There has been a great deal of material in the area of discrete-time transforms that has been published in recent years. This book does an excellent job of presenting important aspects of such material in a clear manner. The book has 11 chapters and a very useful appendix. Seven of these chapters are essentially devoted to the Fourier series/transform, discrete Fourier transform, fast Fourier transform (FFT), and applications of the FFT in the area of spectral estimation. Chapters 8 through 10 deal with many other discrete-time transforms and algorithms to compute them. Of these transforms, the KarhunenLoeve, the discrete cosine, and the Walsh-Hadamard transform are perhaps the most well-known. A lucid discussion of number theoretic transforms i5 presented in Chapter 11. This reviewer feels that the authors have done a fine job of compiling the pertinent material and presenting it in a concise and clear manner. There are a number of problems at the end of each chapter, an appreciable number of which are challenging. The authors have included a comprehensive set of references at the end of the book. In brief, this book is a valuable contribution to the general areas of signal processing and communications. It can be used for a graduate level course in perhaps two ways. One would be to cover the first seven chapters in great detail. The other would be to cover the whole book by focussing on different topics in a selective manner. This book  by  Elliott  and Rao  is extremely  useful to researchers/engineers who are working  in the areas of signal processing and communications. It i s also an excellent reference book, and hence a valuable addition to one’s library

http://ieeexplore.ieee.org/iel5/5/31347/01457444.pdf

Multirate digital signal processing

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[서평]「Computer Organization and Design, The Hardware/Software Interface」

Multiagent Systems is the title of a collection of papers dedicated to surveying specific themes of Multiagent Systems (MAS) and Distributed Artificial Intelligence (DAI). All of them authored by leading researchers of this dynamic multidisciplinary field.

/pdf/multiagent-systems-a-modern-approach-to-distributed-5fon0donq1.pdf

Multiagent systems: a modern approach to distributed artificial intelligence

A survey of communication/networking in Smart Grids

We present a new design for a 1-b full adder featuring hybrid-CMOS design style. The quest to achieve a good-drivability, noise-robustness, and low-energy operations for deep submicrometer guided our research to explore hybrid-CMOS style design. Hybrid-CMOS design style utilizes various CMOS logic style circuits to build new full adders with desired performance. This provides the designer a higher degree of design freedom to target a wide range of applications, thus significantly reducing design efforts. We also classify hybrid-CMOS full adders into three broad categories based upon their structure. Using this categorization, many full-adder designs can be conceived. We will present a new full-adder design belonging to one of the proposed categories. The new full adder is based on a novel xor-xnor circuit that generates xor and xnor full-swing outputs simultaneously. This circuit outperforms its counterparts showing 5%-37% improvement in the power-delay product (PDP). A novel hybrid-CMOS output stage that exploits the simultaneous xor-xnor signals is also proposed. This output stage provides good driving capability enabling cascading of adders without the need of buffer insertion between cascaded stages. There is approximately a 40% reduction in PDP when compared to its best counterpart. During our experimentations, we found out that many of the previously reported adders suffered from the problems of low swing and high noise when operated at low supply voltages. The proposed full adder is energy efficient and outperforms several standard full adders without trading off driving capability and reliability. The new full-adder circuit successfully operates at low voltages with excellent signal integrity and driving capability. To evaluate the performance of the new full adder in a real circuit, we embedded it in a 4- and 8-b, 4-operand carry-save array adder with final carry-propagate adder. The new adder displayed better performance as compared to the standard full adders

/pdf/design-of-robust-energy-efficient-full-adders-for-deep-5ba51kaofh.pdf

Design of Robust, Energy-Efficient Full Adders for Deep-Submicrometer Design Using Hybrid-CMOS Logic Style

Recurrent Neural Networks (RNNs) have become a popular method for learning sequences of data. It is sometimes tough to parallelize all RNN computations on conventional hardware due to its recurrent nature. One challenge of RNN is to find its optimal structure for RNN because of computing complex hidden units that exist. This brief presents a new approach to Long Short-Term Memory (LSTM) that aims to reduce the cost of the computation unit. The proposed Economic LSTM (ELSTM) is designed using a few hardware units to perform its functionality. ELSTM has fewer units compared to the existing LSTM versions which makes it very attractive in processing speed and hardware design cost. The proposed approach is tested using three datasets and compared with other methods. The simulation results show the proposed method has comparable accuracy with other methods. At the hardware level, the proposed method is implemented on Altera FPGA.

Economic LSTM Approach for Recurrent Neural Networks

We present several designs for 1-bit full adder cell featuring hybrid CMOS logic style. These designs are based on a novel XOR-XNOR circuit that simultaneously produces XOR and XNOR full-swing outputs and outperforms its best counterpart showing 39% improvement in PDP. The new full-adder designs are also categorized in three main categories depending upon the implementation of the logic expression for sum and carry outputs. The results show that all the proposed designs prove to be energy-efficient and outperform several standard full-adder designs. All the designs are able to operate at low voltages without significant loss in signal integrity. The improvement in terms of PDP obtained by the best full-adder cell as compared the best standard design amounts to 24%.

On the design of low-energy hybrid CMOS 1-bit full adder cells

Hardware failures are undesired but a common problem in circuits. Such failures are inherently due to the aging of circuitry or variation in circumstances. In critical systems, customers demand the system never to fail. Several self-healing and fault tolerance techniques have been proposed in the literature for recovering a circuitry from a fault. Such techniques come to the rescue when a fault has already occurred but they are typically uninformed about the possibility of an impending failure (i.e., fault prediction), which can be used as a pre-stage to fault tolerance and self-healing. This paper presents an approach to early fault prediction of circuits. The proposed method uses Fast Fourier Transform (FFT) to get the fault frequency signature, Principal Component Analysis (PCA) to get the most important data with reduced dimension, and Convolutional Neural Network (CNN) to learn and classify the fault. The proposed method is validated for working in different circuits by testing it using two circuits: comparator and amplifier. The comparator and amplifier are implemented using 45 nm technology on HSPICE to extract the failures dataset in terms of voltage, current, temperature, noise, and delay. This extracted data is used for training the proposed approach using Tensorflow. To the best of our knowledge, this is the first work of fault prediction at the transistor level for hardware system. The proposed approach considers aging, short-circuit, and open-circuit faults, and it provides a fault prediction accuracy of 98.93% and 98.91% for comparator and amplifier circuits, respectively. The proposed method is tested for two different circuits for its validation, and it consumes 1.08 W for Altera Arria 10 GX FPGA device.

Ashok Kumar

Papers

Design of Robust, Energy-Efficient Full Adders for Deep-Submicrometer Design Using Hybrid-CMOS Logic Style

Economic LSTM Approach for Recurrent Neural Networks

On the design of low-energy hybrid CMOS 1-bit full adder cells

Machine Learning-Based Approach for Hardware Faults Prediction

A network of sensor-based framework for automated visual surveillance