Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Convolutional Neural Networks for Three-Dimensional Reconstruction of the Geometry of Objects in the Image

Automatic creation of three-dimensional prototypes and digital copies of three-dimensional objects of the real world is a revolutionary innovation that is actively used today in many spheres of human activity, for example, for identification of a person for various purposes, in intelligent transport systems, in visualization systems, as well as in digital systems for design, management and monitoring. This trend has intensified at the present time, when additive technologies have become available to a wide range of users, and large-scale repositories of three-dimensional objects are becoming increasingly popular and widespread. The problem of recognizing images of objects of the surrounding world and understanding their scale and volume by two-dimensional projections is one of the most urgent and studied problems solved by computer vision methods. However, this class of tasks is quite difficult to formalize, which makes their solution time-consuming to develop and implement. The article describes the development of a software complex that reconstructs three-dimensional scenes according to their projections using neural network machine learning methods: the basics of three-dimensional reconstruction are considered, a model of the overall architecture of the hardware and software complex is proposed the architecture of the developed neural network is given, as well as the results of training and technical experiments.
Reconstruction of Fouling Distribution from Aggregate Observations

Ultrasonic guided waves (UGWs) can propagate over long distances with little attenuation and are suitable for non-destructive evaluation of a large area. However, localizing fouling inside the closed systems (e.g., pipes, engines) remains to be challenging. Here we propose to use UGWs that propagate along the pipe circumference along multiple helical paths, thus, providing denser structural information without increasing the number of sensors. When the path crosses fouled area the UGWs propagation properties change, generating unique features into each helical wave. Such aggregate information together with the known structural geometry are then used to fit a latent variable Gaussian process to recover the fouling distribution map in a probabilistic manner.
Deep learning-based recognition of Chinese dishes in a waiterless restaurant

The outbreak of COVID-19 makes it danger going out dining. Building waiterless restaurants becomes meaningful and urgent. Because automate and accurate recognition of dishes is indispensable for ordering and charging self-served food, this study investigates the feasibility of a prototype system that uses deep networks for recognizing Chinese dishes in a self-serving restaurant. Specifically, ≈ 17,000 images and 45,000 instances of 28 categories of dishes are collected using a fixed micro-camera equipment, and 5 deep learning networks are explored for dish recognition. Experimental results on the prototype system reveal that 3 transferred deep learning networks achieve high performance, and the accuracy, mean average precision and recall are larger than 97.0%. Real-time, accurate dish recognition in an automate fashion is highly related to a great customer experience, and the real-life evaluation in this study suggests that transferred deep networks are promising to fulfill this task.
Novel Junction Temperature Optimized Operation of Dual Active Bridge Converter Using Extended-Phase-Shift Modulation Featuring SiC MOSFETs

This paper presents the operation of a dual active bridge (DAB) converter using extended-phase-shift (EPS) modulation with a focus on controlling the junction temperatures of the 1.2 kV silicon carbide (SiC) metal-oxide-semiconductor field-effect-transistor (MOSFET) half-bridge power modules. The peak-to-peak variations in the junction temperatures between subsequent operating points will be minimized, consequently softening the impact of junction temperature variations on the module's reliability. Though SiC MOSFETs exhibit several advantages, their packaging technology is still in the initial development phase, and the SiC devices have not reached the reliability levels of their traditional counterparts. Therefore, this scheme will be vital for applications that encounter frequent load/source variations, especially in the converters featuring SiC power devices. This paper discusses the benefit of such a control scheme with the help of the simulation results. It has been shown that using this proposed scheme, an average junction temperature variation of 18 degrees Celsius or less has been achieved for a wide load range apart from the maximum power point under constant voltage operation mode.
Illustration of the dependence of the electrohydrodynamic force on the electrode configuration in a positive corona discharge

In the present work, the spatial distribution of the electric field, the space charge density and the electrohydrodynamic force (EHD) of a stationary DC positive corona discharge in air are evaluated with the aim of characterizing their effects on the electric wind generated by the electrical discharge. Two electrode configurations have been considered: (a) wire-to-plane, and (b) two wires-to-plane, with three different values of the distance between the wires (0.2 cm, 1 cm, and 2 cm). Each wire is subjected to a high positive voltage and the plane is grounded. The electric field and the space charge density have been obtained by solving numerically the continuity equation for the charged particles coupled to Poisson equation. These two physical quantities allow to calculate the EHD force and to illustrate the effect of the interaction between the two wires on the EHD force distribution.
Real-Time Scheduling of DAG Tasks in Self-Powered Sensors with Scavenged Energy

Energy harvesting sensors move into the mainstream because they are regarded as a perfect match for the monitoring applications. Nonetheless, their implementation is only feasible if the computing unit, generally a microcontroller, is capable of running the real-time tasks using sophisticated scheduling and power management techniques. In this paper, we address the hard real-time scheduling problem of Directed Acyclic Graph (DAG) tasks in a uniprocessor RTEH (Real Time Energy Harvesting) computing system. In this model, a task is defined as a set of dependent sub-tasks that execute under precedence constraints with processing time and energy requirements. We demonstrate that the energy aware ED-H algorithm provides an optimal solution. We show that the scheduling policy directly includes a feasibility analysis by enforcing the precedence constraints, thus making it possible to get rid of semaphore synchronization.
Bimorph Piezoelectric MEMS Microphone with Tractive Structure

In this paper, a bimorph piezoelectric MEMS microphone with a tractive structure is proposed. The structure presents an octagon with eight cantilever beams, all connected to a small octagon in the center of the device. The tractive structure adopts the electrical connection of a series-parallel combination without the problem of leading electrical signals to pads. The proposed piezoelectric MEMS microphone is simulated using Finite Element Method (FEM). Compared with fixed boundaries and unimorph structures, it generates higher stress and larger high-stress area during operation. The simulated resonant frequency and −3 dB roll-off frequency are 11.05 kHz and 20 Hz, respectively. Furthermore, the sensitivity at 1 kHz is −41.52 dB (Ref. 1V/Pa). The proposed microphone has great potential in miniaturized pickup applications with long standby and low power consumption.
Insulation State Evaluation Method of Oil Immersed Paper Bushings Based on Multi-Dimensional Electrical Characteristic Parameters

Oil immersed paper bushing is one of the main parts in the transformers, its insulation condition is related to the safety and stability of the operation of transformer. However, the evaluation methods at present only consider single indicator index of only apply subjective or objective weigh method to evaluate the insulation states of oil immersed paper bushing. Therefore, it is necessary to study the insulation state evaluation method of oil immersed paper bushings with multi-dimensional electrical characteristic parameters based on comprehensive weigh method. This paper studies the insulation state evaluation method of oil immersed paper bushings with multi-dimensional electrical characteristic parameters based on comprehensive weigh method. The conclusions are as follows. First, the hierarchical relationship for oil immersed paper bushing considering 7 indicator indexes is constructed. Second, the insulation states of normal, attention, abnormal and faulted are classified. And the judgement model based on the comprehensive weigh assignment for oil immersed paper bushing is established. At last, five bushings of normal, suspended potential fault, corona discharge fault, damped oil and damped paper fault are scored by this model, verifying the validity of this model.
Investigation of Acoustic Underwater Propulsion with $36^{\circ}$ Y-cut Lithium Niobate Transducer

Underwater propulsion systems, using an acoustic driving force generated by the ultrasonic transducer at the interface between the solid and liquid, are studied widely. There are no moving parts such as fins or screws. In addition, the acoustic underwater propulsion of a self-propelled robot is expected to meet the requirements of higher power density and smaller size. In this work, the single-crystal 36° Y-cut lithium niobate ($\text{LiNbO}_{3}$, LN) substrates, which have lead-free and excellent high-power characteristics, are used to investigate underwater acoustic propulsion.
Resonance Frequency Identification of Non-contact Integrated Permanent Magnet Vernier Motor Based on FFT

Due to superior features of non-contact torque transmission and maintenance-free, magnetic gear is gradually replacing mechanical gear in industrial applications. However, magnetic gear is equivalent to a two-inertia system, which causes resonance of the speed loop. When the motor system has the problem of resonance, the identification of resonance frequency is very significant to the design of control algorithm. In order to solve this problem, direct speed analysis method was proposed, which compares the error between actual speed and given speed through the feedback loop. The results showed magnetic gear could bring a periodic resonance of speed loop when load changed. Next, an accurate and fast resonant frequency identification algorithm based on Fast Fourier Transform was performed to identify resonant frequency on-line. By analyzing frequency spectrum of speed error, resonant frequency points are obtained directly. Finally, the accuracy and performance of the on-line identification algorithm are verified.
Detection of cyber attack in smart grid: A Comparative Study

Smart grid steady control relies heavily on the communication infrastructure among sensors, actuators, and control systems, which makes it vulnerable to cyber-attacks. Accurate acquisition of dynamic state information is deemed vital for efficient detection of these cyber-attacks on a smart grid. However, several popular state estimation methods at the present stage are restricted in practical use and require some assumptions. In this paper, we investigate the security of smart grid systems. We (1) identify and define the security problem in the smart grid, (2) compare the performance of several state estimate methods including Least Square, Kalman filter, Extend Kalman filter, in identifying smart grid dynamic information using measurements, and (3) investigate the Chi-square detector, Euclidean Distance, and Cosine similarity matching approaches for attack detection.
Competition between acceleration and loss mechanisms of relativistic electrons during geomagnetic storms

[1] We demonstrate quantitatively that whether a geomagnetic storm results in an increase or decrease of relativistic electrons in the outer zone depends on the competition between the physical processes producing the energization and loss of electrons. We construct a simplified one-dimensional model for the electron energy distribution incorporating electron energization by cyclotron resonant interaction with whistler-mode chorus, and electron losses due to pitch angle scattering into the loss cone by combined plasma waves ( in particular, electromagnetic ion cyclotron waves and plasmaspheric hiss). We do not include radial diffusion explicitly since the model applies to the region 3 < L < 5 where radial diffusion is expected to be weak. We treat the chorus wave amplitude and electron loss rate as model input variables and compute the solution for the electron energy distribution as the model output. The extent of the electron flux increase or decrease during the event is found to be sensitively controlled by the magnitudes of the wave amplitude and electron loss rate.
Magnetic Flux Path and Inductance Analysis of Flux-Switching Machines with Different Field and Armature Winding Configurations

Wound-field flux-switching machines (WFFSMs) have both field and armature windings in the stator eliminating permanent magnets in the rotor for a simple and robust machine design. These two windings need to be optimally placed within the limited stator to maximize the power density and thermal performance. However, the field winding introduces additional magnetic flux paths through stator bridges leading to an inefficient and longer flux path and reduced torque density. Therefore, WFFSMs require in-depth magnetic flux analysis depending on the different winding configurations and rotor design. This paper investigates the magnetic flux path of WFFSMs with different winding configurations in comparison with a conventional flux-switching permanent magnet machine having an identical geometry. The energy conversion loop analysis is conducted to visualize and quantify the impact of the longer magnetic flux paths. Using the finite element method (FEM), incremental (dynamic) inductances are calculated considering saturation and cross-coupling effects. The behavior of dq- inductances of both FSPM and WFFS machines are comprehensively studied and compared. To reduce the adverse impact of the longer magnetic flux, a rotor tooth width optimization is conducted.
Improving Public Transport Efficiency in Mauritius using Artificial Intelligence and Internet of Things solutions

Bus transport has been an important driver of the economic growth of Mauritius for decades. In recent years, it has been facing several challenges, such as competition from the Metro Express and an inability to upgrade its services. In addition, public buses still face inefficiencies that discourage the public to travel by them. These include bus drivers tending to drive slowly on purpose or dangerously fast resulting in their schedules at bus stops being unreliable. This paper exploits advanced technologies such as artificial intelligence, Internet of Things, cloud computing and big data analytics to enhance the efficiency of bus operations and provide a better service to the public. It presents the design and implementation of a microcontroller-based system that uses real-time data from onboard camera and GPS to monitor the condition of the road and the bus speed. The system can detect whether the bus is driving too fast or purposely too slow and upload pictures as proof. A web application is also developed to process data uploaded by the system on the cloud to provide the passengers with the exact location of the buses on a map so that they can better plan their trip.
Combined ultrasound and light backscattering spectroscopy for cancer characterization: a proof of concept

Changes in the tissue microstructure occur at the cellular and nuclear scales when a tissue turns malignant. With the aim of assessing these alterations in a quantitative fashion, an ultrasound and a light backscattering technique have been combined to extract the scatterer size distribution from three different tissue-mimicking phantoms (diameter of 10 µ, 20 µm and 60 µm). The light-based technique showed more potential to characterize the 60 µm phantom (agreement with expected model R2 = 0.94) than the ultrasound-based method, which in turn better models and discriminates the 10 µm and the 20 µm phantom (R2 = 0.93 and R2 = 0.84 respectively). Results suggest that the two modalities could have complementary sensitivities regarding the scatterer sizes. The bimodal technique could potentially probe tumor microstructures at various scales, thereby allowing a thorough tissue characterization.
A DLL-Based Offset Calibration Loop Technology for Wake-Up Receivers

A DLL-based offset calibration loop (OCL) is proposed to eliminate the DC offset and low-frequency flicker noise of the two differential paths to optimize the input signal-to-noise ratio before signal demodulation. The loop technology that can effectively calibrate the offset reduces the false alarm rate of the wake-up receiver (WuRX), and improves the sensitivity and robustness. This design uses 65nm LP CMOS process for layout design and simulation verification. With a supply voltage of 0.4V, the DC offset voltage on the signal path is reduced from an initial 5mV to a calibrated 39µV, resulting in a total system power consumption of 7.4nW.
Optimization of Injected Voltage Amplitude for Low-Speed Sensorless Control of PMSM with High-Frequency Pulse Signal Injection

Sensorless control of permanent magnet synchronous machines (PMSMs) in low-speed operation is a challenging task. High-frequency (HF) signal injection methods have been successfully applied for low-speed sensorless control of PMSM. One of the most recent methods is the HF pulse signal injection method in which a series of HF voltage pulses are injected into the d-axis voltage reference. A control period of the sensorless algorithm is segmented into subperiods for injection and subperiod for an update of the field-oriented algorithm. This approach brings new possibilities and research questions as what should be the optimal amplitude value of the injected voltage. In this paper, a design and verification of the optimization procedure by calculating root-mean-square error values of the selected estimated values are presented. The optimal value of the injected voltage amplitude has been found for the given operating point by using MATLAB/Simulink simulations.
LLVM-ACT: Profiling Based Tool for Approximate Computing Technique Selection

Approximate Computing is currently an emerging paradigm that seeks to replace some data accuracy with aspects such as performance and energy efficiency. There are tools within this scope that apply some approximate computation techniques at software computational level. However, these tools are limited in a way that they only cover some specific scope, apply only one of the known techniques and/or need manual code annotations to work out. Thus, this work proposes the implementation of a tool that, according to the application profiling, chooses the most appropriate approximate computing technique to be applied. LLVM-ACT uses the LLVM compilation infrastructure, where each step is implemented as a code analysis or transformation LLVM Pass. The results show that the technique chosen by LLVM-ACT is cost-effective if low error rates and high speedup are taken into account, with an 8x speedup with 22% error rate on average with the Fluidanimate application.
Blind Phase-aberrated Baseband Point Spread Function Estimation Using Complex-valued Convolutional Neural Network

Many methods used to improve clinical ultrasound image quality, e.g. deconvolution, require precise estimation of point spread function (PSF). However, the PSF cannot be well estimated even with prior knowledge of the system setting because the unknown property of inhomogeneous sound velocity in human tissue leads to phase-aberrated PSF. In addition, most image quality improving techniques are performed over beamformed baseband data (i.e., IQ data) and most portable ultrasound systems only allows the access of beamformed baseband data because of limited data transfer bandwidth. Thus, blind phase-aberrated PSF estimation directly from the beamformed baseband data is beneficial for portable ultrasound to leverage these image quality improving techniques. For this purpose, we introduce a novel complex-valued convolutional neural network (CNN) based blind estimator of phase-aberrated PSF using beamformed baseband data. Simulation results show that the proposed complex-valued U-Net estimator produces an aberrated PSF with higher similarity to the ground truth PSF.
A robot for weighing syringes used in reference gas mixture preparation

In the preparation of some types of reference gas mixtures, one of the starting materials is a liquid. The liquid is introduced in the cylinder by means of a syringe. The weighing of these syringes before and after injection is critical for the calculation of the gas composition. A robot has been developed to facilitate this weighing of syringes used in the gravimetric preparation of these gas mixtures. From the validation work, it is clear that the robot performs the measurements more reliably and with better repeatability than the manual method. The percentage of non-conforming gas mixtures dropped from approximately 5% to less than 1%. Subsequent experiments to revisit assumptions made in the preparation process have gained new insight into effects such as convection and evaporation of liquid. Additional preventive measures are taken, to further improve the weighing accuracy, and by implication, the accuracy of the gas composition data calculated from preparation. The gain in uncertainty is different for various types of gas mixtures and depends largely on the thermophysical properties of the liquid and the gas mixture being prepared.
UAV Image Geo-Localization by Point-Line-Patch Feature Matching and ICLK Optimization

Estimating the coordinate positions corresponding to each pixel in the Unmanned Aerial Vehicles (UAV) image is a prerequisite in actual fields. However, GPS devices are vulnerable to interference or attack, making it impossible to obtain accurate position and attitude parameters. Therefore, UAV image geo-localization based on image content has become a research hotspot. This paper proposes a UAV image geo-Localization method in a GPS-denied environment based on deep-learning building semantic feature extraction and point-line-patch (PLP) feature matching, which could correct the longitude and latitude coordinates of each image pixel within the range of several km 2areas. First, we employ an enhanced edge and multi-scale (EEMS) building extraction network to retain building edges and extract multi-scale buildings. Second, we design the PLP feature using the characteristics of buildings and the distribution of adjacent buildings. Then, we design a semantic matching algorithm based on the PLP feature and optimize the result using the inverse compositional Lucas-Kanade (ICLK) method for the UAV image geo-Iocalization. Experiments show that our proposed method can better locate each UAV image pixel's position, benefiting urban and regional spatio-temporal big data fusion and update.