Robust Face Recognition under Varying Illumination and Occlusion Considering ...singing_wei
Robust Face Recognition under Varying Illumination and Occlusion Considering Structured Sparsity, The International Conference on Digital Image Computing: Techniques and Applications (DICTA’12), December 2012, Australia
This document discusses single-pixel imaging via compressive sampling. It introduces compressive sampling which allows recovering a signal even if it is under-sampled by exploiting sparsity and incoherence. A single-pixel camera architecture is presented that uses a photo detector and modulated light sources to sample an image in a compressive manner, allowing reconstruction of the full image from far fewer samples than required by traditional pixel arrays. This technique could enable higher quality cameras at lower costs by needing just one photo detector instead of many.
PhD Thesis Defense Presentation: Robust Low-rank and Sparse Decomposition for...ActiveEon
Thesis submitted by Andrews Cordolino Sobral at Université de La Rochelle to fulfill the degree of Doctor of Philosophy.
Robust Low-rank and Sparse Decomposition for Moving Object Detection - From Matrices to Tensors
This document summarizes simulation results for spectrum sensing using compressive sensing in cognitive radio networks. It shows that an infrastructure-less approach using a consensus algorithm can achieve detection performance close to a centralized approach, and discusses the impact of varying parameters like the number of iterations, link quality between nodes, and number of measurements. Key results include infrastructure-less achieving near-centralized detection accuracy with enough iterations or measurements, and better connectivity and higher SNR improving performance.
Signal Processing Course : Convex OptimizationGabriel Peyré
This document discusses convex optimization and proximal operators. It begins by introducing convex optimization problems with objective functions G mapping from a Hilbert space H to the real numbers. It then discusses properties of convex, lower semi-continuous, and proper functions. Examples are given of regularization problems and total variation denoising. The document covers subdifferentials, proximal operators, proximal calculus including separability and compositions, and relationships between proximal operators and subdifferentials. Gradient descent and subgradient descent algorithms are also briefly discussed.
Robust Face Recognition under Varying Illumination and Occlusion Considering ...singing_wei
Robust Face Recognition under Varying Illumination and Occlusion Considering Structured Sparsity, The International Conference on Digital Image Computing: Techniques and Applications (DICTA’12), December 2012, Australia
This document discusses single-pixel imaging via compressive sampling. It introduces compressive sampling which allows recovering a signal even if it is under-sampled by exploiting sparsity and incoherence. A single-pixel camera architecture is presented that uses a photo detector and modulated light sources to sample an image in a compressive manner, allowing reconstruction of the full image from far fewer samples than required by traditional pixel arrays. This technique could enable higher quality cameras at lower costs by needing just one photo detector instead of many.
PhD Thesis Defense Presentation: Robust Low-rank and Sparse Decomposition for...ActiveEon
Thesis submitted by Andrews Cordolino Sobral at Université de La Rochelle to fulfill the degree of Doctor of Philosophy.
Robust Low-rank and Sparse Decomposition for Moving Object Detection - From Matrices to Tensors
This document summarizes simulation results for spectrum sensing using compressive sensing in cognitive radio networks. It shows that an infrastructure-less approach using a consensus algorithm can achieve detection performance close to a centralized approach, and discusses the impact of varying parameters like the number of iterations, link quality between nodes, and number of measurements. Key results include infrastructure-less achieving near-centralized detection accuracy with enough iterations or measurements, and better connectivity and higher SNR improving performance.
Signal Processing Course : Convex OptimizationGabriel Peyré
This document discusses convex optimization and proximal operators. It begins by introducing convex optimization problems with objective functions G mapping from a Hilbert space H to the real numbers. It then discusses properties of convex, lower semi-continuous, and proper functions. Examples are given of regularization problems and total variation denoising. The document covers subdifferentials, proximal operators, proximal calculus including separability and compositions, and relationships between proximal operators and subdifferentials. Gradient descent and subgradient descent algorithms are also briefly discussed.
127. Reviewer Certificate in BP InternationalManu Mitra
Manu Mitra received Certificate No. BPI/PR/Cert/2811G.33/MAN from the University of Bridgeport in the USA, dated March 12, 2024. The certificate was issued to Manu Mitra by the University of Bridgeport located in the United States of America.
126. Reviewer Certificate in BP InternationalManu Mitra
Manu Mitra received Certificate No. BPI/PR/Cert/_3628G/MAN from the University of Bridgeport, USA, dated March 11, 2024. The certificate was issued to Manu Mitra and is associated with the University of Bridgeport.
125. Reviewer Certificate in BP International [2024]Manu Mitra
Manu Mitra received Certificate No. BPI/PR/Cert/3067G.3/MAN from the University of Bridgeport in the USA, dated March 11, 2024. The certificate was issued to Manu Mitra by the University of Bridgeport located in the United States of America.
123. Reviewer Certificate in BP InternationalManu Mitra
Manu Mitra received Certificate No. BPI/PR/Cert/_7278A/MAN from the University of Bridgeport, USA, dated February 19, 2024. The certificate was issued to Manu Mitra by the University of Bridgeport located in the United States of America.
122. Reviewer Certificate in BP InternationalManu Mitra
Manu Mitra received Certificate No. BPI/PR/Cert/8900A/MAN from the University of Bridgeport, USA, dated February 3, 2024. The certificate was issued to Manu Mitra and lists his name, the certifying institution, the certificate number, and date of issuance.
Manu Mitra received Certificate No. BPI/PR/Cert/7169E/MAN from the University of Bridgeport in the USA on January 3, 2024 for completing a professional certification program. The certificate acknowledges Manu Mitra's participation and achievement in a training program offered through the University of Bridgeport.
Mr. Manu Mitra from the Department of Electrical Engineering at the University of Bridgeport in the USA received the Best Researcher Award at the 2023 International Award Ceremony on Academic Achievers in Higher Educational Institutions for his outstanding contribution to research.
127. Reviewer Certificate in BP InternationalManu Mitra
Manu Mitra received Certificate No. BPI/PR/Cert/2811G.33/MAN from the University of Bridgeport in the USA, dated March 12, 2024. The certificate was issued to Manu Mitra by the University of Bridgeport located in the United States of America.
126. Reviewer Certificate in BP InternationalManu Mitra
Manu Mitra received Certificate No. BPI/PR/Cert/_3628G/MAN from the University of Bridgeport, USA, dated March 11, 2024. The certificate was issued to Manu Mitra and is associated with the University of Bridgeport.
125. Reviewer Certificate in BP International [2024]Manu Mitra
Manu Mitra received Certificate No. BPI/PR/Cert/3067G.3/MAN from the University of Bridgeport in the USA, dated March 11, 2024. The certificate was issued to Manu Mitra by the University of Bridgeport located in the United States of America.
123. Reviewer Certificate in BP InternationalManu Mitra
Manu Mitra received Certificate No. BPI/PR/Cert/_7278A/MAN from the University of Bridgeport, USA, dated February 19, 2024. The certificate was issued to Manu Mitra by the University of Bridgeport located in the United States of America.
122. Reviewer Certificate in BP InternationalManu Mitra
Manu Mitra received Certificate No. BPI/PR/Cert/8900A/MAN from the University of Bridgeport, USA, dated February 3, 2024. The certificate was issued to Manu Mitra and lists his name, the certifying institution, the certificate number, and date of issuance.
Manu Mitra received Certificate No. BPI/PR/Cert/7169E/MAN from the University of Bridgeport in the USA on January 3, 2024 for completing a professional certification program. The certificate acknowledges Manu Mitra's participation and achievement in a training program offered through the University of Bridgeport.
Mr. Manu Mitra from the Department of Electrical Engineering at the University of Bridgeport in the USA received the Best Researcher Award at the 2023 International Award Ceremony on Academic Achievers in Higher Educational Institutions for his outstanding contribution to research.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
