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Node Layout Algorithm based Environmental Monitoring System for Industrial IoT

Node Layout Algorithm Based Environmental Monitoring System ...

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Electronic Information Engineering and Computer Communication (EIECC), international conference on

作者： Xinyi Ren Kangle Li Yangsen Liu Junshu Fei An He Xiying Zhuge School of Computer and Network Security Chengdu University of Technology Chengdu China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

As the internet of Things (IoT) technology continues to revolutionize, modern industrial production systems are undergoing profound changes. Current production methods require the expansion of production space to accommodate more equipment, raw materials and workers. The traditional monitoring systems mostly use a simple sensor monitoring method. This kind of monitoring method has the problems of too much monitoring data which is not easy to manage, and the system can not respond in time after the disaster occurs. based on this situation, this paper proposes a node layout algorithm based industrial IoT environment monitoring system. The system utilizes Zigbee wireless communication technology to construct the network. The master station uses the Loongson 2K1000LA development board as the display and calculation terminal to interact with the cloud platform. The sub-station uses Loongson 1C102 development board as the main control chip to monitor the environmental data. The cloud platform carries out image recognition and sends peripherals control commands. Compared with the traditional monitoring system, this system reduces the peripheral response delay by more than 1.5 seconds, improves the monitoring efficiency by 33%, The peripheral response time difference is reduced by 1.499 seconds, an increase of 61.3%.and facilitates managers to formulate effective security prevention strategies.

关键词： Industries Cloud computing image recognition Layout Zigbee Delays Environmental monitoring Time factors Optimization Industrial internet of Things

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ETIS international conference on Emerging Technologies for Intelligent systems, ETIS 2025

ETIS International Conference on Emerging Technologies for I...

引用

2025 international conference on Emerging Technologies for Intelligent systems, ETIS 2025

ISBN: (纸本)9798331507541

The proceedings contain 104 papers. The topics discussed include: intelligent traffic signal management system;an automated pothole detection and repair system for effective road maintenance and safety;on-board data acquisition system for spacecraft;cyber-physical modeling and simulation of four-axis articulated robot;an intelligent system for prediction of depression based on facial emotions and textual data;evaluating the impact of dataset variability on Malayalam emotion detection system;an automated smart incubator system for poultry farms and hatcheries;image-driven face presentation attack detection in biometric systems;and soft computing approaches in enhancing healthcare data security using deep neural networks and reversible data hiding.

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Medical image Authentication and Self-Recovery Using Fragile Watermarking in the Frequency Domain

Medical Image Authentication and Self-Recovery Using Fragile...

引用

international image Processing, Applications and systems conference (IPAS)

作者： Riadh Bouarroudj Fouzi Harrou Nabil Zerrouki Feryel Souami Fatma Zohra Bellala Ying Sun Department of Computer Science University of Sciences and Technology Houari Boumedienne Algiers Algeria CEMSE Division King Abdullah University of Science and Technology Thuwal Saudi Arabia DI2M Laboratory Center for Development of Advanced Technology Algiers Algeria Department of Computer Science University of Algiers Benyoucef Benkhedda Algiers Algeria

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

The rapid growth in digital image sharing, driven by advancements in internet and communication technologies, has raised concerns about image integrity, especially in sensitive fields like healthcare. This paper presents a fully blind fragile watermarking technique for authenticating and self-recovering color and grayscale medical images. The approach applies the Discrete Wavelet Transform (DWT) to the cover image, and the resulting subbands are divided into 3×3 blocks. One authentication and four recovery watermarks are then embedded into the least significant bits (LSBs) of each block. During the extraction phase, if tampering is detected, the model accurately localizes the altered areas, and a three-level recovery process, including a new inpainting technique, is used to recover the original image. Results based on two publically available datasets demonstrate that this method delivers high-quality watermarked images, achieves optimal watermark extraction accuracy, and maintains high sensitivity to various attacks. Additionally, it provides precise tamper localization and delivers high-quality recovered images, even with tampering rates as high as 60%.

关键词： Location awareness Accuracy image color analysis Authentication Watermarking Transforms Gray-scale Discrete wavelet transforms Cryptography Biomedical imaging

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Cooperative Physical Layer Authentication for Massive MIMO Machine-Type Communications 23

Cooperative Physical Layer Authentication for Massive MIMO M...

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23rd IEEE international conference on Communication technology, ICCT 2023

作者： Zhang, Haotian Wang, Junjie Han, Gangtao Wang, Ning School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou450001 China Southeast University State Key Laboratory of Millimeter Waves Nanjing210096 China

ISBN: (纸本)9798350325959

Aiming to improve the identity security of massive machine-type communications networks, a two-user cooperative physical layer authentication (PLA) scheme is proposed for the uplink transmission of a massive multiple-input multiple-output (MIMO) systems. In the proposed scheme, two single-antenna users synchronously transmit signals to the base station which is equipped with a large number of antennas, and fast varying channels are considered. A space-time modulation scheme is adopted for the two users to implement cooperative PLA, in which tags are selectively embedded into the transmitted signals based on the bit-relationships between the tags and the messages in order to improve the covertness of tags. A minimum Riemannian distance detector is proposed to implement the tag detection, while a fast noncoherent maximum likelihood (ML) detector is introduced to carry out the messages estimation. Binary hypothesis test is carried out for authentication by comparing the detected tags to the regenerated ones, in which the PLAs of the two users are handled as a whole. The closed-form bit error probability of the message is derived, while the authentication performance is analyzed. Computer simulation results demonstrate that the proposed scheme performs better on signal detection as well as identity authentication. © 2023 IEEE.

关键词： cooperative physical layer authentication massive MIMO noncoherent detection selective tag

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Visual Multi Scene Intelligent Car Service Platform based on internet of Things technology 1

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11th international conference on Communications, signal Processing, and systems, CSPS 2022

作者： Pan, Guoqing School of Information Engineering Suzhou University Anhui Suzhou234000 China

ISBN: (数字)9789819926534

ISBN: (纸本)9789819926527

The visual multi scene intelligent car service platform based on internet of things technology is different from any previous machine idea. It is specially designed to serve an industry or a field. What we want to achieve here is a multi scene service state, and switching scenes without switching machines (only switching conversion method). At the same time, in order to meet this requirement, the traditional centralized design scheme can not be used to arrange all tasks in the machine. Therefore, the hardware level is separated from the software level for step-by-step processing, which will not only reduce the pressure of the hardware machine, but also improve its processing efficiency. Further, its processing capacity is no longer limited by the underlying hardware, but determined by the performance of the upper computer, After having a very powerful upper computer, it can realize the control mode of multi lower computer management. At the same time, it can adopt the scheme of time-sharing processing to deal with the multi lower computer, so as to meet its various performance requirements. In this way, we can complete the service mode of more scenarios under the current technical force, and only adopt a machine mode, a scalable and scalable smart car service platform whose efficiency can be improved at any time. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Efficiency

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Automated Waste Classification using Convolutional Neural Network

Automated Waste Classification using Convolutional Neural Ne...

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international conference on Software technology and Engineering (ICSTE)

作者： Rajermani Thinakaran J. Somasekar Vikram Neerugatti P. Guru Saran Faculty of Data Science and Information Technology INTI International University Negeri Sembilan Malaysia Department of Computer Science and Engineering Jain (Deemed-to-be University) Banglore Karnataka India

ISBN: (数字)9798350378955

ISBN: (纸本)9798350378962

Efficient waste management relies on precise waste classification, which facilitates recycling, composting, and appropriate disposal. Conventional waste classification methods often involve manual sorting or simple rule-based systems, which are labor intensive and prone to errors. This study investigates the application of Convolutional Neural Networks (CNNs) for automated waste classification using image data. CNNs have shown significant success in various image classification tasks due to their ability to learn hierarchical features directly from pixel values. The methodology outlined covers data collection, preprocessing, model architecture design, and training procedures for the CNN model. Experimental results demonstrate the effectiveness of the CNN approach in accurately categorizing various waste types. Additionally, we discuss the practical implications of CNN-based waste classification systems for real-world waste management applications. By leveraging CNNs, waste management processes can be optimized, leading to better resource utilization and environmental sustainability. The authors also highlight potential avenues for future research, including exploring advanced CNN architectures and integrating emerging technologies like internet of Things (IoT) sensors for improved waste management solutions. Overall, this study emphasizes the potential of CNNs to transform waste classification practices and enhance the efficiency of waste management systems.

关键词： Waste management Training Computer architecture Transforms Data models Software Sensors Convolutional neural networks internet of Things Sorting

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Design and Development of an IoT-based Plant Growth Monitoring System Using Esp-32 Cam

Design and Development of an IoT-Based Plant Growth Monitori...

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Control & Automation, Electronics, Robotics, internet of Things, and Artificial Intelligence (CERIA), IEEE international conference on

作者： Rex Basuel Arnel Fajardo Novie Dave Perez Graduate School Technological Institute of the Philippines - Quezon City Quezon City Philippines College of Computing Science Information and Communication Technology Isabela State University Cauayan City Philippines College of Engineering and Information Technology Caraga State University Cabadbaran City Cabadbaran City Philippines

ISBN: (数字)9798331511166

ISBN: (纸本)9798331511173

This paper presents a study that combines the advances of technology specifically in the field of internet-of-Things (IoT) and image Processing. Plant growth monitoring is an essential task especially when growing a high value crop like okra. In this study, an IoT-enabled device like the ESP32-Cam was used to capture and record the growth development of a certain plant. ESP32-Cam is a microcontroller integrated with a camera and has the capability of doing IoT. The system was integrated with other modules such as Nitrogen-Phosphorus-Potassium (NPK), soil moisture sensor and soil temperature sensor. The NPK sensor was used to measure the amount of nutrient in the soil, while the soil moisture and temperature sensor detects the moisture content and temperature of the soil respectively. It is also integrated with a $D C$ pump that will be activated to water the plants once the soil moisture drops to its threshold value. Upon evaluation of the developed prototype, it is evident that it functions based on its intended design.

关键词： Temperature sensors Temperature measurement Soil measurements image processing Soil moisture Prototypes Robot sensing systems Nutrients internet of Things Monitoring

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Adaptive Dynamic Programming-based Feature Tracking Control for image Disturbances Visual Servoing Manipulators

Adaptive Dynamic Programming-Based Feature Tracking Control ...

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IEEE international conference on Control Science and systems Engineering (CCSSE)

作者： Xiaolin Ren Haoyu Yan Bing Ma Department of Control Science and Engineering Changchun University of Technology Changchun China

Aiming at the problem of feature point interference and noise in visual servoing (VS) control, an image feature tracking control method based on adaptive dynamic programming (ADP) is proposed. The output signal of the image sensor is used as the feedback signal of the VS manipulators system for closed-loop control. While the adaptive state observer is used to estimate the image disturbances in the VS model of the manipulator, and the cost function is improved according to the observed disturbance values. On the basis of ADP technology, a critic neural network (NN) was constructed to derive the Hamilton-Jacobi-Bellman (HJB) equation. According to Lyapunov theory, it is proved that the visual servoing manipulators system is asymptotically stable. Finally, numerical simulations are given to verify the effectiveness of the algorithm.

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Implementation of a Line Buffer in an Adaptive Gaussian Bilateral Filter

Implementation of a Line Buffer in an Adaptive Gaussian Bila...

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： C. Saranya P. Kumar Boomika R Harish G S Department of Electronics Engineering (VLSI D&T) K. S. Rangasamy College of Technology India Department of Electronics and Communication Engineering K. S. Rangasamy College of Technology India

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

The proposed method presents the design and implementation of an FPGA-based filter architecture for image processing, focusing on the integration of a line buffer within an Adaptive Gaussian Bilateral Filter (AGBF). The AGBF aims to enhance edge preservation and noise reduction in real-time imaging applications. By leveraging FPGA platforms, the design achieves low-latency, high-throughput performance, essential for high-resolution image processing. The proposed architecture incorporates a line buffer to efficiently manage memory usage and facilitate seamless pixel-by-pixel processing, thereby reducing computational complexity and enhancing signal-to-noise ratio (SNR). Experimental results demonstrate the superiority of the proposed FPGA design in terms of power efficiency, processing speed, and image quality compared to traditional filtering approaches.

关键词： Computer vision Accuracy image edge detection Noise reduction Adaptive filters Real-time systems Satellite images Field programmable gate arrays signal to noise ratio Biomedical imaging

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Brain image pathological segmentation method based on a deep network 2

Brain image pathological segmentation method based on a deep...

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2nd international conference on Medical Imaging and Additive Manufacturing, ICMIAM 2022

作者： Zheng, Shuzhan Xu, Gang Fu, Nana Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment Anhui Polytechnic University Anhui China Anhui Key Laboratory of Detection Technology and Energy Saving Devices Anhui Polytechnic University Anhui China

ISBN: (纸本)9781510653375

The automation of the medical industry has become one of the necessary conditions in today's medical field. Radiologists or physicians need this automation technology for accurate diagnosis and treatment. Automatic segmentation of tumor parts from magnetic resonance (MR) brain images is a challenging task. Several methods have been developed to improve the segmentation efficiency of automation systems. However, in the process of medical image segmentation, there is always room for improvement. This paper proposes a brain tumor image segmentation method based on deep learning, which includes the concepts of stationary wavelet transform (SWT) and growing convolutional neural network (GCNN). The vital goal of this work is to improve the accuracy of traditional systems. In this paper, the support vector machine (SVM) and convolutional neural network (CNN) are compared and analyzed. The experimental results show that this method is superior to SVM and CNN inaccuracy, peak signal-To-noise ratio, mean square error, and other performance indicators. © 2022 SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Support vector machines

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