pwc-archive/papers-with-abstracts huggingface dataset:
Deep Learning for Pathological Speech: A Survey
Authors: Shakeel A. Sheikh, Md. Sahidullah, Ina Kodrasi
Date: 2025-01-07 00:00:00
Tasks: Automatic Speech Recognition, Data Augmentation, Deep Learning, speech-recognition, Speech Recognition, Survey

Advancements in spoken language technologies for neurodegenerative speech disorders are crucial for meeting both clinical and technological needs. This overview paper is vital for advancing the field, as it presents a comprehensive review of state-of-the-art methods in pathological speech detection, automatic speech recognition, pathological speech intelligibility enhancement, intelligibility and severity assessment, and data augmentation approaches for pathological speech. It also high-lights key challenges, such as ensuring robustness, privacy, and interpretability. The paper concludes by exploring promising future directions, including the adoption of multimodal approaches and the integration of graph neural networks and large language models to further advance speech technology for neurodegenerative speech disorders
Rethinking domain generalization in medical image segmentation: One image as one domain
Authors: Jin Hong, Bo Liu, Guoli Long, Siyue Li, Khan Muhammad
Date: 2025-01-08 00:00:00
Tasks: Disentanglement, Domain Generalization, Image Segmentation, Medical Image Segmentation, Optic Cup Segmentation, Segmentation, Semantic Segmentation, Single-Source Domain Generalization

Domain shifts in medical image segmentation, particularly when data comes from different centers, pose significant challenges. Intra-center variability, such as differences in scanner models or imaging protocols, can cause domain shifts as large as, or even larger than, those between centers. To address this, we propose the "one image as one domain" (OIOD) hypothesis, which treats each image as a unique domain, enabling flexible and robust domain generalization. Based on this hypothesis, we develop a unified disentanglement-based domain generalization (UniDDG) framework, which simultaneously handles both multi-source and single-source domain generalization without requiring explicit domain labels. This approach simplifies training with a fixed architecture, independent of the number of source domains, reducing complexity and enhancing scalability. We decouple each input image into content representation and style code, then exchange and combine these within the batch for segmentation, reconstruction, and further disentanglement. By maintaining distinct style codes for each image, our model ensures thorough decoupling of content representations and style codes, improving domain invariance of the content representations. Additionally, we enhance generalization with expansion mask attention (EMA) for boundary preservation and style augmentation (SA) to simulate diverse image styles, improving robustness to domain shifts. Extensive experiments show that our method achieves Dice scores of 84.43% and 88.91% for multi-source to single-center and single-center generalization in optic disc and optic cup segmentation, respectively, and 86.96% and 88.56% for prostate segmentation, outperforming current state-of-the-art domain generalization methods, offering superior performance and adaptability across clinical settings.
Color Correction Meets Cross-Spectral Refinement: A Distribution-Aware Diffusion for Underwater Image Restoration
Authors: Laibin Chang, Yunke Wang, Bo Du, Chang Xu
Date: 2025-01-08 00:00:00
Tasks: Computational Efficiency, Denoising, Image Enhancement, Image Generation, Image Restoration, UIE, Underwater Image Restoration

Underwater imaging often suffers from significant visual degradation, which limits its suitability for subsequent applications. While recent underwater image enhancement (UIE) methods rely on the current advances in deep neural network architecture designs, there is still considerable room for improvement in terms of cross-scene robustness and computational efficiency. Diffusion models have shown great success in image generation, prompting us to consider their application to UIE tasks. However, directly applying them to UIE tasks will pose two challenges, \textit{i.e.}, high computational budget and color unbalanced perturbations. To tackle these issues, we propose DiffColor, a distribution-aware diffusion and cross-spectral refinement model for efficient UIE. Instead of diffusing in the raw pixel space, we transfer the image into the wavelet domain to obtain such low-frequency and high-frequency spectra, it inherently reduces the image spatial dimensions by half after each transformation. Unlike single-noise image restoration tasks, underwater imaging exhibits unbalanced channel distributions due to the selective absorption of light by water. To address this, we design the Global Color Correction (GCC) module to handle the diverse color shifts, thereby avoiding potential global degradation disturbances during the denoising process. For the sacrificed image details caused by underwater scattering, we further present the Cross-Spectral Detail Refinement (CSDR) to enhance the high-frequency details, which are integrated with the low-frequency signal as input conditions for guiding the diffusion. This way not only ensures the high-fidelity of sampled content but also compensates for the sacrificed details. Comprehensive experiments demonstrate the superior performance of DiffColor over state-of-the-art methods in both quantitative and qualitative evaluations.
Convergent Primal-Dual Plug-and-Play Image Restoration: A General Algorithm and Applications
Authors: Yodai Suzuki, Ryosuke Isono, Shunsuke Ono
Date: 2025-01-07 00:00:00
Tasks: Image Restoration

We propose a general deep plug-and-play (PnP) algorithm with a theoretical convergence guarantee. PnP strategies have demonstrated outstanding performance in various image restoration tasks by exploiting the powerful priors underlying Gaussian denoisers. However, existing PnP methods often lack theoretical convergence guarantees under realistic assumptions due to their ad-hoc nature, resulting in inconsistent behavior. Moreover, even when convergence guarantees are provided, they are typically designed for specific settings or require a considerable computational cost in handling non-quadratic data-fidelity terms and additional constraints, which are key components in many image restoration scenarios. To tackle these challenges, we integrate the PnP paradigm with primal-dual splitting (PDS), an efficient proximal splitting methodology for solving a wide range of convex optimization problems, and develop a general convergent PnP framework. Specifically, we establish theoretical conditions for the convergence of the proposed PnP algorithm under a reasonable assumption. Furthermore, we show that the problem solved by the proposed PnP algorithm is not a standard convex optimization problem but a more general monotone inclusion problem, where we provide a mathematical representation of the solution set. Our approach efficiently handles a broad class of image restoration problems with guaranteed theoretical convergence. Numerical experiments on specific image restoration tasks validate the practicality and effectiveness of our theoretical results.
A generative approach for lensless imaging in low-light conditions
Authors: Ziyang Liu, Tianjiao Zeng, Xu Zhan, Xiaoling Zhang, Edmund Y. Lam
Date: 2025-01-07 00:00:00

Lensless imaging offers a lightweight, compact alternative to traditional lens-based systems, ideal for exploration in space-constrained environments. However, the absence of a focusing lens and limited lighting in such environments often result in low-light conditions, where the measurements suffer from complex noise interference due to insufficient capture of photons. This study presents a robust reconstruction method for high-quality imaging in low-light scenarios, employing two complementary perspectives: model-driven and data-driven. First, we apply a physic-model-driven perspective to reconstruct in the range space of the pseudo-inverse of the measurement model as a first guidance to extract information in the noisy measurements. Then, we integrate a generative-model based perspective to suppress residual noises as the second guidance to suppress noises in the initial noisy results. Specifically, a learnable Wiener filter-based module generates an initial noisy reconstruction. Then, for fast and, more importantly, stable generation of the clear image from the noisy version, we implement a modified conditional generative diffusion module. This module converts the raw image into the latent wavelet domain for efficiency and uses modified bidirectional training processes for stabilization. Simulations and real-world experiments demonstrate substantial improvements in overall visual quality, advancing lensless imaging in challenging low-light environments.
K-space Diffusion Model Based MR Reconstruction Method for Simultaneous Multislice Imaging
Authors: Ting zhao, Zhuoxu Cui, Congcong Liu, Xingyang Wu, Yihang Zhou, Dong Liang, Haifeng Wang
Date: 2025-01-06 00:00:00

Simultaneous Multi-Slice(SMS) is a magnetic resonance imaging (MRI) technique which excites several slices concurrently using multiband radiofrequency pulses to reduce scanning time. However, due to its variable data structure and difficulty in acquisition, it is challenging to integrate SMS data as training data into deep learning frameworks.This study proposed a novel k-space diffusion model of SMS reconstruction that does not utilize SMS data for training. Instead, it incorporates Slice GRAPPA during the sampling process to reconstruct SMS data from different acquisition modes.Our results demonstrated that this method outperforms traditional SMS reconstruction methods and can achieve higher acceleration factors without in-plane aliasing.
Reducing Inter-user Interference: Precoding over OFDM for Enhanced MTC
Authors: Karim A. Said, A. A. Louis Beex, Elizabeth Bentley, Lingjia Liu
Date: 2025-01-09 00:00:00

In the physical layer (PHY) of modern cellular systems, information is transmitted as a sequence of resource blocks (RBs) across various domains with each resource block limited to a certain time and frequency duration. In the PHY of 4G/5G systems, data is transmitted in the unit of transport block (TB) across a fixed number of physical RBs based on resource allocation decisions. Using sharp band-limiting in the frequency domain can provide good separation between different resource allocations without wasting resources in guard bands. However, using sharp filters comes at the cost of elongating the overall system impulse response which can accentuate inter-symbol interference (ISI). In a multi-user setup, such as in Machine Type Communication (MTC), different users are allocated resources across time and frequency, and operate at different power levels. If strict band-limiting separation is used, high power user signals can leak in time into low power user allocations. The ISI extent, i.e., the number of neighboring symbols that contribute to the interference, depends both on the channel delay spread and the spectral concentration properties of the signaling waveforms. We hypothesize that using a precoder that effectively transforms an OFDM waveform basis into a basis comprised of prolate spheroidal sequences (DPSS) can minimize the ISI extent when strictly confined frequency allocations are used. Analytical expressions for upper bounds on ISI are derived. In addition, simulation results support our hypothesis.
Timing Properties of the Starlink Ku-Band Downlink
Authors: Wenkai Qin, Andrew M. Graff, Zachary L. Clements, Zacharias M. Komodromos, Todd E. Humphreys
Date: 2025-01-09 00:00:00

We develop signal capture and analysis techniques for precisely extracting and characterizing the frame timing of the Starlink constellation's Ku-band downlink transmissions. The aim of this work is to determine whether Starlink frame timing has sufficient short-term stability to support pseudorange-based opportunistic positioning, navigation, and timing (PNT). A second goal is to determine whether frame timing is disciplined to a common time scale such as GPS time. Our analysis reveals several timing characteristics not previously known that carry strong implications for PNT. On the favorable side, periods of ns-level jitter in frame arrival times across all satellite versions indicate that Starlink hardware is fundamentally capable of the short-term stability required to support GPS-like PNT. But there are several unfavorable characteristics that, if not addressed, will make GPS-like PNT impractical: (1) The v1.0 and v1.5 Starlink satellites exhibit once-per-second abrupt frame timing adjustments whose magnitude (as large as 100s of ns) and sign appear unpredictable. Similar discontinuities are also present in the v2.0-Mini frame timing, though smaller and irregularly spaced. (2) Episodic 15-s periods of high frame jitter routinely punctuate the nominal low-jitter frame arrival timing. (3) Starlink frame timing is disciplined to GPS time, but only loosely: to within a few ms by adjustments occurring every 15 s; otherwise exhibiting drift that can exceed 20 ppm. These unfavorable characteristics are essentially incompatible with accurate PNT. Fortunately, they appear to be a consequence of software design choices, not hardware limitations. Moreover, they could be compensated with third-party-provided corrections.
Joint Communications and Sensing for 6G Satellite Networks: Use Cases and Challenges
Authors: Chandan Kumar Sheemar, Prabhu Thiruvasagam, Wali Ullah Khan, Sourabh Solanki, George C. Alexandropoulos, Jorge Querol, Jan Plachy, Oliver Holschke, Symeon Chatzinotas
Date: 2025-01-09 00:00:00
Tasks: Earth Observation

Satellite Networks (SN) have traditionally been instrumental in providing two key services: communications and sensing. Communications satellites enable global connectivity, while sensing satellites facilitate applications such as Earth observation, navigation, and disaster management. However, the emergence of novel use cases and the exponential growth in service demands make the independent evolution of communication and sensing payloads increasingly impractical. Addressing this challenge requires innovative approaches to optimize satellite resources. Joint Communications and Sensing (JCAS) technology represents a transformative paradigm for SN. By integrating communication and sensing functionalities into unified hardware platforms, JCAS enhances spectral efficiency, reduces operational costs, and minimizes hardware redundancies. This paper explores the potential of JCAS in advancing the next-generation space era, highlighting its role in emerging applications. Furthermore, it identifies critical challenges, such as waveform design, Doppler effect mitigation, and multi-target detection, that remain open for future research. Through these discussions, we aim to stimulate further research into the transformative potential of JCAS in addressing the demands of 6G and beyond SN.
Hyperdimensional Computing for ADHD Classification using EEG Signals
Authors: Federica Colonnese, Antonello Rosato, Francesco Di Luzio, Massimo Panella
Date: 2025-01-09 00:00:00
Tasks: Classification, EEG

Following the recent interest in applying the Hyperdimensional Computing paradigm in medical context to power up the performance of general machine learning applied to biomedical data, this study represents the first attempt at employing such techniques to solve the problem of classification of Attention Deficit Hyperactivity Disorder using electroencephalogram signals. Making use of a spatio-temporal encoder, and leveraging the properties of HDC, the proposed model achieves an accuracy of 88.9%, outperforming traditional Deep Neural Networks benchmark models. The core of this research is not only to enhance the classification accuracy of the model but also to explore its efficiency in terms of the required training data: a critical finding of the study is the identification of the minimum number of patients needed in the training set to achieve a sufficient level of accuracy. To this end, the accuracy of our model trained with only $7$ of the $79$ patients is comparable to the one from benchmarks trained on the full dataset. This finding underscores the model's efficiency and its potential for quick and precise ADHD diagnosis in medical settings where large datasets are typically unattainable.
Joint Detection and Angle Estimation for Multiple Jammers in Beamspace Massive MIMO
Authors: Pengguang Du, Cheng Zhang, Changwei Zhang, Zhilei Zhang, Yongming Huang
Date: 2025-01-09 00:00:00

In this paper, we study the joint detection and angle estimation problem for beamspace multiple-input multiple-output (MIMO) systems with multiple random jamming targets. An iterative low-complexity generalized likelihood ratio test (GLRT) is proposed by transforming the composite multiple hypothesis test on the projected vector into a series of binary hypothesis tests based on the spatial covariance matrix. In each iteration, the detector implicitly inhibits the mainlobe effects of the previously detected jammers by utilizing the estimated angles and average jamming-to-signal ratios. This enables the detection of a new potential jammer and the identification of its corresponding spatial covariance. Simulation results demonstrate that the proposed method outperforms existing benchmarks by suppressing sidelobes of the detected jammers and interference from irrelevant angles, especially in medium-to-high jamming-to-noise ratio scenarios.
Infinite Factorial Linear Dynamical Systems for Transient Signal Detection
Authors: Jiadi Bao, Yatong Wang, Yunjie Li, Mengtao Zhu, Shafei Wang
Date: 2025-01-09 00:00:00

Accurately detecting the transient signal of interest from the background signal is one of the fundamental tasks in signal processing. The most recent approaches assume the existence of a single background source and represent the background signal using a linear dynamical system (LDS). This assumption might fail to capture the complexities of modern electromagnetic environments with multiple sources. To address this limitation, this paper proposes a method for detecting the transient signal in a background composed of an unknown number of emitters. The proposed method consists of two main tasks. First, a Bayesian nonparametric model called the infinite factorial linear dynamical system (IFLDS) is developed. The developed model is based on the sticky Indian buffet process and enables the representation and parameter learning of the unbounded number of background sources. This study also designs a parameter learning method for the IFLDS using slice sampling and particle Gibbs with ancestor sampling. Second, the finite moving average (FMA) stopping time is introduced to minimize the worst-case probability of missed detection, and the statistical performance of the stopping time is investigated. To facilitate the computation of the FMA stopping time, this study derives the factorial Kalman forward filtering (FKFF) method and designs a dependence structure for the underlying model, allowing the stopping time to be defined by a recursive function. Numerical simulations demonstrate the effectiveness of the proposed method and the validity of the theoretical results. The experimental results of the pulse signal detection under the condition of communication interference confirm the effectiveness and superiority of the proposed method.
Secure Beamforming for Continuous Aperture Array (CAPA) Systems
Authors: Mingjun Sun, Chongjun Ouyang, Zhaolin Wang, Shaochuan Wu, Yuanwei Liu
Date: 2025-01-09 00:00:00

Continuous aperture array (CAPA) is considered a promising technology for 6G networks, offering the potential to fully exploit spatial DoFs and achieve the theoretical limits of channel capacity. This paper investigates the performance gain of a CAPA-based downlink secure transmission system, where multiple legitimate user terminals (LUTs) coexist with multiple eavesdroppers (Eves). The system's secrecy performance is evaluated using a weighted secrecy sum-rate (WSSR) under a power constraint. We then propose two solutions for the secure current pattern design. The first solution is a block coordinate descent (BCD) optimization method based on fractional programming, which introduces a continuous-function inversion theory corresponding to matrix inversion in the discrete domain. This approach derives a closed-form expression for the optimal source current pattern. Based on this, it can be found that the optimal current pattern is essentially a linear combination of the channel spatial responses, thus eliminating the need for complex integration operations during the algorithm's optimization process. The second solution is a heuristic algorithm based on Zero-Forcing (ZF), which constructs a zero-leakage current pattern using the channel correlation matrix. It further employs a water-filling approach to design an optimal power allocation scheme that maximizes the WSSR. In high SNR regions, this solution gradually approaches the first solution, ensuring zero leakage while offering lower computational complexity. Simulation results demonstrate that: 1) CAPA-based systems achieve better WSSR compared to discrete multiple-input multiple-output systems. 2) The proposed methods, whether optimization-based or heuristic, provide significant performance improvements over existing state-of-the-art Fourier-based discretization methods, while considerably reducing computational complexity.
Rotatable and Movable Antenna-Enabled Near-Field Integrated Sensing and Communication
Authors: Yunan Sun, Hao Xu, Chongjun Ouyang, Hongwen Yang
Date: 2025-01-08 00:00:00
Tasks: Integrated sensing and communication, ISAC

The aim of this article is to investigate the performance of near-field integrated sensing and communication (ISAC) systems using rotatable movable antennas (RMAs). In the proposed RMA-enabled system, the positions and rotations of antennas at the base station (BS) are dynamically adjusted to enhance both communication and sensing capabilities. Two designs are explored: i) a sensing-centric design that minimizes the Cram$\text{\'e}$r-Rao bound (CRB) with signal-to-interference-plus-noise ratio (SINR) constraints, and ii) a communication-centric design that maximizes the sum-rate with a CRB constraint. To solve the formulated optimization problems, two alternating optimization (AO)-based algorithms are proposed capitalizing on the semidefinite relaxation (SDR) method and the particle swarm optimization (PSO) method. Numerical results demonstrate that: i) the proposed RMA-enabled system outperforms the conventional fixed-position antenna and non-rotatable movable antenna systems in both sensing-centric and communication-centric designs and RMAs' rotations show a higher performance gain in communication-centric design; ii) the proposed optimization methods achieve the Pareto boundary in both sensing-centric and communication-centric designs.
A Support Vector Approach in Segmented Regression for Map-assisted Non-cooperative Source Localization
Authors: Hao Sun, Weiming Huang, Xianghao Yu, Junting Chen
Date: 2025-01-08 00:00:00
Tasks: regression

This paper presents a non-cooperative source localization approach based on received signal strength (RSS) and 2D environment map, considering both line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. Conventional localization methods, e.g., weighted centroid localization (WCL), may perform bad. This paper proposes a segmented regression approach using 2D maps to estimate source location and propagation environment jointly. By leveraging topological information from the 2D maps, a support vector-assisted algorithm is developed to solve the segmented regression problem, separate the LOS and NLOS measurements, and estimate the location of source. The proposed method demonstrates a good localization performance with an improvement of over 30% in localization rooted mean squared error (RMSE) compared to the baseline methods.