Miracle-dz/UniFAR-FTUs huggingface dataset:
Full-Dynamics-Based Bilateral Teleoperation of Hydraulic Robotic Manipulators

Teleoperated robotic manipulators can augment human capabilities to remotely operate environments which are hard to reach or too dangerous for humans. Furthermore, in teleoperated tasks where heavy objects are manipulated or great forces are needed, a hydraulic slave manipulator may be the only suitable option. Motivated by the recent advances in nonlinear model-based (NMB) control of hydraulic robotic manipulators, this study proposes a full-dynamics-based bilateral force-reflected teleoperation, which is designed between a multiple degrees-of-freedom (n-DOF) electrical master manipulator and an n-DOF hydraulic slave manipulator. Based on the authors' knowledge, this is the first time that such a system is designed for the teleoperation of hydraulic manipulators. The individual controllers for the master and slave manipulators are designed based on the virtual decomposition control (VDC) approach. Furthermore, a communication channel is designed to couple the two manipulators. Very importantly, this allows arbitrary motion and force scaling between the master and slave manipulators. The performance of the proposed method is demonstrated with a full-scale two-DOF hydraulic slave manipulator.
Non-Linear Iterative Regularization Algorithm for Neural Activity Reconstruction from EEG Data

In this work a novel non-linear iterative regularization algorithm applied to the reconstruction of neuronal activity is presented. A physiologically-based non-linear spatio-temporal constraint is used for solving the dynamic inverse problem associated to the reconstruction of neural activity of the distributed sources. The proposed method includes the spatio-temporal constraint in a cost function based on a $l$ -2 norm. A simulated EEG data-set is used in order to evaluate the performance of the proposed algorithm for three and five simultaneously active sources under several signal-to-noise ratios, by using relative error measurement. A comparison analysis is performed against the MSP and IRA-L2 source reconstruction methods, where the proposed non-linear method improves the reconstruction of neural activity in terms of the relative error.
Modeling of Photovoltaic Panels

Through the photovoltaic effect, photovoltaic cells convert solar energy into electrical energy. Photovoltaic cells are connected in series to reach the desired voltage (around 700-1000V DC), then parallel in order to obtain a higher current (8-16 A). The photovoltaic cells must be used at their maximum power point. The I – U curve of a photovoltaic panel is non-linear, and environmental factors such as temperature or solar irradiation bring considerable variations to the characteristic and location of the maximum power point. This paper presents a method based on the mathematical model of a diode for tracking the maximum power point of a photovoltaic panel or a photovoltaic field under different meteorological conditions using only the data provided by the manufacturers. The model has been validated with experimental data of a commercial photovoltaic module, CHSM6610P Astroenergy.
Automatic 3D segmentation of MRI data for detection of head and neck cancerous lymph nodes

A novel algorithm for automatic 3D segmentation of magnetic resonance imaging (MRI) data for detection of head and neck cancerous lymph nodes (LN)) is presented in this paper. The proposed algorithm pre-processes the MRI data slices to enhance quality and reduce artefacts. A modified Fuzzy c-mean process is performed through all slices, followed by a probability map which refines the clustering results, to detect the approximate position of cancerous lymph nodes. Fourier interpolation is applied to create an isotropic 3D MRI volume. A new 3D level set method segments the tumour from the interpolated MRI volume. The proposed algorithm is tested on synthetic and real MRI data. The results show that the novel cancerous lymph nodes 3D volume extraction algorithm has over 0.9 Dice similarity score on synthetic data and 0.7 on real MRI data. The F-measure is 0.92 on synthetic data and 0.75 on real data.
High Performance Humidity Sensor by Using Silicon Nanowire Array

This paper presents a low-cost and high-performance humidity sensor using silicon nanowire (SiNW) array. The sensing mechanism is based on the resistance variations due to the adsorption/desorption of water vapor on SiNW array. With the help of traditional microfabrication technology, a large number of low-cost as well as highly controllable SiNW arrays were fabricated, the whole fabrication process is compatible with complementary metal oxide semiconductor (CMOS) technology, such as lithography, reactive ion etching and wet etching. The fabricated SiNW array shows rapid response and high sensitivity to different humidity environments. Our experiments indicated that the signal of SiNW array-based humidity sensor can reach stable in 30 seconds when the humidity changes.
Real-Time Detecting Method of Marine Small Object with Underwater Robot Vision

Detection and counting small objects using under-water robot draw an appealing attention because of its urgent demands in marine aquaculture. Because this challenge problem must be solved before the underwater robot can be used to catch seafood in practice instead of diver. This paper proposed a novel method using Faster R-CNN and kernelized correlation filter (KCF) tracking algorithm to detect seafood objects, such as sea cucumber, sea urchin, and scallop and so on in real time. Firstly, we trained an accurate and stable Faster R-CNN detector with VGG model using underwater image database, which is built by ourselves. Next, we recognized and tracked the seafood objects in order to fetch them using underwater robot vision in naturalistic ocean environment. The experimental results show the proposed method can recognized and catch seafood in real time using our integrated underwater robot.
FVRD: Fishing Vessel Relations Discovery Through VMS Trace Analysis

The sailing information of fishing vessels are recorded through Vessel Monitoring Systems (VMS), which provides the basis to discover the spatial-temporal pattern of fishing activities. Previous research calculates the fishing density distribution from VMS traces, however, the relationships between fishing vessels are neglected. These kinds of relations, such as fishing vessel groups, may have some impact on fishing density distributions. This paper reveals the potential to construct fishing vessel relations through VMS trace analysis. It takes the VMS traces of all fishing related vessels in the East China Sea in 2016 as the object. The proposed system exploits the parallel Map-Reduce computation model to evaluate the spatial closeness among vessels in order to construct the vessel relation model over different time periods. After constructing the relation model, some key conclusions are revealed from the relation model. It shows that 72% vessels share the cooperation relations over one week, confirming the intuition that most fishing vessels are sailing together for fishing. Moreover, 42% vessels are in the long-term cooperations (over four weeks), representing the core members in each individual group.
The GPBiCOR Method for Solving the General Matrix Equation and the General Discrete-Time Periodic Matrix Equations

This paper is concerned with the numerical solutions of the general matrix equation <inline-formula> <tex-math notation="LaTeX">$\sum ^{p}_{i=1}{\sum ^{s_{i}}_{j=1} }\,\,{A_{ij}X_{i}}{B_{ij}} = C$ </tex-math></inline-formula>, and the general discrete-time periodic matrix equations <inline-formula> <tex-math notation="LaTeX">$\sum ^{p}_{i=1}\sum ^{s_{i}}_{j=1} (A_{i,j,k}X_{i,k}B_{i,j,k}+ C_{i,j, k}X_{i,k+1}D_{i,j,k}) = M_{k}$ </tex-math></inline-formula>, for <inline-formula> <tex-math notation="LaTeX">$k = 1, 2, \ldots ,t$ </tex-math></inline-formula>, which include the well-known Lyapunov, Stein, and Sylvester matrix equations that appear in a wide range of applications in engineering and mechanical problems. Recently the generalized product-type BiCOR method, denoted as GPBiCOR, has been originally proposed to solve the nonsymmetric linear systems <inline-formula> <tex-math notation="LaTeX">$Ax = b$ </tex-math></inline-formula>, and its significant convergence performance has been confirmed in many numerical results. By applying the Kronecker product and the vectorization operator, we develop a matrix form of the GPBiCOR method to approximate the solutions of the above-mentioned general matrix equation and general discrete-time periodic matrix equations. We present the theoretical background of the extended GPBiCOR method and its main computational aspects. Furthermore, several numerical examples of matrix equations arising in different applications are considered to exhibit the accuracy and the efficiency of the proposed method as compared with other popular methods in the literature.
Adaptive Filter of Seabed Clutter Onboard the AUVs of an Active Multistatic Sonar Network

This work aims at reducing the false alarm rate of a multistatic active sonar working in coastal, shallow waters. The sonar system is implemented through a heterogeneous manned-unmanned network including autonomous underwater vehicles (AUVs) equipped with receive hydrophone arrays. According to a cognitive sonar philosophy, each AUV characterizes the environment by processing in real time the acquired acoustic data, and exploits this contextual information to optimize the sonar performance in terms of increased probability of detection and reduced false alarm rate. The paper presents an algorithm to identify areas of compact clutter on the seabed in order to filter out as false alarms the contacts falling in those areas. Promising experimental results are reported, which were obtained under different environmental conditions (during LCAS16 and DMON17 sea trials).
R-Tree Node-Splitting Algorithm Using Combined Quality Factors and Weights

In this work we introduce a new approach to calculate splitting quality factors using the distribution of objects inside overflown nodes. Finding most proper splits for overflown R-tree nodes leads to better performance, since it determines index final shape; its tree height, nodes count, and overlap percentage between nodes. A linear cost scan of overflown node’s objects identifies the distribution of objects’ locations in relative to its node’s bounding rectangle, then using objects’ locations to calculate the quality factors. Each factor value is normalized and is given a weight, and then all are combined to select the best axis to split the node along it.
Vitamin D3 as adjunctive therapy in the treatment of depression in tuberculosis patients: a short-term pilot randomized double-blind controlled study

Objective We aimed to evaluate whether high-dose cholecalciferol has beneficial effects on depression in pulmonary tuberculosis (PTB) patients. Methods This pilot, randomized, and double-blind trial enrolled 123 recurrent PTB patients (aged ≥18 years) meeting Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV) criteria of major depressive disorder from four hospitals in Southeast China. Patients were randomly assigned to 8-week oral treatment with 100,000 IU/week cholecalciferol (Vit D group) or a matching placebo (control group). The primary outcome was treatment response, defined as a 50% reduction in symptoms and change in scores of the Chinese version of Beck Depression Inventory (BDI) from baseline to 8 weeks. Relative risks of depression were estimated using multivariable logistic regression. Results Finally, 120 patients were enrolled, including 56 test patients and 64 controls. After 8 weeks, the treatment response or BDI scores did not differ significantly between groups. Multivariate logistic regression showed that BDI scores were not significantly improved in the Vit D group after adjustment for age, time to first negative smear, or 25-hydroxyvitamin D level. Conclusion The use of high-dose Vit D3 supplementation may not be warranted for reducing depressive symptoms in the PTB population. Nevertheless, this finding should be validated by further large-scale studies according to different kinds of depression or Vit D receptor polymorphism genotype.
Combination of the tubular retractor and brain spatulas provides an adequate operative field in surgery for deep-seated lesions: Case series and technical note

Background: Surgeries for deep-seated lesions are challenging because making a corridor and observing the interface between lesions and normal brain tissue are difficult. The ViewSite Brain Access System, which is a clear plastic tubular retractor system, is used for resection of deep-seated lesions. However, the tapered shape of this system may result in limitation of the surgical field and cause brain injury to observe the interface between lesions and normal tissue. In this study, we evaluated the usefulness of the combination of ViewSite and brain spatulas. Methods: Nine patients were retrospectively identified who underwent resection of deep-seated lesions with the combination of Viewsite and brain spatulas. We assessed the extent of resection, prognosis, and quantitative brain injury from postoperative diffusion-weighed imaging (DWI). Results: There were four total radiographically confirmed resections. Subtotal resection in four patients and partial resection in one with central neurocytoma were achieved because these tumors were strongly adherent to the choroid plexus and ependymal veins. Only one case of metastatic tumor relapsed 6 months after surgery. The mean postoperative high signal on DWI was 3.68 ± 0.80 cm3. Conclusions: The combination of ViewSite and brain spatulas provides wide and adequate operative fields to observe the interface between lesions and normal tissue, and to prevent brain injury from excessive retraction pressure on the brain derived from repositioning of the ViewSite. Postoperative 3D volumetric analysis shows minimal damage to normal brain tissue. This report may provide new insight into the use of the ViewSite tubular retractor.
From AutomationML to AutomationQL: A By-Example Query Language for CPPS Engineering Models

Model-based engineering is an emerging paradigm to deal with the complexity of multi-disciplinary engineering in CPPS projects. In such projects, different kinds of models are created during the lifecycle of a production system. Automa-tionML is a promising standard to provide a unifying format to represent and connect the different engineering models. Dedicated tool support has been developed for AutomationML in the last years to create and evolve models. However, when it comes to querying AutomationML models, implementation-related query languages have to be currently used. These languages have a certain complexity as they are not directly based on the concepts of AutomationML but on the underlying technological concepts and encodings of AutomationML. This often hinders the formulation of automatically executable queries by domain experts. In this paper, we propose a dedicated query language for AutomationML called Automation Query Language (Au-tomationQL) which is directly derived from AutomationML. Using this query language, queries can be defined in a by-example manner which allows engineers to formulate queries in terms of AutomationML concepts instead of switching to an implementation-oriented query language. We illustrate how AutomationQL is defined, how queries can be formulated as well as how tool support is provided to automatically evaluate the queries and represent their results. Finally, we contrast our solution with existing query languages and derive a roadmap for future research on AutomationQL.
Model-Based Active Impedance Controller Development of the Exoskeleton Rehabilitation Robot (ERRobot) for Lower-Extremity

This paper presents a hierarchy and hybrid control architecture for a wearable exoskeleton lower-limb rehabilitation robot. The highest level is to calculate muscle-tendon net moment around joint based on muscle-model and skeleton rigid mechanical system, do a lower-limb recovery assessment, and finally select a proper rehabilitation exercise (passive, active-assistive, active or resistive exercise). By using the information of exercise type, the middle-level controller makes a decision to implement which type of controller (impedance or torque controller) that creates the torque command to feed low-level controller. The low-level controller is the realization of torque controller for PMSM (Permanent Magnet Synchronous Motor). The paper focuses on the design of active impedance control belonging to the middle-level control module. Active impedance control regulates the transfer of energy between the exoskeleton and the user, and its goal is to improve the dynamic response of the human limbs without sacrificing the user's control authority. The performance of the controller is demonstrated through simulation.
A Centralized and Conflict-Free Routing Table Update Method through Triplets' Lists Vector in SDN Architectures

For a long time, the distributed management protocols have proven their efficiency in managing computers' networks; however, the recent success of centralized management architectures such as software-defined networking (SDN) to supervise large-scale network infrastructures such as the Cloud, is attracting increasing interest from major computer network companies. In this architecture type, the network switches and routers just simply forward packets by following the flow table rules set by a controller, which owns the control plane of the network. Thus, faced with a failure, for example, the concerned nodes follow the rules provided by the same controller. With the IPFRR (IP Fast ReRoute) routing strategy known for its fast rerouting capabilities, these rules are computed and set up in the devices in advance. However, when a link or node failure becomes persistent over time, the controller must recompute the routing paths and update the nodes in order to keep an acceptable quality of service (QoS). In this paper, we propose a conflict-free mechanism for the routing and rerouting tables' updates of the nodes by the controller without disrupting the current traffic. We describe an efficient strategy of choosing the nodes to update, and we define an update scheme for these nodes. We demonstrate through the simulation of our update's scheme on different networks that our strategy improves QoS by reducing packet routing delays and the data loss rate in case of a persistent link failure in the network.
Indoor Positioning Technology based on Deep Neural Networks

With the increasing demand for location based services, indoor positioning technology has become one of the most attractive research fields. Because the large indoor scenarios exist a large number of Wi-Fi signal, the researchers make it possible to use the existing infrastructure for locating in indoor environment and greatly reduce the cost in recent years. However, this positioning scheme also exists serious problem, the change of the indoor environment can affect the results of positioning, it will lead to the instability and low accuracy of positioning system, positioning method based on Wi-Fi has not been widely used at present. This paper proposes an indoor positioning solution based on the deep learning networks with the fusion of multi-source data, we design the deep learning networks model and introduce Restricted Boltzmann Machine to initialize networks and use grid search to optimize the parameters of networks. When data set is built, we consider the problem of whether the APs are moved or closed, this paper also considers the geomagnetic data, iBeacon in indoor environment, thus the dimensions of the input data are increased, and the stability of the indoor positioning system is improved, the method of cross validation fully trained network is utilized in this paper, at the same time, a data smoothing processing based on Kalman Filter (KF) is introduced. The result of the experiment shows that, average positioning error of scheme based on DNN and Kalman filter is 0.29m, the maximum position error is 1.59m, the position error is 96.33% within 1m, position accuracy is 100% within 2m, basically meets the positioning requirement.
Turbulence Observations in the Northern Gulf of Mexico from a Slocum Glider

In this study, we show that a Webb Slocum G2 deepwater glider equipped with a microstructure package can be used to quantify the horizontal and vertical structure of turbulence. We present data obtained during the 2016 Gulf of Mexico Mixing (GoMix) experiment, which took place at the edge of the continental slope of the northern Gulf of Mexico in September 2016. The glider was operated in a yo-yo mode and collected data from 20m (base of the surface mixed layer) to ∼5m above the bottom. The observational results show that turbulence is strongest in the thermocline with maximum values greater than 10<sup>−6</sup>m<sup>2</sup>s<sup>−3</sup>. Enhanced turbulent layers are often measured below the thermocline, revealing the “pancake-like” structure of the turbulent field in the ocean. The turbulent dissipation rate down to about <tex>$3\times 10^{-11}\ \mathbf{m}^{2}\mathbf{s}^{-3}$</tex> is measured in the most quiescent layers indicating extremely low levels of noise of the glider turbulence observations. Criteria have been developed to evaluate the turbulence influence on droplet dynamics in case of a spill. Based on the turbulent velocity scale, for oil droplets with rising speed greater than 10<sup>−2</sup> m s<sup>−1</sup>, the turbulence effect can be ignored. For oil droplets with rising speed less than 10<sup>−4</sup> m s<sup>−1</sup>, their motion will be affected by the turbulent flow at all depths. For oil droplets with rising speed between 10<sup>−4</sup> and 10<sup>−2</sup> m s<sup>−1</sup>, their influence by turbulence is dependent on the strength of local turbulence and water stratification. The enhanced turbulent layers shown in this study are expected to have larger impact on the oil droplet motions when encountered by the oil/gas mixture/hydrate droplets during an oil spill.
Sonar Image Quality Assessment Based on Degradation Measurement

In many (if not only) sonar applications, it is necessary to send sonar images (SIs) to a remote location for further analysis via underwater acoustic channel. Since bad channel condition only provides with limited bandwidth and unstable link, sonar images are very likely to suffer from different distortions via transmission. To this end, we develop a no-reference sonar image quality metric (NSIQM) for perceiving the quality of SIs. This metric evaluates image quality by measuring contour degradation degree between a test image and its associated filtered version. Features containing contour information are extracted from spatial and frequency domains. Besides, a bootstrap aggregating (bagging) based support vector regression (SVR) module is employed to build the relationship between features and subjective qualities of SIs. The results of experiments validate that the proposed metric is competitive with state-of-the-art reference-based quality metrics, and outperforms the latest reference-free competitors.
Improving of the Photovoltaic High Power Plant Generation Using DC/DC Micro Converters

This work presents a test method used in a simulation environment to increase the amount of electrical energy produced by the generation stage of the Photovoltaic Plant located in Salinas, Imbabura Province, Ecuador through the inclusion of DC-DC converters in the solar panel arrays and tracking techniques of the maximum power point. The results indicate that the insertion of the DC-DC converters in the system causes an increase in the amount of active power, when the system is exposed to the variation of solar radiation or changes in the ambient temperature.