pwc-archive/papers-with-abstracts huggingface dataset:
On Distributed and Asynchronous Sampling of Gaussian Processes for Sequential Binary Hypothesis Testing
Authors: Nandan Sriranga, Saikiran Bulusu, Baocheng Geng, Pramod K. Varshney
Date: 2023-09-14 00:00:00
Tasks: Gaussian Processes

In this work, we consider a binary sequential hypothesis testing problem with distributed and asynchronous measurements. The aim is to analyze the effect of sampling times of jointly $\textit{wide-sense stationary}$ (WSS) Gaussian observation processes at distributed sensors on the expected stopping time of the sequential test at the fusion center (FC). The distributed system is such that the sensors and the FC sample observations periodically, where the sampling times are not necessarily synchronous, i.e., the sampling times at different sensors and the FC may be different from each other. The sampling times, however, are restricted to be within a time window and a sample obtained within the window is assumed to be $\textit{uncorrelated}$ with samples outside the window. We also assume that correlations may exist only between the observations sampled at the FC and those at the sensors in a pairwise manner (sensor pairs not including the FC have independent observations). The effect of $\textit{asynchronous}$ sampling on the SPRT performance is analyzed by obtaining bounds for the expected stopping time. We illustrate the validity of the theoretical results with numerical results.
Quantifying Non-linear Dependencies in Blind Source Separation of Power System Signals using Copula Statistics
Authors: Pooja Algikar, Lamine Mili, Kiran Karra, Akash Algikar, Mohsen Ben Hassine
Date: 2023-09-14 00:00:00
Tasks: blind source separation

The dynamics of a power system with a significant presence of renewable energy resources are growing increasingly nonlinear. This nonlinearity is a result of the intermittent nature of these resources and the switching behavior of their power electronic devices. Therefore, it is crucial to address these nonlinearity in the blind source separation methods. In this paper, we propose a blind source separation of a linear mixture of dependent sources based on copula statistics that measure the non-linear dependence between source component signals structured as copula density functions. The source signals are assumed to be stationary. The method minimizes the Kullback-Leibler divergence between the copula density functions of the estimated sources and of the dependency structure. The proposed method is applied to data obtained from the time-domain analysis of the classical 11-Bus 4-Machine system. Extensive simulation results demonstrate that the proposed method based on copula statistics converges faster and outperforms the state-of-the-art blind source separation method for dependent sources in terms of interference-to-signal ratio.
Stochastic Phased Array Performance Indicators for Quality-of-Service-Enhanced Massive MIMO
Authors: Noud Kanters, Andrés Alayón Glazunov
Date: 2023-09-14 00:00:00
Tasks: Scheduling

In this paper, we show that the signal-to-interference-plus-noise ratio (SINR) at a base station (BS) equipped with an arbitrary physical array antenna can be expressed as a function of two fundamental figures-of-merit (FoMs): (I) the instantaneous effective gain (IEG), and (II) the beamforming-channel correlation (BCC). These two FoMs are functions of the array antenna layout, the antenna elements, the propagation channel and the applied signal processing algorithms, and hence they are random variables (RVs) in general. We illustrate that both FoMs provide essential insights for quality-of-service (QoS)-based phased array design by investigating their statistics for BSs applying full-digital (FD) zero forcing (ZF) beamforming. We evaluate various array designs and show that arrays with higher IEGs and a reduced probability of low BCCs can increase the ergodic sum rate and reduce the need for scheduling.
On the Relationship Between Iterated Statistical Linearization and Quasi-Newton Methods
Authors: Anton Kullberg, Martin A. Skoglund, Isaac Skog, Gustaf Hendeby
Date: 2023-09-14 00:00:00

This letter investigates relationships between iterated filtering algorithms based on statistical linearization, such as the iterated unscented Kalman filter (IUKF), and filtering algorithms based on quasi-Newton (QN) methods, such as the QN iterated extended Kalman filter (QN-IEKF). Firstly, it is shown that the IUKF and the iterated posterior linearization filter (IPLF) can be viewed as QN algorithms, by finding a Hessian correction in the QN-IEKF such that the IPLF iterate updates are identical to that of the QN-IEKF. Secondly, it is shown that the IPLF/IUKF update can be rewritten such that it is approximately identical to the QN-IEKF, albeit for an additional correction term. This enables a richer understanding of the properties of iterated filtering algorithms based on statistical linearization.
Unified Linearization-based Nonlinear Filtering
Authors: Anton Kullberg, Isaac Skog, Gustaf Hendeby
Date: 2023-09-14 00:00:00
Tasks: State Estimation

This letter shows that the following three classes of recursive state estimation filters: standard filters, such as the extended Kalman filter; iterated filters, such as the iterated unscented Kalman filter; and dynamically iterated filters, such as the dynamically iterated posterior linearization filters; can be unified in terms of a general algorithm. The general algorithm highlights the strong similarities between specific filtering algorithms in the three filter classes and facilitates an in-depth understanding of the pros and cons of the different filter classes and algorithms. We end with a numerical example showing the estimation accuracy differences between the three classes of filters when applied to a nonlinear localization problem.
On Performance of Fluid Antenna System using Maximum Ratio Combining
Authors: Xiazhi Lai, Tuo Wu, Junteng Yao, Cunhua Pan, Maged Elkashlan, Kai-Kit Wong
Date: 2023-09-14 00:00:00
Tasks: Diversity

This letter investigates a fluid antenna system (FAS) where multiple ports can be activated for signal combining for enhanced receiver performance. Given $M$ ports at the FAS, the best $K$ ports out of the $M$ available ports are selected before maximum ratio combining (MRC) is used to combine the received signals from the selected ports. The aim of this letter is to study the achievable performance of FAS when more than one ports can be activated. We do so by analyzing the outage probability of this setup in Rayleigh fading channels through the utilization of Gauss-Chebyshev integration, lower bound estimation, and high signal-to-noise ratio (SNR) asymptotic approximations. Our analytical results demonstrate that FAS can harness rich spatial diversity, which is confirmed by computer simulations.
Interpretable and Efficient Beamforming-Based Deep Learning for Single Snapshot DOA Estimation
Authors: Ruxin Zheng, Shunqiao Sun, Hongshan Liu, Honglei Chen, Jian Li
Date: 2023-09-14 00:00:00
Tasks: Compressive Sensing, Deep Learning

We introduce an interpretable deep learning approach for direction of arrival (DOA) estimation with a single snapshot. Classical subspace-based methods like MUSIC and ESPRIT use spatial smoothing on uniform linear arrays for single snapshot DOA estimation but face drawbacks in reduced array aperture and inapplicability to sparse arrays. Single-snapshot methods such as compressive sensing and iterative adaptation approach (IAA) encounter challenges with high computational costs and slow convergence, hampering real-time use. Recent deep learning DOA methods offer promising accuracy and speed. However, the practical deployment of deep networks is hindered by their black-box nature. To address this, we propose a deep-MPDR network translating minimum power distortionless response (MPDR)-type beamformer into deep learning, enhancing generalization and efficiency. Comprehensive experiments conducted using both simulated and real-world datasets substantiate its dominance in terms of inference time and accuracy in comparison to conventional methods. Moreover, it excels in terms of efficiency, generalizability, and interpretability when contrasted with other deep learning DOA estimation networks.
On the Terminal Location Uncertainty in Elliptical Footprints: Application in Air-to-Ground Links
Authors: Alexander Vavoulas, Nicholas Vaiopoulos, Harilaos G. Sandalidis, Konstantinos K. Delibasis
Date: 2023-09-13 00:00:00

Wireless transmitters (Txs) radiating directionally downwards often generate circular footprints on the ground. In certain scenarios, using elliptical cells can offer increased flexibility for providing user coverage, owing to the unique network characteristics. For instance, an elliptical footprint can be produced when a practical directional antenna with unequal azimuth and elevation half-power beamwidths is used in high-speed railway networks. Another common scenario involves the production of an elliptical footprint when an airborne Tx radiates at an angle by tilting its directional antenna by a few degrees. This paper aims to investigate, for the first time, the association between the random user location within an elliptical coverage area and the performance of a wireless communication link by considering these scenarios. We assume an unmanned aerial vehicle (UAV) as a Tx, although a tall cellular base station tower could also be employed without losing generality. To better understand the impact of random location, we derive relevant distance metrics and investigate the outage probability of the link for the two scenarios, taking both random terminal location and fading impairments into account. The findings may provide valuable insights into the performance of similar wireless systems.
Beamforming Design and Performance Evaluation for RIS-aided Localization using LEO Satellite Signals
Authors: Lei Wang, Pinjun Zheng, Xing Liu, Tarig Ballal, Tareq Y. Al-Naffouri
Date: 2023-09-13 00:00:00

The growing availability of low-Earth orbit (LEO) satellites, coupled with the anticipated widespread deployment of reconfigurable intelligent surfaces (RISs), opens up promising prospects for new localization paradigms. This paper studies RIS-aided localization using LEO satellite signals. The Cram\'er-Rao bound of the considered localization problem is derived, based on which an optimal RIS beamforming design that minimizes the derived bound is proposed. Numerical results demonstrate the superiority of the proposed beamforming scheme over benchmark alternatives, while also revealing that the synergy between LEO satellites and RISs holds the promise of achieving localization accuracy at the meter or even sub-meter level.
Model-based traffic state estimation for link traffic using moving cameras
Authors: Tanay Rastogi, Michele D. Simoni, Anders Karlström
Date: 2023-09-11 00:00:00
Tasks: State Estimation

Traffic State Estimation (TSE) is the process of inferring traffic conditions based on partially observed data using prior knowledge of traffic patterns. The type of input data used has a significant impact on the accuracy and methodology of TSE. Traditional TSE methods have relied on data from either stationary sensors like loop detectors or mobile sensors such as GPS-equipped floating cars. However, both approaches have their limitations. This paper proposes a method for estimating traffic states on a road link using vehicle trajectories obtained from cameras mounted on moving vehicles. It involves combining data from multiple moving cameras to construct time-space diagrams and using them to estimate parameters for the link's fundamental diagram (FD) and densities in unobserved regions of space-time. The Cell Transmission Model (CTM) is utilized in conjunction with a Genetic Algorithm (GA) to optimize the FD parameters and boundary conditions necessary for accurate estimation. To evaluate the effectiveness of the proposed methodology, simulated traffic data generated by the SUMO traffic simulator was employed incorporating 140 different space-time diagrams with varying lane density and speed. The evaluation of the simulated data demonstrates the effectiveness of the proposed approach, as it achieves a low root mean square error (RMSE) value of 0.0079 veh/m and is comparable to other CTM-based methods. In conclusion, the proposed TSE method opens new avenues for the estimation of traffic state using an innovative data collection method that uses vehicle trajectories collected from on-board cameras.
A Strong and Simple Deep Learning Baseline for BCI MI Decoding
Authors: Yassine El Ouahidi, Vincent Gripon, Bastien Pasdeloup, Ghaith Bouallegue, Nicolas Farrugia, Giulia Lioi
Date: 2023-09-11 00:00:00
Tasks: Deep Learning, EEG, Motor Imagery, Transfer Learning

We propose EEG-SimpleConv, a straightforward 1D convolutional neural network for Motor Imagery decoding in BCI. Our main motivation is to propose a simple and performing baseline to compare to, using only very standard ingredients from the literature. We evaluate its performance on four EEG Motor Imagery datasets, including simulated online setups, and compare it to recent Deep Learning and Machine Learning approaches. EEG-SimpleConv is at least as good or far more efficient than other approaches, showing strong knowledge-transfer capabilities across subjects, at the cost of a low inference time. We advocate that using off-the-shelf ingredients rather than coming with ad-hoc solutions can significantly help the adoption of Deep Learning approaches for BCI. We make the code of the models and the experiments accessible.
Transparency in Sleep Staging: Deep Learning Method for EEG Sleep Stage Classification with Model Interpretability
Authors: Shivam Sharma, Suvadeep Maiti, S. Mythirayee, Srijithesh Rajendran, Raju Surampudi Bapi
Date: 2023-09-10 00:00:00
Tasks: Classification, Decision Making, EEG, Sleep Quality, Sleep Staging

Automated Sleep stage classification using raw single channel EEG is a critical tool for sleep quality assessment and disorder diagnosis. However, modelling the complexity and variability inherent in this signal is a challenging task, limiting their practicality and effectiveness in clinical settings. To mitigate these challenges, this study presents an end-to-end deep learning (DL) model which integrates squeeze and excitation blocks within the residual network to extract features and stacked Bi-LSTM to understand complex temporal dependencies. A distinctive aspect of this study is the adaptation of GradCam for sleep staging, marking the first instance of an explainable DL model in this domain with alignment of its decision-making with sleep expert's insights. We evaluated our model on the publically available datasets (SleepEDF-20, SleepEDF-78, and SHHS), achieving Macro-F1 scores of 82.5, 78.9, and 81.9, respectively. Additionally, a novel training efficiency enhancement strategy was implemented by increasing stride size, leading to 8x faster training times with minimal impact on performance. Comparative analyses underscore our model outperforms all existing baselines, indicating its potential for clinical usage.
ETP: Learning Transferable ECG Representations via ECG-Text Pre-training
Authors: Che Liu, Zhongwei Wan, Sibo Cheng, Mi Zhang, Rossella Arcucci
Date: 2023-09-06 00:00:00
Tasks: Diagnostic, Language Modeling, Language Modelling, Linear evaluation, Representation Learning, Self-Supervised Learning, zero-shot-classification, Zero-Shot Learning

In the domain of cardiovascular healthcare, the Electrocardiogram (ECG) serves as a critical, non-invasive diagnostic tool. Although recent strides in self-supervised learning (SSL) have been promising for ECG representation learning, these techniques often require annotated samples and struggle with classes not present in the fine-tuning stages. To address these limitations, we introduce ECG-Text Pre-training (ETP), an innovative framework designed to learn cross-modal representations that link ECG signals with textual reports. For the first time, this framework leverages the zero-shot classification task in the ECG domain. ETP employs an ECG encoder along with a pre-trained language model to align ECG signals with their corresponding textual reports. The proposed framework excels in both linear evaluation and zero-shot classification tasks, as demonstrated on the PTB-XL and CPSC2018 datasets, showcasing its ability for robust and generalizable cross-modal ECG feature learning.
PyPVRoof: a Python package for extracting the characteristics of rooftop PV installations using remote sensing data
Authors: Yann Tremenbert, Gabriel Kasmi, Laurent Dubus, Yves-Marie Saint-Drenan, Philippe Blanc
Date: 2023-09-05 00:00:00

Photovoltaic (PV) energy grows at an unprecedented pace, which makes it difficult to maintain up-to-date and accurate PV registries, which are critical for many applications such as PV power generation estimation. This lack of qualitative data is especially true in the case of rooftop PV installations. As a result, extensive efforts are put into the constitution of PV inventories. However, although valuable, these registries cannot be directly used for monitoring the deployment of PV or estimating the PV power generation, as these tasks usually require PV systems {\it characteristics}. To seamlessly extract these characteristics from the global inventories, we introduce {\tt PyPVRoof}. {\tt PyPVRoof} is a Python package to extract essential PV installation characteristics. These characteristics are tilt angle, azimuth, surface, localization, and installed capacity. {\tt PyPVRoof} is designed to cover all use cases regarding data availability and user needs and is based on a benchmark of the best existing methods. Data for replicating our accuracy benchmarks are available on our Zenodo repository \cite{tremenbert2023pypvroof}, and the package code is accessible at this URL: \url{https://github.com/gabrielkasmi/pypvroof}.
Design of Recognition and Evaluation System for Table Tennis Players' Motor Skills Based on Artificial Intelligence
Authors: Zhuo-yong Shi, Ye-tao Jia, Ke-xin Zhang, Ding-han Wang, Long-meng Ji, Yong Wu
Date: 2023-09-04 00:00:00
Tasks: Dimensionality Reduction, Feature Engineering

With the rapid development of electronic science and technology, the research on wearable devices is constantly updated, but for now, it is not comprehensive for wearable devices to recognize and analyze the movement of specific sports. Based on this, this paper improves wearable devices of table tennis sport, and realizes the pattern recognition and evaluation of table tennis players' motor skills through artificial intelligence. Firstly, a device is designed to collect the movement information of table tennis players and the actual movement data is processed. Secondly, a sliding window is made to divide the collected motion data into a characteristic database of six table tennis benchmark movements. Thirdly, motion features were constructed based on feature engineering, and motor skills were identified for different models after dimensionality reduction. Finally, the hierarchical evaluation system of motor skills is established with the loss functions of different evaluation indexes. The results show that in the recognition of table tennis players' motor skills, the feature-based BP neural network proposed in this paper has higher recognition accuracy and stronger generalization ability than the traditional convolutional neural network.
Short-term power load forecasting method based on CNN-SAEDN-Res
Authors: Yang Cui, Han Zhu, Yijian Wang, Lu Zhang, Yang Li
Date: 2023-09-02 00:00:00
Tasks: Decoder, Load Forecasting, Time Series

In deep learning, the load data with non-temporal factors are difficult to process by sequence models. This problem results in insufficient precision of the prediction. Therefore, a short-term load forecasting method based on convolutional neural network (CNN), self-attention encoder-decoder network (SAEDN) and residual-refinement (Res) is proposed. In this method, feature extraction module is composed of a two-dimensional convolutional neural network, which is used to mine the local correlation between data and obtain high-dimensional data features. The initial load fore-casting module consists of a self-attention encoder-decoder network and a feedforward neural network (FFN). The module utilizes self-attention mechanisms to encode high-dimensional features. This operation can obtain the global correlation between data. Therefore, the model is able to retain important information based on the coupling relationship between the data in data mixed with non-time series factors. Then, self-attention decoding is per-formed and the feedforward neural network is used to regression initial load. This paper introduces the residual mechanism to build the load optimization module. The module generates residual load values to optimize the initial load. The simulation results show that the proposed load forecasting method has advantages in terms of prediction accuracy and prediction stability.
Masked Transformer for Electrocardiogram Classification
Authors: Ya Zhou, Xiaolin Diao, Yanni Huo, Yang Liu, Xiaohan Fan, Wei Zhao
Date: 2023-08-31 00:00:00
Tasks: Classification, Diagnostic, ECG Classification, Representation Learning, Time Series

Electrocardiogram (ECG) is one of the most important diagnostic tools in clinical applications. With the advent of advanced algorithms, various deep learning models have been adopted for ECG tasks. However, the potential of Transformer for ECG data has not been fully realized, despite their widespread success in computer vision and natural language processing. In this work, we present Masked Transformer for ECG classification (MTECG), a simple yet effective method which significantly outperforms recent state-of-the-art algorithms in ECG classification. Our approach adapts the image-based masked autoencoders to self-supervised representation learning from ECG time series. We utilize a lightweight Transformer for the encoder and a 1-layer Transformer for the decoder. The ECG signal is split into a sequence of non-overlapping segments along the time dimension, and learnable positional embeddings are added to preserve the sequential information. We construct the Fuwai dataset comprising 220,251 ECG recordings with a broad range of diagnoses, annotated by medical experts, to explore the potential of Transformer. A strong pre-training and fine-tuning recipe is proposed from the empirical study. The experiments demonstrate that the proposed method increases the macro F1 scores by 3.4%-27.5% on the Fuwai dataset, 9.9%-32.0% on the PTB-XL dataset, and 9.4%-39.1% on a multicenter dataset, compared to the alternative methods. We hope that this study could direct future research on the application of Transformer to more ECG tasks.