Neural network-based real-time malfunction diagnosis of reactive ion etching using in situ metrology data

To mitigate capital equipment investments and enhance product quality, semiconductor manufactures are turning to advanced process control (APC) methods. With the objective of facilitating APC, this paper investigates a methodology for real-time malfunction diagnosis of reactive ion etching (RIE) employing two types of in situ metrology: optical emission spectroscopy (OES) and residual gas analysis (RGA). Based on metrology data, time series neural networks (TSNNs) are trained to generate evidential belief for potential malfunctions in real time, and Dempster-Shafer (D-S) theory is adopted for evidential reasoning. Successful malfunction diagnosis is achieved, with only a single missed alarm and a single false alarm occurring out of 21 test runs when both sensors are used in tandem. From the results, we conclude that the OES and RGA sensors, in conjunction with the TSNN models, can be effectively used for RIE monitoring and diagnosis. Furthermore, D-S theory is shown to be an appropriate inference methodology.     

Performance Improvement of IS-95 CDMA Uplink Using Decision Feedback and Sub-Optimal Viterbi Decoding

This paper presents a methodology designed to improve the effectiveness of a non-iterative decision feedback (DF) receiver/decoder for IS-95 Code Division Multiple Access (CDMA) uplink in additive white Gaussian noise (AWGN) and Rayleigh fading. The effectiveness of the DF receiver/decoder is linked to the interleaver specification and the decoding delay of the convolutional decoder. Using sub-optimal convolutional decoding the average decoding delay is reduced resulting in more effective decision feedback decoding (DFD). Simulation results of average decoding delay, bit error rate (BER) and frame error rate (FER) are presented for coherent and noncoherent detection of unfaded single-path and Rayleigh fading multipath signals. Instead of the usual performance degradation these results show that the DF receiver/decoder benefits from some forms of sub-optimal Viterbi decoding. The additional performance gain can further improve the quality of service and/or capacity of a cellular IS-95 system.     

A novel fast approach for estimating error propagation in decisionfeedback detectors

The study of error-burst statistics is important for all detection systems, and more so for the decision feedback class. In data storage applications, many detection systems use decision feedback in one form or another. Fixed-delay tree search with decision feedback (FDTS/DF) and decision feedback equalization (DFE) are the direct forms, whereas the partial response detectors such as the reduced state sequence estimator (RSSE) and noise predictive maximum likelihood (NPML) detectors are the other forms. Although DF reduces the system complexity, it is inevitably linked with error propagation (EP), which can be quantified using error-burst statistics. Analytical evaluation of these statistics is difficult, if not impossible, because of the complexity of the problem. Hence, the usual practice is to use computer simulations. However, the computational time in traditional bit-by-bit simulations can be prohibitive at meaningful signal-to-noise ratios. In this paper, we propose a novel approach for fast estimation of error-burst statistics in FDTS/DF detectors, which is also applicable to other detection systems. In this approach, error events are initiated more frequently than natural by artificially injecting noise samples. These noise samples are generated using a transformation that results in significant reduction in computational complexity. Simulation studies show that the EP performance obtained by the proposed method matches closely with those obtained by bit-by-bit simulations, while saving as much as 99% of simulation time     

Estimating the distribution of a sum of independent lognormalrandom variables

Four methods that can be used to approximate the distribution function (DF) of a sum of independent lognormal random variables (RVs) are compared. The aim is to determine the best method to compute the DF considering both accuracy and computational effort. The investigation focuses on values of the dB spread, σ, valid for practical problems in wireless transmission (6 dB ⩽σ⩽12 dB). Contrary to some previous reports, our results show that the simpler Wilkinson's approach gives a more accurate estimate, in some cases of interest, than Schwartz and Yeh's (1982) approach     

Decision feedback sequence estimation for continuous phasemodulation on a linear multipath channel

An approach to reduced-complexity detection of partial response continuous phase modulation (CPM) on a linear multipath channel is presented. The method, referred to as decision feedback sequence estimation (DFSE), is based on a conventional Viterbi algorithm (VA) using a reduced-state trellis combined with decision feedback (DF). By varying the number of states in the VA, the receiver structure can be changed gradually from a DF receiver to the optimal maximum-likelihood sequence estimator (MLSE). In this way different tradeoffs between performance and complexity can be obtained. Results on the receiver performance, based on minimum distance calculations and bit error rate simulations, are given for Gaussian minimum-shift keying modulation on typical mobile radio channels. It is shown that for channels with a long memory, a significant complexity reduction can be achieved at the cost of a moderate degradation in performance     

无线电测向设备测向误差与数据处理方法研究

通过对某型测向设备的测向精度试验获取的试验数据的分析处理发现，现行的标准和规范中采用的测向误差计算方法没有能够正确区分出设备的能力和试验环境的影响。不能完全客观公正地评价测向设备的真实性能。为了获得合理的测向结果，建议严格按照ITU的有关标准选择测向场地。并选择合适的误差计算方法。     

Denoising and copy attacks resilient watermarking by exploitingprior knowledge at detector

Watermarking with both oblivious detection and high robustness capabilities is still a challenging problem. In this paper, we tackle the aforementioned problem. One easy way to achieve blind detection is to use denoising for filtering out the hidden watermark, which can be utilized to create either a false positive (copy attack) or false negative (denoising and remodulation attack). Our basic design methodology is to exploit prior knowledge available at the detector side and then use it to design a "nonblind" embedder. We prove that the proposed scheme can resist two famous watermark estimation-based attacks, which have successfully cracked many existing watermarking schemes. False negative and false positive analyses are conducted to verify the performance of our scheme. The experimental results show that the new method is indeed powerful.     

A hybrid approach of neutrosophic sets and DEMATEL method for developing supplier selection criteria

For any organization, the selection of suppliers is a very important step to increase productivity and profitability. Any organization or company seeks to use the best methodology and the appropriate technology to achieve its strategies and objectives. The present study employs the neutrosophic set for decision making and evaluation method (DEMATEL) to analyze and determine the factors influencing the selection of SCM suppliers. DEMATEL is considered a proactive approach to improve performance and achieve competitive advantages. This study applies the neutrosophic set Theory to adjust general judgment, using a new scale to present each value. A case study implementing the proposed methodology is presented (i.e. selecting the best supplier for a distribution company). This research was designed by neutrosophic DEMATEL data collection survey of experts, interviewing professionals in management, procurement and production. The results analyzed in our research prove that quality is the most influential criterion in the selection of suppliers.     

Synthesis for real time systems: Solutions and challenges

A real time system typically combines a variety of implementation technologies and hardware architectures. Deciding how to partition the system and selecting an architectural technology for the sub-systems is by no means a trivial task. These architectural decisions, which can have a major impact on the quality and performance of the final implementation, have to be made at the early stages in the design process, when the impact of the decisions is unclear and can only be quantified using some primitive measures. p ]In this paper, we present our vision on how a next generation of design environment can aid the designer in this decision process. We first identify the problems of designing a heterogeneous real time system by walking through the design process of a complex speech recognition system. Based on this analysis, we propose a system design methodology build on top of current synthesis tools. Today, DSP synthesis tools are application and/or architecture specific, covering subparts of the application once the partitioning is made. To make them useful in the proposed methodology, a unified view on the underlying architecture assumptions is needed. Secondly, good decision making requires an “as-good-as-possible” estimation of the implications of the decision. Therefore, it is important that current manual estimation be enlarged by high level estimation and performance analysis tools. p ]The HYPERSPACE environment, which is currently under development, therefore, consists of three complementary components: a set of architecture specific compilers, a set of estimation and performance analysis tools and an architecture selection and partitioning framework, steered by the designer.     

基于信任评估的虚拟网故障诊断算法

网络虚拟化环境的新特点(网络拓扑动态变化、动态症状和故障关系、管理域独立和信息不准确性)对故障诊断提出了新的要求,提出一种改进的针对网络虚拟化环境下虚拟网和底层网故障诊断模型和诊断算法,通过虚拟网信任评估算法来提高故障诊断的准确率、降低误报率.仿真研究结果表明,在大规模和噪声大的虚拟网络环境中,提出的故障诊断算法取得了较好的诊断效果.     

Design for diagnosis using a diagnostic evaluation measure

The results demonstrated by the SAN system show how a real fault tolerant system can have more than one configuration with almost the same reliability. However, from a diagnosis perspective, the designs can have different costs of diagnosis at failure time. A method for measuring the cost of diagnosing diagnosis decision trees (DDT) was demonstrated, which is a specialized weighted mean depth of traversing the DDT. This evaluation method incorporates the qualitative (structure) and quantitative (reliability analysis) into one measure for predicting diagnosis cost. DDTs incorporate the same qualitative and quantitative data as the diagnosis cost measure, which makes this measure suitable for evaluating these decision trees. Evaluating and comparing alternative system designs using the diagnosis cost function as we demonstrated is an efficient and simple method for pursuing the design for diagnosis objective.     

基于时差的同类辐射源信号分选定位方法

多站电子侦察系统中,多个观测站接收到的脉冲信号的时差信息可用于信号分选和定位。但当多个参数相近的同类辐射源信号在时域和频域都混叠在一起时,会得到大量虚假时差信息。该文结合目标位置信息场定位法,提出基于时差的同类辐射源信号分选定位方法。该方法首先利用时差窗先验信息,将主站与副站的脉冲进行时差和参数匹配提取同类辐射源的时差信息,然后对得到的所有时差利用目标位置信息场定位法确定目标数目和位置,最后利用定位结果确定真实时差,剔除虚假时差,完成信号分选和目标精确定位。仿真实验验证了该方法的有效性和实用性。     

Depth-first coding for multivalued pictures using bit-planedecomposition

A data compression technique using a bit-plane decomposition strategy of multivalued images is described. Although the bit-plane decomposition is mainly used for image transmission, our method takes the image expression for image database into consideration. It has two merits which are a hierarchical representation using depth-first (DF) expression and a simple noise reduction algorithm for the DF expression that is similar to human perception. The DF expression is useful for image expansion, rotation, etc. We study the information in an image that should be eliminated by noise reduction. Noise-like patterns in an image are uniformalized and the edge and smooth surfaces remain nearly unchanged. They are not blurred, but instead are a little enhanced. We also study the properties of the black-and-white (B/W) boundary points on bit-planes. The algorithm of the uniformalization process with a DF-expression of an image is described. An experiment for real image data is carried out by a comparison to other methods, and the results are discussed     

用AHP方法优化电子装备测试点的BITE与ATE分配

针对故障诊断中测试冗余、测试资源分配不合理的问题,首先用解析冗余关系(ARRs)优选出测试点;分析了影响测试资源选择的因素,用层次分析法(AHP)建立了评价指标集的递阶层次结构,确定了各指标的综合权重,最后用模糊综合决策方法实现了测试点的BITE与ATE分层优化,对提高电子装备的诊断能力、降低测试费用具有指导意义。     

Exact analysis of the multihop multibranch hybrid AF/DF relaying networks

In this paper, the exact analysis of a multihop multibranch (MHMB) relaying network is investigated wherein each relay can operate in amplify‐and‐forward (AF) or decode‐and‐forward (DF) modes depending upon the decoding result of its received signal. If a relay decodes the received signal correctly, it works in DF mode; otherwise, the relay operates in AF mode. Therefore, we name such relaying network as hybrid amplify‐and‐forward and decode‐and‐forward (hybrid AF/DF) relaying network. We first investigate the signal transmission from source to destination node via n number of relays in a hybrid AF/DF MHMB mode. Then, we obtained the statistical features and analyze the end‐to‐end signal‐to‐noise ratio (SNR). Finally, a comprehensive performance analysis is conducted by using maximal ratio combining (MRC) scheme at the destination node. For comparison, we also obtained the results using selection combining (SC) scheme at the destination node. To the best of our knowledge, very few works in the literature have considered a general system model of MHMB relaying network wherein each relay can operate in AF or DF modes, that is, a hybrid AF/DF relaying network. Accordingly, the analysis of our system model is not only novel and exact, but also is comprehensive and can be employed in the future works.     

Managing capacity for telecommunications networks under uncertainty

The existing telecommunications infrastructure in most of the world is adequate to deliver voice and text applications, but demand for broadband services such as streaming video and large file transfer (e.g., movies) is accelerating. The explosion in Internet use has created a huge demand for telecommunications capacity. However, this demand is extremely volatile, making network planning difficult. Modern financial option pricing methods are applied to the problem of network investment decision timing. In particular, we study the optimal decision problem of building new network capacity in the presence of stochastic demand for services. Adding new capacity requires a capital investment, which must be balanced by uncertain future revenues. We study the underlying risk factor in the bandwidth market and then apply real options theory to the upgrade decision problem. We notice that sometimes it is optimal to wait until the maximum capacity of a line is nearly reached before upgrading directly to the line with the highest known transmission rate (skipping the intermediate lines). It appears that past upgrade practice underestimates the conflicting effects of growth and volatility. This explains the current overcapacity in available bandwidth. To the best of our knowledge, this real options approach has not been used previously in the area of network capacity planning. Consequently, we believe that this methodology can offer insights for network management.     

Electrocardiogram based neonatal seizure detection

A method for the detection of seizures in the newborn using the electrocardiogram (ECG) signal is presented. Using a database of eight recordings, a method was developed for automatically annotating each 1-min epoch as "nonseizure" or "seizure". The system uses a linear discriminant classifier to process 41 heartbeat timing interval features. Performance assessment of the method showed that on a patient-specific basis an average accuracy of 70.5% was achieved in detecting seizures with associated sensitivity of 62.2% and specificity of 71.8%. On a patient-independent basis the average accuracy was 68.3% with sensitivity of 54.6% and specificity of 77.3%. Shifting the decision threshold for the patient-independent classifier allowed an increase in sensitivity to 78.4% at the expense of decreased specificity (51.6%), leading to increased false detections. The results of our ECG-based method are comparable with those reported for EEG-based neonatal seizure detection systems and offer the benefit of an easier acquisition methodology for seizure detection.     

A fixed-point algorithm to minimax learning with neural networks

In some real applications, such as medical diagnosis or remote sensing, available training data do not often reflect the true a priori probabilities of the underlying data distribution. The classifier designed from these data may be suboptimal. Building classifiers that are robust against changes in prior probabilities is possible by applying a minimax learning strategy. In this paper, we propose a simple fixed-point algorithm that is able to train a neural minimax classifier [i.e., a classifier minimizing the worst (maximum) possible risk]. Moreover, we present a new parametric family of loss functions that is able to provide the most accurate estimates for the posterior class probabilities near the decision regions, and we also discuss the application of these functions together with a minimax learning strategy. The results of the experiments carried out on different real databases point out the ability of the proposed algorithm to find the minimax solution and produce a robust classifier when the real a priori probabilities differ from the estimated ones.     

Equalization and semi-blind channel estimation for space-time block coded signals over a frequency-selective fading channel

In this paper, we investigate the equalization and channel identification for space-time block coded signals over a frequency-selective multiple-input multiple-output (MIMO) channel. The equalization has been considered by taking into account the cyclostationarity of space-time block coded signals. The minimum mean square error (MMSE) solutions have been derived for the linear and decision feedback (DF) equalizers. The channel estimation is required for the equalization. With known symbols (as pilot symbols), MIMO channels can be estimated. In addition, due to the redundancy induced by space-time block code, it is possible to identify MIMO channels blindly using the subspace method. We consider both blind and semi-blind channel estimation for MIMO channels. It is shown that the semi-blind channel estimate has fewer estimation errors, and it results in less (bit error rate) performance degradation of the MMSE linear and DF equalizers.     

Asynchronous failed sensor node detection method for sensor networks

Detecting failed nodes is important in a sensor network, because a failed node can cause coverage and connectivity holes. Since sensors are resource‐limited devices, lack of a clocking mechanism imposes difficulties in implementing synchronous failure detection techniques. To counter this, asynchronous methods are needed for detecting failed sensor nodes. For example, in traditional distributed systems sent data packets and received acknowledgements are matched to identify any malfunctions. A similar approach can be adopted in sensor networks. An asynchronous failed sensor node detection (AFSD) method is proposed, where a numeric counter variable is used to track the received and sent data packets between active nodes. This variable acts as bounded for an active sensor node and unbounded for an out‐of‐communication or failed node. By observing the value of the counter, a decision can be made on a deployed sensor node. Whenever the counter value of a neighbour crosses a predefined threshold, that neighbour is suspected. However, measuring a counter value can always lead to a false positive. To avoid false suspicion, a consensus protocol is needed. Such a technique is described, which is proved as accurate and complete. The complexity analysis shows that the control, energy, and time overheads of the proposed method are linear and in the order of number of neighbours per node. Copyright © 2011 John Wiley & Sons, Ltd.