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Building a Smart Agriculture System Based on STM32

Building a Smart Agriculture System Based on STM32

引用

Advanced Algorithms and Control Engineering (ICAACE), International conference on

作者： Baichu Peng Jinsen Hu Laiquan Deng Qiuxiang Tao Wen Chen Weilin Yin School of Information Engineering Xinyu University Xinyu China

ISBN: (数字)9798350361445

ISBN: (纸本)9798350361452

The smart agriculture system is based on the STM32 microcontroller as the system core, and uses temperature and humidity sensors, soil moisture detection modules, carbon dioxide modules, and photoresistors to achieve intelligent detection of the agricultural environment. The detected data will be displayed on the OLED display screen. When the detection data exceeds the threshold, the buzzer will sound an alarm, and the threshold can be adjusted and controlled through buttons; Control the operation of fans, water pumps, and stepper motors through relay circuits, and remotely control the operation of fans, water pumps, and stepper motors through mobile apps and view sensor data through wireless Bluetooth communication, achieving remote control; intelligent control will be achieved when the environmental temperature, soil temperature and humidity, light intensity, and carbon dioxide concentration are above or below the *** addition, this article attempts to use graphs to represent learning techniques for handling redundant information, in order to better model. [3]

关键词： Temperature sensors Smart agriculture Wireless communication Wireless sensor networks Temperature distribution Production sensor systems

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Control of Heterogeneous Communication networks in Machine Vision System

Control of Heterogeneous Communication Networks in Machine V...

引用

IEEE information Technology, Networking, Electronic and Automation Control conference

作者： Penghui Li Ma Fangjun Zhihong Yang Ma Weijie Yan Xiong Institute of Intelligent Manufacturing Jianghan University Wuhan China Beijing Lanyou Technology Co. Ltd

ISBN: (数字)9798350370805

ISBN: (纸本)9798350370812

Machine vision systems typically encompass various cameras and sensors, which exhibit significant differences in terms of quantity, communication interfaces, communication protocols, and real-time requirements. To achieve real-time information exchange among all sensors, this paper proposes a heterogeneous communication network control method suitable for machine vision systems. The method is based on the embedded system's multitasking scheduling and management mechanisms to establish the heterogeneous communication network control scheme. Experimental results indicate that the heterogeneous communication network applied to machine vision systems demonstrates stability, reliability, and rapid responsiveness, effectively meeting the operational needs of the system.

关键词： Protocols Machine vision Packet loss Multitasking sensor systems Real-time systems sensors Communication networks Reliability information technology

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Design of a Capsule Robot System for Gastric Hemorrhage Detection using Luminol 20

Design of a Capsule Robot System for Gastric Hemorrhage Dete...

引用

20th IEEE International conference on Mechatronics and Automation, ICMA 2023

作者： Mao, Yuchen Guo, Jian Guo, Shuxiang Fu, Qiang Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Application Complicated Systems Intelligent Robot Laboratory School of Electrical Engineering and Automation Binshui Xidao Extension 391 Tianjin300384 China Shenzhen Institute of Advanced Biomedical Robot Co. Ltd No.12 Ganli Sixth Road Jihua Street Longgang District Shenzhen518100 China Key Laboratory of Convergence Medical Engineering System and Healthcare Technology The Ministry of Industry and Information Technology School of Life Science Beijing Institute of Technology No.5 Zhongguancun South Street Beijing100081 China

ISBN: (纸本)9798350320831

Gastric hemorrhage in upper gastrointestinal bleeding is an obvious indicator of deteriorating gastric disease. This paper proposes a capsule robot system for detecting bleeding in the stomach. The patient ingests a capsule robot containing luminol reagent, which detects bleeding in the stomach via chemiluminescence. The MCU receives the optical sensor signal, and the data is transmitted via radio frequency. The external lower computer receives and forwards the data to the upper computer for processing and display. The data is used to determine the presence and amount of stomach bleeding. Chemiluminescence is an excellent sensor, requiring no external energy supply, and demonstrating excellent sensitivity and selectivity. Combining chemical and electronic sensors presents opportunities for further development. Capsule robot detection reduces the discomfort and risk of secondary injury associated with traditional endoscopy. This paper establishes an experimental platform using porcine blood to simulate human blood, demonstrating the feasibility of the system. © 2023 IEEE.

关键词： Chemiluminescence

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Application and Optimization of Micro/Nano Electronic sensors Based on Deep Learning in Human-Computer Interaction

Application and Optimization of Micro/Nano Electronic Sensor...

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information and Communication Technology (ICTech), International conference of

作者： Aoyu Zhai Zixuan Zhao Electronic Science and Technology Xi'an Jiaotong Liverpool University Computer Science and Technology Xi'an Jiaotong Liverpool University

ISBN: (数字)9798350376258

ISBN: (纸本)9798350376265

The exponential growth of population and the use of electronic and electrical equipment have increased the requirement for energy production management, thereby increasing the need for precise energy management systems that can predict consumer use for future policy formulation. MEMS/NEMS technology, as a mature microstructure manufacturing technology, can form micro sensors and testing systems with small volume, light weight, and high performance. To improve the human-machine interaction experience of smart grids, this article applies micro/nano sensor technology to human-machine interaction optimization and proposes a data fusion model based on deep learning (DL). Based on the principle structure and training method of Deep Neural Network (DNN), a multi-source data system for power grid technical renovation projects has been constructed, which includes technical factors, economic factors, and environmental factors. This system can effectively analyze energy usage and combine multi-source data fusion technology to achieve intelligent evaluation of power grid projects. The experimental results show that the model designed in this paper is effective and superior, and has reference significance for improving the data fusion algorithm of micro and nano sensors in big data environments.

关键词： Human computer interaction Training Data integration Artificial neural networks sensor fusion Data models sensor systems Smart grids intelligent sensors Optimization

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RIVER: A Bio Inspired Routing Protocol For High Data Rate Wireless sensor networks

RIVER: A Bio Inspired Routing Protocol For High Data Rate Wi...

引用

International conference on information Networking

作者： Geetha V Priya Malik Sowmya Kamath S Dept of Information Technology NITK Surathkal Mangaluru Karnataka India

ISBN: (数字)9798350330946

ISBN: (纸本)9798350330953

Wireless sensor networks are deployed to observe the environment such as disaster management facilities and some industrial applications to observe temperature, pressure and humidity etc. The network is dynamic, battery operated and as a result it phases many issues compared to other networks like MANETs. Compared to MANETs, the issue or challenges are more as the nodes in the network has to send data to a common node called sink node. The challenges increases if the data type is other than scalar data. In future WSN gets extended to high data rate applications to sense and transmit multimedia, image or audio files. Normal routing algorithms, which are currently suitable for low data rate WSNs (LWSNs) are not sufficient for HWSNs. As a result, a new type of routing protocol which can solve various issues of HWSN is essential. It has been found that bio inspired algorithms perform very well for optimization and combinatorial problems. Bio-inspired algorithms are inspired from nature and nature has found very good solutions to very complex problems. In this regard, we are proposing and developing a new bio inspired routing protocol for high data rate wireless sensor networks called River which is based on the intelligent Water Drops algorithm related to swarm intelligent techniques. The proposed method is implemented in ns-2, and the results are compared with AODV. The results shows on an average 75% improvement with respect to various parameters.

关键词： Temperature sensors Wireless sensor networks Simulation Routing Routing protocols Ad hoc networks Real-time systems

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Hybrid data aggregation algorithm for energy efficient wireless sensor networks 3

Hybrid data aggregation algorithm for energy efficient wirel...

引用

3rd International conference on intelligent Communication Technologies and Virtual Mobile networks, ICICV 2021

作者： Deepakraj, D. Raja, K. Annamalai University Faculty of Science Dept. of Computer and Information Science Chidambaram Tamilnadu India Annamalai University Faculty of Engineering and Technology Department of Information Technology Chidambaram Tamilnadu India

ISBN: (纸本)9780738111834

The Wireless sensor networks are pertained in several applications like agriculture, security areas that the node density for the gathered data through the various types of sensor which has the problem of redundancy and it must be avoided through data aggregation technique. This paper demonstrates the Hybrid Data Aggregation Algorithm (HDAA) for providing data processing in real-time. Initially, the network is constructed into cluster members that the cluster head has been elected according to the ranking of the sensors according to the location from the base station, energy level. The aggregation is applied to the real-time application that every sensor node has been prioritized in energy-efficient way. The simulation results prove that the proposed technique reduces the energy consumption and communication delays which automatically enhancing the network lifetime. © 2021 IEEE.

关键词： Energy efficiency

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Collision detection method for industrial robots based on multi-channel information fusion

Collision detection method for industrial robots based on mu...

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Robotics, intelligent Control and Artificial Intelligence (RICAI), International conference on

作者： Yue Xia Bohu Zhang Shangbin Long Jionghao Mo School of Mechanical and Electrical Engineering Guangzhou University Guangzhou China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

Collision detection is an important part of ensuring its safety and reliability, and it is a low-cost and low-requirement collision detection method to detect and identify collision events based on the data of sensors or monitoring systems inside the robot. In view of the shortcomings of the existing deep learning methods in processing the robot motion state data, this paper constructs a CA-Transformer network with multi-channel information fusion, takes the real-time load as the judgment index, realizes the fast and effective prediction of the robot load, and integrates a variety of sensor data in turn to improve the analysis ability of the robot state. The research results can provide ideas for the safe operation detection of robots.

关键词： Robot motion Service robots sensor fusion Robot sensing systems Transformers Real-time systems sensors Collision avoidance Robots Load modeling

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Temperature Rise-Aware Compensation Control for Active Magnetic Suspension Bearing System Based on Neural Network Identification

Temperature Rise-Aware Compensation Control for Active Magne...

引用

Cyber-Energy Systems and intelligent Energy (ICCSIE), International conference on

作者： Sen Zhi Chai Yesheng Zhu Dongsheng Yang Yanhong Luo YanRu Chen DaiCheng Li School of Information Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9798350373318

ISBN: (纸本)9798350373325

Significant temperature rise can lead to parameter variations in the rotor of magnetic levitation bearings and cause thermal drift in eddy current sensors, thereby affecting the stability of active magnetic suspension systems. To enhance the stability of magnetic suspension systems under high-temperature conditions, a temperature compensation control method based on neural network identification is proposed. Firstly, a linearized magnetic force model of active magnetic bearings and a single-degree-of-freedom axial magnetic levitation rotor system model are established to analyze the influence of temperature on the stiffness parameters of magnetic suspension rotors, power amplifiers, and displacement sensor amplification gains. Secondly, an offline identification method using neural networks is adopted to identify the resistance values of the stator coil and the probe coil of the eddy current sensor under different temperature conditions. Then, based on the identified resistance values, a temperature rise compensation control scheme for the active magnetic suspension system is designed using a combination of series and parallel compensation. Finally, through Simulink simulationand experimentation, it is demonstrated that the temperature compensation control method based on neural network identification can improve the stability of the system.

关键词： Temperature sensors Resistance Magnetic sensors Neural networks Rotors Stability analysis sensor systems Temperature control Thermal stability Magnetic levitation

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Research on the Construction of Conductive Fiber Network Based on information Fiber and Its External Field Response Performance

Research on the Construction of Conductive Fiber Network Bas...

引用

2023 International conference on Power, Electrical Engineering, Electronics and Control, PEEEC 2023

作者： Xu, Ming Jia, Fei Bao, Hongquan China Building Materials Academy Beijing100024 China

ISBN: (纸本)9798350329124

Conductive polymer composites are made of polymer matrix filled with conductive fillers, which are widely used in automatic temperature control heating materials, sensors, electromagnetic shielding, antistatic materials, etc. Under the action of stress, temperature, gas, liquid and other external fields, it shows rich response behavior. Although CPCs-based stress sensitive materials have excellent characteristics (such as easy processing and low cost), there are still many shortcomings to be solved. Therefore, it is of great significance to construct a new polymer structure to control the performance of CPCs. In order to obtain a more effective conductive network and improve the sensitivity, response rate and stability of composite sensor, CPC with different microstructures was prepared by designing and regulating the conductive network in CPC, the response behavior of CPC to external stimuli was discussed, and the relationship between the structure regulation and performance of CPC was analyzed. As a functional polymer material, it shows obvious response behavior to external stimuli such as temperature, humidity, organic solvents, pressure, etc., which provides great potential for the application of polymer materials in medical health, intelligent machines, aerospace and other fields. © 2023 IEEE.

关键词： Fibers

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End-to-End Surface Roughness Prediction Method Driven by Multi-Source information Fusion

End-to-End Surface Roughness Prediction Method Driven by Mul...

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International conference on Computer and Communications (ICCC)

作者： Beibei Li Xiyue Zhang Minnan Han Haoxuan Luan Shujie Sun Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China School of Electromechanical and Automotive Engineering Yantai University Yantai China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

To address the limitations of traditional data-driven surface roughness prediction approaches that rely on a single information source and require manual feature engineering, a deep learning-based surface roughness prediction model was proposed. The model used static processing parameters and raw signals from multi-sensors as inputs. Parallel convolutional neural networks were employed to extract multi-scale features from the signals, and a hybrid attention mechanism was introduced for feature enhancement to improve prediction accuracy. Experimental results demonstrate that the proposed method which integrates multi-source sensor signals may predict surface roughness effectively, outperforming other methods which are based on a single sensor signal or traditional machine learning models. It provides an effective approach to predict the surface quality of products in intelligent manufacturing rapidly and accurately.

关键词： Solid modeling Attention mechanisms Computational modeling Predictive models Feature extraction Surface roughness Rough surfaces Manufacturing Convolutional neural networks Surface treatment

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