pwc-archive/papers-with-abstracts huggingface dataset:
3D Structural Phenotype of the Optic Nerve Head at the Intersection of Glaucoma and Myopia - A Key to Improving Glaucoma Diagnosis in Myopic Populations
Authors: Swati Sharma, Fabian A. Braeu, Thanadet Chuangsuwanich, Tin A. Tun, Quan V Hoang, Rachel Chong, Shamira Perera, Ching-Lin Ho, Rahat Husain, Martin L. Buist, Tin Aung, Michaël J. A. Girard
Date: 2025-03-24 00:00:00
Tasks: Decoder, Dimensionality Reduction

Purpose: To characterize the 3D structural phenotypes of the optic nerve head (ONH) in patients with glaucoma, high myopia, and concurrent high myopia and glaucoma, and to evaluate their variations across these conditions. Participants: A total of 685 optical coherence tomography (OCT) scans from 754 subjects of Singapore-Chinese ethnicity, including 256 healthy (H), 94 highly myopic (HM), 227 glaucomatous (G), and 108 highly myopic with glaucoma (HMG) cases. Methods: We segmented the retinal and connective tissues from OCT volumes and their boundary edges were converted into 3D point clouds. To classify the 3D point clouds into four ONH conditions, i.e., H, HM, G, and HMG, a specialized ensemble network was developed, consisting of an encoder to transform high-dimensional input data into a compressed latent vector, a decoder to reconstruct point clouds from the latent vector, and a classifier to categorize the point clouds into the four ONH conditions. Results: The classification network achieved high accuracy, distinguishing H, HM, G, and HMG classes with a micro-average AUC of 0.92 $\pm$ 0.03 on an independent test set. The decoder effectively reconstructed point clouds, achieving a Chamfer loss of 0.013 $\pm$ 0.002. Dimensionality reduction clustered ONHs into four distinct groups, revealing structural variations such as changes in retinal and connective tissue thickness, tilting and stretching of the disc and scleral canal opening, and alterations in optic cup morphology, including shallow or deep excavation, across the four conditions. Conclusions: This study demonstrated that ONHs exhibit distinct structural signatures across H, HM, G, and HMG conditions. The findings further indicate that ONH morphology provides sufficient information for classification into distinct clusters, with principal components capturing unique structural patterns within each group.
Foundation Model for Whole-Heart Segmentation: Leveraging Student-Teacher Learning in Multi-Modal Medical Imaging
Authors: Abdul Qayyum, Moona Mazher, Devran Ugurlu, Jose Alonso Solis Lemus, Cristobal Rodero, Steven A Niederer
Date: 2025-03-24 00:00:00
Tasks: Heart Segmentation, Segmentation, Self-Supervised Learning

Whole-heart segmentation from CT and MRI scans is crucial for cardiovascular disease analysis, yet existing methods struggle with modality-specific biases and the need for extensive labeled datasets. To address these challenges, we propose a foundation model for whole-heart segmentation using a self-supervised learning (SSL) framework based on a student-teacher architecture. Our model is pretrained on a large, unlabeled dataset of CT and MRI scans, leveraging the xLSTM backbone to capture long-range spatial dependencies and complex anatomical structures in 3D medical images. By incorporating multi-modal pretraining, our approach ensures strong generalization across both CT and MRI modalities, mitigating modality-specific variations and improving segmentation accuracy in diverse clinical settings. The use of large-scale unlabeled data significantly reduces the dependency on manual annotations, enabling robust performance even with limited labeled data. We further introduce an xLSTM-UNet-based architecture for downstream whole-heart segmentation tasks, demonstrating its effectiveness on few-label CT and MRI datasets. Our results validate the robustness and adaptability of the proposed model, highlighting its potential for advancing automated whole-heart segmentation in medical imaging.
FACE: Few-shot Adapter with Cross-view Fusion for Cross-subject EEG Emotion Recognition
Authors: Haiqi Liu, C. L. Philip Chen, Tong Zhang
Date: 2025-03-24 00:00:00
Tasks: Domain Adaptation, EEG, EEG Emotion Recognition, Emotion Recognition, Few-Shot Learning, Meta-Learning

Cross-subject EEG emotion recognition is challenged by significant inter-subject variability and intricately entangled intra-subject variability. Existing works have primarily addressed these challenges through domain adaptation or generalization strategies. However, they typically require extensive target subject data or demonstrate limited generalization performance to unseen subjects. Recent few-shot learning paradigms attempt to address these limitations but often encounter catastrophic overfitting during subject-specific adaptation with limited samples. This article introduces the few-shot adapter with a cross-view fusion method called FACE for cross-subject EEG emotion recognition, which leverages dynamic multi-view fusion and effective subject-specific adaptation. Specifically, FACE incorporates a cross-view fusion module that dynamically integrates global brain connectivity with localized patterns via subject-specific fusion weights to provide complementary emotional information. Moreover, the few-shot adapter module is proposed to enable rapid adaptation for unseen subjects while reducing overfitting by enhancing adapter structures with meta-learning. Experimental results on three public EEG emotion recognition benchmarks demonstrate FACE's superior generalization performance over state-of-the-art methods. FACE provides a practical solution for cross-subject scenarios with limited labeled data.
Improving Food Image Recognition with Noisy Vision Transformer
Authors: Tonmoy Ghosh, Edward Sazonov
Date: 2025-03-24 00:00:00
Tasks: Food Recognition

Food image recognition is a challenging task in computer vision due to the high variability and complexity of food images. In this study, we investigate the potential of Noisy Vision Transformers (NoisyViT) for improving food classification performance. By introducing noise into the learning process, NoisyViT reduces task complexity and adjusts the entropy of the system, leading to enhanced model accuracy. We fine-tune NoisyViT on three benchmark datasets: Food2K (2,000 categories, ~1M images), Food-101 (101 categories, ~100K images), and CNFOOD-241 (241 categories, ~190K images). The performance of NoisyViT is evaluated against state-of-the-art food recognition models. Our results demonstrate that NoisyViT achieves Top-1 accuracies of 95%, 99.5%, and 96.6% on Food2K, Food-101, and CNFOOD-241, respectively, significantly outperforming existing approaches. This study underscores the potential of NoisyViT for dietary assessment, nutritional monitoring, and healthcare applications, paving the way for future advancements in vision-based food computing. Code for reproducing NoisyViT for food recognition is available at NoisyViT_Food.
Automated diagnosis of lung diseases using vision transformer: a comparative study on chest x-ray classification
Authors: Muhammad Ahmad, Sardar Usman, Ildar Batyrshin, Muhammad Muzammil, K. Sajid, M. Hasnain, Muhammad Jalal, Grigori Sidorov
Date: 2025-03-22 00:00:00
Tasks: Binary Classification, Classification, Diagnostic, Multi-class Classification, Transfer Learning, X-ray Classification

Background: Lung disease is a significant health issue, particularly in children and elderly individuals. It often results from lung infections and is one of the leading causes of mortality in children. Globally, lung-related diseases claim many lives each year, making early and accurate diagnoses crucial. Radiographs are valuable tools for the diagnosis of such conditions. The most prevalent lung diseases, including pneumonia, asthma, allergies, chronic obstructive pulmonary disease (COPD), bronchitis, emphysema, and lung cancer, represent significant public health challenges. Early prediction of these conditions is critical, as it allows for the identification of risk factors and implementation of preventive measures to reduce the likelihood of disease onset Methods: In this study, we utilized a dataset comprising 3,475 chest X-ray images sourced from from Mendeley Data provided by Talukder, M. A. (2023) [14], categorized into three classes: normal, lung opacity, and pneumonia. We applied five pre-trained deep learning models, including CNN, ResNet50, DenseNet, CheXNet, and U-Net, as well as two transfer learning algorithms such as Vision Transformer (ViT) and Shifted Window (Swin) to classify these images. This approach aims to address diagnostic issues in lung abnormalities by reducing reliance on human intervention through automated classification systems. Our analysis was conducted in both binary and multiclass settings. Results: In the binary classification, we focused on distinguishing between normal and viral pneumonia cases, whereas in the multi-class classification, all three classes (normal, lung opacity, and viral pneumonia) were included. Our proposed methodology (ViT) achieved remarkable performance, with accuracy rates of 99% for binary classification and 95.25% for multiclass classification.
A Low-complexity Structured Neural Network Approach to Intelligently Realize Wideband Multi-beam Beamformers
Authors: Hansaka Aluvihare, Sivakumar Sivasankar, Xianqi Li, Arjuna Madanayake, Sirani M. Perera
Date: 2025-03-26 00:00:00

True-time-delay (TTD) beamformers can produce wideband, squint-free beams in both analog and digital signal domains, unlike frequency-dependent FFT beams. Our previous work showed that TTD beamformers can be efficiently realized using the elements of delay Vandermonde matrix (DVM), answering the longstanding beam-squint problem. Thus, building on our work on classical algorithms based on DVM, we propose neural network (NN) architecture to realize wideband multi-beam beamformers using structure-imposed weight matrices and submatrices. The structure and sparsity of the weight matrices and submatrices are shown to reduce the space and computational complexities of the NN greatly. The proposed network architecture has O(pLM logM) complexity compared to a conventional fully connected L-layers network with O(M2L) complexity, where M is the number of nodes in each layer of the network, p is the number of submatrices per layer, and M >> p. We will show numerical simulations in the 24 GHz to 32 GHz range to demonstrate the numerical feasibility of realizing wideband multi-beam beamformers using the proposed neural architecture. We also show the complexity reduction of the proposed NN and compare that with fully connected NNs, to show the efficiency of the proposed architecture without sacrificing accuracy. The accuracy of the proposed NN architecture was shown using the mean squared error, which is based on an objective function of the weight matrices and beamformed signals of antenna arrays, while also normalizing nodes. The proposed NN architecture shows a low-complexity NN realizing wideband multi-beam beamformers in real-time for low-complexity intelligent systems.
Impact of Network-Controlled Repeaters in Integrated Sensing and Communication Systems
Authors: Henrik Åkesson, Diana P. M. Osorio, Erik G. Larsson
Date: 2025-03-26 00:00:00
Tasks: Integrated sensing and communication

Integrating sensing capabilities into existing massive MIMO communication networks has become crucial, stemming from a need for a more interconnected society. Improved coverage and performance can be obtained by incorporating new network components, such as reconfigurable intelligent surfaces or network-controlled repeaters (NCR). Integrating such components into modern networks brings a number of challenges. Thus, this paper contributes with the analysis of NCR impact in integrated sensing and communication networks. Particularly, the Cram\'er-Rao bound for a range estimator is derived where the interference from the repeater is taken into consideration. Additionally, a joint procedure for determining the repeater amplification factor, along with the precoders of the transmitting access point, is proposed.
Novel Deep Neural OFDM Receiver Architectures for LLR Estimation
Authors: Erhan Karakoca, Hüseyin Çevik, İbrahim Hökelek, Ali Görçin
Date: 2025-03-26 00:00:00
Tasks: Computational Efficiency

Neural receivers have recently become a popular topic, where the received signals can be directly decoded by data driven mechanisms such as machine learning and deep learning. In this paper, we propose two novel neural network based orthogonal frequency division multiplexing (OFDM) receivers performing channel estimation and equalization tasks and directly predicting log likelihood ratios (LLRs) from the received in phase and quadrature phase (IQ) signals. The first network, the Dual Attention Transformer (DAT), employs a state of the art (SOTA) transformer architecture with an attention mechanism. The second network, the Residual Dual Non Local Attention Network (RDNLA), utilizes a parallel residual architecture with a non local attention block. The bit error rate (BER) and block error rate (BLER) performance of various SOTA neural receiver architectures is compared with our proposed methods across different signal to noise ratio (SNR) levels. The simulation results show that DAT and RDNLA outperform both traditional communication systems and existing neural receiver models.
Comparative analysis and evaluation of ageing forecasting methods for semiconductor devices in online health monitoring
Authors: Adrian Villalobos, Iban Barrutia, Rafael Pena-Alzola, Tomislav Dragicevic, Jose I. Aizpurua
Date: 2025-03-26 00:00:00
Tasks: valid

Semiconductor devices, especially MOSFETs (Metal-oxide-semiconductor field-effect transistor), are crucial in power electronics, but their reliability is affected by aging processes influenced by cycling and temperature. The primary aging mechanism in discrete semiconductors and power modules is the bond wire lift-off, caused by crack growth due to thermal fatigue. The process is empirically characterized by exponential growth and an abrupt end of life, making long-term aging forecasts challenging. This research presents a comprehensive comparative assessment of different forecasting methods for MOSFET failure forecasting applications. Classical tracking, statistical forecasting and Neural Network (NN) based forecasting models are implemented along with novel Temporal Fusion Transformers (TFTs). A comprehensive comparison is performed assessing their MOSFET ageing forecasting ability for different forecasting horizons. For short-term predictions, all algorithms result in acceptable results, with the best results produced by classical NN forecasting models at the expense of higher computations. For long-term forecasting, only the TFT is able to produce valid outcomes owing to the ability to integrate covariates from the expected future conditions. Additionally, TFT attention points identify key ageing turning points, which indicate new failure modes or accelerated ageing phases.
Comparative analysis of clustering methods for power delay profile in MMW bands and in-vehicle scenarios
Authors: Radek Zavorka, Ales Prokes, Josef Vychodil, Tomas Mikulasek, Petr Horky, Aniruddha Chandra, Christoph Mecklenbrauker, Jan M. Kelner, Cezary Ziolkowski
Date: 2025-03-26 00:00:00
Tasks: Clustering

The spatial statistics of radio wave propagation in specific environments and scenarios, as well as being able to recognize important signal components, are prerequisites for dependable connectivity. There are several reasons why in-vehicle communication is unique, including safety considerations and vehicle-to-vehicle/infrastructure communication.The paper examines the characteristics of clustering power delay profiles to investigate in-vehicle communication. It has been demonstrated that the Saleh-Valenzuela channel model can also be adapted for in-vehicle communication, and that the signal is received in clusters with exponential decay. A measurement campaign was conducted, capturing the power delay profile inside the vehicle cabin, and the reweighted l1 minimization method was compared with the traditional k-means clustering techniques.
GNSS jammer localization and identification with airborne commercial GNSS receivers
Authors: Marco Spanghero, Filip Geib, Ronny Panier, Panos Papadimitratos
Date: 2025-03-26 00:00:00
Tasks: Position

Global Navigation Satellite Systems (GNSS) are fundamental in ubiquitously providing position and time to a wide gamut of systems. Jamming remains a realistic threat in many deployment settings, civilian and tactical. Specifically, in Unmanned Aerial Vehicles (UAVs) sustained denial raises safety critical concerns. This work presents a strategy that allows detection, localization, and classification both in the frequency and time domain of interference signals harmful to navigation. A high-performance Vertical Take Off and Landing (VTOL) UAV with a single antenna and a commercial GNSS receiver is used to geolocate and characterize RF emitters at long range, to infer the navigation impairment. Raw IQ baseband snapshots from the GNSS receiver make the application of spectral correlation methods possible without extra software-defined radio payload, paving the way to spectrum identification and monitoring in airborne platforms, aiming at RF situational awareness. Live testing at Jammertest, in Norway, with portable, commercially available GNSS multi-band jammers demonstrates the ability to detect, localize, and characterize harmful interference. Our system pinpointed the position with an error of a few meters of the transmitter and the extent of the affected area at long range, without entering the denied zone. Additionally, further spectral content extraction is used to accurately identify the jammer frequency, bandwidth, and modulation scheme based on spectral correlation techniques.
Phase-Center-Constrained Beamforming for Minimizing Phase-Center Displacement
Authors: Jan Steckel, Noori BniLam
Date: 2025-03-26 00:00:00

Accurate knowledge and control of the phase center in antenna arrays is essential for high-precision applications such as Global Navigation Satellite Systems (GNSS), where even small displacements can introduce significant localization errors. Traditional beamforming techniques applied to array antennas often neglect the variation of the phase center, resulting in unwanted spatial shifts, and in consequence, localization errors. In this work, we propose a novel beamforming algorithm, called Phase-Center-Constrained Beamforming (PCCB), which explicitly minimizes the displacement of the phase center (Phase Center Offset, PCO) while preserving a chosen directional gain. We formulate the problem as a constrained optimization problem and incorporate regularization terms that enforce energy compactness and beampattern fidelity. The resulting PCCB approach allows for directional gain control and interference nulling while significantly reducing PCO displacement. Experimental validation using a simulated GNSS antenna array demonstrates that our PCCB approach achieves a fivefold reduction in PCO shift compared to the PCO shifts obtained when using conventional beamforming. A stability analysis across multiple random initializations confirms the robustness of our method and highlights the benefit of repeated optimization. These results indicate that our PCCB approach can serve as a practical and effective solution for decreasing phase center variability.
Derivation and analysis of power offset in fiber-longitudinal power profile estimation using pre-FEC hard-decision data
Authors: Du Tang, Yingjie Jiang, Ji Luo, Yu Chen, Bofang Zheng, Yaojun Qiao
Date: 2025-03-26 00:00:00

Utilizing the precise reference waveform regenerated by post-forward error correction (FEC) data, the fiber-longitudinal power profile estimation based on the minimum-mean-square-error method (MMSE-PPE) has been validated as an effective tool for absolute power monitoring. However, when post-FEC data is unavailable, it becomes necessary to rely on pre-FEC hard-decision data, which inevitably introduces hard-decision errors. These hard-decision errors will result in a power offset that undermines the accuracy of absolute power monitoring. In this paper, we present the first analytical expression for power offset in MMSE-PPE when using pre-FEC hard-decision data, achieved by introducing a virtual hard-decision nonlinear perturbation term. Based on this analytical expression, we also establish the first nonlinear relationship between the power offset and the symbol error rate (SER) of M-ary quadrature amplitude modulation (M-QAM) formats based on Gaussian assumptions. Verified in a numerical 130-GBaud single-wavelength coherent optical fiber transmission system, the correctness of the analytical expression of power offset has been confirmed with 4-QAM, 16-QAM, and 64-QAM formats under different SER situations. Furthermore, the nonlinear relationship between the power offset and SER of $M$-QAM formats has also been thoroughly validated under both linear scale (measured in mW) and logarithmic scale (measured in dB). These theoretical insights offer significant contributions to the design of potential power offset mitigation strategies in MMSE-PPE, thereby enhancing its real-time application.
Optimal One- and Two-Sided Multi-Level ASK for Noncoherent SIMO Systems Over Correlated Rician Fading
Authors: Badri Ramanjaneya Reddy, Soumya P. Dash, George C. Alexandropoulos
Date: 2025-03-26 00:00:00

This paper analyzes the performance of a single-input multiple-output (SIMO) wireless communication system employing one- and two-sided amplitude shift keying (ASK) modulation schemes for data transmission and operating under correlated Rician fading channels. The receiver deploys an optimal noncoherent maximum likelihood detector, which exploits statistical knowledge of the channel state information for signal decoding. An optimal receiver structure is derived, from which series-form and closed-form expressions for the union bound on the symbol error probability (SEP) are obtained for general and massive SIMO systems, respectively. Furthermore, an optimization framework to derive the optimal one- and two-sided ASK modulation schemes is proposed, which focuses on minimizing SEP performance under an average transmit energy constraint. The conducted numerical investigations for various system parameters demonstrate that the proposed noncoherent SIMO system with the designed optimal ASK modulation schemes achieves superior error performance compared to traditional equispaced ASK modulation. It is also shown that, when the proposed system employs traditional two-sided ASK modulation, superior error performance from the case of using one-sided ASK is obtained.
Impact of the Pilot Design for OFDM Based Bi-static Integrated Sensing and Communication System
Authors: Cuneyd Ozturk, Cagri Goken
Date: 2025-03-26 00:00:00
Tasks: Integrated sensing and communication, ISAC, Position

A bistatic milimeter-wave (mmWave) ISAC system utilizing OFDM signaling is considered. For a single-target scnenario, closed-form expressions for the Cramer-Rao bounds (CRBs) of range and velocity estimation are derived for a given pilot pattern. The analysis shows that when the target's range and velocity remain within the maximum unambiguous limits, allocating pilot symbols more frequently in time improves position estimation, while increasing their density in frequency enhances velocity estimation. Numerical results further validate that the least squares (LS) channel estimation approach closely follows CRB predictions, particularly in the high-SNR regime.