Learn More
Handbook of Signal Processing Systems provides a standalone, complete reference to signal processing systems organized in four parts. The first part motivates representative applications that drive and apply state‐of‐ the art methods for design and implementation of signal processing systems; the second part discusses architectures for implementing these(More)
This paper proposes to incorporate full covariance matrices into the radial basis function (RBF) networks and to use the Expectation-Maximization (EM) algorithm to estimate the basis function parameters. The resulting networks, referred to as elliptical basis function (EBF) networks, are evaluated through a series of text-independent speaker veriication(More)
The authors advocate digital VLSI architectures for implementing a wide variety of artificial neural nets (ANNs). A programmable systolic array is proposed, which maximizes the strength of VLSI in terms of intensive and pipelined computing, yet circumvents its limitation on communication. The array is meant to be more general-purpose than most other ANN(More)
Two critical issues in back-propagation (BP) learning are the discrimination capability given a number of hidden units and the speed of convergence in learning. The number of hidden units must be sufficient to provide the discriminating capability required by the given application. On the other hand, the training of an excessively large number of synaptic(More)
Summary form only given. A universal digital VLSI design is proposed for implementing a wide variety of artificial neural networks. A programmable systolic array is presented based on a unified iterative neural network model, which maximizes the strength of VLSI in terms of intensive and pipelined computing and yet circumvents the limitation on(More)
Integral imaging is a technique capable of displaying images with continuous parallax in full natural colour. This paper presents a modified multi-baseline method for extracting depth information from unidirectional integral images. The method involves first extracting sub-images from the integral image. A sub-image is constructed by extracting one pixel(More)
In this paper, we design a novel regularized empirical risk minimization technique for classification called Adaptive Margin Slack Minimization (AMSM). The proposed method is based on minimizing a regularized upper bound of the misclassification error. Compared to the cost function of the classical L2-SVM, AMSM can be interpreted as minimizing a tighter(More)
Over the past decades, face recognition has been a problem of critical interest in the machine learning and signal processing communities. However, conventional approaches such as eigenfaces do not protect the privacy of user data, which is emerging as an important design consideration in today's society. In this work, we leverage a supervised-learning(More)