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

Magnesium hydrides (MgH2) have drawn a lot of interest as a promising hydrogen storage material option due to their good reversibility and high hydrogen storage capacity (7.60 wt.%). However, the high hydrogen desorption temperature (more than 400 °C) and slow sorption kinetics of MgH2 are the main obstacles to its practical use. In this research, nickel zinc oxide (Ni0.6Zn0.4O) was synthesized via the solid-state method and doped into MgH2 to overcome the drawbacks of MgH2. The onset desorption temperature of the MgH2–10 wt.% Ni0.6Zn0.4O sample was reduced to 285 °C, 133 °C, and 56 °C lower than that of pure MgH2 and milled MgH2, respectively. Furthermore, at 250 °C, the MgH2–10 wt.% Ni0.6Zn0.4O sample could absorb 6.50 wt.% of H2 and desorbed 2.20 wt.% of H2 at 300 °C within 1 h. With the addition of 10 wt.% of Ni0.6Zn0.4O, the activation energy of MgH2 dropped from 133 kJ/mol to 97 kJ/mol. The morphology of the samples also demonstrated that the particle size is smaller compared with undoped samples. It is believed that in situ forms of NiO, ZnO, and MgO had good catalytic effects on MgH2, significantly reducing the activation energy and onset desorption temperature while improving the sorption kinetics of MgH2.

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Ni0.6Zn0.4O Synthesised via a Solid-State Method for Promoting Hydrogen Sorption from MgH2

As devices shrink, creating integrated circuits (ICs) that work with the required accuracy becomes more difficult due to issues related to device physics. Receivers are part of an area referred to as "mixed-signal design," meaning that both analog and digital circuitry will be on the same IC. This too presents many challenging issues, as the analog circuitry is highly sensitive to disruptions caused by the noisy digital circuitry. Therefore, accurate modeling and simulation is crucial in the design of wireless receivers to ensure the best possible operation of the fabricated IC. Through simulation and modeling a designer can determine if receiver architecture will meet the required specifications and pinpoint the possible problems before valuable time is spent developing the actual circuit. This article will present design issues for multistandard wireless receivers to give the reader an understanding of the challenges involved in link-budget analysis. TITAN (Toolbox for Integrated Transceiver Analysis), a link-budget analysis tool developed at The Ohio State University Analog VLSI Laboratory, will be presented as an example of a tool for receiver simulation. To determine design performance, various requirements must be translated to model parameters. Among the requirements for receivers are noise floor (NF), second- and third-order distortion (IP2 and IP3, respectively), reciprocal mixing, and phase noise. TITAN offers a graphical interface and encapsulated models to the designer, eliminating the possibility of formula corruption. The interface provides a more intuitive and sophisticated way of setting up the simulation and provides the designer with more readable results. Additionally, a blocking profile component allows the architecture to be tested across multiple standards.

Link budget analysis for multistandard receiver architectures

In this chapter, an ECG processor on-chip for full ECG feature extraction and cardiac autonomic neuropathy (CAN) is presented. Absolute value curve length transform (ACLT) is performed for QRS detection, whereas full feature extraction (detecting QRSon, QRSoff, P-, and T-waves) is achieved by low-pass differentiation. Proposed QRS detector attains a sensitivity of 99.37% and predictivity of 99.38%. Extracted RR interval along with QT interval enables CAN severity detector. CAN is cardiac arrhythmia usually seen in diabetic patients and have prevalent effect in sudden cardiac death. In this chapter, the first hardware real-time implementation of the CAN severity detector is proposed. Detection is based on RR variability and QT variability analysis. RR variability metrics are based on mean RR interval and RMSSD of RR interval. The proposed architecture is implemented in 65 nm technology, and it consumes only 75 nW at 0.6 V, when operating at 250 Hz. Ultra-low power dissipation of the system enables it to be integrated into wearable healthcare devices.

Ultra-Low Power CAN Detection and VA Prediction

Since multiple voltage levels are required simultaneously by the proposed wearable device in Fig. 1.12, multiple voltage regulators are needed. The switched capacitor (SC) voltage regulator is widely utilized in low power applications since the small capacitance can be fully integrated on-chip while driving μA current. Conventionally, multiple SC voltage regulators are required to generate simultaneous multiple regulated voltage levels. This way, each block in the chip operates at an optimum voltage level. However, this conventional PMU design suffers from a large area overhead because it is dominated by the area of the SC regulators. The number of SC regulators increases as the number of the required regulated output voltage levels increases. Hence, more passive components (flying and load capacitors) are needed which makes the PMU area large. As a result, it is imperative to find an efficient way to generate multiple output voltages with minimum area overhead that targets μW power devices.

Mohammed Ismail

Papers

Ni0.6Zn0.4O Synthesised via a Solid-State Method for Promoting Hydrogen Sorption from MgH2

Link budget analysis for multistandard receiver architectures

Ultra-Low Power CAN Detection and VA Prediction

Dual-Outputs Switched Capacitor Voltage Regulator

Analysis of Non-linearities in rf CMOS Amplifiers ICECS