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检索条件"任意字段=Conference on Advanced Sensor Systems and Applications IV"

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advanced Camouflage Robot for Military Spying applications using sensor Fusion and Machine Learning 3

Advanced Camouflage Robot for Military Spying Applications u...

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3rd IEEE International conference on Blockchain and Distributed systems Security, ICBDS 2024

作者： Kulkarni, Neeraj P Khan, Subim N. Rathod, Rahul Ramkishan Choure, Ajay Raosaheb Chinchole, Mahesh Gokul Jspm Rajarshi Shahu College of Engineering Tathawade Pune India Soet D Y Patil University Ambi Pune India Bhivarabai Sawant Institute Of Technology And Research E&tc Department Wagholi Pune India Nutan Maharashtra Institute of Engineering and Technology E&tc Department Pune India

ISBN: (纸本)9798350354348

The current defense application scenario. the ability to blend into the environment is crucial for reducing detection and enhancing operational efficiency. This paper presents the development of an advanced camouflage robot to dynamically adapt its color to match its surroundings. The proposed system utilizes a combination of machine learning, computer vision, and sensor fusion to achieve camouflaging using the RGB LED panels mounted on the robot to adapt to the color of the surroundings. The robot is equipped with a video camera system that captures real-time video feed of the environment. The captured video is then processed by a trained machine learning model, which analyzes the color distribution in the surroundings. Simultaneously, the robot uses RGB color sensors to accurately detect the colors present in its immediate vicinity. The data from both the camera and the sensors are integrated to determine the predominant color around the robot. The hybrid camouflaging approach involves feeding the combined data into the machine learning model, which then calculates the color of maximum distribution. The robot then activates its RGB panels, adjusting their color to blend with the identified predominant color in the environment thereby making it difficult to locate. This process allows the robot to effectively camouflage itself, minimizing its visual footprint. KNN and sensor fusion techniques are used to achieve this task. © 2024 IEEE.

关键词： Nearest neighbor search

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sensor Automation for Industrial applications and Prediction of Energy Level for Home Appliances Using Machine Learning Algorithm 9

Sensor Automation for Industrial Applications and Prediction...

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9th International conference on advanced Computing and Communication systems, ICACCS 2023

作者： Duraisamy, Premkumar Natarajan, Yuvaraj Selvakumari Jeya, I Jasmine Niranjani, V. KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Hindusthan College of Engineering and Technology Department of Information Technology Coimbatore India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350397376

In recent years wireless sensor networks have helped with automation in many industry domains. Wireless technology cuts cable costs, deployment time and information to transfer and process from source to destination. To make good intelligent and highly automated systems, sensors are crucial. They make it possible to detect, analyze, measure, and process a variety of alterations that occur in industrial equipment's location, length, height, appearance, displacement, etc. This paper described the industrial standardization of different WSN protocols like Zig-Bee, Wireless HART, UWB, IETF 6LowPAN, ISA100, Bluetooth and Wi-Fi. The work represented a few applications of WSNs in industry and its implementation challenges like safety, security, and availability. The work collected the energy consumption data of different home appliances. We applied the LightGBM machine learning algorithm to find a better energy consumption and prediction result. © 2023 IEEE.

关键词： Energy utilization

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Enhancing Traceability and Security in mHealth systems: A Proximal Policy Optimization-Based Multi-Authority Attribute-Based Encryption Approach 29

Enhancing Traceability and Security in mHealth Systems: A Pr...

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29th International conference on Information Technology, IT 2025

作者： Konda, Bhargavi Yadulla, Akhila Reddy Kasula, Vinay Kumar Yenugula, Mounica Adupa, Chakradhar HRIS Systems Analyst IV AuburndaleMA02466 United States University of the Cumberlands Department of Information Technology WilliamsburgKY40769 United States VISA Inc Sr. Systems Applications Analyst AshburnVA20147 United States SR University Department of Electronics and Communication Engineering TG Hanamkonda506371 India

ISBN: (纸本)9798331517649

Mobile healthcare (mHealth) is an emerging technology that facilitates the sharing of personal health records (PHR), but it also introduces risks related to the security and privacy of PHRs. To address these concerns, Attribute-Based Encryption (ABE) has been proposed as an advanced cryptographic solution to enable fine-grained access control over encrypted data. However, existing attribute-based mHealth systems often lack efficient traceability or are limited to a single authority. This paper presents a novel traceable multi-authority ABE scheme, incorporating Proximal Policy Optimization (PPO), a state-of-the-art reinforcement learning algorithm, to enhance traceability and security. The scheme is built on composite order groups and supports arbitrary monotonic access structures defined by Linear Secret Sharing Schemes (LSSS). The security of the system is validated under adaptive security assumptions, with PPO optimizing the access control decisions and identifying potential vulnerabilities in real-time. Additionally, the performance of the system is evaluated, demonstrating its efficiency, scalability, and availability for practical mHealth applications. This approach not only ensures robust access control but also guarantees high adaptability and traceability, effectively addressing privacy concerns in the sharing of sensitive health data. © 2025 IEEE.

关键词： Electronic health record

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Lora-Enabled System for Smart Farming Management and Oversight, Utilizing Wireless sensor Networks 14

Lora-Enabled System for Smart Farming Management and Oversig...

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14th IEEE International conference on Communication systems and Network Technologies, CSNT 2025

作者： Elumalai, J. Kumuthapriya, K. Hema, R. Sundaram, P. Sakthi Shunmuga Department of Electronics and Communication Engineering S.K.R Engineering College Ponamallee Chennai600123 India Department of Electronics and Communication Engineering Tagore Engineering College Rathinamangalam Kelambakkam - Vandalur Rd Tamil Nadu Chennai600127 India

ISBN: (纸本)9798331531935

The purpose of this study is to create a LoRa-based smart farming management and surveillance system. Utilizing Wireless sensor Networks (WSNs) in rural settings, with the aim of replacing the existing technology in agricultural monitoring systems. A dedicated private network server is established and connected to a gateway, which gathers information or signals from the nodes at the end and sends this data to the cloud without the need for routers. This data can then be utilized for various applications by the end users. The system overcomes issues related to communication failures and energy-efficient data transmission. This advanced farming platform enhances the effectiveness of farming methods. The proposed methodology is the application of resources including as water, fertilizers, and pesticides should be optimized in order to achieve maximum efficiency while simultaneously minimizing waste. Farming productivity and sustainability are both significantly improved because of its contribution, notably in terms of the efficient management of resources. And intelligent cities used to improve agriculture-comparing the planned weather station and the numerous developed patents. The technique here is like a cutting-edge, reasonably priced choice for local weather monitoring. © 2025 IEEE.

关键词： Agricultural robots

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Towards a Greener Future: sensor-Based Material Characterization in Waste Sorting for a Sustainable Recycling Model 10th

Towards a Greener Future: Sensor-Based Material Characteriza...

引用

International conference on New Technologies, Development and Application - advanced Production Processes and Intelligent systems

作者： Banjanovi-Mehmedovic, Lejla Dzelilovic, Nedim Univ Tuzla Fac Elect Engn Tuzla 75000 Bosnia & Herceg

ISBN: (纸本)9783031662706;9783031662713

Waste sorting is a vital part of the circular *** issue of waste sorting is very complex in terms of detecting individual types of waste, with the aim of recycling and using them in new products. This paper investigates the efficient sorting of metals, plastics, and glass through the application of three distinct sensors-inductive, photoelectric, and capacitive sensors-with the objective of enhancing the recycling process for these materials. Our aim is evaluation of an experimental platform for automatic material sorting, emphasizing reliability, speed, and precision, in order to streamline the recycling process and diminish reliance on manual *** a pivotal component of this platform, we incorporated an FPGA Cyclone iv to facilitate decision-making derived from the sensors regarding the sorting of materials. Examining the outcomes of material classification and sorting under various conditions, it is evident that the sorting accuracy circa 90%. Expectations indicate that the deployment of this system will streamline the efficient sorting of metals, plastics, and glass in the recycling process, playing a substantial role in environmental conservation and promoting sustainability within the recycling industry.

关键词： circular economy FPGA Industry 5.0 recycling sustanabilty waste sorting

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Human Activity Recognition Using sensor Fusion and Deep Learning for Ergonomics in Logistics applications

Human Activity Recognition Using Sensor Fusion and Deep Lear...

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2023 IEEE International conference on Internet of Things and Intelligence systems, IoTaIS 2023

作者： Arab, Ahmad Schmidt, Antonius Aufderheide, Dominik South Westphalia University of Applied Sciences Faculty of Electrical Engineering Soest Germany

ISBN: (纸本)9798350319040

The rapid growth within the e-commerce industry and the necessity of hiring more inexperienced labor in logistics made the warehouse safety more critical than ever. Monitoring the workers' activity during warehouse operations is a fundamental task to create an ergonomic workplace and to reduce operational costs. Therefore, an advanced Human Activity Recognition (HAR) solution is needed to monitor such industrial environments. In this paper, the development of an accurate and reliable HAR classifier using sensor fusion methods is investigated. Three Inertial Measurement Units (IMUs) are used to capture body movement while performing typical warehouse tasks. Deep Learning (DL) algorithms are employed to model the raw time series data and two sensor fusion techniques are applied to combine the data from the different sensors. Overall, the optimal HAR classifier was found to be a stacked model, being more practical and less computationally expensive for industrial applications. © 2023 IEEE.

关键词： Ergonomics

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Vehicle Dynamical sensor Monitoring Toward Forest Soil Moisture Measuring System 12

Vehicle Dynamical Sensor Monitoring Toward Forest Soil Moist...

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12th IEEE International conference on Intelligent Data Acquisition and advanced Computing systems: Technology and applications, IDAACS 2023

作者： Kiga, Yoshinori Tazawa, Takumi Iwase, Masami School of Science and Technology for Future Life Tokyo Denki University Department of Robot Mechatronics Tokyo120-8551 Japan

ISBN: (纸本)9798350358056

In this research, we develop a sensor monitoring system for vehicle dynamics aimed at developing a forest soil moisture measurement system for landslide disaster prevention. The identification of landslide hazard warning areas is necessary to prevent landslide disasters. Soil moisture is one of the factors. Common methods of surveying this soil moisture content include borings and satellites. However, borehole investigations may omit areas that truly need to be investigated without a priori knowledge of the locations to be measured. In addition, it is difficult to specify the timing of measurements in satellite surveys. Therefore, this research aims to establish a system that is safe, wide-ranging, and capable of setting the timing and range of measurement as needed. One approach to meet these requirements is to mount sensors on remotely operated vehicles to measure soil moisture content. In general, vehicles for such purposes need to travel in rough environments, and in order to travel well in rough environments, it is necessary to detect abnormal driving conditions such as slipping or skidding. For this purpose, we develop a sensor monitoring for the vehicle traveling in rough terrain and through forest. © 2023 IEEE.

关键词： filtering IMU Vehicle dynamics monitoring

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Review on Communication Technologies used in Electric Vehicles 8

Review on Communication Technologies used in Electric Vehicl...

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8th IEEE International conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Prerana, S. Reddy, Niranjan N. Varghese, Susan G. Sabhahit, Jayalakshmi N. Manipal Institute of Technology MAHE Department of Electrical and Electronics Engineering Manipal576104 India

ISBN: (纸本)9798350350593

The automotive industry relies on wide range of networking protocols to facilitate effective communication and data exchange within vehicles. Local Interconnect Network (LIN) is a cost-effective protocol commonly utilized for non- critical tasks such as controlling interior lighting and transmitting basic sensor data. Similarly, Control Area Network (CAN) is a widely accepted protocol that enables high-speed real-time networking between electronic control units (ECUs), sensors, and actuators. CANopen is a protocol built after CAN, offers standardized communication profiles and advanced features to seamlessly integrate devices in embedded systems across various industries, including automotive. Time- Triggered CAN (TTCAN) enhances traditional CAN by incorporating time-triggered communication capabilities, ensuring deterministic behavior and predictable response times crucial for safety-critical applications. SAE J1939, which is based on CAN is commonly used in heavy-duty vehicles for standardized communication and diagnostics among different vehicle components. FlexRay, known for its high-speed and deterministic characteristics, is used in safety-critical automotive systems like active suspension and steer-by-wire applications, providing fault tolerance and advanced control functionalities. A comprehensive understanding of these automotive networking protocols is vital for establishing reliable and efficient communication networks that support a wide array of functionalities and safety features in modern vehicles. © 2024 IEEE.

关键词： Active suspension systems

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Unlocking Immersive Driving Experiences with Automotive sensor-Powered Extended Reality (XR) 26

Unlocking Immersive Driving Experiences with Automotive Sens...

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26th Irish Machine Vision and Image Processing conference, IMVIP 2024

作者： Manila, Anushalakshmi Negi, Sonam Nowakowski, Christopher Reilhac, Patrice Valeo North America Inc. United States

ISBN: (纸本)9781837242672

While industries like healthcare and manufacturing are harnessing Extended Reality (XR) for novel applications, automotive XR opportunities remain largely untapped. XR holds immense potential beyond the current industry focus on marketing campaigns and virtual showrooms. Modern vehicles are increasingly equipped with multiple sensors for advanced driver assistance and autonomous driving. In this paper, we demonstrate how these automotive sensors and algorithms can be used to create Augmented Reality (AR) effects such as virtual object placement and occlusion, and we describe Valeo Racer, a vehicle-based XR gaming concept built using these methods https://***/jFQ8wxCANic [Valeo, 2024]. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： advanced driver assistance systems

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On Self-Supervised Learning and Prompt Tuning of Vision Transformers for Cross-sensor Fingerprint Presentation Attack Detection

On Self-Supervised Learning and Prompt Tuning of Vision Tran...

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IEEE International Joint conference on Biometrics (IJCB)

作者： Nadeem, Maryam Nandakumar, Karthik MBZUAI Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350337266

Presentation attacks pose a serious threat to the integrity of fingerprint-based biometric systems. Existing methods for fingerprint presentation attack detection (FpPAD) suffer from a lack of generalizability across different sensors and attack instruments, especially those that are not encountered during training. Recently, deep neural networks based on the Vision Transformer (ViT) architecture have demonstrated impressive generalization performance across many image recognition tasks due to their ability to effectively model long-range dependencies between image patches through the self-attention mechanism. While ViT models have been considered for FpPAD, many practical intricacies involved in learning generalizable ViTs for the FpPAD task have not been explored in depth. These include: (i) what is the best way to pre-process a fingerprint image to generate patches required by a ViT?, (ii) how to pre-train the ViT backbone to be used in FpPAD?, (iii) how to finetune the pre-trained ViT backbone for the FpPAD task?, and (iv) what is the most effective classifier design for a ViT-based FpPAD system? In this study, we undertake a thorough empirical study based on two public-domain datasets (LivDet 2015 and MSU-FPAD) in search of answers to the above questions. The key findings of this study are as follows: (i) Using minutia-aligned local patches provides the best PAD performance compared to partitioning the image into fixed number of non-overlapping patches. (ii) Self-supervised pre-training based on Masked Image Modeling (MIM) leads to better generalization performance than multimodal approaches such as image-text alignment. (iii) Visual prompt learning is a more effective way to adapt a pre-trained ViT model for FpPAD compared to full-tuning. (iv) Learning a linear classification head together with the visual prompts provides superior performance compared to linear probing and alignment with fixed text prompts. We hope that the above findings will be useful to the

关键词： Biometrics

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