I. INTRODUCTIONAudio coding or audio compression algorithms are used to obtain compact digital representations of high-fidelity (wideband) audio signals for the purpose of efficient transmission or storage. The central objective in audio coding is to represent the signal with a minimum number of bits while achieving transparent signal reproduction, i.e., generating output audio that cannot be distinguished from the original input, even by a sensitive listener ("golden ears"). This paper gives a review of algorithms for transparent coding of high-fidelity audio.The introduction of the compact disk (CD) in the early eighties [1] brought to the fore all of the advantages of digital audio representation, including unprecedented high-fidelity, dynamic range, and robustness. These advantages, however, came at the expense of high data rates. Conventional CD and digital audio tape (DAT) systems are typically sampled at either 44.1 or 48 kilohertz (kHz) using pulse code modulation (PCM) with a sixteen bit sample resolution. This results in uncompressed data rates of 705.6/768 kilobits per second (kbps) for a monaural channel, or 1.41/1.54 megabits per second (Mbps) for a stereo pair at 44.1/48 kHz, respectively. Although high, these data rates were accommodated successfully in first generation digital audio applications such as CD and DAT. Unfortunately, second generation multimedia applications and wireless systems in particular are often subject to bandwidth and cost constraints that are incompatible with high data rates. Because of the success enjoyed by the first generation, however, end users have come to expect "CD-quality" audio reproduction from any digital system. Therefore, new network and wireless multimedia digital audio systems must reduce data rates without compromising reproduction quality. These and other considerations have motivated considerable research during the last decade towards formulation of compression schemes that can satisfy simultaneously the conflicting demands of high compression ratios and transparent reproduction quality for high-fidelity audio signals

We propose a sparse representation approach for classifying different targets in Synthetic Aperture Radar (SAR) images. Unlike the other feature based approaches, the proposed method does not require explicit pose estimation or any preprocessing. The dictionary used in this setup is the collection of the normalized training vectors itself. Computing a sparse representation for the test data using this dictionary corresponds to finding a locally linear approximation with respect to the underlying class manifold. SAR images obtained from the Moving and Stationary Target Acquisition and Recognition (MSTAR) public database were used in the classification setup. Results show that the performance of the algorithm is superior to using a support vector machines based approach with similar assumptions. Significant complexity reduction is obtained by reducing the dimensions of the data using random projections for only a small loss in performance.

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Single-walled carbon nanotubes (SWNTs) have been used extensively for sensor fabrication due to its high surface to volume ratio, nanosized structure and interesting electronic property. Lack of selectivity is a major limitation for SWNTs-based sensors. However, surface modification of SWNTs with a suitable molecular recognition system can enhance the sensitivity. On the other hand, porphyrins have been widely investigated as functional materials for chemical sensor fabrication due to their several unique and interesting physico-chemical properties. Structural differences between free-base and metal substituted porphyrins make them suitable for improving selectivity of sensors. However, their poor conductivity is an impediment in fabrication of prophyrin-based chemiresistor sensors. The present attempt is to resolve these issues by combining freebase- and metallo-porphyrins with SWNTs to fabricate SWNTs-porphyrin hybrid chemiresistor sensor arrays for monitoring volatile organic carbons (VOCs) in air. Differences in sensing performance were noticed for porphyrin with different functional group and with different central metal atom. The mechanistic study for acetone sensing was done using field-effect transistor (FET) measurements and revealed that the sensing mechanism of ruthenium octaethyl porphyrin hybrid device was governed by electrostatic gating effect, whereas iron tetraphenyl porphyrin hybrid device was governed by electrostatic gating and Schottky barrier modulation in combination. Further, the recorded electronic responses for all hybrid sensors were analyzed using a pattern-recognition analysis tool. The pattern-recognition analysis confirmed a definite pattern in response for different hybrid material and could efficiently differentiate analytes from one another. This discriminating capability of the hybrid nanosensor devices open up the possibilities for further development of highly dense nanosensor array with suitable porphyrin for E-nose application.