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

In this paper, we present the design and FPGA implementation of 11 LDPC codes with code rates 1/4, 1/3, 2/5, 1/2, 3/5, 2/3, 3/4, 4/5, 5/6, 8/9 and 9/10 for normal frame length of 64800 bits as used in DVB-S2. Out of these 11 codes, 7 are regular and 4 are irregular. All of them have been synthesized into Xilinx Virtex-II XC2V8000 FPGA

Design and Implementation of LDPC Codes for DVB-S2

A number of behavioral and cognitive functions of brain differ between male and female. Occurrences of psychiatric disorders, e.g., attention deficit hyperactivity disorder, autism, depression and schizophrenia also vary from male to female. Understanding the unique cognitive expressions in gender-specific brain functions may lead to insights into the risks and associated responses for a certain external simulation or medications. Previously resting-state functional magnetic resonance imaging (r-fMRI) has been used extensively to understand gender differences using functional network connectivity analysis. However, how the brain functional network changes during a cognitive task for different genders is relatively unknown. This paper makes use of a large data set to test whether task-fMRI functional connectivity can be utilized to predict male vs. female. In addition, it also identifies functional connectivity features that are most predictive of gender. The cognitive task-fMRI data consisting 475 healthy controls is taken from the Human Connectome Project (HCP) database. Pearson correlation coefficients are extracted using mean time-series from anatomical brain regions. Partial least squares (PLS) regression with feature selection on the correlation coefficients achieves a classification accuracy of 0.88 for classifying male vs. female using emotion task data. In addition it is found that inter hemispheric connectivity is most important for predicting gender from task-fMRI.

Predicting Male vs. Female from Task-fMRI Brain Connectivity

In this paper we consider the design of a digit-serial multiplier for the implementation of a complex number multiplier-accumulator (CMAC) on FPGAs. In this case, fixed coefficient multipliers and pipelined at LUT-level structures have been considered. Partial products generators based on look-up tables and multibit Booth recoding are used to reduce the area and increase the performance of the circuit. The efficient mapping of the 5 bit Booth recoding to generate the partial products is presented as the optimum multibit recoding when Xilinx FPGA devices are used.

Digit-serial fixed coefficient complex number multiplier-accumulator on FPGAs

Spiking Neural Networks (SNNs) represent a biologically inspired computation model capable of emulating neural computation in human brain and brain-like structures. The main promise is very low energy consumption. Classic Von Neumann architecture based SNN accelerators in hardware, however, often fall short of addressing demanding computation and data transfer requirements efficiently at scale. In this article, we propose a promising alternative to overcome scalability limitations, based on a network of in-memory SNN accelerators, which can reduce the energy consumption by up to 150.25= when compared to a representative ASIC solution. The significant reduction in energy comes from two key aspects of the hardware design to minimize data communication overheads: (1) each node represents an in-memory SNN accelerator based on a spintronic Computational RAM array, and (2) a novel, De Bruijn graph based architecture establishes the SNN array connectivity.

https://dl.acm.org/doi/pdf/10.1145/3475963

Spiking Neural Networks in Spintronic Computational RAM

This paper presents the architecture and implementation of a full-custom 1.2 micron CMOS VLSI chip that executes a shared division/square root algorithm operating on mantissas (23-b in length) of single precision IEEE 754 std. floating point numbers. The division and square root algorithms used in this implementation are the radix 2 signed digit based digit-by-digit schemes. These two algorithms perform quotient/root digit selection using two most-significant digits of the partial remainder and are hence faster than other similar previously proposed radix 2 shared division/square root schemes. This chip runs at a clock rate of about 66 MHz at 5.0 V (from simulations) and requires 29 cycles per divide/square root operation from the time the operands are provided at its pin inputs.

Keshab K. Parhi

Papers

Design and Implementation of LDPC Codes for DVB-S2

Predicting Male vs. Female from Task-fMRI Brain Connectivity

Digit-serial fixed coefficient complex number multiplier-accumulator on FPGAs

Spiking Neural Networks in Spintronic Computational RAM

A floating point radix 2 shared division/square root chip