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

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ISAR-Diff: A High-Resolution ISAR Imaging Method based on Conditional Diffusion Models 2

ISAR-Diff: A High-Resolution ISAR Imaging Method Based on Co...

引用

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

作者： Wang, Yue Bai, Xueru Xidian University National Key Laboratory of Radar Signal Processing Xi'an China

ISBN: (纸本)9798331515669

Due to low signal-to-noise ratio (SNR) and incomplete echoes, the performance of inverse synthetic aperture radar (ISAR) imaging tends to degrade rapidly. To address this issue, this paper proposes a high-resolution ISAR imaging method based on a conditional diffusion model, dubbed as ISAR-Diff. Specifically, an ISAR imaging diffusion model is trained. Then, the complex-valued ISAR image is then inferred using the diffusion model with a guidance function, conditional on complex-valued echoes. In particular, an ISAR imaging guidance function is designed to make ISAR-Diff applicable to different scenarios and to improve the reconstruction accuracy. Experimental results based on Monte Carlo simulation and measured data demonstrate the superiority of ISAR-Diff in low SNR and incomplete echo scenarios. © 2024 ieee.

关键词： Inverse synthetic aperture radar

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CLIFS: CLIP-DRIVEN FEW-SHOT LEARNING FOR BAGGAGE THREAT CLASSIFICATION 31

CLIFS: CLIP-DRIVEN FEW-SHOT LEARNING FOR BAGGAGE THREAT CLAS...

引用

2024 international conference on image Processing

作者： Ahmed, Abdelfatah Velayudhan, Divya ElMezain, Mahmoud Al Radi, Muaz Boudiaf, Abderrahmene Hassan, Taimur Deriche, Mohamed Bennamoun, Mohammed Werghi, Naoufel Khalifa Univ Abu Dhabi U Arab Emirates Abu Dhabi Univ Abu Dhabi U Arab Emirates Ajman Univ Ajman U Arab Emirates Univ Western Australia Perth Australia

ISBN: (纸本)9798350349405;9798350349399

Baggage screening in airports is a cornerstone in airport security measures. The advent of computer vision technologies in recent years has led to the development of several automated systems for identifying security threats in baggage scans. However, existing methods struggle to adapt to new threat categories when faced with a scarcity of data samples, and the rapid emergence of new threats. Hence, in this paper, we propose a novel CLIP-driven few-shot framework (CLIFS) to explore the potential of multi-modality using text-image fusion through contrastive learning to learn relevant contextual features for recognizing security threats with limited samples. By integrating features from GPT-4 generated captions with image features, CLIFS leverages both visual and textual data to significantly improve threat classification performance with limited samples in a few-shot learning context. Our proposed CLIFS was rigorously tested on the SIXray public available baggage X-ray dataset, where it outperformed state-of-the-art by 31.3% in accuracy and 28.40% in F1-score for the challenging 5-shots scenario, demonstrating its robustness and effectiveness in classifying threats from limited data samples.

关键词： Vision-Langugae Model Contrastive Loss Few-shot learning Contrastive Language image Pretraining Baggage threat classification

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AlexNet for Lung Cancer Detection: Leveraging Deep Learning for Diagnosis 2

AlexNet for Lung Cancer Detection: Leveraging Deep Learning ...

引用

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

作者： Simgamsettil, P. Manikanta Tirupal, T. JNTU Anantapur Dept ECE Ananthapuramu Andhra Pradesh India St Martins Engn Coll Dept ECE Secunderabad Telangana India G Pullaiah Coll Engn & Technol Dept ECE Kurnool Andhra Pradesh India

ISBN: (纸本)9798331540661;9798331540678

In the field of medical diagnosis, the early detection of the cancer cells in the Lung is crucial challenge. The lung cancer is considered as the leading cancer in the worldwide for the death rates, effective and accurate detection in the early stage is essential. To address this, deep learning techniques are deployed in the recent year but not getting the promising results in the analysis of image diagnosis. In this paper, we proposed a novel approach for lung cancer detection utilizing the AlexNet convolutional neural network architecture. The network model is trained with a large dataset by learning the discriminative features indicating the presence of lung cancer. Preprocessing techniques such as data augmentation and normalization are employed to enhance model generalization and performance. The trained model achieves an impressive accuracy of 95% in detecting lung cancer, demonstrating its efficacy in clinical applications. Furthermore, the model's performance is evaluated through rigorous validation procedures, including cross-validation and testing on an independent dataset, ensuring robustness and reliability. The proposed deep learning-based approach holds great potential for early detection of lung cancer, thereby facilitating timely interventions and improving patient outcomes.

关键词： image Processing Convolutional Neural Networks Multi-Level Wavelet CNN Dilated Filtering Wavelet Transform Computational Efficiency

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Intraocular Pressure Monitoring Using IoT 2

Intraocular Pressure Monitoring Using IoT

引用

2nd international conference on Emerging Trends in Information technology and Engineering, ic-ETITE 2024

作者： Moorthi, M. Dhanush, H.S. Ashok, A. Sathish Kumar, S. Vanitha Lakshmi, M. Lau, Chee Yong Alexander, C.H.C. Biomedical Engineering Saveetha Engineering College Chennai India School of Engineering Asia Pacific University of Technology and Innovation Kuala Lumpur Malaysia Ece School of Engineering Presidency University Bangalore India

ISBN: (纸本)9798350328202

An overabundance of aqueous fluid in the eye can induce glaucoma by raising intraocular pressure to a level that is too high for the eyeball to withstand, which in turn damages the optic nerve. The glaucoma treatments of the present generation are either ineffective or have undesirable side effects. An essential physiological signal in glaucoma patients is the intraocular pressure signal, particularly in those with severe glaucoma and those who always have their eyes open. Continuous and real-time monitoring of this signal is required. For this reason, precise, ongoing, real-time intraocular pressure monitoring is crucial to the identification and management of glaucoma patients. In order to remotely monitor and operate objects more quickly and accurately, the globe is turning to internet of things technology. Healthcare practitioners can monitor patients remotely, increase the effectiveness of healthcare systems, and improve patient outcomes by utilizing IoT. © 2024 ieee.

关键词： internet of things

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internet of Things-based Botnet Traffic Detection and Analysis using Deep Convolutional Neural Network

Internet of Things-Based Botnet Traffic Detection and Analys...

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2023 international conference on Artificial Intelligence Robotics, signal and image Processing, AIRoSIP 2023

作者： Majed, Doaa Mohammad Abdulhameed, Abbas Abdulazeez Gaata, Methaq Talib Mustansiriyah University College of Sciences Department of Computer Science Baghdad Iraq

ISBN: (纸本)9798350327472

Botnets are among the biggest dangers on the internet. They are intentionally used to weaken the cornerstones of network security. specifically, Confidentiality, Integrity, and Availability, to effectively find and protect against botnets. We have proposed deep learning methods namely Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) used to detect botnets and assessed them using the BOT- IOT botnet dataset. And we use a variety of performance metrics, including accuracy, sensitivity, specificity, precision, and F1 score to assess how well each model performs when it comes to the classification of known botnet traffic patterns. The results shows the efficacy and accuracy of our suggested method for detecting known botnet traffic. © 2023 ieee.

关键词： Bot (internet)

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DBCTfuse: A Dual-Branch CNN-Transformer feature extraction network for Infrared and Visible image Fusion 17

DBCTfuse: A Dual-Branch CNN-Transformer feature extraction n...

引用

17th ieee international conference on signal Processing, ICSP 2024

作者： Li, Yujing Ma, Xiaole Chen, Xiaoxuan Hu, Shaohai Beijing Jiaotong University School of Computer Science and Technology Beijing100044 China

ISBN: (纸本)9798350387384

The process of fusing infrared and visible images necessitates integrating thermal radiation information from infrared image with the edge and texture detail captured by visible images. Most current fusion methods are based on convolutional neural networks, which can effectively extract local features from source images but are somewhat lacking in global features. To address this, we propose a dual-branch feature extraction fusion network based on convolutional neural network and Transformer - DBCTfuse, to effectively synthesize both local details and global contextual information from source images. Comprehensive experiments conclusively demonstrate the superiority of our DBCTfuse method in infrared and visible image fusion, surpassing contemporary state-of-the-art algorithms in both qualitative and quantitative assessments. © 2024 ieee.

关键词： Convolutional neural networks

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Reduce the Handicap: Performance Estimation for AI systems Safety Certification 21

Reduce the Handicap: Performance Estimation for AI Systems S...

引用

ieee 21st international conference on Industrial Informatics (INDIN)

作者： Pfrommer, Julius Poyer, Matthieu Kiroriwal, Saksham Fraunhofer Inst Optron Syst Technol & Image Explo Karlsruhe Germany

ISBN: (纸本)9781665493130

The safety validation of AI and ML-based systems is challenging, as (i) analytical validation needs to include the interaction with a complex and stochastic physical environment and (ii) empirical validation needs to observe very long time-horizons to get enough "statistical signal" for the typically very low safety-related incident rate. This paper proposes an approach that amplifies the empirical evidence by introducing a handicap that reduces the system performance-making safety-related failures empirically more visible in a controlled environment-and gradually removing the handicap so that the convergence to the final incident rate can be estimated. Two numerical case studies are used to support and exemplify the approach.

关键词： Stochastic systems

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IRLSG: INVARIANT REPRESENTATION LEARNING FOR SINGLE-DOMAIN GENERALIZATION IN MEDICAL image SEGMENTATION 49

IRLSG: INVARIANT REPRESENTATION LEARNING FOR SINGLE-DOMAIN G...

引用

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

作者： Niu, Ziwei Sun, Hao Ouyang, Shuyi Xie, Shiao Chen, Yen-Wei Tong, Ruofeng Lin, Lanfen Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China Ritsumeikan Univ Coll Informat Sci & Engn Kyoto Japan

ISBN: (纸本)9798350344868;9798350344851

Single-domain generalization (SDG) can efficiently enhance model generalization while avoiding high annotation costs and privacy concerns. However, existing SDG methods are mainly based on data manipulation and meta-learning, which are not efficient enough due to the limited generalization performance and complex inference. In response to these challenges, we present a novel single domain-invariant representation learning approach for medical image segmentation, called IRLSG, with two appealing designs: (1) A Classscale Photo-metric Augmentation is first proposed to simulate unseen target domain that is sufficient in diversity and informativeness. After that, a Dual-Consistency Framework is further designed to constrain the consistency of intermediate features and segmentation results between the original and the augmented images, which helps to explore the domain-invariant representation. (2) A simple and effective Style Feature Whitening is designed to decouple and remove the domain-specific style from higher-order covariance statistics, which can further improve the modeling and generalization capability of the network. Experimental results on different benchmarks demonstrate that our IRLSG outperforms the current state-of-the-art methods in tackling single-domain generalization.

关键词： Domain generalization domain invariant representation medical image segmentation

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Fault Analysis of Aircraft systems Indicated on a Cockpit Panel by Using image Processing 1

Fault Analysis of Aircraft Systems Indicated on a Cockpit Pa...

引用

1st ieee international conference on Advances in signal Processing, Power, Communication, and Computing, ASPCC 2024

作者： Chavali, Joseph Chakravarthi Chandy, D. Abraham Tamil Nadu Coimbatore India Karunya Institute of Technology and Sciences Electronics and Com. Engg Tamil Nadu Coimbatore India

ISBN: (纸本)9798350355093

image Processing finds an ocean of applications in the Research. This paper presents a different application area in image processing of a Control Panel of an automated control or display system. Fault detection and analysis could be performed on the control or display system in a Vehicle or automated manufacturing facility using image processing. Specifically, this paper relates to the image processing of a Cockpit Panel for fault detection and analysis. The Cockpit Panel under study is constantly video-recorded by an airborne image recording system. The Cockpit panel displays normal, failure, and malfunction indications of different Aircraft systems through Color light indications such as Red, white, Blue, and Yellow (Amber). The time of failure or malfunction is also critical in Aviation. This paper presents a recording of the different Color indications and the display time. The image processing could generally be done by comparison of the image frames obtained from the video at the pixel level. This paper proposes a shape-centric comparison of images. The results are compared, and the best image processing method is arrived at for fault analysis of the cockpit panel. © 2024 ieee.

关键词： Cockpits (aircraft)

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Storage Solution and Security Transmission in image Sensing Using Blockchain technology in internet of Things

Storage Solution and Security Transmission in Image Sensing ...

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2024 international conference on Communication, Computer Sciences and Engineering, IC3SE 2024

作者： Gunturu, Vijaya Renu, Nekkanti Dhaliwal, Navdeep Sumalatha, Ippa Anandhi, R.J. Dutta, Gourab School Of Engineering Sr University Department Of Ece Telangana Warangal India Department Of Computer Science And Engineering Visakhapatnam Duvvada India Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Dundigal Hyderabad India New Horizon College Of Engineering Department Of Information Science Engineering Bangalore India Brainware University Department Of Computational Sciences West Bengal Kolkata700125 India University Of Kalyani Department Of Computer Science & Engineering West Bengal Kalyani741245 India

ISBN: (纸本)9798350366846

The number of internet of Things (IoT) devices has grown dramatically with the technology's rapid development. Higher security standards have so been proposed for the administration, transfer, and archiving of vast amounts of IoT data. But security problems like data theft and forgeries are likely to happen while IoT data is being transmitted. Furthermore, the majority of data storage options now in use are centralized, meaning that a centralized server handles both data maintenance and storage. The confidentiality of IoT data would be seriously jeopardized once a hostile assault targets the server. Given the aforementioned security concerns, a secure transmission as well as storage solution for blockchain sensing images in the internet of Things is put forth. Therefore, to enable effective secure data storage in internet of Things-related smart computing systems, develop and build a novel blockchain-based artificial intelligence model. We also demonstrated the operation of the system framework. Upon conducting a thorough security study, we have determined that our suggested solution possesses a strong potential to address the majority of security issues that conventional systems encounter. Furthermore, our suggested method can be used for any file-changing wireless internet of things network that requires the exchange of multimedia data, including traffic data from smart cities, wearable device data, healthcare data, etc. © 2024 ieee.

关键词： internet of things

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