pwc-archive/papers-with-abstracts huggingface dataset:
The effect of changing training data on a fixed deep learning detection model
Authors: Aram Nasser, Ammar Alsabbagh1 and Husi Géza
Date: 2019-09-17 00:00:00
Proceeding: Annual Session of Scientific Papers "IMT ORADEA 2019" 30–31 May 2019, Oradea, Felix SPA, Romania 2019 9
Tasks: Object Detection, Object Detection In Indoor Scenes

Within the lack of accurate data, for some computer vision applications, researchers usually use other pictures collected from different sources for the training. To know the effect of these added data, we compare the detection results of a customized dataset of objects, using the same detection model, while changing the training data fed into the network. For our work, we run the detection on images captured by the Microsoft Kinect sensor after training the network on different combinations of training data. The first part of the training data is captured by the Kinect itself, and the second is collected from several sources from the internet, referred to as collected images. We then change the distribution of these images between training and validation to feed them into the fixed training model. The results prove that this distribution of data can considerably affect training and detection results under the same model parameters. In addition, mixing the captured images with other collected ones can improve these results.
Deconvolution in Fluorescence Lifetime imaging microscopy (FLIM)
Authors: Varun Mannam, Xiaotong Yuan, Scott Howard
Date: 2022-01-16 00:00:00

Fluorescence lifetime imaging microscopy (FLIM) is an important technique to understand the chemical micro-environment in cells and tissues since it provides additional contrast compared to conventional fluorescence imaging. When two fluorophores within a diffraction limit are excited, the resulting emission leads to non-linear spatial distortion and localization effects in intensity (magnitude) and lifetime (phase) components. To address this issue, in this work, we provide a theoretical model for convolution in FLIM to describe how the resulting behavior differs from conventional fluorescence microscopy. We then present a Richardson-Lucy (RL) based deconvolution including total variation (TV) regularization method to correct for the distortions in FLIM measurements due to optical convolution, and experimentally demonstrate this FLIM deconvolution method on a multi-photon microscopy (MPM)-FLIM images of fluorescent-labeled fixed bovine pulmonary arterial endothelial (BPAE) cells.
Dynamics of an SIRWS model with waning of immunity and varying immune boosting period
Authors: Richmond Opoku-Sarkodie, Ferenc A. Bartha, Mónika Polner, Gergely Röst
Date: 2022-01-18 00:00:00

SIRS models capture transmission dynamics of infectious diseases for which immunity is not lifelong. Extending these models by a W compartment for individuals with waning immunity, the boosting of the immune system upon repeated exposure may be incorporated. Previous analyses assumed identical waning rates from R to W and from W to S. This implicitly assumes equal length for the period of full immunity and of waned immunity. We relax this restriction, and allow an asymmetric partitioning of the total immune period. Stability switches of the endemic equilibrium are investigated with a combination of analytic and numerical tools. Then, continuation methods are applied to track bifurcations along the equilibrium branch. We find rich dynamics: Hopf bifurcations, endemic double bubbles, and regions of bistability. Our results highlight that the length of the period in which waning immunity can be boosted is a crucial parameter significantly influencing long term epidemiological dynamics.
Dynamics of Bitcoin mining
Authors: Nemo Semret
Date: 2022-01-16 00:00:00

What happens to mining when the Bitcoin price changes, when there are mining supply shocks, the price of energy changes, or hardware technology evolves? We give precise answers based on the technical forces and incentives in the system. We then build on these dynamics to consider value: what is the cost and purpose of mining, and is it worth it? Does it use too much energy, is it bad for the environment? Finally we extend our analysis to the long term: is mining economically feasible forever? What will the global hash rate be in 40 years? How is mining impacted by the limits of computation and energy? Is it physically sustainable in the long run? From first principles, we derive a fundamental scale-invariant feasibility constraint, which enables us to analyze the interlocking dynamics, find key invariants, and answer these questions mathematically.
Master Equation for Discrete-Time Stackelberg Mean Field Games with single leader
Authors: Deepanshu Vasal, Randall Berry
Date: 2022-01-16 00:00:00

In this paper, we consider a discrete-time Stackelberg mean field game with a leader and an infinite number of followers. The leader and the followers each observe types privately that evolve as conditionally independent controlled Markov processes. The leader commits to a dynamic policy and the followers best respond to that policy and each other. Knowing that the followers would play a mean field game based on her policy, the leader chooses a policy that maximizes her reward. We refer to the resulting outcome as a Stackelberg mean field equilibrium (SMFE). In this paper, we provide a master equation of this game that allows one to compute all SMFE. Based on our framework, we consider two numerical examples. First, we consider an epidemic model where the followers get infected based on the mean field population. The leader chooses subsidies for a vaccine to maximize social welfare and minimize vaccination costs. In the second example, we consider a technology adoption game where the followers decide to adopt a technology or a product and the leader decides the cost of one product that maximizes his returns, which are proportional to the people adopting that technology
Channel Modeling in RIS-Empowered Wireless Communications
Authors: Ibrahim Yildirim, Ertugrul Basar
Date: 2022-01-17 00:00:00

One of the most critical aspects of enabling next-generation wireless technologies is developing an accurate and consistent channel model to be validated effectively with the help of real-world measurements. From this point of view, remarkable research has recently been conducted to model propagation channels involving the modification of the wireless propagation environment through the inclusion of reconfigurable intelligent surfaces (RISs). This study mainly aims to present a vision on channel modeling strategies for the RIS-empowered communications systems considering the state-of-the-art channel and propagation modeling efforts in the literature. Moreover, it is also desired to draw attention to open-source and standard-compliant physical channel modeling efforts to provide comprehensive insights regarding the practical use-cases of RISs in future wireless networks.
Cooperative constrained motion coordination of networked heterogeneous vehicles
Authors: Zhiyong Sun, Marcus Greiff, Anders Robertsson, Rolf Johansson, Brian D. O. Anderson
Date: 2022-01-17 00:00:00
Tasks: Motion Generation

We consider the problem of cooperative motion coordination for multiple heterogeneous mobile vehicles subject to various constraints. These include nonholonomic motion constraints, constant speed constraints, holonomic coordination constraints, and equality/inequality geometric constraints. We develop a general framework involving differential-algebraic equations and viability theory to determine coordination feasibility for a coordinated motion control under heterogeneous vehicle dynamics and different types of coordination task constraints. If a coordinated motion solution exists for the derived differential-algebraic equations and/or inequalities, a constructive algorithm is proposed to derive an equivalent dynamical system that generates a set of feasible coordinated motions for each individual vehicle. In case studies on coordinating two vehicles, we derive analytical solutions to motion generation for two-vehicle groups consisting of car-like vehicles, unicycle vehicles, or vehicles with constant speeds, which serve as benchmark coordination tasks for more complex vehicle groups. The motion generation algorithm is well-backed by simulation data for a wide variety of coordination situations involving heterogeneous vehicles. We then extend the vehicle control framework to deal with the cooperative coordination problem with time-varying coordination tasks and leader-follower structure. We show several simulation experiments on multi-vehicle coordination under various constraints to validate the theory and the effectiveness of the proposed schemes.
A Machine Learning Based Algorithm for Joint Improvement of Power Control, link adaptation, and Capacity in Beyond 5G Communication systems
Authors: Jafar Norolahi, Paeiz Azmi
Date: 2022-01-08 00:00:00

In this study, we propose a novel machine learning based algorithm to improve the performance of beyond 5 generation (B5G) wireless communication system that is assisted by Orthogonal Frequency Division Multiplexing (OFDM) and Non-Orthogonal Multiple Access (NOMA) techniques. The non-linear soft margin support vector machine (SVM) problem is used to provide an automatic modulation classifier (AMC) and a signal power to noise and interference ratio (SINR) estimator. The estimation results of AMC and SINR are used to reassign the modulation type, codding rate, and transmit power through frames of eNode B connections. The AMC success rate versus SINR, total power consuming, and sum capacity are evaluated for OFDM-NOMA assisted 5G system. Results show improvement of success rate compared of some published method. Furthermore, the algorithm directly computes SINR after signal is detected by successive interference cancellation (SIC) and before any signal decoding. Moreover, because of the direct sense of physical channel, the presented algorithm can discount occupied symbols (overhead signaling) for channel quality information (CQI) in network communication signaling. The results also prove that the proposed algorithm reduces the total power consumption and increases the sum capacity through the eNode B connections. Simulation results in compare to other algorithms show more successful AMC, efficient SINR estimator, easier practical implantation, less overhead signaling, less power consumption, and more capacity achievement.
Forecasting Loss of Signal in Optical Networks with Machine Learning
Authors: Wenjie Du, David Cote, Chris Barber, Yan Liu
Date: 2022-01-08 00:00:00
Tasks: BIG-bench Machine Learning, Classification on Time Series with Missing Data, Time Series Classification

Loss of Signal (LOS) represents a significant cost for operators of optical networks. By studying large sets of real-world Performance Monitoring (PM) data collected from six international optical networks, we find that it is possible to forecast LOS events with good precision 1-7 days before they occur, albeit at relatively low recall, with supervised machine learning (ML). Our study covers twelve facility types, including 100G lines and ETH10G clients. We show that the precision for a given network improves when training on multiple networks simultaneously relative to training on an individual network. Furthermore, we show that it is possible to forecast LOS from all facility types and all networks with a single model, whereas fine-tuning for a particular facility or network only brings modest improvements. Hence our ML models remain effective for optical networks previously unknown to the model, which makes them usable for commercial applications.
How Bad Are Artifacts?: Analyzing the Impact of Speech Enhancement Errors on ASR
Authors: Kazuma Iwamoto, Tsubasa Ochiai, Marc Delcroix, Rintaro Ikeshita, Hiroshi Sato, Shoko Araki, Shigeru Katagiri
Date: 2022-01-18 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Speech Enhancement, speech-recognition, Speech Recognition

It is challenging to improve automatic speech recognition (ASR) performance in noisy conditions with single-channel speech enhancement (SE). In this paper, we investigate the causes of ASR performance degradation by decomposing the SE errors using orthogonal projection-based decomposition (OPD). OPD decomposes the SE errors into noise and artifact components. The artifact component is defined as the SE error signal that cannot be represented as a linear combination of speech and noise sources. We propose manually scaling the error components to analyze their impact on ASR. We experimentally identify the artifact component as the main cause of performance degradation, and we find that mitigating the artifact can greatly improve ASR performance. Furthermore, we demonstrate that the simple observation adding (OA) technique (i.e., adding a scaled version of the observed signal to the enhanced speech) can monotonically increase the signal-to-artifact ratio under a mild condition. Accordingly, we experimentally confirm that OA improves ASR performance for both simulated and real recordings. The findings of this paper provide a better understanding of the influence of SE errors on ASR and open the door to future research on novel approaches for designing effective single-channel SE front-ends for ASR.
Comparison of COVID-19 Prediction Performances of Normalization Methods on Cough Acoustics Sounds
Authors: Yunus Emre Erdoğan, Ali Narin
Date: 2022-01-16 00:00:00

The disease called the new coronavirus (COVID19) is a new viral respiratory disease that first appeared on January 13, 2020 in Wuhan, China. Some of the symptoms of this disease are fever, cough, shortness of breath and difficulty in breathing. In more serious cases, death may occur as a result of infection. COVID19 emerged as a pandemic that affected the whole world in a little while. The most important issue in the fight against the epidemic is the early diagnosis and follow-up of COVID19 (+) patients. Therefore, in addition to the RT-PCR test, medical imaging methods are also used when identifying COVID 19 (+) patients. In this study, an alternative approach was proposed using cough data, one of the most prominent symptoms of COVID19 (+) patients. The performances of z-normalization and min-max normalization methods were investigated on these data. All features were obtained using discrete wavelet transform method. Support vector machines (SVM) was used as classifier algorithm. The highest performances of accuracy and F1-score were obtained as 100% and 100% using the min-max normalization, respectively. On the other hand, the highest accuracy and highest F1-score performances were obtained as 99.2 % and 99.0 % using the z-normalization, respectively. In light of the results, it is clear that cough acoustic data will contribute significantly to controlling COVID19 cases.
Sensitivity analysis of a mean-value exergy-based internal combustion engine model
Authors: Gabriele Pozzato, Denise Rizzo, Simona Onori
Date: 2022-01-18 00:00:00
Tasks: Sensitivity

In this work, we conduct a sensitivity analysis of the mean-value internal combustion engine exergy-based model, recently developed by the authors, with respect to different driving cycles, ambient temperatures, and exhaust gas recirculation rates. Such an analysis allows to assess how driving conditions and environment affect the exergetic behavior of the engine, providing insights on the system's inefficiency. Specifically, the work is carried out for a military series hybrid electric vehicle.
Reconfigurable Control of a Class of Multicopters
Authors: Hafiz Zeeshan Iqbal Khan, Jahanzeb Rajput, Jamshed Riaz
Date: 2022-01-18 00:00:00
Tasks: Fault Detection

In this paper, a reconfigurable control scheme for a generalized class of multicopters is presented to overcome single or multiple rotor failures. In a multicopter, the rotor failure heavily affects its dynamics and thus its controllability. Limited controllability leads to limited or no reconfigurability. \emph{Available Control Authority Index} has been recently developed \cite{Du2015} as a measure of controllability of multicopters. In this work, the notion of \emph{Available Reduced-Control Authority Index} (ArCAI) is introduced, which shows that in some uncontrollable failures it is still possible to control reduced set of states. Based on this notion, a reconfigurable control scheme is presented, which comprises a \emph{Nonlinear Dynamic Inversion} based baseline control law, a constrained control allocation scheme along with some modifications to incorporate reconfiguration, and a simplified fault detection and isolation technique. Simulation results for a commonly used configuration of Hexacopter are presented in the presence of single and multiple rotor failures.
Sensor Scheduling Design for Complex Networks under a Distributed State Estimation Framework
Authors: Peihu Duan, Lidong He, Lingying Huang, Guanrong Chen, Ling Shi
Date: 2022-01-18 00:00:00
Tasks: Scheduling, State Estimation

This paper investigates sensor scheduling for state estimation of complex networks over shared transmission channels. For a complex network of dynamical systems, referred to as nodes, a sensor network is adopted to measure and estimate the system states in a distributed way, where a sensor is used to measure a node. The estimates are transmitted from sensors to the associated nodes, in the presence of one-step time delay and subject to packet loss. Due to limited transmission capability, only a portion of sensors are allowed to send information at each time step. The goal of this paper is to seek an optimal sensor scheduling policy minimizing the overall estimation errors. Under a distributed state estimation framework, this problem is reformulated as a Markov decision process, where the one-stage reward for each node is strongly coupled. The feasibility of the problem reformulation is ensured. In addition, an easy-to-check condition is established to guarantee the existence of an optimal deterministic and stationary policy. Moreover, it is found that the optimal policies have a threshold, which can be used to reduce the computational complexity in obtaining these policies. Finally, the effectiveness of the theoretical results is illustrated by several simulation examples.
Distributed $H_{\infty}$ Edge Weight Synthesis for Cooperative Systems
Authors: Baris Taner, Kamesh Subbarao
Date: 2022-01-18 00:00:00

This paper studies distributed edge weight synthesis of a cooperative system for a fixed topology to improve $H_{\infty}$ performance, considering that disturbances are injected at interconnection channels. This problem is cast into a linear matrix inequality problem by replacing original cooperative system with an equivalent ideal cooperative system. Derivations of the method relies on dissipative system framework. Proposed method provides an upper bound for the induced $\mathcal{L}_{2}$ norm of the original lumped cooperative system while reducing the computation time. A comparison for computation time illustrates the advantage of the proposed method against the lumped counterpart.
Sensitivity of Single-Pulse Radar Detection to Aircraft Pose Uncertainties
Authors: Austin Costley, Randall Christensen, Greg Droge, Robert C. Leishman
Date: 2022-01-18 00:00:00
Tasks: Sensitivity

Mission planners for aircraft that operate in radar detection environments are often concerned the probability of detection. The probability of detection is a nonlinear function of the aircraft pose and radar position. Current path planning techniques for this application assume that the aircraft pose is deterministic. In practice, however, the aircraft pose is estimated using a navigation filter and therefore contains uncertainty. The uncertainty in the aircraft pose induces uncertainty in the probability of detection, but this phenomenon is generally not considered when path planning. This paper provides a method for combining aircraft pose uncertainty with single-pulse radar detection models to aid mission planning efforts. The method linearizes the expression for the probability of detection and three radar cross section models. The linearized models are then used to determine the variability of the probability of detection induced by uncertainty in the aircraft pose. The results of this paper validate the linearization using Monte Carlo analysis and explore the sensitivity of the probability of detection to aircraft pose uncertainty.
Limited Information Shared Control: A Potential Game Approach
Authors: Balint Varga, Jairo Inga, Soeren Hohmann
Date: 2022-01-17 00:00:00

This paper presents a systematic method for the design of a limited information shared control (LISC). LISC is used in applications where not all system states or reference trajectories are measurable by the automation. Typical examples are partially human-controlled systems, in which some subsystems are fully controlled by automation while others are controlled by a human. The proposed systematic design method uses a novel class of games to model human-machine interaction: the near potential differential games (NPDG). We provide a necessary and sufficient condition for the existence of an NPDG and derive an algorithm for finding a NPDG that completely describes a given differential game. The proposed design method is applied to the control of a large vehicle-manipulator system, in which the manipulator is controlled by a human operator and the vehicle is fully automated. The suitability of the NPDG to model differential games is verified in simulations, leading to a faster and more accurate controller design compared to manual tuning. Furthermore, the overall design process is validated in a study with sixteen test subjects, indicating the applicability of the proposed concept in real applications.
Minimax Multi-Agent Persistent Monitoring of a Network System
Authors: Samuel C. Pinto, Shirantha Welikala, Sean B. Andersson, Julien M. Hendrickx, Christos G. Cassandras
Date: 2022-01-17 00:00:00
Tasks: Traveling Salesman Problem

We investigate the problem of optimally observing a finite set of targets using a mobile agent over an infinite time horizon. The agent is tasked to move in a network-constrained structure to gather information so as to minimize the worst-case uncertainty about the internal states of the targets. To do this, the agent has to decide its sequence of target-visits and the corresponding dwell-times at each visited target. For a given visiting sequence, we prove that in an optimal dwelling time allocation the peak uncertainty is the same among all the targets. This allows us to formulate the optimization of dwelling times as a resource allocation problem and to solve it using a novel efficient algorithm. Next, we optimize the visiting sequence using a greedy exploration process, using heuristics inspired by others developed in the context of the traveling salesman problem. Numerical results are included to illustrate the contributions.
Stochastic Entry Guidance
Authors: Jack Ridderhof, Panagiotis Tsiotras, Breanna J. Johnson
Date: 2021-03-09 00:00:00

In this paper, closed-loop entry guidance in a randomly perturbed atmosphere, using bank angle control, is posed as a stochastic optimal control problem. The entry trajectory, as well as the closed-loop controls, are both modeled as random processes with statistics determined by the entry dynamics, the entry guidance, and the probabilistic structure of altitude-dependent atmospheric density variations. The entry guidance, which is parameterized as a sequence of linear feedback gains, is designed to steer the probability distribution of the entry trajectories while satisfying bounds on the allowable control inputs and on the maximum allowable state errors. Numerical simulations of a Mars entry scenario demonstrate improved range targeting performance with approximately 50% lower 1st and 99th percentile final range errors when using the developed stochastic guidance scheme as compared to the existing Apollo final phase algorithm.