pwc-archive/papers-with-abstracts huggingface dataset:
Performance Analysis of Relay-Assisted OWC Over Foggy Channel with Pointing Error
Authors: Tejas Nimish Shah, Ziyaur Rahman, S. M. Zafaruddin
Date: 2020-06-12 00:00:00

Signal fading due to atmospheric channel impairments and pointing error is a major bottleneck for the performance of optical wireless communication (OWC). In this paper, we consider an amplify-and-forward (AF) optical relaying to enhance the performance of the OWC system with a negligible line-of-sight (LOS) link under the combined effect of fog and pointing error. We analyze the end-to-end performance of the relay-assisted system, which consists of complicated probability distribution functions. We derive analytical expressions of the outage probability, average signal-to-noise ratio (SNR), and ergodic rate in terms of OWC system parameters. We also develop an exact integral-form expression of these performance metrics using the half-harmonic mean of individual SNRs to validate the tightness of the derived analytical expressions. The numerical and simulation analysis shows that the proposed dual-hop relaying has significant performance improvement when comparing to the direct transmission over considered channel impairments. Compared to the direct transmission, the relay-assisted system requires almost $30$ times less transmission power to achieve the same outage probability. The considered system also provides a significant gain in the average SNR and ergodic rate for practical scenarios of OWC deployment.
Expectation-Maximization Algorithm for Identification of Mesh-based Compartment Thermal Model of Power Modules
Authors: Jakub Ševčík, Václav Šmídl, Ondřej Straka
Date: 2020-06-12 00:00:00
Tasks: Management

Accurate knowledge of temperatures in power semiconductor modules is crucial for proper thermal management of such devices. Precise prediction of temperatures allows to operate the system at the physical limit of the device avoiding undesirable over-temperatures and thus improve reliability of the module. Commonly used thermal models can be based on detailed expert knowledge of the device's physical structure or on precise and complete temperature distribution measurements. The latter approach is more often used in the industry. Recently, we have proposed a linear time invariant state-space thermal model based on a compartment representation and its identification procedure that is based on the Expectation-Maximization algorithm from incomplete temperature data. However, the model still requires to measure temperatures of all active elements. In this contribution, we aim to relax the need for all measurements. Therefore, we replace the previous dark gray-box approach with a structured compartment model. The structure of the model is designed by a mesh-based discretization of the physical layout of the module. All compartments are assumed to share parameters that are identified from the data of the measured elements. Temperatures of the unmeasured elements are predicted using the shared parameters. Identification of the parameters is possible only with suitable regularization due to limited amount of the data. In particular, the model tightening is accomplished by sharing parameters among compartments and by constraining the process covariance matrix of the model in this contribution. Applicability of the proposed identification procedure is discussed in terms of growing state-space and therefore speeding up of the identification algorithm is suggested. Performance of the proposed approach is tested and demonstrated on simulated data.
First exit-time analysis for an approximate Barndorff-Nielsen and Shephard model with stationary self-decomposable variance process
Authors: Shantanu Awasthi, Indranil SenGupta
Date: 2020-06-12 00:00:00

In this paper, an approximate version of the Barndorff-Nielsen and Shephard model, driven by a Brownian motion and a L\'evy subordinator, is formulated. The first-exit time of the log-return process for this model is analyzed. It is shown that with certain probability, the first-exit time process of the log-return is decomposable into the sum of the first exit time of the Brownian motion with drift, and the first exit time of a L\'evy subordinator with drift. Subsequently, the probability density functions of the first exit time of some specific L\'evy subordinators, connected to stationary, self-decomposable variance processes, are studied. Analytical expressions of the probability density function of the first-exit time of three such L\'evy subordinators are obtained in terms of various special functions. The results are implemented to empirical S&P 500 dataset.
COVID-19 and Digital Resilience: Evidence from Uber Eats
Authors: Manav Raj, Arun Sundararajan, Calum You
Date: 2020-06-12 00:00:00

Using order-level data from Uber Technologies, we study how the COVID-19 pandemic and the ensuing shutdown of businesses in the United States in 2020 affected small business restaurant supply and demand on the Uber Eats platform. We find evidence that small restaurants experience significant increases in activity on the platform following the closure of the dine-in channel. We document how locality- and restaurant-specific characteristics moderate the size of the increase in activity through the digital channel and explain how these increases may be due to both demand- and supply-side shock. We observe an increase in the intensity of competitive effects following the economic shock and show that growth in the number of providers on a platform induces both market expansion and heightened inter-provider competition. Higher platform activity in response to the shock does not only have short-run implications: restaurants with larger demand shocks had a higher on-platform survival rate one year after the lockdown, suggesting that the platform channel contributes towards long-run resilience following a crisis. Our findings document the heterogeneous effects of platforms during the pandemic, underscore the critical role that digital technologies play in enabling business resilience in the economy, and provide insight into how platforms can manage competing incentives when balancing market expansion and growth goals with the competitive interests of their incumbent providers.
A novel fusion Python application of data mining techniques to evaluate airborne magnetic datasets
Authors: John Stephen Kayode, Yusri Yusup
Date: 2020-06-12 00:00:00

A novel fusion python application of data mining techniques (DMT) was designed and implemented to locate, identify, and delineate the subsurface structural pattern (SSP) of source rocks for the features of interest underlain the study area. The techniques of machine learning tools (MLT) helped to define magnetic anomaly source (MAS) rock and the various depths of these subsurface source rock features. The principal objective is to use straightforward DMT to locate magnetic anomaly features of interest that host mineralization. The required geo-referenced radiometric data, which facilitated the delineation of SSP, were sufficiently covered by combining the application of the Oasis Montaj\c{opyright} 2014 source parameter imaging functions. Relevance basic filtering techniques of data reduction were used to improve the signal-to-noise (S/N) ratio and hence automatically determine depths to the various engrossed features from gridded geo-referenced airborne magnetic datasets before the DMT application was performed. Geological source rock models (GSRM) (i.e., rock contacts, dykes) served as the delineated features based on their structural index (SI) values. The anomalies were perpendicularly oriented, with few inconsequential nonvertical features, and all were generally aligned in NNE-SSW and NE-SW directions. The DMT approach showed that magnetic anomaly patterns (MAP) control the SSP and the ground surface stratigraphy (GSS) on a geological time-scale (GTS) by fusing the subsurface gravitational structural features (SGSF) in the area. The DMT facilitated the determination of depths to these subsurface geological source rock features with a maximum depth of approximately 1.277 km using a 3x3 window size to map the concealed features of interest.
Wideband Modal Orthogonality: A New Approach for Broadband DOA Estimation
Authors: Shervin Amirsoleimani, Ali Olfat
Date: 2020-06-12 00:00:00
Tasks: Numerical Integration

Wideband direction of arrival (DOA) estimation techniques for sensors array have been studied extensively in the literature. Nevertheless, needing prior information on the number and directions of sources or demanding heavy computational load makes most of these techniques less useful in practice. In this paper, a low complexity subspace-based framework for DOA estimation of broadband signals, named as wideband modal orthogonality (WIMO), is proposed and accordingly two DOA estimators are developed. First, a closed-form approximation of spatial-temporal covariance matrix (STCM) in the uniform spectrum case is presented. The eigenvectors of STCM associated with non-zero eigenvalues are modal components of the wideband source in a given bandwidth and direction. WIMO idea is to extract these eigenvectors at desired DOAs from the approximated STCM and test their orthogonality to estimated noise subspace. In the non-uniform spectrum case, WIMO idea can be applied by approximating STCM through numerical integration. Fortunately, STCM approximation and modal extraction can be performed offline. WIMO provides DOA estimation without the conventional prerequisites, such as spectral decomposition, focusing procedure and, a priori information on the number of sources and their DOAs. Several numerical examples are conducted to compare the WIMO performance with the state-of-the-art methods. Simulations demonstrate that the two proposed DOA estimators achieve superior performance in terms of probability of resolution and estimation error along with orders of magnitude runtime speedup.
Effects of Regional Trade Agreement to Local and Global Trade Purity Relationships
Authors: Siyu Huang, Wensha Gou, Hongbo Cai, Xiaomeng Li, Qinghua Chen
Date: 2020-05-29 00:00:00

In contrast to the rapid integration of the world economy, many regional trade agreements (RTAs) have also emerged since the early 1990s. This seeming contradiction has encouraged scholars and policy makers to explore the true effects of RTAs, including both regional and global trade relationships. This paper defines synthesized trade resistance and decomposes it into natural and artificial factors. Here, we separate the influence of geographical distance, economic volume, overall increases in transportation and labor costs and use the expectation maximization algorithm to optimize the parameters and quantify the trade purity indicator, which describes the true global trade environment and relationships among countries. This indicates that although global and most regional trade relations gradually deteriorated during the period 2007-2017, RTAs generate trade relations among members, especially contributing to the relative prosperity of EU and NAFTA countries. In addition, we apply the network to reflect the purity of the trade relations among countries. The effects of RTAs can be analyzed by comparing typical trade unions and trade communities, which are presented using an empirical network structure. This analysis shows that the community structure is quite consistent with some trade unions, and the representative RTAs constitute the core structure of international trade network. However, the role of trade unions has weakened, and multilateral trade liberalization has accelerated in the past decade. This means that more countries have recently tended to expand their trading partners outside of these unions rather than limit their trading activities to RTAs.
A method of online traction parameter identification and mapping
Authors: Alexander Kobelski, Pavel Osinenko, Stefan Streif
Date: 2020-06-12 00:00:00

Fuel consumption of heavy-duty vehicles such as tractors, bulldozers etc. is comparably high due to their scope of operation. The operation settings are usually fixed and not tuned to the environmental factors, such as ground conditions. Yet exactly the ground-to-propelling-unit properties are decisive in energy efficiency. Optimizing the latter would require a means of identifying those properties. This is the central matter of the current study. More specifically, the goal is to estimate the ground conditions from the available measurements, such as drive train signals, and to establish a map of those. The ground condition parameters are estimated using an adaptive unscented Kalman filter. A case study is provided with the actual and estimated ground condition maps. Such a mapping can be seen as a crucial milestone in optimal operation control of heavy-duty vehicles.
PMD-Tolerant 20 krad/s Endless Polarization and Phase Control for BB84-Based QKD with TDM Pilot Signals
Authors: Benjamin Koch, Reinhold Noe
Date: 2020-05-20 00:00:00

TDM-based polarization and differential phase control with 35ps PMD tolerance and 20krad/s tracking speed is demonstrated. 600ns intervals are reserved for QKD and for 0{\deg}- and 45{\deg}-polarized pilot signals. ECLs are modulated directly, with high extinction. Power budget is 17dB, fiber length is 63km.
Time-Domain Analysis of PWM Inverters
Authors: Siddharth Tyagi, Isaak Mayergoyz
Date: 2020-06-12 00:00:00

The time-domain analysis of pulse width modulated (PWM) single-phase inverters is presented for different load circuits. It is demonstrated that this analysis can be reduced to the solution of linear simultaneous algebraic equations with two diagonal matrices. Analytical solutions of such equations are easily found which leads to the explicit expressions for the output voltages and currents in terms of switching time-instants. This technique is presented for second and third order circuits, however it can be used in principle for any higher-order linear load circuit subject to pulse width modulated voltages.
Continuous and discrete phasor analysis of binned or time-gated periodic decays
Authors: Xavier Michalet
Date: 2020-06-13 00:00:00

Time-resolved analysis of periodically excited luminescence decays by the phasor method in the presence of time-gating or binning is revisited. Analytical expressions for discrete configurations of square gates are derived and the locus of the phasors of such modified periodic single-exponential decays is compared to the canonical uni-versal semicircle. The effects of IRF offset, decay truncation and gate shape are also discussed. Finally, modified expressions for the phase and modulus lifetimes are pro-vided for some simple cases. A discussion of a modified phasor calibration approach is presented, and illustration of the new concepts with examples from the literature conclude this work.
Enabling Joint Communication and Radar Sensing in Mobile Networks -- A Survey
Authors: J. Andrew Zhang, Md Lushanur Rahman, Kai Wu, Xiaojing Huang, Y. Jay Guo, Shanzhi Chen, Jinhong Yuan
Date: 2020-06-13 00:00:00
Tasks: Information Retrieval, parameter estimation, Philosophy

Mobile network is evolving from a communication-only network towards one with joint communication and radar/radio sensing (JCAS) capabilities, that we call perceptive mobile network (PMN). In PMNs, JCAS integrates sensing into communications, sharing a majority of system modules and the same transmitted signals. The PMN is expected to provide a ubiquitous radio sensing platform and enable a vast number of novel smart applications, whilst providing non-compromised communications. In this paper, we present a broad picture of the motivation, methodologies, challenges, and research opportunities of realizing PMN, by providing a comprehensive survey for systems and technologies developed mainly in the last ten years. Beginning by reviewing the work on coexisting communication and radar systems, we highlight their limits on addressing the interference problem, and then introduce the JCAS technology. We then set up JCAS in the mobile network context and envisage its potential applications. We continue to provide a brief review of three types of JCAS systems, with particular attention to their differences in design philosophy. We then introduce a framework of PMN, including the system platform and infrastructure, three types of sensing operations, and signals usable for sensing. Subsequently, we discuss required system modifications to enable sensing on current communication-only infrastructure. Within the context of PMN, we review stimulating research problems and potential solutions, organized under nine topics: performance bounds, waveform optimization, antenna array design, clutter suppression, sensing parameter estimation, resolution of sensing ambiguity, pattern analysis, networked sensing under cellular topology, and sensing-assisted communications. We conclude the paper by listing key open research problems for the aforementioned topics and sharing some lessons that we have learned.
Line-of-Sight MIMO for High Capacity Millimeter Wave Backhaul in FDD Systems
Authors: Ye Xue, Xuanyu Zheng, Vincent Lau
Date: 2020-06-13 00:00:00

Wireless backhaul is considered to be the key part of the future wireless network with dense small cell traffic and high capacity demand. In this paper, we focus on the design of a high spectral efficiency line-of-sight (LoS) multiple-input multiple-output (MIMO) system for millimeter wave (mmWave) backhaul using dual-polarized frequency division duplex (FDD). High spectral efficiency is very challenging to achieve for the system due to various physical impairments such as phase noise (PHN), timing offset (TO) as well as the poor condition number of the LoS MIMO. In this paper, we propose a holistic solution containing TO compensation, PHN estimation, precoder/decorrelator optimization of the LoS MIMO for wireless backhaul, and the interleaving of each part. We show that the proposed solution has robust performance with end-to-end spectral efficiency of 60 bits/s/Hz for 8x8 MIMO.
Expert Elicitation on Wind Farm Control
Date: 2020-06-16 00:00:00

Wind farm control is an active and growing field of research in which the
control actions of individual turbines in a farm are coordinated, accounting
for inter-turbine aerodynamic interaction, to improve the overall performance
of the wind farm and to reduce costs. The primary objectives of wind farm
control include increasing power production, reducing turbine loads, and
providing electricity grid support services. Additional objectives include
improving reliability or reducing external impacts to the environment and
communities. In 2019, a European research project (FarmConners) was started
with the main goal of providing an overview of the state-of-the-art in wind
farm control, identifying consensus of research findings, data sets, and best
practices, providing a summary of the main research challenges, and
establishing a roadmap on how to address these challenges. Complementary to the
FarmConners project, an IEA Wind Topical Expert Meeting (TEM) and two rounds of
surveys among experts were performed. From these events we can clearly identify
an interest in more public validation campaigns. Additionally, a deeper
understanding of the mechanical loads and the uncertainties concerning the
effectiveness of wind farm control are considered two major research gaps.
Backward Deep BSDE Methods and Applications to Nonlinear Problems
Authors: Yajie Yu, Bernhard Hientzsch, Narayan Ganesan
Date: 2020-06-13 00:00:00

In this paper, we present a backward deep BSDE method applied to Forward Backward Stochastic Differential Equations (FBSDE) with given terminal condition at maturity that time-steps the BSDE backwards. We present an application of this method to a nonlinear pricing problem - the differential rates problem. To time-step the BSDE backward, one needs to solve a nonlinear problem. For the differential rates problem, we derive an exact solution of this time-step problem and a Taylor-based approximation. Previously backward deep BSDE methods only treated zero or linear generators. While a Taylor approach for nonlinear generators was previously mentioned, it had not been implemented or applied, while we apply our method to nonlinear generators and derive details and present results. Likewise, previously backward deep BSDE methods were presented for fixed initial risk factor values $X_0$ only, while we present a version with random $X_0$ and a version that learns portfolio values at intermediate times as well. The method is able to solve nonlinear FBSDE problems in high dimensions.
Tempered Stable Processes with Time Varying Exponential Tails
Authors: Young Shin Kim, Kum-Hwan Roh, Raphael Douady
Date: 2020-06-13 00:00:00
Tasks: Time Series, Time Series Analysis

In this paper, we introduce a new time series model having a stochastic exponential tail. This model is constructed based on the Normal Tempered Stable distribution with a time-varying parameter. The model captures the stochastic exponential tail, which generates the volatility smile effect and volatility term structure in option pricing. Moreover, the model describes the time-varying volatility of volatility. We empirically show the stochastic skewness and stochastic kurtosis by applying the model to analyze S&P 500 index return data. We present the Monte-Carlo simulation technique for the parameter calibration of the model for the S&P 500 option prices. We can see that the stochastic exponential tail makes the model better to analyze the market option prices by the calibration.
Optimal Attention Management: A Tractable Framework
Authors: Elliot Lipnowski, Laurent Mathevet, Dong Wei
Date: 2020-06-13 00:00:00
Tasks: Management

A well-intentioned principal provides information to a rationally inattentive agent without internalizing the agent's cost of processing information. Whatever information the principal makes available, the agent may choose to ignore some. We study optimal information provision in a tractable model with quadratic payoffs where full disclosure is not optimal. We characterize incentive-compatible information policies, that is, those to which the agent willingly pays full attention. In a leading example with three states, optimal disclosure involves information distortion at intermediate costs of attention. As the cost increases, optimal information abruptly changes from downplaying the state to exaggerating the state.
Metric of Shock Severity
Authors: Yinzhong Yan, Q. M. Li
Date: 2020-06-13 00:00:00

Shock response spectrum (SRS) is a widely accepted method for shock testing specification. However, SRS, as a supremum, is over-conservative and cannot fully describe the relative severity for various shock environments. This study introduces shock response matrix, from which shock severity infimum (SSI) can be extracted using singular value decomposition method. Based on SRS and SSI, dual spectra are introduced to determine the range of shock severity between its supremum and infimum. The evaluation of the relative severity of various shock signals is discussed and validated by finite element simulations.
The Benefits of Using the S-Band in Optical Fiber Communications and How to Get There
Authors: Daniel Semrau, Eric Sillekens, Robert I. Killey, Polina Bayvel
Date: 2020-06-13 00:00:00

The throughput gains of extending the optical transmission bandwidth to the S+C+L-band are quantified using a Gaussian Noise model that accounts for inter-channel stimulated Raman scattering (ISRS). The impact of potential ISRS mitigation strategies, such as dynamic gain equalization and power optimization, are investigated.