Miracle-dz/UniFAR-FTUs huggingface dataset:
Continuous Sensing Ability of Robot Finger Joints with Tactile Sensors

Human symbiotic robots are required to support daily activities of aging societies. However, deficient sensing ability on the current robot hands makes it difficult to perform grasping actions and efficiently accomplish manipulation tasks. Therefore, sufficient data and higher sensing ability are required for operating robot hands. In this research, a new robot finger mechanism is developed. This mechanism has the center of rotation located about the edge of the soft skin. The finger is actuated with an arrangement of a slider, linear motor, and a link mechanism. Hereby, robot finger can attach flexible skin and sensor continuously. Also, sensing ability comparison of other robot finger and the new finger with proposed mechanism is evaluated. As a result, the ability of proposed finger mechanism is confirmed.
Predictors of Performance and of Psychological Well-Being in Engineering Career Aspirants

Educational deficiencies in Peru generate that many adolescents who wish to study engineering in public universities have to study at pre-university centers, this being difficult and unsatisfactory. In this context, this study aimed to identify the predictors of academic performance and psychological well-being factors in aspiring students to engineering at a prestigious public university in Peru. The sample was of 157 students (120 men (76.4%) and 37 women (23.6%)) between 15 and 22 years of age $(M_{age} =\mathit{17.65},\ SD= \mathit{1.31})$, between 6 months to 4 years of pre-university studies. The results show that the educational level of the mother predicts negatively the test anxiety. Studies' time and self-efficacy positively predict academic performance. In addition, time of studies and the test anxiety predict negatively to psychological well-being. Finally, the self-efficacy predicted positively to psychological well-being.
Automated Path Planning for Plug & Produce in a Cutting-tool Changing Application

In this paper, a path planning algorithm is designed and tested with a real robot for a Plug & Produce demonstrator. The demonstrator is divided into modules that can be connected and removed. Modules are used for various processes like tool change and storage. This paper focuses on the process of cutting-tool change for the production industry. The Plug & Produce demonstrator uses a multi-agent system where parts and resources are agents. A part agent, e.g., a cutting-tool, can request a robot to perform skills like transportation. This requires the robot to be autonomous. The aim of this paper is to automate the path planning for industrial robotics in a Plug & Produce system. This is done by implementing a sampling based RRT algorithm combined with a collision detection function in RobotStudio. With various real time scenarios, the path planning execution time is observed and presented in the paper.
A comparative study for the selection of an entropy technique to classify bearing faults

The diagnosis of faults in ball bearings has been a major challenge especially in the non-stationary environment. This paper presents a comparative study to select a method among the approximate entropy, Sample entropy and Multi-Scale entropy that can successfully be employed as a feature vector for fault classification of ball bearings. The acquired vibration signals from the normal and faulty condition of ball bearing are firstly de-noised and then disintegrated by Ensemble empirical mode decomposition method to various intrinsic mode decompositions. From selected intrinsic mode functions the signals are reconstructed and entropies are computed by the proposed methods. The results demonstrate the effectiveness of the Multi-scale entropy technique been able to effectively differentiate the condition of bearing and can be considered to act as an important fault feature vector.
Distributed Output Feedback Consensus of Uncertain Nonlinear Multi-Agent Systems With Limited Data Rate

This paper considers the distributed output feedback consensus problem of uncertain nonlinear multi-agent systems with limited data rate. Each agent is modeled by an nth order integrator with unmeasurable states and unknown nonlinear dynamics. An (n+2)th order extended state observer (ESO) is first designed to estimate the unmeasurable agent states and the unknown nonlinear dynamics. Based on the output of the ESO, a distributed consensus protocol based on dynamic encoding and decoding is proposed. It is shown that, for a connected undirected network with nth order uncertain nonlinear agents, consensus can be guaranteed with merely one bit information exchange between each pair of adjacent agents at each time step.
Numerical Simulation of a Direct Current Mode Stirred Reverberation Chamber

The Direct Current Mode Stirred method (DCMS) is a new method which combines the advantages of Direct Current Injection (DCI) on surfaces of test objects and the advantages of a Reverberation Chamber (RC). The idea of this combination is motivated by the growing requirements of high field level susceptibility testing and the resulting significant costs. The RC already fulfils this efficiency requirement and needs quite low power to generate high field levels, but is always limited in the lowest usable frequency, which ranges typically at some hundred Megahertz. In contrast to this, the DCI (or HLDD, High level Direct Drive) technique works very effectively in the low frequency range, but is limited at higher frequencies to some hundred Megahertz. A combination thereof allows the extension of the useful frequency range from kilohertz to gigahertz and is therefore usable for EMC qualification without frequency limitation.This paper outlines the numerical simulation approach for the DCMS-System and shows the numerical models which were used. It compares the results which were gained by the numerical simulations and compares this to already available measurement data. Results with an acceptable level of uncertainty have been gained but improvements for the numerical model are foreseen in future work.
Improving Deep Learning Based Object Detection of Mobile Robot Vision by HSI Preprocessing Method and CycleGAN Method Under Inconsistent Illumination Conditions in Real Environment

The family mobile robot aims to provide more intelligent services for human home life. Each sensor has its own unique features, of which the camera can be used for object detection, recognition, tracking and more. However, the effect of object detection is easily affected by the outside world such as: inconsistent illumination, object occlusion, and so on. In order to explore the effect of inconsistent illumination on object detection algorithm about robot vision in an actual home environment, the effect of illumination on object detection confidence of robot vision was studied first and the low confidence of object detection was found under the dark environment in real environment. This paper proposes a method of HSI preprocessing to improve the brightness value of HSI space to improve the effect of object detection of robot vision in dark environment. Meanwhile, specific method of brightness migration based on CycleGAN is applied to improve the detection effect of robot vision in dark environment. Compared to HSI preprocessing method, the method of brightness migration based on CycleGAN can obtain better detection effect in robot vision.
An Improvement for Video-based Heart Rate Variability Measurement

Remote photoplethysmography (RPPG) is a technique to measure the physiological signs (such as Heart Rate) remotely. Compared with the conventional photoplethysmography (PPG), the RPPG provides long term monitoring without contact equipment and makes the application possible and comfortable in daily life. Heart Rate Variability (HRV) is a medical index which is calculated as the interval of the heartbeats. It has been proved that the HRV can be used as a biomarker for the autonomic nervous system (ANS) so that the features of HRV are possible to be utilized to detect the human stress and other emotion states [1] [2]. To make the RPPG method works more effectively in human emotion states detection, the measurement of HRV should be improved. In this paper, we propose to use the slope sum function (SSF) to improve the interbeat interval (IBI) detection. The performance of this new method was evaluated with the HRV features which can be used for emotion detection. The results showed that this new method has improved the accuracy of the HRV measurement in both frequency domain and time domain.
Comparison of Deep Learning Architectures on Embedded Devices and Generalized Fixed-point Conversion Algorithm

In recent years, deep neural networks have become the state of the art for a variety of tasks such as image classification, object detection and localization, and speech recognition. Due to the significant computational load, deep neural networks are traditionally trained on high-power CPU or GPU systems while inference can be performed on less powerful hardware. However, deployment of trained deep neural networks on low-power embedded hardware remains challenging. This is because state-of-the-art deep neural networks have a large memory footprint and require many floating point operations, and because efficient compression schemes – while being effective – require custom datatypes and compilers which limit their applicability. In this article, we present a benchmark comparison of common deep learning architectures and a novel architecture for deployment on different embedded platforms showing that further gains in accuracy and speed-up can be achieved with an optimized network design. We also propose a generalized fixed-point conversion scheme that does not require custom datatypes or compilers. Both are of interest for engineering applications as computational power in applications and expert knowledge about efficient neural network implementations are typically limited.
Land Cover Classification Using Sentinel-1 SAR Data

With the development of remote sensing techniques, optical images become more efficient compare to field survey. However, the quality of optical images would influenced by cloud. Radar is known to be very sensitive to vegetation physiognomy and biomass. The sensitivity of synthetic aperture radar (SAR) to the structural features of terrain leads to landcover classification into simple and easily interpreted structural classes. In this paper, the potential of using free of charge Sentinel-1 SAR imagery for land cover mapping in the Moravian-Silesian region, is investigated. The images recorded in 2018 were used for a per-pixel and object-based classification of agricultural land. The per-pixel classification was performed by the maximum likelihood algorithm, the object-based classification then using the support vector machine algorithm. The legend was taken from the Land Parcel Identification System (LPIS) and contained the following three classes – grassland, arable land and a class that involves hop fields, vineyards, and orchards. Post processing of the classification results has been done using the confusion matrix (also known as error matrix) and corresponding overall accuracy and Kappa coefficients of all the classification methods have been calculated. Significantly better results were achieved through object-oriented classification. In both areas of interest, the highest processing and user precision was achieved for the arable land class.
Valid Data Augmentation by Patch Alpha Matting

In this study, we designed a new data augmentation method by matting for object detection and segmentation tasks. This method first uses a trained model to retrieve easy samples from the training data. After the easy dataset is built, a new patch matting algorithm is used to obtain object’s fine border, and then a set of photo montage strategies is used to generate a new dataset. For natural image matting, obtaining accurate borders around very similarly colored foreground and background regions is a hard challenge. This problem is often encountered in reality and causes visual flaws in composite images. To avoid poor composite images, we proposed a new matting method based on color and semantic features on patches to obtain visually acceptable images in such challenging regions and used it in the data augmentation method above. Unlike the traditional data augmentation method, which can only generate similar images, our method can effectively improve the ability of models to distinguish objects from the background. In experiments, we generated 20,000 new images and added them to the original COCO dataset to train a MaskRCNN model. As a result, the performances of our model are superior to the original model on all evaluating indexes of COCO.
Image-enhanced Adaptive Learning Rate Handwritten Vision Processing Algorithm Based on CNN

Handwritten images are an important source of information in the human world, and machine learning has always been an important method for dealing with handwritten visual problems. Most of today’s machine learning uses shallow structures such as SVM (Support Vector Machine) and kernel regression. These algorithms cannot be complex. The image information is more accurately predicted, and the recognition method of machine learning has quite high requirements on the quality of the image. This paper takes the MNIST data set of the National Institute of Standards and Technology as an example to enhance the data of the handwritten image data. The Convolutional Neural Network (CNN) algorithm builds the model and uses the adaptive learning rate algorithm (Adadelta) to enhance the model accuracy. The experimental results on the MNIST dataset show that the proposed method can effectively improve the recognition of handwritten images and quickly and correctly identify handwritten images. Its accuracy of 99.6% is significantly better than neural network and logistic algorithm.
Synthesis of Gold Nanodroplets with Field Enhancement of 105 at their Tips using a Simple Wet-Chemical Method

Strong local field enhancement by plasmonic nanostructures has made them potential candidates in sensing, surface-enhanced Raman spectroscopy, and nanoscale microscopy [1]. Anisotropic nanostructures with sharp tips lead to strong field enhancements [2]. In this paper, we report the wet-chemical synthesis of nanodroplets (AuND), a new nanostructure that has a sharp tip at one end and a spherical base at the other. The AuNDs have two plasmon resonances corresponding to plasmon oscillations along the transverse (base diameter) and longitudinal (height) directions. The longitudinal plasmon resonance is tunable over 800–900 nm by changing the silver salt concentration during synthesis. Numerical simulations indicate that strong field enhancement of 105 can be achieved at the tip of the AuND when excited at the longitudinal plasmon resonance.
Diagnosis of Lumbar Spondylolisthesis via Convolutional Neural Networks

Spondylolisthesis, due to bilateral defects of the posterior vertebral arch, refers to a slippage of a vertebral body over another, usually with the superior vertebral body slipping anteriorly relative to an adjacent inferior vertebral body. The most common site for spondylolisthesis is L4 or L5. As with most spinal diseases, radiological findings are the main ingredients in diagnosing spondylolisthesis. Therefore, computer-assisted systems can be used in diagnosing spondylolisthesis when adequate experience doctors in the outpatient clinics are not present. In this paper, we looked for a solution to the problem of diagnosis of spondylolisthesis by using two well-known artificial neural networks AlexNet and GoogleLeNet. The data set consists of 272 X-ray images in total. 136 of these images belong to patients suffering from spondylolisthesis, and 136 images without spondylolisthesis. Experimental results demonstrate that GoogleLeNet is 93.87% accuracy and performs slightly better than AlexNet with an accuracy of 91.67 %.
Ripple-Free Sampled-Data Output Regulation: Stability Analysis and Sampling Period Estimation

Consider the robust output regulation of linear continuous-time multivariable systems by sampled-error feedback. It is known that by the internal model principle for sampled-data systems, a controller should incorporate a continuous internal model of the exogenous signals for validating the ripple-free error response condition. This study revisits the problem, and particularly, we reformulate the relevant closed-loop regulation system as a specific hybrid representation. This enables us to address a monodromy matrix-based direct method for the stability analysis. Moreover, we can estimate the valid sampling periods assuring the system stability. We also observe that the set of all the allowable sampling periods in general is not connected in the time axis. As a consequence, we propose a line-search algorithm for estimating the local maximum allowable sampling periods (LMASPs).
A Performance Comparison of Routing Protocols for Tramcars in Mining Industry

The tramcar is a significant transportation mode in the mining industry to move the coals from underground mining field to ground. With the applications of advanced wireless sensors in the mining industry, the information of the underground fixed objects can be captured instantly. However, the current mobile communication is difficult to support the information collection of mobile tramcars. In particular, the autonomous-driven tramcars could move in high-speed where the current mobile protocols are not proved to support this scenario. This paper compares the most well-known routing protocols of mobile ad-hoc networks (MANETs) including AODV (Ad hoc On-Demand Distance Vector Routing), DSDV (Destination-Sequenced Distance-Vector Routing) and DSR (Dynamic Source Routing), by employing NS-2, with aiming to examine whether the performance of these protocols can fulfill the combination requirements of Coal Mining Industry. Besides, a comprehensive analysis is given to discussing the possibility of applying existing MANET routing schemes in the underground scenarios of the mining industry.
Semi-automatic quality inspection of solar cell based on Convolutional Neural Networks

Quality control of solar cells is a very important part of the production process. A little crack or joint failure can cause bad performance of the cell in the future, partly because the defective areas can be electrically disconnected from the active zones. Nowadays, one of the techniques to carry out this control is electroluminescence (EL), which allows obtaining high-resolution images of the cells where a visual and non-invasive inspection of defects can be done. This inspection is mostly performed by trained human operators. However, as the eyes become tired after a working day and the subjectivity of the operators, the accuracy with which the defect detection is done may be compromised. In order to solve this problem, a method to assist the operator in the inspection of polycrystalline silicon solar cells surface from EL images based on Convolutional Neural Networks is proposed. The method would classify the cells as defective and non-defective, and suggest those cells that are defective for re-inspection. Also, it would propose a segmentation map of the defects in the cell. To compensate for the lack of image samples in the dataset, each cell image is divided into regions by a sliding window. Then, each region is classified as defective or non-defective. And finally, all classifications related to the cell are resembled obtaining a segmented image of defective areas in the cell.
Enhanced Energy Density and Optical Absorption in a 3D Photonic Band Gap Cavity with Finite Support

The confinement of light to a tiny volume is an important drive in nanophotonics. Notable applications such as trapping of photons, sensing for biotechnology, control of spontaneous emission and Cherenkov radiation, lasing, and cavity QED [1–3]. While much work has been done on microspheres, micropillars, microdisks, plasmonic cavities, toroidal rings, or 2D photonic crystals, there are a few studies on cavities in a three-dimensional (3D) photonic band gap crystal. Therefore, we study the transport and storage of light in a cavity in a 3D photonic band gap crystal slab. The crystal has finite support as it is surrounded by free space, as in experiments. We employ the finite element method (on a computer cluster) to model the diamond-like inverse woodpile crystal that consists of two orthogonal arrays of pores in a high-index dielectric such as silicon and that has experimentally been realized by CMOS-compatible methods [4]. The resonant cavity is formed in the proximal region of two selected orthogonal pores with a radius smaller than the ones in the bulk of the crystal [5].
Frequency-and-polarization-controllable High-power THz Wave Generation Using Organic Nonlinear Optical Crystal

Arbitrary control of high-power THz polarization and frequency is studied by the numerical calculation based on the coupled wave approach. THz wave generation using organic crystals via optical rectification provides high conversion efficiency. To overcome the limitation of the generated THz field direction which comes from their optical anisotropy, we propose the synthesis of THz wave generated from two crystals whose optical axis are orthogonal each other. By the calculation, the linearly and circularly polarized THz wave generation by changing the time delay of two pump pulses is demonstrated.
Plate Springed Parallel Elastic Actuators for Efficient Movement of a Planar Snake Robot

This paper presents plate springed parallel elastic actuators (PEAs) for snake robots. In our previous study, compression springs are mounted on both lateral sides of the PEA-based snake robot to reduce the energy consumption. However, it grows in size and the spring might touch the outdoor environments, which disturbs the movement of the snake robot. To solve this problem, the tension spring was replaced by a plate spring. In the present paper, after analyzing the shape and force of the plate spring under the constraint of the robot joint and applying them to the dynamics, we found the optimal stiffness combination in each moving speed. Finally, the energy consumption was evaluated by the measured motor current in experiments.
The terahertz near-field response of graphene layers and graphene structures

We report on the experimental study of the scattering and the near-field interaction of a THz electromagnetic field with graphene layers near the probe of a terahertz near-field microscope. There are resonance lines in the spectrum for a graphene monolayer-based transistor structure, which is possibly due to the strong plasmon response at terahertz frequencies and the manifestation of plasmon interference in the graphene strip.
Internal Structure Research of 3D Printed Cellular Structures by Laser-ultrasonic Structuroscopy

Broadband ultrasonic spectroscopy is used for locating of damages due to ballistic impacts about 50 $\mu$m wide and more. The method allows the depth of occurrence of defects and the length of cracks to be determined with an accuracy of no more than 200 $\mu$m and no less than 500 $\mu$m, respectively. Metal articles may be up to 20 cm thick with a cross-sectional area of 3 to 10 m2.
Temperature Noncritical Phase Matching for Borate Crystals

The borate crystals are often used for laser radiation frequency conversion in visible and ultraviolet (UV) areas via sum-frequency generation (SFG) and near infrared (IR) band via different-frequency generation (DFG). In order to obtain the complete data for all possible frequency conversion processes the presentation was proposed in [1] in the form of diagram for figure of optical merit (FOM: FOM2=(deff)2/(n1n2n3)) with maximum values of the effective nonlinear coefficient deff along the phase matching directions for every combination of interacting wavelengths. It is also possible to use this diagram for finding interacting wavelengths combinations with noncritical angular and spectral phase matching.