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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Amrutha, K. Shreekumar, T. Resmi, K.R. Vineetha, K.R. Dept. of Computer Science Bangalore India Mite Dept of Computer Science and Engineering Mangalore India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354218

Fatigue during driving significantly impairs a driver's reaction time, awareness, and decision-making abilities, leading to an increased risk of accidents. Recognising and mitigating driver fatigue is crucial for enhancing road safety and preventing potential collisions. This research introduces a non-intrusive method to reduce road accidents caused by driver fatigue. Recognising that accidents often occur due to lapses in driver alertness, the system utilises an affordable, high-quality webcam and dashcam. The approach involves extracting essential information through a multi-stage monitoring process to evaluate the driver's state thoroughly. The study includes a detailed analysis of webcam specifications, such as resolution, frame rate, and additional features. It also compares the traditional cascading technique with the advanced MediaPipe method, incorporating facial recognition, eye movement tracking, and head position analysis to detect driver fatigue accurately. © 2024 IEEE.

关键词： Eye movements

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Revitalizing the single batch environment: a ‘Quest’ to achieve fairness and efficiency

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International Journal of computers and Applications 2024年第8期46卷 651-665页

作者： Manna, Supriya Mudigonda, Krishna Siva Prasad Dept. of Computer Science and Engineering SRM University AP Mangalagiri Andhra Pradesh India

In the realm of computer systems, efficient utilization of the CPU (Central Processing Unit) has always been a paramount concern. Researchers and engineers have long sought ways to optimize process execution on the CPU, leading to the emergence of CPU scheduling as a field of study. In this research, we have analyzed the single offline batch processing and investigated other sophisticated paradigms such as time-sharing operating systems and wildly used algorithms, and their shortcomings. Our work is directed toward two fundamental aspects of scheduling: efficiency and fairness. We propose a novel algorithm for batch processing that operates on a preemptive model, dynamically assigning priorities based on a robust ratio, employing a dynamic time slice, and utilizing periodic sorting to achieve fairness. By engineering this responsive and fair model, the proposed algorithm strikes a delicate balance between efficiency and fairness, providing an optimized solution for batch scheduling while ensuring system responsiveness. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Efficiency

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Asynchronous Telegate and Teledata Protocols for Distributed Quantum Computing 10

Asynchronous Telegate and Teledata Protocols for Distributed...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Peckham, Jacob Makaroff, Dwight Rayan, Steven University of Saskatchewan Dept.of Computer Science Saskatoon Canada Dept. of Mathematics and Statistics Saskatoon Canada

ISBN: (纸本)9798350373011

The cost of distributed quantum operations like the telegate and teledata protocols is high due to latencies from distributing entangled photons and classical information. This paper proposes an extension to the telegate and teledata protocols to allow for asynchronous classical communication which hides the cost of distributed quantum operations. We then discuss the benefits and limitations of these asynchronous protocols and propose a potential way to improve these asynchronous protocols using nonunitary operators. Finally, a quantum network card is described as an example of how asynchronous quantum operations could be used. © 2024 IEEE.

关键词： Quantum communication

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Smart Commerce: Unleashing Machine Learning for Optimal Customer Experiences

Smart Commerce: Unleashing Machine Learning for Optimal Cust...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Agarwal, Rachna Agarwal, Shipra Mittal, Varsha Graphic Era Deemed to be University Dept.of Commerce Dehradun India Graphic Era Deemed to be University Dept of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350378092

Machine learning is a branch of Artificial Intelligence (AI) and computer technology which address on the usage or application of data and Algorithms to emulate the style that humans learn, gradually improving its accuracy. By understanding this statement humans are not associated with machine learning, once the system is deployed by them. Once the whole system is installed in the software through algorithm or coding, the machine adapts the order and continues accordingly without hindering little things like Sleeping, fatigue like humans and can work for long time and can operate it 24/7, with repetition. But the system must be kept up-to-date. E-commerce or electronic commerce is the trading of goods and services, or transmitting of funds or data, over an electronic network, primarily in internet. In this paper, we will explore the crucial role that machine learning plays in the e-commerce sector, highlighting its impact on various aspects of digital commerce and its importance for organizations in the contemporary business landscape. © 2024 IEEE.

关键词： Adversarial machine learning

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Driver Fatigue Countermeasure System 4

Driver Fatigue Countermeasure System

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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Gonuguntla, Ahalya Rani Charan, Vemula Sai Likhitha, Ventrapati Srivalli, Yeddu Shivdeep, Yarmally Saikiran, Veerbhadrapolla Dept.of Computer Science Engineering B V Raju Institute of Technology TelanganaMedak Narsapur India

ISBN: (纸本)9798350305258

One of the major contributing factors to traffic accidents is driver weariness and drowsiness. Automated driver sleepiness detection is a significant computer vision challenge since accidents involving fatigued drivers have resulted in millions of fatalities in recent years. A real-time driver sleepiness detection system is created to address this issue and is based on Facial Landmark Detection. A significant barrier still exists in accurately identifying driver fatigue and forecasting it in real-time scenarios. In the proposed method, drowsiness detection is done by the fusion of eye state recognition and yawn detection. This study describes a non-intrusive driver fatigue detection system that can identify upcoming driver drowsiness and send out timely warnings that could be helpful. The system relies on visual cue, here mouth and eye to characterize the driver's alertness *** detect the mouth and eyes from the full face region, the system uses a Haar cascade classifier function and is followed by extraction of mouth and eyes from images. It also uses image processing techniques to extract the driver's mouth and eyes to determine their states. The proposed model is built using Python language and Open CV. © 2023 IEEE.

关键词： Driver Fatigue Eye Aspect Ratio(EAR) Eye Detection Facial Landmarks Haar Cascade Classifier Mouth Aspect Ratio(MAR) Mouth Detection Threshold value

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An Overview on IoT in Electric Vehicles: Efficiency and Sustainability 2

An Overview on IoT in Electric Vehicles: Efficiency and Sust...

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

作者： Gupta, Shashank Sounkhla, Indu Dubey, Ranjeet Kumar Yadav, Mithilesh Kumar Singh Yadav, Sandeep NIT Dept. of Computer Science and Engineering Jalandhar India KIPM Dept. of Computer Science and Engineering Gorakhpur India Buddha Institute of Technology Dept. of Computer Science and Engineering Gorakhpur India

ISBN: (纸本)9798350368109

Internal combustion engine (ICE) vehicles are very polluting and release high nitrogen oxides, carcinogens, and soot into the environment. ICE vehicles are the best choice in this era, but replacing ICE vehicles with electric vehicles (EVs) and automated vehicle control is a potential transition process. The most important turning point in the history of the automobile occurred with the entry of EVs into the vehicle market. The rise of the Internet of Things (IoT) paradigm in electric vehicles (EVs) has changed the way we look at large-scale transportation by making it intelligent and more sustainable. Recently, with the advent of connectivity through IoT platforms, IoT in EVs has improved performance and reduced environmental impact: from any on-off switch and adoption of IoT through our smartphones placed on the charging base. We can implement smart charging solutions for better energy consumption management, reduction of grid load, and battery life enhancement. IoT allows electric vehicles to communicate directly with charging stations and even the power grid, providing a better way to create balanced transportation networks. But implementing IoT in electric vehicles also raises some issues, including data security and user privacy. With all of these issues, there is huge potential in IoT usage with electric vehicles to help produce cleaner transportation and create a more sustainable future. © 2024 IEEE.

关键词： Electric Vehicles Internal Combustion engine Internet of Things

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Automated Convict Identification Through Machine Learning-Based Facial Recognition

Automated Convict Identification Through Machine Learning-Ba...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Zubary, Nadim N. Khondoker, Shanzida Ferdows, Rubayea Ahmed, Fuad IUBAT-International University of Business Agriculture and Technology Dept of Computer Science and Engineering Bangladesh Dept of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798331529765

In a world riddled with criminal incidents, the swift identification of wrongdoers remains an ongoing challenge. Criminals have become adept at erasing biological and fingerprint traces from crime scenes, rendering traditional investigative methods ineffective. Modern facial identification technologies offer a rapid solution to this problem. With the widespread use of CCTV cameras, driven by security technology advancements, a unique opportunity arises. Our research seeks to automatically detect and identify convicts from video footage, providing a powerful tool for law enforcement. This technology extends to identifying suspects, criminals, missing persons, and more, saving valuable time and effort. Our study focuses on developing a machine learning-based criminal face detection system to streamline law enforcement by automating criminal identification. Employing a mixed methods approach involving data collection and focus group discussions, we gain insights from field practitioners. Our survey reveals a significant challenge in identifying culprits, with nearly 60% of cases analyzed yielding unidentified perpetrators. We propose a novel machine learning based automated method, which integrates with a comprehensive database containing individuals' information and diverse images. Once identified, the system conducts real-time database comparisons, providing immediate results when a match is found. We explore techniques for criminal identification from photographs and introduce innovative ideas. Our research comprehensively covers various models, offering a promising solution to the challenges of locating and identifying convicts in modern law enforcement. © 2024 IEEE.

关键词： Crime

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De-convolution and De-noising of SAR Based GPS Images Using Hybrid Particle Swarm Optimization

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Chinese Journal of Electronics 2023年第1期32卷 166-176页

作者： Rizwan Sadiq Muhammad Bilal Qureshi Muhammad Mohsin Khan Dept.of Electrical and Electronic Engineering Koc University Dept.of Electrical and Computer Engineering COMSATS University Dept.of Radiology and Biomedical Imaging Yale University Sino-Pak Center for Artificial Intelligence (SPCAI) Pak-Austria Fachhochschule:Institute of Applied Sciences and Technology (PAF-IAST)

Synthetic aperture radar(SAR) imaging is an efficient strategy which exploits the properties of microwaves to capture images. A major concern in SAR imaging is the reconstruction of image from back scattered signals in the presence of noise. The reflected signal consist of more noise than the target signal and it is a challenging problem to reduce the noise in the collected signal for better reconstruction of an image. Current studies mostly focus on filtering techniques for noise removal. This can result in an undesirable point spread function causing extreme smearing effect in the desired image. In order to handle this problem, a computational technique, particle swarm optimization(PSO) is used for de-noising purpose and later the target performance is further improved by an amalgamation of Wiener ***, to improve the de-noising performance we have exploited the singular value decomposition based morphological filtering. To justify the proposed improvements we have simulated the proposed techniques and results are compared with the conventional existing models. The proposed method revealed considerable decrease in mean square error compared to Wiener filter and PSO techniques. Quantitative analysis of image restoration quality are also presented in comparison with Wiener filter and PSO based on the improvement in signal to noise ratio and peak signal to noise ratio.

关键词： Radar remote sensing Wiener filters Filtering Statistical analysis Radar polarimetry Particle swarm optimization Synthetic aperture radar

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Smart Street Lighting for Sustainable Urban Environments: Sensor-Based Automation and Efficiency

Smart Street Lighting for Sustainable Urban Environments: Se...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Afrin, Sumaiya Abdi, Khalid Ferdows, Rubayea Ahmed, Fuad IUBAT-International University of Business Agriculture and Technology Dept of Computer Science and Engineering Bangladesh Dept of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798331529765

In recent times, many streetlights have been wasting energy by staying on unnecessarily during the evenings and nights. This not only harms the environment but also costs a lot of money. The goal of Smart Street Lighting is to stop this wasteful energy use and make better use of resources. While other researchers have worked on this issue, we have a specific aim: to create a smart street lighting system that only turns on when it's needed and turns off when it's not. To achieve this, we're using laser sensors, ZigBee wireless technology, and a powerful microprocessor. Our system identifies when there are vehicles on the road using sensors. It then turns on the streetlights only where they are needed. This saves energy by not lighting empty areas. We can control and monitor each streetlight remotely through ZigBee technology, and everything is connected to a computer that makes the streetlights work smarter. Our sensors are good at detecting objects and people with an accuracy of 89.87 percent. This innovation can reduce electricity consumption by 38.1 percent and cut lighting maintenance costs by 48 percent. © 2024 IEEE.

关键词： Zigbee

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An Area Coverage Aware Clustering Based Solution for MCMS Problem in Directional Sensor Networks 6

An Area Coverage Aware Clustering Based Solution for MCMS Pr...

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6th International Conference on Sustainable Technologies for Industry 5.0, STI 2024

作者： Muntaha, Maisha Al Amin Hossain, Md. Hasan, Fahim Yousuf Siam, Md. Abu Sharmin, Selina Pathan, Md. Naimul Nakshi, Sagufta Sabah Green University of Bangladesh Dept of Computer Science and Engineering Dhaka Bangladesh Jagannath University Dept of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798331531973

Wireless Sensor Networks (WSNs) are increasingly employed in applications requiring high reliability, such as industrial monitoring, military operations, and structural health evaluations. One of the main research issues in Directional Wireless Sensor Networks (DSNs) is area coverage. The area coverage problem, also known as MCMS, is a well-known fundamental problem which involves maximizing the sensing area coverage percentage of perception area with the minimum possible sensors. Also, area coverage is a significant difficulty for Directional Wireless Sensor Networks (DSNs). DSNs, unlike omnidirectional sensors, require careful selection of both sensor nodes and detection orientations to maximize coverage. This paper suggests a grid-based method for choosing the cluster head (CH) and setting up its communication sector to get the best area coverage, thus we propose an area coverage aware grid based clustering (ACAC) solution. In this study, we aim to achieve two main objectives: first, selecting CH to collect and transmit data from the sensor to the sink node. Second, choosing feasible communication sector based on the grid count values among multiple sectors. Furthermore, we demonstrate the effectiveness of our algorithms through existing work. © 2024 IEEE.

关键词： Sensor nodes

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