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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Harshavardhan D Saisree K Ragavarshini S S. Sridevi Department of Computer Science Engineering SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

Accurate diagnosis of epilepsy from electroencephalography (EEG) signals is a pressing concern in neurology, prompting the adoption of advanced deep learning techniques for automated seizure detection. However, the lack of interpretability in these models poses a significant challenge. To tackle this issue, recent research has integrated graph representations of EEG signals with graph neural network (GNN) architectures. However, existing methods encounter obstacles, including the high time and space complexity of graph construction and the inability of GNNs to effectively utilize information from both time and frequency domain graphs. In response, this study introduces a novel approach leveraging the Weighted Neighbourhood Field Graph (WNFG) representation, enhancing both efficiency and information retention compared to dense counterparts. Furthermore, we propose a comprehensive pipeline encompassing data preprocessing steps such as filtering and Independent Component Analysis (ICA), followed by the WNFG-based graph representation. The core of our method lies in the integration of the Deep Recurrent Convolutional Neural Network (DRCNN) architecture, which facilitates feature extraction and classification of seizures.

关键词： Accuracy Epilepsy Independent component analysis computer architecture Feature extraction Electroencephalography Graph neural networks Telecommunication computing Convolutional neural networks Principal component analysis

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Wave scattering by a circular cylinder over a porous bed

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Archive of Applied Mechanics 2024年第3期94卷 555-570页

作者： Kushwaha, Aman Kumar Behera, Harekrushna Gupta, Vinay Kumar Department of Mathematics Indian Institute of Technology Indore Indore453552 India Department of Mathematics SRM Institute of Science and Technology Kattankulathur Chennai603203 India Center of Excellence for Ocean Engineering National Taiwan Ocean University Keelung202301 Taiwan

The dispersion of waves over a permeable seabed due to the presence of a vertically floating rigid circular cylinder is investigated under the assumptions of the linearized water wave theory and small-amplitude surface waves. Using the radiation condition and boundary conditions, the unknown potentials for the free-surface region and for the region covered by the cylinder are determined. The velocity potentials of the incident and scattered waves are expanded using the Bessel and Hankel functions for each region in the problem. The eigenfunction expansion technique is employed to solve the resulting boundary value problem. The effectiveness of the study is accessed by demonstrating the wave forces on the circular cylinder and the flow distribution along with the temporal simulation of the flow for different parameters. It is shown that the presence of a floating circular cylinder over the porous seabed reduces the amplitude of a surface wave on the leeside of the structure significantly. It is also illustrated that the radius and height of the cylinder play a significant role in reducing the wave forces on the cylinder and, hence, in creating a calm region in the leeside of the structure. The proposed setup has the potential to help design offshore structures that are capable of reducing the impact of the wave forces on the coastal structures, which can then safely be used for various marine operations. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Circular cylinders

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NAS for Automated ML Deployment on Extreme Edge Devices 3rd

NAS for Automated ML Deployment on Extreme Edge Devices

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3rd International Conference on Signal and Data Processing, ICSDP 2023

作者： Mahendran, Gayathri Thirumaran, Gokhulnath Ali, Syed Imam Jadon, Vishwas Raj SSN College of Engineering Tamil Nadu Chennai India SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Amity School of Engineering and Technology Noida India

ISBN: (纸本)9789819795772

Machine Learning (ML) and Artificial Intelligence are omnipresent topics nowadays. Especially since small personal devices, such as smartphones, are capable of running computation intensive tasks on their own, they can improve our lives in many different ways, e.g. computer vision or speech detection. Deploying neural network models on tiny and therefore performance, memory and power-constrained devices, such as microcontrollers, is currently an important research topic. For mobile and other embedded target devices, Neural Architecture Search, or NAS for short, is a novel technique to find appropriate network models without requiring manual error-prone adjustments by engineers, which allows to automate the workflow from the definition of a real work problem to a deployable solution. While there are a lot of comprehensive surveys on NAS methods in general, none of them considers the application of those ideas on edge devices like microcontrollers (MCUs). This work provides an overview of the recent developments and compares different approaches, which appeared over the last few years, against one another and with well-known reference applications intended for the usage on mobile phones. In the end some opportunities for future work on this topic are discussed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： AutoML MCUNet Once-for-All SpArSe The MicroNet models

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ASMA: An Advanced Secure Messaging Application

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Recent Advances in computer science and Communications 2022年第5期15卷 786-793页

作者： Gupta, Ankur Prabhat, Purnendu Sharma, Arun Department of Computer Science and Engineering Model Institute of Engineering and Technology Jammu India

Background: User-generated digital content, typically from mobile devices, can be of a personal nature. There have been several instances in which this personal content, including but not limited to, photographs, videos, messages and personal information, has been misused by the recipients. The misuse has ranged from violating privacy through unauthorized sharing to manipulating/modifying the original content and finally forgery and fraud. Objectives: The study aims to create an Advanced Secure Messaging Application (ASMA), allowing personal digital content to be shared across mobile devices in a secure, controlled and privacy-preserving manner. The application allows the user to explicitly specify a micro-policy to control the way digital content sent by the user, is consumed, shared or modified by the recipient(s). The users should be able to check the veracity of a shared news item besides controlling group formation and enforcing content sharing guidelines within a group. Methods: A micro-policy based novel mechanism is introduced for exercising fine-grained control over the manner in which digital content generated by users is shared and distributed among their contacts. Fact-checking is supported by cross-checking the message content against credible news sources. Results: Measurements of message throughput show a consistent and manageable overhead, due to a per-message micro-policy travelling with the message data compared to traditional message sharing applications. The high user-ratings received from users in a pilot test establish acceptance of the app and the need for its advanced features. Moreover, the overheads in implementing additional security features are imperceptible at the user level. Conclusion: ASMA is a highly secure and privacy-preserving messaging application, as demonstrated by experimental results and the pilot usability study. It offers novel security and content control features, which are not available in existing mobile messaging appl

关键词： Digital devices

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Autotrack: a framework for query-based vehicle tracking and retrieval from CCTV footages using machine learning at the edge

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International Journal of Information technology (Singapore) 2023年第7期15卷 3827-3837页

作者： Sreedhar, Sreela Philip, Abin Oommen Sreeja, M.U. Department of Computer Science and Engineering Toc H Institute of Science and Technology Ernakulam India Department of Electronics Cochin University of Science and Technology Kochi India Department of Computer Science and Engineering-Cybersecurity Indian Institute of Information Technology Kottayam (IIIT Kottayam) Kottayam India

Automobile theft and hit and run incidents have become ever rampant than before urging the need for automated tracking systems. However, prompt detection of the vehicle involved in incident and recovery of the stolen vehicle is crucial as these are immediately forged to reduce the chances of getting caught. Hence, the proposed model puts forth a query-based framework to intelligently detect and track vehicles from CCTV surveillance footages using queries involving custom input features such as model, make, colour and license plate number. The model uses a combination of techniques for detecting the vehicle make and model, tracking vehicle, and for identifying the license plate number based on You Only Look Once (YOLO), Kalman filters, and Tesseract respectively. Further, the model performs vehicle detection and tracking from multiple surveillance videos which can ensure that the suspicious vehicle which is reported of either theft, license plate forgery or any unusual activity like hit and run cases is located immediately. The detection model is trained and deployed on edge device integrated to CCTVs and hence overcoming the drawbacks associated with a centralized system. The performance of the proposed vehicle detection model compared to baseline models on the basis of popular classification metrics precision, recall and f1-score proves the superiority of the proposed model compared to the existing ones. Additional experimental analysis of the entire framework further establishes the relevance of the combination of selected input features as well as emphasizes the significance of query-based models over its generic counterparts. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： Edge computing Kalman filter Object detection Tracking Vehicle detection YOLO

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Evaluating door quality using CAE analysis and a LabView-controlled pneumatic system 2

Evaluating door quality using CAE analysis and a LabView-con...

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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Pramod, Sredha Beena Sai Neeraj, Kuruvella Vineetha, M. Sreedharan, Pramod Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Mechanical Engineering India SRM Institute of Science and Technology Department of DSBS India

ISBN: (纸本)9798350368109

In this paper, we have developed a door analysis program which is pneumatically mechanised controlled by Lab- View programming to analyze the performance of the door. The impact test evaluates the validity of the structure, the ease of use, and the sustainability of the door, and mimics the actual situation of the flakes. A mechanism by pneumatic system that simulates and controls the force and impact of a door being closed forcefully. LabView software automates the force required for slamming, duration and frequency of the slam test. The evaluation procedure is further enhanced by the addition of computer-aided engineering (CAE) analysis to test the strength of the doors. © 2024 IEEE.

关键词： Software testing

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Employment of Applications of WSN in Agriculture Domain: A Review

Employment of Applications of WSN in Agriculture Domain: A R...

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Saxena, Kumud Srivastav, Akanksha Murugan, R. Department of Computer Science Engineering Noida Institute Of Engineering and Technology Uttar Pradesh Greater Noida India Department of Computer Science and Engineering Tula's Institute Dehradun India Bangalore India

ISBN: (纸本)9798350355338

With the progress of technology and the shrinking of sensor size, sensors are now being utilized in nearly every domain of life. The field of agriculture is one area where sensors and associated networks are effectively utilized to obtain several advantages. This study aims to assess the necessity of wireless sensors in the field of Agriculture, explore the technology of Wireless Sensor Networks (WSN), and examine their use in many parts of agriculture. Additionally, it aims to provide several system frameworks that are currently accessible for this specific domain. © 2024 IEEE.

关键词： sensor and actuator network and Agriculture wireless sensors

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Automated Driving Licensing System Based on Wireless Sensor Networks and IoT

Automated Driving Licensing System Based on Wireless Sensor ...

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Prasanna, R. Jenath, M. Prathaban, Banu Priya Masood, M.H. Habiba, H. Umma Kanna, Rajesh R. Srm Institute of Science and Technology Department of Electronics and Communication Engineering Ramapuram Campus Chennai India Sri Sai Ram Engineering College Department of Electronics and Communication Engineering Chennai India Bharath Institute of Higher Education and Research Department of Electronics and Communication Engineering Chennai Tambaram India Srm Institute of Science and Technology Department of Computer and Science Engineering Ramapuram Campus Chennai India

ISBN: (纸本)9798350394580

The advent of autonomous vehicles has revolutionized the automotive industry, offering promising advancements in safety, efficiency, and mobility. To integrate these autonomous vehicles into our society seamlessly, it is imperative to establish a robust licensing system that ensures the safe operation of automated vehicles on public roads. This paper presents an effective Automated Driving Licensing System (ADLS), a digital framework designed to regulate and license autonomous vehicles. The ADLS leverages cutting-edge technology, including artificial intelligence, machine learning, and secure data management, to evaluate and validate the capabilities of autonomous vehicle systems. Through a comprehensive assessment process, the system assesses the vehicle's ability to navigate various road conditions, respond to dynamic environments, and prioritize safety for passengers and other road users. The automated licensing issuance system ensures that only vehicles meeting predefined safety and performance criteria are granted licenses. Successful licensing is a testament to the vehicle's readiness for autonomous operation within specified geographic regions and under predefined conditions. The proposed system offers a comprehensive approach to safely integrate autonomous vehicles into our transportation ecosystem. Furthermore, the ADLS establishes a framework for continuous monitoring and updates, enabling ongoing assessments to keep pace with evolving technology and safety standards. © 2023 IEEE.

关键词： Automated driving Licensing system Locationing Micro strip Sensors UWB antenna

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Road Marking Detection in Challenging Environments using Optimized YOLOv7 3

Road Marking Detection in Challenging Environments using Opt...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Gupta, Rajeev Kumar Pateriy, R.K. Swain, Debabrata Kumar, Abhijit Department of Computer Science and Engineering Pandit Deendayal Energy University Gujarat Gandhinagar India Department of Computer Science and Engineering Maulana Azad National Institute of Technology Bhopal India School of Computer Science UPES Dehradun India

Road marking detection area avails research with the application of computer vision and machine learning in the identification and analysis of various types of road markings such as lane markers, crosswalks, and road signs. It is very important to enhance safety on the roads by making these markings visible to drivers, cyclists, and pedestrians. Therefore, this study was designed to further enhance the efficiency and performance of the road marking detection models by incorporating the advanced AdamW optimizer and proposing a novel architecture known as Multi-Stage Cross-Convolutional Bottleneck Network-MSCCBN further-efficient in describing complex patterns and shapes from road markings. The testing was performed on a newly developed benchmark dataset containing 2,887 high-resolution images divided into training and test sets, with a total of 11 distinct road marking categories. Applying the Ceymo dataset and YOLOv7 detection framework, the result of the proposed model was an mAP50 score of 0.889, which means very high accuracy in object detection. This model indeed had a high precision of 0.886 and recall of 0.83, hence effectively minimizing both false positives and false negatives. © 2025 Elsevier B.V.. All rights reserved.

关键词： Deep learning

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Resstanet: deep residual spatio-temporal attention network for violent action recognition

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International Journal of Information technology (Singapore) 2024年第5期16卷 2891-2900页

作者： Pandey, Ajeet Kumar, Piyush Computer Science and Engineering National Institute of Technology Patna Ashok Rajpath Bihar Patna 800005 India

Violent Action Recognition (VAR) is a critical domain of research within computer vision and artificial intelligence, aiming to automatically detect violent behaviors in videos. Most of the current VAR methods are not able to extract spatial and temporal features simultaneously, which is crucial for human action recognition. This paper introduces a unique Residual Spatio-Temporal Attention Network (ResSTANet) model for robust violent action recognition. The ResSTANet uses a residual 3-Dimensional Convolutional Neural Network (3D-CNN) for effectively capturing spatiotemporal dynamics simultaneously. A residual connection is incorporated to improve information flow and control critical spatial-temporal features. The output of the residual 3D-CNN is subjected to a Multi-Head Attention (Mu-HA) process, increasing the focus on crucial features. Subsequently, multiple dense and dropout layers are applied to refine feature selection and reduce noise in the representation. Finally, a softmax layer is applied to perform action recognition, achieving state-of-the-art performance in VAR tasks. © The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Convolutional neural network Multi-head attention Residual connection Violent action recognition

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