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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671-680 订阅

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Research on the Intelligent System of Hospital Medical Examination based on Computer internet technology

Research on the Intelligent System of Hospital Medical Exami...

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2023 international conference on Networking, Informatics and Computing, ICNETIC 2023

作者： Xiao, Fei Shaoyang University School of Medical Technology Hunan422000 China

ISBN: (纸本)9798350313314

This article analyses the physical basis and registration principles of mutual information registration and proposes a mutual information measure using differential image entropy. It compares the mutual information measure of a variety of representative medical examination CT images and uses the neighbourhood the information improves the mutual information of poor medical examination images. The test results show that the mutual information measurement combined with airspace information has better noise resistance. © 2023 ieee.

关键词： Intelligent systems

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X-Transfer: A Transfer Learning-based Framework for GAN-Generated Fake image Detection

X-Transfer: A Transfer Learning-Based Framework for GAN-Gene...

引用

international Joint conference on Neural Networks (IJCNN)

作者： Zhang, Lei Chen, Hao Hu, Shu Zhu, Bin Lin, Ching-Sheng Wu, Xi Hu, Jinrong Wang, Xin Chengdu Univ Informat Technol Chengdu Peoples R China Purdue Univ Indianapolis IN USA Microsoft Res Asia Beijing Peoples R China Tunghai Univ Taichung Taiwan SUNY Albany Albany NY 12222 USA

ISBN: (纸本)9798350359329;9798350359312

Generative adversarial networks (GANs) have remarkably advanced in diverse domains, especially image generation and editing. However, the misuse of GANs for generating deceptive images, such as face replacement, raises significant security concerns, which have gained widespread attention. Therefore, it is urgent to develop effective detection methods to distinguish between real and fake images. Current research centers around the application of transfer learning. Nevertheless, it encounters challenges such as knowledge forgetting from the original dataset and inadequate performance when dealing with imbalanced data during training. To alleviate this issue, this paper introduces a novel GAN-generated image detection algorithm called X-Transfer, which enhances transfer learning by utilizing two neural networks that employ interleaved parallel gradient transmission. In addition, we combine AUC loss and cross-entropy loss to improve the model's performance. We carry out comprehensive experiments on multiple facial image datasets. The results show that our model outperforms the general transferring approach, and the best metric achieves 99.04%, which is increased by approximately 10%. Furthermore, we demonstrate excellent performance on non-face datasets, validating its generality and broader application prospects.

关键词： Generative Adversarial Network digital image forensics deep learning transfer learning

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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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Hardware Implementation of Low-latency image Denoising with Wavelet Shrinkage and Noise2Noise-based Learning for Ultra-high Definition Cameras

Hardware Implementation of Low-latency Image Denoising with ...

引用

2024 ieee international conference on Consumer Electronics, ICCE 2024

作者： Kikuchi, Kodai Tomioka, Kohei Honji, Akira Usui, Takenobu Hirakawa, Keigo Kitamura, Kazuya Science and Technology Research Laboratories Japan Broadcasting Corporation Tokyo Japan University of Dayton Department of Electrical and Computer Engineering OH United States

ISBN: (纸本)9798350324136

There is a growing demand for real-Time image denoising in low-light shooting with ultra-high definition cameras. This paper presents a denoising method that incorporates Haar-wavelet shrinkage denoising and a minimum mean square error estimation based on an extension of the Noise2Noise learning technique, which can perform low-latency processing and can be adapted to non-linear processed signals without prior knowledge of noise and signal distributions. By introducing a two-dimensional kernel to reduce memory usage in the coring function estimator, we demonstrate real-Time hardware denoising for luminance signals of 4K 60 fps video with a delay of less than one millisecond. © 2024 ieee.

关键词： FPGA Haar-wavelet image denoising MMSE estimation Noise2Noise UHD

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image Copy-Move Forgery Detection via Deep Cross-Scale PatchMatch

Image Copy-Move Forgery Detection via Deep Cross-Scale Patch...

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ieee international conference on Multimedia and Expo (ICME)

作者： He, Yingjie Li, Yuanman Chen, Changsheng Li, Xia Shenzhen Univ Guangdong Key Lab Intelligent Informat Proc Coll Elect & Informat Engn Shenzhen Peoples R China

ISBN: (纸本)9781665468916

The recently developed deep algorithms achieve promising progress in the field of image copy-move forgery detection (CMFD). However, they have limited generalizability in some practical scenarios, where the copy-move objects may not appear in the training images or cloned regions are from the background. To address the above issues, in this work, we propose a novel end-to-end CMFD framework by integrating merits from both conventional and deep methods. Specifically, we design a deep cross-scale patchmatch method tailored for CMFD to localize copy-move regions. In contrast to existing deep models, our scheme aims to seek explicit and reliable point-to-point matching between source and target regions using features extracted from high-resolution scales. Further, we develop a manipulation region location branch for source/target separation. The proposed CMFD framework is completely differentiable and can be trained in an end-to-end manner. Extensive experimental results demonstrate the high generalizability of our method to different copy-move contents, and the proposed scheme achieves significantly better performance than existing approaches.

关键词： image forensics copy-move forgery multimedia security differentiable patchmatch

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Machine Learning-driven Antenna Selection Strategies for Fading MIMO systems 2

Machine Learning-driven Antenna Selection Strategies for Fad...

引用

2nd ieee international conference on Advancements and Key Challenges in Green Energy and Computing, AKGEC 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Madhav Institute of Technology & Science Deemed University Centre for Internet of Things Gwalior India

ISBN: (纸本)9798350390179

In this study, we investigate better performing antenna selection strategies in wireless communication systems based on machine learning algorithms. Reinforcement learning as well as neural networks among others are analysed for different metrics such a bit error rate (BER), signal-to-noise ratio (SNR) and data throughput. These results show that these methods are working well against the dynamic changing of channel conditions to provide reliable and efficient. The computational complexity analysis suggests affordable overhead, allowing the feasibility of real-time implementation. Although this method can simulate the scenario in a real environment, it is not scalable due to simulation fidelity and antenna array. On the one hand, future research will refine and expand upon these ML models with real-time adaptation capabilities - On the other hand significant field trials are needed to put this work into practice for specific applications making sure that we develop robust adaptive wireless communication systems in practice. © 2024 ieee.

关键词： Adversarial machine learning

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Artificial Intelligence-Powered internet of Things for Farming and Plant Research 3

Artificial Intelligence-Powered Internet of Things for Farmi...

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3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Padma, A. Suresh, P. Narsimlu, G. Amarnath, M. Sreedhar, B.R. Chaitanya Bharathi Institute of Technology Department of Mathematics India

ISBN: (纸本)9798331509675

The wearable fiber optic sensors for plants to monitor microenvironment and growth wearable fiber optic plant sensors for microclimate and growth monitoring. The plant agricultural sectors are facing challenges because to population expansion and climate ***-powered computer vision-based plant growth monitoring system the goal of this research project is to use computer vision to create an AI-based plant growth monitoring system. The technology will allow for automatic and real-time evaluation of plant growth and health by utilizing computer vision algorithms and artificial intelligence approaches. Conventional techniques for tracking plant growth require a lot of work and time. By employing image processing techniques to analyze plant photos and extract pertinent features leaf area, plant height, and width, the suggested solution will overcome these drawbacks. Using the collected information, machine learning methods will be used to train models that can identify patterns and forecast plant development. The system will be made to adapt to various plant species and environmental circumstances. This AI-based plant growth monitoring system will help farmers, researchers, and plant enthusiasts by offering precise and timely insights into plant health and growth. This will allow them to make well-informed decisions about plant care and optimize growth conditions. By providing an economical, scalable, and effective system with numerous applications in horticulture, agriculture, and environmental research, this research project seeks to transform plant growth monitoring. © 2025 ieee.

关键词： Smart agriculture

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MQ-CODER INSPIRED ARITHMETIC CODER FOR SYNTHETIC DNA DATA STORAGE 30

MQ-CODER INSPIRED ARITHMETIC CODER FOR SYNTHETIC DNA DATA ST...

引用

30th ieee international conference on image Processing (ICIP)

作者： Pic, Xavier Dimopoulou, Melpomeni San Antonio, Eva Gil Antonini, Marc Cote Azur Univ I3S Lab Nice France CNRS UMR 7271 Sophia Antipolis France

ISBN: (纸本)9781728198354

Over the past years, the ever-growing trend on data storage demand, more specifically for "cold" data (i.e. rarely accessed), has motivated research for alternative systems of data storage. Because of its biochemical characteristics, synthetic DNA molecules are now considered as serious candidates for this new kind of storage. This paper introduces a novel arithmetic coder for DNA data storage, and presents some results on a lossy JPEG 2000 based image compression method adapted for DNA data storage that uses this novel coder. The DNA coding algorithms presented here have been designed to efficiently compress images, encode them into a quaternary code, and finally store them into synthetic DNA molecules. This work also aims at making the compression models better fit the problematic that we encounter when storing data into DNA, namely the fact that the DNA writing, storing and reading methods are error prone processes. The main take away of this work is our arithmetic coder and it's integration into a performant image codec.

关键词： image compression DNA JPEG 2000 arithmetic coder

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Machine Learning based Approach by image Recognition for Detection of Islanding

Machine Learning Based Approach by Image Recognition for Det...

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2024 international conference on Smart systems for Applications in Electrical Sciences, ICSSES 2024

作者： Pal, Kumaresh Goswami, Subhadip Benia, Tapas Kumar Bhuyan, Anshuman Manna, Pranendu Kumbhakar, Shyamal National Institute of Technology Department of Electrical Engineering Jamshedpur India National Institute of Technology Department of Electrical Engineering Arunachal Pradesh India Gandhi Institute for Education & Technology Department of Electrical Engineering Bhubaneswar India Techno India University Department of Electrical Engineering Kolkata India

ISBN: (纸本)9798350364040

Excessive penetration of DG sources poses a significant difficulty known as islanding. The act of islanding has the potential to inflict harm upon both customers and their equipment. As per the ieee 1547 DG interconnection criteria, it is necessary to identify islanding within a two-second timeframe and then deactivate the DG. The present study introduces a novel methodology for identifying islanding through the utilisation of visual pattern recognition and Machine Learning classification techniques. The Histogram of Oriented Gradient (HOG) is employed as a feature descriptor in order to perform pattern recognition on images, with the objective of identifying both non-islanding and islanding occurrences. Therefore, the spectrogram images are acquired from the time-series signal that exists at the point of common coupling. The provided photos are employed for the purpose of extracting features using the HOG algorithm. These extracted features are subsequently used as input for the k-Nearest Neighbors (KNN) classifier, both for training and testing purposes. The spectrogram image is obtained by using the rate of change of the negative sequence voltage component. The efficacy of the KNN classifier is evaluated using 5,10,20, and 25 -fold cross-validations. The classification findings indicate that the utilisation of the KNN classifier and HOG feature for visual pattern recognition yielded exceptional outcomes, with an accuracy rate of 96.75% and a detection time of 211 ms. © 2024 ieee.

关键词： image recognition

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Content based image Search Engine with Grey Wolf Optimized Neural Network 1

Content based Image Search Engine with Grey Wolf Optimized N...

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1st ieee international conference on Innovations in High Speed Communication and signal Processing, IHCSP 2023

作者： Marathe, Shilpa Arosh, Sirshendu School of Interdisciplinary Studies and Research DY Patil International University Akurdi Maharashtra Pune411044 India Department of Electrical Engineering West Bengal Asansol713305 India

ISBN: (纸本)9798350345957

The systems with Content-based image retrieval (CBIR) approach enable the search and retrieval of images that are comparable to a particular query image by utilizing attributes that indicate the content visualization of such images. The present study created a mechanism for recovering images indexed in databases based on their visual information without the necessity for linguistic annotations. Feature vectors were obtained from the hybrid of DWT and LBP visual contents. The classification is achieved by using Grey Wolf optimized Neural Network (GWO-NN) technique. The proposed strategy yielded 95.59% accurate findings on publicly available databases. © 2023 ieee.

关键词： Search engines

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