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Electrical distribution system suffers from various problems, such as reactive power burden, unbalanced loading, voltage regulation, and harmonic distortion. Though DSTATCOMS are ideal solutions for these systems, they can be costly and have complexity compared to other reactive power compensation solutions. Phasewise-balanced reactive power compensations are required for fast-changing loads needing dynamic power factor correcting devices leading to terminal voltage stabilization. Static var compensators (SVCs) are preferred for these loads due to low cost and simple control strategy. These SVCs, while correcting power factor, inherently create harmonics due to the nonsinusoidal currents caused by the operation of thyristor-controlled reactors. This paper proposes minimizing the harmonics injected into the distribution systems with the operation of TSC-TCR-type SVC used in conjunction with fast-changing loads at the LV distribution level. The fuzzy logic system and ANNare used to solve this nonlinear problem, giving optimum triggering delay angles used to trigger thyristors in TCR. The scheme is attractive and can be used at SVC installations in distribution systems for steady-state reactive power compensation. [ABSTRACT FROM AUTHOR]

Esophagitis is essentially inflammation of the esophageal squamous mucosa. One of the major reasons for cause of Esophagitis is the acid reflux from the stomach. This condition is observed in the process of upper gastro-intestinal tract endoscopy and the diagnosis is arrived at by examining the images of the esophagus. The diagnosis is based on the observation of the lesions and coloration of the digestive mucosa. Our paper reports an implementation of Decision Support System (DSS) for diagnosis of Esophagitis based on the analysis of color and texture features of the images captured during the process of endoscopy. The Hue Saturation and Intensity color model is adapted. The statistical features of the Hue and Saturation form the color features and the texture features are determined by Discrete Cosine Transform coefficients of the image. The decision making structure is a feed forward neural network. The DSS has been tested and results are reported. [ABSTRACT FROM AUTHOR]

The purpose of this study was to evaluate the role of diffusion weighted-magnetic resonance imaging (DW-MRI) in the examination and classification of brain tumors, namely, glioma and meningioma. Our hypothesis was that as signal intensity variations on diffusion weighted (DW) images depend on histology and cellularity of the tumor, analysing the signal intensity characteristics on DW images may allow differentiating between the tumor types. Towards this end the signal intensity variations on DW images of the entire tumor volume data of 20 subjects with glioma and 12 subjects with meningioma were investigated and quantified using signal intensity gradient (SIG) parameter. The relative increase in the SIG values (RSIG) for the subjects with glioma and meningioma was in the range of 10.08-28.36 times and 5.60-9.86 times, respectively, compared to their corresponding SIG values on the contralateral hemisphere. The RSIG values were significantly different between the subjects with glioma and meningioma (P < 0.01), with no overlap between RSIG values across the two tumors. The results indicate that the quantitative changes in the RSIG values could be applied in the differential diagnosis of glioma and meningioma, and their adoption in clinical diagnosis and treatment could be helpful and informative.

Breast cancer is one of the major causes of death among women. Early detection of breast cancer is possible by the detection of clustered microcalcifications on X-ray mammograms. Texture is an important characteristic used in identifying objects or region of interest in a digitised mammogram. This work focuses on a statistical texture analysis method called Surrounding Region Dependence Method (Kim and Park, 1999) - based on second order histogram in two surrounding regions. Six textural features are extracted and are used to classify region of interests into positive ROIs, containing clustered microcalcifications and negative ROIs composed of normal breast tissues. A 3-layer backpropagation neural network is used as a classifier. Results are evaluated using Receiver Operating Characteristics analysis.