pwc-archive/papers-with-abstracts huggingface dataset:
Approximate Sensitivity Conditioning and Singular Perturbation Analysis for Power Converters
Authors: Kaushik Gajula, Lalit Kishore Marepalli, Luis Herrera
Date: 2022-03-31 00:00:00
Tasks: Sensitivity

A sensitivity conditioning control strategy is analyzed in this paper and it is applied to power electronic converters. The term is used to improve closed loop systems, such as power converters with cascaded inner and outer loop controllers. The impact of the sensitivity term is analyzed using singular perturbation theory. In addition, the implementation of the control term is addressed for practical systems, where the number of inputs is generally not sufficient for exact sensitivity conditioning. Simulation results are presented for a buck converter with output capacitor voltage regulation and a Permanent Magnet Synchronous Machine (PMSM), used as a generator with an active rectifier. Finally, experimental results are presented for the buck converter, demonstrating the advantages and feasibility in implementing the approximate sensitivity conditioning term for closed loop power converters.
Approximation by Simple Poles -- Part II: System Level Synthesis Beyond Finite Impulse Response
Authors: Michael W. Fisher, Gabriela Hug, Florian Dörfler
Date: 2022-03-31 00:00:00

In Part I, a novel Galerkin-type method for finite dimensional approximations of transfer functions in Hardy space was developed based on approximation by simple poles. In Part II, this approximation is applied to system level synthesis, a recent approach based on a clever reparameterization, to develop a new technique for optimal control design. To solve system level synthesis problems, prior work relies on finite impulse response approximations that lead to deadbeat control, and that can experience infeasibility and increased suboptimality, especially in systems with large separation of time scales. The new design method does not result in deadbeat control, is convex and tractable, always feasible, can incorporate prior knowledge, and works well for systems with large separation of time scales. Suboptimality bounds with convergence rate depending on the geometry of the pole selection are provided. An example demonstrates superior performance of the method.
Resilient Distribution System Restoration with Communication Recovery by Drone Small Cells
Authors: Haochen Zhang, Chen Chen, Shunbo Lei, Zhaohong Bie
Date: 2022-03-31 00:00:00

Distribution system (DS) restoration after natural disasters often faces the challenge of communication failures to feeder automation (FA) facilities, resulting in prolonged load pick-up process. This letter discusses the utilization of drone small cells for wireless communication recovery of FA, and proposes an integrated DS restoration strategy with communication recovery. Demonstrative case studies are conducted to validate the proposed model, and its advantages are illustrated by comparing to benchmark strategies.
Spatio-Temporal Graph Convolutional Neural Networks for Physics-Aware Grid Learning Algorithms
Authors: Tong Wu, Ignacio Losada Carreno, Anna Scaglione, Daniel Arnold
Date: 2022-03-31 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper proposes a model-free Volt-VAR control (VVC) algorithm via the spatio-temporal graph ConvNet-based deep reinforcement learning (STGCN-DRL) framework, whose goal is to control smart inverters in an unbalanced distribution system. We first identify the graph shift operator (GSO) based on the power flow equations. Then, we develop a spatio-temporal graph ConvNet (STGCN), testing both recurrent graph ConvNets (RGCN) and convolutional graph ConvNets (CGCN) architectures, aimed at capturing the spatiotemporal correlation of voltage phasors. The STGCN layer performs the feature extraction task for the policy function and the value function of the reinforcement learning architecture, and then we utilize the proximal policy optimization (PPO) to search the action spaces for an optimum policy function and to approximate an optimum value function. We further utilize the low-pass property of voltage graph signal to introduce an GCN architecture for the the policy whose input is a decimated state vector, i.e. a partial observation. Case studies on the unbalanced 123-bus systems validate the excellent performance of the proposed method in mitigating instabilities and maintaining nodal voltage profiles within a desirable range.
Cyberattack Detection for Nonlinear Leader-Following Multi-Agent Systems Using Set-Membership Fuzzy Filtering
Authors: Mahshid Rahimifard, Amir M. Moradi Sizkouhi, Rastko R. Selmic
Date: 2022-03-30 00:00:00
Tasks: Prediction

This paper is concerned with cyberattack detection in discrete-time, leader-following, nonlinear, multi-agent systems subject to unknown but bounded (UBB) system noises. The Takagi-Sugeno (T-S) fuzzy model is employed to approximate the nonlinear systems over the true value of the state. A distributed cyberattack detection method, based on a new fuzzy set-membership filtering method, which consists of two steps, namely a prediction step and a measurement update step, is developed for each agent to identify two types of cyberattacks at the time of their occurrence. The attacks are replay attacks and false data injection attacks affecting the leader-following consensus. We calculate an estimation ellipsoid set by updating the prediction ellipsoid set with the current sensor measurement data. Two criteria are provided to detect cyberattacks based on the intersection between the ellipsoid sets. If there is no intersection between the prediction set and the estimation set of an agent at the current time instant, a cyberattack on its sensors is declared. Control signal or communication signal data of an agent are under a cyberattack if its prediction set has no intersection with the estimation set updated at the previous time instant. Recursive algorithms for solving the consensus protocol and calculating the two ellipsoid sets for detecting attacks are proposed. Simulation results are provided to demonstrate the effectiveness of the proposed method.
Going Beyond RF: How AI-enabled Multimodal Beamforming will Shape the NextG Standard
Authors: Debashri Roy, Batool Salehi, Stella Banou, Subhramoy Mohanti, Guillem Reus-Muns, Mauro Belgiovine, Prashant Ganesh, Carlos Bocanegra, Chris Dick, Kaushik Chowdhury
Date: 2022-03-30 00:00:00
Tasks: Dataset Generation, Edge-computing

Incorporating artificial intelligence and machine learning (AI/ML) methods within the 5G wireless standard promises autonomous network behavior and ultra-low-latency reconfiguration. However, the effort so far has purely focused on learning from radio frequency (RF) signals. Future standards and next-generation (nextG) networks beyond 5G will have two significant evolutions over the state-of-the-art 5G implementations: (i) massive number of antenna elements, scaling up to hundreds-to-thousands in number, and (ii) inclusion of AI/ML in the critical path of the network reconfiguration process that can access sensor feeds from a variety of RF and non-RF sources. While the former allows unprecedented flexibility in 'beamforming', where signals combine constructively at a target receiver, the latter enables the network with enhanced situation awareness not captured by a single and isolated data modality. This survey presents a thorough analysis of the different approaches used for beamforming today, focusing on mmWave bands, and then proceeds to make a compelling case for considering non-RF sensor data from multiple modalities, such as LiDAR, Radar, GPS for increasing beamforming directional accuracy and reducing processing time. This so called idea of multimodal beamforming will require deep learning based fusion techniques, which will serve to augment the current RF-only and classical signal processing methods that do not scale well for massive antenna arrays. The survey describes relevant deep learning architectures for multimodal beamforming, identifies computational challenges and the role of edge computing in this process, dataset generation tools, and finally, lists open challenges that the community should tackle to realize this transformative vision of the future of beamforming.
Excitation and Measurement Patterns for the Identifiability of Directed Acyclic Graphs
Authors: Eduardo Mapurunga, Michel Gevers, Alexandre S. Bazanella
Date: 2022-03-30 00:00:00

This paper deals with the design of Excitation and Measurement Patterns (EMP) for the identification of a class of dynamical networks whose topology has the structure of a Directed Acyclic Graph (DAG). In addition to the by now well known condition that the identifiabiltiy of any dynamical network requires that the sources be excited, the sinks be measured, and all other nodes be either excited or measured, we show that for DAGs two other types of nodes have special excitation and measurement requirements. Armed with this result, we propose a systematic procedure for the design of EMPs that guarantee identifiability of a network with DAG topology.
Adaptive Observer for a Class of Systems with Switched Unknown Parameters Using DREM
Authors: Tong Liu, Zengjie Zhang, Fangzhou Liu, Martin Buss
Date: 2022-03-30 00:00:00
Tasks: parameter estimation

In this note, we develop an adaptive observer for a class of nonlinear systems with switched unknown parameters to estimate the states and parameters simultaneously. The main challenge lies in how to eliminate the disturbance effect of zero-input responses caused by the switching on the parameter estimation. These responses depend on the unknown states at switching instants (SASI) and constitute an additive disturbance to the parameter estimation, which obstructs parameter convergence to zero. Our solution is to treat the zero-input responses as excitations instead of disturbances. This is realized by first augmenting the system parameter with the SASI and then developing an estimator for the augmented parameter using the \textit{dynamic regression extension and mixing} (DREM) technique. Thanks to its property of element-wise parameter adaptation, the system parameter estimation is decoupled from the SASI. As a result, the estimation errors of system states and parameters converge to zero asymptotically. Furthermore, the robustness of the proposed adaptive observer is guaranteed in the presence of disturbances and noise. A numerical example validates the effectiveness of the proposed approach.
ShipsEar: An underwater vessel noise database
Authors: David Santos-Domínguez, Soledad Torres-Guijarro, Antonio Cardenal-López, Antonio Pena-Gimenez
Date: 2016-06-22 00:00:00
Proceeding: Applied Acoustics 2016 6

There is a manifest shortage of audio databases available to underwater acoustics researchers. With the aim of palliating this situation, ShipsEar, a database of underwater recordings of ship and boat sounds, has been made available to the research community at http://atlanttic.uvigo.es/underwaternoise/. The database is currently composed of 90 records representing sounds from 11 vessel types. It includes detailed information on technical aspects of the recordings and environmental and other conditions during acquisition. To demonstrate the usefulness of ShipsEar, a vessel classifier was developed, based on cepstral coefficients and Gaussian mixture models. It was tested on a subset of ShipsEar database in which the original 11 vessel types were merged into 4 vessel size classes. The system yielded an overall classification rate of 75.4%, and 100% accuracy in detecting vessel presence. ShipsEar is potentially useful for the development and testing of applications based on processing underwater vessel sound.
Optimal and Robust Disclosure of Public Information
Authors: Takashi Ui
Date: 2022-03-31 00:00:00

A policymaker discloses public information to interacting agents who also acquire costly private information. More precise public information reduces the precision and cost of acquired private information. Considering this effect, what disclosure rule should the policymaker adopt? We address this question under two alternative assumptions using a linear quadratic Gaussian game with arbitrary quadratic material welfare and convex information costs. First, the policymaker knows the cost of private information and adopts an optimal disclosure rule to maximize the expected welfare. Second, the policymaker is uncertain about the cost and adopts a robust disclosure rule to maximize the worst-case welfare. Depending on the elasticity of marginal cost, an optimal rule is qualitatively the same as that in the case of either a linear information cost or exogenous private information. The worst-case welfare is strictly increasing if and only if full disclosure is optimal under some information costs, which provides a new rationale for central bank transparency.
Efficient Non-Autoregressive GAN Voice Conversion using VQWav2vec Features and Dynamic Convolution
Authors: Mingjie Chen, Yanghao Zhou, Heyan Huang, Thomas Hain
Date: 2022-03-31 00:00:00
Tasks: Voice Conversion

It was shown recently that a combination of ASR and TTS models yield highly competitive performance on standard voice conversion tasks such as the Voice Conversion Challenge 2020 (VCC2020). To obtain good performance both models require pretraining on large amounts of data, thereby obtaining large models that are potentially inefficient in use. In this work we present a model that is significantly smaller and thereby faster in processing while obtaining equivalent performance. To achieve this the proposed model, Dynamic-GAN-VC (DYGAN-VC), uses a non-autoregressive structure and makes use of vector quantised embeddings obtained from a VQWav2vec model. Furthermore dynamic convolution is introduced to improve speech content modeling while requiring a small number of parameters. Objective and subjective evaluation was performed using the VCC2020 task, yielding MOS scores of up to 3.86, and character error rates as low as 4.3\%. This was achieved with approximately half the number of model parameters, and up to 8 times faster decoding speed.
Improving Language Identification of Accented Speech
Authors: Kunnar Kukk, Tanel Alumäe
Date: 2022-03-31 00:00:00
Tasks: Language Identification, speech-recognition, Speech Recognition, Spoken language identification

Language identification from speech is a common preprocessing step in many spoken language processing systems. In recent years, this field has seen fast progress, mostly due to the use of self-supervised models pretrained on multilingual data and the use of large training corpora. This paper shows that for speech with a non-native or regional accent, the accuracy of spoken language identification systems drops dramatically, and that the accuracy of identifying the language is inversely correlated with the strength of the accent. We also show that using the output of a lexicon-free speech recognition system of the particular language helps to improve language identification performance on accented speech by a large margin, without sacrificing accuracy on native speech. We obtain relative error rate reductions ranging from to 35 to 63% over the state-of-the-art model across several non-native speech datasets.
Hierarchical Attention Network for Evaluating Therapist Empathy in Counseling Session
Authors: Dehua Tao, Tan Lee, Harold Chui, Sarah Luk
Date: 2022-03-31 00:00:00

Counseling typically takes the form of spoken conversation between a therapist and a client. The empathy level expressed by the therapist is considered to be an essential quality factor of counseling outcome. This paper proposes a hierarchical recurrent network combined with two-level attention mechanisms to determine the therapist's empathy level solely from the acoustic features of conversational speech in a counseling session. The experimental results show that the proposed model can achieve an accuracy of $72.1\%$ in classifying the therapist's empathy level as being ``high" or ``low". It is found that the speech from both the therapist and the client are contributing to predicting the empathy level that is subjectively rated by an expert observer. By analyzing speaker turns assigned with high attention weights, it is observed that $2$ to $6$ consecutive turns should be considered together to provide useful clues for detecting empathy, and the observer tends to take the whole session into consideration when rating the therapist empathy, instead of relying on a few specific speaker turns.
Restoring Vision through Retinal Implants -- A Systematic Literature Review
Authors: Magali Andreia Rossi, Sylviane da Silva Vitor
Date: 2022-03-31 00:00:00
Tasks: Systematic Literature Review

This work presents a bunched of promising technologies to treat blind people: the bionic eyes. The strategy is to combine a retina implant with software capable to interpret the information received. Along this line of thinking, projects such as Retinal Prosthetic Strategy with the Capacity to Restore Normal Vision from Weill Medical College of Cornell University Project, Update on Retinal Prosthetic Research from The Boston Retinal Implant Project, and Restoration of Vision Using Wireless Cortical Implants from Monash Vision Group Project, have shown in a different context the use of technologies that commits to bring the vision through its use.
Plug-and-Play Methods for Integrating Physical and Learned Models in Computational Imaging
Authors: Ulugbek S. Kamilov, Charles A. Bouman, Gregery T. Buzzard, Brendt Wohlberg
Date: 2022-03-31 00:00:00

Plug-and-Play Priors (PnP) is one of the most widely-used frameworks for solving computational imaging problems through the integration of physical models and learned models. PnP leverages high-fidelity physical sensor models and powerful machine learning methods for prior modeling of data to provide state-of-the-art reconstruction algorithms. PnP algorithms alternate between minimizing a data-fidelity term to promote data consistency and imposing a learned regularizer in the form of an image denoiser. Recent highly-successful applications of PnP algorithms include bio-microscopy, computerized tomography, magnetic resonance imaging, and joint ptycho-tomography. This article presents a unified and principled review of PnP by tracing its roots, describing its major variations, summarizing main results, and discussing applications in computational imaging. We also point the way towards further developments by discussing recent results on equilibrium equations that formulate the problem associated with PnP algorithms.
Revisiting Document Image Dewarping by Grid Regularization
Authors: Xiangwei Jiang, Rujiao Long, Nan Xue, Zhibo Yang, Cong Yao, Gui-Song Xia
Date: 2022-03-31 00:00:00
Proceeding: CVPR 2022 1
Tasks: Local Distortion, Optical Flow Estimation

This paper addresses the problem of document image dewarping, which aims at eliminating the geometric distortion in document images for document digitization. Instead of designing a better neural network to approximate the optical flow fields between the inputs and outputs, we pursue the best readability by taking the text lines and the document boundaries into account from a constrained optimization perspective. Specifically, our proposed method first learns the boundary points and the pixels in the text lines and then follows the most simple observation that the boundaries and text lines in both horizontal and vertical directions should be kept after dewarping to introduce a novel grid regularization scheme. To obtain the final forward mapping for dewarping, we solve an optimization problem with our proposed grid regularization. The experiments comprehensively demonstrate that our proposed approach outperforms the prior arts by large margins in terms of readability (with the metrics of Character Errors Rate and the Edit Distance) while maintaining the best image quality on the publicly-available DocUNet benchmark.
Global and Asymptotically Efficient Localization from Range Measurements
Authors: Guangyang Zeng, Biqiang Mu, Jiming Chen, Zhiguo Shi, Junfeng Wu
Date: 2022-03-31 00:00:00
Tasks: Position

We consider the range-based localization problem, which involves estimating an object's position by using $m$ sensors, hoping that as the number $m$ of sensors increases, the estimate converges to the true position with the minimum variance. We show that under some conditions on the sensor deployment and measurement noises, the LS estimator is strongly consistent and asymptotically normal. However, the LS problem is nonsmooth and nonconvex, and therefore hard to solve. We then devise realizable estimators that possess the same asymptotic properties as the LS one. These estimators are based on a two-step estimation architecture, which says that any $\sqrt{m}$-consistent estimate followed by a one-step Gauss-Newton iteration can yield a solution that possesses the same asymptotic property as the LS one. The keypoint of the two-step scheme is to construct a $\sqrt{m}$-consistent estimate in the first step. In terms of whether the variance of measurement noises is known or not, we propose the Bias-Eli estimator (which involves solving a generalized trust region subproblem) and the Noise-Est estimator (which is obtained by solving a convex problem), respectively. Both of them are proved to be $\sqrt{m}$-consistent. Moreover, we show that by discarding the constraints in the above two optimization problems, the resulting closed-form estimators (called Bias-Eli-Lin and Noise-Est-Lin) are also $\sqrt{m}$-consistent. Plenty of simulations verify the correctness of our theoretical claims, showing that the proposed two-step estimators can asymptotically achieve the Cramer-Rao lower bound.
Opportunities for Physical Layer Security in UAV Communication Enhanced with Intelligent Reflective Surfaces
Authors: Wali Ullah Khan, Eva Lagunas, Zain Ali, Muhammad Awais Javed, Manzoor Ahmed, Symeon Chatzinotas, Björn Ottersten, Petar Popovski
Date: 2022-03-31 00:00:00

Unmanned Aerial Vehicles (UAVs) are an important component of next-generation wireless networks that can assist in high data rate communications and provide enhanced coverage.Their high mobility and aerial nature offer deployment flexibility and low-cost infrastructure support to existing cellular networks and provide many applications that rely on mobile wireless communications. However, security is a major challenge in UAV communications, and Physical Layer Security (PLS) is an important technique to improve the reliability and security of data shared with the assistance of UAVs. Recently, Intelligent Reflecting Surfaces (IRS) have emerged as a novel technology to extend and/or enhance wireless coverage by re-configuring the propagation environment of communications. This paper provides an overview of how IRS can improve the PLS of UAV networks. We discuss different use cases of PLS for IRS enhanced UAV communications and briefly review the recent advances in this area. Then based on the recent advances, we also present a case study that utilizes alternate optimization to maximize the secrecy capacity for IRS enhanced UAV scenario in the presence of multiple eavesdroppers. Finally, we highlight several open issues and research challenges to realize PLS in IRS enhanced UAV communications.
The bioacoustic proof of the effects of raising awareness of noise pollution among visitors to the Port Cros National Park using binding communication
Authors: David Reymond, Daphné Duvernay, Frédéric Ely, Hervé Glotin
Date: 2022-03-31 00:00:00

Assuming that the anthropogenic impact of visitors to a natural park can be reduced by communication actions, we have drawn up a bioacoustical protocol combined with a protocol to measure the effectiveness of the communication actions. A simple eco-awareness communication method will be compared to a binding communication method. We address the questions: does visitors' eco-awareness drive them to change their attitudes and behavior in order to limit their own noise disturbance, and does commitment improve their awareness? We detail here the main concepts considered and the methodology followed to discuss the preliminary results.