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IEEE International WIE Conference on Electrical and computer engineering (WIECON-ECE)

作者： Sindu M T Ashwin Shyam Sunder V Shivasankaran S Poongothai P R. Ponnusamy Dept. of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu India Dept. of Computer Mathematics Chennai Institute of Technology Tamil Nadu India

ISBN: (数字)9798331535476

ISBN: (纸本)9798331535483

Stress is one of the prevailing issues in today's world, deeply impacting the actual well-being of individuals. In this paper, we introduce an innovative method to detect stress that deviates from the mainstream approaches, which rely solely on physiologic signals and self-reporting. Our non-invasive, efficient, and scalable real-time system, built on the facial image data processed by CNNs, makes real-time human stress detection possible. The system captures facial expressions through video or static images, which it then processes within a CNN model to identify stress-related features. CNNs are particularly innovative in that they are adept.at automatically extracting and learning hierarchical features from extremely complex, unstructured data like facial images. Facial expression datasets were used to train and test the proposed model in different stress conditions. The model achieved a very good accuracy level in differentiating stressed from non-stressed states, thus putting it on par with professionals. Real-time testing of the system's performance was done on edge devices such as smartphones to prove its potential for use in real-life applications related to mobile health. As facial image data is utilised by the system, thereby killing the need for wearable sensors or invasive physiological monitoring, the approach would be more accessible for detecting stress levels. This research showed that CNN models developed based on facial images can transform stress monitoring. Its implications are wide, applicable in health care, workplace settings, and even self-manageable personal stress. In future work, the model should be generalised for diverse populations, the system's real-time capabilities enhanced, and multimodal data further integrated to increase detection accuracy and reliability.

关键词： Accuracy Human factors Feature extraction Real-time systems Data models Convolutional neural networks Biomedical monitoring Monitoring Stress measurement Testing

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Enhancing NextG Wireless Security: A Lightweight Secret Sharing Scheme with Robust Integrity Check for Military Communications

Enhancing NextG Wireless Security: A Lightweight Secret Shar...

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MILCOM, Military Communications Conference

作者： Abhisek Jha SeyedMohammad Kashani Mohammadi Hossein Andre Kirchner Minglong Zhang Rémi A. Chou Sang Wu Kim Hyuck M. Kwon Vuk Marojevic Taejoon Kim Dept. of Computer Science and Engineering The University of Texas at Arlington Arlington TX USA Dept. of Electrical and Computer Engineering Iowa State University Ames IA USA Dept. of Electrical and Computer Engineering Mississippi State University MS USA Dept. of Electrical and Computer Engineering Wichita State University Wichita KS USA School of Electrical Computer and Energy Engineering Arizona State University Tempe AZ USA

ISBN: (数字)9798350374230

ISBN: (纸本)9798350374247

Multipath communication is a promising approach to enhance confidentiality and resilience in NextG wireless communications, particularly in critical military applications. By integrating threshold secret sharing with multipath communication, we can further protect against attacks that target a single path. However, most existing schemes rely on computationally demanding polynomial interpolation, which limits their practicality in real-world scenarios. This paper introduces a novel, lightweight XOR-based secret sharing scheme, coupled with an efficient two-dimensional (2D) integrity check mechanism, specifically designed for multipath communication scenarios. The proposed scheme significantly reduces computational overhead, achieving a 13.42x faster encoding time and a 360x faster decoding speed compared to Shamir’s Secret Sharing, using an input size of 8.2 KB. Our method ensures the secure and resilient transmission of data, even in adversarial environments, by effectively detecting and pinpointing tampered shares.

关键词： Military communication Wireless communication Interpolation Military computing Two-dimensional displays Polynomials Encoding Decoding Cryptography Resilience

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Revolutionizing Facial Feature Extraction with AI: An Innovative Approach

Revolutionizing Facial Feature Extraction with AI: An Innova...

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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： B. Lakshmi Jandhyam Lakshmi Alekhya K Parish Venkata Kumar B. Kishore K. Kamala Nyni K. Likhita Dept. of Computer Applications V.R. Siddhartha Engineering Dept of computer science and engineering V.R. Siddhartha Engineering College Department of Computer Applications V.R. Siddhartha Engineering College Department of Computer Applications

This paper provides a technique for detecting human faces & extracting their features based on segmentation of skin tone & facial structure. The system goes through several stages. The method first searches for regions where faces might be present by segmenting skin tone. After performing various morphological operations on skin, it uses aspect ratio of a face to find viable face blocks. face blocks are then used to extract additional features. Therefore, one may identify and validate a person's image using their face features by using this biometric attribute. To enhance facial recognition, numerous researchers have employed Support Vector Machine (SVM), Principal Component Analysis (PCA), a combination of SVM and PCA, PCA with an Artificial Neural Network, and even the conventional PCA-SVM. Due to PCA-ANN's limited dataset limitation, PCA-SVM is superior to PCA-ANN. To extract features, relative location & size are used. If algorithm is unable to identify even one feature in a prospective face block, it rejects face block.

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VAPOR: Legged Robot Navigation in Unstructured Outdoor Environments using Offline Reinforcement Learning

VAPOR: Legged Robot Navigation in Unstructured Outdoor Envir...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Kasun Weerakoon Adarsh Jagan Sathyamoorthy Mohamed Elnoor Dinesh Manocha Dept. of Electrical and Computer Engineering University of Maryland College Park MD USA Dept. of Computer Science University of Maryland College Park MD USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

We present VAPOR, a novel method for autonomous legged robot navigation in unstructured, densely vegetated outdoor environments using offline Reinforcement Learning (RL). Our method trains a novel RL policy using an actor-critic network and arbitrary data collected in real outdoor vegetation. Our policy uses height and intensity-based cost maps derived from 3D LiDAR point clouds, a goal cost map, and processed proprioception data as state inputs, and learns the physical and geometric properties of the surrounding obstacles such as height, density, and solidity/stiffness. The fully-trained policy’s critic network is then used to evaluate the quality of dynamically feasible velocities generated from a novel contextaware planner. Our planner adapts the robot’s velocity space based on the presence of entrapment including vegetation, and narrow passages in dense environments. We demonstrate our method’s capabilities on a Spot robot in complex real-world outdoor scenes, including dense vegetation. We observe that VAPOR’s actions improve success rates by up to 40%, decrease the average current consumption by up to 2.9%, and decrease the normalized trajectory length by up to 11.2% compared to existing end-to-end offline RL and other outdoor navigation methods.

关键词： Legged locomotion Point cloud compression Costs Three-dimensional displays Laser radar Navigation Vegetation mapping

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An Ensemble Deep Learning Framework for Binary Classification of COVID-19 Cases Using the VGG16 (Visual Geometry Group 16) Architecture: A Comprehensive Analysis and Performance Evaluation

An Ensemble Deep Learning Framework for Binary Classificatio...

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Disruptive Technologies (ICDT), International Conference on

作者： Deepak Upadhyay Mridul Gupta Somesh Mishra Saksham Mittal Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

This paper presents a novel approach to deep learning by putting forth a cooperative system that uses the VGG16 architecture to categorise COVID-19 examples into two groups. Our model is distinguished by its remarkable recall metrics and precision, which achieve a careful balance that is essential for accurate categorization. What's more impressive is how well the model performs in the non-COVID category, effectively differentiating between COVID and non-COVID cases. With a remarkable overall accuracy of 96%, the model successfully classifies cases from both groups, demonstrating the potential of our suggested framework as a useful diagnostic tool useful in various clinical contexts. This work clarifies the effectiveness of deep learning techniques, concentrating on the VGG16 architecture in the crucial job of binary classification for COVID-19 identification. Our results open up new avenues for investigation in the field of accurate medical diagnosis in addition to providing insights into the real-world applications of sophisticated machine learning. The study highlights the ensemble approach's encouraging benefits, showing how it may strengthen diagnostic precision and advance clinical decision-making.

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Nonparametric Estimation of Local Bandwidth from Level Crossings Sampling

Nonparametric Estimation of Local Bandwidth from Level Cross...

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2023 International Conference on Sampling Theory and Applications, SampTA 2023

作者： Pawlak, Miroslaw Rzepka, Dominik Miskowicz, Marek Agh University of Science and Technology Dept. of Measurement and Electronics Krakow Poland University of Manitoba Canada Dept. of Electrical and Computer Engineering WinnipegR3T 5V6 Canada

ISBN: (纸本)9798350328851

We study the problem of a local bandwidth recovery for nonstationary stochastic signals when the measured information is given in terms of level crossings. We propose a kernel estimate of the local bandwidth from samples generated from level crossings of stochastic signals being the time-warped version of stationary Gaussian processes. The positivity and bandlimited nature of the local intensity is captured by the properly selected class of bandlimited and positive kernel functions. The asymptotic properties of the estimator are derived. © 2023 IEEE.

关键词： Railroad crossings

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Sensitivity Analysis for Normal and Canonical Internal Descriptions of a Notch Digital Filter

Sensitivity Analysis for Normal and Canonical Internal Descr...

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IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)

作者： Yoichi Hinamoto Akimitsu Doi Dept. of Electrical and Computer Engineering National Institute of Technology Kagawa College Takamatsu Japan Dept. of Computer Science Hiroshima Institute of Technology Hiroshima Japan

ISBN: (数字)9798350353464

ISBN: (纸本)9798350353471

This paper analyzes coefficient sensitivity for the internal descriptions (state-space models) of a 2nd-order recursive notch digital filter. To this end, a 2nd-order recursive notch digital filter is realized by a normal-form or canonical-form internal description. The coefficient sensitivity for each internal description is then analyzed to derive the improved $l_{2}$ -sensitivity measure. Moreover, the coefficient sensitivity of the normal-form internal description is compared with that of the canonical-form counterpart by means of numerical experiments. The simulation results clarify that the normal-form internal description has low coefficient sensitivity as a whole comparing to the canonical-form counterpart.

关键词： Sensitivity Quantization (signal) Sensitivity analysis Simulation Communications technology State-space methods Fourth Industrial Revolution

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ChatGPT, tell me more about pilots’ opinion on automation

ChatGPT, tell me more about pilots’ opinion on automation

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IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA)

作者： Elodie Bouzekri Pascal E. Fortin Jeremy R. Cooperstock Dept. of Electrical and Computer Engineering McGill University Montréal Canada Dept. of Computer Science Université du Québec à Chicoutimi Saguenay Canada

ISBN: (数字)9798350362817

ISBN: (纸本)9798350362824

To collect feedback in the early stages of research when researchers have to work with expert users, we propose a method based on the large language models and careful elaboration of personas. This method enables low-cost simulation of answers to interviews and provides inputs for decisions on preliminary research directions. To evaluate our method, we propose to simulate two existing studies in the aviation domain. These studies focus on concerns and expectations of airline pilots about future single pilot operations and higher level of automation in the cockpit. Our results show similar level of concerns to human participants in these two simulated studies. However, the results of the simulations include approximations, errors in the actual work of pilots and over-and underestimations of some potential problems of single pilot operations. We conclude that our method can help guide the preliminary stages of research if researchers have sufficient prior knowledge of the work domain and if this method is complemented by human-in-the-loop methods.

关键词： Automation Costs Redundancy Training data Chatbots Human in the loop Resource management

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Reinforcement Learning for Adaptive Traffic Signal Control Using Deep Q-Networks

Reinforcement Learning for Adaptive Traffic Signal Control U...

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Sustainable Energy Technologies and Computational Intelligence (SETCOM), International Conference on

作者： Asha Rani Bohra Thamarai Selvi V Vishal Pawan Sai Sunkara Balaji Sunku Bhavsar Nitya Jignesh Dept. of Computer Science and Engineering New Horizon College of Engineering Bengaluru Karnataka India Dept. of Information Science and Engineering New Horizon College of Engineering Bengaluru Karnataka India

ISBN: (数字)9798331520540

ISBN: (纸本)9798331520557

Traffic congestion is a critical issue in urban areas, contributing to increased travel time, fuel consumption, and environmental pollution. Traditional traffic signal control methods, such as fixed-time systems, cannot adapt to real-time changes in traffic conditions. This paper presents a novel approach using Deep Q-Networks (DQN) to control traffic signals based on realtime traffic data dynamically. Our simulation environment models a four-way intersection, where vehicle densities and wait times are continuously monitored. The DQN agent optimizes green light durations by minimizing vehicle wait times and maximizing traffic throughput. Extensive simulation results demonstrate that our system significantly reduces congestion and improves traffic flow compared to traditional fixed-time systems. Furthermore, the approach shows potential for real-world applications in urban traffic control systems, particularly in smart cities equipped with Internet of Things (IoT) devices.

关键词： Adaptation models Adaptive systems Smart cities Biological system modeling Reinforcement learning Traffic control Throughput Real-time systems Internet of Things Vehicle dynamics

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Blockchain-based certificate authentication system with enabling correction

arXiv

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arXiv 2023年

作者： Rahman, Md. Mijanur Tonmoy, Md Tanzinul Kabir Shihab, Saifur Rahman Farhana, Riya Dept. of Computer Science and Engineering Southeast University Bangladesh

Blockchain has proven to be an emerging technology in the digital world, changing the way everyone thinks about data security and bringing efficiency to several industries. It has already been applied to a wide range of applications, from financial services and supply chain management to voting systems and identity verification. An organization must verify its candidates before selecting them. Choosing an unqualified candidate can ruin an organization’s reputation. In this digital era, many key fraudulent schemes are rampant in many companies and one of them is certificate fraud. It is possible to validate a candidate’s qualifications using traditional methods, but there are drawbacks such as security issues and time consumption. In this paper, a blockchain-based academic certificate authentication system will be used to ensure authenticity and make the assertion of the decentralized system secure. However, the system will generate, authenticate and make corrections on academic certificates. Ultimately, some blockchain-based authentication systems already exist, they can’t correct any errors that occur during generation. The proposed system will help in many ways, such as providing a user-friendly university admission, and smooth job hiring process, etc. In conclusion, our proposed system can permanently eradicate certificate forgeries and create and promote trust in society. © 2023, CC BY.

关键词： Authentication

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