pwc-archive/papers-with-abstracts huggingface dataset:
Coordination of OLTC and Smart Inverters for Optimal Voltage Regulation of Unbalanced Distribution Networks
Date: 2020-06-24 00:00:00
Tasks: Computational Efficiency

Photovoltaic (PV) smart inverters can improve the voltage profile of
distribution networks. A multi-objective optimization framework for
coordination of reactive power injection of smart inverters and tap operations
of on-load tap changers (OLTCs) for multi-phase unbalanced distribution systems
is proposed. The optimization objective is to minimize voltage deviations and
the number of tap operations simultaneously. A novel linearization method is
proposed to linearize power flow equations and to convexify the problem, which
guarantees convergence of the optimization and less computation costs. The
optimization is modeled and solved using mixed-integer linear programming
(MILP). The proposed method is validated against conventional rule-based
autonomous voltage regulation (AVR) on the highly-unbalanced modified IEEE 37
bus test system and a large California utility feeder. Simulation results show
that the proposed method accurately estimates feeder voltage, significantly
reduces voltage deviations, mitigates over-voltage problems, and reduces
voltage unbalance while eliminating unnecessary tap operations. The robustness
of the method is validated against various levels of forecast error. The
computational efficiency and scalability of the proposed approach are also
demonstrated through the simulations on the large utility feeder.
Composition Modulation
Authors: Ferhat Yarkin, Justin P. Coon
Date: 2020-06-25 00:00:00

In this paper, we propose novel modulation concepts that we call weak composition modulation (WCM) and composition modulation (CM). We use weak and strict compositions of an integer to form codewords of WCM and CM, respectively. For the proposed schemes, we depict a practical model using orthogonal frequency division multiplexing (OFDM). We also analyze the error performance of the proposed schemes. It is shown that the proposed schemes are capable of outperforming conventional OFDM and OFDM with index modulation (OFDM-IM) in terms of error performance.
Some considerations on the protection against the health hazards associated with solar ultraviolet radiation
Authors: Boyan H Petkov
Date: 2020-06-25 00:00:00

The present report briefly reviews the basic features of the current strategy for the protection against the health harms caused by solar ultraviolet (UV, 295 - 400 nm). The emphasis has been made upon the erythema as being the best-studied UV harm and the ability of UV irradiance to damage the deoxyribonucleic acid (DNA) molecules, which leads to carcinogenesis. The erythemally weighted UV irradiance that determines the ultraviolet index (UVI), represents a common measure of the solar UV radiation level at the Earth's surface and the current protective messages have been made by using UVI as a basic parameter. However, such an approach seems insufficiently grounded to be used also in the messages against the skin cancer, bearing in mind the different nature of the erythema and DNA lesions. In this context, an enlargement of the strategy basis by including additional biological effects of UV radiation studied during the past years has been discussed. For instance, the weight of the spectral UV-A (315 - 400 nm) band that in practice had been neglected by UVI definition can be enhanced since it was found to play an important role in DNA damaging. In addition, features of the contemporaneous life style can be taken into account together with some people habits.
Artificial Lateral Line Based Relative State Estimation for Two Adjacent Robotic Fish
Authors: Xingwen Zheng, Wei Wang, Liang Li, Guangming Xie
Date: 2020-06-23 00:00:00
Tasks: regression, State Estimation

The lateral line enables fish to efficiently sense the surrounding environment, thus assisting flow-related fish behaviours. Inspired by this phenomenon, varieties of artificial lateral line systems (ALLSs) have been developed and applied to underwater robots. This article focuses on using the pressure sensor arrays based on ALLS-measured hydrodynamic pressure variations (HPVs) for estimating the relative state between two adjacent robotic fish with leader-follower formation. The relative states include the relative oscillating frequency, amplitude, and offset of the upstream robotic fish to the downstream robotic fish, the relative vertical distance, the relative yaw angle, the relative pitch angle, and the relative roll angle between the two adjacent robotic fish. Regression model between the ALLS-measured and the mentioned relative states is investigated, and regression model-based relative state estimation is conducted. Specifically, two criteria are proposed firstly to investigate not only the sensitivity of each pressure sensor to the variations of relative state but also the insufficiency and redundancy of the pressure sensors. And thus the pressure sensors used for regression analysis are determined. Then four typical regression methods, including random forest algorithm, support vector regression, back propagation neural network, and multiple linear regression method are used for establishing regression models between the ALLS-measured HPVs and the relative states. Then regression effects of the four methods are compared and discussed. Finally, random forest-based method, which has the best regression effect, is used to estimate relative yaw angle and oscillating amplitude using the ALLS-measured HPVs and exhibits excellent estimation performance. This work contributes to local relative estimation for a group of underwater robots, which has always been a challenge.
Comparative Statics in Multicriteria Search Models
Date: 2020-06-25 00:00:00

McCall (1970) examines the search behaviour of an infinitely-lived and
risk-neutral job seeker maximizing her lifetime earnings by accepting or
rejecting real-valued scalar wage offers. In practice, job offers have multiple
attributes, and job seekers solve a multicriteria search problem. This paper
presents a multicriteria search model and new comparative statics results.
Wireless Communication Based on Microwave Photon-Level Detection With Superconducting Devices: Achievable Rate Prediction
Authors: Junyu Zhang, Chen Gong, Shangbin Li, Rui Ni, Chengjie Zuo, Jinkang Zhu, Ming Zhao, Zhengyuan Xu
Date: 2020-06-25 00:00:00
Tasks: Sensitivity

Future wireless communication system embraces physical-layer signal detection with high sensitivity, especially in the microwave photon level. Currently, the receiver primarily adopts the signal detection based on semi-conductor devices for signal detection, while this paper introduces high-sensitivity photon-level microwave detection based on superconducting structure. We first overview existing works on the photon-level communication in the optical spectrum as well as the microwave photon-level sensing based on superconducting structure in both theoretical and experimental perspectives, including microwave detection circuit model based on Josephson junction, microwave photon counter based on Josephson junction, and two reconstruction approaches under background noise. In addition, we characterize channel modeling based on two different microwave photon detection approaches, including the absorption barrier and the dual-path Handury Brown-Twiss (HBT) experiments, and predict the corresponding achievable rates. According to the performance prediction, it is seen that the microwave photon-level signal detection can increase the receiver sensitivity compared with the state-of-the-art standardized communication system with waveform signal reception, with gain over $10$dB.
Quantumized Microwave Detection Based on $Λ$-Type Three-level Superconducting System: HMM Modeling and Performance Prediction
Authors: Junyu Zhang, Chen Gong, Shangbin Li, Shanchi Wu, Rui Ni, Chengjie Zuo, Jinkang Zhu, Ming Zhao, Zhengyuan Xu
Date: 2020-06-25 00:00:00
Tasks: Sensitivity

We adopt artificial $\Lambda$-type three-level system with superconducting devices for microwave signal detection, where the signal intensity reaches the level of discrete photons instead of continuous waveform. Based on the state transition principles of the three-level system, we propose a statistical model for microwave signal detection. Moreover, we investigate the achievable transmission rate and signal detection based on the statistical model. It is predicted that the proposed detection can achieve significantly higher sensitivity compared with the currently deployed 4G/5G communication system. We further characterize the received signal considering the saturation phonomenon, which reveals negligible performance degradation caused by saturation under weak received power regime.
Examining the Effect of COVID-19 on Foreign Exchange Rate and Stock Market -- An Applied Insight into the Variable Effects of Lockdown on Indian Economy
Authors: Indrajit Banerjee, Atul Kumar, Rupam Bhattacharyya
Date: 2020-06-23 00:00:00

Since March 25, 2020, India had been under a nation-wide lockdown announced as a response to the spread of SARS-CoV-2 and COVID-19 and has resorted to a process of 'unlocking' the lockdown over the past couple of months. This work attempts to examine the effect of novel coronavirus 2019 (COVID-19) and its resulting disease, the COVID-19, on the foreign exchange rates and stock market performances of India using secondary data over a span of 112 days spanning between March 11 and June 30, 2020. The study explores whether the causal relationships and directions among the growth rate of confirmed cases (GROWTHC), exchange rate (GEX) and SENSEX value (GSENSEX) are remaining the same across different pre and post-lockdown phases, attempting to capture any potential changes over time via the vector autoregressive (VAR) models. A positive correlation is found between the growth rate of confirmed cases and the growth rate of exchange rate, and a negative correlation between the growth rate of confirmed cases and the growth rate of SENSEX value. However, on applying a vector autoregressive (VAR) model, it is observed that an increase in the confirmed COVID-19 cases causes no significant change in the values of the exchange rate and SENSEX index. The result varies if the analysis is split across different time periods - before lockdown, the four phases of lockdown, and the first phase of unlock. Nuanced and sensible interpretations of the numeric results indicate significant variability across time in terms of the relation between the variables of interest. The detailed knowledge about the varying patterns of dependence could potentially help the policy makers and investors of India in order to develop their strategies to cope up with the situation.
Neural Networks Based Beam Codebooks: Learning mmWave Massive MIMO Beams that Adapt to Deployment and Hardware
Authors: Muhammad Alrabeiah, Yu Zhang, Ahmed Alkhateeb
Date: 2020-06-25 00:00:00

Millimeter wave (mmWave) and massive MIMO systems are intrinsic components of 5G and beyond. These systems rely on using beamforming codebooks for both initial access and data transmission. Current beam codebooks, however, generally consist of a large number of narrow beams that scan all possible directions, even if these directions are never used. This leads to very large training overhead. Further, these codebooks do not normally account for the hardware impairments or the possible non-uniform array geometries, and their calibration is an expensive process. To overcome these limitations, this paper develops an efficient online machine learning framework that learns how to adapt the codebook beam patterns to the specific deployment, surrounding environment, user distribution, and hardware characteristics. This is done by designing a novel complex-valued neural network architecture in which the neuron weights directly model the beamforming weights of the analog phase shifters, accounting for the key hardware constraints such as the constant-modulus and quantized-angles. This model learns the codebook beams through online and self-supervised training avoiding the need for explicit channel state information. This respects the practical situations where the channel is either unavailable, imperfect, or hard to obtain, especially in the presence of hardware impairments. Simulation results highlight the capability of the proposed solution in learning environment and hardware aware beam codebooks, which can significantly reduce the training overhead, enhance the achievable data rates, and improve the robustness against possible hardware impairments.
Interference Cancellation Based Channel Estimation for Massive MIMO Systems with Time Shifted Pilots
Authors: Bule Sun, Yiqing Zhou, Jinhong Yuan, Jinglin Shi
Date: 2020-06-25 00:00:00
Tasks: compressed sensing, Playing the Game of 2048

In massive multiple-input multiple-output (MIMO) systems with time shifted pilot (TSP) schemes, the inter-group interference caused by the pilot contamination can be eliminated when the number of base station (BS) antennas M approaches infinity. However, M is finite in practice and the effectiveness of the TSP is limited by channel estimation errors. In this paper, it is analytically shown that the mean square channel estimation error (MSCEE) of the TSP is dominated by the intergroup data interference. To reduce the MSCEE in the finite antenna massive MIMO systems, an interference cancellation based channel estimation for the TSP (IC-TSP) is proposed, where the dominant inter-group data interference is canceled based on BS cooperation. To show the advantage of the IC-TSP, the additional overhead of IC-TSP is evaluated by considering different M and the coherence time of BS-BS channels. Furthermore, the impact of sectorization and compressed sensing based BS-BS channel estimation are also discussed. We show that when 128 < M < 2048, with the inter-group data interference from the nearest two cell layers being canceled, the IC-TSP achieves a spectral efficiency gain of more than 1.2 bps/Hz over the TSP.
Smart and Secure Wireless Communications via Reflecting Intelligent Surfaces: A Short Survey
Authors: Abdullateef Almohamad, Anas M. Tahir, Ayman Al-Kababji, Haji M. Furqan, Tamer Khattab, Mazen O. Hasna, Huseyin Arslan
Date: 2020-06-25 00:00:00

With the emergence of the internet of things (IoT) technology, wireless connectivity should be more ubiquitous than ever. In fact, the availability of wireless connection everywhere comes with security threats that, unfortunately, cannot be handled by conventional cryptographic solutions alone, especially in heterogeneous and decentralized future wireless networks. In general, physical layer security (PLS) helps in bridging this gap by taking advantage of the fading propagation channel. Moreover, the adoption of reconfigurable intelligent surfaces (RIS) in wireless networks makes the PLS techniques more efficient by involving the channel into the design loop. In this paper, we conduct a comprehensive literature review on the RIS-assisted PLS for future wireless communications. We start by introducing the basic concepts of RISs and their different applications in wireless communication networks and the most common PLS performance metrics. Then, we focus on the review and classification of RIS-assisted PLS applications, exhibiting multiple scenarios, system models, objectives, and methodologies. In fact, most of the works in this field formulate an optimization problem to maximize the secrecy rate (SR) or secrecy capacity (SC) at a legitimate user by jointly optimizing the beamformer at the transmitter and the RIS's coefficients, while the differences are in the adopted methodology to optimally/sub-optimally approach the solution. We finalize this survey by presenting some insightful recommendations and suggesting open problems for future research extensions.
Graph Convolutional Neural Networks for analysis of EEG signals, BCI application
Authors: Mirfarid Musavian Ghazani, Anh Huy Phan
Date: 2020-06-16 00:00:00
Tasks: Deep Learning, EEG, Electroencephalogram (EEG), Feature Engineering

Decoding brain signals has gained many attention and has found much applications in recent years such as Brain Computer Interfaces, communicating with controlling external devices using the user's intentions, occupies an emerging field with the potential of changing the world, with diverse applications from rehabilitation to human augmentation. This being said brain signal analysis, EEG brain signal analysis in particular, is a challenging task. With the advances and achievements in the field of deep learning in problem solving with using only raw data, few attempts has been carried in recent years, to apply deep learning to tackle EEG among other types of brain signals. In this study, we propose a novel loss function, called DeepCSP to extend the classical Common Spatial Patterns to a non linear, differentiable module to serve as the loss function to enforce linearly separable latent representations of EEG signals belonging to different classes in an end to end manner on raw signals without the need to perform extensive feature engineering. With recent generalizations of deep learning methods to work on arbitrarily structured graphs and the introduced loss we have proposed two light weight models to decode EEG signals and carried experiments to show their performance.
Machine Learning-Assisted UAV Operations with UTM: Requirements, Challenges, and Solutions
Authors: Aly Sabri Abdalla, Vuk Marojevic
Date: 2020-06-24 00:00:00
Tasks: BIG-bench Machine Learning, Management

Unmanned aerial vehicles (UAVs) are emerging in commercial spaces and will support many applications and services, such as smart agriculture, dynamic network deployment, and network coverage extension, surveillance and security. The unmanned aircraft system (UAS) traffic management (UTM) provides a framework for safe UAV operation integrating UAV controllers and central data bases via a communications network. This paper discusses the challenges and opportunities for machine learning (ML) for effectively providing critical UTM services. We introduce the four pillars of UTM---operation planning, situational awareness, status and advisors and security---and discuss the main services, specific opportunities for ML and the ongoing research. We conclude that the multi-faceted operating environment and operational parameters will benefit from collected data and data-driven algorithms, as well as online learning to face new UAV operation situations.
Habit learning supported by efficiently controlled network dynamics in naive macaque monkeys
Date: 2020-06-25 00:00:00

Primates display a marked ability to learn habits in uncertain and dynamic
environments. The associated perceptions and actions of such habits engage
distributed neural circuits. Yet, precisely how such circuits support the
computations necessary for habit learning remain far from understood. Here we
construct a formal theory of network energetics to account for how changes in
brain state produce changes in sequential behavior. We exercise the theory in
the context of multi-unit recordings spanning the caudate nucleus, prefrontal
cortex, and frontal eyefields of female macaque monkeys engaged in 60-180
sessions of a free scan task that induces motor habits. The theory relies on
the determination of effective connectivity between recording channels, and on
the stipulation that a brain state is taken to be the trial-specific firing
rate across those channels. The theory then predicts how much energy will be
required to transition from one state into another, given the constraint that
activity can spread solely through effective connections. Consistent with the
theory's predictions, we observed smaller energy requirements for transitions
between more similar and more complex trial saccade patterns, and for sessions
characterized by less entropic selection of saccade patterns. Using a virtual
lesioning approach, we demonstrate the resilience of the observed relationships
between minimum control energy and behavior to significant disruptions in the
inferred effective connectivity. Our theoretically principled approach to the
study of habit learning paves the way for future efforts examining how behavior
arises from changing patterns of activity in distributed neural circuitry.
Over-the-Air Membership Inference Attacks as Privacy Threats for Deep Learning-based Wireless Signal Classifiers
Authors: Yi Shi, Kemal Davaslioglu, Yalin E. Sagduyu
Date: 2020-06-25 00:00:00
Tasks: Inference Attack, Membership Inference Attack

This paper presents how to leak private information from a wireless signal classifier by launching an over-the-air membership inference attack (MIA). As machine learning (ML) algorithms are used to process wireless signals to make decisions such as PHY-layer authentication, the training data characteristics (e.g., device-level information) and the environment conditions (e.g., channel information) under which the data is collected may leak to the ML model. As a privacy threat, the adversary can use this leaked information to exploit vulnerabilities of the ML model following an adversarial ML approach. In this paper, the MIA is launched against a deep learning-based classifier that uses waveform, device, and channel characteristics (power and phase shifts) in the received signals for RF fingerprinting. By observing the spectrum, the adversary builds first a surrogate classifier and then an inference model to determine whether a signal of interest has been used in the training data of the receiver (e.g., a service provider). The signal of interest can then be associated with particular device and channel characteristics to launch subsequent attacks. The probability of attack success is high (more than 88% depending on waveform and channel conditions) in identifying signals of interest (and potentially the device and channel information) used to build a target classifier. These results show that wireless signal classifiers are vulnerable to privacy threats due to the over-the-air information leakage of their ML models
On the Feasibility of Exploiting Traffic Collision Avoidance System Vulnerabilities
Authors: Paul M. Berges, Basavesh Ammanaghatta Shivakumar, Timothy Graziano, Ryan Gerdes, Z. Berkay Celik
Date: 2020-06-25 00:00:00
Tasks: Collision Avoidance

Traffic Collision Avoidance Systems (TCAS) are safety-critical systems required on most commercial aircrafts in service today. However, TCAS was not designed to account for malicious actors. While in the past it may have been infeasible for an attacker to craft radio signals to mimic TCAS signals, attackers today have access to open-source digital signal processing software, like GNU Radio, and inexpensive software defined radios (SDR) that enable the transmission of spurious TCAS messages. In this paper, methods, both qualitative and quantitative, for analyzing TCAS from an adversarial perspective are presented. To demonstrate the feasibility of inducing near mid-air collisions between current day TCAS-equipped aircraft, an experimental Phantom Aircraft generator is developed using GNU Radio and an SDR against a realistic threat model.
Active Learning Pipeline for Brain Mapping in a High Performance Computing Environment
Date: 2020-06-25 00:00:00
Tasks: Active Learning, BIG-bench Machine Learning

This paper describes a scalable active learning pipeline prototype for
large-scale brain mapping that leverages high performance computing power. It
enables high-throughput evaluation of algorithm results, which, after human
review, are used for iterative machine learning model training. Image
processing and machine learning are performed in a batch layer. Benchmark
testing of image processing using pMATLAB shows that a 100$\times$ increase in
throughput (10,000%) can be achieved while total processing time only increases
by 9% on Xeon-G6 CPUs and by 22% on Xeon-E5 CPUs, indicating robust
scalability. The images and algorithm results are provided through a serving
layer to a browser-based user interface for interactive review. This pipeline
has the potential to greatly reduce the manual annotation burden and improve
the overall performance of machine learning-based brain mapping.