Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Deep Learning

THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Computational geometry

The authors discuss high-voltage power conversion. Conventional series connection and three-level voltage source inverter techniques are reviewed and compared. A novel versatile multilevel commutation cell is introduced: it is shown that this topology is safer and more simple to control, and delivers purer output waveforms. The authors show how this technique can be applied to either choppers or voltage-source inverters and generalized to any number of switches.<<ETX>>

Multi-level conversion : high voltage choppers and voltage-source inverters

Nanotechnology is the science of understanding matter and the control of matter at dimensions of 100 nm or less. Encompassing nanoscale science, engineering, and technology, nanotechnology involves imaging, measuring, modeling, and manipulation of matter at this level of precision. An important aspect of research in nanotechnology involves precision control and manipulation of devices and materials at a nanoscale, i.e., nanopositioning. Nanopositioners are precision mechatronic systems designed to move objects over a small range with a resolution down to a fraction of an atomic diameter. The desired attributes of a nanopositioner are extremely high resolution, accuracy, stability, and fast response. The key to successful nanopositioning is accurate position sensing and feedback control of the motion. This paper presents an overview of nanopositioning technologies and devices emphasizing the key role of advanced control techniques in improving precision, accuracy, and speed of operation of these systems.

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A Survey of Control Issues in Nanopositioning

Emerging technology in semiconductor devices and circuits has enabled ultra-low power systems in medical, structural (bridges), and hard to reach places which fueled the energy scavenging research and its interface circuitry. The energy can be harvested from ambient sources such as vibration, wireless, thermal and solar. To harvest the energy, special interface circuit needs to be designed for suitable energy conversion such as DC-DC converters, power management unit, and storage elements. In this paper, we present a survey about the state-of-the-art thermal energy harvesting systems focusing on the interface circuitry, and explaining the tradeoffs in human body-based harvesting applications.

A survey of thermal energy harvesting techniques and interface circuitry

The use of a four-parameter MOS model to characterize drain current mismatch is discussed. Guidelines for the accurate and repeatable measurement of transistor parameter mismatch are presented, along with two extraction methodologies based upon these guidelines. Parameter mismatch measurements made on 430 NMOS and 430 PMOS transistor pairs fabricated in a 0.8-/spl mu/m process are used to predict the drain current mismatch of the same population of transistors. Comparisons are made between the predicted and measured values. >

Characterization of transistor mismatch for statistical CAD of submicron CMOS analog circuits

The Internet of Things (IoT) presents opportunities to address a variety of systemic, metabolic healthcare issues. Cardiovascular disease and diabetes are among the greatest contributors to premature death worldwide. Wireless wearable continuous monitoring systems such as ECG sensors connected to the IoT can greatly decrease the risk of death related to cardiac issues by providing valuable long-term information to physicians, as well as immediate contact with emergency services in the event of a heart attack or stroke. In this report we discuss the fabrication, characterization and validation of composite fabric ECG sensors made from Nylon® coated with reduced graphene oxide (rGOx) as part of a self-powered wearable IoT sensor. We utilize an electronic probing station to measure electrical properties, take live ECG data to measure signal reliability, and provide detailed surface characterization through scanning electron microscopy. Finally, bonding between the layers of the composite and between composite and the Nylon® is analyzed by Fourier transform Infrared spectroscopy. Furthermore, a low power analog front end circuit designed in 65 nm CMOS process is presented to interface the sensor with a system on chip used in a wearable IoT healthcare device.

Graphene oxide: Nylon ECG sensors for wearable IoT healthcare—nanomaterial and SoC interface

A 1.8 V sigma-delta modulator with a 4 bit quantizer has been designed for GSM/WCDMA/WLAN receivers in a 0.18 um CMOS process. The modulator makes use of low-distortion sigma-delta modulator architecture and Pseudo-Data-Weighted-Averaging technique to attain high linearity over a wide bandwidth. Power dissipation is minimized by optimizing the architecture and by a careful design of analog circuitry. In GSM mode, the modulator achieves 96/104 dB peak SNR/SFDR over 100 kHz bandwidth and dissipates 18 mW at a sampling frequency of 32 MHz. The modulator achieves 92/68 dB peak SFDR and 77/54 dB peak SNR over a 2 MHz/10 MHz bandwidth and dissipates 23/39 mW at a sampling frequency of 64 MHz/160 MHz in WCDMA/WLAN.

A Triple-Mode Sigma-Delta Modulator for Multi-Standard Wireless Radio Receivers

The T and P waves of electrocardiogram signals are excellent indicators in the analysis and interpretation of cardiac arrhythmia. As such, the need to address and develop an accurate delineation technique for the detection of these waves is necessary. In this paper, we present a novel robust and adaptive T and P wave delineation method for real-time analysis and nonstandard ECG morphologies. The proposed method is based on ECG signal filtering, value estimation of different fiducial points, applying backward and forward search windows as well as adaptive thresholds. Simulations and evaluations prove the accuracy of the proposed technique in comparison to those proposed techniques in the literature. The mean error for the T peak, T offset, P peak and P offset values are found to be 9.8, 2.3, 7.3 and 3.5 milliseconds, respectively, based on the Physionet QT database, rendering our algorithm as an excellent candidate for ECG signal analysis.

Mohammed Ismail

Papers

A survey of thermal energy harvesting techniques and interface circuitry

Characterization of transistor mismatch for statistical CAD of submicron CMOS analog circuits

Graphene oxide: Nylon ECG sensors for wearable IoT healthcare—nanomaterial and SoC interface

A Triple-Mode Sigma-Delta Modulator for Multi-Standard Wireless Radio Receivers

Adaptive technique for P and T wave delineation in electrocardiogram signals.