pwc-archive/papers-with-abstracts huggingface dataset:
Integrative Deep Learning Framework for Parkinson's Disease Early Detection using Gait Cycle Data Measured by Wearable Sensors: A CNN-GRU-GNN Approach
Authors: Alireza Rashnu, Armin Salimi-Badr
Date: 2024-04-09 00:00:00
Tasks: Binary Classification, Graph Neural Network

Efficient early diagnosis is paramount in addressing the complexities of Parkinson's disease because timely intervention can substantially mitigate symptom progression and improve patient outcomes. In this paper, we present a pioneering deep learning architecture tailored for the binary classification of subjects, utilizing gait cycle datasets to facilitate early detection of Parkinson's disease. Our model harnesses the power of 1D-Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Graph Neural Network (GNN) layers, synergistically capturing temporal dynamics and spatial relationships within the data. In this work, 16 wearable sensors located at the end of subjects' shoes for measuring the vertical Ground Reaction Force (vGRF) are considered as the vertices of a graph, their adjacencies are modelled as edges of this graph, and finally, the measured data of each sensor is considered as the feature vector of its corresponding vertex. Therefore, The GNN layers can extract the relations among these sensors by learning proper representations. Regarding the dynamic nature of these measurements, GRU and CNN are used to analyze them spatially and temporally and map them to an embedding space. Remarkably, our proposed model achieves exceptional performance metrics, boasting accuracy, precision, recall, and F1 score values of 99.51%, 99.57%, 99.71%, and 99.64%, respectively.
Performance Enhancement via Real-time Image-based Beam Tracking for WA-OWC with Dynamic Waves and Mobile Receivers
Authors: Yujie Di, Anzi Xu, Lian-Kuan Chen
Date: 2024-04-09 00:00:00

Intensified underwater activities have driven the escalating demand for reliable, flexible, and high data-rate underwater communication links. Optical wireless communication (OWC) emerges as the most promising technology for short- to medium-range communication, facilitating the real-time high-speed transmission of information from undersea to an aerial vehicle which can subsequently relay the information to a terrestrial station. However, establishing a robust water-air link confronts two primary challenges: (i) beam wandering due to the time-varying refraction when the light beam passes through the undulating ocean surface and (ii) the drone's movement when it hovers above the ocean surface. This paper experimentally demonstrated a real-time imaged-based beam tracking system to mitigate beam misalignment due to dynamic waves and receiver movement over a 0.14-m underwater and 1.83-m free-space OWC channel. Experimental results evince a notable reduction in the standard deviation of the received light spot offset from the receiver. Moreover, the tracking system can proficiently accommodate receiver velocities of up to 150 cm/s while maintaining a paltry packet loss rate (PLR) below 10%. By addressing the combined effects of dynamic waves and moving receivers, the proposed beam tracking system successfully enables a 70% reduction in PLR and an order of magnitude decrease in bit error rate (BER). This results in a substantial 17-fold surge in maximum throughput, from 50 Mbps to 850 Mbps. The experimental results validate the feasibility and effectiveness of the beam tracking system for vanquishing the detrimental effects in the complex water-air OWC (WA-OWC) channel and supporting high-speed data transmission.
EB-GAME: A Game-Changer in ECG Heartbeat Anomaly Detection
Authors: Juneyoung Park, Da Young Kim, Yunsoo Kim, Jisu Yoo, Tae Joon Kim
Date: 2024-04-08 00:00:00
Tasks: Anomaly Detection

Cardiologists use electrocardiograms (ECG) for the detection of arrhythmias. However, continuous monitoring of ECG signals to detect cardiac abnormal-ities requires significant time and human resources. As a result, several deep learning studies have been conducted in advance for the automatic detection of arrhythmia. These models show relatively high performance in supervised learning, but are not applicable in cases with few training examples. This is because abnormal ECG data is scarce compared to normal data in most real-world clinical settings. Therefore, in this study, GAN-based anomaly detec-tion, i.e., unsupervised learning, was employed to address the issue of data imbalance. This paper focuses on detecting abnormal signals in electrocardi-ograms (ECGs) using only labels from normal signals as training data. In-spired by self-supervised vision transformers, which learn by dividing images into patches, and masked auto-encoders, known for their effectiveness in patch reconstruction and solving information redundancy, we introduce the ECG Heartbeat Anomaly Detection model, EB-GAME. EB-GAME was trained and validated on the MIT-BIH Arrhythmia Dataset, where it achieved state-of-the-art performance on this benchmark.
Clinical translation of machine learning algorithms for seizure detection in scalp electroencephalography: systematic review
Authors: Nina Moutonnet, Steven White, Benjamin P Campbell, Saeid Sanei, Toshihisa Tanaka, Hong Ji, Danilo Mandic, Gregory Scott
Date: 2024-04-08 00:00:00
Tasks: Diagnostic, EEG, Electroencephalogram (EEG), Seizure Detection, Translation

Machine learning algorithms for seizure detection have shown considerable diagnostic potential, with recent reported accuracies reaching 100%. Yet, only few published algorithms have fully addressed the requirements for successful clinical translation. This is, for example, because the properties of training data may limit the generalisability of algorithms, algorithm performance may vary depending on which electroencephalogram (EEG) acquisition hardware was used, or run-time processing costs may be prohibitive to real-time clinical use cases. To address these issues in a critical manner, we systematically review machine learning algorithms for seizure detection with a focus on clinical translatability, assessed by criteria including generalisability, run-time costs, explainability, and clinically-relevant performance metrics. For non-specialists, the domain-specific knowledge necessary to contextualise model development and evaluation is provided. It is our hope that such critical evaluation of machine learning algorithms with respect to their potential real-world effectiveness can help accelerate clinical translation and identify gaps in the current seizure detection literature.
Anchor Pair Selection in TDOA Positioning Systems by Door Transition Error Minimization
Authors: Marcin Kolakowski, Jozef Modelski
Date: 2024-04-07 00:00:00

This paper presents an adaptive anchor pairs selection algorithm for UWB (ultra-wideband) TDOA-based (Time Difference of Arrival) indoor positioning systems. The method assumes dividing the system operation area into zones. The most favorable anchor pairs are selected by minimizing the positioning errors in doorways leading to these zones where possible users' locations are limited to small, narrow areas. The sets are determined separately for going in and out of the zone to take users' body shadowing into account. The determined anchor pairs are then used to calculate TDOA values and localize the user moving around the apartment with an Extended Kalman Filter based algorithm. The method was tested experimentally in a furnished apartment. The results have shown that the adaptive selection of the anchor pairs leads to an increase in the user's localization accuracy. The median trajectory error was about 0.32 m.
Greedy Capon Beamformer
Authors: Esa Ollila
Date: 2024-04-07 00:00:00

We propose greedy Capon beamformer (GCB) for direction finding of narrow-band sources present in the array's viewing field. After defining the grid covering the location search space, the algorithm greedily builds the interference-plus-noise covariance matrix by identifying a high-power source on the grid using Capon's principle of maximizing the signal to interference plus noise ratio while enforcing unit gain towards the signal of interest. An estimate of the power of the detected source is derived by exploiting the unit power constraint, which subsequently allows to update the noise covariance matrix by simple rank-1 matrix addition composed of outerproduct of the selected steering matrix with itself scaled by the signal power estimate. Our numerical examples demonstrate effectiveness of the proposed GCB in direction finding where it performs favourably compared to the state-of-the-art algorithms under a broad variety of settings. Furthermore, GCB estimates of direction-of-arrivals (DOAs) are very fast to compute.
A Low-Complexity Design for IRS-Assisted Secure Dual-Function Radar-Communication System
Authors: Yi-Kai Li, Athina Petropulu
Date: 2024-04-06 00:00:00

In dual-function radar-communication (DFRC) systems the probing signal contains information intended for the communication users, which makes that information vulnerable to eavesdropping by the targets. We study the security of a DFRC system aided by an intelligent reflecting surface (IRS) from the physical layer security (PLS) perspective. The IRS helps overcome path loss or blockage and introduces more degrees of freedom for system design, however, it also makes the design problem more challenging. In the system considered, the radar embeds artificial noise (AN) in the probing waveform, and the radar waveform, the AN noise and the IRS parameters are designed to optimize the communication secrecy rate while meeting radar signal-to-noise ratio (SNR) constraints. The contribution of the paper is a novel, low complexity approach to solve the underlying optimization problem and obtain the design parameters. In particular, we consider an alternating optimization approach, where in each iteration, the problem is decomposed into two sub-problems, namely, one that designs the IRS parameters, and another that jointly designs the radar waveform and the AN. The challenges in those sub-problems are the fractional objective, the SNR being a quartic function of the IRS parameters, and the unit-modulus constraint on the IRS parameters. A fractional programming technique is used to transform the fractional form objective into a more tractable non-fractional polynomial form. A closed-form based approach is proposed for the IRS design problem, which results in low complexity IRS design. Numerical results are provided to demonstrate the convergence properties of the proposed system design method, the secrecy rate and beamforming performance of the designed system.
5G-Advanced AI/ML Beam Management: Performance Evaluation with Integrated ML Models
Authors: Nalin Jayaweera, Andrea Bonfante, Mark Schamberger, Amir Mehdi Ahmadian Tehrani, Tachporn Sanguanpuak, Preetish Tilak, Keeth Jayasinghe, Frederick W. Vook, Nandana Rajatheva
Date: 2024-04-06 00:00:00
Tasks: Beam Prediction, Management, Prediction

The legacy beam management (BM) procedure in 5G introduces higher measurement and reporting overheads for larger beam codebooks resulting in higher power consumption of user equipment (UEs). Hence, the 3rd generation partnership project (3GPP) studied the use of artificial intelligence (AI) and machine learning (ML) in the air interface to reduce the overhead associated with the legacy BM procedure. The usage of AI/ML in BM is mainly discussed with regard to spatial-domain beam prediction (SBP) and time-domain beam prediction (TBP). In this study, we discuss different sub-use cases of SBP and TBP and evaluate the beam prediction accuracy of AI/ML models designed for each sub-use case along with AI/ML model generalization aspects. Moreover, a comprehensive system-level performance evaluation is presented in terms of user throughput with integrated AI/ML models to a 3GPP-compliant system-level simulator. Based on user throughput evaluations, we present AI/ML BM design guidelines for the deployment of lightweight, low-complexity AI/ML models discussed in this study.
Advanced simulation-based predictive modelling for solar irradiance sensor farms
Authors: José L. Risco-Martín, Ignacio-Iker Prado-Rujas, Javier Campoy, María S. Pérez, Katzalin Olcoz
Date: 2024-04-05 00:00:00
Tasks: Management

As solar power continues to grow and replace traditional energy sources, the need for reliable forecasting models becomes increasingly important to ensure the stability and efficiency of the grid. However, the management of these models still needs to be improved, and new tools and technologies are required to handle the deployment and control of solar facilities. This work introduces a novel framework named Cloud-based Analysis and Integration for Data Efficiency (CAIDE), designed for real-time monitoring, management, and forecasting of solar irradiance sensor farms. CAIDE is designed to manage multiple sensor farms simultaneously while improving predictive models in real-time using well-grounded Modeling and Simulation (M&S) methodologies. The framework leverages Model Based Systems Engineering (MBSE) and an Internet of Things (IoT) infrastructure to support the deployment and analysis of solar plants in dynamic environments. The system can adapt and re-train the model when given incorrect results, ensuring that forecasts remain accurate and up-to-date. Furthermore, CAIDE can be executed in sequential, parallel, and distributed architectures, assuring scalability. The effectiveness of CAIDE is demonstrated in a complex scenario composed of several solar irradiance sensor farms connected to a centralized management system. Our results show that CAIDE is scalable and effective in managing and forecasting solar power production while improving the accuracy of predictive models in real time. The framework has important implications for the deployment of solar plants and the future of renewable energy sources.
Transportation mode recognition based on low-rate acceleration and location signals with an attention-based multiple-instance learning network
Authors: Christos Siargkas, Vasileios Papapanagiotou, Anastasios Delopoulos
Date: 2024-04-05 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Multiple Instance Learning

Transportation mode recognition (TMR) is a critical component of human activity recognition (HAR) that focuses on understanding and identifying how people move within transportation systems. It is commonly based on leveraging inertial, location, or both types of signals, captured by modern smartphone devices. Each type has benefits (such as increased effectiveness) and drawbacks (such as increased battery consumption) depending on the transportation mode (TM). Combining the two types is challenging as they exhibit significant differences such as very different sampling rates. This paper focuses on the TMR task and proposes an approach for combining the two types of signals in an effective and robust classifier. Our network includes two sub-networks for processing acceleration and location signals separately, using different window sizes for each signal. The two sub-networks are designed to also embed the two types of signals into the same space so that we can then apply an attention-based multiple-instance learning classifier to recognize TM. We use very low sampling rates for both signal types to reduce battery consumption. We evaluate the proposed methodology on a publicly available dataset and compare against other well known algorithms.
Detection of direct path component absence in NLOS UWB channel
Authors: Marcin Kolakowski, Jozef Modelski
Date: 2024-04-02 00:00:00

In this paper a novel NLOS (Non-Line-of-Sight) identification technique is proposed. In comparison to other methods described in the literature, it discerns a situation when the delayed direct path component is available from when it's totally blocked and introduced biases are much higher and harder to mitigate. In the method, NLOS identification is performed using Support Vector Machine (SVM) algorithm based on various signal features. The paper includes description of the method and the results of performed experiment.
Realtime Person Identification via Gait Analysis
Authors: Shanmuga Venkatachalam, Harideep Nair, Prabhu Vellaisamy, Yongqi Zhou, Ziad Youssfi, John Paul Shen
Date: 2024-04-02 00:00:00

Each person has a unique gait, i.e., walking style, that can be used as a biometric for personal identification. Recent works have demonstrated effective gait recognition using deep neural networks, however most of these works predominantly focus on classification accuracy rather than model efficiency. In order to perform gait recognition using wearable devices on the edge, it is imperative to develop highly efficient low-power models that can be deployed on to small form-factor devices such as microcontrollers. In this paper, we propose a small CNN model with 4 layers that is very amenable for edge AI deployment and realtime gait recognition. This model was trained on a public gait dataset with 20 classes augmented with data collected by the authors, aggregating to 24 classes in total. Our model achieves 96.7% accuracy and consumes only 5KB RAM with an inferencing time of 70 ms and 125mW power, while running continuous inference on Arduino Nano 33 BLE Sense. We successfully demonstrated realtime identification of the authors with the model running on Arduino, thus underscoring the efficacy and providing a proof of feasiblity for deployment in practical systems in near future.
Fusing Pretrained ViTs with TCNet for Enhanced EEG Regression
Authors: Eric Modesitt, Haicheng Yin, Williams Huang Wang, Brian Lu
Date: 2024-04-02 00:00:00
Tasks: EEG, Electroencephalogram (EEG), regression

The task of Electroencephalogram (EEG) analysis is paramount to the development of Brain-Computer Interfaces (BCIs). However, to reach the goal of developing robust, useful BCIs depends heavily on the speed and the accuracy at which BCIs can understand neural dynamics. In response to that goal, this paper details the integration of pre-trained Vision Transformers (ViTs) with Temporal Convolutional Networks (TCNet) to enhance the precision of EEG regression. The core of this approach lies in harnessing the sequential data processing strengths of ViTs along with the superior feature extraction capabilities of TCNet, to significantly improve EEG analysis accuracy. In addition, we analyze the importance of how to construct optimal patches for the attention mechanism to analyze, balancing both speed and accuracy tradeoffs. Our results showcase a substantial improvement in regression accuracy, as evidenced by the reduction of Root Mean Square Error (RMSE) from 55.4 to 51.8 on EEGEyeNet's Absolute Position Task, outperforming existing state-of-the-art models. Without sacrificing performance, we increase the speed of this model by an order of magnitude (up to 4.32x faster). This breakthrough not only sets a new benchmark in EEG regression analysis but also opens new avenues for future research in the integration of transformer architectures with specialized feature extraction methods for diverse EEG datasets.
Molecular simulations to investigate the impact of N6-methylation in RNA recognition: Improving accuracy and precision of binding free energy prediction
Authors: Valerio Piomponi, Miroslav Krepl, Jiri Sponer, Giovanni Bussi
Date: 2024-04-23 00:00:00

N6-methyladenosine (m6A) is a prevalent RNA post-transcriptional modification that plays crucial roles in RNA stability, structural dynamics, and interactions with proteins. The YT521-B (YTH) family of proteins, which are notable m6A readers, function through their highly conserved YTH domain. Recent structural investigations and molecular dynamics (MD) simulations have shed light on the recognition mechanism of m6A by the YTHDC1 protein. Despite advancements, using MD to predict the stabilization induced by m6A on the free energy of binding between RNA and YTH proteins remains challenging, due to inaccuracy of the employed force field and limited sampling. For instance, simulations often fail to sufficiently capture the hydration dynamics of the binding pocket. This study addresses these challenges through an innovative methodology that integrates metadynamics, alchemical simulations, and force-field refinement. Importantly, our research identifies hydration of the binding pocket as giving only a minor contribution to the binding free energy and emphasizes the critical importance of precisely tuning force-field parameters to experimental data. By employing a fitting strategy built on alchemical calculations, we refine the m6A partial charges parameters, thereby enabling the simultaneous reproduction of N6 methylation on both the protein binding free energy and the thermodynamic stability of nine RNA duplexes. Our findings underscore the sensitivity of binding free energies to partial charges, highlighting the necessity for thorough parameterization and validation against experimental observations across a range of structural contexts.
Multi-scale modeling of Snail-mediated response to hypoxia in tumor progression
Authors: Giulia Chiari, Martina Conte, Marcello Delitala
Date: 2024-04-25 00:00:00

Tumor cell migration within the microenvironment is a crucial aspect for cancer progression and, in this context, hypoxia has a significant role. An inadequate oxygen supply acts as an environmental stressor inducing migratory bias and phenotypic changes. In this paper, we propose a novel multi-scale mathematical model to analyze the pivotal role of Snail protein expression in the cellular responses to hypoxia. Starting from the description of single-cell dynamics driven by the Snail protein, we construct the corresponding kinetic transport equation that describes the evolution of the cell distribution. Subsequently, we employ proper scaling arguments to formally derive the equations for the statistical moments of the cell distribution, which govern the macroscopic tumor dynamics. Numerical simulations of the model are performed in various scenarios with biological relevance to provide insights into the role of the multiple tactic terms, the impact of Snail expression on cell proliferation, and the emergence of hypoxia-induced migration patterns. Moreover, quantitative comparison with experimental data shows the model's reliability in measuring the impact of Snail transcription on cell migratory potential. Through our findings, we shed light on the potential of our mathematical framework in advancing the understanding of the biological mechanisms driving tumor progression.