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Design of Secure Multi-User Coded-SSK with Index Selecting Capability

IEEE Transactions on Communications 2025年

作者： Hassan, Sumeyra Panayirci, Erdal Helleseth, Tor Vincent Poor, H. Kadir Has University Department of Electrical and Electronics Engineering Istanbul34083 Turkey University of Bergen Department of Informatics Bergen Norway Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States

We propose a new coded space shift keying (CSSK) signaling technique for multi-user (MU), multiple-input multiple-output (MIMO) communication systems incorporating physical layer security (PLS). Besides its error correction ability, the designed linear code is capable of choosing transmit antenna indices automatically and selecting the best set of antenna combinations that minimizes the bit error rate (BER). Results obtained for the single-user (SU) schemes are then extended to a general single-cell downlink MU CSSK setting. A precoder design is proposed with a maximum ratio combining (MRC) technique to eliminate the multi-user interference (MUI) entirely by taking advantage of channel state information (CSI) at the transmitter. It is shown that the same precoding provides a very effective jamming signal for the PLS against passive eavesdroppers, degrading their signal-to-interference-plus-noise ratio (SINR) severely. A closed-form expression for the achievable secrecy rates is derived and it is maximized by the proposed power allocation algorithm. Finally, it is shown analytically and by computer simulations that substantially better BER performance is achieved by each user over interference-free transmission compared to an SU transmission with a maximum likelihood (ML) detector. © 1972-2012 IEEE.

关键词： Signal to noise ratio

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LATUP-Net: A lightweight 3D attention U-Net with parallel convolutions for brain tumor segmentation

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computers in Biology and Medicine 2025年 184卷 109353-109353页

作者： Alwadee, Ebtihal J. Sun, Xianfang Qin, Yipeng Langbein, Frank C. School of Computer Science and Informatics Cardiff University CardiffCF24 4AG United Kingdom Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia

Early-stage 3D brain tumor segmentation from magnetic resonance imaging (MRI) scans is crucial for prompt and effective treatment. However, this process faces the challenge of precise delineation due to the tumors’ complex heterogeneity. Moreover, energy sustainability targets and resource limitations, especially in developing countries, require efficient and accessible medical imaging solutions. The proposed architecture, a Lightweight 3D ATtention U-Net with Parallel convolutions, LATUP-Net, addresses these issues. It is specifically designed to reduce computational requirements significantly while maintaining high segmentation performance. By incorporating parallel convolutions, it enhances feature representation by capturing multi-scale information. It further integrates an attention mechanism to refine segmentation through selective feature recalibration. LATUP-Net achieves promising segmentation performance: the average Dice scores for the whole tumor, tumor core, and enhancing tumor on the BraTS 2020 dataset are 88.41%, 83.82%, and 73.67%, and on the BraTS 2021 dataset, they are 90.29%, 89.54%, and 83.92%, respectively. Hausdorff distance metrics further indicate its improved ability to delineate tumor boundaries. With its significantly reduced computational demand using only 3.07 M parameters, about 59 times fewer than other state-of-the-art models, and running on a single NVIDIA GeForce RTX3060 12 GB GPU, LATUP-Net requires just 15.79 GFLOPs. This makes it a promising solution for real-world clinical applications, particularly in settings with limited resources. Investigations into the model's interpretability, utilizing gradient-weighted class activation mapping and confusion matrices, reveal that while attention mechanisms enhance the segmentation of small regions, their impact is nuanced. Achieving the most accurate tumor delineation requires carefully balancing local and global features. The code is available at https://***/ca/code-bca. © 2024 T

关键词： Magnetic resonance imaging

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Pose Estimation of Spacecraft Using Dual Transformers and Efficient Bayesian Hyperparameter Optimization

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 629-644页

作者： Raja, N. Kannaiya Ramesh, Janjhyam Venkata Naga El-Ebiary, Yousef A. Baker Muniyandy, Elangovan Reddy, N. Konda Kumar, Vanipenta Ravi Devarasetty, Prasad School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Bhopal466114 India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed To Be University Uttarakhand Dehradun248002 India Faculty of Informatics and Computing UniSZA University Malaysia Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Engineering Mathematics K L University Greenfields Guntur Dist Andhra Pradesh Vaddeswaram522302 India Department of Mathematics Annamacharya University New Boyanapalli Rajampet India Department of Computer Science and Engineering DVR & Dr HS MIC College of Technology Andhra Pradesh Kanchikacherla India

Spacecraft pose estimation is an essential contribution to facilitating central space mission activities like autonomous navigation, rendezvous, docking, and on-orbit servicing. Nonetheless, methods like Convolutional Neural Networks (CNNs), Simultaneous Localization and Mapping (SLAM), and Particle Filtering suffer significant drawbacks when implemented in space. Such techniques tend to have high computational complexity, low domain generalization capacity for varied or unknown conditions (domain generalization problem), and accuracy loss with noise from the space environment causes such as fluctuating lighting, sensor limitations, and background interference. In order to overcome these challenges, this study suggests a new solution through the combination of a Dual-Channel Transformer Network with Bayesian Optimization methods. The innovation is at the center with the utilization of EfficientNet, augmented with squeeze-and-excitation attention modules, to extract feature-rich representations without sacrificing computational efficiency. The dual-channel architecture dissects satellite pose estimation into two dedicated streams—translational data prediction and orientation estimation via quaternion-based activation functions for rotational precision. Activation maps are transformed into transformer-compatible sequences via 1×1 convolutions, allowing successful learning in the transformer's encoder-decoder system. To maximize model performance, Bayesian Optimization with Gaussian Process Regression and the Upper Confidence Bound (UCB) acquisition function makes the optimal hyperparameter selection with fewer queries, conserving time and resources. This entire framework, used here in Python and verified with the SLAB Satellite Pose Estimation Challenge dataset, had an outstanding Mean IOU of 0.9610, reflecting higher accuracy compared to standard models. In total, this research sets a new standard for spacecraft pose estimation, by marrying the versatility of deep le

关键词： Convolutional neural networks

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Method of Automated Dataset Collection for Microwave Filters Synthesis

Method of Automated Dataset Collection for Microwave Filters...

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Systems of Signals Generating and Processing in the Field of on Board Communications

作者： O. V. Arinin D. M. Bakhmach A. I. Katsnelson A. S. Kurbeteva A. A. Yelizarov Moscow Technical University of Communications and Informatics Moscow Russia Electronic Engineering Department HSE University Moscow Russia Computer Engineering Department HSE University Moscow Russia

ISBN: (数字)9798331532635

ISBN: (纸本)9798331532642

This research discusses the method of dataset collection automatization for microwave filter synthesis by integrating machine learning techniques, thus reducing development time. Utilizing the 3D electromagnetic analysis software package, the study involves simulation and collecting geometric parameters and amplitude-frequency characteristics from three variants of passband highly selective microstrip tworesonator combined filters with stepped impedance resonators. In this paper, specific items and restrictions on filter topology creation in the simulation program are discussed, as well the review of the script used for dataset collection and its requirements is given. In addition, a dataset structure and format are described.

关键词： Microstrip filters Three-dimensional displays Network topology Resonator filters Machine learning Microwave communication Information filters Topology Microwave filters Optimization

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A Multi-Scale Deep Learning Framework Combining MobileViT-ECA and LSTM for Accurate ECG Analysis

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IEEE Access 2025年 13卷 85473-85492页

作者： Ba Mahel, Abduljabbar S. Al-Gaashani, Mehdhar S. A. M. Alkanhel, Reem Ibrahim Hassan, Dina S. M. Muthanna, Mohammed Saleh Ali Muthanna, Ammar Aziz, Ahmed University of Electronic Science and Technology of China School of Life Science and Technology Chengdu610054 China University of Electronic Science and Technology of China School of Resources and Environment Chengdu610056 China Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Technology P.O. Box 84428 Riyadh11671 Saudi Arabia Tashkent State University of Economics Department of International Business Management Tashkent Uzbekistan RUDN University Department of Applied Probability and Informatics Peoples’Friendship University of Russia 6 Miklukho-Maklaya St Moscow117198 Russia Faculty of Computer and Artificial Intelligence Benha University Department of Computer Science Egypt Central Asian University Engineering school Tashkent Uzbekistan

Electrocardiogram (ECG) analysis is crucial for diagnosing cardiovascular diseases (CVD), especially atrial fibrillation (AF), a prevalent cardiac rhythm abnormality. However, the variability and complexity of ECG signals make AF classification challenging, highlighting the need for more accurate and reliable methods. This paper introduces a novel deep learning (DL) architecture designed to enhance the processing, feature extraction, and analysis of ECG signals. The proposed model utilizes a hybrid framework that combines standard and dilated convolutional networks, advanced attention mechanisms, and temporal sequence learning to address the complexities of ECG data. A parallel architecture integrates a MobileViT block for efficient spatial feature extraction and an Efficient Channel Attention (ECA) module to refine feature representations. Simultaneously, a Long Short-Term Memory (LSTM) network captures temporal dependencies in the data. The concatenation of the outputs from both the MobileViT-ECA block and the LSTM network allows for the extraction of both local and global features. Fully connected layers, along with dropout regularization, ensure robust performance and effective classification. We analyzed ECG signals of three different lengths (1, 2, and 3 seconds) to investigate how segment length affects model performance. Additionally, we conducted various classification scenarios and an ablation study to simulate real-world conditions and identify key components of the architecture. Our experimental findings show that the model attains high accuracy in ECG classification and AF identification, with average metrics including accuracy of 87.80%, recall of 87.80%, F1-score of 87.45%, and specificity of 95.66%. The proposed model outperforms existing methods on the same AF dataset, demonstrating its superior performance. This method accurately classifies ECG signals and detects AF, highlighting its potential for clinical application. The results help move forwar

关键词： Electrocardiography

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FEDERATED INVERSE PROBABILITY TREATMENT WEIGHTING FOR INDIVIDUAL TREATMENT EFFECT ESTIMATION

arXiv

引用

arXiv 2025年

作者： Yin, Changchang Chen, Hong-You Chao, Wei-Lun Zhang, Ping Department of Computer Science and Engineering The Ohio State University ColumbusOH United States Department of Computer Science and Engineering Department of Biomedical Informatics Translational Data Analytics Institute The Ohio State University ColumbusOH United States

Individual treatment effect (ITE) estimation is to evaluate the causal effects of treatment strategies on some important outcomes, which is a crucial problem in healthcare. Most existing ITE estimation methods are designed for centralized settings. However, in real-world clinical scenarios, the raw data are usually not shareable among hospitals due to the potential privacy and security risks, which makes the methods not applicable. In this work, we study the ITE estimation task in a federated setting, which allows us to harness the decentralized data from multiple hospitals. Due to the unavoidable confounding bias in the collected data, a model directly learned from it would be inaccurate. One well-known solution is Inverse Probability Treatment Weighting (IPTW), which uses the conditional probability of treatment given the covariates to re-weight each training example. Applying IPTW in a federated setting, however, is non-trivial. We found that even with a well-estimated conditional probability, the local model training step using each hospital’s data alone would still suffer from confounding bias. To address this, we propose FED-IPTW, a novel algorithm to extend IPTW into a federated setting that enforces both global (over all the data) and local (within each hospital) decorrelation between covariates and treatments. We validated our approach on the task of comparing the treatment effects of mechanical ventilation on improving survival probability for patients with breadth difficulties in the intensive care unit (ICU). We conducted experiments on both synthetic and real-world eICU datasets and the results show that FED-IPTW outperform state-of-the-art methods on all the metrics on factual prediction and ITE estimation tasks, paving the way for personalized treatment strategy design in mechanical ventilation usage. © 2025, CC BY-NC-SA.

关键词： Inverse problems

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Secure and Reliable 6G Communications with BCH-Based GLDPC and Kyber

Secure and Reliable 6G Communications with BCH-Based GLDPC a...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Linh Nguyen Cheol-Hong Min Hanho Lee Tuy Tan Nguyen School of Informatics Computing and Cyber Systems Northern Arizona University USA Department of Electrical. and Computer Engineering University of St. Thomas USA Department of Electrical and Computer Engineering Inha University Republic of Korea

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Channel coding in sixth-generation (6G) networks must attain exceptionally low bit error rate (BER), typically in the range of 10– 6 to 10– 9 , to ensure the requisite level of reliability. While fifth-generation (5G) New Radio (NR) coding techniques have made substantial progress, the inherent limitations of the low error floor impede the attainment of such stringent BER targets. Compared to the low-density parity check (LDPC) codes employed in 5G communications, generalized low-density parity check (GLDPC) codes offer a more advantageous tradeoff between computational complexity and error correction performance in 6G networks. GLDPC codes enhance traditional LDPC structures by incorporating more complex local decoding units within their check nodes. When coupled with cryptographic techniques, GLDPC codes can establish a robust information security framework. In this paper, we propose a new security-based GLDPC that uses a number of BCH matrices for the GLDPC decoding process combined with the Kyber algorithm (BCH-based KyGLDPC) before transmitting data to increase reli-ability and solve the security problem against quantum computer attacks. The system's performance is assessed using both image data and data encrypted prior to transmission in a white additive Gaussian noise environment. The results indicate that the system provides strong error correction and data security.

关键词： 6G mobile communication Quantum computing computer network reliability System performance Bit error rate Parity check codes Error correction codes Decoding Reliability Channel coding

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A Pipeline of Augmentation and Sequence Embedding for Classification of Imbalanced Network Traffic

arXiv

引用

arXiv 2025年

作者： Shokri, Matin Hasibi, Ramin Department of Electrical and Computer Engineering K. N. Toosi University of Technology Iran Department of Informatics University of Bergen Bergen Norway

Network Traffic Classification (NTC) is one of the most important tasks in network management. The imbalanced nature of classes on the internet presents a critical challenge in classification tasks. For example, some classes of applications are much more prevalent than others, such as HTTP. As a result, machine learning classification models do not perform well on those classes with fewer data. To address this problem, we propose a pipeline to balance the dataset and classify it using a robust and accurate embedding technique. First, we generate artificial data using Long Short-Term Memory (LSTM) networks and Kernel Density Estimation (KDE). Next, we propose replacing one-hot encoding for categorical features with a novel embedding framework based on the"Flow as a Sentence" perspective, which we name FS-Embedding. This framework treats the source and destination ports, along with the packet’s direction, as one word in a flow, then trains an embedding vector space based on these new features through the learning classification task. Finally, we compare our pipeline with the training of a Convolutional Recurrent Neural Network (CRNN) and Transformers, both with imbalanced and sampled datasets, as well as with the one-hot encoding approach. We demonstrate that the proposed augmentation pipeline, combined with FS-Embedding, increases convergence speed and leads to a significant reduction in the number of model parameters, all while maintaining the same performance in terms of accuracy. © 2025, CC BY.

关键词： Network embeddings

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Onboard Perception-Assisted High Fidelity Simulation Framework for Autonomous Planetary Soft-Landing

Onboard Perception-Assisted High Fidelity Simulation Framewo...

引用

AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Alibekov, Ulugbek Banerjee, Avijit Satpute, Sumeet Gajanan Nikolakopoulos, George ERASMUS MUNDUS JOINT MASTER in Intelligent Field Robotic Systems Faculty of Informatics Eötvös Loránd University Budapest Hungary Robotics and AI research group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This article presents an onboard perception-assisted high-fidelity simulation framework for autonomous planetary soft-landing, enabling visual information processing tightly integrated with advanced onboard guidance system in a realistic simulation environments. Utilizing the open source Unity game engine, a physics-based simulation toolbox integrated with the Robotic Operating System (ROS2), we effectively emulate the motion of spacecraft approaching planetary terrain. The simulation environment features a realistic 3D terrain model and a lander spacecraft equipped with onboard camera to effectively capture detailed local terrain images. The simulation engine accurately captures the spacecraft’s 6-degrees of freedom motion, seamlessly integrating space craft orientation with the associated camera feed while incorporating various illumination conditions using a light source representing the Sun. To demonstrate the efficacy of the framework, we implement a recently developed attitude-constrained minimum jerk guidance algorithm to emulate spacecraft motion. Concurrently, the visual feed from the onboard RGB camera is processed through a gradient extraction-based perception system, providing the visual odometer. This system identifies provably safe landing sites, effectively avoiding potential risks of landing on uneven terrain. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Planetary landers

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Millimeter-Wave Gap Waveguide-Based Broadband Slant-Polarized Slot Array Antenna

Millimeter-Wave Gap Waveguide-Based Broadband Slant-Polarize...

引用

European Conference on Antennas and Propagation, EuCAP

作者： Davood Zarifi Ali Farahbakhsh Michal Mrozowski Department of Microwave and Antenna Engineering Faculty of Electronics Telecommunications and Informatics Gdansk University of Technology Gdansk Poland School of Electrical and Computer Engineering University of Kashan Kashan Iran Department of Electrical and Computer Engineering Graduate University of Kerman Kerman Iran

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

This research deals with the design of a broadband, slant-polarized antenna array for 26–40 GHz frequency band. The design employs gap waveguide technology to minimize unwanted energy leakage, ensuring high gain and efficiency. In this configuration, the radiating slots are angled at 45°, and a two-layer feed network, utilizing compact folded E-plane groove gap waveguide power dividers, is used to energize the slots. Key advantages of the proposed antenna array include its low profile, low loss, wide bandwidth, and suitability for millimeter-wave applications.

关键词： Power dividers Slot antennas Millimeter wave technology Optical device fabrication Gap waveguide Bandwidth Millimeter wave propagation Broadband communication Broadband antennas Antenna arrays

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