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SSL-VoxPart: A Novel Solid-State LiDAR-Tailored Voxel Partition Approach for 3D Perception

SSL-VoxPart: A Novel Solid-State LiDAR-Tailored Voxel Partit...

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IEEE sensors conference

作者： Leuze, Nico Schaub, Henry Hoh, Maximilian Schoettl, Alfred Univ Appl Sci Munich Dept Elect Engn & Informat Technol Inst Applicat Machine Learning & Intelligent Syst D-80335 Munich Germany

ISBN: (纸本)9798350303872

LiDAR-based 3D point cloud segmentation is a crucial component in the perception system of many applications that rely on a thorough 3D environment understanding. LiDAR point clouds are unstructured, sparsely scattered in 3D space and have non-uniform densities, which restricts the use of ordinary convolutional neural networks. Tackling the point clouds irregular format, several approaches are introduced that divide the 3D space into a discretised volumetric grid. However, voxelization inevitably abandons the 3D topology of point clouds and suffers from information loss. In this paper, we propose SSL-VoxPart, a novel approach of scan-pattern related partitioning for voxel-wise semantic segmentation suitable to MEMS-actuated SolidState LiDARs. We argue scan-pattern related voxel partitioning is worth being considered by showing improvements in extracting semantic information for voxel-wise point cloud segmentation, while reducing lossy cell-label encoding. We specifically conduct to the problems of crowded scene analysis. Crowds represent locally high density distributions of people with heavy occlusion effects, demanding for fine-grained predictions. We introduce a SSL data generation pipeline tailored to surveillance scenarios and validate our proposed method on the resulting dataset.

关键词： Semantic Segmentation

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Smart Agriculture: Leveraging IoT and Machine Learning in Wireless sensor networks for Precision Farming

Smart Agriculture: Leveraging IoT and Machine Learning in Wi...

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2024 International conference on Innovative Computing, intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Sheela, M. Sahaya Leonid, T. Thomas Aravindhraj, N. Ravi, S. Giri, Pallavi Amer, Ayman Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India Kcg College of Technology Department of Cse Chennai India Kongu Engineering College Department of Cse Erode India Department of Vlsi Microelectronics Chennai India Laxminarayan Innovation Technological University Department of Civil Engineering Nagpur India Zarqa University Department of Energy Engineer Zarqa Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

This research explores the challenge of incorporating IoT with Machine Learning (ML) applications to Precision Agriculture, particularly the use of wireless sensors for irrigation control. information from sensors that monitor key parameters including temperature, humidity, moisture, and water levels are used to forecast water requirements and control pumps. Support vector machine (SVM), Decision tree (DT), Random Forest (RF) and Naïve Bayes (NB) techniques are adopted for the analysis to find out their accuracy levels. It is observed from all the above-performed models that among all, the SVM has the maximum prediction rate of the given dataset and is slightly outperformed by only the DT and RF. The confusion matrix which points to the misclassification patterns of each model gives an insight into identifying the best ML approach for use. The convergence of IoT and ML guarantees flexibility and responsiveness when controlling and designing agricultural systems. It is this approach that has the potential to enhance water resources management as well as address issues precipitated by climate change and growing worldwide food demand. © 2024 IEEE.

关键词： Smart agriculture

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Detecting and Securing Internet of Things from Wormhole attacks in a Wireless sensor networks 6

Detecting and Securing Internet of Things from Wormhole atta...

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6th International conference on intelligent Computing and Control Systems, ICICCS 2022

作者： Thangavel, Karthikeyan Gayathri Lakshmi, M. Priya, S. Sathiya Srinivasan, Sathiyandrakumar University of Technology and Applied Sciences Muscat Oman Saveetha Engineering College India Panimalar Institute of Technology India Kalasalingam Academy of Research and Education India

ISBN: (纸本)9781665410359

A IoT based on Wireless sensor networks (WSN) includes some little sensors to gather the information and offer it with the focal storehouses. These sensors are battery-driven and asset limited gadgets that consume most of the energy in detecting or gathering the information and communicating it. During information sharing, security is a significant worry in such organizations as they are inclined to numerous dangers, of which the deadliest is the wormhole assault. These assaults are sent off without gaining the fundamental data of the organization and they profoundly compromise the correspondence, security, and execution of the network. In the IoT-based network climate, its relief turns out to be more testing due to the low asset accessibility in the detecting gadgets. This research study has played out a broad writing investigation of the current procedures against the wormhole assault and arranged them as per their approach. The investigation of writing has propelled our exploration. This calculation has been intended to be basic, furthermore less convoluted to stay away from the overheads and the waste of energy in its activity. The reproduction aftereffects of our procedure show cutthroat outcomes for the identification rate and parcel conveyance proportion. It additionally gives an expanded throughput, a diminished start to finish delay, and a much-decreased utilization of energy. © 2022 IEEE.

关键词： Wireless sensor networks

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Deep Reinforcement Learning Based Secure Communication and Computing Resource Allocation for Grid Cyber-Physical System 1

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2nd International conference on Internet of Things, Communication and intelligent Technology, IoTCIT 2023

作者： Sun, Qiangqiang Lian, Gengxiong Cao, Zhiwei Zeng, Xiangsheng Lv, Zhiyao Liu, Lei Ju, Ying Zheng, Tong-Xing Shenzhen Power Supply Company China Southern Power Grid Shenzhen518028 China School of Information and Communications Engineering Xi’an Jiaotong University Xi’an710049 China School of Telecommunications Engineering Xidian University Xi’an710077 China

ISBN: (数字)9789819727575

ISBN: (纸本)9789819727568

Grid Cyber-Physical System (CPS) improves the intelligence of the grid by combining computing, communication and control technologies, but this new grid CPS system may also have some new security risks, such as new types of attacks on the connection between the physical and information networks. In this paper, we propose a deep reinforcement learning-based joint optimization scheme to improve the security and resource efficiency of multiple grid sensors by exploiting physical layer security (PLS) techniques in a scenario where a malicious eavesdropper can wiretap confidential grid information. We use Wyner’s wiretap coding scheme to prevent confidential information from being decoded and eavesdropped by malicious eavesdroppers. We minimize the system processing latency while securing the wireless communication process by jointly optimizing the transmit power of the grid sensor and the allocation of computing resource blocks. The optimization problem in this paper is formulated as a multi-agent cooperative optimal decision problem and is solved using a double deep Q-network algorithm. Simulation results demonstrate the robustness and effectiveness of the scheme in ensuring information security and reducing delay. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reinforcement learning

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Distributed Monitoring and Control System of Gas Concentrations in Different Environment 24

Distributed Monitoring and Control System of Gas Concentrati...

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24th International conference on Control Systems and Computer Science, CSCS 2023

作者： Furdu, Iulian Pruteanu, Eusebiu Nitu, Costica University Vasile Alecsandri of Bacau Department of Mathematics and Informatics Bacau Romania University Vasile Alecsandri of Bacau Department of Energy and Computer Science Bacau Romania Polytechnic University Department of Intelligent Robotics Bucharest Romania

ISBN: (纸本)9798350313390

Wireless sensor Systems (WSN) is a broad, exciting area with new perspectives and growing growth over the past decades, where more research is being done. WSNs contain many (hundreds of thousands) of micro-sized, cheap chips, powered by low-cost wireless interconnected batteries. These chips are called nodes, which could be of several types, including acoustic, radar, low-frequency magnetic, thermal, and visual sampling frequencies. Many applications are based nowadays on WSN, such as environmental control, smart cities, wildlife monitoring, vehicles and infrastructure, natural disasters, home security, underwater investigations, military, airplane surveillance and drone body sensors. One of the aims of the paper is to develop a smart architecture based on intelligent microprocessor sensors, WSN802GCA/GPA and the software needed to operate with a TINI control unit. TINI allows this system to be connected to Ethernet via TCP/IP © 2023 IEEE.

关键词： Wireless sensor networks

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Agri-Guard: IoT-Based Network for Agricultural Health Monitoring with Fault Detection 10

Agri-Guard: IoT-Based Network for Agricultural Health Monito...

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10th International conference on Internet of Things, Big Data and Security, IoTBDS 2025

作者： Singh, Kushagra Momin, Kafil Abbas Nishal, M. Sultania, Chinmay Rao, Madhav Dept. of Electronics and Communication Engineering International Institute of Information Technology Bangalore Karnataka India

ISBN: (纸本)9789897587504

Agricultural sector is increasingly adopting advanced technologies to enhance crop productivity and sustainability. Precision agriculture leverages IoT devices, sensors, and data analytics to monitor and manage various environmental parameters, addressing challenges such as global food demand, climate change, and resource optimization. Previous research has demonstrated the efficacy of wireless sensor networks (WSNs) and remote sensing technologies in improving irrigation efficiency and early disease detection. However, these systems often assume that all components continue to operate, thereby offering an incomplete view. This study presents an advanced agricultural monitoring system referred to as Agri-Guard that integrates a wide array of sensors to measure temperature, humidity, soil moisture, and gases like CO2, methane and ammonia. By utilizing an ESP8266 microcontroller and IoT connectivity, the system ensures seamless data transmission and real-time processing. Additionally, a centralized hub, equipped with a Raspberry Pi 5 and a thermal camera, enhances the detection of crop anomalies, and an inoperative sensor hub. The sensor hub in the form of a cone is optimally designed to detect environmental parameters besides being rainproof. The proposed Agri-Gaurd setup clearly demonstrated the lack of manure and water from the sensors’ data, whereas thermal imaging showcased the classification of 92.7% between a dead and alive plant. The anomaly between an operating and non-operating Agri-cone was found to be in complete agreement (100%). The proposed system represents a significant improvement over existing solutions, empowering farmers with precise data and faulty hub detection, leading to quick recovery and more sustainable farming practices. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Agricultural robots

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Optimization of Neural networks using Swarm Intelligence Techniques for Achieving Energy Efficiency in Smart Building Architecture 1

Optimization of Neural Networks using Swarm Intelligence Tec...

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1st IEEE International conference on Cognitive Robotics and intelligent Systems, ICC - ROBINS 2024

作者： Raja, M. Karthick Shermatova, Makhbuba Al Ansari, Mohammed Saleh Abdufattokhov, Shokhjakhon Rao, Vuda Sreenivasa Raj, I. Infant Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Turin Polytechnic University in Tashkent Civil Engineering and Architecture Department Tashkent Uzbekistan University of Bahrain College of Engineering Department of Chemical Engineering Bahrain Turin Polytechnic University Automatic Control and Computer Engineering Dept Italy Tashkent Intl. University of Education Dept of Information Technologies Tashkent Uzbekistan Koneru Lakshmaiah Education Fou. Green Fields Department of Computer Science and Engineering A.P Vaddeswaram522302 India K. Ramakrishnan College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy621 112 India

ISBN: (纸本)9798350372748

The increasing prevalence of smart building architectures, driven by the integration of Internet of Things (IoT) devices and automation systems, has led to a surge in energy consumption. This research explores the application of swarm intelligence techniques as an innovative approach to optimize neural networks, aiming to strike a balance between maintaining the desired performance levels and minimizing energy consumption. The study investigates the integration of swarm-based optimization algorithms, such as Particle Swarm Optimization (PSO) into the training and operation of neural networks. These algorithms enable the networks to dynamically adapt and optimize their parameters in response to changing environmental conditions and user requirements. The research focuses on developing a comprehensive framework that considers the specific challenges posed by smart building architectures, including real-time data processing, sensor integration, and adaptive control. The proposed approach aims to achieve optimal neural network configurations that minimize energy consumption while ensuring reliable and responsive operation of smart building systems. The results demonstrate the potential of swarm intelligence to significantly improve the energy efficiency of neural network-enabled smart building architectures, providing a promising avenue for sustainable and intelligent infrastructure. The proposed model has an accuracy of 98.23% which is 7.64% higher than that of the traditional approaches. © 2024 IEEE.

关键词： Energy efficiency

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Data Fusion and processing of Smart sensors in Mechatronics Devices

Data Fusion and Processing of Smart Sensors in Mechatronics ...

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Data Analytics, Computing and Artificial Intelligence (ICDACAI), International conference on

作者： Qiang Yu Changjian Song Zhengyu Xia Chongxin Wang Chenglin Wang Shandong Jianzhu University Jinan China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

With the rapid development of mechatronics equipment, smart sensors play an increasingly important role in various applications. The diversity and complexity of smart sensors make data processing and fusion a key link in improving system performance. This paper studies the application and performance of embedded smart sensor networks in smart electronic systems, analyzes the fundamentals, working principles and communication protocols of embedded smart sensor networks, reveals its key technologies in smart electronic systems, focuses on its application scenarios and advantages in smart electronic systems, and demonstrates its role in improving system intelligence, optimizing performance, and reducing energy consumption through case studies. Including small molecule semiconductors, polymer semiconductors and conductive polymers and other organic sensing materials, as well as chemical sensors, temperature sensors, optical sensors and mechanical sensors and other organic intelligent sensor devices of cutting-edge applications, the results show that the effective application of data fusion and processing methods can significantly improve the level of intelligent mechatronics equipment and operational efficiency, for the further development of the relevant fields to provide a theoretical basis and practical guidance. Theoretical foundation and practical guidance for the further development of related fields.

关键词： Temperature sensors Technological innovation Energy consumption Mechatronics System performance Data integration Solids Robustness Polymers intelligent sensors

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Application of Wearable sensors in Physical education for biomedical surveillance and human-machine interface

Application of Wearable Sensors in Physical education for bi...

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2023 Annual International conference on Emerging Research Areas: International conference on intelligent Systems, AICERA/ICIS 2023

作者： Alsalhy, Mohammed Jameel Ahmed, Ibrahem Hussien, Naseer Ali Hussain, Angham Khalid Hussian, Ahmed Al-Shaikhli, Taha Raad National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Medical Instruments Engineering Techniques Al-farahidi University Baghdad Iraq Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Thi-Qar Iraq Computer Technologies Engineering Al-Turath University College Baghdad Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Computer Engineering Techniques Al-Nisour University College Baghdad Iraq

ISBN: (纸本)9798350303452

Continuous biological surveillance requires less mechanical aid and affordable wearable technology and sensors. The health industry benefits greatly from the rapid development and expansion of the Internet of Things (IoT). Measuring the accuracy of physical education systems is a major difficulty for fitness and sports trackers, as human dependability is a primary concern for these technologies. This study supports the use of a portable sensor device for monitoring activities in athletes' training. The proposed method is achieved with the help of monitoring physical education using a wearable sensor device(MPE-Wsd)to track their health. With the Service Level Agreement (SLA) method, processing power requirements are estimated with the help of a server. The article opened with a unique and compelling model for health. The article offers a portable sensor for use at the human-machine interface. It's convenient for both the participants and the doctor to have access to the various sources of information. With this new field of study, policymakers may make the most efficient use of hardware and software designed for the Internet of Things. Provides the network with instantaneous access to large-scale resources while reducing operational and capital expenditures. Many mental and physiological diseases are rising as the global population ages because point-of-care (POC) treatment and continuous disease monitoring are important. The results of the tests validate the technique as being both convenient and economical to employ regularly. © 2023 IEEE.

关键词： Internet of things

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Design of an intelligent Voice-Controlled Home System Based on Raspberry Pi 25

Design of an Intelligent Voice-Controlled Home System Based ...

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25th IEEE International conferences on High Performance Computing and Communications, 9th International conference on Data Science and Systems, 21st IEEE International conference on Smart City and 9th IEEE International conference on Dependability in sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Wu, Kaiyi Li, Jiaying Yu, Jiabin Hua, Yuhao Xiao, Binggang Wang, Wu College of Information Engineering China Jiliang University Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province Hangzhou China School of Computer Science and Technology Xidian University Xi'an China School of Psychological and Cognitive Sciences Peking University Multisensory Integration Laboratory Beijing China

ISBN: (纸本)9798350330014

A complete intelligent voice-controlled home system should include voice capture, voice recognition, authentication, voice result processing, and other processes. Based on the intelligent voice interaction model of Raspberry Pi 3b+ and Alibaba Cloud platform, this paper realized a high recognition accurate, stable, and efficient intelligent voice-controlled home system. We used the Raspberry Pi to control the hardware interface, collected voice data through an external microphone, and performed command control with external sensors and controllable devices in the hardware part;The software was based on Alibaba Cloud intelligent voice interaction platform to realize voice data recognition. The system constructed can achieve the expected function after repeatedly testing the controlled device. The test results show that the recognition accuracy and recognition efficiency of the system's voice control instructions are both high in a relatively quiet environment. © 2023 IEEE.

关键词： Speech recognition

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