A Wavelet neural network for detection of signals in communications

Our objective is the design and simulation of an efficient system for detection of signals in communications in terms of speed and computational complexity. The proposed scheme takes advantage of two powerful frameworks in signal processing: Wavelets and Neural Networks. The decision system will take a decision based on the computation of the a priori probabilities of the input signal. For the estimation of such probability density functions, a Wavelet Neural Network (WNN) has been chosen. The election has arosen under the following considerations: (a) neural networks have been established as a general approximation tool for fitting nonlinear models from input/output data and (b) the increasing popularity of the wavelet decomposition as a powerful tool for approximation. The integration of the above factors leads to the wavelet neural network concept. This network preserve the universal approximation property of wavelet series, with the advantage of the speed and efficient computation of a neural network architecture. The topology and learning algorithm of the network will provide an efficient approximation to the required probability density functions

Representation Learning in Sensory Cortex: a theory

We review and apply a computational theory of the feedforward path of the ventral stream in visual cortex based on the hypothesis that its main function is the encoding of invariant representations of images. A key justification of the theory is provided by a theorem linking invariant representations to small sample complexity for recognition – that is, invariant representations allows learning from very few labeled examples. The theory characterizes how an algorithm that can be implemented by a set of ”simple” and ”complex” cells – a ”HW module” – provides invariant and selective representations. The invariance can be learned in an unsupervised way from observed transformations. Theorems show that invariance implies several properties of the ventral stream organization, including the eccentricity dependent lattice of units in the retina and in V1, and the tuning of its neurons. The theory requires two stages of processing: the first, consisting of retinotopic visual areas such as V1, V2 and V4 with generic neuronal tuning, leads to representations that are invariant to translation and scaling; the second, consisting of modules in IT, with class- and object-specific tuning, provides a representation for recognition with approximate invariance to class specific transformations, such as pose (of a body, of a face) and expression. In the theory the ventral stream main function is the unsupervised learning of ”good” representations that reduce the sample complexity of the final supervised learning stage.This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF - 1231216

Parallel and Streaming Wavelet Neural Networks for Classification and Regression under Apache Spark

Wavelet neural networks (WNN) have been applied in many fields to solve regression as well as classification problems. After the advent of big data, as data gets generated at a brisk pace, it is imperative to analyze it as soon as it is generated owing to the fact that the nature of the data may change dramatically in short time intervals. This is necessitated by the fact that big data is all pervasive and throws computational challenges for data scientists. Therefore, in this paper, we built an efficient Scalable, Parallelized Wavelet Neural Network (SPWNN) which employs the parallel stochastic gradient algorithm (SGD) algorithm. SPWNN is designed and developed under both static and streaming environments in the horizontal parallelization framework. SPWNN is implemented by using Morlet and Gaussian functions as activation functions. This study is conducted on big datasets like gas sensor data which has more than 4 million samples and medical research data which has more than 10,000 features, which are high dimensional in nature. The experimental analysis indicates that in the static environment, SPWNN with Morlet activation function outperformed SPWNN with Gaussian on the classification datasets. However, in the case of regression, the opposite was observed. In contrast, in the streaming environment i.e., Gaussian outperformed Morlet on the classification and Morlet outperformed Gaussian on the regression datasets. Overall, the proposed SPWNN architecture achieved a speedup of 1.32-1.40.Comment: 25 pages; 2 Tables; 7 Figure

Investigation of Different Video Compression Schemes Using Neural Networks

Image/Video compression has great significance in the communication of motion pictures and still images. The need for compression has resulted in the development of various techniques including transform coding, vector quantization and neural networks. this thesis neural network based methods are investigated to achieve good compression ratios while maintaining the image quality. Parts of this investigation include motion detection, and weight retraining. An adaptive technique is employed to improve the video frame quality for a given compression ratio by frequently updating the weights obtained from training. More specifically, weight retraining is performed only when the error exceeds a given threshold value. Image quality is measured objectively, using the peak signal-to-noise ratio versus performance measure. Results show the improved performance of the proposed architecture compared to existing approaches. The proposed method is implemented in MATLAB and the results obtained such as compression ratio versus signalto- noise ratio are presented

An auditory classifier employing a wavelet neural network implemented in a digital design

This thesis addresses the problem of classifying audio as either voice or music. The goal was to solve this problem by means of digital logic circuit, capable of performing the classification in real time. Since digital audio is essentially a discrete non-periodic timeseries, it was necessary to extract features from the audio which are suitable for classification. The discrete wavelet transform combined with a feature extraction method was found to produce such features. The task of classifying these features was found to be best performed by an artificial neural network. Collectively known as a wavelet neural network, the digital logic design implementation of this architecture was effective in correctly identifying the test data sets. The wavelet neural network was first implemented as a software model, to develop the network architecture and parameters, and to determine ideal results. The unconstrained software simulation was capable of correctly classifying test data sets with greater than 90% accuracy. This model was not feasible as a digital logic design however, as the size of the implementation would have been prohibitive. The size of the resulting hardware model was constrained by reducing the widths of the data paths and storage registers. The hardware implementation of the wavelet processor consisted of a novel pipelined design with a novel data-flow control structure. The neural network training was performed entirely in software by way of a novel training algorithm, and the resulting weights were made to be available to be uploaded to the hardware model. The digital design of the wavelet neural network was modeled in VHDL and was synthesized with Synplicity Synplify, using Actel ProASICPlus APA600 synthesized library cells with a target clock frequency of 11.025 KHz, to match the sampling rate of the digital audio. The results of the synthesis indicated that the design could operate at 15.6 MHz, and required 96,265 logic cells. The resulting constrained wavelet neural network processor was capable of correctly classifying test data sets with greater than 70% accuracy. Additional modeling showed that with a reasonable increase in hardware size, greater than 86% accuracy is attainable. This thesis focused on classifying audio as either voice or music, and future research could readily extend this work to the problem of speaker recognition and multimedia indexing