pwc-archive/papers-with-abstracts huggingface dataset:
Bayesian-based Symbol Detector for Orthogonal Time Frequency Space Modulation Systems
Authors: Xinwei Qu, Alva Kosasih, Wibowo Hardjawana, Vincent Onasis, Branka Vucetic
Date: 2021-10-27 00:00:00

Recently, the orthogonal time frequency space (OTFS) modulation is proposed for 6G wireless system to deal with high Doppler spread. The high Doppler spread happens when the transmitted signal is reflected towards the receiver by fast moving objects (e.g. high speed cars), which causes inter-carrier interference (ICI). Recent state-of-the-art OTFS detectors fail to achieve an acceptable bit-error-rate (BER) performance as the number of mobile reflectors increases which in turn, results in high inter-carrier-interference (ICI). In this paper, we propose a novel detector for OTFS systems, referred to as the Bayesian based parallel interference and decision statistics combining (B-PIC-DSC) OTFS detector that can achieve a high BER performance, under high ICI environments. The B-PIC-DSC OTFS detector employs the PIC and DSC schemes to iteratively cancel the interference, and the Bayesian concept to take the probability measure into the consideration when refining the transmitted symbols. Our simulation results show that in contrast to the state-of-the-art OTFS detectors, the proposed detector is able to achieve a BER of less than $10^{-5}$, when SNR is over $14$ dB, under high ICI environments.
Over-the-Air Aggregation for Federated Learning: Waveform Superposition and Prototype Validation
Authors: Huayan Guo, Yifan Zhu, Haoyu Ma, Vincent K. N. Lau, Kaibin Huang, Xiaofan Li, Huabin Nong, Mingyu Zhou
Date: 2021-10-27 00:00:00
Tasks: Federated Learning

In this paper, we develop an orthogonal-frequency-division-multiplexing (OFDM)-based over-the-air (OTA) aggregation solution for wireless federated learning (FL). In particular, the local gradients in massive IoT devices are modulated by an analog waveform and are then transmitted using the same wireless resources. To this end, achieving perfect waveform superposition is the key challenge, which is difficult due to the existence of frame timing offset (TO) and carrier frequency offset (CFO). In order to address these issues, we propose a two-stage waveform pre-equalization technique with a customized multiple access protocol that can estimate and then mitigate the TO and CFO for the OTA aggregation. Based on the proposed solution, we develop a hardware transceiver and application software to train a real-world FL task, which learns a deep neural network to predict the received signal strength with global positioning system information. Experiments verify that the proposed OTA aggregation solution can achieve comparable performance to offline learning procedures with high prediction accuracy.
SiWa: See into Walls via Deep UWB Radar
Authors: Tianyue Zheng, Zhe Chen, Jun Luo, Lin Ke, Chaoyang Zhao, Yaowen Yang
Date: 2021-10-27 00:00:00

Being able to see into walls is crucial for diagnostics of building health; it enables inspections of wall structure without undermining the structural integrity. However, existing sensing devices do not seem to offer a full capability in mapping the in-wall structure while identifying their status (e.g., seepage and corrosion). In this paper, we design and implement SiWa as a low-cost and portable system for wall inspections. Built upon a customized IR-UWB radar, SiWa scans a wall as a user swipes its probe along the wall surface; it then analyzes the reflected signals to synthesize an image and also to identify the material status. Although conventional schemes exist to handle these problems individually, they require troublesome calibrations that largely prevent them from practical adoptions. To this end, we equip SiWa with a deep learning pipeline to parse the rich sensory data. With an ingenious construction and innovative training, the deep learning modules perform structural imaging and the subsequent analysis on material status, without the need for parameter tuning and calibrations. We build SiWa as a prototype and evaluate its performance via extensive experiments and field studies; results confirm that SiWa accurately maps in-wall structures, identifies their materials, and detects possible failures, suggesting a promising solution for diagnosing building health with lower effort and cost.
Beamforming Feedback-based Model-Driven Angle of Departure Estimation Toward Legacy Support in WiFi Sensing: An Experimental Study
Authors: Sohei Itahara, Sota Kondo, Kota Yamashita, Takayuki Nishio, Koji Yamamoto, Yusuke Koda
Date: 2021-10-27 00:00:00

This study experimentally validated the possibility of angle of departure (AoD) estimation using multiple signal classification (MUSIC) with only WiFi control frames for beamforming feedback (BFF), defined in IEEE 802.11ac/ax. The examined BFF-based MUSIC is a model-driven algorithm, which does not require a pre-obtained database. This contrasts with most existing BFF-based sensing techniques, which are data-driven and require a pre-obtained database. Moreover, the BFF-based MUSIC affords an alternative AoD estimation method without access to channel state information (CSI). Specifically, the extensive experimental and numerical evaluations demonstrated that the BFF-based MUSIC successfully estimates the AoDs for multiple propagation paths. Moreover, the evaluations performed in this study revealed that the BFF-based MUSIC achieved a comparable error of AoD estimation to the CSI-based MUSIC, while BFF is a highly compressed version of CSI in IEEE 802.11ac/ax.
Improving Cell-Free Massive MIMO Detection Performance via Expectation Propagation
Authors: Alva Kosasih, Vera Miloslavskaya, Wibowo Hardjawana, Victor Andrean, Branka Vucetic
Date: 2021-10-27 00:00:00

Cell-free (CF) massive multiple-input multiple-output (M-MIMO) technology plays a prominent role in the beyond fifth-generation (5G) networks. However, designing a high performance CF M-MIMO detector is a challenging task due to the presence of pilot contamination which appears when the number of pilot sequences is smaller than the number of users. This work proposes a CF M-MIMO detector referred to as CF expectation propagation (CF-EP) that incorporates the pilot contamination when calculating the posterior belief. The simulation results show that the proposed detector achieves significant improvements in terms of the bit-error rate and sum spectral efficiency performances as compared to the ones of the state-of-the-art CF detectors.
Data-driven decomposition of brain dynamics with principal component analysis in different types of head impacts
Authors: Xianghao Zhan, Yuzhe Liu, Nicholas J. Cecchi, Olivier Gevaert, Michael M. Zeineh, Gerald A. Grant, David B. Camarillo
Date: 2021-10-27 00:00:00

Strain and strain rate are effective traumatic brain injury predictors. Kinematics-based models estimating these metrics suffer from significant different distributions of both kinematics and the injury metrics across head impact types. To address this, previous studies focus on the kinematics but not the injury metrics. We have previously shown the kinematic features vary largely across head impact types, resulting in different patterns of brain deformation. This study analyzes the spatial distribution of brain deformation and applies principal component analysis (PCA) to extract the representative patterns of injury metrics (maximum principal strain (MPS), MPS rate (MPSR) and MPSXMPSR) in four impact types (simulation, football, mixed martial arts and car crashes). We apply PCA to decompose the patterns of the injury metrics for all impacts in each impact type, and investigate the distributions among brain regions using the first principal component (PC1). Furthermore, we developed a deep learning head model (DLHM) to predict PC1 and then inverse-transform to predict for all brain elements. PC1 explained >80% variance on the datasets. Based on PC1 coefficients, the corpus callosum and midbrain exhibit high variance on all datasets. We found MPSXMPSR the most sensitive metric on which the top 5% of severe impacts further deviates from the mean and there is a higher variance among the severe impacts. Finally, the DLHM reached mean absolute errors of <0.018 for MPS, <3.7 (1/s) for MPSR and <1.1 (1/s) for MPSXMPSR, much smaller than the injury thresholds. The brain injury metric in a dataset can be decomposed into mean components and PC1 with high explained variance. The brain dynamics decomposition enables better interpretation of the patterns in brain injury metrics and the sensitivity of brain injury metrics across impact types. The decomposition also reduces the dimensionality of DLHM.
A C-Band Fully Polarimetric Automotive Synthetic Aperture Radar
Authors: Jason Merlo, Jeffrey A. Nanzer
Date: 2021-10-27 00:00:00

Due to the rapid increase in 76 GHz automotive spectrum use in recent years, wireless interference is becoming a legitimate area of concern. However, the recent rise in interest of automated vehicles (AVs) has also spurred new growth and adoption of low frequency vehicle-to-everything (V2X) communications in and around the 5.8 GHz unlicensed bands, opening the possibility for communications spectrum reuse in the form of joint radar-communications (JRC). In this work, we present a low frequency 5.9 GHz side-looking polarimetric synthetic aperture radar (SAR) for automotive use, utilizing a ranging waveform in a common low frequency V2X communications band. A synthetic aperture technique is employed to address the angular resolution concerns commonly associated with radars at lower frequencies. Three side-looking fully polarimetric SAR images in various urban scenes are presented and discussed to highlight the unique opportunities for landmark inference afforded through measurement of co- and cross-polarized scattering.
r-local sensing: Improved algorithm and applications
Authors: Ahmed Ali Abbasi, Abiy Tasissa, Shuchin Aeron
Date: 2021-10-26 00:00:00

The unlabeled sensing problem is to solve a noisy linear system of equations under unknown permutation of the measurements. We study a particular case of the problem where the permutations are restricted to be r-local, i.e. the permutation matrix is block diagonal with r x r blocks. Assuming a Gaussian measurement matrix, we argue that the r-local permutation model is more challenging compared to a recent sparse permutation model. We propose a proximal alternating minimization algorithm for the general unlabeled sensing problem that provably converges to a first order stationary point. Applied to the r-local model, we show that the resulting algorithm is efficient. We validate the algorithm on synthetic and real datasets. We also formulate the 1-d unassigned distance geometry problem as an unlabeled sensing problem with a structured measurement matrix.
Noninvasive ultrasound for Lithium-ion batteries state estimation
Authors: Simon Montoya-Bedoya, Miguel Bernal, Laura A. Sabogal-Moncada, Hader V. Martinez-Tejada, Esteban Garcia-Tamayo
Date: 2021-10-26 00:00:00
Tasks: State Estimation

Lithium-ion battery degradation estimation using fast and noninvasive techniques is a crucial issue in the circular economy framework of this technology. Currently, most of the approaches used to establish the battery-state (i.e., State of Charge (SoC), State of Health (SoH)) require time-consuming processes. In the present preliminary study, an ultrasound array was used to assess the influence of the SoC and SoH on the variations in the time of flight (TOF) and the speed of sound (SOS) of the ultrasound wave inside the batteries. Nine aged 18650 Lithium-ion batteries were imaged at 100% and 0% SoC using a Vantage-256 system (Verasonics, Inc.) equipped with a 64-element ultrasound array and a center frequency of 5 MHz (Imasonic SAS). It was found that second-life batteries have a complex ultrasound response due to the presence of many degradation pathways and, thus, making it harder to analyze the ultrasound measurements. Although further analysis must be done to elucidate a clear correlation between changes in the ultrasound wave properties and the battery state estimation, this approach seems very promising for future nondestructive evaluation of second-life batteries.
Learning Stable Deep Dynamics Models for Partially Observed or Delayed Dynamical Systems
Authors: Andreas Schlaginhaufen, Philippe Wenk, Andreas Krause, Florian Dörfler
Date: 2021-10-27 00:00:00
Proceeding: NeurIPS 2021 12

Learning how complex dynamical systems evolve over time is a key challenge in system identification. For safety critical systems, it is often crucial that the learned model is guaranteed to converge to some equilibrium point. To this end, neural ODEs regularized with neural Lyapunov functions are a promising approach when states are fully observed. For practical applications however, partial observations are the norm. As we will demonstrate, initialization of unobserved augmented states can become a key problem for neural ODEs. To alleviate this issue, we propose to augment the system's state with its history. Inspired by state augmentation in discrete-time systems, we thus obtain neural delay differential equations. Based on classical time delay stability analysis, we then show how to ensure stability of the learned models, and theoretically analyze our approach. Our experiments demonstrate its applicability to stable system identification of partially observed systems and learning a stabilizing feedback policy in delayed feedback control.
A Subgame Perfect Equilibrium Reinforcement Learning Approach to Time-inconsistent Problems
Authors: Nixie S. Lesmana, Chi Seng Pun
Date: 2021-10-27 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

In this paper, we establish a subgame perfect equilibrium reinforcement learning (SPERL) framework for time-inconsistent (TIC) problems. In the context of RL, TIC problems are known to face two main challenges: the non-existence of natural recursive relationships between value functions at different time points and the violation of Bellman's principle of optimality that raises questions on the applicability of standard policy iteration algorithms for unprovable policy improvement theorems. We adapt an extended dynamic programming theory and propose a new class of algorithms, called backward policy iteration (BPI), that solves SPERL and addresses both challenges. To demonstrate the practical usage of BPI as a training framework, we adapt standard RL simulation methods and derive two BPI-based training algorithms. We examine our derived training frameworks on a mean-variance portfolio selection problem and evaluate some performance metrics including convergence and model identifiability.
Hybrid consensus for multi-agent systems with time-driven jumps
Authors: Andrea Cristofaro, Mattia Mattioni
Date: 2021-10-27 00:00:00

In this paper, the behavior of scalar multi-agent systems over networks subject to time-driven jumps. Assuming that all agents communicate through distinct communication digraphs at jump and flow times, the asymptotic multi-consensus behavior of the hybrid network is explicitly characterized. The hybrid multi-consensus is shown to be associated with a suitable partition that is almost equitable for both the jump and flow communication digraphs. In doing so, no assumption on the underlying digraphs is introduced. Finally, the coupling rules making the multi-consensus subspace attractive are established. Several simulation examples illustrate the theoretical results.
Kalman-Like Filter under Binary Sensors
Authors: Zhongyao Hu, Bo Chen, Yuchen Zhang, Li Yu
Date: 2021-10-27 00:00:00

This paper is concerned with the linear/nonlinear Kalman-like filtering problem under binary sensors. Since innovation represents new information in the sensor measurement and serves to correct the prediction for the Kalman-like filter (KLF), a novel uncertain measurement model is proposed such that the innovation generated from binary sensor can be captured. When considering linear dynamic systems, a conservative estimation error covariance with adjustable parameters is constructed by matrix inequality, and then an optimal filter gain is derived by minimizing its trace. Meanwhile, the optimal selection criterion of an adjustable parameter is developed by minimizing the upper bound of the conservative estimation error covariance. When considering nonlinear dynamic systems, a conservative estimation error covariance with adjustable parameters is also constructed via unscented transform and matrix inequalities. Then, following the idea of designing KLF in linear dynamic systems, the nonlinear filter gain and the optimal adjustable parameter are designed. Finally, $O_2$ content estimation and nonlinear numerical system are employed to show the effectiveness and advantages of the proposed methods.
Fast Distributed Stochastic Scheduling for A Multi-Energy Industrial Park
Authors: Dafeng Zhu, Bo Yang, Zhaojian Wang, Chengbin Ma, Kai Ma, Shanying Zhu
Date: 2021-10-27 00:00:00
Tasks: energy management, Management, Scheduling, Stochastic Optimization

The multi-energy management framework of industrial parks advocates energy conversion and scheduling, which takes full advantage of the compensation and temporal availability of multiple energy. However, how to exploit elastic loads and compensate inelastic loads to match multiple generators and storage is still a key problem under the uncertainty of demand and supply. To solve the issue, the energy management problem is constructed as a stochastic optimization problem. The optimization aims are to minimize the time-averaged energy cost and improve the energy efficiency while respecting the energy constraints. To achieve the distributed implementation in real time without knowing any priori knowledge of underlying stochastic process, a distributed stochastic gradient algorithm based on dual decomposition and a fast scheme are proposed. The numerical results based on real data show that the industrial park, by adopting the proposed algorithm, can achieve social welfare maximization asymptotically.
Control of a Floating Wind Turbine on a Novel Actuated Platform
Authors: David Stockhouse, Mandar Phadnis, Elenya Grant, Kathryn Johnson, Rick Damiani, Lucy Pao
Date: 2021-10-27 00:00:00
Tasks: Motion Compensation

Designing a floating offshore wind turbine (FOWT) controller requires solving engineering challenges not found for fixed-bottom turbines. This paper applies several methods from the growing body of FOWT control literature to the 10-MW Ultraflexible Smart FLoating Offshore Wind Turbine (USFLOWT) baseline generator-speed controller. USFLOWT aims to reduce capital expenses using the lightweight SpiderFLOAT platform, a novel smart floating substructure with built-in distributed actuators for direct platform tilt and heave control. In this work, the USFLOWT baseline controller is improved through detuning and parallel compensation with both blade pitch and generator torque. The SpiderFLOAT platform additionally allows motion compensation through distributed platform actuators. Two proposed SpiderFLOAT actuator types are considered for active platform control: a low-bandwidth actuator that uses variable floater ballast to bring a heeling platform to a steady-state upright position, and a high-bandwidth actuator that dynamically changes the substructure geometry to actively reject transient platform motion. Each control approach is tested for USFLOWT using the open-source aero-hydro-servo-elastic wind turbine simulation tool OpenFAST. Performance results for each approach are compared across a range of above-rated wind speeds, and promising combined approaches are further evaluated to recommend future multi-parameter optimization pathways.
Newtonian Mechanics Based Transient Stability PART V: Inner-group Machine
Authors: Songyan Wang, Jilai Yu, Aoife Foley, Jingrui Zhang
Date: 2021-10-27 00:00:00

This paper analyzes the mechanisms of the inner-group machine. It is first clarified that the inner-group machine is created from the difference between the equivalent system and the original system. The inner-group machine stability is analyzed based on the machine paradigms. In particular, strict correlation between the inner-group machine trajectory and the inner-group machine transient energy conversion is established through the I-CR system modeling. Then, the transient characteristics of the inner-group machine are analyzed. It is clarified that the inner-group motions might be inseparable or separable, and the inner-group machine DLP will occur later than the EDLP and IDLP. Simulation results show that the severity of the original system cannot be simply evaluated through its equivalent system once any inner-group motion becomes fierce.
Towards Model Reduction for Power System Transients with Physics-Informed PDE
Authors: Laurent Pagnier, Michael Chertkov, Julian Fritzsch, Philippe Jacquod
Date: 2021-10-26 00:00:00
Tasks: Physics-informed machine learning

This manuscript reports the first step towards building a robust and efficient model reduction methodology to capture transient dynamics in a transmission level electric power system. Such dynamics is normally modeled on seconds-to-tens-of-seconds time scales by the so-called swing equations, which are ordinary differential equations defined on a spatially discrete model of the power grid. Following Seymlyen (1974) and Thorpe, Seyler, and Phadke (1999), we suggest to map the swing equations onto a linear, inhomogeneous Partial Differential Equation (PDE) of parabolic type in two space and one time dimensions with time-independent coefficients and properly defined boundary conditions. We illustrate our method on the synchronous transmission grid of continental Europe. We show that, when properly coarse-grained, i.e., with the PDE coefficients and source terms extracted from a spatial convolution procedure of the respective discrete coefficients in the swing equations, the resulting PDE reproduces faithfully and efficiently the original swing dynamics. We finally discuss future extensions of this work, where the presented PDE-based modeling will initialize a physics-informed machine learning approach for real-time modeling, $n-1$ feasibility assessment and transient stability analysis of power systems.
How will electric vehicles affect traffic congestion and energy consumption: an integrated modelling approach
Authors: Artur Grigorev, Tuo Mao, Adam Berry, Joachim Tan, Loki Purushothaman, Adriana-Simona Mihaita
Date: 2021-10-26 00:00:00
Tasks: energy management, Management

This paper explores the impact of electric vehicles (EVs) on traffic congestion and energy consumption by proposing an integrated bi-level framework comprising of: a) a dynamic micro-scale traffic simulation suitable for modelling current and hypothetical traffic and charging demand scenarios and b) a queue model for capturing the impact of fast charging station use, informed by traffic flows, travel distances, availability of charging infrastructure and estimated vehicle battery state of charge. To the best of our knowledge, this paper represents the first integrated analysis of potential traffic congestion and energy infrastructure impacts linked to EV uptake, based on real traffic flows and the placement and design of existing fast-charging infrastructure. Results showcase that the integrated queue-energy-transport modelling framework can predict correctly the limitations of the EV infrastructure as well as the traffic congestion evolution. The modelling approach identifies concrete pain points to be addressed in both traffic and energy management and planning. The code for this project can be found at : https://github.com/Future-Mobility-Lab/EV-charging-impact