A separable method for incorporating imperfect fault-coverage intocombinatorial models

This paper presents a new method for incorporating imperfect FC (fault coverage) into a combinatorial model. Imperfect FC, the probability that a single malicious fault can thwart automatic recovery mechanisms, is important to accurate reliability assessment of fault-tolerant computer systems. Until recently, it was thought that the consideration of this probability necessitated a Markov model rather than the simpler (and usually faster) combinatorial model. SEA, the new approach, separates the modeling of FC failures into two terms that are multiplied to compute the system reliability. The first term, a simple product, represents the probability that no uncovered fault occurs. The second term comes from a combinatorial model which includes the covered faults that can lead to system failure. This second term can be computed from any common approach (e.g. fault tree, block diagram, digraph) which ignores the FC concept by slightly altering the component-failure probabilities. The result of this work is that reliability engineers can use their favorite software package (which ignores the FC concept) for computing reliability, and then adjust the input and output of that program slightly to produce a result which includes FC. This method applies to any system for which: the FC probabilities are constant and state-independent; the hazard rates are state-independent; and an FC failure leads to immediate system failure     

Thermal reliability prediction and analysis for high-density electronic systems based on the Markov process

Thermal-mechanical fatigue is one of the main failure modes for electronic systems, particularly for high-density electronic systems with high-power components. Thermal reliability estimation and prediction have been an increasing concern for improving the safety and reliability of electronic systems. In this paper, we propose a stochastic process prediction model to estimate the thermal reliability of an electronic system based on Markov theory. We first divided the high-density electronic systems into four modules: the energy transformation and protection module, the electronic control module, the connection module, and the signal transmission and transformation module. By integrating failure and repair characteristics of the four modules, a stochastic model of thermal reliability analysis and prediction for a whole electronic system was built based on the Markov process. The feature parameters of thermal reliability evaluation, including thermal reliability, thermal failure probability, mean time between thermal faults, and thermal stable availability, were derived based on our comprehensive model. Finally, we applied the model to an indoor electronic system of DC frequency conversion conditioning. The thermal reliability was estimated and predicted using tested failure and debugging repair data. Effective methods for improving thermal reliability are presented and analyzed based on the comprehensive Markov model.     

Estimation, Hypothesis Testing and Parameter Correlation for Markov Chains

In this report we will discuss four problems in statistical inference for Markov chains. Specifically, techniques are described to do the following: 1) estimate the transition probabilities of first- and second-order stationary Markov chains; 2) test the hypothesis that a stationary Markov chain is of first order against the alternate hypothesis that the chain is of second order; and 3) test the hypothesis that a first-order Markov chain has stationary transition probabilities against the alternate hypothesis that the transition probabilities are not stationary. A technique is also developed which can be used in testing to determine whether two parameters of a single electronic component drift independently of each other. The results of these tests are used to draw some inference about continuous-time Markov processes.     

Frequency tracking using hidden Markov models with amplitude andphase information

It is demonstrated how the hidden Markov model (HMM) frequency tracker can be extended by the addition of amplitude and phase information. The HMM tracker as originally formulated uses a gate of spectral bins from fast Fourier transform (FFT) processing, and associates each cell with a state of the hidden Markov chain. A measurement sequence based on the output of a simple threshold detector forms the input to the HMM tracker. Two extensions to the original tracker are proposed. The first, the HMM/A tracker, incorporates the FFT amplitudes in the cells of the measurement sequence. The second, the HMM/AP tracker, does not use a measurement sequence, but uses instead the FFT amplitude and phase values in all cells within the gate. A comparison of the results obtained in using the three HMM-based trackers with simulated data reveals that the extended trackers outperform the original. An analysis of the effect of parameter mismatch for the three trackers is presented. Their use as detectors is also discussed     

Comparison of two optimum rates for noiseless encoding of anonstationary information source

A nonstationary finite-alphabet information source μ is noiselessly encoded, first by adaptive block to variable-length codes and then by finite-state codes of this kind. It is shown that if μ belongs to a certain class of sources that includes those with finite-order Markov memory, then a stationary input-restricted channel exists such that in the first case the optimum encoding rate is equal to the largest conditional entropy for channel input given channel output, whereas in the second case it is the largest channel output entropy. A sufficient condition for the two rates to be equal is also given     

Stochastic analysis of systems with primary and secondary failures

This paper presents the stochastic analysis of repairable systems involving primary as well as secondary failures. To this end, two models are considered. The first model represents a system with two identical warm standbys. The failure rates of units and the system are constant and independent while the repair times are arbitrarily distributed. The second system modeled consists of three repairable regions. The system operates normally if all three regions are operating, otherwise it operates at a derated level unless all three regions fail. The failure rates and repair times of the regions are constant and independent. The first model is analyzed using the supplemental variable technique while the second model is analyzed using the regenerative point technique in the Markov renewal process. Various expressions including system availability, system reliability and mean time to system failure are obtained.     

LED显示屏条状缺陷原因判定及修复验证的质量鉴定应用案例

为了解决LED显示屏的条状缺陷问题,通过对电路结构进行观察分析,并进行实验测试,发现条状缺陷通常是由于串联模组电流输入端的片个LED模块出现故障。从而导致LED显示屏出现条状缺陷,因此．LED显示屏出现条状缺陷,其原刚通常是该条状模组的电流输入端的首个LED出现故障,如断路问题：     

Lossless compression of waveform data for efficient storage andtransmission

A two-stage technique for lossless waveform data compression is described. The first stage is a modified form of linear prediction with discrete coefficients, and the second stage is bilevel sequence coding. The linear predictor generates an error or residue sequence in a way such that exact reconstruction of the original data sequence can be accomplished with a simple algorithm. The residue sequence is essentially white Gaussian with seismic or other similar waveform data. Bilevel sequence coding, in which two sample sizes are chosen and the residue sequence is encoded into subsequences that alternate from one level to the other, further compresses the residue sequence. The algorithm is lossless, allowing exact, bit-for-bit recovery of the original data sequence. The performance of the algorithm at each stage is analyzed. Applications of the two-stage technique to typical seismic data indicates that an average number of compressed bits per sample close to the lower bound is achievable in practical situations     

Quantization error and step-size distributions in ADPCM

An adaptive differential pulse code modulator (ADPCM) with a finite number of possible step-sizes and a leaky integrator in the feedback loop is considered. A zero-mean unit-ariance first-order Markov sequence is chosen as the input to thc system, and the leak parameter of the ADPCM accumulator is made equal to the intersample correlation of the input sequence. Using this fundamental structure, a method is presented for computing the exact joint probability distribution function of quantization error and step-size in ADPCM. From the joint distribution, marginal quantization error, and step-size distributions are obtained for Gauss-Markov, exponential-Markov, and uniform-Markov input signals. Empirical distributions obtained from simulations agree very well with their theoretical counterparts.     

EEG signal modeling using adaptive Markov process amplitude

In this paper, an adaptive Markov process amplitude algorithm is used to model and simulate electroencephalogram (EEG) signals. EEG signal modeling is used as a tool to identify pathophysiological EEG changes potentially useful in clinical diagnosis. The least mean square algorithm is adopted to continuously estimate the parameters of a first-order Markov process model. EEG signals recorded from rodent brains during injury and recovery following global cerebral ischemia are utilized as input signals to the model. The EEG was recorded in a controlled experimental brain injury model of hypoxic-ischemic cardiac arrest. The signals from the injured brain during various phases of injury and recovery were modeled. Results show that the adaptive model is accurate in simulating EEG signal variations following brain injury. The dynamics of the model coefficients successfully capture the presence of spiking and bursting in EEG.     

Design of high-speed digital filters suitable for multi-DSP implementation

A multipath signal processing scheme is proposed to overcome the limitation on throughput rate of present-day LSI devices using a number of digital signal processors. Two methods are proposed to realize a given transfer function H(z) for a digital filter with a throughput rate speed that is N times higher than in conventional methods. The first method, the delayed multipath approach, uses an N-path structure as a building element. These N elements are connected successively with increasing delay units to realize a given transfer function. The second method preprocesses the input signal sequence with an FFT processor and follows it up by N of constituent transfer functions derived from H(z) having real coefficients. The output of these N constituent transfer functions is finally post processed by an inverse FFT processor to obtain the desired output signal. The number of the constituent transfer functions is double for a special case when the transfer function to be implemented has complex valued coefficients.     

A new information theoretic test of the Markov property of block errors in fading channels

This paper introduces a new and effective information theoretic test of the accuracy of the Markov property of block errors in fading channels. We apply the test to verify the validity of a first-order Markov model for block error processes on Rayleigh and Ricean fading channels. Unlike previous work, we address the effects of maximal ratio diversity-combining at the receiver. As a second application, we investigate the behavior of the block success/failure process in transmission systems that incorporate closed-loop power control. This topic is of great interest in the design of next-generation wireless networks. Numerical results show that our approach provides significant improvements in accuracy over previously proposed methods.     

Quasi-Random Testing

Our paper proposes an implementable procedure for using the method of quasi-random sequences in software debug testing. In random testing, the sequence of tests (if considered as points in an -dimensional unit hypercube) will give rise to regions where there are clusters of points, as well as underpopulated regions. Quasi-random sequences, also known as low-discrepancy or low-dispersion sequences, are sequences of points in such a hypercube that are spread more evenly throughout. Based on the observation that program faults tend to lead to contiguous failure regions within a program's input domain, and that an even spread of random tests enhances the failure detection effectiveness for certain failure patterns, we examine the use of quasi-random sequences as a replacement for random sequences in automated testing. Because there are only a small number of quasi-random sequence generation algorithms, and each of them can only generate a small number of distinct sequences, the applicability of quasi-random sequences in testing real programs is severely restricted. To alleviate this problem, we examine the use of two types of randomized quasi-random sequences, which are quasi-random sequences permuted in a nondeterministic fashion in such a way as to retain their low discrepancy properties. We show that testing using randomized quasi-random sequences is often significantly more effective than random testing.     

Interval algorithm for random number generation

The problem of generating a random number with an arbitrary probability distribution by using a general biased M-coin is studied. An efficient and very simple algorithm based on the successive refinement of partitions of the unit interval (0, 1), which we call the interval algorithm, is proposed. A fairly tight evaluation on the efficiency is given. Generalizations of the interval algorithm to the following cases are investigated: (1) output sequence is independent and identically distributed (i.i.d.); (2) output sequence is Markov; (3) input sequence is Markov; (4) input sequence and output sequence are both subject to arbitrary stochastic processes     

Adaptive noise reduction of InSAR images based on a complex-valuedMRF model and its application t o phase unwrapping problem

We propose a new adaptive noise reduction method for interferometric synthetic aperture radar (InSAR) complex-amplitude images. In the proposed method, we detect residues (singular points) in the phase image as well as their neighbors at first. Normal areas that contain no residue are used for the estimation of correct pixel values at the marked residues according to 5th order non-causal complex-valued Markov random field (CMRF) model. The process is performed block-wise with the assumption of a locally stationary condition of statistics. Using a CMRF lattice complex-valued neural-network, the error energy defined as the squared norm of distance between signal and estimated values is minimized by LMS steepest descent algorithm. Eventually, the number of residues is decreased. An application is also presented. An InSAR image around Mt. Fuji is processed by the proposed technique and then phase-unwrapped by the branch-cut method. It is found that after the application of the proposed method, a better phase unwrapped image can be obtained successfully     

Fast mersenne number transforms for the computation of discrete fourier transforms

In this paper we present results on the computation of Discrete Fourier Transforms (DFT) using Mersenne Number Transforms (MNT). It is shown that in the case of Mersenne-composite Number Transforms, the number of multiplications per point for real input data is never more than one, even for sequence lengths exceeding one thousand points. The computation time per point for a length (2P + 1)-point DFT is simply equal to the time for one MNT multiplication and 3 MNT additions if a high-speed, parallel hardware module is used to implement the MNT unit. This new approach allows a large choice of wordlengths and in addition the control of data flow is extremely simple. We also present the results obtained by using Winograd's Fourier Transform Algorithm and the nested MNT to compute efficiently the DFT's of long sequences. We also show that the number of additions can be reduced significantly if Pseudo Mersenne-Number Transforms are used for the computation of DFTs.     

PNS modules for the synthesis of parallel self-organizing hierarchical neural networks

The PNS module is discussed as the building block for the synthesis of parallel, self-organizing, hierarchical, neural networks (PSHNNs). The PNS module contains three submodules (units), the first two of which are created as simple neural network constructs and the last of which is a statistical unit. The first two units are fractile in nature, meaning that each such unit may itself consist of a number of parallel PNS modules in a fractile fashion. Through a mechanism of statistical acceptance or rejection of input vectors for classification, the sample space is divided into a number of regions. The input vectors belonging to each region are classified by a dedicated set of PNS modules. This strategy results in considerably higher accuracy of classification and better generalization as compared to previous neural network models. If the delta rule network is used to generate the first two units, each region approximates a linearly separable region. In this sense, the total system becomes similar to a piecewise linear model. The various regions are determined nonlinearly by the first and third units of the PNS modules.     

Fault-Tolerant Software

Limitations in the current capabilities for verifying programs by formal proof or by exhaustive testing have led to the investigation of fault-tolerance techniques for applications where the consequence of failure is particularly severe. Two current approaches, N-version programming and the recovery block, are described. A critical feature in the latter is the acceptance test, and a number of useful techniques for constructing these are presented. A system model for the recovery block is introduced, and conclusions derived from this model that affect the design of fault-tolerant software are discussed.     

一种输入级模块复用的In-Zn-O薄膜晶体管行集成驱动电路

提出了一种新型的基于In-Zn-O薄膜晶体管(IZO TFT)的行集成驱动电路。该电路采用了输入级模块复用的驱动方法,即一级输入级驱动多级输出级,因此可以显著地减少输入级模块TFT的数量,缩减电路的面积,满足高分辨率显示屏设计,同时也可以迎合显示屏窄边框的审美需求。电路的输入级模块工作时间是输出级模块的n倍(n是一级输入级模块驱动输出级模块的级数),因此输入级尺寸可以做得更小。另外,该电路的驱动时钟频率是传统结构中一级输入级模块驱动一级输出级模块时钟频率的1/n,有效地降低了电路的动态耦合功耗。我们制作了20级的行集成驱动电路,一级输入级模块驱动两级输出级模块,该电路的尺寸为宽730μm,高为164μm,满足窄边框的要求。从实验测结果表明,该电路很好地满足300PPI的AMLCD或AMOLED显示屏的需求。     

An IIR median hybrid filter

A new class of nonlinear filters, the multidirectional infinite impulse response median hybrid (MIMH) filters, is presented and analyzed. The input signal is processed twice using a linear shift-invariant (LSI) infinite impulse response (IIR) filtering module: once with normal causality and a second time with inverted causality. The final output of the MIMH filter is the median of the two-directional outputs and the original input signal. Thus, the MIMH filter is a concatenation of linear filtering (a LSI IIR filtering module) and nonlinear filtering (a median filtering module). Because of this unique scheme, the MIMH filter possesses many desirable properties which are both proven and analyzed (including impulse removal, step preservation, and noise suppression). A comparison to other medium-type filters is also provided