Intrusion Detection System for Wireless Sensor Networks Using Danger Theory Immune-Inspired Techniques

An IDS framework inspired in the Human Immune System to be applied in the wireless sensor network context is proposed. It uses an improved decentralized and customized version of the Dendritic Cell Algorithm, which allows nodes to monitor their neighborhood and collaborate to identify an intruder. The work was implemented and tested both in simulation and in real sensor platform scenarios, comparing them to each other and was also compared to a Negative Selection Theory implementation in order to demonstrate its efficiency in detecting a denial-of-sleep attack and in energy consumption. Results demonstrated the success of the proposal.     

全自动平衡机系统中的EMC研究

介绍了全自动平衡机系统的组成及体系结构,并结合系统的运行环境、运行效果要求分析了系统的电磁兼容性,从电磁干扰三要素(干扰源、噪声耦合路径和敏感设备)着手,提出了保障系统电磁兼容性的若干手段.在此基础上进行了电磁兼容测试,给出了试验结果及结果分析.试验及实践结果表明,全自动平衡机系统达到了电磁兼容性要求.     

纠错算术码的检错时延分布研究

算术码是一种高效的熵编码,但是对误码十分敏感,由此引入了纠错算术码。以往研究中发现基于比特填充法的纠错算术码,其检错时延分布近似几何分布。文中通过研究基于比特跟随法的纠错算术码的检错时延分布,建立了符号检错时延分布的伽马分布模型,并通过曲线拟合得到了伽马分布的参数与影响因素之间的数学关系。拟合计算得出的数据与实验数据比较证实了新模型的可靠性。     

Constructing Proper Codes for Error Detection

It is shown that for any alphabet size q and any code size M, there exist proper codes for error detection on a q-ary symmetric channel for all sufficiently large lengths. The stronger condition zero-strong proper code is defined. It is shown that such codes can only exist for q dividing M, and if this is the case they are shown to exist for sufficiently large lengths. The existence proofs are constructive.     

Support Vector Machines for Automated Recognition of Obstructive Sleep Apnea Syndrome From ECG Recordings

Obstructive sleep apnea syndrome (OSAS) is associated with cardiovascular morbidity as well as excessive daytime sleepiness and poor quality of life. In this study, we apply a machine learning technique [support vector machines (SVMs)] for automated recognition of OSAS types from their nocturnal ECG recordings. A total of 125 sets of nocturnal ECG recordings acquired from normal subjects (OSAS- ) and subjects with OSAS (OSAS+), each of approximately 8 h in duration, were analyzed. Features extracted from successive wavelet coefficient levels after wavelet decomposition of signals due to heart rate variability (HRV) from RR intervals and ECG-derived respiration (EDR) from R waves of QRS amplitudes were used as inputs to the SVMs to recognize OSAS +/- subjects. Using leave-one-out technique, the maximum accuracy of classification for 83 training sets was found to be 100% for SVMs using a subset of selected combination of HRV and EDR features. Independent test results on 42 subjects showed that it correctly recognized 24 out of 26 OSAS + subjects and 15 out of 16 OSAS - subjects (accuracy = 92.85%; Cohen's kappa value of 0.85). For estimating the relative severity of OSAS, the posterior probabilities of SVM outputs were calculated and compared with respective apnea/hypopnea index. These results suggest superior performance of SVMs in OSAS recognition supported by wavelet-based features of ECG. The results demonstrate considerable potential in applying SVMs in an ECG-based screening device that can aid a sleep specialist in the initial assessment of patients with suspected OSAS.     

Automated Design Error Debugging of Digital VLSI Circuits

Journal of Electronic Testing - As the complexity and scope of VLSI designs continue to grow, fault detection processes in the pre-silicon stage have become crucial to guaranteeing reliability in...     

Deadlock Detection for a Class of Communicating Finite State Machines

LetMandNbe two communicating finite state machines which exchange one type of message. We develope a polynomial algorithm to detect whether or notMandNcan reach a deadlock. The time complexity of the algorithm isO(m^{3}n^{3}and its space isO(mn)wheremandnare the numbers of states inMandN, respectively. The algorithm can also be used to verify that two communicating machines which exchange many types of messages are deadlock-free.     

Comparison of AdaBoost and Support Vector Machines for Detecting Alzheimer's Disease Through Automated Hippocampal Segmentation

We compared four automated methods for hippocampal segmentation using different machine learning algorithms: 1) hierarchical AdaBoost, 2) support vector machines (SVM) with manual feature selection, 3) hierarchical SVM with automated feature selection (Ada-SVM), and 4) a publicly available brain segmentation package (FreeSurfer). We trained our approaches using T1-weighted brain MRIs from 30 subjects [10 normal elderly, 10 mild cognitive impairment (MCI), and 10 Alzheimer's disease (AD)], and tested on an independent set of 40 subjects (20 normal, 20 AD). Manually segmented gold standard hippocampal tracings were available for all subjects (training and testing). We assessed each approach's accuracy relative to manual segmentations, and its power to map AD effects. We then converted the segmentations into parametric surfaces to map disease effects on anatomy. After surface reconstruction, we computed significance maps, and overall corrected $p$-values, for the 3-D profile of shape differences between AD and normal subjects. Our AdaBoost and Ada-SVM segmentations compared favorably with the manual segmentations and detected disease effects as well as FreeSurfer on the data tested. Cumulative $p$-value plots, in conjunction with the false discovery rate method, were used to examine the power of each method to detect correlations with diagnosis and cognitive scores. We also evaluated how segmentation accuracy depended on the size of the training set, providing practical information for future users of this technique.      

Selective Error Detection for Error-Resilient Wavelet-Based Image Coding

This paper introduces the concept of a similarity check function for error-resilient multimedia data transmission. The proposed similarity check function provides information about the effects of corrupted data on the quality of the reconstructed image. The degree of data corruption is measured by the similarity check function at the receiver, without explicit knowledge of the original source data. The design of a perceptual similarity check function is presented for wavelet-based coders such as the JPEG2000 standard, and used with a proposed ldquoprogressive similarity-based ARQrdquo (ProS-ARQ) scheme to significantly decrease the retransmission rate of corrupted data while maintaining very good visual quality of images transmitted over noisy channels. Simulation results with JPEG2000-coded images transmitted over the binary symmetric channel, show that the proposed ProS-ARQ scheme significantly reduces the number of retransmissions as compared to conventional ARQ-based schemes. The presented results also show that, for the same number of retransmitted data packets, the proposed ProS-ARQ scheme can achieve significantly higher PSNR and better visual quality as compared to the selective-repeat ARQ scheme.     

DCT网络基于算法的并发错误检测结构

本文提出了一种离散余弦变换（ＤＣＴ）电路的并发错误检测结构,并在此基础上又提出一改进结构,ＤＣＴ采用Ｂ．Ｇ．Ｌｅｅ算法蝶型结构实现,检测采用的方法是基于算法的并发错误检测容错方法,结构中由于采用两组检测机制同时检测,在不增加硬件冗余的同时,吞吐量达到１００％,且故障覆盖率提高,对所提出的结构,文中分析了系统的绑亲笔 率,并给出了结论。     

One-to-Many: Context-Oriented Code for Concurrent Error Detection

The paper deals with context-oriented codes for concurrent error detection. We consider a fault model for which, in the presence of a fault, the values on the circuit’s output are arbitrary. This model allows one to design an error detection code without analyzing sensitive parts or error cones in the synthesized circuit. Conventional coding schemes are based on a one-to-one mapping between an original output vector (information word) and a codeword. In this paper, we introduce a different approach, which we call one-to-many coding. In one-to-many code, each codeword comprises a predefined set of words. The functional unit is referred to as an encoder enabling each activation to map an information word to a different word. This flexible mapping system results in a lower implementation cost of the functional unit and its checker.     

利用冗余进程实现MPI程序错误检测

关注MPI并行程序的运行时错误检测,提出了一种基于冗余进程的检错方法REDReP,能够检测MPI并行程序在运行过程中由于硬件故障导致的数据错误.介绍了REDReP的基本思想,讨论了一些关键问题,最后给出了实验结果,表明REDReP具有较低的检错开销.     

High Frequency Resolution Techniques for Rotor Fault Detection of Induction Machines

Motor current signature analysis (MCSA) is the reference method for the diagnosis of medium–large machines in industrial applications. However, MCSA is still an open research topic, as some signatures may be created by different phenomena, wherein it may become sensitive to load and inertia variations, and with respect to an oscillating load torque, although suitable data normalization can be applied. Recently, the topic of diagnostic techniques for drives and low to medium size machines is becoming attractive, as the procedure can be embedded in the drive at no additional thanks to a dedicated firmware, provided that a suitable computational cost is available. In this paper, statistical time-domain techniques are used to track grid frequency and machine slip. In this way, either a lower computational cost or a higher accuracy than traditional discrete Fourier transform techniques can be obtained. Then, the knowledge of both grid frequency and machine slip is used to tune the parameters of the zoom fast Fourier transform algorithm that either increases the frequency resolution, keeping constant the computational cost, or reduces the computational cost, keeping constant the frequency resolution. The proposed technique is validated for rotor faults.      

An Enhanced Automated Signature Generation Algorithm for Polymorphic Malware Detection

Polymorphic malware is a secure menace for application of computer network systems because hacker can evade detection and launch stealthy attacks. In this paper, a novel enhanced automated signature generation (EASG) algorithm to detect polymorphie malware is proposed. The EASG algorithm is composed of enhanced-expectation maximum algorithm and enhanced K-means clustering algorithm. In EASG algorithm, the fixed threshold value is replaced by the decision threshold of interval area. The false positive ratio can be controlled at low level, and the iterative operations and the execution time are effectively reduced. Moreover, the centroid updating is realized by application of similarity metric of Mahalanobis distance and incremental learning. Different malware group families are partitioned by the centroid updating.     

Deep Learning for Automated Occlusion Edge Detection in RGB-D Frames

Occlusion edges correspond to range discontinuity in a scene from the point of view of the observer. Detection of occlusion edges is an important prerequisite for many machine vision and mobile robotic tasks. Although they can be extracted from range data, extracting them from images and videos would be extremely beneficial. We trained a deep convolutional neural network (CNN) to identify occlusion edges in images and videos with just RGB, RGB-D and RGB-D-UV inputs, where D stands for depth and UV stands for horizontal and vertical components of the optical flow field respectively. The use of CNN avoids hand-crafting of features for automatically isolating occlusion edges and distinguishing them from appearance edges. Other than quantitative occlusion edge detection results, qualitative results are provided to evaluate input data requirements and to demonstrate the trade-off between high resolution analysis and frame-level computation time that is critical for real-time robotics applications.     

An Error Detection Model for Application During Software Development

A variation of the Jelinski/Moranda model is described. The main feature of this new model is that the variable (growing) size of a developing program is accommodated, so that the quality of a program can be estimated by analyzing an initial segment of the written code. Two parameters are estimated from the data. The data are: a) time separations between error detections, b) the number of errors per written instruction, c)the failure rate (or finding rate) of a single error, and d) a time record of the number of instructions under test. This model permits predictions of MTTF and error content of any software package which is homogenous with respect to its complexity (error making/finding). It assists in determining the quality, as measured by error contents, early on, and could eliminate the present practice of applying models to the wrong regimes (decreasing failure rate models applied to growing-in-size software packages). The growth model is very tractable analytically. The important requirement for applications is that the error-making rate must be constant across the entire software program.     

False Error Vulnerability Study of On-line Soft Error Detection Mechanisms

With technology scaling, vulnerability to soft errors in random logic is increasing. There is a need for on-line error detection and protection for logic gates even at sea level. The error checker is the key element for an on-line detection mechanism. We compare three different checkers for error detection from the point of view of area, power and false error detection rates. We find that the Double Sampling checker (used in Razor), is the simplest and most area and power efficient, but suffers from very high false detection rates of 1.15 times higher than the actual error rates. We also find that the alternate approaches of Triple Sampling and Integrate & Sample method can be designed to have zero false detection rates, but at an increased area, power and implementation complexity. The Triple Sampling method has about 1.74 times the area and 1.83 times the power as compared to the Double Sampling method and also needs a complex clock generation scheme. The Integrate & Sample method needs about 6% more power and is 0.58 times the area of Double Sampling. It comes with more stringent implementation constraints as it requires detection of small voltage swings. We also analyse for Double Transient Faults (DTFs) and show that all the methods are prone to DTFs, with Integrate & Sample method being more vulnerable.     

全自动晶圆磨边机平面度误差测量与评定

提出了在全自动晶圆磨边设备中对晶圆平面度误差的等角度单圆周测量方案,讲述了最小二乘法的算法原理;并根据最小二乘法的算法原理,基于Matlab软件编写平面度误差评定及其结果的可视化程序;运行程序对全自动磨边机测量的数据进行处理,程序运行结果表明,该算法运行稳定可靠,能够快速确定最小二乘法平面方程和评定平面度误差。