This project aims to build an accurate, smallfootprint, low-latency Speech Command Recognition system that is capable of detecting predefined keywords. Using the Speech Commands Dataset provided by Google’s TensorFlow and AIY teams, we have implemented different architectures using different machine learning algorithms. Our models include: Vanilla Single-Layer softmax model, Deep Neural Network...

Machine learning techniques have become increasingly popular in the field of resting state fMRI (functional magnetic resonance imaging) network based classification. However, the application of convolutional networks has been proposed only very recently and has remained largely unexplored. In this paper we describe a convolutional neural network architecture for functional connectome classifica...

In this paper, we investigate the usage of machine learning in the detection and recognition of double stars. To do this, numerous images including one star and double stars are simulated. Then, 100 terms of Zernike expansion with random coefficients are considered as aberrations to impose on the aforementioned images. Also, a telescope with a specific aperture is simulated. In this work, two k...

We present in this paper a simple, yet efficient convolutional neural network (CNN) architecture for robust audio event recognition. Opposing to deep CNN architectures with multiple convolutional and pooling layers topped up with multiple fully connected layers, the proposed network consists of only three layers: convolutional, pooling, and softmax layer. Two further features distinguish it fro...

The process of determining which disease or condition explains a person’s symptoms and signs can be very complicated and may be inaccurate in some cases. The general belief is that diagnosing diseases relies on doctors’ keen intuition, rich experience and professional equipment. In this work, we employ ideas from recent advances in plantar pressure research and from the powerful capacity of the...

Power gating technique reduces leakage power in the circuit. However, power gating leads to large voltage fluctuation on the power rail during power gating mode to active mode due to the package inductance in the Printed Circuit Board. This voltage fluctuation may cause unwanted transitions in neighboring circuits. In this work, a power gating architecture is developed for minimizing power in a...

This study is an analysis of scene recognition in a pre-trained convolutional network, to evaluate the information the network uses to distinguish scene categories. We are particularly interested in how the network is related to various areas in the human brain that are involved in different modes of scene recognition. Results of several experiments suggest that the convolutional network relies...

Online social networks like Instagram are places for communication. Also, these media produce rich metadata which are useful for further analysis in many fields including health and cognitive science. Many researchers are using these metadata like hashtags, images, etc. to detect patterns of user activities. However, there are several serious ambiguities like how much reliable are these informa...

Several methods have recently been proposed to detect salient input features for outputs of neural networks. Those methods offer a qualitative glimpse at feature importance, but they fall short of providing quantifiable attributions that can be compared across decisions and measures of the expected quality of their explanations. To address these shortcomings, we present an attentive mixture of ...