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This paper presents a new approach in the field of electrocardiogram (ECG) feature extraction system based on the discrete wavelet transform (DWT) coefficients using Daubechies Wavelets. Real ECG signals recorded in lead II configuration are chosen for processing. The ECG signal was acquired by a battery operated, portable ECG data acquisition and signal processing module. In the second step the ECG signal was denoised using soft thresholding with Symlet4 wavelet. Further denoising was achieved by removing the corresponding wavelet coefficients at higher levels of decomposition. Later the ECG data files were converted to .txt files and subsequently to. mat files before being imported into the Matlab 7.4.0 environment for the computation of the decomposition coefficients. The QRS complexes were grouped as normal or myocardial ischaemic ones based on these decomposition coefficients. The algorithm developed by us was evaluated with control database comprising 120 records and validated using 60 records making up test database. By using the DWT coefficients, we have successfully achieved the myocardial ischaemia detection rates up to 97.5% with the technique developed by us for control data and up to 100% for validation test data.

This paper proposes a novel cache architecture -Way Halted Prediction -to reduce energy consumption and effective access time of set associative caches. This is achieved with the help of halt tag array and prediction circuit. Experimental evaluation of various SPEC benchmark programs on CACTI 5.3 and CASIM simulators reveal that the proposed architecture offers 33%, 6% and 3% savings in dynamic energy consumption and 1.80%, 6.13% and -1.95% saving in effective access time over conventional, way predicting and way halting cache architectures respectively.

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In the cryptographic steganography system, the message will first be converted into unreadable cipher and then this cipher will be embedded into an image file.