비만아 부모의 돌봄 경험: q 방법론

After two decades of research and development, elliptic curve cryptography now has widespread exposure and acceptance. Industry, banking, and government standards are in place to facilitate extensive deployment of this efficient public-key mechanism. Anchored by a comprehensive treatment of the practical aspects of elliptic curve cryptography (ECC), this guide explains the basic mathematics, describes state-of-the-art implementation methods, and presents standardized protocols for public-key encryption, digital signatures, and key establishment. In addition, the book addresses some issues that arise in software and hardware implementation, as well as side-channel attacks and countermeasures. Readers receive the theoretical fundamentals as an underpinning for a wealth of practical and accessible knowledge about efficient application. Features & Benefits: * Breadth of coverage and unified, integrated approach to elliptic curve cryptosystems * Describes important industry and government protocols, such as the FIPS 186-2 standard from the U.S. National Institute for Standards and Technology * Provides full exposition on techniques for efficiently implementing finite-field and elliptic curve arithmetic* Distills complex mathematics and algorithms for easy understanding* Includes useful literature references, a list of algorithms, and appendices on sample parameters, ECC standards, and software toolsThis comprehensive, highly focused reference is a useful and indispensable resource for practitioners, professionals, or researchers in computer science, computer engineering, network design, and network data security.

https://cdn.preterhuman.net/texts/cryptography/Hankerson,%20Menezes,%20Vanstone.%20Guide%20to%20elliptic%20curve%20cryptography%20(Springer,%202004)(ISBN%20038795273X)(332s)_CsCr_.pdf

Guide to Elliptic Curve Cryptography

National Institute of Standards and Technology における超伝導研究及び生活

Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:

http://ieeexplore.ieee.org/iel5/5/31307/01456462.pdf

Linear systems

Ad hoc wireless networks enable new and exciting applications, but also pose significant technical challenges. In this article we give a brief overview of ad hoc wireless networks and their applications with a particular emphasis on energy constraints. We then discuss advances in the link, multiple access, network, and application protocols for these networks. We show that cross-layer design of these protocols is imperative to meet emerging application requirements, particularly when energy is a limited resource.

Design challenges for energy-constrained ad hoc wireless networks

This paper presents a novel computer-aided system for assisting schizophrenia (SZ) diagnosis. Power Spectral Density Ratios (PSDRs) covering 7 brain regions and 5 frequency sub-bands are extracted as features, from single-trial magnetoencephalography (MEG) recorded while subjects read sentence stimuli silently. A two-stage feature selection algorithm combining F-score and Adaptive Boosting (Adaboost) model is proposed to rank the features. The top ranked features are used to build a boosted non-linear classifier using linear decision stumps as the base classifiers. A majority voting scheme is employed to combine single trial classification results from each test subject to make final classification decisions. Following a leave-one-out cross validation procedure, the proposed system achieves 82.61% classification accuracy (92.31% specificity and 70% sensitivity) on 13 healthy controls and 10 SZ patients. The most discriminating PSDR features are selected from the right temporal, right parietal and right frontal regions and are related to alpha (8-13Hz) and beta (13-30Hz) frequency ranges. This information may help in gaining knowledge about the abnormal neural oscillations associated with sentence-level language disorder in SZ.

Classification of single-trial MEG during sentence processing for automated schizophrenia screening

In this paper a novel multi-dimensional (MD) folding transformation technique is formalized which can be used to synthesize control circuits for pipelined architectures which implement a specific class of MD DSP algorithms. Feasibility constraints are derived for folding a 2-D single rate data-flow graph (DFG) onto an available set of hardware functional units according to a given schedule. Retiming is introduced as a tool to facilitate feasibility of the folding constraints and minimization of the storage requirements for the folded architecture. We also derive expressions for the exact storage requirements for the folded architecture. Detailed design example of a non-separable 2-D IIR filter is provided.

Synthesis of folded multi-dimensional DSP systems

In orthogonal frequency division multiplexing (OFDM) systems, the impulse noise causes catastrophic accuracy degradation since the impulse noise affects all the subcarriers in a symbol due to the fast Fourier transform (FFT) operations at the receiver. Potential causes of impulse noise include erasure channel, power switching, and circuit failure in integrated circuits. In this paper, from a practical observation, a novel iterative impulse error correction scheme is proposed. This scheme is referred to as the impulse noise location and value search algorithm, which is based on the crucial observation of the relationship of the impulse noise and the symbol constellation. In a 512-FFT OFDM system at 25 dB additive white Gaussian noise signal-to-noise ratio, for quadrature amplitude modulation (QAM)-4 and QAM-8 modulation, simulation results show that our proposed novel scheme can effectively correct impulse errors that corrupt up to 20.7 % and 13.9 % of the received time-domain signal at known locations. In addition, without the knowledge of impulse noise location, the proposed scheme still can correct at least 9.96 % of the received time-domain signal for QAM-4 modulation.

Impulse Noise Correction in OFDM Systems

Signal estimation from functional magnetic resonance imaging data (fMRI) is a difficult and challenging task that involves carefully chosen models that can be validated by domain experts. This paper explores constrained tensor decomposition methods for model-free estimation of signals from task fMRI. Using a number of constrained tensor decompositions, the signals are estimated as Rank -1 tensor(s). The mutli-subject fMRI data is stored as a three-way tensor (voxel $\times$ time $\times$ subject). First, the signal is decomposed using traditional PARAFAC modeling. Second, the spatio-temporal maps in the PARAFAC formulation are constrained to be non-negative. Third, using domain knowledge of brain activation pattern in spatial domain for fMRI and loading of the spatio-temporal maps of each individual the paper proposes an optimization model for solving the signal estimation problem from task fMRI data. Three different optimization techniques are also used for solving the optimization problems. The decomposed signal portion includes the brain spatial activation maps and corresponding time courses for each individual during task. The solutions of the optimization are evaluated based on similarity of the task signal (the ground truth) to time courses of the decomposed signal as well as by inspecting the spatial maps visually.

Constrained Tensor Decomposition Optimization With Applications To Fmri Data Analysis

This paper considers implementation of finite field multiplication data paths in a domain-specific programmable digital signal processor (DS-PDSP), where special hardware units and corresponding instructions are assumed to be used to program finite field multiplication operation. These multiplication data paths are designed to accommodate programmability with respect to the primitive polynomial as well as the field order. Three types of multipliers are considered; these include semi-systolic array (in both least-significant-bit first and most-significant-bit first modes), fully-parallel, and the proposed approach where polynomial multiplication and polynomial module operations are implemented separately and two instructions, MAC and DEGRGD are assigned to them, respectively. Two approaches are considered for achieving programmability with respect to the field order, either with special control circuitry, or with pre- and post-logical shifting operations. It is concluded that the one-level pipelined fully-parallel multiplier without control circuitry consumes the least energy at component level when only one multiplication is considered. However, at system level, when vector-vector multiplications, common in most DSP algorithms, are considered, the proposed approach is able to achieve 70% energy reduction at the expense of increasing the total instruction count by one.

Keshab K. Parhi

Papers

Classification of single-trial MEG during sentence processing for automated schizophrenia screening

Synthesis of folded multi-dimensional DSP systems

Impulse Noise Correction in OFDM Systems

Constrained Tensor Decomposition Optimization With Applications To Fmri Data Analysis

Low-energy programmable finite field data path architectures