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This paper proposes fusion of synchronous germ computing (SGC) with twin swarm intelligence (TSI) technique named as SGCTSI to enhance quality of global solutions with faster convergence of multimodal functions. In this paper, initially the authors tried to increase the speed of bacteria by updating bacteria positions synchronously, which is treated as SGC. In SGC, all the bacteria update their positions...
The paper provides a novel approach to emotion recognition from facial expression and voice of subjects. The subjects are asked to manifest their emotional exposure in both facial expression and voice, while uttering a given sentence. Facial features including mouth-opening, eye-opening, eyebrow-constriction, and voice features including, first three formants: F1, F2, and F3, and respective powers...
The paper provides a novel approach to emotion recognition from facial expression and voice of subjects. The subjects are asked to manifest their emotional exposure in both facial expression and voice, while uttering a given sentence. Facial features including mouth-opening, eye-opening, eyebrow-constriction, and voice features including, first three formants: F1, F2, and F3, and respective powers...
This paper describes a method of unsupervised color texture segmentation by efficiently combining different features obtained from multi-channel and multi-resolution filters. The DWT and DCT features are extracted separately from 3 color bands of the image and then fused together for optimal performance. The features are then ranked according to a selection criteria. We propose a new correlation measure...
Intelligent diagnostic reasoning system (IDRS), developed by Lockheed Martin Simulation, Training & Support (LM STS), implements a Bayesian model that is able to reduce the time and cost to diagnose failures by isolating faults[1]. As is the case with all learning systems, the quality of diagnosis is expected to increase with time as more data is presented and more knowledge is absorbed by the...
When data objects that are the subject of analysis using machine learning techniques are described by a large number of feature (i.e. the data is high dimension) it is often beneficial to reduce the dimension of the data. dimensionality reduction (DR) can be beneficial not only reasons of computational efficiency but also because it can improve the accuracy of the analysis. Now we have tried to introduce...
In wireless sensor networks, localization in indoor environment suffers from non-LOS (line-of-sight) and multi-path fading. In this paper, a new localization algorithm is proposed, which makes essential use of distributed space-time-codes (DSTC) to combat fading. By cleverly devising DSTC and employing a novel ML synchronization technique, the proposed algorithm exploits the full benefits of TDOA...
This paper describes a method for improving the final accuracy and the convergence speed of Particle Swarm Optimization (PSO) by adapting its inertia factor in the velocity updating equation and also by adding a new coefficient to the position updating equation. These modifications do not impose any serious requirements on the basic algorithm in terms of the number of Function Evaluations (FEs). The...
The Nyquist rate was derived using infinite time interval. But all our applications are based on finite time intervals. In digital communications we repeat our processes usually over the symbol time interval. The Nyquist rate will provide very few samples on this small interval. It will be very difficult to recover the symbol function from so few samples. In this paper we provide a mathematical proof...
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