pwc-archive/papers-with-abstracts huggingface dataset:
Clock Rigidity and Joint Position-Clock Estimation in Ultra-Wideband Sensor Networks
Authors: Ruixin Wen, Eric Schoof, Airlie Chapman
Date: 2021-06-04 00:00:00
Tasks: Position, Translation

Joint position and clock estimation is crucial in many wireless sensor network applications, especially in distance-based estimation with time-of-arrival (TOA) measurement. In this work, we consider a TOA-based ultra-wideband (UWB) sensor network, propose a novel clock rigidity theory and investigate the relation between the network graph properties and the feasibility of clock estimation with TOA timestamp measurements. It is shown that a clock framework can be uniquely determined up to a translation of clock offset and a scaling of all clock parameters if and only if it is infinitesimally clock rigid. We further prove that a clock framework is infinitesimally clock rigid if its underlying graph is generically bearing rigid in 2-dimensional space with at least one redundant edge. Combined with distance rigidity, clock rigidity provides a graphical approach for analyzing the joint position and clock problem. It is shown that a position-clock framework can be uniquely determined up to some trivial variations corresponding to both position and clock if and only if it is infinitesimally joint rigid. Simulation results are presented to demonstrate the clock estimation and joint position-clock estimation.
Functional observers with linear error dynamics for discrete-time nonlinear systems, with application to fault diagnosis
Authors: Sunjeev Venkateswaran, Benjamin A. Wilhite, Costas Kravaris
Date: 2021-06-04 00:00:00
Tasks: Fault Detection, Fault Diagnosis

This work deals with the problem of designing observers for the estimation of a single function of the states for discrete-time nonlinear systems. Necessary and sufficient conditions for the existence of lower order functional observers with linear dynamics and linear output map are derived. The results provide a direct generalization to Luenberger's linear theory of functional observers. The design methodology is tested on a non-isothermal CSTR case study.
Rotor Thermal Monitoring Scheme for Direct-Torque-Controlled Interior Permanent Magnet Synchronous Machines via High-Frequency Rotating Flux or Torque Injection
Authors: Shen Zhang, Sufei Li, Lijun He, Jose A. Restrepo, Thomas G. Habetler
Date: 2021-06-03 00:00:00

Interior permanent magnet synchronous machine drives are widely employed in electric traction systems and various industrial processes. However, prolonged exposure to high temperatures while operating can demagnetize the permanent magnets to the point of irreversible demagnetization. In addition, direct measurements with infrared sensors or contact-type sensors with wireless communication can be expensive and intrusive to the motor drive systems. This paper thus proposes a nonintrusive thermal monitoring scheme for the permanent magnets inside the direct-torque-controlled interior permanent magnet synchronous machines. By applying an external high-frequency rotating flux or torque signal to the hysteresis torque controller in the motor drive, the high-frequency currents can be injected into the stator windings. The permanent magnet temperature can thus be monitored based on the induced high-frequency resistance. The nonintrusive nature of the method is indicated by the elimination of the extra sensors and no hardware change to the existing system. Finally, the effectiveness of the proposed method is validated with experimental results.
Exponential Convergence of Piecewise-Constant Parameters Identification under Finite Excitation Condition
Authors: Anton Glushchenko, Vladislav Petrov, Konstantin Lastochkin
Date: 2021-06-03 00:00:00

A problem of identification of piecewise-constant unknown parameters of a linear regression equation (LRE) is considered. Such parameters change their values over the interval of the regressor finite (rather than persistent) excitation. To solve it, the previously proposed I-DREM procedure and the integral-based filtering method with the exponential forgetting and resetting are improved: the integral of the filter equations is taken over the finite time intervals, which belong to the finite excitation time range. This allows one to obtain an exponentially bounded identification error over the excitation interval, and, when the LRE parameters are constant outside such interval, to provide exponential convergence of the identification error to zero. In addition, the applied method of the regressor excitation normalization makes it possible to obtain the same rate of convergence of the parameter error for the regressors of various amplitudes. The stability and all the above-mentioned properties are proved for the derived identification method. The results of numerical experiments (including the case of the noise-contaminated regressor and output measurements) fully support the obtained theoretical results.
Modeling sequence-space exploration and emergence of epistatic signals in protein evolution
Authors: Matteo Bisardi, Juan Rodriguez-Rivas, Francesco Zamponi, Martin Weigt
Date: 2021-06-04 00:00:00

During their evolution, proteins explore sequence space via an interplay between random mutations and phenotypic selection. Here we build upon recent progress in reconstructing data-driven fitness landscapes for families of homologous proteins, to propose stochastic models of experimental protein evolution. These models predict quantitatively important features of experimentally evolved sequence libraries, like fitness distributions and position-specific mutational spectra. They also allow us to efficiently simulate sequence libraries for a vast array of combinations of experimental parameters like sequence divergence, selection strength and library size. We showcase the potential of the approach in re-analyzing two recent experiments to determine protein structure from signals of epistasis emerging in experimental sequence libraries. To be detectable, these signals require sufficiently large and sufficiently diverged libraries. Our modeling framework offers a quantitative explanation for the variable success of recently published experiments. Furthermore, we can forecast the outcome of time- and resource-intensive evolution experiments, opening thereby a way to computationally optimize experimental protocols.
Simulation of deterministic compartmental models for infectious diseases dynamics
Authors: Antonio M Batista, Silvio L T Souza, Kelly C Iarosz, Alexandre C L Almeida, José D Szezech, Enrique C Gabrick, Michele Mugnaine, Gefferson L dos Santos, Iberê L Caldas
Date: 2021-06-03 00:00:00

Infectious diseases are caused by pathogenic microorganisms and can spread through different ways. Mathematical models and computational simulation have been used extensively to investigate the transmission and spread of infectious diseases. In other words, mathematical model simulation can be used to analyse the dynamics of infectious diseases, aiming to understand the effects and how to control the spread. In general, these models are based on compartments, where each compartment contains individuals with the same characteristics, such as susceptible, exposed, infected, and recovered. In this paper, we cast further light on some classical epidemic models, reporting possible outcomes from numerical simulation. Furthermore, we provide routines in a repository for simulations.
Testing the Goodwin Growth Cycles with Econophysics Approach in 2002-2019 Period in Turkey
Authors: Kerim Eser Afşar, Mehmet Özyigit, Yusuf Yüksel, Ümit Akıncı
Date: 2021-06-04 00:00:00

The Turkish economy between 2002-2019 period has been investigated within the econophysical approach. From the individual income data obtained from the Household Budget Survey, the Gompertz-Pareto distribution for each year and Goodwin cycle for the mentioned period have been obtained. For this period, in which thirteen elections were held under the single-party rule, it has been observed that the income distribution fits well with the Gompertz-Pareto distribution which shows the two-class structure of the Turkish economy. The variation of the threshold value $x_t$ (which separates these two classes) as well as Pareto coefficient have been obtained. Besides, Goodwin cycle has been observed within this period, centered at $(u,v)\cong (66.30,83.40)$ and a period of $T=18.30$ years. It has been concluded that these observations are consistent with the economic and social events experienced in the mentioned period.
Cupid's Invisible Hand: Social Surplus and Identification in Matching Models
Authors: Alfred Galichon, Bernard Salanié
Date: 2021-06-04 00:00:00

We investigate a model of one-to-one matching with transferable utility and general unobserved heterogeneity. Under a separability assumption that generalizes Choo and Siow (2006), we first show that the equilibrium matching maximizes a social gain function that trades off exploiting complementarities in observable characteristics and matching on unobserved characteristics. We use this result to derive simple closed-form formulae that identify the joint matching surplus and the equilibrium utilities of all participants, given any known distribution of unobserved heterogeneity. We provide efficient algorithms to compute the stable matching and to estimate parametric versions of the model. Finally, we revisit Choo and Siow's empirical application to illustrate the potential of our more general approach.
ToyADMOS2: Another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions
Authors: Noboru Harada, Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Masahiro Yasuda, Shoichiro Saito
Date: 2021-06-04 00:00:00
Tasks: Anomaly Detection, Fault Diagnosis

This paper proposes a new large-scale dataset called "ToyADMOS2" for anomaly detection in machine operating sounds (ADMOS). As did for our previous ToyADMOS dataset, we collected a large number of operating sounds of miniature machines (toys) under normal and anomaly conditions by deliberately damaging them but extended with providing controlled depth of damages in anomaly samples. Since typical application scenarios of ADMOS often require robust performance under domain-shift conditions, the ToyADMOS2 dataset is designed for evaluating systems under such conditions. The released dataset consists of two sub-datasets for machine-condition inspection: fault diagnosis of machines with geometrically fixed tasks and fault diagnosis of machines with moving tasks. Domain shifts are represented by introducing several differences in operating conditions, such as the use of the same machine type but with different machine models and parts configurations, different operating speeds, microphone arrangements, etc. Each sub-dataset contains over 27 k samples of normal machine-operating sounds and over 8 k samples of anomalous sounds recorded with five to eight microphones. The dataset is freely available for download at https://github.com/nttcslab/ToyADMOS2-dataset and https://doi.org/10.5281/zenodo.4580270.
Joint Scheduling and Throughput Maximization in Self-backhauled Millimeter Wave Cellular Networks
Authors: Chao Fang, Charitha Madapatha, Behrooz Makki, Tommy Svensson
Date: 2021-06-04 00:00:00
Tasks: Scheduling

Integrated access and backhaul (IAB) networks have the potential to provide high data rate in both access and backhaul networks by sharing the same spectrum. Due to the dense deployment of small base stations (SBSs), IAB networks connect SBSs to the core network in a wireless manner without the deployment of high-cost optical fiber. As large spectrum is available in mmWave bands and high data rate is achieved by using directional beamforming, the access and backhaul links can be integrated in the same frequency band while satisfying quality-of-service constraints. In this work, we optimize the scheduling of access and backhaul links such that the minimum throughput of the access links is maximized based on the revised simplex method. By considering a probability based line-ofsight (LOS) and non-line-of-sight (NLOS) path loss model and the antenna array gains, we compare the achievable minimum access throughput of the IAB network with the network with only macro base stations, and study the effect of the network topology and antenna parameters on the achievable minimum throughput. Simulation results show that, for a broad range of parameter settings, the implementation of IABs improves the access minimum achievable throughput.
A Robust and Accurate Deep Learning based Pattern Recognition Framework for Upper Limb Prosthesis using sEMG
Authors: Sidharth Pancholi, Amit M. Joshi, Deepak Joshi
Date: 2021-06-04 00:00:00
Tasks: CPU, Deep Learning, Position

In EMG based pattern recognition (EMG-PR), deep learning-based techniques have become more prominent for their self-regulating capability to extract discriminant features from large data-sets. Moreover, the performance of traditional machine learning-based methods show limitation to categorize over a certain number of classes and degrades over a period of time. In this paper, an accurate, robust, and fast convolutional neural network-based framework for EMG pattern identification is presented. To assess the performance of the proposed system, five publicly available and benchmark data-sets of upper limb activities were used. This data-set contains 49 to 52 upper limb motions (NinaPro DB1, NinaPro DB2, and NinaPro DB3), Data with force variation, and data with arm position variation for intact and amputated subjects. The classification accuracies of 91.11% (53 classes), 89.45% (49 classes), 81.67% (49 classes of amputees), 95.67% (6 classes with force variation), and 99.11% (8 classes with arm position variation) have been observed during the testing and validation. The performance of the proposed system is compared with the state of art techniques in the literature. The findings demonstrate that classification accuracy and time complexity have improved significantly. Keras, TensorFlow's high-level API for constructing deep learning models, was used for signal pre-processing and deep-learning-based algorithms. The suggested method was run on an Intel 3.5GHz Core i7, 7th Gen CPU with 8GB DDR4 RAM.
Distortion measure of spectrograms for classification of respiratory diseases
Authors: Jeremy Levy, Alexander Naitsat, Yehoshua Y. Zeevi
Date: 2021-06-04 00:00:00
Tasks: Classification, Descriptive

A new method for the classification of respiratory diseases is presented. The method is based on a novel class of features, extracted from pulmonary sounds, by parameterizing their spectrograms that are represented as surfaces, and by utilizing geometrical distortions defined with reference to these surfaces. This method yields a set of highly descriptive features for the analysis of pulmonary sound recordings. Furthermore, by combining these features with Mel-frequency cepstral coefficients, we introduce a powerful model for the automatic diagnosis of common respiratory pathologies. Compared with baseline methods, our model achieves superior results for binary and multi-class classifications of common respiratory diseases. Our new approach to the classification of one-dimensional signals is applicable to other signals in the context of their representations in combined spaces or manifolds.
EEG changes and motor deficits in Parkinson's disease patients: Correlation of motor scales and EEG power bands
Authors: Aleksandar Miladinović, Miloš Ajčević, Pierpaolo Busan, Joanna Jarmolowska, Manuela Deodatod, Susanna Mezzarobba, Piero Paolo Battaglini, Agostino Accardo
Date: 2021-06-04 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Relation

Over the years motor deficit in Parkinson's Disease (PD) patients was largely studied, however, no consistent pattern of relations between quantitative electroencephalography (qEEG) and motor scales emerged. There is a general lack of information on the relation between EEG changes and scales related to specific motor deficits. Therefore, the study aimed to investigate the relation between brain oscillatory activity alterations (EEG power bands) and most used PD-related motor deficit scales. A positive correlation was found between the freezing of the gait questionnaire (FOGQ) and delta spectral power band (rho=0.67; p=0.008), while a negative correlation with the same scale was observed in the alpha spectral power band (rho=-0.59, p=0.027). Additionally, motor scores measure by motor part of Unified Parkinson's Disease Rating Scale (UPDRS) correlated directly with theta (rho=0.55, p=0.040) and inversely with beta EEG power band (rho=-0.77, p=0.001). No significant correlation was found between spectral powers and Hoehn and Yahr (H&Y), BERG (Berg K. et. al. 1995), Modified Parkinson Activity Scale (MPAS), Six-Minute Walk Test (6MWT) and Timed Up and Go Test (TUG). In conclusion, our study supports the earlier findings suggesting a link between EEG slowing and motor decline, providing more insight into the relation between EEG alteration and deficits in different motor domains. These findings indicate that EEG assessment may be a useful biomarker for objective monitoring of progression and neurophysiological effect of rehabilitation approaches in PD's.
First demonstration of C + L band CDCROADM with simple node configuration using multiband switching devices
Authors: Shuto Yamamoto, Hiroki Taniguchi, Yoshiaki Kisaka, Stefano Camatel, Yiran Ma, Daisuke Ogawa, Koichi Hadama, Mitsunori Fukutoku, Takashi Goh, Kenya Suzuki
Date: 2021-06-04 00:00:00

While ultrahigh-baud-rate optical signals are effective for extending the transmission distance of large capacity signals, they also reduce the number of wavelengths that can be arranged in a band because of their wider bandwidth. This reduces the flexibility of optical path configuration in reconfigurable optical add/drop multiplexing (ROADM) networks. In colorless, directionless and contentionless (CDC)-ROADM in particular, the effect reduces the add/drop ratio at a node. Multiband ROADM systems are an effective countermeasure for overcoming this issue, but they make the node configuration more complicated and its operation more difficult. In this paper, we analyze the challenges of C + L band CDC-ROADM and show that optical switch devices that operate over multiple bands are effective in meeting them. For this purpose, we built a C + L band CDC-ROADM node based on C + L band wavelength selective switches (WSSs) and multicast switches (MCSs) and confirmed its effectiveness experimentally. In particular, to simplify the node configuration, we propose a reduction in the number of optical amplifiers used for node loss compensation and experimentally verify its feasibility.
A C-V2X Platform Using Transportation Data and Spectrum-Aware Sidelink Access
Authors: Lin Chia-Hung, Lin Shih-Chun, Wang Chien-Yuan, Chase Thomas
Date: 2021-06-04 00:00:00
Tasks: Autonomous Vehicles, Management

Intelligent transportation systems and autonomous vehicles are expected to bring new experiences with enhanced efficiency and safety to road users in the near future. However, an efficient and robust vehicular communication system should act as a strong backbone to offer the needed infrastructure connectivity. Deep learning (DL)-based algorithms are widely adopted recently in various vehicular communication applications due to their achieved low latency and fast reconfiguration properties. Yet, collecting actual and sufficient transportation data to train DL-based vehicular communication models is costly and complex. This paper introduces a cellular vehicle-to-everything (C-V2X) verification platform based on an actual traffic simulator and spectrum-aware access. This integrated platform can generate realistic transportation and communication data, benefiting the development and adaptivity of DL-based solutions. Accordingly, vehicular spectrum recognition and management are further investigated to demonstrate the potentials of dynamic slidelink access. Numerical results show that our platform can effectively train and realize DL-based C-V2X algorithms. The developed slidelink communication can adopt different operating bands with remarkable spectrum detection performance, validating its practicality in real-world vehicular environments.
Causal Digital Twin from Multi-channel IoT
Authors: PG Madhavan
Date: 2021-06-03 00:00:00
Tasks: Time Series, Time Series Analysis

Treating data from each sensor in an IoT installation on its own separately is wasteful. This article shows how to treat them as a multi-channel time series and introduces the State-space model formulation of Structural Vector Autoregressive (SVAR) model and the use of time-varying Kalman Filter for optimal estimation of causal parameters. Ladder graphs are introduced as a powerful visualization tool for SVAR estimates where both instantaneous and lagged causal factors are displayed and interactions analyzed. Ladder Graph IS the Causal Digital Twin (CDT); its use for multiple IoT applications that involve multi-channel time series are explored briefly. The main takeaway is that the NEXT STEP in IoT ML is the utilization of data from multiple sensors collectively as a single multi-channel time series. This article shows the way to do it and extract high-order (causal) information via our ladder graph based Causal Digital Twin.
Linear regression with partially mismatched data: local search with theoretical guarantees
Authors: Rahul Mazumder, Haoyue Wang
Date: 2021-06-03 00:00:00
Tasks: regression

Linear regression is a fundamental modeling tool in statistics and related fields. In this paper, we study an important variant of linear regression in which the predictor-response pairs are partially mismatched. We use an optimization formulation to simultaneously learn the underlying regression coefficients and the permutation corresponding to the mismatches. The combinatorial structure of the problem leads to computational challenges. We propose and study a simple greedy local search algorithm for this optimization problem that enjoys strong theoretical guarantees and appealing computational performance. We prove that under a suitable scaling of the number of mismatched pairs compared to the number of samples and features, and certain assumptions on problem data; our local search algorithm converges to a nearly-optimal solution at a linear rate. In particular, in the noiseless case, our algorithm converges to the global optimal solution with a linear convergence rate. Based on this result, we prove an upper bound for the estimation error of the parameter. We also propose an approximate local search step that allows us to scale our approach to much larger instances. We conduct numerical experiments to gather further insights into our theoretical results, and show promising performance gains compared to existing approaches.