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7th International Conference on Signal Processing and Integrated Networks, SPIN 2020

作者： Navdeep Singh, Vijander Rani, Asha Goyal, Sonal Delhi University Department of Instrumentation and Control Engineering Netaji Subhas Institute of Technology Delhi India

ISBN: (纸本)9781728154756

Depth images generally have low resolution, which leads to poor edge map from conventional edge detection methods. This necessitates the requirement of a consistent edge detection method for depth image applications. Therefore in this paper, improved depth local binary pattern (IDLBP) edge detection method based on local binary pattern is proposed for depth images. Experimentation is performed on synthetic and depth images to verify the validity of suggested technique. It is revealed that IDLBP is a fast, accurate and non-destructive edge detection technique. The depth local binary pattern, improved local binary pattern, hyper smoothing function based local binary pattern, Canny, Sobel and Laplacian of Gaussian edge detection techniques are also realized for comparative analysis. The effectiveness of suggested IDLBP algorithm is evaluated in terms of accuracy, Jaccard similarity index, specificity, sensitivity and dice similarity coefficient. © 2020 IEEE.

关键词： Local binary pattern

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Mental arithmetic task load recognition using EEG signal and Bayesian optimized K-nearest neighbor

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International Journal of Information technology (Singapore) 2021年第6期13卷 2363-2369页

作者： Sharma, Lakhan Dev Chhabra, Himanshu Chauhan, Urvashi Saraswat, Ritesh Kumar Sunkaria, Ramesh Kumar School of Electronics Engineering VIT-AP University Amaravati India Electronics and Communication Engineering M.L.V. Textile and Engineering College Bhilwara India Instrumentation and Control Engineering Division Netaji Subhas University of Technology Delhi India Electronics and Communication Engineering Dr. B. R. Ambedkar National Institute of Technology Jalandhar India

Cognitive load recognition during mental arithmetic activity facilitates to observe and identify the brain’s response towards stress stimulus. As a result, an efficient mental load characterization approach using electroencephalogram (EEG) signal and Bayesian optimized K-Nearest Neighbor (BO-KNN) has been proposed in this work. The study has been conducted on a recorded EEG dataset of 30 healthy subjects who were exposed to an arithmetic questioner. To obtain artifacts free EEG signal, the Savitzky–Golay filtering approach has been utilized. Further, the decomposition of the extracted EEG signal has been carried out using stationary wavelength transform. In this work, the entropy based feature extraction has been performed followed by F-score based feature selection. Top 40 features having the highest precedence have been used for classification using BO-KNN. The rigorous experimental analysis has been performed to analyze the effectiveness of the proposed method over other state-of-the-art methods and it shows that the classification accuracy is substantially improved. © 2021, Bharati Vidyapeeth's institute of Computer Applications and Management.

关键词： Bayesian optimized K-nearest neighbor (BO-KNN) Cognitive task EEG Stationary wavelet transformation

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Estimation of Parameters in Fractional order Financial Chaotic system with Nature Inspired Algorithms 1

Estimation of Parameters in Fractional order Financial Chaot...

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1st International Conference on Smart Sustainable Intelligent Computing and Applications, ICITETM 2020

作者： Gupta, Sangeeta Gautam, Gaurava Deepak Vats, Divyanshu Varshney, Pragya Srivastava, Smriti Department of Instrumentation and Control Engineering Netaji Subhas Institute of Technology Dwarka Sec-3 New Delhi110078 India

Chaotic system parameter modeling is one of the significant topics in the field of non-linear science and therefore has attracted the attention of many researchers in this field. This paper discusses parameter recognition of Fractional Order Financial Chaotic System by employing different metaheuristic algorithms. The implemented algorithms include Artificial bee colony, Grey wolf optimizer, Whale optimization algorithm and Ant colony optimizer. Mean Square Error is employed as objective function to estimate parameters of system under consideration. Results of optimized parameters using the above-mentioned optimization are compared with each other and discussed in the paper. The overall outcome shows that Whale optimization algorithm gives more accurate and robust results with higher convergence rate as compared with other algorithms. © 2020 The Authors. Published by Elsevier B.V.

关键词： Ant colony optimization

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Image Analysis for E-Healthcare Systems using Multi-Biometric Recognition Model

Image Analysis for E-Healthcare Systems using Multi-Biometri...

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Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： Naman Bansal Pranav Arora Deepak Kumar Sharma Koyel Datta Gupta Chandana Kuntala Department of Instrumentation and Control Engineering Netaji Subhas Institute of Technology University of Delhi New Delhi India Department of Information Technology Indira Gandhi Delhi Technical University for Women New Delhi India Department of Computer Science and Engineering Maharaja Surajmal Institute of Technology New Delhi India Department of Information Technology Indira Gandhi Delhi Technical University for Women New Delhi India

With the dawn of e-Healthcare systems, Medical Record Management has become an important research problem. The storage and organization of medical records have made relatively little progress in a world of constantly emerging new technologies and continuous innovation. Many hospitals keep the records on paper, which raises many challenges, including but not limited to a significant amount of time for searching and retrieving a specific record, high maintenance costs, lack of backup, and limited security. Although the inclusion of technology has made this task far more efficient and secure, there is still much that can be done to improve it. This paper proposes a double index-based approach for mapping medical records directly to a patient’s biometrics which would take advantage of the uniqueness of biometrics to identify a patient. The proposed multi-biometric model achieves an accuracy of 97% and an F 1 score of 0.9814.

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MeFunX: A novel meta-learning-based deep learning architecture to detect fungal infection directly from microscopic images

Franklin Open

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Franklin Open 2024年 6卷

作者： Rawat, Shubhankar Bisht, Bhanvi Bisht, Virender Rawat, Nitin Rawat, Aditya Department of Instrumentation and Control Engineering Netaji Subhas University of Technology India Department of Electronics and Communication Engineering Indira Gandhi Delhi Technical University For Women India Department of Mathematics Al-Falah University India Department of Industrial Management Indian Institute of Management Mumbai India School of Engineering and the Built Environment Birmingham City University United Kingdom

Fungal infections are a growing threat to human health. They can lead to a range of health problems and can be life-threatening. There are many impediments to the traditional diagnosis of fungal infections, such as a diminishing number of clinical mycologists, expensive procedures, high time consumption, and requirements for sensitivity and specificity. Therefore, early diagnosis of fungal infection is critical to effective treatment. In this paper, a novel meta-learning-based deep learning architecture, termed MeFunX, is proposed for the early detection of fungal infections from microscopic images. MeFunX architecture consisted of two convolutional neural network-based models as base learners and XGBoost as the meta-learner. To assess the proposed approach, standard metrics namely, accuracy, precision, recall and f1-score, were used. The fungal disease identification performance of MeFunX was compared with state-of-the-art architectures like VGG16, InceptionV3, ResNet, AlexNet, DenseNet, and EfficientNet. In addition, MeFunX was also benchmarked against its base learners and other meta-learning model with EfficientNet and ResNet as the base learners to demonstrate the effectiveness of the meta-learning architecture. Rigorous experimentation clearly signifies the superior performance of MeFunX, which achieved an overall accuracy of 92.49 % for the early diagnoses of fungal infections in microscopic images. © 2023 The Authors

关键词： Deep learning Early detection Fungal infection Meta-learning XGBoost

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Whale Optimization Algorithm (WOA) Based control of Nonlinear Systems 2

Whale Optimization Algorithm (WOA) Based Control of Nonlinea...

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2nd International Conference on Power Energy Environment and Intelligent control, PEEIC 2019

作者： Srivastava, Vishal Srivastava, Smriti Netaji Subhas Institute of Technology Instrumentation Control Division New Delhi India

ISBN: (纸本)9781728117935

An efficient optimization technique based controller is proposed, for non-linear systems like inverted pendulum and robotic arm manipulator which are bench mark problems in control engineering. The optimization based controller is optimizing the gain parameters by using the Whale optimization algorithm (WOA). WOA is applied on Conventional PID controller to optimize their parameters i.e. mathbf{K-{p}}, mathbf{K-{i}} and mathbf{K}-{mathbf{d}}. Mean Square Error (MSE) is taken as the objective function. The Simulation results show substantial improvement in the performance of the nonlinear systems. © 2019 IEEE.

关键词： Mean square error

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A Modified Incremental Conductance Maximum Power Point Technique for Standalone PV System

A Modified Incremental Conductance Maximum Power Point Techn...

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International Conference on Signal Processing and Integrated Networks (SPIN)

作者： Urvashi Chauhan Asha Rani Vijander singh Bhavnesh Kumar Division of Instrumentation and Control Engineering Netaji Subhas Institute of Technology University of Delhi New Delhi India

ISBN: (数字)9781728154756

ISBN: (纸本)9781728154763

The main aim of this work is to design an effective MPPT algorithm so that the power utilization efficiency of PV array is enhanced. Therefore improved incremental conductance MPPT controller i.e. INC-PID MPPT is proposed which utilizes the concept of traditional incremental conductance algorithm and proportion-integral-derivative controller. The conventional MPPT methods i.e. incremental conductance and perturb & observation have simple structure and may be implemented easily. However these algorithms suffer from low tracking efficiency and steady state oscillations. The proposed technique overcomes these drawbacks and also preserves the simple structure. The controller parameters are optimized using grey wolf optimization (GWO). The proposed algorithm is applied to multi crystal solar panel, KC130GT by M/S Kyocera, and power tracking capability of INC-PID MPPT is evaluated for various patterns of irradiance. The suggested technique proves to be superior than existing techniques in terms of settling time, undershoot and rise time.

关键词： Maximum power point trackers Signal processing algorithms Photovoltaic systems Mathematical model Optimization Oscillators Genetic algorithms

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Estimation of Parameters in Fractional order Financial Chaotic system with Nature Inspired Algorithms

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Procedia Computer Science 2020年 173卷 18-27页

作者： Sangeeta Gupta Gaurava Gautam Deepak Divyanshu Vats Pragya Varshney Smriti Srivastava Department of Instrumentation and Control Engineering Netaji Subhas Institute of Technology Dwarka Sec-3 New Delhi -110078 India

关键词： Optimization Techniques Fractional Calculus Fractional Order Fractional Order Financial Chaotic system Whale Optimization Algorithm Ant colony optimization Grey wolf optimization Artificial bee colony optimization

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Identification of the most efficient algorithm to find Hamiltonian Path in practical conditions

Identification of the most efficient algorithm to find Hamil...

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International Conference on Confluence The Next Generation Information technology Summit (Confluence)

作者： Karanjot Singh Simran Kaur Bedi Prerna Gaur Department of Instrumentation and Control Engineering Netaji Subhas Institute of Technology New Delhi India

ISBN: (数字)9781728127910

ISBN: (纸本)9781728127927

Travelling Salesman Problem (TSP) is a real-world Non-deterministic polynomial-time hard - combinatorial optimization problem. Given several points (cities) to be visited, the objective of the problem is to find the shortest possible route (called Hamiltonian Path) that visits each point exactly once and returns back to the starting point. Several exact, approximate and heuristic algorithms have been proposed to solve the TSP. The objective of this paper is to compare 10 such different algorithms on the basis of cost of the path found and time taken to find that solution in order to identify an algorithm which works most efficiently and thus, can be used in practical scenarios. Therefore, the comparative analysis has been made without time constraints as a preliminary test and then with a constraint of 1 second to determine the most efficient algorithm. This algorithm was then used at the core of the web-based tool (a practical use case) developed for release in public domain which helps users find an optimal round-trip route (i.e. Hamiltonian Path) among the points marked on the map. Google Maps API was used for providing map interface and obtaining real-time distance/duration data (matrix) in the web-application front end.

关键词： Conferences Cloud computing Data science

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Fault Detection and Correction in Omni Bundle Robot Using EKF

Fault Detection and Correction in Omni Bundle Robot Using EK...

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International Conference on Manufacturing, Advanced Computing, Renewable Energy and Communication, MARC 2018

作者： Rana, Rohit Agarwal, Vijyant Gaur, Prerna Parthasarathy, Harish Instrumentation and Control Engineering Division Netaji Subhas Institute of Technology Sec-03 Dwarka110078 India Manufacturing Process and Automation Engineering Division Netaji Subhas Institute of Technology Sec-03 Dwarka110078 India Electronics and Communication Engineering Division Netaji Subhas Institute of Technology Sec-03 Dwarka110078 India

ISBN: (纸本)9789811367717

In this paper, the entire focus is on fault detection and correction using extended Kalman filter (EKF) on industrially used omni bundle robot. Here, the authors considered a problem where a robot does monotonous job and repetitively traverse the same trajectory every time. Due to wear and tear or any fluctuation in input signals, the robot may not track the desired trajectory. Faults may arise at various levels in robots, for example, in actuator, encoder, and dynamical parameters. Actuator fault detection using encoder sensor will be the main focus in this research paper. The dynamics of robotic arm link is modeled using a DC motor attached with the link, and the encoder attached with the motor will provide position feedback. The fault is detected using EKF state estimation, and the fault is corrected using a PID controller feedback into the motor input torque. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Extended Kalman filters

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