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A new method for computing the optimal control, i.e., minimizing a given performance index, thereby steering the given initial state of a system to some point in the state space which may or may not be specified, is proposed here. The classical Calculus of Variations approach and the modern approach of variation in control that leads to Pontriagin's principle have been first compared. Moreover, based on the ideas in the derivation of the Maximum Principle of Pontryagin, we have derived the maximum principle of Pontryagin using the approach of Classical Calculus of Variations modified along the line of brief perturbation and derived an expression for the change in the value of performance index over each discretized interval. First, we derived an expression for the difference in the performance index due to perturbation in control u that differs slightly from u ∗ . Then, using the calculus of variations once again, we derived the formula for calculating the difference in the performance index using the brief perturbation suggested by Agashe. Also, we have given three examples of linear and non-linear systems for establishing the expediency of our derivations. Two examples are on the calculus of variations using the perturbation approach and one based on the derivation of Pontryagin's principle. [ABSTRACT FROM AUTHOR]

Almost all of the automatic accident detection (AAD) system suffers from the tradeoff between computational overhead and detection accuracy. Recent advances in detection and classification methodologies have shown phenomenal improvements in accuracy but these systems require a huge number of computational resources making them unviable for deployment requiring real-time feedback. This paper proposes a methodology to develop a reliable and computationally inexpensive real-time automatic accident detection system that can be deployed with minimum hardware requirements. Specifically, we split our AAD system into three major stages (Detection, Tracking and Classification) and propose algorithms for each stage with reduced computational need. For the detection stage, we propose Mini-YOLO, a deep learning model architecture trained using knowledge distillation that has comparable accuracy with its counterpart YOLO(You-Only-Look-Once) with reduced model size and computational overhead. Mini-YOLO achieves an average precision (AP) score of 34.2 on the MS-COCO dataset while outperforming all other detection algorithms in runtime complexity, achieving a staggering 28 frames per second on a low-end machine. For the tracking stage, we adopt SORT (Simple Online Real-time Tracking) and for classification stage, we compare multiple machine learning algorithms and show that a support vector machine with radial basis kernel performs the best with an area under the curve (AUC) score of 0.98, model size of 448 KB (kilobytes) and 12.73 ms (milliseconds) latency. [ABSTRACT FROM AUTHOR]

In this paper, a noise-robust palmprint recognition system is discussed with a novel feature extraction technique: two-dimensional Cochlear transform (2D-CT) based on the textural analysis of image sample. Orthogonality of 2D-CT is proved which shows the high robustness of the proposed 2D-CT to noise. To validate the proposed feature extraction technique, palmprint recognition has been tested on both left and right palm of IITD database of 230 persons, CASIA palmprint database of 312 persons, polyU palmprint database of 386 persons and achieved high accuracy. The proposed 2D-CT method is compared with discriminative and robust competitive code, double orientation code, competitive coding, ordinal coding, Gabor transform, Gaussian membership-based features, absolute average deviation and mean features. Further, K-nearest neighbor is used to validate the matching stage. The results show superiority of the proposed method over other feature extraction methods. [ABSTRACT FROM AUTHOR]