pwc-archive/papers-with-abstracts huggingface dataset:
Remote atrial fibrillation burden estimation using deep recurrent neural network
Authors: Armand Chocron, Julien Oster, Shany Biton, Mandel Franck, Meyer Elbaz, Yehoshua Y. Zeevi, Joachim Behar
Date: 2020-08-05 00:00:00
Tasks: Electrocardiography (ECG), Prognosis, Time Series, Time Series Analysis

The atrial fibrillation burden (AFB) is defined as the percentage of time spend in atrial fibrillation (AF) over a long enough monitoring period. Recent research has demonstrated the added prognosis value that becomes available by using the AFB as compared with the binary diagnosis. We evaluate, for the first time, the ability to estimate the AFB over long-term continuous recordings, using a deep recurrent neutral network (DRNN) approach. Methods: The models were developed and evaluated on a large database of p=2,891 patients, totaling t=68,800 hours of continuous electrocardiography (ECG) recordings acquired at the University of Virginia heart station. Specifically, 24h beat-to-beat time series were obtained from a single portable ECG channel. The network, denoted ArNet, was benchmarked against a gradient boosting (XGB) model, trained on 21 features including the coefficient of sample entropy (CosEn) and AFEvidence. Data were divided into training and test sets, while patients were stratified by the presence and severity of AF. The generalizations of ArNet and XGB were also evaluated on the independent test PhysioNet LTAF database. Results: the absolute AF burden estimation error |E_AF|, median and interquartile, on the test set, was 1.2 (0.1-6.7) for ArNet and 3.1 (0.0-11.7) for XGB for AF individuals. Generalization results on LTAF were consistent with E_AF of 2.6 (1.1-14.7) for ArNet and 3.6 (1.0-16.7) for XGB. Conclusion: This research demonstrates the feasibility of AFB estimation from 24h beat-to-beat interval time series utilizing recent advances in DRNN. Significance: The novel data-driven approach enables robust remote diagnosis and phenotyping of AF.
Fully Self-Gated Whole-Heart 4D Flow Imaging from a Five-Minute Scan
Authors: Aaron Pruitt, Adam Rich, Yingmin Liu, Ning Jin, Lee Potter, Matthew Tong, Saurabh Rajpal, Orlando Simonetti, Rizwan Ahmad
Date: 2020-04-19 00:00:00
Tasks: compressed sensing

Purpose: To develop and validate an acquisition and processing technique that enables fully self-gated 4D flow imaging with whole-heart coverage in a fixed five-minute scan. Theory and Methods: The data are acquired continuously using Cartesian sampling and sorted into respiratory and cardiac bins using the self-gating signal. The reconstruction is performed using a recently proposed Bayesian method called ReVEAL4D. ReVEAL4D is validated using data from eight healthy volunteers and two patients and compared with a compressed sensing technique, L1-SENSE. Results: Healthy subjects -- Compared to 2D phase-contrast MRI (2D-PC), flow quantification from ReVEAL4D shows no significant bias. In contrast, the peak velocity and peak flow rate for L1-SENSE are significantly underestimated. Compared to traditional parallel MRI-based 4D flow imaging, ReVEAL4D demonstrates small but significant biases in net flow and peak flow rate, with no significant bias in peak velocity. All three indices are significantly and more markedly underestimated by L1-SENSE. Patients -- Flow quantification from ReVEAL4D agrees well with the 2D-PC reference. In contrast, L1-SENSE markedly underestimated peak velocity. Conclusions: The combination of highly accelerated five-minute Cartesian acquisition, self-gating, and ReVEAL4D enables whole-heart 4D flow imaging with accurate flow quantification.
A Low-Cost Algorithm for Adaptive Sampling and Censoring in Diffusion Networks
Authors: Daniel G. Tiglea, Renato Candido, Magno T. M. Silva
Date: 2020-08-05 00:00:00

Distributed signal processing has attracted widespread attention in the scientific community due to its several advantages over centralized approaches. Recently, graph signal processing has risen to prominence, and adaptive distributed solutions have also been proposed in the area. Both in the classical framework and in graph signal processing, sampling and censoring techniques have been topics of intense research, since the cost associated with measuring and/or transmitting data throughout the entire network may be prohibitive in certain applications. In this paper, we propose a low-cost adaptive mechanism for sampling and censoring over diffusion networks that uses information from more nodes when the error in the network is high and from less nodes otherwise. It presents fast convergence during transient and a significant reduction in computational cost and energy consumption in steady state. As a censoring technique, we show that it is able to noticeably outperform other solutions. We also present a theoretical analysis to give insights about its operation, and to help the choice of suitable values for its parameters.
Resource Allocation in 6G Optical Wireless Systems
Authors: Osama Zwaid Alsulami, T. E. H. El-Gorashi, Jaafar M. H. Elmirghani
Date: 2020-08-05 00:00:00
Tasks: Diversity

The abundant optical spectrum is a promising part of the electromagnetic spectrum for 6G communication systems. The visible light spectrum which is a part of the optical spectrum, can be used to provide communication and illumination simultaneously. Visible light communication (VLC) systems have been widely researched, however, little work has focused on the area of multiple access. This chapter studies wavelength division multiple access (WDMA) techniques in VLC systems to support multiple users. In addition, the optimization of resource allocation is considered in this chapter by developing a mixed-integer linear programming (MILP) model that can be used to maximize the signal to noise and interference ratio (SINR) while supporting multiple users. The optimized resource allocation results in the best assignment of access points (APs) and wavelengths to users. Different indoor environments such as office, data center and aircraft cabins are evaluated in this chapter. A laser diode (LD) with four wavelengths (red, green, yellow and blue) is used to provide high bandwidth for communication and white light for illumination. Also, an angle diversity receiver (ADR) is utilized to receive signals and reduce noise and interference by exploiting the spatial domain.
Personal Identification Using Ultrawideband Radar Measurement of Walking and Sitting Motions and a Convolutional Neural Network
Authors: Takuya Sakamoto
Date: 2020-08-05 00:00:00
Tasks: BIG-bench Machine Learning

This study proposes a personal identification technique that applies machine learning with a two-layered convolutional neural network to spectrogram images obtained from radar echoes of a target person in motion. The walking and sitting motions of six participants were measured using an ultrawideband radar system. Time-frequency analysis was applied to the radar signal to generate spectrogram images containing the micro-Doppler components associated with limb movements. A convolutional neural network was trained using the spectrogram images with personal labels to achieve radar-based personal identification. The personal identification accuracies were evaluated experimentally to demonstrate the effectiveness of the proposed technique.
Max-Min Fair Precoder Design and Power Allocation for MU-MIMO NOMA
Authors: Ahmet Zahid Yalcin, Mustafa Kagan Cetin, Melda Yuksel
Date: 2019-11-21 00:00:00
Tasks: Fairness

In this paper, a downlink multiple input multiple output (MIMO) non-orthogonal multiple access (NOMA) wireless communication system is considered. In NOMA systems, the base station has unicast data for all users, and multiple users in a group share the same resources. The objective is to design transmit precoders and power allocation coefficients jointly that provide max-min fairness (MMF) among the strongest users in each group, while maintaining minimum target rates for all the other users. The problem is solved via two main iterative approaches. The first method is based on semi-definite relaxation (SDR) and successive convex approximation (SCA), and the second method is based on the equivalency between achievable rate and minimum mean square error (MMSE) expressions. For the latter approach, Karush-Kuhn-Tucker (KKT) optimality conditions are derived and the expressions satisfied by the optimal receivers, MMSE weights and the optimal precoders are obtained. Proposed algorithms are compared with rate-splitting (RS), orthogonal multiple access (OMA) and multi-user linear precoding (MULP) schemes in terms of MMF rates, energy efficiency and complexity. It is shown that while RS has the best MMF rates and energy efficiency, the MMSE approach based on KKT optimality conditions has the least complexity. Moreover, the SDR/SCA approach offers an excellent tradeoff. It offers high MMF rates, low complexity and superior energy efficiency.
Time-Stretched Femtosecond Lidar Using Microwave Photonic Signal Processing
Authors: Lijie Zhao, Haiyun Xia, Yihua Hu, Tengfei Wu, Zhen Zhang, Jibo Han, Yunbin Wu, Tiancheng Luo
Date: 2020-05-29 00:00:00

A real-time ranging lidar with 0.1 Mega Hertz update rate and few-micrometer resolution incorporating dispersive Fourier transformation and instantaneous microwave frequency measurement is proposed and demonstrated. As time-stretched femtosecond laser pulse passing through an all-fiber Mach-Zehnder Interferometer, where the detection light beam is inserted into the optical path of one arm, the displacement is encoded to the frequency variation of the temporal interferogram. To deal with the challenges in storage and real-time processing of the microwave pulse generated on a photodetector, we turn to all-optical signal processing. A carrier wave is modulated by the time-domain interferogram using an intensity modulator. After that, the frequency variation of the microwave pulse is uploaded to the first order sidebands. Finally, the frequency shift of the sidebands is turned into transmission change through a symmetric-locked frequency discriminator. In experiment, A real-time ranging system with adjustable dynamic range and detection sensitivity is realized by incorporating a programmable optical filter. Standard deviation of 7.64 {\mu}m, overall mean error of 19.10 {\mu}m over 15 mm detection range and standard deviation of 37.73 {\mu}m, overall mean error of 36.63 {\mu}m over 45 mm detection range are obtained respectively.
SER Analysis for SWIPT-Enabled Differential Decode-and-Forward Relay Networks
Authors: Yuxin Lu, Wai Ho Mow
Date: 2020-07-27 00:00:00

In this paper, we analyze the symbol error rate (SER) performance of the simultaneous wireless information and power transfer (SWIPT) enabled three-node differential decode-and-forward (DDF) relay networks, which adopt the power splitting (PS) protocol at the relay. The use of non-coherent differential modulation eliminates the need for sending training symbols to estimate the instantaneous channel state information (CSI) at all network nodes, and therefore improves the power efficiency, as compared with the coherent modulation. However, performance analysis results are not yet available for the state-of-the-art detectors such as the maximum-likelihood detector (MLD) and approximate MLD. Existing works rely on the Monte-Carlo simulation method to show the existence of an optimal PS ratio that minimizes the overall SER. In this work, we propose a near-optimal detector with linear complexity with respect to the modulation size. We derive an approximate SER expression and prove that the proposed detector achieves the full diversity order. Based on our expression, the optimal PS ratio can be accurately estimated without requiring any Monte-Carlo simulation. We also extend the proposed detector and its SER analysis for adopting the time switching (TS) protocol at the relay. Simulation results verify the effectiveness of our proposed detector and the accuracy of our SER results in various network scenarios for both PS and TS protocols.
The agnostic sampling transceiver
Authors: Arijit Misra, Janosch Meier, Karanveer Singh, Stefan Preussler, Thomas Schneider
Date: 2020-08-05 00:00:00

Increasing capacity demands in the access networks require inventive concepts for the transmission and distribution of digital as well as analog signals over the same network. Here a new transceiver system, which is completely agnostic for the signals to be transmitted is presented. Nyquist sampling and time multiplexing of N phase and intensity modulated digital and analog channels with one single modulator, as well as the transmission and demultiplexing with another modulator have been demonstrated. The aggregate symbol rate corresponds to the modulator bandwidth and can be further increased by a modification of the setup. No high-speed electronic signal processing or high bandwidth photonics is required. Apart from its simplicity and the possibility to process high bandwidth signals with low bandwidth electronics and photonics, the method has the potential to be easily integrated into any platform and thus, might be a solution for the increasing data rates in future access networks.
Max-min Fairness of K-user Cooperative Rate-Splitting in MISO Broadcast Channel with User Relaying
Authors: Yijie Mao, Bruno Clerckx, Jian Zhang, Victor O. K. Li, Mohammed Arafah
Date: 2019-10-17 00:00:00
Tasks: Fairness, Scheduling

Cooperative Rate-Splitting (CRS) strategy, relying on linearly precoded rate-splitting at the transmitter and opportunistic transmission of the common message by the relaying user, has recently been shown to outperform typical Non-cooperative Rate-Splitting (NRS), Cooperative Non-Orthogonal Multiple Access (C-NOMA) and Space Division Multiple Access (SDMA) in a two-user Multiple Input Single Output (MISO) Broadcast Channel (BC) with user relaying. In this work, the existing two-user CRS transmission strategy is generalized to the K-user case. We study the problem of jointly optimizing the precoders, message split, time slot allocation, and relaying user scheduling with the objective of maximizing the minimum rate among users. An efficient self-organizing relaying protocol is first proposed followed by a Successive Convex Approximation (SCA)-based algorithm to jointly optimize time slot, precoders and message split. Numerical results show that the worst-case achievable rate achieved by CRS is significantly increased over that of NRS and SDMA in a wide range of network loads and user deployments. Importantly, the proposed SCA-based algorithm dramatically reduces the computational complexity without any rate loss compared with the conventional algorithm in the literature of CRS. Therefore, we conclude that the proposed K-user CRS is more powerful than the existing transmission schemes.
Deep Reinforcement Learning for Field Development Optimization
Authors: Yusuf Nasir
Date: 2020-08-05 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The field development optimization (FDO) problem represents a challenging mixed-integer nonlinear programming (MINLP) problem in which we seek to obtain the number of wells, their type, location, and drilling sequence that maximizes an economic metric. Evolutionary optimization algorithms have been effectively applied to solve the FDO problem, however, these methods provide only a deterministic (single) solution which are generally not robust towards small changes in the problem setup. In this work, the goal is to apply convolutional neural network-based (CNN) deep reinforcement learning (DRL) algorithms to the field development optimization problem in order to obtain a policy that maps from different states or representation of the underlying geological model to optimal decisions. The proximal policy optimization (PPO) algorithm is considered with two CNN architectures of varying number of layers and composition. Both networks obtained policies that provide satisfactory results when compared to a hybrid particle swarm optimization - mesh adaptive direct search (PSO-MADS) algorithm that has been shown to be effective at solving the FDO problem.
Optimal Pooling Matrix Design for Group Testing with Dilution (Row Degree) Constraints
Authors: Jirong Yi, Myung Cho, Xiaodong Wu, Raghu Mudumbai, Weiyu Xu
Date: 2020-08-05 00:00:00

In this paper, we consider the problem of designing optimal pooling matrix for group testing (for example, for COVID-19 virus testing) with the constraint that no more than $r>0$ samples can be pooled together, which we call "dilution constraint". This problem translates to designing a matrix with elements being either 0 or 1 that has no more than $r$ '1's in each row and has a certain performance guarantee of identifying anomalous elements. We explicitly give pooling matrix designs that satisfy the dilution constraint and have performance guarantees of identifying anomalous elements, and prove their optimality in saving the largest number of tests, namely showing that the designed matrices have the largest width-to-height ratio among all constraint-satisfying 0-1 matrices.
Ergodic capacity analysis of reconfigurable intelligent surface assisted wireless systems
Authors: Alexandros-Apostolos A. Boulogeorgos, Angeliki Alexiou
Date: 2020-08-05 00:00:00

This paper presents the analytic framework for evaluating the ergodic capacity (EC) of the reconfigurable intelligent surface (RIS) assisted systems. Moreover, high-signal-to-noise-ratio and high-number of reflection units (RUs) approximations for the EC are provided. Finally, the special case in which the RIS is equipped with a single RU is investigated. Our analysis is verified through respective Monte Carlo simulations, which highlight the accuracy of the proposed framework.
Machine Learning and Feature Engineering for Predicting Pulse Status during Chest Compressions
Authors: Diya Sashidhar, Heemun Kwok, Jason Coult, Jen Blackwood, Peter Kudenchuck, Shiv Bhandari, Thomas Rea, J. Nathan Kutz
Date: 2020-08-05 00:00:00
Tasks: BIG-bench Machine Learning, Feature Engineering, Rhythm

Objective: Current resuscitation protocols require pausing chest compressions during cardiopulmonary resuscitation (CPR) to check for a pulse. However, pausing CPR during a pulseless rhythm can worsen patient outcome. Our objective is to design an ECG-based algorithm that predicts pulse status during uninterrupted CPR and evaluate its performance. Methods: We evaluated 383 patients being treated for out-of-hospital cardiac arrest using defibrillator data. We collected paired and immediately adjacent ECG segments having an organized rhythm. Segments were collected during the 10s period of ongoing CPR prior to a pulse check, and 5s segments without CPR during the pulse check. ECG segments with or without a pulse were identified by the audio annotation of a paramedic's pulse check findings and recorded blood pressures. We developed an algorithm to predict the clinical pulse status based on the wavelet transform of the bandpass-filtered ECG, applying principle component analysis. We then trained a linear discriminant model using 3 principle component modes. Model performance was evaluated on test group segments with and without CPR using receiver operating curves and according to the initial arrest rhythm. Results: There were 230 patients (540 pulse checks) in the training set and 153 patients (372 pulse checks) in the test set. Overall 38% (351/912) of checks had a spontaneous pulse. The areas under the receiver operating characteristic curve (AUCs) for predicting pulse status with and without CPR on test data were 0.84 and 0.89, respectively. Conclusion: A novel ECG-based algorithm demonstrates potential to improve resuscitation by predicting presence of a spontaneous pulse without pausing CPR. Significance: Our algorithm predicts pulse status during uninterrupted CPR, allowing for CPR to proceed unimpeded by pauses to check for a pulse and potentially improving resuscitation performance.
Secrecy Limits of Energy Harvesting IoT Networks under Channel Imperfections
Authors: Furqan Jameel, Zheng Chang, Riku Jantti
Date: 2020-08-04 00:00:00

Simultaneous wireless information and power transfer (SWIPT) has recently gathered much research interest from both academia and industry as a key enabler of energy harvesting Internet-of-things (IoT) networks. Due to a number of growing use cases of such networks, it is important to study their performance limits from the perspective of physical layer security (PLS). With this intent, this work aims to provide a novel analysis of the ergodic secrecy capacity of a SWIPT system is provided for Rician and Nakagami-m faded communication links. For a realistic evaluation of the system, the imperfections of channel estimations for different receiver designs of the SWIPT-based IoT systems have been taken into account. Subsequently, the closedform expressions of the ergodic secrecy capacities for the considered scenario are provided and, then, validated through extensive simulations. The results indicate that an error ceiling appears due to imperfect channel estimation at high values of signal-to-noise ratio (SNR). More importantly, the secrecy capacity under different channel conditions stops increasing beyond a certain limit, despite an increase of the main link SNR. The in-depth analysis of secrecy-energy trade-off has also been performed and a comparison has been provided for imperfect and perfect channel estimation cases. As part of the continuous evolution of IoT networks, the results provided in this work can help in identifying the secrecy limits of IoT networks in the presence of multiple eavesdroppers.
Voronoi Shaping with Efficient Encoding
Authors: H. Buglia, R. R. Lopes
Date: 2020-04-20 00:00:00

In this letter, we propose a Voronoi shaping method with reduced encoding complexity. The method works for integer shaping and coding lattices satisfying the chain $\Lambda_s \subseteq \textbf{K}\mathbb{Z}^n \subseteq \Lambda_c$, with $\textbf{K}$ an integer diagonal matrix. This assumption is easily satisfied for lattices obtained from error-correcting codes. For those lattices, using this strategy, an explicit set of coset representatives is obtained and lattice encoding complexity is reduced to the linear code encoding complexity. Our proposal is illustrated in construction-D lattices with Gosset ($E_8$) and Leech ($\Lambda_{24}$) lattices as shaping lattices.
Radar Adaptive Detection Architectures for Heterogeneous Environments
Authors: Jun Liu, Davide Massaro, Danilo Orlando, Alfonso Farina
Date: 2020-08-04 00:00:00

In this paper, four adaptive radar architectures for target detection in heterogeneous Gaussian environments are devised. The first architecture relies on a cyclic optimization exploiting the Maximum Likelihood Approach in the original data domain, whereas the second detector is a function of transformed data which are normalized with respect to their energy and with the unknown parameters estimated through an Expectation-Maximization-based alternate procedure. The remaining two architectures are obtained by suitably combining the estimation procedures and the detector structures previously devised. Performance analysis, conducted on both simulated and measured data, highlights that the architecture working in the transformed domain guarantees the constant false alarm rate property with respect to the interference power variations and a limited detection loss with respect to the other detectors, whose detection thresholds nevertheless are very sensitive to the interference power.