Search results for: M. Kalamani

Authors: C. Kalamani, K. Paramasivam

Abstract:

In VLSI, testing plays an important role. Major problem in testing are test data volume and test power. The important solution to reduce test data volume and test time is test data compression. The Proposed technique combines the bit maskdictionary and 2n pattern run length-coding method and provides a substantial improvement in the compression efficiency without introducing any additional decompression penalty. This method has been implemented using Mat lab and HDL Language to reduce test data volume and memory requirements. This method is applied on various benchmark test sets and compared the results with other existing methods. The proposed technique can achieve a compression ratio up to 86%.

Keywords: Bit Mask dictionary, 2n pattern run length code, system-on-chip, SOC, test data compression.

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Authors: C. Kalamani, S. Sowmiya, S. Dheivambigai, G. Harihara Sudhan

Abstract:

Radio Frequency Identification (RFID) is a blooming technology which uses radio frequency to track the objects. This technology transmits signals between tag and reader to fetch information from the tag with a unique serial identity. Generally, the drawbacks of RFID technology are high cost, high consumption of power and weak authentication systems between a reader and a tag. The proposed protocol utilizes less dynamic power using reversible truncated multipliers which are implemented in RFID tag-reader with mutual authentication protocol system to reduce both leakage and dynamic power consumption. The proposed system was simulated using Xilinx and Cadence tools.

Keywords: Mutual authentication, protocol, reversible gates, RFID.

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Authors: M. Kalamani, S. Valarmathy, M. Krishnamoorthi

Abstract:

In this paper, Fuzzy C-Means clustering with Expectation Maximization-Gaussian Mixture Model based hybrid modeling algorithm is proposed for Continuous Tamil Speech Recognition. The speech sentences from various speakers are used for training and testing phase and objective measures are between the proposed and existing Continuous Speech Recognition algorithms. From the simulated results, it is observed that the proposed algorithm improves the recognition accuracy and F-measure up to 3% as compared to that of the existing algorithms for the speech signal from various speakers. In addition, it reduces the Word Error Rate, Error Rate and Error up to 4% as compared to that of the existing algorithms. In all aspects, the proposed hybrid modeling for Tamil speech recognition provides the significant improvements for speechto- text conversion in various applications.

Keywords: Speech Segmentation, Feature Extraction, Clustering, HMM, EM-GMM, CSR.

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Authors: C. Kalamani, V. Abishek Karthick, S. Anitha, K. Kavin Kumar

Abstract:

The fault tolerant system plays a crucial role in the critical applications which are being used in the present scenario. A fault may change the functionality of circuits. Aim of this paper is to design multiplier using fault tolerant hybrid full adder. Fault tolerant hybrid full adder is designed to check and repair any fault in the circuit using self-checking circuit and the self-repairing circuit. Further, the use of conventional logic circuits may result in more area, delay as well as power consumption. In order to reduce these parameters of the circuit, GDI (Gate Diffusion Input) techniques with less number of transistors are used compared to conventional full adder circuit. This reduces the area, delay and power consumption. The proposed method solves the major problems occurring in the most crucial and critical applications.

Keywords: Gate diffusion input, hybrid full adder, self-checking, fault tolerant.

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Authors: M. Kalamani, S. Valarmathy, M. Krishnamoorthi

Abstract:

In this paper, Least Mean Square (LMS) adaptive noise reduction algorithm is proposed to enhance the speech signal from the noisy speech. In this, the speech signal is enhanced by varying the step size as the function of the input signal. Objective and subjective measures are made under various noises for the proposed and existing algorithms. From the experimental results, it is seen that the proposed LMS adaptive noise reduction algorithm reduces Mean square Error (MSE) and Log Spectral Distance (LSD) as compared to that of the earlier methods under various noise conditions with different input SNR levels. In addition, the proposed algorithm increases the Peak Signal to Noise Ratio (PSNR) and Segmental SNR improvement (ΔSNRseg) values; improves the Mean Opinion Score (MOS) as compared to that of the various existing LMS adaptive noise reduction algorithms. From these experimental results, it is observed that the proposed LMS adaptive noise reduction algorithm reduces the speech distortion and residual noise as compared to that of the existing methods.

Keywords: LMS, speech enhancement, speech quality, residual noise.

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Authors: C. Kalamani, K. Paramasivam

Abstract:

Test data compression is an efficient method for reducing the test application cost. The problem of reducing test data has been addressed by researchers in three different aspects: Test Data Compression, Built-in-Self-Test (BIST) and Test set compaction. The latter two methods are capable of enhancing fault coverage with cost of hardware overhead. The drawback of the conventional methods is that they are capable of reducing the test storage and test power but when test data have redundant length of runs, no additional compression method is followed. This paper presents a modified Run Length Coding (RLC) technique with Multilevel Selective Huffman Coding (MLSHC) technique to reduce test data volume, test pattern delivery time and power dissipation in scan test applications where redundant length of runs is encountered then the preceding run symbol is replaced with tiny codeword. Experimental results show that the presented method not only improves the test data compression but also reduces the overall test data volume compared to recent schemes. Experiments for the six largest ISCAS-98 benchmarks show that our method outperforms most known techniques.

Keywords: Modified run length coding, multilevel selective Huffman coding, built-in-self-test modified selective Huffman coding, automatic test equipment.

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