205 research outputs found
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
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
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
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