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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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AN IOT based FRAMEWORK FOR PREDICTION OF ENVIORNMENT QUALITY USING ARTIFICIAL INTELLIGENCE 7

AN IOT BASED FRAMEWORK FOR PREDICTION OF ENVIORNMENT QUALITY...

引用

IEEE 7th international conference on Advanced Technologies, signal and image Processing (ATSIP)

作者： Alqahtani, Omar Saad Kshirsagar, Pravin Ramdas King Khalid Univ Dept Comp Sci Abha Saudi Arabia JD Coll Engn & Management Dept Elect & Telecommun Engn Nagpur Maharashtra India

ISBN: (纸本)9798350351491;9798350351484

Environmental phenomena affect our everyday life greatly. This included flexible structures the resources of life, including the pure water and air of our planet. The word climate is characterized as a platform that provides numerous facilities for several atmospheric applications in the areas of water allocation, poor air quality, meteorological conditions, radioactive detection, wastewater treatment, catastrophic event as well as many other predisposing factors. Monitoring system, modeling and management provide a better knowledge of key development and environmental changing management methods. Over time, the problem of emissions has increased owing to a number of reasons, such as increasing population and usage of cars, urban development, which has a significant impact on workers' health. This article proposes AI and IoT for the predictions and modeling of the environmental quality assessment system. The Artificial Intelligence method can evaluate data extremely efficiently and take accurate choices on services in many kinds. The IoT gateway includes the entire application software from sensor level to environmental information systems. The test data suggest that the new approach offers an efficient means of analyzing environmental information over a lengthy period of time.

关键词： Environment Quality Monitoring System internet of Things[IoT] Artificial Intelligence [AI] Air Quality Water Pollution Radiation Pollution Accuracy Efficiency Performance

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Semantic Segmentation-based Label Position Defect Detection

Semantic Segmentation-based Label Position Defect Detection

引用

9th international conference on signal and image Processing (ICSIP)

作者： Yue, Hongwei Jin, Yingying Guangdong Univ Educ Sch Phys & Informat Engn Guangzhou Peoples R China Guangzhou Panyu Polytech Dept Gen Required Courses Guangzhou Peoples R China

ISBN: (纸本)9798350350920

In the label manufacturing industry, accurately identifying label positioning defects is of great significance for ensuring product quality. However, traditional defect detection techniques have faced significant challenges due to the diversity of label materials and the complexity of imaging conditions. In recent years, semantic segmentation technology has made breakthrough progress in the field of image processing, providing innovative solutions to the aforementioned issues. This study proposes a semantic segmentation framework that integrates ResNet50 and Global Context (GCNet) modules, aiming to accurately identify label positioning defects. Through the training of a deep learning model, this framework achieves precise delineation of the label area and effectively determines positioning defects. The experiment was conducted using a dataset composed of 1653 manually annotated images, and the results revealed that the proposed algorithm has significant advantages in terms of segmentation accuracy and algorithm robustness. The algorithm developed in this study demonstrates high efficiency in detecting label positioning defects, providing an innovative technical approach for automated quality control systems.

关键词： labels image segmentation attention mechanism GCNet

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Artificial Intelligence image Detection System based on internet of Things 2

Artificial Intelligence Image Detection System Based on Inte...

引用

2nd international conference on 3D Immersion, Interaction and Multi-Sensory Experiences, ICDIIME 2023

作者： Liu, Guangcai Zhang, Wenbin Sun, Li Chen, Xianya Wu, Liqin Lin, Zhongjie Hao, Liang North Automatic Control Technology Institute Shanxi Taiyuan China

ISBN: (纸本)9798350323818

With the development of image recognition technology and computer technology, artificial intelligence is more and more applied in people's production and life. In addition, the internet of Things technology can be seen everywhere in our daily life. The purpose of this paper is to design an image detection system based on the internet of Things technology, which uses CMOS image sensor to obtain real-time images, obtains candidate flame areas through flame judgment of image pixels, and uses flame detection method of in-depth study of neural network for flame detection. It communicates through the internet of Things module and sends fire alarms and alarm messages to the server platform based on the flame detection results. The results show that the system designed in this paper meets the design requirements. © 2023 IEEE.

关键词： systems analysis

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internet of Things Data Aggregation based on image Information Hiding 23

Internet of Things Data Aggregation Based on Image Informati...

引用

7th international conference on Digital signal Processing, ICDSP 2023

作者： Yang, Yanxiang University of Melbourne Australia

ISBN: (纸本)9781450398626

In the era of intelligent information technology of 5G, smart buildings, promoted by the internet of Things, enhance the ability of indoor monitoring and equipment management. The acquisition and transmission of Architectural Physical Environment Data (APED) is a crucial part of the internet of Things (IoT). Traditional methods rely on many sensors and multiple types of network channels, resulting in problems such as many data transmissions and increased communication bandwidth requirements. Therefore, a data aggregation strategy based on data hiding technology is proposed. All the temporal data is hidden in the existing image through a customized lightweight codec scheme and communication protocol based on binary coding, and the data is aggregated into image(s) for uploading without reducing the fidelity of the image, to improve the efficiency of data transmission. The paper uses APED to conduct simulation experiments to verify the feasibility of the technique. The scheme can be extended to other similar internet of Things applications. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： internet of things

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Driving with Deep Learning: A Robust Traffic Sign Detection and Classification System 2

Driving with Deep Learning: A Robust Traffic Sign Detection ...

引用

2nd international conference on Intelligent Data Communication Technologies and internet of Things, IDCIoT 2024

作者： Patel, Harshita Chippa, Ishaan Kumar, Devansh Maske, Vinay Ravindra Satish Bagal, Aakanksha Singh Rajput, Dharmendra School of Information Technology and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350327533

Intelligent Transport systems, such as self-driving automobiles, represent a prime example of applications utilizing traffic sign recognition technology. The increasing demand for autonomous vehicles highlights a significant challenge: Their ability to adhere to traffic regulations without human intervention for road safety. As technological advancements pave the way for increased automation, this study addresses the detection and identification of traffic sign boards under varying conditions, including lighting, direction, and diverse weather scenarios. Focusing on scenarios where the traffic sign is the sole object in an image with a minimal background, image processing techniques are employed for traffic sign recognition. Furthermore, the identified and classified sign is associated with its specific geospatial coordinates, using the device's location data, and subsequently stored in a database. This enables tracking of updates or changes to traffic signs along a designated route or area, with original information being replaced or overwritten upon any alteration at a particular location. © 2024 IEEE.

关键词： CNN Geospatial coordinates Intelligent Transport systems Traffic signal Database

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A Review of Artificial Intelligence-Enhanced Security Solutions for the internet of Things 4

A Review of Artificial Intelligence-Enhanced Security Soluti...

引用

4th international conference on Artificial Intelligence, Virtual Reality and Visualization, AIVRV 2024

作者： Yue, Qingqing Tu, Shanshan Li, Wenlong Li, Hongchen Beijing University of Technology Faculty of Information Technology Beijing100124 China

ISBN: (纸本)9798331528744

The rapid development of the internet of Things (IoT) is fundamentally transforming various industries, with many IoT systems increasingly driven by artificial intelligence (AI). Advances in AI introduce new changes and breakthroughs in IoT, while the swift progress of IoT technology also presents significant security and privacy challenges. This paper reviews the major security issues and solutions within AI-driven IoT environments, focusing on key management protocols, intrusion detection systems, physical layer authentication, and data encryption. It examines the advantages and limitations of symmetric and asymmetric key management, the roles of network-based and host-based intrusion detection systems, and how physical layer authentication enhances device security through wireless signal characteristics. Additionally, the paper discusses the role of homomorphic encryption and privacy protection technologies, suggesting that integrating these methods represents an effective strategy for addressing IoT security challenges. Future research directions include developing scalable security protocols, advanced threat detection systems, and incorporating AI to enhance the resilience and efficiency of IoT security measures. © 2024 IEEE.

关键词： Artificial Intelligence internet of Things security and privacy

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AI-GENERATED image DETECTION WITH WASSERSTEIN DISTANCE COMPRESSION AND DYNAMIC AGGREGATION 31

AI-GENERATED IMAGE DETECTION WITH WASSERSTEIN DISTANCE COMPR...

引用

2024 international conference on image Processing

作者： Lyu, Zihang Xiao, Jun Zhang, Cong Lam, Kin-Man Hong Kong Polytech Univ Dept Elect & Elect Engn Kowloon Peoples R China

ISBN: (纸本)9798350349405;9798350349399

With the rapid advancement of generative models, image detectors for AI-generated content have become an increasingly necessary technology in computer vision, attracting significant attention from researchers. This technology aims to detect whether an image is naturally generated by imaging systems (e.g., digital cameras) or generated by advanced AI techniques. Despite the promising performance achieved by recent fake detection methods, they are typically trained on millions of redundant images with similar characteristics, leading to inefficient training. Furthermore, the performances of existing detectors often deteriorate when the training datasets are imbalanced. To address these challenges, we propose a novel AI-generated image detector based on dynamic aggregation and information compression with the Wasserstein distance. Experimental results show that our proposed method significantly outperforms state-of-the-art models that generalize across different generative models, with an increase of +1.86% average accuracy and +0.14% average precision, while substantially reducing the training time. On imbalanced datasets, our proposed method leads to a +14.46% accuracy improvement, clearly demonstrating its robustness on imbalanced datasets.

关键词： Fake image Detection Efficient Training

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ISP2DLA: Automated Deep Learning Accelerator Design for On-Sensor image signal Processing 35

ISP2DLA: Automated Deep Learning Accelerator Design for On-S...

引用

35th IEEE international conference on Application-Specific systems, Architectures and Processors (ASAP)

作者： Won, Dong-eon Kim, Yeeun Lee, Janghwan Lee, Minjae Bae, Jonghyun Park, Jongjoo Song, Jeongyong Choi, Jungwook Hanyang Univ Seoul South Korea SK Hynix Inc Icheon Gyeonggi Do South Korea

ISBN: (纸本)9798350349641;9798350349634

Deep neural network-based image signal processing (ISP-DNN) improves image quality with techniques such as demosaicing, but these models pose substantial computational and memory challenges when implemented on CMOS image sensors, particularly due to the high-resolution inputs that increase memory requirements for activations. Layer fusion reduces memory usage by combining consecutive processing steps, yet it increases computational demands, a critical issue in resource-limited on-sensor environments. To address these challenges, we introduce ISP2DLA, an automated deep learning accelerator design framework that balances computational and memory demands for on-sensor ISP. This framework optimizes hardware designs by adjusting line buffer sizes and the number of MAC units, reducing gate counts by 14-79% across two ISP-DNN models, thus enabling efficient on-sensor ISP model inference within constrained resources.

关键词： image signal processing On-sensor deep learning acceleration Automatic accelerator design

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Multimodal Sign Language and Big Data Integration Research 9

Multimodal Sign Language and Big Data Integration Research

引用

9th international conference on Communication, image and signal Processing, CCISP 2024

作者： Song, Fengqian Yang, Huiyu Zhang, Yangyang Internet Academy Anhui University Hefei China Daguan Data Co. Ltd. Shanghai China

ISBN: (纸本)9798350356656

Sign language is the primary means of communication for the deaf and mute community. The regional and national differences in sign language have led to barriers between different sign language systems, making communication between deaf and mute individuals from different regions challenging. This paper proposes a vision-tactile fusion approach to recognize sign language. The scheme applies a flexible sensor data glove to monitor the angle changes of finger joints, as well as visual image processing technology, to integrate tactile and visual data for sign language recognition. The integration is performed through three methods: weighted average, weighted multiplication, and attention weights, with accuracy rates of 95.55%, 97.87%, and 98.83%, respectively. © 2024 IEEE.

关键词： Data integration

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Acoustic signal Processing using Deep Learning for Intelligent Feed Monitoring of Dicentrarchus Labrax 17

Acoustic Signal Processing using Deep Learning for Intellige...

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17th international conference on signal-image technology and internet-based systems, SITIS 2023

作者： Bies, Laura Guruprasad, Shreyas Mysore Ingale, Mrudula Werth, Dirk August-Wilhelm Scheer Institute Saarbrücken Germany

ISBN: (纸本)9798350370911

Acoustic signal processing holds significant promise for real-time fish feeding intensity estimation in aquaculture. Unlike traditional methods reliant on visual cues or sensor data, acoustic analysis provides valuable insights into feeding behavior and demand. By capturing indicators such as water splashing, acoustic techniques can estimate the current feeding demand of fish. Acoustic techniques in aquaculture remain under explored, especially those delving into temporal information within acoustic spectrograms. This paper presents an intelligent monitoring approach using deep learning and acoustic signals. It investigates the perceptual domain of fish feeding acoustic spectrum recognition, extracting insights from Mel Spectrogram feature maps. Employing a supervised machine learning method with a multi-instance multi-label technique, the study classifies audio events during operational scenarios. Furthermore, the research assesses the effectiveness of the proposed neural network (NN) models for multi-label classification by comparing it with established NN architectures like AlexNet, ResNet18, and VGG11, showcasing its superior performance. © 2023 IEEE.

关键词： Feeding

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