pwc-archive/papers-with-abstracts huggingface dataset:
Real-Time Distributed Model Predictive Control with Limited Communication Data Rates
Authors: Yujia Yang, Ye Wang, Chris Manzie, Ye Pu
Date: 2022-08-26 00:00:00
Tasks: Distributed Optimization, Model Predictive Control, Quantization

The application of distributed model predictive controllers (DMPC) for multi-agent systems (MASs) necessitates communication between agents, yet the consequence of communication data rates is typically overlooked. This work focuses on developing stability-guaranteed control methods for MASs with limited data rates. Initially, a distributed optimization algorithm with dynamic quantization is considered for solving the DMPC problem. Due to the limited data rate, the optimization process suffers from inexact iterations caused by quantization noise and premature termination, leading to sub-optimal solutions. In response, we propose a novel real-time DMPC framework with a quantization refinement scheme that updates the quantization parameters on-line so that both the quantization noise and the optimization sub-optimality decrease asymptotically. To facilitate the stability analysis, we treat the sub-optimally controlled MAS, the quantization refinement scheme, and the optimization process as three interconnected subsystems. The cyclic-small-gain theorem is used to derive sufficient conditions on the quantization parameters for guaranteeing the stability of the system under a limited data rate. Finally, the proposed algorithm and theoretical findings are demonstrated in a multi-AUV formation control example.
Extending the practical applicability of the Kalman Filter
Authors: J Humberto Ramos
Date: 2022-08-26 00:00:00

A Schmidt filter is a modification of the Kalman filter that allows to append system parameters as states and considers their uncertainty effect in the filtering process without attempting to estimate such parameters. The states that are only considered but not estimated, are generally known as \textit{consider} or \textit{considered} states. The main contributions of this research are the formulations of a Schmidt-Kalman filter that incorporates the numerical robustness of the well-known square root and factorized filtering forms plus the capacity of actively attempting to update the \textit{considered} states. The filters formulations proposed in this research are a fundamental extension of the Kalman filter. Therefore, the formulations of this work also apply within the Extended Kalman filter framework. More importantly, they are shown to handle nonlinearities, larger initial uncertainties, and poorly conditioned systems better than a typical Extended or Schmidt Kalman filter. Because the new filters are directly based on the Schmidt filter, they offer a novel and straight-forward filtering framework, allowing the use of a more simple filter where a more advanced or elaborated technique could have been needed.
Improved Pump Setpoint Selection Using a Calibrated Hydraulic Model of a High-Pressure Irrigation System
Authors: Ye Wang, Qi Zhao, Wenyan Wu, Ailsa Willis, Angus R. Simpson, Erik Weyer
Date: 2022-08-26 00:00:00
Tasks: Management

This paper presents a case study of the operational management of the Robinvale high-pressure piped irrigation water delivery system (RVHPS) in Australia. Based on datasets available, improved pump setpoint selection using a calibrated hydraulic model is investigated. The first step was to implement pre-processing of measured flow and pressure data to identify errors in the data and possible faulty sensors. An EPANET hydraulic simulation model was updated with calibrated pipe roughness height values by using the processed pressure and flow data. Then, new pump setpoints were selected using the calibrated model given the actual measured demands such that the pressures in the network were minimized subject to required customer service standards. Based on a two-day simulation, it was estimated that 4.7% savings in pumping energy cost as well as 4.7% reduction in greenhouse gas emissions can be achieved by applying the new pump setpoints.
A General Model-Based Extended State Observer with Built-In Zero Dynamics
Authors: Jinfeng Chen, Zhiqiang Gao, Yu Hu, Sally Shao
Date: 2022-08-25 00:00:00

A general model-based extended state observer (GMB-ESO) is proposed for single-input single-output linear time-invariant systems with a given state space model, where the total disturbance, a lump sum of model uncertainties and external disturbances, is defined as an extended state in the same manner as in the original formulation of ESO. The conditions for the existence of such an observer, however, are shown for the first time as 1) the original plant is observable; and 2) there is no invariant zero between the plant output and the total disturbance. Then, the finite-step convergence and error characteristics of GMB-ESO are shown by exploiting its inherent connection to the well-known unknown input observer (UIO). Furthermore, it is shown that, with the relative degree of the plant greater than one and the observer eigenvalues all placed at the origin, GMB-ESO produces the identical disturbance estimation as that of UIO. Finally, an improved GMB-ESO with built-in zero dynamics is proposed for those plants with zero dynamics, which is a problem that has not been addressed in all existing ESO designs.
The Effect of Frequency Droop Damping on System Parameters and Battery Sizing During Load Change Condition
Authors: Mohammed F. Allehyani, Mohamed Abuagreb, Brian K. Johnson
Date: 2022-08-25 00:00:00

Inverter-based resources (IBR) have been widely studied for their advantages on the current power systems. This increase in the penetration of renewable energy has raised some concerns about the stability of the existing grid. Historically, power systems are dominated by synchronous generators that can easily react to system instability due to high inertia and damping characteristics. However, with IBR, the control of the inverter plays a crucial role in contributing to the system stability and enhancing the functionality of the inverters. One of these novel control methods is droop control. Droop characteristics are used to control voltage, frequency, and active and reactive power. This paper presents the impact of frequency droop damping on system frequency, real power, and the rate of change of frequency with distributed energy resources. Also, battery sizing is suggested based on the results. The results also show the need for optimal selection for the frequency droop damping to fulfill the appropriate battery size in terms of cost and performance. The simulations are carried out in an electromagnetic transient program (EMTP)
Active wetting of epithelial tissues: modeling considerations
Authors: Ivana Pajic-Lijakovic, Milan Milivojevic
Date: 2022-08-26 00:00:00

Morphogenesis, tissue regeneration and cancer invasion involve transitions in tissue morphology. These transitions, caused by collective cell migration (CCM), have been interpreted as active wetting/de-wetting transitions. This phenomenon is considered on model system such as wetting of cell aggregate on rigid substrate which includes cell aggregate movement and isotropic/anisotropic spreading of cell monolayer around the aggregate depending on the substrate rigidity and aggregate size. This model system accounts for the transition between 3D epithelial aggregate and 2D cell monolayer as a product of: (1) tissue surface tension, (2) surface tension of substrate matrix, (3) cell-matrix interfacial tension, (4) interfacial tension gradient, (5) viscoelasticity caused by CCM, and (6) viscoelasticity of substrate matrix. These physical parameters depend on the cell contractility and state of cell-cell and cell matrix adhesion contacts, as well as, the stretching/compression of cellular systems caused by CCM. Despite extensive research devoted to study cell wetting, we still do not understand interplay among these physical parameters which induces oscillatory trend of cell rearrangement. This review focuses on these physical parameters in governing the cell rearrangement in the context of epithelial aggregate wetting.de-wetting, and on the modelling approaches aimed at reproducing and understanding these biological systems. In this context, we do not only review previously-published bio-physics models for cell rearrangement caused by CCM, but also propose new extensions of those models in order to point out the interplay between cell-matrix interfacial tension and epithelial viscoelasticity and the role of the interfacial tension gradient in cell spreading.
Modelling spatio-temporal trends of air pollution in Africa
Authors: Paterne Gahungu, Jean Remy Kubwimana, Lionel Jean Marie Benjamin Muhimpundu, Egide Ndamuzi
Date: 2022-08-21 00:00:00
Tasks: Gaussian Processes

Atmospheric pollution remains one of the major public health threat worldwide with an estimated 7 millions deaths annually. In Africa, rapid urbanization and poor transport infrastructure are worsening the problem. In this paper, we have analysed spatio-temporal variations of PM2.5 across different geographical regions in Africa. The West African region remains the most affected by the high levels of pollution with a daily average of 40.856 $\mu g/m^3$ in some cities like Lagos, Abuja and Bamako. In East Africa, Uganda is reporting the highest pollution level with a daily average concentration of 56.14 $\mu g/m^3$ and 38.65 $\mu g/m^3$ for Kigali. In countries located in the central region of Africa, the highest daily average concentration of PM2.5 of 90.075 $\mu g/m^3$ was recorded in N'Djamena. We compare three data driven models in predicting future trends of pollution levels. Neural network is outperforming Gaussian processes and ARIMA models.
VEViD: Vision Enhancement via Virtual diffraction and coherent Detection
Authors: Callen MacPhee, Bahram Jalali
Date: 2022-08-25 00:00:00
Tasks: 4k, Image Enhancement, object-detection, Object Detection

The history of computing started with analog computers consisting of physical devices performing specialized functions such as predicting the trajectory of cannon balls. In modern times, this idea has been extended, for example, to ultrafast nonlinear optics serving as a surrogate analog computer to probe the behavior of complex phenomena such as rogue waves. Here we discuss a new paradigm where physical phenomena coded as an algorithm perform computational imaging tasks. Specifically, diffraction followed by coherent detection, not in its analog realization but when coded as an algorithm, becomes an image enhancement tool. Vision Enhancement via Virtual diffraction and coherent Detection (VEViD) introduced here reimagines a digital image as a spatially varying metaphoric light field and then subjects the field to the physical processes akin to diffraction and coherent detection. The term "Virtual" captures the deviation from the physical world. The light field is pixelated and the propagation imparts a phase with an arbitrary dependence on frequency which can be different from the quadratic behavior of physical diffraction. Temporal frequencies exist in three bands corresponding to the RGB color channels of a digital image. The phase of the output, not the intensity, represents the output image. VEViD is a high-performance low-light-level and color enhancement tool that emerges from this paradigm. The algorithm is interpretable and computationally efficient. We demonstrate image enhancement of 4k video at 200frames per second and show the utility of this physical algorithm in improving the accuracy of object detection by neural networks without having to retrain model for low-light conditions. The application of VEViD to color enhancement is also demonstrated.
Large Volatility Matrix Analysis Using Global and National Factor Models
Authors: Sung Hoon Choi, Donggyu Kim
Date: 2022-08-25 00:00:00

Several large volatility matrix inference procedures have been developed, based on the latent factor model. They often assumed that there are a few of common factors, which can account for volatility dynamics. However, several studies have demonstrated the presence of local factors. In particular, when analyzing the global stock market, we often observe that nation-specific factors explain their own country's volatility dynamics. To account for this, we propose the Double Principal Orthogonal complEment Thresholding (Double-POET) method, based on multi-level factor models, and also establish its asymptotic properties. Furthermore, we demonstrate the drawback of using the regular principal orthogonal component thresholding (POET) when the local factor structure exists. We also describe the blessing of dimensionality using Double-POET for local covariance matrix estimation. Finally, we investigate the performance of the Double-POET estimator in an out-of-sample portfolio allocation study using international stocks from 20 financial markets.
Can Desegregation Close the Racial Gap in High School Coursework?
Authors: Ritika Sethi
Date: 2022-08-25 00:00:00
Tasks: Vocal Bursts Intensity Prediction

This paper examines the interplay between desegregation, institutional bias, and individual behavior in education. Using a game-theoretic model that considers race-heterogeneous social incentives, the study investigates the effects of between-school desegregation on within-school disparities in coursework. The analysis incorporates a segregation measure based on entropy and proposes an optimization-based approach to evaluate the impact of student reassignment policies. The results highlight that Black and Hispanic students in predominantly White schools, despite receiving less encouragement to apply to college, exhibit higher enrollment in college-prep coursework due to stronger social incentives from their classmates' coursework decisions.
Strategy-proof aggregation rules in median semilattices with applications to preference aggregation
Authors: Ernesto Savaglio, Stefano Vannucci
Date: 2022-08-26 00:00:00

Two characterizations of the whole class of strategy-proof aggregation rules on rich domains of locally unimodal preorders in finite median join-semilattices are provided. In particular, it is shown that such a class consists precisely of generalized weak sponsorship rules induced by certain families of order filters of the coalition poset. It follows that the co-majority rule and many other inclusive aggregation rules belong to that class. The co-majority rule for an odd number of agents is characterized and shown to be equivalent to a Condorcet-Kemeny median rule. Applications to preference aggregation rules including Arrowian social welfare functions are also considered. The existence of strategy-proof anonymous, weakly neutral and unanimity-respecting social welfare functions which are defined on arbitrary profiles of total preorders and satisfy a suitably relaxed independence condition is shown to follow from our characterizations.
Sub-aperture SAR Imaging with Uncertainty Quantification
Authors: Victor Churchill, Anne Gelb
Date: 2022-08-25 00:00:00
Tasks: Uncertainty Quantification

In the problem of spotlight mode airborne synthetic aperture radar (SAR) image formation, it is well-known that data collected over a wide azimuthal angle violate the isotropic scattering property typically assumed. Many techniques have been proposed to account for this issue, including both full-aperture and sub-aperture methods based on filtering, regularized least squares, and Bayesian methods. A full-aperture method that uses a hierarchical Bayesian prior to incorporate appropriate speckle modeling and reduction was recently introduced to produce samples of the posterior density rather than a single image estimate. This uncertainty quantification information is more robust as it can generate a variety of statistics for the scene. As proposed, the method was not well-suited for large problems, however, as the sampling was inefficient. Moreover, the method was not explicitly designed to mitigate the effects of the faulty isotropic scattering assumption. In this work we therefore propose a new sub-aperture SAR imaging method that uses a sparse Bayesian learning-type algorithm to more efficiently produce approximate posterior densities for each sub-aperture window. These estimates may be useful in and of themselves, or when of interest, the statistics from these distributions can be combined to form a composite image. Furthermore, unlike the often-employed lp-regularized least squares methods, no user-defined parameters are required. Application-specific adjustments are made to reduce the typically burdensome runtime and storage requirements so that appropriately large images can be generated. Finally, this paper focuses on incorporating these techniques into SAR image formation process. That is, for the problem starting with SAR phase history data, so that no additional processing errors are incurred.
IRIS: Integrated Retinal Functionality in Image Sensors
Authors: Zihan Yin, Md Abdullah-Al Kaiser, Lamine Ousmane Camara, Mark Camarena, Maryam Parsa, Ajey Jacob, Gregory Schwartz, Akhilesh Jaiswal
Date: 2022-08-14 00:00:00
Tasks: Decision Making

Neuromorphic image sensors draw inspiration from the biological retina to implement visual computations in electronic hardware. Gain control in phototransduction and temporal differentiation at the first retinal synapse inspired the first generation of neuromorphic sensors, but processing in downstream retinal circuits, much of which has been discovered in the past decade, has not been implemented in image sensor technology. We present a technology-circuit co-design solution that implements two motion computations occurring at the output of the retina that could have wide applications for vision based decision making in dynamic environments. Our simulations on Globalfoundries 22nm technology node show that, by taking advantage of the recent advances in semiconductor chip stacking technology, the proposed retina-inspired circuits can be fabricated on image sensing platforms in existing semiconductor foundries. Integrated Retinal Functionality in Image Sensors (IRIS) technology could drive advances in machine vision applications that demand robust, high-speed, energy-efficient and low-bandwidth real-time decision making.
Joint Performance Metrics for Integrated Sensing and Communication Systems in Automotive Scenarios
Authors: François De Saint Moulin, Charles Wiame, Luc Vandendorpe, Claude Oestges
Date: 2022-08-26 00:00:00
Tasks: Integrated sensing and communication

In this paper, multiple metrics are presented in order to jointly evaluate the performance of the radar and communication functions in scenarios involving Dual Function Radar Communication (DFRC) systems using stochastic geometry. These metrics are applied in an automotive scenario involving a two-lane road with vehicles and smart traffic lights, both equipped with DFRC systems. First, the performance achieved with these metrics are validated using Monte-Carlo (MC) simulations. Additionally, optimisation w.r.t. the power of the vehicles and smart traffic lights is performed based on the metrics. Then, the model is extended to include interference cancellation for the radar and/or communication function in all the metrics. Either full interference cancellation is applied, or a new model is proposed for imperfect interference cancellation.
Reinforcement Learning based Multi-connectivity Resource Allocation in Factory Automation Systems
Authors: Mohammad Farzanullah, Hung V. Vu, Tho Le-Ngoc
Date: 2022-08-26 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

We propose joint user association, channel assignment and power allocation for mobile robot Ultra-Reliable and Low Latency Communications (URLLC) based on multi-connectivity and reinforcement learning. The mobile robots require control messages from the central guidance system at regular intervals. We use a two-phase communication scheme where robots can form multiple clusters. The robots in a cluster are close to each other and can have reliable Device-to-Device (D2D) communications. In Phase I, the APs transmit the combined payload of a cluster to the cluster leader within a latency constraint. The cluster leader broadcasts this message to its members in Phase II. We develop a distributed Multi-Agent Reinforcement Learning (MARL) algorithm for joint user association and resource allocation (RA) for Phase I. The cluster leaders use their local Channel State Information (CSI) to decide the APs for connection along with the sub-band and power level. The cluster leaders utilize multi-connectivity to connect to multiple APs to increase their reliability. The objective is to maximize the successful payload delivery probability for all robots. Illustrative simulation results indicate that the proposed scheme can approach the performance of the centralized algorithm and offer a substantial gain in reliability as compared to single-connectivity (when cluster leaders are able to connect to 1 AP).
Graph-based sequential beamforming
Authors: Yongsung Park, Florian Meyer, Peter Gerstoft
Date: 2022-08-26 00:00:00
Tasks: Bayesian Inference

This paper presents a Bayesian estimation method for sequential direction finding. The proposed method estimates the number of directions of arrivals (DOAs) and their DOAs performing operations on the factor graph. The graph represents a statistical model for sequential beamforming. At each time step, belief propagation predicts the number of DOAs and their DOAs using posterior probability density functions (pdfs) from the previous time and a different Bernoulli-von Mises state transition model. Variational Bayesian inference then updates the number of DOAs and their DOAs. The method promotes sparse solutions through a Bernoulli-Gaussian amplitude model, is gridless, and provides marginal posterior pdfs from which DOA estimates and their uncertainties can be extracted. Compared to nonsequential approaches, the method can reduce DOA estimation errors in scenarios involving multiple time steps and time-varying DOAs. Simulation results demonstrate performance improvements compared to state-of-the-art methods. The proposed method is evaluated using ocean acoustic experimental data.
Sparse Array Beamformer Design via ADMM
Authors: Huiping Huang, Hing Cheung So, Abdelhak M. Zoubir
Date: 2022-08-25 00:00:00

In this paper, we devise a sparse array design algorithm for adaptive beamforming. Our strategy is based on finding a sparse beamformer weight to maximize the output signal-to-interference-plus-noise ratio (SINR). The proposed method utilizes the alternating direction method of multipliers (ADMM), and admits closed-form solutions at each ADMM iteration. The algorithm convergence properties are analyzed by showing the monotonicity and boundedness of the augmented Lagrangian function. In addition, we prove that the proposed algorithm converges to the set of Karush-Kuhn-Tucker stationary points. Numerical results exhibit its excellent performance, which is comparable to that of the exhaustive search approach, slightly better than those of the state-of-the-art solvers, including the semidefinite relaxation (SDR), its variant (SDR-V), and the successive convex approximation (SCA) approaches, and significantly outperforms several other sparse array design strategies, in terms of output SINR. Moreover, the proposed ADMM algorithm outperforms the SDR, SDR-V, and SCA methods, in terms of computational complexity.