pwc-archive/papers-with-abstracts huggingface dataset:
Efficient DOA Estimation Method for Reconfigurable Intelligent Surfaces Aided UAV Swarm
Authors: Peng Chen, Zhimin Chen, Beixiong Zheng, Xianbin Wang
Date: 2022-03-19 00:00:00
Tasks: Position

The conventional direction of arrival (DOA) estimation methods are performed with multiple receiving channels. In this paper, a changeling DOA estimation problem is addressed in a different scenario with only one full-functional receiving channel. A new unmanned aerial vehicle (UAV) swarm system using multiple lifted reconfigurable intelligent surface (RIS) is proposed for the DOA estimation. The UAV movement degrades the DOA estimation performance significantly, and the existing atomic norm minimization (ANM) methods cannot be used in the scenario with array perturbation. Specifically, considering the position perturbation of UAVs, a new atomic norm-based DOA estimation method is proposed, where an atomic norm is defined with the parameter of the position perturbation. Then, a customized semi-definite programming (SDP) method is derived to solve the atomic norm-based method, where different from the traditional SDP method, an additional transforming matrix is formulated. Moreover, a gradient descent method is applied to refine the estimated DOA and the position perturbation further. Simulation results show that the proposed method achieves much better DOA estimation performance in the RIS-aided UAV swarm system with only one receiving channel than various benchmark schemes.
Off-Grid DOA Estimation Using Sparse Bayesian Learning in MIMO Radar With Unknown Mutual Coupling
Authors: Peng Chen, Zhenxin Cao, Zhimin Chen, Xianbin Wang
Date: 2018-04-12 00:00:00
Tasks: compressed sensing

In the practical radar with multiple antennas, the antenna imperfections degrade the system performance. In this paper, the problem of estimating the direction of arrival (DOA) in multiple-input and multiple-output (MIMO) radar system with unknown mutual coupling effect between antennas is investigated. To exploit the target sparsity in the spatial domain, the compressed sensing (CS)-based methods have been proposed by discretizing the detection area and formulating the dictionary matrix, so an \emph{off-grid} gap is caused by the discretization processes. In this paper, different from the present DOA estimation methods, both the off-grid gap due to the sparse sampling and the unknown mutual coupling effect between antennas are considered at the same time, and a novel sparse system model for DOA estimation is formulated. Then, a novel sparse Bayesian learning (SBL)-based method named sparse Bayesian learning with the mutual coupling (SBLMC) is proposed, where an expectation-maximum (EM)-based method is established to estimate all the unknown parameters including the noise variance, the mutual coupling vectors, the off-grid vector and the variance vector of scattering coefficients. Additionally, the prior distributions for all the unknown parameters are theoretically derived. With regard to the DOA estimation performance, the proposed SBLMC method can outperform state-of-the-art methods in the MIMO radar with unknown mutual coupling effect, while keeping the acceptable computational complexity.
Contribution of Different Handwriting Modalities to Differential Diagnosis of Parkinson's Disease
Authors: Peter Drotár, Jiří Mekyska, Zdeněk Smékal, Irena Rektorová, Lucia Masarová, Marcos Faundez-Zanuy
Date: 2022-03-18 00:00:00

In this paper, we evaluate the contribution of different handwriting modalities to the diagnosis of Parkinson's disease. We analyse on-surface movement, in-air movement and pressure exerted on the tablet surface. Especially in-air movement and pressure-based features have been rarely taken into account in previous studies. We show that pressure and in-air movement also possess information that is relevant for the diagnosis of Parkinson's Disease (PD) from handwriting. In addition to the conventional kinematic and spatio-temporal features, we present a group of the novel features based on entropy and empirical mode decomposition of the handwriting signal. The presented results indicate that handwriting can be used as biomarker for PD providing classification performance around 89% area under the ROC curve (AUC) for PD classification.
Transmit Precoder Design Approaches for Dual-Function Radar-Communication Systems
Authors: Jacob Pritzker, James Ward, Yonina C. Eldar
Date: 2022-03-17 00:00:00

As radio-frequency (RF) antenna, component and processing capabilities increase, the ability to perform multiple RF system functions from a common aperture is being realized. Conducting both radar and communications from the same system is potentially useful in vehicular, health monitoring, and surveillance settings. This paper considers multiple-input-multiple-output (MIMO) dual-function radar-communication (DFRC) systems in which the radar and communication modes use distinct baseband waveforms. A transmit precoder provides spatial multiplexing and power allocation among the radar and communication modes. Multiple precoder design approaches are introduced for a radar detection mode in which a total search volume is divided into dwells to be searched sequentially. The approaches are designed to enforce a reliance on radar waveforms for sensing purposes, yielding improved approximation of desired ambiguity functions over prior methods found in the literature. The methods are also shown via simulation to enable design flexibility, allowing for prioritization of either subsystem and specification of a desired level of radar or communication performance.
Distributed Estimation in Large Scale Wireless Sensor Networks via a Two Step Group-based Approach
Authors: Shan Zhang, Pranay Sharma, Baocheng Geng, Pramod K. Varshney
Date: 2022-03-17 00:00:00

We consider the problem of collaborative distributed estimation in a large scale sensor network with statistically dependent sensor observations. In collaborative setup, the aim is to maximize the overall estimation performance by modeling the underlying statistical dependence and efficiently utilizing the deployed sensors. To achieve greater sensor transmission and estimation efficiency, we propose a two step group-based collaborative distributed estimation scheme, where in the first step, sensors form dependence driven groups such that sensors in the same group are highly dependent, while sensors from different groups are independent, and perform a copula-based maximum a posteriori probability (MAP) estimation via intragroup collaboration. In the second step, the estimates generated in the first step are shared via inter-group collaboration to reach an average consensus. A merge based K-medoid dependence driven grouping algorithm is proposed. Moreover, we further propose a group-based sensor selection scheme using mutual information prior to the estimation. The aim is to select sensors with maximum relevance and minimum redundancy regarding the parameter of interest under certain pre-specified energy constraint. Also, the proposed group-based sensor selection scheme is shown to be equivalent to the global/non-group based selection scheme with high probability, but computationally more efficient. Numerical experiments are conducted to demonstrate the effectiveness of our approach.
EEG based Emotion Recognition: A Tutorial and Review
Authors: Xiang Li, Yazhou Zhang, Prayag Tiwari, Dawei Song, Bin Hu, Meihong Yang, Zhigang Zhao, Neeraj Kumar, Pekka Marttinen
Date: 2022-03-16 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Emotion Recognition

Emotion recognition technology through analyzing the EEG signal is currently an essential concept in Artificial Intelligence and holds great potential in emotional health care, human-computer interaction, multimedia content recommendation, etc. Though there have been several works devoted to reviewing EEG-based emotion recognition, the content of these reviews needs to be updated. In addition, those works are either fragmented in content or only focus on specific techniques adopted in this area but neglect the holistic perspective of the entire technical routes. Hence, in this paper, we review from the perspective of researchers who try to take the first step on this topic. We review the recent representative works in the EEG-based emotion recognition research and provide a tutorial to guide the researchers to start from the beginning. The scientific basis of EEG-based emotion recognition in the psychological and physiological levels is introduced. Further, we categorize these reviewed works into different technical routes and illustrate the theoretical basis and the research motivation, which will help the readers better understand why those techniques are studied and employed. At last, existing challenges and future investigations are also discussed in this paper, which guides the researchers to decide potential future research directions.
Learning to Optimize Resource Assignment for Task Offloading in Mobile Edge Computing
Authors: Yurong Qian, Jindan Xu, Shuhan Zhu, Wei Xu, Lisheng Fan, George K. Karagiannidis
Date: 2022-03-15 00:00:00
Tasks: Edge-computing

In this paper, we consider a multiuser mobile edge computing (MEC) system, where a mixed-integer offloading strategy is used to assist the resource assignment for task offloading. Although the conventional branch and bound (BnB) approach can be applied to solve this problem, a huge burden of computational complexity arises which limits the application of BnB. To address this issue, we propose an intelligent BnB (IBnB) approach which applies deep learning (DL) to learn the pruning strategy of the BnB approach. By using this learning scheme, the structure of the BnB approach ensures near-optimal performance and meanwhile DL-based pruning strategy significantly reduces the complexity. Numerical results verify that the proposed IBnB approach achieves optimal performance with complexity reduced by over 80%.
One-Bit Compressive Sensing: Can We Go Deep and Blind?
Authors: Yiming Zeng, Shahin Khobahi, Mojtaba Soltanalian
Date: 2022-03-13 00:00:00
Tasks: Compressive Sensing

One-bit compressive sensing is concerned with the accurate recovery of an underlying sparse signal of interest from its one-bit noisy measurements. The conventional signal recovery approaches for this problem are mainly developed based on the assumption that an exact knowledge of the sensing matrix is available. In this work, however, we present a novel data-driven and model-based methodology that achieves blind recovery; i.e., signal recovery without requiring the knowledge of the sensing matrix. To this end, we make use of the deep unfolding technique and develop a model-driven deep neural architecture which is designed for this specific task. The proposed deep architecture is able to learn an alternative sensing matrix by taking advantage of the underlying unfolded algorithm such that the resulting learned recovery algorithm can accurately and quickly (in terms of the number of iterations) recover the underlying compressed signal of interest from its one-bit noisy measurements. In addition, due to the incorporation of the domain knowledge and the mathematical model of the system into the proposed deep architecture, the resulting network benefits from enhanced interpretability, has a very small number of trainable parameters, and requires very small number of training samples, as compared to the commonly used black-box deep neural network alternatives for the problem at hand.
Forward Link Analysis for Full-Duplex Cellular Networks with Low Resolution ADC/DAC
Authors: Elyes Balti, Brian L. Evans
Date: 2022-03-07 00:00:00
Tasks: Quantization

In this work, we consider a full-duplex (FD) massive multiple-input multiple-output (MIMO) cellular network with low resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs). Our first contribution is to propose a unified framework for forward link analysis where matched filter precoders are applied at the FD base stations (BSs) under channel hardening. Second, we derive expressions for the signal-to-quantization-plus-interference-plus-noise ratio (SQINR) for general and special cases. Finally, we quantify effects of quantization error, pilot contamination, and full duplexing for a hexagonal cell lattice on spectral efficiency and cumulative distribution function (CDF) to show that FD outperforms half duplex (HD) in a wide variety of scenarios.
Efficient FFT Computation in IFDMA Transceivers
Authors: Yuyang Du, Soung Chang Liew, Yulin Shao
Date: 2022-03-05 00:00:00
Tasks: Scheduling

Interleaved Frequency Division Multiple Access (IFDMA) has the salient advantage of lower Peak-to-Average Power Ratio (PAPR) than its competitors like Orthogonal FDMA (OFDMA). A recent research effort put forth a new IFDMA transceiver design significantly less complex than conventional IFDMA transceivers. The new IFDMA transceiver design reduces the complexity by exploiting a certain correspondence between the IFDMA signal processing and the Cooley-Tukey IFFT/FFT algorithmic structure so that IFDMA streams can be inserted/extracted at different stages of an IFFT/FFT module according to the sizes of the streams. Although the prior work has laid down the theoretical foundation for the new IFDMA transceiver's structure, the practical realization of the transceiver on specific hardware with resource constraints has not been carefully investigated. This paper is an attempt to fill the gap. Specifically, this paper puts forth a heuristic algorithm called multi-priority scheduling (MPS) to schedule the execution of the butterfly computations in the IFDMA transceiver with the constraint of a limited number of hardware processors. The resulting FFT computation, referred to as MPS-FFT, has a much lower computation time than conventional FFT computation when applied to the IFDMA signal processing. Importantly, we derive a lower bound for the optimal IFDMA FFT computation time to benchmark MPS-FFT. Our experimental results indicate that when the number of hardware processors is a power of two: 1) MPS-FFT has near-optimal computation time; 2) MPS-FFT incurs less than 44.13\% of the computation time of the conventional pipelined FFT.
Multiple Estimation Models for Discrete-time Adaptive Iterative Learning Control
Authors: Ram Padmanabhan, Rajini Makam, Koshy George
Date: 2022-03-22 00:00:00
Tasks: LEMMA

This article focuses on making discrete-time Adaptive Iterative Learning Control (ILC) more effective using multiple estimation models. Existing strategies use the tracking error to adjust the parametric estimates. Our strategy uses the last component of the identification error to tune these estimates of the model parameters. We prove that this strategy results in bounded estimates of the parameters, and bounded and convergent identification and tracking errors. We emphasize that the proof does not use the key technical lemma. Rather, it uses the properties of square-summable sequences. We extend this strategy to include multiple estimation models and show that all the signals are bounded, and the errors converge. It is also shown that this works whether we switch between the models at every instant and every iteration or at the end of every iteration. Simulation results demonstrate the efficacy of the proposed method with a faster convergence using multiple estimation models.
An Optimal Transport Formulation of Bayes' Law for Nonlinear Filtering Algorithms
Authors: Amirhossein Taghvaei, Bamdad Hosseini
Date: 2022-03-22 00:00:00

This paper presents a variational representation of the Bayes' law using optimal transportation theory. The variational representation is in terms of the optimal transportation between the joint distribution of the (state, observation) and their independent coupling. By imposing certain structure on the transport map, the solution to the variational problem is used to construct a Brenier-type map that transports the prior distribution to the posterior distribution for any value of the observation signal. The new formulation is used to derive the optimal transport form of the Ensemble Kalman filter (EnKF) for the discrete-time filtering problem and propose a novel extension of EnKF to the non-Gaussian setting utilizing input convex neural networks. Finally, the proposed methodology is used to derive the optimal transport form of the feedback particle filler (FPF) in the continuous-time limit, which constitutes its first variational construction without explicitly using the nonlinear filtering equation or Bayes' law.
Neural System Level Synthesis: Learning over All Stabilizing Policies for Nonlinear Systems
Authors: Luca Furieri, Clara Lucía Galimberti, Giancarlo Ferrari-Trecate
Date: 2022-03-22 00:00:00
Tasks: All

We address the problem of designing stabilizing control policies for nonlinear systems in discrete-time, while minimizing an arbitrary cost function. When the system is linear and the cost is convex, the System Level Synthesis (SLS) approach offers an effective solution based on convex programming. Beyond this case, a globally optimal solution cannot be found in a tractable way, in general. In this paper, we develop a parametrization of all and only the control policies stabilizing a given time-varying nonlinear system in terms of the combined effect of 1) a strongly stabilizing base controller and 2) a stable SLS operator to be freely designed. Based on this result, we propose a Neural SLS (Neur-SLS) approach guaranteeing closed-loop stability during and after parameter optimization, without requiring any constraints to be satisfied. We exploit recent Deep Neural Network (DNN) models based on Recurrent Equilibrium Networks (RENs) to learn over a rich class of nonlinear stable operators, and demonstrate the effectiveness of the proposed approach in numerical examples.
Analysis Method of Strapdown Inertial Navigation Error Distribution Based on Covariance Matrix Decomposition
Authors: Xiaokang Yang, Gongmin Yan, Fan Liu, Bofan Guan, Sihai Li
Date: 2022-03-22 00:00:00

Error distribution analysis is an important assistant technology for the research of SINS(Strapdown Inertial Navigation System). Error distribution result can provide the contribution of different errors to final navigation error, which is helpful for modifying and optimizing SINS. To realize decomposing the navigation error into parts that caused by each error source, the SINS error state space model is established and covariance matrix is decomposed according to error sources. The proposed error distribution analysis method based on 34-dimension SINS error model can quantitatively analyze the contribution to the end navigation error of initial errors, IMU(Inertial Measurement Unit) bias, IMU scale factor errors, mounting errors of gyroscopes and accelerometers, and IMU stochastic errors. The simulations in static condition and single axis rotation condition indict that the distribution result of proposed analysis method accords with the law of error propagation. After trajectory determined, the corresponding error distribution result will be calculated with the proposed method. Compared with the Monte-Carlo method and other method based on covariance matrix, the proposed method uses more complete error model, considers the interaction effect of error sources and can be easily realized with less computation.
COSTARICA estimator for rollback-less systems handling in iterative co-simulation algorithms
Authors: Yohan Eguillon, Bruno Lacabanne, Damien Tromeur-Dervout
Date: 2022-03-22 00:00:00

Co-simulation is widely used in the industry due to the emergence of modular dynamical models made up of interconnected, black-boxed systems. Several co-simulation algorithms have been developed, each with different properties and different levels of accuracy and robustness. Among them, the most accurate and reliable ones are the iterative ones, although they have a main drawback in common: the involved systems are required to be capable of rollback. The latter denotes the ability of a system to integrate over a co-simulation time step that has already been simulated. Non-rollback-capable system can only go forward in time and every integrated step is definitive. In practice, the industrial modelling and simulation platforms rarely produce rollback-capable systems. This paper proposes a solution that slightly changes the co-simulation methodology and that enables to use iterative co-simulation methods on a modular model which contains non-rollback-capable systems in case the latter represent ordinary differential equations. The idea is to replace such a system by a simplified version, which is used to estimate the results of the integrations instead of integrating the real system. Once the co-simulation method's surrogate iterations on these estimators predict the convergence on the co-simulation step, the non-rollback-capable systems genuinely integrate the step using the estimated solution on the other systems before moving forward, transforming the iterative co-simulation method into a non-iterative one.
Accelerating Extremum Seeking Convergence by Richardson Extrapolation Methods
Authors: Jan-Henrik Metsch, Jonathan Neuhauser, Jerome Jouffroy, Taous-Meriem Laleg-Kirati, Johann Reger
Date: 2022-03-22 00:00:00

In this paper, we propose the concept of accelerated convergence that has originally been developed to speed up the convergence of numerical methods for extremum seeking (ES) loops. We demonstrate how the dynamics of ES loops may be analyzed to extract structural information about the generated output of the loop. This information is then used to distil the limit of the loop without having to wait for the system to converge to it.
Unknown Piecewise Constant Parameters Identification with Exponential Rate of Convergence
Authors: Anton Glushchenko, Konstantin Lastochkin
Date: 2022-03-22 00:00:00
Tasks: regression

The scope of this research is the identification of unknown piecewise constant parameters of linear regression equation under the finite excitation condition. Compared to the known methods, to make the computational burden lower, only one model to identify all switching states of the regression is used in the developed procedure with the following two-fold contribution. First of all, we propose a new truly online estimation algorithm based on a well-known DREM approach to detect switching time and preserve time alertness with adjustable detection delay. Secondly, despite the fact that a switching signal function is unknown, the adaptive law is derived that provides global exponential convergence of the regression parameters to their true values in case the regressor is finitely exciting somewhere inside the time interval between two consecutive parameters switches. The robustness of the proposed identification procedure to the influence of external disturbances is analytically proved. Its effectiveness is demonstrated via numerical experiments, in which both abstract regressions and a second-order plant model are used.
Safety of Sampled-Data Systems with Control Barrier Functions via Approximate Discrete Time Models
Authors: Andrew J. Taylor, Victor D. Dorobantu, Ryan K. Cosner, Yisong Yue, Aaron D. Ames
Date: 2022-03-22 00:00:00

Control Barrier Functions (CBFs) have been demonstrated to be a powerful tool for safety-critical controller design for nonlinear systems. Existing design paradigms do not address the gap between theory (controller design with continuous time models) and practice (the discrete time sampled implementation of the resulting controllers); this can lead to poor performance and violations of safety for hardware instantiations. We propose an approach to close this gap by synthesizing sampled-data counterparts to these CBF-based controllers using approximate discrete time models and Sampled-Data Control Barrier Functions (SD-CBFs). Using properties of a system's continuous time model, we establish a relationship between SD-CBFs and a notion of practical safety for sampled-data systems. Furthermore, we construct convex optimization-based controllers that formally endow nonlinear systems with safety guarantees in practice. We demonstrate the efficacy of these controllers in simulation.