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Electromyography (EMG) signals can be used to integrate with machines and form one assistive system such as a powered exoskeleton. This paper focuses on the design and development of a low-cost elbow joint powered exoskeleton for human power augmentation, controlled by the EMG. Majority of the hardware has been designed and developed in-house, without using expensively available hardware. A theoretical...
This paper presents a methodology to implement large Neural Networks based classifiers in low-cost FPGAs. The idea is to divide the large Neural Network into several smaller networks which can easily be implemented in small devices. Then, a Multiple Classifier Ensemble is used to joint the results of each small network and thus provide the output of the system. To validate the proposal a classification...
A new supervised learning algorithm, SNN/LP, is proposed for Spiking Neural Networks. This novel algorithm uses limited precision for both synaptic weights and synaptic delays; 3 bits in each case. Also a genetic algorithm is used for the supervised training. The results are comparable or better than previously published work. The results are applicable to the realization of large-scale hardware neural...
The hardware layer of the Si elegans EU FP7 project is a massively parallel architecture designed to accurately emulate the C. elegans nematode in biological real-time. The C. elegans nematode is one of the simplest and well characterized Biological Nervous Systems (BNS) yet many questions related to basic functions such as movement and learning remain unanswered. The hardware layer includes a Hardware...
Some next generation computing devices may consist of resistive memory arranged as a crossbar. Currently, the dominant approach is to use crossbars as the weight matrix of a neural network, and to use learning algorithms that require small incremental weight updates, such as gradient descent (for example Backpropagation). Using real-world measurements, we demonstrate that resistive memory devices...
The Vienna Neural Network Specification Language (ViNNSL) is an XML based domain specific language for the description of neural network objects. It proved very well as communication framework in service-oriented architecture based neural network simulation environments, as N2Sky. N2Sky is a virtual organization (VO) environment based on the sky computing paradigm. It allows the creation, training,...
The goal of neuromorphic computing is to understand brains better and thereby build better computers. In this paper, we describe a special-purpose hardware architecture for neural network simulation systems called neuron machine, and propose novel schemes that can be used effectively for large-scale neuromorphic simulations. A neuron machine system consists of a single digital hardware neuron, which...
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