Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Automatic Classification of Fragmented Rock Piles via Proprioceptive Sensing and Wavelet Analysis

In this paper, we describe a method for classifying rock piles characterized by different size distributions by using accelerometer data and wavelet analysis. Size distribution (frag-mentation) estimates are used in the mining and aggregates industries to ensure the rock that enters the crushing and grinding circuits meet input design specifications. Current technologies use exteroceptive sensing to estimate size distributions from, for example, camera images. Our approach instead proposes the use of signals acquired from the process of loading equipment that are used to transport fragmented rock. The experimental setup used a laboratory-sized mock up of a haul truck with two inertial measurement units (IMUs) for data collection. Results utilizing wavelet analysis are provided that show how accelerometers could be used to distinguish between piles with different size distributions.
Research on Feedforward Control Based on Robot Dynamics Parameters Identification

Accurate robot dynamics model can effectively improve the motion control performance of robot, and accurate dynamics parameters are the basis for establishing dynamics model. This paper discusses the parameter identification method of the tandem manipulator and the feedforward control scheme based on dynamics. First, we deduced the basic parameters of the first three joints of the UR5 manipulator, and identified the parameters through experimental sampling and calculation. Secondly, we established a feedforward control mechanism based on dynamics using the Simulink platform, and simulated and verified the feedforward control of the robot three-axis system. The experimental results show that the feedforward control scheme based on robot dynamics can effectively improve the error convergence performance, which proves the effectiveness of the method.
Learning Bionic Motions by Imitating Animals

Motion control algorithms for quadruped robots undergo rapid development in recent years. Interactive quadruped robots have demonstrated they may positively enhance the effect of psychotherapy in the treatment of patients with cognitive impairment, which requires them to have more interactive capabilities than traditional quadruped robots. In this study, we focus on enabling interactive quadruped robots to imitate real animal motions extracted from videos, by which the design of robotic motion controllers can be simplified and the bionic degree and the interactive capabilities of the robots can be enhanced. The motion capture data, however, cannot be directly utilized by the motion controllers since the robots and the real animals differ in their respective body geometries, motion dynamics and the numbers of DOF. To address these differences, we propose two strategies for imitating two different kind of motions. For ordinary motions (head scratching, waving, etc.), we first apply a scaling method to motion captured data and then use an inverse kinematic algorithm for imitation. Furthermore, to minimize the error of motion trajectories between the real animals and the robots, we then transform motion trajectories into a nonlinear optimization problem. For walking motions, we first analyze a classical SLIP model-based walking control algorithm for quadruped robots, and then apply the parameters extracted from motion captured data to the walking control algorithm. Experiments based on an interactive quadruped robot we developed demonstrate that our proposed strategies have great potential in improving the imitation capability of robots on the motions of real animals.
Research on On-line Reliability State Detection Method of Power Electronic Devices Based on Turn-off Losses

Insulated gate bipolar transistor (IGBT) has been widely used in photovoltaic power generation, aerospace, electric vehicles, ships and many other fields. As the core component of the equipment in these fields, the reliability of IGBT has always been an important research direction of power electronics. The existing research results show that the reliability of IGBT is closely related to its junction temperature change. How to estimate or measure the junction temperature of IGBT is the key point of its reliability research. Based on the need of reliability research, this paper proposes a junction temperature extraction method based on the turn-off losses, which uses the single loss Eoff in the process of IGBT turn off as the thermal sensitive parameter. In this paper, a non-contact state detection method is proposed, which uses the input and output voltage and current information of single inverter to predict the junction temperature without directly measuring the IGBT. This paper has certain theoretical significance and engineering value for the follow-up IGBT online reliability state detection.
Multiport Converter With Independent Control of AC and DC Power Flows for Bipolar DC Distribution

A multiport ac–dc converter topology is proposed that offers bidirectional power transfer capability between the two dc ports and the ac port. The decoupling of power flows can be exploited as a pole voltage balancing mechanism to accommodate unbalanced dc side loads in bipolar dc distribution systems. The proposed topology gains bipolar operation by using two three-phase two-level voltage-sourced converters connected in a differential fashion with a center-tapped transformer. The analysis shows that no penalties are incurred in terms of increased power ratings for either the grid interface transformer or the semiconductors; only access to the center point of the converter side winding is required. A control scheme for the topology is proposed with the ability to independently control power flows between the different ports. Both the topology and control scheme are validated by extensive simulations and experiments.
Marker-Based Shape Estimation of a Continuum Manipulator Using Binocular Vision and Its Error Compensation

Robot-assisted laparoscopy endoscopic single-site surgery robot (LESS) procedures typically involve highly flexible and continuum manipulators to get access to the surgical targets through a small keyhole incision and then perform complicated operations within the abdomen or chest cavity. However, the inherent deformable design and inevitable collisions with the anatomy cause both active and passive deformation of the distal continuum manipulator, which challenges the accurate shape estimation in both structured and unstructured environment. Therefore, it is essential to achieve real-time and accurate and shape tracking of the continuum manipulator for precise and reliable motion control. A new method is proposed in this paper for solving in the problem of real-time shape sensing for the distal continuous robot using binoculars by extracting position information of each markers. However, to cope with the problems of visual occlusion and limited algorithm stability, an error compensation method based on machine learning algorithm is realized. The performance of the proposed algorithm was verified by experiments, and the accuracy of the reconstruction after the correction by the learning algorithm was improved by about 70%.
Deep Learning-Based Approach for Sign Language Gesture Recognition With Efficient Hand Gesture Representation

Hand gesture recognition is an attractive research field with a wide range of applications, including video games and telesurgery techniques. Another important application of hand gesture recognition is the translation of sign language, which is a complicated structured form of hand gestures. In sign language, the fingers’ configuration, the hand’s orientation, and the hand’s relative position to the body are the primitives of structured expressions. The importance of hand gesture recognition has increased due to the prevalence of touchless applications and the rapid growth of the hearing-impaired population. However, developing an efficient recognition system needs to overcome the challenges of hand segmentation, local hand shape representation, global body configuration representation, and gesture sequence modeling. In this paper, a novel system is proposed for dynamic hand gesture recognition using multiple deep learning architectures for hand segmentation, local and global feature representations, and sequence feature globalization and recognition. The proposed system is evaluated on a very challenging dataset, which consists of 40 dynamic hand gestures performed by 40 subjects in an uncontrolled environment. The results show that the proposed system outperforms state-of-the-art approaches, demonstrating its effectiveness.
Excessive Enterprise Social Media Use Behavior at Work: Role of Communication Visibility and Perspective of Uses and Gratifications Theory

As the popularity of enterprise social media (ESM) increases in organizations, recent studies have shown that ESM may be excessively used at work, triggering negative consequences for employees and organizations. Drawing on uses and gratifications theory (UGT), we propose a theoretical model to test how the communication visibility—message transparency and network translucence—enabled by ESM affects employees’ excessive use of ESM at work by satisfying their different needs (i.e., social, content, and hedonic needs); the gratifications of these needs are reflected through employees’ perceived values, including perceived social, information, and hedonic values. We analyzed a valid sample consisted of 301 Chinese employees using ESM through smart-PLS to verify the model. The data analysis results indicate that both message transparency and network translucence are positively correlated with employees’ perceived social, information, and hedonic values. Perceived social value and perceived information value exert positive influences on excessive ESM use by employees, whereas perceived hedonic value has no significant effect on employees’ excessive use of ESM. The findings also show that the relationship between ESM communication visibility affordance and employees’ excessive ESM use is fully mediated by their perceived social value and perceived information value. These findings enrich the existing research on ESM and excessive use behavior and help inspire organizations to guide and manage the use of ESM by employees.
COBIT 5 for Analysing Information Technology Governance Maturity Level on Masterplan E-Government

The information and communication technology development has increased to fulfil processes of an organization. It needs to be structured to create a clean, competent, evident and liable government and a quality and reliable civil service. With the existence of a system of electronic-based government system (SPBE), one of the regional governments that implemented SPBE is East Java Province by generating SPBE index value of 2.92. The SPBE value shows the SPBE implementation quality is still below the expected value, which is 3. By using COBIT 5 Framework, this research obtains levels of selected process capabilities, i.e. EDM05, APO01, APO04, APO06, are below 3rd level (Established). This research also gives recommendations for improving the level of the process capability.
Population dynamics of the main phytophagous and predatory mites associated with rubber tree plantations in the State of Bahia, Brazil

Abstract Rubber trees, Hevea brasiliensis, are the main source of latex in the world, being cultivated in several states in Brazil. Calacarus heveae Feres and Tenuipalpus heveae Baker are considered the main pest mites of this crop. In this study, we evaluated the population dynamics of these two phytophagous species and of the most common predatory species during a one-year period on crops of rubber tree clones FDR 5788, CDC 312 and PMB 01, located in Igrapiúna, State of Bahia, northeastern Brazil. The results indicate that outbreaks of C. heveae and T. heveae occur from January to May. Population densities vary among clones, being lower on FDR 5788. Amblyseius perditus Chant and Baker (Phytoseiidae), Parapronematus sp. (Iolinidae) and Agistemus pallinii Matioli, Tavares and Pallini (Stigmaeidae) were the most abundant and frequently encountered predacious species. The population dynamics of these predatory species were related to the abundance of the phytophagous mites. However, the efficacy of these predators to mitigate infestations of phytophagous mites under natural conditions is unresolved due to the high reproductive rate of the phytophagous species.
Mobility Support for Energy and QoS aware IoT Services Placement in the Fog

Fog computing has emerged as a strong distributed computation paradigm to support applications with stringent latency requirements. It offers almost ubiquitous computation capacities over a large geographical area. However, Fog systems are highly heterogeneous and dynamic which makes services placement decision quite challenging considering nodes mobility that may decrease the placement decision quality over time. This paper proposes a Mobility-aware Genetic Algorithm (MGA) for services placement in the Fog which aims at supporting nodes’ mobility while ensuring both infrastructures energy-efficiency and applications Quality of Service (QoS) requirements. We have compared this approach with two variants of Shortest Access Point migration strategy (SAP) from the literature, a proposed Mobility Greedy Heuristic (MGH) and a baseline Simple Genetic Algorithm (SGA). Experiments conducted with MyiFogSim simulator have shown that MGA ensures good performances in terms of energy and delay violations minimization compared to other methods.
Linearity Analysis of CMOS Parametric Upconverters

This paper applies a conversion matrix approach to the linearity analysis of a varactor-based 36-GHz CMOS parametric upconverter. The nonlinear model of the upconverter is explained and derived. The comparison between the measurements, simulations, and theoretical calculation is presented to show excellent agreements: 0.5- and 1.5-dB differences in conversion gains for lower-sideband and upper-sideband upconversion, and less than 2.9-dB in both <inline-formula> <tex-math notation="LaTeX">$IIP_{2}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$IIP_{3}$ </tex-math></inline-formula> when converting a 1-GHz signal to 36-GHz output.
A Fully Integrated High-Power-Supply-Rejection Linear Regulator With an Output-Supplied Voltage Reference

This study proposes an output capacitor-less linear regulator with high power supply rejection (PSR) for a wireless power transmission system. To achieve high PSR with a noisy input voltage, a fully integrated linear regulator with its reference circuit supplied by the output voltage is proposed. The proposed technique can isolate the reference circuit from the noisy <inline-formula> <tex-math notation="LaTeX">$\text{V}_{IN}$ </tex-math></inline-formula>, thereby reducing the requirements of the conventional bulky low-pass filter loading the reference voltage node while achieving superior PSR performance. The regulator uses an N-type pass transistor and a dual-feedback structure to achieve wideband ripple-filtering and fast transient responses. The proposed regulator is compatible with typical biomedical implants requiring a 10mA load current at 1.1V output voltage while consuming a total quiescent current of <inline-formula> <tex-math notation="LaTeX">$276~\mu $ </tex-math></inline-formula> A. A PSR performance was measured to be −48 and −56 dB against the <inline-formula> <tex-math notation="LaTeX">$\text{V}_{IN}$ </tex-math></inline-formula> and charge pump at 10 MHz, respectively. The unity gain bandwidth (UGB) of the regulator was 291MHz. The proposed regulator was fabricated using commercial TSMC 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS technology with an area of 0.1054 mm<sup>2</sup> including the reference circuit.
Automatic Safe Data Reuse Detection for the WCET Analysis of Systems With Data Caches

accesses, provides both temporal and spatial reuse information, and does not require the exploration of explicit data access sequences. As a proof of concept we analyze the TACLeBench benchmark suite, showing that most loads/stores present data reuse, and how compiler optimizations affect it. Using a simple hit/miss estimation on our reuse results, the time devoted to data accesses in the worst case is reduced to 27% compared to an always-miss system, equivalent to a data hit ratio of 81%. With compiler optimization, such time is reduced to 6.5%.
Hybrid Beamforming for mmWave Massive MIMO Systems Employing DFT-Assisted User Clustering

Hybrid analog-digital precoding has been recognized as a promising potential solution to facilitate implementation of massive multiple-input multiple-output systems in the millimeter wave spectrum. Hybrid transceivers reduce the radio frequency chain count and cost by dividing the task of precoding between the digital and analog processing domains, but near-optimal design of the hybrid precoders is non-trivial and their optimal design is analytically intractable. This is due to the joint optimization of digital and analog precoding matrices as well as extra constraints for analog precoders due to their realization by analog phase shifters. Additionally, hybrid transceivers can be implemented via two architectures, either fully-connected or partially-connected. In this paper, we make use of projection algorithms to greatly simplify the design problem of the digital baseband and analog precoders into two optimization subproblems whose optimal solutions can be found. We also incorporate in the design a simplified user clustering algorithm based on the discrete Fourier transform, where users within the same and overlapping angular bins in the beam-space domain are grouped together. Simulation results demonstrate the proposed hybrid precoding and user clustering algorithm outperforms existing algorithms.
Research on the Analysis and Check of Electrical Secondary PDF Drawings Based on Deep Learning

Aiming at the problems such as the large amount of manual checking, the influence of the quality of checking on the technical level of the personnel, the quantity of the project and the working attitude, and the influence of the quality of the drawings on the progress and quality of the engineering construction, a method for analyzing and checking PDF drawings based on deep learning was proposed. Using artificial intelligence and image processing technology to analyze and identify drawings, and designing automatic checking of drawings, the quality and efficiency of drawing checking were improved. Among them, the analysis method of drawings mainly includes the conversion of PDF files into picture files, the identification of components in the drawings, the identification and screening of lines, the segmentation of long lines according to the location of components, the segmentation of line according to the relationship between lines and the output of wires and components. It is of great significance to quickly analyze and study the PDF drawings. To some extent, it can avoid the non-standard situation after drawing, improve the quality of engineering design, and provide a strong guarantee for the follow-up checking work.
Tracking Pandemics: A MEC-Enabled IoT Ecosystem with Learning Capability

The COVID-19 pandemic has resulted in unprecedented challenges to global society and the healthcare system in particular. The main objective of this article is to introduce an end-to-end Internet of Things (IoT) ecosystem for healthcare that uses an open source hardware and interoperable IoT standard for eHealth monitoring in general, and COVID-19 symptoms (e.g., fever, coughing, and fatigue) in particular. The system is designed to monitor the physical conditions of human subjects and send the data to a hierarchical multi-access edge computing (MEC) framework. Such a system is expected to be cognizant, taskable (i.e., tasks can be assigned to any computing process in the system), and adaptable. To this end, we demonstrate how a learning method can be introduced in the ecosystem to achieve taskability and efficiency. Specifically, the proposed system utilizes a shared representation learning process to extract actionable information from large volumes of high-dimensional data obtained from IoT edge devices. These edge devices are enabled with tri-sensors for real-time monitoring of COVID-19 symptoms. The feasibility of the proposed system is evaluated by testing real datasets.
Computed Torque Control for an Aerial Robot Equipped with Two Coaxial Rotors Having a 2-DOF Tilt Mechanism

Since all of the rotors’ configuration in conventional drone are fixed to the fuselage, the attitude of the drone needs to be tilted during revel flight motion in the application of human transportation, delivery of goods, etc. It is also difficult for such drones to maintain a stable flight in the proximity inspections of bridges, buildings, etc. Therefore, we have already proposed an aerial robot that has two tilted coaxial rotors so as to realize multifunctional locomotion modes, where some excrements have been conducted to verify the omnidirectional locomotion modes for the robot to traveling on the ground. In this research, we present a computed torque control strategy for the robot, because a model of the robot can be simplified to a fully actuated model, which has six actuator inputs and six generalized coordinate outputs. Thus, a control allocation problem of the system is solvable, where a coordinate transformation is used to simply the allocation problem. Some simulations are demonstrated to verify the effectiveness of the computed torque control strategy for the robot.
Start-up Stage for an Electric Field Assisted Fused Deposition

Electric field assisted Fused Deposition Modeling (E-FDM) is a promising technique in the field of 3D printing. However, large nodes often appear in the start-up stage of printing. When the interval between thin lines is small, large nodes will seriously affect the subsequent printing. In this paper, we will present a method to avoid large nodes in start-up printing according to studies the start-up stage of the printing. The startup stage is a process of liquid gradually deforming and initially contacting the substrate under the action of electric stress. Polycaprolactone (PCL), a popular material for bio-medicine, is selected as the printing material. With a home-build E-FDM system, the nozzle substrate distance is fixed, and the nozzle and substrate temperatures are holding steady. With a photography system, the process of meniscus deformation is recorded. And by image processing methods, the meniscus length and volume of liquid at the nozzle can be obtained. At a set of initial liquid volumes (Vi), nozzle voltage is ramped to a fixed value at a fixed rate. The effects of Vi on the meniscus deformation during the start-up stage of the printing are examined. For sufficiently small Vi, the meniscus deforms into a cone shape (Taylor cone), and a jet with a diameter much smaller than the nozzle diameter appears. For sufficiently large Vi, the meniscus exhibits a spindle shape when it touches the substrate. Through this study, it is suggested that for high resolution printing, ramping the voltage at small Vi is preferable.