pwc-archive/papers-with-abstracts huggingface dataset:
Estimation of Blood Flow Parameters in the Left Atrial Appendage from 4DCT Dynamic Contrast Enhancement
Authors: Lauren M. Severance, Andrew M. Kahn, Juan C. del Alamo, Elliot R. McVeigh
Date: 2025-02-04 00:00:00

Cardiac CT is often used clinically in electrophysiology to evaluate cardiac morphology. One such case is to evaluate patients with Atrial Fibrillation (AF). AF can cause regions of slow blood flow and blood stasis through the left atrial appendage (LAA), and therefore, it may be preferable to evaluate blood flow through the LAA in addition to morphology. Although CT cannot measure flow directly, CT data has been used to estimate flow using modeling approaches such as Computational Fluid Dynamics, which take into account the cardiac geometry to simulate flow. Advances in CT technology now enable high-resolution imaging of the whole heart with low radiation doses. With multi-heartbeat imaging during a contrast injection, we can obtain 4-dimentional CT (4DCT) images to measure dynamic contrast enhancement directly. In this study, we use high-resolution 4DCT to acquire images of contrast enhancement across the LAA over multiple heartbeats. The CT contrast signal at each voxel over time is used to create dynamic contrast enhancement maps of parameters derived from a gamma-variate fit. These contrast enhancement maps enable quantification and visualization of spatial-temporal characteristics of flow parameters across the LAA.
Muographic Image Upsampling with Machine Learning for Built Infrastructure Applications
Authors: William O'Donnell, David Mahon, Guangliang Yang, Simon Gardner
Date: 2025-02-04 00:00:00
Tasks: Generative Adversarial Network, Semantic Segmentation, SSIM

The civil engineering industry faces a critical need for innovative non-destructive evaluation methods, particularly for ageing critical infrastructure, such as bridges, where current techniques fall short. Muography, a non-invasive imaging technique, constructs three-dimensional density maps by detecting interactions of naturally occurring cosmic-ray muons within the scanned volume. Cosmic-ray muons provide deep penetration and inherent safety due to their high momenta and natural source. However, the technology's reliance on this source results in constrained muon flux, leading to prolonged acquisition times, noisy reconstructions and image interpretation challenges. To address these limitations, we developed a two-model deep learning approach. First, we employed a conditional Wasserstein generative adversarial network with gradient penalty (cWGAN-GP) to perform predictive upsampling of undersampled muography images. Using the Structural Similarity Index Measure (SSIM), 1-day sampled images matched the perceptual qualities of a 21-day image, while the Peak Signal-to-Noise Ratio (PSNR) indicated noise improvement equivalent to 31 days of sampling. A second cWGAN-GP model, trained for semantic segmentation, quantitatively assessed the upsampling model's impact on concrete sample features. This model achieved segmentation of rebar grids and tendon ducts, with Dice-S{\o}rensen accuracy coefficients of 0.8174 and 0.8663. Notably, it could mitigate or remove z-plane smearing artifacts caused by muography's inverse imaging problem. Both models were trained on a comprehensive Geant4 Monte-Carlo simulation dataset reflecting realistic civil infrastructure scenarios. Our results demonstrate significant improvements in acquisition speed and image quality, marking a substantial step toward making muography more practical for reinforced concrete infrastructure monitoring applications.
Optimal No-Fly Zone Design for the Coexistence of Drone and Satellite Networks
Authors: Xiangliu Tu, Chiranjib Saha, Harpreet S. Dhillon
Date: 2025-02-05 00:00:00

Constructing a no-fly zone (NFZ) is a straightforward and effective way to facilitate the coexistence of unmanned aerial vehicles (drones) and existing systems (typically satellite systems). However, there has been little work on understanding the optimal design of such NFZs. In the absence of this design, one invariably ends up overestimating this region, hence significantly limiting the allowed airspace for the drones. To optimize the volume of the NFZ, we formulate this task as a variational problem and utilize the calculus of variations to rigorously obtain the NFZ as a function of the antenna pattern of victim receivers and the spatial distribution of drones. This approach parallels the matched filter design in the sense that the NFZ extends in directions where the antenna gain and/or the density of drones is high. Numerical simulations demonstrate the effectiveness of our optimal design compared to the known baselines in reducing the volume of the NFZ without compromising the protective performance.
Decoding Human Attentive States from Spatial-temporal EEG Patches Using Transformers
Authors: Yi Ding, Joon Hei Lee, Shuailei Zhang, Tianze Luo, Cuntai Guan
Date: 2025-02-06 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG)

Learning the spatial topology of electroencephalogram (EEG) channels and their temporal dynamics is crucial for decoding attention states. This paper introduces EEG-PatchFormer, a transformer-based deep learning framework designed specifically for EEG attention classification in Brain-Computer Interface (BCI) applications. By integrating a Temporal CNN for frequency-based EEG feature extraction, a pointwise CNN for feature enhancement, and Spatial and Temporal Patching modules for organizing features into spatial-temporal patches, EEG-PatchFormer jointly learns spatial-temporal information from EEG data. Leveraging the global learning capabilities of the self-attention mechanism, it captures essential features across brain regions over time, thereby enhancing EEG data decoding performance. Demonstrating superior performance, EEG-PatchFormer surpasses existing benchmarks in accuracy, area under the ROC curve (AUC), and macro-F1 score on a public cognitive attention dataset. The code can be found via: https://github.com/yi-ding-cs/EEG-PatchFormer .
Stability and performance guarantees for misspecified multivariate score-driven filters
Authors: Simon Donker van Heel, Rutger-Jan Lange, Dick van Dijk, Bram van Os
Date: 2025-02-07 00:00:00

We address the problem of tracking multivariate unobserved time-varying parameters under potential model misspecification. Specifically, we examine implicit and explicit score-driven (ISD and ESD) filters, which update parameter predictions using the gradient of the postulated logarithmic observation density (commonly referred to as the score). For both filter types, we derive novel sufficient conditions that ensure the invertibility of the filtered parameter path and the existence of a finite mean squared error (MSE) bound relative to the pseudo-true parameter path. Our (non-)asymptotic MSE bounds rely on mild moment conditions on the data-generating process, while our invertibility result is agnostic about the true process. For the ISD filter, concavity of the postulated log density combined with simple parameter restrictions is sufficient (though not necessary) to guarantee stability. In contrast, the ESD filter additionally requires the score to be Lipschitz continuous. We validate our theoretical findings and highlight the superior stability and performance of ISD over ESD filters through extensive simulation studies. Finally, we demonstrate the practical relevance of our approach through an empirical application to U.S. Treasury-bill rates.
Impact of Model Mismatch on DOA Estimation with MUSIC: Near-Field and Far-Field
Authors: Don-Roberts Emenonye, Harpreet S. Dhillon, R. Michael Buehrer
Date: 2025-02-07 00:00:00

There has been substantial work on developing variants of the multiple signal classification (MUSIC) algorithms that take advantage of the information present in the near-field propagation regime. However, it is not always easy to determine the correct propagation regime, which opens the possibility of incorrectly applying simpler algorithms (meant for far-field) in the near-field regime. Inspired by this, we use simulation results to investigate the performance drop when there is a mismatch between the signal model in the MUSIC algorithm and the propagation regime. For direction of arrival (DOA) estimation, we consider the cases when the receiver is in the near-field region but uses i) the near-field model, ii) the approximate near-field model (ANM) model, and iii) the far-field model to design the beamforming matrix in the MUSIC algorithm. We also consider the case when the receiver is in the far-field region, and we use the correct far-field model to design the beamforming matrix in the MUSIC algorithm. One contribution is that in the near-field, we have quantified the loss in performance when the ANM and the far-field model are used to create the beamforming matrix for the MUSIC algorithm, causing a reduction in estimation accuracy compared to the case when the correct near-field model is used to design the beamforming matrix. Another result is that in the near-field, when we incorrectly assume that the receiver is in the far-field and subsequently use the far-field beamforming matrix, we underestimate the DOA estimation error. Finally, we show that the MUSIC algorithm can provide very accurate range estimates for distances less than the Fraunhofer distance. This estimate gradually becomes inaccurate as the distances exceed the Fraunhofer distance.
Towards Smarter Sensing: 2D Clutter Mitigation in RL-Driven Cognitive MIMO Radar
Authors: Adam Umra, Aya Mostafa Ahmed, Aydin Sezgin
Date: 2025-02-07 00:00:00
Tasks: Integrated sensing and communication, Reinforcement Learning (RL)

Motivated by the growing interest in integrated sensing and communication for 6th generation (6G) networks, this paper presents a cognitive Multiple-Input Multiple-Output (MIMO) radar system enhanced by reinforcement learning (RL) for robust multitarget detection in dynamic environments. The system employs a planar array configuration and adapts its transmitted waveforms and beamforming patterns to optimize detection performance in the presence of unknown two-dimensional (2D) disturbances. A robust Wald-type detector is integrated with a SARSA-based RL algorithm, enabling the radar to learn and adapt to complex clutter environments modeled by a 2D autoregressive process. Simulation results demonstrate significant improvements in detection probability compared to omnidirectional methods, particularly for low Signal-to-Noise Ratio (SNR) targets masked by clutter.
Explainable and externally validated machine learning for neuropsychiatric diagnosis via electrocardiograms
Authors: Juan Miguel Lopez Alcaraz, Ebenezer Oloyede, David Taylor, Wilhelm Haverkamp, Nils Strodthoff
Date: 2025-02-07 00:00:00

Electrocardiogram (ECG) analysis has emerged as a promising tool for identifying physiological changes associated with neuropsychiatric conditions. The relationship between cardiovascular health and neuropsychiatric disorders suggests that ECG abnormalities could serve as valuable biomarkers for more efficient detection, therapy monitoring, and risk stratification. However, the potential of the ECG to accurately distinguish neuropsychiatric conditions, particularly among diverse patient populations, remains underexplored. This study utilized ECG markers and basic demographic data to predict neuropsychiatric conditions using machine learning models, with targets defined through ICD-10 codes. Both internal and external validation were performed using the MIMIC-IV and ECG-View datasets respectively. Performance was assessed using AUROC scores. To enhance model interpretability, Shapley values were applied to provide insights into the contributions of individual ECG features to the predictions. Significant predictive performance was observed for conditions within the neurological and psychiatric groups. For the neurological group, Alzheimer's disease (G30) achieved an internal AUROC of 0.813 (0.812-0.814) and an external AUROC of 0.868 (0.867-0.868). In the psychiatric group, unspecified dementia (F03) showed an internal AUROC of 0.849 (0.848-0.849) and an external AUROC of 0.862 (0.861-0.863). Discriminative features align with known ECG markers but also provide hints on potentially new markers. ECG offers significant promise for diagnosing and monitoring neuropsychiatric conditions, with robust predictive performance across internal and external cohorts. Future work should focus on addressing potential confounders, such as therapy-related cardiotoxicity, and expanding the scope of ECG applications, including personalized care and early intervention strategies.
Deep Learning Models for Physical Layer Communications
Authors: Nunzio A. Letizia
Date: 2025-02-07 00:00:00
Tasks: Deep Learning

The increased availability of data and computing resources has enabled researchers to successfully adopt machine learning (ML) techniques and make significant contributions in several engineering areas. ML and in particular deep learning (DL) algorithms have shown to perform better in tasks where a physical bottom-up description of the phenomenon is lacking and/or is mathematically intractable. Indeed, they take advantage of the observations of natural phenomena to automatically acquire knowledge and learn internal relations. Despite the historical model-based mindset, communications engineering recently started shifting the focus towards top-down data-driven learning models, especially in domains such as channel modeling and physical layer design, where in most of the cases no general optimal strategies are known. In this thesis, we aim at solving some fundamental open challenges in physical layer communications exploiting new DL paradigms. In particular, we mathematically formulate, under ML terms, classic problems such as channel capacity and optimal coding-decoding schemes, for any arbitrary communication medium. We design and develop the architecture, algorithm and code necessary to train the equivalent DL model, and finally, we propose novel solutions to long-standing problems in the field.
Terahertz Integrated Sensing and Communication-Empowered UAVs in 6G: A Transceiver Design Perspective
Authors: Ruoyu Zhang, Wen Wu, Xiaoming Chen, Zhen Gao, Yueming Cai
Date: 2025-02-07 00:00:00
Tasks: Integrated sensing and communication, ISAC

Due to their high maneuverability, flexible deployment, and low cost, unmanned aerial vehicles (UAVs) are expected to play a pivotal role in not only communication, but also sensing. Especially by exploiting the ultra-wide bandwidth of terahertz (THz) bands, integrated sensing and communication (ISAC)-empowered UAV has been a promising technology of 6G space-air-ground integrated networks. In this article, we systematically investigate the key techniques and essential obstacles for THz-ISAC-empowered UAV from a transceiver design perspective, with the highlight of its major challenges and key technologies. Specifically, we discuss the THz-ISAC-UAV wireless propagation environment, based on which several channel characteristics for communication and sensing are revealed. We point out the transceiver payload design peculiarities for THz-ISAC-UAV from the perspective of antenna design, radio frequency front-end, and baseband signal processing. To deal with the specificities faced by the payload, we shed light on three key technologies, i.e., hybrid beamforming for ultra-massive MIMO-ISAC, power-efficient THz-ISAC waveform design, as well as communication and sensing channel state information acquisition, and extensively elaborate their concepts and key issues. More importantly, future research directions and associated open problems are presented, which may unleash the full potential of THz-ISAC-UAV for 6G wireless networks.
The Wideband Analysis of the Impact of I/Q Imbalance on THz Communication
Authors: Dogus Can Sevdiren, Aydin Sezgin, Mohammad Soleymani
Date: 2025-02-07 00:00:00

The terahertz (THz) band is a promising solution to the increasing data traffic demands of future wireless networks. However, developing transceivers for THz communication is a complex and toilsome task due to the difficulty in designing devices that operate at this frequency and the impact of hardware impairments on performance. This paper investigates the impact of radio frequency (RF) impairment, in-phase/quadrature imbalance (IQI). To this end, we express an IQI model for the THzspecific array-of-subarrays (AoSA) architecture considering the unique features of THz communication; vast bandwidth, severe power drawdown, and pencil-like beams. We further model the impact of IQI in the power limited regime in order to investigate the power and ultra-wideband trade-off. To achieve this, we express the spectral efficiency in terms of wideband slope and bit energy to noise ratio which are the two important information theoretic metrics that reveals the performance of the ultrawideband systems as in THz communication. Our results show that THz systems with IQI have a strict limit in achievable rate although they provide immense spectrum. We also demonstrate with our simulation results that compared to low frequencies, IQI is a more serious concern in THz links.
DULRTC-RME: A Deep Unrolled Low-rank Tensor Completion Network for Radio Map Estimation
Authors: Yao Wang, Xin Wu, Lianming Xu, Na Liu, Li Wang
Date: 2025-02-07 00:00:00

Radio maps enrich radio propagation and spectrum occupancy information, which provides fundamental support for the operation and optimization of wireless communication systems. Traditional radio maps are mainly achieved by extensive manual channel measurements, which is time-consuming and inefficient. To reduce the complexity of channel measurements, radio map estimation (RME) through novel artificial intelligence techniques has emerged to attain higher resolution radio maps from sparse measurements or few observations. However, black box problems and strong dependency on training data make learning-based methods less explainable, while model-based methods offer strong theoretical grounding but perform inferior to the learning-based methods. In this paper, we develop a deep unrolled low-rank tensor completion network (DULRTC-RME) for radio map estimation, which integrates theoretical interpretability and learning ability by unrolling the tedious low-rank tensor completion optimization into a deep network. It is the first time that algorithm unrolling technology has been used in the RME field. Experimental results demonstrate that DULRTC-RME outperforms existing RME methods.
Affine Frequency Division Multiplexing: Extending OFDM for Scenario-Flexibility and Resilience
Authors: Haoran Yin, Yanqun Tang, Ali Bemani, Marios Kountouris, Yu Zhou, Xingyao Zhang, Yuqing Liu, Gaojie Chen, Kai Yang, Fan Liu, Christos Masouros, Shuangyang Li, Giuseppe Caire, Pei Xiao
Date: 2025-02-07 00:00:00
Tasks: Diversity

Next-generation wireless networks are conceived to provide reliable and high-data-rate communication services for diverse scenarios, such as vehicle-to-vehicle, unmanned aerial vehicles, and satellite networks. The severe Doppler spreads in the underlying time-varying channels induce destructive inter-carrier interference (ICI) in the extensively adopted orthogonal frequency division multiplexing (OFDM) waveform, leading to severe performance degradation. This calls for a new air interface design that can accommodate the severe delay-Doppler spreads in highly dynamic channels while possessing sufficient flexibility to cater to various applications. This article provides a comprehensive overview of a promising chirp-based waveform named affine frequency division multiplexing (AFDM). It is featured with two tunable parameters and achieves optimal diversity order in doubly dispersive channels (DDC). We study the fundamental principle of AFDM, illustrating its intrinsic suitability for DDC. Based on that, several potential applications of AFDM are explored. Furthermore, the major challenges and the corresponding solutions of AFDM are presented, followed by several future research directions. Finally, we draw some instructive conclusions about AFDM, hoping to provide useful inspiration for its development.
OTFS-ISAC System with Sub-Nyquist ADC Sampling Rate
Authors: Henglin Pu, Xuefeng Wang, Ajay Kumar, Lu Su, Husheng Li
Date: 2025-02-07 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) has emerged as a pivotal technology for next-generation wireless communication and radar systems, enabling high-resolution sensing and high-throughput communication with shared spectrum and hardware. However, achieving a fine radar resolution often requires high-rate analog-to-digital converters (ADCs) and substantial storage, making it both expensive and impractical for many commercial applications. To address these challenges, this paper proposes an orthogonal time frequency space (OTFS)-based ISAC architecture that operates at reduced ADC sampling rates, yet preserves accurate radar estimation and supports simultaneous communication. The proposed architecture introduces pilot symbols directly in the delay-Doppler (DD) domain to leverage the transformation mapping between the DD and time-frequency (TF) domains to keep selected subcarriers active while others are inactive, allowing the radar receiver to exploit under-sampling aliasing and recover the original DD signal at much lower sampling rates. To further enhance the radar accuracy, we develop an iterative interference estimation and cancellation algorithm that mitigates data symbol interference. We propose a code-based spreading technique that distributes data across the DD domain to preserve the maximum unambiguous radar sensing range. For communication, we implement a complete transceiver pipeline optimized for reduced sampling rate system, including synchronization, channel estimation, and iterative data detection. Experimental results from a software-defined radio (SDR)-based testbed confirm that our method substantially lowers the required sampling rate without sacrificing radar sensing performance and ensures reliable communication.
TQ-DiT: Efficient Time-Aware Quantization for Diffusion Transformers
Authors: Younghye Hwang, Hyojin Lee, Joonhyuk Kang
Date: 2025-02-06 00:00:00
Tasks: Computational Efficiency, Quantization

Diffusion transformers (DiTs) combine transformer architectures with diffusion models. However, their computational complexity imposes significant limitations on real-time applications and sustainability of AI systems. In this study, we aim to enhance the computational efficiency through model quantization, which represents the weights and activation values with lower precision. Multi-region quantization (MRQ) is introduced to address the asymmetric distribution of network values in DiT blocks by allocating two scaling parameters to sub-regions. Additionally, time-grouping quantization (TGQ) is proposed to reduce quantization error caused by temporal variation in activations. The experimental results show that the proposed algorithm achieves performance comparable to the original full-precision model with only a 0.29 increase in FID at W8A8. Furthermore, it outperforms other baselines at W6A6, thereby confirming its suitability for low-bit quantization. These results highlight the potential of our method to enable efficient real-time generative models.