pwc-archive/papers-with-abstracts huggingface dataset:
SynthFM: Training Modality-agnostic Foundation Models for Medical Image Segmentation without Real Medical Data
Authors: Sourya Sengupta, Satrajit Chakrabarty, Keerthi Sravan Ravi, Gopal Avinash, Ravi Soni
Date: 2025-04-11 00:00:00
Tasks: Decoder, Image Segmentation, Medical Image Segmentation, Semantic Segmentation, Synthetic Data Generation, Zero Shot Segmentation

Foundation models like the Segment Anything Model (SAM) excel in zero-shot segmentation for natural images but struggle with medical image segmentation due to differences in texture, contrast, and noise. Annotating medical images is costly and requires domain expertise, limiting large-scale annotated data availability. To address this, we propose SynthFM, a synthetic data generation framework that mimics the complexities of medical images, enabling foundation models to adapt without real medical data. Using SAM's pretrained encoder and training the decoder from scratch on SynthFM's dataset, we evaluated our method on 11 anatomical structures across 9 datasets (CT, MRI, and Ultrasound). SynthFM outperformed zero-shot baselines like SAM and MedSAM, achieving superior results under different prompt settings and on out-of-distribution datasets.
SlicerNNInteractive: A 3D Slicer extension for nnInteractive
Authors: Coen de Vente, Kiran Vaidhya Venkadesh, Bram van Ginneken, Clara I. Sánchez
Date: 2025-04-07 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

SlicerNNInteractive integrates nnInteractive, a state-of-the-art promptable deep learning-based framework for 3D image segmentation, into the widely used 3D Slicer platform. Our extension implements a client-server architecture that decouples computationally intensive model inference from the client-side interface. Therefore, SlicerNNInteractive eliminates heavy hardware constraints on the client-side and enables better operating system compatibility than existing plugins for nnInteractive. Running both the client and server-side on a single machine is also possible, offering flexibility across different deployment scenarios. The extension provides an intuitive user interface with all interaction types available in the original framework (point, bounding box, scribble, and lasso prompts), while including a comprehensive set of keyboard shortcuts for efficient workflow.
Channel Estimation and Hybrid Precoding for Massive MIMO-OTFS System With Doubly Squint
Authors: Mingming Duan, Pengfei Zhang, Shun Zhang, Yao Ge, Octavia A. Dobre, Chau Yuen
Date: 2025-04-11 00:00:00

Orthogonal time frequency space (OTFS) modulation and massive multi-input multi-output (MIMO) are promising technologies for next generation wireless communication systems for their abilities to counteract the issue of high mobility with large Doppler spread and mitigate the channel path attenuation, respectively. The natural integration of massive MIMO with OTFS in millimeter-wave systems can improve communication data rate and enhance the spectral efficiency. However, when transmitting wideband signals with large-scale arrays, the beam squint effect may occur, causing discrepancies in beam directions across subcarriers in multi-carrier systems. Moreover, the high-mobility wideband millimeter wave communications can induce the Doppler squint effect, leading to different Doppler shifts among the subcarriers. Both beam squint effect and Doppler squint effect (denoted as doubly squint effect) can degrade communication performance significantly. In this paper, we present an efficient channel estimation and hybrid precoding scheme to address the doubly squint effect in massive MIMO-OTFS systems. We first characterize the wideband channel model and the input-output relationship for massive MIMO-OTFS transmission considering doubly squint effect. We then mathematically derive the impact of channel parameters on chirp pilots under the doubly squint effect. Additionally, we develop a peak-index-based channel estimation scheme. By leveraging the results from channel estimation, we propose a hybrid precoding method to mitigate the doubly squint effect in downlink transmission scenarios. Finally, simulation results validate the effectiveness of our proposed scheme and show its superiority over the existing schemes.
Distributed Uplink Joint Transmission for 6G Communication
Authors: Kumar Sai Bondada, Usama Saeed, Yibin Liang, Daniel J. Jakubisin, Lingjia Liu, R. Michael Buehrer
Date: 2025-04-11 00:00:00

This paper investigates the spectral efficiency achieved through uplink joint transmission, where a serving user and the network users (UEs) collaborate by jointly transmitting to the base station (BS). The analysis incorporates the resource requirements for information sharing among UEs as a critical factor in the capacity evaluation. Furthermore, coherent and non-coherent joint transmission schemes are compared under various transmission power scenarios, providing insights into spectral and energy efficiency. A selection algorithm identifying the optimal UEs for joint transmission, achieving maximum capacity, is discussed. The results indicate that uplink joint transmission is one of the promising techniques for enabling 6G, achieving greater spectral efficiency even when accounting for the resource requirements for information sharing.
STF-GCN: A Multi-Domain Graph Convolution Network Method for Automatic Modulation Recognition via Adaptive Correlation
Authors: Mingyuan Shao, Zhengqiu Fu, Dingzhao Li, Fuqing Zhang, Yilin Cai, Shaohua Hong, Lin Cao, Yuan Peng, Jie Qi
Date: 2025-04-11 00:00:00
Tasks: Automatic Modulation Recognition

Automatic Modulation Recognition (AMR) is an essential part of Intelligent Transportation System (ITS) dynamic spectrum allocation. However, current deep learning-based AMR (DL-AMR) methods are challenged to extract discriminative and robust features at low signal-to-noise ratios (SNRs), where the representation of modulation symbols is highly interfered by noise. Furthermore, current research on GNN methods for AMR tasks generally suffers from issues related to graph structure construction and computational complexity. In this paper, we propose a Spatial-Temporal-Frequency Graph Convolution Network (STF-GCN) framework, with the temporal domain as the anchor point, to fuse spatial and frequency domain features embedded in the graph structure nodes. On this basis, an adaptive correlation-based adjacency matrix construction method is proposed, which significantly enhances the graph structure's capacity to aggregate local information into individual nodes. In addition, a PoolGAT layer is proposed to coarsen and compress the global key features of the graph, significantly reducing the computational complexity. The results of the experiments confirm that STF-GCN is able to achieve recognition performance far beyond the state-of-the-art DL-AMR algorithms, with overall accuracies of 64.35%, 66.04% and 70.95% on the RML2016.10a, RML2016.10b and RML22 datasets, respectively. Furthermore, the average recognition accuracies under low SNR conditions from -14dB to 0dB outperform the state-of-the-art (SOTA) models by 1.20%, 1.95% and 1.83%, respectively.
AI-Driven Smart Sportswear for Real-Time Fitness Monitoring Using Textile Strain Sensors
Authors: Chenyu Tang, Wentian Yi, Zibo Zhang, Edoardo Occhipinti, Luigi G. Occhipinti
Date: 2025-04-11 00:00:00

Wearable biosensors have revolutionized human performance monitoring by enabling real-time assessment of physiological and biomechanical parameters. However, existing solutions lack the ability to simultaneously capture breath-force coordination and muscle activation symmetry in a seamless and non-invasive manner, limiting their applicability in strength training and rehabilitation. This work presents a wearable smart sportswear system that integrates screen-printed graphene-based strain sensors with compact electronics for wireless data transfer and a deep learning framework for real-time classification of exercise execution quality. By leveraging 1D ResNet-18 for feature extraction, the system achieves 92.1% classification accuracy across six exercise conditions, distinguishing between breathing irregularities and asymmetric muscle exertion. Additionally, t-SNE analysis and Grad-CAM-based explainability visualization confirm that the network accurately captures biomechanically relevant features, ensuring robust interpretability. The proposed system establishes a foundation for next-generation AI-powered sportswear, with applications in fitness optimization, injury prevention, and adaptive rehabilitation training.
Artifact detection and localization in single-channel mobile EEG for sleep research using deep learning and attention mechanisms
Authors: Khrystyna Semkiv, Jia Zhang, Maria Laura Ferster, Walter Karlen
Date: 2025-04-11 00:00:00
Tasks: Artifact Detection, EEG, Electroencephalogram (EEG), Sensitivity, Specificity

Artifacts in the electroencephalogram (EEG) degrade signal quality and impact the analysis of brain activity. Current methods for detecting artifacts in sleep EEG rely on simple threshold-based algorithms that require manual intervention, which is time-consuming and impractical due to the vast volume of data that novel mobile recording systems generate. We propose a convolutional neural network (CNN) model incorporating a convolutional block attention module (CNN-CBAM) to detect and identify the location of artifacts in the sleep EEG with attention maps. We benchmarked this model against six other machine learning and signal processing approaches. We trained/tuned all models on 72 manually annotated EEG recordings obtained during home-based monitoring from 18 healthy participants with a mean (SD) age of 68.05 y ($\pm$5.02). We tested them on 26 separate recordings from 6 healthy participants with a mean (SD) age of 68.33 y ($\pm$4.08), with contained artifacts in 4\% of epochs. CNN-CBAM achieved the highest area under the receiver operating characteristic curve (0.88), sensitivity (0.81), and specificity (0.86) when compared to the other approaches. The attention maps from CNN-CBAM localized artifacts within the epoch with a sensitivity of 0.71 and specificity of 0.67. This work demonstrates the feasibility of automating the detection and localization of artifacts in wearable sleep EEG.
Successive Jump and Mode Decomposition
Authors: Mojtaba Nazari, Anders Rosendal Korshøj, Naveed Ur Rehman
Date: 2025-04-11 00:00:00
Tasks: Computational Efficiency

We propose fully data-driven variational methods, termed successive jump and mode decomposition (SJMD) and its multivariate extension, successive multivariate jump and mode decomposition (SMJMD), for successively decomposing nonstationary signals into amplitude- and frequency-modulated (AM-FM) oscillations and jump components. Unlike existing methods that treat oscillatory modes and jump discontinuities separately and often require prior knowledge of the number of components (K) -- which is difficult to obtain in practice -- our approaches employ successive optimization-based schemes that jointly handle AM-FM oscillations and jump discontinuities without the need to predefine K. Empirical evaluations on synthetic and real-world datasets demonstrate that the proposed algorithms offer superior accuracy and computational efficiency compared to state-of-the-art methods.
Poisson multi-Bernoulli mixture filter for trajectory measurements
Authors: Marco Fontana, Ángel F. García-Fernández, Simon Maskell
Date: 2025-04-11 00:00:00

This paper presents a Poisson multi-Bernoulli mixture (PMBM) filter for multi-target filtering based on sensor measurements that are sets of trajectories in the last two-time step window. The proposed filter, the trajectory measurement PMBM (TM-PMBM) filter, propagates a PMBM density on the set of target states. In prediction, the filter obtains the PMBM density on the set of trajectories over the last two time steps. This density is then updated with the set of trajectory measurements. After the update step, the PMBM posterior on the set of two-step trajectories is marginalised to obtain a PMBM density on the set of target states. The filter provides a closed-form solution for multi-target filtering based on sets of trajectory measurements, estimating the set of target states at the end of each time window. Additionally, the paper proposes computationally lighter alternatives to the TM-PMBM filter by deriving a Poisson multi-Bernoulli (PMB) density through Kullback-Leibler divergence minimisation in an augmented space with auxiliary variables. The performance of the proposed filters are evaluated in a simulation study.
An Empirical Investigation of Reconstruction-Based Models for Seizure Prediction from ECG Signals
Authors: Mohammad Reza Chopannavaz, Foad Ghaderi
Date: 2025-04-11 00:00:00
Tasks: EEG, Prediction, Seizure prediction, Specificity

Epileptic seizures are sudden neurological disorders characterized by abnormal, excessive neuronal activity in the brain, which is often associated with changes in cardiovascular activity. These disruptions can pose significant physical and psychological challenges for patients. Therefore, accurate seizure prediction can help mitigate these risks by enabling timely interventions, ultimately improving patients' quality of life. Traditionally, EEG signals have been the primary standard for seizure prediction due to their precision in capturing brain activity. However, their high cost, susceptibility to noise, and logistical constraints limit their practicality, restricting their use to clinical settings. In order to overcome these limitations, this study focuses on leveraging ECG signals as an alternative for seizure prediction. In this paper, we present a novel method for predicting seizures based on detecting anomalies in ECG signals during their reconstruction. By extracting time-frequency features and leveraging various advanced deep learning architectures, the proposed method identifies deviations in heart rate dynamics associated with seizure onset. The proposed approach was evaluated using the Siena database and could achieve specificity of 99.16\%, accuracy of 76.05\%, and false positive rate (FPR) of 0.01/h, with an average prediction time of 45 minutes before seizure onset. These results highlight the potential of ECG-based seizure prediction as a patient-friendly alternative to traditional EEG-based methods.
Flip-KLJN: Random Resistance Flipping for Noise-Driven Secure Communication
Authors: Recep Akif Tasci, Ibrahim Yildirim, Ertugrul Basar
Date: 2025-04-11 00:00:00

The information-theoretically (unconditionally) secure Kirchhoff-law-Johnson-noise (KLJN) bit exchange protocol uses two identical resistor pairs with high (H) and low (L) resistance values, driven by Gaussian noise generators emulating Johnson noise with a high common temperature. The resulting mean-square noise voltage on the wire connecting Alice and Bob has three levels: low (L/L), intermediate (H/L or L/H), and high (H/H), and secure key sharing is achieved at the intermediate level (L/H or H/L). This paper introduces the Flip-KLJN scheme, where a pre-agreed intermediate level, such as H/L, triggers a flip of the bit map value during the bit exchange period. For Eve, the bit map flips appear random. Thus, the formerly discarded H/H and L/L situations can also have a pre-agreed bit value mapping, which flips together with the original bit mapping. Thus, Flip-KLJN doubles the key rate and ensures that all three levels on the wire are indistinguishable for Eve. Bit error probabilities are addressed through analytic calculations and computer simulations.
Distributed Kalman Filter with Ultimately Accurate Fused Measurement Covariance
Authors: Tuo Yang, Jiachen Qian, Zhisheng Duan, Zhiyong Sun
Date: 2025-04-11 00:00:00
Tasks: State Estimation

This paper investigates the distributed Kalman filter (DKF) for linear systems, with specific attention on measurement fusion, which is a typical way of information sharing and is vital for enhancing stability and improving estimation accuracy. We show that it is the mismatch between the fused measurement and the fused covariance that leads to performance degradation or inconsistency in previous consensus-based DKF algorithms. To address this issue, we introduce two fully distributed approaches for calculating the exact covariance of the fused measurements, building upon which the modified DKF algorithms are proposed. Moreover, the performance analysis of the modified algorithms is also provided under rather mild conditions, including the steady-state value of the estimation error covariance. We also show that due to the guaranteed consistency in the modified DKF algorithms, the steady-state estimation accuracy is significantly improved compared to classical DKF algorithms. Numerical experiments are carried out to validate the theoretical analysis and show the advantages of the proposed methods.
General Theory of Coupled Characteristic Mode: An Eigen Subspace Approach
Authors: Rakesh Sinha, Sandip Ghosal
Date: 2025-04-11 00:00:00

In this work, the problem of characteristic mode analysis using eigendecomposition of the method of moments impedance matrix has been simplified using the eigen-subspace approach. The idea behind the eigen-subspace arises from the physical properties of antenna or scatterers, where only a few eigenmodes are enough to characterize the antenna or scatterer. Therefore, entire space eigenanalysis is a waste of computational resources, and eigen-subspace analysis with few modes is good enough to characterize antennas and scatterers. It has been assumed that there is an eigen-subspace (or hyperplane) of coupled characteristic mode, which coincides with the eigen-hyperplane of uncoupled characteristic mode. We can say the coupled characteristic modes are linear combinations of isolated modes based on this assumption. The linear combination is mapped via modal coupling matrix. Using the modal coupling matrix, we can explain the behavior of arbitrarily shaped antennas and scatterers. A computationally efficient method is developed to compute coupled characteristic modes of two mutually coupled scatterers or antennas using the eigen-subspace. The method is summarized as a theorem of two-body coupled characteristic mode. The theorem of two-body coupled characteristic mode has been extended to the N-body coupled characteristic mode. Two algorithms have been developed for the two-body multimode coupled characteristic mode and N-body multimode coupled characteristic mode. Two numerical examples are provided to validate the proposed concepts.
Examining Different Placement Strategies for Indoor Environmental Quality Sensors in Office Environments
Authors: Riccardo Talami, Xinhao Hu, Ilyas Dawoodjee, Ali Ghahramani
Date: 2025-04-11 00:00:00
Tasks: Position

Collecting Indoor Environmental Quality (IEQ) data from an occupant's immediate surroundings can provide personalized insights for healthy environmental conditions aligned with occupant preferences, but effective sensor placement for data accuracy and reliability has not been thoroughly explored. This paper explores various positioning of IEQ multi-sensing devices at individual workstations in typical office settings, aiming to identify sensor placements that most accurately reflect the environmental conditions experienced by occupants. We examined five unique positions close to an occupant (above and below the monitor, right side of the desk, ceiling, and chair backrest), two orientations, and three desk locations characterized by different lighting levels, thermal and airflow conditions. Data on temperature, humidity, carbon dioxide (CO2), particulate matters (PM1, PM2.5, PM10), illuminance, and sound were collected over a 2-week longitudinal experiment, followed by short-term experiments simulating common pollution events such as coughing and sneezing. Principal Component Analysis, Spearman's rank correlation, R2, and Mean Absolute Error were applied to identify the position and orientation that best captures the most information and matches breathing zone measurements. It was found that above the monitor position, facing the occupant, best captures the IEQ conditions experienced by the occupant.
Communication-Efficient Cooperative Localization: A Graph Neural Network Approach
Authors: Yinan Zou, Christopher G. Brinton, Vishrant Tripathi
Date: 2025-04-10 00:00:00
Tasks: Graph Neural Network

Cooperative localization leverages noisy inter-node distance measurements and exchanged wireless messages to estimate node positions in a wireless network. In communication-constrained environments, however, transmitting large messages becomes problematic. In this paper, we propose an approach for communication-efficient cooperative localization that addresses two main challenges. First, cooperative localization often needs to be performed over wireless networks with loopy graph topologies. Second is the need for designing an algorithm that has low localization error while simultaneously requiring a much lower communication overhead. Existing methods fall short of addressing these two challenges concurrently. To achieve this, we propose a vector quantized message passing neural network (VQ-MPNN) for cooperative localization. Through end-to-end neural network training, VQ-MPNN enables the co-design of node localization and message compression. Specifically, VQ-MPNN treats prior node positions and distance measurements as node and edge features, respectively, which are encoded as node and edge states using a graph neural network. To find an efficient representation for the node state, we construct a vector quantized codebook for all node states such that instead of sending long messages, each node only needs to transmit a codeword index. Numerical evaluations demonstrates that our proposed VQ-MPNN approach can deliver localization errors that are similar to existing approaches while reducing the overall communication overhead by an order of magnitude.
A Construction of Pairwise Co-prime Integer Matrices of Any Dimension and Their Least Common Right Multiple
Authors: Guangpu Guo, Xiang-Gen Xia
Date: 2025-04-10 00:00:00

Compared with co-prime integers, co-prime integer matrices are more challenging due to the non-commutativity. In this paper, we present a new family of pairwise co-prime integer matrices of any dimension and large size. These matrices are non-commutative and have low spread, i.e., their ratios of peak absolute values to mean absolute values (or the smallest non-zero absolute values) of their components are low. When matrix dimension is larger than $2$, this family of matrices differs from the existing families, such as circulant, Toeplitz matrices, or triangular matrices, and therefore, offers more varieties in applications. In this paper, we first prove the pairwise coprimality of the constructed matrices, then determine their determinant absolute values, and their least common right multiple (lcrm) with a closed and simple form. We also analyze their sampling rates when these matrices are used as sampling matrices for a multi-dimensional signal. The proposed family of pairwise co-prime integer matrices may have applications in multi-dimensional Chinese remainder theorem (MD-CRT) that can be used to determine integer vectors from their integer vector remainders modulo a set of integer matrix moduli, and also in multi-dimensional sparse sensing and multirate systems.