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U-shaped Glass Rod as Key Construction Material in the Extrinsic Fiber Optic sensor to Determine Various Parameters of Liquids at the Operating Wavelength of 660nm

U-shaped Glass Rod as Key Construction Material in the Extri...

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International conference on Mechanical, Electronics and Computer Engineering (ICMECE) - Materials Science

作者： Srinivasulu, S. Rao, S. Venkateswara JNTUH Coll Engn Hyderabad Dept Phys Hyderabad Telangana India

The growth of fiber optic communications and optoelectronics industries has revolutionized two major product areas over the past 50years. The industry of optical fiber revolutionized the telecommunication with reliable links by offering greater performance. Laser printers, Compact disk systems, Laser pointers, Bar code devices are some of the products revolutionized by optoelectronics industries. The optoelectronic technology and fiber optic communication technology have been used in the progress of optical fiber sensing technology. In the present work a novel sensing technique has been used in the configuration of an advanced sensor using two 200/230lm PCS fibers of 50cm each in length, a SciTech power meter, laser light source of 660nm and a U-shaped borosilicate glass rod of specific dimensions. The combination of two volatile liquids (Toluene and 1-Butanol) have been chosen as test liquids to calibrate the sensor having the capability of measuring the various parameters of liquids either transparent or dark in the various dynamic ranges at room temperature (30 degrees C). The sensor has been analyzed on the lines of its geometrical parameters and sensitivity aspects. This sensor is expected to play a major role in future industrial, medical, military, civilian, scientific and consumer applications by providing higher sensitivities, reliability, ruggedness and other advantages in comparison with conventional sensors. (c) 2020 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International conference on Mechanical, Electronics and Computer Engineering 2020: Materials Science. All rights reserved.

关键词： Calibration Operating wavelengths of 660nm PCS fibers sensor Toluene U-shaped borosilicate glass rod 1-Butanol

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Deep Learning Based Low Light Enhancements for advanced Driver-Assistance systems at Night

Deep Learning Based Low Light Enhancements for Advanced Driv...

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International conference on Industrial and Information systems, ICIIS

作者： Pamudu Ranasinghe Dilshan Muthukuda Primash Morapitiya Maheshi B Dissanayake H.K.I.S. Lakmal Dept. Electrical & Electronic Eng. University of Peradeniya Peradeniya Sri Lanka

In the modern automobile industry, full/partial autonomous driving has become a common phenomenon. These systems heavily rely on sensors, both visual and non-visual, such as cameras and LIDAR, to interpret their surroundings and aid the decision-making process. Recently, visual-only sensing devices, mainly cameras, have emerged as the primary sensor for obtaining an immersive feeling in autonomous driving. However, the performance of such systems is heavily constrained by fluctuating light intensity and shadows. The primary goal of the presented research is to develop an effective low-light image enhancement system that improves the perception and inter-pretability of images taken in low-light driving environments. The developed image enhancement architecture demonstrates remarkable capabilities, surpassing existing models in terms of real-time suitability, PSNR (Peak Signal-to-Noise Ratio), and SSIM (Structural Similarity Index) values. Additionally, the model has been specifically designed to have low resource usage, making it an ideal solution for deployment on low-end devices. With significantly lower inference time, the model is exceptionally well-suited for real-time operation in autonomous driving applications. Furthermore, this architecture provides an efficient and effective solution not only for autonomous driving but also for a range of applications in video/image processing and computer vision, such as surveillance systems, medical imaging, astronomy, underwater imaging, and the mining industry. Finally, the relatively short training time makes it an attractive option for researchers and developers. Overall, this architecture represents a significant advancement in low-light image enhancement and autonomous vehicles. A demo of the system and the source code of the proposed LLE_UNET can be found on https://***/pamudu123ILow_Light_Image_Enhancement.

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Securing Internet of Medical Things (IoMT) Devices with Deep Learning Techniques

Securing Internet of Medical Things (IoMT) Devices with Deep...

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Emerging Research in Computational Science (ICERCS), International conference on

作者： Mohsen Aued Farhan Aqeel Al-Hilali Nadhema Ahmed Jaff Ali Hilal Mutlag Al-Nisour University College Baghdad Iraq Department of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad Iraq Department of Business Administration College of Administrative and Financial Sciences Knowledge University Erbil Iraq Department of Medical Engineering Al Kitab University Kirkuk Iraq

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

The extensive utilization of biosensors exemplifies how the Internet of Things has propelled individuals toward the use of entirely digitalized e-health services. A diverse array of commercially available sensors now assists individuals in monitoring their fitness development, blood glucose levels, and various intelligent home diagnostics consistently. Issues such as protracted governmental approvals, inadequate communication between patients and physicians, and cultural opposition to the integration of sensors into daily life arise from an improper classification of the devices. Currently, IoMT is utilized across numerous clinical environments and applications. In particular important applications, machine intelligence has transitioned from an academic curiosity to practical technology due to the swift advancement of Deep Learning (DL) in recent years. Consequently, deep learning approaches are crucial to the integrity of the Internet of Medical Things, transitioning from merely enabling secure device interactions to establishing security as a fundamental principle for information systems. The objective is to deliver a comprehensive study and advanced deep learning techniques for improving the security of IoMT systems. Imminent threats and potential hazards inside the IoT framework are delineated at each instance, whereas security vulnerabilities associated with inherent or emerging risks are presented in the IoMT system. These opportunities and difficulties may serve as the foundation for future research direction. The IMT-DL approach yields findings of data security preservation at 84.25%, healthcare system assessment at 92.30%, memory space enhancement at 93.25%, patient energy constraint resolution at 92.70%, and personal surveillance system improvement at 84.50%.

关键词： Deep learning Wireless LAN Head Data security Internet of Medical Things Bandwidth Magnetic heads Intelligent sensors Medical diagnostic imaging sensor arrays

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Research on Fault Prediction and Maintenance of RGL System Combining Electrical and Electronic Technology and Artificial Intelligence

Research on Fault Prediction and Maintenance of RGL System C...

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Image Processing and Computer applications (ICIPCA), IEEE International conference on

作者： Jianjun Liu Chao Gao Kaibing He Yi Liu Xiaolei Zhao Jiancheng Liu Xincai Chang Taishan Nuclear Power Joint Venture Co. Ltd Taishan China CGN Digital Technology Co. Ltd CGNPC Beijing China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

The uninterrupted functioning of modern society heavily relies on the reliability and stability of power equipment. The integration of electrical and electronic technologies with artificial intelligence in the form of the RGL system introduces innovative concepts and methodologies for managing power infrastructure efficiently. This paper aims to study an RGL (power equipment operation management) system that combines electrical and electronic technology and artificial intelligence, focusing on its fault prediction and maintenance. First, this article provides an in-depth analysis of the structure and principles of the RGL system, focusing on its advantages in power equipment monitoring and control. By introducing advanced sensor technology and data collection methods, the RGL system can obtain the operating status data of power equipment in real time, and use artificial intelligence algorithms to perform real-time analysis to achieve early identification of potential faults. Secondly, the paper studies the application of RGL system in fault prediction in detail. By establishing a fault prediction model based on big data and machine learning, the RGL system can comprehensively monitor and analyze power equipment and discover factors that may cause equipment failure in advance. This not only helps improve equipment reliability, but also reduces the impact of sudden failures on the power system. Finally, this article explores the practical application of RGL systems in fault maintenance. By introducing intelligent maintenance strategies, the RGL system can formulate accurate maintenance plans based on the actual operating status of the equipment and predicted fault information, improve maintenance efficiency, and reduce maintenance costs.

关键词： Machine learning algorithms Focusing Power system stability Predictive models Prediction algorithms Real-time systems Stability analysis Maintenance Power system reliability Monitoring

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Data Association for Multi-Object Tracking Using Assignment Algorithms

Data Association for Multi-Object Tracking Using Assignment ...

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2021 International conference Engineering and Telecommunication, En and T 2021

作者： Thanh, Le Ba Moscow Institute of Physics and Technology Department of Radio Engineering and Control Systems Dolgoprudniy Russia

ISBN: (纸本)9781728183909

Multi-object tracking (MOT) plays a very important role in advanced applications such as driver assistance systems, autonomous vehicles, aircraft monitoring systems. One of the fundamental problems of multi-object tracking is the unknown data association. At each time step, we get multiple sensor measurements. However, we don't know where these sensor measurements come from. Each measurement can come from one of the objects detected in the previous time step, from a new object entering the field of view (FOV), or from clutter. As the number of objects and measurements increases, the combinatorial nature causes the number of data association hypotheses to increase rapidly, making it impossible to consider all data associations [1,2]. One approach to solving the data association problem is to transform it into an optimal assignment problem. One approach to solving the data association problem is to use algorithms of the optimal assignment problem. In this paper, we will introduce the method of using optimal assignment algorithms to solve the data association problem in MOT. At the same time, we also compare the performance of these algorithms in terms of data association accuracy and computational complexity. The results showed that using assignment algorithms like Gibbs sampling brings high performance in data association. © 2021 IEEE.

关键词： Object detection

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OD-PPS: An On-Demand Path Planning Scheme for Maximizing Data Completeness in Multi-modal UWSNs 16th

OD-PPS: An On-Demand Path Planning Scheme for Maximizing Dat...

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16th International conference on Wireless Algorithms, systems, and applications (WASA)

作者： Yu, Tao Liu, Chunfeng Qu, Wenyu Zhao, Zhao Tianjin Univ Coll Intelligence & Comp Tianjin Key Lab Adv Networking Tianjin Peoples R China

ISBN: (纸本)9783030859282;9783030859275

Data collection based on autonomous underwater vehicle (AUV) brings significant advantages for underwater wireless sensor networks (UWSNs) to save and balance energy consumption. For some marine monitoring applications, not only the value of information (VoI) of the event but also the timeliness of the event needs to be considered in order to collect the sensed event data as comprehensively as possible to ensure the data completeness in the monitoring area. However, existing research works often ignore the latter, which may lead to inaccurate decisions made by marine applications due to missing data completeness. Therefore, in response to the on-demand scenario where events generate dynamically, we propose an on-demand path planning scheme (OD-PPS) to maximize the completeness of the data collected by the AUV. In the scheme, we first apply iterative local search method to obtain a collection order of the nodes. After optimizing the visiting points of the nodes to shorten the path length, a node re-insert algorithm is proposed to reduce the data loss. Then, for new collection requests generated during the AUV collection process, we update the AUV path according to the location of the node that sent the request. Finally, extensive simulations verify the effectiveness of the proposed scheme.

关键词： Underwater wireless sensor networks Autonomous underwater vehicle Data collection Path planning Data completeness

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Enhancing UAV Reliability and Security: A Machine Learning-Based Framework for Anomaly Detection and Classification

Enhancing UAV Reliability and Security: A Machine Learning-B...

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Trends in Engineering systems and Technologies (ICTEST), International conference on

作者： Samhitha B Ragesh G K Pretty Joy Venu Madhav Seth Randive Dept.of CSE Cyber Security IIIT Kottayam Kerala

ISBN: (数字)9798331505370

ISBN: (纸本)9798331505387

Unmanned Aerial Vehicles have become integral to sectors like defense, agriculture, logistics, and surveillance due to their versatility and autonomy. However, their widespread adoption has introduced challenges, particularly concerning operational anomalies and cyber-security threats. Anomalies, including hardware failures, sensor malfunctions, and cyber intrusions, can critically affect Unmanned Aerial Vehicles reliability, leading to mission failures, financial losses, and data breaches. This paper presents a machine learning (ML)-based approach to enhance Unmanned Aerial Vehicles operations, focusing on anomaly detection and security. The proposed system leverages self-supervised learning alongside advanced ML models such as Support Vector Machines, Recurrent Neural Networks, and autoencoders to identify and classify anomalies. The methodology employs a two-stage approach: first, detecting anomalies using self-supervised learning models trained on extensive Unmanned Aerial Vehicles datasets, and second, classifying the anomalies into hardware, software, or cyber attack related issues. This ensures precise identification and mitigation of operational threats in real time. Additionally, the system addresses cyber-security concerns, including risks like unauthorized control and data breaches. By analyzing cyber-physical datasets, the proposed method detects and mitigates digital vulnerabilities, ensuring secure Unmanned Aerial Vehicles operations. Experimental results demonstrate significant improvements in metrics like accuracy, precision, and F1 score when employing self-supervised learning for anomaly detection. The findings highlight the potential of integrating ML techniques to enhance Unmanned Aerial Vehicles reliability, security, and autonomy. This work paves the way for safer and more efficient Unmanned Aerial Vehicles operations across diverse applications, addressing the critical need for robust anomaly and intrusion detection systems in contemporary Unm

关键词： Measurement Biological system modeling Intrusion detection Self-supervised learning Machine learning Autonomous aerial vehicles Market research Real-time systems Security Anomaly detection

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An Improved Third-Order Generalized Integrator Based Novel Sliding Mode Observer for Piston Stroke Estimation of Linear Oscillatory Machine

An Improved Third-Order Generalized Integrator Based Novel S...

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Electrical Engineering (CIYCEE), IEEE China International Youth conference on

作者： Mudassar Abbass Wei Xu Abdul Khalique Junejo Kaiju Liao Khalil Ur Rehman Abdur Rahman State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China Key Laboratory of High-Density Electromagnetic Power and Systems (Chinese Academy of Sciences) Institute of Electrical Engineering Chinese Academy of Sciences Haidian District China

ISBN: (数字)9798331530174

ISBN: (纸本)9798331530181

Linear compressors are being widely used in a huge variety of applications due to their higher efficiency than traditional rotary compressors. The linear compressor is driven by the Linear Oscillatory Machine (LOM), and the sensor-less control of LOM is very important to make linear compressors more efficient and compatible auxiliary for big systems like refrigerators, air conditioners, automatic gates etc. A novel piston stroke estimation method has been proposed in this article to obtain the sensor less control of the LOM. This method utilizes an improved third-order generalized integrator (TOGI) based novel sliding mode observer (SMO) for LOM piston stroke estimation. In addition to this, an absolute reaching law with a chosen sliding surface has been implemented to further reduce the effects of sliding chattering. The piston stroke signal suffers DC offsets and harmonics due to mismatched machine parameters, nonlinearities of the converter, and first-order integration of back-electromotive force (EMF). In some recent studies, the second-order generalized integrator (SOGI) has been used to reduce pure integral saturation problem, but it still has a limited attenuation ability against DC components and has considerable steady-state error. Therefore, in order to improve the accuracy of the estimated stroke signals, a new method has been proposed in this article, which can not only solve the integral saturation problem, but also reduce the steady-state error of the estimated piston stroke, effectively. The simulation results and the numerical analysis prove the authenticity of the proposed method.

关键词： Pistons Accuracy Software packages Numerical analysis Simulation Force Estimation Observers Compressors Steady-state

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A Lossless Image, Video and Audio Encryption Method Based Hybrid Chaotic Map and DNA Encryption 3rd

A Lossless Image, Video and Audio Encryption Method Based H...

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3rd International conference on Advancements in Smart Computing and Information Security, ASCIS 2024

作者： Khaladkar, Vrushali Kumar, Manish Maharashtra Pune India Department of Mathematics Birla Institute of Technology and Science Pilani Hyderabad Campus Jawahar Nagar Kapra Mandal Medchal District Telangana Hyderabad500078 India

ISBN: (纸本)9783031863042

The suggested lightweight video encryption strategy offers a promising method for safeguarding multimedia data in cyberspace. The proposed strategy is based on a hybrid chaotic map and DNA encryption. Due to the fewer dimensions of the chaotic map, it can be adapted to secure real-time data generated using closed-circuit television and other Wireless sensors network tools. The strategy provides a strong level of security and resistance against different attacks by utilizing advanced cryptographic techniques including hybrid chaotic systems and DNA encryption. The proposed work is decently compared with other contemporary competing encryption schemes which shows that the proposed one can be adapted for the encryption of videos in lightweight applications. The thorough assessment using simulation results and statistical experiments highlights the practical applicability and efficiency of the proposed encryption technique. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Wireless sensor networks

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advanced Underwater Wireless Optical Communication System Assisted by Deep Echo State Network

Advanced Underwater Wireless Optical Communication System As...

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Asia Communications and Photonics conference and Exhibition (ACP)

作者： Kexin Wang Yihong Gao Mauro Dragone Yvan Petillot Xu Wang School of Engineering and Physical Sciences Heriot-Watt University Edinburgh UK

The underwater wireless sensor network (UWSN) has been widely employed in scientific and commercial applications. The underwater wireless optical communication (UWOC) provides high transmission data rate, high security, and low latency communication to link the nodes in a UWSN. However, the link length of the UWOC systems is severely affected by large absorption and scattering losses that reduce the received optical signal-to-noise ratio (OSNR). The advanced signal processing technologies are hereby highly desired in UWOC systems to improve the electrical signal-to-noise (SNR) from the optical signal. In this paper, for the first time we have applied the Deep Echo State Network (DeepESN) in high-speed UWOC system to assist signal processing for both PAM and QAM-OFDM signals. We have experimentally verified the effectiveness of DeepESN in UWOC systems and demonstrated over 40.5 meters error-free underwater transmission with 100-167 Mbps data-rate.

关键词： Wireless communication Wireless sensor networks Optical distortion Adaptive optics Optical scattering Optical sensors Optical modulation

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