pwc-archive/papers-with-abstracts huggingface dataset:
A 2-bit Wideband 5G mm-Wave RIS with Low Side Lobe Levels and no Quantization Lobe
Authors: Ruiqi Wang, Yiming Yang, Atif Shamim
Date: 2025-02-22 00:00:00
Tasks: Quantization

Reconfigurable intelligent surface (RIS) with 1-bit phase resolution suffers from high side lobe levels (SLLs) in the near field and pronounced quantization lobe levels (QLLs) in the far field, which is detrimental for the quality of wireless communication. RIS design is further challenging in the mm-wave bands, where typically large bandwidths are required. To address these limitations, this work proposes a novel wideband 5G mm-Wave RIS with 2-bit phase quantization, capable of covering the entire 5G n258 band. The proposed unit cell design is a combination of a grounded main slot, a parasitic slot, and a coupling patch, utilizing only two PIN diodes. This design achieves a 2-bit bandwidth (BW) of 24.1--27.7 GHz (13.9\%) and an effective 1-bit BW of 20.0--28.9 GHz (36.4\%). The unit cell features a compact size of 0.39$\lambda$ $\times$ 0.39$\lambda$, providing decent angular stability of $\pm30^\circ$ as well as eliminating the grating lobes. Based on this unit cell design, a 20 $\times$ 20 RIS array has been designed, fabricated, and experimentally characterized. The measured results demonstrate that, within the 5G n258 band, the proposed 2-bit 5G mm-Wave RIS achieves an SLL of -15.4 dB in the near field, representing a 7.6 dB improvement compared to its 1-bit counterpart. Furthermore, it has almost negligible QLL (-14.6 dB) in the far field, providing a suppression of 13.3 dB relative to the 1-bit design. Thus, the proposed 2-bit mm-Wave RIS offers wideband performance, low SLL, negligible QLL, decent angular stability, and a broad beam scanning range of 50$^\circ$, making it a promising solution for high-resolution and low-interference mm-Wave wireless communication systems.
Importance-Aware Source-Channel Coding for Multi-Modal Task-Oriented Semantic Communication
Authors: Yi Ma, Chunmei Xu, Zhenyu Liu, Siqi Zhang, Rahim Tafazolli
Date: 2025-02-22 00:00:00
Tasks: Semantic Communication

This paper explores the concept of information importance in multi-modal task-oriented semantic communication systems, emphasizing the need for high accuracy and efficiency to fulfill task-specific objectives. At the transmitter, generative AI (GenAI) is employed to partition visual data objects into semantic segments, each representing distinct, task-relevant information. These segments are subsequently encoded into tokens, enabling precise and adaptive transmission control. Building on this frame work, we present importance-aware source and channel coding strategies that dynamically adjust to varying levels of significance at the segment, token, and bit levels. The proposed strategies prioritize high fidelity for essential information while permitting controlled distortion for less critical elements, optimizing overall resource utilization. Furthermore, we address the source-channel coding challenge in semantic multiuser systems, particularly in multicast scenarios, where segment importance varies among receivers. To tackle these challenges, we propose solutions such as rate-splitting coded progressive transmission, ensuring flexibility and robustness in task-specific semantic communication.
Using Graph Convolutional Networks to Address fMRI Small Data Problems
Authors: Thomas Screven, Andras Necz, Jason Smucny, Ian Davidson
Date: 2025-02-19 00:00:00
Tasks: Prognosis

Although great advances in the analysis of neuroimaging data have been made, a major challenge is a lack of training data. This is less problematic in tasks such as diagnosis, where much data exists, but particularly prevalent in harder problems such as predicting treatment responses (prognosis), where data is focused and hence limited. Here, we address the learning from small data problems for medical imaging using graph neural networks. This is particularly challenging as the information about the patients is themselves graphs (regions of interest connectivity graphs). We show how a spectral representation of the connectivity data allows for efficient propagation that can yield approximately 12\% improvement over traditional deep learning methods using the exact same data. We show that our method's superior performance is due to a data smoothing result that can be measured by closing the number of triangle inequalities and thereby satisfying transitivity.
Multimodal Sleep Stage and Sleep Apnea Classification Using Vision Transformer: A Multitask Explainable Learning Approach
Authors: Kianoosh Kazemi, Iman Azimi, Michelle Khine, Rami N. Khayat, Amir M. Rahmani, Pasi Liljeberg
Date: 2025-02-18 00:00:00
Tasks: Classification, Sleep Quality, Sleep Staging

Sleep is an essential component of human physiology, contributing significantly to overall health and quality of life. Accurate sleep staging and disorder detection are crucial for assessing sleep quality. Studies in the literature have proposed PSG-based approaches and machine-learning methods utilizing single-modality signals. However, existing methods often lack multimodal, multilabel frameworks and address sleep stages and disorders classification separately. In this paper, we propose a 1D-Vision Transformer for simultaneous classification of sleep stages and sleep disorders. Our method exploits the sleep disorders' correlation with specific sleep stage patterns and performs a simultaneous identification of a sleep stage and sleep disorder. The model is trained and tested using multimodal-multilabel sensory data (including photoplethysmogram, respiratory flow, and respiratory effort signals). The proposed method shows an overall accuracy (cohen's Kappa) of 78% (0.66) for five-stage sleep classification and 74% (0.58) for sleep apnea classification. Moreover, we analyzed the encoder attention weights to clarify our models' predictions and investigate the influence different features have on the models' outputs. The result shows that identified patterns, such as respiratory troughs and peaks, make a higher contribution to the final classification process.
Urinary Tract Infection Detection in Digital Remote Monitoring: Strategies for Managing Participant-Specific Prediction Complexity
Authors: Kexin Fan, Alexander Capstick, Ramin Nilforooshan, Payam Barnaghi
Date: 2025-02-18 00:00:00
Tasks: Clustering, Fairness

Urinary tract infections (UTIs) are a significant health concern, particularly for people living with dementia (PLWD), as they can lead to severe complications if not detected and treated early. This study builds on previous work that utilised machine learning (ML) to detect UTIs in PLWD by analysing in-home activity and physiological data collected through low-cost, passive sensors. The current research focuses on improving the performance of previous models, particularly by refining the Multilayer Perceptron (MLP), to better handle variations in home environments and improve sex fairness in predictions by making use of concepts from multitask learning. This study implemented three primary model designs: feature clustering, loss-dependent clustering, and participant ID embedding which were compared against a baseline MLP model. The results demonstrated that the loss-dependent MLP achieved the most significant improvements, increasing validation precision from 48.92% to 72.60% and sensitivity from 27.44% to 70.52%, while also enhancing model fairness across sexes. These findings suggest that the refined models offer a more reliable and equitable approach to early UTI detection in PLWD, addressing participant-specific data variations and enabling clinicians to detect and screen for UTI risks more effectively, thereby facilitating earlier and more accurate treatment decisions.
ConSense: Continually Sensing Human Activity with WiFi via Growing and Picking
Authors: Rong Li, Tao Deng, Siwei Feng, MingJie Sun, Juncheng Jia
Date: 2025-02-18 00:00:00
Tasks: Activity Recognition, class-incremental learning, Class Incremental Learning, Exemplar-Free, Human Activity Recognition, Incremental Learning

WiFi-based human activity recognition (HAR) holds significant application potential across various fields. To handle dynamic environments where new activities are continuously introduced, WiFi-based HAR systems must adapt by learning new concepts without forgetting previously learned ones. Furthermore, retaining knowledge from old activities by storing historical exemplar is impractical for WiFi-based HAR due to privacy concerns and limited storage capacity of edge devices. In this work, we propose ConSense, a lightweight and fast-adapted exemplar-free class incremental learning framework for WiFi-based HAR. The framework leverages the transformer architecture and involves dynamic model expansion and selective retraining to preserve previously learned knowledge while integrating new information. Specifically, during incremental sessions, small-scale trainable parameters that are trained specifically on the data of each task are added in the multi-head self-attention layer. In addition, a selective retraining strategy that dynamically adjusts the weights in multilayer perceptron based on the performance stability of neurons across tasks is used. Rather than training the entire model, the proposed strategies of dynamic model expansion and selective retraining reduce the overall computational load while balancing stability on previous tasks and plasticity on new tasks. Evaluation results on three public WiFi datasets demonstrate that ConSense not only outperforms several competitive approaches but also requires fewer parameters, highlighting its practical utility in class-incremental scenarios for HAR.
MVCNet: Multi-View Contrastive Network for Motor Imagery Classification
Authors: Ziwei Wang, Siyang Li, Xiaoqing Chen, Wei Li, Dongrui Wu
Date: 2025-02-18 00:00:00
Tasks: Brain Computer Interface, Contrastive Learning, Data Augmentation, EEG, Eeg Decoding, Informativeness, Motor Imagery

Electroencephalography (EEG)-based brain-computer interfaces (BCIs) enable neural interaction by decoding brain activity for external communication. Motor imagery (MI) decoding has received significant attention due to its intuitive mechanism. However, most existing models rely on single-stream architectures and overlook the multi-view nature of EEG signals, leading to limited performance and generalization. We propose a multi-view contrastive network (MVCNet), a dual-branch architecture that parallelly integrates CNN and Transformer models to capture both local spatial-temporal features and global temporal dependencies. To enhance the informativeness of training data, MVCNet incorporates a unified augmentation pipeline across time, frequency, and spatial domains. Two contrastive modules are further introduced: a cross-view contrastive module that enforces consistency of original and augmented views, and a cross-model contrastive module that aligns features extracted from both branches. Final representations are fused and jointly optimized by contrastive and classification losses. Experiments on five public MI datasets across three scenarios demonstrate that MVCNet consistently outperforms seven state-of-the-art MI decoding networks, highlighting its effectiveness and generalization ability. MVCNet provides a robust solution for MI decoding by integrating multi-view information and dual-branch modeling, contributing to the development of more reliable BCI systems.
Low-Interference Near-Field Multi-User Communication Enabled by Spatially Converging Multi-Mode Vortex Waves
Authors: Yufei Zhao, Qihao Lv, Yuanbin Chen, Afkar Mohamed Ismail, Yong Liang Guan, Chau Yuen
Date: 2025-02-18 00:00:00

This paper proposes a multi-user Spatial Division Multiplexing (SDM) near-field access scheme, inspired by the orthogonal characteristics of multi-mode vortex waves. A Reconfigurable Meta-surface (RM) is ingeniously employed as the gateway for information transmission. This RM not only receives spatially overlapping multiplexed multi-mode vortex beams but also converts them into focused point beams in the near field. Specifically, a multi-port microstrip array method is utilized to generate multiple orthogonal vortex electromagnetic wave modes. Different ports serve as feeding points for baseband signals, allowing independent modulated data to be flexibly loaded onto different modes. After being adjusted by the RM, the vortex electromagnetic waves are converted into energy-focusing point beams, which can be directed to arbitrary 3D positions in the RM's near-field region and received by different users. Since the spatial positions of the point beams are non-overlapping, this approach not only ensures energy concentration but also significantly reduces inter-user interference. Near-field scanning results in a microwave anechoic chamber validate the effectiveness of this method, while real-time communication demonstrations confirm the system's capability for low-interference information multiplexing and transmission in practical scenarios.
Frequency-Aware Masked Autoencoders for Human Activity Recognition using Accelerometers
Authors: Niels R. Lorenzen, Poul J. Jennum, Emmanuel Mignot, Andreas Brink-Kjaer
Date: 2025-02-17 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

Wearable accelerometers are widely used for continuous monitoring of physical activity. Supervised machine learning and deep learning algorithms have long been used to extract meaningful activity information from raw accelerometry data, but progress has been hampered by the limited amount of labeled data that is publicly available. Exploiting large unlabeled datasets using self-supervised pretraining is a relatively new and underexplored approach in the field of human activity recognition (HAR). We used a time-series transformer masked autoencoder (MAE) approach to self-supervised pretraining and propose two novel spectrogram-based loss functions: the log-scale meanmagnitude (LMM) and log-scale magnitude variance (LMV) losses. We compared these losses with the mean squared error (MSE) loss for MAE training. We leveraged the large unlabeled UK Biobank accelerometry dataset (n = 109k) for pretraining and evaluated downstream HAR performance using a linear classifier in a smaller labelled dataset. We found that pretraining with the LMM loss improved performance compared to an MAE pretrained with the MSE loss, with 12.7% increase in subject-wise F1 score when using linear probing. Compared with a state-of-the-art ResNet-based HAR model, our LMM-pretrained transformer models performed better (+9.8% F1) with linear probing and comparably when fine-tuned using an LSTM classifier. The addition of the LMV to the LMM loss decreased performance compared to the LMM loss alone. These findings establish the LMM loss as a robust and effective method for pretraining MAE models on accelerometer data for HAR and show the potential of pretraining sequence-based models for free-living HAR.
Fusion of ECG Foundation Model Embeddings to Improve Early Detection of Acute Coronary Syndromes
Authors: Zeyuan Meng, Lovely Yeswanth Panchumarthi, Saurabh Kataria, Alex Fedorov, Jessica Zègre-Hemsey, Xiao Hu, Ran Xiao
Date: 2025-02-17 00:00:00
Tasks: Contrastive Learning, Self-Supervised Learning

Acute Coronary Syndrome (ACS) is a life-threatening cardiovascular condition where early and accurate diagnosis is critical for effective treatment and improved patient outcomes. This study explores the use of ECG foundation models, specifically ST-MEM and ECG-FM, to enhance ACS risk assessment using prehospital ECG data collected in ambulances. Both models leverage self-supervised learning (SSL), with ST-MEM using a reconstruction-based approach and ECG-FM employing contrastive learning, capturing unique spatial and temporal ECG features. We evaluate the performance of these models individually and through a fusion approach, where their embeddings are combined for enhanced prediction. Results demonstrate that both foundation models outperform a baseline ResNet-50 model, with the fusion-based approach achieving the highest performance (AUROC: 0.843 +/- 0.006, AUCPR: 0.674 +/- 0.012). These findings highlight the potential of ECG foundation models for early ACS detection and motivate further exploration of advanced fusion strategies to maximize complementary feature utilization.
ECG-Expert-QA: A Benchmark for Evaluating Medical Large Language Models in Heart Disease Diagnosis
Authors: Xu Wang, Jiaju Kang, Puyu Han, Yubao Zhao, Qian Liu, Liwenfei He, Lingqiong Zhang, Lingyun Dai, Yongcheng Wang, Jie Tao
Date: 2025-02-16 00:00:00
Tasks: Diagnostic, Rhythm

We present ECG-Expert-QA, a comprehensive multimodal dataset for evaluating diagnostic capabilities in electrocardiogram (ECG) interpretation. It combines real-world clinical ECG data with systematically generated synthetic cases, covering 12 essential diagnostic tasks and totaling 47,211 expert-validated QA pairs. These encompass diverse clinical scenarios, from basic rhythm recognition to complex diagnoses involving rare conditions and temporal changes. A key innovation is the support for multi-turn dialogues, enabling the development of conversational medical AI systems that emulate clinician-patient or interprofessional interactions. This allows for more realistic assessment of AI models' clinical reasoning, diagnostic accuracy, and knowledge integration. Constructed through a knowledge-guided framework with strict quality control, ECG-Expert-QA ensures linguistic and clinical consistency, making it a high-quality resource for advancing AI-assisted ECG interpretation. It challenges models with tasks like identifying subtle ischemic changes and interpreting complex arrhythmias in context-rich scenarios. To promote research transparency and collaboration, the dataset, accompanying code, and prompts are publicly released at https://github.com/Zaozzz/ECG-Expert-QA
Estimating Time Delays between Signals under Mixed Noise Influence with Novel Cross- and Bispectral Methods
Authors: Tin Jurhar, Franziska Pellegrini, Ana I. Nuñes del Toro, Tilman Stephani, Guido Nolte, Stefan Haufe
Date: 2025-02-16 00:00:00

A common problem to signal processing are biases introduced by correlated noise. When quantifying time delays between two signals, mixed noise introduces a bias towards zero delay in conventional delay estimates based on the cross- or bispectrum. Here we propose two novel time delay estimators that address these shortcomings: (1) A cross-spectrum based approach that relies on estimating the periodicity of the phase spectrum rather than its slope, and (2) a bispectrum based approach, bispectral antisymmetrization, which removes contributions from not just Gaussian but all independent sources. In a simulation study, we compare conventional and novel TDE approaches and resolve differences in performance with respect to noise Gaussianity and auto-correlation structure. As a proof-of concept, we also perform TDE analysis on a neural stimulation dataset (n=3). We find that antisymmetrization consistently outperforms conventional bispectral methods at low signal-to-noise ratios (SNR) and prevents spurious zero-delay estimates in all mixed-noise environments. Time delay estimation based on phase periodicity also improves signal sensitivity compared to conventional cross-spectral methods. These observations are stable with respect to the magnitude of the delay and the statistical properties of the noise.
Model-Based Learning for DOA Estimation with One-Bit Single-Snapshot Sparse Arrays
Authors: Yunqiao Hu, Shunqiao Sun, Yimin D. Zhang
Date: 2025-02-15 00:00:00
Tasks: Binary Classification

We address the challenging problem of estimating the directions-of-arrival (DOAs) of multiple off-grid signals using a single snapshot of one-bit quantized measurements. Conventional DOA estimation methods face difficulties in tackling this problem effectively. This paper introduces a domain-knowledge-guided learning framework to achieve high-resolution DOA estimation in such a scenario, thus drastically reducing hardware complexity without compromising performance. We first reformulate DOA estimation as a maximum a posteriori (MAP) problem, unifying on-grid and off-grid scenarios under a Laplacian-type sparsity prior to effectively enforce sparsity for both uniform and sparse linear arrays. For off-grid signals, a first-order approximation grid model is embedded into the one-bit signal model. We then reinterpret one-bit sensing as a binary classification task, employing a multivariate Bernoulli likelihood with a logistic link function to enhance stability and estimation accuracy. To resolve the non-convexity inherent in the MAP formulation, we develop augmented algorithmic frameworks based on majorization-minimization principles. Further, we design model-based inference neural networks by deep unrolling these frameworks, significantly reducing computational complexity while preserving the estimation precision. Extensive simulations demonstrate the robustness of the proposed framework across a wide range of input signal-to-noise ratio values and off-grid deviations. By integrating the unified model-based priors with data-driven learning, this work bridges the gap between theoretical guarantees and practical feasibility in one-bit single-snapshot DOA estimation, offering a scalable, hardware-efficient solution for next-generation radar and communication systems.
In-sensor 24 classes HAR under 850 Bytes
Authors: Ahmed. S Benmessaoud, Wassim Kezai, Farida Medjani, Khalid Bouaita, Tahar Kezai
Date: 2025-02-13 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

The year 2023 was a key year for tinyML unleashing a new age of intelligent sensors pushing intelligence from the MCU into the source of the data at the sensor level, enabling them to perform sophisticated algorithms and machine learning models in real-time. This study presents an innovative approach to Human Activity Recognition (HAR) using Intelligent Sensor Processing Units (ISPUs), demonstrating the feasibility of deploying complex machine learning models directly on ultra-constrained sensor hardware. We developed a 24-class HAR model achieving 85\% accuracy while operating within an 850-byte stack memory limit. The model processes accelerometer and gyroscope data in real time, reducing latency, enhancing data privacy, and consuming only 0.5 mA of power. To address memory constraints, we employed incremental class injection and feature optimization techniques, enabling scalability without compromising performance. This work underscores the transformative potential of on-sensor processing for applications in healthcare, predictive maintenance, and smart environments, while introducing a publicly available, diverse HAR dataset for further research. Future efforts will explore advanced compression techniques and broader IoT integration to push the boundaries of TinyML on constrained devices.