pwc-archive/papers-with-abstracts huggingface dataset:
Elastic 3D Wavefield Simulation on budget GPUs using the GLSL shading language
Authors: Emanuel Trabes, Silvana Spagnotto, Orlando Alvarez Pontoriero, Julio Daniel Dondo Gazzano, Carlos Federico Sosa Paez
Date: 2021-12-30 00:00:00
Tasks: CPU, GPU

Forward wavefield simulation is an important step in Full Waveform Inversion systems. Fast simulations are instrumental to get inversion result in reasonable time frames. Most of research and software aims towards utilizing costly computer clusters composed of multiple CPUs and numerous high end GPUs to shorten the forward simulation time. Using this type of hardware has some disadvantages as: high cost, complex programming models and unavailability of resources. In this work, we present a finite difference elastic 3D wavefield forward simulation that takes advantage of any modern low end GPU, by using the GLSL shading language.Some of the advantages of using GLSL are: runs in any modern GPU, has a simplified computing and memory model and provides state of art performance thanks to its very well optimized vendor developed drivers. We show that our GLSL implementation easily outperforms a multicore CPU implementation in a modern PC. We further benchmark our result using a real seismic event, and show that we can get accurate simulations in reasonable time using our system.
Constrained Wrapped Least Squares: A Tool for High Accuracy GNSS Attitude Determination
Authors: Xing Liu, Tarig Ballal, Hui Chen, Tareq Y. Al-Naffouri
Date: 2021-12-29 00:00:00

Attitude determination is a popular application of Global Navigation Satellite Systems (GNSS). Many methods have been developed to solve the attitude determination problem with different performance offerings. We develop a constrained wrapped least-squares (C-WLS) method for high-accuracy attitude determination. This approach is built on an optimization model that leverages prior information related to the antenna array and the integer nature of the carrier-phase ambiguities in an innovative way. The proposed approach adopts an efficient search strategy to estimate the vehicle's attitude parameters using ambiguous carrier-phase observations directly, without requiring prior carrier-phase ambiguity fixing. The performance of the proposed method is evaluated via simulations and experimentally utilizing data collected using multiple GNSS receivers. The simulation and experimental results demonstrate excellent performance, with the proposed method outperforming the ambiguity function method, the constrained LAMBDA and multivariate constrained LAMBDA methods, three prominent attitude determination algorithms.
Cooperation in Space: HAPS-Aided Optical Inter-Satellite Connectivity with Opportunistic Scheduling
Authors: Eylem Erdogan, Ibrahim Altunbas, Gunes Karabulut Kurt, Halim Yanikomeroglu
Date: 2021-12-28 00:00:00
Tasks: Position, Scheduling

Issues with tracking, precision pointing, and Doppler shift are the major sources of performance loss in laser inter-satellite communication that can severely decrease the coverage and overall performance of satellite constellations. As a solution to these problems, we propose a cooperation strategy in which a high altitude platform station (HAPS) staying at a quasi-stationary position contributes to the inter-satellite connectivity. In this setup, the HAPS node uses two different scheduling approaches: one that relies on the zenith angle; the other on instantaneous signal-to-noise ratio. To quantify the performance of the proposed scheme, overall outage probabilities for the two scheduling methods are obtained. In addition, guidelines for the design of practical inter-satellite networks are provided.
A Gridless Fourth-order Cumulant-Based DOA Estimation Method under Unknown Colored Noise
Authors: Jiawen Yuan
Date: 2021-12-28 00:00:00

To reduce the adverse impacts of the unknown colored noise on the performance degradation of the direction-of-arrival (DOA) estimation, we propose a new gridless DOA estimation method based on fourth-order cumulant (FOC)in this letter. We first introduce the non-redundancy single measurement vector (SMV) through FOC, which is capable of suppressing the Gaussian colored noise. Next, we analyze the distribution of the estimation error and design an estimation error tolerance scheme for it. We then combine the atomic norm minimization of the non-redundancy SMV with the above constraint scheme. This combination poses the stability of the sparsest solution. Finally, the DOA estimation is retrieved through rotational invariance techniques. Moreover, this method extends the gridless DOA estimation to the sparse linear array. Numerical simulations validate the effectiveness of the proposed method.
TeraHertz Band Communication: An Old Problem Revisited and Research Directions for the Next Decade
Authors: Ian F. Akyildiz, Chong Han, Zhifeng Hu, Shuai Nie, Josep M. Jornet
Date: 2021-12-25 00:00:00

Terahertz (THz) band communications are envisioned as a key technology for 6G and Beyond. As a fundamental wireless infrastructure, THz communication can boost abundant promising applications. In 2014, our team published two comprehensive roadmaps for the development and progress of THz communication networks [1], [2], which helped the research community to start research on this subject afterwards. The topic of THz communications became very important and appealing to the research community due to 6G wireless systems design and development in recent years. Many papers are getting published covering different aspects of wireless systems using the THz band. With this paper, our aim is looking back to the last decade and revisiting the old problems and pointing out what has been achieved in the research community so far. Furthermore, in this paper, open challenges and new research directions still to be investigated for the THz band communication systems are presented, by covering diverse topics ranging from devices, channel behavior, communication and networking, to physical testbeds and demonstration systems. The key aspects presented in this paper will enable THz communications as a pillar of 6G and Beyond wireless systems in the next decade.
Noninvasive Fetal Electrocardiography: Models, Technologies and Algorithms
Authors: Reza Sameni
Date: 2021-12-24 00:00:00
Tasks: blind source separation

The fetal electrocardiogram (fECG) was first recorded from the maternal abdominal surface in the early 1900s. During the past fifty years, the most advanced electronics technologies and signal processing algorithms have been used to convert noninvasive fetal electrocardiography into a reliable technology for fetal cardiac monitoring. In this chapter, the major signal processing techniques, which have been developed for the modeling, extraction and analysis of the fECG from noninvasive maternal abdominal recordings are reviewed and compared with one another in detail. The major topics of the chapter include: 1) the electrophysiology of the fECG from the signal processing viewpoint, 2) the mathematical model of the maternal volume conduction media and the waveform models of the fECG acquired from body surface leads, 3) the signal acquisition requirements, 4) model-based techniques for fECG noise and interference cancellation, including adaptive filters and semi-blind source separation techniques, and 5) recent algorithmic advances for fetal motion tracking and online fECG extraction from few number of channels.
Machine Learning-based Efficient Ventricular Tachycardia Detection Model of ECG Signal
Authors: Pampa Howladar, Manodipan Sahoo
Date: 2021-12-24 00:00:00
Tasks: BIG-bench Machine Learning

In primary diagnosis and analysis of heart defects, an ECG signal plays a significant role. This paper presents a model for the prediction of ventricular tachycardia arrhythmia using noise filtering, a unique set of ECG features, and a machine learning-based classifier model. Before signal feature extraction, we detrend and denoise the signal to eliminate the noise for detecting features properly. After that necessary features have been extracted and necessary parameters related to these features are measured. Using these parameters, we prepared one efficient multiclass classifier model using a machine learning approach that can classify different types of ventricular tachycardia arrhythmias efficiently. Our results indicate that Logistic regression and Decision tree-based models are the most efficient machine learning models for detecting ventricular tachycardia arrhythmia. In order to diagnose heart diseases and find care for a patient, an early, reliable diagnosis of different types of arrhythmia is necessary. By implementing our proposed method, this work deals with the problem of reducing the misclassification of the critical signal related to ventricular tachycardia very efficiently. Experimental findings demonstrate satisfactory enhancements and demonstrate high resilience to the algorithm that we have proposed. With this assistance, doctors can assess this type of arrhythmia of a patient early and take the right decision at the proper time.
Supraventricular Tachycardia Detection and Classification Model of ECG signal Using Machine Learning
Authors: Pampa Howladar, Manodipan Sahoo
Date: 2021-12-24 00:00:00
Tasks: BIG-bench Machine Learning

Investigation on the electrocardiogram (ECG) signals is an essential way to diagnose heart disease since the ECG process is noninvasive and easy to use. This work presents a supraventricular arrhythmia prediction model consisting of a few stages, including filtering of noise, a unique collection of ECG characteristics, and automated learning classifying model to classify distinct types, depending on their severity. We de-trend and de-noise a signal to reduce noise to better determine functionality before extractions are performed. After that, we present one R-peak detection method and Q-S detection method as a part of necessary feature extraction. Next parameters are computed that correspond to these features. Using these characteristics, we have developed a classification model based on machine learning that can successfully categorize different types of supraventricular tachycardia. Our findings suggest that decision-tree-based models are the most efficient machine learning models for supraventricular tachycardia arrhythmia. Among all the machine learning models, this model most efficiently lowers the crucial signal misclassification of supraventricular tachycardia. Experimental results indicate satisfactory improvements and demonstrate a superior efficiency of the proposed approach with 97% accuracy.
An Asymptotically MSE-Optimal Estimator based on Gaussian Mixture Models
Authors: Michael Koller, Benedikt Fesl, Nurettin Turan, Wolfgang Utschick
Date: 2021-12-23 00:00:00

This paper investigates a channel estimator based on Gaussian mixture models (GMMs) in the context of linear inverse problems with additive Gaussian noise. We fit a GMM to given channel samples to obtain an analytic probability density function (PDF) which approximates the true channel PDF. Then, a conditional mean estimator (CME) corresponding to this approximating PDF is computed in closed form and used as an approximation of the optimal CME based on the true channel PDF. This optimal CME cannot be calculated analytically because the true channel PDF is generally unknown. We present mild conditions which allow us to prove the convergence of the GMM-based CME to the optimal CME as the number of GMM components is increased. Additionally, we investigate the estimator's computational complexity and present simplifications based on common model-based insights. Further, we study the estimator's behavior in numerical experiments including multiple-input multiple-output (MIMO) and wideband systems.
Deep Proximal Learning for High-Resolution Plane Wave Compounding
Authors: Nishith Chennakeshava, Ben Luijten, Massimo Mischi, Yonina C. Eldar, Ruud J. G. van Sloun
Date: 2021-12-23 00:00:00
Tasks: Inductive Bias, Vocal Bursts Intensity Prediction

Plane Wave imaging enables many applications that require high frame rates, including localisation microscopy, shear wave elastography, and ultra-sensitive Doppler. To alleviate the degradation of image quality with respect to conventional focused acquisition, typically, multiple acquisitions from distinctly steered plane waves are coherently (i.e. after time-of-flight correction) compounded into a single image. This poses a trade-off between image quality and achievable frame-rate. To that end, we propose a new deep learning approach, derived by formulating plane wave compounding as a linear inverse problem, that attains high resolution, high-contrast images from just 3 plane wave transmissions. Our solution unfolds the iterations of a proximal gradient descent algorithm as a deep network, thereby directly exploiting the physics-based generative acquisition model into the neural network design. We train our network in a greedy manner, i.e. layer-by-layer, using a combination of pixel, temporal, and distribution (adversarial) losses to achieve both perceptual fidelity and data consistency. Through the strong model-based inductive bias, the proposed architecture outperforms several standard benchmark architectures in terms of image quality, with a low computational and memory footprint.
Temporal Analysis of Functional Brain Connectivity for EEG-based Emotion Recognition
Authors: Ensieh Khazaei, Hoda Mohammadzade
Date: 2021-12-23 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG), Emotion Classification, Emotion Recognition, Functional Connectivity

EEG signals in emotion recognition absorb special attention owing to their high temporal resolution and their information about what happens in the brain. Different regions of brain work together to process information and meanwhile the activity of brain changes during the time. Therefore, the investigation of the connection between different brain areas and their temporal patterns plays an important role in neuroscience. In this study, we investigate the emotion classification performance using functional connectivity features in different frequency bands and compare them with the classification performance using differential entropy feature, which has been previously used for this task. Moreover, we investigate the effect of using different time periods on the classification performance. Our results on publicly available SEED dataset show that as time goes on, emotions become more stable and the classification accuracy increases. Among different time periods, we achieve the highest classification accuracy using the time period of 140s-end. In this time period, the accuracy is improved by 4 to 6% compared to using the entire signal. The mean accuracy of about 88% is obtained using any of the Pearson correlation coefficient, coherence, and phase locking value features and SVM. Therefore, functional connectivity features lead to better classification accuracy than DE features (with the mean accuracy of 84.89%) using the proposed framework. Finally, in a relatively fair comparison, we show that using the best time interval and SVM, we achieve better accuracy than using Recurrent Neural Networks which need large amount of data and have high computational cost.
Learning shared neural manifolds from multi-subject FMRI data
Authors: Jessie Huang, Erica L. Busch, Tom Wallenstein, Michal Gerasimiuk, Andrew Benz, Guillaume Lajoie, Guy Wolf, Nicholas B. Turk-Browne, Smita Krishnaswamy
Date: 2021-12-22 00:00:00
Tasks: Brain Computer Interface, Decoder

Functional magnetic resonance imaging (fMRI) is a notoriously noisy measurement of brain activity because of the large variations between individuals, signals marred by environmental differences during collection, and spatiotemporal averaging required by the measurement resolution. In addition, the data is extremely high dimensional, with the space of the activity typically having much lower intrinsic dimension. In order to understand the connection between stimuli of interest and brain activity, and analyze differences and commonalities between subjects, it becomes important to learn a meaningful embedding of the data that denoises, and reveals its intrinsic structure. Specifically, we assume that while noise varies significantly between individuals, true responses to stimuli will share common, low-dimensional features between subjects which are jointly discoverable. Similar approaches have been exploited previously but they have mainly used linear methods such as PCA and shared response modeling (SRM). In contrast, we propose a neural network called MRMD-AE (manifold-regularized multiple decoder, autoencoder), that learns a common embedding from multiple subjects in an experiment while retaining the ability to decode to individual raw fMRI signals. We show that our learned common space represents an extensible manifold (where new points not seen during training can be mapped), improves the classification accuracy of stimulus features of unseen timepoints, as well as improves cross-subject translation of fMRI signals. We believe this framework can be used for many downstream applications such as guided brain-computer interface (BCI) training in the future.
Compressive Scanning Transmission Electron Microscopy
Authors: Daniel Nicholls, Alex Robinson, Jack Wells, Amirafshar Moshtaghpour, Mounib Bahri, Angus Kirkland, Nigel Browning
Date: 2021-12-22 00:00:00
Tasks: Compressive Sensing, Dictionary Learning

Scanning Transmission Electron Microscopy (STEM) offers high-resolution images that are used to quantify the nanoscale atomic structure and composition of materials and biological specimens. In many cases, however, the resolution is limited by the electron beam damage, since in traditional STEM, a focused electron beam scans every location of the sample in a raster fashion. In this paper, we propose a scanning method based on the theory of Compressive Sensing (CS) and subsampling the electron probe locations using a line hop sampling scheme that significantly reduces the electron beam damage. We experimentally validate the feasibility of the proposed method by acquiring real CS-STEM data, and recovering images using a Bayesian dictionary learning approach. We support the proposed method by applying a series of masks to fully-sampled STEM data to simulate the expectation of real CS-STEM. Finally, we perform the real data experimental series using a constrained-dose budget to limit the impact of electron dose upon the results, by ensuring that the total electron count remains constant for each image.
Carrier Phase Ranging for Indoor Positioning with 5G NR Signals
Authors: Liang Chen, Xin Zhou, Feifei Chen, Lie-Liang Yang, Ruizhi Chen
Date: 2021-12-22 00:00:00

Indoor positioning is one of the core technologies of Internet of Things (IoT) and artificial intelligence (AI), and is expected to play a significant role in the upcoming era of AI. However, affected by the complexity of indoor environments, it is still highly challenging to achieve continuous and reliable indoor positioning. Currently, 5G cellular networks are being deployed worldwide, the new technologies of which have brought the approaches for improving the performance of wireless indoor positioning. In this paper, we investigate the indoor positioning under the 5G new radio (NR), which has been standardized and being commercially operated in massive markets. Specifically, a solution is proposed and a software defined receiver (SDR) is developed for indoor positioning. With our SDR indoor positioning system, the 5G NR signals are firstly sampled by universal software radio peripheral (USRP), and then, coarse synchronization is achieved via detecting the start of the synchronization signal block (SSB). Then, with the assistance of the pilots transmitted on the physical broadcasting channel (PBCH), multipath acquisition and delay tracking are sequentially carried out to estimate the time of arrival (ToA) of received signals. Furthermore, to improve the ToA ranging accuracy, the carrier phase of the first arrived path is estimated. Finally, to quantify the accuracy of our ToA estimation method, indoor field tests are carried out in an office environment, where a 5G NR base station (known as gNB) is installed for commercial use. Our test results show that, in the static test scenarios, the ToA accuracy measured by the 1-{\sigma} error interval is about 0.5 m, while in the pedestrian mobile environment, the probability of range accuracy within 0.8 m is 95%.
Energy efficient deployment solutions in high density heterogeneous networks
Authors: Nguyen Doan Hieu, Dao Le Thu Thao, Tran Manh Hoang
Date: 2021-12-22 00:00:00
Tasks: Vocal Bursts Intensity Prediction

This study deals with the problem of optimizing transmit power in high density heterogeneous networks. In the communication network, effective methods of allocating transmit power, in order to reduce the total transmit power, but still ensure the quality of service of the user equipment, is a big challenge. number of power consumption optimization problems in core station links, with the goal of maximizing network energy efficiency while ensuring user experience. To solve this non-convex optimization problem, the authors first propose some iterative algorithms to find the convergence point such as the "descent" method, the "Lagrange" method. Then, the authors evaluate the convergence point of each method as well as consider the complexity of each algorithm when put into application. Finally, the simulation results will show the convergence value and compare the performance with the technologies being used today to confirm the effectiveness of the proposed algorithm.
Data-Driven Outage Restoration Time Prediction via Transfer Learning with Cluster Ensembles
Authors: Dingwei Wang, Yuxuan Yuan, Rui Cheng, Zhaoyu Wang
Date: 2021-12-21 00:00:00
Tasks: Computational Efficiency, Transfer Learning

This paper develops a data-driven approach to accurately predict the restoration time of outages under different scales and factors. To achieve the goal, the proposed method consists of three stages. First, given the unprecedented amount of data collected by utilities, a sparse dictionary-based ensemble spectral clustering (SDESC) method is proposed to decompose historical outage datasets, which enjoys good computational efficiency and scalability. Specifically, each outage sample is represented by a linear combination of a small number of selected dictionary samples using a density-based method. Then, the dictionary-based representation is utilized to perform the spectral analysis to group the data samples with similar features into the same subsets. In the second stage, a knowledge-transfer-added restoration time prediction model is trained for each subset by combining weather information and outage-related features. The transfer learning technology is introduced with the aim of dealing with the underestimation problem caused by data imbalance in different subsets, thus improving the model performance. Furthermore, to connect unseen outages with the learned outage subsets, a t-distributed stochastic neighbor embedding-based strategy is applied. The proposed method fully builds on and is also tested on a large real-world outage dataset from a utility provider with a time span of six consecutive years. The numerical results validate that our method has high prediction accuracy while showing good stability against real-world data limitations.