pwc-archive/papers-with-abstracts huggingface dataset:
Graph Laplacian Diffusion Localization of Connected and Automated Vehicles
Authors: Nikos Piperigkos, Aris S. Lalos, Kostas Berberidis
Date: 2021-08-24 00:00:00
Tasks: Autonomous Driving

In this paper, we design distributed multi-modal localization approaches for Connected and Automated vehicles. We utilize information diffusion on graphs formed by moving vehicles, based on Adapt-then-Combine strategies combined with the Least-Mean-Squares and the Conjugate Gradient algorithms. We treat the vehicular network as an undirected graph, where vehicles communicate with each other by means of Vehicle-to- Vehicle communication protocols. Connected vehicles perform cooperative fusion of different measurement modalities, including location and range measurements, in order to estimate both their positions and the positions of all other networked vehicles, by interacting only with their local neighborhood. The trajectories of vehicles were generated either by a well-known kinematic model, or by using the CARLA autonomous driving simulator. The various proposed distributed and diffusion localization schemes significantly reduce the GPS error and do not only converge to the global solution, but they even outperformed it. Extensive simulation studies highlight the benefits of the various approaches, outperforming the accuracy of the state of the art approaches. The impact of the network connections and the network latency are also investigated.
Scorpiano -- A System for Automatic Music Transcription for Monophonic Piano Music
Authors: Bojan Sofronievski, Branislav Gerazov
Date: 2021-08-24 00:00:00
Tasks: Music Transcription, Onset Detection

Music transcription is the process of transcribing music audio into music notation. It is a field in which the machines still cannot beat human performance. The main motivation for automatic music transcription is to make it possible for anyone playing a musical instrument, to be able to generate the music notes for a piece of music quickly and accurately. It does not matter if the person is a beginner and simply struggles to find the music score by searching, or an expert who heard a live jazz improvisation and would like to reproduce it without losing time doing manual transcription. We propose Scorpiano -- a system that can automatically generate a music score for simple monophonic piano melody tracks using digital signal processing. The system integrates multiple digital audio processing methods: notes onset detection, tempo estimation, beat detection, pitch detection and finally generation of the music score. The system has proven to give good results for simple piano melodies, comparable to commercially available neural network based systems.
Sonic: A Sampling-based Online Controller for Streaming Applications
Authors: Yan Pei, Keshav Pingali
Date: 2021-08-15 00:00:00
Tasks: Bayesian Optimization

Many applications in important problem domains such as machine learning and computer vision are streaming applications that take a sequence of inputs over time. It is challenging to find knob settings that optimize the run-time performance of such applications because the optimal knob settings are usually functions of inputs, computing platforms, time as well as user's requirements, which can be very diverse. Most prior works address this problem by offline profiling followed by training models for control. However, profiling-based approaches incur large overhead before execution; it is also difficult to redeploy them in other run-time configurations. In this paper, we propose Sonic, a sampling-based online controller for long-running streaming applications that does not require profiling ahead of time. Within each phase of a streaming application's execution, Sonic utilizes the beginning portion to sample the knob space strategically and aims to pick the optimal knob setting for the rest of the phase, given a user-specified constrained optimization problem. A hybrid approach of machine learning regressions and Bayesian optimization are used for better overall sampling choices. Sonic is implemented independent of application, device, input, performance objective and constraints. We evaluate Sonic on traditional parallel benchmarks as well as on deep learning inference benchmarks across multiple platforms. Our experiments show that when using Sonic to control knob settings, application run-time performance is only 5.3% less than if optimal knob settings were used, demonstrating that Sonic is able to find near-optimal knob settings under diverse run-time configurations without prior knowledge quickly.
Communication Modes with Large Intelligent Surfaces in the Near Field
Authors: Nicolò Decarli, Davide Dardari
Date: 2021-08-24 00:00:00
Tasks: valid

This paper proposes a practical method for the definition of communication modes when antennas operate in the near-field region, by realizing ad-hoc beams exploiting the focusing capability of large antennas. The beamspace modeling proposed to define the communication modes is then exploited to derive expressions for their number (i.e., the degrees of freedom) in a generic setup, beyond the traditional paraxial scenario, together with closed-form definitions for the basis set at the transmitting and receiving antennas for several cases of interest, such as for the communication between a large antenna and a small antenna. Numerical results show that quasi-optimal communication can be obtained starting from focusing functions. This translates into the possibility of a significant enhancement of the channel capacity even in line-of-sight channel condition, without the need of implementing optimal but complex phase/amplitude profiles on transmitting/receiving antennas as well as resorting to intensive numerical solutions. Traditional results valid under paraxial approximation are revised in light of the proposed modeling, showing that similar conclusions can be obtained from different perspectives.
MIMO OFDM Dual-Function Radar-Communication Under Error Rate and Beampattern Constraints
Authors: Jeremy Johnston, Luca Venturino, Emanuele Grossi, Marco Lops, Xiaodong Wang
Date: 2021-08-24 00:00:00

In this work we consider a multiple-input multiple-output (MIMO) dual-function radar-communication (DFRC) system, which senses multiple spatial directions and serves multiple users. Upon resorting to an orthogonal frequency division multiplexing (OFDM) transmission format and a differential phase shift keying (DPSK) modulation, we study the design of the radiated waveforms and of the receive filters employed by the radar and the users. The approach is communication-centric, in the sense that a radar-oriented objective is optimized under constraints on the average transmit power, the power leakage towards specific directions, and the error rate of each user, thus safeguarding the communication quality of service (QoS). We adopt a unified design approach allowing a broad family of radar objectives, including both estimation- and detection-oriented merit functions. We devise a suboptimal solution based on alternating optimization of the involved variables, a convex restriction of the feasible search set, and minorization-maximization, offering a single algorithm for all of the radar merit functions in the considered family. Finally, the performance is inspected through numerical examples.
On the Efficiency of 5(4) RK-Embedded Pairs with High Order Compact Scheme and Robin Boundary Condition for Options Valuation
Authors: Chinonso Nwankwo, Weizhong Dai
Date: 2021-08-23 00:00:00

When solving the American options with or without dividends, numerical methods often obtain lower convergence rates if further treatment is not implemented even using high-order schemes. In this article, we present a fast and explicit fourth-order compact scheme for solving the free boundary options. In particular, the early exercise features with the asset option and option sensitivity are computed based on a coupled of nonlinear PDEs with fixed boundaries for which a high order analytical approximation is obtained. Furthermore, we implement a new treatment at the left boundary by introducing a third-order Robin boundary condition. Rather than computing the optimal exercise boundary from the analytical approximation, we simply obtain it from the asset option based on the linear relationship at the left boundary. As such, a high order convergence rate can be achieved. We validate by examples that the improvement at the left boundary yields a fourth-order convergence rate without further implementation of mesh refinement, Rannacher time-stepping, and/or smoothing of the initial condition. Furthermore, we extensively compare, the performance of our present method with several 5(4) Runge-Kutta pairs and observe that Dormand and Prince and Bogacki and Shampine 5(4) pairs are faster and provide more accurate numerical solutions. Based on numerical results and comparison with other existing methods, we can validate that the present method is very fast and provides more accurate solutions with very coarse grids.
A Techno-Economic Cost Framework for Satellite Networks Applied to Low Earth Orbit Constellations: Assessing Starlink, OneWeb and Kuiper
Authors: Osoro B. Ogutu, Edward J. Oughton
Date: 2021-08-24 00:00:00

Delivering broadband connectivity to unconnected areas is extremely challenging. The emergence of Low Earth Orbit (LEO) satellite systems has been seen as a potential solution for connecting remote areas. Despite the hype around these new technologies, we still lack an open-source modeling framework for assessing the techno-economics of satellite broadband connectivity which is therefore the purpose of this paper. Firstly, a generalizable techno-economic model is presented to assess the engineering-economics of satellite constellations. Secondly, the approach is applied to assess the three main competing LEO constellations which include Starlink, OneWeb and Kuiper. This involves simulating the impact on coverage, capacity and cost as both the number of satellites and quantity of subscribers increases. Finally, a global assessment is undertaken visualizing the potential capacity and cost per user via different subscriber scenarios. The results demonstrate how limited the capacity will be once resources are spread across users in each satellite coverage area. For example, if there is 1 user per 10 km^2 we estimate a mean per user capacity of 24.94 Mbps, 1.01 Mbps and 10.30 Mbps for Starlink, OneWeb and Kuiper respectively in the busiest hour of the day. But if the subscriber density increases to 1 user per km^2, then the mean per user capacity drops significantly to 2.49 Mbps, 0.10 Mbps and 1.02 Mbps for Starlink, OneWeb and Kuiper respectively. LEO broadband will be an essential part of the connectivity toolkit, but the results reveal that these mega-constellations will most likely have to operate below 0.1 users per km^2 to provide a service that outcompetes other broadband connectivity options. The open-source codebase which the paper contributes is provided with the hope that other engineers will access, use, and further develop the satellite assessment capability.
Economic and technical study for the construction of a 1 MW grid-connected solar power plant in southern Iran
Authors: Mahmoud Makkiabadi
Date: 2021-08-24 00:00:00

Renewable energy such as solar and wind energy can solve the major problems of humanity such as electricity and fresh water. The renewable energy sources are promising to take a significant share in the energy sector as a viable option for integration with conventional fossil fuel power plants. In this paper, the production of 1 MW of electricity for several households in the city of Sirjan in southern Iran has been studied. The actual data required by the model including solar irradiation, air temperature, load profile, cost of energy for Sirjan, Iran have been utilized in the proposed model. Considering the Iranian market, the fixed and current costs of building this power plant have been studied. Then, using Hummer software, the amount of electricity production per month has been studied. Results showed that Scaled data were used for calculations in HOMER. It had a scaled annual average of 1127 kWh/day and the peak load was 0.467 kW. The maximum electricity energy is obtained in July.
A Note on Surgical Eigenstructure Assignment via State Feedback
Authors: Abdullah Al Maruf, Sandip Roy
Date: 2020-10-05 00:00:00

Assignability of all eigenvalues and a subset of key eigenvectors/generalized eigenvectors of a linear time-invariant system via state feedback is considered. We clarify that, if the key eigenvectors/generalized eigenvectors and their associated eigenvalues satisfy the classical conditions for full eigenstructure assignment, the remaining eigenvalues can be assigned at will.
Nonhomogeneous Stochastic Geometry Analysis of Massive LEO Communication Constellations
Authors: Niloofar Okati, Taneli Riihonen
Date: 2021-08-24 00:00:00

Providing truly ubiquitous connectivity requires development of low Earth orbit (LEO) satellite Internet, whose theoretical study is lagging behind network-specific simulations. In this paper, we derive analytical expressions for the downlink coverage probability and average data rate of a massive inclined LEO constellation in terms of total interference power's Laplace transform in the presence of fading and shadowing, ergo presenting a stochastic geometry based analysis. We assume the desired link to experience Nakagami m fading, which serves to represent different fading scenarios by varying integer m, while the interfering channels can follow any fading model without an effect on analytical tractability. To take into account the inherent nonuniform distribution of satellites across different latitudes, we model the LEO network as a nonhomogeneous Poisson point process with its intensity being a function of satellites' actual distribution in terms of constellation size, the altitude of the constellation, and the inclination of orbital planes. From the numerical results, we observe optimum points for both the constellation altitude and the number of orthogonal frequency channels; interestingly, the optimum user's latitude is greater than the inclination angle due to the presence of fewer interfering satellites. Overall, the presented study facilitates general stochastic evaluation and planning of satellite Internet constellations without specific orbital simulations or tracking data on satellites' exact positions in space.
Data-driven predictive control with improved performance using segmented trajectories
Authors: Edward O'Dwyer, Eric C. Kerrigan, Paola Falugi, Marta A. Zagorowska, Nilay Shah
Date: 2021-08-24 00:00:00
Tasks: energy management, LEMMA, Management, Point Tracking, Prediction, Segmentation

A class of data-driven control methods has recently emerged based on Willems' fundamental lemma. Such methods can ease the modelling burden in control design but can be sensitive to disturbances acting on the system under control. In this paper, we propose a restructuring of the problem to incorporate segmented prediction trajectories. The proposed segmentation leads to reduced tracking error for longer prediction horizons in the presence of unmeasured disturbance and noise when compared to an unsegmented formulation. The performance characteristics are illustrated in a set-point tracking case study in which the segmented formulation enables more consistent performance over a wide range of prediction horizons. The method is then applied to a building energy management problem using a detailed simulation environment. The case studies show that good tracking performance is achieved for a range of horizon choices, whereas performance degrades with longer horizons without segmentation.
Design and Performance Evaluation of Joint Sensing and Communication Integrated System for 5G MmWave Enabled CAVs
Authors: Qixun Zhang, Xinna Wang, Zhenhao Li, Zhiqing Wei
Date: 2021-08-24 00:00:00

The safety of connected automated vehicles (CAVs) relies on the reliable and efficient raw data sharing from multiple types of sensors. The 5G millimeter wave (mmWave) communication technology can enhance the environment sensing ability of different isolated vehicles. In this paper, a joint sensing and communication integrated system (JSCIS) is designed to support the dynamic frame structure configuration for sensing and communication dual functions based on the 5G New Radio protocol in the mmWave frequency band, which can solve the low latency and high data rate problems of raw sensing data sharing among CAVs. To evaluate the timeliness of raw sensing data transmission, the best time duration allocation ratio of sensing and communication dual functions for one vehicle is achieved by modeling the M/M/1 queuing problem using the age of information (AoI) in this paper. Furthermore, the resource allocation optimization problem among multiple CAVs is formulated as a non-cooperative game using the radar mutual information as a key indicator. And the feasibility and existence of pure strategy Nash equilibrium (NE) are proved theoretically, and a centralized time resource allocation (CTRA) algorithm is proposed to achieve the best feasible pure strategy NE. Finally, both simulation and hardware testbed are designed, and the results show that the proposed CTRA algorithm can improve the radar total mutual information by 26%, and the feasibility of the proposed JSCIS is achieved with an acceptable radar ranging accuracy within 0.25 m, as well as a stable data rate of 2.8 Gbps using the 28 GHz mmWave frequency band.
A Sensitivity Matrix Approach Using Two-Stage Optimization for Voltage Regulation of LV Networks with High PV Penetration
Authors: A. S. Jameel Hassan, Umar Marikkar, G. W. Kasun Prabhath, Aranee Balachandran, W. G. Chaminda Bandara, Parakrama B. Ekanayake, Roshan I. Godaliyadda, Janaka B. Ekanayake
Date: 2021-08-23 00:00:00
Tasks: Management, Sensitivity

The occurrence of voltage violations are a major deterrent for absorbing more roof-top solar power to smart Low Voltage Distribution Grids (LVDG). Recent studies have focused on decentralized control methods to solve this problem due to the high computational time in performing load flows in centralized control techniques. To address this issue a novel sensitivity matrix is developed to estimate voltages of the network by replacing load flow simulations. In this paper, a Centralized Active, Reactive Power Management System (CARPMS) is proposed to optimally utilize the reactive power capability of smart photo-voltaic inverters with minimal active power curtailment to mitigate the voltage violation problem. The developed sensitivity matrix is able to reduce the time consumed by 48% compared to load flow simulations, enabling near real-time control optimization. Given the large solution space of power systems, a novel two-stage optimization is proposed, where the solution space is narrowed down by a Feasible Region Search (FRS) step, followed by Particle Swarm Optimization (PSO). The performance of the proposed methodology is analyzed in comparison to the load flow method to demonstrate the accuracy and the capability of the optimization algorithm to mitigate voltage violations in near real-time. The deviation of mean voltages of the proposed methodology from load flow method was; 6.5*10^-3 p.u for reactive power control using Q-injection, 1.02*10^-2 p.u for reactive power control using Q-absorption, and 0 p.u for active power curtailment case.
Risk-based Classical Failure Mode and Effect Analysis (FMEA) of Microgrid Cyber-physical Energy Systems
Authors: Shravan Kumar Akula, Hossein Salehfar
Date: 2021-08-23 00:00:00

Modern microgrids are networked systems comprising physical and cyber components for networking, computation, and monitoring. These cyber components make microgrids more reliable but increase the system complexity. Therefore, risk assessment methods are an imperative technology for cyber-physical systems to ensure safe and secure operations. The authors propose a reliability approach that utilizes the failure modes of power and cyber network key components to perform the risk analysis in microgrids. In this work, the authors have used the Failure Mode and Effect Analysis (FMEA) approach for risk assessment of microgrid systems and determine the influence of various failure modes on their performance. FMEA is one of the most effective methods to assess the risk involving the cyber components. It is the method of examining components, modules, and subsystems to determine failure modes in a system and their causes and consequences. A novel approach is proposed to calculate risk priority numbers based on factors like severity, occurrence, and detection. A risk matrix is calculated from the proposed FMEA worksheet, which acts as a graphical representation for evaluating components risk based on risk priority number or criticality metrics.
Time Dependence in Kalman Filter Tuning
Authors: Zhaozhong Chen, Christoffer Heckman, Simon Julier, Nisar Ahmed
Date: 2021-08-21 00:00:00
Tasks: Bayesian Optimization, Prediction Intervals

In this paper, we propose an approach to address the problems with ambiguity in tuning the process and observation noises for a discrete-time linear Kalman filter. Conventional approaches to tuning (e.g. using normalized estimation error squared and covariance minimization) compute empirical measures of filter performance and the parameter are selected manually or selected using some kind of optimization algorithm to maximize these measures of performance. However, there are two challenges with this approach. First, in theory, many of these measures do not guarantee a unique solution due to observability issues. Second, in practice, empirically computed statistical quantities can be very noisy due to a finite number of samples. We propose a method to overcome these limitations. Our method has two main parts to it. The first is to ensure that the tuning problem has a single unique solution. We achieve this by simultaneously tuning the filter over multiple different prediction intervals. Although this yields a unique solution, practical issues (such as sampling noise) mean that it cannot be directly applied. Therefore, we use Bayesian Optimization. This technique handles noisy data and the local minima that it introduces.
Spatial scale of agglomeration and dispersion: Number, spacing, and the spatial extent of cities
Authors: Takashi Akamatsu, Tomoya Mori, Minoru Osawa, Yuki Takayama
Date: 2019-12-11 00:00:00

Cities have become fewer, bigger, and flatter in many countries. Better transport access has fostered the concentration of economic activities toward fewer, bigger cities, while each city has become more decentralized. We show that two types of dispersion forces against spatial agglomeration explain the dual evolution of cities. Dispersion forces within a city, such as those arising from the consumption of limited land, affect only the city's residents. A decrease in transport costs reduces the relative benefits of agglomeration in a congested central location, making the population distribution in each city flatter and more spread out to achieve a balance between the costs and benefits of agglomeration. Dispersion forces that spill over beyond a city, such as market crowding, make locations near the city unattractive. Lower transport costs promote competition for a wider area around each city, leaving fewer cities with larger distances between them. A model's comparative static results, and thus its policy implications, depend on the type of dispersion forces it incorporates.