Miracle-dz/UniFAR-FTUs huggingface dataset:
RRT-Based Path Planning Considering Initial and Final Pose under Curvature Constraints for Nonholonomic Wheeled Robot

Rapidly-exploring random trees(RRT) are popular algorithms in path planning, because they provide efficient solutions to singe-query problems and possess probabilistic completeness. Its modifications, such as RRT* and Informed RRT*, extend RRT to asymptotically find optimal solutions as the number of sampling approaches infinity. These algorithms, however, give no considerations to robot's poses and kinematic constraints, and therefore their results can be unfeasible for a nonholonomic robot with given initial pose and desired final pose. In this paper, we present another modification of RRT* for nonholonomic path-planning with not only kinematic constraints but also initial and final pose constraints. The proposed method constructs the search tree as a directed graph of which each node retains the position (x, y) plus the robot pose $\theta$, and a segment of clothoid curve and line is used for connecting and evaluating the cost between two nodes. Furthermore, by building two trees from both initial and final poses and executing bidirectional search, this method can find a path containing crosscut point. We experimentally show that our approach calculates smoother, more feasible paths than RRT* and satisfy the given constraints on curvature.
Hybrid PV Array Configurations for Mitigating the Mismatching Power loss and Number of Peaks in the Output Characteristics under various PSCs

The photovoltaic (PV) central inverter architecture comprises various array configurations. The conventional configurations are series-parallel (S-P), total-cross-tied (T-C-T), bridge-linked (B-L), and honey-comb (H-C). These configurations produce the higher mismatching power loss and also represents the number of peak power points (PPPs) in the output characteristics (I- V & P - V curves) under partial shading conditions (PSCs). This paper proposes the 6 × 6 hybrid configurations, such as series-parallel: total-cross-tied (S-P: T-C-T), bridge-linked: total-cross-tied (B-L: T-C-T), honey-comb: total-cross-tied (H-C: T-C-T) and bridge-linked: honey-comb (B-L: H-C). The hybrid configurations are formed by combining any two of the conventional configurations. The two-diode model of the PV module is used for modelling the hybrid configurations in MATLAB/Simulink software tool. The performance of the hybrid configurations is determined under various PSCs and compared with the conventional configurations. The amount of power generated at the global-PPP (G-PPP), the mismatching power loss and the number of local-PPPs (L-PPPs) generated in each configuration under-considered PSCs are noted.
Automatic steel bar counting in production line based on laser triangulation

Within the steel industry, hot rolling mill is the most important process in terms of quantity and variety of products. The intrinsic characteristics of such a harsh environment reduce the quantity of machine vision systems installed in this industry. Specifically, there is not industrial system in the world capable of counting reinforcing bars of any diameter with 100% of accuracy. This paper presents the development and implementation of a 3D vision system for counting reinforcing bars in a hot rolling mill overcoming the limitations of mechanical counting in existing hot rolling mills. Experimental results obtained in a real industrial environment are shown, proving the viability of the system. The proposed solution can be applied to count different diameter in actual manufacturing conditions with variable lighting conditions.
A mobile ultrasound system for majority detection

Human trafficking for the purpose of sexual exploitation is a problem worldwide and the proportion of underage victims is significant. To identify the minority of a person, the measurement of bone age using X-ray imaging is the gold-standard procedure. Beside several technical and judicial problems like radiation, this technique still requires medical diagnosis and limits the acceptance and practicability. Existing ultrasound systems for bone age determination evaluate changes of speed of sound through bone showing unreliable results for majority determining. In this work a mobile ultrasound system for the purpose of fast and reliable majority determination is developed and clinically evaluated using machine learning for classification without the need of a physician trained for age determination. In women, the complete fusion of the distal growth plate in ulna and radius bones correlates with reaching the age of 18. The existence of this growth plate is measured using the developed mobile multichannel ultrasound system. Its main parts are a low cost and low power FPGA, a fully integrated 8 channel transceiver and a WIFI module for data transfer to a mobile device. A pair of ultrasound array transducers (4 elements with 11x11 mm aperture each, 1 MHz center frequency) is positioned and moved up to 15 mm for reflection and transmission measurements through the forearm resulting in measurements of multiple paths through an existing growth plate leading to more robust results. The detection of growth plate existence was done using machine learning approaches. Different types of artificial neural nets were used. The system has been evaluated successfully in a clinical study with 149 women at Saarland University Medical Center. Classical signal processing analyzing the change of ultrasound signals during transducer movement over the growth plate showed that a difference between underage and legal age women is contained in the data, but classification had to be done using machine learning methods. The best performance for binary majority classification was achieved using a ResNet with an F1 score of approximately 84 % showing the capabilities of the setup. Currently, a multi-center study started to increase the number of data used for training the artificial neural nets, improving these results further while adapting the technology even to male subject groups.
Cascaded H-Bridge Multilevel Inverter with Single DC Source for Medium-Voltage Drive Applications

A cascaded H-bridge multilevel inverter is presented in this paper. The inverter uses only one H-bridge that is excited from an isolated DC source and will be connected in cascade with a number of H-bridges whose dc links are formed by floating capacitors. Floating capacitors work on the principle of level doubling and have the capability to converge to the desired steady-state voltages without demanding any closed loop control techniques. In steady-state, all the DC link voltages maintain a binary asymmetric ratio. The inverter generates a seventeen-level phase voltage and a thirty-three-level line voltage at unity modulation index. Regardless of the modulation index and power factor, self-balancing of capacitor voltage is achieved. Simulation of the proposed converter is carried out in MATLAB/ SIMULINK.
On Control Design and Implementation of Multilevel SSBC STATCOM Using FPGA

Multilevel converters prove to be well-suitable for high and medium voltage systems due to their lower cost and high-redundance design. For STATCOM, Single-star Bridge-cell (SSBC) topology is well applied for its satisfactory performance and superior component count. However, when number of levels increases, more switches are required to be parallelly controlled. Therefore, software-based microcontrollers (e.g. DSPs) shows limitations in terms of I/O built-in pins as well as performance. Alternatively, field programmable gate arrays (FPGAs), with their parallelism capability, are suitable for implementing control of SSBC-STATCOM. Multilevel SSBC-STATCOM control is composed of three layers which are output voltage control layer, internal current control layer and capacitor voltage balancing layer. A dq control based on PI regulators are commonly used for its simplicity and ease in implementation. In this work, detailed control design and implementation of SSBC-STATCOM using FPGA is discussed. Cyclone V FPGA with 50 MHz clock was used to implement control blocks and a hardware set-up of 142 V SSBC-STATCOM was designed to verify the results.
A Novel Method to Detect Multiple Arrhythmias Based on Time-Frequency Analysis and Convolutional Neural Networks

Electrocardiogram (ECG) is an efficient and commonly used tool for detecting arrhythmias. With the development of dynamic ECG monitoring, an effective and simple algorithm is needed to deal with large quantities of ECG data. In this study, we proposed a method to detect multiple arrhythmias based on time-frequency analysis and convolutional neural networks. For a short-time (10 s) single-lead ECG signal, the time-frequency distribution matrix of the signal was first obtained using a time-frequency transform method, and then a convolutional neural network was used to discriminate the rhythm of the signal. ECG data in multiple databases were used and were divided into 12 classes. Finally, the performance of three kinds of time-frequency transform methods are evaluated, including short-time Fourier transform (STFT), continuous wavelet transform (CWT), and pseudo Wigner-Ville distribution (PWVD). The best result was obtained by STFT, with an accuracy of 96.65%, an average sensitivity of 96.47%, an average specificity of 99.68%, and an average F1 score of 96.27%, respectively. Especially, the area under curve (AUC) value is 0.9987. The proposed method in this work may be efficient and valuable to detect multiple arrhythmias for dynamic ECG monitoring.
Energy Optimization for a Smart Home with Renewable Generation

This article looks at optimizing cost of electricity for a small residential microgrid. Each household has renewable generation capability and daily load is portioned into essential and schedulable loads. Dual tariffs exist, for buying and the other for in-feed into the grid. The optimization consist of scheduling decisions for suitable loads as well amount of power to sell or procure from the utility depending on availability and prevailing pricing. Assuming availability of time-variant energy parameters, a global optimization problem is formulated whose solutions leads to quantification of the optimal quantity of energy purchased and sold for each of the households. When the unrealistic assumption of availability of information is removed from the implementation of the global optimization, an online algorithm that only requires the current values of the time varying supply and demand processes shows by simulation that the distributed algorithm can realise credible scheduling of prosumer household electricity usage.
Wave Speeds and Green’s Tensors for Shear Wave Propagation in Incompressible, Hyperelastic Materials with Uniaxial Stretch

Assessing elastic material properties from shear wave propagation following an acoustic radiation force impulse (ARFI) excitation is difficult in anisotropic materials because of the complex relations among the propagation direction, shear wave polarizations, and material symmetries. In this paper, we describe a method to calculate shear wave signals using Green’s tensor methods in an incompressible, hyperelastic material with uniaxial stretch. Phase and group velocities are determined for SH and SV propagation modes as a function of stretch by constructing the equation of motion from the Cauchy stress tensor determined from the strain energy density. The Green’s tensor is expressed as the sum of contributions from the SH and SV propagation modes with the SH contribution determined using a closed-form expression and the SV contribution determined by numerical integration. Results are presented for a Mooney-Rivlin material model with a tall Gaussian excitation similar to an ARFI excitation. For an experimental configuration with a tilted material symmetry axis, results show that shear wave signals exhibit complex structures such as shear splitting that are characteristic of both the SH and SV propagation modes.
Rolling Bearing Initial Fault Detection Using Long Short-Term Memory Recurrent Network

The complete failure of the rolling bearing is a deterioration process from the initial minor fault to the serious fault, it is meaningless for guiding maintenance when the serious fault is alarmed. This work presents a novel initial fault diagnosis framework based on sliding window stacked denoising auto-encoder (SDAE) and long short-term memory (LSTM) model. In this approach, multiple vibration value of the rolling bearings are entered into SDAE by sliding window processing. Then, multiple vibration value of the rolling bearings of the next period is predicted from the signal reconstructed by the trained SDAE in the previous period using LSTM. For the given input data, the reconstruction errors between the next period data and the output data generated by trained LSTM are used to detect initial anomalous conditions. The proposed method not only utilizes the ability of SDAE to learn the inherent distribution of data, but also ensures that LSTM can extract timing relationships between data cycles, and the model is built using only normal data. The initial fault detection as a key difficulty in the operating condition monitoring and performance degradation assessment of the rolling bearing is effectively solved. Experimental and classic rotating machinery datasets have been employed to testify the effectiveness of the proposed method and its preponderance over some state-of-the-art methods. The experiment results indicate that the proposed method can effectively detect the initial anomalies of the rolling bearing and accurately describe the deterioration trend with strong robustness, and have high significance for maintenance guiding.
Strategies for Sustainable Use of Indigenous Cattle Genetic Resources in Southern Africa

Indigenous cattle breeds are the most important livestock species in the Southern African Development Community (SADC) region owing to their role in human food, nutrition, income, and social security. Despite the role of these breeds in the household and national economies, they are currently underutilised, their productivity remains low, and populations are faced with extinction. In addition, there are insufficient measures taken to secure their present and future value. The current review highlights strategies for sustainable use of indigenous cattle genetic resources in the region, including the use of novel production and marketing practices, women and youth empowerment, and development of the appropriate capacity building, legislative, and policy structures. At present, the lack of coordination among the different stakeholders still poses a challenge to the implementation of these strategies. To this end, partnerships, collaboration, and stakeholders’ participation are recommended to effectively implement strategies for sustainable use of indigenous cattle breeds.
Neural Interaction Detection

We develop a method of detecting statistical interactions in data by interpreting the trained weights of a feedforward multilayer neural network. With sparsity regularization applied to the weights, our method can achieve high interaction detection performance without searching an exponential solution space of possible interactions. We obtain our computational savings by first observing that interactions between input features are created by the non-additive effect of nonlinear activation functions, and that interacting paths are encoded in weight matrices. We use these observations to develop a way of identifying both pairwise and higher-order interactions with a simple traversal over the input weight matrix. In experiments on simulated and real-world data, we demonstrate the performance of our method and the importance of discovered interactions.
A New 5-Level ANPC Switched Capacitor Inverter Topology for Photovoltaic Applications

In this paper, a new active neutral point clamped (ANPC) five-level (5L) inverter is proposed. The conventional ANPC output voltage is half of the input voltage. To rectify the above problem, a floating capacitor is used to boost the output voltage equal to the input voltage, which further reduces the size of the dc-link capacitors. In addition to this, the proposed topology does not need any sensors to balance the floating capacitor, which minimize the complexity of the inverter. Further, in the proposed topology, the neutral point is directly connected to the mid-point of dc-link capacitors, which reduces the leakage current and common mode voltage due to the presence of two clamping diodes. Moreover, a new carrier signal is introduced, and the advantages are discussed. The performance of proposed 5L inverter topology is analysed in terms of dynamic loading condition. To show the superiority of the proposed topology, it is simulated in MATLAB and results are discussed.
Mestrado Integrado em Engenharia Química Investigating Cellular Response to Engineered Hydrogel Substrates

The study of cell-cell interactions in co-culture systems has revealed difficult to manipulate accurately in previous tissue engineering reports. However, microfabrication technology has evolved and stereolithography techniques can be used as tools to control cell-cell interactions of interest. Previous studies with micropatterned co-cultures were based in the selectivity of two distinct cell types to adhere to different bioactive regions of the material. However, those studies may present some challenges when studying the interaction between two types of neurons, for example. The main innovative idea behind this project is to design selectively degradable materials where any two cell types can be cultured for the study of cell-cell interactions in vitro. Using laser microstereolithography (μSL) defined multi-component micropatterned architectures of poly(ethylene glycol) – dimethacrylate (PEG-DM) and carboxymethylcellulose aminoethyl methacrylate (CMC-MA) were developed on top of chemically modified glass surfaces. Since both polymer surfaces are resistant to cell and protein adsorption, the first cell type is forced to adhere to the free glass spaces. The degradation of the CMC-MA patterns by the enzymatic action of cellulase will provide new adhesive regions in the material where a second cell type can be seeded. With this technique it is possible to control cell-cell contact and spacing between two different cell types at the microscale. This model can be further used as a tool for the study and clarification of intercellular signalling mechanisms. From degradation studies with CMC-MA under cellulase as from cell viability studies of 3T3 fibroblasts under the same enzyme, it was possible to conclude that the optimal concentration of enzyme for the degradation of CMC-MA in the micropatterns is 0.5 U/mL. It has been proven that PEG-DM is not degraded by this enzyme and the cell compatibility of the materials was confirmed. The full experiment with the culture of 3T3 fibroblasts, the degradation of the CMC-MA pattern and the culture of PC12 cells was conducted on macro PEG-DM/CMC-MA patterns and once the same experiment is conducted on the micropatterns, the applicability of the technique can be proven. Key-words: Tissue engineering; Micropatterning; Co-cultures; Glass surface modification; Polyethylene glycol; Carboxymethyl cellulose.
Nonconvex Low Tubal Rank Tensor Minimization

In the sparse vector recovery problem, the $L_{0}$ -norm can be approximated by a convex function or a nonconvex function to achieve sparse solutions. In the low-rank matrix recovery problem, the nonconvex matrix rank can be replaced by a convex function or a nonconvex function on the singular value of matrix to achieve low-rank solutions. Although the convex relaxation can easily lead to the optimal solution, the nonconvex approximation tends to yield more sparse or lower rank local solutions. As a natural extension of vector and matrix to high order structure, tensor can better represent the essential structure of data for modeling the high-dimensional data. In this paper, we study the low tubal rank tensor recovery problem by nonconvex optimization. Instead of using convex tensor nuclear norm, we use nonconvex surrogate functions to approximate the tensor tubal rank, and propose a tensor based iteratively reweighted nuclear norm solver. We further provide the convergence analysis of our new solver. Sufficient experiments on synthetic data and real images verify the effectiveness of our new method.
Circularly Polarized Array with Enhanced Isolation Using Magnetic Metamaterials

This article proposes a 2 × 2 circularly polarized array with enhanced isolation that could be used in the adaptive anti-jamming system for China’s Beidou Navigation Satellite System (BDS), and a compact dual-band microstrip antenna for GPS and GLONASS is designed at the center part of the array for multiple modes operation. The substrate integrated split ring resonators with negative effective permeability are used as the magnetic metamaterials. Four groups of the magnetic metamaterials are placed between the BDS antenna elements, which could constrain the EM filed in the area of the active element, and then significantly reduce the mutual coupling among the elements. The results tell that the isolation among the BDS elements are improved by more than 10 dB, and the performance of radiation patterns and the axial ratio of each BDS elements are also apparently improved compared to the array without magnetic metamaterials.
Diabetes is a group of metabolic diseases characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both

INTRODUCTION: Diabetes is a group of metabolic diseases characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both. The chronic hyperglycemia of diabetes is associated with long-term damage, dysfunction, and failure of different organs, especially the eyes, kidneys, nerves, heart, and blood vessels (1) . The severity of the metabolic abnormality can progress, regress, or stay the same. Thus, the degree of hyperglycemia reflects the severity of the underlying metabolic process and its treatment more than the nature of the process itself (2) .
LT/Quartz HAL SAW Resonator with Large LT Thickness over Severalfold Wavelength

A Hetero Acoustic Layer (HAL) surface acoustic wave (SAW) device is a new class of SAW device using a thin LT or LN layer rigidly mounted on a substrate. In the past reports, the LT layer was as thin as 0.13-0.221 (<0.7 µm for 1 GHz devices) to obtain a high impedance ratio (e.g. >80 dB), but this range of thickness is not easy to control in mass-production. In this study, we investigated the design space at larger LT thickness by FEM simulation. In the design space near the conventional one, in-band ripples and/or out-of-band spurious responses are generated. If a 1.4 times thicker Al or Cu electrode is combined with a LT layer thicker than a specific value (1λ for Al and 4λ for Cu), however, the ripples and spurious responses almost disappear and the deterioration of impedance ratio is limited. On the other hand, by using further thicker electrodes, a higher mode of SAW is excited preferentially at 2-2.6 times higher frequency than the fundamental mode.