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

Traditionally equalization is performed individually for 10GBASE-T, and FEXT is treated as noise to be cancelled at the receiver. However, FEXT contains information about the symbols transmitted from remote transmitters and it can be viewed as a signal rather than noise to facilitate signal recovery. This paper proposes to use MIMO (multi-input multi output) equalization technique to deal with FEXT in 10GBASE-T. In the proposed MIMO technique, FEXT is treated as signal, which improves SNR. Instead of using long FEXT cancellers, MIMO-DFE with short length is used to remove post-cursor ISI. Our simulation results show that, by using the proposed MIMO equalization, we are able to achieve SNR (signal to noise ratio) improvement around 0.5~9dB with 13% less complexity than the traditional equalization technique in twisted-pair channel environment.

MIMO Equalization and Cancellation for 10Gbase-T 1

It is shown that the minimum-noise lattice filter can be obtained from the normalized lattice filter by applying the modified scaling method It is concluded that the minimum noise lattice filter can also be referred to as the l/sub 2/ scaled normalized lattice filter A review of the scaling method using state variables is provided >

The scaled normalized lattice digital filter

Hyperdimensional (HD) computing holds promise for classifying two groups of data. This paper explores seizure detection from electroencephalogram (EEG) from subjects with epilepsy using HD computing based on power spectral density (PSD) features. Publicly available intra-cranial EEG (iEEG) data collected from 4 dogs and 8 human patients in the Kaggle seizure detection contest are used in this paper. This paper explores two methods for classification. First, few ranked PSD features from small number of channels from a prior classification are used in the context of HD classification. Second, all PSD features extracted from all channels are used as features for HD classification. It is shown that for about half the subjects small number features outperform all features in the context of HD classification, and for the other half, all features outperform small number of features. HD classification achieves above 95% accuracy for six of the 12 subjects, and between 85-95% accuracy for 4 subjects. For two subjects, the classification accuracy using HD computing is not as good as classical approaches such as support vector machine classifiers.

Seizure Detection Using Power Spectral Density via Hyperdimensional Computing

We present a unified framework to compare and optimize various adders in the literature including carry-ripple adders, carry-look-ahead adders, prefix-adders, canonic adders, block-based carry-look-ahead adders, carry-skip adders, conditional-sum adders, carry-select adders and hybrid adders. The logic of all those adders can be separated into two parts: the carry logic and the sum logic, while carries can be generated by a uniformed carry operator implemented by any of the prefix operator, the FCO operator, MUX, the AND-OR gates and pass-transistors. The most efficient way to generate carries is by layers instead of by groups, where layers are non-consecutive non-equal length collections. We demonstrate that the conditional-sum adders and carry-select adders have redundant sum logic and sub-optimal carry logic; therefore they should be eliminated. We show how to design efficient carry logic by layers of carries and improve some prefix addition algorithms such as the Brent-Kung's adder for carry generation. The ideas discussed here are independent of the implementation technology and therefore useful for all implementations and technologies.

A unified adder design

In this paper, a set of variables which can be easily computed in the course of iterative decoding of turbo decoders named turbo decoding metrics (TDMs) are introduced. TDMs are measured twice per iteration. According to the measured values, a lot of information other than the signal-to-noise ratio (SNR) in the received data, such as how good (or bad) the current decoding block is and how close to the convergence the current decoding is, can be obtained. Detailed discussions are provided regarding why these variables are considered. Based on the measured TDMs after the first iteration, an approximate channel reliability value (denoted as L/sub c/) can be obtained for MAP-based turbo decoders. Simulation results show that there is no performance degradation if approximated L/sub c/ values are used instead of using exact values. It is also shown that the adaptive decoding approach employing TDMs is more efficient than all existing methods in terms of hardware and latency. Other applications of TDMs are also pointed out.

Keshab K. Parhi

Papers

MIMO Equalization and Cancellation for 10Gbase-T 1

The scaled normalized lattice digital filter

Seizure Detection Using Power Spectral Density via Hyperdimensional Computing

A unified adder design

On-line extraction of soft decoding information and applications in VLSI turbo decoding