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HA-Net: bare soil extraction from optical remote sensing images

HA-Net: bare soil extraction from optical remote sensing ima...

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2024 international conference on Algorithms, High Performance computing, and Artificial Intelligence, AHPCAI 2024

作者： Zhao, Junqi Chen, Lifu Chen, Haoda Jin, Yuchen School of Electrical and Information Engineering Changsha University of Science and Technology Changsha410114 China

ISBN: (纸本)9781510685840

Bare soil will cause the soil erosion and contribute to air pollution through the generation of dust, making timely and effective monitoring of bare soil an urgent requirement for environmental management. Though there have been some researches in bare soil extraction using high-resolution remote sensing images, great challenges still need to be solved, such as complex background interference and multi-scale problem. In this regard, the Hybrid Attention Network (HA-Net) is proposed for automatic extraction of bare soil from high-resolution remote sensing images, which includes the encoder and the decoder. In the encoder, HA-Net initially utilizes BoTNet50 for primary feature extraction, producing four-level features. The extracted highest-level features are then input into the constructed Spatial Information Perception Module (SIPM) and the Channel Information Enhancement Module (CIEM), to emphasize the spatial and channel dimensions of bare soil information adequately. During the decoder, the Semantic Restructuring-based Upsampling Module (SRUM) is proposed to leverage the semantic information of input features, and compensate for the loss of detailed information during the down-sampling in the encoder. Experiment is performed based on high-resolution remote sensing images from the China-Pakistan Resources Satellite 04A. The results show that HA-Net obviously outperforms several excellent semantic segmentation networks in bare soil extraction. The IoU and Recall of HA-Net in test scene can reach 81.4% and 87.4%, respectively, which demonstrates the excellent performance of HA-Net. It embodies the powerful ability of HA-Net for suppressing the interference from complex background and solving the multi-scale issue. © 2024 SPIE.

关键词： Semantic Segmentation

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Cyber-Attack detection of Bias Injection Attacks using Extended State Kalman Filters

Cyber-Attack detection of Bias Injection Attacks using Exten...

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2023 international automatic control conference, CACS 2023

作者： Altius, Marnel Liang, Shu-Hao National Taiwan University of Science and Technology Department of Electrical Engineering Taipei106335 Taiwan National Taiwan University of Science and Technology Taipei106335 Taiwan

ISBN: (纸本)9798350306354

Cyber-Attacks against Cyber-Physical Systems (CPSs) in Industry have increased in frequency and scale over the past decade. Successful attacks have damaged infrastructure and the quality of life for many. To help remedy this situation, we evaluate the viability of using a bank of Extended State Kalman Filters (ESKFs) to detect bias attacks, a class of False Data Injection attacks, on a linear time-invariant system CPS with mismatched disturbance. Due to its ubiquity in robotics, we used a DC motor model as the example model. Then, we designed a Dynamic Surface control (DSC) based controller and an Extended State Observer to accurately control the motor's position under the effect of the load disturbance. The sensors' signals were then attacked by injecting a bias into the model's output. Finally, the various estimates produced by the ESKFs were examined to identify traits possessed by an attack. © 2023 IEEE.

关键词： Simulink

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Design of wireless transmission system for intelligent agricultural greenhouse data acquisition based on modbus communication protocol 3

Design of wireless transmission system for intelligent agric...

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2024 3rd international conference on electrical, control and Information Technology, ECITech 2024

作者： Peng, Zhaoxia Cui, Zhiyong Zhong, Dongbo School of Economics and Management Jiangxi Agricultural University Nanchang330045 China School of Software Engineering Jiangxi University of Science and Technology Nanchang330013 China School of Automation Nanjing University of Science and Technology Nanjing210000 China

Traditional agricultural greenhouses rely too much on manual labor and have a poor ability to adjust the key factors that affect crop growth, such as temperature, humidity, and illumination, resulting in low production efficiency and unstable product quality. Based on the MODBUS communication protocol, a set of intelligent agricultural greenhouse data acquisition wireless transmission systems is designed in this paper. Intelligent instruments, sensors, wireless transmission equipment, and smart control equipment (PLC) have been introduced in agricultural greenhouses. These sensors and instruments automatically collect data and wirelessly transmit it to the intelligent control system in the control room, which is located a few kilometers away. The intelligent control system processes the data and generates various control instructions which are then transmitted to the executive mechanism in agricultural greenhouses for implementation. We complete automatic adjustment of temperature, humidity, illuminance, and other parameters and operations. The intelligent, large-scale, and unmanned agricultural greenhouses are realized. It greatly improves the production efficiency and product quality of crops. © 2024 Published under licence by IOP Publishing Ltd.

关键词： Data acquisition

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A Novel Method for Studying Mosquito Oviposition Behaviour Using Computer Vision and Deep Learning Algorithm 5

A Novel Method for Studying Mosquito Oviposition Behaviour U...

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5th international conference on Artificial Intelligence and Data sciences, AiDAS 2024

作者： Ong, Song-Quan Isawasan, Pradeep Nair, Gomesh Salleh, Khairulliza Ahmad Institute for Tropical Biology and Conservation Universiti Malaysia Sabah Sabah Malaysia College of Computing Informatics and Mathematics Universiti Teknologi Mara Perak Branch Tapah Campus Malaysia School of Electrical and Electronics Engineering Universiti Sains Malaysia Penang Malaysia

ISBN: (纸本)9798331528553

Understanding the oviposition behaviour of mosquitoes is crucial for developing a vector surveillance program and a control strategy. To study this behaviour, in situ observation is one of the ways to determine the details of oviposition preference. However, this method of data collection is time-consuming and labour-intensive, and the presence of human observers often causes odour nuisance, which can lead to bias. We demonstrated a novel method that able to study this behaviour, which we named automatic Mosquito Oviposition Study System (AMOSS) that automatically detects and measures mosquito oviposition activity and collects data without human intervention. The system consists of a microcomputer with an infrared camera that records time-lapse video in a dark environment, and a post-record processing component for detecting the activity by using a deep learning algorithm. We used the system to study the oviposition activity of Aedes mosquitoes on a disposable mask and the result was consistent with the standard oviposition testing - egg counting bioassay. This technology could be an additional tool to determine mosquito preference for a particular substrate, which is very helpful in developing a push-and-pull strategy for mosquito control. © 2024 IEEE.

关键词： Deep learning

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IoT and Cloud-based Solutions for Energy-Efficient Restroom Lighting Systems 4

IoT and Cloud-based Solutions for Energy-Efficient Restroom ...

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4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Manthena, Kishore Varma Jothi, M. Visumathi, J. Mohankumar, N. Maheshwari, S. Sujatha, S. Department of Computer Science and Engineering Andhra Pradesh Bhimavaram India School of Electrical and Electronics Engineering Srm Institute of Science and Technology SRM Nagar Tamil Nadu Trichy India Veltech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Information Technology Tamil Nadu Chennai India Nagpur Campus Maharashtra Pune India Sri Sai Ranganathan Engineering College Department of Information Technology Tamil Nadu Coimbatore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331540364

This research study introduces a new way to improve the energy efficiency of restroom lighting systems by combining cloud-based solutions with Internet of Things (IoT) technologies. Traditional toilet lighting systems waste much energy since flexible or optimized. Our suggested solution is to use IoT devices like ambient light sensors and motion detectors to regulate lighting in response to occupancy and daylight smartly. In real-time, advanced techniques examine trends in the data sent by these sensors and optimize the lighting schedule on a platform in the cloud. Our technology optimizes energy usage while simultaneously improving user comfort and convenience via the dynamic adjustment of lighting levels and the automatic switching of lights in unoccupied areas. Using simulations and real-world trials shows that our method is feasible and successful, with particular emphasis on improving sustainability in restrooms while significantly reducing energy use. Its findings highlight the promise of IoT and cloud computing in promoting sustainability in various settings and developing more energy-efficient building management systems. The challenges include providing continuous connection, maintaining sensor accuracy, controlling data security, seamlessly integrating technologies, and providing various user needs in diverse environments. © 2024 IEEE.

关键词： Cloud platforms

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A Smart and Systematic Vehicle Headlight Operations controlling System based on Light Dependent Resistor 2

A Smart and Systematic Vehicle Headlight Operations Controll...

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2nd IEEE international conference on Intelligent and Innovative Technologies in computing, electrical and Electronics, ICIITCEE 2024

作者： Sumithra, J. Sumalatha, A. Vanitha, A. Sivadharshini, B. Pandi, V. Samuthira Ravi, S. Rmd Engineering College Department of Ece Tamil Nadu Chennai India Kristu Jayanti College Department of Computer Science Bengaluru India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Biomedical Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Tamil Nadu Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India Simats Saveetha School of Engineering Department of Vlsi Microelectronics Tamil Nadu Chennai India

ISBN: (纸本)9798350306415

A knowledgeable management system based on a single integrated device is developed for the automobile lighting system in order to enhance the smart operation of vehicle headlamps and increase the security and dependability of vehicle driving in a wide range of weather conditions, particularly with dust, fog, and haze. When driving at night, the vast majority of drivers turn on their brightest beam. The inconvenience this provides to the individual coming from the opposite route is obvious. To prevent such occurrences, two vehicles are needed to demonstrate the proposed system, which uses a Light Dependent Resistor (LDR) sensor and related communication to help drivers avoid collisions by adjusting their high beams in response to oncoming traffic. To prevent this glare, engineers devised a prototype automated headlamp intensity adjustment system. People driving at night are temporarily rendered blind by this beam, increasing the risk of collisions. When it detects an oncoming vehicle, the high beam automatically changes to the low beam. The manual switching by the driver is unnecessary thanks to this model's philosophy. The primary objective of this technology is to use an Arduino to control the vehicle's headlights in a sophisticated manner, taking into account ambient light levels via a resistor that depends on light and the presence of oncoming traffic via an ultrasonic sensor. Consequently, the system automatically controls the strength of the car's headlights and establishes an appropriate intensity quality throughout the night, taking into account the ambient light level and the presence of a vehicle approaching from the opposite way. © 2024 IEEE.

关键词： Driver Assistance LDR Light Dependent Resistor Roadside Assistance Smart Vehicle Vehicle Headlight control Vehicle Safety

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Oracle recognition based on improved YOLOv7

Oracle recognition based on improved YOLOv7

引用

2023 international conference on Computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2023

作者： Liu, Junya Huang, Ting Li, Rujia Yang, Zhen School of Foreign Languages Jiangxi Science and Technology Normal University Nanchang Jiangxi330038 China School of Information and Mechanical and Electrical Engineering Jiangxi Science and Technology Normal University Nanchang Jiangxi330038 China

ISBN: (纸本)9781510677685

To realize the automatic recognition of oracle bones, this paper presents an oracle bone recognition technology based on the development of Chinese characters. In the experimental process, the model of YOLOv7 algorithm is used to realize the recognition function of oracle script. The model of YOLOv7 algorithm is improved by adding the Coordconv structure and replacing the traditional NMS (Non-maximum Suppression) with the matrix NMS, which effectively improves the recognition accuracy and speed of the oracle script. Moreover, in this work, oracle recognition is introduced based on the semi-supervised learning of training, learning, Jinwen, the relationship between the seal script and the clerical script, and more script features are used to search existing words. In the experiment, the recognition accuracy of the oracle script reached 81.12%. This also proved that there is a certain relationship between the oracle script characters in the process of evolution, which is a basis for the research of oracle script characters based on the evolution of Chinese characters. © 2024 SPIE.

关键词： Machine learning

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Resource Provisioning in Cloud computing using Fuzzy Logic control System: An adaptive approach 2

Resource Provisioning in Cloud Computing using Fuzzy Logic C...

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2nd international conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Alnawayseh, Saif. E. A. Muhammad, Muhammad Hassan Ghulam Hassan, Zahid Fatima, Mayraj Aslam, Muhammad Shoukat Ibrahim, Amer Ateeq, Karamath Mutah Univesity Faculty of Engineering Electrical Engineering Department Jordan NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan PMAS-Arid Agriculture University Rawalpindi Pakistan American University in Th Emirates College of Computer and Information Technology Dubai United Arab Emirates Skyline University BSIT Faculty Sharjah United Arab Emirates

ISBN: (纸本)9798350335644

The need for cloud computing has been increasing widely in this era since last two decades due to its continual benefits include security, disaster recovery, increased collaboration, cost saving and virtualization etc. This innovation introduces an enormous transformation of conventional applications in business industry. However, cloud users indulge themselves not acquiring a sufficient amount of computing resources caused by resource confliction. A challenging and significant problem in cloud computing is Resource Provisioning in virtualization environment that relates with selecting, deploying and run time management of software and hardware resources. On the contrary, such situations also exist where resources are found in virtual servers either under or over-utilization. In order to deal with these problems, proposed framework in this paper is demonstrated using Fuzzy Logic control System towards characterization the processes' priorities by means of employing input parameters (workload, processing time, and process age) keeping in view of the fact that expert judgment is required for taking into account these parameters. © 2023 IEEE.

关键词： Virtualization

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Training with a Neurofeedback System for the control of a Drone Using Electroencephalographic Signals 19

Training with a Neurofeedback System for the Control of a Dr...

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19th international conference on electrical engineering, computing science and automatic control, CCE 2022

作者： Gonzalez-Munoz, Leasly Atzuri Orozco-Magdaleno, Luis Ortiz-Yescas, Vania Alice Octavio Ramirez-Morales, Adrian Tovar-Corona, Blanca Zacatelco-Barrios, Luis Brayan Instituto Politecnico Nacional Upiita Mexico City Mexico

ISBN: (数字)9781665455084

ISBN: (纸本)9781665455084

In the present work, a portable electroencephalography system was developed in order to monitor the attention coefficient during a neurofeedback training using a drone as a visual stimulus. A four channels EEG signal acquisition system was developed using the 10-20 international standard, positioned at FP1, FP2, F3 and F4. The Power Spectral Density was calculated using the Modified Covariance method, and the attention coefficient was calculated using the ratio of the area under the curve between the high Beta and Theta bands. With the help of the Neurofeedback system and the drone, a series of eight tests of ten minutes each was carried out with a group of eleven volunteers. Only one session per day was carried out. In each test, the attention coefficient was calculated while the subject watched the drone performing one of the 16 determined trajectories. If the attention coefficient exceeded a threshold, the drone could continue moving, otherwise the drone remained static. As a result of using this neurofeedback system, it was found an increase in sustained attention time from 7.7 to 9.4 s, as well as a reduction in inattention time from 6.0 to 4.8 s. © 2022 IEEE.

关键词： Electroencephalography

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Intelligent information processing of Fiber Bragg Grating biosensor for stress monitoring in microfluidics system 3

Intelligent information processing of Fiber Bragg Grating bi...

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3rd international computing Imaging conference, CITA 2023

作者： Zhang, Zijun Lu, Zhenghao Wang, Jing Pan, Han Li, Peiyun Li, Xiaoyao Department of Electrical and Electronic Engineering University of Nottingham Ningbo China Ningbo China Departmen of Physics and Astronomy University College London London United Kingdom Key Laboratory of More Electric Aircraft Technology of Zhejiang Province University of Nottingham Ningbo China Ningbo China Departmen of Applied Computational Science and Engineering Imperial College London London United Kingdom

ISBN: (纸本)9781510671409

A Fiber Bragg Grating (FBG) biosensor with good biocompatibility promises label-free, anti-electromagnetic interference, and non-destructive detection of stress monitoring, it holds the potential to rapidly identify the stress changes in microfluidics systems. Based on signal processing, the correlation between wavelength shift in FBG and flowrate is discussed, an alternative to manual processing of wavelength offset calculation is analyzed, and the automatic selection of FBG wavelength change under high noise conditions is realized. In addition, we propose some technologies of intelligent processing algorithms such as machine learning (BP—back propagation neural network) and moving average. Finally, the feasibility and stability of the signal processing method are verified by experimental validation and multiple data sets testing. This study provides a promising intelligent information processing strategy for rapid and accurate stress sensing of microfluidics systems. © 2023 SPIE.

关键词： Fiber Bragg gratings

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