Searched for: contributor:"Kumar, Sumeet (graduation committee)"
(1 - 8 of 8)
document
Spessot, Davide (author)
Recent trends in platforms for the consumer market increased the need for low-power and reliable classification engines. Spiking Neural Network (SNN) is a new technology that promises to deliver 4 orders of magnitude more performance per watt than competing solutions. Moreover, the adoption of RADAR for gesture detection provides higher...
master thesis 2019
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Vyas, Rahul (author)
Autonomous vehicle (AV technology) relies heavily on vision based applications like object recognition, obstacle/collision avoidance etc. In order to achieve this, understanding and estimating the dynamics in the environment is extremely important. LIDARs are proven to detect both shape as well as the speed/movement of the objects in the scene...
master thesis 2019
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Lauriks, Joppe (author)
Spiking Neural Networks have opened new doors in the world of Neural Networks. This study implements and shows a viable architecture to detect and classify blob-like input data. An architecture consisting of three parts a region proposal network, weight calculations, and the classifier is discussed and implemented. The region proposal network is...
master thesis 2019
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Joshi, Ninad (author)
Traditional Artificial Neural Networks(ANNs)like CNNs have shown tremendous opportunities in various domains like autonomous cars, disease diagnosis, etc. Proven learning algorithms like backpropagation help ANNs in achieving higher accuracy. But there is a serious challenge with the increasing popularity of traditional ANNs is of energy...
master thesis 2019
document
Coenen, Joris (author)
One of the challenges of neuromorphic computing is efficiently routing spikes from neurons to their connected synapses. The aim of this thesis is to design a spike-routing architecture for flexible connections on single-chip neuromorphic systems. A model for estimating area, power consumption, memory, spike latency and link utilisation for...
master thesis 2019
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Mes, Johan (author)
The Self-Organizing Map (SOM) is an unsupervised neural networktopology that incorporates competitive learning for the classicationof data. In this thesis we investigate the design space of a system incorporating such a topology based on Spiking Neural Networks (SNNs), and apply it to classifying electrocardiogram (ECG) beats. We present novel...
master thesis 2018
document
Kolağasioğlu, Eralp (author)
Cardiovascular diseases are the leading cause of death in the devel- oped world. Preventing these deaths, require long term monitoring and manual inspection of ECG signals, which is a very time consum- ing process. Consequently, a wearable system that can automatically categorize beats is essential.<br/>Neuromorphic machines have been introduced...
master thesis 2018
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Jeyachandra, Evelyn Rashmi (author)
As technology scaling enters the nanometer regime, device aging effects cause quality and reliability issues in CMOS Integrated Circuits (ICs), which in turn shorten its lifetime. Evaluating system aging through circuit simulations is very complex and time consuming. In this thesis, a framework is proposed, which allows for the evaluation of...
master thesis 2017
Searched for: contributor:"Kumar, Sumeet (graduation committee)"
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