pwc-archive/papers-with-abstracts huggingface dataset:
Cramér-Rao Bounds for Holographic Positioning
Authors: Antonio A. D'Amico, Andrea de Jesus Torres, Luca Sanguinetti, Moe Win
Date: 2021-11-03 00:00:00
Tasks: Position

Multiple antennas arrays play a key role in wireless networks for communications but also for localization and sensing applications. The use of large antenna arrays at high carrier frequencies (in the mmWave range) pushes towards a propagation regime in which the wavefront is no longer plane but spherical. This allows to infer the position and orientation of a transmitting source from the received signal without the need of using multiple anchor nodes, located in known positions. To understand the fundamental limits of large antenna arrays for localization, this paper combines wave propagation theory with estimation theory, and computes the Cram\'er-Rao Bound (CRB) for the estimation of the source position on the basis of the three Cartesian components of the electric field, observed over a rectangular surface area. The problem is referred to as holographic positioning and is formulated by taking into account the radiation angular pattern of the transmitting source, which is typically ignored in standard signal processing models. We assume that the source is a Hertzian dipole, and address the holographic positioning problem in both cases, that is, with and without a priori knowledge of its orientation. To simplify the analysis and gain further insights, we also consider the case in which the dipole is located on the line perpendicular to the surface center. Numerical and asymptotic results are given to quantify the CRBs, and to quantify the effect of various system parameters on the ultimate estimation accuracy. It turns out that surfaces of practical size may guarantee a centimeter-level accuracy in the mmWave bands.
Distributed Learning over a Wireless Network with FSK-Based Majority Vote
Authors: Alphan Sahin, Bryson Everette, Safi Shams Muhtasimul Hoque
Date: 2021-11-02 00:00:00

In this study, we propose an over-the-air computation (AirComp) scheme for federated edge learning (FEEL). The proposed scheme relies on the concept of distributed learning by majority vote (MV) with sign stochastic gradient descend (signSGD). As compared to the state-of-the-art solutions, with the proposed method, edge devices (EDs) transmit the signs of local stochastic gradients by activating one of two orthogonal resources, i.e., orthogonal frequency division multiplexing (OFDM) subcarriers, and the MVs at the edge server (ES) are obtained with non-coherent detectors by exploiting the energy accumulations on the subcarriers. Hence, the proposed scheme eliminates the need for channel state information (CSI) at the EDs and ES. By taking path loss, power control, cell size, and the probabilistic nature of the detected MVs in fading channel into account, we prove the convergence of the distributed learning for a non-convex function. Through simulations, we show that the proposed scheme can provide a high test accuracy in fading channels even when the time-synchronization and the power alignment at the ES are not ideal. We also provide insight into distributed learning for location-dependent data distribution for the MV-based schemes.
Verifying Switched System Stability With Logic
Authors: Yong Kiam Tan, Stefan Mitsch, André Platzer
Date: 2021-11-02 00:00:00

Switched systems are known to exhibit subtle (in)stability behaviors requiring system designers to carefully analyze the stability of closed-loop systems that arise from their proposed switching control laws. This paper presents a formal approach for verifying switched system stability that blends classical ideas from the controls and verification literature using differential dynamic logic (dL), a logic for deductive verification of hybrid systems. From controls, we use standard stability notions for various classes of switching mechanisms and their corresponding Lyapunov function-based analysis techniques. From verification, we use dL's ability to verify quantified properties of hybrid systems and dL models of switched systems as looping hybrid programs whose stability can be formally specified and proven by finding appropriate loop invariants, i.e., properties that are preserved across each loop iteration. This blend of ideas enables a trustworthy implementation of switched system stability verification in the KeYmaera X prover based on dL. For standard classes of switching mechanisms, the implementation provides fully automated stability proofs, including searching for suitable Lyapunov functions. Moreover, the generality of the deductive approach also enables verification of switching control laws that require non-standard stability arguments through the design of loop invariants that suitably express specific intuitions behind those control laws. This flexibility is demonstrated on three case studies: a model for longitudinal flight control by Branicky, an automatic cruise controller, and Brockett's nonholonomic integrator.
Mechanochemical models for calcium waves in embryonic epithelia
Authors: Katerina Kaouri, Paul E. Méndez, Ricardo Ruiz-Baier
Date: 2021-11-03 00:00:00
Tasks: Math

In embryogenesis, epithelial cells, acting as individual entities or as coordinated aggregates in a tissue, exhibit strong coupling between chemical signalling and mechanical responses to internally or externally applied stresses. Intercellular communication in combination with such coordination of morphogenetic movements can lead to drastic modifications in the calcium distribution in the cells. In this paper we extend the recent mechanochemical model in [K. Kaouri, P.K. Maini, P.A. Skourides, N. Christodoulou, S.J. Chapman. J. Math. Biol., 78 (2019) 2059--2092], for an epithelial continuum in one dimension, to a more realistic multi-dimensional case. The resulting parametrised governing equations consist of an advection-diffusion-reaction system for calcium signalling coupled with active-stress linear viscoelasticity and equipped with pure Neumann boundary conditions. We implement a mixed finite element method for the simulation of this complex multiphysics problem. Special care is taken in the treatment of the stress-free boundary conditions for the viscoelasticity in order to eliminate rigid motions from the space of admissible displacements. The stability and solvability of the continuous weak formulation is shown using fixed-point theory. We investigate numerically the solutions of this system and show that solitary waves and periodic wavetrains of calcium propagate through the embryonic epithelial sheet. We analyse the bifurcations of the system guided by the bifurcation analysis of the one-dimensional model. We also demonstrate the nucleation of calcium sparks into synchronous calcium waves coupled with contraction. This coupled model can be employed to gain insights into recent experimental observations in the context of embryogenesis, but also in other biological systems such as cancer cells, wound healing, keratinocytes, or white blood cells.
A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion
Authors: Benjamin van Niekerk, Marc-André Carbonneau, Julian Zaïdi, Mathew Baas, Hugo Seuté, Herman Kamper
Date: 2021-11-03 00:00:00
Tasks: Representation Learning, Voice Conversion

The goal of voice conversion is to transform source speech into a target voice, keeping the content unchanged. In this paper, we focus on self-supervised representation learning for voice conversion. Specifically, we compare discrete and soft speech units as input features. We find that discrete representations effectively remove speaker information but discard some linguistic content - leading to mispronunciations. As a solution, we propose soft speech units. To learn soft units, we predict a distribution over discrete speech units. By modeling uncertainty, soft units capture more content information, improving the intelligibility and naturalness of converted speech. Samples available at https://ubisoft-laforge.github.io/speech/soft-vc/. Code available at https://github.com/bshall/soft-vc/.
Reduction of Subjective Listening Effort for TV Broadcast Signals with Recurrent Neural Networks
Authors: Nils L. Westhausen, Rainer Huber, Hannah Baumgartner, Ragini Sinha, Jan Rennies, Bernd T. Meyer
Date: 2021-11-02 00:00:00
Tasks: Audio Source Separation, Speech Enhancement

Listening to the audio of TV broadcast signals can be challenging for hearing-impaired as well as normal-hearing listeners, especially when background sounds are prominent or too loud compared to the speech signal. This can result in a reduced satisfaction and increased listening effort of the listeners. Since the broadcast sound is usually premixed, we perform a subjective evaluation for quantifying the potential of speech enhancement systems based on audio source separation and recurrent neural networks (RNN). Recently, RNNs have shown promising results in the context of sound source separation and real-time signal processing. In this paper, we separate the speech from the background signals and remix the separated sounds at a higher signal-to-noise ratio. This differs from classic speech enhancement, where usually only the extracted speech signal is exploited. The subjective evaluation with 20 normal-hearing subjects on real TV-broadcast material shows that our proposed enhancement system is able to reduce the listening effort by around 2 points on a 13-point listening effort rating scale and increases the perceived sound quality compared to the original mixture.
Virtual reference feedback tuning with robustness constraints: A swarm intelligence solution
Authors: L. V. Fiorio, C. L. Remes, P. Wheeler, Y. R. de Novaes
Date: 2021-11-03 00:00:00
Tasks: Metaheuristic Optimization

The simplified modeling of a complex system allied with a low-order controller structure can lead to poor closed-loop performance and robustness. A feasible solution is to avoid the necessity of a model by using data for the controller design. The Virtual Reference Feedback Tuning (VRFT) is a data-driven design method that only requires a single batch of data and solves a reference tracking problem, although with no guarantee of robustness. In this work, the inclusion of an $\mathcal{H}_{\infty}$ robustness constraint to the VRFT cost function is addressed. The estimation of the $\mathcal{H}_{\infty}$ norm of the sensitivity transfer function is extended to maintain the one-shot characteristic of the VRFT. Swarm intelligence algorithms are used to solve the non-convex cost function. The proposed method is applied in two real-world inspired problems with four different swarm intelligence algorithms, which are compared with each other through a Monte Carlo experiment of 50 executions. The obtained results are satisfactory, achieving the desired robustness criteria.
Online Service Provisioning in NFV-enabled Networks Using Deep Reinforcement Learning
Authors: Ali Nouruzi, Abolfazl Zakeri, Mohamad Reza Javan, Nader Mokari, Rasheed Hussain, Ahsan Syed Kazmi
Date: 2021-11-03 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

In this paper, we study a Deep Reinforcement Learning (DRL) based framework for an online end-user service provisioning in a Network Function Virtualization (NFV)-enabled network. We formulate an optimization problem aiming to minimize the cost of network resource utilization. The main challenge is provisioning the online service requests by fulfilling their Quality of Service (QoS) under limited resource availability. Moreover, fulfilling the stochastic service requests in a large network is another challenge that is evaluated in this paper. To solve the formulated optimization problem in an efficient and intelligent manner, we propose a Deep Q-Network for Adaptive Resource allocation (DQN-AR) in NFV-enable network for function placement and dynamic routing which considers the available network resources as DQN states. Moreover, the service's characteristics, including the service life time and number of the arrival requests, are modeled by the Uniform and Exponential distribution, respectively. In addition, we evaluate the computational complexity of the proposed method. Numerical results carried out for different ranges of parameters reveal the effectiveness of our framework. In specific, the obtained results show that the average number of admitted requests of the network increases by 7 up to 14% and the network utilization cost decreases by 5 and 20 %.
Adaptive observers for biophysical neuronal circuits
Authors: Thiago B. Burghi, Rodolphe Sepulchre
Date: 2021-11-03 00:00:00
Tasks: parameter estimation

This paper presents adaptive observers for online state and parameter estimation of a class of nonlinear systems motivated by biophysical models of neuronal circuits. We first present a linear-in-the-parameters design that solves a classical recursive least squares problem. Then, building on this simple design, we present an augmented adaptive observer for models with a nonlinearly parameterized internal dynamics, the parameters of which we interpret as structured uncertainty. We present a convergence and robustness analysis based on contraction theory, and illustrate the potential of the approach in neurophysiological applications by means of numerical simulations.
Homoscatter: Towards efficient connectivity for ZigBee backscatter system
Authors: Zhaoyuan Xu, Wei Gong
Date: 2021-11-03 00:00:00
Tasks: TAG, Translation

Recent advances in backscatter open a promising direction for ultra-low power communication. However, the state-of-art ZigBee backscatter system, Interscatter, has several drawbacks to deploy. Its backscatter tag and exciting source, Bluetooth, can hardly decode packets from other ZigBee nodes, which left Interscatter one-way communication. Besides, it adopts instantaneous phase change to modulate information, producing obvious sidelobes and interfering devices working on neighboring channels severely. To address the problems mentioned above, we introduce Homoscatter, a novel ZigBee backscatter system that adopts specific ZigBee devices to generate a single tone and leverages continuous phase change to modulate information, which eliminates spectral leakage. It also does codeword translation on the packet header of exciting packets, improving the utilization of ambient signal. The prototype of Homoscatter consists of a microchip radio, a backscatter tag, and a commodity receiver. The evaluations show that the occupied bandwidth of Homoscatter achieves 3x smaller than Interscatter. When the channel capacity is 17.5 kbps, the continuous phase change modulation achieves 13 kbps with the codeword translation on the excitation header. Based on the widely spread IoT devices, Homoscatter is a practical way to build an efficient connection between IoT devices.
Distributed Extended Object Tracking Information Filter Over Sensor Networks
Authors: Zhifei Li, Yan Liang, Linfeng Xu, Shuli Ma
Date: 2021-11-03 00:00:00
Tasks: Object, Object Tracking

This work aims to design a distributed extended object tracking (EOT) system over a realistic network, where both the extent and kinematics are required to retain consensus within the entire network. To this end, we resort to the multiplicative error model (MEM) that allows the extent parameters of perpendicular axis-symmetric objects to have individual uncertainty. To incorporate the MEM into the information filter (IF) style, we use the moment-matching technique to derive two pair linear models with only additive noise. The separation is merely in a fashion, and the cross-correlation between states is preserved as parameters in each other's model. As a result, the closed-form expressions are transferred into an alternating iteration of two linear IFs. With the two models, a centralized IF is proposed wherein the measurements are converted into a summation of innovation parts. Later, under a sensor network with the communication nodes and sensor nodes, we present two distributed IFs through the consensus on information and consensus on measurement schemes, respectively. Moreover, we prove the estimation errors of the proposed filter are exponentially bounded in the mean square. The benefits are testified by numerical experiments in comparison to state-of-the-art filters in literature.
Load Restoration in Islanded Microgrids: Formulation and Solution Strategies
Authors: Shourya Bose, Yu Zhang
Date: 2021-11-03 00:00:00
Tasks: Model Predictive Control

Adverse circumstances such as extreme weather events can cause significant disruptions to normal operation of electric distribution systems (DS), which includes isolating parts of the DS due to damaged transmission equipment. In this paper, we consider the problem of load restoration in a microgrid (MG) that is islanded from the upstream DS. The MG contains sources of distributed generation such as microturbines and renewable energy sources, as well as energy storage systems (ESS). We formulate the load restoration task as a non-convex optimization problem. This problem embodies the physics of the MG by leveraging a branch flow model, while incorporating salient phenomenon in islanded MGs such as the need for internal frequency regulation, and complementarity requirements arising in ESS operations. Since the formulated optimization problem is non-convex, we introduce a convex relaxation which can be solved through model predictive control as a baseline method. However, in order to solve the problem considering its full non-convexity, we leverage a policy-learning method called constrained policy optimization, a tailored version of which is used as our proposed algorithm. The aforementioned approaches, along with an additional deep learning method are compared through extensive simulations.
A Supervised-Learning based Hour-Ahead Demand Response of a Behavior-based HEMS approximating MILP Optimization
Authors: Huy Truong Dinh, Kyu-haeng Lee, Daehee Kim
Date: 2021-11-03 00:00:00
Tasks: Multi-agent Reinforcement Learning, Scheduling

The demand response (DR) program of a traditional HEMS usually intervenes appliances by controlling or scheduling them to achieve multiple objectives such as minimizing energy cost and maximizing user comfort. In this study, instead of intervening appliances and changing resident behavior, our proposed strategy for hour-ahead DR firstly learns appliance use behavior of residents and then silently controls ESS and RES to minimize daily energy cost based on its knowledge. To accomplish the goal, our proposed deep neural networks (DNNs) models approximate MILP optimization by using supervised learning. The datasets for training DNNs are created from optimal outputs of a MILP solver with historical data. After training, at each time slot, these DNNs are used to control ESS and RES with real-time data of the surrounding environment. For comparison, we develop two different strategies named multi-agent reinforcement learning-based strategy, a kind of hour-ahead strategy and forecast-based MILP strategy, a kind of day-ahead strategy. For evaluation and verification, our proposed strategies are applied at three different real-world homes with real-world real-time global horizontal irradiation and real-world real-time prices. Numerical results verify that the proposed MILP-based supervised learning strategy is effective in term of daily energy cost and is the best one among three proposed strategies
Optimal Parameter Inflation to Enhance the Availability of Single-Frequency GBAS for Intelligent Air Transportation
Authors: Halim Lee, Sam Pullen, Jiyun Lee, Byungwoon Park, Moonseok Yoon, Jiwon Seo
Date: 2021-11-03 00:00:00
Tasks: Position

Ground-based Augmentation System (GBAS) augments Global Navigation Satellite Systems (GNSS) to support the precision approach and landing of aircraft. To guarantee integrity, existing single-frequency GBAS utilizes position-domain geometry screening to eliminate potentially unsafe satellite geometries by inflating one or more broadcast GBAS parameters. However, GBAS availability can be drastically impacted in low-latitude regions where severe ionospheric conditions have been observed. Thus, we developed a novel geometry-screening algorithm in this study to improve GBAS availability in low-latitude regions. Simulations demonstrate that the proposed method can provide 5-8 percentage point availability enhancement of GBAS at Gale\~ao airport near Rio de Janeiro, Brazil, compared to existing methods.
Stochastic simulation of residential building occupant-driven energy use in a bottom-up model of the U.S. housing stock
Authors: Jianli Chen, Rajendra Adhikari, Eric Wilson, Joseph Robertson, Anthony Fontanini, Ben Polly, Opeoluwa Olawale
Date: 2021-11-02 00:00:00

The residential buildings sector is one of the largest electricity consumers worldwide and contributes disproportionally to peak electricity demand in many regions. Strongly driven by occupant activities at home, household energy consumption is stochastic and heterogeneous in nature. However, most residential building energy models applied by industry use homogeneous, deterministic occupant activity schedules, which work well for predictions of annual energy consumption, but can result in unrealistic hourly or sub-hourly electric load profiles, with exaggerated or muted peaks. This mattered less in the past, but the increasing proportion of variable renewable energy generators in power systems means that representing the heterogeneity and stochasticity of occupant behavior is crucial for reliable energy planning. This is particularly true for systems that include distributed energy resources, such as grid-interactive efficient buildings, solar photovoltaics, and battery storage. This work presents a stochastic occupant behavior simulator that models the energy use behavior of individual household members. It also presents an integration with a building stock model to simulate residential building loads more accurately at community, city, state, and national scales. More specifically, we first employ clustering techniques to identify distinct patterns of occupant behavior. Then, we combine time-inhomogeneous Markov chain simulations with probabilistic sampling of event durations to realistically simulate occupant behaviors. This stochastic simulator is integrated with ResStock, a large-scale residential building stock simulation tool, to demonstrate the capability of stochastic residential building load modeling at scale. The simulation results were validated against both American Time Use Survey data and measured end-use electricity data for accuracy and reliability.
Resilient Interval Observer for Simultaneous Estimation of States, Modes and Attack Policies
Authors: Mohammad Khajenejad, Zeyuan Jin, Sze Zheng Yong
Date: 2021-11-02 00:00:00

This paper considers the problem of designing interval observers for hidden mode switched nonlinear systems with bounded noise signals that are compromised by false data injection and switching attacks. The proposed observer consists of three components: i) a bank of mode-matched observers, which simultaneously estimates the corresponding mode-matched continuous states and discrete states (modes), as well as learns a model of the unknown attack policy, ii) a mode observer that eliminates the incompatible modes based on a residual-based set-membership criterion, and iii) a global fusion observer that combines the outputs of i) and ii). Moreover, in addition to showing the correctness, stability and convergence of the mode-matched estimates, we provide sufficient conditions to guarantee that all false modes will be eliminated after sufficiently large finite time steps, i.e., the system is mode-detectable under the proposed observer.