Miracle-dz/UniFAR-FTUs huggingface dataset:
Delay robust control design of under-actuated PDE-ODE-PDE systems

We propose a controller design for a system composed of two sets of linear heterodirectional hyperbolic PDEs, with actuation at one boundary, and coupled at the other boundary by ODEs in a PDE-ODE-PDE configuration. The design approach employs a backstepping transformation, however, the under-actuation limits the choice of target system. To deal with this limitation, we propose to use stability results from delay systems to find a stable target system. Furthermore, we propose to use a high-pass filter of the proximal reflection, in place of cancellation, to obtain controllers that are robust to small delays.
1-D Electronic Beam-Steering Partially Reflective Surface Antenna

A 1-D electronic beam-steering partially reflective surface (PRS) antenna using a new reconfigurable PRS unit cell is proposed in this paper. The proposed work addresses the challenge to achieve a large beam steering angle with small gain variation and a small number of active/lumped elements by using a reconfigurable PRS superstrate only. The PRS unit cell consists of two back-to-back T-shaped strips with one PIN diode inserted between them and a pair of trapezoid patches (a rectangular patch and a pair of triangle parasitic patches). Beam steering is achieved by controlling the different states of PIN diodes. Thanks to the trapezoid patches, the proposed unit cell can generate a larger phase difference between different states, thereby leading to a larger beam steering angle. Furthermore, due to the addition of more degrees of freedom in the proposed unit cell, the phase difference can be easily manipulated. Moreover, since the T-shaped strips in each unit cell is connected with adjacent ones, the biasing network is very simple without needing a large number of lumped elements and dc biasing lines. The beam steering property is analyzed by using phased array theory. An antenna prototype with a main beam direction towards 0°, −18° and 18° operating at 5.5 GHz in the H-plane is fabricated and measured. Good agreement between the predicted simulation and measurement results for the input reflection coefficients and radiation patterns is achieved, which validates the feasibility of the design. The measured realized gains are over 11 dBi for all states with a 0.8 dBi gain variation.
Finite-interval kernel-based identification and state estimation for LTI systems with noisy output data* This work was supported by The National Science & Engineering Research Council of Canada

This note extends previous results pertaining to algebraic state and parameter estimation of linear systems based on a special construction of kernel system representations that incorporate system differential invariants. Main results include explicit expressions for the kernel functions for single-input, single-output LTI systems of arbitrary order. A recursive regression type algorithm is also proposed for the purpose of joint system identification and finite interval filtering. As compared with previous results the proposed non-asymptotic estimation method proves remarkably robust to Gaussian noise in output measurements. The approach has been shown to extend to linear time-varying and linear parameter-varying systems in a multivariate setting. The idea of system-related kernels can further be employed to enhance convergence properties of moving-window and minimum energy nonlinear filtering methods.
Horizontal and Vertical Scaling of Container-Based Applications Using Reinforcement Learning

Software containers are changing the way distributed applications are executed and managed on cloud computing resources. Interestingly, containers offer the possibility of handling workload fluctuations by exploiting both horizontal and vertical elasticity "on the fly". However, most of the existing control policies consider horizontal and vertical scaling as two disjointed control knobs. In this paper, we propose Reinforcement Learning (RL) solutions for controlling the horizontal and vertical elasticity of container-based applications with the goal to increase the flexibility to cope with varying workloads. Although RL represents an interesting approach, it may suffer from a possible long learning phase, especially when nothing about the system is known a-priori. To speed up the learning process and identify better adaptation policies, we propose RL solutions that exploit different degrees of knowledge about the system dynamics (i.e., Q-learning, Dyna-Q, and Model-based). We integrate the proposed policies in Elastic Docker Swarm, our extension that introduces self-adaptation capabilities in the container orchestration tool Docker Swarm. We demonstrate the effectiveness and flexibility of model-based RL policies through simulations and prototype-based experiments.
Confinement of Vibrotactile Stimuli in Narrow Plates

On a touch surface, providing a local vibrotactile feedback enables multiusers and multitouch interactions. While the vibration propagation usually impedes this localization, we show in this paper that narrow strip-shaped plates constitute waveguides in which bending waves below a cut-off frequency do not propagate. We provide a theoretical explanation of the phenomenon and experimental validations. We thus show that vibrations up to 2 kHz are well confined on top of the actuated area with vibration amplitude over 1 µm that can be felt by the fingers. The principle was validated with piezoelectric actuators of various shapes and a vibration motor.
Analysis of Message Generation Rules for Collective Perception in Connected and Automated Driving

Collective Perception (CP) or cooperative sensing enables vehicles and infrastructure nodes to exchange sensor information to improve their perception of the driving environment. CP enables vehicles to detect objects (e.g. non-connected vehicles, pedestrians, obstacles, etc.) beyond their local sensing capabilities. ETSI is currently developing the European standards for collective perception or cooperative sensing. This includes defining which information should be exchanged about the detected objects, and how often it should be exchanged. To this aim, different CP generation rules for collective perception are currently under analysis, and this paper presents an in-depth analysis of their performance and efficiency. The conducted analysis highlights the existing tradeoffs between performance (capacity to detect surrounding objects) and efficiency (redundant detection and transmission of the same detected objects). It also demonstrates the need to design advanced policies that dynamically control the redundancy on the wireless channel while ensuring the capacity to reliably detect the driving environment.
Research on Intelligent Diagnosis Strategy and Treatment Method of ev Charging Fault

With electric cars, large-scale development, in order to reduce the potential safety hazard in the process of electric vehicle charging, in this paper, by analyzing the power battery, charging pile, power supply equipment integration between safety mechanism, puts forward the integration of fault logic judgment business process, established a multistage equipment integration on-line fault diagnosis of the fault tree, making the integration of fault judging module and fault processing module, information mining and integrated warning index pretreatment technology is studied, which can effectively improve the electric car charging security, promote the healthy development of electric vehicles.
Objective and Subjective Assessment of Algorithms for Reducing Three-Axis Vibrations to One-Axis Vibrations

A typical approach to creating realistic vibrotactile feedback is reducing 3D vibrations recorded by an accelerometer to 1D signals that can be played back on a haptic actuator, but some of the information is often lost in this three-to-one (321) dimensional reduction process. This paper describes five real-time 321 algorithms and proposes four performance metrics that indicate the average value and the variability of the output signal’s spectral match and temporal match across 3D rotations of the input vector. These four performance metrics were applied to four texture recordings, and the method utilizing the discrete Fourier transform (DFT) was found to be the best regardless of the sensing axis. We also recruited 16 human subjects to assess the perceptual similarity achieved by each 321 algorithm in real time. We found the four metrics correlated well with the subjectively rated similarities for four of the dimensional reduction algorithms, with the exception of taking the 3D vector magnitude, which was perceived to be good despite its low average spectral and temporal match metrics.
Phase Noise Compensation for OFDM Systems Exploiting Coherence Bandwidth

Millimeter-wave (mmWave) systems must overcome severe phase noise impairment to exploit its full advantage (e.g. high data rate). However, the estimation of phase noise combined with wireless channel requires not only high-computational complexity, but also does not guarantee a stable solution. In this paper, the fact that the coherence bandwidth of a mmWave system is much larger than those of conventional systems is exploited to acquire the knowledge regarding phase noise by linear estimator. The channel coefficients are assumed to be piecewise-constant over Ncb successive subcarriers within coherence bandwidth. Utilizing the coherence structure, we propose a new algorithm for phase noise compensation on orthogonal frequency division multiplexing (OFDM) systems. Numerical results with phase noise generated as a Wiener process demonstrate that the proposed algorithm can provide improved performance even in severe phase noise environments.
Performing an assortment of tasks on Machine Learning and Benchmarking based Clinical Time Series Data

Conceptual— Health care is one of the most exciting borders in data mining and machine learning. Appropriation of electronic health records (EHRs) made a blast in advanced clinical information which is accessible for examination, but progress in machine learning for healthcare research has been complicated to measure because of the absence of openly available benchmark data sets. In this paper we propose three clinical expectation benchmarks to overcome the issue of utilizing the information got from the freely accessible Medical Information Mart for Intensive Care (Emulate III) database. These assignments cover a scope of clinical issues counting demonstrating danger of mortality, anticipating length of remain and distinguishing physiologic decay. MIMIC-III (Medical Information Mart for Intensive Care III) is a considerable, openly accessible database containing de-identified wellbeing related information related with more than forty thousand patients who remained in basic consideration units of the Beth Israel Deaconess Medical Center somewhere in the range of 2001 and 2012. Our plan is to perform various tasks with an objective to mutually take in a variety of clinically important forecast assignments based on similar time arrangement information.
Query Data Inconsistency for Business Processes

Business processes are designed to achieve business goals under procedural rules by orchestrating tasks, information and documents. Managing data inconsistency in business processes is a challenging task. If not managed properly, business will face negative financial consequences. From literatures, BPMN modelling approaches deal with inconsistency problem by patterns; data provenance approaches analyze data generated in business process and investigate the reachability between data points. Although substantial works have been done, the data inconsistency problem has not been properly resolved. In particular, it is still lacking of modelling language and resolution for inconsistency caused by multiple starting points of business processes and dynamics of business processes execution. This paper provides data consistency solution in two aspects: a business process modelling in enriched business workflow notation with data states and temporal properties, and a workflow query algorithm to discovery data inconsistency issue.
DL-SLAM: Direct 2.5D LiDAR SLAM for Autonomous Driving

Precisely localizing a vehicle in the GNSS-denied urban area is crucial for autonomous driving. The occupancy grid-based 2D LiDAR SLAM methods scale poorly to outdoor road scenarios, while the 3D point cloud-based LiDAR SLAM methods suffer from huge computation and storage costs. Aiming at the precise real-time LiDAR SLAM for both indoor and outdoor, this paper proposed a direct 2.5D heightmap-based SLAM system. This system extended our previously proposed DLO (the direct 2.5D LiDAR odometry) method by introducing the 2.5D segment features for efficient loop closure detection. We experimented our SLAM method on the KITTI datasets and shown it superior performance compared with the existing LiDAR SLAM methods.
Multi-AUV Fixed-point and Positioning Control Based on Virtual Leader

With the development of AUV, a special task has emerged: if multiple AUVs perform marine target rescue in the way of formation trajectory tracking, they must be stabilized to the target in the desired position and posture, so that the subsequent rescue mission can be smoothly carried out. The multi-AUV fixed-point positioning problem based on the virtual leader can be described: using the control law to enable the multi-AUV to stabilize to the desired target position and target posture. In this paper, the multi-AUV fixed-point positioning control is based on the directional communication topology, using the virtual leader control strategy and guidance-control structure to achieve fixed-point positioning control of multiple AUVs on known trajectories. For the fixed-point positioning control system, it generally deals with some emergency rescue or search round-up situations, so it requires fast response under the premise of stability. In summary, when designing the controller and selecting the control method, a fast response speed is required under the premise of stability. Therefore, chooses a new control method combining linear time-invariant backstep method and linear time-varying sliding mode variable structure method (namely anti-step sliding mode method) to design multi-AUV fixed-point positioning control based on virtual leader. Multi-AUV can reach the desired pose stably, accurately and quickly in the process of trajectory tracking.
Aerial Image Matching Based Relative Localization of a UAV in Urban Environments

This paper presents a method for localizing an Unmanned Aerial Vehicle (UAV) using geo-referenced aerial images, in case of GPS failure. When flying in urban environments in low altitudes the inaccurate GPS localization has become a major problem. The proposed method fuses measurements from inertial sensors and visual odometer and perform image registration process using the images captured by the onboard down looking camera. A pre-built local orthomosaic map with geotagged aerial images is used for the registration. The problem of unreliable matches caused by the dynamic objects is addressed in our approach. The experiment results show that it is possible to localize the UAV in urban environments with a low margin of error by using our method.
A Practical Bayesian Optimization Approach for the Optimal Estimation of the Rotor Effective Wind Speed

Modern wind turbines require careful tuning of controller and estimator parameters. However, tuning requires expert control experience, and is therefore in practice often performed by a trial-and-error brute-force approach. The contribution of this work is twofold. Firstly, a framework for tuning the parameters for conventional control and estimator architectures with Bayesian optimization is proposed. Secondly, the proposed scheme is applied to the problem of tuning Kalman filter parameters for the estimation of the rotor effective wind speed. For accomplishing the beforementioned task, the Bayesian optimization machine learning algorithm uses entropy search as utility function. The NREL 5-MW reference wind turbine is used in high-fidelity simulation software to show the efficacy of the proposed methodology. The Bayesian optimized Kalman filter configuration, is shown to estimate the rotor effective wind speed with a root mean square error smaller than 5 %, with respect to the actual effective wind speed over all load cases.
Satellite Imagery Spectral Bands Subpixel Equalization Based on Ground Classes’ Topology

The technique of multispectral images processing for the spatial resolution equalization is presented. Land cover fuzzy logic classification using ground-truth spectral signatures measurements is performed. Spatial topology relationships were taking into account. Basic types of subpixel connection are allocated. Multiple-valued logic mathematical approach was used for make a decision about class changing according to class similarity matrix. Analysis by modulation transfer function demonstrates increase in physical resolution of the original image after equalization.
TCP BBR for Ultra-Low Latency Networking: Challenges, Analysis, and Solutions

With the new emerging throughput-intensive ultralow latency applications, there is a need for a transport layer protocol that can achieve high throughput with low latency. One promising candidate is TCP BBR, a protocol developed by Google, with the aim of achieving high throughput and low latency by operating around the Bandwidth Delay Product (BDP) of the bottleneck link. Google reported significant throughput gains and much lower latency relative to TCP Cubic following the deployment of BBR in their high-speed wide area wired network. As most of these emerging applications will be supported by Millimeter Wave (mmWave) wireless networks, BBR should achieve both high throughput and ultra-low latency in these settings. However, in our preliminary experiments with BBR over a mmWave wireless link operating at 60 GHz, we observed a severe degradation in throughput that we were able to attribute to high delay variation on the link. In this paper, we show that “throughput collapse” occurs when BBR's estimate of minimum RTT is less than half of the average RTT of the uncongested link (as when delay jitter is large). We demonstrate this phenomenon and explain the underlying reasons for it using a series of controlled experiments on the CloudLab testbed. We also present a mathematical analysis of BBR, which matches our experimental results closely. Based on our analysis, we propose and experimentally evaluate potential solutions that can overcome the throughput collapse without addina sianificant latency.
Dissipative Stabilization of Nonlinear Repetitive Processes with an Iterative Learning Control Application

Repetitive processes arise in the modeling of physical systems and have a 2D systems structure as there are two directions of information propagation, one of which is spatial and the other is time over a finite duration. Recently, a stability theory for nonlinear repetitive processes has been developed. This paper uses nonlinear stability theory to develop a parameterized control law for linear dynamics. Applied to iterative learning control law design, this law allows for gain scheduling tuning to achieve better performance. The new design is illustrated by application to an example where the model representing the dynamics has been obtained by frequency response test data obtained from a physical example.
Asymptotic Average Secrecy Rate for MISO Free-Space Optical Wiretap Channels

Lately, it has been demonstrated that free-space optical (FSO) communication systems are not interception-free. For that reason, the scientific community is paying special attention to study these links from a physical layer security point of view. In this work, some potential eavesdropping attacks are studied for multiple-input/single-output (MISO) FSO communication systems with equal gain combining (EGC) reception over gamma-gamma (GG) and misalignment fading channels. A novel asymptotic solution at high signal-to-noise-ratio (SNR) is obtained to compute the average secrecy rate over turbulence channels. The robustness of FSO technology is once again proven by employing multiple laser sources at the transmitter side and, hence increasing the secrecy rate and obtaining a performance close to the capacity with no eavesdropper.
Eigenvalue Assignment for the Laplacian Matrix of Directed Graphs

This paper considers the problem of designing the edge weights of directed graphs such that their Laplacian matrix has a prescribed spectrum. We provide a parametrization of the Laplacian matrix which is suitable for solving the problem numerically. We show how the edge weights can be further optimized to achieve secondary design goals and give an application example by designing the communication topology of a multi-agent system. Besides the general case of graphs with arbitrarily many vertices, we consider some special cases for small graphs and provide deeper insight into the solution to the problem in these cases.
University of Birmingham Exogenous cueing of visual attention using small, directional, tactile cues applied to the fingertip

— The deployment of visual spatial attention can be significantly influenced in an exogenous, presumably bottom-up manner. Traditionally, spatial cueing paradigms have been utilized to come to such conclusions. Although these paradigms have primarily made use of visual cues, spatially correspondent tactile cues have also been successfully employed. However, one property of tactile cues not thoroughly explored in this context is the influence of their specific directionality on the subsequent deployment of visual attention. Thus, the current study sought to evaluate the potential utility of small, directional tactile cues as a means to exogenously direct visual spatial attention. Tactile cues were employed by a small shearing of the fingertip’s skin in either the leftward of rightward direction. A modified spatial cueing paradigm was used to compare reaction time performance across both traditional-visual and directional-tactile cues at cue-target onset asynchronies of 100, 200, 400 and 800 ms. The results indicated that both visual and tactile cues mediated the deployment of exogenous visual spatial attention. However, differences between the two modalities were observed in terms of both the magnitude and the pattern of the associated cueing effects. Further, there appeared to be a general rightward bias in performance irrespective of cue modality. Overall, the current work offers preliminary evidence that small, directional tactile stimulation may influence the allocation of attention across space in a manner at least partially distinct to traditional visual cueing tasks. Yet, further research will be required to explicitly determine the underlying mechanisms.
Data Analysis of Building Sensors for Efficient Energy Management and Future Trends in the EU

Thermal comfort is an important aspect of home and office environments for maintaining proper human health and wellbeing. This paper presents the results of data analysis techniques employed on measured data of building sensors. The study underscores the importance of data pre-processing in data analysis. The data analysis reveals hidden characteristics of room temperature evolution which are influenced by different physical parameters recorded by sensors installed in a room. This influence is made clearer when seasonal data and different data pre-processing methods are considered. Space heating and cooling accounts for a significant portion of a building's energy consumption. Thus, deep analysis of room temperature data could prove a useful parameter to unlock sustainable energy efficiency measures using future-oriented technologies and solutions, such as Information and Communication Technology (ICT), Internet of Things (IoT), blockchain with smart contracts.