pwc-archive/papers-with-abstracts huggingface dataset:
Chaos and order in event-triggered control
Authors: Gabriel de Albuquerque Gleizer, Manuel Mazo Jr
Date: 2022-01-12 00:00:00

Event-triggered control (ETC) is claimed to provide enormous reductions in sampling frequency when compared to periodic sampling, but little is formally known about its generated traffic. This work shows that ETC can exhibit very complex, even chaotic traffic, especially when the triggering condition is aggressive in reducing communications. First, we characterize limit traffic patterns by observing invariant lines and planes through the origin, as well as their attractivity. Then, we present abstraction-based methods to compute limit metrics, such as limit average and limit inferior inter-sample time (IST) of periodic ETC (PETC), with considerations to the robustness of such metrics, as well as measuring the emergence of chaos. The methodology and tools allow us to find ETC examples that provably outperform periodic sampling in terms of average IST. In particular for PETC, we prove that this requires aperiodic or chaotic traffic.
Robust path-following control design of heavy vehicles based on multiobjective evolutionary optimization
Authors: Gustavo Alves Prudencio de Morais, Lucas Barbosa Marcos, Filipe Marques Barbosa, Bruno Henrique Groenner Barbosa, Marco Henrique Terra, Valdir Grassi Jr
Date: 2020-10-14 00:00:00
Tasks: Autonomous Navigation, Multiobjective Optimization

The ability to deal with systems parametric uncertainties is an essential issue for heavy self-driving vehicles in unconfined environments. In this sense, robust controllers prove to be efficient for autonomous navigation. However, uncertainty matrices for this class of systems are usually defined by algebraic methods which demand prior knowledge of the system dynamics. In this case, the control system designer depends on the quality of the uncertain model to obtain an optimal control performance. This work proposes a robust recursive controller designed via multiobjective optimization to overcome these shortcomings. Furthermore, a local search approach for multiobjective optimization problems is presented. The proposed method applies to any multiobjective evolutionary algorithm already established in the literature. The results presented show that this combination of model-based controller and machine learning improves the effectiveness of the system in terms of robustness, stability and smoothness.
Online Voltage Control for Unbalanced Distribution Networks Using Projected Newton Method
Authors: Rui Cheng, Zhaoyu Wang, Yifei Guo, Qianzhi Zhang
Date: 2022-01-11 00:00:00

This paper proposes an online voltage control strategy of distributed energy resources (DERs), based on the projected Newton method (PNM), for unbalanced distribution networks. The optimal Volt/VAr control (VVC) problem is formulated as an optimization program, with the goal of maintaining the voltage profile across the network by coordinating the VAr outputs of DERs. To overcome the slow convergence rate of conventional gradient-based methods, a PNM-based solution algorithm is developed to solve this VVC problem. It utilizes a non-diagonal symmetric positive definite matrix, developed from the Hessian matrix of the objective, to scale the gradient, and thus a fast convergence performance can be expected in this Newton-like algorithm. Moreover, taking advantage of the instantaneous feedback of voltage measurements, the online implementation of the PNM-based VVC is further designed to deal with fast system variations. In this online PNM-based VVC scheme, each bus agent communicates the instantaneous voltage measurements to the central agent, and the central agent communicates the VAr output commands of DERs back to each bus agent. The fast convergence performance of PNM results in a stronger capability to track the system variations in real time. Finally, numerical case studies are performed to validate the effectiveness, superiority, and scalability of the proposed method.
Model-less Robust Voltage Control in Active Distribution Networks using Sensitivity Coefficients Estimated from Measurements
Authors: Rahul Gupta, Fabrizio Sossan, Mario Paolone
Date: 2022-01-11 00:00:00
Tasks: Sensitivity

Measurement-rich power distribution networks may enable distribution system operators (DSOs) to adopt model-less and measurement-based monitoring and control of distributed energy resources (DERs) for mitigating grid issues such as over/under voltages and lines congestions. However, measurement-based monitoring and control applications may lead to inaccurate control decisions due to measurement errors. In particular, estimation models relying on regression-based schemes result in significant errors in the estimates (e.g., nodal voltages) especially for measurement devices with high Instrument Transformer (IT) classes. The consequences are detrimental to control performance since this may lead to infeasible decisions. This work proposes a model-less robust voltage control accounting for the uncertainties of measurement-based estimated voltage sensitivity coefficients. The coefficients and their uncertainties are obtained using a recursive least squares (RLS)-based online estimation, updated whenever new measurements are available. This formulation is applied to control distributed controllable photovoltaic (PV) generation in a distribution network to restrict the voltage within prescribed limits. The proposed scheme is validated by simulating a CIGRE low-voltage system interfacing multiple controllable PV plants.
The Observability in Unobservable Systems
Authors: Wei Kang, Liang Xu, Hong Zhou
Date: 2022-01-11 00:00:00

In this paper, we introduce the concept of observability of targeted state variables for systems that may not be fully observable. For their estimation, we introduce and exemplify a deep filter, which is a neural network specifically designed for the estimation of targeted state variables without computing the trajectory of the entire system. The observability definition is quantitative rather than a yes or no answer so that one can compare the level of observability between different sensor locations.
Ultrasound Strain Imaging using ADMM
Authors: Md Ashikuzzaman, Hassan Rivaz
Date: 2022-01-12 00:00:00

Ultrasound strain imaging, which delineates mechanical properties to detect tissue abnormalities, involves estimating the time-delay between two radio-frequency (RF) frames collected before and after tissue deformation. The existing regularized optimization-based time-delay estimation (TDE) techniques suffer from at least one of the following drawbacks: 1) The regularizer is not aligned with tissue deformation physics due to taking only the first-order displacement derivative into account. 2) The L2-norm of the displacement derivatives, which oversmooths the estimated time-delay, is utilized as the regularizer. 3) The absolute value function should be approximated by a smooth function to facilitate the optimization of L1-norm. Herein, to resolve these shortcomings, we propose employing the alternating direction method of multipliers (ADMM) for optimizing a novel cost function consisting of L2-norm data fidelity term and L1-norm first- and second-order spatial continuity terms. ADMM empowers the proposed algorithm to use different techniques for optimizing different parts of the cost function and obtain high-contrast strain images with smooth background and sharp boundaries. We name our technique ADMM for totaL variaTion RegUlarIzation in ultrasound STrain imaging (ALTRUIST). In extensive simulation, phantom, and in vivo experiments, ALTRUIST substantially outperforms GLUE, OVERWIND, and L1-SOUL, three recently-published TDE algorithms, both qualitatively and quantitatively. ALTRUIST yields 118%, 104%, and 72% improvements of contrast-to-noise ratio over L1-SOUL for simulated, phantom, and in vivo liver cancer datasets, respectively. We will publish the ALTRUIST code after the acceptance of this paper at http://code.sonography.ai.
Accelerated Projected Gradient Method for the Optimization of Cell-Free Massive MIMO Downlink
Authors: Muhammad Farooq, Hien Quoc Ngo, Le-Nam Tran
Date: 2022-01-12 00:00:00
Tasks: Second-order methods

We consider the downlink of a cell-free massive multiple-input multiple-output (MIMO) system where large number of access points (APs) simultaneously serve a group of users. Two fundamental problems are of interest, namely (i) to maximize the total spectral efficiency (SE), and (ii) to maximize the minimum SE of all users. As the considered problems are non-convex, existing solutions rely on successive convex approximation to find a sub-optimal solution. The known methods use off-the-shelf convex solvers, which basically implement an interior-point algorithm, to solve the derived convex problems. The main issue of such methods is that their complexity does not scale favorably with the problem size, limiting previous studies to cell-free massive MIMO of moderate scales. Thus the potential of cell-free massive MIMO has not been fully understood. To address this issue, we propose an accelerated projected gradient method to solve the considered problems. Particularly, the proposed solution is found in closed-form expressions and only requires the first order information of the objective, rather than the Hessian matrix as in known solutions, and thus is much more memory efficient. Numerical results demonstrate that our proposed solution achieves far less run-time, compared to other second-order methods.
Optimal Fractional Fourier Filtering in Time-vertex Graphs signal processing
Authors: Zirui Ge, Haiyan Guo, Tingting Wang, Zhen Yang
Date: 2022-01-12 00:00:00

Graph signal processing (GSP) is an effective tool in dealing with data residing in irregular domains. In GSP, the optimal graph filter is one of the essential techniques, owing to its ability to recover the original signal from the distorted and noisy version. However, most current research focuses on static graph signals and ordinary space/time or frequency domains. The time-varying graph signals have a strong ability to capture the features of real-world data, and fractional domains can provide a more suitable space to separate the signal and noise. In this paper, the optimal time-vertex graph filter and its Wiener-Hopf equation are developed, using the product graph framework. Furthermore, the optimal time-vertex graph filter in fractional domains is also developed, using the graph fractional Laplacian operator and graph fractional Fourier transform. Numerical simulations on real-world datasets will demonstrate the superiority of the optimal time-vertex graph filter in fractional domains over the optimal time-vertex graph filter in ordinary domains and the optimal static graph filter in fractional domains.
Joint Hybrid and Passive RIS-Assisted Beamforming for MmWave MIMO Systems Relying on Dynamically Configured Subarrays
Authors: Chenghao Feng, Wenqian Shen, Jianping An, Lajos Hanzo
Date: 2022-01-12 00:00:00

Reconfigurable intelligent surface (RIS) assisted millimeter-wave (mmWave) communication systems relying on hybrid beamforming structures are capable of achieving high spectral efficiency at a low hardware complexity and low power consumption. In this paper, we propose an RIS-assisted mmWave point-to-point system relying on dynamically configured sub-array connected hybrid beamforming structures. More explicitly, an energy-efficient analog beamformer relying on twin-resolution phase shifters is proposed. Then, we conceive a successive interference cancelation (SIC) based method for jointly designing the hybrid beamforming matrix of the base station (BS) and the passive beamforming matrix of the RIS. Specifically, the associated bandwidth-efficiency maximization problem is transformed into a series of sub-problems, where the sub-array of phase shifters and RIS elements are jointly optimized for maximizing each sub-array's rate. Furthermore, a greedy method is proposed for determining the phase shifter configuration of each sub-array. We then propose to update the RIS elements relying on a complex circle manifold (CCM)-based method. The proposed dynamic sub-connected structure as well as the proposed joint hybrid and passive beamforming method strikes an attractive trade-off between the bandwidth efficiency and power consumption. Our simulation results demonstrate the superiority of the proposed method compared to its traditional counterparts.
Boosted-SpringDTW for Comprehensive Feature Extraction of Physiological Signals
Authors: Jonathan Martinez, Kaan Sel, Bobak J. Mortazavi, Roozbeh Jafari
Date: 2022-01-11 00:00:00
Tasks: Dynamic Time Warping, parameter estimation

Goal: To achieve-high quality comprehensive feature extraction from physiological signals that enables precise physiological parameter estimation despite evolving waveform morphologies. Methods: We propose Boosted-SpringDTW, a probabilistic framework that leverages dynamic time warping (DTW) and minimal domain-specific heuristics to simultaneously segment physiological signals and identify fiducial points that represent cardiac events. An automated dynamic template adapts to evolving waveform morphologies. We validate Boosted-SpringDTW performance with a benchmark PPG dataset whose morphologies include subject- and respiratory-induced variation. Results: Boosted-SpringDTW achieves precision, recall, and F1-scores over 0.96 for identifying fiducial points and mean absolute error values less than 11.41 milliseconds when estimating IBI. Conclusion: Boosted-SpringDTW improves F1-Scores compared to two baseline feature extraction algorithms by 35 percent on average for fiducial point identification and mean percent difference by 16 percent on average for IBI estimation. Significance: Precise hemodynamic parameter estimation with wearable devices enables continuous health monitoring throughout a patients' daily life.
Modeling Homophily in Dynamic Networks with Application to HIV Molecular Surveillance
Authors: V. DeGruttola, M. Nakazawa, J. Liu, X. Tu, S. Little, S. Mehta
Date: 2021-12-29 00:00:00

This paper describes a novel approach to modeling homphily, i.e. the tendency of nodes that share (or differ in) certain attributes to be linked; we consider dynamic networks in which nodes can be added over time but not removed. Our application is to HIV genetic linkage analysis that has been used to investigate HIV transmission dynamics. In this setting, two HIV sequences from different persons with HIV (PWH) are said to be linked if the genetic distance between these sequences is less than a given threshold. Such linkage suggests that that the nodes representing the two infected PWH, are close to each other in a transmission network; such proximity would imply that either one of the infected people directly transmitted the virus to the other or indirectly transmitted it through a small number of intermediaries. These viral genetic linkage networks are dynamic in the sense that, over time, a group or cluster of genetically linked viral sequences may increase in size as new people are infected by those in the cluster either directly or through intermediaries. Our approach makes use of a logistic model to describe homophily with regard to demographic and behavioral characteristics that is we investigate whether similarities (or differences) between PWH in these characteristics impacts the probability that their sequences are be linked. Such analyses provide information about HIV transmission dynamics within a population.
Seizure prediction with long-term iEEG recordings: What can we learn from data nonstationarity?
Authors: Hongliu Yang, Matthias Eberlein, Jens Müller, Ronald Tetzlaff
Date: 2022-01-11 00:00:00
Tasks: Prediction, Seizure prediction, Time Series, Time Series Analysis

Repeated epileptic seizures impair around 65 million people worldwide and a successful prediction of seizures could significantly help patients suffering from refractory epilepsy. For two dogs with yearlong intracranial electroencephalography (iEEG) recordings, we studied the influence of time series nonstationarity on the performance of seizure prediction using in-house developed machine learning algorithms. We observed a long-term evolution on the scale of weeks or months in iEEG time series that may be represented as switching between certain meta-states. To better predict impending seizures, retraining of prediction algorithms is therefore necessary and the retraining schedule should be adjusted to the change in meta-states. There is evidence that the nature of seizure-free interictal clips also changes with the transition between meta-states, accwhich has been shown relevant for seizure prediction.
Brain Signals Analysis Based Deep Learning Methods: Recent advances in the study of non-invasive brain signals
Authors: Almabrok Essa, Hari Kotte
Date: 2021-12-30 00:00:00
Tasks: Computed Tomography (CT), EEG, Electroencephalogram (EEG)

Brain signals constitute the information that are processed by millions of brain neurons (nerve cells and brain cells). These brain signals can be recorded and analyzed using various of non-invasive techniques such as the Electroencephalograph (EEG), Magneto-encephalograph (MEG) as well as brain-imaging techniques such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and others, which will be discussed briefly in this paper. This paper discusses about the currently emerging techniques such as the usage of different Deep Learning (DL) algorithms for the analysis of these brain signals and how these algorithms will be helpful in determining the neurological status of a person by applying the signal decoding strategy.
In vitro evaluation of the effect of Ceftiofur Sodium and of a new Gentamycin Sulfate formulation on the viability of Marek disease virus
Authors: J. Chacon, M. Pimentel, A. Pedroso, A. Ferreira, D. Martinez, C. Ruelas
Date: 2022-01-12 00:00:00

The present study evaluated In vitro effect of gentamicin sulfate and ceftiofur sodium on the viability of the Marek's disease virus. The titer of cell associated turkey herpesvirus (HVT) vaccine was not appreciably reduced when incubated with 50 mg/ml of gentamicin sulfate or ceftiofur sodium. Statistic difference was not found between the number of plaqueforming units (PFU) of reconstituted vaccine associated with both antibiotics 0, 15, 30 and 60 minutes after reconstitution of vaccine. The antibiotics did not considerably alter the pH values. There was a significative decrease of the titer of all vaccinal solutions when they were inoculated 30 and 60 minutes after the reconstitution of the vaccine. Nevertheless, these titers are higher than the required titers to protectect against the Marek disease.
Effect of an antimicrobial association (Respirend-M) on productive performance of broilers
Authors: Mario Chuy, Marcial Cumpa, Diego Martinez
Date: 2022-01-12 00:00:00

The objective of this study was to determine the effect of feeding an antimicrobial association to males broiler chickens from 0 to 14 days of age on the performance and clinical signs of bacterial processes. Two treatments were tested: a control treatment without Respirend-M, and a treatment with the antimicrobial (300 g/t Respirend-M, 0-14 d). 398 male Cobb 500 chicks were randomly assigned to two treatments and four replications per treatment. Body weight at 14 d, body weight gain, feed intake, feed conversion ratio, and health status were evaluated. The results showed feeding 300 g/t Respirend-M significantly (P<0.05) improved the final body weight, the body weight gain and the feed intake. No statistically significant (P>0.05) effect was detected on the feed conversion ratio.
Effect of a mixture of amoxicillin and norfloxacin in piglets feeding on productive performance and clinical signs
Authors: Diego Martinez, Enrique Alvarado, Carlos Vilchez
Date: 2022-01-12 00:00:00

An experiment was conducted to evaluate the effect of feeding post-weaned piglets different levels of a commercial amoxicillin plus norfloxacin formula (Respirend) on the performance, clinical evidence of sickness, and the cost:benefit ratio. 454 PIC x Camborough female and castrated male, weaned at 21 d of age, with an average initial weight of 6.19 kg, were used in this experiment. Treatment 1 consisted in feeding a basal diet supplemented with 1000 g of a commercial tilmicosin product (Pulmotil) per metric tonne of feed (control group, from 21 to 35 d of age) and Treatments 2), 3) and 4) consisted in feeding the basal diet supplement with 500, 300 y 100 g of Respirend per metric tonne of feed, respectively, from 21 to 42 d of age. The results did not show significant differences (P<0.05) in mortality nor in parenteral medicine administration cost in none of the periods. Nevertheless, 500 g of Respirend per metric tonne of feed improved significantly (P<0.05) the general health status in the period between 21 and 35 d of age. For the whole evaluation period (21 to 42 d of age), this level improved significantly (P<0.05) the final body weight, the average daily weight gain, the average daily feed intake, the feed:gain ratio and optimized the cost:benefit ratio.
Inferring characteristics of bacterial swimming in biofilm matrix from time-lapse confocal laser scanning microscopy
Authors: Guillaume Ravel, Michel Bergmann, Alain Trubuil, Julien Deschamps, Romain Briandet, Simon Labarthe
Date: 2022-01-12 00:00:00

Biofilms are spatially organized microorganism colonies embedded in a self-produced matrix, conferring to the microbial community resistance to environmental stresses. Motile bacteria have been observed swimming in the matrix of pathogenic exogeneous host biofilms. This observation opened new promising routes for deleterious biofilms biocontrol: these bacterial swimmers enhance biofilm vascularization for chemical treatment or could deliver biocontrol agent by microbial hitchhiking or local synthesis. %\cite{muok2021microbial,yu2020hitchhiking,samad2017swimming}. Hence, characterizing swimmer trajectories in the biofilm matrix is of particular interest to understand and optimize its biocontrol.In this study, a new methodology is developed to analyze time-lapse confocal laser scanning images to describe and compare the swimming trajectories of bacterial swimmers populations and their adaptations to the biofilm structure. The method is based on the inference of a kinetic model of swimmer population including mechanistic interactions with the host biofilm. After validation on synthetic data, the methodology is implemented on images of three different motile {Bacillus species swimming in a Staphylococcus aureus biofilm. The fitted model allows to stratify the swimmer populations by their swimming behavior and provides insights into the mechanisms deployed by the micro-swimmers to adapt their swimming traits to the biofilm matrix.
Riemannian Geometry and Molecular Surfaces I: Spectrum of the Laplacian
Authors: Daniel J. Cole, Stuart J. Hall, Rachael Pirie
Date: 2022-01-10 00:00:00
Tasks: Drug Discovery

Ligand-based virtual screening aims to reduce the cost and duration of drug discovery campaigns. Shape similarity can be used to screen large databases, with the goal of predicting potential new hits by comparing to molecules with known favourable properties. This paper presents the theory underpinning RGMolSA, a new alignment-free and mesh-free surface-based molecular shape descriptor derived from the mathematical theory of Riemannian geometry. The treatment of a molecule as a series of intersecting spheres allows the description of its surface geometry using the Riemannian metric, obtained by considering the spectrum of the Laplacian. This gives a simple vector descriptor constructed of the weighted surface area and eight non-zero eigenvalues, which capture the surface shape. We demonstrate the potential of our method by considering a series of PDE5 inhibitors that are known to have similar shape as an initial test case. RGMolSA displays promise when compared to existing shape descriptors and in its capability to handle different molecular conformers. The code and data used to produce the results are available via GitHub: https://github.com/RPirie96/RGMolSA.