Miracle-dz/UniFAR-FTUs huggingface dataset:
Theoretical Derivation and Realization of Adaptive Grasping Based on Rotational Incipient Slip Detection

Manipulating objects whose physical properties are unknown remains one of the greatest challenges in robotics. Controlling grasp force is an essential aspect of handling unknown objects without slipping or crushing them. Although extensive research has been carried out on grasp force control, unknown object manipulation is still difficult because conventional approaches assume that object properties (mass, center of gravity, friction coefficient, etc.) are known for grasp force control. One of the approaches to address this issue is incipient slip detection. However, there has been few detailed investigations of robust detection and control of incipient slip on rotational case. This study makes contributions on deriving the theoretical model of incipient slip and proposes a new algorithm to detect incipient slip. Additionally, a novel sensor configuration and a grasp force control algorithm based on the derived theoretical model are proposed. Finally, the proposed algorithm is evaluated by grasping objects with different weights and moments including a fragile pastry (éclair).
Weakly Supervised Silhouette-based Semantic Scene Change Detection

This paper presents a novel semantic scene change detection scheme with only weak supervision. A straightforward approach for this task is to train a semantic change detection network directly from a large-scale dataset in an end-to-end manner. However, a specific dataset for this task, which is usually labor-intensive and time-consuming, becomes indispensable. To avoid this problem, we propose to train this kind of network from existing datasets by dividing this task into change detection and semantic extraction. On the other hand, the difference in camera viewpoints, for example, images of the same scene captured from a vehicle-mounted camera at different time points, usually brings a challenge to the change detection task. To address this challenge, we propose a new siamese network structure with the introduction of correlation layer. In addition, we create a publicly available dataset for semantic change detection to evaluate the proposed method. The experimental results verified both the robustness to viewpoint difference in change detection task and the effectiveness for semantic change detection of the proposed networks. Our code and dataset are available at https://github.com/xdspacelab/sscdnet.
A Variable-Fractional Order Admittance Controller for pHRI

In today’s automation driven manufacturing environments, emerging technologies like cobots (collaborative robots) and augmented reality interfaces can help integrating humans into the production workflow to benefit from their adaptability and cognitive skills. In such settings, humans are expected to work with robots side by side and physically interact with them. However, the trade-off between stability and transparency is a core challenge in the presence of physical human robot interaction (pHRI). While stability is of utmost importance for safety, transparency is required for fully exploiting the precision and ability of robots in handling labor intensive tasks. In this work, we propose a new variable admittance controller based on fractional order control to handle this trade-off more effectively. We compared the performance of fractional order variable admittance controller with a classical admittance controller with fixed parameters as a baseline and an integer order variable admittance controller during a realistic drilling task. Our comparisons indicate that the proposed controller led to a more transparent interaction compared to the other controllers without sacrificing the stability. We also demonstrate a use case for an augmented reality (AR) headset which can augment human sensory capabilities for reaching a certain drilling depth otherwise not possible without changing the role of the robot as the decision maker.
Tension Sensor with Soft Optical Fiber for Tendon-Driven Robotic Hand

Tendon-driven robot's tension is important for accurately controlling. This paper presents the design philosophy of a stretchable optical sensor that is used to measure tendon tension between finger and actuator. The proposed sensor consists of flexible fiber, shell, light-emitting diode (LED) and PhotoDetector (PD). The tension sensor exploits the principle of total reflection of light to measure the length variation of flexible fiber. It has several advantages including low-cost, simple construction and direct measurement. Then manufacturing process and the circuit of the sensor are presented. Finally, the calibration experiments are implemented and the results show the effectiveness of the sensor.
Device and Circuit-Level Performance Comparison of Vertically Grown All-Si and Ge/Si Hetero-Junction TFET

This work reports device and circuit-level performance comparison between Ge/Si heterojunction vertical tunnel field effect transistor (TFET) and All-Si vertical tunnel field effect transistor (TFET). To improve the subthreshold performance, a source pocket is sandwiched between source and channel region of the presented TFETs. Germanium which has lower bandgap than silicon is considered in the source region of the proposed TFET to reduce the tunneling width which in turn inhibits more electrons to tunnel through the interface between source and channel region. Device-level performance benefits in terms of DC and RF parameters of the vertically grown Ge/Si heterojunction TFET is thoroughly investigated and compared with the All-Si vertical TFET using SILVACO ATLASTM TCAD tool. In addition, circuit-level performance comparison of the presented TFETs is made by considering some basic digital circuits like an inverter and a five-stage ring oscillator using CADENCE Virtuoso tool.
High-Flexibility Locomotion and Whole-Torso Control for a Wheel-Legged Robot on Challenging Terrain*

In this paper, we propose a parallel six-wheel-legged robot that can traverse irregular terrain while carrying objectives to do heavy-duty work. This robot is equipped with six Stewart platforms as legs and tightly integrates the additional degrees of freedom introduced by the wheels. The presented control strategy with physical system used to adapt the diverse degrees of each leg to irregular terrain such that robot increases the traversability, and simultaneously to maintain the horizontal whole-torso pose. This strategy makes use of Contact Scheduler (CS) and Whole-Torso Control (WTC) to control the multiple degrees of freedom (DOF) leg for performing high-flexibility locomotion and adapting the rough terrain like actively parallel suspension system. We conducted experiments on flat, slope, soft and sandgravel surface, which validate the proposed control method and physical system. Especially, we attempt to traverse over sand-gravel terrain with 3 people about 240kg payload.
Improvements in Insertion of Auxiliary Parity Segments in WIPL-D All-Quad Meshing Algorithm

This paper provides a new technique that improves the all-quad meshing algorithm implemented in the WIPL-D software. The technique allows for the insertion of required auxiliary segments to achieve parity of a number of segments over all closed loop faces. The aim of the improvement is to reduce the total number of mesh elements in the final mesh. The efficiency of the proposed technique is compared to the traditional one mentioned earlier. A reduction in the number of mesh elements of about 15% has been achieved when applied to a log-periodic antenna WIPL-D model.
Bandwidth Spread of Microwave Absorber by Integrating Spoof Plasmonic structure and Resistive Metasurface

Bandwidth spread of microwave absorber attracts much attention nowadays. In this paper, we proposed and demonstrated a broadband microwave absorber which combines resistive metasurface and spoof plasmonic structure. Our hybrid absorber consists of a resistive metasurface on the top of a gradual corrugated metal strips based plasmonic structures. By adjusting the unit cell parameters, the different absorption mechanisms of the resistive metasurface and spoof plasmonic structures can be combined together to achieve a broad absorption bandwidth. Compared with the simple SSPP absorbers, this hybrid absorber has a spread absorption bandwidth. The absorber sample was fabricated based on optimized results and experimental results indicate that the presented absorber can absorb the incident microwave over 90% in the band of 5.6-14.4 GHz with the thickness of 7 mm.
Driving-Cycle-Oriented Design Optimization of a Permanent Magnet Hub Motor Drive System for a Four-Wheel-Drive Electric Vehicle

The electrical drive system is crucial to the drive performance and safety of electric vehicles (EVs). In contrast to the traditional two-wheel-driven EVs, the hub motor four-wheel-drive system can steer the vehicle by controlling the torque and speed of each wheel independently, yielding a very simple distributed drivetrain with high efficiency and reliability. This article presents a system-level design optimization method for a permanent magnet hub motor drive system for a campus patrol EV based on a practical driving cycle. An outer rotor permanent-magnet synchronous hub motor (PMSHM) and an improved model predicate current control are proposed for the drive system. Due to the lack of reducers, the direct-drive PMSHM needs to face more complex working conditions and design constraints. In the implementation, the motor design requirements are obtained through the collection of practical EV driving cycles on the campus. Based on these requirements, two models are proposed as the preliminary designs for the PMSHM. To improve their performance, an efficient multiobjective optimization method is employed to the motor considering different operational conditions. The finite-element model and thermal network model are employed to verify the performance of the optimized PMSHM. An optimal design scheme is selected by comparing the comprehensive performance of the two optimized motors. In addition, a duty-cycle model predictive current control is adopted to drive the motor. Finally, a prototype is developed and tested, and the experimental results are presented.
Design and Implementation of an Inspection Robot for Non-Destructive Testing of Aluminum Conductor Composite Core Wires

In order to detect the damage in the core of Aluminum Conductor Composite Core (ACCC) wires in the power grid, the design and implementation of an inspection robot based on non-destructive testing technology is introduced in this paper. First, the mechanical optimization design of the driving wheel is discussed. Second, the obstacle crossing capability of the inspection robot is analyzed by calculating the maximum climbing angle and the maximum surmountable obstacle size. Thus, the rationality of the structure design of the robot is verified. Finally, experiments were carried out on the experimental transmission lines and the overhead transmission lines in the grid. The results show that the robot can meet the requirements for in-situ mobile flaw detection of overhead ACCC wires.
Recurrent Embedded Hourglass Network for Single Image Super-Resolution

The single image super-resolution has become an important topic of discussion due to the demand for high-quality digital images in the field of visual artificial intelligence. The deep learning-based approach has achieved great success because of its excellent ability to control complex features. Using simply extending or deepening the network, performance can be improved slightly, but not significantly. There are still blurry edges and rough texture details. In this paper, we propose a Recurrent Embedded Hourglass Network (SRRHN) for super-resolution reconstruction. We use the hourglass network to combine deep and shallow features and embed Gated Recurrent Unit (GRU) in each layer of the hourglass network to improve the long-range correlations. Finally, sub-pixel convolution is adopted to avoid image distortion during up-sampling. Extensive experiments on several standard benchmarks show that our proposed method achieves better performance compared with state-of-the-art methods.
Robot Programming without Coding

An approach toward intuitive and easy robot programming, consists to transfer skills from humans to machines, through demonstration. A vast literature exists on learning from multiple demonstrations.This paper, on the other hand, tackles the problem of providing all needed information to execute a certain task by resorting to one single demonstration - hence, a problem closer to programming than to learning. We use wearable consumer devices - but no keyboard nor coding - as programming tools, to let the programmer tele-operate the robot, which in turn records the most salient features and affordances from the object, environment, robot, and human.To enable this goal we combine off-the-shelf soft-articulated robotic components with the framework of Dynamic Movement Primitives, which we contribute to extend to generalize human trajectories and impedance regulation skills.This framework enables to teach robot quickly and in a intuitive way without coding. Experimental tests have been performed on a dual-arm system composed by two 7-dofs collaborative robots equipped with anthropomorphic end-effectors. Experiments show the functionality of the framework and verify the effectiveness of the impedance extension.
Recent Progress on the Convergence of the Internet of Things and Artificial Intelligence

The overwhelming increase of ubiquitous data, connections, and services brings serious challenges, in particular facing the demanding requirements of the Internet of Things (IoT). In order to seek better solutions and achieve more efficient information retrieval, artificial intelligence (AI) serves as a strong technical earthquake and contributes a lot to data analysis and decision making. It plays a compelling role in prompting digital and intelligent services. In this article, we focus on and emphasize the great significance pertaining to the convergence of IoT and AI. We first elaborate two typical forms of AI, namely knowledge-enabled AI and data-driven AI, with a comparison between respective advantages and disadvantages. Then we survey recent progress relating to the convergence of AI throughout the IoT architecture, from the sensing layer through the network layer to the application layer. In addition, a case study of a smart city is presented illustrating the convergence between IoT and AI. Furthermore, we point out open issues worth further research. The convergence of IoT and AI marries the merits of both and enables strong capability of resolving a broad range of problems.
Indoor High Precision Three-Dimensional Positioning System Based on Visible Light Communication Using Improved Hybrid Bat Algorithm

To solve problems of low positioning accuracy and long time-consuming in indoor visible light three-dimensional positioning system, a visible light three-dimensional positioning method based on the improved hybrid bat algorithm (IHBA) is proposed in this paper. Firstly, some beacon points are set at the beginning of the IHBA to reduce the number of iterations. Secondly, weight coefficient is defined to improve positioning accuracy when the fitness function is constructed. Thirdly, aiming at controlling the search speed reasonably, an adaptive search factor is introduced while the bat individual update formula is designed. Finally, the chaotic perturbation operation is used to avoid the algorithm falling into local optimum. In indoor simulation environment, the size of which is 5 m×5 m × 3 m, the average positioning error of the IHBA is 1.16 cm and the positioning time is 1.85s. To verify the actual effectiveness of IHBA, a receiver is placed in the experimental environment, the size of which is 1.5 m × 1.2 m × 2 m. Through the optical power detected by the receiver, the processor calculate its coordinates with IHBA. The average positioning error of the IHBA is 3.64 cm and the positioning time is 0.89 s. The proposed algorithm has practical application value in existing indoor positioning methods.
Where is the Learning in Learning Analytics? A Systematic Literature Review on the Operationalization of Learning-Related Constructs in the Evaluation of Learning Analytics Interventions

Learning technologies enable interventions in the learning process aiming to improve learning. Learning analytics provides such interventions based on analysis of learner data, which are believed to have beneficial effects on both learning and the learning environment. Literature reporting on the effects of learning analytics interventions on learning allows us to assess in what way learning analytics improves learning. No standard set of operational definitions for learning affected by learning analytics interventions is available. We performed a systematic literature review of 1932 search hits, which yielded 62 key studies. We analyzed how affected learning was operationalized in these key studies and classified operational definitions into three categories: 1) learning environment; 2) learning process; and 3) learning outcome. A deepening analysis yielded a refined classification scheme with 11 subcategories. Most of the analyzed studies relate to either learning outcome or learning process. Only nine of the key studies relate to more than one category. Given the complex nature of applying learning analytics interventions in practice, measuring the effects on a wider spectrum of aspects can give more insight into the workings of learning analytics interventions on the different actors, processes, and outcomes involved. Based on the results of our review, we recommend making deliberate decisions on the (multiple) aspects of learning one tries to improve by applying learning analytics. Our refined classification with examples of operational definitions may help both academics and practitioners doing so, as it allows for a more structured, grounded, and comparable positioning of learning analytics benefits.
Iterative Learning based feedforward control for Transition of a Biplane-Quadrotor Tailsitter UAS

This paper provides a real time on-board algorithm for a biplane-quadrotor to iteratively learn a forward transition maneuver via repeated flight trials. The maneuver is controlled by regulating the pitch angle and propeller thrust according to feedforward control laws that are parameterized by polynomials. Based on a nominal model with simplified aerodynamics, the optimal coefficients of the polynomials are chosen through simulation such that the maneuver is completed with specified terminal conditions on altitude and air speed. In order to compensate for modeling errors, repeated flight trials are performed by updating the feedforward control paramters according to an iterative learning algorithm until the maneuver is perfected. A geometric attitude controller, valid for all flight modes is employed in order to track the pitch angle according to the feedforward law. Further, a high-fidelity thrust model of the propeller for varying advance-ratio and orientation angle is obtained from wind tunnel data which is captured using a neural network model. This facilitates accurate application of feedforward thrust for varying flow conditions during transition. Experimental flight trials are performed to demonstrate the robustness and rapid convergence of the proposed learning algorithm.
Codeword Generation for a Combined Cryptography and LDPC Coding System

This paper describes a method of generating the cyphertext for a combined cryptographic and LDPC encoding system. This combined system is used for the purpose of enhancing privacy. However, to create the cyphertext, 2 matrix inversions must occur and one of these matrices may sometimes be singular. In addition to this inversion issue, this combined system may use many more PRNG bits per message bit than the standard XORing system. Both issues are addressed here.
5G mmWAVE Technology Design Challenges and Development Trends

The explosive growth in areas of mobile subscriber, wireless network access, mobile services and applications shows evidences of the continuous expansion of cellular communication bandwidth requirement. The 5G (fifth generation) mobile communication standard aims to solve the substantially increased requirement of data rate and the avalanche of traffic volume. One of the promising solutions is to use the millimeter wave frequency band to get the wide available spectrum. To overcome these unfavorable channel properties, i.e. high path loss, propagation loss, rain fading…,e.t. c. , the beam-forming, beam tracking together with phased array antenna are the most crucial key technologies.In this paper, we describe the system specifications and the design considerations of the architecture for the RF and beam forming of the 5G mmWave communication system. Then the design challenges and development trends of the 5G mmWave front-end module are presented. Finally, ITRI’s developed 28GHz 5G millimeter-wave development and verification platform is introduced which adopts the hybrid beam-forming architecture designed with a 8/16/32/64-antenna phased antenna array for gNB and/or UE to implement the beam-forming technology, also a beam-tracking algorithm is developed to support the mobile transmission over 100Km/hr.
State of Art: Energy Efficient Protocols for Self-Powered Wireless Sensor Network in IIoT to Support Industry 4.0

Up gradation of manufacturing systems in industries by means of implementing innovative manufacturing techniques that captures real time data, applies machine learning algorithms, makes entire system self-decisive and provides inter connectivity to the whole system is the prime focus of the Industry 4.0. It is aimed at bringing new industrial revolution with the help of internet of things technology due to its considerable influence in the industrial manufacturing process. Though impact of internet of things in industrial sector is huge, a practical implementation incorporates challenges in energy efficiency, self-powered sensor nodes and security. For processing of gathered data self-powered sensor nodes may sinks energy from ambient energy sources. A considerable amount of efforts has been put by researchers to address the challenges for development of energy efficient routing protocol for such nodes. The presented survey is aimed at analyzing the protocols for contribution towards the goals of industry 4.0 Energy efficient protocol will support the system to consume least energy for its operation. Henceforth combination of self-powered wireless sensor network and energy efficient protocol will be useful to power up many industrial IoT applications.
Design Space Exploration Framework for Tensilica-Based Digital Audio Processors in Hearing Aids

Choosing a suitable processor architecture for a hearing aid is a difficult task. Various aspects have to be taken into account, like power consumption and silicon area. Also, the computational performance and flexibility of an architecture are essential. Therefore, a wide variety of design goals must be weighted against each other before a final decision for the architecture can be made. In this paper, several configurable audio processors are evaluated, using five commonly known acoustic beamforming algorithms. In order to reduce the exploration time, this paper presents a partly automated design space exploration framework. The hearing aid algorithms are implemented in fixedpoint representation to reduce the computational complexity. This framework includes a fixed-point analysis and an automated reference code generation using MATLAB tools. With the Xtensa Xplorer, different configurations of the Tensilica-based processor architecture are profiled. Finally, a case study is presented to show the usability of the proposed framework.