Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

https://users.encs.concordia.ca/~ymzhang/publications/ARC32-2-98Zhang_pp.229-252.pdf

Bibliographical review on reconfigurable fault-tolerant control systems

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

With advanced computer technology, systems are getting larger to fulfill more complicated tasks: however, they are also becoming less reliable. Consequently, testing is an indispensable part of system design and implementation; yet it has proved to be a formidable task for complex systems. This motivates the study of testing finite stare machines to ensure the correct functioning of systems and to discover aspects of their behavior. A finite state machine contains a finite number of states and produces outputs on state transitions after receiving inputs. Finite state machines are widely used to model systems in diverse areas, including sequential circuits, certain types of programs, and, more recently, communication protocols. In a testing problem we have a machine about which we lack some information; we would like to deduce this information by providing a sequence of inputs to the machine and observing the outputs produced. Because of its practical importance and theoretical interest, the problem of testing finite state machines has been studied in different areas and at various times. The earliest published literature on this topic dates back to the 1950's. Activities in the 1960's mid early 1970's were motivated mainly by automata theory and sequential circuit testing. The area seemed to have mostly died down until a few years ago when the testing problem was resurrected and is now being studied anew due to its applications to conformance testing of communication protocols. While some old problems which had been open for decades were resolved recently, new concepts and more intriguing problems from new applications emerge. We review the fundamental problems in testing finite state machines and techniques for solving these problems, tracing progress in the area from its inception to the present and the stare of the art. In addition, we discuss extensions of finite state machines and some other topics related to testing.

Principles and methods of testing finite state machines-a survey

Ultrathin films of single-walled carbon nanotubes (SWNTs) represent an attractive, emerging class of material, with properties that can approach the exceptional electrical, mechanical, and optical characteristics of individual SWNTs, in a format that, unlike isolated tubes, is readily suitable for scalable integration into devices. These features suggest the potential for realistic applications as conducting or semiconducting layers in diverse types of electronic, optoelectronic and sensor systems. This article reviews recent advances in assembly techniques for forming such films, modeling and experimental work that reveals their collective properties, and engineering aspects of implementation in sensors and in electronic devices and circuits with various levels of complexity. A concluding discussion provides some perspectives on possibilities for future work in fundamental and applied aspects.

http://www.ifc.dicp.ac.cn/library/cailiao/pdf1/11.pdf

Ultrathin Films of Single-Walled Carbon Nanotubes for Electronics and Sensors: A Review of Fundamental and Applied Aspects

Reliability is an important feature in the design and maintenance of a large-scale system. Usually, for a two-terminal network reliability is a measure for the connectivity between the source and sink nodes. Various approaches have been presented to evaluate system reliability; however, they become cumbersome or prohibitive due to the large computational complexity, especially when multiple states are considered for nodes. In this study, a stochastic approach is presented for estimating corresponding reliability. Randomly permuted sequences with fixed numbers of multiple values are generalized from non-Bernoulli binary sequences to model the multi-state property. State probabilities are represented by randomly permuted sequences to improve both computational efficiency and accuracy. Stochastic models are then constructed for arcs and nodes with different capacities. The proposed stochastic analysis is capable of predicting reliability at high accuracy and without a need for constructing the commonly-used but complex multi-state minimal cut vectors. Non-exponential distributions and correlated signals are readily handled by the stochastic approach for a general network. Results obtained through the stochastic analysis are compared with exact values and those found by Monte Carlo simulation. The accuracy, efficiency and scalability of the stochastic approach are assessed by evaluating several case studies.

Approximate reliability of multi-state two-terminal networks by stochastic analysis

We give three new algorithms to compute tests for faults in the interconnects of random access memories (RAM) using only read and write operations. Diagnosis of address line faults is the most difficult step. Our Adaptive Diagnosis Algorithm (ADA) considers each address line separately. Single line faults are easy to diagnose, so the objective is to ascertain which address lines are free of faults, thereby pruning impossible multi-line faults. Our Consecutive Diagnosis Algorithm (CDA) uses a more complicated and lengthier test sequence. However, with CDA, interpreting the test results is easier and the diagnostic resolution is superior. Our third algorithm, Adaptive Diagnosis Algorithm with Repair (ADAR), relies upon additional testing after repair in order to diagnose more faults than would otherwise be possible. ADAR has three test stages with two repair stages between them.

Adaptive Fault Detection and Diagnosis of RAM Interconnects

We give two algorithms for maximal diagnosis of wiring networks without repair under a general fault model. Maximal diagnosis consists of identifying all diagnosable faults under the assumptions that each net can have multiple drivers and receivers and can be affected by any number of short and open faults. This process is equivalent to verifying all connections between inputs and outputs. Matrices represent the connections in fault-free and faulty networks. We present two new algorithms and discuss prior algorithms. All algorithms discussed are adaptive and have their tests divided into two phases. Our first new algorithm exploits a unique condition for verifying the connections; our second new algorithm maps the connection verification problem to a bipartite graph. All algorithms discussed use an independent test set for the first test phase. Simulation results show that the proposed algorithms outperform previous algorithms for maximal diagnosis in terms of the number of tests. The total time complexity for computing the test sequences and analyzing the output response is polynomial.

Adaptive algorithms for maximal diagnosis of wiring interconnects

In this paper, the H-field and E-field sensors as well as the tunable devices are fabricated by utilizing single-phase magnetoelectric (ME) hexaferrite thin films on a silicon substrate. A buffer layer of indium–tin oxide is introduced between the Si wafer and the ME film. The required voltage excitations are reduced to values below 1 V at room temperature, hence making these devices compatible with planar integrated circuits (ICs) and CMOS. A planar spiral coil is utilized in a frequency range of 100 Hz to 10 MHz for sensing applications by applying an ac field (electric or magnetic) for excitation of the ME film. For H-field (E-field) sensing, a sensitivity of up to 1000 Vm $^{-1}$ /G ( $10^{-7}$ G/Vm $^{-1})$ is attained. The corresponding sensitivity for H-field is $4 \times 10^{4}$ up to $10^{7}$ V/T for 3 kHz up to 10 MHz. Tunability of up to 60% is achieved for tunable inductor applications; moreover, a change of 26% in voltage is observed at a single frequency by applying a voltage of 3 V on the ME film, thus performing as a single frequency detector. The proposed devices are made of single-phase materials, so they are simple to fabricate and capable to operate at the voltages of few volts; these features make them viable as a new generation of the ME devices for integration in ICs.

Field Sensors and Tunable Devices Using Magnetoelectric Hexaferrite on Silicon Substrates

The silicon-on-insulator (SOI) MOS as an alternative to the bulk (silicon-based M circuits for applications requiring low-voltag operation. Fully depleted SOI (FDSOI) ben current driven ability; so, this techn advantageous features, such as steep characteristics and small short channel ef presents a comprehensive assessment of differ Random Access Memory) cells utilizing diff transistors (i.e. 8 and 9). These cells are eval for different performance metrics (such as stability, critical charge, power consumption voltage threshold variation) at the 22nm techn

/pdf/fdsoi-sram-cells-for-low-power-design-at-22nm-technology-im77qfpa0o.pdf

Fabrizio Lombardi

Papers

Approximate reliability of multi-state two-terminal networks by stochastic analysis

Adaptive Fault Detection and Diagnosis of RAM Interconnects

Adaptive algorithms for maximal diagnosis of wiring interconnects

Field Sensors and Tunable Devices Using Magnetoelectric Hexaferrite on Silicon Substrates

FDSOI SRAM cells for low power design at 22nm technology node