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Artificial intelligence in physiological characteristics recognition for internet of things authentication

Digital communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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Fine Tuned DBN Model for Food Ingredient Recognition: Introduction to Self-improved Tasmanian Devil Optimization Algorithm

Fine Tuned DBN Model for Food Ingredient Recognition: Introd...

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2023 IEEE International Conference on Integrated Circuits and communication Systems, ICICACS 2023

作者： Madival, Sharanabasappa A. Jawaligi, Shivkumar S. Sharnbasva University Faculty of Engineering & Technology Department of Computer Science and Engineering Kalburgi India Sharnbasva University Faculty of Engineering & Technology Department of Electronic and Communication Engineering Kalburgi India

ISBN: (纸本)9798350398465

Food recognition has captivated much significance for health-related appliances. The traditional schemes mainly concern on the classification of food depending on its names and disregards the basic ingredient compositions. However, owing to shortage of datasets with labels of ingredient, the issues on food ingredient recognition (FIR) is often unnoticed. This work develops a new FIR approach, where, textual features, SIFT and deep features are derived. Then, the DBN classifier is deployed for the recognition of food ingredients. To get a precise recognition, tuning the weights of DBN is done via Improved TDO (ITDO) model. © 2023 IEEE.

关键词： FIR filters

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Semi-Blind Channel Estimation and Achievable Rate Analysis for Uplink RIS-Enhanced Multi-User Networks

IEEE Open Journal of Vehicular Technology

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IEEE Open Journal of Vehicular Technology 2025年 6卷 1108-1139页

作者： Farghaly, Samar I. Ismail, Mohamed I. Fouda, Mostafa M. Alwakeel, Ahmed S. Tanta University Department of Electronics and Electrical Communications Engineering Faculty of Engineering Tanta31111 Egypt Idaho State University Department of Electrical and Computer Engineering College of Science and Engineering PocatelloID83209 United States Modern Academy for Engineering Department of Electronic Engineering and Communication Technology Cairo Governorate 4411602 Egypt Center for Advanced Energy Studies Idaho FallsID83401 United States Suez University Department of Electrical Engineering Faculty of Engineering Suez43221 Egypt

Future wireless networks could benefit from the energy-efficient, low-latency, and scalable deployments that Reconfigurable Intelligent Surfaces (RISs) offer. However, the creation of an effective low overhead channel estimate technique is a major obstacle in RIS-assisted systems, especially given the high number of RIS components and intrinsic hardware constraints. This research examines the uplink of a RIS-empowered multi-user MIMO communication system and presents a novel semi-blind channel estimate approach. Unlike current approaches, which rely on pilot-based channel estimation, our methodology uses data to estimate channels, considerably enhancing the achievable rate. We provide a closed-form deterministic expression for the uplink achievable rate in actual settings where the channel state information (CSI) must be estimated rather than assumed perfect. The results of the simulations show that the formula obtained is accurate, with a close alignment between the deterministic and actual achievable rates (generally between 2 5% deviations). The proposed approach outperforms traditional approaches, resulting in rate increases of up to 35–40%, especially in instances with more RIS elements. These findings illustrate RIS technology's tremendous potential to improve system capacity and coverage, providing useful insights for optimizing RIS adoption in future wireless networks. © 2020 IEEE.

关键词： Channel state information

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Video and image quality improvement using an enhanced optimized dehazing technique

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Multimedia Tools and Applications 2024年 1-19页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. EL-Rabaie, El-Sayed M. Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

The detrimental effects of atmospheric haze frequently plague outdoor imagery. This phenomenon arises from the scattering of light by minute particles within the ambient environment surrounding the scene to be imaged. Haze engenders an overall whitening of the image, leading to diminished contrast. To address these issues and enhance the quality of hazy images and videos, an enhanced dehazing technique is proposed. The proposed technique includes image enhancement before the optimal dehazing process. The enhancement stage entails the implementation of both homomorphic processing and Contrast Limited Adaptive Histogram Equalization (CLAHE), serving to control the image dynamic range, while concurrently heightening the image contrast. The culminating stage encompasses an optimized dehazing technique, adept at expunging haze-induced artifacts from images. The homomorphic processing and CLAHE, applied in the pre-processing step, establish a foundation for the subsequent dehazing procedure. This proposed methodology is systematically applied to a gamut of visual outputs, including visible videos, Near-Infrared (NIR) frames, and authentic hazy images. Comparative evaluations of the proposed technique, homomorphic-processing-based enhanced dehazing, and standalone dehazing techniques is undertaken on different video types encompassing five frames. It is evident that the proposed technique, synergizing the homomorphic processing, CLAHE, and dehazing, outperforms alternative strategies. Furthermore, the proposed technique is subjected to a comparative study with various existing dehazing techniques over real hazy images. The assessment depends on Peak Signal-to-Noise Ratio (PSNR), correlation, and entropy metrics. The results underscore the efficacy of the proposed technique, particularly in terms of spectral entropy enhancement of dehazed frames. For both visible and NIR frames, the percentages of enhancement by the proposed technique are 17.66% and 118.48%, respectively. ©

关键词： Image enhancement

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Automatic Semantic Segmentation and Classification of Remote Sensing Image Data for Flood Detection Using Novel LSTM Neural Network

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SN computer Science 2024年第8期5卷 1-10页

作者： Sonavale, Amruta Chakkaravarthy, Midhun Srinivasa Rao, Surampudi Salleh, Hishamuddin Bin M. Jadhav, Jagannath Department of Computer Science and Multimedia Lincoln University College Kota Bharu Malaysia Faculty of Computer Science and Multimedia Lincoln University College Kota Bharu Malaysia ECE Malla Reddy College of Engineering and Technology Hyderabad India Faculty of Electronics and Communication Engineering KLE College of Engineering and Technology Chikodi India

Floods are one of the main threats to human life and the property of natural disasters, especially in highly populated urban areas. Fast and accurate extraction of submerged area risky to supporting emergency planning and providing damage assessment in spatial and temporal measurements. Satellite multispectral images have limited bands, low resolution, and information that can be analyzed. The bands are merged to form a unified image that incorporates data from all bands. However, current contouring techniques are affected by chromatic aberrations. This study uses intensity-hue-saturation and higher-order statistics to enhance the spatial and spectral information of remote sensing images in combination with segmentation and classification methods. High performance can be shown by implementing edge detection technology to identify objects using structure in Remote Sensing Images (RSI). The parameters used to evaluate the implementation of the proposed edge detection method included root mean square error, correlation coefficient, structural similarity index measure, and the error associated with the mean spectral analysis. The dimensionality of multiband RSI can be reduced based on higher-order data statistics using independent component analysis. Furthermore, images can be clustered using high-resolution panchromatic RSI models. The proposed technique can convert the pixel powers into the sign powers of adjacent higher-order partitions during region segmentation. Pixel intensities can be analyzed using preprocessing techniques such as denoising, which gradually stabilizes the object’s mean value. This method is shown not to impact the original seed level. In this proposed work wiener filtering is *** process, the structural region is extracted from the preprocessing image. After detecting structural regions in the RSI, the different feature values are obtained for further classification. The propos

关键词： Adaptive median filtering Flood detection LSTM Neural network algorithm Structural object son the RSI Versatile Linear Edge Detection (VLED) algorithm

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Enhancing visual clarity in hazy media: a comprehensive approach through preprocessing and feature fusion attention-based dehazing

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Multimedia Tools and Applications 2024年 1-23页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. Rabaie, El-Sayed M. Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt

Particles in the atmosphere, such as dust and smoke, can cause visual clarity problems in both images and videos. Haze is the result of the interaction between airborne particles and light, which is scattered and attenuated. Hazy media present difficulties in a variety of applications due to the reduced contrast and loss of essential information. In response, dehazing techniques have been introduced to bring hazy videos and images back to clarity. Here, we provide a novel technique for eliminating haze. It comprises preprocessing steps before dehazing. Preprocessing is applied to hazy images through homomorphic processing and Contrast Limited Adaptive Histogram Equalization (CLAHE). We present a dehazing technique referred to as the pre-trained Feature Fusion Attention Network (FFA-Net) that directly lets dehazed images be restored from hazy or preprocessed hazy inputs without requiring the determination of atmospheric factors, such as air light and transmission maps. The FFA-Net architecture incorporates a Feature Attention (FA) method to do this task. We assess the proposed technique in a variety of circumstances, including visible frames, Near-Infrared (NIR) frames, and real-world hazy images. Evaluation criteria like entropy, correlation, and Peak Signal-to-Noise Ratio (PSNR) are used to compare the quality of dehazed frames or images to their hazy counterparts. Furthermore, histogram analysis and spectral entropy are adopted to determine the effectiveness of the proposed technique in comparison to existing dehazing techniques. Comparative results are presented for both real-world and simulated environments. The benefits of the proposed technique are demonstrated by a comparison of the results obtained from the standalone pre-trained FFA-Net and the proposed comprehensive methodology. Moreover, a thorough assessment is carried out for comparing the effectiveness of the proposed FFA-Net technique to those of some current dehazing techniques on real hazy images. T

关键词： Image enhancement

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Evaluation and Design Criterion for Pulse-shaped AFDM

Evaluation and Design Criterion for Pulse-shaped AFDM

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2024 IEEE Global communications Conference, GLOBECOM 2024

作者： Yin, Haoran Tang, Yanqun Li, Shuangyang Zhou, Yu Yi, Cong Sun Yat-sen University School of Electronics and Communication Engineering Shenzhen China Technical University of Berlin Faculty of Electrical Engineering and Computer Science Berlin Germany

ISBN: (纸本)9798350351255

Affine frequency division multiplexing (AFDM) is a promising chirp-based waveform designed for communications in high-mobility scenarios. In this paper, the pulse shaping for AFDM over doubly selective channels (DSC) is investigated. We first develop the pulse-shaped AFDM (PS-AFDM) system, where different transmit pulses and receive pulses can be used for each chirp carrier. Based on that, we formulate the impacts of pulse shaping on the input-output relationship of PS-AFDM system with fractional delay and fractional Doppler shifts. In particular, we reveal that there exists inter-pulse interference (IPI) within the pilot region and inter-region interference (IRI) between the pilot region and the data region in the AFDM/PS-AFDM received symbols. To provide an instructive guideline for interference suppression, we elaborate how the adopted transmit and receive pulses determine the IPI and IRI. Furthermore, we demonstrate that applying the pulse-shaping window with low sidelobe levels in PS-AFDM can suppress the IPI and IRI, facilitating the channel estimation and signal detection processes significantly. Simulations verify that the proposed PS-AFDM systems can achieve lower overhead and higher accuracy channel estimation compared to the conventional AFDM systems. © 2024 IEEE.

关键词： Channel estimation

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Generating synthetic mixed-type tabular data by decoding samples from a latent-space: a case study in healthcare 28th

Generating synthetic mixed-type tabular data by decoding sam...

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28th International Conference on Knowledge Based and Intelligent information and engineering Systems, KES 2024

作者： Drapala, Jaroslaw Swiatek, Jerzy Department of Computer Science and Systems Engineering Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wroclaw50-370 Poland

Medical data are subject to privacy regulations, which severely limit AI specialists who wish to construct decision support systems for medicine. Large amounts of this data are tabular, indicating that they are organized into a table format, where patient records are represented in rows and measured variables in columns. Furthermore, the variables come in different types-some are numerical, while others are categorical. In this work, we introduce a novel method for constructing generators of synthetic tabular data with mixed types. The key point of our approach is the explicit utilization of a latent space to represent the original data. A case study using real medical data is presented. © 2024 The Authors.

关键词： Data privacy

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Evaluating 10Gbps Wireless Optical communication Using NRZ Modulation: Analyzing BER, Signal Quality, and System Performance 7

Evaluating 10Gbps Wireless Optical Communication Using NRZ M...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Khant, Shailesh Makwana, Mayur Patel, Atul Faculty of Computer Science and Applications Charusat Changa Anand India Dept. Electronics and Communication Engineering Charusat Changa Anand India

ISBN: (纸本)9798350350067

Free space optical (FSO) communication is a fast and wireless data transfer technique that serves as a viable alternative to traditional fiber-optic systems. This study illustrates how FSO can effectively address the growing demand for rapid data transmission and suggests its potential applicability in various scenarios. FSO can be employed to establish connections between buildings within a corporate campus without the need for physical wiring, or it can provide high-speed internet access in locations where installing cables is challenging. We used Optsim software to assess critical parameters such as Bit Error Rate (BER), eye diagrams, power, and signal quality. The results offer valuable insights into enhancing the efficiency and reliability of FSO systems. FSO can find applications in cellular networks to interconnect network equipment, as well as in high-definition video surveillance. These findings highlight the areas in which FSO can advance communication technology and provide valuable insights for future developments. © 2024 IEEE.

关键词： Bit error rate

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Smart Weapon/Emotion Recognition System to Reduce Suicide Attempts

Smart Weapon/Emotion Recognition System to Reduce Suicide At...

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2024 Arab ICT Conference, AICTC 2024

作者： Hagecg, Christel Al Jebran, Mital Harb, John Aoun, Elio Kassem, Abdallah Hamad, Mustapha Notre Dame University-Louaize Department of Computer and Communication Engineering Faculty of Engineering Zouk Mosbeh P.O. Box: 72 Zouk Mikael Lebanon

ISBN: (纸本)9798350342086

Acting towards decreasing the numbers of suicides is a must. Suicide rates have increased notably through the years;therefore, this paper describes a system whose purpose is to decrease those rates by trying to delay people from committing suicide until help arrives. This can be done using Artificial Intelligence, Machine Learning, and Deep Learning. The result is that the a camera will detect the situation on case of suicide, a message will be sent to specific person in order to get help. © 2024 IEEE.

关键词： Emotion Recognition

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