Miracle-dz/UniFAR-FTUs huggingface dataset:
Vibration suppression of a pneumatic drive flexible manipulator using adaptive phase adjusting controller

Generally, a low damping system has a small phase margin. One should deal with the problems of long time delay and unmodeled dynamics for active vibration control of a low damping system sometimes. For a pneumatic drive flexible manipulator system, time delay or phase lag and unmodeled nonlinear dynamics problems have raised the level of concern. It is vital but difficult to regulate the phase of vibration controller, to compensate for the time-varying phase lag or time delay due to the gas compression and parametric uncertainty of the pneumatic control system. The objective of this investigation is to formulate an active vibration control law with practical approach of phase adjusting for a pneumatic drive flexible manipulator system. A control strategy named adaptive phase adjusting control is designed and applied. The adaptive phase adjusting controller is implemented by using the phase angles as explicit parameters. These parameters are tuned online, depending on the corresponding control performance index. To evaluate the control performance of the designed adaptive phase adjusting technique, experiments are conducted on a pneumatic drive flexible manipulator experimental setup. The experimental results demonstrate the adaptive phase adjusting controller has the ability to optimize the phase angle accordingly. Vibration suppression is accomplished by using the designed adaptive phase adjusting controller, even with time delay and unmodeled dynamics in the system.
Explainable Prediction of Academic Failure Using Bayesian Networks

Currently, academic dropout is a crucial problem of higher education institutions in Chile due to the high social and economic costs that it entails. Usually, dropout prediction is performed using computational methods analyzing the student’s personal information as well as their academic indicators during their studies, which require a period of time to know them. Moreover, this event is highly correlated to the academic failure of several key courses. An attractive alternative is to apply an initial test to achieve this prediction without requiring completion of the typical study periods. In this work we propose the use of Bayesian networks to make the explainable predictions of academic failure from an initial test considering the incorporation of knowledge from experts in educational management in the computational model. In particular, different configurations of Bayesian network models are applied to first-year engineering students at a Chilean university during the year 2019. The results indicate that this approach generally obtains 85% accuracy where the Bayesian networks show complex relationships between the variables. This work shows that the Bayesian network model can eventually detect the students most likely to drop out at the beginning of their studies. As future work, we plan to incorporate external variables as the student socio-economic data.
A new calibration method for tri-axial field sensors in strap-down navigation systems

This paper presents a novel calibration method for tri-axial field sensors, such as magnetometers and accelerometers, in strap-down navigation systems. Strap-down tri-axial sensors have been widely used as they have the advantages of small size and low cost, but they need to be calibrated in order to ensure their accuracy. The most commonly used calibration method for a tri-axial field sensor is based on ellipsoid fitting, which has no requirement for external references. However, the self-calibration based on ellipsoid fitting is unable to determine and compensate the mutual misalignment between different sensors in a multi-sensor system. Therefore, a novel calibration method that employs the invariance of the dot product of two constant vectors is introduced in this paper. The proposed method, which is named dot product invariance method, brings a complete solution for the error model of tri-axial field sensors, and can solve the problem of alignment in a multi-sensor system. Its effectiveness and superiority over the ellipsoid fitting method are illustrated by numerical simulations, and its application on a digital magnetic compass shows significant enhancement of the heading accuracy.
Deep Learning-based Task Offloading and Time Allocation for Edge Computing WBANs

The limited energy resource and low computing capability of tiny sensor nodes have been longstanding challenges for the deployment of Wireless Body Area Networks (WBANs). The emergence of Wireless Power Transfer (WPT) and Mobile Edge Computing (MEC) provide a potential solution. Therefore, we devote this paper to developing an efficient task offloading and time allocation scheme based on WPT and MEC for WBANs. Technically, we first propose an MEC-aided system model with multiple WBANs where WBANs can harvest energy from Radio Frequency (RF) signals wirelessly and an edge server can process the tasks offloaded by WBANs. We formulate an non-convex optimization problem with the objective to maximize the computation rate, and propose a deep learning-based task offloading and time allocation algorithm, named DNN-TOTA. DNN-TOTA adopts the Deep Neural Network (DNN) and the Order-Preserving Quantization (OPQ) method to generate the candidate offloading decision sets, and uses the convex optimization to solve the time allocation problem. The simulation results demonstrate that the computation rate of our proposed DNN-TOTA can reach to 98% of the optimal solution.
Codebook Design for Large Reconfigurable Refractive Surface Enabled Holographic MIMO Systems

Holographic multiple-input multiple-output (HMI-MO) has recently motivated its potential use to handle the exponentially increasing data transmission demands by achieving a spatially continuous aperture. With densely-packed metamaterial elements, the reconfigurable refractive surfaces (RRSs) serving as antennas emerge to enable HMIMO. In this paper, we consider a multi-user system where an RRS is employed as the transmit antenna array at the base station (BS). To avoid the high overhead of acquiring perfect channel state information (CSI), a codebook and beam training mechanism is required to develop the beamforming scheme. Given the large physical dimension of the RRS, users are likely to distribute in both the near field and the far field of the BS, making the codebook design more difficult. To address this issue, we design a hybrid near-far field codebook which applies to all users in any location with low overhead. To further reduce the training overhead, each codeword is designed to cover multiple spatial areas, enabling a multi-user beam training mechanism which can be performed for all users simultaneously. Under a general setting including both the near-field and far-field users, simulation results show that the proposed scheme significantly reduces the overhead and achieves a higher sum rate compared to the state-of-the-art codebooks, which performs very close to that of the perfect CSI case.
Testing for Coincidences between Time Varying Poisson Processes

Multivariate point processes have a long history of application to areas such as queueing theory, neural coding, high frequency finance. However the dominant models exclude coincidences i.e. of events in two different point processes occurring simultaneously. Here we consider testing for simultaneous occurrence of events between two point processes. Though this kind of problem occurs widely in practice it has been largely ignored. We develop a simple test based on cross covariance type statistics and illustrate it with simulations and on real data.
Energy Efficiency and Power Allocation Optimization in Hardware-Impaired Full-Duplex Access Point

Full-duplex (FD) communications have been recognized as one of the promising wireless transmission candidates for the future of wireless communication and networking technologies, thanks to their ability to greatly improve spectral efficiency (SE) and dramatically enhance energy efficiency (EE). In this paper, we study the influence of hardware impairments (HWIs) on single-input single-output (SISO) FD access point (AP). More precisely, we study the EE when the system's terminals have impaired transceivers. Optimization problem for EE maximization is formulated to fulfill quality of service (QoS) and power budget constraints. We propose an algorithm to solve the optimization problem by using the fractional programming theory and Karush-Kuhn-Tucker (KKT) conditions technique. The results unveil that excellent power allocation is crucial in FD communication networks and the proposed algorithm outperforms the maximum power transmission scheme. The results also reveal that HWIs degrade both EE and SE performance. Moreover, at high power region, no further improvements can be obtained by increasing the transmission power. Finally, we assume that all fading channels demonstrate Nakagami-m distribution.
Invariant Sets for Assume-Guarantee Contracts

Contract theory is a powerful tool to reason on systems that are interacting with an external environment, possibly made of other systems. Formally, a contract is usually given by assumptions and guarantees, which specify the expected behavior of the system (the guarantees) in a certain context (the assumptions). In this work, we present a verification framework for discrete-time dynamical systems with assume-guarantee contracts. We first introduce a class of assume-guarantee contracts with their satisfaction semantics parameterized by a time-horizon over which assumptions are evaluated. We then show that the problem of verifying whether such contracts are satisfied is equivalent to show the existence of a positive invariant set for an auxiliary system. This allows us to leverage the extensive literature on invariant set computation. A simple illustrative example is provided to show the effectiveness of our approach.
Real-time Data Acquisition and Processing under Mobile Edge Computing-assisted UAV System

Owing to the rapid development of unmanned aerial vehicles (UAVs), UAV-enabled data collection has emerged as a promising technology. However, for the scenarios where the UAVs are dispatched to gather surrounding information actively and dynamically, the existing data collection methods can hardly fulfill the corresponding demands. To this end, we aim to study the paradigm data acquisition, where the UAV dynamically gathers information by on-board sensors. In this paper, we consider the scenario where a UAV acquires data in real time, which needs to be timely processed with the assistance of a mobile edge computing server. To address the real-time data acquisition characteristics, we construct a novel data acquisition rate model, which is with respect to the UAV speed. Next, we formulate a UAV energy consumption minimization problem that jointly considers UAV trajectory, transmission power, and CPU frequency. To tackle the highly complex problem, we propose an efficient iterative algorithm, and rigorously derive a closed-form solution for the UAV transmission power and computation resources allocation. With the obtained numerical results, we further validate the superiority of the proposed system design and the effectiveness of our algorithm against benchmark schemes.
A Novel General Inverse Kinematics Optimization-Based Solution for Legged Robots in Dynamic Walking by a Heuristic Approach

This work presents a new optimization-based solution to the Inverse Kinematics Problem (IKP) of legged robots, including a modified Walking Pattern Generator that automatically avoids singularity configurations regarding position. The approach uses a numeric constrained problem solved with heuristic algorithms, where the joint vector is calculated to minimize the position errors, while orientation errors are handled as equality constraints, and threshold values are used to set the maximum error for each foot along the trajectories. The optimization model can generalize the IKP for robots with any number of legs and any number of poses within the physically consistent trajectories for the Center of Mass and the feet, considering the dynamics of the robot for a stable walking. The case study was a 12-DOF biped robot, and the resulting joint trajectories were validated by a dynamic simulation, using a Gazebo-ROS platform, where the walking was successfully performed without requiring a feedback control for correcting the torso tilt, showing the solution quality.
DRL-based Channel Access in NR Unlicensed Spectrum for Downlink URLLC

To improve the capacity of cellular systems without additional expenses on licensed frequency bands, the 3rd Gen-eration Partnership Project (3GPP) has proposed New Radio Unlicensed (NR-U). It should be noted that each node in NR-U has to perform the Listen-Before- Talk (LBT) operation before transmission to avoid collisions by other unlicensed radio access technologies (e.g., WiFi). Thus, packets transmissions are prone to delay due to the LBT channel access mechanism. How to achieve Ultra-Reliable and Low-Latency Communications (URLLC) requirements in NR-U networks under the coexistence with WiFi networks is of importance and extremely challenging. In this paper, we develop a novel deep reinforcement learning (DRL) framework to optimize the downlink URLLC trans-mission in the NR-U and WiFi coexistence system through dynamically adjusting the energy detection (ED) thresholds. Our results have shown that the NR-U system reliability has been improved significantly via the DRL compared to that without learning approaches, but with the sacrifice of WiFi system reliability. To address this, we redesigned the reward to take fairness into account, which guarantees the WiFi system reliability while improvina the NR- U system reliability.
CrossCas: A Novel Cross-Platform Approach for Predicting Cascades in Online Social Networks with Hidden Markov Model

Information sharing through online social networks (OSNs) facilitates quick discovery and consumption of information online. Many OSNs such as Facebook, Twitter provide resharing or reposting features, which allows users to share others' content with their own friends or followers. As content is shared from person to person, cascades of information-sharing can occur. There are many existing works focusing on analyzing and characterizing the cascades in OSNs. However, previous works focus on the analysis and characterization of cascades without providing a solution to accurately predict cascades. Although some methods for cascade prediction have been proposed recently, their methods work in social networks such as Facebook (or Twitter), and do not work well simultaneously in multiple OSNs such as Software Social Network (SSN) GitHub, Twitter and Reddit because GitHub, Twitter and Reddit have different social activity patterns. In this paper, we first perform a thorough analysis of cascades in multiple OSNs: GitHub, Twitter and Reddit, and identify the cascades of information-sharing. We then propose CrossCas, a novel cross-platform approach for predicting cascades in multiple OSNs with Hidden Markov Model (HMM). The experimental results show that our proposed method achieves high performance.
Computer Vision-Driven Non-Destructive Multi-Layered 3D Reconstruction with Broadband Terahertz and Millimeter-Wave Imagers

This paper reports on combining computer vision-driven simple 3D reconstruction and broadband non-destructive terahertz and millimeter-wave imaging. The presenting technique visualizes the arbitrary hierarchical 3D images, based on a silhouette reconstruction method, of multi-layered target objects concealed with opaque coatings. The use of carbon nanotube (CNT) film allows high-efficient broad absorption of terahertz and millimeter-waves. Employing CNT film-based broadband sensor thus facilitates multi-wavelength testing, and detecting differences in the transmittance of the target against each wavelength irradiation results in the visualization of respective components.
Opacity and its Trade-offs with Security in Linear Systems

Opacity and attack detectability are important properties for any system as they allow the states to remain private and malicious attacks to be detected, respectively. In this paper, we show that a fundamental trade-off exists between these properties for a linear dynamical system, in the sense that one cannot have an opaque system without making it vulnerable to undetectable attacks. We first characterize the opacity conditions for the system in terms of its weakly unobservable subspace (WUS) and show that the number of opaque states is proportional to the size of the WUS. Further, we establish conditions under which increasing the opaque sets also increases the set of undetectable attacks. This highlights a fundamental trade-off between security and privacy. We demonstrate our results on a team of delivery UAVs.
Statistical Analysis of Remote Health Monitoring Based IoT Security Models & Deployments From a Pragmatic Perspective

Remote health monitoring-based Internet of Things (IoT) network security is a multi-domain task, that involves identification of network attack, evaluation of mitigation strategies, design of performance aware data security models, integration of privacy models, and modeling of device-level security methods. Internal designs for each of these models is highly complex, and varies in terms of quantitative & qualitative performance measures. This is due to their variation in terms of design nuances, functional advantages, context-based limitations, and possible deployment-specific future scopes. Due to this variation, it is highly ambiguous to select these models for performance-specific IoT deployments. Moreover, these models also vary in terms of security level, Quality of Service (QoS) parameters, scalability performance, computational complexity, deployment costs, and other performance metrics. Thus, to identify optimum models, researchers & network designers are required to test & validate multiple security models for their deployments. Due to which, the cost & time to market for IoT devices is increased, thereby affecting viability of IoT products. To overcome these selection issues, an empirical survey of different IoT security models including block-chains, encryption techniques, hashing models, privacy preservation techniques, machine learning based security methods, etc. are discussed in this text. This text also discusses various attack mitigation models that provide node-level security, network-level security, physical security, & route-level security. This discussion will assist in initially evaluating different operating characteristics of these models, which will allow readers to identify most suited models for their application-specific use cases. This article also assesses the models’ performance in terms of computational latency, energy consumption, security levels, deployment complexity, and scalability measures. These metrics are compared between different security models, which will further assist readers to identify optimum models for their performance-specific use cases. To further assist in model selection, this text proposes evaluation of a novel IoT Security Performance Rank (ISRP), that combines various performance metrics to form a singular rank which can be used to describe overall performance of these models. Readers will be able to consider optimal security approaches for new and current IoT installations based on this ranking.
Trajectory Optimization for Fast Sensor Energy Replenishment using UAVs as RF sources

The problem of the lifetime of connected objects, in most use cases (Industrial Internet of Things (IIoT), disaster management, etc.) is an essential element of the proposed solutions. Radio frequency (RF) harvesting of sensor batteries is an attractive solution, however, it does not scale up if it has to be done by human operators, and becomes impossible if the objects are located in unreachable places. An innovative solution consists of using fleets of drones to take care of this regular recharge. In this paper, we focus on the self-organised deployment of a fleet of drones to solve this problem, taking into account the multiple constraints involved. We propose a two-step optimization framework based on an optimal orchestration solution to reduce the recharging time of a complete sensor system, by optimizing the number of drones, the overall flight time and their energy consumption. We illustrate the performance of our framework that ensures the drones avoid conflicts to guarantee a higher energy harvesting efficiency (establishment of optimal drone positions and planning of the global flight plan).
Artificial Noise Assisted Interference Alignment for Physical Layer Security Enhancement

Secure transfer of wireless information is becoming a critical issue in multi-user interference networks. In this paper, we consider secure transmission from a source (Alice) to a legitimate destination (Bob), coexisting with a passive eaves-dropper (Eve) and $K$ transceiver pairs. By assuming that only statistical channel state information (CSI) of Eve and local CSIs of legitimate users are known, a secrecy beamforming scheme with artificial noise (AN) is designed for secure transmission, and a modified interference alignment (IA) scheme is proposed for secrecy enhancement. Unlike the conventional AN-aided IA approaches which may lead to private signal cancellation, we propose a novel design modification with security protection. Moreover, a definite connection between improperness and in-feasibility of IA is established, to provide guiding insights on IA requirements. Based on a strict mathematical analysis, we further characterize the impact of transmit power on transceiver design and secrecy performance. Numerical results confirm that our design enables high transmission security with performance guarantee, and thus is suitable and stable for physical layer security (PLS) in multi-user interference networks.
Assessing the combination of differential flatness and deterministic automata for controllable hybrid systems

Hybrid automata are a powerful model class to analyze and control technical systems with nonlinear continuous dynamics and discrete-event dynamics. However, due to their complexity, controllability is generally hard to achieve in hybrid automata, although it is a desirable property of engineered systems. In the present paper, we assess a novel model class of controllable deterministic hybrid automata, which we call Flat Hybrid Automata. This model class is based on the concept of differential flatness for the nonlinear continuous dynamics and on the property of determinism and controllability for the discrete event dynamics. We review the formal definition and we give the respective conditions and a proof for its controllability. An electrical network example illustrates the methodology and shows its potential regarding modelling, control and system design.
A MPI programming model for fast bird nest detection on the railway catenary

We propose a MIP programming model for the bird’s nest detection on the railway catenary, which performs coarse-to-fine strategy based on a cascaded YOLO network, and calculates the coarse-level and fine-level detection in parallel for different detected images. Due to the optimization of the parallel pipeline acceleration model, the deep learning network has a running speed equivalent to that of the single-stage network, which can perform real-time detection of bird’s nest.