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Robust Indoor RF Signal LOS Classification: An Exploit of Modulation and Beamforming

IEEE Internet of Things Journal 2025年第9期12卷 12723-12734页

作者： Zhang, Tingwei Kalathas, Paris Wang, Guangxin Liu, Huaping Oregon State University School of Electrical Engineering and Computer Science CorvallisOR97331 United States

Indoor positioning systems (IPS) have gained significant attention in research due to the growing need for reliable location-based services in indoor settings where global navigation satellite systems are unreliable or unavailable. A major challenge in developing dependable IPS lies in accurately classifying line-of-sight (LOS) and non-LOS (NLOS) propagation conditions, which heavily influence positioning accuracy. Existing solutions often demand high-computational resources or are constrained by specific assumptions about the environment, limiting their broad applicability. This article proposes a novel mechanism that leverages the histogram characteristic of the signal phase in common signal modulation methods like quadrature amplitude modulation and phase shift keying to differentiate between LOS and NLOS conditions. We posit that the phase modulation forms a distinctive pattern in the histogram, serving as a reliable identifier for LOS/NLOS classification. Our approach’s effectiveness is validated through a series of experiments, demonstrating up to 100% real-time accuracy in LOS/NLOS identifications, resilience to noise, extensive operational range, and free of computing-intensive machine learning models. © 2014 IEEE.

关键词： Location based services

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Work in Progress: Introducing electrical and computer engineering to High School Math and science Students

Work in Progress: Introducing Electrical and Computer Engine...

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Frontiers in Education (FIE) Conference

作者： Michael J. Rust Bartley D. Richardson Karen C. Davis Suzanne W. Soled Pamela Heckel Department of Electrical & Computer Engineering and Computer Science University of Cincinnati Cincinnati OH USA Department of Teacher Education & School Leadership Northern Kentucky University Highland Heights KY USA Department of Civil and Environmental Engineering University of Cincinnati Cincinnati OH USA

This paper describes the recent activities of electrical and computer engineering doctoral students in the design, development, and implementation of lessons for high school mathematics and science classes. The graduate students, called fellows, worked in secondary classrooms in the Cincinnati Public Schools District as a part of Project STEP at the University of Cincinnati, which is funded by the National science Foundation GK-12 Program. The fellows formed partnerships with secondary math and science teachers to generate new lessons, activities, and resources to enhance the STEM skills of high school students. Additionally, the Fellows used their engineering expertise to bring authentic learning experiences into the classroom and introduced concepts in their field of engineering to underserved student populations. This paper discusses observations and reflections by the fellows regarding aspects of the activities that had the most impact on student learning and interest in engineering, which was measured by self-reported student surveys

关键词： Educational institutions Design engineering Mathematics computer science engineering profession Education Educational technology Recruitment computer aided instruction Linear programming

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Divergence Maximizing Linear Projection for Supervised Dimension Reduction

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IEEE Transactions on Information Theory 2025年第3期71卷 2104-2115页

作者： Chen, Biao Kortje, Joshua Syracuse University Department of Electrical Engineering and Computer Science SyracuseNY13244 United States

This paper proposes two linear projection methods for supervised dimension reduction using only first- and second-order statistics. The methods, each catering to a different parameter regime, are derived under the general Gaussian model by maximizing the Kullback-Leibler divergence between the two classes in the projected sample for a binary classification problem. They subsume existing linear projection approaches developed under simplifying assumptions of Gaussian distributions, such as these distributions might share an equal mean or covariance matrix. As a by-product, we establish that the multi-class linear discriminant analysis, a celebrated method for classification and supervised dimension reduction, is provably optimal for maximizing pairwise Kullback-Leibler divergence when the Gaussian populations share an identical covariance matrix. For the case when the Gaussian distributions share an equal mean, we establish conditions under which the optimal subspace remains invariant regardless of how the Kullback-Leibler divergence is defined, despite the asymmetry of the divergence measure itself. Such conditions encompass the classical case of signal plus noise, where both signal and noise have zero mean and arbitrary covariance matrices. Experiments are conducted to validate the proposed solutions, demonstrate their superior performance over existing alternatives, and illustrate the procedure for selecting the appropriate linear projection solution. © 1963-2012 IEEE.

关键词： Discriminant analysis

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Neural acceleration of incomplete factorization preconditioning

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Neural Computing and Applications 2025年第2期37卷 1009-1026页

作者： Booth, Joshua Dennis Sun, Hongyang Garnett, Trevor Computer Science University of Alabama in Huntsville HuntsvilleAL35899 United States Electrical Engineering & Computer Science University of Kansas LawrenceKS66046 United States

The solution of a sparse system of linear equations is ubiquitous in scientific applications. Iterative methods, such as the preconditioned conjugate gradient (PCG) method and the generalized minimal residuals (GMRES) method, are normally chosen over direct methods due to memory and computational complexity constraints. However, the efficiency of these methods depends largely on the preconditioner utilized. The development of a preconditioner normally requires some insight into the sparse linear system and the desired trade-off between generating the preconditioner and the reduction in the number of iterations. Incomplete factorization is a popular black box method to generate these preconditioners. However, it may fail for several reasons, including numerical issues that require searching for adequate scaling, shifting, and fill-in while utilizing a difficult-to-parallelize algorithm. With a move toward heterogeneous computing, many sparse applications find GPUs that are optimized for dense tensor applications like training neural networks being underutilized. In this work, we demonstrate that a simple artificial neural network trained either at compile time or in parallel to the running application on a GPU can provide an incomplete LLT factorization that can be used as a preconditioner. This generated preconditioner is as good as or better than the ones found using multiple preconditioning techniques such as scaling and shifting in terms of reduction in number of iterations. Moreover, the generated method also works and never fails to produce a preconditioner that does not reduce the iteration count. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Conjugate gradient method

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Generalized Fractional Repetition Codes for Binary Coded Computations

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IEEE Transactions on Information Theory 2025年第3期71卷 2170-2194页

作者： Charalambides, Neophytos Mahdavifar, Hessam Hero, Alfred O. University of California at San Diego Department of Computer Science and Engineering La Jolla CA92093 United States Northeastern University Department of Electrical and Computer Engineering BostonMA02115 United States University of Michigan Department of Electrical Engineering and Computer Science Ann ArborMI48104 United States

This paper addresses the gradient coding and coded matrix multiplication problems in distributed optimization and coded computing. We present a computationally efficient coding method which overcomes the drawbacks of the Fractional Repetition Coding gradient coding method proposed by Tandon et al., and can also be leveraged by coded computing networks whose servers are of heterogeneous nature. Specifically, we propose a construction for fractional repetition gradient coding;while ensuring that the generator matrix remains close to perfectly balanced for any set of coding parameters, as well as a low complexity decoding step. The proposed binary encoding avoids operations over the real and complex numbers which inherently introduce numerical and rounding errors, thereby enabling accurate distributed encodings of the partial gradients. We then make connections between gradient coding and coded matrix multiplication. Specifically, we show that any gradient coding scheme can be extended to coded matrix multiplication. Furthermore, we show how the proposed binary gradient coding scheme can be used to construct two different coded matrix multiplication schemes, each achieving different trade-offs. © 1963-2012 IEEE.

关键词： Encoding (symbols)

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Photovoltaic power one-day and multistep-hourly AI predictions using node-by-node evolved binomial tree structures to form L-transformed PDE modules

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Soft Computing 2025年第5期29卷 2483-2495页

作者： Zjavka, Ladislav Department of Computer Science Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava 17. Listopadu 15/2172 Ostrava Czech Republic

Photovoltaic (PV) power is generated by two common types of solar components that are primarily affected by fluctuations and development in cloud structures as a result of uncertain and chaotic processes. Local PV forecasting is unavoidable in supply and load planning necessary in integration of smart systems into electrical grids. Intra- or day-ahead modelling of weather patterns based on Artificial Intelligence (AI) allows one to refine available 24 h. cloudiness forecast or predict PV production at a particular plant location during the day. AI usually gets an adequate prediction quality in shorter-level horizons, using the historical meteo- and PV record series as compared to Numerical Weather Prediction (NWP) systems. NWP models are produced every 6 h to simulate grid motion of local cloudiness, which is additionally delayed and usually scaled in a rough less operational applicability. Differential Neural Network (DNN) is based on a newly developed neurocomputing strategy that allows the representation of complex weather patterns analogous to NWP. DNN parses the n-variable linear Partial Differential Equation (PDE), which describes the ground-level patterns, into sub-PDE modules of a determined order at each node. Their derivatives are substituted by the Laplace transforms and solved using adapted inverse operations of Operation Calculus (OC). DNN fuses OC mathematics with neural computing in evolution 2-input node structures to form sum modules of selected PDEs added step-by-step to the expanded composite model. The AI multi- 1…9-h and one-stage 24-h models were evolved using spatio-temporal data in the preidentified daily learning sequences according to the applied input–output data delay to predict the Clear Sky Index (CSI). The prediction results of both statistical schemes were evaluated to assess the performance of the AI models. Intraday models obtain slightly better prediction accuracy in average errors compared to those applied in the second-day-ahead

关键词： Laplace transforms

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A Comprehensive Data Description for LoRaWAN Path Loss Measurements in an Indoor Office Setting: Effects of Environmental Factors

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IEEE Access 2025年 13卷 83148-83170页

作者： Obiri, Nahshon Mokua Laerhoven, Kristof Van University of Siegen Department of Electrical Engineering and Computer Science Germany

This paper presents a comprehensive dataset of LoRaWAN technology path loss measurements collected in an indoor office environment, focusing on quantifying the effects of environmental factors on signal propagation. Utilizing a network of six strategically placed LoRaWAN end devices (EDs) and a single indoor gateway (GW) at the University of Siegen's Hölderlinstraße Campus in the City of Siegen, Germany, we systematically measured signal strength indicators such as the Received Signal Strength Indicator (RSSI) and the Signal-to-Noise Ratio (SNR) under various environmental conditions, including temperature, relative humidity, carbon dioxide (CO₂) concentration, barometric pressure, and particulate matter levels (PM_2.5). Our empirical analysis confirms that transient phenomena such as reflections, scattering, interference, occupancy patterns (induced by environmental parameter variations), and furniture rearrangements can alter signal attenuation by as much as 10.58 dB, highlighting the dynamic nature of indoor propagation. As an example of how this dataset can be utilized, we tested and evaluated a refined Log-Distance Path Loss and Shadowing Model that integrates both structural obstructions (Multiple Walls) and Environmental Parameters (LDPLSM-MW-EP). Compared to a baseline model that considers only Multiple Walls (LDPLSMMW), the enhanced approach reduced the root mean square error (RMSE) from 10.58 dB to 8.04 dB and increased the coefficient of determination (R2) from 0.6917 to 0.8222. By capturing the extra effects of environmental conditions and occupancy dynamics, this improved model provides valuable insights for optimizing power usage and prolonging device battery life, enhancing network reliability in indoor Internet of Things (IoT) deployments, among other applications. The dataset, accessible upon reasonable request, offers a solid foundation for future research and development in indoor wireless communication. © 2013 IEEE.

关键词： dataset environmental factors indoor propagation internet of things (IoT) LoRaWAN low-power wide area network (LPWAN) path loss

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Deep Learning-Based Cloud Security: Innovative Attack Detection and Privacy Focused Key Management

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IEEE Transactions on computers 2025年第6期74卷 1978-1989页

作者： Ahmad, Shahnawaz Arif, Mohd Mehfuz, Shabana Ahmad, Javed Nazim, Mohd Bennett University School of Computer Science Engineering and Technology Greater Noida201310 India Galgotias University School of Computer Science & Engineering Greater Noida203201 India Jamia Millia Islamia Department of Electrical Engineering New Delhi110025 India Sharda University Department of Computer Science and Engineering Sharda School of Engineering and Technology Greater Noida201310 India Noida Institute of Engineering and Technology Department of Computer Science and Engineering School of Computer Science and IT Greater Noida201306 India

Cloud Computing (CC) is widely adopted in sectors like education, healthcare, and banking due to its scalability and cost-effectiveness. However, its internet-based nature exposes it to cyber threats, necessitating advanced security frameworks. Traditional models suffer from high false positives and limited adaptability. To address these challenges, VECGLSTM, an attack detection model integrating Variable Long Short-Term Memory (VLSTM), capsule networks, and the Enhanced Gannet Optimization Algorithm (EGOA), is introduced. This hybrid approach enhances accuracy, reduces false positives, and dynamically adapts to evolving threats. EGOA is employed for its superior optimization capability, ensuring faster convergence and resilience. Additionally, Chaotic Cryptographic Pelican Tunicate Swarm Optimization (CCPTSO) is proposed for privacy-preserving key management. This model combines chaotic cryptographic techniques with the Pelican Tunicate Swarm Optimization Algorithm (PTSOA), leveraging the pelican algorithm’s exploration strength and the tunicate swarm’s exploitation ability for optimal encryption security. Performance evaluation demonstrates 99.675% accuracy, 99.5175% recall, 99.7075% precision, and 99.615% F1-score, along with reduced training (1.79s), encryption (0.986s), and decryption (1.029s) times. This research significantly enhances CC security by providing a scalable, adaptive framework that effectively counters evolving cyber threats while ensuring efficient key management. © 1968-2012 IEEE.

关键词： Cloud security

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Optimized COVID-19 detection using sparse deep learning models from multimodal imaging data

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Multimedia Tools and Applications 2025年第13期84卷 11451-11474页

作者： Moradi, MohammadMahdi Hassanzadeh, Alireza Haghanifar, Arman Ko, Seok Bum Faculty of Computer Science and Software Engineering Concordia University MontrealQC Canada Faculty of Electrical Engineering Shahid Beheshti University Tehran Tehran Iran Department of Electrical & Computer Engineering University of Saskatchewan SaskatoonSK Canada

This paper explores the global spread of the COVID-19 virus since 2019, impacting 219 countries worldwide. Despite the absence of a definitive cure, the utilization of artificial intelligence (AI) methods for disease diagnosis has demonstrated commendable effectiveness in promptly diagnosing patients and curbing infection transmission. The study introduces a deep learning-based model tailored for COVID-19 detection, leveraging three prevalent medical imaging modalities: computed tomography (CT), chest X-ray (CXR), and Ultrasound. Various deep Transfer Learning Convolutional Neural Network-based (CNN) models have undergone assessment for each imaging modality. For each imaging modality, this study has selected the two most accurate models based on evaluation metrics such as accuracy and loss. Additionally, efforts have been made to prune unnecessary weights from these models to obtain more efficient and sparse models. By fusing these pruned models, enhanced performance has been achieved. The models have undergone rigorous training and testing using publicly available real-world medical datasets, focusing on classifying these datasets into three distinct categories: Normal, COVID-19 Pneumonia, and non-COVID-19 Pneumonia. The primary objective is to develop an optimized and swift model through strategies like Transfer Learning, Ensemble Learning, and reducing network complexity, making it easier for storage and transfer. The results of the trained network on test data exhibit promising outcomes. The accuracy of these models on the CT scan, X-ray, and ultrasound datasets stands at 99.4%, 98.9%, and 99.3%, respectively. Moreover, these models’ sizes have been substantially reduced and optimized by 51.93%, 38.00%, and 69.07%, respectively. This study proposes a computer-aided-coronavirus-detection system based on three standard medical imaging techniques. The intention is to assist radiologists in accurately and swiftly diagnosing the disease, especially during the screen

关键词： COVID-19

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Super-resolution reconstruction of off-axis digital holography based on Real-ESRGAN

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Applied Optics 2025年第9期64卷 2245-2254页

作者： Chen, Geng Chen, Jie Liao, Houzhang Liu, Keke Tang, Xin Kong, Yong School of Electric and Electrical Engineering Shanghai University of Engineering Science Shanghai201620 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

Off-axis digital holography plays a crucial role in high-precision three-dimensional imaging. However, high-resolution phase images are often affected by the limited pixel size of the sensor. To address this issue, this study proposes, for the first time, to the best of our knowledge, the application of Real-ESRGAN for the super-resolution reconstruction of off-axis digital holography images. By directly applying super-resolution processing to the hologram, the limitations of the sensor's pixel size are overcome, followed by phase reconstruction to obtain high-resolution phase images. The Real-ESRGAN model enhances the texture information of the hologram through a deep network with multiple residual-in-residual dense blocks (RRDBs), restoring a clearer hologram. A U-Net architecture with spectral normalization, offering stronger discriminative capability, is used to provide detailed per-pixel feedback for the generator. The experimental results demonstrate that this method can be applied to multi-scale holograms, outperforming comparison networks in both visual quality and quantitative metrics, thus providing an innovative solution for the super-resolution reconstruction of off-axis digital holography. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Holograms

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