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 incremental-precision classification approach that leads to a reduction in energy consumption of linear classifiers for IoT applications. Features are first input to a low-precision classifier. If the classifier successfully classifies the sample, then the process terminates. Otherwise, the classification performance is incrementally improved by using a classifier of higher precision. This process is repeated until the classification is complete. The argument is that many samples can be classified using the low-precision classifier, leading to a reduction in energy. To achieve incremental-precision, a novel data-path decomposition is proposed to design of fixed-width adders and multipliers. These components improve the precision without recalculating the outputs, thus reducing energy. Using a linear classification example, it is shown that the proposed incremental-precision based multi-level classifier approach can reduce energy by about 41% while achieving comparable accuracies as that of a full-precision system.

https://dl.acm.org/doi/pdf/10.1145/3194554.3194603

Low-Energy Architectures of Linear Classifiers for IoT Applications using Incremental Precision and Multi-Level Classification

This paper presents a compilation strategy and a toolkit for biochemical reactions that perform sequential arithmetic computation on protein quantities, analogous to register-based computation in digital systems. From a Verilog-like input specification file, we generate biochemical reactions that produce output quantities as a function of input quantities, performing operations such as addition, subtraction, and multiplication. Sequential operations are implemented by transferring quantities between protein types, based on a clocking mechanism. Synthesis first is performed at a conceptual level, in terms of abstract biochemical reactions — a task analogous to technology-independent logic synthesis in circuit design. Then the results are mapped onto specific biochemical reactions, selected from libraries — a task analogous to technology mapping in circuit design. Our method targets the universal DNA substrate developed by Erik Win-free's group at Caltech as the experimental chassis. We demonstrate the algorithm on the synthesis of a variety of standard sequential functions: signal processing functions (FIR filters and IIR filters), vector multiplication, integration and differentiation. The designs are validated through transient stochastic simulation of the chemical kinetics.

Synthesizing sequential register-based computation with biochemistry

Molecular Sensing and Computing Systems (Invited Paper)

The present invention relates to data processing techniques in multi-channel data transmission systems. In this invention, a novel approach is proposed to deal with FEXT interferences in the application of high/ultra-high speed Ethernet systems. Compared with the traditional FEXT cancellation approaches, the proposed FEXT canceller can deal with the non-causal part of FEXT, and thus can achieve better cancellation performance. Instead of using the conventional DFE, structure, TH precoding technique is incorporated into the proposed design to alleviate the error propagation problem. The resulting FEXT cancellers do not contain feedback loops which makes the high speed VLSI implementation easy. A modified design is also developed by using a finite signal as the input to the FEXT canceller such that the hardware complexity of the proposed FEXT canceller can be reduced.

System for fext cancellation of multi-channel transceivers with precoding

In deep-submicron technology, global interconnect capacitances have started reaching several orders of magnitude greater than the intrinsic capacitances of the CMOS gates. The dynamic power consumption of a CMOS gate driving a global wire is the sum of the power dissipated due to (dis)charging (i) the intrinsic capacitance of the gate, and (ii) the wire capacitance. The latter is referred to as on-chip signaling power consumption.

/pdf/energy-efficient-signaling-in-deep-submicron-technology-48nnr5i6uc.pdf

Keshab K. Parhi

Papers

Low-Energy Architectures of Linear Classifiers for IoT Applications using Incremental Precision and Multi-Level Classification

Synthesizing sequential register-based computation with biochemistry

Molecular Sensing and Computing Systems (Invited Paper)

System for fext cancellation of multi-channel transceivers with precoding

Energy efficient signaling in deep-submicron technology