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 high-speed multi-pair wireline communication systems, such as 10 gigabit Ethernet over Copper (10GBASE-T), far-end crosstalk (FEXT) becomes a major impairment and needs to be suppressed to increase data rates. Conventional techniques based on crosstalk cancellation are not suitable for FEXT due to the fact that the disturbing source of FEXT is generally unknown to the victims. This paper presents two different approaches to efficiently deal with FEXT over unshielded twisted-pair (UTP) copper cables. To eliminate the error propagation problem in practice, both approaches use the Tomlinson-Harashima precoding (THP) technique which however makes the transceiver design nontrivial. In the first approach, FEXT is treated as noise and a new feedforward FEXT canceller is proposed. Compared with conventional techniques, the proposed FEXT canceller can mitigate the non-causal part of FEXT, thus leading to better FEXT cancellation performance. In the second approach, FEXT is treated as signal, and the general multi-input multi-output (MIMO) equalization technique is combined with the TH precoding technique to deal with both intersymbol interference (ISI) and FEXT. Different from the existing works, the proposed designs comply with the 10GBASE-T standard and they are suitable in real applications. Simulation results verify that the proposed approaches can achieve much better performance in terms of decision-point signal-to-noise ratio (DP-SNR) than conventional techniques. It is also shown that the hardware complexity of the transceiver can be reduced by about 37.2% by utilizing the increased DP-SNR in the proposed designs.

Novel FEXT Cancellation and Equalization for High Speed Ethernet Transmission

Correlation between initial vision and vision improvement with automatically calculated retinal cyst volume in treated DME after resolution

Bit parallel bit level recursive filter architecture.

In this paper, we propose low complexity implementations of sum-product algorithm (SPA) for decoding low-density parity-check (LDPC) codes. The finite precision implementations for SPA have an important tradeoff between the decoding performance and hardware complexity caused by two dominant area consuming factors. One is the memory for updated messages storage and the other is the look-up table (LUT) for implementation of nonlinear function ?(x). The proposed two types of variable quantization schemes result in significant reductions of LUT sizes. Computer simulation results indicate that the performance losses of SPA with reduced LUT by proposed schemes are less than 0.05 dB compared to that of SPA with original LUT. Moreover, the proposed implementation offers a large reduction in the finite wordlength for storage of updated messages using a kind of compression technique without performance losses. As a result, using the proposed implementation based on variable quantization scheme and data compression technique, the novel proposed implementation for SPA decoder architecture offers significant reductions of 75% and 33% in hardware complexities of LUT and memories, respectively, without significant performance degradations over conventional uniform quantization scheme. Furthermore, since each LUT with variable (6:3) quantization scheme II has only 8 entries, its hardware implementation can be replaced with a small combination circuit instead of read-only-memory (ROM) for LUT.

Low Complexity Implementations of Sum-Product Algorithm for Decoding Low-Density Parity-Check Codes

This paper presents a novel approach to estimate delay differences of each stage in a standard MUX-based physical unclonable function (PUF). Test data collected from PUFs fabricated using 32 nm process are used to train a linear model. The delay differences of the stages directly correspond to the model parameters. These parameters are trained by using a least mean square (LMS) adaptive algorithm. The accuracy of the response using the proposed model is around 97.5% and 99.5% for two different PUFs. Second, the PUF is also modeled by a perceptron. The perceptron has almost 100% classification accuracy. A comparison shows that the perceptron model parameters are scaled versions of the model derived by the LMS algorithm. Thus, the delay differences can be estimated from the perceptron model where the scaling factor is computed by comparing the models of the LMS algorithm and the perceptron. Because the delay differences are challenge independent, these parameters can be stored on the server. This will enable the server to issue random challenges whose responses need not be stored. An analysis of the proposed model confirms that the delay differences of all stages of the PUFs on the same chip belong to the same Gaussian probability density function.

Keshab K. Parhi

Papers

Novel FEXT Cancellation and Equalization for High Speed Ethernet Transmission

Correlation between initial vision and vision improvement with automatically calculated retinal cyst volume in treated DME after resolution

Bit parallel bit level recursive filter architecture.

Low Complexity Implementations of Sum-Product Algorithm for Decoding Low-Density Parity-Check Codes

Estimating delay differences of arbiter PUFs using silicon data