Miracle-dz/UniFAR-FTUs huggingface dataset:
Error Sources and Accuracy of Vertical Velocities Computed from Multiple-Doppler Radar Measurements in Deep Convective Storms

Abstract Actual data are used in one case to investigate the nature and source of vertical velocity errors resulting from analyses of multiple-Doppler radar measurements. Consistent with earlier analytical works, larger errors are found than would be expected from previous theoretical studies. It is shown that the reconstructed maximum updraft speed in strong updrafts (>20 m s−1) is accurate, on the average, to within about 10% (standard deviation of 10%). Storm advection, incomplete sampling of low-altitude divergence caused by the radar horizon, top boundary errors, and uneven terrain are studied and all are dismissed as dominant sources of error in the case considered here. The inability to determine a dominant error source has important consequences for the formulation of vertical velocity adjustment schemes.
Distributed Relatively Smooth Optimization

Smoothness conditions, either on the cost itself or its gradients, are ubiquitous in the development and study of gradient-based algorithms for optimization and learning. In the context of distributed optimization and multi-agent systems, smoothness conditions and gradient bounds are additionally central to controlling the effect of local heterogeneity. We deviate from this paradigm and study distributed learning problems in relatively smooth environments, where cost functions may grow faster than a quadratic, and gradients need not be bounded. We generalize gradient noise conditions to cover this setting, and present convergence guarantees in relatively smooth and relatively convex environments. Numerical results corroborate the findings.
Imaging technique for three-dimensional microstructure by digital holography

Digital holography is a whole-field, non-contact, and highly sensitive interferometric imaging and testing technology. It is more suitable for microscopic measurement owing to digitalization and flexibility in holograms recording, storage, reconstruction and transmittance. This paper analyzes the factors which lead to the phase aberrations in the off-axis lensless Fourier transform digital holography firstly. Then a method, which is obtained by borrowing ideas from T. Colomb, is presented to correct the phase aberrations automatically during the numerical reconstruction. This is implemented by multiplying a phase mask directly with the reconstructed wave field. The phase mask is obtained by an iterative procedure computing automatically without the pre-knowledge of the physical parameters, such as the off-set of the reference point source and the recording distance. This method enables one to reconstruct the relative correct and accurate phase-contrast image, even in the presence of the noise, which is needed to be smoothed by a median filter. In order to achieve an accurate phase image, the procedure described here is applied iteratively, starting from the initial values provided by the first evaluation. We present and analyze the simulation results of the phase images based on a special three-dimensional micro object. The results show that for a weak noise the above method is very effective; while for strong noise the common phase-unwrapping method must be applied. This indicates that it is very important to record high quality hologram and to suppress the noise in phase data.
Data-driven Characterization of Recovery Energy in Controlled Dynamical Systems using Koopman Operator

A key aspect of a resilient dynamical system is its ability to recover from large disruptions. In this paper, we define and characterize recovery energy as a quantitative measure of the minimum effort required by a control dynamical system to return to a safe operating region after large disruptions. The problem of steering the system state to a pre-specified safe region with minimum recovery energy is posed as an optimization problem which turns out to be a nonconvex due to the nonlinear nature of the system dynamics. To this end, we use the Koopman operator theoretic framework to obtain a convex reformulation in a lifted observable space. Finally, we demonstrate the approach on an IEEE 123-bus test feeder and quantify its ability to recover from disruptions.
SIC-based Precoding Scheme with Sub-connected Architecture for MIMO VLC Systems

High spatial correlation is one of the main factors limiting the communication performance of multiple-input multiple-output (MIMO) visible light communication (VLC), which can be alleviate by precoding. However, most existing precoding algorithms require a dedicated baseband chain for each light emitting diode (LED), which may result in high energy consumption and hardware complexity, especially when LEDs are densely deployed. To solve this issue, a successive interference cancellation-based precoding scheme with sub-connected architecture (SIC-SA) is proposed, where each baseband chain is connected to an LED sub-array. In this case, since the SIC-based precoding can only determine the signal of each baseband chain for an LED sub-array, the electrical/optical power of each LED must be jointly optimized to alleviate the spatial correlation among individual LEDs. This joint SIC-based precoding, power allocation and direct current offset design problem is formulated as an achievable sum rate maximization problem under dimming control and electrical power constraints. To solve this problem, the original problem is separated into two subproblems. In the first subproblem, the SIC-based precoding is calculated by successive null space computation. With the obtained SIC-based precoding, the second subproblem optimizes the power allocation and direct-current offset. Finally, these two subproblems are iteratively solved to obtain a convergent solution. Simulation results demonstrate that the proposed SIC-SA achieves 0.1306 bps/Hz/W and 0.1340 bps/Hz/W improvements in terms of energy efficiency compared to minimum mean square error precoding with fully-connected architecture (FA) and zero-forcing precoding with FA, respectively, when the signal-to-noise ratio is 30 dB.
DotView: A Low-Cost Compact Tactile Sensor for Pressure, Shear, and Torsion Estimation

Tactile sensation is one of the most important sensing methods of humans, which helps humans complete complex decisions and actions by itself or in cooperation with other sensations. Similarly, tactile sensors are also crucial for robots to interact with the environment and fulfill dexterous object manipulation. In this study, we present DotView, a compact, low-cost, and high-performance tactile sensor that utilizes a biomimetic micro-structured soft layer to generate rich, high-resolution tactile images captured by a commercialized capacitive sensing array, which exhibits sensitive and robust responses under pressure, shear and torsion applied by different indenters. Furthermore, we utilize a neural network to estimate multiaxial contact forces and torques, and achieve good accuracy in different contact geometries. Finally, we demonstrate the capability of DotView in a dexterous manipulation scenario by performing the task of guiding a tool through a maze with only tactile feedback.
Near-Field Channel Estimation for RIS-Assisted Wideband Terahertz Systems

Terahertz (THz) communication has been widely considered as a key enabler for future wireless systems. However, due to the strong directivity and severe attenuation of THz signals, the communication performance relies heavily on the existence of a line-of-sight (LoS) link. To deal with this problem, a reconfigurable intelligent surface (RIS) that modifies the wireless channel through intelligent signal reflection has gained much attention recently. In this paper, we propose an efficient near-field RIS-assisted wideband THz channel estimation scheme. Key idea of the proposed scheme, referred to as the polar-domain frequency-dependent RIS-assisted channel estimation (PF -RCE), is to exploit the polar-domain sparsity of the near-field channel and common support property of the wideband THz channel. To the best of our knowledge, this is the first work that investigates the characteristics of RIS-assisted wideband THz channel in the near- filed region and provides an efficient channel estimation scheme. From the numerical evaluations, we demonstrate that the proposed PF-RCE scheme achieves a significant performance gain over the conventional THz channel estimation schemes in terms of the normalized mean square error (NMSE).
Detection of High-Resistance Connections in Industrial Power Systems During Induction Motor Start-up

We propose a methodology for detecting and isolating high-resistance connections (HRC) in industrial power systems during induction motor (IM) start-up. Negative sequence components are analyzed during the IM start-up transient in different points of the electric system of an industrial plant. The effects of HRC are evaluated using an analytical model of an industrial power system in steady state and validated with numerical simulations obtained of a transient model. These results show not only the possibility to detect HRC but also its isolation from unbalance voltage conditions of the grid. In addition, an approximation of the total power of motors affected by the HRC is calculated with our proposal. This power and the electric one-line diagram of the industrial plant allow us to determine the location of HRC. Finally, the proposal is validated by using experimental results obtained in a meat processing industry.
Drag and propulsion of large satellites in the ionosphere: An Alfvén propulsion engine in space

There is a motionally induced charge separation in a conductor moving across magnetic field lines. This charge may be conducted away, resulting in a dc current flow through the conductor if it moves through a plasma. The generation of Alfven waves is a mechanism particularly effective for circulating the charge for very large conductors moving in or above the earth's ionosphere. This mechanism is studied in this paper and when applied to the analysis of the orbit of the Echo satellite is found to give rise to a significant damping of the motion as mechanical energy is converted to that of Alfven radiation. The calculated drag is comparable to that observed for the orbit of Echo 1 and attributed in earlier studies entirely to the mechanical drag of considerable nonionized atmospheric density. Perturbations in electron density associated with this current flow may in appropriate circumstances be detectable even thousands of kilometers away from such a high altitude satellite. The drag can be changed to a propulsion mechanism when a source of electrical power is available on the satellite. Up to fifty per cent of the expended power is available for pushing a space vehicle across an ambient magnetic field.
Synchronization of oscillators by nonlinear measurements with application to VLC

This note studies the problem of masterslave phase synchronization between two oscillators described by Andronov-Hopf equations, where pulselike signals, which disturbances can be heavily corrupt, are used for communication. The synchronization conditions are established using the Lyapunov function theory, and the synchronization error bounds are evaluated. The results are motivated and illustrated by designing a synchronization scheme for visible light communication (VLC), where a periodic sequence is usually sent at the beginning of the communication to synchronize the transmitter and the receiver clocks.
Learning-based Balancing of Model-based and Feedback Control for Second-order Mechanical Systems

High-performance tracking control of mechanical systems typically requires model-based control as it enables to counteract undesirable dynamics in a timely fashion. The quality of the compensation depends on the accuracy of the underlying model. However, the dynamics are often (partially) infeasible for complex systems in a-priori unknown environments. Due to the favorable properties of model-based control, the goal is to apply feedback control for error compensation only when necessary. In this paper, we present an online learning-based balancing (OLBB) framework to adapt the feedback gains such that the controlled system with state-dependent uncertainties satisfies given performance specifications. For this purpose, an oracle predicts the unknown dynamics and an error model adapts the feedback gains. The framework can be easily applied to existing control approaches to improve the safety and performance of the closed-loop.
Keck spectroscopy of Cosmic Lens All‐Sky Survey gravitational lenses

We present the optical spectra of four newly discovered gravitational lenses from the Cosmic Lens All-Sky Survey (CLASS). These observations were carried out using the Low-Resolution Imaging Spectrograph on the W. M. Keck-I Telescope as part of a programme to study galaxy-scale gravitational lenses. From our spectra, we found the redshift of the background source in CLASS B0128+437 (z s = 3.1240 ± 0.0042) and the lensing galaxy redshifts in CLASS B0445+123 (z 1 = 0.5583 ± 0.0003) and CLASS B0850+054 (z 1 = 0.5883 ± 0.0006). Intriguingly, we also discovered that CLASS B0631+519 may have two lensing galaxies (z 1,1 = 0.0896 ± 0.0001, z 1,2 = 0.6196 ± 0.0004). We also found a single unidentified emission line from the lensing galaxy in CLASS B0128+437 and the lensed source in CLASS B0850+054. We find the lensing galaxies in CLASS B0445+123 and CLASS B0631+519 (1, 2) to be early-type galaxies with Einstein radii of 2.8-3.0 h -1 kpc. The deflector in CLASS B0850+054 is a late-type galaxy with an Einstein radius of 1.6 h -1 kpc.
Metric Relations and Displacement Groups in Mechanism and Robot Kinematics

This paper presents a systematic theory for metric relations between the invariant properties of displacement groups, and shows this theory application to mechanism kinematics. Displacement groups, their invariant properties and operations are briefly described. Kinematic constraints are then introduced as tools for relating abstract group properties to actual mechanism constraints. Criteria and operating rules to employ metric relations for the generation of a meaningful set of closure equations for kinematic chains are detailed.
Whispering gallery mode resonators: An alternate platform for Ti:sapphire lasers and amplifiers

Titanium doped sapphire (Ti:sapphire) is a ubiquitous gain medium. Due to its broadband gain it has been in use in the solid state laser industry for over three decades while still going strong. We recently demonstrated a Ti:sapphire laser using the whispering gallery mode (WGM) resonator platform. Here, we review some of our previous work, shedding light on the theoretical and experimental lasing threshold of this first high quality Ti:sapphire WGM laser and review amplification with this new platform as well. We also report on new results of multi-mode lasing observed with this system. These results can potentially be utilised in future to train neural networks to control the experimental parameters used to observe lasing.
Machine learning based condition monitoring of a DC-link capacitor in a Back-to-Back converter

The utilization of power electronic converters has increased, and the significance of continuous operation is essential in various applications. Therefore, proper condition monitoring (CM) is vital for power converters to eradicate unpredictable maintenance. However, the existing CM techniques may require additional sensors or injection of controlled voltage to the converters. The following machine learning algorithms, such as a K-nearest neighbors (KNN), Support Vector Machine (SVM), and Naive Bayes (NB), have been proposed to monitor the condition of the dc-link capacitor in a Back-to-Back (BTB) converter. The dc-link voltage is measured, and a wavelet decomposition is employed for the feature extraction. Moreover, the performance index evaluates the efficacy of the different classifiers. Further, different datasets have been considered for the evaluation of the classifiers. From this analysis, it is found that the SVM classifier performs better than others.
Achieving consensus in networks of increasingly stubborn voters

We study opinion evolution in networks of stubborn agents discussing a sequence of issues, modeled through the so called concatenated Friedkin-Johnsen (FJ) model. It is concatenated in the sense that agents’ opinions evolve for each issue, and the final opinion is then taken as a starting point for the next issue. We consider the scenario where agents also take a vote at the end of each issue and propose a feedback mechanism from the result (based on the median voter) to the agents’ stubbornness. Specifically, agents become increasingly stubborn during issue s+1 the more they disagree with the vote at the end of issue s. We analyze this model for a number of special cases and provide sufficient conditions for convergence to consensus stated in terms of permissible initial opinion and stubbornness. In the opposite scenario, where agents become less stubborn when disagreeing with the vote result, we prove that consensus is achieved, and we demonstrate the faster convergence of opinions compared to constant stubbornness.
Analysis, Modeling, and optimization of Single-Inductor Multiple-Output Continuous Conduction Mode Buck Converter with Arbitrary Number of Outputs

Single-Inductor Multiple-Output (SIMO) regulators have recently gained momentum as viable solutions that can provide multiple domains to Systems-on-Chip (SoCs) in a miniaturized area. For portables, area is a primary cost metric for achieving high efficiencies and power densities. A best-area-utilization technique is, therefore, of importance. This paper outlines an approach to modeling and optimization of SIMO buck converters running in continuous conduction mode (CCM) and subject to an area constraint. Results are assessed against both behavioral and circuit-level simulations.
On the Dynamic Parameter Identification of Collaborative Manipulators: Application to a KUKA iiwa

This paper deals with the dynamic parameter identification of the KUKA LBR iiwa R820, a robotic manipulator with flexible joints intended for collaborative applications. The control software interface of this robot provides different signals that allow the parameter identification of not only the real robot, but also of the model integrated in the robot's controller, in a reverse engineering process. By analyzing the successive positions and torques computed by the controller, an accurate set of inertial parameters referring to the robot's model integrated in the controller is obtained. A percent error in the torque reconstruction of less than 1% for every joint, validates the process. Then, this set of parameters is useful to complete and compare the set of parameters that is obtained with real physical measurements which is more prone to errors due to noise and uncertainties. Finally, the torque reconstruction using the measured signals is compared with models present in bibliography, showing a better performance.
AI-Enabled Health 4.0: An IoT-Based COVID-19 Diagnosis Use-Case

The Internet of Things (IoT) has revamped service-oriented architectures by enabling edge-based devices to collect and share information that is vital for the service provisioning process. IoT devices have evolved from simple data acquirers and have become part of the service provisioning process. These devices are now able to sense, acquire, communicate, and process data in an intelligent manner. With the support of Artificial Intelligence (AI), IoT devices can now support users with minimal reliance on centralized entities, such as the Cloud. IoT devices are now able to share raw and processed information securely, without or with minimal reliance on centralized devices. This paper proposes a general framework for Health 4.0 to provide edge-based health services with the support of AI. IoT devices collect and share patient information in a secure manner to enable user-side disease diagnosis. The solution enables both federated and centralized learning to coexist under one framework. As a proof-of-concept, the solution considers a COVID-19 diagnosis use-case. A Machine Learning (ML) web-based user application is developed to analyze frontal chest X-ray (CXR) images and make predictions on whether patients' lungs are damaged. The solution provides an experimental study on mechanisms and approaches needed to increase learning accuracy with reduced dataset sizes and image quality through Federated Learning (FL).
Reliable Distributed Fuzzy Control for Leader-Following Consensus of Nonlinear Multi-gent Systems with Time-varying Communication Delays

In this paper, a leader-following consensus problem of nonlinear multi-agent systems (MASs) with external disturbances and time-varying communication delays is addressed by proposing distributed fuzzy control technique. Unlike previous studies, a novel reliable control technique for nonlinear MASs that does not need to delay each agent’s own state value required for feedback is proposed. To represent the nonlinear MAS, Takagi-Sugeno (T–S) fuzzy model is employed, and the stabilization conditions are derived in terms of linear matrix inequalities (LMIs) to ensure robust stability and $H_{\infty}$ performance. Finally, the validity of the proposed method is verified through the simulation example.
A Microvision-Based Motion Measurement System for Nanopositioners Using the Feature-to-Phase Method

The development of automation at the nanoscale has been calling for precision motion sensing for robotic nanopositioners. This study presents a microvision-based measurement system for the accurate and efficient motion sensing of three-degree-of-freedom (DOF) nanopositioners. In this measurement system, an optimized target searching (OTS) scheme is proposed for automatic tracking target selection. A strategy is designed to combine feature matching with phase correlation to balance the measurement accuracy and efficiency. By proposing a multiple target tracking scheme, high-precision angular measurement is achieved, and velocity estimation is also provided in this microvision-based measurement system. Subsequently, offline simulations and online experiments are performed to comprehensively evaluate the performances of the microvision-based system with comparisons to traditional instrumentation. The simulation and experimental results demonstrate that the proposed system and marker-free method can realize excellent extensibility, strong robustness, and high precision of motion tracking for the nanopositioners, with measurement accuracy higher than 93 nm and 96 $\mu $ rad.