A Survey: Face Recognition by Sparse Representation

Face recognition is very helpful in many applications such as video surveillance, forensic applications criminal investigations, and in many other fields. The most common methods includes PCA approach based Eigenface, Linear Discriminant Analysis(LDA), Hidden Markov Model(HMM),DWT, geometry based and template matching approaches.In this paper we are using sparse representation approach to attain more robustness to variation in lighting, directions and expressions. This survey paper performs analysis on different approaches and factors affecting the face recognition

Effective crop categorization using wavelet transform based optimized long short-term memory technique

Effective crop categorization is important for keeping track of how crops grow and how much they produce in the future. Gathering crop data on categories, regions, and space distribution in a timely and accurate way could give a scientifically sound reason for changes to the way crops are organized. Polarimetric synthetic aperture radar dataset provides sufficient information for accurate crop categorization. It is essential to classify crops in order to successfully. This article presents wavelet transform (WT) based optimizedlong short-term memory (LSTM) deep learning (DL) for effective crop categorization. Image denoising is performed by WT. Denoising algorithms for images attempt to find a middle ground between totally removing all of the image’s noise and preserving essential, signal-free components of the picture in their original state. After denoising of images, crop image classification is achieved by LSTM and support vector machine (SVM) algorithm. LSTM has achieved 99.5% accuracy

Level-set based adaptive-active contour segmentation technique with long short-term memory for diabetic retinopathy classification

Diabetic Retinopathy (DR) is a major type of eye defect that is caused by abnormalities in the blood vessels within the retinal tissue. Early detection by automatic approach using modern methodologies helps prevent consequences like vision loss. So, this research has developed an effective segmentation approach known as Level-set Based Adaptive-active Contour Segmentation (LBACS) to segment the images by improving the boundary conditions and detecting the edges using Level Set Method with Improved Boundary Indicator Function (LSMIBIF) and Adaptive-Active Counter Model (AACM). For evaluating the DR system, the information is collected from the publically available datasets named as Indian Diabetic Retinopathy Image Dataset (IDRiD) and Diabetic Retinopathy Database 1 (DIARETDB 1). Then the collected images are pre-processed using a Gaussian filter, edge detection sharpening, Contrast enhancement, and Luminosity enhancement to eliminate the noises/interferences, and data imbalance that exists in the available dataset. After that, the noise-free data are processed for segmentation by using the Level set-based active contour segmentation technique. Then, the segmented images are given to the feature extraction stage where Gray Level Co-occurrence Matrix (GLCM), Local ternary, and binary patterns are employed to extract the features from the segmented image. Finally, extracted features are given as input to the classification stage where Long Short-Term Memory (LSTM) is utilized to categorize various classes of DR. The result analysis evidently shows that the proposed LBACS-LSTM achieved better results in overall metrics. The accuracy of the proposed LBACS-LSTM for IDRiD and DIARETDB 1 datasets is 99.43% and 97.39%, respectively which is comparably higher than the existing approaches such as Three-dimensional semantic model, Delimiting Segmentation Approach Using Knowledge Learning (DSA-KL), K-Nearest Neighbor (KNN), Computer aided method and Chronological Tunicate Swarm Algorithm with Stacked Auto Encoder (CTSA-SAE)

Investigation of De Speckling Techniques for Echocardiographic Images: A Review

Speckle noise corrupt the major part of ultrasound image, because of which the quality deteriorate and loss of valuable information leads to false diagnosis. A large community of images like synthetic aperture radar (SAR) image, Synthetic image, and simulated ultrasound image, require despeckling at pre-processing stage for better processing. Cleaning the speckle from image and preserving the edge details is a vital task. Nowadays not only despeckling is considered as an important process but also preserving information at boundary and edges of image is also important. As most of the algorithms able to remove speckle noise but fails to preserve the details of edges. This paper covers several recent methods for removal of speckle noise along with various metrics opted for comparisons. The distinctive part of this paper is, a mathematical and parametric review has been done. Also a table is also included which summarizes the entire paper.</jats:p

“Power Transmission through Solar Power Satellite to Earth Surface with Minimum Power Loss”

Now days, expanding needs of photovoltaic cells and other solar oriented power establishments are in administration around the globe and in space. These utilizations extend from essential electric power source for satellites, remote site logical experiment and towns in creating nations to enhancing the commercial electric grid and giving fractional power for individual organization. In space, power produced by photovoltaic cells after conversion from sunlight will become mainstay of power source for Earth and geostationary satellite bodies. The conspicuous reason is daylight on earth is excessively untrustworthy, whereas in space energy can be harvest by 24 hours. The challenge is to harvest maximum energy and transmit the energy from space to earth in the form of microwave frequency with minimum power loss. In this work to harvest maximum power,s we change the directivity range of receiving antenna to -40 dB, Received microwave frequency from transmitting antenna is in the form of beam of rectangular array, In previous work, directivity ranges from 0 to -8 dB for receiving frequency 70.00 MHz causes cutoff of transmitted frequency for below 2.45 GHz, if directivity ranges increase from 0 to -40 dB then transmitting frequency ranges till 2.00 GHz will be utilized by which after conversion in rectenna we get total 223.42 MW power.</jats:p