5,407 research outputs found

    Intelligent intrusion detection in low power IoTs

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    Middleware and Services for Dynamic Adaptive Neural Network Arrays

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    Dynamic Adaptive Neural Network Arrays (DANNAs) are neuromorphic systems that exhibit spiking behaviors and can be designed using evolutionary optimization. Array elements are rapidly reconfigurable and can function as either neurons or synapses with programmable interconnections and parameters. Visualization applications can examine DANNA element connections, parameters, and functionality, and evolutionary optimization applications can utilize DANNA to speedup neural network simulations. To facilitate interactions with DANNAs from these applications, we have developed a language-agnostic application programming interface (API) that abstracts away low-level communication details with a DANNA and provides a high-level interface for reprogramming and controlling a DANNA. The library has also been designed in modules in order to adapt to future changes in the design of DANNA, including changes to the DANNA element design, DANNA communication protocol, and connection. In addition to communicating with DANNAs, it is also beneficial for applications to store networks with known functionality. Hence, a Representational State Transfer (REST) API with a MongoDB database back-end has been developed to encourage the collection and exploration of networks

    Load balancing in distributed query management at web enabled systems

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    Every day more and more Business are using web as compulsory medium to provide services. Ever increase in technological advancement leads more devices and applications are accessing web based application around the clock. So single node web applications are prone collapse, designing an application with Distributed frame work and mange those application often reduce the risk of single point failure. In that strategy load balancers plays an important role to direct the traffic to multiple Servers. Existing load balancers are Prone to fail and centralized strategy to distribute the load among the server. Our proposed heuristic based load balancer follows Decentralized approach to solve the problem and our ANN based supervised back propagation technique gives optimized results that existing Load balancer

    Synthesis of neural networks for spatio-temporal spike pattern recognition and processing

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    The advent of large scale neural computational platforms has highlighted the lack of algorithms for synthesis of neural structures to perform predefined cognitive tasks. The Neural Engineering Framework offers one such synthesis, but it is most effective for a spike rate representation of neural information, and it requires a large number of neurons to implement simple functions. We describe a neural network synthesis method that generates synaptic connectivity for neurons which process time-encoded neural signals, and which makes very sparse use of neurons. The method allows the user to specify, arbitrarily, neuronal characteristics such as axonal and dendritic delays, and synaptic transfer functions, and then solves for the optimal input-output relationship using computed dendritic weights. The method may be used for batch or online learning and has an extremely fast optimization process. We demonstrate its use in generating a network to recognize speech which is sparsely encoded as spike times.Comment: In submission to Frontiers in Neuromorphic Engineerin
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