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

Polar codes are among the most promising error correction codes due to their ability to achieve the symmetric capacities of the binary-input discrete memoryless channels (B-DMCs). However, how to design successive cancellation (SC) decoders which can maximize the hardware utilization efficiency is still challenging due to the inherent serial nature of SC decoding algorithm. To this end, in this paper, formal design approaches for designing both the time-constrained and resource-constrained interleaved SC decoders are proposed. Compared with the state-of-the-art design, the proposed interleaved decoders can achieve more than 50% reduction in term of area-time product.

Interleaved successive cancellation polar decoders

This paper presents novel architectures for machine learning based classifiers using stochastic logic. Two types of classifier architectures are presented. These include: linear support vector machine (SVM) and artificial neural network (ANN). Stochastic computing systems require fewer logic gates and are inherently fault-tolerant. Thus, these structures are well suited for nanoscale CMOS technologies. These architectures are validated using seizure prediction from electroencephalogram (EEG) as an application example. To improve the accuracy of proposed stochastic classifiers, a novel approach based on linear transformation of input data is proposed for EEG signal classification using linear SVM classifiers. Simulation results in terms of the classification accuracy are presented for the proposed stochastic computing and the traditional binary implementations based datasets from one patient. Compared to conventional binary implementation, the accuracy of the proposed stochastic ANN is improved by 5.89%. Synthesis results are also presented for EEG signal classification. Compared to the traditional binary linear SVM, the hardware complexity, power consumption and critical path of the stochastic implementation are reduced by 78%, 74% and 53%, respectively. The hardware complexity, power consumption and critical path of the stochastic ANN classifier are reduced by 92%, 88% and 47%, respectively, compared to the conventional binary implementation.

Machine learning classifiers using stochastic logic

Mental disorders are a major source of disability, with few effective treatments. It has recently been argued that these diseases might be effectively treated by focusing on decision-making, and specifically remediating decision-making deficits that act as “ingredients” in these disorders. Prior work showed that direct electrical brain stimulation can enhance human cognitive control, and consequently decision-making. This raises a challenge of detecting cognitive control lapses directly from electrical brain activity. Here, we demonstrate approaches to overcome that challenge. We propose a novel method, referred to as maximal variance node merging (MVNM), that merges nodes within a brain region to construct informative inter-region brain networks. We employ this method to estimate functional (correlational) and effective (causal) networks using local field potentials (LFP) during a cognitive behavioral task. The effective networks computed using convergent cross mapping differentiate task engagement from background neural activity with 85% median classification accuracy. We also derive task engagement networks (TENs): networks that constitute the most discriminative inter-region connections. Subsequent graph analysis illustrates the crucial role of the dorsolateral prefrontal cortex (dlPFC) in task engagement, consistent with a widely accepted model for cognition. We also show that task engagement is linked to prefrontal cortex theta (4-8 Hz) oscillations. We, therefore, identify objective biomarkers associated with task engagement. These approaches may generalize to other cognitive functions, forming the basis of a network-based approach to detecting and rectifying decision deficits.

/pdf/region-level-functional-and-effective-network-analysis-of-2w2n403ba6.pdf

Region-Level Functional and Effective Network Analysis of Human Brain During Cognitive Task Engagement

The Carrierless AM/PM (CAP) modulation scheme is a viable alternative for high speed digital transmission for the HDSL/ADSL/VHDSL environments. In this paper, the conventional 2-D CAP system is expanded to a 3-D space. A 50% increase in the system throughput is achieved at the expense of added receiver complexity and some performance degradation. The 3-D signaling is obtained through a minimax optimization process to match the characteristics of the original 2-D system and to achieve perfect reconstruction (PR) condition. The 3-D CAP system is simulated over unshielded twisted pair (UTP) cable environment for baud rate of 150 Mbps.

Three-dimensional carrierless AM/PM line code for the unshielded twisted pair cables

True random number generators (TRNGs) are crucial components for the security of cryptographic systems. In contrast to pseudo--random number generators (PRNGs), TRNGs provide higher security by extracting randomness from physical phenomena. To evaluate a TRNG, statistical properties of the circuit model and raw bitstream should be studied. In this article, a model for the beat frequency detector--based high-speed TRNG (BFD-TRNG) is proposed. The parameters of the model are extracted from the experimental data of a test chip. A statistical analysis of the proposed model is carried out to derive mean and variance of the counter values of the TRNG. Our statistical analysis results show that mean of the counter values is inversely proportional to the frequency difference of the two ring oscillators (ROSCs), whereas the dynamic range of the counter values increases linearly with standard deviation of environmental noise and decreases with increase of the frequency difference. Without the measurements from the test data, a model cannot be created; similarly, without a model, performance of a TRNG cannot be predicted. The key contribution of the proposed approach lies in fitting the model to measured data and the ability to use the model to predict performance of BFD-TRNGs that have not been fabricated. Several novel alternate BFD-TRNG architectures are also proposed; these include parallel BFD, cascade BFD, and parallel-cascade BFD. These TRNGs are analyzed using the proposed model, and it is shown that the parallel BFD structure requires less area per bit, whereas the cascade BFD structure has a larger dynamic range while maintaining the same mean of the counter values as the original BFD-TRNG. It is shown that 3.25M and 4M random bits can be obtained per counter value from parallel BFD and parallel-cascade BFD, respectively, where M counter values are computed in parallel. Furthermore, the statistical analysis results illustrate that BFD-TRNGs have better randomness and less cost per bit than other existing ROSC-TRNG designs. For example, it is shown that BFD-TRNGs accumulate 150p more jitter than the original two-oscillator TRNG and that parallel BFD-TRNGs require one-third power and one-half area for same number of random bits for a specified period.

Keshab K. Parhi

Papers

Interleaved successive cancellation polar decoders

Machine learning classifiers using stochastic logic

Region-Level Functional and Effective Network Analysis of Human Brain During Cognitive Task Engagement

Three-dimensional carrierless AM/PM line code for the unshielded twisted pair cables

Beat Frequency Detector--Based High-Speed True Random Number Generators: Statistical Modeling and Analysis