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Kernel Correlation Filter Algorithm and Its Research in Small Target and Occlusion Fields

Kernel Correlation Filter Algorithm and Its Research in Smal...

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intelligent Robotics and Automatic control (IRAC), international conference on

作者： Wenhu Li Yanfei Tang Youling Xiong Fei Wang Computer and Network College of Anhui Postal and Telecommunications Vocational and Technical College Hefei China School of Financial Technology Anhui Vocational and Technical College of Commerce Wuhu China

ISBN: (数字)9798350389807

ISBN: (纸本)9798350389814

This paper focuses on the improvement of kernel filtering algorithms and their applications, particularly on enhancing the accuracy of target recognition and tracking in modern intelligent environmental monitoring. The Kernel Correlation Filter (KCF) algorithm, due to its efficiency, high precision, and strong robustness, holds significant potential for application in surveillance environments. However, the performance of the KCF algorithm is limited in complex scenarios such as occlusion, scale changes, similar targets, and rapid *** address these issues, this paper proposes two improved KCF algorithms. Firstly, a scale pool algorithm is introduced, which enhances the algorithm's adaptability to target scale changes by performing multi-scale feature extraction and matching, thereby improving tracking accuracy, efficiency, and speed. Experiments demonstrate that the tracking accuracy of the improved algorithm is significantly enhanced on the OTB50 and OTB100 datasets. In experiments using a self-constructed dataset, the success rates for feature extraction, scale changes, and sampling rates were improved by 40%, 13.6%, and 29.76%, ***, to address the occlusion problem, an improved joint tracking algorithm combining the modified KCF with ByteTrack is proposed. The high-quality detection results of the improved KCF are utilized as input for ByteTrack, which optimizes the handling of occluded objects through its multiple matching strategies. The joint algorithm exhibits excellent performance on the OTB50 and OTB100 datasets. In the self-constructed dataset, the success rate for handling occlusion scenarios is improved by 50% compared to the original KCF, effectively solving issues such as target template updating, feature changes, and target disappearance.

关键词： Target tracking Accuracy Correlation Target recognition Surveillance Filtering algorithms Feature extraction Robustness Kernel Robots

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Decision-Making for Remote control of Emergency Mobile Robot

Decision-Making for Remote Control of Emergency Mobile Robot

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international conference on Modern Electrical and Energy Systems (MEES)

作者： Oleksandr Tsymbal Oleh Posashkov Department of Computer-Integrated Technologies Automation and Mechatronics Kharkiv National University of Radio Electronics Kharkiv Ukraine

The proposed paper analyses the decision-making for remote control of emergency mobile robotic platforms. There is described a model of robot’s decision-making, based on predicate logics, which combines local and remote procedures of goals and actions determination. The developed model uses the description of local emergency workspace added with remote-supplied information, based on distributed computer vision system. In core of decision-making system is problem-solving engine, which includes the set of action procedures, goals, specifications of robot and its sensor system. The procedure of action typically contains the result of action, the set of subgoals to solve, set of added facts and removed facts on workspace. Facts of considered workspace includes the descriptions of objects inside workspace with their coordinates, dimensions, and probable identification. To describe the emergency workspace here is proposed information model, based on set theory descriptions. While emergency robot is mostly considered as autonomous system with independent decision-making, the role of remote control is still essential for fast recognition of unknown or damaged objects, fast generation of movements and actions in emergency workspace, fast solution on actions in dangerous situations, for fast information support from other robots and people. The key role of remote control system can be executed by human operator with help of intelligent tools of other remote control systems, which provide monitoring of emergency workspace, based on computer-vision of acting robot and other information sources. The proposed ideas can be considered as a basis for real emergency robot control system implementation.

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(POSTER) A Holistic IoT-Enabled Approach for Indoor Air Quality control

(POSTER) A Holistic IoT-Enabled Approach for Indoor Air Qual...

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international conference on distributed Computing in Sensor Systems (DCOSS)

作者： Fotios Kalioras Gabriel Filios Stylianos Karatzas Sotiris Nikoletseas Department of Computer Engineering and Informatics University of Patras Greece Department of Computer Engineering and Informatics University of Patras Computer Technology Institute and Press “Diophantus” Greece Department of Civil Engineering University of Patras Greece

Indoor air quality (IAQ) is an important yet often overlooked aspect of public health, with poor IAQ contributing to a significant number of diverse health problems worldwide. Existing air quality standards have failed to fully address the problem, however emerging technologies in the field of IoT have demonstrated promise in effectively addressing this issue. This concept paper advocates a hybrid hierarchy system for IAQ monitoring that incorporates both offline and online connectivity. The system utilizes sensing elements that can perform a wide range of functions, including autocalibration, pollution event detection, and more, autonomously from the network. By combining a gateway device, the system's capabilities are enhanced, providing increased data granularity, additional calibration and sensing options as well as connection with Building Management Systems (BMS). Furthermore, by connecting the gateway to the cloud, the system can perform more advanced data analysis and machine learning, providing users with insights into not only air quality but also general environmental quality. Key features of the envisioned system include the integration of subjective perception of pollution via crowdsourcing, a TinyML technology at the edge, digital twins, and an optimised redundancy of sensors that can alleviate poor accuracy and drift as well as capture the spatial dispersion of pollution.

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Path Planning based on Reinforcement Learning with Improved APF model for Synergistic Multi-UAVs

Path Planning based on Reinforcement Learning with Improved ...

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international conference on computer Supported Cooperative Work in Design

作者： Qun Ding Xiaolong Xu Wenming Gui Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Jinling Institute of Technology Nanjing China

As the emerging technology of Unmanned Aerial Vehicles (UAVs) becomes mature, UAVs are widely used in environmental monitoring, communication and other fields. In view of this, this paper analyzed the task of synergistic multi-UAVs exploration of unknown environments, and proposed a path planning method for them based on reinforcement learning. Firstly, the path planning task of the UAVs was divided into two parts: the path travel strategy module and the information exploration strategy module. Models of the two modules were based on the Deep Deterministic Policy Gradient algorithm (DDPG), and an improved Artificial Potential Field (APF) force traction mechanism was introduced in the path travel strategy module. Its aim was to assist in guiding the generation of UAV flight path trajectories. Also it could enhance the learning capability of the model. The path travel strategy module would generate the complete flight path of the whole cluster in a distributed manner. A series of temporary target points provided by the information exploration strategy module helped. In maps with 21.5%, 25.3% and 29.6% of obstacles, multi-UAVs could achieve 84.2%, 76.7% and 69.9% of environmental exploration by the designed method. Compared with the APF method, the A star method and the Breath First Search (BFS) method, the proposed method is not only able to plan feasible paths in a more complex map model, but also the curvature of the planned paths is smoother, thus achieving the goal of reducing the energy cost of UAVs.

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Review of Smart Agricultural Technologies for Precision Pest control and Sustainable Water in Cotton Farming

Review of Smart Agricultural Technologies for Precision Pest...

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international conference on Inventive Computation Technologies (ICICT)

作者： Prince Charles M Pandiselvam Pandiyarajan Department of Computer Application Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Cotton production is extremely susceptible to pests, disease, and irrigation mismanagement, resulting in considerable yield loss and environmental degradation. Conventional practices like manual monitoring of pests and blanket spraying of pesticides are time-consuming, ecologically detrimental, and frequently inadequate in averting timely infestations. The intersection of Artificial Intelligence (AI), the Internet of Things (IoT), and smart water management offers a revolutionary chance towards sustainable agriculture. This paper discusses the cooperative function of IoT and AI in cotton cultivation, particularly real-time pest identification, disease classification, and dynamic irrigation management. Deep learning techniques precisely detect pests at all stages of development, whereas IoT sensors track environmental data like soil moisture, temperature, and humidity. The sensor data are used as input to machine learning algorithms that adjust irrigation schedules and predict outbreaks of disease. Furthermore, adaptive learning methods such as federated and incremental learning optimize system performance through time, allowing for ongoing improvement without violating data privacy. Major findings indicate that the integration of these technologies decreases pesticide use, saves water, and enhances crop resistance. Yet, the absence of complete integrated systems prevents the full potential from being realized. This research highlights the necessity for integrated frameworks that address pest management and water management simultaneously in real time. Closing these gaps will open the door to innovations that increase yield, enhance sustainable agriculture practices, and make cotton farming sustainable in the long term.

关键词： Temperature sensors Irrigation Production Pesticides Real-time systems Cotton Internet of Things Artificial intelligence intelligent sensors Diseases

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A Structured Reasoning Framework for Unbalanced Data Classification Using Probabilistic Models

A Structured Reasoning Framework for Unbalanced Data Classif...

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Consumer Electronics and computer Engineering (ICCECE), 2021 IEEE international conference on

作者： Junliang Du Shiyu Dou Bohuan Yang Jiacheng Hu Tai An Shanghai Jiao Tong University Shanghai China Yale University New Haven USA University of California San Diego San Diego USA Tulane University New Orleans USA University of Rochester Rochester China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

This paper studies a Markov network model for unbalanced data, aiming to solve the problems of classification bias and insufficient minority class recognition ability of traditional machine learning models in environments with uneven class distribution. By constructing joint probability distribution and conditional dependency, the model can achieve global modeling and reasoning optimization of sample categories. The study introduced marginal probability estimation and weighted loss optimization strategies, combined with regularization constraints and structured reasoning methods, effectively improving the generalization ability and robustness of the model. In the experimental stage, a real credit card fraud detection dataset was selected and compared with models such as logistic regression, support vector machine, random forest and XGBoost. The experimental results show that the Markov network performs well in indicators such as weighted accuracy, F1 score, and AUC-ROC, significantly outperforming traditional classification models, demonstrating its strong decision-making ability and applicability in unbalanced data scenarios. Future research can focus on efficient model training, structural optimization, and deep learning integration in large-scale unbalanced data environments and promote its wide application in practical applications such as financial risk control, medical diagnosis, and intelligent monitoring.

关键词： Deep learning Support vector machines Adaptation models Decision making distributed databases Probabilistic logic Data models Cognition Optimization Markov random fields

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Design of intelligent light control system based on NB-IoT

Design of intelligent light control system based on NB-IoT

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Wearables, Sports and Lifestyle Management (WSLM), international conference on

作者： Xiaoling Zeng Jianping Zhang Chongqing Electronic Information College Chongqing China

ISBN: (纸本)9781665408202

At this stage, the level of urbanization in China is constantly improving, and the construction of “smart city” is gradually included in the ranks of national urban planning. As an important part of smart cities, intelligent street light are welcoming unprecedented development opportunities. The traditional street light control system generally adopts the methods of centralized line control, human inspection, and computer-side web monitoring. Aim at the lag of traditional street lamp management, and lack of real-time monitoring and remote control, this thesis designs a set of intelligent street light monitoring system based on NB-IoT (Narrow Band-Internet of Things). The system realizes the real-time control of street lighting system, environmental monitoring, spray cooling and dust suppression, etc. Cloud technology is used to calibrate the geographic location information and working status information of each street lamp. The test results show that the system is stable in operation and reliable in data transmission, reducing the cost and energy consumption of the traditional street lamp control nodes, and realizing the remote monitoring and intelligent management of the status of urban street lamps by the terminal equipment.

关键词： Cloud computing Smart cities Wearable computers Web and internet services Urban planning control systems Real-time systems

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IoT-Driven Genomic Ecology: Exploring Genetic Adaptation in Changing Environments using CNN Model

IoT-Driven Genomic Ecology: Exploring Genetic Adaptation in ...

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Electrical, Electronics, Information and Communication Technologies (ICEEICT), international conference on

作者： Chitra Sabapathy Ranganathan Arshadh Ariff Mohamed Abuthahir Riazulhameed Santosh Kumar Sahoo V. Sumathi G. Sudhakar Anantha Raman Rathinam Mphasis Corporation Chennai Tamil Nadu India Blackstraw Technologies Pvt Ltd Chennai Tamil Nadu India Department of Engineering and Technology VIT University Bhopal Madhya Pradesh India Department of Mathematics Sri Sairam Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Sri Sai Ranganathan Engineering College Coimbatore Tamil Nadu India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vijayawada India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369090

The Internet of Things (IoT) technology and Convolutional Neural Networks (CNNs) catalyzes this investigation, which dives into the complex relationship between genomes and the surrounding environment. It monitors various environmental factors in real-time using a distributed network of Internet of Things sensors installed across various natural and urban locations. While doing this, this system collects genomic data from creatures that live in or interact with these habitats. This allows us to explore the intriguing dynamics of genetic adaptation in response to environmental changes. The research sheds light on the genetic factors responsible for an organism's capacity to flourish in constantly changing environments. The system can shed light on patterns of genetic mutations, disease prevalence, and variations in biodiversity by integrating environmental data collected from the Internet of Things with genomic information. These realizations contribute to our comprehension of the mechanisms that drive evolution; help create conservation plans for species at risk of extinction, inform disease epidemiology, and assist sustainable farming practices. The article "IoT-Driven Genomic Ecology" demonstrates a multidisciplinary approach to converging genomics, environmental science, and IoT technology. Its purpose is to shed light on the underlying principles that control life's adaptability to a world that is always changing.

关键词： Temperature sensors Genomics Water quality Sensors Internet of Things Convolutional neural networks Bioinformatics environmental management monitoring Diseases

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Research on application of real-time database for air quality automatic monitoring system 5

Research on application of real-time database for air qualit...

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5th international conference on Energy Engineering and environmental Protection, EEEP 2020

作者： Sun, Peiwen Liu, Wenfeng Xu, Yijin Wang, Lingyun School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China

With the increase of city air monitoring sites and monitoring project, monitoring instruments and monitoring data are increasing rapidly. When using the traditional relational database in processing huge real-time data, the throughput and timeliness of monitoring system has been unable to meet the needs of the business. In order to solve this problem, this paper introduces the real-time database into the air quality automatic monitoring system. According to the design principles, the paper outlines the four aspects of the design: the composition of the system, the overall structure of the system, the data flow and the network topology. Aim at assembling the embedded system architecture, distributed system, database technology, network browser/server computing mode, client/server network computing technology, and large-scale computer application system of virtual private network connection based on public network. In the specific design, this paper design substation and central station architecture based on the real-time databases and complete the function of field data collection and storage, equipment control, data transmission, human-computer interaction, data exchange interface with third party system and other function. Finally, according to the observation results of the city air quality automatic monitoring system, the project realizes the functions of the system and improve the real-time quality of environmental air monitoring and the capacity of instrument supervision. © Published under licence by IOP Publishing Ltd.

关键词： monitoring

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Marine intelligent distributed Temperature and Humidity Collection System Based on Narrow-Band IoT Architecture 10th

Marine Intelligent Distributed Temperature and Humidity Coll...

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10th international conference on computer Engineering and Networks, CENet 2020

作者： Hu, Congliang Wan, Huaqing Ding, Wei 719 Research Institute Wuhan China

ISBN: (纸本)9789811584619

In recent years, the rapid development of the shipping industry at sea has become an essential part of Chinas economic development. Large ships carry more items, and changes in temperature and humidity in the closed cabin are especially crucial for preserving items. To grasp the change of temperature and humidity in the cabin in time, an intelligent distributed temperature and humidity collection system based on narrowband Internet of things is designed. The system combines microprocessor module (STM32), sensor module, and narrowband Internet of things to realize a real-time collection of temperature and humidity in the cabin and alarm function when the change exceeds the threshold. The mobile APP is used to realize remote monitoring and data collection monitoring. narrowband internet of things;temperature and humidity collection;STM32;remote control. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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