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

Lumped-element analysis and design of CMOS distributed amplifiers with image impedance termination

Most CMOS inverter models in the literature express the delay in terms of a load capacitance from which the effect of changing the sizes of the transistors is not immediately clear. In this paper, we introduce a first-order formulation based on the widths of the transistors for quick evaluation and optimization of the delay and energy in digital CMOS circuits. We then apply the formulation to obtain simple, closed-form expressions for a number of important parameters. These parameters include the optimum transistor sizing for minimizing the propagation delay, rising delay, falling delay, and energy-delay product. Furthermore, we demonstrate the convenience of using the model by applying it to a number of circuit scenarios.

A formulation for quick evaluation and optimization of digital CMOS circuits

This paper presents a new methodology for the design of low noise amplifiers for ultra wideband applications. Using the unwanted effect of the gate-drain capacitor of transistors on the input impedance to our benefit, the operation of the conventional narrowband LNA is extended to provide a very good input matching from 3 GHz to 10 GHz. Using a triple-resonance circuit as the drain impedance, a relatively flat gain is also achieved over the same operation band. A power gain of 8 dB, with good input and output matching (S11< -14 dB and S22 < -14 dB) is achieved over a 3 to 10 GHz band in 0.13 mum CMOS technology

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A 3-10 GHz Low-Noise Amplifier for Ultra-Wideband Applications

Digital VLSI systems

This paper presents a four-stage CMOS distributed amplifier (DA) design implemented in standard 0.18 mum CMOS technology. The proposed design eliminates the need for transmission line capacitors and, consequently, uses significantly smaller spiral inductors compared with the previous designs. Using the minimum size inductor, the bandwidth of the amplifiers is extended, and the quality factors of the on-chip inductor are improved. Proposed DA occupies the smallest die area (0.3mum*0.8mum) amongst the DAs reported with the same performance. A unity gain bandwidth of 10 GHz and a gain of 15 dB are measured. DC power dissipation is 56 mW

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Mohamed I. Elmasry

Papers

Lumped-element analysis and design of CMOS distributed amplifiers with image impedance termination

A formulation for quick evaluation and optimization of digital CMOS circuits

A 3-10 GHz Low-Noise Amplifier for Ultra-Wideband Applications

Digital VLSI systems

A 10-GHz 15-dB Four-Stage Distributed Amplifier in 0.18μm CMOS Process

Mohamed I. Elmasry

Papers

Lumped-element analysis and design of CMOS distributed amplifiers with image impedance termination

A formulation for quick evaluation and optimization of digital CMOS circuits

A 3-10 GHz Low-Noise Amplifier for Ultra-Wideband Applications

Digital VLSI systems

A 10-GHz 15-dB Four-Stage Distributed Amplifier in 0.18&#956;m CMOS Process

A 10-GHz 15-dB Four-Stage Distributed Amplifier in 0.18μm CMOS Process