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检索条件"任意字段=International Conference on Signal Image Technology and Internet Based Systems"

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Patrol: An Intelligent Surveillance System for Mobile-Free Environments

Patrol: An Intelligent Surveillance System for Mobile-Free E...

引用

international Communication systems and Networks and Workshops, COMSNETS

作者： Aritra Dutta Banik Trisagni Mandal Anubhab Mukherjee Sharnabho Chatterjee Raja Karmakar Department of CSE (IoT & Cyber Security Including Block Chain Technology) Heritage Institute of Technology Kolkata India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

The widespread and unauthorized use of mobile phones in no-mobile zones such as examination halls, government offices, and hospitals presents significant security, confidentiality, and productivity challenges. Traditional signal-based detection methods fail to detect phones in airplane mode or disconnected from networks, leaving critical gaps in enforcement. To address this issue, we propose "Phone Patrol," a deep learning-based system designed to detect and report unauthorized mobile phone usage. Phone Patrol uses You Only Look Once version 5 (YOLOv5) for detecting individuals with mobile phones and You Only Look Once version 9 (YOLOv9) for face recognition, enabling identification of violators. Deployed on a Raspberry Pi 5, the system processes real-time video feeds and sends automated SMS alerts to offenders, offering a compact, standalone internet of Things (IoT) solution. Experimental results demonstrate that YOLOv5 achieved a mean Average Precision at 50% Intersection over Union (mAP50) score of 0.992, showcasing its accuracy in detecting mobile phones. Although the current prototype has limitations in scalability and optimization for larger environments, it presents a robust proof-of-concept for image-based detection independent of network signals, providing a more comprehensive solution to enforce no-mobile zones effectively.

关键词： YOLO Productivity Surveillance Scalability Prototypes Streaming media Mobile handsets Real-time systems internet of Things Security

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Automated Blood Group Detection System Using image Processing for Non- Invasive Antigen Feature Extraction and Classification

Automated Blood Group Detection System Using Image Processin...

引用

Intelligent Data Communication Technologies and internet of Things (IDCIoT), international conference on

作者： Gopu Chandrasekhar Reddy Gajavada SaiKrishna A. Annie Micheal Department of CSE with AIML Sathyabama Institute of Science and Technology Chennai India Department of CSE Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Blood group detection is essential in medical diagnostics and transfusion medicine but often relies on invasive methods requiring specialized infrastructure. This research introduces an automated, non-invasive blood group detection system using YOLOv8 object detection and advanced image processing. The system addresses challenges such as human error, lack of accessibility in resource-limited settings, and delays in traditional methods. By analyzing high-resolution blood sample images, the system extracts antigen features and classifies agglutination patterns for A, B, and D blood groups. With a mean average precision (mAP) of 97.4% and an inference time of 15 milliseconds per image, the system delivers scalable, real-time, and robust diagnostics. This research advances healthcare accessibility and precision medicine objectives, offering an efficient and practical alternative to traditional laboratory-based methods.”

关键词： Antigens Precision medicine Object detection Feature extraction Real-time systems Delays Medical diagnosis internet of Things Blood Medical diagnostic imaging

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Enhancing image Search Engine Accuracy Using Psychological Intention Diagrams

Enhancing Image Search Engine Accuracy Using Psychological I...

引用

Integrated Circuits and Communication systems (ICICACS), IEEE international conference on

作者： Aswathi KP Sunita Yadwad D. Nirmal Raj Shailesh Rastogi Department of Computer Science and Engineering Chennai Institute of Technology Chennai India Department of Computer science and Engineering Faculty of Engineering and Technology Parul University Vadodara India Balaji Institute of Management and Human Resource Development (BIMHRD) Pune India Symbiosis Institute of Business Management Nagpur Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

As the internet becomes increasingly image-centric, users face substantial challenges in efficiently locating desired images due to the inherent limitations of image search engines in interpreting visual content and keyword ambiguity. This paper introduces an innovative solution employing psychological intention diagrams to enhance search accuracy. These diagrams harness historical data to depict the search intentions associated with specific keywords, reflecting both user and web designer perspectives. By integrating these diagrams, our proposed search engine significantly refines its ability to predict and retrieve the most relevant images. The implementation of this system involves constructing two types of intention diagrams: one based on past user queries and another derived from the intentions of web designers for cases without historical user data. This dual approach not only captures the semantic essence of images more effectively but also addresses the multi-faceted meanings of search keywords. Our experimental results confirm that incorporating psychological dimensions into image search mechanisms substantially improves precision, facilitating more accurate and user-aligned image retrieval. This pioneering approach is the first to apply psychological profiling in enhancing image search engine functionalities, marking a significant advancement in digital image retrieval technologies.

关键词： Visualization Accuracy Digital images Semantics image retrieval Psychology Machine learning Search engines internet Standards

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Performance Measurement of LoRa Modules for Extending Smart Applications to the Things Network

Performance Measurement of LoRa Modules for Extending Smart ...

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Advances in Electrical, Electronics and Computing technology (EECT), international conference on

作者： Afifi Khoury Abdallah Al-Zoubi National Electric Power Company Amman Jordan Princess Sumaua University for Technology Amman Jordan

ISBN: (数字)9798331541545

ISBN: (纸本)9798331541552

The performance of three LoRa modules with similar characteristics and designs was tested to select suitable options for smart applications. The modules were tested in a specific location in Jordan, which has unique terrains and weather conditions. A GPS module was added to simplify measurements of location and elevation. The data collected included the signal-to-noise ratio (SNR), the received signal strength indicator (RSSI), and benchmark sensor values for temperature and humidity. A Blynk server and Google Sheets scripts facilitated the real-time tracking process and automation of data collection. The results on signal behavior with distance should provide a guide in LoRa module design specification. A low-cost LoRa development board, which achieved over 7.5 Km in range and provided adequate signal stability, was utilized with a gateway to create a LoRaWAN-based system for smart applications. Distribution system. Further integration to The Things Network (TTN) produced promising outcomes for the LoRaWAN-based system, highlighting its potential for sustainable water management solutions.

关键词： Temperature measurement Temperature sensors Performance evaluation Logic gates Real-time systems internet Servers Received signal strength indicator signal to noise ratio Global Positioning System

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Enhanced Data Hiding based on First, Middle, and Last Order Pixel in Spatial Domain images

Enhanced Data Hiding Based on First, Middle, and Last Order ...

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international conference on signal Processing and Communication (ICSC)

作者： Rahmad Hidayat Adifa Widyadhani Chanda D'Layla Ntivuguruzwa Jean De La Croix Tohari Ahmad Department of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia STAIN Sultan Abdurrahman Kepulauan Riau Indonesia Department of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia University of Rwanda Kigali Rwanda Department of Informatics Intitut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

The rapid advancement of internet technology has revolutionized global information sharing, enabling faster and more efficient digital communication. However, the increased reliance on online platforms for exchanging sensitive information raises significant concerns about data security, as unauthorized access and misuse of transmitted data pose severe risks, including identity theft and financial fraud. Steganography has emerged as a promising technique for securing data by embedding it imperceptibly into digital media. This study introduces a novel steganographic method that utilizes the difference between pixels in the cover image to embed secret messages while maintaining high image quality. The proposed scheme divides image pixels into blocks of 9. It calculates the difference between pixels with each block's first, middle, or last pixel to ensure minimal distortion and superior visual quality post-embedding. Experimental results validate the effectiveness of the method, achieving consistently higher PSNR (Peak signal-to-Noise Ratio) values compared to a baseline approach, with a PSNR of 64.9205 dB for a 10 KB payload and similarly superior results for payloads of 20 KB and 40 KB.

关键词： image quality Steganography Visualization PSNR Scalability Media Distortion Robustness internet Payloads

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Comparative Analysis of Object Detection Accuracy in Diverse Background Conditions

Comparative Analysis of Object Detection Accuracy in Diverse...

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international conference on signal Processing and Communication (ICSC)

作者： Aparajita Sheoran Aman Tripathi Ankur Bharadwaj Electronics and Communication Jaypee Institute of Information Technology Noida India

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

Object detection models must perform effectively across diverse environmental conditions to support autonomous navigation and surveillance systems. YOLOv5 and MobileNetV2 were evaluated using a custom dataset of 100,000 images captured under varied conditions, including daylight, fog, rain, and nighttime. YOLOv5 achieved an F1 score of 0.89 and a precision of 0.91 in low-light and rainy scenarios, demonstrating robustness in challenging environments. MobileNetV2 delivered faster inference speeds of up to 45 FPS on devices like the Raspberry Pi 5 and Google Coral Dev Board, with an F1 score of 0.82 in clear and foggy conditions, making it a strong candidate for resource-constrained deployments. The evaluation highlights YOLOv5's advanced feature extraction for high-accuracy detection and MobileNetV2's lightweight architecture for computational efficiency. These findings enable informed decision-making when selecting models for edge-based object detection applications, particularly in cost-sensitive and real-world scenarios.

关键词： YOLO Rain Surveillance image edge detection Decision making signal processing Feature extraction Robustness internet Edge computing

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Power internet of Things Access System based on Low Power Communication technology

Power Internet of Things Access System Based on Low Power Co...

引用

international conference on Communication systems and Network Technologies (CSNT)

作者： Zhigang Jiang Liming Ren Bing Zhu Jiafeng Jiang School of Computer Science and Technology University of Science and Technology of China Hefei China School of Department of Electronic Information and Electrical Engineering Hefei University Hefei China School of Computer and Information Hefei University of Technology Hefei China School of Information Engineering Inner Mongolia University of Science and Technology Baotou China

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

In response to the issues of data transmission energy efficiency and network coverage in the power internet of Things system, this study introduces an access scheme based on low-power communication technology. By optimizing the network architecture and communication protocol, the aim is to improve the energy efficiency of sensor nodes in the power internet of Things and reduce communication latency. Firstly, an access scheme based on low power wide area network (LPWAN) technology is designed. LoRa (Long Range Radio) or NB-IoT (Narrow Band internet of Thing) is selected as the communication protocol. By optimizing the working mode of sensor nodes, periodic transmission and event-driven mechanism are adopted. Secondly, an adaptive data transmission mechanism is designed to dynamically adjust the transmission frequency and rate according to environmental changes to ensure reliability under different network conditions. Furthermore, deep sleep mode and power consumption scheduling algorithm are adopted, and the network architecture adopts star topology to ensure the efficiency of signal coverage and data forwarding, and the secure transmission of data information is guaranteed by encryption and authentication mechanism. Finally, through simulation and actual deployment, the performance of the scheme in the actual environment is verified, and its stability and data transmission efficiency under different network scales are evaluated. The battery life of periodic wake-up mode is significantly lower than that of deep sleep mode. Under low-frequency transmission (once an hour), the battery life is 320 days. In periodic wake-up mode, the average power consumption of sensor nodes is significantly higher than that of deep sleep mode. At low frequency transmission, average power consumption is 3.5 mW and battery life drops to 75 days. The power internet of Things access system based on low-power communication technology proposed in this work effectively solves the bottleneck pr

关键词： Power demand Protocols Network architecture Communications technology Energy efficiency Stability analysis Delays Batteries internet of Things Thermal stability

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IoT based Line of Control Monitoring System

IoT based Line of Control Monitoring System

引用

Machine Learning and Autonomous systems (ICMLAS), international conference on

作者： Taniya Petkar Department of Computer Science And Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

This paper presents a novel line-of-control (LoC) monitoring system that leverages the internet of Things (IoT) to improve border security. The system creates a strong infrastructure for real-time monitoring throughout critical border regions, especially the Line of Control, by utilizing IoT devices, sensors, and communication technologies. The coverage, timeliness, and data quality of conventional border surveillance systems are frequently compromised. As opposed to this, the suggested system makes use of a wirelessly networked network of intelligent sensors, such as infrared, acoustic, and video cameras. By utilizing sophisticated image processing algorithms and machine learning to analyze the gathered data, the system is able to differentiate between regular activities and any security risks. To help security staff make decisions quickly, real-time updates are transmitted to a centralized control center. Integration with cloud computing also makes data analysis, retrieval, and storage simpler and guarantees data accessibility for forensic investigations and trend research.

关键词： Data analysis Surveillance Scalability Machine learning Cameras Real-time systems Sensor systems Security internet of Things Intelligent sensors

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MEDIAN: Adaptive Intermediate-grained Aggregation Network for Composed image Retrieval

MEDIAN: Adaptive Intermediate-grained Aggregation Network fo...

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Qinlei Huang Zhiwei Chen Zixu Li Chunxiao Wang Xuemeng Song Yupeng Hu Liqiang Nie School of Software Shandong University Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) School of Computer Science and Technology Shandong University School of Computer Science and Technology Harbin Institute of Technology (Shenzhen)

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The Composed image Retrieval (CIR) task aims to retrieve a target image that meets the requirements based on a given multimodal query (includes a reference image and modification text). Most existing works align multimodal semantics at both local and global granularity. However, they have failed to consider the mining of semantic correspondences at the intermediate-grained level, which has resulted in sub-optimal model performance. In this paper, we propose an adaptive interMEDiate-graIned Aggregation Network (MEDIAN). Compared with the conventional CIR models, MEDIAN is capable of generating intermediate-grained feature aggregation supervised signals and constructing graph attention networks to extract intermediate-grained features. Concurrently, MEDIAN also devises cross-modal semantic correspondence aligning guided by the target image, which in turn enables accurate multi-grained feature composition. The superiority of MEDIAN is demonstrated by extensive experiments on three benchmark datasets. Our code is available at https://***/***/.

关键词： Adaptation models Adaptive systems Accuracy Codes Semantics image retrieval Benchmark testing signal processing Feature extraction Speech processing

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Adaptive KOA: Leaf disease classification using hyperspectral images for internet of Things (IoT)-based sustainable agriculture

Adaptive KOA: Leaf disease classification using hyperspectra...

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Emerging systems and Intelligent Computing (ESIC), international conference on

作者： Pradeep Kumar Mallick Annapareddy V N Reddy Manas Ranjan Mohanty Biswajit Sahoo Alok Kumar Jagadev School of Computer Engineering Kalinga Institute of Industrial Technology(KIIT) DU Bhubaneswar Odisha Department of Information Technology Lakireddy Bali Reddy College of Engg. Mylavaram Andhra Pradesh

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

In recent times, the crops as well as the agriculture management is one of the important field to watch out, therefore the image processing business provide more benefit to the crops along with support precaution needs. In this research, an adaptive Kookaburra Optimization Algorithm (Adaptive KOA) is implemented for leaf disease classification using hyperspectral images for Sustainable Agriculture in *** first, the IoT modelis replicated and the routing is conductedto transmit the data from source to the Base Station (BS). Moreover, in BS the following steps are conducted. The input hyperspectral image (HSI)is subjected to a band selection module. The band selection is done using the implemented Adaptive Kookaburra Optimization Algorithm (Adaptive KOA). The developed Adaptive KOA is devised by incorporating Adaptive concept with Kookaburra Optimization Algorithm (KOA). The Selected bands are fed into the leaf segmentation module, where the Deep Embedded Clustering (DEC) technique is utilized for segmentation. Fusion of segmented image from different bands is carried out by the majority voting method. Finally, Leaf disease classification is carried out based on the 3D-Convolutional Neural Network (3DCNN) into normal and abnormal cases. The developed Adaptive KOA-3DCNN method has the maximum value for accuracy as 0.917%, highest value for True negative rate(TNR)as 0.927%, highest value for True positive rate (TPR) as 0.907%.

关键词： image segmentation Plant diseases Adaptation models Clustering algorithms Routing Agriculture Classification algorithms internet of Things Optimization Hyperspectral imaging

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