pwc-archive/papers-with-abstracts huggingface dataset:
Relay Selection for 5G New Radio Via Artificial Neural Networks
Authors: Saud Aldossari, Kwang-cheng Chen
Date: 2020-05-02 00:00:00

Millimeter-wave supplies an alternative frequency band of wide bandwidth to better realize pillar technologies of enhanced mobile broadband (eMBB) and ultra-reliable and lowlatency communication (uRLLC) for 5G - new radio (5G-NR). When using mmWave frequency band, relay stations to assist the coverage of base stations in radio access network (RAN) emerge as an attractive technique. However, relay selection to result in the strongest link becomes the critical technology to facilitate RAN using mmWave. A alternative approach toward relay selection is to take advantage of existing operating data and apply appropriate artificial neural networks (ANN) and deep learning algorithms to alleviate severe fading in mmWave band. In this paper, we apply classification techniques using ANN with multilayer perception to predict the path loss of multiple transmitted links and base on a certain loss level, and thus execute effective relay selection, which also recommends the handover to an appropriate path. ANN with multilayer perceptions are compared with other ML algorithms to demonstrate the effectiveness for relay selection in 5G-NR.
Utilizing Pulse Pileup Effect in Development of Robust Low-SNR Covert Communication Links
Authors: Alexei V. Nikitin, Ruslan L. Davidchack
Date: 2020-05-02 00:00:00

In contrast to other spread-spectrum techniques, wideband pulse trains with relatively low pulse arrival rates may be considered unsuitable for covert communications. The high crest factor of such trains can be extremely burdensome for the transmitter hardware, and it makes the pulse trains easily detectable even at very low signal-to-noise ratios. In addition, it may appear that sharing the wideband channel by multiple users would require explicit allocation of the pulse arrival times for each sub-channel, which would be impractical in most cases. On the other hand, messaging by wideband pulse trains has many appealing features. Among those are the ease of synchronous as well as asynchronous pulse detection, and on-the-fly channel reconfigurability (e.g. changing the spreading factor). Favorably, the crest factor of a pulse train, as well as its apparent temporal and amplitude structure, can be easily, and reversibly, controlled by simple linear filtering. For example, a transmitted pulse train can be made statistically indistinguishable from the Gaussian component of the channel noise (e.g. the thermal noise) observed in the same spectral band, while the received signal will be the designed high-crest-factor wideband pulse train. In this paper, we utilize the so-called pulse pileup effect to perform such reversible control of the pulse train structure, enabling a wider use of this approach for synthesis of robust low-SNR covert communication links. We place a particular focus on the synchronous pulse detection in the receiver, that provides a better utilization of the channel spectrum.
Automotive-Radar-Based 50-cm Urban Positioning
Authors: Lakshay Narula, Peter A. Iannucci, Todd E. Humphreys
Date: 2020-05-02 00:00:00
Tasks: Position, Translation

Deployment of automated ground vehicles (AGVs) beyond the confines of sunny and dry climes will require sub-lane-level positioning techniques based on radio waves rather than near-visible-light radiation. Like human sight, lidar and cameras perform poorly in low-visibility conditions. This paper develops and demonstrates a novel technique for robust 50-cm-accurate urban ground positioning based on commercially-available low-cost automotive radars. The technique is computationally efficient yet obtains a globally-optimal translation and heading solution, avoiding local minima caused by repeating patterns in the urban radar environment. Performance is evaluated on an extensive and realistic urban data set. Comparison against ground truth shows that, when coupled with stable short-term odometry, the technique maintains 95-percentile errors below 50 cm in horizontal position and 1 degree in heading.
Connectivity estimation of high dimensional data recorded from neuronal cells
Authors: Stefano De Blasi
Date: 2020-05-01 00:00:00
Tasks: Connectivity Estimation, Vocal Bursts Intensity Prediction

The main result of this thesis is the development of a novel connectivity estimation method, called Total Spiking Probability Edges (TSPE). Based on cross-correlation and edge filtering at different time scales this method is proposed and the theoretical framework is outlined in this work. TSPE enables the classification between inhibitory and excitatory connections by using recorded action potentials. To compare this method learning about state of the art algorithms to estimate connectivity is necessary. After a research, promising algorithms are implemented and evaluated for further research topics, among others in the biomems lab of UAS Aschaffenburg. To evaluate these algorithms in silico networks are used, because of their known connectivity. This makes it possible to validate the correctness of our algorithm results. Therefore, a biophysically representative neuronal network simulation is needed first. Datasets were simulated in different ways and analysed in order to develop an evaluation framework. After a successful evaluation with in silico networks, in vitro experiments and their analyses complete this project.
Next Generation Terahertz Communications: A Rendezvous of Sensing, Imaging, and Localization
Authors: Hadi Sarieddeen, Nasir Saeed, Tareq Y. Al-Naffouri, Mohamed-Slim Alouini
Date: 2019-09-19 00:00:00

Terahertz (THz)-band communications are celebrated as a key enabling technology for next-generation wireless systems that promises to integrate a wide range of data-demanding and delay-sensitive applications. Following recent advancements in optical, electronic, and plasmonic transceiver design, integrated, adaptive, and efficient THz systems are no longer far-fetched. In this paper, we present a progressive vision of how the traditional "THz gap" will transform into a "THz rush" over the next few years. We posit that the breakthrough that the THz band will introduce will not be solely driven by achievable high data rates, but more profoundly by the interaction between THz sensing, imaging, and localization applications. We first detail the peculiarities of each of these applications at the THz band. Then, we illustrate how their coalescence results in enhanced environment-aware system performance in beyond-5G use cases. We further discuss the implementation aspects of this merging of applications in the context of shared and dedicated resource allocation, highlighting the role of machine learning.
Design and Convergence Analysis of an IIC-based BICM-ID Receiver for FBMC-QAM Systems
Authors: Sumaila Mahama, Yahya J. Harbi, Alister G. Burr, David Grace
Date: 2020-05-01 00:00:00
Tasks: Decoder

In this paper, an iterative interference cancellation (IIC) based BICM-ID receiver is designed to cancel the intrinsic interference in FBMC-QAM systems. The proposed receiver consist of an inner decoder, which combines iterative demodulation and interference cancellation, and an outer decoder which is a low density parity check (LDPC) channel decoder. To evaluate the convergence behaviour of the proposed receiver, extrinsic information transfer (EXIT) chart analysis is employed, where EXIT curves for the components of the iterative decoder are derived. Numerical results show that the intrinsic interference in FBMC-QAM systems can be removed by adopting the proposed IIC-based BICM-ID receiver.
Energy-Efficient Wireless Communications with Distributed Reconfigurable Intelligent Surfaces
Authors: Zhaohui Yang, Mingzhe Chen, Walid Saad, Wei Xu, Mohammad Shikh-Bahaei, H. Vincent Poor, Shuguang Cui
Date: 2020-05-01 00:00:00

This paper investigates the problem of resource allocation for a wireless communication network with distributed reconfigurable intelligent surfaces (RISs). In this network, multiple RISs are spatially distributed to serve wireless users and the energy efficiency of the network is maximized by dynamically controlling the on-off status of each RIS as well as optimizing the reflection coefficients matrix of the RISs. This problem is posed as a joint optimization problem of transmit beamforming and RIS control, whose goal is to maximize the energy efficiency under minimum rate constraints of the users. To solve this problem, two iterative algorithms are proposed for the single-user case and multi-user case. For the single-user case, the phase optimization problem is solved by using a successive convex approximation method, which admits a closed-form solution at each step. Moreover, the optimal RIS on-off status is obtained by using the dual method. For the multi-user case, a low-complexity greedy searching method is proposed to solve the RIS on-off optimization problem. Simulation results show that the proposed scheme achieves up to 33\% and 68\% gains in terms of the energy efficiency in both single-user and multi-user cases compared to the conventional RIS scheme and amplify-and-forward relay scheme, respectively.
Methodology based on higher-order statistics and genetic algorithms for the classification of power quality disturbances
Authors: Luis Alejandro Romero-Ramirez1, David Alejandro Elvira-Ortiz1, Arturo Y. Jaen-Cuellar1, Daniel Morinigo- Sotelo2, Roque A. Osornio-Rios1, Rene de J. Romero-Troncoso
Date: 2020-09-04 00:00:00

The present work proposes a methodology for the detection and classification of some transient power quality
disturbances. It uses a genetic algorithm for estimating the amplitude, frequency, and phase of the fundamental component in
an optimum way to suppress it from an electric signal. By using this pre-processing strategy, it is possible to remove a single
frequency component instead of removing a whole frequency band as other methodologies do. Once the fundamental
component is suppressed, it is possible to perform a better identification of an anomalous condition in a signal because its high
energy does not hide the presence of a disturbance. After the suppression, the proposed methodology computes three higher-
order statistics (variance, kurtosis, and sixth-order cumulant) from the resulting signal and uses them as inputs for a fuzzy-based
classifier. Higher-order statistics are selected because they are insensible to the presence of Gaussian noise adding robustness
to the proposed methodology. Experimentation is performed using both synthetic and real signals. Real signals come from a
photovoltaic generation plant and a hospital facility, both located in Spain. Results prove that the proposed methodology allows
enhancing the results delivered by other methodologies up to 30%.
Signalling boosts the evolution of cooperation in repeated group interactions
Authors: Luis A. Martinez-Vaquero, Francisco C. Santos, Vito Trianni
Date: 2021-04-13 00:00:00

Many biological and social systems show significant levels of collective action. Several cooperation mechanisms have been proposed, yet they have been mostly studied independently. Among these, direct reciprocity supports cooperation on the basis of repeated interactions among individuals. Signals and quorum dynamics may also drive cooperation. Here, we resort to an evolutionary game theoretical model to jointly analyse these two mechanisms and study the conditions in which evolution selects for direct reciprocity, signalling, or their combination. We show that signalling alone leads to higher levels of cooperation than when combined with reciprocity, while offering additional robustness against errors. Specifically, successful strategies in the realm of direct reciprocity are often not selected in the presence of signalling, and memory of past interactions is only exploited opportunistically in case of earlier coordination failure. Differently, signalling always evolves, even when costly. In the light of these results, it may be easier to understand why direct reciprocity has been observed only in a limited number of cases among non-humans, whereas signalling is widespread at all levels of complexity.
Towards agrobots: Identification of the yaw dynamics and trajectory tracking of an autonomous tractor
Authors: Erkan Kayacan, Erdal Kayacan, Herman Ramon, Wouter Saeys
Date: 2021-04-14 00:00:00
Tasks: Model Predictive Control

More efficient agricultural machinery is needed as agricultural areas become more limited and energy and labor costs increase. To increase their efficiency, trajectory tracking problem of an autonomous tractor, as an agricultural production machine, has been investigated in this study. As a widely used model-based approach, model predictive control is preferred in this paper to control the yaw dynamics of the tractor which can deal with the constraints on the states and the actuators in a system. The yaw dynamics is identified by using nonlinear least squares frequency domain system identification. The speed is controlled by a proportional-integral-derivative controller and a kinematic trajectory controller is used to calculate the desired speed and the desired yaw rate signals for the subsystems in order to minimize the tracking errors in both the longitudinal and transversal directions. The experimental results show the accuracy and the efficiency of the proposed control scheme in which the euclidean error is below $40$ cm for time-based straight line trajectories and $60$ cm for time-based curved line trajectories, respectively.
Audio-based cough counting using independent subspace analysis
Authors: Paul Leamy, Ted Burke, Dan Barry, David Dorran
Date: 2021-04-14 00:00:00

In this paper, an algorithm designed to detect characteristic cough events in audio recordings is presented, significantly reducing the time required for manual counting. Using time-frequency representations and independent subspace analysis (ISA), sound events that exhibit characteristics of coughs are automatically detected, producing a summary of the events detected. Using a dataset created from publicly available audio recordings, this algorithm has been tested on a variety of synthesized audio scenarios representative of those likely to be encountered by subjects undergoing an ambulatory cough recording, achieving a true positive rate of 76% with an average of 2.85 false positives per minute.
Iterative Geometry Calibration from Distance Estimates for Wireless Acoustic Sensor Networks
Authors: Tobias Gburrek, Joerg Schmalenstroeer, Reinhold Haeb-Umbach
Date: 2020-12-11 00:00:00
Tasks: Position

In this paper we present an approach to geometry calibration in wireless acoustic sensor networks, whose nodes are assumed to be equipped with a compact microphone array. The proposed approach solely works with estimates of the distances between acoustic sources and the nodes that record these sources. It consists of an iterative weighted least squares localization procedure, which is initialized by multidimensional scaling. Alongside the sensor node locations, also the positions of the acoustic sources are estimated. Furthermore, we derive the Cramer-Rao lower bound (CRLB) for source and sensor position estimation, and show by simulation that the estimator is efficient.
Learning Metrics from Mean Teacher: A Supervised Learning Method for Improving the Generalization of Speaker Verification System
Authors: Ju-ho Kim, Hye-jin Shim, Jee-weon Jung, Ha-Jin Yu
Date: 2021-04-14 00:00:00
Tasks: Speaker Verification

Most speaker verification tasks are studied as an open-set evaluation scenario considering the real-world condition. Thus, the generalization power to unseen speakers is of paramount important to the performance of the speaker verification system. We propose to apply \textit {Mean Teacher}, a temporal averaging model, to extract speaker embeddings with small intra-class variance and large inter-class variance. The mean teacher network refers to the temporal averaging of deep neural network parameters; it can produces more accurate and stable representations than using weights after the training finished. By learning the reliable intermediate representation of the mean teacher network, we expect that the proposed method can explore more discriminatory embedding spaces and improve the generalization performance of the speaker verification system. Experimental results on the VoxCeleb1 test set demonstrate that the proposed method relatively improves performance by 11.61\%, compared to a baseline system.
Systems-Theoretic Safety Assessment of Teleoperated Road Vehicles
Authors: Simon Hoffmann, Dr. Frank Diermeyer
Date: 2021-04-14 00:00:00

Teleoperation is becoming an essential feature in automated vehicle concepts, as it will help the industry overcome challenges facing automated vehicles today. Teleoperation follows the idea to get humans back into the loop for certain rare situations the automated vehicle cannot resolve. Teleoperation therefore has the potential to expand the operational design domain and increase the availability of automated vehicles. This is especially relevant for concepts with no backup driver inside the vehicle. While teleoperation resolves certain issues an automated vehicle will face, it introduces new challenges in terms of safety requirements. While safety and regulatory approval is a major research topic in the area of automated vehicles, it is rarely discussed in the context of teleoperated road vehicles. The focus of this paper is to systematically analyze the potential hazards of teleoperation systems. An appropriate hazard analysis method (STPA) is chosen from literature and applied to the system at hand. The hazard analysis is an essential part in developing a safety concept (e.g., according to ISO26262) and thus far has not been discussed for teleoperated road vehicles.
Optimization-Based Path-Planning for Connected and non-Connected Automated Vehicles
Authors: Panagiotis Typaldos, Markos Papageorgiou, Ioannis Papamichail
Date: 2021-04-14 00:00:00
Tasks: Model Predictive Control

A path-planning algorithm for connected and non-connected automated road vehicles on multilane motorways is derived from the opportune formulation of an optimal control problem. In this framework, the objective function to be minimized contains appropriate respective terms to reflect: the goals of vehicle advancement; passenger comfort; and avoidance of collisions with other vehicles, of road departures and of negative speeds. Connectivity implies that connected vehicles are able to exchange with each other (V2V) or the infrastructure (V2I), real-time information about their last generated path. For the numerical solution of the optimal control problem, an efficient feasible direction algorithm is used. To ensure high-quality local minima, a simplified Dynamic Programming algorithm is also conceived to deliver the initial guess trajectory for the feasible direction algorithm. Thanks to low computation times, the approach is readily executable within a model predictive control (MPC) framework. The proposed MPC-based approach is embedded within the Aimsun microsimulation platform, which enables the evaluation of a plethora of realistic vehicle driving and advancement scenarios. Results obtained on a multilane motorway stretch indicate higher efficiency of the optimally controlled vehicles in driving closer to their desired speed, compared to ordinary Aimsun vehicles. Increased penetration rates of automated vehicles are found to increase the efficiency of the overall traffic flow, benefiting manual vehicles as well. Moreover, connected controlled vehicles appear to be more efficient compared to the corresponding non-connected controlled vehicles, due to the improved real-time information and short-term prediction.
Towards Distributed Accommodation of Covert Attacks in Interconnected Systems
Authors: Angelo Barboni, Thomas Parisini
Date: 2020-07-06 00:00:00

The problem of mitigating maliciously injected signals in interconnected systems is dealt with in this paper. We consider the class of covert attacks, as they are stealthy and cannot be detected by conventional means in centralized settings. Distributed architectures can be leveraged for revealing such stealthy attacks by exploiting communication and local model knowledge. We show how such detection schemes can be improved to estimate the action of an attacker and we propose an accommodation scheme in order to mitigate or neutralize abnormal behavior of a system under attack.
Out-of-Step Detection Based On an Improved Line Potential Energy Criterion
Authors: Xiao Li, Chongru Liu, Jin Ma
Date: 2021-04-14 00:00:00

The line potential energy in the cutset is used as the criterion for monitoring the generator instability, but the criterion has the following two limitations due to narrowly defined conditions. The assumption of an ideal constant power load is difficult to satisfy, and the condition of the critical cutset is too narrow. The limitations are addressed by analyzing the relationship between the power of the load elements and the power of the cutset based on the two-machine equivalent. Modifications of the theoretical derivation steps of this criterion are presented to extend the applications of the criterion and provide theoretical support for its online use. Two simple strategies are proposed to ensure early detection of the generator instability.
Attitude Observation for Second Order Attitude Kinematics
Authors: Yonhon Ng, Pieter van Goor, Robert Mahony, Tarek Hamel
Date: 2021-04-14 00:00:00
Tasks: Object

This paper addresses the problem of estimating the attitude and angular velocity of a rigid object by exploiting its second order kinematic model. The approach is particularly useful in cases where angular velocity measurements are not available and the attitude and angular velocity of an object need to be estimated from accelerometers and magnetometers. We propose a novel sensor modality that uses multiple accelerometers to measure the angular acceleration of an object as well as using magnetometers to measure partial attitude. We extend the approach of equivariant observer design to second order attitude kinematics by demonstrating that the special Euclidean group acts as a symmetry group on the system considered. The observer design is based on the lifted kinematics and we prove almost global asymptotic stability and local uniform exponential stability of the estimation error. The performance of the observer is demonstrated in simulation.
Towards Optimal Coordination between Regional Groups: HVDC Supplementary Power Control
Authors: Andrea Tosatto, Georgios Misyris, Adrià Junyent-Ferré, Fei Teng, Spyros Chatzivasileiadis
Date: 2020-11-25 00:00:00

With Europe dedicated to limiting climate change and greenhouse gas emissions, large shares of Renewable Energy Sources (RES) are being integrated in the national grids, phasing out conventional generation. The new challenges arising from the energy transition will require a better coordination between neighboring system operators to maintain system security. To this end, this paper studies the benefit of exchanging primary frequency reserves between asynchronous areas using the Supplementary Power Control (SPC) functionality of High-Voltage Direct-Current (HVDC) lines. First, we focus on the derivation of frequency metrics for asynchronous AC systems coupled by HVDC interconnectors. We compare two different control schemes for HVDC converters, which allow for unilateral or bilateral exchanges of reserves between neighboring systems. Second, we formulate frequency constraints and include them in a unit commitment problem to ensure the N-1 security criterion. A data-driven approach is proposed to better represent the frequency nadir constraint by means of cutting hyperplanes. Our results suggest that the exchange of primary reserves through HVDC can reduce up to 10% the cost of reserve procurement while maintaining the system N-1 secure.