Application of Geographic Information System for the Installation of Surge Arrestors on over head 132 k-v Power Line

Power system consists of generation, transmission and distribution of electrical energy. Transmission lines transport the desired amount of electrical power from one place to another. The Protection of Power line is a very important factor in smooth transfer of electric power. Lightning is a major cause of overhead line faults.It is necessary to protect the power apparatus from over volts in electric system, namely lighting over voltages & switching over voltages. The objectives of this study is to protect the power system equipment's from lightning using geographic information system approach A Geographical Information System (GIS) is a collection of software's that allows you to create, visualize, query and analyse geographic data.This paper presents the idea of installing line surge arrestors by marking the exact location of towers using a multispectral satellite image and image processing software with the help of gps points taken on the ground. A case study of 132 k-v existing double circuit line from Sheik Muhammadi 500 k-v grid to 132 k-v city grid Peshawar is considered for results where as input data to GIS is in the form of spot-5 satellite image having 2.5m resolution

Higher order conditional entropy-constrained trellis-coded RVQ withapplication to pyramid image coding

This paper introduces an extension of conditional entropy-constrained residual vector quantization (CEC-RVQ) to include quantization cell shape gain. The method is referred to as conditional entropy-constrained trellis-coded RVQ (CEC-TCRVQ). The new design is based on coding image vectors by taking into account their 2D correlation and employing a higher order entropy model with a trellis structure. We employed CEC-TCRVQ to code image subbands at low bit rate. The CEC-TCRVQ coded images do well in term of preserving low-magnitude textures present in some image

GGM classifier with multi-scale line detectors for retinal vessel segmentation

Persistent changes in the diameter of retinal blood vessels may indicate some chronic eye diseases. Computer-assisted change observation attempts may become challenging due to the emergence of interfering pathologies around blood vessels in retinal fundus images. The end result is lower sensitivity to thin vessels for certain computerized detection methods. Quite recently, multi-scale line detection method proved to be worthy for improved sensitivity toward lower-caliber vessels detection. This happens largely due to its adaptive property that responds more to the longevity patterns than width of a given vessel. However, the method suffers from the lack of a better aggregation process for individual line detectors. This paper investigates a scenario that introduces a supervised generalized Gaussian mixture classifier as a robust solution for the aggregate process. The classifier is built with class-conditional probability density functions as a logistic function of linear mixtures. To boost the classifier’s performance, the weighted scale images are modeled as Gaussian mixtures. The classifier is trained with weighted images modeled on a Gaussian mixture. The net effect is increased sensitivity for small vessels. The classifier’s performance has been tested with three commonly available data sets: DRIVE, SATRE, and CHASE_DB1. The results of the proposed method (with an accuracy of 96%, 96.1% and 95% on DRIVE, STARE, and CHASE_DB1, respectively) demonstrate its competitiveness against the state-of-the-art methods and its reliability for vessel segmentation

Design and analysis of entropy-constrained reflected residual vector quantization

Residual vector quantization (RVQ) is a vector quantization (VQ) paradigm which imposes structural constraints on the encoder in order to reduce the encoding search burden and memory storage requirements of an unconstrained VQ. Jointly optimized RVQ (JORVQ) is an effective design algorithm for minimizing the overall quantization error. Reflected residual vector quantization (RRVQ) is an alternative design algorithm for the RVQ structure with a smaller computation burden. RRVQ works by imposing an additional symmetry constraint on the RVQ codebook design. Savings in computation were accompanied by an increase in distortion. However, an RRVQ codebook, being structured in nature, is expected to provide lower output entropy. Therefore, we generalize RRVQ to include noiseless entropy coding. The method is referred to as entropy-constrained RRVQ (EC-RRVQ). Simulation results show that EC-RRVQ outperforms RRVQ by 4 dB for memoryless Gaussian and Laplacian sources. In addition, for the same synthetic sources, EC-RRVQ provided an improvement over other entropy-constrained designs, such as entropy-constrained JORVQ (EC-JORVQ). The design performed equally well on image data. In comparison with EC-JORVQ, EC-RRVQ is simpler and outperforms the EC-JORVQ

Image coding using entropy-constrained reflected residual vector quantization

Residual vector quantization (RVQ) is a structurally constrained vector quantization (VQ) paradigm. RVQ employs multipath search and has higher encoding cost as compared to sequential single-path search. Reflected residual vector quantization (Ref-RVQ), a design with additional symmetry on the codebook, was developed later to a jointly optimized RVQ structure with single-path search. The constrained Ref-RVQ codebook exhibits an increase in distortion. However, it was conjectured that the Ref-RVQ codebook has a lower output entropy than that of the multipath RVQ codebook. Therefore, the Ref-RVQ design was generalized to include noiseless entropy coding. We apply it to image coding. The method is referred to as entropy-constrained Ref-RVQ (EC-Ref-RVQ). Since the RVQ scheme is able to implement very large dimensional vector quantization designs like 16/spl times/16 and 32/spl times/32 VQs, it is found highly successful in extracting linear and non-linear correlation among image pixels. We intend to implement these large dimensional vectors with the EC-Ref-RVQ scheme to realize a computationally less demanding image-RVQ design. Simulation results demonstrate that EC-Ref-RVQ, while maintaining single path search, provides 1 dB improvement in PSNR for image data over the multipath EC-RVQ

Design and analysis of entropy-constrained reflected residual vector quantization

Residual vector quantization (RVQ) is a vector quantization (VQ) paradigm which imposes structural constraints on the encoder in order to reduce the encoding search burden and memory storage requirements of an unconstrained VQ. Jointly optimized RVQ (JORVQ) is an effective design algorithm for minimizing the overall quantization error. Reflected residual vector quantization (RRVQ) is an alternative design algorithm for the RVQ structure with a smaller computation burden. RRVQ works by imposing an additional symmetry constraint on the RVQ codebook design. Savings in computation were accompanied by an increase in distortion. However, an RRVQ codebook, being structured in nature, is expected to provide lower output entropy. Therefore, we generalize RRVQ to include noiseless entropy coding. The method is referred to as entropy-constrained RRVQ (EC-RRVQ). Simulation results show that EC-RRVQ outperforms RRVQ by 4 dB for memoryless Gaussian and Laplacian sources. In addition, for the same synthetic sources, EC-RRVQ provided an improvement over other entropy-constrained designs, such as entropy-constrained JORVQ (EC-JORVQ). The design performed equally well on image data. In comparison with EC-JORVQ, EC-RRVQ is simpler and outperforms the EC-JORVQ

Lung nodule classification utilizing support vector machines

Lung cancer is one of the deadly and most common diseases in the world. Radiologists fail to diagnose small pulmonary nodules in as many as 30% of positive cases. Many methods have been proposed in the literature such as neural network algorithms. Recently, support vector machines (SVMs) had received increasing attention for pattern recognition. The advantage of SVM lies in better modeling the recognition process. The objective of this paper is to apply support vector machines SVMs for classification of lung nodules. The SVM classifier is trained with features extracted from 30 nodule images and 20 non-nodule images, and is tested with features out of 16 nodule/non-nodule images. The sensitivity of SVM classifier is found to be 87.5%. We intend to automate the pre-processing detection process to further enhance the overall classification

Competitive learning/reflected residual vector quantization for coding angiogram images

Medical images need to be compressed for the purpose of storage/transmission of a large volume of medical data. Reflected residual vector quantization (RRVQ) has emerged recently as one of the computationally cheap compression algorithms. RRVQ, which is a lossy compression scheme, was introduced as an alternative design algorithm for residual vector quantization (RVQ) structure (a structure famous for providing progressive quantization). However, RRVQ is not guaranteed to reach global minimum. It was found that it has a higher probability to diverge when used with nonGaussian and nonLaplacian image sources such as angiogram images. By employing competitive learning neural network in the codebook design process, we tried to obtain a stable and convergent algorithm. This paper deals with employing competitive learning neural network in RRVQ design algorithm that results in competitive learning RRVQ algorithm for the RVQ structure. Simulation results indicate that the new proposed algorithm is indeed convergent with high probability and provides peak signal-to-noise ratio (PSNR) of approximately 32 dB for an-giogram images at an average encoding bit rate of 0.25 bits per pixel