Research

Key Words: Deep learning, convolutional neural networks, digital signals processing

convolutional neural network - uses little preprocessing, recognizes and learns filters, not hand-engineered

Modulation Classification Using Convolutional Neural Network Based Deep Learning Model

"Abstract—Deep learning (DL) is a powerful classification technique that has great success in many application domains. However, its usage in communication systems has not been well explored. In this paper, we address the issue of using DL in communication systems, especially for modulation classification. Convolutional neural network (CNN) is utilized to complete the classification task. We convert the raw modulated signals into images that have a grid-like topology and feed them to CNN for network training. Two existing approaches, including cumulant and support vector machine (SVM) based classification algorithms, are involved for performance comparison. Simulation results indicate that the proposed CNN based modulation classification approach achieves comparable classification accuracy without the necessity of manual feature selection. "

Simple convolutional neural network on image classification

"Abstract: In recent years, deep learning has been used in image classification, object tracking, pose estimation, text detection and recognition, visual saliency detection, action recognition and scene labeling. Auto Encoder, sparse coding, Restricted Boltzmann Machine, Deep Belief Networks and Convolutional neural networks is commonly used models in deep learning. Among different type of models, Convolutional neural networks has been demonstrated high performance on image classification. In this paper we bulided a simple Convolutional neural network on image classification. This simple Convolutional neural network completed the image classification. Our experiments are based on bench marking data sets minist [1] and cifar-10. On the basis of the Convolutional neural network, we also analyzed different methods of learning rate set and different optimization algorithm of solving the optimal parameters of the influence on image classification."

Optimizing convolutional neural network on DSP

" Abstract: Deep learning techniques like Convolutional Neural Networks (CNN) are getting traction for classification of objects (e.g. traffic signs, pedestrian, vehicles etc.) in Advanced Driver Assistance Systems (ADAS). Typical CNN based trained networks poses huge computational complexity in feed forward path during operation due to multiple layers and within layer operations like 2D convolution, spatial pooling and non-linear mapping. The paper proposes optimization techniques to efficiently map such networks on Digital Signal processors (DSP). These techniques consist of fixed point conversion, data re-organization, weight placement and LUT usage resulting in optimal utilization of resources on C66x™ DSP. The proposed kernels are developed and simulated on Texas Instruments (TI)'s Driver Assist TDA3X platform with optimal utilization of compute and data resources inside DSP. These optimization techniques are applicable for multiple network topologies published in the literature. "

Deep Learning

"Abstract: Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech."

Deep Learning for Content-Based Image Retrieval: A Comprehensive Study

"Abstract: Learning effective feature representations and similarity measures are crucial to the retrieval performance of a content-based image retrieval (CBIR) system. Despite extensive research efforts for decades, it remains one of the most challenging open problems that considerably hinders the successes of real-world CBIR systems. The key challenge has been attributed to the well-known ``semantic gap'' issue that exists between low-level image pixels captured by machines and high-level semantic concepts perceived by human. Among various techniques, machine learning has been actively investigated as a possible direction to bridge the semantic gap in the long term. Inspired by recent successes of deep learning techniques for computer vision and other applications, in this paper, we attempt to address an open problem: if deep learning is a hope for bridging the semantic gap in CBIR and how much improvements in CBIR tasks can be achieved by exploring the state-of-the-art deep learning techniques for learning feature representations and similarity measures. Specifically, we investigate a framework of deep learning with application to CBIR tasks with an extensive set of empirical studies by examining a state-of-the-art deep learning method (Convolutional Neural Networks) for CBIR tasks under varied settings. From our empirical studies, we find some encouraging results and summarize some important insights for future research. "

MatConvNet: Convolutional Neural Networks for MATLAB

Abstract: "MatConvNet is an implementation of Convolutional Neural Networks (CNNs) for MATLAB. The toolbox is designed with an emphasis on simplicity and flexibility. It exposes the building blocks of CNNs as easy-to-use MATLAB functions, providing routines for computing linear convolutions with filter banks, feature pooling, and many more. In this manner, MatConvNet allows fast prototyping of new CNN architectures; at the same time, it supports efficient computation on CPU and GPU allowing to train complex models on large datasets such as ImageNet ILSVRC. This document provides an overview of CNNs and how they are implemented in MatConvNet and gives the technical details of each computational block in the toolbox."

Asynchronous Methods for Deep Reinforcement Learning

Abstract: "We propose a conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient descent for optimization of deep neural network controllers. We present asynchronous variants of four standard reinforcement learning algorithms and show that parallel actor-learners have a stabilizing effect on training allowing all four methods to successfully train neural network controllers. The best performing method, an asynchronous variant of actor-critic, surpasses the current state-of-the-art on the Atari domain while training for half the time on a single multi-core CPU instead of a GPU. Furthermore, we show that asynchronous actor-critic succeeds on a wide variety of continuous motor control problems as well as on a new task of navigating random 3D mazes using a visual input."