Yashwant Kurmi

Microstrip planer antenna array by proximity fed for complete S band applications

A naval single band antenna presented is a combination of four element patches, printed on a substrate, for 3.4 GHz frequency operation. The array of antenna uses the ground at the bottom of substrate, a patch at top of substrate fed with proximity coupling. This array has a very simple structure; with size of 50×50mmexcited by discrete port through proxy feeding, which is also having efficient radiator to contribute the resonance for antenna to cover the complete S-Band. It provides the impedance bandwidth of 1.72GHz and Gain of 7.39dB. This antenna is coupled with 50 ohm where modeled through the CST microwave studio antenna software, where it uses finite element method for designing the antenna. The proposed microstrip antenna shows the good impedance bandwidth matching, high gain, high radiation efficiency and high directivity. Results are precisely obtained and validation of model is also verified.

Multifeature-based medical image segmentation

Histopathology image segmentation is an important area in the field of computer aided diagnosis using image processing. This study presents a local feature-based novel technique for the segmentation of histopathology images. It mainly focuses on a system that segments overlapped nuclei (OLN) without affecting the general non-OLN segmentation performance. The proposed method suggests a three-stage system. The initial segmentation is done by using local features for the demarcation of nuclei regions. In the second stage, salient-based active contour is applied for complete nucleus-region identification. In the final step, the OLN are identified and segmented using a Gaussian distribution and entropy maximisation. The performance of the proposed segmentation method is evaluated on the basis of precision, recall, accuracy, and F 1 -score. The proposed method is simulated on animal diagnostics laboratory histopathology image dataset and reported 90.3% average accuracy with average F 1-score 0.937. Simulation results confirm the superiority of the proposed method as compared with the existing state-of-art methods.

Fractal image compression with optimized domain pool size

Fractal image compression is an inventive method with a large number of advantages. Fractal process utilizes the existence of self-similarity in images. In practical applications, widely used approach of fractal image compressing has very high computational and time requirement of compression phase due to large number of image level comparison between sub-sections of image. It leads to a very high compression time. This paper presents optimization of domain pool size by aimed to provide acceptable retrieved image quality with affordable encoding time.