Research Article Support Vector Machine Based Pades Approximant for Diabetic Retinal Eye Detection

Abstract: Diabetic Retina (DR), a problem of formation of blood clot must be diagnosed at an early stage for laser therapy. A number of automated diagnosis methods based on image segmentation of fundus image is present which can diagnose DR at late mild proliferative stage. Proposed work is aimed to detect DR at early mild proliferative stage. Method uses feature extraction of fundus image using 2D Gabor filtering and pre-classification for feature vector extraction using Pades approximation. The Padesvector are then again classified using SVM by forming a dual of convex quadratic type minimization problem for linearly separable hyper plane. The performance of the proposed work is tested with set of images taken from fundus camera

Buck-boost single-stage microinverter for building integrated photovoltaic systems

Microinverters for Building Integrated Photovoltaic (BIPV) systems must have had a small number of components, be efficient, and be reliable. In this context, a single-phase Buck-Boost Single-stage Microinverter (BBSM) for grid-connected BIPV systems is presented. The concept of topology is extracted from the buck-boost converter. The leakage current in the system is kept under control. It uses an optimal number of active and passive components to function at a high-efficiency level. The suggested topology provides a high level of reliability due to the absence of shoot-through problems. To validate the findings, a simulation in combination with an experimental system for a 70 W system is developed with the design approach. The efficiency of the microinverter, total harmonic distortion of the grid current are measured as 96.4% and 4.09% respectively. Finally, a comparison study has indicated the advantages and disadvantages of the suggested inverter

SDLFormer: A Sparse and Dense Locality-enhanced Transformer for Accelerated MR Image Reconstruction

Transformers have emerged as viable alternatives to convolutional neural networks owing to their ability to learn non-local region relationships in the spatial domain. The self-attention mechanism of the transformer enables transformers to capture long-range dependencies in the images, which might be desirable for accelerated MRI image reconstruction as the effect of undersampling is non-local in the image domain. Despite its computational efficiency, the window-based transformers suffer from restricted receptive fields as the dependencies are limited to within the scope of the image windows. We propose a window-based transformer network that integrates dilated attention mechanism and convolution for accelerated MRI image reconstruction. The proposed network consists of dilated and dense neighborhood attention transformers to enhance the distant neighborhood pixel relationship and introduce depth-wise convolutions within the transformer module to learn low-level translation invariant features for accelerated MRI image reconstruction. The proposed model is trained in a self-supervised manner. We perform extensive experiments for multi-coil MRI acceleration for coronal PD, coronal PDFS and axial T2 contrasts with 4x and 5x under-sampling in self-supervised learning based on k-space splitting. We compare our method against other reconstruction architectures and the parallel domain self-supervised learning baseline. Results show that the proposed model exhibits improvement margins of (i) around 1.40 dB in PSNR and around 0.028 in SSIM on average over other architectures (ii) around 1.44 dB in PSNR and around 0.029 in SSIM over parallel domain self-supervised learning. The code is available at https://github.com/rahul-gs-16/sdlformer.gitComment: Accepted at MICCAI workshop MILLanD 2023 Medical Image Learning with noisy and Limited Dat

Spatiotemporal communication with synchronized optical chaos

We propose a model system that allows communication of spatiotemporal information using an optical chaotic carrier waveform. The system is based on broad-area nonlinear optical ring cavities, which exhibit spatiotemporal chaos in a wide parameter range. Message recovery is possible through chaotic synchronization between transmitter and receiver. Numerical simulations demonstrate the feasibility of the proposed scheme, and the benefit of the parallelism of information transfer with optical wavefronts.Comment: 4 pages, 5 figure

Physics and Applications of Laser Diode Chaos

An overview of chaos in laser diodes is provided which surveys experimental achievements in the area and explains the theory behind the phenomenon. The fundamental physics underpinning this behaviour and also the opportunities for harnessing laser diode chaos for potential applications are discussed. The availability and ease of operation of laser diodes, in a wide range of configurations, make them a convenient test-bed for exploring basic aspects of nonlinear and chaotic dynamics. It also makes them attractive for practical tasks, such as chaos-based secure communications and random number generation. Avenues for future research and development of chaotic laser diodes are also identified.Comment: Published in Nature Photonic

Multilocational testing of pigeonpea for broad-based resistance to sterility mosaic in India

During 1978-83, 88 pigeon pea lines resistant to sterility mosaic (SM) from different research centres in India were tested at 10 locations (Badnapur, Bangalore, Dholi, Pantnagar, Faizabad, Kanpur, Ludhiana, Patancheru, Vamban and Varanasi) to identify lines with stable and broad-based resistance. The multilocation evaluation was carried out through the joint Indian Council of Agricultural Research and the ICRISAT Uniform Trial for Pigeon Pea Sterility Mosaic Resistance. SM resistant genotypes were identified at each of the 10 locations. Lines ICP 7867, ICP 10976 and ICP 10977 were resistant or tolerant at all 10 locations. These lines are now being used by breeders at ICRISAT as well as in the Indian national programme for developing SM resistant and high yielding cultivars