pwc-archive/papers-with-abstracts huggingface dataset:
Antenna Health-Aware Selective Beamforming for Hardware-Constrained DFRC Systems II
Authors: Anis Hamadouche, Tharm Ratnarajah, Christos Masouros, John Thompson, Mathini Sellathurai
Date: 2024-12-23 00:00:00

This study introduces an innovative beamforming design approach that incorporates the reliability of antenna array elements into the optimization process, termed "antenna health-aware selective beamforming". This method strategically focuses transmission power on more reliable antenna elements, thus enhancing system resilience and operational integrity. By integrating antenna health information and individual power constraints, our research leverages advanced optimization techniques such as the Group Proximal-Gradient Dual Ascent (GPGDA) to efficiently address nonconvex challenges in sparse array selection. Applying the proposed technique to a Dual-Functional Radar-Communication (DFRC) system, our findings highlight that increasing the sparsity promotion weight ($\rho_s$) generally boosts spectral efficiency and communication data rate, achieving perfect system reliability at higher $\rho_s$ values but also revealing a performance threshold beyond which further sparsity is detrimental. This underscores the importance of balanced sparsity in beamforming for optimizing performance, particularly in critical communication and defense applications where uninterrupted operation is crucial. Additionally, our analysis of the time complexity and power consumption associated with GPGDA underscores the need for optimizing computational resources in practical implementations.
Antenna Health-Aware Selective Beamforming for Hardware-Constrained DFRC Systems I
Authors: Anis Hamadouche, Tharm Ratnarajah, Christos Masouros, John Thompson, Mathini Sellathurai
Date: 2024-12-23 00:00:00
Tasks: Fairness, Integrated sensing and communication, ISAC

This paper addresses the optimization challenges in dual-functional radar-communication (DFRC) systems with a focus on array selection and beamforming in dynamic and heterogeneous operational contexts. We propose a novel array selection criterion that integrates antenna health information into the optimization process, distinguishing our approach from traditional methods. Our methodology employs gradient dual ascent and dual proximal-gradient ascent for tackling the constrained non-convex and non-smooth nature of sparse array selection problems. A key feature of our strategy is the implementation of proportional fairness among communication users, which aligns with system resource limitations while meeting the minimum rate requirements for all users. This facet of our method not only enhances system efficiency and responsiveness but also ensures a fair distribution of resources. Through extensive simulations, the efficacy of the proposed solutions in optimizing DFRC system performance is validated, illustrating their applicability in integrated sensing and communication (ISAC) scenarios. Our findings contribute to the evolving field of DFRC systems, offering new perspectives and solutions for the challenges in array selection and beamforming optimisation.
Underwater Acoustic Reconfigurable Intelligent Surfaces: from Principle to Practice
Authors: Yu Luo, Lina Pu, Junming Diao, Chun-Hung Liu, Aijun Song
Date: 2024-12-21 00:00:00

This article explores the potential of underwater acoustic reconfigurable intelligent surfaces (UA-RIS) for facilitating long-range and eco-friendly communication in marine environments. Unlike radio frequency-based RIS (RF-RIS), which have been extensively investigated in terrestrial contexts, UA-RIS is an emerging field of study. The distinct characteristics of acoustic waves, including their slow propagation speed and potential for noise pollution affecting marine life, necessitate a fundamentally different approach to the architecture and design principles of UA-RIS compared to RF-RIS. Currently, there is a scarcity of real systems and experimental data to validate the feasibility of UA-RIS in practical applications. To fill this gap, this article presents field tests conducted with a prototype UA-RIS consisting of 24 acoustic elements. The results demonstrate that the developed prototype can effectively reflect acoustic waves to any specified directions through passive beamforming, thereby substantially extending the range and data rate of underwater communication systems.
EnhancePPG: Improving PPG-based Heart Rate Estimation with Self-Supervision and Augmentation
Authors: Luca Benfenati, Sofia Belloni, Alessio Burrello, Panagiotis Kasnesis, Xiaying Wang, Luca Benini, Massimo Poncino, Enrico Macii, Daniele Jahier Pagliari
Date: 2024-12-20 00:00:00
Tasks: Data Augmentation, Heart rate estimation, Photoplethysmography (PPG), Self-Supervised Learning

Heart rate (HR) estimation from photoplethysmography (PPG) signals is a key feature of modern wearable devices for health and wellness monitoring. While deep learning models show promise, their performance relies on the availability of large datasets. We present EnhancePPG, a method that enhances state-of-the-art models by integrating self-supervised learning with data augmentation (DA). Our approach combines self-supervised pre-training with DA, allowing the model to learn more generalizable features, without needing more labelled data. Inspired by a U-Net-like autoencoder architecture, we utilize unsupervised PPG signal reconstruction, taking advantage of large amounts of unlabeled data during the pre-training phase combined with data augmentation, to improve state-of-the-art models' performance. Thanks to our approach and minimal modification to the state-of-the-art model, we improve the best HR estimation by 12.2%, lowering from 4.03 Beats-Per-Minute (BPM) to 3.54 BPM the error on PPG-DaLiA. Importantly, our EnhancePPG approach focuses exclusively on the training of the selected deep learning model, without significantly increasing its inference latency
Parkinson Disease Detection Based on In-air Dynamics Feature Extraction and Selection Using Machine Learning
Authors: Jungpil Shin, Abu Saleh Musa Miah, Koki Hirooka, Md. Al Mehedi Hasan, Md. Maniruzzaman
Date: 2024-12-19 00:00:00

Parkinson's disease (PD) is a progressive neurological disorder that impairs movement control, leading to symptoms such as tremors, stiffness, and bradykinesia. Many researchers analyzing handwriting data for PD detection typically rely on computing statistical features over the entirety of the handwriting task. While this method can capture broad patterns, it has several limitations, including a lack of focus on dynamic change, oversimplified feature representation, lack of directional information, and missing micro-movements or subtle variations. Consequently, these systems face challenges in achieving good performance accuracy, robustness, and sensitivity. To overcome this problem, we proposed an optimized PD detection methodology that incorporates newly developed dynamic kinematic features and machine learning (ML)-based techniques to capture movement dynamics during handwriting tasks. In the procedure, we first extracted 65 newly developed kinematic features from the first and last 10% phases of the handwriting task rather than using the entire task. Alongside this, we also reused 23 existing kinematic features, resulting in a comprehensive new feature set. Next, we enhanced the kinematic features by applying statistical formulas to compute hierarchical features from the handwriting data. This approach allows us to capture subtle movement variations that distinguish PD patients from healthy controls. To further optimize the feature set, we applied the Sequential Forward Floating Selection method to select the most relevant features, reducing dimensionality and computational complexity. Finally, we employed an ML-based approach based on ensemble voting across top-performing tasks, achieving an impressive 96.99\% accuracy on task-wise classification and 99.98% accuracy on task ensembles, surpassing the existing state-of-the-art model by 2% for the PaHaW dataset.
Zero-Shot Generalization for Blockage Localization in mmWave Communication
Authors: Rafaela Scaciota, Malith Gallage, Sumudu Samarakoon, Mehdi Bennis
Date: 2024-12-18 00:00:00
Tasks: Self-Supervised Learning, Zero-shot Generalization

This paper introduces a novel method for predicting blockages in millimeter-wave (mmWave) communication systems towards enabling reliable connectivity. It employs a self-supervised learning approach to label radio frequency (RF) data with the locations of blockage-causing objects extracted from light detection and ranging (LiDAR) data, which is then used to train a deep learning model that predicts object`s location only using RF data. Then, the predicted location is utilized to predict blockages, enabling adaptability without retraining when transmitter-receiver positions change. Evaluations demonstrate up to 74% accuracy in predicting blockage locations in dynamic environments, showcasing the robustness of the proposed solution.
Canine EEG Helps Human: Cross-Species and Cross-Modality Epileptic Seizure Detection via Multi-Space Alignment
Authors: Z. Wang, S. Li, Dongrui Wu
Date: 2024-12-18 00:00:00
Tasks: Brain Computer Interface, Diagnostic, Domain Adaptation, EEG, Electroencephalogram (EEG), Knowledge Distillation, Seizure Detection

Epilepsy significantly impacts global health, affecting about 65 million people worldwide, along with various animal species. The diagnostic processes of epilepsy are often hindered by the transient and unpredictable nature of seizures. Here we propose a multi-space alignment approach based on cross-species and cross-modality electroencephalogram (EEG) data to enhance the detection capabilities and understanding of epileptic seizures. By employing deep learning techniques, including domain adaptation and knowledge distillation, our framework aligns cross-species and cross-modality EEG signals to enhance the detection capability beyond traditional within-species and with-modality models. Experiments on multiple surface and intracranial EEG datasets of humans and canines demonstrated substantial improvements in the detection accuracy, achieving over 90% AUC scores for cross-species and cross-modality seizure detection with extremely limited labeled data from the target species/modality. To our knowledge, this is the first study that demonstrates the effectiveness of integrating heterogeneous data from different species and modalities to improve EEG-based seizure detection performance. The approach may also be generalizable to different brain-computer interface paradigms, and suggests the possibility to combine data from different species/modalities to increase the amount of training data for large EEG models.
Constraint-Based Model in Multimodal Learning to Improve Ventricular Arrhythmia Prediction
Authors: Evariste Njomgue Fotso, Buntheng Ly, Hubert Cochet, Maxime Sermesant
Date: 2024-12-18 00:00:00
Tasks: Computed Tomography (CT), Diagnostic, Sensitivity

Cardiac disease evaluation depends on multiple diagnostic modalities: electrocardiogram (ECG) to diagnose abnormal heart rhythms, and imaging modalities such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and echocardiography to detect signs of structural abnormalities. Each of these modalities brings complementary information for a better diagnosis of cardiac dysfunction. However, training a machine learning (ML) model with data from multiple modalities is a challenging task, as it increases the dimension space, while keeping constant the number of samples. In fact, as the dimension of the input space increases, the volume of data required for accurate generalisation grows exponentially. In this work, we address this issue, for the application of Ventricular Arrhythmia (VA) prediction, based on the combined clinical and CT imaging features, where we constrained the learning process on medical images (CT) based on the prior knowledge acquired from clinical data. The VA classifier is fed with features extracted from a 3D myocardium thickness map (TM) of the left ventricle. The TM is generated by our pipeline from the imaging input and a Graph Convolutional Network is used as the feature extractor of the 3D TM. We introduce a novel Sequential Fusion method and evaluate its performance against traditional Early Fusion techniques and single-modality models. The crossvalidation results show that the Sequential Fusion model achieved the highest average scores of 80.7% $\pm$ 4.4 Sensitivity and 73.1% $\pm$ 6.0 F1 score, outperforming the Early Fusion model at 65.0% $\pm$ 8.9 Sensitivity and 63.1% $\pm$ 6.3 F1 score. Both fusion models achieved better scores than the single-modality models, where the average Sensitivity and F1 score are 62.8% $\pm$ 10.1; 52.1% $\pm$ 6.5 for the clinical data modality and 62.9% $\pm$ 6.3; 60.7% $\pm$ 5.3 for the medical images modality.
SCFNet:A Transferable IIIC EEG Classification Network
Authors: Weijin Xu
Date: 2024-12-16 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG)

Epilepsy and epileptiform discharges are common harmful brain activities, and electroencephalogram (EEG) signals are widely used to monitor the onset status of patients. However, due to the lack of unified EEG signal acquisition standards, there are many obstacles in practical applications, especially the difficulty in transferring and using models trained on different numbers of channels. To address this issue, we proposes a neural network architecture with a single-channel feature extraction (Singal Channel Feature) model backend fusion (SCFNet). The feature extractor of the model is an RCNN network with single-channel input, which does not depend on other channels, thereby enabling easier migration to data with different numbers of channels. Experimental results show that on the IIIC-Seizure dataset, the accuracy of EEG-SCFNet has improved by 4% compared to the baseline model and also increased by 1.3% compared to the original RCNN neural network model. Even with only fine-tuning the classification head, its performance can still maintain a level comparable to the baseline. In addition, in terms of cross-dataset transfer, EEG-SCFNet can still maintain certain performance even if the channel leads are different.
EEG-GMACN: Interpretable EEG Graph Mutual Attention Convolutional Network
Authors: Haili Ye, Stephan Goerttler, Fei He
Date: 2024-12-15 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG)

Electroencephalogram (EEG) is a valuable technique to record brain electrical activity through electrodes placed on the scalp. Analyzing EEG signals contributes to the understanding of neurological conditions and developing brain-computer interface. Graph Signal Processing (GSP) has emerged as a promising method for EEG spatial-temporal analysis, by further considering the topological relationships between electrodes. However, existing GSP studies lack interpretability of electrode importance and the credibility of prediction confidence. This work proposes an EEG Graph Mutual Attention Convolutional Network (EEG-GMACN), by introducing an 'Inverse Graph Weight Module' to output interpretable electrode graph weights, enhancing the clinical credibility and interpretability of EEG classification results. Additionally, we incorporate a mutual attention mechanism module into the model to improve its capability to distinguish critical electrodes and introduce credibility calibration to assess the uncertainty of prediction results. This study enhances the transparency and effectiveness of EEG analysis, paving the way for its widespread use in clinical and neuroscience research.
Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data
Authors: Christian Flores, Marcelo Contreras, Ichiro Macedo, Javier Andreu-Perez
Date: 2024-12-14 00:00:00
Tasks: Brain Computer Interface, Transfer Learning

Machine learning and deep learning advancements have boosted Brain-Computer Interface (BCI) performance, but their wide-scale applicability is limited due to factors like individual health, hardware variations, and cultural differences affecting neural data. Studies often focus on uniform single-site experiments in uniform settings, leading to high performance that may not translate well to real-world diversity. Deep learning models aim to enhance BCI classification accuracy, and transfer learning has been suggested to adapt models to individual neural patterns using a base model trained on others' data. This approach promises better generalizability and reduced overfitting, yet challenges remain in handling diverse and imbalanced datasets from different equipment, subjects, multiple centres in different countries, and both healthy and patient populations for effective model transfer and tuning. In a setting characterized by maximal heterogeneity, we proposed P300 wave detection in BCIs employing a convolutional neural network fitted with adaptive transfer learning based on Poison Sampling Disk (PDS) called Active Sampling (AS), which flexibly adjusts the transition from source data to the target domain. Our results reported for subject adaptive with 40% of adaptive fine-tuning that the averaged classification accuracy improved by 5.36% and standard deviation reduced by 12.22% using two distinct, internationally replicated datasets. These results outperformed in classification accuracy, computational time, and training efficiency, mainly due to the proposed Active Sampling (AS) method for transfer learning.
MANGO: Multimodal Acuity traNsformer for intelliGent ICU Outcomes
Authors: Jiaqing Zhang, Miguel Contreras, Sabyasachi Bandyopadhyay, Andrea Davidson, Jessica Sena, Yuanfang Ren, Ziyuan Guan, Tezcan Ozrazgat-Baslanti, Tyler J. Loftus, Subhash Nerella, Azra Bihorac, Parisa Rashidi
Date: 2024-12-13 00:00:00

Estimation of patient acuity in the Intensive Care Unit (ICU) is vital to ensure timely and appropriate interventions. Advances in artificial intelligence (AI) technologies have significantly improved the accuracy of acuity predictions. However, prior studies using machine learning for acuity prediction have predominantly relied on electronic health records (EHR) data, often overlooking other critical aspects of ICU stay, such as patient mobility, environmental factors, and facial cues indicating pain or agitation. To address this gap, we present MANGO: the Multimodal Acuity traNsformer for intelliGent ICU Outcomes, designed to enhance the prediction of patient acuity states, transitions, and the need for life-sustaining therapy. We collected a multimodal dataset ICU-Multimodal, incorporating four key modalities, EHR data, wearable sensor data, video of patient's facial cues, and ambient sensor data, which we utilized to train MANGO. The MANGO model employs a multimodal feature fusion network powered by Transformer masked self-attention method, enabling it to capture and learn complex interactions across these diverse data modalities even when some modalities are absent. Our results demonstrated that integrating multiple modalities significantly improved the model's ability to predict acuity status, transitions, and the need for life-sustaining therapy. The best-performing models achieved an area under the receiver operating characteristic curve (AUROC) of 0.76 (95% CI: 0.72-0.79) for predicting transitions in acuity status and the need for life-sustaining therapy, while 0.82 (95% CI: 0.69-0.89) for acuity status prediction...
High-resolution urban air pollution and thermal comfort mapping: an application of drive mobile sensing platform for smart city services
Authors: Hui Zhong, Hongliang Lu, Ting Gan, Yonghong Liu, Xinhu Zheng
Date: 2024-12-13 00:00:00

Air pollutant exposure exhibits significant spatial and temporal variability, with localized hotspots, particularly in traffic microenvironments, posing health risks to commuters. Although widely used for air quality assessment, fixed-site monitoring stations are limited by sparse distribution, high costs, and maintenance needs, making them less effective in capturing on-road pollution levels. This study utilizes a fleet of 314 taxis equipped with sensors to measure NO\textsubscript{2}, PM\textsubscript{2.5}, and PM\textsubscript{10} concentrations and identify high-exposure hotspots. The findings reveal disparities between mobile and stationary measurements, map the spatiotemporal exposure patterns, and highlight local hotspots. These results demonstrate the potential of mobile monitoring to provide fine-scale, on-road air pollution assessments, offering valuable insights for policymakers to design targeted interventions and protect public health, particularly for sensitive populations.
A Beginner's Guide to Power and Energy Measurement and Estimation for Computing and Machine Learning
Authors: Akshaya Jagannadharao, Nicole Beckage, Sovan Biswas, Hilary Egan, Jamil Gafur, Thijs Metsch, Dawn Nafus, Giuseppe Raffa, Charles Tripp
Date: 2024-12-11 00:00:00

Concerns about the environmental footprint of machine learning are increasing. While studies of energy use and emissions of ML models are a growing subfield, most ML researchers and developers still do not incorporate energy measurement as part of their work practices. While measuring energy is a crucial step towards reducing carbon footprint, it is also not straightforward. This paper introduces the main considerations necessary for making sound use of energy measurement tools and interpreting energy estimates, including the use of at-the-wall versus on-device measurements, sampling strategies and best practices, common sources of error, and proxy measures. It also contains practical tips and real-world scenarios that illustrate how these considerations come into play. It concludes with a call to action for improving the state of the art of measurement methods and standards for facilitating robust comparisons between diverse hardware and software environments.
Decoding individual words from non-invasive brain recordings across 723 participants
Authors: Stéphane d'Ascoli, Corentin Bel, Jérémy Rapin, Hubert Banville, Yohann Benchetrit, Christophe Pallier, Jean-Rémi King
Date: 2024-12-11 00:00:00
Tasks: EEG

Deep learning has recently enabled the decoding of language from the neural activity of a few participants with electrodes implanted inside their brain. However, reliably decoding words from non-invasive recordings remains an open challenge. To tackle this issue, we introduce a novel deep learning pipeline to decode individual words from non-invasive electro- (EEG) and magneto-encephalography (MEG) signals. We train and evaluate our approach on an unprecedentedly large number of participants (723) exposed to five million words either written or spoken in English, French or Dutch. Our model outperforms existing methods consistently across participants, devices, languages, and tasks, and can decode words absent from the training set. Our analyses highlight the importance of the recording device and experimental protocol: MEG and reading are easier to decode than EEG and listening, respectively, and it is preferable to collect a large amount of data per participant than to repeat stimuli across a large number of participants. Furthermore, decoding performance consistently increases with the amount of (i) data used for training and (ii) data used for averaging during testing. Finally, single-word predictions show that our model effectively relies on word semantics but also captures syntactic and surface properties such as part-of-speech, word length and even individual letters, especially in the reading condition. Overall, our findings delineate the path and remaining challenges towards building non-invasive brain decoders for natural language.