Cryptosystem designers frequently assume that secrets will be manipulated in closed, reliable computing environments. Unfortunately, actual computers and microchips leak information about the operations they process. This paper examines specific methods for analyzing power consumption measurements to find secret keys from tamper resistant devices. We also discuss approaches for building cryptosystems that can operate securely in existing hardware that leaks information.

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Differential Power Analysis

This monograph provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks. The application areas are chosen with the following three criteria in mind: (1) expertise or knowledge of the authors; (2) the application areas that have already been transformed by the successful use of deep learning technology, such as speech recognition and computer vision; and (3) the application areas that have the potential to be impacted significantly by deep learning and that have been experiencing research growth, including natural language and text processing, information retrieval, and multimodal information processing empowered by multi-task deep learning.

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Deep Learning: Methods and Applications

This ebook is the first authorized digital version of Kernighan and Ritchie's 1988 classic, The C Programming Language (2nd Ed.). One of the best-selling programming books published in the last fifty years, "K&R" has been called everything from the "bible" to "a landmark in computer science" and it has influenced generations of programmers. Available now for all leading ebook platforms, this concise and beautifully written text is a "must-have" reference for every serious programmers digital library.

As modestly described by the authors in the Preface to the First Edition, this "is not an introductory programming manual; it assumes some familiarity with basic programming concepts like variables, assignment statements, loops, and functions. Nonetheless, a novice programmer should be able to read along and pick up the language, although access to a more knowledgeable colleague will help."

The C programming language

Recently, the hybrid deep neural network (DNN)- hidden Markov model (HMM) has been shown to significantly improve speech recognition performance over the conventional Gaussian mixture model (GMM)-HMM. The performance improvement is partially attributed to the ability of the DNN to model complex correlations in speech features. In this paper, we show that further error rate reduction can be obtained by using convolutional neural networks (CNNs). We first present a concise description of the basic CNN and explain how it can be used for speech recognition. We further propose a limited-weight-sharing scheme that can better model speech features. The special structure such as local connectivity, weight sharing, and pooling in CNNs exhibits some degree of invariance to small shifts of speech features along the frequency axis, which is important to deal with speaker and environment variations. Experimental results show that CNNs reduce the error rate by 6%-10% compared with DNNs on the TIMIT phone recognition and the voice search large vocabulary speech recognition tasks.

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Convolutional neural networks for speech recognition

From the Publisher:
Connectionist Speech Recognition: A Hybrid Approach describes the theory and implementation of a method to incorporate neural network approaches into state-of-the-art continuous speech recognition systems based on Hidden Markov Models (HMMs) to improve their performance. In this framework, neural networks (and in particular, multilayer perceptrons or MLPs) have been restricted to well-defined subtasks of the whole system, i.e., HMM emission probability estimation and feature extraction. The book describes a successful five year international collaboration between the authors. The lessons learned form a case study that demonstrates how hybrid systems can be developed to combine neural networks with more traditional statistical approaches. The book illustrates both the advantages and limitations of neural networks in the framework of a statistical system. Using standard databases and comparing with some conventional approaches, it is shown that MLP probability estimation can improve recognition performance. Other approaches are discussed, though there is no such unequivocal experimental result for these methods. Connectionist Speech Recognition: A Hybrid Approach is of use to anyone intending to use neural networks for speech recognition or within the framework provided by an existing successful statistical approach. This includes research and development groups working in the field of speech recognition, both with standard and neural network approaches, as well as other pattern recognition and/or neural network researchers. This book is also suitable as a text for advanced courses on neural networks or speech processing.

Connectionist Speech Recognition: A Hybrid Approach

Latchup and performance degradation due to the parasitic elements in merged BiCMOS structures are studied. Circuit simulations using HSPICE and analytical characterisation are used to describe the parasitic components and model their effects on the output voltage switching characteristics and delay time. A latchup factor is defined to relate the degree of performance degradation to the device and key circuit parameters. >

Analysis of latchup and parasitic effects in merged BiCMOS structures

VLSI design requires design methodologies which are tailored to the implementation technology. Symbolic layout has been addressed in the past for MOS technology, while bipolar technology has largely been ignored. This paper describes a novel symbolic design technique which addresses both bipolar and MOS technologies. The technique allows the designer to symbolically layout nMOS, CMOS, and bipolar circuit structures. The symbol set is used for both MOS and bipolar devices in an integrated and consistent way. It is closely related to the mask layout information rather than circuit schematics. Thus, it allows the creation of any circuit structure based on bipolar, MOS, CMOS, BIMOS, and BICMOS technologies. Design examples are given.

Symbolic Layout for Bipolar and MOS VLSI

An integrated design and test synthesis methodology is presented with area, delay, and test cost constraints. This methodology, called CATREE2, differs from other approaches by simultaneously synthesizing the normal and test mode design behavior. It is particularly suited for top-down system design where a VLSI component's behavior and test method have been fully specified. Preliminary results show that better design solutions are obtained by considering test incorporation during design synthesis than other methods which incorporate test after a structural design solution is formed. >

Integrated design and test synthesis

Modular switched-resistor ANN chip for character recognition using novel parallel VLSI architecture.

The past decade has witnessed rapid developments in a new form of computing in the area of information processing. The developments were inspired by the architecture and operation of the biological brains, and hence they were called Artificial Neural Networks (ANNs). The computations in these systems are distinguished by their ability to learn by themselves through repetitive presentation of training data. Moreover, they are built with a large number of processing nodes which are heavily interconnected with each other and operate concurrently. These characteristics have enabled them to be excellent information processing systems and hence they have found wide use in applications such as speech processing, pattern recognition, image processing, signal processing, and robotics control.

Mohamed I. Elmasry

Papers

Analysis of latchup and parasitic effects in merged BiCMOS structures

Symbolic Layout for Bipolar and MOS VLSI

Integrated design and test synthesis

Modular switched-resistor ANN chip for character recognition using novel parallel VLSI architecture.

A Design Automation Environment for Mixed Analog/Digital ANNs