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the Electronic Healthcare Record (EHR) is the patient’s health care data history, which retains the information about the diseases and their DICOM images reports. This application widely used in the medical field and thereby an employing a Iossless entropy coding technique adaptively to remove the redundancy of an image at a precise level. Run-Length Encoding (RLE) is the basic entropy coding method to adopt in the DICOM images. To accelerate the entropy coding process deployed the parallel architecture CUDA with the GPU technology. The parallelized RLE entropy coding technique implemented on GPGPU platform by using the parallelized CUDA programming. Here we proposed a parallel RLE entropy coding technique to accelerate up to 5X times faster than the serial RLE and 2. 25X times faster than parallel Huffman coding. The parallel RLE runs at O(log N) only.

In this paper design and development of an S Band linearly polarized microstrip patch antenna for high temperature applications is presented with simulated and measured results. The antenna requires dielectric cover over radiating portion to provide the protection from heat, physical damage and environment. This antenna configuration is mostly useful in applications like missiles, aircrafts, spacecrafts and satellites. The proposed antenna configuration is designed and optimized by simulation studies using IE3D which is based on the Finite Element Method (FEM). The radiation performance of single antenna, 4 element array mounted on vehicle body is carried out at S Band frequency.

Often in forensic scenarios, need arises to map a partial or poor quality finger print to the identity of an individual. The general image matching methods found in computer vision perform poorly as they are sensitive to local distortions like broken ridge patterns and incomplete information. To address this issue, we propose to use a weak descriptor to capture local structures at a higher abstract level. The goal here is to mine a large set of initial correspondence through weak description and then rely on robust estimator scheme to prune false matches. By coupling weak local descriptor with robust estimator, we minimize the affect of broken ridge patterns and also obtain a dense set of matches for a given pair. We evaluate the performance of the proposed method against SIFT as per the Fingerprint Verification Competition guidelines. We also report superior performance of the current methods rotation, scale, noise and overlap handling capabilities.

Face recognition is a complex numerical technology in computer systems that can assist recognition of human faces using principal component analysis (PCA) by comparing the given facial uniqueness with the already available face database. Human faces are usually straight, so they can be treated as 2D images relatively rather than 3D. The 2D facial image can be transformed into a 1D vector of pixels and projected into the principal components of the feature space called the eigenspace projection, which is evaluated from the eigenvectors of the covariance matrix derived from a set of facial images. The projections of the given face are then compared with the available training set and the face is identified. In this paper, we introduce a novel approach of dimensionality reduction of the covariance matrix and apply this algorithm with the training sets of JNTU face database and nonface database. Recognition rate versus Euclidean distance and number of eigenfaces versus recognition rate and eigenvalue variation is presented in this paper.