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

共 20484 条记录，以下是291-300 订阅

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Fast NMPC Design for image-based Visual Servoing of Autonomous Underwater Vehicles 7

Fast NMPC Design for Image-Based Visual Servoing of Autonomo...

引用

ieee 7th international conference on Industrial Cyber-Physical systems (ICPS)

作者： Gu, Hang Shen, Chao Carleton Univ Dept Syst & Comp Engn Ottawa ON Canada

ISBN: (纸本)9798350363029;9798350363012

This paper presents a new approach to image-based visual servoing (IBVS) for Autonomous Underwater Vehicles (AUVs) with the goal of improved performance and computational efficiency. Traditional IBVS methods, when combined with Model Predictive Control (MPC), face high computational demands due to the nonlinear dynamics and the large degrees of freedom (DOFs) in the variables of the associated optimization problem. Our method addresses this by reducing the DOFs of the optimization variable in the cost function while maintaining a good control performance. To further consider the smoothness of the MPC control signal, a soft constraint handling method is developed. The fast nonlinear MPC, combined with smoother control trajectories and effective constraint handling, makes our method particularly suitable for AUV IBVS applications in dynamic environments. Comparisons with standard strategies confirm the improved performance of our approach in terms of both speed and trajectory quality. Simulation results show that our approach can achieve an improved computation up to 100 times faster than conventional MPC-based IBVS methods, which highlights the great potential for real-time IBVS applications.

关键词： Model Predictive Control Visual Servoing Computational Efficiency Robotics Optimization Algorithms

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A Lightweight and Effective Multi-View Knowledge Distillation Framework for Text-image Retrieval

A Lightweight and Effective Multi-View Knowledge Distillatio...

引用

international Joint conference on Neural Networks (IJCNN)

作者： Song, Yuxiang Zheng, Yuxuan Zhao, Shangqing Liu, Shu Zhuang, Xinlin Long, Zhaoguang Sun, Changzhi Zhou, Aimin Lan, Man East China Normal Univ Sch Comp Sci & Technol Shanghai Peoples R China China Jiangxi Radio & TV Stn Nanchang Jiangxi Peoples R China East China Normal Univ Shanghai Inst AI Educ Shanghai Peoples R China

ISBN: (纸本)9798350359329;9798350359312

Large-scale dual-stream Vision-Language Pre-training (VLP) models provide an efficient solution for text-image retrieval tasks. Despite this, their performance often falls short of the most current single-stream models, primarily due to limited fine-grained text-image interactions. Recent trends indicate a union of these two types of networks. Some methods adopt a retrieve and rerank strategy, their performance improvements largely hinge on the single-stream encoder during inference. Other approaches utilize knowledge distillation to strengthen either the single-stream encoder or the dual-stream encoder, surpassing their previous capabilities. However, existing distillation techniques typically focus on a single knowledge type, neglecting the richer insights available in the teacher model. To bridge this gap, we introduce a Lightweight and Effective Multi-View Knowledge Distillation approach, named LEMKD, for text-image retrieval. This method effectively utilizes response-based, feature-based and relation-based knowledge, transferring the knowledge from the single-stream encoder to the dual-stream encoder. Our approach is executed on the widely used MS-COCO and Flickr30K datasets. Results demonstrate that LEMKD not only matches the exceptional performance of the most advanced single-stream models but also excels in dual-stream encoder performance amidst the recent integration of single-stream and dual-stream models.

关键词： text-image retrieval knowledge distillation multimodal

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SBSN: Weakly Supervised image Segmentation based on Shape Prior 2

SBSN: Weakly Supervised Image Segmentation Based on Shape Pr...

引用

2nd ieee international conference on signal, Information and Data Processing, ICSIDP 2024

作者： Yuan, Shuangjie Wang, Kaixuan Shao, Jinliang Yang, Lu University of Electronic Science and Technology of China China

ISBN: (纸本)9798331515669

In the domain of multimedia signal processing, shape priors serve as crucial cues for guiding model training. However, many existing techniques struggle to effectively represent complex shape priors in multimedia content. To address this challenge, we introduce a Shape-based image Segmentation Network (SBSN) that leverages a Convolutionally Constrained Boltzmann Machine (CRBM) for shape prior extraction from multimedia signals. The SBSN employs CRBM to explicitly model the shape probability distribution through rigorous mathematical formulations and utilizes Wasserstein distance as a metric to quantify the disparity between the segmentation output and the extracted shape prior. For multimedia signal segmentation, SBSN incorporates an advanced feature fusion architecture, utilizing ResNet for hierarchical feature extraction and transposed convolution for signal upsampling and reconstruction. This process culminates in high-resolution, pixel-level segmentation of multimedia content. Notably, SBSN is designed to operate effectively with a limited set of labeled multimedia samples during the training phase. Extensive evaluations were conducted against state-of-the-art weakly-supervised methods across diverse multimedia objects: human figures, horses, airplanes, and surface cracks. The results demonstrate that SBSN achieves a significant 0.8% improvement in average AUROC and secures the second rank in average ABO performance, underscoring its efficacy in processing and analyzing complex multimedia signals. © 2024 ieee.

关键词： image segmentation

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RATTLE: Train Identification through Audio Fingerprinting 10

RATTLE: Train Identification through Audio Fingerprinting

引用

international conference on Smart Computing (SMARTCOMP)

作者： Ciabattini, Leonardo Sciullo, Luca Esposito, Alfonso Zyrianoff, Ivan Di Felice, Marco Univ Bologna Dept Comp Sci & Engn Bologna Italy

ISBN: (纸本)9798350349955;9798350349948

Train model identification can enhance the structural monitoring of railway infrastructures by providing contextual information about train passages. While approaches relying on timetables are impractical due to delays, camera-based solutions present challenges related to deployment costs and privacy concerns. In this paper, we propose RATTLE, a self-contained framework for train tracking and identification based on audio signal fingerprinting. We have developed a prototype IoT system tailored for train tracking and ground truth assessment, enabling the acquisition of a real-world dataset spanning four months of measurements. Then, we conducted a comparative analysis of several traditional Machine Learning (ML) and Deep Learning (DL) algorithms for audio features classification, mel spectrogram classification, and image classification (serving as baselines). Our findings highlight that mel-trained CNN algorithms achieve high accuracy (97%) comparable to the best video-based DL solution, while substantially reducing model size. Furthermore, we explored the potential for migrating the classification task to the edge through quantisation techniques.

关键词： internet of Things (IoT) Machine Learning (ML) Deep Learning (DL) Audio fingerprinting Edge Computing Testbed

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TAG ANTENNA STRUCTURE CALIBRATED BACKSCATTERING signal DETECTION 49

TAG ANTENNA STRUCTURE CALIBRATED BACKSCATTERING SIGNAL DETEC...

引用

49th ieee international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Goay, Amus Chee Yuen Mishra, Deepak Murch, Ross Seneviratne, Aruna Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW Australia Hong Kong Univ Sci & Technol Dept ECE Hong Kong Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Backscatter Communication (BackCom) is gaining popularity due to its potential for sustainable and low-cost internet of Things (IoT) applications. However, due to the limited resources of passive tags, optimizing the backscatter modulation is critical for the widespread use of this technology. Current backscatter modulation designs ignore the impact of the tag's antenna structure, which we show in this paper to have a negative effect on system performance and lead to design discrepancies. We investigate the impact of the antenna structure parameter on the backscattered signal characteristics. Then, we propose a novel signal subtraction technique that effectively calibrates the received signal based on the tag's antenna structure to enable accurate detection. Our simulation results demonstrate that different values of this critical parameter result in different backscattered signals, which influence the signal decoding efficiency at the receiver. Furthermore, our work provides insights for optimized system design and enhanced BackCom performance.

关键词： Backscattering Radar Cross Section Antenna Structural Scattering Calibration Reflection Coefficient

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Tape Barcode Recognition and Plane Positioning based on image Processing 6

Tape Barcode Recognition and Plane Positioning Based on Imag...

引用

6th international conference on internet of Things, Automation and Artificial Intelligence, IoTAAI 2024

作者： Qi, Wenping Zeng, Yong Zhang, Shuai Li, Liutong China Centre for Resources Satellite Data and Application Beijing China

ISBN: (纸本)9798350386974

In order to achieve unattended tape storage management, this article designs a tape barcode recognition and positioning technology based on video and image. The algorithm uses the YOLOV5s network model to quickly recognize the tape barcode in the image and the QR code used to record the actual position of the plane, and then uses the ZBAR toolkit to decode it. Finally, the QR code is used as a geometric correction control point for the image, and the actual position of the plane is calculated by the pixel position of the tape barcode. Through testing on the dataset, the recognition time for each tape is 0.007s, and the detection rate refers to the proportion of tapes that can be found and correctly recognized, which is 97.13%. This achieves the goal of efficient inventory of tapes. At the same time, when the position of the camera remains unchanged, after geometric correction processing of the image, the positioning accuracy of the tape can reach 99.99%. From the experimental results, it can be seen that this article has achieved its goal well. © 2024 ieee.

关键词： Tapes

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Optimizing Autonomous Cargo Transport with Quantum Algorithms and Satellite image Fusion 2

Optimizing Autonomous Cargo Transport with Quantum Algorithm...

引用

2nd international conference on Intelligent Cyber Physical systems and internet of Things (ICoICI)

作者： Prabhu, Bennet Muthukumar, B. Sathyabama Inst Sci & Technol Dept Informat Technol Chennai Tamil Nadu India Rajalakshmi Engn Coll Dept Informat Technol Chennai Tamil Nadu India

ISBN: (纸本)9798331540661;9798331540678

Railways are popular for moving goods because they are fast and can carry a lot. Due to technological development, researchers are now thinking about using self-driving systems instead of regular trains to make transporting goods more efficient and safer. Current train control systems sometimes make slow decisions which can be unsafe. This research introduces a new way to make these decisions faster and better for self-driving trains. We propose an algorithm is called the Near-field scene Quantum optimization (NFQO) algorithm. This uses live satellite images to help the train understand its surroundings. Additionally, a Hexagonal Grid (HG) tool, it helps the train pick the best route quickly. The main advantage of developing quantum algorithm is used to make decisions super-fast and accurate. When combined NFQO and HG with satellite images, improves reliable and effective self-driving cargo trains transportation. We've tested NFQO, and it's better than other available tools. This research, which combines quantum technology, satellite images, and self-driving systems, points to exciting future developments in train transportation.

关键词： Autonomous Cargo Transport Quantum Optimization Algorithm Satellite image Fusion Near-field Scene Analysis and Decision-making systems

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Dynamic Traffic signal Optimization Congestion Management and Emergency Vehicle Prioritization 3

Dynamic Traffic Signal Optimization Congestion Management an...

引用

3rd international conference on Intelligent Data Communication Technologies and internet of Things, IDCIoT 2025

作者： Deoghare, Seema U. Dhond, Yash Gaikwad, Saakshi Gorkhe, Parth Department of Electronics and Telecommunication Pimpri Chinchwad College of Engineering Akurdi India

ISBN: (纸本)9798331527549

These delays have been linked to the traditional traffic signal systems that are used as they are time bound with pre-set schedules that do not take into account changing operational surroundings. The real life integration of technology into traffic management has the potential of alleviating these issues. In this paper, a Smart Traffic Management System that uses YOLOv8 object detection, IoT inter-signal communication and Real Time adaptive signal control is examined. The proposed system does automatic adjustments to signal times according to the density of the vehicles and gives higher priority to emergency vehicles to promote seamless traffic. Through the examination of existing systems, an analysis is conducted pointing out the issues revolving around those systems which this research aims to solve. The STMS incorporates including but not limited to USB cameras, Raspberry Pi, and sophisticated machine learning models to do real life identification and decision making. In consideration of modern day urban traffic troubles, it can be said with confidence that this system is able to terminate congestion with the help of emergency vehicles and also provide limitlessly scalable solutions. Future considerations will include identifying field implementation limitations and addressing them during the research. © 2025 ieee.

关键词： Decision making

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SPEECH GUIDEDMASKED imageMODELING FOR VISUALLY GROUNDED SPEECH 49

SPEECH GUIDEDMASKED IMAGEMODELING FOR VISUALLY GROUNDED SPEE...

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49th ieee international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Woo, Jongbhin Ryu, Hyeonggon Senocak, Arda Chung, Joon Son Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9798350344868;9798350344851

The objective of this study is to investigate the learning process of Visually Grounded Speech (VGS) models through joint learning that combines contrastive learning and masked image modeling. Typically, VGS models aim to establish audio-visual alignment between images and their spoken captions within a contrastive learning framework. Building upon this seminal concept, in this work, we explore whether visual reconstruction with the help of cross-modality can enhance alignment, given that spoken captions describe visual appearances. To achieve this, we extend the contrastive learning-based VGS models by incorporating a masked autoencoder that utilizes cross-attention in the decoder. Through this cross-modal interaction in the decoder, spoken caption features guide the model to reconstruct the masked patches and capture correspondence between the two modalities. Our findings suggest that integrating cross-modal reconstruction within the contrastive learning framework enhances audio-visual feature alignment. Consequently, our proposed method gives comparable performance to existing models that utilize prior knowledge or other modalities, such as object region proposals or Contrastive Language-image Pretraining (CLIP).

关键词： Visually Grounded Speech Self-supervised Learning Masked Autoencoder Contrastive Learning

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Energy Harvesting-enabled Cognitive Radio-internet of Things Using Machine Learning

Energy Harvesting-enabled Cognitive Radio-Internet of Things...

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ieee international conference on Consumer Electronics (ICCE)

作者： Sipon Miah, Md Garcia Armada, Ana UC3M Dept Signal Theory & Commun Madrid 28911 Spain

ISBN: (纸本)9781665491303

The cognitive radio (CR) is a modern technology in cognitive radio-internet of things (CR-IoT) networks. In contrast, each CR-IoT user is unable to achieve both a better sensing gain, and an enhanced system sum rate in conventional energy detection technique based CR-IoT networks with the present energy harvesting (EH) and security threats due to under-utilized the reporting framework. For this reason, we proposed EH-enabled CR-IoT networks using machine learning (ML) algorithms in which each normal CR-IoT user is assisted by finite capacity battery systems and energy harvested. In this paper, Firstly, the proposed hybrid detection technique based on EH-enabled CRIoT networks using ML algorithms is separating the trusted (normal) and untrusted (malicious users) CR-IoT users where all untrusted CR-IoT users are not participating in spectrum sensing due to they degraded the performance like sensing gain and system sum rate;Secondly, the proposed scheme is utilized the reporting framework where only trusted CR-IoT users are obtained longer sensing time slot which enhanced the sensing gain, the EH, and the sum rate;and Finally, the simulation results show that the proposed hybrid scheme outperformed the conventional schemes in terms of security, sensing gain, EH, and system sum rate.

关键词： Energy detection p-norm detection spectrum sensing internet of things energy harvesting system sum rate machine learning

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