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Color image restoration by filtering methods: a review

Soft Computing 2024年第13-14期28卷 7755-7782页

作者： Salamat, Nadeem Missen, Malik Muhammad Saad Akhtar, Nadeem Mustahsan, Muhammad Surya Prasath, V.B. Department of Mathematics Khawaja Fareed University of Engineering and Information Technology Rahim Yar Khan64200 Pakistan Department of Computer Science and IT The Islamia University of Bahawalpur Bahawalpur63100 Pakistan Division of Biomedical Informatics Cincinnati Children’s Hospital Medical Center CincinnatiOH45229 United States Department of Computer Science Pediatrics and Biomedical Informatics University of Cincinnati CincinnatiOH45221 United States

Digital images are corrupted with noise, and image denoising is an important step in image processing modules. In this review, the latest developments in filtering methods for color image restoration are analyzed. These algorithms are compared in terms of objective image quality measures and divided into major classes, such as spatial domain, switching and wavelet filtering methods. These classes are based on the particular methodology used in image denoising algorithms and further subdivided to show their classification in terms of noise models utilized, application style, and stages the filters applied in images. In particular, we present a review of filtering methods in color image denoising, published over the past two decades. Our classification and succinct descriptions of color image restoration by these mathematical filtering techniques and their characterizations can help choose the appropriate ones for various downstream image processing tasks. © The Author(s) 2024.

关键词： Restoration

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Real Time Stock Inventory Management System

Real Time Stock Inventory Management System

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International Conference on Inventive Systems and Control (ICISC)

作者： Steve Caleb Roosevelt Ebenezer Veemaraj Stewart Kirubakaran Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu

ISBN: (数字)9798350386578

ISBN: (纸本)9798350386585

For companies looking to stay competitive in the dynamic and fast-paced business world of today, effective stock inventory management has become critical. In order to provide organizations with an all-inclusive and flexible inventory control solution, this paper presents a Real-Time Stock Inventory Management System. The system provides real-time stock inventory tracking, monitoring, and management by utilizing cutting-edge technologies including cloud computing, data analytics, and the Internet of Things. Through the utilization of Internet of Things devices, the Real-Time Stock Inventory Management System’s cloud-based infrastructure enhances scalability, accessibility, and flexibility while providing accurate real-time tracking of inventory quantities, locations, and statuses.

关键词： Cloud computing Data analysis Scalability Companies Inventory management Inventory control Control systems

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10th IEEE Smart World Congress, SWC 2024

作者： Chen, Shuang Xuan, Shichang Shen, Jiaxing The Hong Kong Polytechnic University Research Institute for Artificial Intelligence of Things Hong Kong Harbin Engineering University College of Computer Science and Technology China Lingnan University Division of Artificial Intelligence Hong Kong

ISBN: (纸本)9798331520861

Recently, industrial digital twins have been widely adopted to create a digital replica of the industrial sites for real-time simulation, intelligent decision-making, and automatic feedback control. However, the bi-directional information flows between the physical twin and digital twin are facing unique challenges in guaranteeing data authenticity and improving data searchability. Existing studies propose employing blockchain-empowered industrial digital twins to authenticate the information flow. They also consider improving data security and privacy and deploying blockchain-empowered industrial digital twins on emerging 6G and edge networks. However, they neglect the provision of high-efficiency data query services. This work is the first to consider the searchability issue in blockchain-empowered industrial digital twins. We propose a general system architecture of blockchain-empowered industrial digital twins applicable for various applications. The key novelty of this work lies in the proof-of-similarity blockchain consensus mechanism that significantly improves the search efficiency of industrial digital twins data. More specifically, proof of similarity guarantees that the generated block contains semantically similar data and a block summary. Then, the blockchain queries will be compared with the block summaries before entering the transactions in the blocks. In this way, up to 9 2. 0 % of the blocks can be skipped during blockchain query, resulting in an 87.4% reduction of the average query completion time. © 2024 IEEE.

关键词： Blockchain

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SHIS: secure healthcare intelligent scheme in internet of multimedia vehicular environment

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Multimedia Tools and Applications 2024年第40期83卷 87781-87800页

作者： Mangla, Cherry Rani, Shalli Dhiman, Gaurav Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India Bikram Government College Punjab Patiala India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Division of Research and Development Lovely Professional University Phagwara144411 India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun248002 India MEU research Unit Middle East University Amman Jordan

For providing a safe and secure healthcare environment in vehicular multimedia networks has been emerged as a promising area because of data exchange in multimedia format. Because of properties of Long Term Evolution (LTE) with low delay, high data rate, high mobility, an efficient safe environment can be provided to the in healthcare environment for multimedia content (images, audio, text, video). Despite these benefits, due to the high mobility and data transmission using an open channel, i.e., Internet, multimedia vehicular networks are facing many security breaches in recent years. The Internet of Multimedia Vehicular Environment calls for the establishment of a safe and intelligent healthcare system, and the proposed work problem lays the groundwork for those requirements. One of the security breaches is the Denial of Service (DoS) attack in healthcare. To mitigate this issue, in this paper, we propose, SHIS, a model for detection of Imposter location using two parameters (i) packet counter and (ii) average ratio of packet delivery. It helps to detect attacks faster in comparison to the existing approach (MEC-DoS) for healthcare data. We proposed SHIS to detect denial of service attack to find the location of malicious nodes in the multimedia network. Simulation results showed that SHIS outperformed the existing techniques as it has 20% improvement in detection of attacks as compared to the existing techniques. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Denial-of-service attack

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Incremental Instance Segmentation for Cluttered Baggage Threat Detection

Incremental Instance Segmentation for Cluttered Baggage Thre...

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2023 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2023

作者： Nasim, Ammara Velayudhan, Divya Ahmed, Abdelfatah Hassan Hassan, Taimur Akcay, Samet Akram, Muhammad Usman Werghi, Naoufel National University of Sciences and Technology Department of Computer and Software Engineering Pakistan Khalifa University KUCARS and C2PS Department of Electrical Engineering and Computer Science United Arab Emirates Abu Dhabi University Department of Electrical and Computer Engineering Abu Dhabi United Arab Emirates Intel Corporation RandD Division United Kingdom

ISBN: (纸本)9781665434454

Identification of contraband items from highly oc-cluded baggage of air travelers is a challenging task even for human experts with very high experience. Many researchers have been working rigorously to develop computer vision-based techniques for baggage screening through X-ray images. Nu-merous machine learning and deep learning-based frameworks have been proposed by researchers in the last two decades. However, all of these techniques face limitations in segmenting prohibited items from highly occluded and cluttered baggage. In this paper, we propose a novel framework based on semantic segmentation to automatically detect concealed prohibited items from X-ray baggage scans. Furthermore, to detect different overlapping instances of the same contraband item, we propose an instance-Aware segmentation model that enables the semantic segmentation model to identify multiple instances of the same threat category through incremental learning without requiring additional overhead. The proposed framework is computationally lighter compared to other similar approaches as it requires min-imal training examples and leverages previous knowledge. The proposed model has outperformed state-of-The-Art instance seg-mentation techniques when tested on publicly available GDXray and SIXray datasets, giving mean average precision scores of 0.50 and 0.47, respectively. In addition, the proposed framework leads other instance segmentation baseline models in terms of mean inference time. © 2023 IEEE.

关键词： Semantic Segmentation

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Hemochromatosis Pathogenesis and Its Association with Liver Disease: An Analysis Through Machine Learning 4

Hemochromatosis Pathogenesis and Its Association with Liver ...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Imtiaz, Isra Rehman, Attique Ur Javaid, Sabeen Ali, Tahir Muhammad Mir, Azka Masud, Mehedi Kumar, Deepak University of Sialkot Department of Software Engineering Sialkot Pakistan Gulf University for Science and Technology Department of Computer Science Kuwait Taif University Department of Computer Science Saudi Arabia Lovely Professional University Division of Research and Development Phagwara144411 India KKR and KSR Institute of Technology and Sciences Andhra Pradesh Guntur India

ISBN: (纸本)9798350353266

A significant risk factor for the development of liver disease is hemochromatosis, an inherited iron overload illness, but the complex pathophysiology behind this link is still unknown. To understand the intricate interactions between hemochromatosis and liver disease, this study uses a cutting-edge strategy that incorporates machine learning techniques. Promising prediction skills are demonstrated by machine learning models, which open up new possibilities for early detection and individualized intervention approaches. The results shed important light on the relationship between hemochromatosis and liver disease and improve our understanding of the pathogenesis of the disease. This study clarifies the pathophysiology of hemochromatosis and its subsequent connection to liver disease, highlighting the revolutionary power of machine learning in understanding intricate biological processes. This selected dataset contains 15 parameters and has 202 dated records with a target value of 'Disease Name' which has 5 different categories Type 1, 2, 2A, 2B,2C, 3, 4, and Type5. The experiment is performed using two classifiers including KNN and Naïve Bayes. The k-fold cross-validation technique is also used to improve the performance. After the comparative analysis of the resulting accuracy percentage of different classifiers, we have proposed the model with the classifier with the highest accuracy. Our proposed model has achieved an accuracy rate of 98.00% using a K-NN classifier. Rapid miner platform is used for the application of the machine learning tools and techniques for this research These discoveries have consequences that go beyond clarifying the mechanisms behind disease;they also open the door to better clinical care and tailored treatment therapies for high-risk people. © 2024 IEEE.

关键词： Potassium alloys

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Decentralized Adaptive Routing Framework using Q-Learning and Blockchain

Decentralized Adaptive Routing Framework using Q-Learning an...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Esswari S Salaja Silas G. Jaspher W. Kathrine Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore Division of CSE Karunya Institute of Technology and Sciences Coimbatore

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

Efficient and reliable routing in decentralized networks remains a persistent challenge, particularly in environments characterized by dynamic and unpredictable conditions. Traditional routing protocols often lack the adaptability required to respond effectively to rapid changes, leading to suboptimal performance and increased latencies [1] [2]. A decentralized routing framework is proposed, integrating Proof-of-Authority (PoA) blockchain for secure and transparent transaction logging [3] with a multi-neighbor Q-learning algorithm for adaptive decision-making [4] [5]. By leveraging Q-learning, nodes dynamically optimize their routing choices based on latency feedback while balancing exploration and exploitation [6]. The framework is evaluated within a "2-step ring" topology, demonstrating its ability to adaptively select neighbors while maintaining security and decentralization [7]. Results illustrate significant improvements in routing efficiency, adaptability, and decision robustness across multiple operational cycles, offering a scalable solution for modern network challenges [8] [9]

关键词： Q-learning Adaptive systems Heuristic algorithms Routing Stability analysis Blockchains Topology Security Tuning Thermal stability

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Imitation Game for Adversarial Disillusion With Chain-of-Thought Reasoning in Generative AI

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IEEE Access 2025年 13卷 95085-95093页

作者： Chang, Ching-Chun Chen, Fan-Yun Gu, Shih-Hong Gao, Kai Wang, Hanrui Echizen, Isao National Institute of Informatics Information and Society Research Division Tokyo101-8430 Japan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan School of Multidisciplinary Sciences Kanagawa240-0193 Japan The University of Tokyo Graduate School of Information Science and Technology Tokyo113-8656 Japan

As the cornerstone of artificial intelligence, machine perception confronts a fundamental threat posed by adversarial illusions. These adversarial attacks manifest in two primary forms: deductive illusion, where specific stimuli are crafted based on the victim model’s general decision logic, and inductive illusion, where the victim model’s general decision logic is shaped by specific stimuli. The former exploits the model’s decision boundaries to create a stimulus that, when applied, interferes with its decision-making process. The latter reinforces a conditioned reflex in the model, embedding a backdoor during its learning phase that, when triggered by a stimulus, causes aberrant behaviors. The multifaceted nature of adversarial illusions calls for a unified defence framework, addressing vulnerabilities across various forms of attack. In this study, we propose a disillusion paradigm based on the concept of an imitation game. At the heart of the imitation game lies a multimodal generative agent, steered by chain-of-thought reasoning, which observes, internalizes and reconstructs the semantic essence of a sample, liberated from the classic pursuit of reversing the sample to its original state. As a proof of concept, we conduct experimental simulations using a multimodal generative dialogue agent and evaluates the methodology under a variety of attack scenarios. Experimental results demonstrate that the proposed framework consistently neutralizes both deductive and inductive adversarial illusions across diverse white-box and black-box attack scenarios. © 2013 IEEE.

关键词： Intelligent systems

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Advanced extended-term simulation approach with flexible quas-isteady-state and dynamic semi-analytical simulation engines

iEnergy

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iEnergy 2022年第1期1卷 124-132页

作者： Rui Yao Dongbo Zhao A.P.Sakis Meliopoulos Chanan Singh Joydeep Mitra Feng Qiu Division of Energy Systems Argonne National LaboratoryLemontIL 60439USA School of Electrical and Computer Engineering Georgia Institute of TechnologyAtlantaGA 30332USA Department of Electrical and Computer Engineering Texas A&M UniversityCollege Sta-tionTX 77843USA Department of Electrical and Computer Engineering Michigan State UniversityEast LansingMI 48824USA

Power system simulations that extend over a time period of minutes,hours,or even longer are called extendedterm *** power systems evolve into complex systems with increasing interdependencies and richer dynamic behaviors across a wide range of timescales,extendedterm simulation is needed for many power system analysis tasks(e.g.,resilience analysis,renewable energy integration,cascading failures),and there is an urgent need for efficient and robust extendedterm simulation *** conventional approaches are insufficient for dealing with the extendedterm simulation of multitimescale *** paper proposes an extendedterm simulation approach based on the semianalytical simulation(SAS)*** accuracy and computational efficiency are backed by SAS's high accuracy in eventdriven simulation,larger and adaptive time steps,and flexible switching between fulldynamic and quasisteadystate(QSS)*** used this proposed extendedterm simulation approach to evaluate bulk power system restoration plans,and it demonstrates satisfactory accuracy and efficiency in this complex simulation task.

关键词： Extended-term simulation resilience multi-timescale simulation event-driven simulation dynamics quasi-steady-state(QSS) hybrid simulation holomorphic embedding(HE) semi-analytical simulation(SAS)

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Low Noise Millimeter-Wave Generation with Integrated Lasers and a Compact Fabry-Perot

Low Noise Millimeter-Wave Generation with Integrated Lasers ...

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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Groman, William Kudelin, Igor Lind, Alexander Lee, Dahyeon Nakamura, Takuma Kelleher, Megan McLemore, Charles A. Guo, Joel Jin, Warren Bowers, John Quinlan, Franklyn Diddams, Scott A. Department of Physics University of Colorado BoulderCO United States Time and Frequency Division National Institute of Standards and Technology BoulderCO United States Department of Electrical and Computer Engineering University of California Santa BarbaraCA United States Department of Electrical Computer and Energy Engineering University of Colorado BoulderCO United States

We generate millimeter-waves up to 118 GHz via heterodyning two photonic chip lasers which are phase-locked to the same miniature F-P cavity. Phase noise as low as −120 dBc/Hz at 40 kHz offset is achieved. © Opti... 详细信息

关键词： Fabry-Perot interferometers

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