Miracle-dz/UniFAR-FTUs huggingface dataset:
A Learning-based Methodology for Scenario-aware Mapping of Soft Real-time Applications onto Heterogeneous MPSoCs

Soft real-time streaming applications often process input data that evoke varying workloads for their tasks. This may lead to high energy consumption or deadline misses in case their mapping onto a heterogeneous MPSoC target architecture is not adapted, e.g., when tasks with high execution times for the current input are assigned to resources of low computational power. To handle the vast variety of different input data, we propose to cluster data with similar execution characteristics into so-called data scenarios for which we determine specialized mappings by performing a scenario-aware design space exploration (DSE). A runtime manager (RTM) uses these mappings to adapt the execution of the running applications to their upcoming input by first identifying their best-suited scenarios. Subsequently, the RTM selects mappings considering their identified scenarios, which minimize the total number of deadline misses and the consumed energy. We embed the RTM into hybrid application mapping (HAM); ergo, performing time-consuming optimizations offline. In this article, we propose a novel data-scenario-aware HAM methodology that can cope with multiple applications and comprises two novel scenario-based mapping selection algorithms: Inter-Application Resource Mediation Mapping introduces barely any runtime overhead. Adaptive multi-app mapping selection is highly adaptive to changes in the application workload but imposes a small runtime overhead. Our HAM approach is fully automated and uses machine-learning techniques to learn the selection of suitable mappings from training data sequences at design time. Experiments on three differently complex target architectures show that our proposed approach consistently outperforms existing state-of-the-art solutions regarding the number of deadline misses and consumed energy.
Concentric Annular Leaky Wave Antenna Array and Its Application to Self-Sensing Smart Antenna for Radio Wave Coverage in 2-D Confined Spaces

A beam-changeable concentric annular Leaky-Wave Antenna (LWA) array is proposed, which contains two annular LWAs with constant-interval slots. By changing the excitations of the two LWAs, the radiation beam can be changed to different shapes and in different directions i.e. single beam to normal direction, conical beam to 40° and a synthetic beam. A theoretical model for analyzing the radiation properties of the LWA array is put forward, and a prototype of the array with the radius of 42.6 mm and the height of 4 mm is fabricated. By comparing simulation and the measurement results, it is found that the proposed LWA array exhibits a very well expected performance. The proposed antenna array is then applied to construct a self-sensing smart antenna which can provide flexible radio wave coverage according to the positions of users. Image-aware function is integrated into the smart antenna, and a series of tests including received power and Error Vector Magnitude (EVM) measurements are carried out in indoor scenarios. The results verify the effectiveness of the design.
Design of a Rigid-Flexible Coupling Origami Gripper

This paper presents the design a pneumatic rigid-flexible coupling origami gripper. The mechanism of the gripper is designed from the waterbomb origami, and the stiffness of the gripper is increased by attaching steel sheets to the facets of the origami gripper. Then the gripper realizes the adaptivity of soft grippers and high load capacity of rigid grippers. Three pressure sensors are arranged on the gripper as its sensory system. The sensory system gives distinct responses to objects of different shapes, and therefore enables the gripper to recognize grasping objects.
Towards Data-driven Optimal Control: A Systematic Review of the Landscape

This literature review extends and contributes to research on the development of data-driven optimal control. Previous reviews have documented the development of model-based and data-driven control in isolation and have not critically reviewed reinforcement learning approaches for adaptive data-driven optimal control frameworks. The presented review discusses the development of model-based to model-free adaptive controllers, highlighting the use of data in control frameworks. In data-driven control frameworks, reinforcement learning methods may be used to derive the optimal policy for dynamical systems. Attractive characteristics of these methods include not requiring a mathematical model of complex systems, their inherent adaptive control capabilities, being an unsupervised learning technique and their decision-making abilities, which are both an advantage and motivation behind this approach. This review considers previous reviews on these topics, including recent work on data-driven control methods. In addition, this review shows the use of data to derive system dynamics, determine the control policy using feedback information, and tune fixed controllers. Furthermore, the review summarises various data-driven methods and their corresponding characteristics. Finally, the review provides a taxonomy, a timeline and a concise narrative of the development of model-based to model-free data-driven adaptive control and underlines the limitations of these techniques due to the lack of theoretical analysis. Areas of further work include theoretical analysis on stability and robustness for data-driven control systems, explainability of black-box policy learning techniques and an evaluation of the impact of the extension of system simulators to include digital twins.
A bio-inspired continuum robot for out-pipe climbing and confined space navigating

Pipe robots are being developed to climb the areas that are hard to reach for human beings. Generally, there are few out-pipe robots that can both climb among pipes and pass through confined space. To this end, we propose a solution based on a continuum robot with several compliant mechanisms. This robot can imitate the locomotion principles of both caterpillars and inch-worms. The kinematics of the two-segment continuum robot is analyzed. Simulations including task space motion and confined space navigating are performed using a flexible multi-body dynamics method are performed for further discussion. Lastly, we showcase primary experiments of our proposed continuum robot for narrow gap exploration.
Design and Analysis of Scissor Extendable Airframe for a Morphing Multirotor

This paper presents a general class of scissor extendable airframes (SEA) for adjusting the size of multirotor in flight to various specific tasks and environments. The proposed SEA is a single degree-of-freedom mechanism, which consists of multiple extendable rotor arms (ERAs) symmetrically arranged around a closed-loop central linkage mechanism. All ERAs are hinged to the central linkage mechanism and can extend or contract simultaneously due to the synchronized effect of the central linkage mechanism. The kinematic characteristics of SEA are investigated, and the influence of design parameters on performance metrics is also studied. Based on these, we can intuitively obtain the performance of an SEA. An SEA satisfying desired performance metrics is designed and fabricated. Experimental results show that, the morphing response time of SEA is only 0.5 s, which enables multirotor to have rapid morphing capability.
Relative State Estimation with Observer-based Intermittent Kalman Filter and Radial Basis Function Neural Network

Relative localization is a prerequisite for aerial swarms. Vision is an important method of relative localization. However, the loss of visual information caused by the limited field of view and occlusion limits the performance of vision-based relative localization. This paper proposes a novel method to solve visual loss in relative localization. The method utilizes the intermittent Kalman filter to fuse the visual information with inertial measurement unit (IMU) data. Then, we design an IMU observer to reduce estimation error. Furthermore, to decrease abrupt oscillation of estimation, we employ the RBF neural network to fuse Visual-Inertial Odometry (VIO), Ultra Wide Band (UWB) information, and the result of the Observer-based Intermittent Kalman Filter (OIKF). Real-world experiments show that the proposed method for solving the visual loss problem reduces the mean square error by more than 30% compared with an optimization method.
Visual Localization in a Prior 3D LiDAR Map Combining Points and Lines

Robot localization in a prior map is critical for vision-based navigation. Currently, visual and visual-inertial odometry (VO&VIO) research has made great progress, but in the absence of closed loop, drift inevitably accumulates. The localization method using only 3D points had low accuracy and scale drift, and the localization algorithm using only straight lines had weak generalization ability and poor robustness. We propose a innovative and efficient visual localization in a 3D LiDAR map combining points and lines in this article. First, 3D lines are extracted offline from the 3D lidar map, and robust 2D lines in the raw image are extracted online. Prediction by Pose and reconstructed sparse 3D points from VIO or VO, we can efficiently obtain points and lines coarse correspondences respectively. The camera poses and correspondences are then iteratively optimized by minimizing the reprojection errors of 2D-3D lines and 3D-3D points. Our algorithm shows better results compared to state-of-the-art methods on the EuRoC MAV dataset, self-collection dataset and KITTI dataset without accumulated drifts.
Predicting Electricity Consumption in Microgrid-based Educational Building using Google Trends, Google Mobility, and COVID-19 Data in the Context of COVID-19 Pandemic

Electricity demand has been disrupted in various countries since many governments imposed comprehensive social restriction policies to control the COVID-19 pandemic. Obtaining accurate electricity consumption predictions in this highly uncertain period is particularly important for building operators to improve the corresponding operational planning efficacy. Nevertheless, developing accurate electricity consumption prediction models for buildings within the COVID-19 context is a nontrivial task. Correspondingly, this research focuses on incorporating publicly available internet data (i.e., Google Trends, Google Mobility, and COVID-19 data) to develop accurate electricity consumption prediction models for microgrid-based buildings during the COVID 19 pandemic. For this purpose, we developed extreme gradient boosting (XGBoost), support vector regression (SVR), and autoregressive integrated moving average with explanatory variable (ARIMAX) models. As a case study, we analyzed a real-life electricity consumption dataset of a six-floor microgrid-designed educational building at a technological university in Bandung, West Java, Indonesia. The findings show that incorporating publicly online data positively impacts prediction accuracy. The accuracy increases, even more when we use the lagged value of the predictors. XGBoost models utilizing lagged historical values of the electricity consumption, Google Trends, and COVID-19 data of the previous days is the best performing model. However, adding more lagged predictors does not necessarily increase SVR models’ accuracy. Lastly, the ARIMAX models become the worst-performing models compared to XGBoost and SVR models.
Fraud Detection in Insurance Claim System: A Review

Insurance fraud has existed since the inception of insurance companies. These are a wide range of crimes that go undiscovered and cost the insurance business billions of dollars each year. Due to economic growth, increased awareness, and stronger distribution channels, the Indian insurance business is predicted to reach US${\$}$280 billion by 2020. India is ranked 10th in terms of gross premiums earned for life insurance and 15th for non-life insurance products. For that reason, wen’re introducing a blockchain-based framework for enabling secure transactions and data exchange among various interacting agents in the insurance network. Blockchain is a distributed peer-to-peer technology that allows for the safe, immutable, and transparent validation of healthcare claims. Also, discuss how blockchain and smart contracts can be used together to improve organizational operations. More particularly, it will show how these technologies can be utilized to create a system that prevents certain types of fraud in the areas of vehicle, health-care, and life insurance claims, among others. In this review will discuss about types of Fraud Detection in Insurance Claim System and its classification based on different machine learning methods. Also gives the future direction for Fraud Detection in Insurance Claim System.
Smart Homes adoption in India – Value-based Adoption Approach

Due to the emergence of technology, smart homes will be the next big thing in India. The study adopted a Value-based Adoption Model (VAM) to predict the consumer intention to use smart homes services in India. We used three constructs to measure the sacrifices and benefits: privacy risk, Technicality, perceived fees, usefulness, social inclusion, and status symbol. The effect of perceived value on Intention to use smart homes was predicted. The construct adopted from the previous studies was used to develop a structured questionnaire. We sent an electronic questionnaire to 98 respondents who were mostly aware of the technology of smart homes. The empirical model was tested by multiple linear regression with the help of SPSS software. The study concluded that the usefulness, perceived fees, and status symbol determine the perceived value, and perceived value successfully determining the Intention to use smart home services.
Distributed Entrapping Control of Multiple Second-order Mobile Agents Under a Directed Network

In this paper, for second-order multi-agent systems, the distributed entrapping control problem under a direct network is investigated. For the target, the ideal time-varying entrapping formation has good elasticity and rotation. Thus, the agents are able to not only move in a certain area, but also ensure that the target can still be captured. We adopt the leader-follower structure. The formation shape parameter which is known to only some agents, is viewed as the leader. The remaining agents use observers to estimate shape parameters. With the position, velocity measurements and observers, we design a dynamic control law, which forms a closed-loop system with the second-order system. The closed-loop system can reach asymptotically stable. Finally, simulation example illustrates the theoretic result.
Design of a Hopping Robot with Its Kinetics and Dynamics Analysis

Referring to hopping animals in nature, this paper presents a newly designed hopping robot. This robot can adapt to a variety of complex terrains more effectively than wheeled robots. The bionic jumping mechanism could enlarge the take-off velocity using a six-bar linkage, which can increase the jumping length as well as height. The hopping robot’s kinetic model is established and analyzed since we need to magnify the ratio of kinetic energy (related to initial take-off speed) to elastic potential energy (stored in torsion spring). Subsequently, we have successfully built an interactive simulation platform on Adams software, which enables us to run five sets of takeoff angles’ simulations by stroboscopic shooting. The distance and height of each leap are recorded for further analysis. Since the hopping robot will probably roll over when taking off, the landing impact may lead to the robot’s malfunction. Therefore, we introduce a PID attitude-control method into the robot, referring to the satellite attitude-adjustment method. Based on the kinetic model, a Matlab-Adams collaborative simulation platform is built to simulate this procedure. As a result, the hopping robot could jump according to the preprogrammed distance and height without rolling over.
A New Scheme for Cooperative Hunting Tasks with Multiple Targets in Dynamic Environments

Multiple robot cooperation has been playing more and more significant role in scientific and engineering applications. The cooperative hunting task with multiple targets is a critical issue in multiple robot problem. To successfully hunt multiple preys, predator robots not only need to settle the problem on how to cooperatively hunt a prey, but also find an efficient task allocation to assign each predator to hunt certain prey. This paper proposes a cooperative hunting scheme based on the multi-target k-winner-take-all (k-WTA) algorithm and the wolf-pack-particle-based model. Based on the competitive mechanism, the multi-target k-WTA algorithm is used for task allocation with an optimized hunting strategy and achieves the allocation within a short period of time in dynamic environments. With simple rules, the wolf-pack-particle-based model can trigger collective behaviors and enable predator robots to encircle the prey. The simulative experiment results show that the proposed approach is capable of efficient task allocation and guiding predator robots to round up multiple moving targets in dynamic environments.
An Electroluminescent driven with Class E Resonant Inverter

Electroluminescent device (EL) is the one of light emitting devices that have been widely used to design the light emitting in various shapes. The AC current voltage of 160 Vrms and frequency of 400 Hz were supplied by power supply for EL devices in this study. The purpose of this research is to design and fabricate the class-E-resonant inverter circuit for EL devices with the size of 59x84 cm2. The electrical and optical properties of EL devices was investigated, which has a direct effect to the class-E-resonant inverter circuit. For the result, when the class-E-resonant inverter circuit was supplied by input supplying (220 V and 50 Hz) and output supplying (300 Vp-p and 400 Hz), the sine wave signal was obtained. Therefore, it is possible to fabricate the EL devices along with the optimum properties (electrical and optical) and can be obtained smaller EL devices together with high efficiency.
Novel Dual-Band Impedance Matching Circuits Implemented by Complementary Compact Microstrip Resonant Cells (CCMRCs)

This paper presents a novel dual-band impedance matching circuit (IMC) based on the quarter-wave-like transformer (QWLT), which can be effectively implemented by complementary compact microstrip resonant cells (CCMRCs). In this study, the dual-band IMC is designed at 3.3 GHz and 4.4 GHz for 5G technology applications in Thailand. In this study, a QWLT implemented by an asymmetric CCMRC can be systematically designed using the ABCD parameters of the CCMRC by solving associated equations of impedance matching simultaneously at both frequencies of interest. It is found that the asymmetric CCMRC can provide the length reduction of 14.8 % with excellent impedance matching at both frequencies of interest, compared to that of the symmetric case.
Pneumatically Actuated MR-Safe Parallel Robot for Deep Brain Stimulation Electrode Implantation

Deep brain stimulation (DBS) has been widely used in clinical practice as an effective therapy for neurological disorders. MR-conditional robot-assisted electrode implantation has attracted more attention for DBS because of its great accuracy and high-quality guiding images by using MRI. This paper develops a pneumatically actuated MR-safe parallel robot for deep brain electrode implantation. Its fully plastic components and MR-safe pneumatic actuators enable the robotic system to be MR-safe, posing no known hazards in MR environment. A 6-DOF parallel robot is developed using six slider-crank mechanisms for prismatic motions. Inverse kinematics is built to investigate its workspace and achieve the precise control. Experiments are carried out to validate the feasibility, and results shows that the average error of the motion is 0.11 mm. The developed parallel robot shows the potential advantages in the automatic and precise robot-assisted neurosurgical surgery in future.
Continuous Finite-Time Trajectory Tracking Control for Unmanned Quadrotor Transportation Systems

Nowadays, studies on unmanned quadrotor transportation system have aroused widespread interests of many researchers. In this paper, a continuous tracking control method based on terminal sliding mode (TSM) is developed considering the coupling relationship between the payload swing and quadrotor translation. Finite-time stability of the closed-loop system is guaranteed by Lyapunov techniques. The proposed method can achieve fast convergent rate and high-precision tracking performance under the influence of external disturbances and noises. The validity of the proposed method is supported by simulation results compared with the PD controller and the regular linear quadratic regulator (LQR) control scheme.
An anthropomorphic surgical simulator arm based on series elastic actuators with haptic feedback

We present a surgical simulator (Epsilon-1, which was designed using series elastic actuators (SEA) with off-the-shelf components. This low-cost alternative provides surgical training to surgeons by providing haptic feedback from an environment simulated by physics engines. We describe the hardware and software architecture of the surgical trainer arm in this paper. This is the first Anthropomorphic surgical arm because its dimensions and motions are of anthropomorphic nature. We present here our intuitive software simulation environment that gives multiple views for the comfort of the surgeon trainee.
Impact Parameter Test of Virtual Prototype based on Experiment and SQP Optimization Algorithm

In the process of virtual prototype modeling by means of CAD software Pro/E and mechanical system dynamic software ADAMS,the parameter setting is a very important part.For there are many kinds of materials in physical prototype but ADAMS only provides a few material impact parameter,the impact parameter of copper and steel was tested through the method of comparison between experiment and virtual prototype.Ideal collision parameters were gotten and the method can be used for other materials.
An intrusion recognition method based on the combination of One-dimensional CNN and DenseNet with DAS system

An intrusion recognition method based on the combination of one-dimensional convolution neural network (1-D CNN) and DenseNet network is proposed and demonstrated. Assisted with fiber optic distributed acoustic sensing (DAS) system, the average recognition accuracy of 98.4% for six types of events and 2ms processing time were achieved in the field test.
Analysis of End Effect on Thrust Ripple in Linear Brushless Doubly-Fed Machine

The linear brushless doubly-fed machine (LBDFM) as a novel type of machine has attracted extensive attention in recent years. Due to the complex magnetic fields and end effect, there is rich low-frequency thrust ripple in LBDFM. In order to make the physical process of thrust ripple clear, the end effect on the winding magnetomotive force (MMF) is studied. Firstly, the shifted slot vector affected by the end is obtained. Then, a negative sequence magnetic motive force is analyzed with the certain winding configuration. Finally, the generation of low-frequency thrust ripple is explained. The analysis results are verified by finite element algorithm (FEA) and the proposed method can be applied to most linear machines with short primary.