Removal of redundant dependences in DOACROSS loops with constantdependences

An efficient algorithm to remove redundant dependences in simple loops with constant dependences is presented. Dependences constrain the parallel execution of programs and are typically enforced by synchronization instructions. The synchronization instructions represent a significant part of the overhead in the parallel execution of a program. Some program dependences are redundant because they are covered by other dependences. It is shown that unlike with single loops, in the case of nested loops, a particular dependence may be redundant at some iterations but not redundant at others, so that the redundancy of a dependence may not be uniform over the entire iteration space. A sufficient condition for the uniformity of redundancy in a doubly nested loop is developed     

Two stage emotion recognition based on speaking rate

This paper proposes two stage speech emotion recognition approach using speaking rate. The emotions considered in this study are anger, disgust, fear, happy, neutral, sadness, sarcastic and surprise. At the first stage, based on speaking rate, eight emotions are categorized into 3 broad groups namely active (fast), normal and passive (slow). In the second stage, these 3 broad groups are further classified into individual emotions using vocal tract characteristics. Gaussian mixture models (GMM) are used for developing the emotion models. Emotion classification performance at broader level, based on speaking rate is found to be around 99% for speaker and text dependent cases. Performance of overall emotion classification is observed to be improved using the proposed two stage approach. Along with spectral features, the formant features are explored in the second stage, to achieve robust emotion recognition performance in case of speaker, gender and text independent cases.     

Thermodynamic optimization of a solar system for cogeneration of water heating and absorption cooling

This paper presents a contribution to understanding the behavior of solar‐powered air conditioning and refrigeration systems with a view to determining the manner in which refrigeration rate, mass flows, heat transfer areas, and internal architecture are related. A cogeneration system consisting of a solar concentrator, a cavity‐type receiver, a gas burner, and a thermal storage reservoir is devised to simultaneously produce heat (hot water) and cooling (absorption refrigerator system). A simplified mathematical model, which combines fundamental and empirical correlations, and principles of classical thermodynamics, mass and heat transfer, is developed. The proposed model is then utilized to simulate numerically the system transient and steady‐state response under different operating and design conditions. A system global optimization for maximum performance (or minimum exergy destruction) in the search for minimum pull‐down and pull‐up times, and maximum system second law efficiency is performed with low computational time. Appropriate dimensionless groups are identified and the results are presented in normalized charts for general application. The numerical results show that the three‐way maximized system second law efficiency, η_{II,max,max,max}, occurs when three system characteristic mass flow rates are optimally selected in general terms as dimensionless heat capacity rates, i.e. (ψ_ss, ψ_wxwx, ψ_Hs)_opt=(0.335, 0.28, 0.2). The minimum pull‐down and pull‐up times, and maximum second law efficiencies found with respect to the optimized operating parameters are sharp and, therefore, important to be considered in actual design. As a result, the model is expected to be a useful tool for simulation, design, and optimization of solar energy systems in the context of distributed power generation. Copyright © 2008 John Wiley & Sons, Ltd.     

Characterization and recognition of emotions from speech using excitation source information

This paper explores the excitation source features of speech production mechanism for characterizing and recognizing the emotions from speech signal. The excitation source signal is obtained from speech signal using linear prediction (LP) analysis, and it is also known as LP residual. Glottal volume velocity (GVV) signal is also used to represent excitation source, and it is derived from LP residual signal. Speech signal has high signal to noise ratio around the instants of glottal closure (GC). These instants of glottal closure are also known as epochs. In this paper, the following excitation source features are proposed for characterizing and recognizing the emotions: sequence of LP residual samples and their phase information, parameters of epochs and their dynamics at syllable and utterance levels, samples of GVV signal and its parameters. Auto-associative neural networks (AANN) and support vector machines (SVM) are used for developing the emotion recognition models. Telugu and Berlin emotion speech corpora are used to evaluate the developed models. Anger, disgust, fear, happy, neutral and sadness are the six emotions considered in this study. About 42 % to 63 % of average emotion recognition performance is observed using different excitation source features. Further, the combination of excitation source and spectral features has shown to improve the emotion recognition performance up to 84 %.     

Emotion recognition from speech using sub-syllabic and pitch synchronous spectral features

In this work, spectral features extracted from sub-syllabic regions and pitch synchronous analysis are proposed for speech emotion recognition. Linear prediction cepstral coefficients, mel frequency cepstral coefficients and the features extracted from high amplitude regions of spectrum are used to represent emotion specific spectral information. These features are extracted from consonant, vowel and transition regions of each syllable to study the contribution of these regions toward recognition of emotions. Consonant, vowel and the transition regions are determined using vowel onset points. Spectral features extracted from each pitch cycle, are also used to recognize emotions present in speech. The emotions used in this study are: anger, fear, happy, neutral and sad. The emotion recognition performance using sub-syllabic speech segments are compared with the results of conventional block processing approach, where entire speech signal is processed frame by frame. The proposed emotion specific features are evaluated using simulated emotion speech corpus, IITKGP-SESC (Indian Institute of Technology, KharaGPur-Simulated Emotion Speech Corpus). The emotion recognition results obtained using IITKGP-SESC are compared with the results of Berlin emotion speech corpus. Emotion recognition systems are developed using Gaussian mixture models and auto-associative neural networks. The purpose of this study is to explore sub-syllabic regions to identify the emotions embedded in a speech signal, and if possible, to avoid processing of entire speech signal for emotion recognition without serious compromise in the performance.     

Distribution of Proteolytic Activity in the Different Protein Fractions of Tropical Shrimp Head Waste

Ammonium sulfate fractionated protein fractions at levels of 25%, 30%, 35%, 40%, 42.5% and 45% ammonium sulfate were recovered from the head waste of tropical marine tiger (MTS), culture tiger (CTS), white (WS) and brown shrimp (BS) and then characterized for protease activity. Distribution of buffer (pH 7.1) extracted protein among ammonium sulfate fractions showed that total protein in 25% fraction of MTS, CTS, WS and BS was 48, 54, 34 and 24 times more than that in the respective 42.5% fraction of the head waste of these shrimps. The highest proteolytic activity was observed in 42.5% (NH₄)₂SO₄ protein fraction of the head waste of MTS, CTS, WS and BS, the values were 19, 1.7, 11.6 and 2 times, respectively, more than that of the corresponding 25% (NH₄)₂SO₄ protein fractions. Highest caseinolytic activity (pH 8.5) and gelatinolytic activity (pH 7.1) was observed in the 42.5% fraction of the head waste of CTS and WS, respectively; but the highest albuminolytic activity (pH 8) was observed in the same fraction of the head waste of both MTS and BS. The optimum pH for highest gelatinolytic and albuminolytic activity of the 42.5% (NH₄)₂SO₄ protein fraction of the head waste of MTS and BS was 4; the same for highest gelatinolytic activity of the same protein fraction of the head waste of WS, and these fractions included acid proteases such as pepsin, the optimum pH for the above activity of the same fraction of CTS was 6-8.5, and the fraction was an alkaline protease such as chymotrypsin. The SDS-PAGE pattern of 42.5% (NH₄)₂SO₄ protein fraction of the head waste of BS, CTS, WS and MTS was almost similar with a dark band close to the marker band 20kDa. Proteases make up to 48% of industrial enzymes and are mostly used in detergents, leather production and food industry.     

Optimisation of process conditions for lactose hydrolysis in paneer whey with cold‐active β‐galactosidase from psychrophilic Thalassospira frigidphilosprofundus

The present study was conducted to analyse the physiochemical properties of Indian paneer whey. High concentration of minerals such as potassium, calcium, zinc and sodium, as NaCl, were observed which indicates the suitability of paneer whey in the preparation of beverages. A central composite rotatable design (CCRD) of response surface methodology (RSM) was employed to optimise the hydrolysis of lactose from whey using cold‐active β‐galactosidase of Thalassospira frigidphilosprofundus. Results indicated that 80% of lactose was hydrolysed at pH of 6.5 at 20 °C in 40 min in comparison with 40% at 30 °C. This emphasises the potential use of cold‐active β‐galactosidase in dairy industry.     

Distribution of Proteolytic Activity in the Different Protein Fractions of Tropical Shrimp Head Waste

Ammonium sulfate fractionated protein fractions at levels of 25%, 30%, 35%, 40%, 42.5% and 45% ammonium sulfate were recovered from the head waste of tropical marine tiger (MTS), culture tiger (CTS), white (WS) and brown shrimp (BS) and then characterized for protease activity. Distribution of buffer (pH 7.1) extracted protein among ammonium sulfate fractions showed that total protein in 25% fraction of MTS, CTS, WS and BS was 48, 54, 34 and 24 times more than that in the respective 42.5% fraction of the head waste of these shrimps. The highest proteolytic activity was observed in 42.5% (NH₄)₂SO₄ protein fraction of the head waste of MTS, CTS, WS and BS, the values were 19, 1.7, 11.6 and 2 times, respectively, more than that of the corresponding 25% (NH₄)₂SO₄ protein fractions. Highest caseinolytic activity (pH 8.5) and gelatinolytic activity (pH 7.1) was observed in the 42.5% fraction of the head waste of CTS and WS, respectively; but the highest albuminolytic activity (pH 8) was observed in the same fraction of the head waste of both MTS and BS. The optimum pH for highest gelatinolytic and albuminolytic activity of the 42.5% (NH₄)₂SO₄ protein fraction of the head waste of MTS and BS was 4; the same for highest gelatinolytic activity of the same protein fraction of the head waste of WS, and these fractions included acid proteases such as pepsin, the optimum pH for the above activity of the same fraction of CTS was 6–8.5, and the fraction was an alkaline protease such as chymotrypsin. The SDS-PAGE pattern of 42.5% (NH₄)₂SO₄ protein fraction of the head waste of BS, CTS, WS and MTS was almost similar with a dark band close to the marker band 20kDa. Proteases make up to 48% of industrial enzymes and are mostly used in detergents, leather production and food industry.     

Thermal conversion efficiency of producing hydrogen enriched syngas from biomass steam gasification

This paper presents the results from an experimental study on the energy conversion efficiency of producing hydrogen enriched syngas through uncatalyzed steam biomass gasification. Wood pellets were gasified using a 100 kW_th fluidized bed gasifier at temperatures up to 850 °C. The syngas hydrogen concentration and cold gas efficiency were found to increase with both bed temperature and steam to biomass weight ratio, reaching a maximum of 51% and 124% respectively. The overall energy conversion to syngas (based on heating value) also increased with bed temperature but was inversely proportional to the steam to biomass ratio. The maximum energy conversion to syngas was found to be 68%. The conversion of energy to hydrogen (by heating value) increased with gasifier temperature and gas residence time, but was found to be independent of the S/B ratio. The maximum conversion of all energy sources to hydrogen was found to be 25%.     

Dynamic allocation of communicating tasks in computational grids

This paper studies task allocation in computational grids operating in a dynamic and uncertain environment. Computational grids consist of loosely coupled heterogeneous resources or agents with finite buffer capacities. These grids are primarily used to process large-scale applications consisting of several interdependent tasks. The task allocation problem is modeled as an infinite horizon Markov decision process, with the resource service times and the task arrivals following general probability distributions. We explicitly consider the communication cost between agents incurred in coordinating the execution of interdependent tasks. We show that a stationary optimal policy exists for this task allocation problem. Furthermore, we develop an action elimination procedure for reducing the complexity of computational methods in finding the optimal policy. We also present a real-time heuristic policy based on certain structural properties of the problem. Finally, computational results are presented that compare the performance of the heuristic policy with respect to other approaches for allocating tasks in the grid. Results from this paper are also applicable to the task allocation problem in manufacturing and service areas such as distributed design, project management and supplier coalitions.