pwc-archive/papers-with-abstracts huggingface dataset:
Electromagnetic Modelling of Extended Targets in a Distributed Antenna System
Authors: Baptiste Sambon, François De Saint Moulin, Guillaume Thiran, Claude Oestges, Luc Vandendorpe
Date: 2025-04-30 00:00:00
Tasks: Integrated sensing and communication, ISAC

Traditional radar and integrated sensing and communication (ISAC) systems often approximate targets as point sources, a simplification that fails to capture the essential scattering characteristics for many applications. This paper presents a novel electromagnetic (EM)-based framework to accurately model the near-field (NF) scattering response of extended targets, which is then applied to three canonical shapes : a flat rectangular plate, a sphere and a cylinder. Mathematical expressions for the received signal are provided in each case. Based on this model, the influence of bandwidth, carrier frequency and target distance on localisation accuracy is analysed, showing how higher bandwidths and carrier frequencies improve resolution. Additionally, the impact of target curvature on localisation performance is studied. Results indicate that detection performance is slightly enhanced when considering curved objects. A comparative analysis between the extended and point target models shows significant similarities when targets are small and curved. However, as the target size increases or becomes flatter, the point target model introduces estimation errors owing to model mismatch. The impact of this model mismatch as a function of system parameters is analysed, and the operational zones where the point abstraction remains valid and where it breaks down are identified. These findings provide theoretical support for experimental results based on point-target models in previous studies.
Three-Stage Composite Outlier Identification of Wind Power Data: Integrating Physical Rules with Regression Learning and Mathematical Morphology
Authors: Limengqian Zheng, Lipeng Zhu, Weijia Wen, Jiayong Li, Cong Zhang
Date: 2025-04-30 00:00:00

Existing studies on identifying outliers in wind speed-power datasets are often challenged by the complicated and irregular distributions of outliers, especially those being densely stacked yet staying close to normal data. This could degrade their identification reliability and robustness in practice. To address this defect, this paper develops a three-stage composite outlier identification method by systematically integrating three complementary techniques, i.e., physical rule-based preprocessing, regression learning-enabled detection, and mathematical morphology-based refinement. Firstly, the raw wind speed-power data are preprocessed via a set of simple yet efficient physical rules to filter out some outliers obviously going against the physical operating laws of practical wind turbines. Secondly, a robust wind speed-power regression learning model is built upon the random sample consensus algorithm. This model is able to reliably detect most outliers with the help of an adaptive threshold automatically set by the interquartile range method. Thirdly, by representing the wind speed-power data distribution with a two-dimensional image, mathematical morphology operations are applied to perform refined outlier identification from a data distribution perspective. This technique can identify outliers that are not effectively detected in the first two stages, including those densely stacked ones near normal data points. By integrating the above three techniques, the whole method is capable of identifying various types of outliers in a reliable and adaptive manner. Numerical test results with wind power datasets acquired from distinct wind turbines in practice and from simulation environments extensively demonstrate the superiority of the proposed method as well as its potential in enhancing wind power prediction.
Waveform Design Based on Mutual Information Upper Bound For Joint Detection and Estimation
Authors: Ruofeng Yu, Caiguang Zhang, Chenyang Luo, Mengdi Bai, Shangqu Yan, Wei Yang, Yaowen Fu
Date: 2025-04-30 00:00:00
Tasks: parameter estimation, Radar waveform design

Adaptive radar waveform design grounded in information-theoretic principles is critical for advancing cognitive radar performance in complex environments. This paper investigates the optimization of phase-coded waveforms under constant modulus constraints to jointly enhance target detection and parameter estimation. We introduce a unified design framework based on maximizing a Mutual Information Upper Bound (MIUB), which inherently reconciles the trade-off between detection sensitivity and estimation precision without relying on ad hoc weighting schemes. To model realistic, potentially non-Gaussian statistics of target returns and clutter, we adopt Gaussian Mixture Distributions (GMDs), enabling analytically tractable approximations of the MIUB's constituent Kullback-Leibler divergence and mutual information terms. To address the resulting non-convex problem, we propose the Phase-Coded Dream Optimization Algorithm (PC-DOA), a tailored metaheuristic that leverages hybrid initialization and adaptive exploration-exploitation mechanisms specifically designed for phase-variable optimization. Numerical simulations demonstrate the effectiveness of the proposed method in achieving modestly better detection-estimation trade-off.
Generalised Label-free Artefact Cleaning for Real-time Medical Pulsatile Time Series
Authors: Xuhang Chen, Ihsane Olakorede, Stefan Yu Bögli, Wenhao Xu, Erta Beqiri, Xuemeng Li, Chenyu Tang, Zeyu Gao, Shuo Gao, Ari Ercole, Peter Smielewski
Date: 2025-04-29 00:00:00
Tasks: Decision Making, Photoplethysmography (PPG), Time Series, Time Series Analysis

Artefacts compromise clinical decision-making in the use of medical time series. Pulsatile waveforms offer probabilities for accurate artefact detection, yet most approaches rely on supervised manners and overlook patient-level distribution shifts. To address these issues, we introduce a generalised label-free framework, GenClean, for real-time artefact cleaning and leverage an in-house dataset of 180,000 ten-second arterial blood pressure (ABP) samples for training. We first investigate patient-level generalisation, demonstrating robust performances under both intra- and inter-patient distribution shifts. We further validate its effectiveness through challenging cross-disease cohort experiments on the MIMIC-III database. Additionally, we extend our method to photoplethysmography (PPG), highlighting its applicability to diverse medical pulsatile signals. Finally, its integration into ICM+, a clinical research monitoring software, confirms the real-time feasibility of our framework, emphasising its practical utility in continuous physiological monitoring. This work provides a foundational step toward precision medicine in improving the reliability of high-resolution medical time series analysis
Energy Efficient Wireless Communications by Harnessing Huygens' Metasurfaces
Authors: Maryam Rezvani, Raviraj Adve, Akram Bin Sediq, Amr El-Keyi
Date: 2025-04-29 00:00:00

Ambitions for the next generation of wireless communication include high data rates, low latency, ubiquitous access, ensuring sustainability (in terms of consumption of energy and natural resources), all while maintaining a reasonable level of implementation complexity. Achieving these goals necessitates reforms in cellular networks, specifically in the physical layer and antenna design. The deployment of transmissive metasurfaces at basestations (BSs) presents an appealing solution, enabling beamforming in the radiated wave domain, minimizing the need for energy-hungry RF chains. Among various metasurface-based antenna designs, we propose using Huygens' metasurface-based antennas (HMAs) at BSs. Huygens' metasurfaces offer an attractive solution for antennas because, by utilizing Huygens' equivalence principle, they allow independent control over both the amplitude and phase of the transmitted electromagnetic wave. In this paper, we investigate the fundamental limits of HMAs in wireless networks by integrating electromagnetic theory and information theory within a unified analytical framework. Specifically, we model the unique electromagnetic characteristics of HMAs and incorporate them into an information-theoretic optimization framework to determine their maximum achievable sum rate. By formulating an optimization problem that captures the impact of HMA's hardware constraints and electromagnetic properties, we quantify the channel capacity of HMA-assisted systems. We then compare the performance of HMAs against phased arrays and other metasurface-based antennas in both rich scattering and realistic 3GPP channels, highlighting their potential in improving spectral and energy efficiency.
Deep Learning Characterizes Depression and Suicidal Ideation from Eye Movements
Authors: Kleanthis Avramidis, Woojae Jeong, Aditya Kommineni, Sudarsana R. Kadiri, Marcus Ma, Colin McDaniel, Myzelle Hughes, Thomas McGee, Elsi Kaiser, Dani Byrd, Assal Habibi, B. Rael Cahn, Idan A. Blank, Kristina Lerman, Takfarinas Medani, Richard M. Leahy, Shrikanth Narayanan
Date: 2025-04-29 00:00:00
Tasks: Deep Learning, Response Generation

Identifying physiological and behavioral markers for mental health conditions is a longstanding challenge in psychiatry. Depression and suicidal ideation, in particular, lack objective biomarkers, with screening and diagnosis primarily relying on self-reports and clinical interviews. Here, we investigate eye tracking as a potential marker modality for screening purposes. Eye movements are directly modulated by neuronal networks and have been associated with attentional and mood-related patterns; however, their predictive value for depression and suicidality remains unclear. We recorded eye-tracking sequences from 126 young adults as they read and responded to affective sentences, and subsequently developed a deep learning framework to predict their clinical status. The proposed model included separate branches for trials of positive and negative sentiment, and used 2D time-series representations to account for both intra-trial and inter-trial variations. We were able to identify depression and suicidal ideation with an area under the receiver operating curve (AUC) of 0.793 (95% CI: 0.765-0.819) against healthy controls, and suicidality specifically with 0.826 AUC (95% CI: 0.797-0.852). The model also exhibited moderate, yet significant, accuracy in differentiating depressed from suicidal participants, with 0.609 AUC (95% CI 0.571-0.646). Discriminative patterns emerge more strongly when assessing the data relative to response generation than relative to the onset time of the final word of the sentences. The most pronounced effects were observed for negative-sentiment sentences, that are congruent to depressed and suicidal participants. Our findings highlight eye tracking as an objective tool for mental health assessment and underscore the modulatory impact of emotional stimuli on cognitive processes affecting oculomotor control.
Bayesian Deep End-to-End Learning for MIMO-OFDM System with Delay-Domain Sparse Precoder
Authors: Nilesh Kumar Jha, Huayan Guo, Vincent K. N. Lau
Date: 2025-04-29 00:00:00

This paper introduces a novel precoder design aimed at reducing pilot overhead for effective channel estimation in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) applications utilizing high-order modulation. We propose an innovative demodulation reference signal scheme that achieves up to an 8x reduction in overhead by implementing a delay-domain sparsity constraint on the precoder. Furthermore, we present a deep neural network (DNN)-based end-to-end architecture that integrates a propagation channel estimation module, a precoder design module, and an effective channel estimation module. Additionally, we propose a Bayesian model-assisted training framework that incorporates domain knowledge, resulting in an interpretable datapath design. Simulation results demonstrate that our proposed solution significantly outperforms various baseline schemes while exhibiting substantially lower computational complexity.
Performance Evaluation of Efficient Hybrid Compression Methods For Devanagiri-Encoded Hindi Text Using Lossless Algorithms
Authors: Mukesh Sahu, Jeebananda Panda
Date: 2025-04-29 00:00:00

This research paper provides a comprehensive performance analysis of five standard lossless compression algorithms-LZMA, Zstd, Brotli, Bzip2, and LZ4HC-alongside sixty hybrid combinations on UTF-8 encoded Hindi text datasets, written in the Devanagari script, across varying sizes-small, medium, and large. The evaluation covers compression ratio, compression speed, decompression speed, and a derived weighted normalized efficiency score. Hybrid algorithms-especially those combining Zstd with LZ4HC or Brotli-demonstrate superior performance across all individual parameters. Independent Zstd achieves the highest efficiency score for the medium-sized dataset, illustrating how a well-balanced algorithm can excel in overall efficiency despite not leading in any single metric. However, in terms of compression ratio, speed, and decompression speed, hybrid methods consistently outperform standalone approaches. Notably, the hybrid combination Zstd + LZ4HC achieves the highest efficiency, with scores of 0.6764 for small files and 0.8597 for large files. Independent algorithms such as Zstd and Bzip2 perform well but are consistently surpassed by strategic hybrid pairings. The analysis underscores the significance of combining fast and high-ratio compression techniques, particularly for language-specific data. However, not all hybrids yielded significant improvements, with some introducing computational overhead. This comparative analysis highlights the potential of hybrid compression in applications such as cloud storage, mobile messaging, and NLP-based Hindi text processing.
Parametrized Stacked Intelligent Metasurfaces for Bistatic Integrated Sensing and Communications
Authors: Kuranage Roche Rayan Ranasinghe, Iván Alexander Morales Sandoval, Giuseppe Thadeu Freitas de Abreu, George C. Alexandropoulos
Date: 2025-04-29 00:00:00
Tasks: compressed sensing, ISAC, parameter estimation

We consider stacked intelligent metasurfaces (SIMs) as a tool to improve the performance of bistatic integrated sensing and communications (ISAC) schemes. To that end, we optimize the SIMs and design a radar parameter estimation (RPE) scheme aimed at enhancing radar sensing capabilities as well as communication performance under ISAC-enabling waveforms known to perform well in doubly-dispersive (DD) channels. The SIM optimization is done via a min-max problem formulation solved via steepest ascent with closed-form gradients, while the RPE is carried out via a compressed sensing-based probabilistic data association (PDA) algorithm. Our numerical results indicate that the design of waveforms suitable to mitigating the effects of DD channels is significantly impacted by the emerging SIM technology.
Disjoint Delay-Doppler Estimation in OTFS ISAC with Deep Learning-aided Path Detection
Authors: Mauro Marchese, Musa Furkan Keskin, Pietro Savazzi, Henk Wymeersch
Date: 2025-04-29 00:00:00
Tasks: ISAC

In this work, the problem of communication and radar sensing in orthogonal time frequency space (OTFS) with reduced cyclic prefix (RCP) is addressed. A monostatic integrated sensing and communications (ISAC) system is developed and, it is demonstrated that by leveraging the cyclic shift property inherent in the RCP, a delay-Doppler (DD) channel matrix that encapsulates the effects of propagation delays and Doppler shifts through unitary matrices can be derived. Consequently, a novel low-complexity correlation-based algorithm performing disjoint delay-Doppler estimation is proposed for channel estimation. Subsequently, this estimation approach is adapted to perform radar sensing on backscattered data frames. Moreover, channel estimation is complemented by a deep learning (DL) architecture that improves path detection and accuracy under low signal-to-noise ratio (SNR) conditions, compared to stopping criterion (SC) based multipath detection. Simulation results indicate that the proposed estimation scheme achieves lower normalized mean squared error (NMSE) compared to conventional channel estimation algorithms and sensing performance close to the Cramer-Rao lower bound (CRLB). Furthermore, an iterative data detection algorithm based on matched filter (MF) and combining is developed by exploiting the unitary property of delay-Doppler parameterized matrices. Simulation results reveal that this iterative scheme achieves performance comparable to that of the linear minimum mean squared error (LMMSE) estimator while significantly reducing computational complexity.
Cell-free Fluid Antenna Multiple Access Networks
Authors: Tianyu Han, Yongxu Zhu, Kai-Kit Wong, Gan Zheng, Hyundong Shin
Date: 2025-04-29 00:00:00

Fluid antenna enables position reconfigurability that gives transceiver access to a high-resolution spatial signal and the ability to avoid interference through the ups and downs of fading channels. Previous studies investigated this fluid antenna multiple access (FAMA) approach in a single-cell setup only. In this paper, we consider a cell-free network architecture in which users are associated with the nearest base stations (BSs) and all users share the same physical channel. Each BS has multiple fixed antennas that employ maximum ratio transmission (MRT) to beam to its associated users while each user relies on its fluid antenna system (FAS) on one radio frequency (RF) chain to overcome the inter-user interference. Our aim is to analyze the outage probability performance of such cell-free FAMA network when both large- and small-scale fading effects are considered. To do so, we derive the distribution of the received \textcolor{black}{magnitude} for a typical user and then the interference distribution under both fast and slow port switching techniques. The outage probability is finally obtained in integral form in each case. Numerical results demonstrate that in an interference-limited situation, although fast port switching is typically understood as the superior method for FAMA, slow port switching emerges as a more effective solution when there is a large antenna array at the BS. Moreover, it is revealed that FAS at each user can serve to greatly reduce the burden of BS in terms of both antenna costs and CSI estimation overhead, thereby enhancing the scalability of cell-free networks.
Communications-Centric Secure ISAC with Hybrid Reconfigurable Intelligent Surfaces
Authors: Ioannis Gavras, George C. Alexandropoulos
Date: 2025-04-29 00:00:00
Tasks: ISAC

Hybrid reconfigurable intelligent surfaces (HRISs) constitute an emerging paradigm of metasurfaces that empowers the concept of smart wireless environments, inherently supporting simultaneously communications and sensing. Very recently, some preliminary HRIS designs for Integrated Sensing And Communications (ISAC) have appeared, however, secure ISAC schemes are still lacking. In this paper, we present a novel communications-centric secure ISAC framework capitalizing on the dual-functional capability of HRISs to realize bistatic sensing simultaneously with secure downlink communications. In particular, we jointly optimize the BS precoding vector and the HRIS reflection and analog combining configurations to enable simultaneous accurate estimation of both a legitimate user and an eavesdropper, while guaranteeing a predefined threshold for the secrecy spectral efficiency, with both operations focused within an area of interest. The presented simulation results validate the effectiveness of the proposed secure ISAC design, highlighting the interplay among key system design parameters as well as quantifying the trade-offs between the HRIS's absorption and reflection coeffcients.
Sense-then-Charge: Wireless Power Transfer to Unresponsive Devices with Unknown Location
Authors: Amirhossein Azarbahram, Onel L. A. López, Richard D. Souza, Petar Popovski, Matti Latva-aho
Date: 2025-04-29 00:00:00

This paper explores a multi-antenna dual-functional radio frequency (RF) wireless power transfer (WPT) and radar system to charge multiple unresponsive devices. We formulate a beamforming problem to maximize the minimum received power at the devices without prior location and channel state information (CSI) knowledge. We propose dividing transmission blocks into sensing and charging phases. First, the location of the devices is estimated by sending sensing signals and performing multiple signal classification and least square estimation on the received echo. Then, the estimations are used for CSI prediction and RF-WPT beamforming. Simulation results reveal that there is an optimal number of blocks allocated for sensing and charging depending on the system setup. Our sense-then-charge (STC) protocol can outperform CSI-free benchmarks and achieve near-optimal performance with a sufficient number of receive antennas and transmit power. However, STC struggles if using insufficient antennas or power as device numbers grow.
A Deep Generative Model for Five-Class Sleep Staging with Arbitrary Sensor Input
Authors: Hans van Gorp, Merel M. van Gilst, Pedro Fonseca, Fokke B. van Meulen, Johannes P. van Dijk, Sebastiaan Overeem, Ruud J. G. van Sloun
Date: 2024-08-09 00:00:00
Tasks: EEG, Sleep Staging

Gold-standard sleep scoring is based on epoch-based assignment of sleep stages based on a combination of EEG, EOG and EMG signals. However, a polysomnographic recording consists of many other signals that could be used for sleep staging, including cardio-respiratory modalities. Leveraging this signal variety would offer important advantages, for example increasing reliability, resilience to signal loss, and application to long-term non-obtrusive recordings. We developed a deep generative model for automatic sleep staging from a plurality of sensors and any -- arbitrary -- combination thereof. We trained a score-based diffusion model using a dataset of 1947 expert-labelled overnight recordings with 36 different signals, and achieved zero-shot inference on any sensor set by leveraging a novel Bayesian factorization of the score function across the sensors. On single-channel EEG, the model reaches the performance limit in terms of polysomnography inter-rater agreement (5-class accuracy 85.6%, Cohen's kappa 0.791). Moreover, the method offers full flexibility to use any sensor set, for example finger photoplethysmography, nasal flow and thoracic respiratory movements, (5-class accuracy 79.0%, Cohen's kappa of 0.697), or even derivations very unconventional for sleep staging, such as tibialis and sternocleidomastoid EMG (5-class accuracy 71.0%, kappa 0.575). Additionally, we propose a novel interpretability metric in terms of information gain per sensor and show this is linearly correlated with classification performance. Finally, our model allows for post-hoc addition of entirely new sensor modalities by merely training a score estimator on the novel input instead of having to retrain from scratch on all inputs.