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arXiv 2024年

作者： Li, Lin Gou, Jianping Yu, Baosheng Du, Lan Yi, Zhang Tao, Dacheng The School of Computer Science and Communication Engineering Jiangsu Key Laboratory of Security Tech. for Industrial Cyberspace Jiangsu University Zhenjiang212013 China The College of Computer and Information Science College of Software Southwest University Chongqing400715 China The School of Computer Science Faculty of Engineering The University of Sydney DarlingtonNSW2008 Australia The Faculty of Information Technology Monash University ClaytonVIC Australia The School of Computer Science Sichuan University Chengdu610065 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

Federated Learning (FL) seeks to train a model collaboratively without sharing private training data from individual clients. Despite its promise, FL encounters challenges such as high communication costs for large-scale models and the necessity for uniform model architectures across all clients and the server. These challenges severely restrict the practical applications of FL. To address these limitations, the integration of knowledge distillation (KD) into FL has been proposed, forming what is known as Federated Distillation (FD). FD enables more flexible knowledge transfer between clients and the server, surpassing the mere sharing of model parameters. By eliminating the need for identical model architectures across clients and the server, FD mitigates the communication costs associated with training large-scale models. This paper aims to offer a comprehensive overview of FD, highlighting its latest advancements. It delves into the fundamental principles underlying the design of FD frameworks, delineates FD approaches for tackling various challenges, and provides insights into the diverse applications of FD across different scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Distillation

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A Multi-Stage Security Solution for Medical Color Images in Healthcare Applications

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computer Systems Science & engineering 2023年第9期46卷 3599-3618页

作者： Walid El-Shafai Fatma Khallaf El-Sayed M.El-Rabaie Fathi E.Abd El-Samie Iman Almomani Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electrical Engineering Faculty of EngineeringAhram Canadian UniversityGizaEgypt Computer Science Department King Abdullah II School of Information TechnologyThe University of JordanJordan11942Jordan

This paper presents a robust multi-stage security solution based on fusion,encryption,and watermarking processes to transmit color healthcare images,*** presented solution depends on the features of discrete cosine transform(DCT),lifting wavelet transform(LWT),and singular value decomposition(SVD).The primary objective of this proposed solution is to ensure robustness for the color medical watermarked images against transmission *** watermark embedding,the host color medical image is transformed into four sub-bands by employing three stages of *** resulting low-frequency sub-band is then transformed by employing three stages of DCT followed by SVD ***,a fusion process is used for combining different watermarks into a single watermark *** single fused image is then ciphered using Deoxyribose Nucleic Acid(DNA)encryption to strengthen the ***,the DNA-ciphered fused watermark is embedded in the host medical image by applying the suggested watermarking technique to obtain the watermarked *** main contribution of this work is embedding multiple watermarks to prevent identity *** the presence of different multimedia attacks,several simulation tests on different colormedical images have been *** results prove that the proposed security solution achieves a decent imperceptibility quality with high Peak Signal-to-Noise Ratio(PSNR)values and high correlation between the extracted and original watermark ***,the watermark image extraction process succeeds in achieving high efficiency in the presence of attacks compared with related works.

关键词： Medical images DNA encryption digital image watermarking fusion healthcare applications

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A Leaky-Wave Beam Scanning Antenna with PIN Diodes

A Leaky-Wave Beam Scanning Antenna with PIN Diodes

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IEEE International Conference on Electronic information and Communication technology (ICEICT)

作者： Gengbo Xiao Botao Feng Xiao Ding Li Deng Kwok L. Chung Wenzhe Gu College of Electronics and Information Engineering Shenzhen University Shenzhen China Faculty of Humanities and Arts Macau University of Science and Technology Macau China College of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China School of Computer Science and Engineering Huizhou University Huizhou P. R. China School of Computer Science and Engineering Huizhou University Huizhou China

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

In this paper, a reconfigurable digital modulation leaky-wave antenna (LWA) is proposed. This antenna does not require additional ADC/DAC modules and mixing circuits, which simplifies the antenna structure design. By introducing diodes, the proposed antenna realizes digital beam scanning and digital modulation of the leaky-wave. By inputting different digital codes, the multi-beam switching functionality is achieved, thereby realizing the purpose of beam reconfigurability. A low-cost and easy-to-manufacture metasurface radiator is fabricated, which can achieve the leaky-wave radiation effect. In summary, the proposed antenna is a promising candidate for realizing beam reconfigurability in future intelligent wireless communications.

关键词： Wireless communication Radiation effects Codes PIN photodiodes Digital modulation Switches Metasurfaces

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Revolutionizing waste management: harnessing the power of IoT

Revolutionizing waste management: harnessing the power of Io...

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7th IET Smart Cities Symposium (SCS 2023)

作者： Ankita Karale Anand Singh Rajawat S. B. Goyal Computer Engineering Sandip Foundation's Sandip Institute of Research Center Nashik India School of Computer Science & Engineering Sandip University Nashik India Faculty of Information Technology City University Petaling Jaya Malaysia

Waste management has become a pressing problem in a world that is becoming more and more urbanised. Traditional ways of dealing with trash are usually inefficient and bad, leading to pollution, health problems, and wasted resources. As an answer to these problems, the Internet of Things (IoT) has changed the way trash is handled in a big way. This abstract looks at how smart waste management based on the Internet of Things could totally change the business of garbage disposal. Through a network of sensors and devices, the Internet of Things (IoT) connects trash cans, garbage trucks, and waste processing facilities so that they can be monitored and improved in real time. A smart waste management system that is built on IoT has many benefits. With the real-time information it gives about how much trash is being made, routes for collecting trash can be made more efficient in terms of time and fuel use. This cuts down on the number of times trash needs to be picked up, reduces the amount of greenhouse gases released, and saves money. Sensors in trash cans can also find dangerous or flammable items, so they can be dealt with right away and any problems can be avoided. Also, the Internet of Things makes it possible for predictive analytics, which help waste management officials predict how much trash will be made and use their resources well. Using smart waste sorting methods like optical sensors and machine learning algorithms, recyclable and non- recyclable trash can be correctly found and sorted. This helps people recycle more and puts less pressure on landfills. Smart trash management systems that are based on the Internet of Things (IoT) get more people involved and help them understand. Mobile apps and smart devices can tell a person when their trash will be picked up, where the nearest recycling sites are, and how much trash they make. People are encouraged to act responsibly and throw away trash in ways that are good for the environment. Smart waste management bas

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Dual-Band Printed Antenna Design for Bluetooth and WLAN Applications

Dual-Band Printed Antenna Design for Bluetooth and WLAN Appl...

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IEEE International Conference on Electronic information and Communication technology (ICEICT)

作者： Naveed Hamid Nawaz Khan Botao Feng Xiao Ding Wenzhe Gu Li Deng Kwok L. Chung College of Electronics and Information Engineering Shenzhen University Shenzhen China Faculty of Humanities and Arts Macau University of Science and Technology Macau China School of Computer Science and Engineering Huizhou University Huizhou China College of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China School of Computer Science and Engineering Huizhou University Huizhou P. R. China

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

This study presents the fabrication process of a printed antenna on a FR-4 substrate with accurate measurements of 25×37×1 mm 3 . The antenna design, known for its simplicity, has four semicircles with four small square patches at each end. In addition, it includes two rectangular patches that are fitted with pin diode switches in order to enable the ability to operate in two different frequency bands. The antenna's ground has a carefully placed opening. The antenna being examined demonstrates resonance at both the frequencies of 2.4 GHz and 5 GHz, with reflection coefficients of -19.6 dB and -16.5 dB correspondingly. The VSWR for both working frequencies is below 2, satisfying the requirements of Bluetooth and WLAN applications. The tiny size and creative design elements of this antenna allow for versatility and adaptation in many wireless communication systems.

关键词： Wireless communication Antenna measurements Wireless LAN Bluetooth PIN photodiodes Resonant frequency Dual band

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Early Detection of Interproximal Caries using YOLOv8 Convolutional Neural Network on Bitewing Radiographs

Early Detection of Interproximal Caries using YOLOv8 Convolu...

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Data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： Soundar Ida Mahizha Joseph Annrose Jeyebalaji Mano Christaine Angelo Israel Domilin Shyni Department of Information Technology St. Xavier’s Catholic College of Engineering Nagercoil India Faculty of Dentistry Sri Ramachandra Institute of Higher Education and Research Chennai India Department of Computer Science and Engineering DMI College of Engineering India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

Early childhood caries is a common oral health issue in both developed and developing countries. Identifying caries lesions early can help in implementing minimally invasive treatment plans, preserving the remaining tooth structure. Interproximal lesions are typically found between the adjacent surfaces of two teeth. The first step is visual inspection, followed by examination using dental probes to detect the dental caries. Usually, radiographic examination is done for early detection of dental caries. This process is time-consuming. In recent years, AI has been used to detect early caries from X-ray images. In dentistry, proximal caries is difficult to identify from periapical and panoramic radiographs, so Bitewing Radiographs (BWR) are used to detect the interproximal caries. Since the X-ray source is parallel to the sensor, Bitewing Radiographs has least distortion and help in the detection of early proximal caries. Several studies have shown that Bitewing Radiographs have high sensitivity in detecting interproximal caries occurring in existing restorations compared to visual clinical detection. The main objective of this study is to detect dental caries and classify them into various classes enamel, dentin and restorations. In this study, a dataset was collected from Mendeley data and Kaggle. The caries classification utilized a Deep Convolutional Neural Network. A YOLOv8 algorithm was developed based on the severity of the lesions observed in bitewing digital radiographs, enabling convenient and reliable detection of these lesions. The performance of these classifications is evaluated. This study aimed to evaluate the performance of the deep neural network for detecting interproximal caries lesions on Bitewing Radiographs (BWR). Accurate detection and diagnosis of dental caries are crucial for reducing the cost of oral health management and improving the likelihood of preserving natural teeth, leading to better patient outcomes.

关键词： Visualization Sensitivity Teeth Dentistry Image restoration Lesions Convolutional neural networks Diagnostic radiography X-ray imaging Surface treatment

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Meander-Line Slots Based Miniaturized Inset-Fed Patch-Sensor Antenna for Water Characterization Technique

Meander-Line Slots Based Miniaturized Inset-Fed Patch-Sensor...

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IEEE International Conference on System engineering and technology

作者： Aya Khalid Hadi Mahmood Farhan Mosleh Adham R. Azeez Raed A. Abd-Alhameed Department of Computer Techniques Engineering University of Middle Technique Baghdad Iraq Department of Media Technology and Communications Engineering College of Engineering University of Information Technology and Communications Baghdad Iraq Faculty of Engineering and Informatics University of Bradford Bradford United Kingdom

This article presents a miniaturized size Microstrip-patch sensor structure based on meander-line slot for water-quality and salinity measurement applications. The HFSS-software tool is employed to design and simulate the proposed sensor. The proposed sensor is printed on $60\times 50\times 1~mm^{3}$ a dielectric material of FR-4 type. The meander-line slot resonator is inserted in the patch-sensor for miniaturize with the sensitivity enhancement. In order to contain the water sample that wants to be tested, a receptacle with dimensions of $47.75\times 50\times 4~mm^{3}$ is mounted on the sensor structure. The receptacle is made from PTFE-Teflon dielectric material. The miniaturize sensor is noticed resonant at 2.74 GHz for unloaded-state. Furthermore, it’s found resonant at; 2.44 GHz, 2.49 GHz, and 2.5 GHz for DI-water, fresh-water and sea-water respectfully. The miniaturized-sensor is showed good sensing values that equals to 0.901 for DI-water, 0.751 for fresh-water, and 0.721 for Sea-water. Finally, a good agreement is showed between the measurement results and those from simulation.

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Proposal of Multi-Agent Robotic Fire Extinguishing Model for Industrial Premises

Proposal of Multi-Agent Robotic Fire Extinguishing Model for...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Franjo Filipović Josip Balen Davor Damjanovic Rajesh Rimal Muhammad Moazam Azeem Adamya Shukla Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia TTTech Auto Osijek Croatia Department of Computer Science and Engineering Qatar University Doha Qatar Volvo Group GTO Gothenburg Sweden

Protecting equipment and objects from fire in warehouses and industry premises is essential due to a high number of people present, valuable equipment or inventory and possibly dangerous goods. It is mostly often conducted by using smoke and fire sensors along with extinguishing systems. Still, in facilities with valuable inventory and equipment, it is also necessary to protect equipment from damage caused by water. This paper proposes a multi-agent robotic fire extinguishing model consisted of autonomous robots divided into two groups: Patrol Robots and Fire Extinguishing (FE) robots, called PR-FER model. The main methodology of the PR-FER model is using small patrol autonomous robots as a surveillance robots that will first map the surveillance area, determine the patrol paths, and conduct surveillance according to the predefined schedule. They are equipped with the devices for detecting fire and high temperatures such as RGB, IR and Thermal Imaging cameras and various sensors. Furthermore, they will have high capacity batteries which will enable them a long autonomy. In case of a detection of too high temperatures and fire, patrol robots would map the dangerous location and call FE robots to extinguish it. The FE robots are more robust and heavier robots equipped with fire extinguishers. During the fire extinguishing procedure the patrol robots would notify the fire department and enable them a video streaming in order to get an early awareness of the situation on the field.

关键词： Temperature sensors Industries Service robots Surveillance Streaming media Robot sensing systems Iron

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Expression of Concern for: Utilizing Machine Learning Algorithms to Optimize Real-Time Data Analysis

Expression of Concern for: Utilizing Machine Learning Algori...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Vaishali Singh Eesha Gupta D Little Femilin Jana Gopal B. Deshmukh Abhishek Singla K. Gopala Krishna Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Department of Electronics and Communication Engineering Faculty of Engineering and Technology Bangalore Karnataka India

ISBN: (数字)9798350319125

ISBN: (纸本)9798350319132

This paper offers a way for optimizing actual-time records analysis the usage of a variety of machine gaining knowledge of algorithms. The algorithms paintings by creating predictive fashions which extract useful information from datasets, follow appropriate preprocessing techniques to make sure the high-quality of the records, and model the information to deduce meaningful capabilities from the data. The predictive models are then used to discover applicable developments, find patterns, and summarize the data for clean analysis. specifically, this paper outlines the technique of using supervised and unsupervised learning algorithms, consisting of Random wooded area, Naïve Bayes, and k-approach clustering, to extract meaningful styles and relationships from facts points in real-time. Additionally, the paper discusses wonderful capacity packages of the proposed technique, including fraud detection and prediction of stock costs. Such capability programs demonstrate the fee of actual-time records analysis for organizations and organizations.

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Automated COVID-19 screening framework via Deep Convolutional Neural Network with Chest X-ray Medical Images

Automated COVID-19 screening framework via Deep Convolutiona...

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International Conference on information technology (INCIT)

作者： Waris Damkham Tipajin Thaipisutikul Akara Supratak Jidapa Kraisangka Pattanasak Mongkolwat Jja-Ching Wang Faculty of Information and Communication Technology Mahidol University Thailand Department of Computer Science and Information Engineering National Central University Taiwan

COVID-19 screening using chest X-rays plays a significant role in the early diagnosis of COVID-19 illness during the ongoing pandemic. Manually identifying this infection from chest X-ray films is a challenging and time-consuming technique due to time restrictions and the competence of radiologists. Also, the manual Covid-19 identification technique is made much more difficult and opaquer by the feature similarity between positive and negative chest X-ray images. Therefore, we propose an automated COVID-19 screening framework that utilizes artificial intelligence techniques with a transfer learning approach for COVID-19 diagnosis using chest X-ray images. Specifically, we employ the transfer learning concept for feature extraction before further processing with modified deep neural networks. Also, Grad-CAM visualization is used for our case study to support the predicted diagnosis. The results of the experiments on the publicly accessible dataset show that the convolutional neural network model, which is simple yet effective, performs significantly better than other deep learning techniques across all metrics, including accuracy, precision, recall, and F-measure.

关键词： COVID-19 Deep learning Measurement Visualization Pandemics Transfer learning Neural networks

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