pwc-archive/papers-with-abstracts huggingface dataset:
Experimental Validation of a Dynamic Equivalent Model for Microgrids
Authors: F. Conte, F. D'Agostino, S. Massucco, F. Silvestro, C. Bossi, M. Cabiati
Date: 2021-03-05 00:00:00

The goal of this paper is the experimental validation of a gray-box equivalent modeling approach applied to microgrids. The main objective of the equivalent modeling is to represent the dynamic response of a microgrid with a simplified model. The main contribution of this work is the experimental validation of a two-step process, composed by the definition of a nonlinear equivalent model with operational constraints, adapted to the microgrid environment, and the identification procedure used to define the model parameters. Once the parameters are identified, the simplified model is ready to reproduce the microgrid behavior to voltage and frequency variations, in terms of active and reactive power exchanges at the point of common coupling. To validate the proposed approach, a set of experimental tests have been carried out on a real LV microgrid considering different configurations, including both grid-connected and islanded operating conditions. Results show the effectiveness of the proposed technique and the applicability of the model to perform dynamic simulations.
Multilingual aspect clustering for sentiment analysis
Authors: Lucas Rafael Costella Pessutto, Danny Suarez Vargas, Viviane P. Moreira
Date: 2019-12-09 00:00:00
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Aspect Extraction, Clustering, Sentiment Analysis, Word Embeddings

In the last few years, there has been growing interest in aspect-based sentiment analysis, which
deals with extracting, clustering, and rating the overall opinion about the features of the entity being
evaluated. Techniques for aspect extraction can produce an undesirably large number of aspects —
with many of those relating to the same product feature. Hence, aspect clustering becomes necessary.
Current solutions for aspect clustering are monolingual, but in many practical situations, reviews for
a given entity are available in several languages, calling for multilingual integration. In this article,
we address the novel task of multilingual aspect clustering, which aims at grouping semantically
related aspects extracted from reviews written in several languages. Our method is unsupervised
and relies on the contextual information of the aspects, which is represented by word embeddings.
This representation allied with a suitable similarity measure allows clustering related aspects. Our
experiments on two datasets with five languages each showed that our unsupervised clustering
technique achieves results that outperform monolingual baselines adapted to work with multilingual
data. We also show the benefits of the multilingual approach compared to using languages in isolation.
Dynamic Student Classiffication on Memory Networks for Knowledge Tracing
Authors: Sein Minn, Michel C. Desmarais, Feida Zhu, Jing Xiao, Jianzong Wang
Date: 2019-03-22 00:00:00
Tasks: Knowledge Tracing

Knowledge Tracing (KT) is the assessment of student’s knowledge state and predicting whether that student may or may not answer the next problem correctly based on a number of previous practices and outcomes in their learning process. KT leverages machine learning and data mining techniques to provide better assessment, supportive learning feedback and adaptive instructions. In this paper, we propose anovel model called Dynamic Student Classification on Memory Networks(DSCMN) for knowledge tracing that enhances existing KT approachesby capturing temporal learning ability at each time interval in student’s long-term learning process. Experimental results confirm that the proposed model is significantly better at predicting student performancethan well known state-of-the-art KT modelling techniques.
Population-scale testing can suppress the spread of infectious disease
Authors: Jussi Taipale, Ioannis Kontoyiannis, Sten Linnarsson
Date: 2021-04-14 00:00:00

Major advances in public health have resulted from disease prevention. However, prevention of a new infectious disease by vaccination or pharmaceuticals is made difficult by the slow process of vaccine and drug development. We propose an additional intervention that allows rapid control of emerging infectious diseases, and can also be used to eradicate diseases that rely almost exclusively on human-to-human transmission. The intervention is based on (1) testing every individual for the disease, (2) repeatedly, and (3) isolation of infected individuals. We show here that at a sufficient rate of testing, the reproduction number is reduced below 1.0 and the epidemic will rapidly collapse. The approach does not rely on strong or unrealistic assumptions about test accuracy, isolation compliance, population structure or epidemiological parameters, and its success can be monitored in real time by following the test positivity rate. In addition to the compliance rate and false negatives, the required rate of testing depends on the design of the testing regime, with concurrent testing outperforming random sampling. Provided that results are obtained rapidly, the test frequency required to suppress an epidemic is monotonic and near-linear with respect to R0, the infectious period, and the fraction of susceptible individuals. The testing regime is effective against both early phase and established epidemics, and additive to other interventions (e.g. contact tracing and social distancing). It is also robust to failure: any rate of testing reduces the number of infections, improving both public health and economic conditions. These conclusions are based on rigorous analysis and simulations of appropriate epidemiological models. A mass-produced, disposable test that could be used at home would be ideal, due to the optimal performance of concurrent tests that return immediate results.
Neural-network-based MDG and Optical SNR Estimation in SDM Transmission
Authors: Ruby S B Ospina, Menno van den Hout, Sjoerd van der Heide, Chigo Okonkwo, Darli A A Mello
Date: 2021-04-14 00:00:00

We propose a neural network model for MDG and optical SNR estimation in SDM transmission. We show that the proposed neural-network-based solution estimates MDG and SNR with high accuracy and low complexity from features extracted after DSP.
Two-Dimensional DOA Estimation for L-shaped Nested Array via Tensor Modeling
Authors: Feng Xu, Sergiy A. Vorobyov
Date: 2021-04-14 00:00:00
Tasks: Tensor Decomposition

The problem of two-dimensional (2-D) direction-of-arrival (DOA) estimation for the L-shaped nested array is considered. Typically, the multi-dimensional structure of the received signal in co-array domain is ignored in the problem considered. Moreover, the cross term generated by the correlated signal and noise components degrades the 2-D DOA estimation performance seriously. To tackle these issues, an iterative 2-D DOA estimation approach based on tensor modeling is proposed. To develop such approach, a higher-order tensor is constructed, whose factor matrices contain the sources azimuth and elevation information. By exploiting the Vandermonde structure of the factor matrix, a computationally efficient tensor decomposition method is then developed to estimate the sources DOA information in each dimension independently. The pair-matching of the azimuth and elevation angles is conducted via the cross-correlation matrix (CCM) of the received signals. An iterative method is further designed to improve the DOA estimation performance. Specifically, the cross term is estimated and removed in the next step of such iterative procedure on the basis of the DOA estimates originated from the tensor decomposition in the previous step. As a consequence, the DOA estimation with better accuracy and higher resolution is obtained. The proposed iterative 2-D DOA estimation method for the L-shaped nested array can resolve more sources than the number of real elements, which is superior to conventional approaches. Simulation results validate the performance improvement of the proposed 2-D DOA estimation method as compared to existing state-of-the-art DOA estimation techniques for the L-shaped nested array.
Towards Off-the-grid Algorithms for Total Variation Regularized Inverse Problems
Authors: Yohann de Castro, Vincent Duval, Romain Petit
Date: 2021-04-14 00:00:00

We introduce an algorithm to solve linear inverse problems regularized with the total (gradient) variation in a gridless manner. Contrary to most existing methods, that produce an approximate solution which is piecewise constant on a fixed mesh, our approach exploits the structure of the solutions and consists in iteratively constructing a linear combination of indicator functions of simple polygons.
Non-Terrestrial Networks for UAVs: Base Station Service Provisioning Schemes with Antenna Tilt
Authors: Seongjun Kim, Minsu Kim, Jong Yeol Ryu, Jemin Lee, Tony Q. S. Quek
Date: 2021-04-14 00:00:00

By focusing on unmanned aerial vehicle (UAV) communications in non-terrestrial networks (NTNs), this paper provides a guideline on the appropriate base station (BS) service provisioning scheme with considering the antenna tilt angle of BS. Specifically, two service provisioning schemes are considered including the inclusive-service BS (IS-BS) scheme, which makes BSs serve both ground users (GUs) and aerial users (AUs) (i.e., UAVs) simultaneously, and the exclusive-service BS (ES-BS) scheme, which has BSs for GUs and BSs for AUs. By considering the antenna tilt angle-based channel gain, we derive the network outage probability for both IS-BS and ES-BS schemes, and show the existence of the optimal tilt angle that minimizes the network outage probability after analyzing the conflict impact of the antenna tilt angle. We also analyze the impact of various network parameters, including the ratio of GUs to total users and densities of total and interfering BSs, on the network outage probability. Finally, we analytically and numerically show in which environments each service provisioning scheme can be superior to the other one.
Semi-supervised Learning Framework for UAV Detection
Authors: Olusiji O Medaiyese, Martins Ezuma, Adrian P Lauf, Ismail Guvenc
Date: 2021-04-14 00:00:00

The use of supervised learning with various sensing techniques such as audio, visual imaging, thermal sensing, RADAR, and radio frequency (RF) have been widely applied in the detection of unmanned aerial vehicles (UAV) in an environment. However, little or no attention has been given to the application of unsupervised or semi-supervised algorithms for UAV detection. In this paper, we proposed a semi-supervised technique and architecture for detecting UAVs in an environment by exploiting the RF signals (i.e., fingerprints) between a UAV and its flight-controller communication under wireless inference such as Bluetooth and WiFi. By decomposing the RF signals using a two-level wavelet packet transform, we estimated the second moment statistic (i.e., variance) of the coefficients in each packet as a feature set. We developed a local outlier factor model as the UAV detection algorithm using the coefficient variances of the wavelet packets from WiFi and Bluetooth signals. When detecting the presence of RF-based UAV, we achieved an accuracy of 96.7$\%$ and 86$\%$ at a signal-to-noise ratio of 30~dB and 18~dB, respectively. The application of this approach is not limited to UAV detection as it can be extended to the detection of rogue RF devices in an environment.
Energy-Efficient 3D Deployment of Aerial Access Points in a UAV Communication System
Authors: Nithin Babu, Constantinos B. Papadias, Petar Popovski
Date: 2021-03-27 00:00:00
Tasks: Position

In this letter, we propose an energy-efficient 3-dimensional placement of multiple aerial access points (AAPs), in the desired area, acting as flying base stations for uplink communication from a set of ground user equipment (UE). The globally optimal energy-efficient vertical position of AAPs is derived analytically by considering the inter-cell interference and AAP energy consumption. The horizontal position of AAPs which maximize the packing density of the AAP coverage area are determined using a novel regular polygon-based AAP placement algorithm. We also determine the maximum number of non-interfering AAPs that can be placed in the desired area. The effect of the AAP energy consumption on the optimal placement and the analytic findings are verified via numerical simulations.
Differential chaos shift keying-based wireless power transfer
Authors: Priyadarshi Mukherjee, Constantinos Psomas, Ioannis Krikidis
Date: 2021-03-25 00:00:00

In this work, we investigate differential chaos shift keying (DCSK), a communication-based waveform, in the context of wireless power transfer (WPT). Particularly, we present a DCSK-based WPT architecture, that employs an analog correlator at the receiver in order to boost the energy harvesting (EH) performance. By taking into account the nonlinearities of the EH process, we derive closed-form analytical expressions for the peak-to-average-power-ratio of the received signal as well as the harvested power. Nontrivial design insights are provided, where it is shown how the parameters of the transmitted waveform affects the EH performance. Furthermore, it is demonstrated that the employment of a correlator at the receiver achieves significant EH gains in DCSK-based WPT systems.
High Accuracy Visible Light Positioning Based on Multi-target Tracking Algorithm
Authors: Linyi Huang, Wentao Yang, ShangSheng Wen, Manxi Liu, Weipeng Guan
Date: 2021-03-01 00:00:00
Tasks: Vocal Bursts Intensity Prediction

In this paper, we propose a multi-target image tracking algorithm based on continuously apative mean-shift (Cam-shift) and unscented Kalman filter. We improved the single-lamp tracking algorithm proposed in our previous work to multi-target tracking, and achieved better robustness in the case of occlusion, the real-time performance to complete one positioning and relatively high accuracy by dynamically adjusting the weights of the multi-target motion states. Our previous algorithm is limited to the analysis of tracking error. In this paper, the results of the tracking algorithm are evaluated with the tracking error we defined. Then combined with the double-lamp positioning algorithm, the real position of the terminal is calculated and evaluated with the positioning error we defined. Experiments show that the defined tracking error is 0.61cm and the defined positioning error for 3-D positioning is 3.29cm with the average processing time of 91.63ms per frame. Even if nearly half of the LED area is occluded, the tracking error remains at 5.25cm. All of this shows that the proposed visible light positioning (VLP) method can track multiple targets for positioning at the same time with good robustness, real-time performance and accuracy. In addition, the definition and analysis of tracking errors and positioning errors indicates the direction for future efforts to reduce errors.
Potential Advantages of Peak Picking Multi-Voltage Threshold Digitizer in Energy Determination in Radiation Measurement
Authors: Kezhang Zhu, Junhua Mei, Yuming Su, Pingping Dai, Nicola D'Ascenzo, Hao Wang, Peng Xiao, Lin Wan, Qingguo Xie
Date: 2021-03-08 00:00:00

The Multi-voltage Threshold (MVT) method, which samples the signal by certain reference voltages, has been well developed as being adopted in pre-clinical and clinical digital positron emission tomography(PET) system. To improve its energy measurement performance, we propose a Peak Picking MVT(PP-MVT) Digitizer in this paper. Firstly, a sampled Peak Point(the highest point in pulse signal), which carries the values of amplitude feature voltage and amplitude arriving time, is added to traditional MVT with a simple peak sampling circuit. Secondly, an amplitude deviation statistical analysis, which compares the energy deviation of various reconstruction models, is used to select adaptive reconstruction models for signal pulses with different amplitudes. After processing 30,000 randomly-chosen pulses sampled by the oscilloscope with a 22Na point source, our method achieves an energy resolution of 17.50% within a 450-650 KeV energy window, which is 2.44% better than the result of traditional MVT with same thresholds; and we get a count number at 15225 in the same energy window while the result of MVT is at 14678. When the PP-MVT involves less thresholds than traditional MVT, the advantages of better energy resolution and larger count number can still be maintained, which shows the robustness and the flexibility of PP-MVT Digitizer. This improved method indicates that adding feature peak information could improve the performance on signal sampling and reconstruction, which canbe proved by the better performance in energy determination in radiation measurement.
Coexistence of Communications and Cognitive MIMO Radar: Waveform Design and Prototype
Authors: Mohammad Alaee-Kerahroodi, Ehsan Raei, Sumit Kumar, Bhavani Shankar Mysore Rama Rao
Date: 2021-03-08 00:00:00
Tasks: Radar waveform design

New generation of radar systems will need to coexist with other radio frequency (RF) systems, anticipating their behavior and reacting appropriately to avoid interference. In light of this requirement, this paper designs, implements, and evaluates the performance of phase-only sequences (with constant power) for intelligent spectrum utilization using the custom built cognitive Multiple Input Multiple Output (MIMO) radar prototype. The proposed transmit waveforms avoid the frequency bands occupied by narrowband interferers or communication links, while simultaneously have a small cross-correlation among each other to enable their separability at the MIMO radar receiver. The performance of the optimized set of sequences obtained through solving a non-convex bi-objective optimization problem, is compared with the state-of-the-art counterparts, and its applicability is illustrated by the developed prototype. A realistic Long Term Evolution (LTE) downlink is used for the communications, and the real-time system implementation is validated and evaluated through the throughput calculations for communications and the detection performance measurement for the radar system.
Secrecy Performance of Small-Cell Networks with Transmitter Selection and Unreliable Backhaul under Spectrum Sharing Environment
Authors: Jinghua Zhang, Chinmoy Kundu, Octavia A. Dobre, Emi Garcia-Palacios, Nguyen-Son Vo
Date: 2021-03-07 00:00:00

We investigate the secrecy performance of an underlay small-cell cognitive radio network under unreliable backhaul connections. The small-cell network shares the same spectrum with the primary network, ensuring that a desired outage probability constraint is always met in the primary network. {To improve the security of the small-cell cognitive network, we propose three sub-optimal small-cell transmitter selection schemes,} namely sub-optimal transmitter selection, minimal interference selection, and minimal eavesdropping selection. Closed-form expressions of the non-zero secrecy rate, secrecy outage probability, and ergodic secrecy capacity are provided for the schemes along with asymptotic expressions. {We also propose an optimal selection scheme and compare performances with the sub-optimal selection schemes.} {Computable expressions for the non-zero secrecy rate and secrecy outage probability are presented for the optimal selection scheme.} Our results show that by increasing the primary transmitter's power and the number of small-cell transmitters, the system performance improves. The selection scheme, the backhaul reliability, and the primary user quality-of-service constraint also have a significant impact on secrecy performance.
Secrecy Outage of Dual-hop Regenerative Multi-relay System with Relay Selection
Authors: Chinmoy Kundu, Sarbani Ghose, Ranjan Bose
Date: 2021-03-07 00:00:00

Relay selection is considered to enhance the secrecy of a dual-hop regenerative multi-relay system with an eavesdropper. Without assuming perfect decoding at the relays, the secrecy outage probability of a single relay system is obtained first. Secrecy outage of optimal, traditional and suboptimal relay selection schemes is then evaluated. To reduce the power consumption, partial relay selection schemes based only on either of the source-relay or relay-destination instantaneous channel state information (ICSI) are introduced. Its secrecy outage is evaluated and compared with the other schemes. Secrecy outage of all the selection schemes are obtained in closed-form. An optimal relay selection scheme is proposed using secrecy outage which does not require any ICSI. Asymptotic and diversity gain analysis of the secrecy outage is presented when source-relay and relay-destination average SNRs are same or different. We observe that the improvement in eavesdropper link quality affects the secrecy outage more when required secrecy rate is low as compared to the case when rate is high. We also observe that relay selection improves performance more when number of relays are more. It is important to note that either of the source-relay or the relay-destination link quality can equally limit the secrecy outage performance even if the other link quality is infinitely good.
Ergodic Secrecy Rate of Optimal Source Selection in a Multi-Source System with Unreliable Backhaul
Authors: Chinmoy Kundu, Mark F. Flanagan
Date: 2021-03-07 00:00:00

The use of multiple source nodes with wireless backhaul is considered for secrecy enhancement through source node selection in future wireless networks. The ergodic secrecy rate (ESR) of {optimal source node selection in the presence of multiple eavesdroppers} over independent non-identically distributed (INID) Rayleigh fading channels is evaluated in closed-form. At high signal-to-noise ratio (SNR), {the ESR is expressed as a simple weighted summation where each term relates to the contribution of an individual source and eavesdropper}. An asymptotic analysis shows the effect of the system parameters and backhaul reliability on the performance. {The proposed method can provide a generalized solution for the ESR of optimal \textit{transmit antenna} selection in multi-antenna systems, optimal \textit{source node} selection, and optimal \textit{relay selection} with or without unreliable backhaul.
Learning Latent Interactions for Event classification via Graph Neural Networks and PMU Data
Authors: Yuxuan Yuan, Zhaoyu Wang, Yanchao Wang
Date: 2020-10-04 00:00:00
Tasks: Graph Learning

Phasor measurement units (PMUs) are being widely installed on power systems, providing a unique opportunity to enhance wide-area situational awareness. One essential application is the use of PMU data for real-time event identification. However, how to take full advantage of all PMU data in event identification is still an open problem. Thus, we propose a novel method that performs event identification by mining interaction graphs among different PMUs. The proposed interaction graph inference method follows an entirely data-driven manner without knowing the physical topology. Moreover, unlike previous works that treat interactive learning and event identification as two different stages, our method learns interactions jointly with the identification task, thereby improving the accuracy of graph learning and ensuring seamless integration between the two stages. Moreover, to capture multi-scale event patterns, a dilated inception-based method is investigated to perform feature extraction of PMU data. To test the proposed data-driven approach, a large real-world dataset from tens of PMU sources and the corresponding event logs have been utilized in this work. Numerical results validate that our method has higher classification accuracy compared to previous methods.