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 logistic regression based approach to predict the soft-response for a challenge using the total delay-difference as an input. This approach enables us to determine whether a challenge is stable or not. Soft-response is the probability of response bit corresponding to the challenge being 1. The total delay-difference is computed from the input challenge by assuming that the delay-difference of the stages are known. The approach learns a logistic function based on the total delay-difference which has just 3 parameters. Therefore, this is a simple approach which gives comparable performance against a more complex approach based on artificial neural network (ANN) models. The model demonstrates good sensitivity and precision but poor specificity. Furthermore, we use scaling parameter of the logistic function to study its relation to the arbiter's timing parameters like setup and hold time.

Predicting Soft-Response of MUX PUFs via Logistic Regression of Total Delay Difference

QR decomposition based multi-channel least square lattice (QRD-MLSL) algorithm possesses a good numerical property and regularity which are attractive for VLSI implementation. However, due to the presence of a local recursive loop in its implementation, the algorithm's speed is limited to the computation time of each computation cell. In this paper, a novel approach for pipelining the QRD-MLSL adaptive filtering algorithm is developed. The proposed architecture is pipelined at fine-grain level, thus it can be operated at arbitrarily high speed. Furthermore, it provides an exact least square solution, so there is no performance degradation. Computer simulations are presented to demonstrate the functionality of the proposed pipelining methodology.

Pipelined QR decomposition based multi-channel least square lattice adaptive filter architectures

Prediction of seizures is a difficult problem as the EEG patterns are not wide-sense stationary and change from seizure to seizure, electrode to electrode, and from patient to patient. This paper presents a novel patient-specific algorithm for prediction of seizures in epileptic patients. Cross-correlation coefficients are extracted every 2 seconds using a 4-second window with 50% overlap from focus electrodes identified by the epileptologist. Features are further processed by a second-order Kalman filter and then input to three different classifiers which include AdaBoost, radial basis function kernel support vector machine (RBF-SVM) and artificial neural network (ANN). The algorithm is tested on the long-term intra-cranial EEG (iEEG) database collected at the UMN epilepsy clinic. This database includes EEG recordings from 2 patients sampled from varying number of electrodes sampled at 2kHz. It is shown that the proposed algorithm achieves a high sensitivity and a low false positive rate.

Seizure prediction using cross-correlation and classification

This paper presents an area-efficient architecture for stochastic low-density parity-check (LDPC) decoder with high throughput and excellent bit-error-rate (BER) performance The correlation effects of a stochastic Sum-Product Algorithm (SPA) are analyzed Based on this analysis, a variable node (VN) structure is proposed and its similarity with a correlation divider (CORDIV) is pointed out Based on the properties of CORDIV, the area of probability tracer in the VN is reduced significantly In order to achieve more accurate results when the check-to-variable (C2V) messages are not strong enough, the 3-3 input grouping sub-node is replaced by an adder-based 5-1 input grouping sub-node of the degree-6 VN for (2048,1723) code An unbiased stochastic sequence generator is adopted to get more accurate results from the smaller probability tracer Furthermore, the soft bit-flipping prior-processing and the C2V-based hard decision updating method are combined in VN to reduce the decoding latency A (2048,1723) stochastic LDPC decoder is designed in the TSMC 65 nm process to demonstrate the proposed decoder architecture With the aid of early termination, the decoder occupies 234 mm2 core area and can achieve 11617 Gb/s at 44 dB and 46199 Gb/s at 55 dB under 970 MHz with better decoding performance Compared with the state-of-the-art stochastic IEEE 8023an LDPC decoders, the proposed architecture can achieve the best throughput, throughput-to-area ratio, and BER performance

A High-Performance Stochastic LDPC Decoder Architecture Designed via Correlation Analysis

This paper describes a novel approach to synthesize molecular reactions to compute a radial basis function (RBF) support vector machine (SVM) kernel. The approach is based on fractional coding where a variable is represented by two molecules. The synergy between fractional coding in molecular computing and stochastic logic implementations in electronic computing is key to translating known stochastic logic circuits to molecular computing. Although inspired by prior stochastic logic implementation of the RBF-SVM kernel, the proposed molecular reactions require non-obvious modifications. This paper introduces a new explicit bipolar-to-unipolar molecular converter for intermediate format conversion. Two designs are presented; one is based on the explicit and the other is based on implicit conversion from prior stochastic logic. When 5 support vectors are used, it is shown that the DNA RBF-SVM realized using the explicit format conversion has orders of magnitude less regression error than that based on implicit conversion. CCS CONCEPTS • Applied computing → Molecular structural biology; •Hardware → Biology-related information processing;

Keshab K. Parhi

Papers

Predicting Soft-Response of MUX PUFs via Logistic Regression of Total Delay Difference

Pipelined QR decomposition based multi-channel least square lattice adaptive filter architectures

Seizure prediction using cross-correlation and classification

A High-Performance Stochastic LDPC Decoder Architecture Designed via Correlation Analysis

Computing Radial Basis Function Support Vector Machine using DNA via Fractional Coding