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Driver Drowsiness Detection Using Deep Learning 4

Driver Drowsiness Detection Using Deep Learning

引用

4th International Conference for Emerging technology, INCET 2023

作者： Ojha, Dhiren Pawar, Amit Kasliwal, Gaurav Raut, Roshani Devkar, Anita Pimpri Chinchwad College of Engineering Dept. of Information Technology Pune India

ISBN: (纸本)9798350335750

Driver drowsiness is a major cause of road accidents worldwide, and a drowsy driver is a serious threat to road safety [1]. To address this issue, researchers have developed various driver drowsiness detection systems that can alert the driver before a mishap occurs. In this paper, we present a comprehensive review of driver drowsiness detection systems, including their underlying techniques, advantages, and limitations. We examine the most commonly used techniques for detecting driver drowsiness, such as physiological measures, eye-tracking, and machine learning approaches. We also highlight the challenges associated with the development and implementation of such systems, including variability in individual sleep patterns, changing environmental conditions, and the trade-off between accuracy and user-friendliness. This review aims to provide a critical analysis of the state-of-the-art in driver drowsiness detection systems, and to identify research gaps and future directions for improving road safety. The document describes the functional and non-functional requirements of the system, including video input, eye detection, blink detection, EAR calculation, drowsiness detection, alert mechanism, user settings, accuracy, speed, robustness, portability, security, and user-friendliness. This study presents a driver drowsiness detection system based on machine learning algorithms and physiological signals, including electroencephalogram (EEG) and electrocardiogram (ECG). © 2023 IEEE.

关键词： Eye tracking

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Exploring Impact of 5G on IoT 2

Exploring Impact of 5G on IoT

引用

2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Amale, Siddhi Satish Nandu Kotkar, Akshay Jadhav, Devyani Sanjivani College of Engineering Dept. Information Technology Maharashtra Kopargaon India

ISBN: (纸本)9798350325799

5G is wireless technology and changing the way of connecting and communication in the world. As IoT is widely used everywhere, this study is focused on how 5G will make a positive impact on society. This study focuses on the establishment and necessity of IoT over 5G, as well as the aspect of IoT on updated 5G technology. 5G has the ability to transmit data more quickly and support further links, which has been a challenge for IoT solutions. Also, this study has discussed about the establishment and necessity of IoT over 5G along with the aspect of IoT on updated 5G technology. Through the usage of smart devices, Internet of Things (IoT) is playing a significant role in enabling the provision of low-cost, environmentally friendly energy throughout the world. This study provides an overview of the contributions of 5G and IoT to the development of power systems. © 2023 IEEE.

关键词： Internet of things

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Distributed Compressive Power Spectrum Sensing for Cognitive Radio

Distributed Compressive Power Spectrum Sensing for Cognitive...

引用

2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications technology, IAICT 2023

作者： Wiryawan, I Made Adhi Ariananda, Dyonisius Dony Wibowo, Sigit Basuki Universitas Gadjah Mada Dept. Electrical Engineering and Information Technology Indonesia

ISBN: (纸本)9798350313635

In cognitive radio (CR) networks, secondary users (SUs) might be required to gauge a wide frequency band to find frequency holes that they can use for signal transmission. When this spectrum sensing process is conducted digitally, a high sampling rate might be needed to satisfy the Nyquist rate. However, the existence of the frequency holes can be concluded by simply constructing the power spectral density (PSD) instead of the original signal. In fact, the Nyquist criterion is not applicable when we aim to reconstruct the PSD (and not the original analog signal). This paper introduces a distributed wideband power spectrum sensing using multiple SUs to first estimate the power spectrum of signals received from sources in a collaborative manner. Each SU samples the received signal at sub-Nyquist rate and reconstructs the local PSD estimate based on the received digital samples. The local PSD estimate is then exchanged between SUs based on the consensus approach without fusion center. Once convergence on the PSD is reached, the detection on the existence of PUs is conducted. We found that for a PU signal power of 4 mW, noise power of 1 mW, and Rayleigh fading with the variance of -1 dB, the probability of detection can be at least 0.9 for the probability of a false alarm of 0.1 if the number of SUs is at least 40 or the compression rate is at least 0.4. © 2023 IEEE.

关键词： Cognitive radio

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From Heat to Power: Assessing Thermoelectric Energy Harvesting for Self-sustainable Sensors

From Heat to Power: Assessing Thermoelectric Energy Harvesti...

引用

2023 IEEE International Workshop on Technologies for Defense and Security, TechDefense 2023

作者： Lenz, Chiara Vostrikov, Sergei Mayer, Philipp Magno, Michele Eth Dept. of Information Technology and Electrical Engineering Zürich Switzerland

ISBN: (纸本)9798350319392

Autonomy and self-sustainability are essential for the next generation of Internet of Things (IoT) and sensing devices. Harnessing electrical energy from temperature gradients, thermoelectric generators (TEGs) have great potential to power these devices for extended time periods. Analyzing the performance of transducers and converters is crucial for designing compact, low-power systems. This work highlights the importance of a comprehensive experimental evaluation of advanced low-voltage, high-efficiency conversion architectures in conjunction with TEGs. Characterization of multiple transducers and converters demonstrates the potential and limitations of thermoelectric energy harvesting (EH), showcasing the possibility of supplying a power of 0.65 μ W/cm2 harvested from a temperature gradient of only 1K and 150 μ W/cm2 for 10K. Conversion efficiencies of 75% are achievable despite high voltage conversion ratios of 1:60. © 2023 IEEE.

关键词： Energy harvesting

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Fingerprint-Based Vote Marking System for Elector Identification

Fingerprint-Based Vote Marking System for Elector Identifica...

引用

2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2023

作者： John Baptist, G. Vishnu, K. Sneha, S. Arya, K.G. Silpa Sangeeth, L.R. Government Engineering College Dept. of Information Technology Kerala Palakkad India

ISBN: (纸本)9798350312126

India being the largest democracy in the world, it is crucial to conduct a free, fair and secure election. This paper aims to develop a fingerprint-based vote marking system. The Fingerprint based vote marking system is a mobile application that helps the government to conduct polls smoothly. The objective is to develop a system that decreases fraud and safeguard voting through fingerprint verification. Voter information is saved in a database along with their fingerprints. The system retrieves the electoral information of the respective voter if the fingerprint matches. The officer verifies the elector ID and identifies the person as a valid voter. Thus, our system provides a reliable, secure, fast and cost-effective solution in the election process. © 2023 IEEE.

关键词： Cost effectiveness

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Automated Detection of Anomalous Events in Surveillance Video Scenes Using Deep Learning Techniques 7

Automated Detection of Anomalous Events in Surveillance Vide...

引用

7th IEEE International Conference on Computational Systems and information technology for Sustainable Solutions, CSITSS 2023

作者： Rao, Abhishek S. Karthik Pai, B.H. Rao, Sathvik R Samarth Prathap, M. Anjani Prabhu, K. Rakshitha, K. Nmam Institute of Technology Dept. of Information Science & Engineering Nitte India

ISBN: (纸本)9798350343144

The identification of anomalous events in surveillance camera situations is a critical issue in public safety and security. Traditional surveillance systems, reliant on human operators, are labor-intensive and prone to errors. To address this challenge, we propose a deep learning approach for the automated detection of unusual events in surveillance video scenes. Utilizing the remarkable performance of deep learning techniques in computer vision, we extract spatiotemporal information from video frames and classify them as normal or unusual using a combination of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. The objective of this project is to develop a deep learning approach that overcomes the limitations of traditional video surveillance systems by automatically detecting and classifying unusual events. The proposed approach combines CNNs to extract spatial features and LSTMs to model temporal dependencies between frames, effectively capturing both spatial and temporal characteristics of video scenes. Experiments were conducted on two public datasets for abnormal event detection, and the proposed approach outperformed state-of-the-art methods, achieving an accuracy of 80%. The significant implications of this approach for public safety and security lie in its ability to improve the accuracy and efficiency of video surveillance systems while reducing the workload of human operators. By automating the detection of unusual events, this deep learning approach can enhance overall surveillance capabilities and help prevent critical security issues. © 2023 IEEE.

关键词： Long short-term memory

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Investigation of mmWave Radar technology For Non-contact Vital Sign Monitoring

Investigation of mmWave Radar Technology For Non-contact Vit...

引用

2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023

作者： Marty, Steven Pantanella, Federico Ronco, Andrea Dheman, Kanika Magno, Michele Eth Zürich Dept. of Information Technology and Electrical Engineering Switzerland

ISBN: (纸本)9781665493840

Non-contact vital sign monitoring has many advantages over conventional methods in being comfortable, unobtrusive and without any risk of spreading infection. The use of millimeter-wave (mmWave) radars is one of the most promising approaches that enable contact-less monitoring of vital signs. Novel low-power implementations of this technology promise to enable vital sign sensing in embedded, battery-operated devices. The nature of these new low-power sensors exacerbates the challenges of accurate and robust vital sign monitoring and especially the problem of heart-rate tracking. This work focuses on the investigation and characterization of three Frequency Modulated Continuous Wave (FMCW) low-power radars with different carrier frequencies of 24 GHz, 60 GHz and 120 GHz. The evaluation platforms were first tested on phantom models that emulated human bodies to accurately evaluate the baseline noise, error in range estimation, and error in displacement estimation. Additionally, the systems were also used to collect data from three human subjects to gauge the feasibility of identifying heartbeat peaks and breathing peaks with simple and lightweight algorithms that could potentially run in low-power embedded processors. The investigation revealed that the 24 GHz radar has the highest baseline noise level, 0.04mm at 0° angle of incidence, and an error in range estimation of 3.45 ± 1.88 cm at a distance of 60 cm. At the same distance, the 60 GHz and the 120 GHz radar system shows the least noise level, 0.0lmm at 0° angle of incidence, and error in range estimation 0.64 ± 0.01 cm and 0.04 ± 0.0 cm respectively. Additionally, tests on humans showed that all three radar systems were able to identify heart and breathing activity but the 120 GHz radar system outperformed the other two. © 2023 IEEE.

关键词： Millimeter waves

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Energy Conserving Trust Routing Based-Artificial Rabbits Optimization in WSN

Energy Conserving Trust Routing Based-Artificial Rabbits Opt...

引用

2024 International Conference on Advancement in Renewable Energy and Intelligent Systems, AREIS 2024

作者： Maradona, N. Jarin, T. Jaya, T. Parimala Geetha, T.K. Bommy, B. Saveetha Engineering College Dept. Information and Communication Engineering Chennai India Jyothi Engineering College Dept of Cse Thrissur India Saveetha Engineering College Dept. of Eee Chennai India Christian College of Engineering and Technology Oddanchatram India Csi Institute of Technology Dept. of Eee Thovalai India

ISBN: (纸本)9798350387230

Developing a routing model for WSNs is a challenging task, especially in terms of energy efficiency. Many energy-efficient routing strategies have been proposed to handle data packet forwarding through Cluster Heads (CHs) along secure paths. However, network longevity and scalability are major challenges. Most traditional trust-based routing solutions fail to provide adequate defense against attacks;hence, proper trust management becomes a significant concern. An algorithm known as Trust Routing based Artificial Rabbits Optimization (TR-ARO), which refers to Trust Routing based on Artificial Rabbits Optimization, is presented. The TR-ARO algorithm takes into consideration the observed trust, third-party trust, current trust, and probability values that will be utilized to optimize routing through CHs. TR-ARO, by using criteria for fitness evaluation, identifies the best and most secure routes for data transmission. The performance of the proposed model is analyzed using a variety of parameters such as network lifetime, 3050 rounds;delay, 0.03 seconds;trust level, 0.9;and packet delivery ratio, 0.98. A comparative analysis with other models reveals the efficiency of the TR-ARO algorithm to enhance both energy efficiency and security. © 2024 IEEE.

关键词： Routing algorithms

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Smart Medication Monitoring in Medical IoT 2

Smart Medication Monitoring in Medical IoT

引用

2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Ramani, K. Kumar, P. Vinay Jahnavi, N. Karthik, A. Yadav, Nitesh Rahul, U.S. Sree Vidyanikethan Engineering College Dept. of Information Technology Tirupati India

ISBN: (纸本)9798350347982

Elderly and patients with alzheimer's disease may miss their regular pill taking schedules. In this paper an IoT based intelligent medication reminder and monitoring system is developed to help needy people to consume the prescribed medicine at the scheduled time and also displays an alert message to the mobile. It produces a voice output through a speaker that helps the patient to intake the pills regularly. This information will also reflect in the Cloud, mobile app and is also intimated to the caretaker. A notification will be sent to the doctor and the caretaker, If the measured slot value which is revised in the Cloud is less than some default slot value. Then it's the responsibility of the doctor to decide whether the patient must consume the same dose of the medicine or provide them with the other set of new medicine. A continuous monitoring of the pulse rate and temperature of the patient will be updated to the caretaker and doctor. After deciding, the doctor will again update the prescription and provide new medication if required. If any crucial signs or during emergency situation is noticed, then the messages are sent to the guardian along with their live location. © 2023 IEEE.

关键词： Neurodegenerative diseases

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An Image Measurement System for Detecting Dents and Scratches on the Surface of Used Car Body Parts 8

An Image Measurement System for Detecting Dents and Scratche...

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8th International Conference on Business and Industrial Research, ICBIR 2023

作者： Sikun, Wang Ying, Yang Cunwei, Lu Fukuoka Institute of Technology Dept. of Electronic Information Engineering Fukuoka Japan

ISBN: (纸本)9798350399646

As abandoned cars have become a huge social problem, procedures to reuse them are urgently required. Before reusing used car body parts, it is necessary to inspect them for defects such as dents and scratches. Currently, most companies around the world perform manual inspections. Manually detection of these used cars is time consuming and over-relies on individual judgement. In this paper, an automatic used car body parts detection system is proposed based on image measurement. A two-times shooting technique is taken by this system to distinguish the dent and scratch on the car body surface, and at the same time, a neural network-based target detection method is proposed to locate the position of dent. Furthermore, a scratch extraction method is also involved. The experiment result shows that the proposed method can be used to detect car body parts with reflective surfaces. And the two-times shooting technique reduces the time used to take images. © 2023 IEEE.

关键词： Railroad cars

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