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eess.SP updates on arXiv.org

ECG-biometrics-bench: A Unified Framework for Reproducible Benchmarking of ECG Biometrics Physiology-Aware Masked Cross-Modal Reconstruction for Biosignal Representation Learning Towards Improving Speaker Distance Estimation through Generative Impulse Response Augmentation Federated Learning with Hypergradient-based Online Update of Aggregation Weights Soft Graph Diffusion Transformer for MIMO Detection SPLICE: Latent Diffusion over JEPA Embeddings for Conformal Time-Series Inpainting Sequential Inference for Gaussian Processes: A Signal Processing Perspective Statistical Channel Fingerprint Construction for Massive MIMO: A Unified Tensor Learning Framework Recent Advances in mm-Wave and Sub-THz/THz Oscillators for FutureG Technologies Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods Super-resolution Multi-signal Direction-of-Arrival Estimation by Hankel-structured Sensing and Decomposition Hankel and Toeplitz Rank-1 Decomposition of Arbitrary Matrices with Applications to Signal Direction-of-Arrival Estimation Adaptive Transform Coding for Semantic Compression EdgeSpike: Spiking Neural Networks for Low-Power Autonomous Sensing in Edge IoT Architectures Sparse Graph Learning from Sparse Data via Fiedler Number Maximization A Deep Learning Model for Battery State Prediction towards Intelligent Energy Management Transfer Learning for Tonal Noise Prediction in VRF Units Using Thermodynamic and Vibration Signals EVT-Based Generative AI for Tail-Aware Channel Estimation Monitoring exposure-length variations in submarine power cables using distributed fiber-optic sensing BandRouteNet: An Adaptive Band Routing Neural Network for EEG Artifact Removal Phase-Separated Complex Hilbert PCA on Markerless 3D Pose Estimation Data: A Global Phase Network and Its Extension to a Continuous Field on the Body Surface Selective Correlation Based Knowledge Distillation for Ground Reaction Force Estimation Deep Learning-Enabled Dissolved Oxygen Sensing in Biofouling Environments for Ocean Monitoring Speech Enhancement Based on Drifting Models Robust and Clinically Reliable EEG Biomarkers: A Cross Population Framework for Generalizable Parkinson's Disease Detection An AI-Based Supervisory Measurement Integrity Validation Layer for Cyber-Resilient AC/DC Protection in Inverter-Based Microgrids Explainable AI in Speaker Recognition -- Making Latent Representations Understandable Time-Localized Parametric Decomposition of Respiratory Airflow for Sub-Breath Analysis NAKUL-Med: Spectral-Graph State Space Models with Dynamics Kernels for Medical Signals An Algorithm for On-Sensor Agnostic Detection of Changes in Human Activity for Ultra-Low-Power Applications
Analysis and Development of SiC MOSFET Boost Converter as...
A. Bharathi sankar, R. Seyezhai · 2018-10-24 · via eess.SP updates on arXiv.org

Renewable energy source such as photovoltaic (PV) cell generates power from the sun light by converting solar power to electrical power with no moving parts and less maintenance. A single photovoltaic cell produces voltage of low level. In order to boost up the voltage, a DC-DC boost converter is used. In order to use this DC-DC converter for high voltage and high frequency applications, Silicon Carbide (SiC) device is most preferred because of larger current carrying capability, higher voltage blocking capability, high operating temperature and less static and dynamic losses than the traditional silicon (Si) power switches. In the proposed work, the static and dynamic characteristics of SiC MOSFET for different temperatures are observed. A SiC MOSFET based boost converter is investigated which is powered by PV source. This DC - DC converter is controlled using a Pulse-Width Method (PWM) and the duty cycle d is calculated for tracking the maximum power point using incremental conductance algorithm of the PV systems implemented in FPGA. Simulation studies are carried out in MATLAB/SIMULINK.A prototype of the SiC converter is built and the results are verified experimentally. The performance parameters of the proposed converter such as output voltage ripple input current ripple and losses are computed and it is compared with the classical silicon (Si) MOSFET converter.