The evolution of the embedded cores-based design paradigm in recent times has created numerous challenging problems for the test design community. To develop suitable test environment and appropriate test methodologies for digital cores-based system-on-chip (SOC) is a fascinating area of research today. This paper attempts to develop viable solution option to these problems based on cores constructed from the International Symposium on Circuits and Systems (ISCAS) 85 combinational and ISCAS 89 sequential benchmark circuits. The wrapper that separates the core under test from other cores is assumed to be Institute of Electrical and Electronics Engineers P1500-compliant. The cores and test access mechanism (TAM) are described in the paper using Verilog hardware description language, while TAM is implemented as a plain signal transport medium, being shared by all the cores in the SOC. The fault injection is done by a simulator that also generates the tests for the core. The fault simulation process is carried out after successful compilation of the cores, the individual core selection being done by the program running in the background.

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Induction machines are employed as prime movers in several applications in the present day industries. The major contributors of faults in an Induction machine are the bearings. The Incipient Bearing Failure defines the fault at an early stage and is a potential danger to the catastrophic failure of the electrical machine. One of the common faults found in bearings is the Outer Race Fault (ORF). The aim of this paper is to identify ORF in the induction machine at different load conditions. The vibration data is acquired and analysis is done by computing the statistical parameters, probability density function, and the cumulative distribution function. Finally, the chi-square test is used to validate the data and findings.

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This paper presents an algorithm to extract symbolic rules from trained artificial neural networks (ANNs), called ERANN. In many applications, it is desirable to extract knowledge from ANNs for the users to gain a better understanding of how the networks solve the problems. Although ANN usually achieves high classification accuracy, the obtained results sometimes may be incomprehensible, because the knowledge embedded within them is distributed over the activation functions and the connection weights. This problem can be solved by extracting rules from trained ANNs. To do so, a rule extraction algorithm has been proposed in this paper to extract symbolic rules from trained ANNs. A standard three-layer feedforward ANN with four-phase training is the basis of the proposed algorithm. Extensive experimental studies on a set of benchmark classification problems, including breast cancer, iris, diabetes, wine, season, golfplaying, and lenses classification, demonstrates the applicability of the proposed method. Extracted rules are comparable with other methods in terms of number of rules, average number of conditions for a rule, and the rules accuracy. The proposed method achieved accuracy values 96.28%, 98.67%, 76.56%, 91.01%, 100%, 100%, and 100% for the above problems, respectively. It has been seen that these results are one of the best results comparing with results obtained from related previous studies.

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Usage of Automatic Speech Recognition (ASR) systems is increasing day-by-day for voice centric applications in mobile handheld and Voice over Internet Protocol (VoIP) devices. The necessity is also increasing to find out the ASR performance under different network impediments. Among them, speech and audio coding standards is the one, which affects the ASR performance greatly, when, using them with different sampling and bit rates in the practical systems. Another common impediment which influences the ASR accuracy is the bit errors in the wireless networks and packet drop conditions in the VoIP networks. ASR performance with some of the speech coding standards under noise conditions for the wireless networks is reported in the literature. However, each study is reporting the ASR performance for few narrowband codecs with different speech databases and different ASR toolkits like RAPHEL, HTK, SPHINX, etc. In this paper, the analysis on ASR performance while using both narrowband and wideband speech and audio coding standards, which are currently accepted for GSM mobile and VoIP networks, using the common speech database-TIMIT, and using ASR toolkit-SPHINX, is presented. The Mean Opinion Score (MOS), which is the generally accepted speech quality measurement technique, is also analyzed for all the speech and audio coding standards, using the same speech database. The results of the studies carried out for the ASR word accuracies and MOS values for different narrowband and wideband speech and audio codecs under no-loss conditions are presented. Results for different rates of packet drop condition which is the common noise scenario in wired networks such as VoIP (which is also merging with wireless networks) are also presented. The observation is that though some of the codecs are showing poor MOS performance at lower bit rates, the corresponding ASR performance is comparable with other codecs at higher bit rates.

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Chaos-based encryption may offer new quality in secure data transmission. A recently proposed Chaotic Key-Based Algorithm (CKBA) has been shown to be unavoidably susceptible to chosen/known plaintext attacks and ciphertext-only attacks. In this paper, Discrete wavelet transform and Modified Chaotic Key-Based Algorithm are proposed to enhance the security of CKBA. The enhanced security of the proposed algorithm is analyzed through cryptanalysis. Furthermore, Very large scale integration (VLSI) architecture of the proposed algorithm is designed. The VLSI architecture is implemented on an image using ModelSim to demonstrate the effectiveness of the proposed scheme.

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Novel designs of third-order recursive wideband digital integrator and differentiator are presented. The integrator is obtained by interpolating two digital integration techniques, the Schneider-Kaneshige-Groutage (SKG) rule and the Trapezoidal rule. The differentiator is obtained by inverting the transfer function of the designed integrator after necessary modifications. The proposed integrator and differentiator approximate their ideal counterparts with absolute magnitude errors less than 0.02 and 0.14, respectively, over the entire frequency spectrum. Their performance compares favorably with existing integrators and differentiators.

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The impact of gate-to-source/drain overlap length on performance and variability of 65 nm CMOS is presented. The device and circuit variability is investigated as a function of three significant process parameters, namely gate length, gate oxide thickness, and halo dose. The comparison is made with three different values of gate-to-source/drain overlap length namely 5 nm, 0 nm, and -5 nm and at two different leakage currents of 10 nA and 100 nA. The Worst-Case-Analysis approach is used to study the inverter delay fluctuations at the process corners. The drive current of the device for device robustness and stage delay of an inverter for circuit robustness are taken as performance metrics. The design trade-off between performance and variability is demonstrated both at the device level and circuit level. It is shown that larger overlap length leads to better performance, while smaller overlap length results in better variability. Performance trades with variability as overlap length is varied. An optimal value of overlap length of 0 nm is recommended at 65 nm gate length, for a reasonable combination of performance and variability.

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This paper proposes a new automatic personalized document summarization using pseudo relevance feedback (PRF) and semantic features to extract meaningful sentences from retrieval documents in the Internet. The proposed method uses generic summarization based on the semantic features of non-negative matrix factorization to extract sentences that well reflect the major topics of the searched documents. In addition, this method reduces the semantic gap between the low level of summarizing search results and the high level of user's perception; the method uses query-based summarization depending on PRF and semantic features. The method improves the quality of personalized document summarization because the sentences most relevant to the given query are extracted efficiently by using a combination of generic and query-based summarization. The experimental results demonstrate that the proposed method achieves a better document summarization performance than do other methods.

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This document proposes a new algorithm for Motion Estimation using the Kalman Filter. The algorithm predicts the motion vectors for the next frame by using the motion vectors of the current frame. The new algorithm is compared with the already existing search algorithms such as Exhaustive search, Three-Step Search, and Four-Step Search and show that the prediction using the Kalman Filter leads to reduction in the computational complexity.

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