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International Conference on Biosignals, Images and Instrumentation (ICBSII)

作者： Krithika S Ajay Kumar Reddy Poreddy Thunakala Balakrishna Priyanka Kokil Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India Department of Electronics and Communication Engineering Advanced Signal and Image Processing (ASIP) Lab Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai

ISBN: (数字)9798350350951

ISBN: (纸本)9798350350968

Anemia, characterized by a deficiency in red blood corpuscles or hemoglobin, poses a significant global health challenge, particularly affecting vulnerable populations. Traditional diagnostic methods often involve invasive procedures, posing challenges in resource-limited settings. This study aims to explore non-invasive anemia detection using fingernail images and convolutional neural networks (CNNs) as a promising alternative to conventional diagnostic approaches. The study utilizes a dataset of fingernail images collected from hospitals in Ghana, comprising both anemic and non-anemic cases. The dataset undergoes preprocessing, including selective enhancement of red components, conversion to the CIElab color space, and feature extraction. A multi-input Deep Neural Network (DNN) framework employing pre-trained CNNs is proposed for anemia classification. The pre-trained CNN architectures include EfficientNet B1, EfficientNet B4, and MobileNet V3. The framework’s performance was assessed using two methodologies: The first involved random shuffling of the dataset, followed by division into training, testing, and validation sets, with evaluation metrics including Accuracy, Precision, F1 scores, and a Confusion Matrix. The second employed five-fold cross-validation, measured using accuracy. The evaluation of the proposed DNN framework using both of the methodologies indicates that EfficientNet B4 achieved the highest testing accuracy (97.87%), precision (97.88%), recall (97.87%), and F1 score (97.88%) and a cross-validation accuracy of 97.37% for the first and second methodologies respectively making it best fit for the proposed DNN framework. The findings demonstrate that the proposed framework yields promising results, especially under the second approach, and opens avenues for further exploration in transfer learning, fine-tuning of deep neural networks for multi-input feature integration, and cross-validation.

关键词： Training Accuracy Transfer learning Sociology Nails Quality assessment Convolutional neural networks

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Joint Design of Probabilistic Constellation Shaping and Precoding for Multi-user VLC Systems

Joint Design of Probabilistic Constellation Shaping and Prec...

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IEEE Conference on Global communications (GLOBECOM)

作者： Thang K. Nguyen Thanh V. Pham Hoang D. Le Chuyen T. Nguyen Anh T. Pham Computer Communications Lab. The University of Aizu Japan Department of Mathematical and Systems Engineering Shizuoka University Japan School of Electrical and Electronic Engineering Hanoi University of Science & Technology Vietnam

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

This paper proposes a joint design of probabilistic constellation shaping (PCS) and precoding to enhance the sum-rate performance of multi-user visible light communications (VLC) broadcast channels subject to signal amplitude constraint. In the proposed design, the transmission probabilities of bipolar M-pulse amplitude modulation (M-PAM) symbols for each user and the transmit precoding matrix are jointly optimized to improve the sum-rate performance. The joint design problem is shown to be a complex non-convex problem due to the non-convexity of the objective function. To tackle the problem, the firefly algorithm (FA), a nature-inspired heuristic optimization approach, is employed to solve a local optima to the original non-convex optimization problem. The FA-based approach, however, suffers from high computational complexity. Therefore, we propose a low-complexity design based on zero-forcing (ZF) precoding, which is solved using an alternating optimization (AO) approach. Simulation results reveal that the proposed joint design with PCS significantly improves the sum-rate performance compared to the conventional design with uniform signaling. Some insights into the optimal symbol distributions of the two joint design approaches are also provided.

关键词： Precoding Simulation Heuristic algorithms Symbols Probabilistic logic Numerical simulation Linear programming Global communication Optimization Visible light communication

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Joint Design of Probabilistic Constellation Shaping and Precoding for Multi-user VLC Systems

arXiv

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arXiv 2024年

作者： Nguyen, Thang K. Pham, Thanh V. Le, Hoang D. Nguyen, Chuyen T. Pham, Anh T. Computer Communications Lab. The University of Aizu Japan Department of Mathematical and Systems Engineering Shizuoka University Japan School of Electrical and Electronic Engineering Hanoi University of Science & Technology Viet Nam

关键词： Convex optimization

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Instruction Tuning-free Visual Token Complement for Multimodal LLMs

arXiv

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arXiv 2024年

作者： Wang, Dongsheng Cui, Jiequan Li, Miaoge Lin, Wang Chen, Bo Zhang, Hanwang College of Computer Science and Software Engineering Shenzhen University China School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computing Hong Kong Polytechnic University Hong Kong College of Computer Science and Technology Zhejiang University China National Key Lab of Radar Signal Processing Xidian University China

As the open community of large language models (LLMs) matures, multimodal LLMs (MLLMs) have promised an elegant bridge between vision and language. However, current research is inherently constrained by challenges such as the need for high-quality instruction pairs and the loss of visual information in image-to-text training objectives. To this end, we propose a Visual Token Complement framework (VTC) that helps MLLMs regain the missing visual features and thus improve response accuracy. Specifically, our VTC integrates text-to-image generation as a guide to identifying the text-irrelevant features, and a visual selector is then developed to generate complementary visual tokens to enrich the original visual input. Moreover, an iterative strategy is further designed to extract more visual information by iteratively using the visual selector without any additional training. Notably, the training pipeline requires no additional image-text pairs, resulting in a desired instruction tuning-free property. Both qualitative and quantitative experiments demonstrate the superiority and efficiency of our VTC. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual BASIC

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Evaluation of LoRaWAN for Remote Health Monitoring: A Preliminary Study

Evaluation of LoRaWAN for Remote Health Monitoring: A Prelim...

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IEEE Conference on Standards for communications and Networking (CSCN)

作者： Konstantinos Katzis Nina Evetovic Brian Cloteaux Kamran Sayrafian Dragana Bajić Department of Computer Science and Engineering European University Cyprus Nicosia Cyprus Department of Communications & Signal Processing University of Novi Sad Novi Sad Serbia Currently with the Institute of Measurement science Slovak Academy of Sciences Information Technology Laboratory National Institute of Standards & Technology Maryland USA

ISBN: (数字)9798331507428

ISBN: (纸本)9798331507435

Lack of sufficient patient monitoring data patterns and scenarios are among the challenges for accurate traffic modeling and network performance optimization when considering IoT medical devices. This paper examines how health data-traffic models can be used to investigate the capability and proper configuration of Long-Range Wide Area Networks (LoRaWAN) for remotely monitoring patients in a hospital environment or at home. For the hospital environment, it is assumed that there are two types of patients at any given time i.e., patients in the intensive care unit (ICU) or surgical wards and those located in the general medical wards. The transmission times of the health data from patients in the ICU/Surgical wards and those in general wards are randomly distributed within one and four-hour periods, respectively. For personal health monitoring devices that people use at their homes, a two-state Markov chain is used to model the generated traffic pattern. Preliminary results indicate that LoRaWAN may be used to support a significant number of nodes with sufficient reliability in both scenarios.

关键词： Wide area networks Patient monitoring Medical devices Hospitals LoRaWAN Surgery Traffic control Reliability Standards Optimization

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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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Novel Personalized Multimedia Recommendation Systems Using Tensor Singular-Value-Decomposition

Novel Personalized Multimedia Recommendation Systems Using T...

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2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB 2023

作者： Chang, Shih Yu Wu, Hsiao-Chun Yan, Kun Chen, Xinjia Wu, Yiyan San Jose State University Department of Applied Data Science San JoseCA95192 United States Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Guilin University of Electronic Technology Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin541004 China Guilin University of Electronic Technology Department of Information and Telecommunication Guilin541004 China Northwestern State University of Louisiana Department of Engineering Technology NatchitochesLA71497 United States Communications Research Centre Canada OttawaONK2H 8S2 Canada

ISBN: (纸本)9798350321524

Nowadays, multimedia data are often produced by various sources, e.g., Internet of Things (IoT), social media, customer databases, etc. As tremendous multimedia data are produced every day, users or E-commerce customers cannot infer information from such data by themselves and therefore recommender systems have been developed to help users to select the products or services which better fit users' preferences or requirements. Nonetheless, there exists few works on the incorporation of side information or multiple attributes about items into the design of a more robust recommender system. In this work, we propose a novel approach based on the third-order tensor singular-value-decomposition (T3-SVD) to design new personalized multimedia recommender systems (PMRSs) for internet users. A PMRS can dynamically adjust its recommendation strategy subject to a particular user's on-line transaction behavior. To evaluate the effectiveness of our proposed PMRS based on T3-SVD, we compare the performances of our proposed new PMRS and two other existing tensor-based recommendation systems over realworld data in terms of normalized root-mean-square error (NRMSE). As a result, our proposed new PMRS greatly outperforms the other two existing systems. © 2023 IEEE.

关键词： Recommender systems

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Measurement-based Characterization of Physical Layer Security for RIS-assisted Wireless Systems

Measurement-based Characterization of Physical Layer Securit...

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IEEE Conference on Vehicular Technology (VTC)

作者： Samed Keşir Sefa Kayraklık İbrahim Hökelek Ali Emre Pusane Ertugrul Basar Ali Görçin TUBITAK BILGEM Communications and Signal Processing Research (HISAR) Lab. Kocaeli Turkey Department of Electrical and Electronics Engineering Boğaziçi University Istanbul Turkey Department of Electrical and Electronics Engineering CoreLab Koç University Istanbul Turkey Department of Electronics and Communication Engineering Yildiz Technical University Istanbul Turkey

There have been recently many studies demonstrating that the performance of wireless communication systems can be significantly improved by a reconfigurable intelligent surface (RIS), which is an attractive technology due to its low power requirement and low complexity. This paper presents a measurement-based characterization of RISs for providing physical layer security, where the transmitter (Alice), the intended user (Bob), and the eavesdropper (Eve) are deployed in an indoor environment. Each user is equipped with a software-defined radio connected to a horn antenna. The phase shifts of reflecting elements are software controlled to collaboratively determine the amount of received signal power at the locations of Bob and Eve in such a way that the secrecy capacity is aimed to be maximized. An iterative method is utilized to configure a Greenerwave RIS prototype consisting of 76 passive reflecting elements. computer simulation and measurement results demonstrate that an RIS can be an effective tool to significantly increase the secrecy capacity between Bob and Eve.

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Automated Colorectal Polyps Detection from Endoscopic Images using MultiResUNet Framework with Attention Guided Segmentation

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Human-Centric Intelligent Systems 2024年第2期4卷 299-315页

作者： Ahamed, Md. Faysal Islam, Md. Rabiul Nahiduzzaman, Md. Chowdhury, Muhammad E. H. Alqahtani, Abdulrahman Murugappan, M. Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Department of Electrical & Computer Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Department of Electrical Engineering Qatar University Doha Qatar Department of Biomedical Technology College of Applied Medical Sciences Al-Kharj Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Department of Medical Equipment Technology College of Applied Medical Science Majmaah University Majmaah City Saudi Arabia Intelligent Signal Processing (ISP) Research Lab Department of Electronics and Communication Engineering Kuwait College of Science and Technology Doha Kuwait Department of Electronics and Communication Engineering Vels Institute of Sciences Technology and Advanced Studies Chennai India DCenter for Unmanned Autonomous Systems (CoEUAS) Universiti Malaysia Perlis Perlis Malaysia

The early detection of colorectal polyps is crucial for the reduction of mortality rates. However, manually identifying polyps is time-consuming and expensive, increasing the risk of missing them. Our paper aims to address this issue by presenting an automated segmentation approach for colorectal polyps. This paper proposes a method that combines a skip connection with hybrid attention guidance (AG) using attention guidance (AG) and residual path frameworks to identify salient features. Furthermore, we augment test samples using original, horizontal flip, and vertical flip transformations to enhance model robustness through Test Time Augmentation (TTA). The model was trained with Kvasir-seg samples and evaluated on Kvasir-seg and CVC-ClinicDB datasets to gauge generalizability. A significant accuracy (0.9546), a Dice Similarity Coefficient (DSC) of 0.8557, a Cross-section over Union (IoU) of 0.8824, a Recall (0.8221), a Precision (0.8922), an area under Receiver Operating Characteristics (ROC-AUC) of 0.9454, and an area under Precision-Recall (AUC-PR) of 0.8717 were achieved without TTA. Through TTA integration, accuracy (0.9993), DSC (0.8663), IoU (0.8277), Recall (0.8060), Precision (0.9364), and ROC-AUC (0.9587) have been improved. A comparison of our framework with state-of-the-art models demonstrated its effectiveness and segmentation capabilities. Additionally, the proposed model contains only 0.47 million parameters and a weight size of 6.71 MB, illustrating its potential for clinical diagnostics. A computer-aided diagnosis (CAD) system improves patient outcomes by detecting colorectal polyps early and improving segmentation accuracy.

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A Novel Hyper Chaotic-Cellular Automata Based Medical Image Encryption Technique

A Novel Hyper Chaotic-Cellular Automata Based Medical Image ...

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Electronic engineering (ICEEM), International Conference on

作者： Walid El-Shafai Ahmed Naguib Mona Shokair Computer Science Department Security Engineering Lab Prince Sultan University Riyadh Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt Faculty of Electrical Engineering 6 October University Egypt

The security of healthcare and telemedicine systems stands as a matter of paramount importance, necessitating extensive research. In the upcoming years, the medical industry envisions widespread adoption of advanced telemedicine applications. With healthcare smart devices becoming increasingly interconnected through the Internet, enabling convenient access from any location, the protection of sensitive and private information, such as personal medical images, emerges as a critical focal point. As a result, the encryption of medical images assumes a pivotal role within the domain of telemedicine and healthcare applications. This article introduces a highly efficient cryptographic system designed to enhance the protection of medical images by harnessing the power of cellular automata techniques in conjunction with hyper-chaotic procedures. In the proposed medical image encryption technique, a layered cellular automata approach is embraced, and a pseudorandom sequence derived from a four-dimensional hyper-chaotic system is employed as the cryptographic key. The study encompasses both grayscale and color images. Several assessment criteria, including the Number of Changing Pixel Rate (NPCR), Unified Averaged Changed Intensity (UACI), average entropy, correlation coefficients, Structural Similarity (SSIM), Feature Similarity (FSIM), and Peak signal-to-Noise Ratio (PSNR), are employed to evaluate the suggested cryptosystem. These evaluations vividly underscore the robust security performance exhibited by the proposed approach.

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