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

共 20489 条记录，以下是821-830 订阅

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Stochastic Gradient-based Optimization of Control Parameters for Uncertain Linear systems with Sliding Mode Control and LQR

Stochastic Gradient-Based Optimization of Control Parameters...

引用

2024 international conference on Control, Automation and Diagnosis, ICCAD 2024

作者： Raghda, Jouirou Mouna, Dali Hassan Anouar, Benamor University of Monastir Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir 5019 Tunisia

ISBN: (纸本)9798350361025

The objective of this article is to investigate the efficacy of an optimal control method for linear systems using first-order sliding mode control, along with the incorporation of a Linear Quadratic Regulator (LQR), in controlling the behavior of a pendulum. Our focus is on developing a control law that is both optimal and robust, capable of ensuring precision, convergence, speed, and asymptotic stabilization of the system even in the presence of uncertainties and perturbations, while minimizing chattering. To validate our proposed control strategy, we present simulation results that demonstrate its usefulness and effectiveness. © 2024 ieee.

关键词： Linear systems

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Strategies for Strengthening Security in image Watermarking: A Critical Review 7

Strategies for Strengthening Security in Image Watermarking:...

引用

7th international conference on image Information Processing, ICIIP 2023

作者： Dewangan, Prachee Swain, Debabala Swain, Monalisa Rama Devi Women's University Dept. of Computer Science Bhubaneswar India Shailabala Women's College Dept. of Computer Science Cuttack India

ISBN: (纸本)9798350371406

The propagation of digital content and its ease of distribution over the internet have raised concerns regarding the protection of intellectual property and its prevention from unauthorized usage. One such effective method for safeguarding digital images is watermarking. However, as digital manipulation and counterfeiting techniques continue to advance, there is a growing need to strengthen the security of image watermarking systems. This paper provides an overview of such key strategies used to enhance security in images using watermarking. © 2023 ieee.

关键词： image watermarking

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A Novel Lightweight and Robust Source-Channel Coding Solution for MIoT Communication based on DL Denoising/Super Resolution Model 16

A Novel Lightweight and Robust Source-Channel Coding Solutio...

引用

16th international conference on signal-image technology and internet-based systems (SITIS)

作者： Noura, Hassan Azar, Joseph Couturier, Raphael Salman, Ola UFC FEMTO ST Inst CNRS Belfort France Amer Univ Beirut Elect & Comp Engn Dept Beirut 11072020 Lebanon

ISBN: (纸本)9781665464956

Multimedia internet of Things (MIoT) network is prone to a variety of challenging constraints, especially in terms of performance and security. Employed MIoT devices can be limited in terms of power, computation, and memory, which make them suffer with the high volume of collected multimedia data. In this context, data compression is one solution to reduce the size of communicated data. However, existing lossy multimedia compression algorithms, such as JPEG and BPG, impose a practical challenge for several MIoT devices since they require high computation and memory resources. Another challenge is the errors that can occur during transmission due to channel errors, which require re-transmitting the erroneous data. In this case, channel coding is one solution. However, channel coding solutions impose overhead in terms of computation and communication resources. To reduce this overhead, in this paper, we propose a lightweight source and channel coding solution that should be applied only on the application server. This solution consists of down-scaling each input image with a factor alpha >= 2 at the MIoT device. This reduces the computation, communicated data size, and memory consumption, which would consequently reduce both energy consumption and latency. However, the down-scaled image might be corrupted by channel errors due to the reliance on the wireless connection. In addition, if the down-scaled image was encrypted, an error in one data block will propagate to other data blocks after the decryption process at the application server. Thus, to avoid costly data re-transmission or redundancy, our solution proposes to apply a Deep Learning (DL) denoising/super-resolution model at the server-side to recuperate high-quality images. This model plays the role of source and channel coding algorithm. The obtained results show the effectiveness of the proposed solution, especially in terms of enhancing the visual quality of the reconstructed high-resolution images from dow

关键词： Multimedia IoT devices Communication/ computation overhead Deep learning image denoising/super-resolution

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A Decision Tree-based Spectrum Sensing Approach in IoT-based Software-Defined Networks 11

A Decision Tree-based Spectrum Sensing Approach in IoT-based...

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11th ieee international conference on Software Defined systems, SDS 2024

作者： Hussienat, Ayyoub Salameh, Haythem Bany Al-Balqa Applied University Department of Electrical Engineering Department Salt Jordan Al Ain University College of Engineering Al Ain United Arab Emirates

ISBN: (纸本)9798331518325

This paper presents a novel machine learning (ML)-based approach for spectrum sensing (SS) in software-defined networks (SDNs) that coexists with licensed non-orthogonal multiple access (NOMA)-based wireless systems. The proposed solution leverages feature-based SS along with an ML approach to effectively detect idle licensed channels that IoT-based SDNs can opportunistically utilize. The SS framework employs a decision tree (DT) ML classifier, significantly enhancing detection efficiency in internet-of-things (IoT) environments that typically experience low operating SNR levels. The proposed DT-based approach ensures reliable SS in dynamic IoT settings, where maintaining detection performance in low SNR scenarios is often a significant challenge. The effectiveness of the DT classifier is evaluated based on its detection capabilities. Simulation results demonstrate that the ML-based approach significantly increases the detection probability compared to traditional feature-based methods at low SNR levels, specifically in ranges of -12 dB to 0 dB, offering a robust and scalable solution to improve spectrum utilization in IoT-based SDNs. © 2024 ieee.

关键词： signal to noise ratio

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RL-IFF: Indoor Localization via Reinforcement Learning-based Information Fusion 48

RL-IFF: Indoor Localization via Reinforcement Learning-Based...

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48th ieee international conference on Acoustics, Speech and signal Processing, ICASSP 2023

作者： Salimibeni, Mohammad Mohammadi, Arash Montreal Canada University of Toronto Electrical and Computer Engineering Toronto Canada

ISBN: (纸本)9781728163277

The paper is motivated by the importance of the Smart Cities (SC) concept for future management of global urbanization. Among all internet of Things (IoT)-based communication technologies, Blue-tooth Low Energy (BLE) plays a vital role in city-wide decision making and services. Extreme fluctuations of the Received signal Strength Indicator (RSSI), however, prevent this technology from being a reliable solution with acceptable accuracy in the dynamic indoor tracking/localization approaches for ever-changing SC environments. The latest version of the BLE v.5.1 introduced a better possibility for tracking users by utilizing the direction finding approaches based on the Angle of Arrival (AoA), which is more reliable. There are still some fundamental issues remaining to be addressed. Existing works mainly focus on implementing stand-alone models overlooking potentials fusion strategies. The paper addresses this gap and proposes a novel Reinforcement Learning (RL)-based information fusion framework (RL-IFF) by coupling AoA with RSSI-based particle filtering and Inertial Measurement Unit (IMU)-based Pedestrian Dead Reckoning (PDR) frameworks. The proposed RL-IFF solution is evaluated through a comprehensive set of experiments illustrating superior performance compared to its counterparts. © 2023 ieee.

关键词： Angle of Arrival (AoA) Bluetooth Low Energy (BLE) Convolutional Neural Network (CNN) Indoor Localization internet of Things (IoT)

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An optical character recognition algorithm for documents with cascading structures 4

An optical character recognition algorithm for documents wit...

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4th international conference on Computer Vision, image and Deep Learning, CVIDL 2023

作者： Ni, Wuguang Li, Haoru Lu, Lidong Qian, Kehan College of Internet of Things Engineering Hohai University Changzhou China

ISBN: (纸本)9798350326444

Document recognition is an important area of optical character recognition(OCR) technology. In this paper, an OCR recognition algorithm for document based on the three-stage text recognition approach is proposed, and is tested with a self-built dataset. The experimental results show that the recognition accuracy of the proposed algorithm is improved by 5.8% compared to the PP OCR V3 algorithm. © 2023 ieee.

关键词： Optical character recognition

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DETECTING STABLE DIFFUSION GENERATED imageS USING FREQUENCY ARTIFACTS: A CASE STUDY ON DISNEY-STYLE ART 30

DETECTING STABLE DIFFUSION GENERATED IMAGES USING FREQUENCY ...

引用

30th ieee international conference on image Processing (ICIP)

作者： Zhang, Junbin Wang, Yixiao Tohidypour, Hamid Reza Nasiopoulos, Panos Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada

ISBN: (纸本)9781728198354

The use of Stable Diffusion models to generate realistic images has become a popular topic in recent years. However, this technology has also raised concerns about the potential harm it may cause to the copyright holders, particularly in the realm of art where these synthesized images can closely resemble the original work. As these synthesized images are hard for humans to distinguish from authentic ones, it is of great importance to develop methods that may identify them. In this paper, we propose a deep learning-based approach to detect synthesized images using information in the frequency domain. Since there exists no well-established dataset of images synthesized by stable diffusion models, in order to train and evaluate our network we generated a representative dataset consisting of carefully selecting human-created authentic images and synthesized animation images generated by the Stable Diffusion models. We chose to use Disney-style animated content for our case study, given its significance in the realm of intellectual property protection. Experimental results demonstrated that our proposed model outperforms humans and other state-of-the-art methods, achieving an accuracy rate of 99.46%.

关键词： Synthesized image detection Stable Diffusion Discrete cosine transform

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Cross-Dataset Evaluation of an Effective Face Anti-Spoofing Algorithm 5

Cross-Dataset Evaluation of an Effective Face Anti-Spoofing ...

引用

5th ieee international conference on Pattern Recognition and Machine Learning, PRML 2024

作者： You, Xingyi Zhao, Xiaohu China University of Mining and Technology School of Information and Control Engineering National and Local Joint Engineering Laboratory of Internet Applied Technology on Mines Xuzhou China

ISBN: (纸本)9798350355925

Face anti-spoofing (FAS) is a kind of face-liveness detection technology that is the key to ensuring the reliability of face recognition results. In recent years, some deep learning-based methods have performed well in intra-tests. However, their limited ability to extract intrinsic features from face images often hinders generalization to attacks in unknown domains and leads to poor performance. In this paper, we propose a novel FAS method based on domain generalization, comprising an image encoder, two lightweight feature extractors (identity (I-FT) and style (S-FT), and a modulation factor. Initially, deep features extracted from the ResNet34 network are utilized to convert input face images into high-dimensional feature vectors. Subsequently, two lightweight feature extraction modules learn distinct hints related to identity and style information from the image encoder. Finally, an adjustment factor is adopted that adjusts the extracted feature parameters for enhancing model performance on domain generalization tasks for anti-fraud purposes involving faces. After the verification of a large number of experimental results, we find that the proposed method has good domain generalization ability and shows excellent performance on different datasets. © 2024 ieee.

关键词： Cross-Dataset Face anti-spoofing feature extractor

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Utilization of Non-Orthogonal Multiple Access (NOMA) for Spectral Efficiency in Mixed Fading Environments 2

Utilization of Non-Orthogonal Multiple Access (NOMA) for Spe...

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2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications systems, ICMACC 2024

作者： Upadhyay, Deepak Upadhyay, Abhay Sharma, Rashmi Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Symbiosis Institute of Technology Department of CSE Nagpur Campus India Pune India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India

ISBN: (纸本)9798350366570

We explore in this paper a potential application of Non-Orthogonal Multiple Access (NOMA) to improve spectral efficiency in multi-fading environments that are ubiquitous in wireless communication systems. This is enabled by NOMA, which distributes the transmit power over different users according to their channel conditions and effectively utilizes the spectrum resources. The performance of NOMA is evaluated by extensive simulations and analytical results are provided to analyze the gains of NOMA over Orthogonal Multiple Access (OMA) in various fading models such as Rayleigh, Rician, and Nakagami-m. The following key metrics including Spectral Efficiency, Bit Error Rate (BER), Capacity, Outage Probability, Throughput and Cumulative Distribution Function (CDF) of User Rates are analyzed to measure the performance gains provided by NOMA for system throughput improvement, reduced errors, support for additional users as well as a fair resource allocation. Simulation results show the superiority of NOMA technique in comparison to OMA, especially for high density users and different channel models, making NOMA an important candidate as a future radio access scheme. © 2024 ieee.

关键词： Distribution functions

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Hindi Text-to-image Generation: A Diverse Data Collection Methods, Annotation Approaches and Challenges 2

Hindi Text-to-Image Generation: A Diverse Data Collection Me...

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2nd international conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Mudiraj, Nakkala Srinivas Singh, Satwinder Central University of Punjab Department of Computer Science & Technology Ghudda Bathinda India

ISBN: (纸本)9798331519056

The rapid development in the field of artificial intelligence reshapes industries and humankind every day-a remarkable evolution. The machines can think and make decisions because of the availability of a large amount of data. The advancements in technology and the ease with which internet users can generate vast amounts of data daily, the visual representation of the data has a high impact. image generation based on text is a revolutionary change in AI research advancements, in which machines can interpret and generate visual representations of text. This work aims to collect the data set to build the text-to-image model for Indian scenarios. The diverse data set that pairs textual descriptions with the image content is collected for the various parts of India with different environments to make a robust system. A systematic approach to the collection of data is applied to this work. Further, it explains the annotation approaches and challenges faced during the data collection and annotation stages. © 2024 ieee.

关键词： Data collection

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