Miracle-dz/UniFAR-FTUs huggingface dataset:
E2E Service Class Mapping in Heterogeneous IOUT: SDN-Based Architecture

Over the past few years, the Internet-of-Underwater-Things (IoUT) became a prominent technology for investigating the underwater conditions. Moreover, the communication networks became heterogeneous over the past few years due to deployment of various technologies between the source and target nodes on the end-to-end (E2E) path. In heterogeneous IoUT networks, the nodes on an E2E path may consist of different traffic classes (TCs). Hence, the legacy model of networking and the default software-defined networking (SDN) will not be effective with inter-domain communication and E2E mapping of TCs. In this study, we propose a state-of-the art methodology utilizing SDN to fulfill the E2E service needs among domains with various TCs on the E2E path for the heterogeneous IoUT networks. We propose a module in the controller based upon a multi-criteria decision-making strategy i.e. simple additive weighting module (SAWM) to select a significant service class from the TCs. Moreover, we map the TCs in the global programmable controller (GPC) to meet an E2E quality-of-service (QoS) that meets application demands. Finally, we validate the supremacy of our proposed scheme through comparisons for E2E TCs mapping in terms of network performance metrics. The proof-of-concept experiments validate the effectiveness of the proposed strategy.
Power-efficient and Distance-adaptive Disaster Protection for Service Function Chain Provisioning

Network function virtualization (NFV) in Elastic Optical Inter-DataCenter Networks (EO-DCNs) enables a flex-ible, adaptive, effective, and economic network services de-ployment and upgrade. However, it is facing critical threats from large-scale network failures, due to natural disasters. This is driving the need for efficient network protection schemes of service function chain (SFC) provisioning. In this paper, we investigate the disaster-resilient SFC provisioning problem leveraging power-efficient path protection with distance-adaptive modulation format (MF) assignment, concerning virtual network function (VNF) placement, SFC mapping, path protection, and spectrum allocation simultaneously. An integer linear program (ILP) model is formulated to jointly minimize power consumption and spectrum usage, subject to disaster resilience. A heuristic algorithm is also developed for the sake of scalability. Numerical Simulation results demonstrate that the proposed disaster pro-tection schemes enable saving up to 32.05 % power consumption.
Machine Learning Classifiers Based on HoG Features Extracted from Locomotive Neutral Section Images

This paper presents a comparative study on machine learning algorithms for neutral section image classification. The classifiers are trained by employing the Histogram of Oriented Gradient features that are extracted from the neutral section dataset [1]. A neutral section is a phase break that is used on the Transnet freight rail system to separate the single-phase supply from the 25kV three-phase overhead traction supply. The 25kV is a stepped-down voltage from an 8SkV three-phase supply coming from the national grid. While the main purpose of the neutral section is to separate phase voltages, electric locomotives can traverse through these phases by switching On and Off. This auto-switching is possible through induction magnets installed in between the rails and with magnet detection sensors installed underneath the locomotives. However, a computer vision model has been developed, trained, and tested with a neutral section dataset containing images having open and close markers [1]. This paper, therefore, utilises this dataset to provide performance comparison on several machine learning classification algorithms viz. Decision Tree, Discriminant Analysis, Support Vector Machine, K-Nearest Neighbors, Ensemble, Naïve Bayes, and Convolutional Neural Network. A confusion matrix, F1-measure and computation time are employed to measure the performance of each classifier. The MATLAB Classification Learner application was used to obtain the results. The results show that the Linear Support Vector Machine performs best when considering performance and prediction speed. The Linear Support Vector Machine achieved a training accuracy of 93.40% with a test accuracy reaching 94% at a prediction speed of 75 objects per second (computation time).
Generating Minimal Controller Sets for Mixing MMAC

Multiple model adaptive control (MMAC) is an adaptive control method designed for plant parameter uncertainty given both linear and non-linear plant models. For a system subject to varying operating conditions, the number of controllers necessary to guarantee stable control under nominal-plant uncertainty, or under multiple operating conditions, are both unknown. We propose a learning-based controller synthesis approach that can guarantee stability of a system subject to varying operating conditions. We adopt a convex hull (CH)-based multiple-model controller estimation algorithm that only requires N + M + 1 controllers, where N is the dimension of a compact nominal uncertainty parameter set Θ, and M is the number of additive faults. We empirically validate this result for a quadcopter, which is subject to faults in rotors and sensors as well as to adverse wind conditions.
Consensus of Open Multi-agent Systems over Dynamic Undirected Graphs with Preserved Connectivity and Collision Avoidance

We address the consensus problem with collision avoidance for multi-agent systems under limited sensing ranges, in the case where new interconnections and agents may be added at any time. The graph topology is represented by a dynamic undirected graph, assumed to be connected only at an initial time, and the open multi-agent system is modeled via a multidimensional impulsive switched representation. We propose a barrier-Lyapunov-function-based consensus control law that guarantees inter-agent collision-avoidance and connectivity maintenance and, relying on the edge-agreement framework, we establish almost-everywhere asymptotic stability of the consensus manifold. The obtained results are also readily applicable to closed multi-agent systems with edge addition. A numerical simulation illustrates the effectiveness of the proposed approach.
Assessment of Quadrotor PID Control Algorithms using six-Degrees of Freedom CFD simulations

The evolution of technology has made increasingly advantageous the introduction of Unmanned Aerial Systems (UASs) in various applications, especially by exploiting their ability for autonomous flight. This paper presents an innovative approach to simulating UAS maneuvers that integrates a Computational Fluid Dynamics (CFD) model and a closed-loop control algorithm for both position and attitude dynamics. We chose the Proportional-Integrative-Derivative (PID) controller for this preliminary research activity because of its simple implementation and widespread employment in commercial autopilot systems. The numerical simulation of the UAS aerodynamics allows for performing an accurate analysis in critical situations. These include, for example, ground effect or wind gusts scenarios, which require an enhanced propulsive model to capture the interaction between vehicle dynamics, aerodynamics, and environmental conditions. The coupled CFD/PID framework can be a virtual testing environment for UAS platforms. Here we report on its validation. The paper compares such an innovative in-the-loop CFD approach and a classical simplified propulsive model that adopts constant thrust and torque coefficients.
On the problem of weight factors and functions in fusion applications

Sensor fusion plays an important role in many applications. The abundance of sensory data from different sources does not guarantee better quality information. It is reasonable to assume that not all sensors have the same levels of accuracy and precision. This needs to be taken into consideration when sensor fusion or integration is performed. In many applications it is difficult to measure the sensors’ accuracy and precision, which could be dynamic variables. This paper explores different approaches to address the problems related to competitive sensor fusion. The paper focuses on optimal weight assignment and nonlinear weight functions using artificial neural networks as well as simultaneous fusion and calibration of multi-sensor systems. Optimal weight assignment is formulated as a constrained optimization problem and solved using the Karush-Kuhn-Tucker conditions to minimize the variance. Single and multi-layer artificial neural networks are used to determine linear and nonlinear weight functions for sensor fusion. Although they require training data, neural networks are particularly effective when little statistical information about the sensors is available. Several examples are presented to demonstrate the proposed methods.
Practical stability and attractors of systems with bounded perturbations

The classical Lyapunov analysis of stable fixed points is extended to perturbed dynamical systems that may not have any fixed point due to perturbations. Practical stability is meant here to assess the convergence of such systems. This is achieved by investigating a parametric optimization problem encoding some worst-case Lie derivative. Key properties of this parametric optimization problem are formulated. The proposed framework is finally applied to a class of perturbed linear systems tracking a highly nonlinear reference.
Adaptive Greedy Quasi-Newton with Superlinear Rate and Global Convergence Guarantee

Though quasi-Newton methods have been extensively studied in the literature, they either suffer from local convergence or use a series of line searches for global convergence. In this work, we propose a line search free greedy quasi-Newton method with adaptive steps and establish explicit nonasymptotic bounds for both the global convergence rate and local superlinear rate. Our novel idea lies in the design of multiple greedy quasi-Newton updates to control the Hessian approximation error and a simple mechanism to adjust stepsizes to ensure function improvement per iteration. The global superlinear convergence 1 of our method is validated via numerical experiments.
3DLoc: A Non-Line-of-Sight 3D Indoor Localization System in Wireless Sensor Networks

Indoor localization systems in wireless sensor networks (WSNs), such as WiFi BLE, are still not widely adopted despite years of continuous research. One possible impediment is that many prior systems perform worse in non-line-of-sight (NLoS) indoor scenarios than in line-of-sight (LoS) scenarios or even fail to work. Nevertheless, realizing localization in both LoS and NLoS scenarios is indispensable for a viable system working in indoor environments. Since existing state-of-the-art solutions mainly aim to localize a target in LoS indoor scenarios, we present 3DLoc that uses multipath channel parameters to achieve 3D localization with a single AP in indoor NLoS scenarios. 3DLoc introduces deployable and simple custom-designed radio relays to assist localization, which can receive signals from the target and forward them to the AP in NLoS scenarios. Field trials conducted in a real NLoS scenario show that our system achieves a median 3D localization accuracy of 86 cm with a uniform circular array (UCA) in NLoS conditions.
SNR Based Detection and Estimation of AWN Channel Parameters Changes

Here we consider the case of a feedback control loop stabilized over an additive white noise (AWN) channel subject to a Signal-to-Noise Ratio (SNR) constraint. On that premise the initially known AWN channel may change its mean and/or variance due to changing transmission circumstances. Based on the channel input measurement we propose here the detection of such changes and the estimation of the new mean and/or variance values based on the channel SNR estimation, in particular for the AWN channel variance changes. We study the cases of the AWN channel when it is assumed to be located either over the direct path (between the controller and the plant model), or the feedback path (between the plant and the controller model).
A Compact Multiband FICA Antenna with Coupled-Fed Mechanism for Smartphone Applications

A novel coupled-fed Folded Inverted Conformal Antenna (FICA) with compact size for smartphone application is presented in this paper. The proposed antenna is mounted at the bottom side of the PCB, which consists of a closed slot. , a T-shaped driven branch for the coupled-fed, a shorting pin, and an impedance matching network. Three resonant modes have been generated to cover the lower band from 824 to 960 MHz and upper band from 1.71 to 2.82 GHz. In addition, both of the clearance and height of the proposed antenna are only 4 mm, which makes the antenna very compact, low-profile and suitable for the narrow frame smartphone applications.
A new accelerated gradient-based estimating sequence technique for solving large-scale optimization problems with composite structure

Various problems arising in control and data analysis can be formulated as large-scale convex optimization problems with a composite objective structure. Within the black-box optimization framework, such problems are typically solved by using accelerated first-order methods. The celebrated examples of such methods are the Fast Gradient Method and the Accelerated Multistep Gradient Method, designed by using the estimating sequences framework. In this work, we present a new class of estimating sequences, which are constructed by making use of a tighter lower bound on the objective function together with the gradient mapping technique. Based on the newly introduced estimating sequences, we construct a new method, which is also equipped with an efficient line-search strategy that provides robustness to the imperfect knowledge of the Lipschitz constant. Our proposed method enjoys the accelerated convergence rate, and our theoretical results are corroborated by numerical experiments conducted on real-world datasets. The experimental results also demonstrate the robustness of the initialization of the proposed method to the imperfect knowledge of the strong convexity parameter of the objective function.
Energy Saving on Constrained 12-Leads Real-Time ECG Monitoring

Continuous real-time electrocardiogram (ECG) monitoring can detect arrhythmia and provide early warning for heart attacks. Effective power management signals and controlling the mode of operation to reduce the need for full fidelity ECG signal. This work studies the impact of the base-delta compression technique for different cardiac conditions on power consummation. It also aims to evaluate operational strategies and their effect on the battery life when the ECG patch can switch between different operating modes (e.g., varying the number of leads according to the cardiac conditions). We use a binary classifier to inform the decision of switching between different operational strategies. Both scenarios are evaluated in terms of execution time, Bluetooth Low Energy (BLE) communication airtime, power consumption, and energy-saving ratios on a Texas Instruments CC2650 Micro-controller Unit (MCU). We compare the performance of the base-delta compression and changing the mode of operation scenarios on various cardiac abnormalities. Performance evaluation shows that operational strategies outperforms data compression in power saving for normal ECG readings by a double fold. In contrast, operational strategies incurs an additional overhead of 1011 ms during an abnormal status. However, base-delta satisfies the embedded platform constraints on execution time and airtime with 25 ms and 20 ms, respectively in the MCU environment.
Determination of Cointegrating Rank in Partially Non-Stationary Processes Via a Generalised Von-Neumann Criterion

In this paper we show that it is possible to characterise the cointegrating structure of a partially non-stationary, cointegrated, I(1) time series via the canonical correlations between the future and, the present and past, of the first differences of that series. This leads to a consideration of different model free non-parametric methodologies for identifying the cointegrating rank. An adaptation of existing techniques using a novel method of spectral estimation gives rise to both a new applied tool and an alternative analytical framework that unifies current hypothesis-testing approaches. An investigation of the eigenstructure of a multivariate version of von-Neumann's ratio also leads to the development of an entirely new model free cointegrating rank selection criterion. All the procedures considered are easily implemented and the practical relevance of the theoretical results obtained, which are founded on asymptotic arguments, is demonstrated by means of a simulation study. Copyright Royal Economic Socciety 2004
Comparison of Drone Classification Accuracy for CNN Models Using UWB Radar

We have previously proposed a method for recognizing various drones from images of ultra-wideband (UWB) radar range profile by using a convolutional neural network (CNN). In general, the classification accuracy for CNN tends to improve as the layer becomes deeper. Therefore, in this paper, we examine the classification accuracy of drones for four types of CNN models (AlexNet, GoogleNet, ResNet-50, and ResNet-101) with different depths. We have investigated the classification performances for five types of drones (Matrice 600, 3DR Solo, Phantom 3, Mavic pro, and Bebop drone) and a radio-controlled flapping bird (Bionic bird) by each of the CNN models. As a result, we have confirmed that all the CNN models can recognize each target with an average accuracy of 90% or more. Furthermore, we have also confirmed that ResNet-50 consisting of 50 layers can recognize all the targets with equal or better accuracy than that by AlexNet which consists of 8 layers.
Trustworthy Communication Channel for the IoT Sensor Nodes Using Reinforcement Learning

IoT has been deployed in different fields to enhance the quality of human life. However, the IoT has become an appealing source for intruders to penetrate the smart premises of users. As security technology grows, cybercriminals also enable themselves to launch the most sophisticated attacks. Therefore, to maintain the protection of IoT devices, there is need for a responsive security system that can efficiently encounter novel attacks. This paper proposes a security mechanism to tackle cyberattacks by employing Reinforcement Learning (RL). Through RL, we can efficiently detect any ordinary or novel attacks as the RL agent learns by its own without human instructions. So, it educates the algorithm against any sophisticated attack. Dataset UNSW-NB is incorporated to evaluate the performance of the proposed study. The performance and detection rate of the model was enhanced selecting optimal features of the dataset. The proposed RL approach achieves an average accuracy of 97.29%. Results reveal that the proposed study has the potential to be deployed as a security mechanism against cyberattacks.
Exploring doctors' satisfaction with electronic health records and their features: A nationwide survey study

Electronic Health Records (EHR) have revolutionized care delivery. Studies focus more on improving patients’ experiences as direct consumers of the technologies provided. However, doctors’ satisfaction with the systems remains of similar importance. In this study, we explore the services and usages of EHRs that impact providers’ satisfaction with the use of such computerized systems in their organizations. We used nationally representative data (National Electronic Health Records Survey extended survey) to analyze the EHR’s features and services impacting providers’ satisfaction with the computerized systems used. We ran all statistical analyses using Python 3.7, and (n=1,524 doctors) responded to the surveys. Most respondents were satisfied with the EHR systems used (72.15% of all respondents). We explored the systems’ factors that impacted this satisfaction. We found that strategies that allow patients to view, download, exchange, or upload health information through online medical records made doctors more satisfied with EHRs (OR=3.68, 4.05, 4.53, and 5; P=0.002, 0.01, 0,005, 0.01). However, the certification criteria of EHR tools did not significantly impact their satisfaction (P=0.13). Recording information, safety, and quality measurement did not significantly correlate with the doctors’ satisfaction. However, patient involvement and communication-related usage greatly impacted it. For instance, doctors who use EHRs to exchange secure messages with their patients and create shared care plans that are available across clinical care teams were more satisfied with the computerized systems (OR=3.54, 3.70; P=0.001, respectively 0.003). This study serves as an excellent reference to the EHR designers and organizations that adopt EHR to organize information flow within healthcare organizations. As providers’ EHR satisfaction is principally correlated with the ability of the systems to support communication with patients and teams, designers and EHR vendors should consider designing the systems to satisfy the needs of the users. It is time for them to consider interoperability and connectivity as design opportunities and a must-have. To conclude, this study revealed that higher levels of satisfaction were associated with the perceived positive effect of EHR on patient’s access to and with the management of the information flow within the care team. Future research should focus on improving the design and implementation of EHRs to score better performance with fewer errors and optimized satisfaction of all healthcare processes actors.
Multilevel Adaptive Algorithm for Multiscale Analysis of Heterogeneous Materials

AbstractA multilevel adaptive FEM is proposed for multiscale mechanical analysis of heterogeneous materials. The algorithm is developed based on the extended multiscale FEM (EMSFEM), which has been proven to be an efficient method for multiscale mechanical analysis. A residual force-based error estimate is introduced, which is able to detect both global and local errors economically. The concept of microscopic numerical base function (NBF) is introduced, which can be used to refine the local results at the regions with a high-stress gradient level by level. Two efficient methods for updating the microscopic NBFs were developed, i.e., the hierarchical NBFs scheme and the residual force NBFs scheme. A hierarchical linear solver is designed to solve the multilevel equation system in each iteration step. In the algorithm, the calculations at the current iteration step take full advantage of the results of previous iteration steps, which is helpful to save computational resources. The numerical examples demons.
Image Measurement Method for Automatic Insertion of Forks into Inclined Pallet

In order to insert a fork into a hole of a pallet by a forklift located in front of a pallet, it is necessary to control the height position, reach position, and tilt angle of the fork to match the position and orientation of the hole of the pallet. In order to make AGF (Autonomous Guided Forklift) do this automatically, we propose an image measurement method to measure the pitch inclination of the pallet in the camera coordinate system from an image obtained by using a wide-angle camera. In addition, we propose an image measurement method to easily acquire the calibration information between the camera coordinate system and the fork coordinate system necessary to apply the measurements in the camera coordinate system to the fork control. In the experiment space, a wide-angle camera was fixed at the backrest of a reach type forklift. The wide-angle images taken by placing a pallet in front of the camera were processed. As a result of evaluating the error by comparing the image measurement value with the hand measurement value when changing the pitch inclination angle of the pallet, the relative height of the pallet and the fork, and whether the pallet is loaded or not, it was confirmed that the error was within the allowable range for safely inserting the fork.