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

共 7686 条记录，以下是301-310 订阅

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Benchmarking Semantic Communications for image Transmission Over MIMO Interference Channels 16

Benchmarking Semantic Communications for Image Transmission ...

引用

16th international conference on Wireless Communications and signal Processing, WCSP 2024

作者： Wang, Yanhu Guo, Shuaishuai Dong, Anming Zhao, Hui School of Control Science and Engineering Shandong University China School of Computer Science and Technology Qilu University of Technology China Eurecom Communication Systems Department France

ISBN: (纸本)9798350390643

Semantic communications offer promising prospects for enhancing data transmission efficiency. However, existing schemes have predominantly concentrated on point-to-point transmissions. In this paper, we aim to investigate the validity of this claim in interference scenarios compared to baseline approaches. Specifically, our focus is on general multiple-input multiple-output (MIMO) interference channels, where we propose an interference-robust semantic communication (IRSC) scheme. This scheme involves the development of transceivers based on neural networks (NNs), which integrate channel state information (CSI) either solely at the receiver or at both transmitter and receiver ends. Moreover, we establish a composite loss function for training IRSC transceivers, along with a dynamic mechanism for updating the weights of various components in the loss function to enhance system fairness among users. Experimental results demonstrate that the proposed IRSC scheme effectively learns to mitigate interference and outperforms baseline approaches, particularly in low signal-tonoise (SNR) regimes. © 2024 IEEE.

关键词： Radio transceivers

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Interference Resisted Cross-dimensional Information Aggregation Local Refinement Network for Colposcopic image Lesion Segmentation 16

Interference Resisted Cross-dimensional Information Aggregat...

引用

16th international conference on signal Processing systems, ICSPS 2024

作者： Zheng, Zhipeng Huang, Guoheng Yang, Xiaohui Tan, Zhengguang Guangdong University of Technology China The Third Affiliated Hospital of Sun Yat-sen University China

ISBN: (纸本)9781510689251

In recent years, contour instance segmentation methods have rapidly developed due to advantages such as low computational costs, independence from detection boxes, and the absence of the need to process the internal pixel points of target objects. However, current contour instance segmentation methods still face issues like human-designed rough initial contours and overlapping contour vertex regression paths, which greatly limit the performance of contour instance segmentation algorithm models and increase the difficulty of learning for these models. Additionally, due to objective conditions such as high-light reflection areas and complex lesion edges in colposcopic images, existing models generally exhibit under-segmentation or over-segmentation phenomena. This paper proposes an end-to-end contour instance segmentation algorithm for colposcopic images, based on cross-dimensional attention mechanisms, autonomous initial contour generation, and multi-directional collaborative contour adjustment, effectively reducing the impact of the aforementioned issues and enhancing segmentation accuracy. On the cervical acetic acid map dataset provided by Dr. Yang Xiaohui from the Third Affiliated Hospital of Sun Yat-sen University, this paper's method has achieved improvements of 0.075, 0.047, and 0.013 in the Dice index, IoU index, and AP index respectively compared to the E2EC model. On the cervical iodine map dataset, the improvements in each index are 0.089, 0.079, and 0.069 respectively. © 2025 SPIE.

关键词： image segmentation

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2023 6th international conference on signal Processing and Information Security, ICSPIS 2023

2023 6th International Conference on Signal Processing and I...

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6th international conference on signal Processing and Information Security, ICSPIS 2023

ISBN: (纸本)9798350329599

The proceedings contain 44 papers. The topics discussed include: blockchain-enabled DNS: enhancing security and mitigating attacks in domain name systems;deep learning-based human tracking, face mask and social distance monitoring, systems using YOLOv5;IMU-based human activity recognition using machine learning and deep learning models;enhancing brain tumor aid diagnosis with augmented reality rendering-based 6 DoF object pose estimation;secure key exchange scheme and blockchain-oriented data protection in the internet of vehicles;an intelligent edge-deployable indoor air quality monitoring and activity recognition approach;DiffT: a novel approach for privacy preserving data analytics;using data integration platform for effective location-aware service development platform;hybrid whale-mud-ring optimization for precise color skin cancer image segmentation;and an extensive evaluation of plant disease detection using diverse machine learning approaches.

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An Efficacious Classifier for Recognition of Traffic Symbols 1

引用

7th international conference on internet of Things and Connected Technologies, ICIoTCT 2022

作者： Virmani, Deepali Parikh, Ketan School of Engineering & Technology Vivekananda Institute of Professional Studies-Technical Campus New Delhi India Department of Computer Science Engineering Bhagwan Parshuram Institute of Technology New Delhi India

ISBN: (数字)9789811997198

ISBN: (纸本)9789811997181

Information gathering refers to collecting information from different sources in order to process it to obtain useful information. In particular, image classification refers to the use of several computer algorithms to classify images to extract useful information and as to what they symbolize. It is one of the deep learning methods. Traffic symbols are important to be noticed while driving any vehicle. This paper proposes an image classifier to classify and detect traffic symbols from the dataset of traffic symbols. The proposed algorithm for image classification provides an efficient and accurate way to classify the traffic signal images into classes and the obtained results have great precision. This image classifier can detect the traffic symbols in real time with an accuracy of 88.76%. Various datasets can be chosen to perform experiments with the resulting image classifier. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image classification

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A Secure Watermarking based image Integrity Verification in IoMT 4

A Secure Watermarking Based Image Integrity Verification in ...

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4th IEEE international conference on Computing, Communication, and Intelligent systems, ICCCIS 2023

作者： Sulfi, Saeeda Neena Raj, N.R. College of Engineering Trivandrum Department of Computer Science and Engineering Thiruvananthapuram India Mar Baselios College of Engineering and Technology Department of Computer Science and Engineering Thiruvananthapuram India

ISBN: (纸本)9798350306118

The internet of Medical Things in healthcare necessitates a secure way to transfer medical images due to the rise of telemedicine. Security breaches in public networks lead to falsification of medical images and further incorrect diagnoses. To address this issue, we propose a multiwatermarking scheme in which logo is embedded in region of interest and text data is concealed in regions of non-interest for providing authentication and integrity verification. Fractal encoding technique is used to ensure the secure transmission of watermarked medical image. Experimental results demonstrate that proposed scheme ensures high visual quality, lossless recovery of text data and high security. The proposed scheme provides better watermarked image quality as compared with state-of-The-Art schemes. © 2023 IEEE.

关键词： Authentication

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An In-Depth Analysis of Application Protocols Performances in Various IoT Network Environments 8

An In-Depth Analysis of Application Protocols Performances i...

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8th IEEE international conference on image and signal Processing and their Applications, ISPA 2024

作者： Belkheir, Mohammed Rouissat, Mehdi Boukhobza, Mohamed Achraf Mokaddem, Allel Belkhira, Hichame Sid Ahmed Lorenz, Pascal Bouziani, Merahi Beneddine, Messaouda Reguieg, Aya Nour Bachir University Center LIMA Laboratory El-Bayadh Algeria Nour Bachir University Center El-Bayadh Algeria Haute Alsace University Mulhouse France Djilali Liabes University Sidi Bel Abbes Algeria

ISBN: (纸本)9798350309249

The internet of Things (IoT) has emerged as a transformative technology, revolutionizing critical domains such as healthcare, transportation, and energy management by enabling seamless connectivity, real-time data insights, and enhanced automation. Therefore, considerations for both intrinsic and extrinsic constraints are essential in the design of application protocols for IoT networks. IoT nodes often have limited processing power, memory capacity, and battery life. Furthermore, these nodes are interconnected wirelessly within a topology that undergoes dynamic and random changes. This in-depth study aims to analyze the performance of MQTT-SN (Message Queuing Telemetry Protocol for Sensor Networks) and CoAP (Constrained Application Protocol), which are widely regarded as prominent options for the IoT application layer. The analysis focuses on evaluating the control overhead and total energy consumption associated with these protocols under various scenarios of topologies and densities of IoT *** simulation results reveal a significant difference in the generation of control packets at the network level between CoAP and MQTT-SN. Specifically, CoAP exhibits a higher control overhead, which varies based on the network density, while maintaining equivalent energy consumption for both protocols. Moreover, CoAP demonstrates superior efficiency in transmitting application data messages compared to MQTT- SN under similar network constraints. © 2024 IEEE.

关键词： internet of things

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A Deep Assessment of Thermal image-Base Object Detection for a Wide Range of Applications 2

A Deep Assessment of Thermal Image-Base Object Detection for...

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2nd international conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Pandurangan, Kamaleswari Nagappan, Krishnaraj SRM University School of Computing Dept. of Networking and Communication SRM Institute Science and Technology Kattankulathur India

ISBN: (纸本)9798350349221

Computer vision enables for the detection of things even in densely populated areas. This work investigates autonomous object detection and activity recognition in static and dynamic images, as well as their potential and restrictions. Several object identification challenges are addressed in relation to illness diagnosis, identifying wild animals, and identifying industrial activities, such as processing, segmentation, and extraction techniques for various object features. It is also critical to highlight the limitations, gaps, technical problems, applicability, and state-of-the-art of the various techniques. Shape-based detection, skin color extraction, edge identification, and an object detector are all approaches we use to identify and classify objects. This contrasts with motion detection, which is less precise when utilized at night. Thermal imaging has been used to track objects in a variety of applications, including surveillance systems and the identification of people and wildlife. We plan to evaluate new object detectors such as YOLO and RCNN. Another route for future research was suggested: investigate various object identification systems, particularly those that deal with thermal video or image detection. © 2024 IEEE.

关键词： Object detection

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An Advanced IoT and GPS-GSM based Real-Time Automated Data Monitoring Robot with image Processing and Live Streaming for Human Life and Land Mine Detection systems

An Advanced IoT and GPS-GSM Based Real-Time Automated Data M...

引用

2024 international conference on Computer, Electrical and Communication Engineering, ICCECE 2024

作者： Islam, Md. Muhaiminul Akter, Farzana Hossain, Md. Rakib Faruk Hossain, Md. Mahedy Hasan, S.M. Yakin Srizon, Md. Azmain Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi6204 Bangladesh Department of Electrical & Electronic Engineering Rajshahi6204 Bangladesh Department of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350386479

An era of automation is currently being experienced, where everything is becoming more automated day by day. Automation technology has been applied everywhere, from smaller to larger scales. Moreover, real-time communication between complicated human-machine interfaces is one of the difficulties of the fourth industrial revolution. That is why robots are essential in this field because they improve human lifestyles, lighten workloads, and carry out several dangerous tasks. This research presents a groundbreaking advancement in the form of an automated real-time IoT-based data monitoring robot in an era characterized by the proliferation of automation and the internet of Things (IoT). This ground-breaking system combines GPS-GSM functionality, human life detection, land mine detection, image processing, and live streaming in an efficient manner. The robot utilizes its IoT framework and a variety of embedded sensors and technology to gather, process, and transmit realtime data from its surroundings. Notably, it supports disaster response, military operations, and safety measures by performing the vital tasks of detecting human life and land mines. The ability to categorize and process visual data is improved by the image processing feature, and situational awareness is provided by live streaming © 2024 IEEE.

关键词： Global positioning system

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A Statistical Approach for Improving image Quality and Cell Quantification from Imaging Mass Cytometry 9

A Statistical Approach for Improving Image Quality and Cell ...

引用

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

作者： Xiao, Xu Lin, Yating Zhang, Lei Yang, Wenxian Yu, Rongshan School of Informatics Xiamen University Xiamen China Aginome Scientific Xiamen China

ISBN: (纸本)9798350356656

Imaging Mass Cytometry (IMC), a multiplexed imaging technology, has become a valuable tool in biomedical research due to its capability to measure over 100 markers theoretically. However, the presence of noise in IMC images can affect cell phenotyping and the precision of clinical analysis. To address this challenge, we propose IMCell, a highly effective and generalizable method that enhances image quality and cell quantification by leveraging biological priors. IMCell begins by generating decoy cells using Monte Carlo sampling and incorporates cell shape information for each protein channel. Subsequently, it estimates background noise from the expression of decoy cells by fitting Gaussian mixture models with the EM algorithm. Additionally, it identifies positive cell expressions by comparing distribution patterns between real cells and decoy cells. based on the analysis results from IMCell, we further introduce an image quality index for comprehensive image quality assessment by combining noise and positive cell expressions. Experiment results demonstrate the efficacy of our proposed approach in enhancing image quality and facilitating downstream analysis. By improving the quality of IMC images, IMCell holds the potential to enhance our understanding of complex biological systems and disease mechanisms. © 2024 IEEE.

关键词： Biomedical imaging EM algorithm Gaussian mixture model image quality enhancement IMC

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Ray Tracing based RIS-Assisted Corridor Environment Channel Reconstruction at 300GHz 12

Ray Tracing Based RIS-Assisted Corridor Environment Channel ...

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12th IEEE international conference on Information and Communication Networks, ICICN 2024

作者： Zheng, Wenlong Lu, Hao College of Information Science and Engineering Hohai University Nanjing China

ISBN: (纸本)9798350355802

Reconfigurable intelligent surfaces (RIS) have emerged as a crucial technology for sixth-generation (6G) communication systems. Mastering the modeling and analysis methods of RIS is essential for its effective implementation. In this paper, a channel reconstruction model is proposed based on ray tracing (RT) simulations in an indoor corridor environment at 300 GHz. It can reconstruct the extracted multipath components (MPCs) to enhance their accuracy and alignment with real-world measurement environments. By analyzing the power delay profile (PDP), power angular profile (PAP), and path loss (PL) before and after integrating RIS, the accuracy and effectiveness of the proposed method are verified. It has also confirmed that RIS significantly improves the transmission efficiency and extends the signal coverage of communication systems. © 2024 IEEE.

关键词： image reconstruction

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