pwc-archive/papers-with-abstracts huggingface dataset:
Finger-to-Chest Style Transfer-assisted Deep Learning Method For Photoplethysmogram Waveform Restoration with Timing Preservation
Authors: Sara Maria Pagotto, Federico Tognoni, Matteo Rossi, Dario Bovio, Caterina Salito, Luca Mainardi, Pietro Cerveri
Date: 2025-03-11 00:00:00
Tasks: Generative Adversarial Network, Style Transfer

Wearable measurements, such as those obtained by photoplethysmogram (PPG) sensors are highly susceptible to motion artifacts and noise, affecting cardiovascular measures. Chest-acquired PPG signals are especially vulnerable, with signal degradation primarily resulting from lower perfusion, breathing-induced motion, and mechanical interference from chest movements. Traditional restoration methods often degrade the signal, and supervised deep learning (DL) struggles with random and systematic distortions, requiring very large datasets for successful training. To efficiently restore chest PPG waveform, we propose a style transfer-assisted cycle-consistent generative adversarial network, called starGAN, whose performance is evaluated on a three-channel PPG signal (red, green,and infrared) acquired by a chest-worn multi-modal sensor, called Soundi. Two identical devices are adopted, one sensor to collect the PPG signal on the chest, considered to feature low quality and undergoing restoration, and another sensor to obtain a high-quality PPG signal measured on the finger, considered the reference signal. Extensive validation over some 8,000 5-second chunks collected from 40 subjects showed about 90% correlation of the restored chest PPG with the reference finger PPG, with a 30% improvement over raw chest PPG. Likewise, the signal-to-noise ratio improved on average of about 125%, over the three channels. The agreement with heart-rate computed from concurrent ECG was extremely high, overcoming 84% on average. These results demonstrate effective signal restoration, comparable with findings in recent literature papers. Significance: PPG signals collected from wearable devices are highly susceptible to artifacts, making innovative AI-based techniques fundamental towards holistic health assessments in a single device.
TransECG: Leveraging Transformers for Explainable ECG Re-identification Risk Analysis
Authors: Ziyu Wang, Elahe Khatibi, Kianoosh Kazemi, Iman Azimi, Sanaz Mousavi, Shaista Malik, Amir M. Rahmani
Date: 2025-03-11 00:00:00
Tasks: Age Classification, Gender Classification

Electrocardiogram (ECG) signals are widely shared across multiple clinical applications for diagnosis, health monitoring, and biometric authentication. While valuable for healthcare, they also carry unique biometric identifiers that pose privacy risks, especially when ECG data shared across multiple entities. These risks are amplified in shared environments, where re-identification threats can compromise patient privacy. Existing deep learning re-identification models prioritize accuracy but lack explainability, making it challenging to understand how the unique biometric characteristics encoded within ECG signals are recognized and utilized for identification. Without these insights, despite high accuracy, developing secure and trustable ECG data-sharing frameworks remains difficult, especially in diverse, multi-source environments. In this work, we introduce TransECG, a Vision Transformer (ViT)-based method that uses attention mechanisms to pinpoint critical ECG segments associated with re-identification tasks like gender, age, and participant ID. Our approach demonstrates high accuracy (89.9% for gender, 89.9% for age, and 88.6% for ID re-identification) across four real-world datasets with 87 participants. Importantly, we provide key insights into ECG components such as the R-wave, QRS complex, and P-Q interval in re-identification. For example, in the gender classification, the R wave contributed 58.29% to the model's attention, while in the age classification, the P-R interval contributed 46.29%. By combining high predictive performance with enhanced explainability, TransECG provides a robust solution for privacy-conscious ECG data sharing, supporting the development of secure and trusted healthcare data environment.
Analysis of Learning-based Offshore Wind Power Prediction Models with Various Feature Combinations
Authors: Linhan Fang, Fan Jiang, Ann Mary Toms, Xingpeng Li
Date: 2025-03-10 00:00:00
Tasks: Prediction

Accurate wind speed prediction is crucial for designing and selecting sites for offshore wind farms. This paper investigates the effectiveness of various machine learning models in predicting offshore wind power for a site near the Gulf of Mexico by analyzing meteorological data. After collecting and preprocessing meteorological data, nine different input feature combinations were designed to assess their impact on wind power predictions at multiple heights. The results show that using wind speed as the output feature improves prediction accuracy by approximately 10% compared to using wind power as the output. In addition, the improvement of multi-feature input compared with single-feature input is not obvious mainly due to the poor correlation among key features and limited generalization ability of models. These findings underscore the importance of selecting appropriate output features and highlight considerations for using machine learning in wind power forecasting, offering insights that could guide future wind power prediction models and conversion techniques.
Event-Driven Implementation of a Physical Reservoir Computing Framework for superficial EMG-based Gesture Recognition
Authors: Yuqi Ding, Elisa Donati, Haobo Li, Hadi Heidari
Date: 2025-03-10 00:00:00
Tasks: Gesture Recognition, Low-latency processing

Wearable health devices have a strong demand in real-time biomedical signal processing. However traditional methods often require data transmission to centralized processing unit with substantial computational resources after collecting it from edge devices. Neuromorphic computing is an emerging field that seeks to design specialized hardware for computing systems inspired by the structure, function, and dynamics of the human brain, offering significant advantages in latency and power consumption. This paper explores a novel neuromorphic implementation approach for gesture recognition by extracting spatiotemporal spiking information from surface electromyography (sEMG) data in an event-driven manner. At the same time, the network was designed by implementing a simple-structured and hardware-friendly Physical Reservoir Computing (PRC) framework called Rotating Neuron Reservoir (RNR) within the domain of Spiking neural network (SNN). The spiking RNR (sRNR) is promising to pipeline an innovative solution to compact embedded wearable systems, enabling low-latency, real-time processing directly at the sensor level. The proposed system was validated by an open-access large-scale sEMG database and achieved an average classification accuracy of 74.6\% and 80.3\% using a classical machine learning classifier and a delta learning rule algorithm respectively. While the delta learning rule could be fully spiking and implementable on neuromorphic chips, the proposed gesture recognition system demonstrates the potential for near-sensor low-latency processing.
FLP-XR: Future Location Prediction on Extreme Scale Maritime Data in Real-time
Authors: George S. Theodoropoulos, Andreas Patakis, Andreas Tritsarolis, Yannis Theodoridis
Date: 2025-03-10 00:00:00
Tasks: Collision Avoidance, Management

Movements of maritime vessels are inherently complex and challenging to model due to the dynamic and often unpredictable nature of maritime operations. Even within structured maritime environments, such as shipping lanes and port approaches, where vessels adhere to navigational rules and predefined sea routes, uncovering underlying patterns is far from trivial. The necessity for accurate modeling of the mobility of maritime vessels arises from the numerous applications it serves, including risk assessment for collision avoidance, optimization of shipping routes, and efficient port management. This paper introduces FLP-XR, a model that leverages maritime mobility data to construct a robust framework that offers precise predictions while ensuring extremely fast training and inference capabilities. We demonstrate the efficiency of our approach through an extensive experimental study using three real-world AIS datasets. According to the experimental results, FLP-XR outperforms the current state-of-the-art in many cases, whereas it performs 2-3 orders of magnitude faster in terms of training and inference.
Cascade of one-class classifier ensemble and dynamic naive Bayes classifier applied to the myoelectric-based upper limb prosthesis control with contaminated channels detection
Authors: Pawel Trajdos, Marek Kurzynski
Date: 2025-03-10 00:00:00
Tasks: One-class classifier

Modern upper limb bioprostheses are typically controlled by sEMG signals using a pattern recognition scheme in the control process. Unfortunately, the sEMG signal is very susceptible to contamination that deteriorates the quality of the control system and reduces the usefulness of the prosthesis in the patient's everyday life. In the paper, the authors propose a new recognition system intended for sEMG-based control of the hand prosthesis with detection of contaminated sEMG signals. The originality of the proposed solution lies in the co-operation of two recognition systems working in a cascade structure: (1) an ensemble of one-class classifiers used to recognise contaminated signals and (2) a naive Bayes classifier (NBC) which recognises the patient's intentions using the information about contaminations produced by the ensemble. Although in the proposed approach, the NBC model is changed dynamically, due to the multiplicative form of the classification functions, training can be performed in a one-shot procedure. Experimental studies were conducted using real sEMG signals. The results obtained confirm the hypothesis that the use of the one-class classifier ensemble and the dynamic NBC model leads to improved classification quality.
Onboard Terrain Classification via Stacked Intelligent Metasurface-Diffractive Deep Neural Networks from SAR Level-0 Raw Data
Authors: Mengbing Liu, Xin Li, Jiancheng An, Chau Yuen
Date: 2025-03-10 00:00:00

This paper introduces a novel approach for real-time onboard terrain classification from Sentinel-1 (S1) level-0 raw In-phase/Quadrature (IQ) data, leveraging a Stacked Intelligent Metasurface (SIM) to perform inference directly in the analog wave domain. Unlike conventional digital deep neural networks, the proposed multi-layer Diffractive Deep Neural Network (D$^2$NN) setup implements automatic feature extraction as electromagnetic waves propagate through stacked metasurface layers. This design not only reduces reliance on expensive downlink bandwidth and high-power computing at terrestrial stations but also achieves performance levels around 90\% directly from the real raw IQ data, in terms of accuracy, precision, recall, and F1 Score. Our method therefore helps bridge the gap between next-generation remote sensing tasks and in-orbit processing needs, paving the way for computationally efficient remote sensing applications.
Machine learning for triage of strokes with large vessel occlusion using photoplethysmography biomarkers
Authors: Márton Á. Goda, Helen Badge, Jasmeen Khan, Yosef Solewicz, Moran Davoodi, Rumbidzai Teramayi, Dennis Cordato, Longting Lin, Lauren Christie, Christopher Blair, Gagan Sharma, Mark Parsons, Joachim A. Behar
Date: 2025-03-09 00:00:00
Tasks: Binary Classification, Photoplethysmography (PPG)

Objective. Large vessel occlusion (LVO) stroke presents a major challenge in clinical practice due to the potential for poor outcomes with delayed treatment. Treatment for LVO involves highly specialized care, in particular endovascular thrombectomy, and is available only at certain hospitals. Therefore, prehospital identification of LVO by emergency ambulance services, can be critical for triaging LVO stroke patients directly to a hospital with access to endovascular therapy. Clinical scores exist to help distinguish LVO from less severe strokes, but they are based on a series of examinations that can take minutes and may be impractical for patients with dementia or those who cannot follow commands due to their stroke. There is a need for a fast and reliable method to aid in the early identification of LVO. In this study, our objective was to assess the feasibility of using 30-second photoplethysmography (PPG) recording to assist in recognizing LVO stroke. Method. A total of 88 patients, including 25 with LVO, 27 with stroke mimic (SM), and 36 non-LVO stroke patients (NL), were recorded at the Liverpool Hospital emergency department in Sydney, Australia. Demographics (age, sex), as well as morphological features and beating rate variability measures, were extracted from the PPG. A binary classification approach was employed to differentiate between LVO stroke and NL+SM (NL.SM). A 2:1 train-test split was stratified and repeated randomly across 100 iterations. Results. The best model achieved a median test set area under the receiver operating characteristic curve (AUROC) of 0.77 (0.71--0.82). \textit{Conclusion.} Our study demonstrates the potential of utilizing a 30-second PPG recording for identifying LVO stroke.
Using Test-Time Data Augmentation for Cross-Domain Atrial Fibrillation Detection from ECG Signals
Authors: Majid Soleimani, Maedeh H. Toosi, Siamak Mohammadi, Babak Hossein Khalaj
Date: 2025-03-06 00:00:00
Tasks: Atrial Fibrillation Detection, Data Augmentation

Atrial fibrillation (AF) detection from electrocardiogram (ECG) signals is crucial for early diagnosis and management of cardiovascular diseases. However, deploying robust AF detection models across different datasets with significant domain variations remains a challenge. In this paper, we use test-time data augmentation (TTA) to address the cross-domain problem and enhance AF detection performance. We use a publicly available dataset for training - Physionet Computing in Cardiology Challenge 2017 -, while collecting a distinct test set, creating a cross-domain scenario. We employ a neural network architecture that integrates transformer-based encoding of ECG signals and convolutional layers for spectrogram feature extraction. The model combines the latent representations obtained from both encoders to classify the input signals. By incorporating TTA during inference, we enhance the model's performance, achieving an F1 score of 76.6\% on our test set. Furthermore, our experiments demonstrate that the model becomes more resilient to perturbations in the input signal, enhancing its robustness. We show that TTA can be effective in addressing the cross-domain problem, where training and test data originate from disparate sources. This work contributes to advancing the field of AF detection in real-world scenarios.
PiEEG kit -- bioscience Lab in home for your Brain and Body
Authors: Ildar Rakhmatulin
Date: 2025-03-05 00:00:00
Tasks: EEG

PiEEG kit is a multifunctional, compact, and mobile device that allows measure EEG, EMG, EOG, and EKG signals. The PiEEG Box incorporates the Raspberry Pi-based PiEEG shield, an EEG electrode cap, a display screen, additional sensors about body parameters and the environment, and other necessary peripherals, software, and an SDK course to learn signal processing into a single, portable unit. This integrated solution addresses the need for a compact, user-friendly, and accessible EEG measurement tool for researchers and hobbyists. The PiEEG Box builds upon the open-source foundation of the original PiEEG device, offering 8-channel EEG recording capabilities. By combining all required elements into one package, the PiEEG Box significantly reduces setup time and complexity, potentially broadening the application of EEG technology in various fields including neuroscience research, brain-computer interfaces, and educational settings.
WVEmbs with its Masking: A Method For Radar Signal Sorting
Authors: Xianan Hu, Fu Li, Kairui Niu, Peihan Qi, Zhiyong Liang
Date: 2025-03-05 00:00:00

Our study proposes a novel embedding method, Wide-Value-Embeddings (WVEmbs), for processing Pulse Descriptor Words (PDWs) as normalized inputs to neural networks. This method adapts to the distribution of interleaved radar signals, ranking original signal features from trivial to useful and stabilizing the learning process. To address the imbalance in radar signal interleaving, we introduce a value dimension masking method on WVEmbs, which automatically and efficiently generates challenging samples, and constructs interleaving scenarios, thereby compelling the model to learn robust features. Experimental results demonstrate that our method is an efficient end-to-end approach, achieving high-granularity, sample-level pulse sorting for high-density interleaved radar pulse sequences in complex and non-ideal environments.
Advancing Highway Work Zone Safety: A Comprehensive Review of Sensor Technologies for Intrusion and Proximity Hazards
Authors: Ayenew Yihune Demeke, Moein Younesi Heravi, Israt Sharmin Dola, Youjin Jang, Chau Le, Inbae Jeong, Zhibin Lin, Danling Wang
Date: 2025-03-05 00:00:00

Highway work zones are critical areas where accidents frequently occur, often due to the proximity of workers to heavy machinery and ongoing traffic. With technological advancements in sensor technologies and the Internet of Things, promising solutions are emerging to address these safety concerns. This paper provides a systematic review of existing studies on the application of sensor technologies in enhancing highway work zone safety, particularly in preventing intrusion and proximity hazards. Following the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) protocol, the review examines a broad spectrum of publications on various sensor technologies, including GPS, radar, laser, infrared, RFID, Bluetooth, ultrasonic, and infrared sensors, detailing their application in reducing intrusion and proximity incidents. The review also assesses these technologies in terms of their accuracy, range, power consumption, cost, and user-friendliness, with a specific emphasis on their suitability for highway work zones. The findings highlight the potential of sensor technologies to significantly enhance work zone safety. As there are a wide range of sensor technologies to choose from, the review also revealed that selection of sensors for a particular application needs careful consideration of different factors. Finally, while sensor technologies offer promising solutions for enhancing highway work zone safety, their effective implementation requires comprehensive consideration of various factors beyond technological capabilities, including developing integrated, cost-effective, user-friendly, and secure systems, and creating regulatory frameworks to support the rapid development of these technologies.
Radar Pulse Deinterleaving with Transformer Based Deep Metric Learning
Authors: Edward Gunn, Adam Hosford, Daniel Mannion, Jarrod Williams, Varun Chhabra, Victoria Nockles
Date: 2025-03-04 00:00:00
Tasks: Metric Learning, Triplet

When receiving radar pulses it is common for a recorded pulse train to contain pulses from many different emitters. The radar pulse deinterleaving problem is the task of separating out these pulses by the emitter from which they originated. Notably, the number of emitters in any particular recorded pulse train is considered unknown. In this paper, we define the problem and present metrics that can be used to measure model performance. We propose a metric learning approach to this problem using a transformer trained with the triplet loss on synthetic data. This model achieves strong results in comparison with other deep learning models with an adjusted mutual information score of 0.882.
Cross-Subject Depression Level Classification Using EEG Signals with a Sample Confidence Method
Authors: Zhongyi Zhang, Chenyang Xu, LiXuan Zhao, Huirang Hou, Qinghao Meng
Date: 2025-03-04 00:00:00
Tasks: Binary Classification, Depression Detection, EEG, Electroencephalogram (EEG)

Electroencephalogram (EEG) is a non-invasive tool for real-time neural monitoring,widely used in depression detection via deep learning. However, existing models primarily focus on binary classification (depression/normal), lacking granularity for severity assessment. To address this, we proposed the DepL-GCN, i.e., Depression Level classification based on GCN model. This model tackles two key challenges: (1) subjectivity in depres-sion-level labeling due to patient self-report biases, and (2) class imbalance across severity categories. Inspired by the model learning patterns, we introduced two novel modules: the sample confidence module and the minority sample penalty module. The former leverages the L2-norm of prediction errors to progressively filter EEG samples with weak label alignment during training, thereby reducing the impact of subjectivity; the latter automatically upweights misclassified minority-class samples to address imbalance issues. After testing on two public EEG datasets, DepL-GCN achieved accuracies of 81.13% and 81.36% for multi-class severity recognition, outperforming baseline models.Ablation studies confirmed both modules' contributions. We further discussed the strengths and limitations of regression-based models for depression-level recognition.
Robust Detection of Extremely Thin Lines Using 0.2mm Piano Wire
Authors: Jisoo Hong, Youngjin Jung, Jihwan Bae, Seungho Song, Sung-Woo Kang
Date: 2025-03-04 00:00:00
Tasks: Edge Detection, Line Detection, Position

This study developed an algorithm capable of detecting a reference line (a 0.2 mm thick piano wire) to accurately determine the position of an automated installation robot within an elevator shaft. A total of 3,245 images were collected from the experimental tower of H Company, the leading elevator manufacturer in South Korea, and the detection performance was evaluated using four experimental approaches (GCH, GSCH, GECH, FCH). During the initial image processing stage, Gaussian blurring, sharpening filter, embossing filter, and Fourier Transform were applied, followed by Canny Edge Detection and Hough Transform. Notably, the method was developed to accurately extract the reference line by averaging the x-coordinates of the lines detected through the Hough Transform. This approach enabled the detection of the 0.2 mm thick piano wire with high accuracy, even in the presence of noise and other interfering factors (e.g., concrete cracks inside the elevator shaft or safety bars for filming equipment). The experimental results showed that Experiment 4 (FCH), which utilized Fourier Transform in the preprocessing stage, achieved the highest detection rate for the LtoL, LtoR, and RtoL datasets. Experiment 2(GSCH), which applied Gaussian blurring and a sharpening filter, demonstrated superior detection performance on the RtoR dataset. This study proposes a reference line detection algorithm that enables precise position calculation and control of automated robots in elevator shaft installation. Moreover, the developed method shows potential for applicability even in confined working spaces. Future work aims to develop a line detection algorithm equipped with machine learning-based hyperparameter tuning capabilities.