and elastic; silicon wafers are not. Semiconductor technologies that can bridge this gap in form and mechanics will create new opportunities in devices that adopt biologically inspired designs or require intimate integration with the human body. This talk describes the development of ideas for electronics that offer the performance of state-of-the-art, wafer- based systems but with the mechanical properties of a rubber band. We explain the underlying materials science and mechanics of these approaches, and illustrate their use in (1) bio- integrated, ‘tissue-like’ electronics with unique capabilities for mapping cardiac and neural electrophysiology, and (2) bio-inspired, ‘eyeball’ cameras with exceptional imaging properties enabled by curvilinear, Petzval designs.

Bio-Inspired Cyber Security for Smart Grid Deployments

Energy Technology Data Exchange (ETDEWEB)

McKinnon, Archibald D.; Thompson, Seth R.; Doroshchuk, Ruslan A.; Fink, Glenn A.; Fulp, Errin W.

2013-05-01

mart grid technologies are transforming the electric power grid into a grid with bi-directional flows of both power and information. Operating millions of new smart meters and smart appliances will significantly impact electric distribution systems resulting in greater efficiency. However, the scale of the grid and the new types of information transmitted will potentially introduce several security risks that cannot be addressed by traditional, centralized security techniques. We propose a new bio-inspired cyber security approach. Social insects, such as ants and bees, have developed complex-adaptive systems that emerge from the collective application of simple, light-weight behaviors. The Digital Ants framework is a bio-inspired framework that uses mobile light-weight agents. Sensors within the framework use digital pheromones to communicate with each other and to alert each other of possible cyber security issues. All communication and coordination is both localized and decentralized thereby allowing the framework to scale across the large numbers of devices that will exist in the smart grid. Furthermore, the sensors are light-weight and therefore suitable for implementation on devices with limited computational resources. This paper will provide a brief overview of the Digital Ants framework and then present results from test bed-based demonstrations that show that Digital Ants can identify a cyber attack scenario against smart meter deployments.

Bio-Inspired Neural Model for Learning Dynamic Models

Science.gov (United States)

Duong, Tuan; Duong, Vu; Suri, Ronald

2009-01-01

A neural-network mathematical model that, relative to prior such models, places greater emphasis on some of the temporal aspects of real neural physical processes, has been proposed as a basis for massively parallel, distributed algorithms that learn dynamic models of possibly complex external processes by means of learning rules that are local in space and time. The algorithms could be made to perform such functions as recognition and prediction of words in speech and of objects depicted in video images. The approach embodied in this model is said to be "hardware-friendly" in the following sense: The algorithms would be amenable to execution by special-purpose computers implemented as very-large-scale integrated (VLSI) circuits that would operate at relatively high speeds and low power demands.

A bio-inspired electrocommunication system for small underwater robots.

Science.gov (United States)

Wang, Wei; Liu, Jindong; Xie, Guangming; Wen, Li; Zhang, Jianwei

2017-03-29

Weakly electric fishes (Gymnotid and Mormyrid) use an electric field to communicate efficiently (termed electrocommunication) in the turbid waters of confined spaces where other communication modalities fail. Inspired by this biological phenomenon, we design an artificial electrocommunication system for small underwater robots and explore the capabilities of such an underwater robotic communication system. An analytical model for electrocommunication is derived to predict the effect of the key parameters such as electrode distance and emitter current of the system on the communication performance. According to this model, a low-dissipation, and small-sized electrocommunication system is proposed and integrated into a small robotic fish. We characterize the communication performance of the robot in still water, flowing water, water with obstacles and natural water conditions. The results show that underwater robots are able to communicate electrically at a speed of around 1 k baud within about 3 m with a low power consumption (less than 1 W). In addition, we demonstrate that two leader-follower robots successfully achieve motion synchronization through electrocommunication in the three-dimensional underwater space, indicating that this bio-inspired electrocommunication system is a promising setup for the interaction of small underwater robots.

A Review on Development and Applications of Bio-Inspired Superhydrophobic Textiles

Directory of Open Access Journals (Sweden)

Ishaq Ahmad

2016-11-01

Full Text Available Bio-inspired engineering has been envisioned in a wide array of applications. All living bodies on Earth, including animals and plants, have well organized functional systems developed by nature. These naturally designed functional systems inspire scientists and engineers worldwide to mimic the system for practical applications by human beings. Researchers in the academic world and industries have been trying, for hundreds of years, to demonstrate how these natural phenomena could be translated into the real world to save lives, money and time. One of the most fascinating natural phenomena is the resistance of living bodies to contamination by dust and other pollutants, thus termed as self-cleaning phenomenon. This phenomenon has been observed in many plants, animals and insects and is termed as the Lotus Effect. With advancement in research and technology, attention has been given to the exploration of the underlying mechanisms of water repellency and self-cleaning. As a result, various concepts have been developed including Young’s equation, and Wenzel and Cassie–Baxter theories. The more we unravel this process, the more we get access to its implications and applications. A similar pursuit is emphasized in this review to explain the fundamental principles, mechanisms, past experimental approaches and ongoing research in the development of bio-inspired superhydrophobic textiles.

A Review on Development and Applications of Bio-Inspired Superhydrophobic Textiles

Science.gov (United States)

Ahmad, Ishaq; Kan, Chi-wai

2016-01-01

Bio-inspired engineering has been envisioned in a wide array of applications. All living bodies on Earth, including animals and plants, have well organized functional systems developed by nature. These naturally designed functional systems inspire scientists and engineers worldwide to mimic the system for practical applications by human beings. Researchers in the academic world and industries have been trying, for hundreds of years, to demonstrate how these natural phenomena could be translated into the real world to save lives, money and time. One of the most fascinating natural phenomena is the resistance of living bodies to contamination by dust and other pollutants, thus termed as self-cleaning phenomenon. This phenomenon has been observed in many plants, animals and insects and is termed as the Lotus Effect. With advancement in research and technology, attention has been given to the exploration of the underlying mechanisms of water repellency and self-cleaning. As a result, various concepts have been developed including Young’s equation, and Wenzel and Cassie–Baxter theories. The more we unravel this process, the more we get access to its implications and applications. A similar pursuit is emphasized in this review to explain the fundamental principles, mechanisms, past experimental approaches and ongoing research in the development of bio-inspired superhydrophobic textiles. PMID:28774012

Molecular machines with bio-inspired mechanisms.

Science.gov (United States)

Zhang, Liang; Marcos, Vanesa; Leigh, David A

2018-02-26

The widespread use of molecular-level motion in key natural processes suggests that great rewards could come from bridging the gap between the present generation of synthetic molecular machines-which by and large function as switches-and the machines of the macroscopic world, which utilize the synchronized behavior of integrated components to perform more sophisticated tasks than is possible with any individual switch. Should we try to make molecular machines of greater complexity by trying to mimic machines from the macroscopic world or instead apply unfamiliar (and no doubt have to discover or invent currently unknown) mechanisms utilized by biological machines? Here we try to answer that question by exploring some of the advances made to date using bio-inspired machine mechanisms.

Bio-inspired materials engineering using polysaccharide based biotemplates

International Nuclear Information System (INIS)

Zollfrank, C.

2007-01-01

Nano-structured materials with a controlled microstructure and tailored properties at a scale below 100 nm are of interest for applications in micro-mechanical, sensor and biomedical devices. In contrast to top-down manufacturing processes the formation of solid matter structures in nature is templated and directed by biomacromolecules such as polysaccharides and polypeptides. A promising biomimetic route for the directed deposition of ceramic materials is the application of anisotropically structured biomacromolecules as patterned templates. The polysaccharides exhibit a hierarchical multi scale order as well as self-assembly properties. The bio-inspired deposition and formation of ceramic phases on biomolecular polysaccharide templates was investigated. The polysaccharides were used at various structural levels from the molecular scale up to three-dimensional parts in the millimetre range. The versatility of polysaccharide shaping capabilities was explored using dissolved polysaccharide molecules as well as thin films for the or simultaneous or successive formation of inorganic mineral phases. Microalgae with a spherical appearance of 5 micro-m were applied in mineralisation studies. The extracellular polysaccharide (EPS) layers on the microalgae were used as biotemplates for manufacturing of functional ceramics. The obtained results on the mineralisation of inorganic phases on polysaccharides are adapted for novel biomimetic routes used in the fabrication for functional and biomedical ceramics. (author)

8th International Conference on Bio-Inspired Computing : Theories and Applications

CERN Document Server

Pan, Linqiang; Fang, Xianwen

2013-01-01

International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) is one of the flagship conferences on Bio-Computing, bringing together the world’s leading scientists from different areas of Natural Computing. Since 2006, the conferences have taken place at Wuhan (2006), Zhengzhou (2007), Adelaide (2008), Beijing (2009), Liverpool & Changsha (2010), Malaysia (2011) and India (2012). Following the successes of previous events, the 8th conference is organized and hosted by Anhui University of Science and Technology in China. This conference aims to provide a high-level international forum that researchers with different backgrounds and who are working in the related areas can use to present their latest results and exchange ideas. Additionally, the growing trend in Emergent Systems has resulted in the inclusion of two other closely related fields in the BIC-TA 2013 event, namely Complex Systems and Computational Neuroscience. These proceedings are intended for researchers in the fiel...

Brain-inspired Stochastic Models and Implementations

KAUST Repository

Al-Shedivat, Maruan

2015-05-12

One of the approaches to building artificial intelligence (AI) is to decipher the princi- ples of the brain function and to employ similar mechanisms for solving cognitive tasks, such as visual perception or natural language understanding, using machines. The recent breakthrough, named deep learning, demonstrated that large multi-layer networks of arti- ficial neural-like computing units attain remarkable performance on some of these tasks. Nevertheless, such artificial networks remain to be very loosely inspired by the brain, which rich structures and mechanisms may further suggest new algorithms or even new paradigms of computation. In this thesis, we explore brain-inspired probabilistic mechanisms, such as neural and synaptic stochasticity, in the context of generative models. The two questions we ask here are: (i) what kind of models can describe a neural learning system built of stochastic components? and (ii) how can we implement such systems e ̆ciently? To give specific answers, we consider two well known models and the corresponding neural architectures: the Naive Bayes model implemented with a winner-take-all spiking neural network and the Boltzmann machine implemented in a spiking or non-spiking fashion. We propose and analyze an e ̆cient neuromorphic implementation of the stochastic neu- ral firing mechanism and study the e ̄ects of synaptic unreliability on learning generative energy-based models implemented with neural networks.

A bio-inspired approach for in situ synthesis of tunable adhesive

International Nuclear Information System (INIS)

Sun, Leming; Yi, Sijia; Wang, Yongzhong; Pan, Kang; Zhong, Qixin; Zhang, Mingjun

2014-01-01

Inspired by the strong adhesive produced by English ivy, this paper proposes an in situ synthesis approach for fabricating tunable nanoparticle enhanced adhesives. Special attention was given to tunable features of the adhesive produced by the biological process. Parameters that may be used to tune properties of the adhesive will be proposed. To illustrate and validate the proposed approach, an experimental platform was presented for fabricating tunable chitosan adhesive enhanced by Au nanoparticles synthesized in situ. This study contributes to a bio-inspired approach for in situ synthesis of tunable nanocomposite adhesives by mimicking the natural biological processes of ivy adhesive synthesis. (paper)

Multiple Decoupled CPGs with Local Sensory Feedback for Adaptive Locomotion Behaviors of Bio-inspired Walking Robots

DEFF Research Database (Denmark)

Shaker Barikhan, Subhi; Wörgötter, Florentin; Manoonpong, Poramate

2014-01-01

, and their interactions during body and leg movements through the environment. Based on this concept, we present here an artificial bio-inspired walking system. Its intralimb coordination is formed by multiple decoupled CPGs while its interlimb coordination is attained by the interactions between body dynamics...... and the environment through local sensory feedback of each leg. Simulation results show that this bio-inspired approach generates self-organizing emergent locomotion allowing the robot to adaptively form regular patterns, to stably walk while pushing an object with its front legs or performing multiple stepping...

Bio-inspired evaporation through plasmonic film of nanoparticles at the air-water interface.

Science.gov (United States)

Wang, Zhenhui; Liu, Yanming; Tao, Peng; Shen, Qingchen; Yi, Nan; Zhang, Fangyu; Liu, Quanlong; Song, Chengyi; Zhang, Di; Shang, Wen; Deng, Tao

2014-08-27

Plasmonic gold nanoparticles self-assembled at the air-water interface to produce an evaporative surface with local control inspired by skins and plant leaves. Fast and efficient evaporation is realized due to the instant and localized plasmonic heating at the evaporative surface. The bio-inspired evaporation process provides an alternative promising approach for evaporation, and has potential applications in sterilization, distillation, and heat transfer. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Performance Characteristics of Bio-Inspired Metal Nanostructures as Surface-Enhanced Raman Scattered (SERS) Substrates.

Science.gov (United States)

Areizaga-Martinez, Hector I; Kravchenko, Ivan; Lavrik, Nickolay V; Sepaniak, Michael J; Hernández-Rivera, Samuel P; De Jesús, Marco A

2016-09-01

The fabrication of high-performance plasmonic nanomaterials for bio-sensing and trace chemical detection is a field of intense theoretical and experimental research. The use of metal-silicon nanopillar arrays as analytical sensors has been reported with reasonable results in recent years. The use of bio-inspired nanocomposite structures that follow the Fibonacci numerical architecture offers the opportunity to develop nanostructures with theoretically higher and more reproducible plasmonic fields over extended areas. The work presented here describes the nanofabrication process for a series of 40 µm × 40 µm bio-inspired arrays classified as asymmetric fractals (sunflower seeds and romanesco broccoli), bilaterally symmetric (acacia leaves and honeycombs), and radially symmetric (such as orchids and lily flowers) using electron beam lithography. In addition, analytical capabilities were evaluated using surface-enhanced Raman scattering (SERS). The substrate characterization and SERS performance of the developed substrates as the strategies to assess the design performance are presented and discussed. © The Author(s) 2016.

Bio-inspired Artificial Intelligence: А Generalized Net Model of the Regularization Process in MLP

Directory of Open Access Journals (Sweden)

Stanimir Surchev

2013-10-01

Full Text Available Many objects and processes inspired by the nature have been recreated by the scientists. The inspiration to create a Multilayer Neural Network came from human brain as member of the group. It possesses complicated structure and it is difficult to recreate, because of the existence of too many processes that require different solving methods. The aim of the following paper is to describe one of the methods that improve learning process of Artificial Neural Network. The proposed generalized net method presents Regularization process in Multilayer Neural Network. The purpose of verification is to protect the neural network from overfitting. The regularization is commonly used in neural network training process. Many methods of verification are present, the subject of interest is the one known as Regularization. It contains function in order to set weights and biases with smaller values to protect from overfitting.

Three-dimensional trajectory tracking for underactuated AUVs with bio-inspired velocity regulation

Directory of Open Access Journals (Sweden)

Jiajia Zhou

2018-05-01

Full Text Available This paper attempts to address the motion parameter skip problem associated with three-dimensional trajectory tracking of an underactuated Autonomous Underwater Vehicle (AUV using backstepping-based control, due to the unsmoothness of tracking trajectory. Through kinematics concepts, a three-dimensional dynamic velocity regulation controller is derived. This controller makes use of the surge and angular velocity errors with bio-inspired models and backstepping techniques. It overcomes the frequently occurring problem of parameter skip at inflection point existing in backstepping tracking control method and increases system robustness. Moreover, the proposed method can effectively avoid the singularity problem in backstepping control of virtual velocity error. The control system is proved to be uniformly ultimately bounded using Lyapunov stability theory. Simulation results illustrate the effectiveness and efficiency of the developed controller, which can realize accurate three-dimensional trajectory tracking for an underactuated AUV with constant external disturbances. Keywords: Dynamic velocity regulation, Bio-inspired model, Backstepping, Underactuated AUV, Three-dimensional trajectory tracking

Direct numerical simulations of drag reduction in turbulent channel flow over bio-inspired herringbone riblet-texture

NARCIS (Netherlands)

Benschop, H.O.G.; Westerweel, J.; Breugem, W.P.

2015-01-01

The use of drag reducing surface textures is a promising passive method to reduce fuel consumption. Probably most wellknown is the utilisation of shark-skin inspired ridges or riblets parallel to the mean flow. They can reduce drag up to 10%. Recently another bio-inspired texture based on bird

Interactive Learning Environment for Bio-Inspired Optimization Algorithms for UAV Path Planning

Science.gov (United States)

Duan, Haibin; Li, Pei; Shi, Yuhui; Zhang, Xiangyin; Sun, Changhao

2015-01-01

This paper describes the development of BOLE, a MATLAB-based interactive learning environment, that facilitates the process of learning bio-inspired optimization algorithms, and that is dedicated exclusively to unmanned aerial vehicle path planning. As a complement to conventional teaching methods, BOLE is designed to help students consolidate the…

A compact bio-inspired visible/NIR imager for image-guided surgery (Conference Presentation)

Science.gov (United States)

Gao, Shengkui; Garcia, Missael; Edmiston, Chris; York, Timothy; Marinov, Radoslav; Mondal, Suman B.; Zhu, Nan; Sudlow, Gail P.; Akers, Walter J.; Margenthaler, Julie A.; Liang, Rongguang; Pepino, Marta; Achilefu, Samuel; Gruev, Viktor

2016-03-01

Inspired by the visual system of the morpho butterfly, we have designed, fabricated, tested and clinically translated an ultra-sensitive, light weight and compact imaging sensor capable of simultaneously capturing near infrared (NIR) and visible spectrum information. The visual system of the morpho butterfly combines photosensitive cells with spectral filters at the receptor level. The spectral filters are realized by alternating layers of high and low dielectric constant, such as air and cytoplasm. We have successfully mimicked this concept by integrating pixelated spectral filters, realized by alternating silicon dioxide and silicon nitrate layers, with an array of CCD detectors. There are four different types of pixelated spectral filters in the imaging plane: red, green, blue and NIR. The high optical density (OD) of all spectral filters (OD>4) allow for efficient rejections of photons from unwanted bands. The single imaging chip weighs 20 grams with form factor of 5mm by 5mm. The imaging camera is integrated with a goggle display system. A tumor targeted agent, LS301, is used to identify all spontaneous tumors in a transgenic PyMT murine model of breast cancer. The imaging system achieved sensitivity of 98% and selectivity of 95%. We also used our imaging sensor to locate sentinel lymph nodes (SLNs) in patients with breast cancer using indocyanine green tracer. The surgeon was able to identify 100% of SLNs when using our bio-inspired imaging system, compared to 93% when using information from the lymphotropic dye and 96% when using information from the radioactive tracer.

Integration of bio-inspired, control-based visual and olfactory data for the detection of an elusive target

Science.gov (United States)

Duong, Tuan A.; Duong, Nghi; Le, Duong

2017-01-01

In this paper, we present an integration technique using a bio-inspired, control-based visual and olfactory receptor system to search for elusive targets in practical environments where the targets cannot be seen obviously by either sensory data. Bio-inspired Visual System is based on a modeling of extended visual pathway which consists of saccadic eye movements and visual pathway (vertebrate retina, lateral geniculate nucleus and visual cortex) to enable powerful target detections of noisy, partial, incomplete visual data. Olfactory receptor algorithm, namely spatial invariant independent component analysis, that was developed based on data of old factory receptor-electronic nose (enose) of Caltech, is adopted to enable the odorant target detection in an unknown environment. The integration of two systems is a vital approach and sets up a cornerstone for effective and low-cost of miniaturized UAVs or fly robots for future DOD and NASA missions, as well as for security systems in Internet of Things environments.

Bio-inspired composites with functionally graded platelets exhibit enhanced stiffness.

Science.gov (United States)

Tapse, Sanjay; S, Anup

2017-11-09

Unidirectional composites inspired from biological materials such as nacre, are composed of stiff platelets arranged in a staggered manner within a soft matrix. Elaborate analyses have been conducted on the aforementioned composites and they are found to have excellent mechanical properties like stiffness, strength and fracture toughness. The superior properties exhibited by these composites have been proved to be the result of its unique structure. An emerging development in the field of composite structures is Functionally Graded Composites(FGC), whose properties vary spatially and possess enhanced thermo-mechanical properties. In this paper, the platelets are functionally graded with its Young's Modulus varying parabolically along the length. Two different models - namely, Tension Shear Chain Model and Minimisation of Complementary Energy Model have been employed to obtain the stiffness of the overall composite analytically. The effect of various parameters that define the composite model such as overlapping length between any two neighbouring platelets, different gradation parameters and platelet aspect ratio on the overall mechanical properties have been studied. Composites with functionally graded platelets are found to possess enhanced stiffness (upto 14% higher) for certain values of these parameters. The obtained solutions have been validated using Finite Element Analysis. Bio-inspired composites with functionally graded platelets can be engineered for structural applications, such as in automobile, aerospace and aircraft industry, where stiffness plays a crucial role. © 2017 IOP Publishing Ltd.

A Case Study on Neural Inspired Dynamic Memory Management Strategies for High Performance Computing.

Energy Technology Data Exchange (ETDEWEB)

Vineyard, Craig Michael [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Verzi, Stephen Joseph [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2017-09-01

As high performance computing architectures pursue more computational power there is a need for increased memory capacity and bandwidth as well. A multi-level memory (MLM) architecture addresses this need by combining multiple memory types with different characteristics as varying levels of the same architecture. How to efficiently utilize this memory infrastructure is an unknown challenge, and in this research we sought to investigate whether neural inspired approaches can meaningfully help with memory management. In particular we explored neurogenesis inspired re- source allocation, and were able to show a neural inspired mixed controller policy can beneficially impact how MLM architectures utilize memory.

A neurally inspired musical instrument classification system based upon the sound onset.

Science.gov (United States)

Newton, Michael J; Smith, Leslie S

2012-06-01

Physiological evidence suggests that sound onset detection in the auditory system may be performed by specialized neurons as early as the cochlear nucleus. Psychoacoustic evidence shows that the sound onset can be important for the recognition of musical sounds. Here the sound onset is used in isolation to form tone descriptors for a musical instrument classification task. The task involves 2085 isolated musical tones from the McGill dataset across five instrument categories. A neurally inspired tone descriptor is created using a model of the auditory system's response to sound onset. A gammatone filterbank and spiking onset detectors, built from dynamic synapses and leaky integrate-and-fire neurons, create parallel spike trains that emphasize the sound onset. These are coded as a descriptor called the onset fingerprint. Classification uses a time-domain neural network, the echo state network. Reference strategies, based upon mel-frequency cepstral coefficients, evaluated either over the whole tone or only during the sound onset, provide context to the method. Classification success rates for the neurally-inspired method are around 75%. The cepstral methods perform between 73% and 76%. Further testing with tones from the Iowa MIS collection shows that the neurally inspired method is considerably more robust when tested with data from an unrelated dataset.

Training mechanical engineering students to utilize biological inspiration during product development.

Science.gov (United States)

Bruck, Hugh A; Gershon, Alan L; Golden, Ira; Gupta, Satyandra K; Gyger, Lawrence S; Magrab, Edward B; Spranklin, Brent W

2007-12-01

The use of bio-inspiration for the development of new products and devices requires new educational tools for students consisting of appropriate design and manufacturing technologies, as well as curriculum. At the University of Maryland, new educational tools have been developed that introduce bio-inspired product realization to undergraduate mechanical engineering students. These tools include the development of a bio-inspired design repository, a concurrent fabrication and assembly manufacturing technology, a series of undergraduate curriculum modules and a new senior elective in the bio-inspired robotics area. This paper first presents an overview of the two new design and manufacturing technologies that enable students to realize bio-inspired products, and describes how these technologies are integrated into the undergraduate educational experience. Then, the undergraduate curriculum modules are presented, which provide students with the fundamental design and manufacturing principles needed to support bio-inspired product and device development. Finally, an elective bio-inspired robotics project course is present, which provides undergraduates with the opportunity to demonstrate the application of the knowledge acquired through the curriculum modules in their senior year using the new design and manufacturing technologies.

Computational Design of Multi-component Bio-Inspired Bilayer Membranes

Directory of Open Access Journals (Sweden)

Evan Koufos

2014-04-01

Full Text Available Our investigation is motivated by the need to design bilayer membranes with tunable interfacial and mechanical properties for use in a range of applications, such as targeted drug delivery, sensing and imaging. We draw inspiration from biological cell membranes and focus on their principal constituents. In this paper, we present our results on the role of molecular architecture on the interfacial, structural and dynamical properties of bio-inspired membranes. We focus on four lipid architectures with variations in the head group shape and the hydrocarbon tail length. Each lipid species is composed of a hydrophilic head group and two hydrophobic tails. In addition, we study a model of the Cholesterol molecule to understand the interfacial properties of a bilayer membrane composed of rigid, single-tail molecular species. We demonstrate the properties of the bilayer membranes to be determined by the molecular architecture and rigidity of the constituent species. Finally, we demonstrate the formation of a stable mixed bilayer membrane composed of Cholesterol and one of the phospholipid species. Our approach can be adopted to design multi-component bilayer membranes with tunable interfacial and mechanical properties. We use a Molecular Dynamics-based mesoscopic simulation technique called Dissipative Particle Dynamics that resolves the molecular details of the components through soft-sphere coarse-grained models and reproduces the hydrodynamic behavior of the system over extended time scales.

Design of a universal two-layered neural network derived from the PLI theory

Science.gov (United States)

Hu, Chia-Lun J.

2004-05-01

The if-and-only-if (IFF) condition that a set of M analog-to-digital vector-mapping relations can be learned by a one-layered-feed-forward neural network (OLNN) is that all the input analog vectors dichotomized by the i-th output bit must be positively, linearly independent, or PLI. If they are not PLI, then the OLNN just cannot learn no matter what learning rules is employed because the solution of the connection matrix does not exist mathematically. However, in this case, one can still design a parallel-cascaded, two-layered, perceptron (PCTLP) to acheive this general mapping goal. The design principle of this "universal" neural network is derived from the major mathematical properties of the PLI theory - changing the output bits of the dependent relations existing among the dichotomized input vectors to make the PLD relations PLI. Then with a vector concatenation technique, the required mapping can still be learned by this PCTLP system with very high efficiency. This paper will report in detail the mathematical derivation of the general design principle and the design procedures of the PCTLP neural network system. It then will be verified in general by a practical numerical example.

Derivation of the stress-strain behavior of the constituents of bio-inspired layered TiO2/PE-nanocomposites by inverse modeling based on FE-simulations of nanoindentation test.

Science.gov (United States)

Lasko, G; Schäfer, I; Burghard, Z; Bill, J; Schmauder, S; Weber, U; Galler, D

2013-03-01

Owing to the apparent simple morphology and peculiar properties, nacre, an iridescent layer, coating of the inner part of mollusk shells, has attracted considerable attention of biologists, material scientists and engineers. The basic structural motif in nacre is the assembly of oriented plate-like aragonite crystals with a 'brick' (CaCO3 crystals) and 'mortar' (macromolecular components like proteins) organization. Many scientific researchers recognize that such structures are associated with the excellent mechanical properties of nacre and biomimetic strategies have been proposed to produce new layered nanocomposites. During the past years, increasing efforts have been devoted towards exploiting nacre's structural design principle in the synthesis of novel nanocomposites. However, the direct transfer of nacre's architecture to an artificial inorganic material has not been achieved yet. In the present contribution we report on laminated architecture, composed of the inorganic oxide (TiO2) and organic polyelectrolyte (PE) layers which fulfill this task. To get a better insight and understanding concerning the mechanical behaviour of bio-inspired layered materials consisting of oxide ceramics and organic layers, the elastic-plastic properties of titanium dioxide and organic polyelectrolyte phase are determined via FE-modelling of the nanoindentation process. With the use of inverse modeling and based on numerical models which are applied on the microscopic scale, the material properties of the constituents are derived.

A survey on bio inspired meta heuristic based clustering protocols for wireless sensor networks

Science.gov (United States)

Datta, A.; Nandakumar, S.

2017-11-01

Recent studies have shown that utilizing a mobile sink to harvest and carry data from a Wireless Sensor Network (WSN) can improve network operational efficiency as well as maintain uniform energy consumption by the sensor nodes in the network. Due to Sink mobility, the path between two sensor nodes continuously changes and this has a profound effect on the operational longevity of the network and a need arises for a protocol which utilizes minimal resources in maintaining routes between the mobile sink and the sensor nodes. Swarm Intelligence based techniques inspired by the foraging behavior of ants, termites and honey bees can be artificially simulated and utilized to solve real wireless network problems. The author presents a brief survey on various bio inspired swarm intelligence based protocols used in routing data in wireless sensor networks while outlining their general principle and operation.

Bio-mimicked atomic-layer-deposited iron oxide-based memristor with synaptic potentiation and depression functions

Science.gov (United States)

Wan, Xiang; Gao, Fei; Lian, Xiaojuan; Ji, Xincun; Hu, Ertao; He, Lin; Tong, Yi; Guo, Yufeng

2018-06-01

In this study, an iron oxide (FeO x )-based memristor was investigated for the realization of artificial synapses. An FeO x resistive switching layer was prepared by self-limiting atomic layer deposition (ALD). The movement of oxygen vacancies enabled the device to have history-dependent synaptic functions, which was further demonstrated by device modeling and simulation. Analog synaptic potentiation/depression in conductance was emulated by applying consecutive voltage pulses in the simulation. Our results suggest that the ALD FeO x -based memristor can be used as the basic building block for neural networks, neuromorphic systems, and brain-inspired computers.

Bio-inspired Autonomic Structures: a middleware for Telecommunications Ecosystems

Science.gov (United States)

Manzalini, Antonio; Minerva, Roberto; Moiso, Corrado

Today, people are making use of several devices for communications, for accessing multi-media content services, for data/information retrieving, for processing, computing, etc.: examples are laptops, PDAs, mobile phones, digital cameras, mp3 players, smart cards and smart appliances. One of the most attracting service scenarios for future Telecommunications and Internet is the one where people will be able to browse any object in the environment they live: communications, sensing and processing of data and services will be highly pervasive. In this vision, people, machines, artifacts and the surrounding space will create a kind of computational environment and, at the same time, the interfaces to the network resources. A challenging technological issue will be interconnection and management of heterogeneous systems and a huge amount of small devices tied together in networks of networks. Moreover, future network and service infrastructures should be able to provide Users and Application Developers (at different levels, e.g., residential Users but also SMEs, LEs, ASPs/Web2.0 Service roviders, ISPs, Content Providers, etc.) with the most appropriate "environment" according to their context and specific needs. Operators must be ready to manage such level of complication enabling their latforms with technological advanced allowing network and services self-supervision and self-adaptation capabilities. Autonomic software solutions, enhanced with innovative bio-inspired mechanisms and algorithms, are promising areas of long term research to face such challenges. This chapter proposes a bio-inspired autonomic middleware capable of leveraging the assets of the underlying network infrastructure whilst, at the same time, supporting the development of future Telecommunications and Internet Ecosystems.

Biotemplating of Luffa cylindrica sponges to self-supporting hierarchical zeolite macrostructures for bio-inspired structured catalytic reactors

International Nuclear Information System (INIS)

Zampieri, Alessandro; Mabande, Godwin T.P.; Selvam, Thangaraj; Schwieger, Wilhelm; Rudolph, Alexander; Hermann, Ralph; Sieber, Heino; Greil, Peter

2006-01-01

Biomorphic self-supporting MFI-type zeolite frameworks with hierarchical porosity and complex architecture were prepared using a 2-step (in-situ seeding and secondary crystal growth) hydrothermal synthesis in the presence of a biological template (Luffa sponge), employed as a macroscale sacrificial structure builder. The bio-inspired zeolitic replica inherited the complex spongy morphology and the intricate open-porous architecture of the biotemplate. Moreover, it exhibited reasonable mechanical stability in order to study the applicability of the biomorphic catalyst in a technical catalytic process. A bio-inspired catalytic reactor utilising the self-supporting ZSM-5 scaffold in monolithic configuration was developed in order to test the catalytic performance of the material

Biomimetic and bio-inspired uses of mollusc shells.

Science.gov (United States)

Morris, J P; Wang, Y; Backeljau, T; Chapelle, G

2016-06-01

Climate change and ocean acidification are likely to have a profound effect on marine molluscs, which are of great ecological and economic importance. One process particularly sensitive to climate change is the formation of biominerals in mollusc shells. Fundamental research is broadening our understanding of the biomineralization process, as well as providing more informed predictions on the effects of climate change on marine molluscs. Such studies are important in their own right, but their value also extends to applied sciences. Biominerals, organic/inorganic hybrid materials with many remarkable physical and chemical properties, have been studied for decades, and the possibilities for future improved use of such materials for society are widely recognised. This article highlights the potential use of our understanding of the shell biomineralization process in novel bio-inspired and biomimetic applications. It also highlights the potential for the valorisation of shells produced as a by-product of the aquaculture industry. Studying shells and the formation of biominerals will inspire novel functional hybrid materials. It may also provide sustainable, ecologically- and economically-viable solutions to some of the problems created by current human resource exploitation. Copyright © 2016 Elsevier B.V. All rights reserved.

BioMAV : Bio-inspired intelligence for autonomous flight

NARCIS (Netherlands)

Gerke, P.K.; Langevoort, J.; Lagarde, S.; Bax, L.; Grootswagers, T.; Drenth, R.J.; Slieker, V.; Vuurpijl, L.; Haselager, P.; Sprinkhuizen-Kuyper, I.; Van Otterlo, M.; De Croon, G.C.H.E.

2011-01-01

This paper aims to contribute to research on biologically inspired micro air vehicles in two ways: (i) it explores a novel repertoire of behavioral modules which can be controlled through ?nite state machines (FSM) and (ii) elementary movement detectors (EMD) are combined with a center/surround edge

Low-cost autonomous perceptron neural network inspired by quantum computation

Science.gov (United States)

Zidan, Mohammed; Abdel-Aty, Abdel-Haleem; El-Sadek, Alaa; Zanaty, E. A.; Abdel-Aty, Mahmoud

2017-11-01

Achieving low cost learning with reliable accuracy is one of the important goals to achieve intelligent machines to save time, energy and perform learning process over limited computational resources machines. In this paper, we propose an efficient algorithm for a perceptron neural network inspired by quantum computing composite from a single neuron to classify inspirable linear applications after a single training iteration O(1). The algorithm is applied over a real world data set and the results are outer performs the other state-of-the art algorithms.

Bio-inspiring cyber security and cloud services trends and innovations

CERN Document Server

Kim, Tai-Hoon; Kacprzyk, Janusz; Awad, Ali

2014-01-01

This volume presents recent research in cyber security, and reports how organizations can gain competitive advantages by applying the different security techniques in real-world scenarios. The volume provides reviews of cutting–edge technologies, algorithms, applications and insights for bio-inspiring cyber security-based systems. The book will be a valuable companion and comprehensive reference for both postgraduate and senior undergraduate students who are taking a course in cyber security. The volume is organized in self-contained chapters to provide greatest reading flexibility.  

Strong Quantum Confinement Effects and Chiral Excitons in Bio-Inspired ZnO–Amino Acid Cocrystals

KAUST Repository

Muhammed, Madathumpady Abubaker Habeeb; Lamers, Marlene; Baumann, Verena; Dey, Priyanka; Blanch, Adam J.; Polishchuk, Iryna; Kong, Xiang-Tian; Levy, Davide; Urban, Alexander S.; Govorov, Alexander O.; Pokroy, Boaz; Rodrí guez-Ferná ndez, Jessica; Feldmann, Jochen

2018-01-01

of amino acid potential barriers within the ZnO crystal lattice. Overall, our findings indicate that biomolecule cocrystallization can be used as a truly bio-inspired means to induce chiral quantum confinement effects in quasi-bulk semiconductors.

Bio-inspired routes for synthesizing efficient nanoscale platinum electrocatalysts

Energy Technology Data Exchange (ETDEWEB)

Cha, Jennifer N. [Univ. of California, San Diego, CA (United States); Wang, Joseph [Univ. of California, San Diego, CA (United States)

2014-08-31

The overall objective of the proposed research is to use fundamental advances in bionanotechnology to design powerful platinum nanocrystal electrocatalysts for fuel cell applications. The new economically-viable, environmentally-friendly, bottom-up biochemical synthetic strategy will produce platinum nanocrystals with tailored size, shape and crystal orientation, hence leading to a maximum electrochemical reactivity. There are five specific aims to the proposed bio-inspired strategy for synthesizing efficient electrocatalytic platinum nanocrystals: (1) isolate peptides that both selectively bind particular crystal faces of platinum and promote the nucleation and growth of particular nanocrystal morphologies, (2) pattern nanoscale 2-dimensional arrays of platinum nucleating peptides from DNA scaffolds, (3) investigate the combined use of substrate patterned peptides and soluble peptides on nanocrystal morphology and growth (4) synthesize platinum crystals on planar and large-area carbon electrode supports, and (5) perform detailed characterization of the electrocatalytic behavior as a function of catalyst size, shape and morphology. Project Description and Impact: This bio-inspired collaborative research effort will address key challenges in designing powerful electrocatalysts for fuel cell applications by employing nucleic acid scaffolds in combination with peptides to perform specific, environmentally-friendly, simultaneous bottom-up biochemical synthesis and patterned assembly of highly uniform and efficient platinum nanocrystal catalysts. Bulk synthesis of nanoparticles usually produces a range of sizes, accessible catalytic sites, crystal morphologies, and orientations, all of which lead to inconsistent catalytic activities. In contrast, biological systems routinely demonstrate exquisite control over inorganic syntheses at neutral pH and ambient temperature and pressures. Because the orientation and arrangement of the templating biomolecules can be precisely

A bio-inspired N-doped porous carbon electrocatalyst with hierarchical superstructure for efficient oxygen reduction reaction

Science.gov (United States)

Miao, Yue-E.; Yan, Jiajie; Ouyang, Yue; Lu, Hengyi; Lai, Feili; Wu, Yue; Liu, Tianxi

2018-06-01

The bio-inspired hierarchical "grape cluster" superstructure provides an effective integration of one-dimensional carbon nanofibers (CNF) with isolated carbonaceous nanoparticles into three-dimensional (3D) conductive frameworks for efficient electron and mass transfer. Herein, a 3D N-doped porous carbon electrocatalyst consisting of carbon nanofibers with grape-like N-doped hollow carbon particles (CNF@NC) has been prepared through a simple electrospinning strategy combined with in-situ growth and carbonization processes. Such a bio-inspired hierarchically organized conductive network largely facilitates both the mass diffusion and electron transfer during the oxygen reduction reactions (ORR). Therefore, the metal-free CNF@NC catalyst demonstrates superior catalytic activity with an absolute four-electron transfer mechanism, strong methanol tolerance and good long-term stability towards ORR in alkaline media.

Combining two open source tools for neural computation (BioPatRec and Netlab) improves movement classification for prosthetic control.

Science.gov (United States)

Prahm, Cosima; Eckstein, Korbinian; Ortiz-Catalan, Max; Dorffner, Georg; Kaniusas, Eugenijus; Aszmann, Oskar C

2016-08-31

Controlling a myoelectric prosthesis for upper limbs is increasingly challenging for the user as more electrodes and joints become available. Motion classification based on pattern recognition with a multi-electrode array allows multiple joints to be controlled simultaneously. Previous pattern recognition studies are difficult to compare, because individual research groups use their own data sets. To resolve this shortcoming and to facilitate comparisons, open access data sets were analysed using components of BioPatRec and Netlab pattern recognition models. Performances of the artificial neural networks, linear models, and training program components were compared. Evaluation took place within the BioPatRec environment, a Matlab-based open source platform that provides feature extraction, processing and motion classification algorithms for prosthetic control. The algorithms were applied to myoelectric signals for individual and simultaneous classification of movements, with the aim of finding the best performing algorithm and network model. Evaluation criteria included classification accuracy and training time. Results in both the linear and the artificial neural network models demonstrated that Netlab's implementation using scaled conjugate training algorithm reached significantly higher accuracies than BioPatRec. It is concluded that the best movement classification performance would be achieved through integrating Netlab training algorithms in the BioPatRec environment so that future prosthesis training can be shortened and control made more reliable. Netlab was therefore included into the newest release of BioPatRec (v4.0).

A bio-inspired approach for the reduction of left ventricular workload.

Directory of Open Access Journals (Sweden)

Niema M Pahlevan

Full Text Available Previous studies have demonstrated the existence of optimization criteria in the design and development of mammalians cardiovascular systems. Similarities in mammalian arterial wave reflection suggest there are certain design criteria for the optimization of arterial wave dynamics. Inspired by these natural optimization criteria, we investigated the feasibility of optimizing the aortic waves by modifying wave reflection sites. A hydraulic model that has physical and dynamical properties similar to a human aorta and left ventricle was used for a series of in-vitro experiments. The results indicate that placing an artificial reflection site (a ring at a specific location along the aorta may create a constructive wave dynamic that could reduce LV pulsatile workload. This simple bio-inspired approach may have important implications for the future of treatment strategies for diseased aorta.

Natural and bio-inspired underwater adhesives: Current progress and new perspectives

Science.gov (United States)

Cui, Mengkui; Ren, Susu; Wei, Shicao; Sun, Chengjun; Zhong, Chao

2017-11-01

Many marine organisms harness diverse protein molecules as underwater adhesives to achieve strong and robust interfacial adhesion under dynamic and turbulent environments. Natural underwater adhesion phenomena thus provide inspiration for engineering adhesive materials that can perform in water or high-moisture settings for biomedical and industrial applications. Here we review examples of biological adhesives to show the molecular features of natural adhesives and discuss how such knowledge serves as a heuristic guideline for the rational design of biologically inspired underwater adhesives. In view of future bio-inspired research, we propose several potential opportunities, either in improving upon current L-3, 4-dihydroxyphenylalanine-based and coacervates-enabled adhesives with new features or engineering conceptually new types of adhesives that recapitulate important characteristics of biological adhesives. We underline the importance of viewing natural adhesives as dynamic materials, which owe their outstanding performance to the cellular coordination of protein expression, delivery, deposition, assembly, and curing of corresponding components with spatiotemporal control. We envision that the emerging synthetic biology techniques will provide great opportunities for advancing both fundamental and application aspects of underwater adhesives.

Natural and bio-inspired underwater adhesives: Current progress and new perspectives

Directory of Open Access Journals (Sweden)

Mengkui Cui

2017-11-01

Full Text Available Many marine organisms harness diverse protein molecules as underwater adhesives to achieve strong and robust interfacial adhesion under dynamic and turbulent environments. Natural underwater adhesion phenomena thus provide inspiration for engineering adhesive materials that can perform in water or high-moisture settings for biomedical and industrial applications. Here we review examples of biological adhesives to show the molecular features of natural adhesives and discuss how such knowledge serves as a heuristic guideline for the rational design of biologically inspired underwater adhesives. In view of future bio-inspired research, we propose several potential opportunities, either in improving upon current L-3, 4-dihydroxyphenylalanine-based and coacervates-enabled adhesives with new features or engineering conceptually new types of adhesives that recapitulate important characteristics of biological adhesives. We underline the importance of viewing natural adhesives as dynamic materials, which owe their outstanding performance to the cellular coordination of protein expression, delivery, deposition, assembly, and curing of corresponding components with spatiotemporal control. We envision that the emerging synthetic biology techniques will provide great opportunities for advancing both fundamental and application aspects of underwater adhesives.

Bio-Inspired Control of an Arm Exoskeleton Joint with Active-Compliant Actuation System

Directory of Open Access Journals (Sweden)

Michele Folgheraiter

2009-01-01

Full Text Available This paper presents the methodology followed on the design of a multi-contact point haptic interface that uses a bio-inspired control approach and a novel actuation system. The combination of these components aims at creating a system that increases the operability of the target, and, at the same time, enables an intuitive and safe tele-operation of any complex robotic system of any given morphology. The novelty lies on the combination of a thoughtful kinematic structure driven by an active-compliant actuation system and a bio-inspired paradigm for its regulation. Due to the proposed actuation approach, the final system will achieve the condition of wearable system. On that final solution, each joint will be able to change its stiffness depending on the task to be executed, and on the anatomical features of each individual. Moreover, the system provides a variety of safety mechanisms at different levels to prevent causing any harm to the operator. In future, the system should allow the complete virtual immersion of the user within the working scenario.

On the relationships between generative encodings, regularity, and learning abilities when evolving plastic artificial neural networks.

Directory of Open Access Journals (Sweden)

Paul Tonelli

Full Text Available A major goal of bio-inspired artificial intelligence is to design artificial neural networks with abilities that resemble those of animal nervous systems. It is commonly believed that two keys for evolving nature-like artificial neural networks are (1 the developmental process that links genes to nervous systems, which enables the evolution of large, regular neural networks, and (2 synaptic plasticity, which allows neural networks to change during their lifetime. So far, these two topics have been mainly studied separately. The present paper shows that they are actually deeply connected. Using a simple operant conditioning task and a classic evolutionary algorithm, we compare three ways to encode plastic neural networks: a direct encoding, a developmental encoding inspired by computational neuroscience models, and a developmental encoding inspired by morphogen gradients (similar to HyperNEAT. Our results suggest that using a developmental encoding could improve the learning abilities of evolved, plastic neural networks. Complementary experiments reveal that this result is likely the consequence of the bias of developmental encodings towards regular structures: (1 in our experimental setup, encodings that tend to produce more regular networks yield networks with better general learning abilities; (2 whatever the encoding is, networks that are the more regular are statistically those that have the best learning abilities.

Design and Characterization of a Novel Bio-inspired Hair Flow Sensor Based on Resonant Sensing

Science.gov (United States)

Guo, X.; Yang, B.; Wang, Q. H.; Lu, C. F.; Hu, D.

2018-03-01

Flow sensors inspired by the natural hair sensing mechanism have great prospect in the research of micro-autonomous system and technology (MAST) for the three-dimensional structure characteristics with high spatial and quality utilization. A novel bio-inspired hair flow sensor (BHFS) based on resonant sensing with a unique asymmetric design is presented in this paper. A hair transducer and a signal detector which is constituted of a two-stage micro-leverage mechanism and two symmetrical resonators (double ended tuning fork, DETF) are adopted to realize the high sensitivity to air flow. The sensitivity of the proposed BHFS is improved significantly than the published ones due to the high sensitivity of resonators and the higher amplification factor possessed by the two-stage micro-leverage mechanism. The standard deep dry silicon on glass (DDSOG) process is chosen to fabricate the proposed BHFS. The experiment result demonstrates that the fabricated BHFS has a mechanical sensitivity of 5.26 Hz/(m/s)2 at a resonant frequency of 22 kHz with the hair height of 6 mm.

An FEA study on impact resistance of bio-inspired CAD models

OpenAIRE

Page, T; Thorsteinsson, G

2017-01-01

The purpose of this paper is to explore the use of biomimetic methods in the design of armour systems. It focusses on biological structures found in nature that feature both rigid and flexible armours, analysing their structures and determining which are the most widely successful. A study was conducted on three bio-inspired structures built in Creo Parametric and tested using Finite Element Analysis (FEA) software to determine which structure had the best impact resistance. The study was con...

Bio-inspired nano-photodiode for Low Light, High Resolution and crosstalk-free CMOS image sensing

KAUST Repository

Saffih, Faycal; Fitzpatrick, Nathaniel N.; Mohammad, Mohammad Ali; Evoy, S.; Cui, Bo

2011-01-01

photodiode morphology. The proposed bio-inspired nanorod photodiode puts the depletion region length on the path of the incident photon instead of on its width, as the case is with the planar photodiodes. The depletion region has a revolving volume

Bio-inspired grasp control in a robotic hand with massive sensorial input.

Science.gov (United States)

Ascari, Luca; Bertocchi, Ulisse; Corradi, Paolo; Laschi, Cecilia; Dario, Paolo

2009-02-01

The capability of grasping and lifting an object in a suitable, stable and controlled way is an outstanding feature for a robot, and thus far, one of the major problems to be solved in robotics. No robotic tools able to perform an advanced control of the grasp as, for instance, the human hand does, have been demonstrated to date. Due to its capital importance in science and in many applications, namely from biomedics to manufacturing, the issue has been matter of deep scientific investigations in both the field of neurophysiology and robotics. While the former is contributing with a profound understanding of the dynamics of real-time control of the slippage and grasp force in the human hand, the latter tries more and more to reproduce, or take inspiration by, the nature's approach, by means of hardware and software technology. On this regard, one of the major constraints robotics has to overcome is the real-time processing of a large amounts of data generated by the tactile sensors while grasping, which poses serious problems to the available computational power. In this paper a bio-inspired approach to tactile data processing has been followed in order to design and test a hardware-software robotic architecture that works on the parallel processing of a large amount of tactile sensing signals. The working principle of the architecture bases on the cellular nonlinear/neural network (CNN) paradigm, while using both hand shape and spatial-temporal features obtained from an array of microfabricated force sensors, in order to control the sensory-motor coordination of the robotic system. Prototypical grasping tasks were selected to measure the system performances applied to a computer-interfaced robotic hand. Successful grasps of several objects, completely unknown to the robot, e.g. soft and deformable objects like plastic bottles, soft balls, and Japanese tofu, have been demonstrated.

RAIN: A Bio-Inspired Communication and Data Storage Infrastructure.

Science.gov (United States)

Monti, Matteo; Rasmussen, Steen

2017-01-01

We summarize the results and perspectives from a companion article, where we presented and evaluated an alternative architecture for data storage in distributed networks. We name the bio-inspired architecture RAIN, and it offers file storage service that, in contrast with current centralized cloud storage, has privacy by design, is open source, is more secure, is scalable, is more sustainable, has community ownership, is inexpensive, and is potentially faster, more efficient, and more reliable. We propose that a RAIN-style architecture could form the backbone of the Internet of Things that likely will integrate multiple current and future infrastructures ranging from online services and cryptocurrency to parts of government administration.

A Review of Natural Joint Systems and Numerical Investigation of Bio-Inspired GFRP-to-Steel Joints

Directory of Open Access Journals (Sweden)

Evangelos I. Avgoulas

2016-07-01

Full Text Available There are a great variety of joint types used in nature which can inspire engineering joints. In order to design such biomimetic joints, it is at first important to understand how biological joints work. A comprehensive literature review, considering natural joints from a mechanical point of view, was undertaken. This was used to develop a taxonomy based on the different methods/functions that nature successfully uses to attach dissimilar tissues. One of the key methods that nature uses to join dissimilar materials is a transitional zone of stiffness at the insertion site. This method was used to propose bio-inspired solutions with a transitional zone of stiffness at the joint site for several glass fibre reinforced plastic (GFRP to steel adhesively bonded joint configurations. The transition zone was used to reduce the material stiffness mismatch of the joint parts. A numerical finite element model was used to identify the optimum variation in material stiffness that minimises potential failure of the joint. The best bio-inspired joints showed a 118% increase of joint strength compared to the standard joints.

Modular representation of layered neural networks.

Science.gov (United States)

Watanabe, Chihiro; Hiramatsu, Kaoru; Kashino, Kunio

2018-01-01

Layered neural networks have greatly improved the performance of various applications including image processing, speech recognition, natural language processing, and bioinformatics. However, it is still difficult to discover or interpret knowledge from the inference provided by a layered neural network, since its internal representation has many nonlinear and complex parameters embedded in hierarchical layers. Therefore, it becomes important to establish a new methodology by which layered neural networks can be understood. In this paper, we propose a new method for extracting a global and simplified structure from a layered neural network. Based on network analysis, the proposed method detects communities or clusters of units with similar connection patterns. We show its effectiveness by applying it to three use cases. (1) Network decomposition: it can decompose a trained neural network into multiple small independent networks thus dividing the problem and reducing the computation time. (2) Training assessment: the appropriateness of a trained result with a given hyperparameter or randomly chosen initial parameters can be evaluated by using a modularity index. And (3) data analysis: in practical data it reveals the community structure in the input, hidden, and output layers, which serves as a clue for discovering knowledge from a trained neural network. Copyright © 2017 Elsevier Ltd. All rights reserved.

Biologically inspired multi-layered synthetic skin for tactile feedback in prosthetic limbs.

Science.gov (United States)

Osborn, Luke; Nguyen, Harrison; Betthauser, Joseph; Kaliki, Rahul; Thakor, Nitish

2016-08-01

The human body offers a template for many state-of-the-art prosthetic devices and sensors. In this work, we present a novel, sensorized synthetic skin that mimics the natural multi-layered nature of mechanoreceptors found in healthy glabrous skin to provide tactile information. The multi-layered sensor is made up of flexible piezoresistive textiles that act as force sensitive resistors (FSRs) to convey tactile information, which are embedded within a silicone rubber to resemble the compliant nature of human skin. The top layer of the synthetic skin is capable of detecting small loads less than 5 N whereas the bottom sensing layer responds reliably to loads over 7 N. Finite element analysis (FEA) of a simplified human fingertip and the synthetic skin was performed. Results suggest similarities in behavior during loading. A natural tactile event is simulated by loading the synthetic skin on a prosthetic limb. Results show the sensors' ability to detect applied loads as well as the ability to simulate neural spiking activity based on the derivative and temporal differences of the sensor response. During the tactile loading, the top sensing layer responded 0.24 s faster than the bottom sensing layer. A synthetic biologically-inspired skin such as this will be useful for enhancing the functionality of prosthetic limbs through tactile feedback.

Final Report for Bio-Inspired Approaches to Moving-Target Defense Strategies

Energy Technology Data Exchange (ETDEWEB)

Fink, Glenn A.; Oehmen, Christopher S.

2012-09-01

This report records the work and contributions of the NITRD-funded Bio-Inspired Approaches to Moving-Target Defense Strategies project performed by Pacific Northwest National Laboratory under the technical guidance of the National Security Agency’s R6 division. The project has incorporated a number of bio-inspired cyber defensive technologies within an elastic framework provided by the Digital Ants. This project has created the first scalable, real-world prototype of the Digital Ants Framework (DAF)[11] and integrated five technologies into this flexible, decentralized framework: (1) Ant-Based Cyber Defense (ABCD), (2) Behavioral Indicators, (3) Bioinformatic Clas- sification, (4) Moving-Target Reconfiguration, and (5) Ambient Collaboration. The DAF can be used operationally to decentralize many such data intensive applications that normally rely on collection of large amounts of data in a central repository. In this work, we have shown how these component applications may be decentralized and may perform analysis at the edge. Operationally, this will enable analytics to scale far beyond current limitations while not suffering from the bandwidth or computational limitations of centralized analysis. This effort has advanced the R6 Cyber Security research program to secure digital infrastructures by developing a dynamic means to adaptively defend complex cyber systems. We hope that this work will benefit both our client’s efforts in system behavior modeling and cyber security to the overall benefit of the nation.

Bio-inspired Miniature Suction Cups Actuated by Shape Memory Alloy

Directory of Open Access Journals (Sweden)

Hu Bing-shan

2010-02-01

Full Text Available Wall climbing robots using negative pressure suction always employ air pumps which have great noise and large volume. Two prototypes of bio-inspired miniature suction cup actuated by shape memory alloy (SMA are designed based on studying characteristics of biologic suction apparatuses, and the suction cups in this paper can be used as adhesion mechanisms for miniature wall climbing robots without air pumps. The first prototype with a two-way shape memory effect (TWSME extension TiNi spring imitates the piston structure of the stalked sucker; the second one actuated by a one way SMA actuator with a bias has a basic structure of stiff margin, guiding element, leader and elastic element. Analytical model of the second prototype is founded considering the constitutive model of the SMA actuator, the deflection of the thin elastic plate under compound load and the thermo-dynamic model of the sealed air cavity. Experiments are done to test their suction characteristics, and the analytical model of the second prototype is simulated on Matlab/simulink platform and validated by experiments.

Bio-inspired Miniature Suction Cups Actuated by Shape Memory Alloy

Directory of Open Access Journals (Sweden)

Hu Bing-Shan

2009-09-01

Full Text Available Wall climbing robots using negative pressure suction always employ air pumps which have great noise and large volume. Two prototypes of bio-inspired miniature suction cup actuated by shape memory alloy (SMA are designed based on studying characteristics of biologic suction apparatuses, and the suction cups in this paper can be used as adhesion mechanisms for miniature wall climbing robots without air pumps. The first prototype with a two-way shape memory effect (TWSME extension TiNi spring imitates the piston structure of the stalked sucker; the second one actuated by a one way SMA actuator with a bias has a basic structure of stiff margin, guiding element, leader and elastic element. Analytical model of the second prototype is founded considering the constitutive model of the SMA actuator, the deflection of the thin elastic plate under compound load and the thermo-dynamic model of the sealed air cavity. Experiments are done to test their suction characteristics, and the analytical model of the second prototype is simulated on Matlab/simulink platform and validated by experiments.

Bio-Inspired Design and Kinematic Analysis of Dung Beetle-Like Legs

DEFF Research Database (Denmark)

Aditya, Sai Krishna Venkata; Ignasov, Jevgeni; Filonenko, Konstantin

2017-01-01

The African dung beetle Scarabaeus galenus can use its front legs to walk and manipulate or form a dung ball. The interesting multifunctional legs have not been fully investigated or even used as inspiration for robot leg design. Thus, in this paper, we present the development of real dung beetle......-like front legs based on biological investigation. As a result, each leg consists of three main segments which were built using 3D printing. The segments were combined with in total four active DOFs in order to mimic locomotion and object manipulation of the beetle. Kinematics analysis of the leg was also...... performed to identify its workspace as well as to design its trajectory. To this end, the study contributes not only novel multifunctional robotic legs but also the methodology of the bio-inspired leg design....

Copper removal using bio-inspired polydopamine coated natural zeolites

Energy Technology Data Exchange (ETDEWEB)

Yu, Yang; Shapter, Joseph G. [Flinders Centre for Nanoscale Science and Technology, School of Chemical and Physical Sciences, Flinders University, Sturt Road, Bedford Park, Adelaide 5042, SA (Australia); Popelka-Filcoff, Rachel [School of Chemical and Physical Sciences, Flinders University, Sturt Road, Bedford Park, Adelaide 5042, SA (Australia); Bennett, John W. [Centre for Nuclear Applications, Australian Nuclear Science and Technology Organisation, Lucas Heights 2234, NSW (Australia); Ellis, Amanda V., E-mail: Amanda.Ellis@flinders.edu.au [Flinders Centre for Nanoscale Science and Technology, School of Chemical and Physical Sciences, Flinders University, Sturt Road, Bedford Park, Adelaide 5042, SA (Australia)

2014-05-01

Highlights: • Natural zeolites were modified with bio-inspired polydopamine. • A 91.4% increase in Cu(II) ion adsorption capacity was observed. • Atomic absorption and neutron activation analysis gave corroborative results. • Neutron activation analysis was used to provide accurate information on 30+ elements. • Approximately 90% of the adsorbed copper could be recovered by 0.1 M HCl treatment. - Abstract: Herein, for the first time, natural clinoptilolite-rich zeolite powders modified with a bio-inspired adhesive, polydopamine (PDA), have been systematically studied as an adsorbent for copper cations (Cu(II)) from aqueous solution. Fourier transform infrared (FTIR) spectroscopy, X-ray photoelectron spectroscopy (XPS) and thermogravimetric analysis (TGA) revealed successful grafting of PDA onto the zeolite surface. The effects of pH (2–5.5), PDA treatment time (3–24 h), contact time (0 to 24 h) and initial Cu(II) ion concentrations (1 to 500 mg dm{sup −3}) on the adsorption of Cu(II) ions were studied using atomic absorption spectroscopy (AAS) and neutron activation analysis (NAA). The adsorption behavior was fitted to a Langmuir isotherm and shown to follow a pseudo-second-order reaction model. The maximum adsorption capacities of Cu(II) were shown to be 14.93 mg g{sup −1} for pristine natural zeolite and 28.58 mg g{sup −1} for PDA treated zeolite powders. This impressive 91.4% increase in Cu(II) ion adsorption capacity is attributed to the chelating ability of the PDA on the zeolite surface. Furthermore studies of recyclability using NAA showed that over 50% of the adsorbed copper could be removed in mild concentrations (0.01 M or 0.1 M) of either acid or base.

Copper removal using bio-inspired polydopamine coated natural zeolites

International Nuclear Information System (INIS)

Yu, Yang; Shapter, Joseph G.; Popelka-Filcoff, Rachel; Bennett, John W.; Ellis, Amanda V.

2014-01-01

Highlights: • Natural zeolites were modified with bio-inspired polydopamine. • A 91.4% increase in Cu(II) ion adsorption capacity was observed. • Atomic absorption and neutron activation analysis gave corroborative results. • Neutron activation analysis was used to provide accurate information on 30+ elements. • Approximately 90% of the adsorbed copper could be recovered by 0.1 M HCl treatment. - Abstract: Herein, for the first time, natural clinoptilolite-rich zeolite powders modified with a bio-inspired adhesive, polydopamine (PDA), have been systematically studied as an adsorbent for copper cations (Cu(II)) from aqueous solution. Fourier transform infrared (FTIR) spectroscopy, X-ray photoelectron spectroscopy (XPS) and thermogravimetric analysis (TGA) revealed successful grafting of PDA onto the zeolite surface. The effects of pH (2–5.5), PDA treatment time (3–24 h), contact time (0 to 24 h) and initial Cu(II) ion concentrations (1 to 500 mg dm −3 ) on the adsorption of Cu(II) ions were studied using atomic absorption spectroscopy (AAS) and neutron activation analysis (NAA). The adsorption behavior was fitted to a Langmuir isotherm and shown to follow a pseudo-second-order reaction model. The maximum adsorption capacities of Cu(II) were shown to be 14.93 mg g −1 for pristine natural zeolite and 28.58 mg g −1 for PDA treated zeolite powders. This impressive 91.4% increase in Cu(II) ion adsorption capacity is attributed to the chelating ability of the PDA on the zeolite surface. Furthermore studies of recyclability using NAA showed that over 50% of the adsorbed copper could be removed in mild concentrations (0.01 M or 0.1 M) of either acid or base

Characterization of anthocyanin based dye-sensitized organic solar cells (DSSC) and modifications based on bio-inspired ion mobility improvements

Science.gov (United States)

Mawyin, Jose Amador

to a strong Raman anti-Stoke scattering probability). Finally, solutions to the mobility problem of organic photovoltaics were explored. The solutions examined here were based on the bio-inspired neural ionic conduction were nature has overcome the poor ionic mobility in solutions (D ˜ 10-5cm2/ s) to achieve amazingly fast ionic conduction using non-electric field energy gradients. Electric-permeability-graded layers with possibility to create an energy gradient that helps the diffusion DSSC electrolyte diffusion were explored in this work.

A bio-inspired high-authority actuator for shape morphing structures

Science.gov (United States)

Elzey, Dana M.; Sofla, Aarash Y. N.; Wadley, Haydn N. G.

2003-08-01

Lightweight structures capable of changing their shape on demand are of interest for a number of applications, including aerospace, power generation, and undersea vehicles. This paper describes a bio-inspired cellular metal vertebrate structure which relies on shape memory alloy (SMA) faces to achieve fully reversing shape change. The resulting vertebrate actuators can be combined with flexible face sheets to create a load-bearing, shape morphing panel. Performance of the vertebrate actuator in terms of maximum curvature and moment is analyzed and discussed. A recently constructed, prototype shape morphing airfoil is used to illustrate the concept.

Bio-inspired multistructured conical copper wires for highly efficient liquid manipulation.

Science.gov (United States)

Wang, Qianbin; Meng, Qingan; Chen, Ming; Liu, Huan; Jiang, Lei

2014-09-23

Animal hairs are typical structured conical fibers ubiquitous in natural system that enable the manipulation of low viscosity liquid in a well-controlled manner, which serves as the fundamental structure in Chinese brush for ink delivery in a controllable manner. Here, drawing inspiration from these structure, we developed a dynamic electrochemical method that enables fabricating the anisotropic multiscale structured conical copper wire (SCCW) with controllable conicity and surface morphology. The as-prepared SCCW exhibits a unique ability for manipulating liquid with significantly high efficiency, and over 428 times greater than its own volume of liquid could be therefore operated. We propose that the boundary condition of the dynamic liquid balance behavior on conical fibers, namely, steady holding of liquid droplet at the tip region of the SCCW, makes it an excellent fibrous medium to manipulate liquid. Moreover, we demonstrate that the titling angle of the SCCW can also affect its efficiency of liquid manipulation by virtue of its mechanical rigidity, which is hardly realized by flexible natural hairs. We envision that the bio-inspired SCCW could give inspiration in designing materials and devices to manipulate liquid in a more controllable way and with high efficiency.

Facile creation of bio-inspired superhydrophobic Ce-based metallic glass surfaces

Science.gov (United States)

Liu, Kesong; Li, Zhou; Wang, Weihua; Jiang, Lei

2011-12-01

A bio-inspired synthesis strategy was conducted to fabricate superhydrophobic Ce-based bulk metallic glass (BMG) surfaces with self-cleaning properties. Micro-nanoscale hierarchical structures were first constructed on BMG surfaces and then modified with the low surface energy coating. Surface structures, surface chemical compositions, and wettability were characterized by combining scanning electron microscopy, atomic force microscopy, x-ray photoelectron spectroscopy, and contact angle measurements. Research indicated that both surface multiscale structures and the low surface free energy coating result in the final formation of superhydrophobicity.

A tracked robot with novel bio-inspired passive "legs".

Science.gov (United States)

Sun, Bo; Jing, Xingjian

2017-01-01

For track-based robots, an important aspect is the suppression design, which determines the trafficability and comfort of the whole system. The trafficability limits the robot's working capability, and the riding comfort limits the robot's working effectiveness, especially with some sensitive instruments mounted on or operated. To these aims, a track-based robot equipped with a novel passive bio-inspired suspension is designed and studied systematically in this paper. Animal or insects have very special leg or limb structures which are good for motion control and adaptable to different environments. Inspired by this, a new track-based robot is designed with novel "legs" for connecting the loading wheels to the robot body. Each leg is designed with passive structures and can achieve very high loading capacity but low dynamic stiffness such that the robot can move on rough ground similar to a multi-leg animal or insect. Therefore, the trafficability and riding comfort can be significantly improved without losing loading capacity. The new track-based robot can be well applied to various engineering tasks for providing a stable moving platform of high mobility, better trafficability and excellent loading capacity.

New development thoughts on the bio-inspired intelligence based control for unmanned combat aerial vehicle

Institute of Scientific and Technical Information of China (English)

无

2010-01-01

Bio-inspired intelligence is in the spotlight in the field of international artificial intelligence,and unmanned combat aerial vehicle(UCAV),owing to its potential to perform dangerous,repetitive tasks in remote and hazardous,is very promising for the technological leadership of the nation and essential for improving the security of society.On the basis of introduction of bioinspired intelligence and UCAV,a series of new development thoughts on UCAV control are proposed,including artificial brain based high-level autonomous control for UCAV,swarm intelligence based cooperative control for multiple UCAVs,hy-brid swarm intelligence and Bayesian network based situation assessment under complicated combating environments, bio-inspired hardware based high-level autonomous control for UCAV,and meta-heuristic intelligence based heterogeneous cooperative control for multiple UCAVs and unmanned combat ground vehicles(UCGVs).The exact realization of the proposed new development thoughts can enhance the effectiveness of combat,while provide a series of novel breakthroughs for the intelligence,integration and advancement of future UCAV systems.

Mechanical properties of a bio-inspired robotic knifefish with an undulatory propulsor

International Nuclear Information System (INIS)

Curet, Oscar M; Patankar, Neelesh A; MacIver, Malcolm A; Lauder, George V

2011-01-01

South American electric knifefish are a leading model system within neurobiology. Recent efforts have focused on understanding their biomechanics and relating this to their neural processing strategies. Knifefish swim by means of an undulatory fin that runs most of the length of their body, affixed to the belly. Propelling themselves with this fin enables them to keep their body relatively straight while swimming, enabling straightforward robotic implementation with a rigid hull. In this study, we examined the basic properties of undulatory swimming through use of a robot that was similar in some key respects to the knifefish. As we varied critical fin kinematic variables such as frequency, amplitude, and wavelength of sinusoidal traveling waves, we measured the force generated by the robot when it swam against a stationary sensor, and its velocity while swimming freely within a flow tunnel system. Our results show that there is an optimal operational region in the fin's kinematic parameter space. The optimal actuation parameters found for the robotic knifefish are similar to previously observed parameters for the black ghost knifefish, Apteronotus albifrons. Finally, we used our experimental results to show how the force generated by the robotic fin can be decomposed into thrust and drag terms. Our findings are useful for future bio-inspired underwater vehicles as well as for understanding the mechanics of knifefish swimming.

Bio-inspired UAV routing, source localization, and acoustic signature classification for persistent surveillance

Science.gov (United States)

Burman, Jerry; Hespanha, Joao; Madhow, Upamanyu; Pham, Tien

2011-06-01

A team consisting of Teledyne Scientific Company, the University of California at Santa Barbara and the Army Research Laboratory* is developing technologies in support of automated data exfiltration from heterogeneous battlefield sensor networks to enhance situational awareness for dismounts and command echelons. Unmanned aerial vehicles (UAV) provide an effective means to autonomously collect data from a sparse network of unattended ground sensors (UGSs) that cannot communicate with each other. UAVs are used to reduce the system reaction time by generating autonomous collection routes that are data-driven. Bio-inspired techniques for search provide a novel strategy to detect, capture and fuse data. A fast and accurate method has been developed to localize an event by fusing data from a sparse number of UGSs. This technique uses a bio-inspired algorithm based on chemotaxis or the motion of bacteria seeking nutrients in their environment. A unique acoustic event classification algorithm was also developed based on using swarm optimization. Additional studies addressed the problem of routing multiple UAVs, optimally placing sensors in the field and locating the source of gunfire at helicopters. A field test was conducted in November of 2009 at Camp Roberts, CA. The field test results showed that a system controlled by bio-inspired software algorithms can autonomously detect and locate the source of an acoustic event with very high accuracy and visually verify the event. In nine independent test runs of a UAV, the system autonomously located the position of an explosion nine times with an average accuracy of 3 meters. The time required to perform source localization using the UAV was on the order of a few minutes based on UAV flight times. In June 2011, additional field tests of the system will be performed and will include multiple acoustic events, optimal sensor placement based on acoustic phenomenology and the use of the International Technology Alliance (ITA

Biologically-inspired Learning in Pulsed Neural Networks

DEFF Research Database (Denmark)

Lehmann, Torsten; Woodburn, Robin

1999-01-01

Self-learning chips to implement many popular ANN (artificial neural network) algorithms are very difficult to design. We explain why this is so and say what lessons previous work teaches us in the design of self-learning systems. We offer a contribution to the `biologically-inspired' approach......, explaining what we mean by this term and providing an example of a robust, self-learning design that can solve simple classical-conditioning tasks. We give details of the design of individual circuits to perform component functions, which can then be combined into a network to solve the task. We argue...

Three-Dimensional-Printing of Bio-Inspired Composites

Science.gov (United States)

Xiang Gu, Grace; Su, Isabelle; Sharma, Shruti; Voros, Jamie L.; Qin, Zhao; Buehler, Markus J.

2016-01-01

Optimized for millions of years, natural materials often outperform synthetic materials due to their hierarchical structures and multifunctional abilities. They usually feature a complex architecture that consists of simple building blocks. Indeed, many natural materials such as bone, nacre, hair, and spider silk, have outstanding material properties, making them applicable to engineering applications that may require both mechanical resilience and environmental compatibility. However, such natural materials are very difficult to harvest in bulk, and may be toxic in the way they occur naturally, and therefore, it is critical to use alternative methods to fabricate materials that have material functions similar to material function as their natural counterparts for large-scale applications. Recent progress in additive manufacturing, especially the ability to print multiple materials at upper micrometer resolution, has given researchers an excellent instrument to design and reconstruct natural-inspired materials. The most advanced 3D-printer can now be used to manufacture samples to emulate their geometry and material composition with high fidelity. Its capabilities, in combination with computational modeling, have provided us even more opportunities for designing, optimizing, and testing the function of composite materials, in order to achieve composites of high mechanical resilience and reliability. In this review article, we focus on the advanced material properties of several multifunctional biological materials and discuss how the advanced 3D-printing techniques can be used to mimic their architectures and functions. Lastly, we discuss the limitations of 3D-printing, suggest possible future developments, and discuss applications using bio-inspired materials as a tool in bioengineering and other fields. PMID:26747791

Three-Dimensional-Printing of Bio-Inspired Composites.

Science.gov (United States)

Xiang Gu, Grace; Su, Isabelle; Sharma, Shruti; Voros, Jamie L; Qin, Zhao; Buehler, Markus J

2016-02-01

Optimized for millions of years, natural materials often outperform synthetic materials due to their hierarchical structures and multifunctional abilities. They usually feature a complex architecture that consists of simple building blocks. Indeed, many natural materials such as bone, nacre, hair, and spider silk, have outstanding material properties, making them applicable to engineering applications that may require both mechanical resilience and environmental compatibility. However, such natural materials are very difficult to harvest in bulk, and may be toxic in the way they occur naturally, and therefore, it is critical to use alternative methods to fabricate materials that have material functions similar to material function as their natural counterparts for large-scale applications. Recent progress in additive manufacturing, especially the ability to print multiple materials at upper micrometer resolution, has given researchers an excellent instrument to design and reconstruct natural-inspired materials. The most advanced 3D-printer can now be used to manufacture samples to emulate their geometry and material composition with high fidelity. Its capabilities, in combination with computational modeling, have provided us even more opportunities for designing, optimizing, and testing the function of composite materials, in order to achieve composites of high mechanical resilience and reliability. In this review article, we focus on the advanced material properties of several multifunctional biological materials and discuss how the advanced 3D-printing techniques can be used to mimic their architectures and functions. Lastly, we discuss the limitations of 3D-printing, suggest possible future developments, and discuss applications using bio-inspired materials as a tool in bioengineering and other fields.

Post-capture vibration suppression of spacecraft via a bio-inspired isolation system

Science.gov (United States)

Dai, Honghua; Jing, Xingjian; Wang, Yu; Yue, Xiaokui; Yuan, Jianping

2018-05-01

Inspired by the smooth motions of a running kangaroo, a bio-inspired quadrilateral shape (BIQS) structure is proposed to suppress the vibrations of a free-floating spacecraft subject to periodic or impulsive forces, which may be encountered during on-orbit servicing missions. In particular, the BIQS structure is installed between the satellite platform and the capture mechanism. The dynamical model of the BIQS isolation system, i.e. a BIQS structure connecting the platform and the capture mechanism at each side, is established by Lagrange's equations to simulate the post-capture dynamical responses. The BIQS system suffering an impulsive force is dealt with by means of a modified version of Lagrange's equations. Furthermore, the classical harmonic balance method is used to solve the nonlinear dynamical system subject to periodic forces, while for the case under impulsive forces the numerical integration method is adopted. Due to the weightless environment in space, the present BIQS system is essentially an under-constrained dynamical system with one of its natural frequencies being identical to zero. The effects of system parameters, such as the number of layers in BIQS, stiffness, assembly angle, rod length, damping coefficient, masses of satellite platform and capture mechanism, on the isolation performance of the present system are thoroughly investigated. In addition, comparisons between the isolation performances of the presently proposed BIQS isolator and the conventional spring-mass-damper (SMD) isolator are conducted to demonstrate the advantages of the present isolator. Numerical simulations show that the BIQS system has a much better performance than the SMD system under either periodic or impulsive forces. Overall, the present BIQS isolator offers a highly efficient passive way for vibration suppressions of free-floating spacecraft.

Energy evaluation of a bio-inspired gait modulation method for quadrupedal locomotion.

Science.gov (United States)

Fukuoka, Yasuhiro; Fukino, Kota; Habu, Yasushi; Mori, Yoshikazu

2015-08-04

We have proposed a bio-inspired gait modulation method, by means of which a simulated quadruped model can successfully perform smooth, autonomous gait transitions from a walk to a trot to a gallop, as observed in animals. The model is equipped with a rhythm generator called a central pattern generator (CPG) for each leg. The lateral neighbouring CPGs are mutually and inhibitorily coupled, and the CPG network is hardwired to produce a trot. Adding only the simple feedback of body tilt to each CPG, which was based on input from the postural reflex, led to the emergence of un-programmed walking and galloping at low and high speeds, respectively. Although this autonomous gait transition was a consequence of postural adaptation, it coincidentally also resulted in the minimization of energy consumption, as observed in real animals. In simulations at a variety of constant speeds the energy cost was lower for walking at low speeds and for galloping at high speeds than it was for trotting. Moreover, each gait transition occurred at the optimal speed, such that the model minimised its energy consumption. Thus, gait transitions in simulations that included the bio-inspired gait modulation method were similar to those observed in animals, even from the perspective of energy consumption. This method should therefore be a preferred choice for motion generation and control in biomimetic quadrupedal locomotion.

Design and Analysis of a Bio-Inspired Wire-Driven Multi-Section Flexible Robot

Directory of Open Access Journals (Sweden)

Zheng Li

2013-04-01

Full Text Available This paper presents a bio-inspired wire-driven multi-section flexible robot. It is inspired by the snake skeleton and octopus arm muscle arrangements. The robot consists of three sections and each section is made up of several identical vertebras, which are articulated by both spherical joints and a flexible backbone. Each section is driven by two groups of wires, controlling the bending motion in X and Y directions. This design integrates the serpentine robots' structure and the continuum robots' actuation. As a result, it is more compact than traditional serpentine robots and has a higher positioning accuracy than typical continuum soft robots, such as OctArm V. A Kinematics model and a workspace model of the robot are developed based on the piece wise constant curvature assumption. To evaluate the design, a prototype is built and experiments are carried out. The average distal end positioning error is less than 4%. Characteristics of the wire-driven robot are also discussed, including the leverage effect and the manipulability under constraint. These features makes the proposed robot well suited to confined spaces, especially for working in minimally invasive surgery, nuclear reactor pipelines, disaster debris, etc.

Bio-inspired nano tools for neuroscience.

Science.gov (United States)

Das, Suradip; Carnicer-Lombarte, Alejandro; Fawcett, James W; Bora, Utpal

2016-07-01

Research and treatment in the nervous system is challenged by many physiological barriers posing a major hurdle for neurologists. The CNS is protected by a formidable blood brain barrier (BBB) which limits surgical, therapeutic and diagnostic interventions. The hostile environment created by reactive astrocytes in the CNS along with the limited regeneration capacity of the PNS makes functional recovery after tissue damage difficult and inefficient. Nanomaterials have the unique ability to interface with neural tissue in the nano-scale and are capable of influencing the function of a single neuron. The ability of nanoparticles to transcend the BBB through surface modifications has been exploited in various neuro-imaging techniques and for targeted drug delivery. The tunable topography of nanofibers provides accurate spatio-temporal guidance to regenerating axons. This review is an attempt to comprehend the progress in understanding the obstacles posed by the complex physiology of the nervous system and the innovations in design and fabrication of advanced nanomaterials drawing inspiration from natural phenomenon. We also discuss the development of nanomaterials for use in Neuro-diagnostics, Neuro-therapy and the fabrication of advanced nano-devices for use in opto-electronic and ultrasensitive electrophysiological applications. The energy efficient and parallel computing ability of the human brain has inspired the design of advanced nanotechnology based computational systems. However, extensive use of nanomaterials in neuroscience also raises serious toxicity issues as well as ethical concerns regarding nano implants in the brain. In conclusion we summarize these challenges and provide an insight into the huge potential of nanotechnology platforms in neuroscience. Copyright © 2016 Elsevier Ltd. All rights reserved.

Design of a bio-inspired pneumatic artificial muscle with self-contained sensing.

Science.gov (United States)

Erin, Onder; Pol, Nishant; Valle, Luis; Yong-Lae Park

2016-08-01

Pneumatic artificial muscles (PAMs) are one of the most famous linear actuators in bio-inspired robotics. They can generate relatively high linear force considering their form factors and weights. Furthermore, PAMs are inexpensive compared with traditional electromagnetic actuators (e.g. DC motors) and also inherently light and compliant. In robotics applications, however, they typically require external sensing mechanisms due to their nonlinear behaviors, which may make the entire mechanical system bulky and complicated, limiting their use in simple systems. This study presents the design and fabrication of a low-cost McKibben-type PAM with a self-contained displacement and force sensing capability that does not require any external sensing elements. The proposed PAM can detect axial contraction force and displacement at the same time. In this study, the design of a traditional McKibben muscle was modified to include an inductive coil surrounding the muscle fibers. Then, a thin, soft silicone layer was coated outside of the muscle to protect and hold the sensing coil on the actuator. This novel design measures coil inductance change to determine the contraction force and the displacement. The process can be applied to a variety of existing McKibben actuator designs without significantly changing the rigidity of the actuator while minimizing the device's footprint.

Catalytic applications of bio-inspired nanomaterials

Science.gov (United States)

Pacardo, Dennis Kien Balaong

The biomimetic synthesis of Pd nanoparticles was presented using the Pd4 peptide, TSNAVHPTLRHL, isolated from combinatorial phage display library. Using this approach, nearly monodisperse and spherical Pd nanoparticles were generated with an average diameter of 1.9 +/- 0.4 nm. The peptide-based nanocatalyst were employed in the Stille coupling reaction under energy-efficient and environmentally friendly reaction conditions of aqueous solvent, room temperature and very low catalyst loading. To this end, the Pd nanocatalyst generated high turnover frequency (TOF) value and quantitative yields using ≥ 0.005 mol% Pd as well as catalytic activities with different aryl halides containing electron-withdrawing and electron-donating groups. The Pd4-capped Pd nanoparticles followed the atom-leaching mechanism and were found to be selective with respect to substrate identity. On the other hand, the naturally-occurring R5 peptide (SSKKSGSYSGSKGSKRRIL) was employed in the synthesis of biotemplated Pd nanomaterials which showed morphological changes as a function of Pd:peptide ratio. TOF analysis for hydrogenation of olefinic alcohols showed similar catalytic activity regardless of nanomorphology. Determination of catalytic properties of these bio-inspired nanomaterials are important as they serve as model system for alternative green catalyst with applications in industrially important transformations.

A Bio-Inspired Herbal Tea Flavour Assessment Technique

Directory of Open Access Journals (Sweden)

Nur Zawatil Isqi Zakaria

2014-07-01

Full Text Available Herbal-based products are becoming a widespread production trend among manufacturers for the domestic and international markets. As the production increases to meet the market demand, it is very crucial for the manufacturer to ensure that their products have met specific criteria and fulfil the intended quality determined by the quality controller. One famous herbal-based product is herbal tea. This paper investigates bio-inspired flavour assessments in a data fusion framework involving an e-nose and e-tongue. The objectives are to attain good classification of different types and brands of herbal tea, classification of different flavour masking effects and finally classification of different concentrations of herbal tea. Two data fusion levels were employed in this research, low level data fusion and intermediate level data fusion. Four classification approaches; LDA, SVM, KNN and PNN were examined in search of the best classifier to achieve the research objectives. In order to evaluate the classifiers’ performance, an error estimator based on k-fold cross validation and leave-one-out were applied. Classification based on GC-MS TIC data was also included as a comparison to the classification performance using fusion approaches. Generally, KNN outperformed the other classification techniques for the three flavour assessments in the low level data fusion and intermediate level data fusion. However, the classification results based on GC-MS TIC data are varied.

Bio-inspired iron and manganese complexes derived from mixed N,O ligands for the oxidation of olefins

NARCIS (Netherlands)

Moelands, M.A.H.

2014-01-01

This Thesis describes the synthesis and structural analysis of bio-inspired iron and manganese complexes used for the catalytic oxidation of olefin substrates. The development of catalytic systems for oxidation chemistry that are based on first row transition metals and that apply a green oxidant

BioAir: Bio-Inspired Airborne Infrastructure Reconfiguration

Science.gov (United States)

2016-01-01

must also minimize resource usage due to limitations on the amount of processing , memory and power onboard a node. BioAIR assumes the availability of...subsequent maintenance of tentacles, each node will take one of the following roles: “ orphan ”, “free”, “tip”, “backbone” or “extra”. The BioAIR...algorithm dictates that when a node is disconnected from the tentacle or origin it is an orphan , and as such it will change its target to the nearest

Novel bio-inspired smart control for hazard mitigation of civil structures

International Nuclear Information System (INIS)

Kim, Yeesock; Kim, Changwon; Langari, Reza

2010-01-01

In this paper, a new bio-inspired controller is proposed for vibration mitigation of smart structures subjected to ground disturbances (i.e. earthquakes). The control system is developed through the integration of a brain emotional learning (BEL) algorithm with a proportional–integral–derivative (PID) controller and a semiactive inversion (Inv) algorithm. The BEL algorithm is based on the neurologically inspired computational model of the amygdala and the orbitofrontal cortex. To demonstrate the effectiveness of the proposed hybrid BEL–PID–Inv control algorithm, a seismically excited building structure equipped with a magnetorheological (MR) damper is investigated. The performance of the proposed hybrid BEL–PID–Inv control algorithm is compared with that of passive, PID, linear quadratic Gaussian (LQG), and BEL control systems. In the simulation, the robustness of the hybrid BEL–PID–Inv control algorithm in the presence of modeling uncertainties as well as external disturbances is investigated. It is shown that the proposed hybrid BEL–PID–Inv control algorithm is effective in improving the dynamic responses of seismically excited building structure–MR damper systems

A bio-inspired glucose controller based on pancreatic β-cell physiology.

Science.gov (United States)

Herrero, Pau; Georgiou, Pantelis; Oliver, Nick; Johnston, Desmond G; Toumazou, Christofer

2012-05-01

Control algorithms for closed-loop insulin delivery in type 1 diabetes have been mainly based on control engineering or artificial intelligence techniques. These, however, are not based on the physiology of the pancreas but seek to implement engineering solutions to biology. Developments in mathematical models of the β-cell physiology of the pancreas have described the glucose-induced insulin release from pancreatic β cells at a molecular level. This has facilitated development of a new class of bio-inspired glucose control algorithms that replicate the functionality of the biological pancreas. However, technologies for sensing glucose levels and delivering insulin use the subcutaneous route, which is nonphysiological and introduces some challenges. In this article, a novel glucose controller is presented as part of a bio-inspired artificial pancreas. A mathematical model of β-cell physiology was used as the core of the proposed controller. In order to deal with delays and lack of accuracy introduced by the subcutaneous route, insulin feedback and a gain scheduling strategy were employed. A United States Food and Drug Administration-accepted type 1 diabetes mellitus virtual population was used to validate the presented controller. Premeal and postmeal mean ± standard deviation blood glucose levels for the adult and adolescent populations were well within the target range set for the controller [(70, 180) mg/dl], with a percent time in range of 92.8 ± 7.3% for the adults and 83.5 ± 14% for the adolescents. This article shows for the first time very good glucose control in a virtual population with type 1 diabetes mellitus using a controller based on a subcellular β-cell model. © 2012 Diabetes Technology Society.

Synchronization and Inter-Layer Interactions of Noise-Driven Neural Networks.

Science.gov (United States)

Yuniati, Anis; Mai, Te-Lun; Chen, Chi-Ming

2017-01-01

In this study, we used the Hodgkin-Huxley (HH) model of neurons to investigate the phase diagram of a developing single-layer neural network and that of a network consisting of two weakly coupled neural layers. These networks are noise driven and learn through the spike-timing-dependent plasticity (STDP) or the inverse STDP rules. We described how these networks transited from a non-synchronous background activity state (BAS) to a synchronous firing state (SFS) by varying the network connectivity and the learning efficacy. In particular, we studied the interaction between a SFS layer and a BAS layer, and investigated how synchronous firing dynamics was induced in the BAS layer. We further investigated the effect of the inter-layer interaction on a BAS to SFS repair mechanism by considering three types of neuron positioning (random, grid, and lognormal distributions) and two types of inter-layer connections (random and preferential connections). Among these scenarios, we concluded that the repair mechanism has the largest effect for a network with the lognormal neuron positioning and the preferential inter-layer connections.

INVESTIGATING PECTORAL SHAPES AND LOCOMOTIVE STRATEGIES FOR CONCEPTUAL DESIGNING BIO-INSPIRED ROBOTIC FISH

Directory of Open Access Journals (Sweden)

A. I. MAINONG

2017-01-01

Full Text Available This paper describes the performance analysis of a conceptual bio-inspired robotic fish design, which is based on the morphology similar to the boxfish (Ostracion melagris. The robotic fish prototype is driven by three micro servos; two on the pectoral fins, and one on the caudal fin. Two electronic rapid prototyping boards were employed; one for the movement of robotic fish, and one for the force sensors measurements. The robotic fish were built using fused deposition modeling (FDM, more popularly known as the 3D printing method. Several designs of pectoral fins (rectangular, triangular and quarter-ellipse with unchanging the value of aspect ratio (AR employed to measure the performance of the prototype robotic fish in terms of hydrodynamics, thrust and maneuvering characteristics. The analysis of the unmanned robotic system performance is made experimentally and the results show that the proposed bioinspired robotic prototype opens up the possibility of design optimization research for future work.

Nature as an engineer: one simple concept of a bio-inspired functional artificial muscle.

Science.gov (United States)

Schmitt, S; Haeufle, D F B; Blickhan, R; Günther, M

2012-09-01

The biological muscle is a powerful, flexible and versatile actuator. Its intrinsic characteristics determine the way how movements are generated and controlled. Robotic and prosthetic applications expect to profit from relying on bio-inspired actuators which exhibit natural (muscle-like) characteristics. As of today, when constructing a technical actuator, it is not possible to copy the exact molecular structure of a biological muscle. Alternatively, the question may be put how its characteristics can be realized with known mechanical components. Recently, a mechanical construct for an artificial muscle was proposed, which exhibits hyperbolic force-velocity characteristics. In this paper, we promote the constructing concept which is made by substantiating the mechanical design of biological muscle by a simple model, proving the feasibility of its real-world implementation, and checking their output both for mutual consistency and agreement with biological measurements. In particular, the relations of force, enthalpy rate and mechanical efficiency versus contraction velocity of both the construct's technical implementation and its numerical model were determined in quick-release experiments. All model predictions for these relations and the hardware results are now in good agreement with the biological literature. We conclude that the construct represents a mechanical concept of natural actuation, which is suitable for laying down some useful suggestions when designing bio-inspired actuators.

Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot

DEFF Research Database (Denmark)

Grinke, Eduard; Tetzlaff, Christian; Wörgötter, Florentin

2015-01-01

correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors) in the network to generate different turning angles with short-term memory for a walking...... dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a many degrees-of-freedom (DOFs) walking robot is a challenging task. Thus, in this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural...... mechanisms with plasticity, exteroceptive sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent neural network consisting of two fully connected neurons. Online...

Flow Interactions of Two- and Three-Dimensional Networked Bio-Inspired Control Elements in an In-Line Arrangement.

Science.gov (United States)

Kurt, Melike; Moored, Keith

2018-04-19

-dimensions. These results can aid in the design of networked bio-inspired control elements that through integrated sensing can synchronize to three-dimensional flow interactions. © 2018 IOP Publishing Ltd.

Multi-layer micro/nanofluid devices with bio-nanovalves

Science.gov (United States)

Li, Hao; Ocola, Leonidas E.; Auciello, Orlando H.; Firestone, Millicent A.

2013-01-01

A user-friendly multi-layer micro/nanofluidic flow device and micro/nano fabrication process are provided for numerous uses. The multi-layer micro/nanofluidic flow device can comprise: a substrate, such as indium tin oxide coated glass (ITO glass); a conductive layer of ferroelectric material, preferably comprising a PZT layer of lead zirconate titanate (PZT) positioned on the substrate; electrodes connected to the conductive layer; a nanofluidics layer positioned on the conductive layer and defining nanochannels; a microfluidics layer positioned upon the nanofluidics layer and defining microchannels; and biomolecular nanovalves providing bio-nanovalves which are moveable from a closed position to an open position to control fluid flow at a nanoscale.

Bio-inspired Design Approached Antifouling Strategies

Science.gov (United States)

Fitzsimons, L.; Chapman, J.; Lawlor, A.; Regan, F.

2012-04-01

Biofouling exists as the undesirable accumulation of flora and fauna on a given substrate when immersed into an aquatic media. Its presence causes a range of deleterious effects for anyone faced in tackling the problem, which is more than often financially testing. Generally, the initial biofouling stage is stochastic and the attachment of microorganisms held fast in biofilm matrices is irreversible. Stability of the biofilm occurs when exopolymeric substances (EPS) are produced forming a protective surrounding, allowing the cohered microorganisms to colonise and thrive upon the surface. Therefore, if this initial stage of biofilm development can be prevented then it could be possible to prevent subsequent macro events that ensue. Environmental monitoring is one area that faces this challenge and forms the impetus of the work presented herein. In order to improve a monitoring device's lifetime, surface coatings with biocidal agents are applied to counteract these steps. This work shows the development of a range of novel materials, which demonstrate the ability to counteract and inhibit the initial stages of biofouling for monitoring devices. Natural bio-inspired surfaces have been developed using nano-functionalised coatings. All materials are tested in the field and positive results in reducing the biofouling challenge are demonstrated. The results from the deployment of antifouling materials, together with real-time, long-term water quality data from the test site are also shown.

Medical Image Registration by means of a Bio-Inspired Optimization Strategy

Directory of Open Access Journals (Sweden)

Hariton Costin

2012-07-01

Full Text Available Medical imaging mainly treats and processes missing, ambiguous, complementary, redundant and distorted data. Biomedical image registration is the process of geometric overlaying or alignment of two or more 2D/3D images of the same scene, taken at different time slots, from different angles, and/or by different acquisition systems. In medical practice, it is becoming increasingly important in diagnosis, treatment planning, functional studies, computer-guided therapies, and in biomedical research. Technically, image registration implies a complex optimization of different parameters, performed at local or/and global levels. Local optimization methods frequently fail because functions of the involved metrics with respect to transformation parameters are generally nonconvex and irregular. Therefore, global methods are often required, at least at the beginning of the procedure. In this paper, a new evolutionary and bio-inspired approach -- bacterial foraging optimization -- is adapted for single-slice to 3-D PET and CT multimodal image registration. Preliminary results of optimizing the normalized mutual information similarity metric validated the efficacy of the proposed method by using a freely available medical image database.

Improvement to the pattern of control rods of the equilibrium cycle of 18 months for the CLV using bio-inspired algorithms

International Nuclear Information System (INIS)

Perusquia, R.; Ortiz, J.J.; Montes, J.L.

2003-01-01

Nowadays in the National Institute of Nuclear Research are carried out studies with some bio-inspired optimization techniques to improve the performance of the fuel cycles of the boiling water reactors of the Laguna Verde power plant (CLV). In the present work two bio-inspired techniques were applied with the purpose of improving the performance of a balance cycle of 18 months developed for the CLV: genetic algorithms (AG) and systems based on ants colonies (SCH). The design of the reference cycle it represents in several aspects an optimal cycle proposed starting from the experience of several operation decades with the boiling water reactors (BWR initials for Boiling Water Reactor) in the world. To try to improve their performance is beforehand a difficult challenge and it puts on test the feasibility of the optimization methods in the reloads design. The study of the bio-inspired techniques was centered exclusively on the obtaining of the control rod patterns (PBC) trying to overcome the capacity factor reached in the design of the reference cycle. It was fixed the cycle length such that the decrease of the coast down period would represent an increase of the capacity factor of the cycle; so that, it diminishes the annual cost associated with the capital cost of the plant. As consequence of the study, was found that the algorithm based on the ants colonies reaches to diminish the coast down period in five and half days respect to the original balance cycle, what represents an annual saving of $US 74,000. Since the original cycle was optimized, the above-mentioned, shows the ability of the SCH for the optimization of the cycle design. With the AG it was reach to approach to the original balance cycle with a coast down period greater in seven days estimating an annual penalization of $US 130,000. (Author)

A Wireless Fatigue Monitoring System Utilizing a Bio-Inspired Tree Ring Data Tracking Technique

OpenAIRE

Bai, Shi; Li, Xuan; Xie, Zhaohui; Zhou, Zhi; Ou, Jinping

2014-01-01

Fatigue, a hot scientific research topic for centuries, can trigger sudden failure of critical structures such as aircraft and railway systems, resulting in enormous casualties as well as economic losses. The fatigue life of certain structures is intrinsically random and few monitoring techniques are capable of tracking the full life-cycle fatigue damage. In this paper, a novel in-situ wireless real-time fatigue monitoring system using a bio-inspired tree ring data tracking technique is propo...

Distributed Recurrent Neural Forward Models with Neural Control for Complex Locomotion in Walking Robots

DEFF Research Database (Denmark)

Dasgupta, Sakyasingha; Goldschmidt, Dennis; Wörgötter, Florentin

2015-01-01

here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of (1) central pattern generator based control for generating basic rhythmic patterns and coordinated......Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental...... conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain...

Sequential neural models with stochastic layers

DEFF Research Database (Denmark)

Fraccaro, Marco; Sønderby, Søren Kaae; Paquet, Ulrich

2016-01-01

How can we efficiently propagate uncertainty in a latent state representation with recurrent neural networks? This paper introduces stochastic recurrent neural networks which glue a deterministic recurrent neural network and a state space model together to form a stochastic and sequential neural...... generative model. The clear separation of deterministic and stochastic layers allows a structured variational inference network to track the factorization of the model's posterior distribution. By retaining both the nonlinear recursive structure of a recurrent neural network and averaging over...

Formation Control for Water-Jet USV Based on Bio-Inspired Method

Science.gov (United States)

Fu, Ming-yu; Wang, Duan-song; Wang, Cheng-long

2018-03-01

The formation control problem for underactuated unmanned surface vehicles (USVs) is addressed by a distributed strategy based on virtual leader strategy. The control system takes account of disturbance induced by external environment. With the coordinate transformation, the advantage of the proposed scheme is that the control point can be any point of the ship instead of the center of gravity. By introducing bio-inspired model, the formation control problem is addressed with backstepping method. This avoids complicated computation, simplifies the control law, and smoothes the input signals. The system uniform ultimate boundness is proven by Lyapunov stability theory with Young inequality. Simulation results are presented to verify the effectiveness and robust of the proposed controller.

Parallel Processing and Bio-inspired Computing for Biomedical Image Registration

Directory of Open Access Journals (Sweden)

Silviu Ioan Bejinariu

2014-07-01

Full Text Available Image Registration (IR is an optimization problem computing optimal parameters of a geometric transform used to overlay one or more source images to a given model by maximizing a similarity measure. In this paper the use of bio-inspired optimization algorithms in image registration is analyzed. Results obtained by means of three different algorithms are compared: Bacterial Foraging Optimization Algorithm (BFOA, Genetic Algorithm (GA and Clonal Selection Algorithm (CSA. Depending on the images type, the registration may be: area based, which is slow but more precise, and features based, which is faster. In this paper a feature based approach based on the Scale Invariant Feature Transform (SIFT is proposed. Finally, results obtained using sequential and parallel implementations on multi-core systems for area based and features based image registration are compared.

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

Science.gov (United States)

Okuno, Hirotsugu; Yagi, Tetsuya

2012-08-01

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

Nature as an engineer: one simple concept of a bio-inspired functional artificial muscle

International Nuclear Information System (INIS)

Schmitt, S; Haeufle, D F B; Günther, M; Blickhan, R

2012-01-01

The biological muscle is a powerful, flexible and versatile actuator. Its intrinsic characteristics determine the way how movements are generated and controlled. Robotic and prosthetic applications expect to profit from relying on bio-inspired actuators which exhibit natural (muscle-like) characteristics. As of today, when constructing a technical actuator, it is not possible to copy the exact molecular structure of a biological muscle. Alternatively, the question may be put how its characteristics can be realized with known mechanical components. Recently, a mechanical construct for an artificial muscle was proposed, which exhibits hyperbolic force–velocity characteristics. In this paper, we promote the constructing concept which is made by substantiating the mechanical design of biological muscle by a simple model, proving the feasibility of its real-world implementation, and checking their output both for mutual consistency and agreement with biological measurements. In particular, the relations of force, enthalpy rate and mechanical efficiency versus contraction velocity of both the construct’s technical implementation and its numerical model were determined in quick-release experiments. All model predictions for these relations and the hardware results are now in good agreement with the biological literature. We conclude that the construct represents a mechanical concept of natural actuation, which is suitable for laying down some useful suggestions when designing bio-inspired actuators. (paper)

Bio-inspired CO2 reduction by a rhenium tricarbonyl bipyridine-based catalyst appended to amino acids and peptidic platforms: incorporating proton relays and hydrogen-bonding functional groups.

Science.gov (United States)

Chabolla, S A; Machan, C W; Yin, J; Dellamary, E A; Sahu, S; Gianneschi, N C; Gilson, M K; Tezcan, F A; Kubiak, C P

2017-06-02

Herein, we report a new approach to bio-inspired catalyst design. The molecular catalyst employed in these studies is based on the robust and selective Re(bpy)(CO) 3 Cl-type (bpy = 2,2'-bipyridine) homogeneous catalysts, which have been extensively studied for their ability to reduce CO 2 electrochemically or photochemically in the presence of a photosensitizer. These catalysts can be highly active photocatalysts in their own right. In this work, the bipyridine ligand was modified with amino acids and synthetic peptides. These results build on earlier findings wherein the bipyridine ligand was functionalized with amide groups to promote dimer formation and CO 2 reduction by an alternate bimolecular mechanism at lower overpotential (ca. 250 mV) than the more commonly observed unimolecular process. The bio-inspired catalysts were designed to allow for the incorporation of proton relays to support reduction of CO 2 to CO and H 2 O. The coupling of amino acids tyrosine and phenylalanine led to the formation of two structurally similar Re catalyst/peptide catalysts for comparison of proton transport during catalysis. This article reports the synthesis and characterization of novel catalyst/peptide hybrids by molecular dynamics (MD simulations of structural dynamics), NMR studies of solution phase structures, and electrochemical studies to measure the activities of new bio-inspired catalysts in the reduction of CO 2.

Mimicking the cell membrane: bio-inspired simultaneous functions with monovalent anion selectivity and antifouling properties of anion exchange membrane

Science.gov (United States)

Zhao, Yan; Liu, Huimin; Tang, Kaini; Jin, Yali; Pan, Jiefeng; der Bruggen, Bart Van; Shen, Jiangnan; Gao, Congjie

2016-01-01

A new bio-inspired method was applied in this study to simultaneously improve the monovalent anion selectivity and antifouling properties of anion exchange membranes (AEMs). Three-layer architecture was developed by deposition of polydopamine (PDA) and electro-deposition of N-O-sulfonic acid benzyl chitosan (NSBC). The innermost and outermost layers were PDA with different deposition time. The middle layer was prepared by NSBC. Fourier transform infrared spectroscopy and scanning electron microscopy confirmed that PDA and NSBC were successfully modified on the surfaces of AEMs. The contact angle of the membranes indicated an improved hydrophilicity of the modified membranes. A series of electrodialysis experiments in which Cl−/SO42− separation was studied, demonstrating the monovalent anion selectivity of the samples. The Cl−/SO42− permselectivity of the modified membranes can reach up to 2.20, higher than that of the commercial membrane (only 0.78) during 90 minutes in electrodialysis (ED). The increase value of the resistance of the membranes was also measured to evaluate the antifouling properties. Sodium dodecyl benzene sulfonate (SDBS) was used as the fouling material in the ED process and the membrane area resistance of modified membrane increase value of was only 0.08 Ωcm2 30 minutes later. PMID:27853255

Mimicking the cell membrane: bio-inspired simultaneous functions with monovalent anion selectivity and antifouling properties of anion exchange membrane

Science.gov (United States)

Zhao, Yan; Liu, Huimin; Tang, Kaini; Jin, Yali; Pan, Jiefeng; der Bruggen, Bart Van; Shen, Jiangnan; Gao, Congjie

2016-11-01

A new bio-inspired method was applied in this study to simultaneously improve the monovalent anion selectivity and antifouling properties of anion exchange membranes (AEMs). Three-layer architecture was developed by deposition of polydopamine (PDA) and electro-deposition of N-O-sulfonic acid benzyl chitosan (NSBC). The innermost and outermost layers were PDA with different deposition time. The middle layer was prepared by NSBC. Fourier transform infrared spectroscopy and scanning electron microscopy confirmed that PDA and NSBC were successfully modified on the surfaces of AEMs. The contact angle of the membranes indicated an improved hydrophilicity of the modified membranes. A series of electrodialysis experiments in which Cl-/SO42- separation was studied, demonstrating the monovalent anion selectivity of the samples. The Cl-/SO42- permselectivity of the modified membranes can reach up to 2.20, higher than that of the commercial membrane (only 0.78) during 90 minutes in electrodialysis (ED). The increase value of the resistance of the membranes was also measured to evaluate the antifouling properties. Sodium dodecyl benzene sulfonate (SDBS) was used as the fouling material in the ED process and the membrane area resistance of modified membrane increase value of was only 0.08 Ωcm2 30 minutes later.

Obstacle traversal and self-righting of bio-inspired robots reveal the physics of multi-modal locomotion

Science.gov (United States)

Li, Chen; Fearing, Ronald; Full, Robert

Most animals move in nature in a variety of locomotor modes. For example, to traverse obstacles like dense vegetation, cockroaches can climb over, push across, reorient their bodies to maneuver through slits, or even transition among these modes forming diverse locomotor pathways; if flipped over, they can also self-right using wings or legs to generate body pitch or roll. By contrast, most locomotion studies have focused on a single mode such as running, walking, or jumping, and robots are still far from capable of life-like, robust, multi-modal locomotion in the real world. Here, we present two recent studies using bio-inspired robots, together with new locomotion energy landscapes derived from locomotor-environment interaction physics, to begin to understand the physics of multi-modal locomotion. (1) Our experiment of a cockroach-inspired legged robot traversing grass-like beam obstacles reveals that, with a terradynamically ``streamlined'' rounded body like that of the insect, robot traversal becomes more probable by accessing locomotor pathways that overcome lower potential energy barriers. (2) Our experiment of a cockroach-inspired self-righting robot further suggests that body vibrations are crucial for exploring locomotion energy landscapes and reaching lower barrier pathways. Finally, we posit that our new framework of locomotion energy landscapes holds promise to better understand and predict multi-modal biological and robotic movement.

Hardware implementation of stochastic spiking neural networks.

Science.gov (United States)

Rosselló, Josep L; Canals, Vincent; Morro, Antoni; Oliver, Antoni

2012-08-01

Spiking Neural Networks, the last generation of Artificial Neural Networks, are characterized by its bio-inspired nature and by a higher computational capacity with respect to other neural models. In real biological neurons, stochastic processes represent an important mechanism of neural behavior and are responsible of its special arithmetic capabilities. In this work we present a simple hardware implementation of spiking neurons that considers this probabilistic nature. The advantage of the proposed implementation is that it is fully digital and therefore can be massively implemented in Field Programmable Gate Arrays. The high computational capabilities of the proposed model are demonstrated by the study of both feed-forward and recurrent networks that are able to implement high-speed signal filtering and to solve complex systems of linear equations.

Biomimicry as an approach for bio-inspired structure with the aid of compu

Directory of Open Access Journals (Sweden)

Moheb Sabry Aziz

2016-03-01

Full Text Available Biomimicry is the study of emulating and mimicking nature, where it has been used by designers to help in solving human problems. From centuries ago designers and architects looked at nature as a huge source of inspiration. Biomimicry argues that nature is the best, most influencing and the guaranteed source of innovation for the designers as a result of nature’s 3.85 billion years of evolution, as it holds a gigantic experience of solving problems of the environment and its inhabitants. The biomimicry emerging field deals with new technologies honed from bio-inspired engineering at the micro and macro scale levels. Architects have been searching for answers from nature to their complex questions about different kinds of structures, and they have mimicked a lot of forms from nature to create better and more efficient structures for different architectural purposes. Without computers these complex ways and forms of structures couldn’t been mimicked and thus using computers had risen the way of mimicking and taking inspiration from nature because it is considered a very sophisticated and accurate tool for simulation and computing, as a result designers can imitate different nature’s models in spite of its complexity.

Optimal Design of a Bio-Inspired Anthropocentric Shoulder Rehabilitator

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S. K. Mustafa

2006-01-01

Full Text Available This paper presents the design of a bio-inspired anthropocentric 7-DOF wearable robotic arm for the purpose of stroke rehabilitation. The proposed arm rehabilitator synergistically utilizes the human arm structure with non-invasive kinematically under-deterministic cable-driven mechanisms to form a completely deterministic structure. It offers the advantages of being lightweight and having high dexterity. Adopting an anthropocentric design concept also allows it to conform to the human anatomical structure. The focus of this paper is on the analysis and design of the 3-DOF-shoulder module, called the shoulder rehabilitator. The design methodology is divided into three main steps: (1 performance evaluation of the cable-driven shoulder rehabilitator, (2 performance requirements of the shoulder joint based on its physiological characteristics and (3 design optimization of the shoulder rehabilitator based on shoulder joint physiological limitations. The aim is to determine a suitable configuration for the development of a shoulder rehabilitator prototype.

Biologically-inspired On-chip Learning in Pulsed Neural Networks

DEFF Research Database (Denmark)

Lehmann, Torsten; Woodburn, Robin

1999-01-01

Self-learning chips to implement many popular ANN (artificial neural network) algorithms are very difficult to design. We explain why this is so and say what lessons previous work teaches us in the design of self-learning systems. We offer a contribution to the "biologically-inspired" approach......, explaining what we mean by this term and providing an example of a robust, self-learning design that can solve simple classical-conditioning tasks, We give details of the design of individual circuits to perform component functions, which can then be combined into a network to solve the task. We argue...

Design and development of bio-inspired framework for reservoir operation optimization

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Asvini, M. Sakthi; Amudha, T.

2017-12-01

Frameworks for optimal reservoir operation play an important role in the management of water resources and delivery of economic benefits. Effective utilization and conservation of water from reservoirs helps to manage water deficit periods. The main challenge in reservoir optimization is to design operating rules that can be used to inform real-time decisions on reservoir release. We develop a bio-inspired framework for the optimization of reservoir release to satisfy the diverse needs of various stakeholders. In this work, single-objective optimization and multiobjective optimization problems are formulated using an algorithm known as "strawberry optimization" and tested with actual reservoir data. Results indicate that well planned reservoir operations lead to efficient deployment of the reservoir water with the help of optimal release patterns.

Bio-Inspired Energy-Aware Protocol Design for Cooperative Wireless Networks

DEFF Research Database (Denmark)

Perrucci, Gian Paolo; Anggraeni, Puri Novelti; Wardana, Satya Ardhy

2011-01-01

In this work, bio-inspired cooperation rules are applied to wireless communication networks. The main goal is to derive cooperative behaviour rules to improve the energy consumption of each mobile device. A medium access control (MAC) protocol particularly designed for peer-to-peer communication...... be achieved by this architecture using game theoretic approaches. As an extension, this work explores the impact of the MAC protocol on the power saving capabilities. This result shows that standard MAC mechanisms are not optimised for the considered cooperative setup. A new MAC protocol is proposed...... among cooperative wireless mobile devices is described. The work is based on a novel communication architecture, where a group of mobile devices are connected both to a cellular base station and among them using short-range communication links. A prior work has investigated the energy saving that can...

Reservoir-based Online Adaptive Forward Models with Neural Control for Complex Locomotion in a Hexapod Robot

DEFF Research Database (Denmark)

Manoonpong, Poramate; Dasgupta, Sakyasingha; Goldschmidt, Dennis

2014-01-01

Walking animals show fascinating locomotor abilities and complex behaviors. Biological study has revealed that such complex behaviors is a result of a combination of biomechanics and neural mechanisms. While biomechanics allows for flexibility and a variety of movements, neural mechanisms generate...... locomotion, make predictions, and provide adaptation. Inspired by this finding, we present here an artificial bio-inspired walking system which combines biomechanics (in terms of its body and leg structures) and neural mechanisms. The neural mechanisms consist of 1) central pattern generator-based control...... for generating basic rhythmic patterns and coordinated movements, 2) reservoir-based adaptive forward models with efference copies for sensory prediction as well as state estimation, and 3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental...

Real-Coded Quantum-Inspired Genetic Algorithm-Based BP Neural Network Algorithm

Directory of Open Access Journals (Sweden)

Jianyong Liu

2015-01-01

Full Text Available The method that the real-coded quantum-inspired genetic algorithm (RQGA used to optimize the weights and threshold of BP neural network is proposed to overcome the defect that the gradient descent method makes the algorithm easily fall into local optimal value in the learning process. Quantum genetic algorithm (QGA is with good directional global optimization ability, but the conventional QGA is based on binary coding; the speed of calculation is reduced by the coding and decoding processes. So, RQGA is introduced to explore the search space, and the improved varied learning rate is adopted to train the BP neural network. Simulation test shows that the proposed algorithm is effective to rapidly converge to the solution conformed to constraint conditions.

Bio-inspired color sketch for eco-friendly printing

Science.gov (United States)

Safonov, Ilia V.; Tolstaya, Ekaterina V.; Rychagov, Michael N.; Lee, Hokeun; Kim, Sang Ho; Choi, Donchul

2012-01-01

Saving of toner/ink consumption is an important task in modern printing devices. It has a positive ecological and social impact. We propose technique for converting print-job pictures to a recognizable and pleasant color sketches. Drawing a "pencil sketch" from a photo relates to a special area in image processing and computer graphics - non-photorealistic rendering. We describe a new approach for automatic sketch generation which allows to create well-recognizable sketches and to preserve partly colors of the initial picture. Our sketches contain significantly less color dots then initial images and this helps to save toner/ink. Our bio-inspired approach is based on sophisticated edge detection technique for a mask creation and multiplication of source image with increased contrast by this mask. To construct the mask we use DoG edge detection, which is a result of blending of initial image with its blurred copy through the alpha-channel, which is created from Saliency Map according to Pre-attentive Human Vision model. Measurement of percentage of saved toner and user study proves effectiveness of proposed technique for toner saving in eco-friendly printing mode.

The modulation and reconstruction of a BiO layer of cuprate Bi2212

International Nuclear Information System (INIS)

Fan Wei; Zeng, Z

2011-01-01

Studies based on ab initio density functional theory show that the modulated structures of BiO surfaces of cuprate Bi2212 superconductors are spontaneously formed and closely related to the reconstructions of BiO surfaces. The reconstructions of BiO layers occur both on the surface and in the bulk, accompanied with the formations of BiO-zigzag chains and Bi 2 O 2 quadrilaterals. The structural modulations of the BiO surface are along the b axis, perpendicular to the BiO-zigzag chains along the a axis. Our calculations provide a unified understanding of the formation of modulating structures in Bi2212. Another interesting result is that electronic structures of BiO surfaces are significantly influenced by the CuO 2 layer beneath because of the structural modulations and reconstructions.

A bio-inspired methodology of identifying influential nodes in complex networks.

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Cai Gao

Full Text Available How to identify influential nodes is a key issue in complex networks. The degree centrality is simple, but is incapable to reflect the global characteristics of networks. Betweenness centrality and closeness centrality do not consider the location of nodes in the networks, and semi-local centrality, leaderRank and pageRank approaches can be only applied in unweighted networks. In this paper, a bio-inspired centrality measure model is proposed, which combines the Physarum centrality with the K-shell index obtained by K-shell decomposition analysis, to identify influential nodes in weighted networks. Then, we use the Susceptible-Infected (SI model to evaluate the performance. Examples and applications are given to demonstrate the adaptivity and efficiency of the proposed method. In addition, the results are compared with existing methods.

A Bio-Inspired Approach to Alarm Malware Attacks in Mobile Handsets

Science.gov (United States)

Ahn, Taejin; Park, Taejoon

With proliferation of smart handsets capable of mobile Internet, the severity of malware attacks targeting such handsets is rapidly increasing, thereby requiring effective countermeasure for them. However, existing signature-based solutions are not suitable for resource-poor handsets due to the excessive run-time overhead of matching against ever-increasing malware pattern database as well as the limitation of detecting well-known malware only. To overcome these drawbacks, we present a bio-inspired approach to discriminate malware (non-self) from normal programs (self) by replicating the processes of biological immune system. Our proposed approach achieves superior performance in terms of detecting 83.7% of new malware or their variants and scalable storage requirement that grows very slowly with inclusion of new malware, making it attractive for use with mobile handsets.

Network model of chemical-sensing system inspired by mouse taste buds.

Science.gov (United States)

Tateno, Katsumi; Igarashi, Jun; Ohtubo, Yoshitaka; Nakada, Kazuki; Miki, Tsutomu; Yoshii, Kiyonori

2011-07-01

Taste buds endure extreme changes in temperature, pH, osmolarity, so on. Even though taste bud cells are replaced in a short span, they contribute to consistent taste reception. Each taste bud consists of about 50 cells whose networks are assumed to process taste information, at least preliminarily. In this article, we describe a neural network model inspired by the taste bud cells of mice. It consists of two layers. In the first layer, the chemical stimulus is transduced into an irregular spike train. The synchronization of the output impulses is induced by the irregular spike train at the second layer. These results show that the intensity of the chemical stimulus is encoded as the degree of the synchronization of output impulses. The present algorithms for signal processing result in a robust chemical-sensing system.

Engineered bio-inspired coating for reduction of flow separation

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Bocanegra Evans, Humberto; Hamed, Ali M.; Gorumlu, Serdar; Doosttalab, Ali; Aksak, Burak; Chamorro, Leonardo P.; Castillo, Luciano

2017-11-01

Flow control using passive strategies has received notable attention in the last decades as a way to increase mixing and reduce skin drag, among others. Here, we present a bio-inspired coating, composed by uniformly distributed pillars with diverging tips, that is able to reduce the recirculation region in highly separated flows. This is demonstrated with laboratory experiments in a refractive index-matching flume at Reynolds number Reθ 1200 . The flow over an expanding channel following a S835 wing section was characterized with the coating and with smooth walls. High-resolution, wall-normal particle image velocimetry show a significant reduction of the reversed flow with the coating, where the region with reverse flow was reduced by 60 % . The performance of the micro-scale coating is surprising since the size of the fibers are nearly coincident with the viscous length scale (k+ 1). Additionally, the flow control properties of the surface do not depend on hydrophobicity, giving the coating the capability to work in both air and water media.

An Advanced Bio-Inspired PhotoPlethysmoGraphy (PPG) and ECG Pattern Recognition System for Medical Assessment.

Science.gov (United States)

Rundo, Francesco; Conoci, Sabrina; Ortis, Alessandro; Battiato, Sebastiano

2018-01-30

Physiological signals are widely used to perform medical assessment for monitoring an extensive range of pathologies, usually related to cardio-vascular diseases. Among these, both PhotoPlethysmoGraphy (PPG) and Electrocardiography (ECG) signals are those more employed. PPG signals are an emerging non-invasive measurement technique used to study blood volume pulsations through the detection and analysis of the back-scattered optical radiation coming from the skin. ECG is the process of recording the electrical activity of the heart over a period of time using electrodes placed on the skin. In the present paper we propose a physiological ECG/PPG "combo" pipeline using an innovative bio-inspired nonlinear system based on a reaction-diffusion mathematical model, implemented by means of the Cellular Neural Network (CNN) methodology, to filter PPG signal by assigning a recognition score to the waveforms in the time series. The resulting "clean" PPG signal exempts from distortion and artifacts is used to validate for diagnostic purpose an EGC signal simultaneously detected for a same patient. The multisite combo PPG-ECG system proposed in this work overpasses the limitations of the state of the art in this field providing a reliable system for assessing the above-mentioned physiological parameters and their monitoring over time for robust medical assessment. The proposed system has been validated and the results confirmed the robustness of the proposed approach.

Germ layers, the neural crest and emergent organization in development and evolution.

Science.gov (United States)

Hall, Brian K

2018-04-10

Discovered in chick embryos by Wilhelm His in 1868 and named the neural crest by Arthur Milnes Marshall in 1879, the neural crest cells that arise from the neural folds have since been shown to differentiate into almost two dozen vertebrate cell types and to have played major roles in the evolution of such vertebrate features as bone, jaws, teeth, visceral (pharyngeal) arches, and sense organs. I discuss the discovery that ectodermal neural crest gave rise to mesenchyme and the controversy generated by that finding; the germ layer theory maintained that only mesoderm could give rise to mesenchyme. A second topic of discussion is germ layers (including the neural crest) as emergent levels of organization in animal development and evolution that facilitated major developmental and evolutionary change. The third topic is gene networks, gene co-option, and the evolution of gene-signaling pathways as key to developmental and evolutionary transitions associated with the origin and evolution of the neural crest and neural crest cells. © 2018 Wiley Periodicals, Inc.

Mobile robots for localizing gas emission sources on landfill sites: is bio-inspiration the way to go?

Science.gov (United States)

Hernandez Bennetts, Victor; Lilienthal, Achim J; Neumann, Patrick P; Trincavelli, Marco

2011-01-01

Roboticists often take inspiration from animals for designing sensors, actuators, or algorithms that control the behavior of robots. Bio-inspiration is motivated with the uncanny ability of animals to solve complex tasks like recognizing and manipulating objects, walking on uneven terrains, or navigating to the source of an odor plume. In particular the task of tracking an odor plume up to its source has nearly exclusively been addressed using biologically inspired algorithms and robots have been developed, for example, to mimic the behavior of moths, dung beetles, or lobsters. In this paper we argue that biomimetic approaches to gas source localization are of limited use, primarily because animals differ fundamentally in their sensing and actuation capabilities from state-of-the-art gas-sensitive mobile robots. To support our claim, we compare actuation and chemical sensing available to mobile robots to the corresponding capabilities of moths. We further characterize airflow and chemosensor measurements obtained with three different robot platforms (two wheeled robots and one flying micro-drone) in four prototypical environments and show that the assumption of a constant and unidirectional airflow, which is the basis of many gas source localization approaches, is usually far from being valid. This analysis should help to identify how underlying principles, which govern the gas source tracking behavior of animals, can be usefully "translated" into gas source localization approaches that fully take into account the capabilities of mobile robots. We also describe the requirements for a reference application, monitoring of gas emissions at landfill sites with mobile robots, and discuss an engineered gas source localization approach based on statistics as an alternative to biologically inspired algorithms.

Mobile Robots for Localizing Gas Emission Sources on Landfill Sites: Is Bio-Inspiration the Way to Go?

Directory of Open Access Journals (Sweden)

Victor eHernandez Bennetts

2012-01-01

Full Text Available Roboticists often take inspiration from animals for designing sensors, actuators or algorithms that control the behaviour of robots. Bio-inspiration is motivated with the uncanny ability of animals to solve complex tasks like recognizing and manipulating objects, walking on uneven terrains, or navigating to the source of an odour plume. In particular the task of tracking an odour plume up to its source has nearly exclusively been addressed using biologically inspired algorithms and robots have been developed, for example, to mimic the behaviour of moths, dungbeetles, or lobsters. In this paper we argue that biomimetic approaches to gas source localization are of limited use, primarily because animals differ fundamentally in their sensing and actuation capabilities from state-of-the-art gas-sensitive mobile robots. To support our claim, we compare actuation and chemical sensing available to mobile robots to the corresponding capabilities of moths. We further characterize airflow and chemosensor measurements obtained with three different robot platforms (two wheeled robots and one flying micro drone in four prototypical environments and show that the assumption of a constant and unidirectional airflow, which is at the basis of many gas source localization approaches, is usually far from being valid. This analysis should help to identify how underlying principles, which govern the gas source tracking behaviour of animals, can be usefully translated into gas source localization approaches that fully take into account the capabilities of mobile robots. We also describe the requirements for a reference application, monitoring of gas emissions at landfill sites with mobile robots, and discuss an engineered gas source localization approach based on statistics as an alternative to biologically-inspired algorithms.

Multi-Locomotion Robotic Systems New Concepts of Bio-inspired Robotics

CERN Document Server

Fukuda, Toshio; Sekiyama, Kosuke; Aoyama, Tadayoshi

2012-01-01

Nowadays, multiple attention have been paid on a robot working in the human living environment, such as in the field of medical, welfare, entertainment and so on. Various types of researches are being conducted actively in a variety of fields such as artificial intelligence, cognitive engineering, sensor- technology, interfaces and motion control. In the future, it is expected to realize super high functional human-like robot by integrating technologies in various fields including these types of researches. The book represents new developments and advances in the field of bio-inspired robotics research introducing the state of the art, the idea of multi-locomotion robotic system to implement the diversity of animal motion. It covers theoretical and computational aspects of Passive Dynamic Autonomous Control (PDAC), robot motion control, multi legged walking and climbing as well as brachiation focusing concrete robot systems, components and applications. In addition, gorilla type robot systems are described as...

Gas Classification Using Deep Convolutional Neural Networks

Science.gov (United States)

Peng, Pai; Zhao, Xiaojin; Pan, Xiaofang; Ye, Wenbin

2018-01-01

In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP). PMID:29316723

Gas Classification Using Deep Convolutional Neural Networks.

Science.gov (United States)

Peng, Pai; Zhao, Xiaojin; Pan, Xiaofang; Ye, Wenbin

2018-01-08

In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP).

Biologically inspired EM image alignment and neural reconstruction.

Science.gov (United States)

Knowles-Barley, Seymour; Butcher, Nancy J; Meinertzhagen, Ian A; Armstrong, J Douglas

2011-08-15

Three-dimensional reconstruction of consecutive serial-section transmission electron microscopy (ssTEM) images of neural tissue currently requires many hours of manual tracing and annotation. Several computational techniques have already been applied to ssTEM images to facilitate 3D reconstruction and ease this burden. Here, we present an alternative computational approach for ssTEM image analysis. We have used biologically inspired receptive fields as a basis for a ridge detection algorithm to identify cell membranes, synaptic contacts and mitochondria. Detected line segments are used to improve alignment between consecutive images and we have joined small segments of membrane into cell surfaces using a dynamic programming algorithm similar to the Needleman-Wunsch and Smith-Waterman DNA sequence alignment procedures. A shortest path-based approach has been used to close edges and achieve image segmentation. Partial reconstructions were automatically generated and used as a basis for semi-automatic reconstruction of neural tissue. The accuracy of partial reconstructions was evaluated and 96% of membrane could be identified at the cost of 13% false positive detections. An open-source reference implementation is available in the Supplementary information. seymour.kb@ed.ac.uk; douglas.armstrong@ed.ac.uk Supplementary data are available at Bioinformatics online.

Bio-Inspired Vision-Based Leader-Follower Formation Flying in the Presence of Delays

Directory of Open Access Journals (Sweden)

John Oyekan

2016-08-01

Full Text Available Flocking starlings at dusk are known for the mesmerizing and intricate shapes they generate, as well as how fluid these shapes change. They seem to do this effortlessly. Real-life vision-based flocking has not been achieved in micro-UAVs (micro Unmanned Aerial Vehicles to date. Towards this goal, we make three contributions in this paper: (i we used a computational approach to develop a bio-inspired architecture for vision-based Leader-Follower formation flying on two micro-UAVs. We believe that the minimal computational cost of the resulting algorithm makes it suitable for object detection and tracking during high-speed flocking; (ii we show that provided delays in the control loop of a micro-UAV are below a critical value, Kalman filter-based estimation algorithms are not required to achieve Leader-Follower formation flying; (iii unlike previous approaches, we do not use external observers, such as GPS signals or synchronized communication with flock members. These three contributions could be useful in achieving vision-based flocking in GPS-denied environments on computationally-limited agents.

Vibro-Perception of Optical Bio-Inspired Fiber-Skin.

Science.gov (United States)

Li, Tao; Zhang, Sheng; Lu, Guo-Wei; Sunami, Yuta

2018-05-12

In this research, based on the principle of optical interferometry, the Mach-Zehnder and Optical Phase-locked Loop (OPLL) vibro-perception systems of bio-inspired fiber-skin are designed to mimic the tactile perception of human skin. The fiber-skin is made of the optical fiber embedded in the silicone elastomer. The optical fiber is an instinctive and alternative sensor for tactile perception with high sensitivity and reliability, also low cost and susceptibility to the magnetic interference. The silicone elastomer serves as a substrate with high flexibility and biocompatibility, and the optical fiber core serves as the vibro-perception sensor to detect physical motions like tapping and sliding. According to the experimental results, the designed optical fiber-skin demonstrates the ability to detect the physical motions like tapping and sliding in both the Mach-Zehnder and OPLL vibro-perception systems. For direct contact condition, the OPLL vibro-perception system shows better performance compared with the Mach-Zehnder vibro-perception system. However, the Mach-Zehnder vibro-perception system is preferable to the OPLL system in the indirect contact experiment. In summary, the fiber-skin is validated to have light touch character and excellent repeatability, which is highly-suitable for skin-mimic sensing.

Bio-inspired piezoelectric linear motor driven by a single-phase harmonic wave with an asymmetric stator.

Science.gov (United States)

Pan, Qiaosheng; Miao, Enming; Wu, Bingxuan; Chen, Weikang; Lei, Xiujun; He, Liangguo

2017-07-01

A novel, bio-inspired, single-phase driven piezoelectric linear motor (PLM) using an asymmetric stator was designed, fabricated, and tested to avoid mode degeneracy and to simplify the drive mechanism of a piezoelectric motor. A piezoelectric transducer composed of two piezoelectric stacks and a displacement amplifier was used as the driving element of the PLM. Two simple and specially designed claws performed elliptical motion. A numerical simulation was performed to design the stator and determine the feasibility of the design mechanism of the PLM. Moreover, an experimental setup was built to validate the working principles, as well as to evaluate the performance, of the PLM. The prototype motor outputs a no-load speed of 233.7 mm/s at a voltage of 180 V p-p and a maximum thrust force of 2.3 N under a preload of 10 N. This study verified the feasibility of the proposed design and provided a method to simplify the driving harmonic signal and structure of PLMs.

Catalytic Water Oxidation by a Bio-inspired Nickel Complex with Redox Active Ligand

Science.gov (United States)

Wang, Dong; Bruner, Charlie O.

2017-01-01

The oxidation of water to dioxygen is important in natural photosynthesis. One of nature’s strategies for managing such multi-electron transfer reactions is to employ redox active metal-organic cofactor arrays. One prototype example is the copper-tyrosinate active site found in galactose oxidase. In this work, we have implemented such a strategy to develop a bio-inspired nickel-phenolate complex capable of catalyzing the oxidation of water to O2 electrochemically at neutral pH with a modest overpotential. The employment of the redox-active ligand turned out to be a useful strategy to avoid the formation of high-valent nickel intermediates while a reasonable turnover rate (0.15 s−1) is retained. PMID:29099176

Bio-inspired engineering of cell- and virus-like nanoparticles for drug delivery.

Science.gov (United States)

Parodi, Alessandro; Molinaro, Roberto; Sushnitha, Manuela; Evangelopoulos, Michael; Martinez, Jonathan O; Arrighetti, Noemi; Corbo, Claudia; Tasciotti, Ennio

2017-12-01

The engineering of future generations of nanodelivery systems aims at the creation of multifunctional vectors endowed with improved circulation, enhanced targeting and responsiveness to the biological environment. Moving past purely bio-inert systems, researchers have begun to create nanoparticles capable of proactively interacting with the biology of the body. Nature offers a wide-range of sources of inspiration for the synthesis of more effective drug delivery platforms. Because the nano-bio-interface is the key driver of nanoparticle behavior and function, the modification of nanoparticles' surfaces allows the transfer of biological properties to synthetic carriers by imparting them with a biological identity. Modulation of these surface characteristics governs nanoparticle interactions with the biological barriers they encounter. Building off these observations, we provide here an overview of virus- and cell-derived biomimetic delivery systems that combine the intrinsic hallmarks of biological membranes with the delivery capabilities of synthetic carriers. We describe the features and properties of biomimetic delivery systems, recapitulating the distinctive traits and functions of viruses, exosomes, platelets, red and white blood cells. By mimicking these biological entities, we will learn how to more efficiently interact with the human body and refine our ability to negotiate with the biological barriers that impair the therapeutic efficacy of nanoparticles. Copyright © 2017. Published by Elsevier Ltd.

Layers and Multilayers of Self-Assembled Polymers: Tunable Engineered Extracellular Matrix Coatings for Neural Cell Growth.

Science.gov (United States)

Landry, Michael J; Rollet, Frédéric-Guillaume; Kennedy, Timothy E; Barrett, Christopher J

2018-03-12

Growing primary cells and tissue in long-term cultures, such as primary neural cell culture, presents many challenges. A critical component of any environment that supports neural cell growth in vivo is an appropriate 2-D surface or 3-D scaffold, typically in the form of a thin polymer layer that coats an underlying plastic or glass substrate and aims to mimic critical aspects of the extracellular matrix. A fundamental challenge to mimicking a hydrophilic, soft natural cell environment is that materials with these properties are typically fragile and are difficult to adhere to and stabilize on an underlying plastic or glass cell culture substrate. In this review, we highlight the current state of the art and overview recent developments of new artificial extracellular matrix (ECM) surfaces for in vitro neural cell culture. Notably, these materials aim to strike a balance between being hydrophilic and soft while also being thick, stable, robust, and bound well to the underlying surface to provide an effective surface to support long-term cell growth. We focus on improved surface and scaffold coating systems that can mimic the natural physicochemical properties that enhance neuronal survival and growth, applied as soft hydrophilic polymer coatings for both in vitro cell culture and for implantable neural probes and 3-D matrixes that aim to enhance stability and longevity to promote neural biocompatibility in vivo. With respect to future developments, we outline four emerging principles that serve to guide the development of polymer assemblies that function well as artificial ECMs: (a) design inspired by biological systems and (b) the employment of principles of aqueous soft bonding and self-assembly to achieve (c) a high-water-content gel-like coating that is stable over time in a biological environment and possesses (d) a low modulus to more closely mimic soft, compliant real biological tissue. We then highlight two emerging classes of thick material coatings that

A bio-inspired memory model for structural health monitoring

International Nuclear Information System (INIS)

Zheng, Wei; Zhu, Yong

2009-01-01

Long-term structural health monitoring (SHM) systems need intelligent management of the monitoring data. By analogy with the way the human brain processes memories, we present a bio-inspired memory model (BIMM) that does not require prior knowledge of the structure parameters. The model contains three time-domain areas: a sensory memory area, a short-term memory area and a long-term memory area. First, the initial parameters of the structural state are specified to establish safety criteria. Then the large amount of monitoring data that falls within the safety limits is filtered while the data outside the safety limits are captured instantly in the sensory memory area. Second, disturbance signals are distinguished from danger signals in the short-term memory area. Finally, the stable data of the structural balance state are preserved in the long-term memory area. A strategy for priority scheduling via fuzzy c-means for the proposed model is then introduced. An experiment on bridge tower deformation demonstrates that the proposed model can be applied for real-time acquisition, limited-space storage and intelligent mining of the monitoring data in a long-term SHM system

A bio-inspired memory model for structural health monitoring

Science.gov (United States)

Zheng, Wei; Zhu, Yong

2009-04-01

Long-term structural health monitoring (SHM) systems need intelligent management of the monitoring data. By analogy with the way the human brain processes memories, we present a bio-inspired memory model (BIMM) that does not require prior knowledge of the structure parameters. The model contains three time-domain areas: a sensory memory area, a short-term memory area and a long-term memory area. First, the initial parameters of the structural state are specified to establish safety criteria. Then the large amount of monitoring data that falls within the safety limits is filtered while the data outside the safety limits are captured instantly in the sensory memory area. Second, disturbance signals are distinguished from danger signals in the short-term memory area. Finally, the stable data of the structural balance state are preserved in the long-term memory area. A strategy for priority scheduling via fuzzy c-means for the proposed model is then introduced. An experiment on bridge tower deformation demonstrates that the proposed model can be applied for real-time acquisition, limited-space storage and intelligent mining of the monitoring data in a long-term SHM system.

Retina-Inspired Filter.

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Doutsi, Effrosyni; Fillatre, Lionel; Antonini, Marc; Gaulmin, Julien

2018-07-01

This paper introduces a novel filter, which is inspired by the human retina. The human retina consists of three different layers: the Outer Plexiform Layer (OPL), the inner plexiform layer, and the ganglionic layer. Our inspiration is the linear transform which takes place in the OPL and has been mathematically described by the neuroscientific model "virtual retina." This model is the cornerstone to derive the non-separable spatio-temporal OPL retina-inspired filter, briefly renamed retina-inspired filter, studied in this paper. This filter is connected to the dynamic behavior of the retina, which enables the retina to increase the sharpness of the visual stimulus during filtering before its transmission to the brain. We establish that this retina-inspired transform forms a group of spatio-temporal Weighted Difference of Gaussian (WDoG) filters when it is applied to a still image visible for a given time. We analyze the spatial frequency bandwidth of the retina-inspired filter with respect to time. It is shown that the WDoG spectrum varies from a lowpass filter to a bandpass filter. Therefore, while time increases, the retina-inspired filter enables to extract different kinds of information from the input image. Finally, we discuss the benefits of using the retina-inspired filter in image processing applications such as edge detection and compression.

Bio-inspired adaptive feedback error learning architecture for motor control.

Science.gov (United States)

Tolu, Silvia; Vanegas, Mauricio; Luque, Niceto R; Garrido, Jesús A; Ros, Eduardo

2012-10-01

This study proposes an adaptive control architecture based on an accurate regression method called Locally Weighted Projection Regression (LWPR) and on a bio-inspired module, such as a cerebellar-like engine. This hybrid architecture takes full advantage of the machine learning module (LWPR kernel) to abstract an optimized representation of the sensorimotor space while the cerebellar component integrates this to generate corrective terms in the framework of a control task. Furthermore, we illustrate how the use of a simple adaptive error feedback term allows to use the proposed architecture even in the absence of an accurate analytic reference model. The presented approach achieves an accurate control with low gain corrective terms (for compliant control schemes). We evaluate the contribution of the different components of the proposed scheme comparing the obtained performance with alternative approaches. Then, we show that the presented architecture can be used for accurate manipulation of different objects when their physical properties are not directly known by the controller. We evaluate how the scheme scales for simulated plants of high Degrees of Freedom (7-DOFs).

Control of Flow Structure on Non-Slender Delta Wing: Bio-inspired Edge Modifications, Passive Bleeding, and Pulsed Blowing

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Yavuz, Mehmet Metin; Celik, Alper; Cetin, Cenk

2016-11-01

In the present study, different flow control approaches including bio-inspired edge modifications, passive bleeding, and pulsed blowing are introduced and applied for the flow over non-slender delta wing. Experiments are conducted in a low speed wind tunnel for a 45 degree swept delta wing using qualitative and quantitative measurement techniques including laser illuminated smoke visualization, particle image velocimety (PIV), and surface pressure measurements. For the bio-inspired edge modifications, the edges of the wing are modified to dolphin fluke geometry. In addition, the concept of flexion ratio, a ratio depending on the flexible length of animal propulsors such as wings, is introduced. For passive bleeding, directing the free stream air from the pressure side of the planform to the suction side of the wing is applied. For pulsed blowing, periodic air injection through the leading edge of the wing is performed in a square waveform with 25% duty cycle at different excitation frequencies and compared with the steady and no blowing cases. The results indicate that each control approach is quite effective in terms of altering the overall flow structure on the planform. However, the success level, considering the elimination of stall or delaying the vortex breakdown, depends on the parameters in each method.

Human Inspired Self-developmental Model of Neural Network (HIM): Introducing Content/Form Computing

Science.gov (United States)

Krajíček, Jiří

This paper presents cross-disciplinary research between medical/psychological evidence on human abilities and informatics needs to update current models in computer science to support alternative methods for computation and communication. In [10] we have already proposed hypothesis introducing concept of human information model (HIM) as cooperative system. Here we continue on HIM design in detail. In our design, first we introduce Content/Form computing system which is new principle of present methods in evolutionary computing (genetic algorithms, genetic programming). Then we apply this system on HIM (type of artificial neural network) model as basic network self-developmental paradigm. Main inspiration of our natural/human design comes from well known concept of artificial neural networks, medical/psychological evidence and Sheldrake theory of "Nature as Alive" [22].

Design and development of a bio-inspired, under-actuated soft gripper.

Science.gov (United States)

Hassan, Taimoor; Manti, Mariangela; Passetti, Giovanni; d'Elia, Nicolò; Cianchetti, Matteo; Laschi, Cecilia

2015-08-01

The development of robotic devices able to perform manipulation tasks mimicking the human hand has been assessed on large scale. This work stands in the challenging scenario where soft materials are combined with bio-inspired design in order to develop soft grippers with improved grasping and holding capabilities. We are going to show a low-cost, under-actuated and adaptable soft gripper, highlighting the design and the manufacturing process. In particular, a critical analysis is made among three versions of the gripper with same design and actuation mechanism, but based on different materials. A novel actuation principle has been implemented in both cases, in order to reduce the encumbrance of the entire system and improve its aesthetics. Grasping and holding capabilities have been tested for each device, with target objects varying in shape, size and material. Results highlight synergy between the geometry and the intrinsic properties of the soft material, showing the way to novel design principles for soft grippers.

Advances in neural networks computational and theoretical issues

CERN Document Server

Esposito, Anna; Morabito, Francesco

2015-01-01

This book collects research works that exploit neural networks and machine learning techniques from a multidisciplinary perspective. Subjects covered include theoretical, methodological and computational topics which are grouped together into chapters devoted to the discussion of novelties and innovations related to the field of Artificial Neural Networks as well as the use of neural networks for applications, pattern recognition, signal processing, and special topics such as the detection and recognition of multimodal emotional expressions and daily cognitive functions, and  bio-inspired memristor-based networks.  Providing insights into the latest research interest from a pool of international experts coming from different research fields, the volume becomes valuable to all those with any interest in a holistic approach to implement believable, autonomous, adaptive, and context-aware Information Communication Technologies.

Data-Foraging-Oriented Reconnaissance Based on Bio-Inspired Indirect Communication for Aerial Vehicles

Directory of Open Access Journals (Sweden)

Josué Castañeda Cisneros

2017-07-01

Full Text Available In recent years, aerial vehicles have allowed exploring scenarios with harsh conditions. These can conduct reconnaissance tasks in areas that change periodically and have a high spatial and temporal resolution. The objective of a reconnaissance task is to survey an area and retrieve strategic information. The aerial vehicles, however, have inherent constraints in terms of energy and transmission range due to their mobility. Despite these constraints, the Data Foraging problem requires the aerial vehicles to exchange information about profitable data sources. In Data Foraging, establishing a single path is not viable because of dynamic conditions of the environment. Thus, reconnaissance must be focused on periodically searching profitable environmental data sources, as some animals perform foraging. In this work, a data-foraging-oriented reconnaissance algorithm based on bio-inspired indirect communication for aerial vehicles is presented. The approach establishes several paths that overlap to identify valuable data sources. Inspired by the stigmergy principle, the aerial vehicles indirectly communicate through artificial pheromones. The aerial vehicles traverse the environment using a heuristic algorithm that uses the artificial pheromones as feedback. The solution is formally defined and mathematically evaluated. In addition, we show the viability of the algorithm by simulations which have been tested through various statistical hypothesis.

VLSI implementation of a bio-inspired olfactory spiking neural network.

Science.gov (United States)

Hsieh, Hung-Yi; Tang, Kea-Tiong

2012-07-01

This paper presents a low-power, neuromorphic spiking neural network (SNN) chip that can be integrated in an electronic nose system to classify odor. The proposed SNN takes advantage of sub-threshold oscillation and onset-latency representation to reduce power consumption and chip area, providing a more distinct output for each odor input. The synaptic weights between the mitral and cortical cells are modified according to an spike-timing-dependent plasticity learning rule. During the experiment, the odor data are sampled by a commercial electronic nose (Cyranose 320) and are normalized before training and testing to ensure that the classification result is only caused by learning. Measurement results show that the circuit only consumed an average power of approximately 3.6 μW with a 1-V power supply to discriminate odor data. The SNN has either a high or low output response for a given input odor, making it easy to determine whether the circuit has made the correct decision. The measurement result of the SNN chip and some well-known algorithms (support vector machine and the K-nearest neighbor program) is compared to demonstrate the classification performance of the proposed SNN chip.The mean testing accuracy is 87.59% for the data used in this paper.

Aerodynamics of a bio-inspired flexible flapping-wing micro air vehicle

International Nuclear Information System (INIS)

Nakata, T; Liu, H; Nishihashi, N; Wang, X; Sato, A; Tanaka, Y

2011-01-01

MAVs (micro air vehicles) with a maximal dimension of 15 cm and nominal flight speeds of around 10 m s −1 , operate in a Reynolds number regime of 10 5 or lower, in which most natural flyers including insects, bats and birds fly. Furthermore, due to their light weight and low flight speed, the MAVs' flight characteristics are substantially affected by environmental factors such as wind gust. Like natural flyers, the wing structures of MAVs are often flexible and tend to deform during flight. Consequently, the aero/fluid and structural dynamics of these flyers are closely linked to each other, making the entire flight vehicle difficult to analyze. We have recently developed a hummingbird-inspired, flapping flexible wing MAV with a weight of 2.4–3.0 g and a wingspan of 10–12 cm. In this study, we carry out an integrated study of the flexible wing aerodynamics of this flapping MAV by combining an in-house computational fluid dynamic (CFD) method and wind tunnel experiments. A CFD model that has a realistic wing planform and can mimic realistic flexible wing kinematics is established, which provides a quantitative prediction of unsteady aerodynamics of the four-winged MAV in terms of vortex and wake structures and their relationship with aerodynamic force generation. Wind tunnel experiments further confirm the effectiveness of the clap and fling mechanism employed in this bio-inspired MAV as well as the importance of the wing flexibility in designing small flapping-wing MAVs.

(YIP 10) - Bio-Inspired Interfaces for Hybrid Structures

Science.gov (United States)

2013-07-01

vertebrate bones and teeth, mollusk shells and arthropod exoskeletons [1, 2]. Two interesting examples of such biological systems are gecko’s footpad...range from non-wetting painting and smart adhesives [35-41] to intricate bioinspired designs such as nano- and micro- robotics with climbing abilities...smart adhesion. Advanced Materials, 2008. 20(4): p. 711-716. 42. Wood, R.J., The first takeoff of a biologically inspired at-scale robotic insect

A bio-inspired asynchronous skin system for crack detection applications

International Nuclear Information System (INIS)

Sharp, Nathan; Kuntz, Alan; Brubaker, Cole; Amos, Stephanie; Gao, Wei; Gupta, Gautum; Mohite, Aditya; Farrar, Charles; Mascareñas, David

2014-01-01

In many applications of structural health monitoring (SHM) it is imperative or advantageous to have large sensor arrays in order to properly sense the state of health of the structure. Typically these sensor networks are implemented by placing a large number of sensors over a structure and running individual cables from each sensor back to a central measurement station. Data is then collected from each sensor on the network at a constant sampling rate regardless of the current timescales at which events are acting on the structure. These conventional SHM sensor networks have a number of shortfalls. They tend to have a large number of cables that can represent a single point of failure for each sensor as well as add significant weight and installation costs. The constant sampling rate associated with each sensor very quickly leads to large amounts of data that must be analyzed, stored, and possibly transmitted to a remote user. This leads to increased demands on power consumption, bandwidth, and size. It also taxes our current techniques for managing large amounts of data. For the last decade the goal of the SHM community has been to endow structures with the functionality of a biological nervous system. Despite this goal the community has predominantly ignored the biological nervous system as inspiration for building structural nervous systems, choosing instead to focus on experimental mechanics and simulation techniques. In this work we explore the use of a novel, bio-inspired, SHM skin. This skin makes use of distributed computing and asynchronous communication techniques to alleviate the scale of the data management challenge as well as reduce power. The system also periodically sends a ‘heat beat’ signal to provide state-of-health updates. This conductive skin was implemented using conductive ink resistors as well as with graphene-oxide capacitors. (paper)

Distributed Recurrent Neural Forward Models with Synaptic Adaptation and CPG-based control for Complex Behaviors of Walking Robots

Directory of Open Access Journals (Sweden)

Sakyasingha eDasgupta

2015-09-01

Full Text Available Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures with the underlying neural mechanisms. The neural mechanisms consist of 1 central pattern generator based control for generating basic rhythmic patterns and coordinated movements, 2 distributed (at each leg recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and 3 searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Using simulations we show that this bio-inspired approach with adaptive internal models allows the walking robot to perform complex locomotive behaviors as observed in insects, including walking on undulated terrains, crossing large gaps as well as climbing over high obstacles. Furthermore we demonstrate that the newly developed recurrent network based approach to sensorimotor prediction outperforms the previous state of the art adaptive neuron

A one-layer recurrent neural network for constrained nonsmooth invex optimization.

Science.gov (United States)

Li, Guocheng; Yan, Zheng; Wang, Jun

2014-02-01

Invexity is an important notion in nonconvex optimization. In this paper, a one-layer recurrent neural network is proposed for solving constrained nonsmooth invex optimization problems, designed based on an exact penalty function method. It is proved herein that any state of the proposed neural network is globally convergent to the optimal solution set of constrained invex optimization problems, with a sufficiently large penalty parameter. In addition, any neural state is globally convergent to the unique optimal solution, provided that the objective function and constraint functions are pseudoconvex. Moreover, any neural state is globally convergent to the feasible region in finite time and stays there thereafter. The lower bounds of the penalty parameter and convergence time are also estimated. Two numerical examples are provided to illustrate the performances of the proposed neural network. Copyright © 2013 Elsevier Ltd. All rights reserved.

Multi-layer hierarchical array fabricated with diatom frustules for highly sensitive bio-detection applications

International Nuclear Information System (INIS)

Li, Aobo; Cai, Jun; Pan, Junfeng; Wang, Yu; Yue, Yue; Zhang, Deyuan

2014-01-01

Diatoms have delicate porous structures which are very beneficial in improving the absorbing ability in the bio-detection field. In this study, multi-layered hierarchical arrays were fabricated by packing Nitzschia soratensis (N. soratensis) frustules into Cosinodiscus argus (C. argus) frustules to achieve advanced sensitivity in bio-detection chips. Photolithographic patterning was used to obtain N. soratensis frustule arrays, and the floating behavior of C. argus frustules was employed to control their postures for packing N. soratensis frustule array spots. The morphology of the multi-layer C. argus–N. soratensis package array was investigated by scanning electron microscopy, demonstrating that the overall and sub-structures of the diatom frustules were retained. The signal enhancing effect of multi-layer C. argus–N. soratensis packages was demonstrated by fluorescent antibody test results. The mechanism of the enhancement was also analyzed, indicating that both complex hierarchical frustule structures and optimized posture of C. argus frustules were important for improving bio-detection sensitivities. The technique for fabricating multi-layer diatom frustules arrays is also useful for making multi-functional biochips and controllable drug delivery systems. (paper)

Catalytic Water Oxidation by a Bio-inspired Nickel Complex with a Redox-Active Ligand.

Science.gov (United States)

Wang, Dong; Bruner, Charlie O

2017-11-20

The oxidation of water (H 2 O) to dioxygen (O 2 ) is important in natural photosynthesis. One of nature's strategies for managing such multi-electron transfer reactions is to employ redox-active metal-organic cofactor arrays. One prototype example is the copper tyrosinate active site found in galactose oxidase. In this work, we have implemented such a strategy to develop a bio-inspired nickel phenolate complex capable of catalyzing the oxidation of H 2 O to O 2 electrochemically at neutral pH with a modest overpotential. Employment of the redox-active ligand turned out to be a useful strategy to avoid the formation of high-valent nickel intermediates while a reasonable turnover rate (0.15 s -1 ) is retained.

Improvement to the pattern of control rods of the equilibrium cycle of 18 months for the CLV using bio-inspired algorithms; Mejora del patron de barras de control del ciclo de equilibrio de 18 meses para la CLV empleando algoritmos bio-inspirados

Energy Technology Data Exchange (ETDEWEB)

Perusquia, R.; Ortiz, J.J.; Montes, J.L. [ININ, 52045 Ocoyoacac, Estado de Mexico (Mexico)]. e-mail: rpc@nuclear.inin.mx

2003-07-01

Nowadays in the National Institute of Nuclear Research are carried out studies with some bio-inspired optimization techniques to improve the performance of the fuel cycles of the boiling water reactors of the Laguna Verde power plant (CLV). In the present work two bio-inspired techniques were applied with the purpose of improving the performance of a balance cycle of 18 months developed for the CLV: genetic algorithms (AG) and systems based on ants colonies (SCH). The design of the reference cycle it represents in several aspects an optimal cycle proposed starting from the experience of several operation decades with the boiling water reactors (BWR initials for Boiling Water Reactor) in the world. To try to improve their performance is beforehand a difficult challenge and it puts on test the feasibility of the optimization methods in the reloads design. The study of the bio-inspired techniques was centered exclusively on the obtaining of the control rod patterns (PBC) trying to overcome the capacity factor reached in the design of the reference cycle. It was fixed the cycle length such that the decrease of the coast down period would represent an increase of the capacity factor of the cycle; so that, it diminishes the annual cost associated with the capital cost of the plant. As consequence of the study, was found that the algorithm based on the ants colonies reaches to diminish the coast down period in five and half days respect to the original balance cycle, what represents an annual saving of $US 74,000. Since the original cycle was optimized, the above-mentioned, shows the ability of the SCH for the optimization of the cycle design. With the AG it was reach to approach to the original balance cycle with a coast down period greater in seven days estimating an annual penalization of $US 130,000. (Author)

Bio-inspired patterned networks (BIPS) for development of wearable/disposable biosensors

Science.gov (United States)

McLamore, E. S.; Convertino, M.; Hondred, John; Das, Suprem; Claussen, J. C.; Vanegas, D. C.; Gomes, C.

2016-05-01

Here we demonstrate a novel approach for fabricating point of care (POC) wearable electrochemical biosensors based on 3D patterning of bionanocomposite networks. To create Bio-Inspired Patterned network (BIPS) electrodes, we first generate fractal network in silico models that optimize transport of network fluxes according to an energy function. Network patterns are then inkjet printed onto flexible substrate using conductive graphene ink. We then deposit fractal nanometal structures onto the graphene to create a 3D nanocomposite network. Finally, we biofunctionalize the surface with biorecognition agents using covalent bonding. In this paper, BIPS are used to develop high efficiency, low cost biosensors for measuring glucose as a proof of concept. Our results on the fundamental performance of BIPS sensors show that the biomimetic nanostructures significantly enhance biosensor sensitivity, accuracy, response time, limit of detection, and hysteresis compared to conventional POC non fractal electrodes (serpentine, interdigitated, and screen printed electrodes). BIPs, in particular Apollonian patterned BIPS, represent a new generation of POC biosensors based on nanoscale and microscale fractal networks that significantly improve electrical connectivity, leading to enhanced sensor performance.

Interactions of Bio-Inspired Membranes with Peptides and Peptide-Mimetic Nanoparticles

Directory of Open Access Journals (Sweden)

Michael Sebastiano

2015-08-01

Full Text Available Via Dissipative Particle Dynamics (DPD and implicit solvent coarse-grained (CG Molecular Dynamics (MD we examine the interaction of an amphiphilic cell-penetrating peptide PMLKE and its synthetic counterpart with a bio-inspired membrane. We use the DPD technique to investigate the interaction of peptide-mimetic nanoparticles, or nanopins, with a three-component membrane. The CG MD approach is used to investigate the interaction of a cell-penetrating peptide PMLKE with single-component membrane. We observe the spontaneous binding and subsequent insertion of peptide and nanopin in the membrane by using CG MD and DPD approaches, respectively. In addition, we find that the insertion of peptide and nanopins is mainly driven by the favorable enthalpic interactions between the hydrophobic components of the peptide, or nanopin, and the membrane. Our study provides insights into the mechanism underlying the interactions of amphiphilic peptide and peptide-mimetic nanoparticles with a membrane. The result of this study can be used to guide the functional integration of peptide and peptide-mimetic nanoparticles with a cell membrane.

Fusion of nacre, mussel, and lotus leaf: bio-inspired graphene composite paper with multifunctional integration

Science.gov (United States)

Zhong, Da; Yang, Qinglin; Guo, Lin; Dou, Shixue; Liu, Kesong; Jiang, Lei

2013-06-01

Multifunctional integration is an inherent characteristic for biological materials with multiscale structures. Learning from nature is an effective approach for scientists and engineers to construct multifunctional materials. In nature, mollusks (abalone), mussels, and the lotus have evolved different and optimized solutions to survive. Here, bio-inspired multifunctional graphene composite paper was fabricated in situ through the fusion of the different biological solutions from nacre (brick-and-mortar structure), mussel adhesive protein (adhesive property and reducing character), and the lotus leaf (self-cleaning effect). Owing to the special properties (self-polymerization, reduction, and adhesion), dopamine could be simultaneously used as a reducing agent for graphene oxide and as an adhesive, similar to the mortar in nacre, to crosslink the adjacent graphene. The resultant nacre-like graphene paper exhibited stable superhydrophobicity, self-cleaning, anti-corrosion, and remarkable mechanical properties underwater.Multifunctional integration is an inherent characteristic for biological materials with multiscale structures. Learning from nature is an effective approach for scientists and engineers to construct multifunctional materials. In nature, mollusks (abalone), mussels, and the lotus have evolved different and optimized solutions to survive. Here, bio-inspired multifunctional graphene composite paper was fabricated in situ through the fusion of the different biological solutions from nacre (brick-and-mortar structure), mussel adhesive protein (adhesive property and reducing character), and the lotus leaf (self-cleaning effect). Owing to the special properties (self-polymerization, reduction, and adhesion), dopamine could be simultaneously used as a reducing agent for graphene oxide and as an adhesive, similar to the mortar in nacre, to crosslink the adjacent graphene. The resultant nacre-like graphene paper exhibited stable superhydrophobicity, self

Fabricating bio-inspired micro/nano-particles by polydopamine coating and surface interactions with blood platelets

Energy Technology Data Exchange (ETDEWEB)

Ye, Wei [Jiangsu Provincial Key Lab for Interventional Medical Devices, Huaiyin Institute of Technology, Huaian 223003 (China); State Key Laboratory of Polymer Physics and Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022 (China); Shi, Qiang, E-mail: shiqiang@ciac.ac.cn [State Key Laboratory of Polymer Physics and Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022 (China); Hou, Jianwen; Gao, Jian; Li, Chunming; Jin, Jing; Shi, Hengchong [State Key Laboratory of Polymer Physics and Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022 (China); Yin, Jinghua, E-mail: yinjh@ciac.ac.cn [State Key Laboratory of Polymer Physics and Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022 (China)

2015-10-01

Graphical abstract: The particles or particle aggregations activate the blood platelets and provide the physical adhesive sites for platelets adhesion. - Highlights: • Particles with varied sizes and surface properties were fabricated by facile polydopamine (PDA) coating on polystyrene microsphere. • The direct interaction between PDA particles and blood platelets was qualitatively investigated. • The knowledge on platelet–particle interactions provided the basic principle to select biocompatible micro/nano-particles in biomedical field. - Abstract: Although bio-inspired polydopamine (PDA) micro/nano-particles show great promise for biomedical applications, the knowledge on the interactions between micro/nano-particles and platelets is still lacking. Here, we fabricate PDA-coated micro/nano-particles and investigate the platelet–particle surface interactions. Our strategy takes the advantage of facile PDA coating on polystyrene (PS) microsphere to fabricate particles with varied sizes and surface properties, and the chemical reactivity of PDA layers to immobilize fibrinogen and bovine serum albumin to manipulate platelet activation and adhesion. We demonstrate that PS particles activate the platelets in the size-dependent manner, but PDA nanoparticles have slight effect on platelet activation; PS particles promote platelet adhesion while PDA particles reduce platelet adhesion on the patterned surface; Particles interact with platelets through activating the glycoprotein integrin receptor of platelets and providing physical sites for initial platelet adhesion. Our work sheds new light on the interaction between platelets and particles, which provides the basic principle to select biocompatible micro/nano-particles in biomedical field.

A new biarticular actuator design facilitates control of leg function in BioBiped3.

Science.gov (United States)

Sharbafi, Maziar Ahmad; Rode, Christian; Kurowski, Stefan; Scholz, Dorian; Möckel, Rico; Radkhah, Katayon; Zhao, Guoping; Rashty, Aida Mohammadinejad; Stryk, Oskar von; Seyfarth, Andre

2016-07-01

Bioinspired legged locomotion comprises different aspects, such as (i) benefiting from reduced complexity control approaches as observed in humans/animals, (ii) combining embodiment with the controllers and (iii) reflecting neural control mechanisms. One of the most important lessons learned from nature is the significant role of compliance in simplifying control, enhancing energy efficiency and robustness against perturbations for legged locomotion. In this research, we investigate how body morphology in combination with actuator design may facilitate motor control of leg function. Inspired by the human leg muscular system, we show that biarticular muscles have a key role in balancing the upper body, joint coordination and swing leg control. Appropriate adjustment of biarticular spring rest length and stiffness can simplify the control and also reduce energy consumption. In order to test these findings, the BioBiped3 robot was developed as a new version of BioBiped series of biologically inspired, compliant musculoskeletal robots. In this robot, three-segmented legs actuated by mono- and biarticular series elastic actuators mimic the nine major human leg muscle groups. With the new biarticular actuators in BioBiped3, novel simplified control concepts for postural balance and for joint coordination in rebounding movements (drop jumps) were demonstrated and approved.

Dynamic analysis of a bio-inspired climbing robot using ADAMS-Simulink co-simulation

Science.gov (United States)

Chattopadhyay, P.; Dikshit, H.; Majumder, A.; Ghoshal, S.; Maity, A.

2018-04-01

Climbing robot has been an area of interest since the demand of inspection of pipeline, nuclear power plant, and various big structure is growing up rapidly. This paper represents the development of a bio-inspired modular robot which mimics inchworm locomotion during climbing. In the present paper, the climbing motion is achieved only on a flat vertical plane by magnetic adhesion principle. The robot is modelled as a 4-link planar mechanism with three revolute joints actuated by DC servo motors. Sinusoidal gait pattern is used to approximate the motion of an inchworm. The dynamics of the robot is presented by using ADAMS/MATLAB co-simulation methodology. The simulation result gives the maximum value of joint torque during one complete cycle of motion. This torque value is used for the selection of servo motor specifications required to build the prototype.

Tunable band gaps in bio-inspired periodic composites with nacre-like microstructure

Science.gov (United States)

Chen, Yanyu; Wang, Lifeng

2014-08-01

Periodic composite materials have many promising applications due to their unique ability to control the propagation of waves. Here, we report the existence and frequency tunability of complete elastic wave band gaps in bio-inspired periodic composites with nacre-like, brick-and-mortar microstructure. Numerical results show that complete band gaps in these periodic composites derive from local resonances or Bragg scattering, depending on the lattice angle and the volume fraction of each phase in the composites. The investigation of elastic wave propagation in finite periodic composites validates the simulated complete band gaps and further reveals the mechanisms leading to complete band gaps. Moreover, our results indicate that the topological arrangement of the mineral platelets and changes of material properties can be utilized to tune the evolution of complete band gaps. Our finding provides new opportunities to design mechanically robust periodic composite materials for wave absorption under hostile environments, such as for deep water applications.

Role of cellulose functionality in bio-inspired synthesis of nano bioactive glass.

Science.gov (United States)

Gupta, Nidhi; Santhiya, Deenan

2017-06-01

In search of abundant cheaper natural polymer for bio-inspired bioactive glass nanoparticles synthesis, cellulose and its derivatives have been considered as a template. Different templates explored in the present studies are pure cellulose, methyl cellulose and amine grafted cellulose. To the best of our knowledge, for the first time of the considered templates, pure cellulose and amine grafted cellulose results in in situ nano particulate composite formation while interestingly methyl cellulose proves to be an excellent sacrificial template for the synthesis of uniform bioglass nanoparticles of diameter in the range of 55nm. Further, viscoelastic measurements were carried out using dynamic mechanical analyzer. Herein, an attempt has been made to establish structure-mechanical relationship based on the templates. Moreover, in vitro bioactivity is also observed to be affected by the nature of the template molecule used for the synthesis of bioactive glass. Copyright © 2017 Elsevier B.V. All rights reserved.

Soft computing integrating evolutionary, neural, and fuzzy systems

CERN Document Server

Tettamanzi, Andrea

2001-01-01

Soft computing encompasses various computational methodologies, which, unlike conventional algorithms, are tolerant of imprecision, uncertainty, and partial truth. Soft computing technologies offer adaptability as a characteristic feature and thus permit the tracking of a problem through a changing environment. Besides some recent developments in areas like rough sets and probabilistic networks, fuzzy logic, evolutionary algorithms, and artificial neural networks are core ingredients of soft computing, which are all bio-inspired and can easily be combined synergetically. This book presents a well-balanced integration of fuzzy logic, evolutionary computing, and neural information processing. The three constituents are introduced to the reader systematically and brought together in differentiated combinations step by step. The text was developed from courses given by the authors and offers numerous illustrations as

Morphological self stabilization of locomotion gaits: illustration on a few examples from bio-inspired locomotion.

Science.gov (United States)

Chevallereau, Christine; Boyer, Frédéric; Porez, Mathieu; Mauny, Johan; Aoustin, Yannick

2017-06-20

To a large extent, robotics locomotion can be viewed as cyclic motions, named gaits. Due to the high complexity of the locomotion dynamics, to find the control laws that ensure an expected gait and its stability with respect to external perturbations, is a challenging issue for feedback control. To address this issue, a promising way is to take inspiration from animals that intensively exploit the interactions of the passive degrees of freedom of their body with their physical surroundings, to outsource the high-level exteroceptive feedback control to low-level proprioceptive ones. In this case, passive interactions can ensure most of the expected control goals. In this article, we propose a methodological framework to study the role of morphology in the design of locomotion gaits and their stability. This framework ranges from modelling to control aspects, and is illustrated through three examples from bio-inspired locomotion: a three-dimensional micro air vehicle in hovering flight, a pendular planar climber and a bipedal planar walker. In these three cases, we will see how simple considerations based on the morphology of the body can ensure the existence of passive stable gaits without requiring any high-level control.

Carbon-Nanotube-Supported Bio-Inspired Nickel Catalyst and Its Integration in Hybrid Hydrogen/Air Fuel Cells

Energy Technology Data Exchange (ETDEWEB)

Gentil, Solène [Univ. Grenoble Alpes, CNRS, DCM UMR 5250, 38000 Grenoble France; Laboratoire de Chimie et Biologie des Métaux, Univ. Grenoble Alpes, CNRS UMR5249, CEA, 38000 Grenoble France; Lalaoui, Noémie [Univ. Grenoble Alpes, CNRS, DCM UMR 5250, 38000 Grenoble France; Dutta, Arnab [Pacific Northwest National Laboratory, Richland WA 99532 USA; Current address: Chemistry Department, IIT Gandhinagar, Gujarat 382355 India; Nedellec, Yannig [Univ. Grenoble Alpes, CNRS, DCM UMR 5250, 38000 Grenoble France; Cosnier, Serge [Univ. Grenoble Alpes, CNRS, DCM UMR 5250, 38000 Grenoble France; Shaw, Wendy J. [Pacific Northwest National Laboratory, Richland WA 99532 USA; Artero, Vincent [Laboratoire de Chimie et Biologie des Métaux, Univ. Grenoble Alpes, CNRS UMR5249, CEA, 38000 Grenoble France; Le Goff, Alan [Univ. Grenoble Alpes, CNRS, DCM UMR 5250, 38000 Grenoble France

2017-01-12

A biomimetic nickel bis-diphosphine complex incorporating the amino-acid arginine in the outer coordination sphere, was immobilized on modified single-wall carbon nanotubes (SWCNTs) through electrostatic interactions. The sur-face-confined catalyst is characterized by a reversible 2-electron/2-proton redox process at potentials close to the equibrium potential of the H+/H2 couple. Consequently, the functionalized redox nanomaterial exhibits reversible electrocatalytic activity for the H2/2H+ interconversion over a broad range of pH. This system exhibits catalytic bias, analogous to hydrogenases, resulting in high turnover frequencies at low overpotentials for electrocatalytic H2 oxida-tion between pH 0 and 7. This allowed integrating such bio-inspired nanomaterial together with a multicopper oxi-dase at the cathode side in a hybrid bioinspired/enzymatic hydrogen fuel cell. This device delivers ~2 mW cm–2 with an open-circuit voltage of 1.0 V at room temperature and pH 5, which sets a new efficiency record for a bio-related hydrogen fuel cell with base metal catalysts.

Design of neural network model-based controller in a fed-batch microbial electrolysis cell reactor for bio-hydrogen gas production

Science.gov (United States)

Azwar; Hussain, M. A.; Abdul-Wahab, A. K.; Zanil, M. F.; Mukhlishien

2018-03-01

One of major challenge in bio-hydrogen production process by using MEC process is nonlinear and highly complex system. This is mainly due to the presence of microbial interactions and highly complex phenomena in the system. Its complexity makes MEC system difficult to operate and control under optimal conditions. Thus, precise control is required for the MEC reactor, so that the amount of current required to produce hydrogen gas can be controlled according to the composition of the substrate in the reactor. In this work, two schemes for controlling the current and voltage of MEC were evaluated. The controllers evaluated are PID and Inverse neural network (NN) controller. The comparative study has been carried out under optimal condition for the production of bio-hydrogen gas wherein the controller output is based on the correlation of optimal current and voltage to the MEC. Various simulation tests involving multiple set-point changes and disturbances rejection have been evaluated and the performances of both controllers are discussed. The neural network-based controller results in fast response time and less overshoots while the offset effects are minimal. In conclusion, the Inverse neural network (NN)-based controllers provide better control performance for the MEC system compared to the PID controller.

Viscous-Inviscid Methods in Unsteady Aerodynamic Analysis of Bio-Inspired Morphing Wings

Science.gov (United States)

Dhruv, Akash V.

Flight has been one of the greatest realizations of human imagination, revolutionizing communication and transportation over the years. This has greatly influenced the growth of technology itself, enabling researchers to communicate and share their ideas more effectively, extending the human potential to create more sophisticated systems. While the end product of a sophisticated technology makes our lives easier, its development process presents an array of challenges in itself. In last decade, scientists and engineers have turned towards bio-inspiration to design more efficient and robust aerodynamic systems to enhance the ability of Unmanned Aerial Vehicles (UAVs) to be operated in cluttered environments, where tight maneuverability and controllability are necessary. Effective use of UAVs in domestic airspace will mark the beginning of a new age in communication and transportation. The design of such complex systems necessitates the need for faster and more effective tools to perform preliminary investigations in design, thereby streamlining the design process. This thesis explores the implementation of numerical panel methods for aerodynamic analysis of bio-inspired morphing wings. Numerical panel methods have been one of the earliest forms of computational methods for aerodynamic analysis to be developed. Although the early editions of this method performed only inviscid analysis, the algorithm has matured over the years as a result of contributions made by prominent aerodynamicists. The method discussed in this thesis is influenced by recent advancements in panel methods and incorporates both viscous and inviscid analysis of multi-flap wings. The surface calculation of aerodynamic coefficients makes this method less computationally expensive than traditional Computational Fluid Dynamics (CFD) solvers available, and thus is effective when both speed and accuracy are desired. The morphing wing design, which consists of sequential feather-like flaps installed

Constrained VPH+: a local path planning algorithm for a bio-inspired crawling robot with customized ultrasonic scanning sensor.

Science.gov (United States)

Rao, Akshay; Elara, Mohan Rajesh; Elangovan, Karthikeyan

This paper aims to develop a local path planning algorithm for a bio-inspired, reconfigurable crawling robot. A detailed description of the robotic platform is first provided, and the suitability for deployment of each of the current state-of-the-art local path planners is analyzed after an extensive literature review. The Enhanced Vector Polar Histogram algorithm is described and reformulated to better fit the requirements of the platform. The algorithm is deployed on the robotic platform in crawling configuration and favorably compared with other state-of-the-art local path planning algorithms.

Bio-inspired online variable recruitment control of fluidic artificial muscles

Science.gov (United States)

Jenkins, Tyler E.; Chapman, Edward M.; Bryant, Matthew

2016-12-01

This paper details the creation of a hybrid variable recruitment control scheme for fluidic artificial muscle (FAM) actuators with an emphasis on maximizing system efficiency and switching control performance. Variable recruitment is the process of altering a system’s active number of actuators, allowing operation in distinct force regimes. Previously, FAM variable recruitment was only quantified with offline, manual valve switching; this study addresses the creation and characterization of novel, on-line FAM switching control algorithms. The bio-inspired algorithms are implemented in conjunction with a PID and model-based controller, and applied to a simulated plant model. Variable recruitment transition effects and chatter rejection are explored via a sensitivity analysis, allowing a system designer to weigh tradeoffs in actuator modeling, algorithm choice, and necessary hardware. Variable recruitment is further developed through simulation of a robotic arm tracking a variety of spline position inputs, requiring several levels of actuator recruitment. Switching controller performance is quantified and compared with baseline systems lacking variable recruitment. The work extends current variable recruitment knowledge by creating novel online variable recruitment control schemes, and exploring how online actuator recruitment affects system efficiency and control performance. Key topics associated with implementing a variable recruitment scheme, including the effects of modeling inaccuracies, hardware considerations, and switching transition concerns are also addressed.

A Compact VLSI System for Bio-Inspired Visual Motion Estimation.

Science.gov (United States)

Shi, Cong; Luo, Gang

2018-04-01

This paper proposes a bio-inspired visual motion estimation algorithm based on motion energy, along with its compact very-large-scale integration (VLSI) architecture using low-cost embedded systems. The algorithm mimics motion perception functions of retina, V1, and MT neurons in a primate visual system. It involves operations of ternary edge extraction, spatiotemporal filtering, motion energy extraction, and velocity integration. Moreover, we propose the concept of confidence map to indicate the reliability of estimation results on each probing location. Our algorithm involves only additions and multiplications during runtime, which is suitable for low-cost hardware implementation. The proposed VLSI architecture employs multiple (frame, pixel, and operation) levels of pipeline and massively parallel processing arrays to boost the system performance. The array unit circuits are optimized to minimize hardware resource consumption. We have prototyped the proposed architecture on a low-cost field-programmable gate array platform (Zynq 7020) running at 53-MHz clock frequency. It achieved 30-frame/s real-time performance for velocity estimation on 160 × 120 probing locations. A comprehensive evaluation experiment showed that the estimated velocity by our prototype has relatively small errors (average endpoint error < 0.5 pixel and angular error < 10°) for most motion cases.

A neural network model of ventriloquism effect and aftereffect.

Science.gov (United States)

Magosso, Elisa; Cuppini, Cristiano; Ursino, Mauro

2012-01-01

Presenting simultaneous but spatially discrepant visual and auditory stimuli induces a perceptual translocation of the sound towards the visual input, the ventriloquism effect. General explanation is that vision tends to dominate over audition because of its higher spatial reliability. The underlying neural mechanisms remain unclear. We address this question via a biologically inspired neural network. The model contains two layers of unimodal visual and auditory neurons, with visual neurons having higher spatial resolution than auditory ones. Neurons within each layer communicate via lateral intra-layer synapses; neurons across layers are connected via inter-layer connections. The network accounts for the ventriloquism effect, ascribing it to a positive feedback between the visual and auditory neurons, triggered by residual auditory activity at the position of the visual stimulus. Main results are: i) the less localized stimulus is strongly biased toward the most localized stimulus and not vice versa; ii) amount of the ventriloquism effect changes with visual-auditory spatial disparity; iii) ventriloquism is a robust behavior of the network with respect to parameter value changes. Moreover, the model implements Hebbian rules for potentiation and depression of lateral synapses, to explain ventriloquism aftereffect (that is, the enduring sound shift after exposure to spatially disparate audio-visual stimuli). By adaptively changing the weights of lateral synapses during cross-modal stimulation, the model produces post-adaptive shifts of auditory localization that agree with in-vivo observations. The model demonstrates that two unimodal layers reciprocally interconnected may explain ventriloquism effect and aftereffect, even without the presence of any convergent multimodal area. The proposed study may provide advancement in understanding neural architecture and mechanisms at the basis of visual-auditory integration in the spatial realm.

Using a virtual cortical module implementing a neural field model to modulate brain rhythms in Parkinson's disease

Directory of Open Access Journals (Sweden)

Julien Modolo

2010-06-01

Full Text Available We propose a new method for selective modulation of cortical rhythms based on neural field theory, in which the activity of a cortical area is extensively monitored using a two-dimensional microelectrode array. The example of Parkinson's disease illustrates the proposed method, in which a neural field model is assumed to accurately describe experimentally recorded activity. In addition, we propose a new closed-loop stimulation signal that is both space- and time- dependent. This method is especially designed to specifically modulate a targeted brain rhythm, without interfering with other rhythms. A new class of neuroprosthetic devices is also proposed, in which the multielectrode array is seen as an artificial neural network interacting with biological tissue. Such a bio-inspired approach may provide a solution to optimize interactions between the stimulation device and the cortex aiming to attenuate or augment specific cortical rhythms. The next step will be to validate this new approach experimentally in patients with Parkinson's disease.

A biologically inspired neural network model to transformation invariant object recognition

Science.gov (United States)

Iftekharuddin, Khan M.; Li, Yaqin; Siddiqui, Faraz

2007-09-01

Transformation invariant image recognition has been an active research area due to its widespread applications in a variety of fields such as military operations, robotics, medical practices, geographic scene analysis, and many others. The primary goal for this research is detection of objects in the presence of image transformations such as changes in resolution, rotation, translation, scale and occlusion. We investigate a biologically-inspired neural network (NN) model for such transformation-invariant object recognition. In a classical training-testing setup for NN, the performance is largely dependent on the range of transformation or orientation involved in training. However, an even more serious dilemma is that there may not be enough training data available for successful learning or even no training data at all. To alleviate this problem, a biologically inspired reinforcement learning (RL) approach is proposed. In this paper, the RL approach is explored for object recognition with different types of transformations such as changes in scale, size, resolution and rotation. The RL is implemented in an adaptive critic design (ACD) framework, which approximates the neuro-dynamic programming of an action network and a critic network, respectively. Two ACD algorithms such as Heuristic Dynamic Programming (HDP) and Dual Heuristic dynamic Programming (DHP) are investigated to obtain transformation invariant object recognition. The two learning algorithms are evaluated statistically using simulated transformations in images as well as with a large-scale UMIST face database with pose variations. In the face database authentication case, the 90° out-of-plane rotation of faces from 20 different subjects in the UMIST database is used. Our simulations show promising results for both designs for transformation-invariant object recognition and authentication of faces. Comparing the two algorithms, DHP outperforms HDP in learning capability, as DHP takes fewer steps to

Bio-inspired device: a novel smart MR spring featuring tendril structure

International Nuclear Information System (INIS)

Kaluvan, Suresh; Park, Chun-Yong; Choi, Seung-Bok

2016-01-01

Smart materials such as piezoelectric patches, shape memory alloy, electro and magneto rheological fluid, magnetostrictive materials, etc are involved by far to design intelligent and high performance smart devices like injectors, dental braces, dampers, actuators and sensors. In this paper, an interesting smart device is proposed by inspiring on the structure of the bio climber plant. The key enabling concept of this proposed work is to design the smart spring damper as a helical shaped tendril structure using magneto-rheological (MR) fluid. The proposed smart spring consists of a hollow helical structure filled with MR fluid. The viscosity of the MR fluid decides the damping force of helical shaped smart spring, while the fluid intensity in the vine decides the strength of the tendril in the climber plant. Thus, the proposed smart spring can provide a new concept design of the damper which can be applicable to various damping system industries with tuneable damping force. The proposed smart spring damper has several advantageous such as cost effective, easy implementation compared with the conventional damper. In addition, the proposed spring damper can be easily designed to adapt different damping force levels without any alteration. (letter)

Bio-inspired device: a novel smart MR spring featuring tendril structure

Science.gov (United States)

Kaluvan, Suresh; Park, Chun-Yong; Choi, Seung-Bok

2016-01-01

Smart materials such as piezoelectric patches, shape memory alloy, electro and magneto rheological fluid, magnetostrictive materials, etc are involved by far to design intelligent and high performance smart devices like injectors, dental braces, dampers, actuators and sensors. In this paper, an interesting smart device is proposed by inspiring on the structure of the bio climber plant. The key enabling concept of this proposed work is to design the smart spring damper as a helical shaped tendril structure using magneto-rheological (MR) fluid. The proposed smart spring consists of a hollow helical structure filled with MR fluid. The viscosity of the MR fluid decides the damping force of helical shaped smart spring, while the fluid intensity in the vine decides the strength of the tendril in the climber plant. Thus, the proposed smart spring can provide a new concept design of the damper which can be applicable to various damping system industries with tuneable damping force. The proposed smart spring damper has several advantageous such as cost effective, easy implementation compared with the conventional damper. In addition, the proposed spring damper can be easily designed to adapt different damping force levels without any alteration.

Bio-inspired aquatic robotics by untethered piezohydroelastic actuation

International Nuclear Information System (INIS)

Cen, L; Erturk, A

2013-01-01

This paper investigates fish-like aquatic robotics using flexible bimorphs made of macro-fiber composite (MFC) piezoelectric laminates for carangiform locomotion. In addition to noiseless and efficient actuation over a range of frequencies, geometric scalability, and simple design, bimorph propulsors made of MFCs offer a balance between the actuation force and velocity response for performance enhancement in bio-inspired swimming. The experimental component of the presented work focuses on the characterization of an elastically constrained MFC bimorph propulsor for thrust generation in quiescent water as well as the development of a robotic fish prototype combining a microcontroller and a printed-circuit-board amplifier to generate high actuation voltage for untethered locomotion. From the theoretical standpoint, a distributed-parameter electroelastic model including the hydrodynamic effects and actuator dynamics is coupled with the elongated-body theory for predicting the mean thrust in quiescent water. In-air and underwater experiments are performed to verify the incorporation of hydrodynamic effects in the linear actuation regime. For electroelastically nonlinear actuation levels, experimentally obtained underwater vibration response is coupled with the elongated-body theory to predict the thrust output. The measured mean thrust levels in quiescent water (on the order of ∼10 mN) compare favorably with thrust levels of biological fish. An untethered robotic fish prototype that employs a single bimorph fin (caudal fin) for straight swimming and turning motions is developed and tested in free locomotion. A swimming speed of 0.3 body-length/second (7.5 cm s −1 swimming speed for 24.3 cm body length) is achieved at 5 Hz for a non-optimized main body-propulsor bimorph combination under a moderate actuation voltage level. (paper)

Complex biological and bio-inspired systems

Energy Technology Data Exchange (ETDEWEB)

Ecke, Robert E [Los Alamos National Laboratory

2009-01-01

The understanding and characterization ofthe fundamental processes of the function of biological systems underpins many of the important challenges facing American society, from the pathology of infectious disease and the efficacy ofvaccines, to the development of materials that mimic biological functionality and deliver exceptional and novel structural and dynamic properties. These problems are fundamentally complex, involving many interacting components and poorly understood bio-chemical kinetics. We use the basic science of statistical physics, kinetic theory, cellular bio-chemistry, soft-matter physics, and information science to develop cell level models and explore the use ofbiomimetic materials. This project seeks to determine how cell level processes, such as response to mechanical stresses, chemical constituents and related gradients, and other cell signaling mechanisms, integrate and combine to create a functioning organism. The research focuses on the basic physical processes that take place at different levels ofthe biological organism: the basic role of molecular and chemical interactions are investigated, the dynamics of the DNA-molecule and its phylogenetic role are examined and the regulatory networks of complex biochemical processes are modeled. These efforts may lead to early warning algorithms ofpathogen outbreaks, new bio-sensors to detect hazards from pathomic viruses to chemical contaminants. Other potential applications include the development of efficient bio-fuel alternative-energy processes and the exploration ofnovel materials for energy usages. Finally, we use the notion of 'coarse-graining,' which is a method for averaging over less important degrees of freedom to develop computational models to predict cell function and systems-level response to disease, chemical stress, or biological pathomic agents. This project supports Energy Security, Threat Reduction, and the missions of the DOE Office of Science through its efforts to

Bio-inspired micro-nano structured surface with structural color and anisotropic wettability on Cu substrate

International Nuclear Information System (INIS)

Liu, Yan; Li, Shuyi; Niu, Shichao; Cao, Xiaowen; Han, Zhiwu; Ren, Luquan

2016-01-01

Highlights: • We have prepared a biomimetic hydrophobic surface on copper substrate by one-step femtosecond laser technique. • The hydrophobicity mechanism relies on morphology and chemical component on surface. • The hydrophobic surfaces exhibit different structural colors and a anisotropic wettability. - Abstract: Inspired by the unique creatures in the nature, the femtosecond laser technology has been usually used to fabricate the periodic microstructures due to its advantages of rapidness, simplicity, ease of large-area fabrication, and simultaneously offering dual micro/nano-scale structures simply via one-step process for a wide variety of materials. By changing the experimental conditions, multi-functional surfaces which possess superhydrophobicity and structural colors could be achieved on copper substrate. In addition, the apparent contact angle can reach 144.3° without any further modification, which also exhibits the anisotropic wettability. Moreover, it can be inferred that higher laser fluence can lead to a larger CA within a certain range. At the same time, due to the change of laser processing parameters, the obtained surfaces present different structural colors. This study may expand the applications of bio-inspired functional materials because multiple colors and hydrophobicity are both important features in the real life and industrial applications, such as display, decoration, and anti-counterfeiting technology etc.

Bio-inspired micro-nano structured surface with structural color and anisotropic wettability on Cu substrate

Energy Technology Data Exchange (ETDEWEB)

Liu, Yan [Key Laboratory of Bionic Engineering (Ministry of Education), Jilin University, Changchun 130022 (China); State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022 (China); Li, Shuyi; Niu, Shichao [Key Laboratory of Bionic Engineering (Ministry of Education), Jilin University, Changchun 130022 (China); Cao, Xiaowen [Key Laboratory on Integrated Optoelectronics College of Electronic Science and Engineering, Jilin University, Changchun 130012 (China); Han, Zhiwu, E-mail: zwhan@jlu.edu.cn [Key Laboratory of Bionic Engineering (Ministry of Education), Jilin University, Changchun 130022 (China); Ren, Luquan [Key Laboratory of Bionic Engineering (Ministry of Education), Jilin University, Changchun 130022 (China)

2016-08-30

Highlights: • We have prepared a biomimetic hydrophobic surface on copper substrate by one-step femtosecond laser technique. • The hydrophobicity mechanism relies on morphology and chemical component on surface. • The hydrophobic surfaces exhibit different structural colors and a anisotropic wettability. - Abstract: Inspired by the unique creatures in the nature, the femtosecond laser technology has been usually used to fabricate the periodic microstructures due to its advantages of rapidness, simplicity, ease of large-area fabrication, and simultaneously offering dual micro/nano-scale structures simply via one-step process for a wide variety of materials. By changing the experimental conditions, multi-functional surfaces which possess superhydrophobicity and structural colors could be achieved on copper substrate. In addition, the apparent contact angle can reach 144.3° without any further modification, which also exhibits the anisotropic wettability. Moreover, it can be inferred that higher laser fluence can lead to a larger CA within a certain range. At the same time, due to the change of laser processing parameters, the obtained surfaces present different structural colors. This study may expand the applications of bio-inspired functional materials because multiple colors and hydrophobicity are both important features in the real life and industrial applications, such as display, decoration, and anti-counterfeiting technology etc.

Hydrogels from Amorphous Calcium Carbonate and Polyacrylic Acid: Bio-Inspired Materials for "Mineral Plastics".

Science.gov (United States)

Sun, Shengtong; Mao, Li-Bo; Lei, Zhouyue; Yu, Shu-Hong; Cölfen, Helmut

2016-09-19

Given increasing environmental issues due to the large usage of non-biodegradable plastics based on petroleum, new plastic materials, which are economic, environmentally friendly, and recyclable are in high demand. One feasible strategy is the bio-inspired synthesis of mineral-based hybrid materials. Herein we report a facile route for an amorphous CaCO3 (ACC)-based hydrogel consisting of very small ACC nanoparticles physically cross-linked by poly(acrylic acid). The hydrogel is shapeable, stretchable, and self-healable. Upon drying, the hydrogel forms free-standing, rigid, and transparent objects with remarkable mechanical performance. By swelling in water, the material can completely recover the initial hydrogel state. As a matrix, thermochromism can also be easily introduced. The present hybrid hydrogel may represent a new class of plastic materials, the "mineral plastics". © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Artificial Roughness Encoding with a Bio-inspired MEMS-based Tactile Sensor Array

Directory of Open Access Journals (Sweden)

Calogero Maria Oddo

2009-04-01

Full Text Available A compliant 2x2 tactile sensor array was developed and investigated for roughness encoding. State of the art cross shape 3D MEMS sensors were integrated with polymeric packaging providing in total 16 sensitive elements to external mechanical stimuli in an area of about 20 mm2, similarly to the SA1 innervation density in humans. Experimental analysis of the bio-inspired tactile sensor array was performed by using ridged surfaces, with spatial periods from 2.6 mm to 4.1 mm, which were indented with regulated 1N normal force and stroked at constant sliding velocity from 15 mm/s to 48 mm/s. A repeatable and expected frequency shift of the sensor outputs depending on the applied stimulus and on its scanning velocity was observed between 3.66 Hz and 18.46 Hz with an overall maximum error of 1.7%. The tactile sensor could also perform contact imaging during static stimulus indentation. The experiments demonstrated the suitability of this approach for the design of a roughness encoding tactile sensor for an artificial fingerpad.

A Bio-inspired Approach for Power and Performance Aware Resource Allocation in Clouds

Directory of Open Access Journals (Sweden)

Kumar Rajesh

2016-01-01

Full Text Available In order to cope with increasing demand, cloud market players such as Amazon, Microsoft, Google, Gogrid, Flexiant, etc. have set up large sized data centers. Due to monotonically increasing size of data centers and heterogeneity of resources have made resource allocation a challenging task. A large percentage of total energy consumption of the data centers gets wasted because of under-utilization of resources. Thus, there is a need of resource allocation technique that improves the utilization of resources with effecting performance of services being delivered to end users. In this work, a bio-inspired resource allocation approach is proposed with the aim to improve utilization and hence the energy efficiency of the cloud infrastructure. The proposed approach makes use of Cuckoo search for power and performance aware allocation of resources to the services hired by the end users. The proposed approach is implemented in CloudSim. The simulation results have shown approximately 12% saving in energy consumption.

Towards Bio-Inspired Chromatic Behaviours in Surveillance Robots

Directory of Open Access Journals (Sweden)

Sampath Kumar Karutaa Gnaniar

2016-09-01

Full Text Available The field of Robotics is ever growing at the same time as posing enormous challenges. Numerous works has been done in biologically inspired robotics emulating models, systems and elements of nature for the purpose of solving traditional robotics problems. Chromatic behaviours are abundant in nature across a variety of living species to achieve camouflage, signaling, and temperature regulation. The ability of these creatures to successfully blend in with their environment and communicate by changing their colour is the fundamental inspiration for our research work. In this paper, we present dwarf chameleon inspired chromatic behaviour in the context of an autonomous surveillance robot, “PACHONDHI”. In our experiments, we successfully validated the ability of the robot to autonomously change its colour in relation to the terrain that it is traversing for maximizing detectability to friendly security agents and minimizing exposure to hostile agents, as well as to communicate with fellow cooperating robots.

Fabrication of bio-inspired nitinol alloy surface with tunable anisotropic wetting and high adhesive ability.

Science.gov (United States)

Tian, Yan L; Zhao, Yue C; Yang, Cheng J; Wang, Fu J; Liu, Xian P; Jing, Xiu B

2018-10-01

In this paper, micro/nano-scale structures were fabricated on nitinol alloy (NiTi) to realize tunable anisotropic wetting and high adhesive capability. Laser texturing and silanization process are utilized to change the morphological and chemical properties of substrates. It is noted that these treated substrates exhibit the joint characteristics of anisotropic wetting and high adhesive capability. In order to investigate the influences of laser-texturing and silanization processes on NiTi, these surfaces were evaluated using scanning electron microscope (SEM), a white light confocal microscope, X-ray photoelectron spectroscopy (XPS) and goniometer. The relationship between water volume and anisotropic wetting was also established. From the experimental testing, we can obtain the following conclusions: (1) the anisotropic wetting characterized by the difference between the water contact angles (WCAs) in the vertical and parallel directions ranges from 0° to 20.3°, which is far more than the value of natural rice leaves. (2) the water sliding angles (WSAs) kept stable at 180°, successfully mimicking the adhesive ability of rose petals. (3) the silanization process could strengthen the hydrophobicity but weaken anisotropic wetting. These bio-inspired NiTi surfaces have a tremendous potential applications such as microfluidic devices, bio-mimetic materials fabrication and lab on chip. Copyright © 2018 Elsevier Inc. All rights reserved.

Spatial frequency domain spectroscopy of two layer media

Science.gov (United States)

Yudovsky, Dmitry; Durkin, Anthony J.

2011-10-01

Monitoring of tissue blood volume and oxygen saturation using biomedical optics techniques has the potential to inform the assessment of tissue health, healing, and dysfunction. These quantities are typically estimated from the contribution of oxyhemoglobin and deoxyhemoglobin to the absorption spectrum of the dermis. However, estimation of blood related absorption in superficial tissue such as the skin can be confounded by the strong absorption of melanin in the epidermis. Furthermore, epidermal thickness and pigmentation varies with anatomic location, race, gender, and degree of disease progression. This study describes a technique for decoupling the effect of melanin absorption in the epidermis from blood absorption in the dermis for a large range of skin types and thicknesses. An artificial neural network was used to map input optical properties to spatial frequency domain diffuse reflectance of two layer media. Then, iterative fitting was used to determine the optical properties from simulated spatial frequency domain diffuse reflectance. Additionally, an artificial neural network was trained to directly map spatial frequency domain reflectance to sets of optical properties of a two layer medium, thus bypassing the need for iteration. In both cases, the optical thickness of the epidermis and absorption and reduced scattering coefficients of the dermis were determined independently. The accuracy and efficiency of the iterative fitting approach was compared with the direct neural network inversion.

Single-hidden-layer feed-forward quantum neural network based on Grover learning.

Science.gov (United States)

Liu, Cheng-Yi; Chen, Chein; Chang, Ching-Ter; Shih, Lun-Min

2013-09-01

In this paper, a novel single-hidden-layer feed-forward quantum neural network model is proposed based on some concepts and principles in the quantum theory. By combining the quantum mechanism with the feed-forward neural network, we defined quantum hidden neurons and connected quantum weights, and used them as the fundamental information processing unit in a single-hidden-layer feed-forward neural network. The quantum neurons make a wide range of nonlinear functions serve as the activation functions in the hidden layer of the network, and the Grover searching algorithm outstands the optimal parameter setting iteratively and thus makes very efficient neural network learning possible. The quantum neuron and weights, along with a Grover searching algorithm based learning, result in a novel and efficient neural network characteristic of reduced network, high efficient training and prospect application in future. Some simulations are taken to investigate the performance of the proposed quantum network and the result show that it can achieve accurate learning. Copyright © 2013 Elsevier Ltd. All rights reserved.

Working principle of bio-inspired shape memory alloy composite actuators

International Nuclear Information System (INIS)

Smith, Colin; Villanueva, Alex; Joshi, Keyur; Tadesse, Yonas; Priya, Shashank

2011-01-01

Recently, bio-inspired shape memory alloy composite (BISMAC) actuators have been shown to mimic the deformation characteristics of natural jellyfish medusa. In this study, a constant cross-section BISMAC actuator was characterized in terms of bending deflection and force in conjunction with microscopy to understand its deformation mechanism. The actuator showed bending deflection of 111% with respect to the active length along with a blocking force of 0.061 N. The resulting energy density of the composite actuator was 4929 J m −3 at an input voltage and current level of 12 V and 0.7 A, respectively. For a dry-state actuator, this performance is extremely high and represents an optimum combination of force and deflection. Experiments reveal that BISMAC's performance is related to the moment induced from tip attachment of the shape memory alloy (SMA) rather than to friction within the composite structure. A physics-based model of BISMAC structure is presented which shows that the actuator is highly sensitive to the distance between the SMA wire and the incompressible component. While SMA has both stress and strain limitations, the limiting factor in BISMAC actuators is dependent on separation distance. The limiting factor in BISMAC's suitability for mimicking the performance of medusa was experimentally found to be related to the maximum 4% strain of the SMA and not its force generation. (fast track communication)

Drawing inspiration from biological optical systems

Science.gov (United States)

Wolpert, H. D.

2009-08-01

Bio-Mimicking/Bio-Inspiration: How can we not be inspired by Nature? Life has evolved on earth over the last 3.5 to 4 billion years. Materials formed during this time were not toxic; they were created at low temperatures and low pressures unlike many of the materials developed today. The natural materials formed are self-assembled, multifunctional, nonlinear, complex, adaptive, self-repairing and biodegradable. The designs that failed are fossils. Those that survived are the success stories. Natural materials are mostly formed from organics, inorganic crystals and amorphous phases. The materials make economic sense by optimizing the design of the structures or systems to meet multiple needs. We constantly "see" many similar strategies in approaches, between man and nature, but we seldom look at the details of natures approaches. The power of image processing, in many of natures creatures, is a detail that is often overlooked. Seldon does the engineer interact with the biologist and learn what nature has to teach us. The variety and complexity of biological materials and the optical systems formed should inspire us.

A neural network model of ventriloquism effect and aftereffect.

Directory of Open Access Journals (Sweden)

Elisa Magosso

Full Text Available Presenting simultaneous but spatially discrepant visual and auditory stimuli induces a perceptual translocation of the sound towards the visual input, the ventriloquism effect. General explanation is that vision tends to dominate over audition because of its higher spatial reliability. The underlying neural mechanisms remain unclear. We address this question via a biologically inspired neural network. The model contains two layers of unimodal visual and auditory neurons, with visual neurons having higher spatial resolution than auditory ones. Neurons within each layer communicate via lateral intra-layer synapses; neurons across layers are connected via inter-layer connections. The network accounts for the ventriloquism effect, ascribing it to a positive feedback between the visual and auditory neurons, triggered by residual auditory activity at the position of the visual stimulus. Main results are: i the less localized stimulus is strongly biased toward the most localized stimulus and not vice versa; ii amount of the ventriloquism effect changes with visual-auditory spatial disparity; iii ventriloquism is a robust behavior of the network with respect to parameter value changes. Moreover, the model implements Hebbian rules for potentiation and depression of lateral synapses, to explain ventriloquism aftereffect (that is, the enduring sound shift after exposure to spatially disparate audio-visual stimuli. By adaptively changing the weights of lateral synapses during cross-modal stimulation, the model produces post-adaptive shifts of auditory localization that agree with in-vivo observations. The model demonstrates that two unimodal layers reciprocally interconnected may explain ventriloquism effect and aftereffect, even without the presence of any convergent multimodal area. The proposed study may provide advancement in understanding neural architecture and mechanisms at the basis of visual-auditory integration in the spatial realm.

The Difference Se Makes: A Bio-Inspired Dppf-Supported Nickel Selenolate Complex Boosts Dihydrogen Evolution with High Oxygen Tolerance.

Science.gov (United States)

Pan, Zhong-Hua; Tao, Yun-Wen; He, Quan-Feng; Wu, Qiao-Yu; Cheng, Li-Ping; Wei, Zhan-Hua; Wu, Ji-Huai; Lin, Jin-Qing; Sun, Di; Zhang, Qi-Chun; Tian, Dan; Luo, Geng-Geng

2018-06-12

Inspired by the metal active sites of [NiFeSe]-hydrogenases, a dppf-supported nickel(II) selenolate complex (dppf=1,1'-bis(diphenylphosphino)ferrocene) shows high catalytic activity for electrochemical proton reduction with a remarkable enzyme-like H 2 evolution turnover frequency (TOF) of 7838 s -1 under an Ar atmosphere, which markedly surpasses the activity of a dppf-supported nickel(II) thiolate analogue with a low TOF of 600 s -1 . A combined study of electrochemical experiments and DFT calculations shed light on the catalytic process, suggesting that selenium atom as a bio-inspired proton relay plays a key role in proton exchange and enhancing catalytic activity of H 2 production. For the first time, this type of Ni selenolate-containing electrocatalyst displays a high degree of O 2 and H 2 tolerance. Our results should encourage the development of the design of highly efficient oxygen-tolerant Ni selenolate molecular catalysts. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Demonstrations of bio-inspired perching landing gear for UAVs

Science.gov (United States)

Tieu, Mindy; Michael, Duncan M.; Pflueger, Jeffery B.; Sethi, Manik S.; Shimazu, Kelli N.; Anthony, Tatiana M.; Lee, Christopher L.

2016-04-01

Results are presented which demonstrate the feasibility and performance of two concepts of biologically-inspired landing-gear systems that enable bird-sized, unmanned aerial vehicles (UAV's) to land, perch, and take-off from branchlike structures and/or ledges. The first concept follows the anatomy of birds that can grasp ahold of a branch and perch as tendons in their legs are tensioned. This design involves a gravity-activated, cable-driven, underactuated, graspingfoot mechanism. As the UAV lands, its weight collapses a four-bar linkage pulling a cable which curls two opposing, multi-segmented feet to grasp the landing target. Each foot is a single, compliant mechanism fabricated by simultaneouly 3D-printing a flexible thermo-plastic and a stiffer ABS plastic. The design is optimized to grasp structures over a range of shapes and sizes. Quasi-static and flight tests of this landing gear affixed to RC rotorcraft (24 cm to 550 cm in diameter) demonstrate that the aircraft can land, perch, and take-off from a tree branch, rectangular wood board, PVC pipe, metal hand rail, chair armrest, and in addition, a stone wall ledge. Stability tests show that perching is maintained under base and wind disturbances. The second design concept, inspired by roosting bats, is a two-material, 3D-printed hooking mechanism that enables the UAV to stably suspend itself from a wire or small-diameter branch. The design balances structural stiffness for support and flexibility for the perching process. A flight-test demonstrates the attaching and dis-engaging of a small, RC quadcopter from a suspended line.

Distributed Synchronization Technique for OFDMA-Based Wireless Mesh Networks Using a Bio-Inspired Algorithm

Directory of Open Access Journals (Sweden)

Mi Jeong Kim

2015-07-01

Full Text Available In this paper, a distributed synchronization technique based on a bio-inspired algorithm is proposed for an orthogonal frequency division multiple access (OFDMA-based wireless mesh network (WMN with a time difference of arrival. The proposed time- and frequency-synchronization technique uses only the signals received from the neighbor nodes, by considering the effect of the propagation delay between the nodes. It achieves a fast synchronization with a relatively low computational complexity because it is operated in a distributed manner, not requiring any feedback channel for the compensation of the propagation delays. In addition, a self-organization scheme that can be effectively used to construct 1-hop neighbor nodes is proposed for an OFDMA-based WMN with a large number of nodes. The performance of the proposed technique is evaluated with regard to the convergence property and synchronization success probability using a computer simulation.

Distributed Synchronization Technique for OFDMA-Based Wireless Mesh Networks Using a Bio-Inspired Algorithm.

Science.gov (United States)

Kim, Mi Jeong; Maeng, Sung Joon; Cho, Yong Soo

2015-07-28

In this paper, a distributed synchronization technique based on a bio-inspired algorithm is proposed for an orthogonal frequency division multiple access (OFDMA)-based wireless mesh network (WMN) with a time difference of arrival. The proposed time- and frequency-synchronization technique uses only the signals received from the neighbor nodes, by considering the effect of the propagation delay between the nodes. It achieves a fast synchronization with a relatively low computational complexity because it is operated in a distributed manner, not requiring any feedback channel for the compensation of the propagation delays. In addition, a self-organization scheme that can be effectively used to construct 1-hop neighbor nodes is proposed for an OFDMA-based WMN with a large number of nodes. The performance of the proposed technique is evaluated with regard to the convergence property and synchronization success probability using a computer simulation.

Color encoding in biologically-inspired convolutional neural networks.

Science.gov (United States)

Rafegas, Ivet; Vanrell, Maria

2018-05-11

Convolutional Neural Networks have been proposed as suitable frameworks to model biological vision. Some of these artificial networks showed representational properties that rival primate performances in object recognition. In this paper we explore how color is encoded in a trained artificial network. It is performed by estimating a color selectivity index for each neuron, which allows us to describe the neuron activity to a color input stimuli. The index allows us to classify whether they are color selective or not and if they are of a single or double color. We have determined that all five convolutional layers of the network have a large number of color selective neurons. Color opponency clearly emerges in the first layer, presenting 4 main axes (Black-White, Red-Cyan, Blue-Yellow and Magenta-Green), but this is reduced and rotated as we go deeper into the network. In layer 2 we find a denser hue sampling of color neurons and opponency is reduced almost to one new main axis, the Bluish-Orangish coinciding with the dataset bias. In layers 3, 4 and 5 color neurons are similar amongst themselves, presenting different type of neurons that detect specific colored objects (e.g., orangish faces), specific surrounds (e.g., blue sky) or specific colored or contrasted object-surround configurations (e.g. blue blob in a green surround). Overall, our work concludes that color and shape representation are successively entangled through all the layers of the studied network, revealing certain parallelisms with the reported evidences in primate brains that can provide useful insight into intermediate hierarchical spatio-chromatic representations. Copyright © 2018 Elsevier Ltd. All rights reserved.

Dynamic reciprocity in bio-inspired supramolecular materials

NARCIS (Netherlands)

Bastings, M.M.C.

2012-01-01

Dynamic reciprocity, the spatio-temporal bidirectional process between evolving partners in a functional system is not only found in nature, but also applies to supramolecularly assembling architectures. In this thesis, the focus was on the understanding of nature-inspired supramolecular

Surface protection in bio-shields via a functional soft skin layer: Lessons from the turtle shell.

Science.gov (United States)

Shelef, Yaniv; Bar-On, Benny

2017-09-01

The turtle shell is a functional bio-shielding element, which has evolved naturally to provide protection against predator attacks that involve biting and clawing. The near-surface architecture of the turtle shell includes a soft bi-layer skin coating - rather than a hard exterior - which functions as a first line of defense against surface damage. This architecture represents a novel type of bio-shielding configuration, namely, an inverse structural-mechanical design, rather than the hard-coated bio-shielding elements identified so far. In the current study, we used experimentally based structural modeling and FE simulations to analyze the mechanical significance of this unconventional protection architecture in terms of resistance to surface damage upon extensive indentations. We found that the functional bi-layer skin of the turtle shell, which provides graded (soft-softer-hard) mechanical characteristics to the bio-shield exterior, serves as a bumper-buffer mechanism. This material-level adaptation protects the inner core from the highly localized indentation loads via stress delocalization and extensive near-surface plasticity. The newly revealed functional bi-layer coating architecture can potentially be adapted, using synthetic materials, to considerably enhance the surface load-bearing capabilities of various engineering configurations. Copyright © 2017 Elsevier Ltd. All rights reserved.

A bio-inspired hair- based acceleration sensor

NARCIS (Netherlands)

Droogendijk, H.

Crickets use so-called clavate hairs to sense (gravitational) acceleration to obtain information on their orientation. Inspired by this clavate hair system, a one-axis biomimetic accelerometer has been developed and fabricated using surface micromachining and SU- 8 lithography. Measu- rements show

Fusion of nacre, mussel, and lotus leaf: bio-inspired graphene composite paper with multifunctional integration.

Science.gov (United States)

Zhong, Da; Yang, Qinglin; Guo, Lin; Dou, Shixue; Liu, Kesong; Jiang, Lei

2013-07-07

Multifunctional integration is an inherent characteristic for biological materials with multiscale structures. Learning from nature is an effective approach for scientists and engineers to construct multifunctional materials. In nature, mollusks (abalone), mussels, and the lotus have evolved different and optimized solutions to survive. Here, bio-inspired multifunctional graphene composite paper was fabricated in situ through the fusion of the different biological solutions from nacre (brick-and-mortar structure), mussel adhesive protein (adhesive property and reducing character), and the lotus leaf (self-cleaning effect). Owing to the special properties (self-polymerization, reduction, and adhesion), dopamine could be simultaneously used as a reducing agent for graphene oxide and as an adhesive, similar to the mortar in nacre, to crosslink the adjacent graphene. The resultant nacre-like graphene paper exhibited stable superhydrophobicity, self-cleaning, anti-corrosion, and remarkable mechanical properties underwater.

Extensive Investigations on Bio-Inspired Trust and Reputation Model over Hops Coefficient Factor in Distributed Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Vinod Kumar Verma

2014-08-01

Full Text Available Resource utilization requires a substantial consideration for a trust and reputation model to be deployed within a wireless sensor network (WSN. In the evaluation, our attention is focused on the effect of hops coefficient factor estimation on WSN with bio-inspired trust and reputation model (BTRM. We present the state-of-the-art system level evaluation of accuracy and path length of sensor node operations for their current and average scenarios. Additionally, we emphasized over the energy consumption evaluation for static, dynamic and oscillatory modes of BTRM-WSN model. The performance of the hops coefficient factor for our proposed framework is evaluated via analytic bounds and numerical simulations.

A new backpropagation learning algorithm for layered neural networks with nondifferentiable units.

Science.gov (United States)

Oohori, Takahumi; Naganuma, Hidenori; Watanabe, Kazuhisa

2007-05-01

We propose a digital version of the backpropagation algorithm (DBP) for three-layered neural networks with nondifferentiable binary units. This approach feeds teacher signals to both the middle and output layers, whereas with a simple perceptron, they are given only to the output layer. The additional teacher signals enable the DBP to update the coupling weights not only between the middle and output layers but also between the input and middle layers. A neural network based on DBP learning is fast and easy to implement in hardware. Simulation results for several linearly nonseparable problems such as XOR demonstrate that the DBP performs favorably when compared to the conventional approaches. Furthermore, in large-scale networks, simulation results indicate that the DBP provides high performance.

Research on one Bio-inspired Jumping Locomotion Robot for Search and Rescue

Directory of Open Access Journals (Sweden)

Dunwen Wei

2014-10-01

Full Text Available Jumping locomotion is much more effective than other locomotion means in order to tackle the unstructured and complex environment in research and rescue. Here, a bio-inspired jumping robot with a closed-chain mechanism is proposed to achieve the power amplification during taking-off. Through actuating one variable transmission mechanism to change the transmission ratio, the jumping robot reveals biological characteristics in the phase of posture adjustment when adjusting the height and distance of one jump. The kinematics and dynamics of the simplified jumping mechanism model in one jumping cycle sequence are analysed. A compliant contact model considering nonlinear damping is investigated for jumping performance under different terrain characteristics. The numerical simulation algorithm with regard to solving the dynamical equation is described and simulation results are discussed. Finally, one primary prototype and experiment are described. The experimental results show the distance of jumping in the horizontal direction increases with the increasing gear ratio, while the height of jumping decreases in reverse. The jumping robot can enhance the capability to adapt to unknown cluttered environments, such as those encountered in research and rescue, using this strategy.

B-iTRS: A Bio-Inspired Trusted Routing Scheme for Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Mingchuan Zhang

2015-01-01

Full Text Available In WSNs, routing algorithms need to handle dynamical changes of network topology, extra overhead, energy saving, and other requirements. Therefore, routing in WSNs is an extremely interesting and challenging issue. In this paper, we present a novel bio-inspired trusted routing scheme (B-iTRS based on ant colony optimization (ACO and Physarum autonomic optimization (PAO. For trust assessment, B-iTRS monitors neighbors’ behavior in real time, receives feedback from Sink, and then assesses neighbors’ trusts based on the acquired information. For routing scheme, each node finds routes to the Sink based on ACO and PAO. In the process of path finding, B-iTRS senses the load and trust value of each node and then calculates the link load and link trust of the found routes to support the route selection. Moreover, B-iTRS also assesses the route based on PAO to maintain the route table. Simulation results show how B-iTRS can achieve the effective performance compared to existing state-of-the-art algorithms.

Bio-Inspired Supramolecular Chemistry Provides Highly Concentrated Dispersions of Carbon Nanotubes in Polythiophene

Directory of Open Access Journals (Sweden)

Yen-Ting Lin

2016-06-01

Full Text Available In this paper we report the first observation, through X-ray diffraction, of noncovalent uracil–uracil (U–U dimeric π-stacking interactions in carbon nanotube (CNT–based supramolecular assemblies. The directionally oriented morphology determined using atomic force microscopy revealed highly organized behavior through π-stacking of U moieties in a U-functionalized CNT derivative (CNT–U. We developed a dispersion system to investigate the bio-inspired interactions between an adenine (A-terminated poly(3-adeninehexyl thiophene (PAT and CNT–U. These hybrid CNT–U/PAT materials interacted through π-stacking and multiple hydrogen bonding between the U moieties of CNT–U and the A moieties of PAT. Most importantly, the U···A multiple hydrogen bonding interactions between CNT–U and PAT enhanced the dispersion of CNT–U in a high-polarity solvent (DMSO. The morphology of these hybrids, determined using transmission electron microscopy, featured grape-like PAT bundles wrapped around the CNT–U surface; this tight connection was responsible for the enhanced dispersion of CNT–U in DMSO.

Kernel Function Tuning for Single-Layer Neural Networks

Czech Academy of Sciences Publication Activity Database

Vidnerová, Petra; Neruda, Roman

-, accepted 28.11. 2017 (2018) ISSN 2278-0149 R&D Projects: GA ČR GA15-18108S Institutional support: RVO:67985807 Keywords : single-layer neural networks * kernel methods * kernel function * optimisation Subject RIV: IN - Informatics, Computer Science http://www.ijmerr.com/

Antibacterial, anti-inflammatory and neuroprotective layer-by-layer coatings for neural implants

Science.gov (United States)

Zhang, Zhiling; Nong, Jia; Zhong, Yinghui

2015-08-01

Objective. Infection, inflammation, and neuronal loss are common issues that seriously affect the functionality and longevity of chronically implanted neural prostheses. Minocycline hydrochloride (MH) is a broad-spectrum antibiotic and effective anti-inflammatory drug that also exhibits potent neuroprotective activities. In this study, we investigated the development of biocompatible thin film coatings capable of sustained release of MH for improving the long term performance of implanted neural electrodes. Approach. We developed a novel magnesium binding-mediated drug delivery mechanism for controlled and sustained release of MH from an ultrathin hydrophilic layer-by-layer (LbL) coating and characterized the parameters that control MH loading and release. The anti-biofilm, anti-inflammatory and neuroprotective potencies of the LbL coating and released MH were also examined. Main results. Sustained release of physiologically relevant amount of MH for 46 days was achieved from the Mg2+-based LbL coating at a thickness of 1.25 μm. In addition, MH release from the LbL coating is pH-sensitive. The coating and released MH demonstrated strong anti-biofilm, anti-inflammatory, and neuroprotective potencies. Significance. This study reports, for the first time, the development of a bioactive coating that can target infection, inflammation, and neuroprotection simultaneously, which may facilitate the translation of neural interfaces to clinical applications.

Anomalous Diffusion within the Transcriptome as a Bio-Inspired Computing Framework for Resilience

Directory of Open Access Journals (Sweden)

William Seffens

2017-07-01

Full Text Available Much of biology-inspired computer science is based on the Central Dogma, as implemented with genetic algorithms or evolutionary computation. That 60-year-old biological principle based on the genome, transcriptome and proteasome is becoming overshadowed by a new paradigm of complex ordered associations and connections between layers of biological entities, such as interactomes, metabolomics, etc. We define a new hierarchical concept as the “Connectosome”, and propose new venues of computational data structures based on a conceptual framework called “Grand Ensemble” which contains the Central Dogma as a subset. Connectedness and communication within and between living or biology-inspired systems comprise ensembles from which a physical computing system can be conceived. In this framework the delivery of messages is filtered by size and a simple and rapid semantic analysis of their content. This work aims to initiate discussion on the Grand Ensemble in network biology as a representation of a Persistent Turing Machine. This framework adding interaction and persistency to the classic Turing-machine model uses metrics based on resilience that has application to dynamic optimization problem solving in Genetic Programming.

Novel Approaches for Bio-inspired Mechano-Sensors

DEFF Research Database (Denmark)

Drimus, Alin; Bilberg, Arne

2011-01-01

In this paper, we present novel approaches for building tactile- array sensors for use in robotic grippers inspired from biology. We start by describing the sense of touch for humans and we continue by propos- ing dierent methods to build sensors that mimic this behaviour. For the static tactile...

eDNA: A Bio-Inspired Reconfigurable Hardware Cell Architecture Supporting Self-organisation and Self-healing

DEFF Research Database (Denmark)

Boesen, Michael Reibel; Madsen, Jan

2009-01-01

This paper presents the concept of a biological inspired reconfigurable hardware cell architecture which supports self-organisation and self-healing. Two fundamental processes in biology, namely fertilization-to-birth and cell self-healing have inspired the development of this cell architecture...... to simulate our self-organisation and self-healing algorithms and the results obtained from this looks promising....

A one-layer recurrent neural network for constrained nonsmooth optimization.

Science.gov (United States)

Liu, Qingshan; Wang, Jun

2011-10-01

This paper presents a novel one-layer recurrent neural network modeled by means of a differential inclusion for solving nonsmooth optimization problems, in which the number of neurons in the proposed neural network is the same as the number of decision variables of optimization problems. Compared with existing neural networks for nonsmooth optimization problems, the global convexity condition on the objective functions and constraints is relaxed, which allows the objective functions and constraints to be nonconvex. It is proven that the state variables of the proposed neural network are convergent to optimal solutions if a single design parameter in the model is larger than a derived lower bound. Numerical examples with simulation results substantiate the effectiveness and illustrate the characteristics of the proposed neural network.

Development of a bio-inspired UAV perching system

Science.gov (United States)

Xie, Pu

of animals and human arms approaching to a fixed or moving target for grasping or capturing. The autonomous flight control was also implemented through a PID controller. Autonomous flight performance was proved through simulation in SimMechanics. Finally, the prototyping of our designs were conducted in different generations of our bio-inspired UAV perching system, which include the leg prototype, gripper prototype, and system prototype. Both the machined prototype and 3D printed prototype were tried. The performance of these prototypes was tested through experiments.

Growth kinetics of borided layers: Artificial neural network and least square approaches

Science.gov (United States)

Campos, I.; Islas, M.; Ramírez, G.; VillaVelázquez, C.; Mota, C.

2007-05-01

The present study evaluates the growth kinetics of the boride layer Fe 2B in AISI 1045 steel, by means of neural networks and the least square techniques. The Fe 2B phase was formed at the material surface using the paste boriding process. The surface boron potential was modified considering different boron paste thicknesses, with exposure times of 2, 4 and 6 h, and treatment temperatures of 1193, 1223 and 1273 K. The neural network and the least square models were set by the layer thickness of Fe 2B phase, and assuming that the growth of the boride layer follows a parabolic law. The reliability of the techniques used is compared with a set of experiments at a temperature of 1223 K with 5 h of treatment time and boron potentials of 2, 3, 4 and 5 mm. The results of the Fe 2B layer thicknesses show a mean error of 5.31% for the neural network and 3.42% for the least square method.

Bio-inspired carbon electro-catalysis for the oxygen reduction reaction

OpenAIRE

Preuss, Kathrin; Kannuchamy, Vasanth Kumar; Marinovic, Adam; Isaacs, Mark; Wilson, Karen; Abrahams, Isaac; Titirici, Maria-Magdalena

2016-01-01

We report the synthesis, characterisation and catalytic performance of two nature-inspired biomass-derived electro-catalysts for the oxygen reduction reaction in fuel cells. The catalysts were prepared via pyrolysis of a real food waste (lobster shells) or by mimicking the composition of lobster shells using chitin and CaCO3 particles followed by acid washing. The simplified model of artificial lobster was prepared for better reproducibility. The calcium carbonate in both samples acts as a po...

A bio-inspired swarm robot coordination algorithm for multiple target searching

Science.gov (United States)

Meng, Yan; Gan, Jing; Desai, Sachi

2008-04-01

The coordination of a multi-robot system searching for multi targets is challenging under dynamic environment since the multi-robot system demands group coherence (agents need to have the incentive to work together faithfully) and group competence (agents need to know how to work together well). In our previous proposed bio-inspired coordination method, Local Interaction through Virtual Stigmergy (LIVS), one problem is the considerable randomness of the robot movement during coordination, which may lead to more power consumption and longer searching time. To address these issues, an adaptive LIVS (ALIVS) method is proposed in this paper, which not only considers the travel cost and target weight, but also predicting the target/robot ratio and potential robot redundancy with respect to the detected targets. Furthermore, a dynamic weight adjustment is also applied to improve the searching performance. This new method a truly distributed method where each robot makes its own decision based on its local sensing information and the information from its neighbors. Basically, each robot only communicates with its neighbors through a virtual stigmergy mechanism and makes its local movement decision based on a Particle Swarm Optimization (PSO) algorithm. The proposed ALIVS algorithm has been implemented on the embodied robot simulator, Player/Stage, in a searching target. The simulation results demonstrate the efficiency and robustness in a power-efficient manner with the real-world constraints.

Holarchical Systems and Emotional Holons : Biologically-Inspired System Designs for Control of Autonomous Aerial Vehicles

Science.gov (United States)

Ippolito, Corey; Plice, Laura; Pisanich, Greg

2003-01-01

The BEES (Bio-inspired Engineering for Exploration Systems) for Mars project at NASA Ames Research Center has the goal of developing bio-inspired flight control strategies to enable aerial explorers for Mars scientific investigations. This paper presents a summary of our ongoing research into biologically inspired system designs for control of unmanned autonomous aerial vehicle communities for Mars exploration. First, we present cooperative design considerations for robotic explorers based on the holarchical nature of biological systems and communities. Second, an outline of an architecture for cognitive decision making and control of individual robotic explorers is presented, modeled after the emotional nervous system of cognitive biological systems. Keywords: Holarchy, Biologically Inspired, Emotional UAV Flight Control

In Situ Atomic Force Microscopy Studies on Nucleation and Self-Assembly of Biogenic and Bio-Inspired Materials

Directory of Open Access Journals (Sweden)

Cheng Zeng

2017-08-01

Full Text Available Through billions of years of evolution, nature has been able to create highly sophisticated and ordered structures in living systems, including cells, cellular components and viruses. The formation of these structures involves nucleation and self-assembly, which are fundamental physical processes associated with the formation of any ordered structure. It is important to understand how biogenic materials self-assemble into functional and highly ordered structures in order to determine the mechanisms of biological systems, as well as design and produce new classes of materials which are inspired by nature but equipped with better physiochemical properties for our purposes. An ideal tool for the study of nucleation and self-assembly is in situ atomic force microscopy (AFM, which has been widely used in this field and further developed for different applications in recent years. The main aim of this work is to review the latest contributions that have been reported on studies of nucleation and self-assembly of biogenic and bio-inspired materials using in situ AFM. We will address this topic by introducing the background of AFM, and discussing recent in situ AFM studies on nucleation and self-assembly of soft biogenic, soft bioinspired and hard materials.

Biologically Inspired Modular Neural Control for a Leg-Wheel Hybrid Robot

DEFF Research Database (Denmark)

Manoonpong, Poramate; Wörgötter, Florentin; Laksanacharoen, Pudit

2014-01-01

In this article we present modular neural control for a leg-wheel hybrid robot consisting of three legs with omnidirectional wheels. This neural control has four main modules having their functional origin in biological neural systems. A minimal recurrent control (MRC) module is for sensory signal...... processing and state memorization. Its outputs drive two front wheels while the rear wheel is controlled through a velocity regulating network (VRN) module. In parallel, a neural oscillator network module serves as a central pattern generator (CPG) controls leg movements for sidestepping. Stepping directions...... or they can serve as useful modules for other module-based neural control applications....

Modular Neural Networks and Type-2 Fuzzy Systems for Pattern Recognition

CERN Document Server

Melin, Patricia

2012-01-01

This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. Type-2 fuzzy logic is an extension of traditional type-1 fuzzy logic that enables managing higher levels of uncertainty in complex real world problems, which are of particular importance in the area of pattern recognition. The book is organized in three main parts, each containing a group of chapters built around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which are the basis for achieving intelligent pattern recognition. The second part contains chapters with the main theme of using type-2 fuzzy models and modular neural ne...

Gradual DropIn of Layers to Train Very Deep Neural Networks

OpenAIRE

Smith, Leslie N.; Hand, Emily M.; Doster, Timothy

2015-01-01

We introduce the concept of dynamically growing a neural network during training. In particular, an untrainable deep network starts as a trainable shallow network and newly added layers are slowly, organically added during training, thereby increasing the network's depth. This is accomplished by a new layer, which we call DropIn. The DropIn layer starts by passing the output from a previous layer (effectively skipping over the newly added layers), then increasingly including units from the ne...

Atomic Layer Deposition in Bio-Nanotechnology: A Brief Overview.

Science.gov (United States)

Bishal, Arghya K; Butt, Arman; Selvaraj, Sathees K; Joshi, Bela; Patel, Sweetu B; Huang, Su; Yang, Bin; Shukohfar, Tolou; Sukotjo, Cortino; Takoudis, Christos G

2015-01-01

Atomic layer deposition (ALD) is a technique increasingly used in nanotechnology and ultrathin film deposition; it is ideal for films in the nanometer and Angstrom length scales. ALD can effectively be used to modify the surface chemistry and functionalization of engineering-related and biologically important surfaces. It can also be used to alter the mechanical, electrical, chemical, and other properties of materials that are increasingly used in biomedical engineering and biological sciences. ALD is a relatively new technique for optimizing materials for use in bio-nanotechnology. Here, after a brief review of the more widely used modes of ALD and a few of its applications in biotechnology, selected results that show the potential of ALD in bio-nanotechnology are presented. ALD seems to be a promising means for tuning the hydrophilicity/hydrophobicity characteristics of biomedical surfaces, forming conformal ultrathin coatings with desirable properties on biomedical substrates with a high aspect ratio, tuning the antibacterial properties of substrate surfaces of interest, and yielding multifunctional biomaterials for medical implants and other devices.

On the approximation by single hidden layer feedforward neural networks with fixed weights

OpenAIRE

Guliyev, Namig J.; Ismailov, Vugar E.

2017-01-01

International audience; Feedforward neural networks have wide applicability in various disciplines of science due to their universal approximation property. Some authors have shown that single hidden layer feedforward neural networks (SLFNs) with fixed weights still possess the universal approximation property provided that approximated functions are univariate. But this phenomenon does not lay any restrictions on the number of neurons in the hidden layer. The more this number, the more the p...

A one-layer recurrent neural network for constrained nonconvex optimization.

Science.gov (United States)

Li, Guocheng; Yan, Zheng; Wang, Jun

2015-01-01

In this paper, a one-layer recurrent neural network is proposed for solving nonconvex optimization problems subject to general inequality constraints, designed based on an exact penalty function method. It is proved herein that any neuron state of the proposed neural network is convergent to the feasible region in finite time and stays there thereafter, provided that the penalty parameter is sufficiently large. The lower bounds of the penalty parameter and convergence time are also estimated. In addition, any neural state of the proposed neural network is convergent to its equilibrium point set which satisfies the Karush-Kuhn-Tucker conditions of the optimization problem. Moreover, the equilibrium point set is equivalent to the optimal solution to the nonconvex optimization problem if the objective function and constraints satisfy given conditions. Four numerical examples are provided to illustrate the performances of the proposed neural network.

Bio-inspired synthesis and characterization of superparamagnetic particles; Sintese e caracterizacao bioinspirada de particulas superparamagneticas

Energy Technology Data Exchange (ETDEWEB)

Castro, Vinicius F., E-mail: vfc_mg@yahoo.com.br [Universidade Federal de Itajuba (UNIFEI), MG (Brazil); Queiroz, Alvaro A.A. [Universidade Federal de Itajuba (UNIFEI), MG (Brazil). Centro de Estudos e Inovacao em Materiais Biofuncionais Avancados

2012-08-15

This paper discusses the bio-inspired synthesis of type YFeAl ferrites encapsulated into polyglycerol dendrimers (PGLD) generation 3. The structure and morphological properties of the system YFeAl/PGLD was characterized by X-ray diffraction (XRD) and transmission electron microscopy (TEM). The magnetic properties were studied through the techniques of Moessbauer spectroscopy and magnetization. The cytotoxicity of the nanoparticles encapsulated in dendrimers PGLD G3 at the cell membrane was studied against mammalian cell line CHO.K1 measuring the amount of lactate dehydrogenase (LDH) released by the cell damage. Microscopy TEM and XRD analysis indicate that spherical nanoparticles were obtained highly crystalline and monodisperse with size 20 nm

Strong Quantum Confinement Effects and Chiral Excitons in Bio-Inspired ZnO–Amino Acid Cocrystals

KAUST Repository

Muhammed, Madathumpady Abubaker Habeeb

2018-02-20

Elucidating the underlying principles behind band gap engineering is paramount for the successful implementation of semiconductors in photonic and optoelectronic devices. Recently it has been shown that the band gap of a wide and direct band gap semiconductor, such as ZnO, can be modified upon cocrystallization with amino acids, with the role of the biomolecules remaining unclear. Here, by probing and modeling the light-emitting properties of ZnO-amino acid cocrystals, we identify the amino acids\\' role on this band gap modulation and demonstrate their effective chirality transfer to the interband excitations in ZnO. Our 3D quantum model suggests that the strong band edge emission blue-shift in the cocrystals can be explained by a quasi-periodic distribution of amino acid potential barriers within the ZnO crystal lattice. Overall, our findings indicate that biomolecule cocrystallization can be used as a truly bio-inspired means to induce chiral quantum confinement effects in quasi-bulk semiconductors.

Mechanics ofadhesion and contact self-cleaning of bio-inspired microfiberadhesives

Science.gov (United States)

Abusomwan, Uyiosa Anthony

The remarkable attachment system of geckos has inspired the development of dry microfiber adhesives through the last two decades. Some of the notable characteristics of gecko-inspired fibrillar adhesives include: strong, directional, and controllable adhesion to smooth and rough surfaces in air, vacuum, and under water; ability to maintain strong adhesion during repeated use; anti-fouling and self-cleaning after contamination. Given these outstanding qualities, fibrillar adhesives promise an extensive range of use in industrial, robotic, manufacturing, medical, and consumer products. Significant advancements have been made in the design of geckoinspired microfiber adhesives with the characteristic properties listed above, with the exception of the anti-fouling and self-cleaning features. The self-cleaning mechanism of the gecko's adhesion system plays an important role to its ability to remain sticky in various environments. Similarly, enabling self-cleaning capability for synthetic microfiber adhesives will lead to robust performance in various areas of application. Presently, the practical use of fibrillar adhesives is restricted mainly to clean environments, where they are free from contaminants. The goal of this thesis is to conduct a detailed study of the mechanisms and mechanics of contact-based self-cleaning of gecko-inspired microfiber adhesives. This work focuses on contact self-cleaning mechanisms, as a more practical approach to cleaning. Previous studies on the cleaning of microfiber adhesives have mostly focused on mechanisms that involve complete removal of the contaminants from the adhesive. In this thesis, a second cleaning process is proposed whereby particles are removed from the tip of the microfibers and embedded between adjacent microfibers or in grooves patterned onto the adhesive, where they are no longer detrimental to the performance of the adhesive. In this work, a model of adhesion for microfiber adhesives that take the deformation of the

A One-Layer Recurrent Neural Network for Constrained Complex-Variable Convex Optimization.

Science.gov (United States)

Qin, Sitian; Feng, Jiqiang; Song, Jiahui; Wen, Xingnan; Xu, Chen

2018-03-01

In this paper, based on calculus and penalty method, a one-layer recurrent neural network is proposed for solving constrained complex-variable convex optimization. It is proved that for any initial point from a given domain, the state of the proposed neural network reaches the feasible region in finite time and converges to an optimal solution of the constrained complex-variable convex optimization finally. In contrast to existing neural networks for complex-variable convex optimization, the proposed neural network has a lower model complexity and better convergence. Some numerical examples and application are presented to substantiate the effectiveness of the proposed neural network.

Classification of remotely sensed data using OCR-inspired neural network techniques. [Optical Character Recognition

Science.gov (United States)

Kiang, Richard K.

1992-01-01

Neural networks have been applied to classifications of remotely sensed data with some success. To improve the performance of this approach, an examination was made of how neural networks are applied to the optical character recognition (OCR) of handwritten digits and letters. A three-layer, feedforward network, along with techniques adopted from OCR, was used to classify Landsat-4 Thematic Mapper data. Good results were obtained. To overcome the difficulties that are characteristic of remote sensing applications and to attain significant improvements in classification accuracy, a special network architecture may be required.

Assessing bio-available silver released from silver nanoparticles embedded in silica layers using the green algae Chlamydomonas reinhardtii as bio-sensors

Energy Technology Data Exchange (ETDEWEB)

Pugliara, Alessandro [nMat group-CEMES (Centre d' Elaboration de Matériaux et d' Etudes Structurales)-CNRS, Université de Toulouse, 29 rue Jeanne Marvig, BP 94347, F-31055 Toulouse Cedex 4 (France); LAPLACE (LAboratoire PLAsma et Conversion d' Energie), Université de Toulouse, CNRS, UPS, INPT, 118 route de Narbonne, F-31062 Toulouse (France); Makasheva, Kremena; Despax, Bernard [LAPLACE (LAboratoire PLAsma et Conversion d' Energie), Université de Toulouse, CNRS, UPS, INPT, 118 route de Narbonne, F-31062 Toulouse (France); Bayle, Maxime; Carles, Robert; Benzo, Patrizio; BenAssayag, Gérard; Pécassou, Béatrice [nMat group-CEMES (Centre d' Elaboration de Matériaux et d' Etudes Structurales)-CNRS, Université de Toulouse, 29 rue Jeanne Marvig, BP 94347, F-31055 Toulouse Cedex 4 (France); Sancho, Maria Carmen; Navarro, Enrique [IPE (Instituto Pirenaico de Ecología)-CSIC, Avda. Montañana 1005, Zaragoza 50059 (Spain); Echegoyen, Yolanda [I3A, Department of Analytical Chemistry, University of Zaragoza, C/ María de Luna 3, 50018, Zaragoza (Spain); Bonafos, Caroline, E-mail: bonafos@cemes.fr [nMat group-CEMES (Centre d' Elaboration de Matériaux et d' Etudes Structurales)-CNRS, Université de Toulouse, 29 rue Jeanne Marvig, BP 94347, F-31055 Toulouse Cedex 4 (France)

2016-09-15

Silver nanoparticles (AgNPs) because of their strong antibacterial activity are widely used in health-care sector and industrial applications. Their huge surface-volume ratio enhances the silver release compared to the bulk material, leading to an increased toxicity for microorganisms sensitive to this element. This work presents an assessment of the toxic effect on algal photosynthesis due to small (size < 20 nm) AgNPs embedded in silica layers. Two physical approaches were originally used to elaborate the nanocomposite structures: (i) low energy ion beam synthesis and (ii) combined silver sputtering and plasma polymerization. These techniques allow elaboration of a single layer of AgNPs embedded in silica films at defined nanometer distances (from 0 to 7 nm) beneath the free surface. The structural and optical properties of the nanostructures were studied by transmission electron microscopy and optical reflectance. The silver release from the nanostructures after 20 h of immersion in buffered water was measured by inductively coupled plasma mass spectrometry and ranges between 0.02 and 0.49 μM. The short-term toxicity of Ag to photosynthesis of Chlamydomonas reinhardtii was assessed by fluorometry. The obtained results show that embedding AgNPs reduces the interactions with the buffered water free media, protecting the AgNPs from fast oxidation. The release of bio-available silver (impacting on the algal photosynthesis) is controlled by the depth at which AgNPs are located for a given host matrix. This provides a procedure to tailor the toxicity of nanocomposites containing AgNPs. - Highlights: • Controlled synthesis of 2D arrays of silver nanoparticles embedded in silica. • Assessing bio-available silver release using the green algae as bio-sensors. • The Ag release can be controlled by the distance nanoparticles/dielectric surface. • All the Ag released in solution is in the form of Ag{sup +} ions. • Toxicity comparable to similar concentrations of

Cardiac Arrhythmia Classification by Multi-Layer Perceptron and Convolution Neural Networks

Directory of Open Access Journals (Sweden)

Shalin Savalia

2018-05-01

Full Text Available The electrocardiogram (ECG plays an imperative role in the medical field, as it records heart signal over time and is used to discover numerous cardiovascular diseases. If a documented ECG signal has a certain irregularity in its predefined features, this is called arrhythmia, the types of which include tachycardia, bradycardia, supraventricular arrhythmias, and ventricular, etc. This has encouraged us to do research that consists of distinguishing between several arrhythmias by using deep neural network algorithms such as multi-layer perceptron (MLP and convolution neural network (CNN. The TensorFlow library that was established by Google for deep learning and machine learning is used in python to acquire the algorithms proposed here. The ECG databases accessible at PhysioBank.com and kaggle.com were used for training, testing, and validation of the MLP and CNN algorithms. The proposed algorithm consists of four hidden layers with weights, biases in MLP, and four-layer convolution neural networks which map ECG samples to the different classes of arrhythmia. The accuracy of the algorithm surpasses the performance of the current algorithms that have been developed by other cardiologists in both sensitivity and precision.

Cardiac Arrhythmia Classification by Multi-Layer Perceptron and Convolution Neural Networks.

Science.gov (United States)

Savalia, Shalin; Emamian, Vahid

2018-05-04

The electrocardiogram (ECG) plays an imperative role in the medical field, as it records heart signal over time and is used to discover numerous cardiovascular diseases. If a documented ECG signal has a certain irregularity in its predefined features, this is called arrhythmia, the types of which include tachycardia, bradycardia, supraventricular arrhythmias, and ventricular, etc. This has encouraged us to do research that consists of distinguishing between several arrhythmias by using deep neural network algorithms such as multi-layer perceptron (MLP) and convolution neural network (CNN). The TensorFlow library that was established by Google for deep learning and machine learning is used in python to acquire the algorithms proposed here. The ECG databases accessible at PhysioBank.com and kaggle.com were used for training, testing, and validation of the MLP and CNN algorithms. The proposed algorithm consists of four hidden layers with weights, biases in MLP, and four-layer convolution neural networks which map ECG samples to the different classes of arrhythmia. The accuracy of the algorithm surpasses the performance of the current algorithms that have been developed by other cardiologists in both sensitivity and precision.

Nature-inspired design of hybrid intelligent systems

CERN Document Server

Castillo, Oscar; Kacprzyk, Janusz

2017-01-01

This book highlights recent advances in the design of hybrid intelligent systems based on nature-inspired optimization and their application in areas such as intelligent control and robotics, pattern recognition, time series prediction, and optimization of complex problems. The book is divided into seven main parts, the first of which addresses theoretical aspects of and new concepts and algorithms based on type-2 and intuitionistic fuzzy logic systems. The second part focuses on neural network theory, and explores the applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The book’s third part presents enhancements to meta-heuristics based on fuzzy logic techniques and describes new nature-inspired optimization algorithms that employ fuzzy dynamic adaptation of parameters, while the fourth part presents diverse applications of nature-inspired optimization algorithms. In turn, the fifth part investigates applications of fuzzy logic in diverse areas, such as...

Bio-inspired dental fillings

Science.gov (United States)

Deyhle, Hans; Bunk, Oliver; Buser, Stefan; Krastl, Gabriel; Zitzmann, Nicola U.; Ilgenstein, Bernd; Beckmann, Felix; Pfeiffer, Franz; Weiger, Roland; Müller, Bert

2009-08-01

Human teeth are anisotropic composites. Dentin as the core material of the tooth consists of nanometer-sized calcium phosphate crystallites embedded in collagen fiber networks. It shows its anisotropy on the micrometer scale by its well-oriented microtubules. The detailed three-dimensional nanostructure of the hard tissues namely dentin and enamel, however, is not understood, although numerous studies on the anisotropic mechanical properties have been performed and evaluated to explain the tooth function including the enamel-dentin junction acting as effective crack barrier. Small angle X-ray scattering (SAXS) with a spatial resolution in the 10 μm range allows determining the size and orientation of the constituents on the nanometer scale with reasonable precision. So far, only some dental materials, i.e. the fiber reinforced posts exhibit anisotropic properties related to the micrometer-size glass fibers. Dental fillings, composed of nanostructures oriented similar to the natural hard tissues of teeth, however, do not exist at all. The current X-ray-based investigations of extracted human teeth provide evidence for oriented micro- and nanostructures in dentin and enamel. These fundamental quantitative findings result in profound knowledge to develop biologically inspired dental fillings with superior resistance to thermal and mechanical shocks.

Bio-Inspired nacre-like nanolignocellulose-poly (vinyl alcohol)-TiO2 composite with superior mechanical and photocatalytic properties.

Science.gov (United States)

Chen, Yipeng; Wang, Hanwei; Dang, Baokang; Xiong, Ye; Yao, Qiufang; Wang, Chao; Sun, Qingfeng; Jin, Chunde

2017-05-12

Nacre, the gold standard for biomimicry, provides an excellent example and guideline for assembling high-performance composites. Inspired by the layered structure and extraordinary strength and toughness of natural nacre, nacre-like nanolignocellulose/poly (vinyl alcohol)/TiO 2 composites possessed the similar layered structure of natural nacre were constructed through hot-pressing process. Poly (vinyl alcohol) and TiO 2 nanoparticles have been used as nanofillers to improve the mechanical performance and synchronously endow the superior photocatalytic activity of the composites. This research would be provided a promising candidate for the photooxidation of volatile organic compounds also combined with outstanding mechanical property.

DNA derived fluorescent bio-dots for sensitive detection of mercury and silver ions in aqueous solution

Energy Technology Data Exchange (ETDEWEB)

Song, Ting [Laboratory of Environmental Science and Technology, Xinjiang Technical Institute of Physics & Chemistry, Key Laboratory of Functional Materials and Devices for Special Environments, Chinese Academy of Sciences, Urumqi 830011 (China); University of Chinese Academy of Sciences, Beijing 100049 (China); Zhu, Xuefeng, E-mail: zhuxf@ms.xjb.ac.cn [Laboratory of Environmental Science and Technology, Xinjiang Technical Institute of Physics & Chemistry, Key Laboratory of Functional Materials and Devices for Special Environments, Chinese Academy of Sciences, Urumqi 830011 (China); Zhou, Shenghai [Laboratory of Environmental Science and Technology, Xinjiang Technical Institute of Physics & Chemistry, Key Laboratory of Functional Materials and Devices for Special Environments, Chinese Academy of Sciences, Urumqi 830011 (China); Yang, Guang [Electronic Materials Research Laboratory, Key Laboratory of the Ministry of Education and International Center for Dielectric Research, Xi’an Jiaotong University, Xi’an 710049 (China); Gan, Wei [Laboratory of Environmental Science and Technology, Xinjiang Technical Institute of Physics & Chemistry, Key Laboratory of Functional Materials and Devices for Special Environments, Chinese Academy of Sciences, Urumqi 830011 (China); Yuan, Qunhui, E-mail: yuanqh@ms.xjb.ac.cn [Laboratory of Environmental Science and Technology, Xinjiang Technical Institute of Physics & Chemistry, Key Laboratory of Functional Materials and Devices for Special Environments, Chinese Academy of Sciences, Urumqi 830011 (China)

2015-08-30

Highlights: • First application of a DNA derived fluorescent bio-dot for metal sensing. • Bio-dot was conveniently obtained via a mild thermal hydro-thermal synthesis. • Bio-dot was directly used for fluorescent sensing without further modification. • Bio-dot showed good fluorescent sensing property for Hg(II) and Ag(I). • Formation of T–Hg–T and C–Ag–C structures played key roles in sensing. - Abstract: Inspired by the high affinity between heavy metal ions and bio-molecules as well as the low toxicity of carbon-based quantum dots, we demonstrated the first application of a DNA derived carbonaceous quantum dots, namely bio-dots, in metal ion sensing. The present DNA-derived bio-dots contain graphitic carbon layers with 0.242 nm lattice fringes, exhibit excellent fluorescence property and can be obtained via a facile hydrothermal preparation procedure. Hg(II) and Ag(I) are prone to be captured by the bio-dots due to the existence of residual thymine (T) and cytosine (C) groups, resulting in a quenched fluorescence while other heavy metal ions would cause negligible changes on the fluorescent signals of the bio-dots. The bio-dots could be used as highly selective toxic-free biosensors, with two detecting linear ranges of 0–0.5 μM and 0.5–6 μM for Hg(II) and one linear range of 0–10 μM for Ag(I). The detection limits (at a signal-to-noise ratio of 3) were estimated to be 48 nM for Hg(II) and 0.31 μM for Ag(I), respectively. The detection of Hg(II) and Ag(I) could also be realized in the real water sample analyses, with satisfying recoveries ranging from 87% to 100%.

DNA derived fluorescent bio-dots for sensitive detection of mercury and silver ions in aqueous solution

International Nuclear Information System (INIS)

Song, Ting; Zhu, Xuefeng; Zhou, Shenghai; Yang, Guang; Gan, Wei; Yuan, Qunhui

2015-01-01

Highlights: • First application of a DNA derived fluorescent bio-dot for metal sensing. • Bio-dot was conveniently obtained via a mild thermal hydro-thermal synthesis. • Bio-dot was directly used for fluorescent sensing without further modification. • Bio-dot showed good fluorescent sensing property for Hg(II) and Ag(I). • Formation of T–Hg–T and C–Ag–C structures played key roles in sensing. - Abstract: Inspired by the high affinity between heavy metal ions and bio-molecules as well as the low toxicity of carbon-based quantum dots, we demonstrated the first application of a DNA derived carbonaceous quantum dots, namely bio-dots, in metal ion sensing. The present DNA-derived bio-dots contain graphitic carbon layers with 0.242 nm lattice fringes, exhibit excellent fluorescence property and can be obtained via a facile hydrothermal preparation procedure. Hg(II) and Ag(I) are prone to be captured by the bio-dots due to the existence of residual thymine (T) and cytosine (C) groups, resulting in a quenched fluorescence while other heavy metal ions would cause negligible changes on the fluorescent signals of the bio-dots. The bio-dots could be used as highly selective toxic-free biosensors, with two detecting linear ranges of 0–0.5 μM and 0.5–6 μM for Hg(II) and one linear range of 0–10 μM for Ag(I). The detection limits (at a signal-to-noise ratio of 3) were estimated to be 48 nM for Hg(II) and 0.31 μM for Ag(I), respectively. The detection of Hg(II) and Ag(I) could also be realized in the real water sample analyses, with satisfying recoveries ranging from 87% to 100%

Engineering of a polymer layered bio-hybrid heart valve scaffold

Energy Technology Data Exchange (ETDEWEB)

Jahnavi, S., E-mail: jani84@gmail.com [Stem Cell and Molecular Biology Laboratory, Department of Biotechnology, Indian Institute of Technology Madras, Chennai 600036, TN (India); Tissue Culture Laboratory, Biomedical Technology Wing, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Poojappura, Trivandrum, Kerala 695012 (India); Kumary, T.V., E-mail: tvkumary@yahoo.com [Tissue Culture Laboratory, Biomedical Technology Wing, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Poojappura, Trivandrum, Kerala 695012 (India); Bhuvaneshwar, G.S., E-mail: gs.bhuvnesh@gmail.com [Trivitron Innovation Centre, Department of Engineering Design, Indian Institute of Technology Madras, Chennai 600036, TN (India); Natarajan, T.S., E-mail: tsniit@gmail.com [Conducting Polymer laboratory, Department of Physics, Indian Institute of Technology, Madras, Chennai 600036, TN (India); Verma, R.S., E-mail: vermars@iitm.ac.in [Stem Cell and Molecular Biology Laboratory, Department of Biotechnology, Indian Institute of Technology Madras, Chennai 600036, TN (India)

2015-06-01

Current treatment strategy for end stage valve disease involves either valvular repair or replacement with homograft/mechanical/bioprosthetic valves. In cases of recurrent stenosis/ regurgitation, valve replacement is preferred choice of treatment over valvular repair. Currently available mechanical valves primarily provide durability whereas bioprosthetic valves have superior tissue compatibility but both lack remodelling and regenerative properties making their utility limited in paediatric patients. With advances in tissue engineering, attempts have been made to fabricate valves with regenerative potential using various polymers, decellularized tissues and hybrid scaffolds. To engineer an ideal heart valve, decellularized bovine pericardium extracellular matrix (DBPECM) is an attractive biocompatible scaffold but has weak mechanical properties and rapid degradation. However, DBPECM can be modified with synthetic polymers to enhance its mechanical properties. In this study, we developed a Bio-Hybrid scaffold with non-cross linked DBPECM in its native structure coated with a layer of Polycaprolactone-Chitosan (PCL-CH) nanofibers that displayed superior mechanical properties. Surface and functional studies demonstrated integration of PCL-CH to the DBPECM with enhanced bio and hemocompatibility. This engineered Bio-Hybrid scaffold exhibited most of the physical, biochemical and functional properties of the native valve that makes it an ideal scaffold for fabrication of cardiac valve with regenerative potential. - Highlights: • A Bio-Hybrid scaffold was fabricated with PCL-CH blend and DBPECM. • PCL-CH functionally interacted with decellularized matrix without cross linking. • Modified scaffold exhibited mechanical properties similar to native heart valve. • Supported better fibroblast and endothelial cell adhesion and proliferation. • The developed scaffold can be utilized for tissue engineering of heart valve.

Artificial heartbeat: design and fabrication of a biologically inspired pump

International Nuclear Information System (INIS)

Walters, Peter; Stephenson, Robert; Lewis, Amy; Stinchcombe, Andrew; Ieropoulos, Ioannis

2013-01-01

We present a biologically inspired actuator exhibiting a novel pumping action. The design of the ‘artificial heartbeat’ actuator is inspired by physical principles derived from the structure and function of the human heart. The actuator employs NiTi artificial muscles and is powered by electrical energy generated by microbial fuel cells (MFCs). We describe the design and fabrication of the actuator and report the results of tests conducted to characterize its performance. This is the first artificial muscle-driven pump to be powered by MFCs fed on human urine. Results are presented in terms of the peak pumping pressure generated by the actuator, as well as for the volume of fluid transferred, when the actuator was powered by energy stored in a capacitor bank, which was charged by 24 MFCs fed on urine. The results demonstrate the potential for the artificial heartbeat actuator to be employed as a fluid circulation pump in future generations of MFC-powered robots (‘EcoBots’) that extract energy from organic waste. We also envisage that the actuator could in the future form part of a bio-robotic artwork or ‘bio-automaton’ that could help increase public awareness of research in robotics, bio-energy and biologically inspired design. (paper)

Sound Source Localization through 8 MEMS Microphones Array Using a Sand-Scorpion-Inspired Spiking Neural Network.

Science.gov (United States)

Beck, Christoph; Garreau, Guillaume; Georgiou, Julius

2016-01-01

Sand-scorpions and many other arachnids perceive their environment by using their feet to sense ground waves. They are able to determine amplitudes the size of an atom and locate the acoustic stimuli with an accuracy of within 13° based on their neuronal anatomy. We present here a prototype sound source localization system, inspired from this impressive performance. The system presented utilizes custom-built hardware with eight MEMS microphones, one for each foot, to acquire the acoustic scene, and a spiking neural model to localize the sound source. The current implementation shows smaller localization error than those observed in nature.

A bio-inspired spatial patterning circuit.

Science.gov (United States)

Chen, Kai-Yuan; Joe, Danial J; Shealy, James B; Land, Bruce R; Shen, Xiling

2014-01-01

Lateral Inhibition (LI) is a widely conserved patterning mechanism in biological systems across species. Distinct from better-known Turing patterns, LI depend on cell-cell contact rather than diffusion. We built an in silico genetic circuit model to analyze the dynamic properties of LI. The model revealed that LI amplifies differences between neighboring cells to push them into opposite states, hence forming stable 2-D patterns. Inspired by this insight, we designed and implemented an electronic circuit that recapitulates LI patterning dynamics. This biomimetic system serve as a physical model to elucidate the design principle of generating robust patterning through spatial feedback, regardless of the underlying devices being biological or electrical.

Implementing Signature Neural Networks with Spiking Neurons.

Science.gov (United States)

Carrillo-Medina, José Luis; Latorre, Roberto

2016-01-01

Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm-i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data-to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the absence

Biologically Inspired Technology Using Electroactive Polymers (EAP)

Science.gov (United States)

Bar-Cohen, Yoseph

2006-01-01

Evolution allowed nature to introduce highly effective biological mechanisms that are incredible inspiration for innovation. Humans have always made efforts to imitate nature's inventions and we are increasingly making advances that it becomes significantly easier to imitate, copy, and adapt biological methods, processes and systems. This brought us to the ability to create technology that is far beyond the simple mimicking of nature. Having better tools to understand and to implement nature's principles we are now equipped like never before to be inspired by nature and to employ our tools in far superior ways. Effectively, by bio-inspiration we can have a better view and value of nature capability while studying its models to learn what can be extracted, copied or adapted. Using electroactive polymers (EAP) as artificial muscles is adding an important element to the development of biologically inspired technologies.

Influence of neural adaptation on dynamics and equilibrium state of neural activities in a ring neural network

Science.gov (United States)

Takiyama, Ken

2017-12-01

How neural adaptation affects neural information processing (i.e. the dynamics and equilibrium state of neural activities) is a central question in computational neuroscience. In my previous works, I analytically clarified the dynamics and equilibrium state of neural activities in a ring-type neural network model that is widely used to model the visual cortex, motor cortex, and several other brain regions. The neural dynamics and the equilibrium state in the neural network model corresponded to a Bayesian computation and statistically optimal multiple information integration, respectively, under a biologically inspired condition. These results were revealed in an analytically tractable manner; however, adaptation effects were not considered. Here, I analytically reveal how the dynamics and equilibrium state of neural activities in a ring neural network are influenced by spike-frequency adaptation (SFA). SFA is an adaptation that causes gradual inhibition of neural activity when a sustained stimulus is applied, and the strength of this inhibition depends on neural activities. I reveal that SFA plays three roles: (1) SFA amplifies the influence of external input in neural dynamics; (2) SFA allows the history of the external input to affect neural dynamics; and (3) the equilibrium state corresponds to the statistically optimal multiple information integration independent of the existence of SFA. In addition, the equilibrium state in a ring neural network model corresponds to the statistically optimal integration of multiple information sources under biologically inspired conditions, independent of the existence of SFA.

Neuroscience-Inspired Artificial Intelligence.

Science.gov (United States)

Hassabis, Demis; Kumaran, Dharshan; Summerfield, Christopher; Botvinick, Matthew

2017-07-19

The fields of neuroscience and artificial intelligence (AI) have a long and intertwined history. In more recent times, however, communication and collaboration between the two fields has become less commonplace. In this article, we argue that better understanding biological brains could play a vital role in building intelligent machines. We survey historical interactions between the AI and neuroscience fields and emphasize current advances in AI that have been inspired by the study of neural computation in humans and other animals. We conclude by highlighting shared themes that may be key for advancing future research in both fields. Copyright © 2017. Published by Elsevier Inc.

Bio-inspired Iron Catalysts for Hydrocarbon Oxidations

Energy Technology Data Exchange (ETDEWEB)

Que, Jr., Lawrence [Univ. of Minnesota, Minneapolis, MN (United States)

2016-03-22

Stereoselective oxidation of C–H and C=C bonds are catalyzed by nonheme iron enzymes. Inspired by these bioinorganic systems, our group has been exploring the use of nonheme iron complexes as catalysts for the oxidation of hydrocarbons using H2O2 as an environmentally friendly and atom-efficient oxidant in order to gain mechanistic insights into these novel transformations. In particular, we have focused on clarifying the nature of the high-valent iron oxidants likely to be involved in these transformations.

Application of a neural network for reflectance spectrum classification

Science.gov (United States)

Yang, Gefei; Gartley, Michael

2017-05-01

Traditional reflectance spectrum classification algorithms are based on comparing spectrum across the electromagnetic spectrum anywhere from the ultra-violet to the thermal infrared regions. These methods analyze reflectance on a pixel by pixel basis. Inspired by high performance that Convolution Neural Networks (CNN) have demonstrated in image classification, we applied a neural network to analyze directional reflectance pattern images. By using the bidirectional reflectance distribution function (BRDF) data, we can reformulate the 4-dimensional into 2 dimensions, namely incident direction × reflected direction × channels. Meanwhile, RIT's micro-DIRSIG model is utilized to simulate additional training samples for improving the robustness of the neural networks training. Unlike traditional classification by using hand-designed feature extraction with a trainable classifier, neural networks create several layers to learn a feature hierarchy from pixels to classifier and all layers are trained jointly. Hence, the our approach of utilizing the angular features are different to traditional methods utilizing spatial features. Although training processing typically has a large computational cost, simple classifiers work well when subsequently using neural network generated features. Currently, most popular neural networks such as VGG, GoogLeNet and AlexNet are trained based on RGB spatial image data. Our approach aims to build a directional reflectance spectrum based neural network to help us to understand from another perspective. At the end of this paper, we compare the difference among several classifiers and analyze the trade-off among neural networks parameters.

A novel angle computation and calibration algorithm of bio-inspired sky-light polarization navigation sensor.

Science.gov (United States)

Xian, Zhiwen; Hu, Xiaoping; Lian, Junxiang; Zhang, Lilian; Cao, Juliang; Wang, Yujie; Ma, Tao

2014-09-15

Navigation plays a vital role in our daily life. As traditional and commonly used navigation technologies, Inertial Navigation System (INS) and Global Navigation Satellite System (GNSS) can provide accurate location information, but suffer from the accumulative error of inertial sensors and cannot be used in a satellite denied environment. The remarkable navigation ability of animals shows that the pattern of the polarization sky can be used for navigation. A bio-inspired POLarization Navigation Sensor (POLNS) is constructed to detect the polarization of skylight. Contrary to the previous approach, we utilize all the outputs of POLNS to compute input polarization angle, based on Least Squares, which provides optimal angle estimation. In addition, a new sensor calibration algorithm is presented, in which the installation angle errors and sensor biases are taken into consideration. Derivation and implementation of our calibration algorithm are discussed in detail. To evaluate the performance of our algorithms, simulation and real data test are done to compare our algorithms with several exiting algorithms. Comparison results indicate that our algorithms are superior to the others and are more feasible and effective in practice.

Growing adaptive machines combining development and learning in artificial neural networks

CERN Document Server

Bredeche, Nicolas; Doursat, René

2014-01-01

The pursuit of artificial intelligence has been a highly active domain of research for decades, yielding exciting scientific insights and productive new technologies. In terms of generating intelligence, however, this pursuit has yielded only limited success. This book explores the hypothesis that adaptive growth is a means of moving forward. By emulating the biological process of development, we can incorporate desirable characteristics of natural neural systems into engineered designs, and thus move closer towards the creation of brain-like systems. The particular focus is on how to design artificial neural networks for engineering tasks. The book consists of contributions from 18 researchers, ranging from detailed reviews of recent domains by senior scientists, to exciting new contributions representing the state of the art in machine learning research. The book begins with broad overviews of artificial neurogenesis and bio-inspired machine learning, suitable both as an introduction to the domains and as a...

A Wireless Fatigue Monitoring System Utilizing a Bio-Inspired Tree Ring Data Tracking Technique

Directory of Open Access Journals (Sweden)

Shi Bai

2014-03-01

Full Text Available Fatigue, a hot scientific research topic for centuries, can trigger sudden failure of critical structures such as aircraft and railway systems, resulting in enormous casualties as well as economic losses. The fatigue life of certain structures is intrinsically random and few monitoring techniques are capable of tracking the full life-cycle fatigue damage. In this paper, a novel in-situ wireless real-time fatigue monitoring system using a bio-inspired tree ring data tracking technique is proposed. The general framework, methodology, and verification of this intelligent system are discussed in details. The rain-flow counting (RFC method is adopted as the core algorithm which quantifies fatigue damages, and Digital Signal Processing (DSP is introduced as the core module for data collection and analysis. Laboratory test results based on strain gauges and polyvinylidene fluoride (PVDF sensors have shown that the developed intelligent system can provide a reliable quick feedback and early warning of fatigue failure. With the merits of low cost, high accuracy and great reliability, the developed wireless fatigue sensing system can be further applied to mechanical engineering, civil infrastructures, transportation systems, aerospace engineering, etc.

A wireless fatigue monitoring system utilizing a bio-inspired tree ring data tracking technique.

Science.gov (United States)

Bai, Shi; Li, Xuan; Xie, Zhaohui; Zhou, Zhi; Ou, Jinping

2014-03-05

Fatigue, a hot scientific research topic for centuries, can trigger sudden failure of critical structures such as aircraft and railway systems, resulting in enormous casualties as well as economic losses. The fatigue life of certain structures is intrinsically random and few monitoring techniques are capable of tracking the full life-cycle fatigue damage. In this paper, a novel in-situ wireless real-time fatigue monitoring system using a bio-inspired tree ring data tracking technique is proposed. The general framework, methodology, and verification of this intelligent system are discussed in details. The rain-flow counting (RFC) method is adopted as the core algorithm which quantifies fatigue damages, and Digital Signal Processing (DSP) is introduced as the core module for data collection and analysis. Laboratory test results based on strain gauges and polyvinylidene fluoride (PVDF) sensors have shown that the developed intelligent system can provide a reliable quick feedback and early warning of fatigue failure. With the merits of low cost, high accuracy and great reliability, the developed wireless fatigue sensing system can be further applied to mechanical engineering, civil infrastructures, transportation systems, aerospace engineering, etc.

Sound Source Localization Through 8 MEMS Microphones Array Using a Sand-Scorpion-Inspired Spiking Neural Network

Directory of Open Access Journals (Sweden)

Christoph Beck

2016-10-01

Full Text Available Sand-scorpions and many other arachnids perceive their environment by using their feet to sense ground waves. They are able to determine amplitudes the size of an atom and locate the acoustic stimuli with an accuracy of within 13° based on their neuronal anatomy. We present here a prototype sound source localization system, inspired from this impressive performance. The system presented utilizes custom-built hardware with eight MEMS microphones, one for each foot, to acquire the acoustic scene, and a spiking neural model to localize the sound source. The current implementation shows smaller localization error than those observed in nature.

On the synthesis of a bio-inspired dual-cellular fluidic flexible matrix composite adaptive structure based on a non-dimensional dynamics model

International Nuclear Information System (INIS)

Li, Suyi; Wang, K W

2013-01-01

A recent study investigated the dynamic characteristics of an adaptive structure concept featuring dual fluidic flexible matrix composite (F 2 MC) cells inspired by the configuration of plant cells and cell walls. This novel bio-inspired system consists of two F 2 MC cells with different fiber angles connected through internal fluid circuits. It was discovered that the dual F 2 MC cellular structure can be characterized as a two degree of freedom damped mass–spring oscillator, and can be utilized as a vibration absorber or an enhanced actuator under different operation conditions. These results demonstrated that the concept is promising and further investigations are needed to develop methodologies for synthesizing future multi-cellular F 2 MC structural systems. While interesting, the previous study focused on specific case studies and analysis. That is, the outcome did not provide insight that could be generalized, or tools for synthesizing a multiple F 2 MC cellular structure. This paper attempts to address this important issue by developing a non-dimensional dynamic model, which reveals good physical insights as well as identifying crucial constitutive parameters for F 2 MC cellular design. Working with these parameters, rather than physical variables, can greatly simplify the mathematics involved in the study. A synthesis tool is then developed for the dual-cellular structure, and it is found that for each set of achievable target poles and zero, there exist multiple F 2 MC cellular designs, forming a design space. The presented physical insights and synthesis tool for the dual-cellular structure will be the building blocks for future investigation on cellular structures with a larger number of cells. (paper)

Communication analysis for feedback control of civil infrastructure using cochlea-inspired sensing nodes

Science.gov (United States)

Peckens, Courtney A.; Cook, Ireana; Lynch, Jerome P.

2016-04-01

Wireless sensor networks (WSNs) have emerged as a reliable, low-cost alternative to the traditional wired sensing paradigm. While such networks have made significant progress in the field of structural monitoring, significantly less development has occurred for feedback control applications. Previous work in WSNs for feedback control has highlighted many of the challenges of using this technology including latency in the wireless communication channel and computational inundation at the individual sensing nodes. This work seeks to overcome some of those challenges by drawing inspiration from the real-time sensing and control techniques employed by the biological central nervous system and in particular the mammalian cochlea. A novel bio-inspired wireless sensor node was developed that employs analog filtering techniques to perform time-frequency decomposition of a sensor signal, thus encompassing the functionality of the cochlea. The node then utilizes asynchronous sampling of the filtered signal to compress the signal prior to communication. This bio-inspired sensing architecture is extended to a feedback control application in order to overcome the traditional challenges currently faced by wireless control. In doing this, however, the network experiences high bandwidths of low-significance information exchange between nodes, resulting in some lost data. This study considers the impact of this lost data on the control capabilities of the bio-inspired control architecture and finds that it does not significantly impact the effectiveness of control.

Paracoccus denitrificans possesses two BioR homologs having a role in regulation of biotin metabolism.

Science.gov (United States)

Feng, Youjun; Kumar, Ritesh; Ravcheev, Dmitry A; Zhang, Huimin

2015-08-01

Recently, we determined that BioR, the GntR family of transcription factor, acts as a repressor for biotin metabolism exclusively distributed in certain species of α-proteobacteria, including the zoonotic agent Brucella melitensis and the plant pathogen Agrobacterium tumefaciens. However, the scenario is unusual in Paracoccus denitrificans, another closely related member of the same phylum α-proteobacteria featuring with denitrification. Not only does it encode two BioR homologs Pden_1431 and Pden_2922 (designated as BioR1 and BioR2, respectively), but also has six predictive BioR-recognizable sites (the two bioR homolog each has one site, whereas the two bio operons (bioBFDAGC and bioYB) each contains two tandem BioR boxes). It raised the possibility that unexpected complexity is present in BioR-mediated biotin regulation. Here we report that this is the case. The identity of the purified BioR proteins (BioR1 and BioR2) was confirmed with LC-QToF-MS. Phylogenetic analyses combined with GC percentage raised a possibility that the bioR2 gene might be acquired by horizontal gene transfer. Gel shift assays revealed that the predicted BioR-binding sites are functional for the two BioR homologs, in much similarity to the scenario seen with the BioR site of A. tumefaciens bioBFDAZ. Using the A. tumefaciens reporter system carrying a plasmid-borne LacZ fusion, we revealed that the two homologs of P. denitrificans BioR are functional repressors for biotin metabolism. As anticipated, not only does the addition of exogenous biotin stimulate efficiently the expression of bioYB operon encoding biotin transport/uptake system BioY, but also inhibits the transcription of the bioBFDAGC operon resembling the de novo biotin synthetic pathway. EMSA-based screening failed to demonstrate that the biotin-related metabolite is involved in BioR-DNA interplay, which is consistent with our former observation with Brucella BioR. Our finding defined a complex regulatory network for biotin

Robust platforms for creating organic-inorganic nanocomposite microspheres: decorating polymer microspheres containing mussel-inspired adhesion layers with inorganic nanoparticles.

Science.gov (United States)

Satoh, H; Saito, Y; Yabu, H

2014-12-07

We describe a method for creating robust and stable core-shell polymer microspheres decorated with inorganic (IO) nanoparticles (NPs) by a self-organization process and heterocoagulation using a mussel-inspired polymer adhesive layer between the IO NPs and the microspheres.

Temporal neural network for the identification of nuclear power plant transients

International Nuclear Information System (INIS)

Uluyol, O.; Ragheb, M.

1993-01-01

In this paper a layered spatiotemporal neural network is proposed for the identification of nuclear power plant transients. The developed layered spatiotemporal network is inspired by the formal avalanche structure developed by S. Grossberg and offers advantages compared with the stationary pattern approach using the perceptron paradigm. Each layer in the network is trained to recognize a separate time-dependent accident scenario. Within each scenario, the temporal behavior of the relevant parameters such as pressurizer pressure, pressurizer water volume, cold and hot legs temperatures, vessel flow, and power, are considered. Numerical cases are considered where the proposed methodology is applied to two nuclear power plant anticipated transient scenarios: the Station Blackout and the Anticipated Transient without Scram transients in a pressurized water reactor . The transient signatures used were generated by modeling the accidents using RELAP5/MOD2, a best-estimate thermal-hydraulics numerical code. The ability of the proposed layered spatiotemporal network to operate at different noise levels is investigated. Its incorporation within an Insightful Algorithm and Anticipatory Systems context for identifying and in predicting the course of nuclear transients is discussed

The Multi-Layered Perceptrons Neural Networks for the Prediction of Daily Solar Radiation

OpenAIRE

Radouane Iqdour; Abdelouhab Zeroual

2007-01-01

The Multi-Layered Perceptron (MLP) Neural networks have been very successful in a number of signal processing applications. In this work we have studied the possibilities and the met difficulties in the application of the MLP neural networks for the prediction of daily solar radiation data. We have used the Polack-Ribière algorithm for training the neural networks. A comparison, in term of the statistical indicators, with a linear model most used in literature, is also perfo...

Biomimetic Hair Sensor Arrays: From Inspiration To Implementation

NARCIS (Netherlands)

Jaganatharaja, R.K.; Bruinink, C.M.; Kolster, M.L.; Lammerink, Theodorus S.J.; Wiegerink, Remco J.; Krijnen, Gijsbertus J.M.

2010-01-01

In this work, we report on the successful implementation of highly sensitive artificial hair-based flow-sensor arrays for sensing low-frequency air flows. Artificial hair sensors are bio-inspired from crickets’ cercal filiform hairs, one of nature’s best in sensing small air flows. The presented

Brain Inspired Cognitive Model with Attention for Self-Driving Cars

OpenAIRE

Chen, Shitao; Zhang, Songyi; Shang, Jinghao; Chen, Badong; Zheng, Nanning

2017-01-01

Perception-driven approach and end-to-end system are two major vision-based frameworks for self-driving cars. However, it is difficult to introduce attention and historical information of autonomous driving process, which are the essential factors for achieving human-like driving into these two methods. In this paper, we propose a novel model for self-driving cars named brain-inspired cognitive model with attention (CMA). This model consists of three parts: a convolutional neural network for ...

Bio-inspired green synthesis of Fe{sub 3}O{sub 4} spherical magnetic nanoparticles using Syzygium cumini seed extract

Energy Technology Data Exchange (ETDEWEB)

Venkateswarlu, Sada; Natesh Kumar, B.; Prasad, C.H.; Venkateswarlu, P.; Jyothi, N.V.V., E-mail: nvvjyothi01@gmail.com

2014-09-15

A novel and bio-inspired Fe{sub 3}O{sub 4} spherical magnetic nanoparticles (SMNPs) were synthesized using Syzygium cumini (S. cumini) seed extract, which is a non-toxic ecofriendly fruit waste material. S. cumini seed extract acts as a green solvent, reducing and capping agent in which sodium acetate acts as electrostatic stabilizing agent. The green synthesized nanoparticles were characterized with the help of various techniques such as X-ray diffraction (XRD), Raman spectroscopy, transmission electron microscopy (TEM), Energy-dispersive spectroscopy (EDS), Vibrating sample magnetometer (VSM), FTIR spectroscopy and nitrogen adsorption and desorption analysis techniques. The XRD study divulged that the synthesized SMNPs have inverse spinel cubic structure. The hysteresis loop of Fe{sub 3}O{sub 4} nanoparticles shows an excellent ferromagnetic behavior with saturation magnetization value of 13.6 emu/g.

Bio-inspired particle separator design based on the food retention mechanism by suspension-feeding fish

International Nuclear Information System (INIS)

Hung, Tien-Chieh; Piedrahita, Raul H; Cheer, Angela

2012-01-01

A new particle separator is designed using a crossflow filtration mechanism inspired by suspension-feeding fish in this study. To construct the model of the bio-inspired particle separator, computational fluid dynamics techniques are used, and parameters related to separator shape, fluid flow and particle properties that might affect the performance in removing particles from the flow, are varied and tested. The goal is to induce a flow rotation which enhances the separation of particles from the flow, reduce the particle-laden flow that exits via a collection zone at the lower/posterior end of the separator, while at the same time increase the concentration of particles in that flow. Based on preliminary particle removal efficiency tests, an exiting flow through the collection zone of about 8% of the influent flow rate is selected for all the performance tests of the separator including trials with particles carried by air flow instead of water. Under this condition, the simulation results yield similar particle removal efficiencies in water and air but with different particle properties. Particle removal efficiencies (percentage of influent particles that exit through the collection zone) were determined for particles ranging in size from 1 to 1500 µm with a density between 1000 and 1150 kg m −3 in water and 2 and 19 mm and 68 and 2150 kg m −3 in air. As an example, removal efficiencies are 66% and 64% for 707 µm diameter particles with a density of 1040 kg m −3 in water and for 2 mm particles with a density of 68 kg m −3 in air, respectively. No significant performance difference is found by geometrically scaling the inlet diameter of the separator up or down in the range from 2.5 to 10 cm. (paper)

Typology of nonlinear activity waves in a layered neural continuum.

Science.gov (United States)

Koch, Paul; Leisman, Gerry

2006-04-01

Neural tissue, a medium containing electro-chemical energy, can amplify small increments in cellular activity. The growing disturbance, measured as the fraction of active cells, manifests as propagating waves. In a layered geometry with a time delay in synaptic signals between the layers, the delay is instrumental in determining the amplified wavelengths. The growth of the waves is limited by the finite number of neural cells in a given region of the continuum. As wave growth saturates, the resulting activity patterns in space and time show a variety of forms, ranging from regular monochromatic waves to highly irregular mixtures of different spatial frequencies. The type of wave configuration is determined by a number of parameters, including alertness and synaptic conditioning as well as delay. For all cases studied, using numerical solution of the nonlinear Wilson-Cowan (1973) equations, there is an interval in delay in which the wave mixing occurs. As delay increases through this interval, during a series of consecutive waves propagating through a continuum region, the activity within that region changes from a single-frequency to a multiple-frequency pattern and back again. The diverse spatio-temporal patterns give a more concrete form to several metaphors advanced over the years to attempt an explanation of cognitive phenomena: Activity waves embody the "holographic memory" (Pribram, 1991); wave mixing provides a plausible cause of the competition called "neural Darwinism" (Edelman, 1988); finally the consecutive generation of growing neural waves can explain the discontinuousness of "psychological time" (Stroud, 1955).

Bio-inspired ciliary force sensor for robotic platforms

KAUST Repository

Ribeiro, Pedro; Khan, Mohammed Asadullah; Alfadhel, Ahmed; Kosel, Jü rgen; Franco, Fernando; Cardoso, Susana; Bernardino, Alexandre; Schmitz, Alexander; Santos-Victor, Jose; Jamone, Lorenzo

2017-01-01

The detection of small forces is of great interest in any robotic application that involves interaction with the environment (e.g., objects manipulation, physical human-robot interaction, minimally invasive surgery), since it allows the robot to detect the contacts early on and to act accordingly. In this letter, we present a sensor design inspired by the ciliary structure frequently found in nature, consisting of an array of permanently magnetized cylinders (cilia) patterned over a giant magnetoresistance sensor (GMR). When these cylinders are deformed in shape due to applied forces, the stray magnetic field variation will change the GMR sensor resistivity, thus enabling the electrical measurement of the applied force. In this letter, we present two 3 mm × 3 mm prototypes composed of an array of five cilia with 1 mm of height and 120 and 200 μm of diameter for each prototype. A minimum force of 333 μN was measured. A simulation model for determining the magnetized cylinders average stray magnetic field is also presented.

Bio-inspired ciliary force sensor for robotic platforms

KAUST Repository

Ribeiro, Pedro

2017-01-20

The detection of small forces is of great interest in any robotic application that involves interaction with the environment (e.g., objects manipulation, physical human-robot interaction, minimally invasive surgery), since it allows the robot to detect the contacts early on and to act accordingly. In this letter, we present a sensor design inspired by the ciliary structure frequently found in nature, consisting of an array of permanently magnetized cylinders (cilia) patterned over a giant magnetoresistance sensor (GMR). When these cylinders are deformed in shape due to applied forces, the stray magnetic field variation will change the GMR sensor resistivity, thus enabling the electrical measurement of the applied force. In this letter, we present two 3 mm × 3 mm prototypes composed of an array of five cilia with 1 mm of height and 120 and 200 μm of diameter for each prototype. A minimum force of 333 μN was measured. A simulation model for determining the magnetized cylinders average stray magnetic field is also presented.

First controlled vertical flight of a biologically inspired microrobot

Energy Technology Data Exchange (ETDEWEB)

Perez-Arancibia, Nestor O; Ma, Kevin Y; Greenberg, Jack D; Wood, Robert J [School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138 (United States); Galloway, Kevin C, E-mail: nperez@seas.harvard.edu, E-mail: kevinma@seas.harvard.edu, E-mail: kevin.galloway@wyss.harvard.edu, E-mail: jdgreenb@seas.harvard.edu, E-mail: rjwood@eecs.harvard.edu [Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115 (United States)

2011-09-15

In this paper, we present experimental results on altitude control of a flying microrobot. The problem is approached in two stages. In the first stage, system identification of two relevant subsystems composing the microrobot is performed, using a static flapping experimental setup. In the second stage, the information gathered through the static flapping experiments is employed to design the controller used in vertical flight. The design of the proposed controller relies on the idea of treating an exciting signal as a subsystem of the microrobot. The methods and results presented here are a key step toward achieving total autonomy of bio-inspired flying microrobots.

First controlled vertical flight of a biologically inspired microrobot

International Nuclear Information System (INIS)

Perez-Arancibia, Nestor O; Ma, Kevin Y; Greenberg, Jack D; Wood, Robert J; Galloway, Kevin C

2011-01-01

In this paper, we present experimental results on altitude control of a flying microrobot. The problem is approached in two stages. In the first stage, system identification of two relevant subsystems composing the microrobot is performed, using a static flapping experimental setup. In the second stage, the information gathered through the static flapping experiments is employed to design the controller used in vertical flight. The design of the proposed controller relies on the idea of treating an exciting signal as a subsystem of the microrobot. The methods and results presented here are a key step toward achieving total autonomy of bio-inspired flying microrobots.

A Bio-Inspired Model-Based Approach for Context-Aware Post-WIMP Tele-Rehabilitation

Directory of Open Access Journals (Sweden)

Víctor López-Jaquero

2016-10-01

Full Text Available Tele-rehabilitation is one of the main domains where Information and Communication Technologies (ICT have been proven useful to move healthcare from care centers to patients’ home. Moreover, patients, especially those carrying out a physical therapy, cannot use a traditional Window, Icon, Menu, Pointer (WIMP system, but they need to interact in a natural way, that is, there is a need to move from WIMP systems to Post-WIMP ones. Moreover, tele-rehabilitation systems should be developed following the context-aware approach, so that they are able to adapt to the patients’ context to provide them with usable and effective therapies. In this work a model-based approach is presented to assist stakeholders in the development of context-aware Post-WIMP tele-rehabilitation systems. It entails three different models: (i a task model for designing the rehabilitation tasks; (ii a context model to facilitate the adaptation of these tasks to the context; and (iii a bio-inspired presentation model to specify thoroughly how such tasks should be performed by the patients. Our proposal overcomes one of the limitations of the model-based approach for the development of context-aware systems supporting the specification of non-functional requirements. Finally, a case study is used to illustrate how this proposal can be put into practice to design a real world rehabilitation task.

Carbon-Nanotube-Supported Bio-Inspired Nickel Catalyst and Its Integration in Hybrid Hydrogen/Air Fuel Cells.

Science.gov (United States)

Gentil, Solène; Lalaoui, Noémie; Dutta, Arnab; Nedellec, Yannig; Cosnier, Serge; Shaw, Wendy J; Artero, Vincent; Le Goff, Alan

2017-02-06

A biomimetic nickel bis-diphosphine complex incorporating the amino acid arginine in the outer coordination sphere was immobilized on modified carbon nanotubes (CNTs) through electrostatic interactions. The functionalized redox nanomaterial exhibits reversible electrocatalytic activity for the H 2 /2 H + interconversion from pH 0 to 9, with catalytic preference for H 2 oxidation at all pH values. The high activity of the complex over a wide pH range allows us to integrate this bio-inspired nanomaterial either in an enzymatic fuel cell together with a multicopper oxidase at the cathode, or in a proton exchange membrane fuel cell (PEMFC) using Pt/C at the cathode. The Ni-based PEMFC reaches 14 mW cm -2 , only six-times-less as compared to full-Pt conventional PEMFC. The Pt-free enzyme-based fuel cell delivers ≈2 mW cm -2 , a new efficiency record for a hydrogen biofuel cell with base metal catalysts. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Bio-inspired nano-photodiode for Low Light, High Resolution and crosstalk-free CMOS image sensing

KAUST Repository

Saffih, Faycal

2011-05-01

Previous attempts have been devoted to mimic biological vision intelligence at the architectural system level. In this paper, a novel imitation of biological visual system intelligence is suggested, at the device level with the introduction of novel photodiode morphology. The proposed bio-inspired nanorod photodiode puts the depletion region length on the path of the incident photon instead of on its width, as the case is with the planar photodiodes. The depletion region has a revolving volume to increase the photodiode responsivity, and thus its photosensitivity. In addition, it can virtually boost the pixel fill factor (FF) above the 100% classical limit due to decoupling of its vertical sensing area from its limited planar circuitry area. Furthermore, the suggested nanorod photodiode photosensitivity is analytically proven to be higher than that of the planar photodiode. We also show semi-empirically that the responsivity of the suggested device varies linearly with its height; this important feature has been confirmed using Sentaurus simulation. The proposed nano-photorod is believed to meet the increasingly stringent High-Resolution-Low-Light (HRLL) detection requirements of the camera-phone and biomedical imaging markets. © 2011 IEEE.

Comparing Two Methods of Neural Networks to Evaluate Dead Oil Viscosity

Directory of Open Access Journals (Sweden)

Meysam Dabiri-Atashbeyk

2018-01-01

Full Text Available Reservoir characterization and asset management require comprehensive information about formation fluids. In fact, it is not possible to find accurate solutions to many petroleum engineering problems without having accurate pressure-volume-temperature (PVT data. Traditionally, fluid information has been obtained by capturing samples and then by measuring the PVT properties in a laboratory. In recent years, neural network has been applied to a large number of petroleum engineering problems. In this paper, a multi-layer perception neural network and radial basis function network (both optimized by a genetic algorithm were used to evaluate the dead oil viscosity of crude oil, and it was found out that the estimated dead oil viscosity by the multi-layer perception neural network was more accurate than the one obtained by radial basis function network.

Time-recovering PCI-AER interface for bio-inspired spiking systems

Science.gov (United States)

Paz-Vicente, R.; Linares-Barranco, A.; Cascado, D.; Vicente, S.; Jimenez, G.; Civit, A.

2005-06-01

Address Event Representation (AER) is an emergent neuromorphic interchip communication protocol that allows for real-time virtual massive connectivity between huge number neurons located on different chips. By exploiting high speed digital communication circuits (with nano-seconds timings), synaptic neural connections can be time multiplexed, while neural activity signals (with mili-seconds timings) are sampled at low frequencies. Also, neurons generate 'events' according to their activity levels. More active neurons generate more events per unit time, and access the interchip communication channel more frequently, while neurons with low activity consume less communication bandwidth. When building multi-chip muti-layered AER systems it is absolutely necessary to have a computer interface that allows (a) to read AER interchip traffic into the computer and visualize it on screen, and (b) inject a sequence of events at some point of the AER structure. This is necessary for testing and debugging complex AER systems. This paper presents a PCI to AER interface, that dispatches a sequence of events received from the PCI bus with embedded timing information to establish when each event will be delivered. A set of specialized states machines has been introduced to recovery the possible time delays introduced by the asynchronous AER bus. On the input channel, the interface capture events assigning a timestamp and delivers them through the PCI bus to MATLAB applications. It has been implemented in real time hardware using VHDL and it has been tested in a PCI-AER board, developed by authors, that includes a Spartan II 200 FPGA. The demonstration hardware is currently capable to send and receive events at a peak rate of 8,3 Mev/sec, and a typical rate of 1 Mev/sec.

Novel maximum-margin training algorithms for supervised neural networks.

Science.gov (United States)

Ludwig, Oswaldo; Nunes, Urbano

2010-06-01

This paper proposes three novel training methods, two of them based on the backpropagation approach and a third one based on information theory for multilayer perceptron (MLP) binary classifiers. Both backpropagation methods are based on the maximal-margin (MM) principle. The first one, based on the gradient descent with adaptive learning rate algorithm (GDX) and named maximum-margin GDX (MMGDX), directly increases the margin of the MLP output-layer hyperplane. The proposed method jointly optimizes both MLP layers in a single process, backpropagating the gradient of an MM-based objective function, through the output and hidden layers, in order to create a hidden-layer space that enables a higher margin for the output-layer hyperplane, avoiding the testing of many arbitrary kernels, as occurs in case of support vector machine (SVM) training. The proposed MM-based objective function aims to stretch out the margin to its limit. An objective function based on Lp-norm is also proposed in order to take into account the idea of support vectors, however, overcoming the complexity involved in solving a constrained optimization problem, usually in SVM training. In fact, all the training methods proposed in this paper have time and space complexities O(N) while usual SVM training methods have time complexity O(N (3)) and space complexity O(N (2)) , where N is the training-data-set size. The second approach, named minimization of interclass interference (MICI), has an objective function inspired on the Fisher discriminant analysis. Such algorithm aims to create an MLP hidden output where the patterns have a desirable statistical distribution. In both training methods, the maximum area under ROC curve (AUC) is applied as stop criterion. The third approach offers a robust training framework able to take the best of each proposed training method. The main idea is to compose a neural model by using neurons extracted from three other neural networks, each one previously trained by

Deep neural networks rival the representation of primate IT cortex for core visual object recognition.

Directory of Open Access Journals (Sweden)

Charles F Cadieu

2014-12-01

Full Text Available The primate visual system achieves remarkable visual object recognition performance even in brief presentations, and under changes to object exemplar, geometric transformations, and background variation (a.k.a. core visual object recognition. This remarkable performance is mediated by the representation formed in inferior temporal (IT cortex. In parallel, recent advances in machine learning have led to ever higher performing models of object recognition using artificial deep neural networks (DNNs. It remains unclear, however, whether the representational performance of DNNs rivals that of the brain. To accurately produce such a comparison, a major difficulty has been a unifying metric that accounts for experimental limitations, such as the amount of noise, the number of neural recording sites, and the number of trials, and computational limitations, such as the complexity of the decoding classifier and the number of classifier training examples. In this work, we perform a direct comparison that corrects for these experimental limitations and computational considerations. As part of our methodology, we propose an extension of "kernel analysis" that measures the generalization accuracy as a function of representational complexity. Our evaluations show that, unlike previous bio-inspired models, the latest DNNs rival the representational performance of IT cortex on this visual object recognition task. Furthermore, we show that models that perform well on measures of representational performance also perform well on measures of representational similarity to IT, and on measures of predicting individual IT multi-unit responses. Whether these DNNs rely on computational mechanisms similar to the primate visual system is yet to be determined, but, unlike all previous bio-inspired models, that possibility cannot be ruled out merely on representational performance grounds.

Bio-inspired control of joint torque and knee stiffness in a robotic lower limb exoskeleton using a central pattern generator.

Science.gov (United States)

Schrade, Stefan O; Nager, Yannik; Wu, Amy R; Gassert, Roger; Ijspeert, Auke

2017-07-01

Robotic lower limb exoskeletons are becoming increasingly popular in therapy and recreational use. However, most exoskeletons are still rather limited in their locomotion speed and the activities of daily live they can perform. Furthermore, they typically do not allow for a dynamic adaptation to the environment, as they are often controlled with predefined reference trajectories. Inspired by human leg stiffness modulation during walking, variable stiffness actuators increase flexibility without the need for more complex controllers. Actuation with adaptable stiffness is inspired by the human leg stiffness modulation during walking. However, this actuation principle also introduces the stiffness setpoint as an additional degree of freedom that needs to be coordinated with the joint trajectories. As a potential solution to this issue a bio-inspired controller based on a central pattern generator (CPG) is presented in this work. It generates coordinated joint torques and knee stiffness modulations to produce flexible and dynamic gait patterns for an exoskeleton with variable knee stiffness actuation. The CPG controller is evaluated and optimized in simulation using a model of the exoskeleton. The CPG controller produced stable and smooth gait for walking speeds from 0.4 m/s up to 1.57 m/s with a torso stabilizing force that simulated the use of crutches, which are commonly needed by exoskeleton users. Through the CPG, the knee stiffness intrinsically adapted to the frequency and phase of the gait, when the speed was changed. Additionally, it adjusted to changes in the environment in the form of uneven terrain by reacting to ground contact forces. This could allow future exoskeletons to be more adaptive to various environments, thus making ambulation more robust.

Dynamic behaviour of the silica-water-bio electrical double layer in the presence of a divalent electrolyte.

Science.gov (United States)

Lowe, B M; Maekawa, Y; Shibuta, Y; Sakata, T; Skylaris, C-K; Green, N G

2017-01-25

Electronic devices are becoming increasingly used in chemical- and bio-sensing applications and therefore understanding the silica-electrolyte interface at the atomic scale is becoming increasingly important. For example, field-effect biosensors (BioFETs) operate by measuring perturbations in the electric field produced by the electrical double layer due to biomolecules binding on the surface. In this paper, explicit-solvent atomistic calculations of this electric field are presented and the structure and dynamics of the interface are investigated in different ionic strengths using molecular dynamics simulations. Novel results from simulation of the addition of DNA molecules and divalent ions are also presented, the latter of particular importance in both physiological solutions and biosensing experiments. The simulations demonstrated evidence of charge inversion, which is known to occur experimentally for divalent electrolyte systems. A strong interaction between ions and DNA phosphate groups was demonstrated in mixed electrolyte solutions, which are relevant to experimental observations of device sensitivity in the literature. The bound DNA resulted in local changes to the electric field at the surface; however, the spatial- and temporal-mean electric field showed no significant change. This result is explained by strong screening resulting from a combination of strongly polarised water and a compact layer of counterions around the DNA and silica surface. This work suggests that the saturation of the Stern layer is an important factor in determining BioFET response to increased salt concentration and provides novel insight into the interplay between ions and the EDL.

Fundamentals of computational intelligence neural networks, fuzzy systems, and evolutionary computation

CERN Document Server

Keller, James M; Fogel, David B

2016-01-01

This book covers the three fundamental topics that form the basis of computational intelligence: neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation. Discusses single-layer and multilayer neural networks, radial-basi function networks, and recurrent neural networks Covers fuzzy set theory, fuzzy relations, fuzzy logic interference, fuzzy clustering and classification, fuzzy measures and fuzz...

Bio-energy. Innovators talking; Bio-energie. Innovators aan het woord

Energy Technology Data Exchange (ETDEWEB)

NONE

2013-02-15

Qualitative studies have been conducted of the results of completed projects focused on energy innovation, spread over the seven themes of the top sector Energy: Energy saving in industry, Energy conservation in the built environment, Gas, Bio-energy, Smart grids, Offshore Wind, Solar PV. This provides insight into the follow-up activities and lessons of some EOS (Energy Research Subsidy) completed projects with the aim to inspire, connect and strengthen the TKIs (Topconsortia for Knowledge and Innovation) and individual companies and researchers working on energy innovation. This report concerns the research on bio-energy [Dutch] Er is een kwalitatief onderzoek uitgevoerd naar de resultaten van afgeronde projecten gericht op energie-innovatie, verdeeld over de zeven thema's van de topsector Energie: Energiebesparing in de industrie; Energiebesparing in de gebouwde omgeving; Gas; Bio-energie; Smart grids; Wind op zee; Zon-pv. Daarmee wordt inzicht gegeven in de vervolgactiviteiten en lessen van een aantal afgesloten EOS-projecten (Energie Onderzoek Subsidie) met het oog op het inspireren, verbinden en versterken van de TKI's (Topconsortia voor Kennis en Innovatie) en individuele bedrijven en onderzoekers die werken aan energie-innovatie. Dit rapport betreft het onderzoek naar bio-energie.

Bio-energy. Innovators talking; Bio-energie. Innovators aan het woord

Energy Technology Data Exchange (ETDEWEB)

NONE

2013-02-15

Qualitative studies have been conducted of the results of completed projects focused on energy innovation, spread over the seven themes of the top sector Energy: Energy saving in industry, Energy conservation in the built environment, Gas, Bio-energy, Smart grids, Offshore Wind, Solar PV. This provides insight into the follow-up activities and lessons of some EOS (Energy Research Subsidy) completed projects with the aim to inspire, connect and strengthen the TKIs (Topconsortia for Knowledge and Innovation) and individual companies and researchers working on energy innovation. This report concerns the research on bio-energy [Dutch] Er is een kwalitatief onderzoek uitgevoerd naar de resultaten van afgeronde projecten gericht op energie-innovatie, verdeeld over de zeven thema's van de topsector Energie: Energiebesparing in de industrie; Energiebesparing in de gebouwde omgeving; Gas; Bio-energie; Smart grids; Wind op zee; Zon-pv. Daarmee wordt inzicht gegeven in de vervolgactiviteiten en lessen van een aantal afgesloten EOS-projecten (Energie Onderzoek Subsidie) met het oog op het inspireren, verbinden en versterken van de TKI's (Topconsortia voor Kennis en Innovatie) en individuele bedrijven en onderzoekers die werken aan energie-innovatie. Dit rapport betreft het onderzoek naar bio-energie.

Applicability of the Reaction Layer Principle to Nanoparticulate Metal Complexes at a Macroscopic Reactive (Bio)Interface

NARCIS (Netherlands)

Duval, Jérôme F.L.; Town, Raewyn M.; Leeuwen, Van Herman P.

2017-01-01

The reaction layer concept is commonly adopted to estimate the contribution of metal complexes to the flux of free metal ions (M) toward a macroscopic M-accumulating (bio)interface, e.g., a biosurface (microorganism) or a sensor (electrode). This concept is well-established for molecular ligands

Generalization and capacity of extensively large two-layered perceptrons

International Nuclear Information System (INIS)

Rosen-Zvi, Michal; Kanter, Ido; Engel, Andreas

2002-01-01

The generalization ability and storage capacity of a treelike two-layered neural network with a number of hidden units scaling as the input dimension is examined. The mapping from the input to the hidden layer is via Boolean functions; the mapping from the hidden layer to the output is done by a perceptron. The analysis is within the replica framework where an order parameter characterizing the overlap between two networks in the combined space of Boolean functions and hidden-to-output couplings is introduced. The maximal capacity of such networks is found to scale linearly with the logarithm of the number of Boolean functions per hidden unit. The generalization process exhibits a first-order phase transition from poor to perfect learning for the case of discrete hidden-to-output couplings. The critical number of examples per input dimension, α c , at which the transition occurs, again scales linearly with the logarithm of the number of Boolean functions. In the case of continuous hidden-to-output couplings, the generalization error decreases according to the same power law as for the perceptron, with the prefactor being different

Neural networks advances and applications 2

CERN Document Server

Gelenbe, E

1992-01-01

The present volume is a natural follow-up to Neural Networks: Advances and Applications which appeared one year previously. As the title indicates, it combines the presentation of recent methodological results concerning computational models and results inspired by neural networks, and of well-documented applications which illustrate the use of such models in the solution of difficult problems. The volume is balanced with respect to these two orientations: it contains six papers concerning methodological developments and five papers concerning applications and examples illustrating the theoret

Adaptive Bio-Inspired Wireless Network Routing for Planetary Surface Exploration

Science.gov (United States)

Alena, Richard I.; Lee, Charles

2004-01-01

Wireless mobile networks suffer connectivity loss when used in a terrain that has hills, and valleys when line of sight is interrupted or range is exceeded. To resolve this problem and achieve acceptable network performance, we have designed an adaptive, configurable, hybrid system to automatically route network packets along the best path between multiple geographically dispersed modules. This is very useful in planetary surface exploration, especially for ad-hoc mobile networks, where computational devices take an active part in creating a network infrastructure, and can actually be used to route data dynamically and even store data for later transmission between networks. Using inspiration from biological systems, this research proposes to use ant trail algorithms with multi-layered information maps (topographic maps, RF coverage maps) to determine the best route through ad-hoc network at real time. The determination of best route is a complex one, and requires research into the appropriate metrics, best method to identify the best path, optimizing traffic capacity, network performance, reliability, processing capabilities and cost. Real ants are capable of finding the shortest path from their nest to a food source without visual sensing through the use of pheromones. They are also able to adapt to changes in the environment using subtle clues. To use ant trail algorithms, we need to define the probability function. The artificial ant is, in this case, a software agent that moves from node to node on a network graph. The function to calculate the fitness (evaluate the better path) includes: length of the network edge, the coverage index, topology graph index, and pheromone trail left behind by other ant agents. Each agent modifies the environment in two different ways: 1) Local trail updating: As the ant moves between nodes it updates the amount of pheromone on the edge; and 2) Global trail updating: When all ants have completed a tour the ant that found the

Colloidal-based additive manufacturing of bio-inspired composites

Science.gov (United States)

Studart, Andre R.

Composite materials in nature exhibit heterogeneous architectures that are tuned to fulfill the functional demands of the surrounding environment. Examples range from the cellulose-based organic structure of plants to highly mineralized collagen-based skeletal parts like bone and teeth. Because they are often utilized to combine opposing properties such as strength and low-density or stiffness and wear resistance, the heterogeneous architecture of natural materials can potentially address several of the technical limitations of artificial homogeneous composites. However, current man-made manufacturing technologies do not allow for the level of composition and fiber orientation control found in natural heterogeneous systems. In this talk, I will present two additive manufacturing technologies recently developed in our group to build composites with exquisite architectures only rivaled by structures made by living organisms in nature. Since the proposed techniques utilize colloidal suspensions as feedstock, understanding the physics underlying the stability, assembly and rheology of the printing inks is key to predict and control the architecture of manufactured parts. Our results will show that additive manufacturing routes offer a new exciting pathway for the fabrication of biologically-inspired composite materials with unprecedented architectures and functionalities.

Bio-inspired functional surfaces for advanced applications

DEFF Research Database (Denmark)

Malshe, Ajay; Rajurkar, Kamlakar; Samant, Anoop

2013-01-01

, are being evolved to a higher state of intelligent functionality. These surfaces became more efficient by using combinations of available materials, along with unique physical and chemical strategies. Noteworthy physical strategies include features such as texturing and structure, and chemical strategies...... such as sensing and actuation. These strategies collectively enable functional surfaces to deliver extraordinary adhesion, hydrophobicity, multispectral response, energy scavenging, thermal regulation, antibiofouling, and other advanced functions. Production industries have been intrigued with such biological...... surface strategies in order to learn clever surface architectures and implement those architectures to impart advanced functionalities into manufactured consumer products. This keynote paper delivers a critical review of such inspiring biological surfaces and their nonbiological product analogs, where...

Bio-objects and the media: the role of communication in bio-objectification processes.

Science.gov (United States)

Maeseele, Pieter; Allgaier, Joachim; Martinelli, Lucia

2013-06-01

The representation of biological innovations in and through communication and media practices is vital for understanding the nature of "bio-objects" and the process we call "bio-objectification." This paper discusses two ideal-typical analytical approaches based on different underlying communication models, ie, the traditional (science- and media-centered) and media sociological (a multi-layered process involving various social actors in defining the meanings of scientific and technological developments) approach. In this analysis, the latter is not only found to be the most promising approach for understanding the circulation, (re)production, and (re)configuration of meanings of bio-objects, but also to interpret the relationship between media and science. On the basis of a few selected examples, this paper highlights how media function as a primary arena for the (re)production and (re)configuration of scientific and biomedical information with regards to bio-objects in the public sphere in general, and toward decision-makers, interest groups, and the public in specific.

Experimental study of surface pattern effects on the propulsive performance and wake of a bio-inspired pitching panel

Science.gov (United States)

King, Justin; Kumar, Rajeev; Green, Melissa

2016-11-01

Force measurements and stereoscopic particle image velocimetry (PIV) were used to characterize the propulsive performance and wake structure of rigid, bio-inspired trapezoidal pitching panels. In the literature, it has been demonstrated that quantities such as thrust coefficient and propulsive efficiency are affected by changes in the surface characteristics of a pitching panel or foil. More specifically, the variation of surface pattern produces significant changes in wake structure and dynamics, especially in the distribution of vorticity in the wake. Force measurements and PIV data were collected for multiple surface patterns chosen to mimic fish surface morphology over a Strouhal number range of 0.17 to 0.56. Performance quantities are compared with the three-dimensional vortex wake structure for both the patterned and smooth panels to determine the nature and magnitude of surface pattern effects in terms of thrust produced, drag reduced, and wake vortices reshaped and reorganized. This work was supported by the Office of Naval Research under ONR Award No. N00014-14-1-0418.

Methods for discriminating gas-liquid two phase flow patterns based on gray neural networks and SVM

International Nuclear Information System (INIS)

Li Jingjing; Zhou Tao; Duan Jun; Zhang Lei

2013-01-01

Background: The flow patterns of two phase flow will directly influence the heat transfer and mass transfer of the flow. Purpose: By wavelet analysis of the pressure drop experimental data, the wavelet coefficients of different frequency can be obtained. Methods: Get the wavelet energy and then train them in the model of BP neural network to distinguish the flow patterns. Introduced the implant gray neural networks model and use it for the two phase flow for the first time. At the same time, set up the method of training the pressure data and wavelet energy data in the support vector machine. Results: Through treatment of the gray layer, the result of the neural network is more accuracy. It can obviously reduce the effect of data marginalization. The accuracy of the pressure drop Lib-SVM method is 95.2%. Conclusions: The results show that these three methods can make a distinction among the different flow patterns and the Lib-SVM method gets the best result, then the gray neural networks, and at last the BP neural networks. (authors)

A biologically inspired neural network controller for ballistic arm movements

Directory of Open Access Journals (Sweden)

Schmid Maurizio

2007-09-01

Full Text Available Abstract Background In humans, the implementation of multijoint tasks of the arm implies a highly complex integration of sensory information, sensorimotor transformations and motor planning. Computational models can be profitably used to better understand the mechanisms sub-serving motor control, thus providing useful perspectives and investigating different control hypotheses. To this purpose, the use of Artificial Neural Networks has been proposed to represent and interpret the movement of upper limb. In this paper, a neural network approach to the modelling of the motor control of a human arm during planar ballistic movements is presented. Methods The developed system is composed of three main computational blocks: 1 a parallel distributed learning scheme that aims at simulating the internal inverse model in the trajectory formation process; 2 a pulse generator, which is responsible for the creation of muscular synergies; and 3 a limb model based on two joints (two degrees of freedom and six muscle-like actuators, that can accommodate for the biomechanical parameters of the arm. The learning paradigm of the neural controller is based on a pure exploration of the working space with no feedback signal. Kinematics provided by the system have been compared with those obtained in literature from experimental data of humans. Results The model reproduces kinematics of arm movements, with bell-shaped wrist velocity profiles and approximately straight trajectories, and gives rise to the generation of synergies for the execution of movements. The model allows achieving amplitude and direction errors of respectively 0.52 cm and 0.2 radians. Curvature values are similar to those encountered in experimental measures with humans. The neural controller also manages environmental modifications such as the insertion of different force fields acting on the end-effector. Conclusion The proposed system has been shown to properly simulate the development of

Bio-Inspired Photon Absorption and Energy Transfer for Next Generation Photovoltaic Devices

Science.gov (United States)

Magsi, Komal

Nature's solar energy harvesting system, photosynthesis, serves as a model for photon absorption, spectra broadening, and energy transfer. Photosynthesis harvests light far differently than photovoltaic cells. These differences offer both engineering opportunity and scientific challenges since not all of the natural photon absorption mechanisms have been understood. In return, solar cells can be a very sensitive probe for the absorption characteristics of molecules capable of transferring charge to a conductive interface. The objective of this scientific work is the advancement of next generation photovoltaics through the development and application of natural photo-energy transfer processes. Two scientific methods were used in the development and application of enhancing photon absorption and transfer. First, a detailed analysis of photovoltaic front surface fluorescent spectral modification and light scattering by hetero-structure was conducted. Phosphor based spectral down-conversion is a well-known laser technology. The theoretical calculations presented here indicate that parasitic losses and light scattering within the spectral range are large enough to offset any expected gains. The second approach for enhancing photon absorption is based on bio-inspired mechanisms. Key to the utilization of these natural processes is the development of a detailed scientific understanding and the application of these processes to cost effective systems and devices. In this work both aspects are investigated. Dye type solar cells were prepared and tested as a function of Chlorophyll (or Sodium-Copper Chlorophyllin) and accessory dyes. Forster has shown that the fluorescence ratio of Chlorophyll is modified and broadened by separate photon absorption (sensitized absorption) through interaction with nearby accessory pigments. This work used the dye type solar cell as a diagnostic tool by which to investigate photon absorption and photon energy transfer. These experiments shed

A computational model of conditioning inspired by Drosophila olfactory system.

Science.gov (United States)

Faghihi, Faramarz; Moustafa, Ahmed A; Heinrich, Ralf; Wörgötter, Florentin

2017-03-01

Recent studies have demonstrated that Drosophila melanogaster (briefly Drosophila) can successfully perform higher cognitive processes including second order olfactory conditioning. Understanding the neural mechanism of this behavior can help neuroscientists to unravel the principles of information processing in complex neural systems (e.g. the human brain) and to create efficient and robust robotic systems. In this work, we have developed a biologically-inspired spiking neural network which is able to execute both first and second order conditioning. Experimental studies demonstrated that volume signaling (e.g. by the gaseous transmitter nitric oxide) contributes to memory formation in vertebrates and invertebrates including insects. Based on the existing knowledge of odor encoding in Drosophila, the role of retrograde signaling in memory function, and the integration of synaptic and non-synaptic neural signaling, a neural system is implemented as Simulated fly. Simulated fly navigates in a two-dimensional environment in which it receives odors and electric shocks as sensory stimuli. The model suggests some experimental research on retrograde signaling to investigate neural mechanisms of conditioning in insects and other animals. Moreover, it illustrates a simple strategy to implement higher cognitive capabilities in machines including robots. Copyright © 2016 Elsevier Ltd. All rights reserved.

Biologically inspired emotion recognition from speech

Directory of Open Access Journals (Sweden)

Buscicchio Cosimo

2011-01-01

Full Text Available Abstract Emotion recognition has become a fundamental task in human-computer interaction systems. In this article, we propose an emotion recognition approach based on biologically inspired methods. Specifically, emotion classification is performed using a long short-term memory (LSTM recurrent neural network which is able to recognize long-range dependencies between successive temporal patterns. We propose to represent data using features derived from two different models: mel-frequency cepstral coefficients (MFCC and the Lyon cochlear model. In the experimental phase, results obtained from the LSTM network and the two different feature sets are compared, showing that features derived from the Lyon cochlear model give better recognition results in comparison with those obtained with the traditional MFCC representation.

Integrated low noise low power interface for neural bio-potentials recording and conditioning

Science.gov (United States)

Bottino, Emanuele; Martinoia, Sergio; Valle, Maurizio

2005-06-01

The recent progress in both neurobiology and microelectronics suggests the creation of new, powerful tools to investigate the basic mechanisms of brain functionality. In particular, a lot of efforts are spent by scientific community to define new frameworks devoted to the analysis of in-vitro cultured neurons. One possible approach is recording their spiking activity to monitor the coordinated cellular behaviour and get insights about neural plasticity. Due to the nature of neurons action-potentials, when considering the design of an integrated microelectronic-based recording system, a number of problems arise. First, one would desire to have a high number of recording sites (i.e. several hundreds): this poses constraints on silicon area and power consumption. In this regard, our aim is to integrate-through on-chip post-processing techniques-hundreds of bio-compatible microsensors together with CMOS standard-process low-power (i.e. some tenths of uW per channel) conditioning electronics. Each recording channel is provided with sampling electronics to insure synchronous recording so that, for example, cross-correlation between signals coming from different sites can be performed. Extra-cellular potentials are in the range of [50-150] uV, so a comparison in terms of noise-efficiency was carried out among different architectures and very low-noise pre-amplification electronics (i.e. less than 5 uVrms) was designed. As spikes measurements are made with respect to the voltage of a reference electrode, we opted for an AC-coupled differential-input preamplifier provided with band-pass filtering capability. To achieve this, we implemented large time-constant (up to seconds) integrated components in the preamp feedback path. Thus, we got rid also of random slow-drifting DC-offsets and common mode signals. The paper will present our achievements in the design and implementation of a fully integrated bio-abio interface to record neural spiking activity. In particular

Identification of Non-Linear Structures using Recurrent Neural Networks

DEFF Research Database (Denmark)

Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.

Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....

Identification of Non-Linear Structures using Recurrent Neural Networks

DEFF Research Database (Denmark)

Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.

1995-01-01

Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....

Lightweight mechanical amplifiers for rolled dielectric elastomer actuators and their integration with bio-inspired wing flappers

International Nuclear Information System (INIS)

Lau, Gih-Keong; Lim, Hoong-Ta; Teo, Jing-Ying; Chin, Yao-Wei

2014-01-01

Dielectric elastomer actuators (DEAs) are attractive for use in bio-inspired flapping-wing robots because they have high work density (specific energy) and can produce a large actuation strain. Although the active membrane of a dielectric elastomer is lightweight, the support structure that pre-tensions the elastomeric membrane is massive and it lowers the overall work density. If the DEA is to be used successfully to drive flapping-wing robots, its support structure must be as lightweight as possible. In this work, we designed, analysed, and developed a lightweight shell using a cross-ply laminate of carbon fibre reinforced polymer (CFRP) to pre-strain a rolled DEA. The CFRP shell was shown to weigh 24.3% of the total mass for the whole DEA assembly, while providing up to 35.0% axial pre-strain to a rolled DEA (BJB-5005 silicone rubber). This DEA assembly using the CFRP shell achieved 30.9% of the theoretical work density for a BJB-TC5005 membrane at 33.5 MV m −1 . In comparison, spring rolls with a massive spring core were reported with overall work density merely 10–20% of the maximum value. Furthermore, this CFRP shell can amplify an axial DEA stroke into a larger transverse shell deformation. With these deformation characteristics, this CFRP shell and a rolled DEA were successfully integrated with an insect-inspired thoracic mechanism and they were shown to be feasible to drive it for a flapping wing. (paper)

Two uncommon uses of Bio-Oss for GTR and ridge augmentation following extractions: two case reports.

Science.gov (United States)

Pripatnanont, Prisana; Nuntanaranont, Thongchai; Chungpanich, Supis

2002-06-01

Bio-Oss is natural bovine bone mineral, which has the property of bone conduction. It is recommended to be used in two- or three-walled bony defects with an ample supply of pleuripotential cells. Two cases are reported. The first was an intentional replantation, because of previous trauma, of a hopeless tooth affected with severe periodontitis. The tooth was replanted after complete elimination of granulation tissue. Bio-Oss, together with a guided tissue regeneration (GTR) membrane, was used to enhance periodontal regeneration. After 2 years of follow-up, the replanted tooth was quite stable. In the second case, Bio-Oss, together with bone taken from the retromolar area, was used in a sinus lift grafting procedure after the removal of two supernumerary teeth from the floor of the maxillary sinus. Four months after grafting, an orthodontic treatment was applied to move the two adjacent teeth through the grafted site and align them in the proper position. The clinical results of the two cases were satisfactory.

Fabrication, testing and modeling of a new flexible armor inspired from natural fish scales and osteoderms

International Nuclear Information System (INIS)

Chintapalli, Ravi Kiran; Mirkhalaf, Mohammad; Dastjerdi, Ahmad Khayer; Barthelat, Francois

2014-01-01

Crocodiles, armadillo, turtles, fish and many other animal species have evolved flexible armored skins in the form of hard scales or osteoderms, which can be described as hard plates of finite size embedded in softer tissues. The individual hard segments provide protection from predators, while the relative motion of these segments provides the flexibility required for efficient locomotion. In this work, we duplicated these broad concepts in a bio-inspired segmented armor. Hexagonal segments of well-defined size and shape were carved within a thin glass plate using laser engraving. The engraved plate was then placed on a soft substrate which simulated soft tissues, and then punctured with a sharp needle mounted on a miniature loading stage. The resistance of our segmented armor was significantly higher when smaller hexagons were used, and our bio-inspired segmented glass displayed an increase in puncture resistance of up to 70% compared to a continuous plate of glass of the same thickness. Detailed structural analyses aided by finite elements revealed that this extraordinary improvement is due to the reduced span of individual segments, which decreases flexural stresses and delays fracture. This effect can however only be achieved if the plates are at least 1000 stiffer than the underlying substrate, which is the case for natural armor systems. Our bio-inspired system also displayed many of the attributes of natural armors: flexible, robust with ‘multi-hit’ capabilities. This new segmented glass therefore suggests interesting bio-inspired strategies and mechanisms which could be systematically exploited in high-performance flexible armors. This study also provides new insights and a better understanding of the mechanics of natural armors such as scales and osteoderms. (paper)

Failure detection studies by layered neural network

International Nuclear Information System (INIS)

Ciftcioglu, O.; Seker, S.; Turkcan, E.

1991-06-01

Failure detection studies by layered neural network (NN) are described. The particular application area is an operating nuclear power plant and the failure detection is of concern as result of system surveillance in real-time. The NN system is considered to be consisting of 3 layers, one of which being hidden, and the NN parameters are determined adaptively by the backpropagation (BP) method, the process being the training phase. Studies are performed using the power spectra of the pressure signal of the primary system of an operating nuclear power plant of PWR type. The studies revealed that, by means of NN approach, failure detection can effectively be carried out using the redundant information as well as this is the case in this work; namely, from measurement of the primary pressure signals one can estimate the primary system coolant temperature and hence the deviation from the operational temperature state, the operational status identified in the training phase being referred to as normal. (author). 13 refs.; 4 figs.; 2 tabs

Nature-inspired computing and optimization theory and applications

CERN Document Server

Yang, Xin-She; Nakamatsu, Kazumi

2017-01-01

The book provides readers with a snapshot of the state of the art in the field of nature-inspired computing and its application in optimization. The approach is mainly practice-oriented: each bio-inspired technique or algorithm is introduced together with one of its possible applications. Applications cover a wide range of real-world optimization problems: from feature selection and image enhancement to scheduling and dynamic resource management, from wireless sensor networks and wiring network diagnosis to sports training planning and gene expression, from topology control and morphological filters to nutritional meal design and antenna array design. There are a few theoretical chapters comparing different existing techniques, exploring the advantages of nature-inspired computing over other methods, and investigating the mixing time of genetic algorithms. The book also introduces a wide range of algorithms, including the ant colony optimization, the bat algorithm, genetic algorithms, the collision-based opti...

Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot

Directory of Open Access Journals (Sweden)

Eduard eGrinke

2015-10-01

Full Text Available Walking animals, like insects, with little neural computing can effectively perform complex behaviors. They can walk around their environment, escape from corners/deadlocks, and avoid or climb over obstacles. While performing all these behaviors, they can also adapt their movements to deal with an unknown situation. As a consequence, they successfully navigate through their complex environment. The versatile and adaptive abilities are the result of an integration of several ingredients embedded in their sensorimotor loop. Biological studies reveal that the ingredients include neural dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a walking robot is a challenging task. In this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural mechanisms with plasticity, sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent network consisting of two fully connected neurons. Online correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors in the network to generate different turning angles with short-term memory for a biomechanical walking robot. The turning information is transmitted as descending steering signals to the locomotion control which translates the signals into motor actions. As a result, the robot can walk around and adapt its turning angle for avoiding obstacles in different situations as well as escaping from sharp corners or deadlocks. Using backbone joint control embedded in the locomotion control allows the robot to climb over small obstacles. Consequently, it can successfully explore and navigate in complex environments.

Concepts and Relations in Neurally Inspired In Situ Concept-Based Computing.

Science.gov (United States)

van der Velde, Frank

2016-01-01

In situ concept-based computing is based on the notion that conceptual representations in the human brain are "in situ." In this way, they are grounded in perception and action. Examples are neuronal assemblies, whose connection structures develop over time and are distributed over different brain areas. In situ concepts representations cannot be copied or duplicated because that will disrupt their connection structure, and thus the meaning of these concepts. Higher-level cognitive processes, as found in language and reasoning, can be performed with in situ concepts by embedding them in specialized neurally inspired "blackboards." The interactions between the in situ concepts and the blackboards form the basis for in situ concept computing architectures. In these architectures, memory (concepts) and processing are interwoven, in contrast with the separation between memory and processing found in Von Neumann architectures. Because the further development of Von Neumann computing (more, faster, yet power limited) is questionable, in situ concept computing might be an alternative for concept-based computing. In situ concept computing will be illustrated with a recently developed BABI reasoning task. Neurorobotics can play an important role in the development of in situ concept computing because of the development of in situ concept representations derived in scenarios as needed for reasoning tasks. Neurorobotics would also benefit from power limited and in situ concept computing.

Bio-inspired step-climbing in a hexapod robot

International Nuclear Information System (INIS)

Chou, Ya-Cheng; Yu, Wei-Shun; Huang, Ke-Jung; Lin, Pei-Chun

2012-01-01

Inspired by the observation that the cockroach changes from a tripod gait to a different gait for climbing high steps, we report on the design and implementation of a novel, fully autonomous step-climbing maneuver, which enables a RHex-style hexapod robot to reliably climb a step up to 230% higher than the length of its leg. Similar to the climbing strategy most used by cockroaches, the proposed maneuver is composed of two stages. The first stage is the ‘rearing stage,’ inclining the body so the front side of the body is raised and it is easier for the front legs to catch the top of the step, followed by the ‘rising stage,’ maneuvering the body's center of mass to the top of the step. Two infrared range sensors are installed on the front of the robot to detect the presence of the step and its orientation relative to the robot's heading, so that the robot can perform automatic gait transition, from walking to step-climbing, as well as correct its initial tilt approaching posture. An inclinometer is utilized to measure body inclination and to compute step height, thus enabling the robot to adjust its gait automatically, in real time, and to climb steps of different heights and depths successfully. The algorithm is applicable for the robot to climb various rectangular obstacles, including a narrow bar, a bar and a step (i.e. a bar of infinite width). The performance of the algorithm is evaluated experimentally, and the comparison of climbing strategies and climbing behaviors in biological and robotic systems is discussed. (paper)

A novel nature inspired firefly algorithm with higher order neural network: Performance analysis

Directory of Open Access Journals (Sweden)

Janmenjoy Nayak

2016-03-01

Full Text Available The applications of both Feed Forward Neural network and Multilayer perceptron are very diverse and saturated. But the linear threshold unit of feed forward networks causes fast learning with limited capabilities, while due to multilayering, the back propagation of errors exhibits slow training speed in MLP. So, a higher order network can be constructed by correlating between the input variables to perform nonlinear mapping using the single layer of input units for overcoming the above drawbacks. In this paper, a Firefly based higher order neural network has been proposed for data classification for maintaining fast learning and avoids the exponential increase of processing units. A vast literature survey has been conducted to review the state of the art of the previous developed models. The performance of the proposed method has been tested with various benchmark datasets from UCI machine learning repository and compared with the performance of other established models. Experimental results imply that the proposed method is fast, steady, reliable and provides better classification accuracy than others.

Two-chamber configuration of the bio-nano ECRIS

International Nuclear Information System (INIS)

Uchida, T.; Minezaki, H.; Yoshida, Y.; Biri, S.; Racz, R.; Kitagawa, A.; Muramatsu, M.; Kato, Y.; Asaji, T.; Tanaka, K.

2012-01-01

We are studying the application of the electron cyclotron resonance ion source (ECRIS) for the new materials production on nano-scale. Our main target is the endohedral fullerenes. There are several promising approaches to produce the endohedral fullerenes using an ECRIS. One of them is the ion-ion collision reaction of fullerenes and aliens ions to be encapsulated in the mixture plasma of them. Another way is the shooting of ion beam into a pre-prepared fullerene layer. In this study, the new device configuration of the Bio-Nano ECRIS is reported which allows the application of both methods. The basic concept and the preliminary results using Ar gas and fullerenes plasmas are described. The paper is followed by the associated poster. (authors)

A One-Layer Recurrent Neural Network for Real-Time Portfolio Optimization With Probability Criterion.

Science.gov (United States)

Liu, Qingshan; Dang, Chuangyin; Huang, Tingwen

2013-02-01

This paper presents a decision-making model described by a recurrent neural network for dynamic portfolio optimization. The portfolio-optimization problem is first converted into a constrained fractional programming problem. Since the objective function in the programming problem is not convex, the traditional optimization techniques are no longer applicable for solving this problem. Fortunately, the objective function in the fractional programming is pseudoconvex on the feasible region. It leads to a one-layer recurrent neural network modeled by means of a discontinuous dynamic system. To ensure the optimal solutions for portfolio optimization, the convergence of the proposed neural network is analyzed and proved. In fact, the neural network guarantees to get the optimal solutions for portfolio-investment advice if some mild conditions are satisfied. A numerical example with simulation results substantiates the effectiveness and illustrates the characteristics of the proposed neural network.

Two layer powder pressing

International Nuclear Information System (INIS)

Schreiner, H.

1979-01-01

First, significance and advantages of sintered materials consisting of two layers are pointed out. By means of the two layer powder pressing technique metal powders are formed resulting in compacts with high accuracy of shape and mass. Attributes of basic powders, different filling methods and pressing techniques are discussed. The described technique is supposed to find further applications in the field of two layer compacts in the near future

Multistability in bidirectional associative memory neural networks

International Nuclear Information System (INIS)

Huang Gan; Cao Jinde

2008-01-01

In this Letter, the multistability issue is studied for Bidirectional Associative Memory (BAM) neural networks. Based on the existence and stability analysis of the neural networks with or without delay, it is found that the 2n-dimensional networks can have 3 n equilibria and 2 n equilibria of them are locally exponentially stable, where each layer of the BAM network has n neurons. Furthermore, the results has been extended to (n+m)-dimensional BAM neural networks, where there are n and m neurons on the two layers respectively. Finally, two numerical examples are presented to illustrate the validity of our results

Multistability in bidirectional associative memory neural networks

Science.gov (United States)

Huang, Gan; Cao, Jinde

2008-04-01

In this Letter, the multistability issue is studied for Bidirectional Associative Memory (BAM) neural networks. Based on the existence and stability analysis of the neural networks with or without delay, it is found that the 2 n-dimensional networks can have 3 equilibria and 2 equilibria of them are locally exponentially stable, where each layer of the BAM network has n neurons. Furthermore, the results has been extended to (n+m)-dimensional BAM neural networks, where there are n and m neurons on the two layers respectively. Finally, two numerical examples are presented to illustrate the validity of our results.

An IPMC-enabled bio-inspired bending/twisting fin for underwater applications

Science.gov (United States)

Palmre, Viljar; Hubbard, Joel J.; Fleming, Maxwell; Pugal, David; Kim, Sungjun; Kim, Kwang J.; Leang, Kam K.

2013-01-01

This paper discusses the design, fabrication, and characterization of an ionic polymer-metal composite (IPMC) actuator-based bio-inspired active fin capable of bending and twisting motion. It is pointed out that IPMC strip actuators are used in the simple cantilever configuration to create simple bending (flapping-like) motion for propulsion in underwater autonomous systems. However, the resulting motion is a simple 1D bending and performance is rather limited. To enable more complex deformation, such as the flapping (pitch and heaving) motion of real pectoral and caudal fish fins, a new approach which involves molding or integrating IPMC actuators into a soft boot material to create an active control surface (called a ‘fin’) is presented. The fin can be used to realize complex deformation depending on the orientation and placement of the actuators. In contrast to previously created IPMCs with patterned electrodes for the same purpose, the proposed design avoids (1) the more expensive process of electroless plating platinum all throughout the surface of the actuator and (2) the need for specially patterning the electrodes. Therefore, standard shaped IPMC actuators such as those with rectangular dimensions with varying thicknesses can be used. One unique advantage of the proposed structural design is that custom shaped fins and control surfaces can be easily created without special materials processing. The molding process is cost effective and does not require functionalizing or ‘activating’ the boot material similar to creating IPMCs. For a prototype fin (90 mm wide × 60 mm long × 1.5 mm thick), the measured maximum tip displacement was approximately 44 mm and the twist angle of the fin exceeded 10°. Lift and drag measurements in water where the prototype fin with an airfoil profile was dragged through water at a velocity of 21 cm s-1 showed that the lift and drag forces can be affected by controlling the IPMCs embedded into the fin structure. These

Effect of pre-tension on the peeling behavior of a bio-inspired nano-film and a hierarchical adhesive structure

Science.gov (United States)

Peng, Zhilong; Chen, Shaohua

2012-10-01

Inspired by the reversible adhesion behaviors of geckos, the effects of pre-tension in a bio-inspired nano-film and a hierarchical structure on adhesion are studied theoretically. In the case with a uniformly distributing pre-tension in a spatula-like nano-film under peeling, a closed-form solution to a critical peeling angle is derived, below or above which the peel-off force is enhanced or reduced, respectively, compared with the case without pre-tension. The effects of a non-uniformly distributing pre-tension on adhesion are further investigated for both a spatula-like nano-film and a hierarchical structure-like gecko's seta. Compared with the case without pre-tension, the pre-tension, no matter uniform or non-uniform, can increase the adhesion force not only for the spatula-like nano-film but also for the hierarchical structure at a small peeling angle, while decrease it at a relatively large peeling angle. Furthermore, if the pre-tension is large enough, the effective adhesion energy of a hierarchical structure tends to vanish at a critical peeling angle, which results in spontaneous detachment of the hierarchical structure from the substrate. The present theoretical predictions can not only give some explanations on the existing experimental observation that gecko's seta always detaches at a specific angle and no apparent adhesion force can be detected above the critical angle but also provide a deep understanding for the reversible adhesion mechanism of geckos and be helpful to the design of biomimetic reversible adhesives.

Optimal neural computations require analog processors

Energy Technology Data Exchange (ETDEWEB)

Beiu, V.

1998-12-31

This paper discusses some of the limitations of hardware implementations of neural networks. The authors start by presenting neural structures and their biological inspirations, while mentioning the simplifications leading to artificial neural networks. Further, the focus will be on hardware imposed constraints. They will present recent results for three different alternatives of parallel implementations of neural networks: digital circuits, threshold gate circuits, and analog circuits. The area and the delay will be related to the neurons` fan-in and to the precision of their synaptic weights. The main conclusion is that hardware-efficient solutions require analog computations, and suggests the following two alternatives: (i) cope with the limitations imposed by silicon, by speeding up the computation of the elementary silicon neurons; (2) investigate solutions which would allow the use of the third dimension (e.g. using optical interconnections).

Light and colour as analytical detection tools: a journey into the periodic table using polyamines to bio-inspired systems as chemosensors.

Science.gov (United States)

Lodeiro, Carlos; Capelo, José Luis; Mejuto, Juan Carlos; Oliveira, Elisabete; Santos, Hugo M; Pedras, Bruno; Nuñez, Cristina

2010-08-01

This critical review describes some developments on the chemistry of fluorescent and colorimetric molecular probes or chemosensors, based on polyamines and associated compounds having oxygen and/or sulfur as donor atoms. The reported systems are essentially based on some selected published work in this field in the last five years, and in the work developed by the authors from 2000 onwards. Some interesting properties beyond sensing molecules, ions or/and cations by fluorescence, colorimetry as well as by MALDI-TOF MS spectrometry can arise from these systems. A short brief on different examples activated by PET (photoinduced electron transfer), ICT (internal charge transfer) and EET (electronic energy transfer) phenomena will be provided. Finally the introduction of bio-inspired compounds derived from emissive amino acid or short peptide systems and nanoparticle devices to detect metal ions will be reviewed (202 references).

Production and detailed characterization of bio-oil from fast pyrolysis of palm kernel shell

International Nuclear Information System (INIS)

Asadullah, Mohammad; Ab Rasid, Nurul Suhada; Kadir, Sharifah Aishah Syed A.; Azdarpour, Amin

2013-01-01

Bio-oil has been produced from palm kernel shell in a fluidized bed reactor. The process conditions were optimized and the detailed characteristics of bio-oil were carried out. The higher feeding rate and higher gas flow rate attributed to higher bio-oil yield. The maximum mass fraction of biomass (57%) converted to bio-oil at 550 °C when 2 L min −1 of gas and 10 g min −1 of biomass were fed. The bio-oil produced up to 500 °C existed in two distinct phases, while it formed one homogeneous phase when it was produced above 500 °C. The higher heating value of bio-oil produced at 550 °C was found to be 23.48 MJ kg −1 . As GC–MS data shows, the area ratio of phenol is the maximum among the area ratio of identified compounds in 550 °C bio-oil. The UV–Fluorescence absorption, which is the indication of aromatic content, is also the highest in 550 °C bio-oil. -- Highlights: • Maximum 56 wt% yield of bio-oil was obtained at 550 °C from palm kernel shell. • Two layer of bio-oil was observed up to 500 °C, while it was one layer above 500 °C. • Bio-oil from palm kernel shell provides more than 40% area ratio of phenol in GC–MS analysis. • The calorific value of palm kernel shell bio-oil is higher than other bio-oil

Bio-inspired Plasmonic Nanoarchitectured Hybrid System Towards Enhanced Far Red-to-Near Infrared Solar Photocatalysis

Science.gov (United States)

Yan, Runyu; Chen, Min; Zhou, Han; Liu, Tian; Tang, Xingwei; Zhang, Ke; Zhu, Hanxing; Ye, Jinhua; Zhang, Di; Fan, Tongxiang

2016-01-01

Solar conversion to fuels or to electricity in semiconductors using far red-to-near infrared (NIR) light, which accounts for about 40% of solar energy, is highly significant. One main challenge is the development of novel strategies for activity promotion and new basic mechanisms for NIR response. Mother Nature has evolved to smartly capture far red-to-NIR light via their intelligent systems due to unique micro/nanoarchitectures, thus motivating us for biomimetic design. Here we report the first demonstration of a new strategy, based on adopting nature’s far red-to-NIR responsive architectures for an efficient bio-inspired photocatalytic system. The system is constructed by controlled assembly of light-harvesting plasmonic nanoantennas onto a typical photocatalytic unit with butterfly wings’ 3D micro/nanoarchitectures. Experiments and finite-difference time-domain (FDTD) simulations demonstrate the structural effects on obvious far red-to-NIR photocatalysis enhancement, which originates from (1) Enhancing far red-to-NIR (700~1200 nm) harvesting, up to 25%. (2) Enhancing electric-field amplitude of localized surface plasmon (LSPs) to more than 3.5 times than that of the non-structured one, which promotes the rate of electron-hole pair formation, thus substantially reinforcing photocatalysis. This proof-of-concept study provides a new methodology for NIR photocatalysis and would potentially guide future conceptually new NIR responsive system designs.

Enhancing thermal reliability of fiber-optic sensors for bio-inspired applications at ultra-high temperatures

Science.gov (United States)

Kang, Donghoon; Kim, Heon-Young; Kim, Dae-Hyun

2014-07-01

The rapid growth of bio-(inspired) sensors has led to an improvement in modern healthcare and human-robot systems in recent years. Higher levels of reliability and better flexibility, essential features of these sensors, are very much required in many application fields (e.g. applications at ultra-high temperatures). Fiber-optic sensors, and fiber Bragg grating (FBG) sensors in particular, are being widely studied as suitable sensors for improved structural health monitoring (SHM) due to their many merits. To enhance the thermal reliability of FBG sensors, thermal sensitivity, generally expressed as αf + ξf and considered a constant, should be investigated more precisely. For this purpose, the governing equation of FBG sensors is modified using differential derivatives between the wavelength shift and the temperature change in this study. Through a thermal test ranging from RT to 900 °C, the thermal sensitivity of FBG sensors is successfully examined and this guarantees thermal reliability of FBG sensors at ultra-high temperatures. In detail, αf + ξf has a non-linear dependence on temperature and varies from 6.0 × 10-6 °C-1 (20 °C) to 10.6 × 10-6 °C-1 (650 °C). Also, FBGs should be carefully used for applications at ultra-high temperatures due to signal disappearance near 900 °C.

Bio-inspired Plasmonic Nanoarchitectured Hybrid System Towards Enhanced Far Red-to-Near Infrared Solar Photocatalysis.

Science.gov (United States)

Yan, Runyu; Chen, Min; Zhou, Han; Liu, Tian; Tang, Xingwei; Zhang, Ke; Zhu, Hanxing; Ye, Jinhua; Zhang, Di; Fan, Tongxiang

2016-01-28

Solar conversion to fuels or to electricity in semiconductors using far red-to-near infrared (NIR) light, which accounts for about 40% of solar energy, is highly significant. One main challenge is the development of novel strategies for activity promotion and new basic mechanisms for NIR response. Mother Nature has evolved to smartly capture far red-to-NIR light via their intelligent systems due to unique micro/nanoarchitectures, thus motivating us for biomimetic design. Here we report the first demonstration of a new strategy, based on adopting nature's far red-to-NIR responsive architectures for an efficient bio-inspired photocatalytic system. The system is constructed by controlled assembly of light-harvesting plasmonic nanoantennas onto a typical photocatalytic unit with butterfly wings' 3D micro/nanoarchitectures. Experiments and finite-difference time-domain (FDTD) simulations demonstrate the structural effects on obvious far red-to-NIR photocatalysis enhancement, which originates from (1) Enhancing far red-to-NIR (700~1200 nm) harvesting, up to 25%. (2) Enhancing electric-field amplitude of localized surface plasmon (LSPs) to more than 3.5 times than that of the non-structured one, which promotes the rate of electron-hole pair formation, thus substantially reinforcing photocatalysis. This proof-of-concept study provides a new methodology for NIR photocatalysis and would potentially guide future conceptually new NIR responsive system designs.

Righting and turning in mid-air using appendage inertia: reptile tails, analytical models and bio-inspired robots

International Nuclear Information System (INIS)

Jusufi, A; Full, R J; Kawano, D T; Libby, T

2010-01-01

Unlike the falling cat, lizards can right themselves in mid-air by a swing of their large tails in one direction causing the body to rotate in the other. Here, we developed a new three-dimensional analytical model to investigate the effectiveness of tails as inertial appendages that change body orientation. We anchored our model using the morphological parameters of the flat-tailed house gecko Hemidactylus platyurus. The degree of roll in air righting and the amount of yaw in mid-air turning directly measured in house geckos matched the model's results. Our model predicted an increase in body roll and turning as tails increase in length relative to the body. Tails that swung from a near orthogonal plane relative to the body (i.e. 0-30 0 from vertical) were the most effective at generating body roll, whereas tails operating at steeper angles (i.e. 45-60 0 ) produced only half the rotation. To further test our analytical model's predictions, we built a bio-inspired robot prototype. The robot reinforced how effective attitude control can be attained with simple movements of an inertial appendage.

Righting and turning in mid-air using appendage inertia: reptile tails, analytical models and bio-inspired robots

Energy Technology Data Exchange (ETDEWEB)

Jusufi, A; Full, R J [Department of Integrative Biology, University of California, Berkeley, CA 94720-3140 (United States); Kawano, D T [Department of Mechanical Engineering, University of California, Berkeley, CA 94720-1740 (United States); Libby, T, E-mail: ardianj@berkeley.ed [Center for Interdisciplinary Bio-inspiration in Education and Research, University of California, Berkeley, CA 94720-3140 (United States)

2010-12-15

Unlike the falling cat, lizards can right themselves in mid-air by a swing of their large tails in one direction causing the body to rotate in the other. Here, we developed a new three-dimensional analytical model to investigate the effectiveness of tails as inertial appendages that change body orientation. We anchored our model using the morphological parameters of the flat-tailed house gecko Hemidactylus platyurus. The degree of roll in air righting and the amount of yaw in mid-air turning directly measured in house geckos matched the model's results. Our model predicted an increase in body roll and turning as tails increase in length relative to the body. Tails that swung from a near orthogonal plane relative to the body (i.e. 0-30{sup 0} from vertical) were the most effective at generating body roll, whereas tails operating at steeper angles (i.e. 45-60{sup 0}) produced only half the rotation. To further test our analytical model's predictions, we built a bio-inspired robot prototype. The robot reinforced how effective attitude control can be attained with simple movements of an inertial appendage.

A one-layer recurrent neural network for non-smooth convex optimization subject to linear inequality constraints

International Nuclear Information System (INIS)

Liu, Xiaolan; Zhou, Mi

2016-01-01

In this paper, a one-layer recurrent network is proposed for solving a non-smooth convex optimization subject to linear inequality constraints. Compared with the existing neural networks for optimization, the proposed neural network is capable of solving more general convex optimization with linear inequality constraints. The convergence of the state variables of the proposed neural network to achieve solution optimality is guaranteed as long as the designed parameters in the model are larger than the derived lower bounds.

Boundary-Layer Control: In Memory of Bill Reynolds

Science.gov (United States)

Kim, John

2004-11-01

Professor Bill Reynolds (1933-2004) inspired many students and colleagues with his never-ending curiosity and thought-provoking ideas. Bill's relentless energy, together with his hallmark can-do character and do-it-yourself attitude, led to many seminal contributions to mechanical engineering in general, and fluid mechanics in particular. He has left a lasting impact on many of us, especially for those who had the privilege of working closely with him. Some of my current work on boundary-layer control, the use of neural networks in particular, were inspired by many discussions with Bill. He was among the first to see the potential of control-theoretic approaches for flow control, which has become the main thrust of my current research. Without his continued encouragement, I would not have been deeply involved in this line of research; and perhaps, we would not have seen the current flurry of research activities in applying modern control theories to flow control. In memory of Bill Reynolds, who himself has contributed much to flow control, an analysis of boundary-layer control from a linear system perspective will be presented.

A One-Layer Recurrent Neural Network for Pseudoconvex Optimization Problems With Equality and Inequality Constraints.

Science.gov (United States)

Qin, Sitian; Yang, Xiudong; Xue, Xiaoping; Song, Jiahui

2017-10-01

Pseudoconvex optimization problem, as an important nonconvex optimization problem, plays an important role in scientific and engineering applications. In this paper, a recurrent one-layer neural network is proposed for solving the pseudoconvex optimization problem with equality and inequality constraints. It is proved that from any initial state, the state of the proposed neural network reaches the feasible region in finite time and stays there thereafter. It is also proved that the state of the proposed neural network is convergent to an optimal solution of the related problem. Compared with the related existing recurrent neural networks for the pseudoconvex optimization problems, the proposed neural network in this paper does not need the penalty parameters and has a better convergence. Meanwhile, the proposed neural network is used to solve three nonsmooth optimization problems, and we make some detailed comparisons with the known related conclusions. In the end, some numerical examples are provided to illustrate the effectiveness of the performance of the proposed neural network.

HyperCell: A Bio-inspired Design Framework for Real-time Interactive Architectures

Directory of Open Access Journals (Sweden)

Jia-Rey Chang

2018-01-01

Full Text Available This pioneering research focuses on Biomimetic Interactive Architecture using “Computation”, “Embodiment”, and “Biology” to generate an intimate embodied convergence to propose a novel rule-based design framework for creating organic architectures composed of swarm-based intelligent components. Furthermore, the research boldly claims that Interactive Architecture should emerge as the next truly Organic Architecture. As the world and society are dynamically changing, especially in this digital era, the research dares to challenge the Utilitas, Firmitas, and Venustas of the traditional architectural Weltanschauung, and rejects them by adopting the novel notion that architecture should be dynamic, fluid, and interactive. This project reflects a trajectory from the 1960’s with the advent of the avant-garde architectural design group, Archigram, and its numerous intriguing and pioneering visionary projects. Archigram’s non-standard, mobile, and interactive projects profoundly influenced a new generation of architects to explore the connection between technology and their architectural projects. This research continues this trend of exploring novel design thinking and the framework of Interactive Architecture by discovering the interrelationship amongst three major topics: “Computation”, “Embodiment”, and “Biology”. The project aims to elucidate pioneering research combining these three topics in one discourse: “Bio-inspired digital architectural design”. These three major topics will be introduced in this Summary.   “Computation”, is any type of calculation that includes both arithmetical and nonarithmetical steps and follows a well-defined model understood and described as, for example, an algorithm. But, in this research, refers to the use of data storage, parametric design application, and physical computing for developing informed architectural designs. “Form” has always been the most critical focus in

Phase separation of bio-oil produced by co-pyrolysis of corn cobs and polypropylene

Science.gov (United States)

Supramono, D.; Julianto; Haqqyana; Setiadi, H.; Nasikin, M.

2017-11-01

In co-pyrolysis of biomass-plastics, bio-oil produced contains both oxygenated and non-oxygenated compounds. High oxygen composition is responsible for instability and low heating value of bio-oil and high acid content for corrosiveness. Aims of the present work are to evaluate possibilities of achieving phase separation between oxygenated and non-oxygenated compounds in bio-oil using a proposed stirred tank reactor and to achieve synergistic effects on bio-oil yield and non-oxygenated compound layer yield. Separation of bio-oil into two layers, i.e. that containing oxygenated compounds (polar phase) and non-oxygenated compounds (non-polar phase) is important to obtain pure non-polar phase ready for the next processing of hydrogenation and used directly as bio-fuel. There has been no research work on co-pyrolysis of biomass-plastic considering possibility of phase separation of bio-oil. The present work is proposing a stirred tank reactor for co-pyrolysis with nitrogen injection, which is capable of tailoring co-pyrolysis conditions leading to low viscosity and viscosity asymmetry, which induce phase separation between polar phase and non-polar phase. The proposed reactor is capable of generating synergistic effect on bio-oil and non-polar yields as the composition of PP in feed is more than 25% weight in which non-polar layers contain only alkanes, alkenes, cycloalkanes and cycloalkenes.

Appropriateness of Dropout Layers and Allocation of Their 0.5 Rates across Convolutional Neural Networks for CIFAR-10, EEACL26, and NORB Datasets

OpenAIRE

Romanuke Vadim V.

2017-01-01

A technique of DropOut for preventing overfitting of convolutional neural networks for image classification is considered in the paper. The goal is to find a rule of rationally allocating DropOut layers of 0.5 rate to maximise performance. To achieve the goal, two common network architectures are used having either 4 or 5 convolutional layers. Benchmarking is fulfilled with CIFAR-10, EEACL26, and NORB datasets. Initially, series of all admissible versions for allocation of DropOut layers are ...

Electronic and optoelectronic materials and devices inspired by nature

Science.gov (United States)

Meredith, P.; Bettinger, C. J.; Irimia-Vladu, M.; Mostert, A. B.; Schwenn, P. E.

2013-03-01

Inorganic semiconductors permeate virtually every sphere of modern human existence. Micro-fabricated memory elements, processors, sensors, circuit elements, lasers, displays, detectors, etc are ubiquitous. However, the dawn of the 21st century has brought with it immense new challenges, and indeed opportunities—some of which require a paradigm shift in the way we think about resource use and disposal, which in turn directly impacts our ongoing relationship with inorganic semiconductors such as silicon and gallium arsenide. Furthermore, advances in fields such as nano-medicine and bioelectronics, and the impending revolution of the ‘ubiquitous sensor network’, all require new functional materials which are bio-compatible, cheap, have minimal embedded manufacturing energy plus extremely low power consumption, and are mechanically robust and flexible for integration with tissues, building structures, fabrics and all manner of hosts. In this short review article we summarize current progress in creating materials with such properties. We focus primarily on organic and bio-organic electronic and optoelectronic systems derived from or inspired by nature, and outline the complex charge transport and photo-physics which control their behaviour. We also introduce the concept of electrical devices based upon ion or proton flow (‘ionics and protonics’) and focus particularly on their role as a signal interface with biological systems. Finally, we highlight recent advances in creating working devices, some of which have bio-inspired architectures, and summarize the current issues, challenges and potential solutions. This is a rich new playground for the modern materials physicist.

High-performance mussel-inspired adhesives of reduced complexity.

Science.gov (United States)

Ahn, B Kollbe; Das, Saurabh; Linstadt, Roscoe; Kaufman, Yair; Martinez-Rodriguez, Nadine R; Mirshafian, Razieh; Kesselman, Ellina; Talmon, Yeshayahu; Lipshutz, Bruce H; Israelachvili, Jacob N; Waite, J Herbert

2015-10-19

Despite the recent progress in and demand for wet adhesives, practical underwater adhesion remains limited or non-existent for diverse applications. Translation of mussel-inspired wet adhesion typically entails catechol functionalization of polymers and/or polyelectrolytes, and solution processing of many complex components and steps that require optimization and stabilization. Here we reduced the complexity of a wet adhesive primer to synthetic low-molecular-weight catecholic zwitterionic surfactants that show very strong adhesion (∼50 mJ m(-2)) and retain the ability to coacervate. This catecholic zwitterion adheres to diverse surfaces and self-assembles into a molecularly smooth, thin (adhesive for nanofabrication. This study significantly simplifies bio-inspired themes for wet adhesion by combining catechol with hydrophobic and electrostatic functional groups in a small molecule.

Selection of hidden layer nodes in neural networks by statistical tests

International Nuclear Information System (INIS)

Ciftcioglu, Ozer

1992-05-01

A statistical methodology for selection of the number of hidden layer nodes in feedforward neural networks is described. The method considers the network as an empirical model for the experimental data set subject to pattern classification so that the selection process becomes a model estimation through parameter identification. The solution is performed for an overdetermined estimation problem for identification using nonlinear least squares minimization technique. The number of the hidden layer nodes is determined as result of hypothesis testing. Accordingly the redundant network structure with respect to the number of parameters is avoided and the classification error being kept to a minimum. (author). 11 refs.; 4 figs.; 1 tab

Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot.

Science.gov (United States)

Grinke, Eduard; Tetzlaff, Christian; Wörgötter, Florentin; Manoonpong, Poramate

2015-01-01

Walking animals, like insects, with little neural computing can effectively perform complex behaviors. For example, they can walk around their environment, escape from corners/deadlocks, and avoid or climb over obstacles. While performing all these behaviors, they can also adapt their movements to deal with an unknown situation. As a consequence, they successfully navigate through their complex environment. The versatile and adaptive abilities are the result of an integration of several ingredients embedded in their sensorimotor loop. Biological studies reveal that the ingredients include neural dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a many degrees-of-freedom (DOFs) walking robot is a challenging task. Thus, in this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural mechanisms with plasticity, exteroceptive sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent neural network consisting of two fully connected neurons. Online correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors) in the network to generate different turning angles with short-term memory for a walking robot. The turning information is transmitted as descending steering signals to the neural locomotion control which translates the signals into motor actions. As a result, the robot can walk around and adapt its turning angle for avoiding obstacles in different situations. The adaptation also enables the robot to effectively escape from sharp corners or deadlocks. Using backbone joint control embedded in the the locomotion control allows the robot to climb over small obstacles

Hardware Acceleration of Adaptive Neural Algorithms.

Energy Technology Data Exchange (ETDEWEB)

James, Conrad D. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2017-11-01

As tradit ional numerical computing has faced challenges, researchers have turned towards alternative computing approaches to reduce power - per - computation metrics and improve algorithm performance. Here, we describe an approach towards non - conventional computing that strengthens the connection between machine learning and neuroscience concepts. The Hardware Acceleration of Adaptive Neural Algorithms (HAANA) project ha s develop ed neural machine learning algorithms and hardware for applications in image processing and cybersecurity. While machine learning methods are effective at extracting relevant features from many types of data, the effectiveness of these algorithms degrades when subjected to real - world conditions. Our team has generated novel neural - inspired approa ches to improve the resiliency and adaptability of machine learning algorithms. In addition, we have also designed and fabricated hardware architectures and microelectronic devices specifically tuned towards the training and inference operations of neural - inspired algorithms. Finally, our multi - scale simulation framework allows us to assess the impact of microelectronic device properties on algorithm performance.

3D Polygon Mesh Compression with Multi Layer Feed Forward Neural Networks

Directory of Open Access Journals (Sweden)

Emmanouil Piperakis

2003-06-01

Full Text Available In this paper, an experiment is conducted which proves that multi layer feed forward neural networks are capable of compressing 3D polygon meshes. Our compression method not only preserves the initial accuracy of the represented object but also enhances it. The neural network employed includes the vertex coordinates, the connectivity and normal information in one compact form, converting the discrete and surface polygon representation into an analytic, solid colloquial. Furthermore, the 3D object in its compressed neural form can be directly - without decompression - used for rendering. The neural compression - representation is viable to 3D transformations without the need of any anti-aliasing techniques - transformations do not disrupt the accuracy of the geometry. Our method does not su.er any scaling problem and was tested with objects of 300 to 107 polygons - such as the David of Michelangelo - achieving in all cases an order of O(b3 less bits for the representation than any other commonly known compression method. The simplicity of our algorithm and the established mathematical background of neural networks combined with their aptness for hardware implementation can establish this method as a good solution for polygon compression and if further investigated, a novel approach for 3D collision, animation and morphing.

Créer de nouveaux écosystèmes durables par des méthodes bio-inspirées : le cas des toits verts extensifs en région méditerranéenne

Directory of Open Access Journals (Sweden)

DUTOIT, Thierry

2016-05-01

Full Text Available Outre l’aspect esthétique, de nombreuses études ont confirmé l’intérêt des toitures végétales dans une perspective de restauration ou de protection de la biodiversité et de l'environnement en milieu urbain. Leur mise en place exige toutefois quelques recommandations quant aux matériaux et techniques utilisés, qui doivent notamment s'inspirer des habitats naturels écologiquement proches. Les objectifs des travaux présentés ici sont d'élaborer et de tester de nouvelles techniques bio-inspirées pour la création de toits verts extensifs en climat méditerranéen sans reccours à l'irrigation permanente.

Bio-Inspired Sensing and Imaging of Polarization Information in Nature

Science.gov (United States)

2008-05-04

Neurobiology of polarization vision,” Trends Neu- rosci. 12, 353–359 (1989). 32. R. Wehner, “‘Matched filters’: neural models of the external world,” J...degrees of polarization,” J. Exp. Biol. 199, 1467–1475 (1996). 47. T. Labhart and E. P. Meyer, Neural mechanisms in insect navigation: polarization...mm by 0.3–0.5 in. 7.6–12.7 mm while a typical ight bulb is 3 in. 76 mm in diameter and needs to be laced at least 7 in. 177.8 mm from the

The application of artificial neural networks to TLD dose algorithm

International Nuclear Information System (INIS)

Moscovitch, M.

1997-01-01

We review the application of feed forward neural networks to multi element thermoluminescence dosimetry (TLD) dose algorithm development. A Neural Network is an information processing method inspired by the biological nervous system. A dose algorithm based on a neural network is a fundamentally different approach from conventional algorithms, as it has the capability to learn from its own experience. The neural network algorithm is shown the expected dose values (output) associated with a given response of a multi-element dosimeter (input) many times.The algorithm, being trained that way, eventually is able to produce its own unique solution to similar (but not exactly the same) dose calculation problems. For personnel dosimetry, the output consists of the desired dose components: deep dose, shallow dose, and eye dose. The input consists of the TL data obtained from the readout of a multi-element dosimeter. For this application, a neural network architecture was developed based on the concept of functional links network (FLN). The FLN concept allowed an increase in the dimensionality of the input space and construction of a neural network without any hidden layers. This simplifies the problem and results in a relatively simple and reliable dose calculation algorithm. Overall, the neural network dose algorithm approach has been shown to significantly improve the precision and accuracy of dose calculations. (authors)

An Adaptive Neural Mechanism for Acoustic Motion Perception with Varying Sparsity.

Science.gov (United States)

Shaikh, Danish; Manoonpong, Poramate

2017-01-01

Biological motion-sensitive neural circuits are quite adept in perceiving the relative motion of a relevant stimulus. Motion perception is a fundamental ability in neural sensory processing and crucial in target tracking tasks. Tracking a stimulus entails the ability to perceive its motion, i.e., extracting information about its direction and velocity. Here we focus on auditory motion perception of sound stimuli, which is poorly understood as compared to its visual counterpart. In earlier work we have developed a bio-inspired neural learning mechanism for acoustic motion perception. The mechanism extracts directional information via a model of the peripheral auditory system of lizards. The mechanism uses only this directional information obtained via specific motor behaviour to learn the angular velocity of unoccluded sound stimuli in motion. In nature however the stimulus being tracked may be occluded by artefacts in the environment, such as an escaping prey momentarily disappearing behind a cover of trees. This article extends the earlier work by presenting a comparative investigation of auditory motion perception for unoccluded and occluded tonal sound stimuli with a frequency of 2.2 kHz in both simulation and practice. Three instances of each stimulus are employed, differing in their movement velocities-0.5°/time step, 1.0°/time step and 1.5°/time step. To validate the approach in practice, we implement the proposed neural mechanism on a wheeled mobile robot and evaluate its performance in auditory tracking.

Characteristics of bio-oil from the pyrolysis of palm kernel shell in a newly developed two-stage pyrolyzer

International Nuclear Information System (INIS)

Oh, Seung-Jin; Choi, Gyung-Goo; Kim, Joo-Sik

2016-01-01

Pyrolysis of palm kernel shell was performed using a two-stage pyrolyzer consisting of an auger reactor and a fluidized bed reactor within the auger reactor temperature range of ∼290–380 °C at the fluidized bed reactor temperature of ∼520 °C, and with a variable residence time of the feed material in the auger reactor. The highest bio-oil yield of the two-stage pyrolysis was ∼56 wt%. The bio-oil derived from the auger reactor contained degradation products of the hemicelluloses of PKS, such as acetic acid, and furfural, whereas the fluidized bed reactor produced a bio-oil with high concentrations of acetic acid and phenol. The auger reactor temperature and the residence time of PKS in the auger reactor had an influence on the acetic acid concentration in the bio-oil, while their changes did not induce an observable trend on the phenol concentration in the bio-oil derived from the fluidized bed reactor. The maximum concentrations of acetic acid and phenol in bio-oil were ∼78 and 12 wt% dry basis, respectively. As a result, it was possible for the two-stage pyrolyzer to separately produce two different bio-oils in one operation without any costly fractionation process of bio-oils. - Highlights: • The two-stage pyrolyzer is composed of an auger and a fluidized bed reactor. • The two-stage pyrolyzer produced two different bio-oils in a single operation. • The maximum bio-oil yield of the two-stage pyrolysis was ∼56 wt%. • The maximum concentration of acetic acid in bio-oil was ∼78 wt% dry basis. • The maximum concentration of phenol in bio-oil was ∼12 wt% dry basis.

Recent developments in bio-inspired sensors fabricated by additive manufacturing technologies

NARCIS (Netherlands)

Krijnen, Gijsbertus J.M.; Sanders, Remco G.P.

2017-01-01

In our work on micro-fabricated hair-sensors, inspired by the flow-sensitive sensors found on crickets, we have made great progress. Initially delivering mediocre performance compared to their natural counter parts they have evolved into capable sensors with thresholds roughly a factor of 30 larger

Design and Dynamics Analysis of a Bio-Inspired Intermittent Hopping Robot for Planetary Surface Exploration

Directory of Open Access Journals (Sweden)

Long Bai

2012-10-01

Full Text Available A small, bio-inspired and minimally actuated intermittent hopping robot for planetary surface exploration is proposed in this paper. The robot uses a combined-geared six-bar linkage/spring mechanism, which has a possible rich trajectory and metamorphic characteristics and, due to this, the robot is able to recharge, lock/release and jump by using just a micro-power motor as the actuator. Since the robotic system has a closed-chain structure and employs underactuated redundant motion, the constrained multi-body dynamics are derived with time-varying driving parameters and ground unilateral constraint both taken into consideration. In addition, the established dynamics equations, mixed of higher order differential and algebraic expressions, are solved by the immediate integration algorithm. A prototype is implemented and experiments are carried out. The results show that the robot, using a micro-power motor as the actuator and solar cells as the power supply, can achieve a biomimetic multi-body hopping stance and a nonlinearly increasing driving force. Typically, the robot can jump a horizontal distance of about 1 m and a vertical height of about 0.3 m, with its trunk and foot moving stably during takeoff. In addition, the computational and experimental results are consistent as regards the hopping performance of the robot, which suggests that the proposed dynamics model and its solution have general applicability to motion prediction and the performance analysis of intermittent hopping robots.

Enhancing thermal reliability of fiber-optic sensors for bio-inspired applications at ultra-high temperatures

International Nuclear Information System (INIS)

Kang, Donghoon; Kim, Heon-Young; Kim, Dae-Hyun

2014-01-01

The rapid growth of bio-(inspired) sensors has led to an improvement in modern healthcare and human–robot systems in recent years. Higher levels of reliability and better flexibility, essential features of these sensors, are very much required in many application fields (e.g. applications at ultra-high temperatures). Fiber-optic sensors, and fiber Bragg grating (FBG) sensors in particular, are being widely studied as suitable sensors for improved structural health monitoring (SHM) due to their many merits. To enhance the thermal reliability of FBG sensors, thermal sensitivity, generally expressed as α f + ξ f and considered a constant, should be investigated more precisely. For this purpose, the governing equation of FBG sensors is modified using differential derivatives between the wavelength shift and the temperature change in this study. Through a thermal test ranging from RT to 900 °C, the thermal sensitivity of FBG sensors is successfully examined and this guarantees thermal reliability of FBG sensors at ultra-high temperatures. In detail, α f + ξ f has a non-linear dependence on temperature and varies from 6.0 × 10 −6  °C −1 (20 °C) to 10.6 × 10 −6  °C −1 (650 °C). Also, FBGs should be carefully used for applications at ultra-high temperatures due to signal disappearance near 900 °C. (paper)

Modular Neural Tile Architecture for Compact Embedded Hardware Spiking Neural Network

NARCIS (Netherlands)

Pande, Sandeep; Morgan, Fearghal; Cawley, Seamus; Bruintjes, Tom; Smit, Gerardus Johannes Maria; McGinley, Brian; Carrillo, Snaider; Harkin, Jim; McDaid, Liam

2013-01-01

Biologically-inspired packet switched network on chip (NoC) based hardware spiking neural network (SNN) architectures have been proposed as an embedded computing platform for classification, estimation and control applications. Storage of large synaptic connectivity (SNN topology) information in

Hypothetical Pattern Recognition Design Using Multi-Layer Perceptorn Neural Network For Supervised Learning

Directory of Open Access Journals (Sweden)

Md. Abdullah-al-mamun

2015-08-01

Full Text Available Abstract Humans are capable to identifying diverse shape in the different pattern in the real world as effortless fashion due to their intelligence is grow since born with facing several learning process. Same way we can prepared an machine using human like brain called Artificial Neural Network that can be recognize different pattern from the real world object. Although the various techniques is exists to implementation the pattern recognition but recently the artificial neural network approaches have been giving the significant attention. Because the approached of artificial neural network is like a human brain that is learn from different observation and give a decision the previously learning rule. Over the 50 years research now a days pattern recognition for machine learning using artificial neural network got a significant achievement. For this reason many real world problem can be solve by modeling the pattern recognition process. The objective of this paper is to present the theoretical concept for pattern recognition design using Multi-Layer Perceptorn neural networkin the algorithm of artificial Intelligence as the best possible way of utilizing available resources to make a decision that can be a human like performance.

Bio-Inspired Extreme Wetting Surfaces for Biomedical Applications

Science.gov (United States)

Shin, Sera; Seo, Jungmok; Han, Heetak; Kang, Subin; Kim, Hyunchul; Lee, Taeyoon

2016-01-01

Biological creatures with unique surface wettability have long served as a source of inspiration for scientists and engineers. More specifically, materials exhibiting extreme wetting properties, such as superhydrophilic and superhydrophobic surfaces, have attracted considerable attention because of their potential use in various applications, such as self-cleaning fabrics, anti-fog windows, anti-corrosive coatings, drag-reduction systems, and efficient water transportation. In particular, the engineering of surface wettability by manipulating chemical properties and structure opens emerging biomedical applications ranging from high-throughput cell culture platforms to biomedical devices. This review describes design and fabrication methods for artificial extreme wetting surfaces. Next, we introduce some of the newer and emerging biomedical applications using extreme wetting surfaces. Current challenges and future prospects of the surfaces for potential biomedical applications are also addressed. PMID:28787916

An IPMC-enabled bio-inspired bending/twisting fin for underwater applications

International Nuclear Information System (INIS)

Palmre, Viljar; Pugal, David; Kim, Sungjun; Kim, Kwang J; Hubbard, Joel J; Fleming, Maxwell; Leang, Kam K

2013-01-01

This paper discusses the design, fabrication, and characterization of an ionic polymer–metal composite (IPMC) actuator-based bio-inspired active fin capable of bending and twisting motion. It is pointed out that IPMC strip actuators are used in the simple cantilever configuration to create simple bending (flapping-like) motion for propulsion in underwater autonomous systems. However, the resulting motion is a simple 1D bending and performance is rather limited. To enable more complex deformation, such as the flapping (pitch and heaving) motion of real pectoral and caudal fish fins, a new approach which involves molding or integrating IPMC actuators into a soft boot material to create an active control surface (called a ‘fin’) is presented. The fin can be used to realize complex deformation depending on the orientation and placement of the actuators. In contrast to previously created IPMCs with patterned electrodes for the same purpose, the proposed design avoids (1) the more expensive process of electroless plating platinum all throughout the surface of the actuator and (2) the need for specially patterning the electrodes. Therefore, standard shaped IPMC actuators such as those with rectangular dimensions with varying thicknesses can be used. One unique advantage of the proposed structural design is that custom shaped fins and control surfaces can be easily created without special materials processing. The molding process is cost effective and does not require functionalizing or ‘activating’ the boot material similar to creating IPMCs. For a prototype fin (90 mm wide × 60 mm long × 1.5 mm thick), the measured maximum tip displacement was approximately 44 mm and the twist angle of the fin exceeded 10°. Lift and drag measurements in water where the prototype fin with an airfoil profile was dragged through water at a velocity of 21 cm s −1 showed that the lift and drag forces can be affected by controlling the IPMCs embedded into the fin structure

Novel quantum inspired binary neural network algorithm

Indian Academy of Sciences (India)

This parameter is taken as the threshold of neuron for learning of neural network. This algorithm is tested with three benchmark datasets and ... Author Affiliations. OM PRAKASH PATEL1 ARUNA TIWARI. Department of Computer Science and Engineering, Indian Institute of Technology Indore, Indore 453552, India ...

Electronic and optoelectronic materials and devices inspired by nature

International Nuclear Information System (INIS)

Meredith, P; Schwenn, P E; Bettinger, C J; Irimia-Vladu, M; Mostert, A B

2013-01-01

Inorganic semiconductors permeate virtually every sphere of modern human existence. Micro-fabricated memory elements, processors, sensors, circuit elements, lasers, displays, detectors, etc are ubiquitous. However, the dawn of the 21st century has brought with it immense new challenges, and indeed opportunities—some of which require a paradigm shift in the way we think about resource use and disposal, which in turn directly impacts our ongoing relationship with inorganic semiconductors such as silicon and gallium arsenide. Furthermore, advances in fields such as nano-medicine and bioelectronics, and the impending revolution of the ‘ubiquitous sensor network’, all require new functional materials which are bio-compatible, cheap, have minimal embedded manufacturing energy plus extremely low power consumption, and are mechanically robust and flexible for integration with tissues, building structures, fabrics and all manner of hosts. In this short review article we summarize current progress in creating materials with such properties. We focus primarily on organic and bio-organic electronic and optoelectronic systems derived from or inspired by nature, and outline the complex charge transport and photo-physics which control their behaviour. We also introduce the concept of electrical devices based upon ion or proton flow (‘ionics and protonics’) and focus particularly on their role as a signal interface with biological systems. Finally, we highlight recent advances in creating working devices, some of which have bio-inspired architectures, and summarize the current issues, challenges and potential solutions. This is a rich new playground for the modern materials physicist. (review article)

Music Pedagogy as an Aid to Integration? El Sistema-Inspired Music Activity in Two Swedish Preschools

Science.gov (United States)

Gustavsson, Hans-Olof; Ehrlin, Anna

2018-01-01

The study focuses on how preschool and musical school teachers experience working with El Sistema-inspired activity at two municipal preschools in a multicultural district in a medium-sized Swedish town. What, according to the educators,is the most significant aspect of working with El Sistema-inspired activities? The theoretical point of…

Implementation of multi-layer feed forward neural network on PIC16F877 microcontroller

International Nuclear Information System (INIS)

Nur Aira Abd Rahman

2005-01-01

Artificial Neural Network (ANN) is an electronic model based on the neural structure of the brain. Similar to human brain, ANN consists of interconnected simple processing units or neurons that process input to generate output signals. ANN operation is divided into 2 categories; training mode and service mode. This project aims to implement ANN on PIC micro-controller that enable on-chip or stand alone training and service mode. The input can varies from sensors or switches, while the output can be used to control valves, motors, light source and a lot more. As partial development of the project, this paper reports the current status and results of the implemented ANN. The hardware fraction of this project incorporates Microchip PIC16F877A microcontrollers along with uM-FPU math co-processor. uM-FPU is a 32-bit floating point co-processor utilized to execute complex calculation requires by the sigmoid activation function for neuron. ANN algorithm is converted to software program written in assembly language. The implemented ANN structure is three layer with one hidden layer, and five neurons with two hidden neurons. To prove the operability and functionality, the network is trained to solve three common logic gate operations; AND, OR, and XOR. This paper concludes that the ANN had been successfully implemented on PIC16F877a and uM-FPU math co-processor hardware that works accordingly on both training and service mode. (Author)

A one-layer recurrent neural network for constrained pseudoconvex optimization and its application for dynamic portfolio optimization.

Science.gov (United States)

Liu, Qingshan; Guo, Zhishan; Wang, Jun

2012-02-01

In this paper, a one-layer recurrent neural network is proposed for solving pseudoconvex optimization problems subject to linear equality and bound constraints. Compared with the existing neural networks for optimization (e.g., the projection neural networks), the proposed neural network is capable of solving more general pseudoconvex optimization problems with equality and bound constraints. Moreover, it is capable of solving constrained fractional programming problems as a special case. The convergence of the state variables of the proposed neural network to achieve solution optimality is guaranteed as long as the designed parameters in the model are larger than the derived lower bounds. Numerical examples with simulation results illustrate the effectiveness and characteristics of the proposed neural network. In addition, an application for dynamic portfolio optimization is discussed. Copyright © 2011 Elsevier Ltd. All rights reserved.

Peristaltic Wave Locomotion and Shape Morphing with a Millipede Inspired System

Science.gov (United States)

Spinello, Davide; Fattahi, Javad S.

2017-08-01

We present the mechanical model of a bio-inspired deformable system, modeled as a Timoshenko beam, which is coupled to a substrate by a system of distributed elements. The locomotion action is inspired by the coordinated motion of coupling elements that mimic the legs of millipedes and centipedes, whose leg-to-ground contact can be described as a peristaltic displacement wave. The multi-legged structure is crucial in providing redundancy and robustness in the interaction with unstructured environments and terrains. A Lagrangian approach is used to derive the governing equations of the system that couple locomotion and shape morphing. Features and limitations of the model are illustrated with numerical simulations.

A bio-inspired real-time capable artificial lateral line system for freestream flow measurements.

Science.gov (United States)

Abels, C; Qualtieri, A; De Vittorio, M; Megill, W M; Rizzi, F

2016-06-03

To enhance today's artificial flow sensing capabilities in aerial and underwater robotics, future robots could be equipped with a large number of miniaturized sensors distributed over the surface to provide high resolution measurement of the surrounding fluid flow. In this work we show a linear array of closely separated bio-inspired micro-electro-mechanical flow sensors whose sensing mechanism is based on a piezoresistive strain-gauge along a stress-driven cantilever beam, mimicking the biological superficial neuromasts found in the lateral line organ of fishes. Aiming to improve state-of-the-art flow sensing capability in autonomously flying and swimming robots, our artificial lateral line system was designed and developed to feature multi-parameter freestream flow measurements which provide information about (1) local flow velocities as measured by the signal amplitudes from the individual cantilevers as well as (2) propagation velocity, (3) linear forward/backward direction along the cantilever beam orientation and (4) periodicity of pulses or pulse trains determined by cross-correlating sensor signals. A real-time capable cross-correlation procedure was developed which makes it possible to extract freestream flow direction and velocity information from flow fluctuations. The computed flow velocities deviate from a commercial system by 0.09 m s(-1) at 0.5 m s(-1) and 0.15 m s(-1) at 1.0 m s(-1) flow velocity for a sampling rate of 240 Hz and a sensor distance of 38 mm. Although experiments were performed in air, the presented flow sensing system can be applied to underwater vehicles as well, once the sensors are embedded in a waterproof micro-electro-mechanical systems package.

Exploring the Applications of Bio-Eco Architecture for Sustainable Design and Construction process

OpenAIRE

M. M. Naguib; M. A. M. Hanafi

2013-01-01

It has been commonly noted that the main perception of nature influenced forms isbasically aesthetic while little concern is given to the importance of inspiring from naturein the construction and structural performance of buildings as well as in the naturalecological architectural solutions, thus, this paper will focus on bio-inspired architectureapproach which embraces the eco-friendly practices of sustainable construction, the useof natural materials and the energy conservation by mimickin...

Potential usefulness of an artificial neural network for assessing ventricular size

International Nuclear Information System (INIS)

Fukuda, Haruyuki; Nakajima, Hideyuki; Usuki, Noriaki; Saiwai, Shigeo; Miyamoto, Takeshi; Inoue, Yuichi; Onoyama, Yasuto.

1995-01-01

An artificial neural network approach was applied to assess ventricular size from computed tomograms. Three layer, feed-forward neural networks with a back propagation algorithm were designed to distinguish between three degree of enlargement of the ventricles on the basis of patient's age and six items of computed tomographic information. Data for training and testing the neural network were created with computed tomograms of the brains selected at random from daily examinations. Four radiologists decided by mutual consent subjectively based on their experience whether the ventricles were within normal limits, slightly enlarged, or enlarged for the patient's age. The data for training was obtained from 38 patients. The data for testing was obtained from 47 other patients. The performance of the neural network trained using the data for training was evaluated by the rate of correct answers to the data for testing. The valid solution ratio to response of the test data obtained from the trained neural networks was more than 90% for all conditions in this study. The solutions were completely valid in the neural networks with two or three units at the hidden layer with 2,200 learning iterations, and with two units at the hidden layer with 11,000 learning iterations. The squared error decreased remarkably in the range from 0 to 500 learning iterations, and was close to a contrast over two thousand learning iterations. The neural network with a hidden layer having two or three units showed high decision performance. The preliminary results strongly suggest that the neural network approach has potential utility in computer-aided estimation of enlargement of the ventricles. (author)

Neural Network Based Model of an Industrial Oil-Fired Boiler System ...

African Journals Online (AJOL)

A two-layer feed-forward neural network with Hyperbolic tangent sigmoid ... The neural network model when subjected to test, using the validation input data; ... Proportional Integral Derivative (PID) Controller is used to control the neural ...

Appropriateness of Dropout Layers and Allocation of Their 0.5 Rates across Convolutional Neural Networks for CIFAR-10, EEACL26, and NORB Datasets

Directory of Open Access Journals (Sweden)

Romanuke Vadim V.

2017-12-01

Full Text Available A technique of DropOut for preventing overfitting of convolutional neural networks for image classification is considered in the paper. The goal is to find a rule of rationally allocating DropOut layers of 0.5 rate to maximise performance. To achieve the goal, two common network architectures are used having either 4 or 5 convolutional layers. Benchmarking is fulfilled with CIFAR-10, EEACL26, and NORB datasets. Initially, series of all admissible versions for allocation of DropOut layers are generated. After the performance against the series is evaluated, normalized and averaged, the compromising rule is found. It consists in non-compactly inserting a few DropOut layers before the last convolutional layer. It is likely that the scheme with two or more DropOut layers fits networks of many convolutional layers for image classification problems with a plenty of features. Such a scheme shall also fit simple datasets prone to overfitting. In fact, the rule “prefers” a fewer number of DropOut layers. The exemplary gain of the rule application is roughly between 10 % and 50 %.

The equilibrium of neural firing: A mathematical theory

Energy Technology Data Exchange (ETDEWEB)

Lan, Sizhong, E-mail: lsz@fuyunresearch.org [Fuyun Research, Beijing, 100055 (China)

2014-12-15

Inspired by statistical thermodynamics, we presume that neuron system has equilibrium condition with respect to neural firing. We show that, even with dynamically changeable neural connections, it is inevitable for neural firing to evolve to equilibrium. To study the dynamics between neural firing and neural connections, we propose an extended communication system where noisy channel has the tendency towards fixed point, implying that neural connections are always attracted into fixed points such that equilibrium can be reached. The extended communication system and its mathematics could be useful back in thermodynamics.

Usage of neural network to predict aluminium oxide layer thickness.

Science.gov (United States)

Michal, Peter; Vagaská, Alena; Gombár, Miroslav; Kmec, Ján; Spišák, Emil; Kučerka, Daniel

2015-01-01

This paper shows an influence of chemical composition of used electrolyte, such as amount of sulphuric acid in electrolyte, amount of aluminium cations in electrolyte and amount of oxalic acid in electrolyte, and operating parameters of process of anodic oxidation of aluminium such as the temperature of electrolyte, anodizing time, and voltage applied during anodizing process. The paper shows the influence of those parameters on the resulting thickness of aluminium oxide layer. The impact of these variables is shown by using central composite design of experiment for six factors (amount of sulphuric acid, amount of oxalic acid, amount of aluminium cations, electrolyte temperature, anodizing time, and applied voltage) and by usage of the cubic neural unit with Levenberg-Marquardt algorithm during the results evaluation. The paper also deals with current densities of 1 A · dm(-2) and 3 A · dm(-2) for creating aluminium oxide layer.

Usage of Neural Network to Predict Aluminium Oxide Layer Thickness

Directory of Open Access Journals (Sweden)

Peter Michal

2015-01-01

Full Text Available This paper shows an influence of chemical composition of used electrolyte, such as amount of sulphuric acid in electrolyte, amount of aluminium cations in electrolyte and amount of oxalic acid in electrolyte, and operating parameters of process of anodic oxidation of aluminium such as the temperature of electrolyte, anodizing time, and voltage applied during anodizing process. The paper shows the influence of those parameters on the resulting thickness of aluminium oxide layer. The impact of these variables is shown by using central composite design of experiment for six factors (amount of sulphuric acid, amount of oxalic acid, amount of aluminium cations, electrolyte temperature, anodizing time, and applied voltage and by usage of the cubic neural unit with Levenberg-Marquardt algorithm during the results evaluation. The paper also deals with current densities of 1 A·dm−2 and 3 A·dm−2 for creating aluminium oxide layer.

A Theoretical Characterization of Curvature Controlled Adhesive Properties of Bio-Inspired Membranes

DEFF Research Database (Denmark)

Afferante, Luciano; Heepe, Lars; Casdorff, Kirstin

2016-01-01

Some biological systems, such as the tree frog, Litoria caerulea, and the bush-cricket, Tettigonia viridissima, have developed the ability to control adhesion by changing the curvature of their pads. Active control systems of adhesion inspired by these biological models can be very attractive...

Bio-inspired layered chitosan/graphene oxide nanocomposite hydrogels with high strength and pH-driven shape memory effect.

Science.gov (United States)

Zhang, Yaqian; Zhang, Min; Jiang, Haoyang; Shi, Jinli; Li, Feibo; Xia, Yanhong; Zhang, Gongzheng; Li, Huanjun

2017-12-01

The layered nanocomposite hydrogel films containing chitosan (CS) and graphene oxide (GO) have been prepared by water evaporation induced self-assembly and subsequent physical cross-linking in alkaline solution. The layered CS/GO hydrogel films obtained have a nacre-like brick-and-mortar microstructure, which contributes to their excellent mechanical properties. The tensile strength and elongation at break of the hydrogel films with 5wt% GO are 5.35MPa and 193.5%, respectively, which are comparable to natural costal cartilage. Furthermore, the CS/GO hydrogel films exhibited pH-driven shape memory effect, and this unique phenomenon is mainly attributed to the reversible transition of partial physically cross-linking corresponding to hydrogen bondings and hydrophobic interactions between CS polymer chains due to pH changing. Copyright © 2017 Elsevier Ltd. All rights reserved.

Bio-Inspired Multi-Functional Drug Transport Design Concept and Simulations.

Science.gov (United States)

Pidaparti, Ramana M; Cartin, Charles; Su, Guoguang

2017-04-25

In this study, we developed a microdevice concept for drug/fluidic transport taking an inspiration from supramolecular motor found in biological cells. Specifically, idealized multi-functional design geometry (nozzle/diffuser/nozzle) was developed for (i) fluidic/particle transport; (ii) particle separation; and (iii) droplet generation. Several design simulations were conducted to demonstrate the working principles of the multi-functional device. The design simulations illustrate that the proposed design concept is feasible for multi-functionality. However, further experimentation and optimization studies are needed to fully evaluate the multifunctional device concept for multiple applications.

Nature-Inspired Cognitive Evolution to Play MS. Pac-Man

Science.gov (United States)

Tan, Tse Guan; Teo, Jason; Anthony, Patricia

Recent developments in nature-inspired computation have heightened the need for research into the three main areas of scientific, engineering and industrial applications. Some approaches have reported that it is able to solve dynamic problems and very useful for improving the performance of various complex systems. So far however, there has been little discussion about the effectiveness of the application of these models to computer and video games in particular. The focus of this research is to explore the hybridization of nature-inspired computation methods for optimization of neural network-based cognition in video games, in this case the combination of a neural network with an evolutionary algorithm. In essence, a neural network is an attempt to mimic the extremely complex human brain system, which is building an artificial brain that is able to self-learn intelligently. On the other hand, an evolutionary algorithm is to simulate the biological evolutionary processes that evolve potential solutions in order to solve the problems or tasks by applying the genetic operators such as crossover, mutation and selection into the solutions. This paper investigates the abilities of Evolution Strategies (ES) to evolve feed-forward artificial neural network's internal parameters (i.e. weight and bias values) for automatically generating Ms. Pac-man controllers. The main objective of this game is to clear a maze of dots while avoiding the ghosts and to achieve the highest possible score. The experimental results show that an ES-based system can be successfully applied to automatically generate artificial intelligence for a complex, dynamic and highly stochastic video game environment.

iSpike: a spiking neural interface for the iCub robot

International Nuclear Information System (INIS)

Gamez, D; Fidjeland, A K; Lazdins, E

2012-01-01

This paper presents iSpike: a C++ library that interfaces between spiking neural network simulators and the iCub humanoid robot. It uses a biologically inspired approach to convert the robot’s sensory information into spikes that are passed to the neural network simulator, and it decodes output spikes from the network into motor signals that are sent to control the robot. Applications of iSpike range from embodied models of the brain to the development of intelligent robots using biologically inspired spiking neural networks. iSpike is an open source library that is available for free download under the terms of the GPL. (paper)

Feasibility Study of a Bio-inspired Artificial Pancreas in Adults with Type 1 Diabetes

Science.gov (United States)

Herrero, Pau; El Sharkawy, Mohamed; Pesl, Peter; Jugnee, Narvada; Thomson, Hazel; Pavitt, Darrell; Toumazou, Christofer; Johnston, Desmond; Georgiou, Pantelis; Oliver, Nick

2014-01-01

Abstract Background: This study assesses proof of concept and safety of a novel bio-inspired artificial pancreas (BiAP) system in adults with type 1 diabetes during fasting, overnight, and postprandial conditions. In contrast to existing glucose controllers in artificial pancreas systems, the BiAP uses a control algorithm based on a mathematical model of β-cell physiology. The algorithm is implemented on a miniature silicon microchip within a portable hand-held device that interfaces the components of the artificial pancreas. Materials and Methods: In this nonrandomized open-label study each subject attended for a 6-h fasting study followed by a 13-h overnight and post-breakfast study on a separate occasion. During both study sessions the BiAP system was used, and microboluses of insulin were recommended every 5 min by the control algorithm according to subcutaneous sensor glucose levels. The primary outcome was percentage time spent in the glucose target range (3.9–10.0 mmol/L). Results: Twenty subjects (55% male; mean [SD] age, 44 [10] years; duration of diabetes, 22 [12] years; glycosylated hemoglobin, 7.4% [0.7%] [57 (7) mmol/mol]; body mass index, 25 [4] kg/m2) participated in the fasting study, and the median (interquartile range) percentage time in target range was 98.0% (90.8–100.0%). Seventeen of these subjects then participated in the overnight/postprandial study, where 70.7% (63.9–77.4%) of time was spent in the target range and, reassuringly, 0.0% (0.0–2.3%) of time was spent in hypoglycemia (<3.9 mmol/L). Conclusions: The BiAP achieves safe glycemic control during fasting, overnight, and postprandial conditions. PMID:24801544

Bio-inspired photonic-crystal microchip for fluorescent ultratrace detection.

Science.gov (United States)

Hou, Jue; Zhang, Huacheng; Yang, Qiang; Li, Mingzhu; Song, Yanlin; Jiang, Lei

2014-06-02

Ultratrace detection attracts great interest because it is still a challenge to the early diagnosis and drug testing. Enriching the targets from highly diluted solutions to the sensitive area is a promising method. Inspired by the fog-collecting structure on Stenocara beetle's back, a photonic-crystal (PC) microchip with hydrophilic-hydrophobic micropattern was fabricated by inkjet printing. This device was used to realize high-sensitive ultratrace detection of fluorescence analytes and fluorophore-based assays. Coupled with the fluorescence enhancement effect of a PC, detection down to 10(-16) mol L(-1) was achieved. This design can be combined with biophotonic devices for the detection of drugs, diseases, and pollutions of the ecosystem. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The scientific study of inspiration in the creative process: Challenges and opportunities

Directory of Open Access Journals (Sweden)

Victoria C. Oleynick

2014-06-01

Full Text Available Inspiration is a motivational state that compels individuals to bring ideas into fruition. Creators have long argued that inspiration is important to the creative process, but until recently, scientists have not investigated this claim. In this article, we review challenges to the study of creative inspiration, as well as solutions to these challenges afforded by theoretical and empirical work on inspiration over the past decade. First, we discuss the problem of definitional ambiguity, which has been addressed through an integrative process of construct conceptualization. Second, we discuss the challenge of how to operationalize inspiration. This challenge has been overcome by the development and validation of the Inspiration Scale, which may be used to assess trait or state inspiration. Third, we address ambiguity regarding how inspiration differs from related concepts (creativity, insight, positive affect by discussing discriminant validity. Next, we discuss the preconception that inspiration is less important than perspiration (effort, and we review empirical evidence that inspiration and effort both play important—but different—roles in the creative process. Finally, with many challenges overcome, we argue that the foundation is now set for a new generation of research focused on neural underpinnings. We discuss potential challenges to and opportunities for the neuroscientific study of inspiration. A better understanding of the biological basis of inspiration will illuminate the process through which creative ideas fire the soul, such that individuals are compelled to transform ideas into products and solutions that may benefit society.

How does the canine paw pad attenuate ground impacts? A multi-layer cushion system

Directory of Open Access Journals (Sweden)

Huaibin Miao

2017-12-01

Full Text Available Macroscopic mechanical properties of digitigrade paw pads, such as non-linear elastic and variable stiffness, have been investigated in previous studies; however, little is known about the micro-scale structural characteristics of digitigrade paw pads, or the relationship between these characteristics and the exceptional cushioning of the pads. The digitigrade paw pad consists of a multi-layered structure, which is mainly comprised of a stratified epithelium layer, a dermis layer and a subcutaneous layer. The stratified epithelium layer and dermal papillae constitute the epidermis layer. Finite element analyses were carried out and showed that the epidermis layer effectively attenuated the ground impact across impact velocities of 0.05–0.4 m/s, and that the von Mises stresses were uniformly distributed in this layer. The dermis layer encompassing the subcutaneous layer can be viewed as a hydrostatic system, which can store, release and dissipate impact energy. All three layers in the paw pad work as a whole to meet the biomechanical requirements of animal locomotion. These findings provide insights into the biomechanical functioning of digitigrade paw pads and could be used to facilitate bio-inspired, ground-contacting component development for robots and machines, as well as contribute to footwear design.

How does the canine paw pad attenuate ground impacts? A multi-layer cushion system.

Science.gov (United States)

Miao, Huaibin; Fu, Jun; Qian, Zhihui; Ren, Luquan; Ren, Lei

2017-12-15

Macroscopic mechanical properties of digitigrade paw pads, such as non-linear elastic and variable stiffness, have been investigated in previous studies; however, little is known about the micro-scale structural characteristics of digitigrade paw pads, or the relationship between these characteristics and the exceptional cushioning of the pads. The digitigrade paw pad consists of a multi-layered structure, which is mainly comprised of a stratified epithelium layer, a dermis layer and a subcutaneous layer. The stratified epithelium layer and dermal papillae constitute the epidermis layer. Finite element analyses were carried out and showed that the epidermis layer effectively attenuated the ground impact across impact velocities of 0.05-0.4 m/s, and that the von Mises stresses were uniformly distributed in this layer. The dermis layer encompassing the subcutaneous layer can be viewed as a hydrostatic system, which can store, release and dissipate impact energy. All three layers in the paw pad work as a whole to meet the biomechanical requirements of animal locomotion. These findings provide insights into the biomechanical functioning of digitigrade paw pads and could be used to facilitate bio-inspired, ground-contacting component development for robots and machines, as well as contribute to footwear design. © 2017. Published by The Company of Biologists Ltd.

Social transformation of two students in an El Sistema-inspired orchestra program

Directory of Open Access Journals (Sweden)

Christine D’Alexander

2016-07-01

Full Text Available In recent years there has been a substantial increase in El Sistema-inspired programs for young musicians. An overwhelming majority of these programs across the USA are centered in underserved communities where access to instrumental music training would otherwise be sparse. Most often, this is due to budget cuts partially or completely being wiped off from school curricula, or the financial cost associated with instrumental lessons. These programs, often free of cost to families, consist of musical training several days a week in small and large group classes, and claim to promote social change amongst these children’s lives. It is of importance to carefully study if, and how, these young musicians believe their lives are transforming through participation in these El Sistema-inspired programs, and if in fact, social change is occurring. This article reports on a multiple case study which sought to dive deeper into the lives of two children taking part in one El Sistema-inspired orchestral program located in Los Angeles, California. The study focused on the influence and impact this program has had on the lives of students, the exploration and formation of their beliefs surrounding musical learning, as well as experiences they share both as individuals and members of the orchestra.

A comparative study of two neural networks for document retrieval

International Nuclear Information System (INIS)

Hui, S.C.; Goh, A.

1997-01-01

In recent years there has been specific interest in adopting advanced computer techniques in the field of document retrieval. This interest is generated by the fact that classical methods such as the Boolean search, the vector space model or even probabilistic retrieval cannot handle the increasing demands of end-users in satisfying their needs. The most recent attempt is the application of the neural network paradigm as a means of providing end-users with a more powerful retrieval mechanism. Neural networks are not only good pattern matchers but also highly versatile and adaptable. In this paper, we demonstrate how to apply two neural networks, namely Adaptive Resonance Theory and Fuzzy Kohonen Neural Network, for document retrieval. In addition, a comparison of these two neural networks based on performance is also given

Direct and inverse neural networks modelling applied to study the influence of the gas diffusion layer properties on PBI-based PEM fuel cells

Energy Technology Data Exchange (ETDEWEB)

Lobato, Justo; Canizares, Pablo; Rodrigo, Manuel A.; Linares, Jose J. [Chemical Engineering Department, University of Castilla-La Mancha, Campus Universitario s/n, 13004 Ciudad Real (Spain); Piuleac, Ciprian-George; Curteanu, Silvia [Faculty of Chemical Engineering and Environmental Protection, Department of Chemical Engineering, ' ' Gh. Asachi' ' Technical University Iasi Bd. D. Mangeron, No. 71A, 700050 IASI (Romania)

2010-08-15

This article shows the application of a very useful mathematical tool, artificial neural networks, to predict the fuel cells results (the value of the tortuosity and the cell voltage, at a given current density, and therefore, the power) on the basis of several properties that define a Gas Diffusion Layer: Teflon content, air permeability, porosity, mean pore size, hydrophobia level. Four neural networks types (multilayer perceptron, generalized feedforward network, modular neural network, and Jordan-Elman neural network) have been applied, with a good fitting between the predicted and the experimental values in the polarization curves. A simple feedforward neural network with one hidden layer proved to be an accurate model with good generalization capability (error about 1% in the validation phase). A procedure based on inverse neural network modelling was able to determine, with small errors, the initial conditions leading to imposed values for characteristics of the fuel cell. In addition, the use of this tool has been proved to be very attractive in order to predict the cell performance, and more interestingly, the influence of the properties of the gas diffusion layer on the cell performance, allowing possible enhancements of this material by changing some of its properties. (author)

Application of structured support vector machine backpropagation to a convolutional neural network for human pose estimation.

Science.gov (United States)

Witoonchart, Peerajak; Chongstitvatana, Prabhas

2017-08-01

In this study, for the first time, we show how to formulate a structured support vector machine (SSVM) as two layers in a convolutional neural network, where the top layer is a loss augmented inference layer and the bottom layer is the normal convolutional layer. We show that a deformable part model can be learned with the proposed structured SVM neural network by backpropagating the error of the deformable part model to the convolutional neural network. The forward propagation calculates the loss augmented inference and the backpropagation calculates the gradient from the loss augmented inference layer to the convolutional layer. Thus, we obtain a new type of convolutional neural network called an Structured SVM convolutional neural network, which we applied to the human pose estimation problem. This new neural network can be used as the final layers in deep learning. Our method jointly learns the structural model parameters and the appearance model parameters. We implemented our method as a new layer in the existing Caffe library. Copyright © 2017 Elsevier Ltd. All rights reserved.

Computational modeling of spiking neural network with learning rules from STDP and intrinsic plasticity

Science.gov (United States)

Li, Xiumin; Wang, Wei; Xue, Fangzheng; Song, Yongduan

2018-02-01

Recently there has been continuously increasing interest in building up computational models of spiking neural networks (SNN), such as the Liquid State Machine (LSM). The biologically inspired self-organized neural networks with neural plasticity can enhance the capability of computational performance, with the characteristic features of dynamical memory and recurrent connection cycles which distinguish them from the more widely used feedforward neural networks. Despite a variety of computational models for brain-like learning and information processing have been proposed, the modeling of self-organized neural networks with multi-neural plasticity is still an important open challenge. The main difficulties lie in the interplay among different forms of neural plasticity rules and understanding how structures and dynamics of neural networks shape the computational performance. In this paper, we propose a novel approach to develop the models of LSM with a biologically inspired self-organizing network based on two neural plasticity learning rules. The connectivity among excitatory neurons is adapted by spike-timing-dependent plasticity (STDP) learning; meanwhile, the degrees of neuronal excitability are regulated to maintain a moderate average activity level by another learning rule: intrinsic plasticity (IP). Our study shows that LSM with STDP+IP performs better than LSM with a random SNN or SNN obtained by STDP alone. The noticeable improvement with the proposed method is due to the better reflected competition among different neurons in the developed SNN model, as well as the more effectively encoded and processed relevant dynamic information with its learning and self-organizing mechanism. This result gives insights to the optimization of computational models of spiking neural networks with neural plasticity.

Estimation of effective connectivity using multi-layer perceptron artificial neural network.

Science.gov (United States)

Talebi, Nasibeh; Nasrabadi, Ali Motie; Mohammad-Rezazadeh, Iman

2018-02-01

Studies on interactions between brain regions estimate effective connectivity, (usually) based on the causality inferences made on the basis of temporal precedence. In this study, the causal relationship is modeled by a multi-layer perceptron feed-forward artificial neural network, because of the ANN's ability to generate appropriate input-output mapping and to learn from training examples without the need of detailed knowledge of the underlying system. At any time instant, the past samples of data are placed in the network input, and the subsequent values are predicted at its output. To estimate the strength of interactions, the measure of " Causality coefficient " is defined based on the network structure, the connecting weights and the parameters of hidden layer activation function. Simulation analysis demonstrates that the method, called "CREANN" (Causal Relationship Estimation by Artificial Neural Network), can estimate time-invariant and time-varying effective connectivity in terms of MVAR coefficients. The method shows robustness with respect to noise level of data. Furthermore, the estimations are not significantly influenced by the model order (considered time-lag), and the different initial conditions (initial random weights and parameters of the network). CREANN is also applied to EEG data collected during a memory recognition task. The results implicate that it can show changes in the information flow between brain regions, involving in the episodic memory retrieval process. These convincing results emphasize that CREANN can be used as an appropriate method to estimate the causal relationship among brain signals.

2D neural hardware versus 3D biological ones

Energy Technology Data Exchange (ETDEWEB)

Beiu, V.

1998-12-31

This paper will present important limitations of hardware neural nets as opposed to biological neural nets (i.e. the real ones). The author starts by discussing neural structures and their biological inspirations, while mentioning the simplifications leading to artificial neural nets. Going further, the focus will be on hardware constraints. The author will present recent results for three different alternatives of implementing neural networks: digital, threshold gate, and analog, while the area and the delay will be related to neurons' fan-in and weights' precision. Based on all of these, it will be shown why hardware implementations cannot cope with their biological inspiration with respect to their power of computation: the mapping onto silicon lacking the third dimension of biological nets. This translates into reduced fan-in, and leads to reduced precision. The main conclusion is that one is faced with the following alternatives: (1) try to cope with the limitations imposed by silicon, by speeding up the computation of the elementary silicon neurons; (2) investigate solutions which would allow one to use the third dimension, e.g. using optical interconnections.

IR wireless cluster synapses of HYDRA very large neural networks

Science.gov (United States)

Jannson, Tomasz; Forrester, Thomas

2008-04-01

RF/IR wireless (virtual) synapses are critical components of HYDRA (Hyper-Distributed Robotic Autonomy) neural networks, already discussed in two earlier papers. The HYDRA network has the potential to be very large, up to 10 11-neurons and 10 18-synapses, based on already established technologies (cellular RF telephony and IR-wireless LANs). It is organized into almost fully connected IR-wireless clusters. The HYDRA neurons and synapses are very flexible, simple, and low-cost. They can be modified into a broad variety of biologically-inspired brain-like computing capabilities. In this third paper, we focus on neural hardware in general, and on IR-wireless synapses in particular. Such synapses, based on LED/LD-connections, dominate the HYDRA neural cluster.

Learning of N-layers neural network

Directory of Open Access Journals (Sweden)

Vladimír Konečný

2005-01-01

Full Text Available In the last decade we can observe increasing number of applications based on the Artificial Intelligence that are designed to solve problems from different areas of human activity. The reason why there is so much interest in these technologies is that the classical way of solutions does not exist or these technologies are not suitable because of their robustness. They are often used in applications like Business Intelligence that enable to obtain useful information for high-quality decision-making and to increase competitive advantage.One of the most widespread tools for the Artificial Intelligence are the artificial neural networks. Their high advantage is relative simplicity and the possibility of self-learning based on set of pattern situations.For the learning phase is the most commonly used algorithm back-propagation error (BPE. The base of BPE is the method minima of error function representing the sum of squared errors on outputs of neural net, for all patterns of the learning set. However, while performing BPE and in the first usage, we can find out that it is necessary to complete the handling of the learning factor by suitable method. The stability of the learning process and the rate of convergence depend on the selected method. In the article there are derived two functions: one function for the learning process management by the relative great error function value and the second function when the value of error function approximates to global minimum.The aim of the article is to introduce the BPE algorithm in compact matrix form for multilayer neural networks, the derivation of the learning factor handling method and the presentation of the results.

Superhydrophobic gecko feet with high adhesive forces towards water and their bio-inspired materials

Science.gov (United States)

Liu, Kesong; Du, Jiexing; Wu, Juntao; Jiang, Lei

2012-01-01

Functional integration is an inherent characteristic for multiscale structures of biological materials. In this contribution, we first investigate the liquid-solid adhesive forces between water droplets and superhydrophobic gecko feet using a high-sensitivity micro-electromechanical balance system. It was found, in addition to the well-known solid-solid adhesion, the gecko foot, with a multiscale structure, possesses both superhydrophobic functionality and a high adhesive force towards water. The origin of the high adhesive forces of gecko feet to water could be attributed to the high density nanopillars that contact the water. Inspired by this, polyimide films with gecko-like multiscale structures were constructed by using anodic aluminum oxide templates, exhibiting superhydrophobicity and a strong adhesive force towards water. The static water contact angle is larger than 150° and the adhesive force to water is about 66 μN. The resultant gecko-inspired polyimide film can be used as a ``mechanical hand'' to snatch micro-liter liquids. We expect this work will provide the inspiration to reveal the mechanism of the high-adhesive superhydrophobic of geckos and extend the practical applications of polyimide materials.

Accurate estimation of CO2 adsorption on activated carbon with multi-layer feed-forward neural network (MLFNN algorithm

Directory of Open Access Journals (Sweden)

Alireza Rostami

2018-03-01

Full Text Available Global warming due to greenhouse effect has been considered as a serious problem for many years around the world. Among the different gases which cause greenhouse gas effect, carbon dioxide is of great difficulty by entering into the surrounding atmosphere. So CO2 capturing and separation especially by adsorption is one of the most interesting approaches because of the low equipment cost, ease of operation, simplicity of design, and low energy consumption.In this study, experimental results are presented for the adsorption equilibria of carbon dioxide on activated carbon. The adsorption equilibrium data for carbon dioxide were predicted with two commonly used isotherm models in order to compare with multi-layer feed-forward neural network (MLFNN algorithm for a wide range of partial pressure. As a result, the ANN-based algorithm shows much better efficiency and accuracy than the Sips and Langmuir isotherms. In addition, the applicability of the Sips and Langmuir models are limited to isothermal conditions, even though the ANN-based algorithm is not restricted to the constant temperature condition. Consequently, it is proved that MLFNN algorithm is a promising model for calculation of CO2 adsorption density on activated carbon. Keywords: Global warming, CO2 adsorption, Activated carbon, Multi-layer feed-forward neural network algorithm, Statistical quality measures

Mussel-inspired tough hydrogels with self-repairing and tissue adhesion

Science.gov (United States)

Gao, Zijian; Duan, Lijie; Yang, Yongqi; Hu, Wei; Gao, Guanghui

2018-01-01

The mussel-inspired polymeric hydrogels have been attractively explored owing to their self-repairing or adhesive property when the catechol groups of dopamine could chelate metal ions. However, it was a challenge for self-repairing hydrogels owning high mechanical properties. Herein, a synergistic strategy was proposed by combining catechol-Fe3+ complexes and hydrophobic association. The resulting hydrogels exhibited seamless self-repairing behavior, tissue adhesion and high mechanical property. Moreover, the pH-dependent stoichiometry of catechol-Fe3+ and temperature-sensitive hydrophobic association endue hydrogels with pH/thermo responsive characteristics. Subsequently, the self-repairing rate and mechanical property of hydrogels were investigated at different pH and temperature. This bio-inspired strategy would build an avenue for designing and constructing a new generation of self-repairing, tissue-adhesive and tough hydrogel.

Validation of an analytical method for the determination of spiramycin, virginiamycin and tylosin in feeding-stuffs bij thin-layer chromatography and bio-autography

NARCIS (Netherlands)

Vincent, U.; Gizzi, G.; Holst, von C.; Jong, de J.; Michard, J.

2007-01-01

An inter-laboratory validation was carried out to determine the performance characteristics of an analytical method based on thin-layer chromatography (TLC) coupled to microbiological detection (bio-autography) for screening feed samples for the presence of spiramycin, tylosin and virginiamycin.

Bio-robots automatic navigation with graded electric reward stimulation based on Reinforcement Learning.

Science.gov (United States)

Zhang, Chen; Sun, Chao; Gao, Liqiang; Zheng, Nenggan; Chen, Weidong; Zheng, Xiaoxiang

2013-01-01

Bio-robots based on brain computer interface (BCI) suffer from the lack of considering the characteristic of the animals in navigation. This paper proposed a new method for bio-robots' automatic navigation combining the reward generating algorithm base on Reinforcement Learning (RL) with the learning intelligence of animals together. Given the graded electrical reward, the animal e.g. the rat, intends to seek the maximum reward while exploring an unknown environment. Since the rat has excellent spatial recognition, the rat-robot and the RL algorithm can convergent to an optimal route by co-learning. This work has significant inspiration for the practical development of bio-robots' navigation with hybrid intelligence.

Genetic algorithm for neural networks optimization

Science.gov (United States)

Setyawati, Bina R.; Creese, Robert C.; Sahirman, Sidharta

2004-11-01

This paper examines the forecasting performance of multi-layer feed forward neural networks in modeling a particular foreign exchange rates, i.e. Japanese Yen/US Dollar. The effects of two learning methods, Back Propagation and Genetic Algorithm, in which the neural network topology and other parameters fixed, were investigated. The early results indicate that the application of this hybrid system seems to be well suited for the forecasting of foreign exchange rates. The Neural Networks and Genetic Algorithm were programmed using MATLAB«.

Assessing artificial neural network performance in estimating the layer properties of pavements

Directory of Open Access Journals (Sweden)

Gloria Inés Beltran

2014-05-01

Full Text Available A major concern in assessing the structural condition of existing flexible pavements is the estimation of the mechanical properties of constituent layers, which is useful for the design and decision-making process in road management systems. This parameter identification problem is truly complex due to the large number of variables involved in pavement behavior. To this end, non-conventional adaptive or approximate solutions via Artificial Neural Networks – ANNs – are considered to properly map pavement response field measurements. Previous investigations have demonstrated the exceptional ability of ANNs in layer moduli estimation from non-destructive deflection tests, but most of the reported cases were developed using synthetic deflection data or hypothetical pavement systems. This paper presents further attempts to back-calculate layer moduli via ANN modeling, using a database gathered from field tests performed on three- and four-layer pavement systems. Traditional layer structuring and pavements with a stabilized subbase were considered. A three-stage methodology is developed in this study to design and validate an “optimum” ANN-based model, i.e., the best architecture possible along with adequate learning rules. An assessment of the resulting ANN model demonstrates its forecasting capabilities and efficiency in solving a complex parameter identification problem concerning pavements.

Bio-inspired green synthesis of Fe3O4 magnetic nanoparticles using watermelon rinds and their catalytic activity

Science.gov (United States)

Prasad, Ch.; Gangadhara, S.; Venkateswarlu, P.

2016-08-01

Novel and bio-inspired magnetic nanoparticles were synthesized using watermelon rinds (WR) which are nontoxic and biodegradable. Watermelon rind extract was used as a solvent and capping and reducing agent in the synthesis. The Fe3o4 MNPs were characterized by using transmission electron microscopy (TEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) and vibrating sample magnetometer techniques (VSM). XRD studies revealed a high degree of crystalline and monophasic Fe nanoparticles of face-centered cubic stricture. FTIR analysis proved that particles are reduced and stabilized in solution by the capping agent that is likely to be proteins secreted by the biomass. The present process in an excellent candidate for the synthesis of iron nanoparticles that is simple, easy to execute, pollutant free and inexpensive. A practical and convenient method for the synthesis of highly stable and small-sized iron nanoparticles with a narrow distribution from 2 to 20 nm is reported. Also, the MNPs present in higher saturation magnetization (Ms) of 14.2 emu/g demonstrate tremendous magnetic response behavior. However, the synthesized iron nanoparticles were used as a catalyst for the preparation of biologically interesting 2-oxo-1,2,3,4-tetrahydropyrimidine derivatives in high yields. These results exhibited that the synthesized Fe3O4 MNPs could be used as a catalyst in organic synthesis.

Bio-Inspired Polymer Membrane Surface Cleaning

Directory of Open Access Journals (Sweden)

Agnes Schulze

2017-03-01

Full Text Available To generate polyethersulfone membranes with a biocatalytically active surface, pancreatin was covalently immobilized. Pancreatin is a mixture of digestive enzymes such as protease, lipase, and amylase. The resulting membranes exhibit self-cleaning properties after “switching on” the respective enzyme by adjusting pH and temperature. Thus, the membrane surface can actively degrade a fouling layer on its surface and regain initial permeability. Fouling tests with solutions of protein, oil, and mixtures of both, were performed, and the membrane’s ability to self-clean the fouled surface was characterized. Membrane characterization was conducted by investigation of the immobilized enzyme concentration, enzyme activity, water permeation flux, fouling tests, porosimetry, X-ray photoelectron spectroscopy, and scanning electron microscopy.

A locust-inspired miniature jumping robot.

Science.gov (United States)

Zaitsev, Valentin; Gvirsman, Omer; Ben Hanan, Uri; Weiss, Avi; Ayali, Amir; Kosa, Gabor

2015-11-25

Unmanned ground vehicles are mostly wheeled, tracked, or legged. These locomotion mechanisms have a limited ability to traverse rough terrain and obstacles that are higher than the robot's center of mass. In order to improve the mobility of small robots it is necessary to expand the variety of their motion gaits. Jumping is one of nature's solutions to the challenge of mobility in difficult terrain. The desert locust is the model for the presented bio-inspired design of a jumping mechanism for a small mobile robot. The basic mechanism is similar to that of the semilunar process in the hind legs of the locust, and is based on the cocking of a torsional spring by wrapping a tendon-like wire around the shaft of a miniature motor. In this study we present the jumping mechanism design, and the manufacturing and performance analysis of two demonstrator prototypes. The most advanced jumping robot demonstrator is power autonomous, weighs 23 gr, and is capable of jumping to a height of 3.35 m, covering a distance of 1.37 m.

Layer-by-layer self-assembled multilayers on PEEK implants improve osseointegration in an osteoporosis rabbit model.

Science.gov (United States)

Liu, Xilin; Han, Fei; Zhao, Peng; Lin, Chao; Wen, Xuejun; Ye, Xiaojian

2017-05-01

This study aims to fabricate and deposit nanoscale multilayers on polyetheretherketone (PEEK) to improve cell adhesion and osseointegration. Bio-activated PEEK constructs were designed with prepared surface of different layers of polystyrene sulfonate (PSS) and polyallylamine hydrochloride (PAH) multilayers. Irregular morphology was found on the 5 and 10-layer PEEK surfaces, while "island-like" clusters were observed for 20-layer (20 L) multilayers. Besides, the 20 L PEEK showed more hydrophilic feature than native PEEK, and the surface contact angle reduced from 39.7° to 21.7° as layers increased from 5 to 20. In vitro, modified PEEK allowed excellent adhesion and proliferation of bone marrow stromal cells, and induced higher cell growth rate and alkaline phosphatase level. In vivo, this bio-active PEEK exhibited significantly enhanced integration with bone tissue in an osteoporosis rabbit model. This work highlights layer-by-layer self-assembly as a practical method to construct bio-active PEEK implants for enhanced osseointegration. Copyright © 2017 Elsevier Inc. All rights reserved.

Power level control of the TRIGA Mark-II research reactor using the multifeedback layer neural network and the particle swarm optimization

International Nuclear Information System (INIS)

Coban, Ramazan

2014-01-01

Highlights: • A multifeedback-layer neural network controller is presented for a research reactor. • Off-line learning of the MFLNN is accomplished by the PSO algorithm. • The results revealed that the MFLNN–PSO controller has a remarkable performance. - Abstract: In this paper, an artificial neural network controller is presented using the Multifeedback-Layer Neural Network (MFLNN), which is a recently proposed recurrent neural network, for neutronic power level control of a nuclear research reactor. Off-line learning of the MFLNN is accomplished by the Particle Swarm Optimization (PSO) algorithm. The MFLNN-PSO controller design is based on a nonlinear model of the TRIGA Mark-II research reactor. The learning and the test processes are implemented by means of a computer program at different power levels. The simulation results obtained reveal that the MFLNN-PSO controller has a remarkable performance on the neutronic power level control of the reactor for tracking the step reference power trajectories

A biologically inspired neural model for visual and proprioceptive integration including sensory training.

Science.gov (United States)

Saidi, Maryam; Towhidkhah, Farzad; Gharibzadeh, Shahriar; Lari, Abdolaziz Azizi

2013-12-01

Humans perceive the surrounding world by integration of information through different sensory modalities. Earlier models of multisensory integration rely mainly on traditional Bayesian and causal Bayesian inferences for single causal (source) and two causal (for two senses such as visual and auditory systems), respectively. In this paper a new recurrent neural model is presented for integration of visual and proprioceptive information. This model is based on population coding which is able to mimic multisensory integration of neural centers in the human brain. The simulation results agree with those achieved by casual Bayesian inference. The model can also simulate the sensory training process of visual and proprioceptive information in human. Training process in multisensory integration is a point with less attention in the literature before. The effect of proprioceptive training on multisensory perception was investigated through a set of experiments in our previous study. The current study, evaluates the effect of both modalities, i.e., visual and proprioceptive training and compares them with each other through a set of new experiments. In these experiments, the subject was asked to move his/her hand in a circle and estimate its position. The experiments were performed on eight subjects with proprioception training and eight subjects with visual training. Results of the experiments show three important points: (1) visual learning rate is significantly more than that of proprioception; (2) means of visual and proprioceptive errors are decreased by training but statistical analysis shows that this decrement is significant for proprioceptive error and non-significant for visual error, and (3) visual errors in training phase even in the beginning of it, is much less than errors of the main test stage because in the main test, the subject has to focus on two senses. The results of the experiments in this paper is in agreement with the results of the neural model

Shark skin-inspired designs that improve aerodynamic performance.

Science.gov (United States)

Domel, August G; Saadat, Mehdi; Weaver, James C; Haj-Hariri, Hossein; Bertoldi, Katia; Lauder, George V

2018-02-01

There have been significant efforts recently aimed at improving the aerodynamic performance of aerofoils through the modification of their surfaces. Inspired by the drag-reducing properties of the tooth-like denticles that cover the skin of sharks, we describe here experimental and simulation-based investigations into the aerodynamic effects of novel denticle-inspired designs placed along the suction side of an aerofoil. Through parametric modelling to query a wide range of different designs, we discovered a set of denticle-inspired surface structures that achieve simultaneous drag reduction and lift generation on an aerofoil, resulting in lift-to-drag ratio improvements comparable to the best-reported for traditional low-profile vortex generators and even outperforming these existing designs at low angles of attack with improvements of up to 323%. Such behaviour is enabled by two concurrent mechanisms: (i) a separation bubble in the denticle's wake altering the flow pressure distribution of the aerofoil to enhance suction and (ii) streamwise vortices that replenish momentum loss in the boundary layer due to skin friction. Our findings not only open new avenues for improved aerodynamic design, but also provide new perspective on the role of the complex and potentially multifunctional morphology of shark denticles for increased swimming efficiency. © 2018 The Author(s).

Two-layer anti-reflection strategies for implant applications

Science.gov (United States)

Guerrero, Douglas J.; Smith, Tamara; Kato, Masakazu; Kimura, Shigeo; Enomoto, Tomoyuki

2006-03-01

A two-layer bottom anti-reflective coating (BARC) concept in which a layer that develops slowly is coated on top of a bottom layer that develops more rapidly was demonstrated. Development rate control was achieved by selection of crosslinker amount and BARC curing conditions. A single-layer BARC was compared with the two-layer BARC concept. The single-layer BARC does not clear out of 200-nm deep vias. When the slower developing single-layer BARC was coated on top of the faster developing layer, the vias were cleared. Lithographic evaluation of the two-layer BARC concept shows the same resolution advantages as the single-layer system. Planarization properties of a two-layer BARC system are better than for a single-layer system, when comparing the same total nominal thicknesses.

Automatic detection of photoresist residual layer in lithography using a neural classification approach

KAUST Repository

Gereige, Issam

2012-09-01

Photolithography is a fundamental process in the semiconductor industry and it is considered as the key element towards extreme nanoscale integration. In this technique, a polymer photo sensitive mask with the desired patterns is created on the substrate to be etched. Roughly speaking, the areas to be etched are not covered with polymer. Thus, no residual layer should remain on these areas in order to insure an optimal transfer of the patterns on the substrate. In this paper, we propose a nondestructive method based on a classification approach achieved by artificial neural network for automatic residual layer detection from an ellipsometric signature. Only the case of regular defect, i.e. homogenous residual layer, will be considered. The limitation of the method will be discussed. Then, an experimental result on a 400 nm period grating manufactured with nanoimprint lithography is analyzed with our method. © 2012 Elsevier B.V. All rights reserved.

A top-down approach for fabricating free-standing bio-carbon supercapacitor electrodes with a hierarchical structure

OpenAIRE

Yingzhi Li; Qinghua Zhang; Junxian Zhang; Lei Jin; Xin Zhao; Ting Xu

2015-01-01

Biomass has delicate hierarchical structures, which inspired us to develop a cost-effective route to prepare electrode materials with rational nanostructures for use in high-performance storage devices. Here, we demonstrate a novel top-down approach for fabricating bio-carbon materials with stable structures and excellent diffusion pathways; this approach is based on carbonization with controlled chemical activation. The developed free-standing bio-carbon electrode exhibits a high specific ca...

Bio-mimetic mechanisms of natural hierarchical materials: a review.

Science.gov (United States)

Chen, Qiang; Pugno, Nicola M

2013-03-01

Natural selection and evolution develop a huge amount of biological materials in different environments (e.g. lotus in water and opuntia in desert). These biological materials possess many inspiring properties, which hint scientists and engineers to find some useful clues to create new materials or update the existing ones. In this review, we highlight some well-studied (e.g. nacre shell) and newly-studied (e.g. turtle shell) natural materials, and summarize their hierarchical structures and mechanisms behind their mechanical properties, from animals to plants. These fascinating mechanisms suggest to researchers to investigate natural materials deeply and broadly, and to design or fabricate new bio-inspired materials to serve our life. Copyright © 2012 Elsevier Ltd. All rights reserved.

Improved algorithms for circuit fault diagnosis based on wavelet packet and neural network

International Nuclear Information System (INIS)

Zhang, W-Q; Xu, C

2008-01-01

In this paper, two improved BP neural network algorithms of fault diagnosis for analog circuit are presented through using optimal wavelet packet transform(OWPT) or incomplete wavelet packet transform(IWPT) as preprocessor. The purpose of preprocessing is to reduce the nodes in input layer and hidden layer of BP neural network, so that the neural network gains faster training and convergence speed. At first, we apply OWPT or IWPT to the response signal of circuit under test(CUT), and then calculate the normalization energy of each frequency band. The normalization energy is used to train the BP neural network to diagnose faulty components in the analog circuit. These two algorithms need small network size, while have faster learning and convergence speed. Finally, simulation results illustrate the two algorithms are effective for fault diagnosis

A neuro-inspired spike-based PID motor controller for multi-motor robots with low cost FPGAs.

Science.gov (United States)

Jimenez-Fernandez, Angel; Jimenez-Moreno, Gabriel; Linares-Barranco, Alejandro; Dominguez-Morales, Manuel J; Paz-Vicente, Rafael; Civit-Balcells, Anton

2012-01-01

In this paper we present a neuro-inspired spike-based close-loop controller written in VHDL and implemented for FPGAs. This controller has been focused on controlling a DC motor speed, but only using spikes for information representation, processing and DC motor driving. It could be applied to other motors with proper driver adaptation. This controller architecture represents one of the latest layers in a Spiking Neural Network (SNN), which implements a bridge between robotics actuators and spike-based processing layers and sensors. The presented control system fuses actuation and sensors information as spikes streams, processing these spikes in hard real-time, implementing a massively parallel information processing system, through specialized spike-based circuits. This spike-based close-loop controller has been implemented into an AER platform, designed in our labs, that allows direct control of DC motors: the AER-Robot. Experimental results evidence the viability of the implementation of spike-based controllers, and hardware synthesis denotes low hardware requirements that allow replicating this controller in a high number of parallel controllers working together to allow a real-time robot control.

Smear layer removal evaluation of different protocol of Bio Race file and XP- endo Finisher file in corporation with EDTA 17% and NaOCl.

Science.gov (United States)

Zand, Vahid; Mokhtari, Hadi; Reyhani, Mohammad-Frough; Nahavandizadeh, Neda; Azimi, Shahram

2017-11-01

The aim of the present study was to compare the amount of the smear layer remaining in prepared root canals with different protocols of Bio RaCe files and XP-endo Finisher file (XPF) in association with 17% EDTA and sodium hypochlorite solution. A total of 68 extracted single-rooted teeth were randomly divided into 4 experimental groups (n=14) and two control groups (n=6). The root canals were prepared with Bio RaCe files (FKG Dentaire, Switzerland) using the crown-down technique based on manufacturer's instructions and irrigated according to the following irrigation techniques: Group 1: XPF with 2 mL of 2.5% NaOCl for 1 minute. Group 2:, XPF with 1 mL of 17% EDTA for one minute. Group 3: XPF was used for 1 minute in association with normal saline solution. Group 4: XP-endo Finisher file for 30 seconds in association with 2.5% NaOCl and 17% EDTA for 30 seconds. The negative control group: NaOCl (2.5%) was used during root canal preparation, followed by irrigation with 17% EDTA at the end of root canal preparation. The positive control group: Normal saline solution was used for irrigation during root canal preparation. In all the groups, during preparation of the root canals with Bio RaCe file, 20 mL of 2.5% NaOCl was used for root canal irrigation and at the end of the procedural steps 20 mL of normal saline solution was used as a final irrigant. The samples were analyzed under SEM at ×1000‒2000 magnification and evaluated using Torabinejad scoring system. Data were analyzed with non-parametric Kruskal-Wallis test and post hoc Mann-Whitney U test, using SPSS. Statistical significant was defined at P <0.05. The results of the study showed the least amount of the smear layer at coronal, middle and apical thirds of the root canals in groups 2, which was not significantly different from the negative control group ( P <0.5). Under the limitations of the present study, use of a combination of NaOCl and EDTA in association with XPF exhibited the best efficacy for the

Anomalous Signal Detection in ELF Band Electromagnetic Wave using Multi-layer Neural Network with Wavelet Decomposition

Science.gov (United States)

Itai, Akitoshi; Yasukawa, Hiroshi; Takumi, Ichi; Hata, Masayasu

It is well known that electromagnetic waves radiated from the earth's crust are useful for predicting earthquakes. We analyze the electromagnetic waves received at the extremely low frequency band of 223Hz. These observed signals contain the seismic radiation from the earth's crust, but also include several undesired signals. Our research focuses on the signal detection technique to identify an anomalous signal corresponding to the seismic radiation in the observed signal. Conventional anomalous signal detections lack a wide applicability due to their assumptions, e.g. the digital data have to be observed at the same time or the same sensor. In order to overcome the limitation related to the observed signal, we proposed the anomalous signals detection based on a multi-layer neural network which is trained by digital data observed during a span of a day. In the neural network approach, training data do not need to be recorded at the same place or the same time. However, some noises, which have a large amplitude, are detected as the anomalous signal. This paper develops a multi-layer neural network to decrease the false detection of the anomalous signal from the electromagnetic wave. The training data for the proposed network is the decomposed signal of the observed signal during several days, since the seismic radiations are often recorded from several days to a couple of weeks. Results show that the proposed neural network is useful to achieve the accurate detection of the anomalous signal that indicates seismic activity.

Automatic disease diagnosis using optimised weightless neural networks for low-power wearable devices.

Science.gov (United States)

Cheruku, Ramalingaswamy; Edla, Damodar Reddy; Kuppili, Venkatanareshbabu; Dharavath, Ramesh; Beechu, Nareshkumar Reddy

2017-08-01

Low-power wearable devices for disease diagnosis are used at anytime and anywhere. These are non-invasive and pain-free for the better quality of life. However, these devices are resource constrained in terms of memory and processing capability. Memory constraint allows these devices to store a limited number of patterns and processing constraint provides delayed response. It is a challenging task to design a robust classification system under above constraints with high accuracy. In this Letter, to resolve this problem, a novel architecture for weightless neural networks (WNNs) has been proposed. It uses variable sized random access memories to optimise the memory usage and a modified binary TRIE data structure for reducing the test time. In addition, a bio-inspired-based genetic algorithm has been employed to improve the accuracy. The proposed architecture is experimented on various disease datasets using its software and hardware realisations. The experimental results prove that the proposed architecture achieves better performance in terms of accuracy, memory saving and test time as compared to standard WNNs. It also outperforms in terms of accuracy as compared to conventional neural network-based classifiers. The proposed architecture is a powerful part of most of the low-power wearable devices for the solution of memory, accuracy and time issues.

Bio fuel ash in a road construction: impact on soil solution chemistry.

Science.gov (United States)

Thurdin, R T; van Hees, P A W; Bylund, D; Lundström, U S

2006-01-01

Limited natural resources and landfill space, as well as increasing amounts of ash produced from incineration of bio fuel and municipal solid waste, have created a demand for useful applications of ash, of which road construction is one application. Along national road 90, situated about 20 km west of Sollefteå in the middle of Sweden, an experiment road was constructed with a 40 cm bio fuel ash layer. The environmental impact of the ash layer was evaluated from soil solutions obtained by centrifugation of soil samples taken on four occasions during 2001-2003. Soil samples were taken in the ash layer, below the ash layer at two depths in the road and in the ditch. In the soil solutions, pH, conductivity, dissolved organic carbon (DOC) and the total concentration of cations (metals) and anions were determined. Two years after the application of the ash layers in the test road, the concentrations in the ash layer of K, SO4, Zn, and Hg had increased significantly while the concentration of Se, Mo and Cd had decreased significantly. Below the ash layer in the road an initial increase of pH was observed and the concentrations of K, SO4, Se, Mo and Cd increased significantly, while the concentrations of Cu and Hg decreased significantly in the road and also in the ditch. Cd was the element showing a potential risk of contamination of the groundwater. The concentrations of Ca in the ash layer indicated an ongoing hardening, which is important for the leaching rate and the strength of the road construction.

BioCapacitor: A novel principle for biosensors.

Science.gov (United States)

Sode, Koji; Yamazaki, Tomohiko; Lee, Inyoung; Hanashi, Takuya; Tsugawa, Wakako

2016-02-15

Studies regarding biofuel cells utilizing biocatalysts such as enzymes and microorganisms as electrocatalysts have been vigorously conducted over the last two decades. Because of their environmental safety and sustainability, biofuel cells are expected to be used as clean power generators. Among several principles of biofuel cells, enzyme fuel cells have attracted significant attention for their use as alternative energy sources for future implantable devices, such as implantable insulin pumps and glucose sensors in artificial pancreas and pacemakers. However, the inherent issue of the biofuel cell principle is the low power of a single biofuel cell. The theoretical voltage of biofuel cells is limited by the redox potential of cofactors and/or mediators employed in the anode and cathode, which are inadequate for operating any devices used for biomedical application. These limitations inspired us to develop a novel biodevice based on an enzyme fuel cell that generates sufficient stable power to operate electric devices, designated "BioCapacitor." To increase voltage, the enzyme fuel cell is connected to a charge pump. To obtain a sufficient power and voltage to operate an electric device, a capacitor is used to store the potential generated by the charge pump. Using the combination of a charge pump and capacitor with an enzyme fuel cell, high voltages with sufficient temporary currents to operate an electric device were generated without changing the design and construction of the enzyme fuel cell. In this review, the BioCapacitor principle is described. The three different representative categories of biodevices employing the BioCapacitor principle are introduced. Further, the recent challenges in the developments of self-powered stand-alone biodevices employing enzyme fuel cells combined with charge pumps and capacitors are introduced. Finally, the future prospects of biodevices employing the BioCapacitor principle are addressed. Copyright © 2015 The Authors

Tuning Recurrent Neural Networks for Recognizing Handwritten Arabic Words

KAUST Repository

Qaralleh, Esam

2013-10-01

Artificial neural networks have the abilities to learn by example and are capable of solving problems that are hard to solve using ordinary rule-based programming. They have many design parameters that affect their performance such as the number and sizes of the hidden layers. Large sizes are slow and small sizes are generally not accurate. Tuning the neural network size is a hard task because the design space is often large and training is often a long process. We use design of experiments techniques to tune the recurrent neural network used in an Arabic handwriting recognition system. We show that best results are achieved with three hidden layers and two subsampling layers. To tune the sizes of these five layers, we use fractional factorial experiment design to limit the number of experiments to a feasible number. Moreover, we replicate the experiment configuration multiple times to overcome the randomness in the training process. The accuracy and time measurements are analyzed and modeled. The two models are then used to locate network sizes that are on the Pareto optimal frontier. The approach described in this paper reduces the label error from 26.2% to 19.8%.

Prediction of two-phase mixture density using artificial neural networks

International Nuclear Information System (INIS)

Lombardi, C.; Mazzola, A.

1997-01-01

In nuclear power plants, the density of boiling mixtures has a significant relevance due to its influence on the neutronic balance, the power distribution and the reactor dynamics. Since the determination of the two-phase mixture density on a purely analytical basis is in fact impractical in many situations of interest, heuristic relationships have been developed based on the parameters describing the two-phase system. However, the best or even a good structure for the correlation cannot be determined in advance, also considering that it is usually desired to represent the experimental data with the most compact equation. A possible alternative to empirical correlations is the use of artificial neural networks, which allow one to model complex systems without requiring the explicit formulation of the relationships existing among the variables. In this work, the neural network methodology was applied to predict the density data of two-phase mixtures up-flowing in adiabatic channels under different experimental conditions. The trained network predicts the density data with a root-mean-square error of 5.33%, being ∼ 93% of the data points predicted within 10%. When compared with those of two conventional well-proven correlations, i.e. the Zuber-Findlay and the CISE correlations, the neural network performances are significantly better. In spite of the good accuracy of the neural network predictions, the 'black-box' characteristic of the neural model does not allow an easy physical interpretation of the knowledge integrated in the network weights. Therefore, the neural network methodology has the advantage of not requiring a formal correlation structure and of giving very accurate results, but at the expense of a loss of model transparency. (author)

A survey of bio-inspired compliant legged robot designs

International Nuclear Information System (INIS)

Zhou Xiaodong; Bi Shusheng

2012-01-01

The roles of biological springs in vertebrate animals and their implementations in compliant legged robots offer significant advantages over the rigid legged ones in certain types of scenarios. A large number of robotics institutes have been attempting to work in conjunction with biologists and incorporated these principles into the design of biologically inspired robots. The motivation of this review is to investigate the most published compliant legged robots and categorize them according to the types of compliant elements adopted in their mechanical structures. Based on the typical robots investigated, the trade-off between each category is summarized. In addition, the most significant performances of these robots are compared quantitatively, and multiple available solutions for the future compliant legged robot design are suggested. Finally, the design challenges for compliant legged robots are analysed. This review will provide useful guidance for robotic designers in creating new designs by inheriting the virtues of those successful robots according to the specific tasks. (topical review)

NASA's Bio-Inspired Acoustic Absorber Concept

Science.gov (United States)

Koch, L. Danielle

2017-01-01

are encouraged to contact the NASA Glenn Technology Transfer Office, https:technology.grc.nasa.gov. The NASA Glenn Office of Education https:www.nasa.govcentersglenneducationindex.html and the NASA Glenn Virtual Interchange for Nature-Inspired Exploration https:www.grc.nasa.govvine are also helping to make research like this accessible to the public and students of all ages.

An Improved Brain-Inspired Emotional Learning Algorithm for Fast Classification

Directory of Open Access Journals (Sweden)

Ying Mei

2017-06-01

Full Text Available Classification is an important task of machine intelligence in the field of information. The artificial neural network (ANN is widely used for classification. However, the traditional ANN shows slow training speed, and it is hard to meet the real-time requirement for large-scale applications. In this paper, an improved brain-inspired emotional learning (BEL algorithm is proposed for fast classification. The BEL algorithm was put forward to mimic the high speed of the emotional learning mechanism in mammalian brain, which has the superior features of fast learning and low computational complexity. To improve the accuracy of BEL in classification, the genetic algorithm (GA is adopted for optimally tuning the weights and biases of amygdala and orbitofrontal cortex in the BEL neural network. The combinational algorithm named as GA-BEL has been tested on eight University of California at Irvine (UCI datasets and two well-known databases (Japanese Female Facial Expression, Cohn–Kanade. The comparisons of experiments indicate that the proposed GA-BEL is more accurate than the original BEL algorithm, and it is much faster than the traditional algorithm.

A Fusion Face Recognition Approach Based on 7-Layer Deep Learning Neural Network

Directory of Open Access Journals (Sweden)

Jianzheng Liu

2016-01-01

Full Text Available This paper presents a method for recognizing human faces with facial expression. In the proposed approach, a motion history image (MHI is employed to get the features in an expressive face. The face can be seen as a kind of physiological characteristic of a human and the expressions are behavioral characteristics. We fused the 2D images of a face and MHIs which were generated from the same face’s image sequences with expression. Then the fusion features were used to feed a 7-layer deep learning neural network. The previous 6 layers of the whole network can be seen as an autoencoder network which can reduce the dimension of the fusion features. The last layer of the network can be seen as a softmax regression; we used it to get the identification decision. Experimental results demonstrated that our proposed method performs favorably against several state-of-the-art methods.

Cation Effects on the Layer Structure of Biogenic Mn-Oxides

Energy Technology Data Exchange (ETDEWEB)

Zhu, M.; Ginder-Vogel, M; Parikh, S; Feng, X; Sparks, D

2010-01-01

Biologically catalyzed Mn(II) oxidation produces biogenic Mn-oxides (BioMnO{sub x}) and may serve as one of the major formation pathways for layered Mn-oxides in soils and sediments. The structure of Mn octahedral layers in layered Mn-oxides controls its metal sequestration properties, photochemistry, oxidizing ability, and topotactic transformation to tunneled structures. This study investigates the impacts of cations (H{sup +}, Ni(II), Na{sup +}, and Ca{sup 2+}) during biotic Mn(II) oxidation on the structure of Mn octahedral layers of BioMnO{sub x} using solution chemistry and synchrotron X-ray techniques. Results demonstrate that Mn octahedral layer symmetry and composition are sensitive to previous cations during BioMnO{sub x} formation. Specifically, H{sup +} and Ni(II) enhance vacant site formation, whereas Na{sup +} and Ca{sup 2+} favor formation of Mn(III) and its ordered distribution in Mn octahedral layers. This study emphasizes the importance of the abiotic reaction between Mn(II) and BioMnO{sub x} and dependence of the crystal structure of BioMnO{sub x} on solution chemistry.

Convolutional Neural Network for Histopathological Analysis of Osteosarcoma.

Science.gov (United States)

Mishra, Rashika; Daescu, Ovidiu; Leavey, Patrick; Rakheja, Dinesh; Sengupta, Anita

2018-03-01

Pathologists often deal with high complexity and sometimes disagreement over osteosarcoma tumor classification due to cellular heterogeneity in the dataset. Segmentation and classification of histology tissue in H&E stained tumor image datasets is a challenging task because of intra-class variations, inter-class similarity, crowded context, and noisy data. In recent years, deep learning approaches have led to encouraging results in breast cancer and prostate cancer analysis. In this article, we propose convolutional neural network (CNN) as a tool to improve efficiency and accuracy of osteosarcoma tumor classification into tumor classes (viable tumor, necrosis) versus nontumor. The proposed CNN architecture contains eight learned layers: three sets of stacked two convolutional layers interspersed with max pooling layers for feature extraction and two fully connected layers with data augmentation strategies to boost performance. The use of a neural network results in higher accuracy of average 92% for the classification. We compare the proposed architecture with three existing and proven CNN architectures for image classification: AlexNet, LeNet, and VGGNet. We also provide a pipeline to calculate percentage necrosis in a given whole slide image. We conclude that the use of neural networks can assure both high accuracy and efficiency in osteosarcoma classification.

Bio-prospecting of distillery yeasts as bio-control and bio-remediation agents.

Science.gov (United States)

Ubeda, Juan F; Maldonado, María; Briones, Ana I; Francisco, J Fernández; González, Francisco J

2014-05-01

This work constitutes a preliminary study in which the capacity of non-Saccharomyces yeasts isolated from ancient distilleries as bio-control agents against moulds and in the treatment of waste waters contaminated by heavy metals-i.e. bio-remediation-is shown. In the first control assays, antagonist effect between non-Saccharomyces yeasts, their extracts and supernatants against some moulds, analysing the plausible (not exhaustive) involved factors were qualitatively verified. In addition, two enzymatic degrading properties of cell wall plant polymers, quitinolitic and pectinolitic, were screened. Finally, their use as agents of bio-remediation of three heavy metals (cadmium, chromium and lead) was analysed semi-quantitatively. The results showed that all isolates belonging to Pichia species effectively inhibited all moulds assayed. Moreover, P. kudriavzevii is a good candidate for both bio-control and bio-remediation because it inhibited moulds and accumulated the major proportion of the three tested metals.

Ultra-nanocrystalline diamond electrodes: optimization towards neural stimulation applications.

Science.gov (United States)

Garrett, David J; Ganesan, Kumaravelu; Stacey, Alastair; Fox, Kate; Meffin, Hamish; Prawer, Steven

2012-02-01

Diamond is well known to possess many favourable qualities for implantation into living tissue including biocompatibility, biostability, and for some applications hardness. However, conducting diamond has not, to date, been exploited in neural stimulation electrodes due to very low electrochemical double layer capacitance values that have been previously reported. Here we present electrochemical characterization of ultra-nanocrystalline diamond electrodes grown in the presence of nitrogen (N-UNCD) that exhibit charge injection capacity values as high as 163 µC cm(-2) indicating that N-UNCD is a viable material for microelectrode fabrication. Furthermore, we show that the maximum charge injection of N-UNCD can be increased by tailoring growth conditions and by subsequent electrochemical activation. For applications requiring yet higher charge injection, we show that N-UNCD electrodes can be readily metalized with platinum or iridium, further increasing charge injection capacity. Using such materials an implantable neural stimulation device fabricated from a single piece of bio-permanent material becomes feasible. This has significant advantages in terms of the physical stability and hermeticity of a long-term bionic implant.

neural network based model o work based model of an industrial oil

African Journals Online (AJOL)

eobe

technique. g, Neural Network Model, Regression, Mean Square Error, PID controller. ... during the training processes. An additio ... used to carry out simulation studies of the mode .... A two-layer feed-forward neural network with Matlab.

Feature to prototype transition in neural networks

Science.gov (United States)

Krotov, Dmitry; Hopfield, John

Models of associative memory with higher order (higher than quadratic) interactions, and their relationship to neural networks used in deep learning are discussed. Associative memory is conventionally described by recurrent neural networks with dynamical convergence to stable points. Deep learning typically uses feedforward neural nets without dynamics. However, a simple duality relates these two different views when applied to problems of pattern classification. From the perspective of associative memory such models deserve attention because they make it possible to store a much larger number of memories, compared to the quadratic case. In the dual description, these models correspond to feedforward neural networks with one hidden layer and unusual activation functions transmitting the activities of the visible neurons to the hidden layer. These activation functions are rectified polynomials of a higher degree rather than the rectified linear functions used in deep learning. The network learns representations of the data in terms of features for rectified linear functions, but as the power in the activation function is increased there is a gradual shift to a prototype-based representation, the two extreme regimes of pattern recognition known in cognitive psychology. Simons Center for Systems Biology.

Simple Algorithms for Distributed Leader Election in Anonymous Synchronous Rings and Complete Networks Inspired by Neural Development in Fruit Flies.

Science.gov (United States)

Xu, Lei; Jeavons, Peter

2015-11-01

Leader election in anonymous rings and complete networks is a very practical problem in distributed computing. Previous algorithms for this problem are generally designed for a classical message passing model where complex messages are exchanged. However, the need to send and receive complex messages makes such algorithms less practical for some real applications. We present some simple synchronous algorithms for distributed leader election in anonymous rings and complete networks that are inspired by the development of the neural system of the fruit fly. Our leader election algorithms all assume that only one-bit messages are broadcast by nodes in the network and processors are only able to distinguish between silence and the arrival of one or more messages. These restrictions allow implementations to use a simpler message-passing architecture. Even with these harsh restrictions our algorithms are shown to achieve good time and message complexity both analytically and experimentally.

Autonomous self-healing structural composites with bio-inspired design.

Science.gov (United States)

D'Elia, Eleonora; Eslava, Salvador; Miranda, Miriam; Georgiou, Theoni K; Saiz, Eduardo

2016-05-05

Strong and tough natural composites such as bone, silk or nacre are often built from stiff blocks bound together using thin interfacial soft layers that can also provide sacrificial bonds for self-repair. Here we show that it is possible exploit this design in order to create self-healing structural composites by using thin supramolecular polymer interfaces between ceramic blocks. We have built model brick-and-mortar structures with ceramic contents above 95 vol% that exhibit strengths of the order of MPa (three orders of magnitude higher than the interfacial polymer) and fracture energies that are two orders of magnitude higher than those of the glass bricks. More importantly, these properties can be fully recovered after fracture without using external stimuli or delivering healing agents. This approach demonstrates a very promising route towards the design of strong, ideal self-healing materials able to self-repair repeatedly without degradation or external stimuli.

Neuro-inspired computing using resistive synaptic devices

CERN Document Server

2017-01-01

This book summarizes the recent breakthroughs in hardware implementation of neuro-inspired computing using resistive synaptic devices. The authors describe how two-terminal solid-state resistive memories can emulate synaptic weights in a neural network. Readers will benefit from state-of-the-art summaries of resistive synaptic devices, from the individual cell characteristics to the large-scale array integration. This book also discusses peripheral neuron circuits design challenges and design strategies. Finally, the authors describe the impact of device non-ideal properties (e.g. noise, variation, yield) and their impact on the learning performance at the system-level, using a device-algorithm co-design methodology. • Provides single-source reference to recent breakthroughs in resistive synaptic devices, not only at individual cell-level, but also at integrated array-level; • Includes detailed discussion of the peripheral circuits and array architecture design of the neuro-crossbar system; • Focuses on...

Quantum-Inspired Multidirectional Associative Memory With a Self-Convergent Iterative Learning.

Science.gov (United States)

Masuyama, Naoki; Loo, Chu Kiong; Seera, Manjeevan; Kubota, Naoyuki

2018-04-01

Quantum-inspired computing is an emerging research area, which has significantly improved the capabilities of conventional algorithms. In general, quantum-inspired hopfield associative memory (QHAM) has demonstrated quantum information processing in neural structures. This has resulted in an exponential increase in storage capacity while explaining the extensive memory, and it has the potential to illustrate the dynamics of neurons in the human brain when viewed from quantum mechanics perspective although the application of QHAM is limited as an autoassociation. We introduce a quantum-inspired multidirectional associative memory (QMAM) with a one-shot learning model, and QMAM with a self-convergent iterative learning model (IQMAM) based on QHAM in this paper. The self-convergent iterative learning enables the network to progressively develop a resonance state, from inputs to outputs. The simulation experiments demonstrate the advantages of QMAM and IQMAM, especially the stability to recall reliability.

Inductive differentiation of two neural lineages reconstituted in a microculture system from Xenopus early gastrula cells.

Science.gov (United States)

Mitani, S; Okamoto, H

1991-05-01

Neural induction of ectoderm cells has been reconstituted and examined in a microculture system derived from dissociated early gastrula cells of Xenopus laevis. We have used monoclonal antibodies as specific markers to monitor cellular differentiation from three distinct ectoderm lineages in culture (N1 for CNS neurons from neural tube, Me1 for melanophores from neural crest and E3 for skin epidermal cells from epidermal lineages). CNS neurons and melanophores differentiate when deep layer cells of the ventral ectoderm (VE, prospective epidermis region; 150 cells/culture) and an appropriate region of the marginal zone (MZ, prospective mesoderm region; 5-150 cells/culture) are co-cultured, but not in cultures of either cell type on their own; VE cells cultured alone yield epidermal cells as we have previously reported. The extent of inductive neural differentiation in the co-culture system strongly depends on the origin and number of MZ cells initially added to culture wells. The potency to induce CNS neurons is highest for dorsal MZ cells and sharply decreases as more ventrally located cells are used. The same dorsoventral distribution of potency is seen in the ability of MZ cells to inhibit epidermal differentiation. In contrast, the ability of MZ cells to induce melanophores shows the reverse polarity, ventral to dorsal. These data indicate that separate developmental mechanisms are used for the induction of neural tube and neural crest lineages. Co-differentiation of CNS neurons or melanophores with epidermal cells can be obtained in a single well of co-cultures of VE cells (150) and a wide range of numbers of MZ cells (5 to 100). Further, reproducible differentiation of both neural lineages requires intimate association between cells from the two gastrula regions; virtually no differentiation is obtained when cells from the VE and MZ are separated in a culture well. These results indicate that the inducing signals from MZ cells for both neural tube and neural

Two-step fast microwave-assisted pyrolysis of biomass for bio-oil production using microwave absorbent and HZSM-5 catalyst.

Science.gov (United States)

Zhang, Bo; Zhong, Zhaoping; Xie, Qinglong; Liu, Shiyu; Ruan, Roger

2016-07-01

A novel technology of two-step fast microwave-assisted pyrolysis (fMAP) of corn stover for bio-oil production was investigated in the presence of microwave absorbent (SiC) and HZSM-5 catalyst. Effects of fMAP temperature and catalyst-to-biomass ratio on bio-oil yield and chemical components were examined. The results showed that this technology, employing microwave, microwave absorbent and HZSM-5 catalyst, was effective and promising for biomass fast pyrolysis. The fMAP temperature of 500°C was considered the optimum condition for maximum yield and best quality of bio-oil. Besides, the bio-oil yield decreased linearly and the chemical components in bio-oil were improved sequentially with the increase of catalyst-to-biomass ratio from 1:100 to 1:20. The elemental compositions of bio-char were also determined. Additionally, compared to one-step fMAP process, two-step fMAP could promote the bio-oil quality with a smaller catalyst-to-biomass ratio. Copyright © 2016. Published by Elsevier B.V.

Bio-inspired computational heuristics to study Lane-Emden systems arising in astrophysics model.

Science.gov (United States)

Ahmad, Iftikhar; Raja, Muhammad Asif Zahoor; Bilal, Muhammad; Ashraf, Farooq

2016-01-01

This study reports novel hybrid computational methods for the solutions of nonlinear singular Lane-Emden type differential equation arising in astrophysics models by exploiting the strength of unsupervised neural network models and stochastic optimization techniques. In the scheme the neural network, sub-part of large field called soft computing, is exploited for modelling of the equation in an unsupervised manner. The proposed approximated solutions of higher order ordinary differential equation are calculated with the weights of neural networks trained with genetic algorithm, and pattern search hybrid with sequential quadratic programming for rapid local convergence. The results of proposed solvers for solving the nonlinear singular systems are in good agreements with the standard solutions. Accuracy and convergence the design schemes are demonstrated by the results of statistical performance measures based on the sufficient large number of independent runs.

Complex-Valued Neural Networks

CERN Document Server

Hirose, Akira

2012-01-01

This book is the second enlarged and revised edition of the first successful monograph on complex-valued neural networks (CVNNs) published in 2006, which lends itself to graduate and undergraduate courses in electrical engineering, informatics, control engineering, mechanics, robotics, bioengineering, and other relevant fields. In the second edition the recent trends in CVNNs research are included, resulting in e.g. almost a doubled number of references. The parametron invented in 1954 is also referred to with discussion on analogy and disparity. Also various additional arguments on the advantages of the complex-valued neural networks enhancing the difference to real-valued neural networks are given in various sections. The book is useful for those beginning their studies, for instance, in adaptive signal processing for highly functional sensing and imaging, control in unknown and changing environment, robotics inspired by human neural systems, and brain-like information processing, as well as interdisciplina...

Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data.

Science.gov (United States)

Ching, Travers; Zhu, Xun; Garmire, Lana X

2018-04-01

Artificial neural networks (ANN) are computing architectures with many interconnections of simple neural-inspired computing elements, and have been applied to biomedical fields such as imaging analysis and diagnosis. We have developed a new ANN framework called Cox-nnet to predict patient prognosis from high throughput transcriptomics data. In 10 TCGA RNA-Seq data sets, Cox-nnet achieves the same or better predictive accuracy compared to other methods, including Cox-proportional hazards regression (with LASSO, ridge, and mimimax concave penalty), Random Forests Survival and CoxBoost. Cox-nnet also reveals richer biological information, at both the pathway and gene levels. The outputs from the hidden layer node provide an alternative approach for survival-sensitive dimension reduction. In summary, we have developed a new method for accurate and efficient prognosis prediction on high throughput data, with functional biological insights. The source code is freely available at https://github.com/lanagarmire/cox-nnet.

Automatic target recognition using a feature-based optical neural network

Science.gov (United States)

Chao, Tien-Hsin

1992-01-01

An optical neural network based upon the Neocognitron paradigm (K. Fukushima et al. 1983) is introduced. A novel aspect of the architectural design is shift-invariant multichannel Fourier optical correlation within each processing layer. Multilayer processing is achieved by iteratively feeding back the output of the feature correlator to the input spatial light modulator and updating the Fourier filters. By training the neural net with characteristic features extracted from the target images, successful pattern recognition with intra-class fault tolerance and inter-class discrimination is achieved. A detailed system description is provided. Experimental demonstration of a two-layer neural network for space objects discrimination is also presented.

Two-step microalgal biodiesel production using acidic catalyst generated from pyrolysis-derived bio-char

International Nuclear Information System (INIS)

Dong, Tao; Gao, Difeng; Miao, Chao; Yu, Xiaochen; Degan, Charles; Garcia-Pérez, Manuel; Rasco, Barbara; Sablani, Shyam S.; Chen, Shulin

2015-01-01

Highlights: • Highly active catalyst was prepared using bio-char co-produced in Auger pyrolysis. • Catalyst inhibitors in crude oil were effectively removed by a practical refinery process. • Free fatty acids (FFA) content in refined microalgal oil was reduced to less than 0.5%. • A total fatty acid methyl ester (FAME) yield of 99% was obtained via a two-step process. • The inexpensive bio-char catalyst is superior to Amberlyst-15 in pre-esterification. - Abstract: An efficient process for biodiesel production from fast-refined microalgal oil was demonstrated. A low cost catalyst prepared from pyrolysis-derived bio-char, was applied in pre-esterification to reduce free fatty acid (FFA) content. Results showed that the bio-char catalyst was highly active in esterification; however, the performance of the catalyst significantly reduced when crude microalgal oil was used as feedstock. To solve the problem caused by catalyst-fouling, a fast and scalable crude oil refinery procedure was carried out to remove chlorophyll and phospholipids that might degrade the catalyst and the quality of biodiesel. The activity and reusability of bio-char catalyst were remarkably improved in the fast-refined oil. FFA content in the refined microalgal oil was reduced to less than 0.5% after pre-esterification. The bio-char catalyst could be reused for 10 cycles without dramatic loss in activity. The pre-esterification fits the first-order kinetic reaction with activation energy of 42.16 kJ/mol. The activity of bio-char catalyst was superior to commercial Amberlyst-15 under the same reaction condition. A total fatty acid methyl ester (FAME, namely biodiesel) yield of 99% was obtained following the second-step CaO-catalyzed transesterification. The cost-effective bio-char catalyst has great potential for biodiesel production using feedstocks having high FFA content.

Biologically inspired growth of hydroxyapatite crystals on bio-organics-defined scaffolds

Energy Technology Data Exchange (ETDEWEB)

Yang, Chunrong, E-mail: milkhoney3@163.com [Department of Materials Science and Engineering, Fujian University of Technology, Fuzhou 350108 (China); Li, Yuli; Nan, Kaihui [Eye Hospital, Wenzhou Medical College, Wenzhou 325027 (China)

2013-03-15

Graphical abstract: Petal-like crystals were observed to form on the surface of the BG/COL/ChS scaffolds. Highlights: ► Porous scaffolds were prepared using bioglass, collagen and chondroitin sulfate. ► Highly oriented HA crystals were grown on scaffolds using simulated body fluids ► The microstructure and orientation of HA were explained by molecular configuration. - Abstract: Several bio-organics-defined composite scaffolds were prepared using 58s-bioglass (BG), collagen (Col) and chondroitin sulfate (ChS). These scaffolds possess highly porous structure. X-ray diffraction of these scaffolds strongly indicated that hydroxyapatite (HA) crystals formed on their surfaces in simulated body fluids within 3 d, and similar formation process of crystals could be obtained on BG/Col and BG/Col/ChS scaffolds. The morphology and structure of the crystals were further examined by scanning electron microscopy. The results obtained indicate that an apatite with petal-like structure similar to that found on BG/Col scaffolds can be produced on BG/Col/ChS scaffolds through biomimetic synthesis, while that on BG/ChS scaffolds took place differently. The differences could be explained by self-assembly processes and the different macromolecular configurations of the Col and ChS fibrils which self-assemble spontaneously into their fibers. On the other hand, the bio-organics-defined composites have good cell biocompability. The results may be applicable to develop tailored biomaterials for peculiar bone substitute.

Biologically inspired growth of hydroxyapatite crystals on bio-organics-defined scaffolds