Miracle-dz/UniFAR-FTUs huggingface dataset:
An Approach for CNN-Based Feature Matching Towards Real-Time SLAM

Matching keypoints between images showing the same scene under different conditions is a fundamental step for a variety of applications. Recent approaches based on convolutional neural networks show superior results in terms of discriminability compared to well established descriptors like SIFT or ORB. However, there is less previous work which brings the CNNs to automated driving applications like SLAM and analyze the performance in terms of accuracy and runtime. In this work, we take state-of-the-art patch comparison CNNs, train them from scratch and analyze the performance on the KITTI odometry benchmark. For that, we replace the ORBfrontend within the publicly available ORB-SLAM2 framework through our trained CNN variants and compare both. We show that it is necessary to downsize the complexity of the original architectures to achieve real-time capability. Furthermore, our evaluation shows that the downsized models achieve significantly higher matching performance than the ORB descriptor. Moreover, we achieve slightly better results on the KITTI odometry benchmark compared to ORB-SLAM2 while using a CNN-based feature descriptor, which can easily be adapted to different environments.
A Reference-based Source Extraction Algorithm to Extract Movement Related Cortical Potentials for Brain-Computer Interface Applications

Brain-Computer Interface (BCI) systems aim at providing a channel through electroencephalogram (EEG) to control external devices. Movement-Related Cortical Potential (MRCP) is a low-frequency and low-amplitude EEG component that has been recently introduced to control real-time BCI systems. The performance of BCI systems based on MRCP highly depends on the accuracy of single trial MRCP detection, which is a challenging task due to high amount of EEG background noise and various types of artifacts. In this paper, a semi-blind source extraction algorithm based on second order statistics and a reference signal, designed according to the BCI paradigm, is presented to extract MRCP. The performance of the method is investigated during a complex motor task, gait initiation. The reference signal utilizes the temporal information from the training set to extract MRCP. The proposed method is compared with two commonly used spatial filtering techniques as well as two source extraction methods based on second order statistics. Two different training sets including only ankle dorsiflexion data (AD) and ankle dorsiflexion plus gait initiation (GI) data was used to train the proposed algorithm and the methods that require training. The performance of the proposed algorithm and other extraction methods on the extracted MRCP was evaluated by a performance index (PI) quantifying the signal-to-noise ratio of the extracted MRCP. The PI obtained with the proposed algorithm trained with AD only training set $(2.43 \pm 1.23)$ was greater than all other investigated methods. When trained with both AD and GI trials, the performance of the method was the second best among all methods ($2.52 \pm 0.83)$. The results from this study show that the proposed method is robust against differences between training and testing sets and provides high quality MRCP extraction.
Assessment of Knowledge on Health Economics among Medical Professionals in Chennai

Objectives: To measure the level of knowledge of medical professionals on health economics in Chennai. Materials and methods: A cross sectional study was conducted in two randomly selected medical colleges in Chennai using a self administered questionnaire. The first part of the question contained respondent’s general information (age, sex, institution, designation and year of experience etc). The second part of the questionnaire was in multiple closed choice formats with focus from general knowledge of health economics to specific terms related to health economics. Results: 189 doctors who were all present on the date of visit to the colleges and who were willing to participate in the study were included. 73.5% of the doctors said they have heard of the term health economics, but of them only 43.9% said they are familiar with the term health economics. The assessment showed the overall knowledge was poor with lowest score for cost benefit analysis at 12.7% and highest for marginal cost at 59%. None of them are regular readers of articles related to health economics while 39.7% did not read at all. Only 6.3% of doctors used regularly the technique of health economics in their clinical practice while 40.8% did not use them. 92.8% of the doctors felt that subject of health economics should be included in undergraduate, postgraduate or both the curriculum. Conclusion: The outcome of this study has revealed that the knowledge level of doctors in day to day use of health economic concepts was limited. Providing doctors with basic health economics knowledge at various levels of their medical education and training will go a long way in improving their basic knowledge in health economics concepts.
A SVPWM Method With Reduced Switching Frequency Suitable for High Power Three-level NPC Rectifiers

The high power three-level NPC rectifier should reduce the switching frequency as much as possible under the premise of satisfying the current harmonic performance of the grid side and the stability of the DC neutral-point potential. Under the even carrier ratio, the existing SVPWM method that begins with the same type of small vector in each 60-degree sector has a lower switching frequency but a higher even harmonics content, and is not conducive to neutral-point potential balancing. However, P type and N type small vector selected alternatively as starting vector in different sectors has a lower even harmonics content and neutral-point potential self-balancing capability but a higher switching frequency. To address this deficiency, this paper presents a new SVPWM method, which chooses P type small vector as starting vector in the corresponding half fundamental period of three phases, and N type small vector as starting vector in the other half period, thus obtaining three different switching sequences. The three-phase average switching frequency is reduced and the even harmonics are eliminated by cycling three switching sequences. Established a three-level NPC rectifier experimental platform and the experimental results verify the effectiveness of this new SVPWM method.
Computational and Experimental Studies of Adsorption of $\mathrm{CrO}_{4}\ ^{2-}$ Molecular Anions on The Surface of Carbon Nanostructures

Search for new adsorbent materials which can allow economically-efficient schemes of the heavy metal removal is a topical research task. The Density Functional Theory (DFT) computations of the electronic structures of undoped, B- and N-doped graphene sheets with adsorbed chromate anions $\mathrm{CrO}_{4}\ ^{2-}$ were performed within molecular cluster approach. Relaxed geometries and binding energieswere calculated using B3LYP hybrid exchange-correlation functional. Oscillator strengths of electronic transitions of $\mathrm{CrO}_{4}\ ^{2-}$ anions adsorbed on graphene surface were calculated by TD-DFT method. The optical absorption spectra of aqueous solutions containing mixes of carbon nanotubes and chromate anions were measured. The luminescence emission spectra of carbon nanostructures with adsorbed chromate anions deposited on silicon glass substrate were measured. Two stable configurations were found for adsorption of $\mathrm{CrO}_{4}\ ^{2-}$ anions on undoped graphene sheets. The calculated energy of the lowest-energy transition of the adsorbed $\mathrm{CrO}_{4}\ ^{2-}$ anion is reduced by 0.5 eV in comparison with corresponding transition energy of free anion. Spectra of optical absorbance and luminescence of the systems containing carbon nanostructures measured before and after adsorption of chromate anions reveal substantial difference that confirms the principal possibility of monitoring the $\mathrm{CrO}_{4}\ ^{2-}$ anion adsorption on carbon nanostructures.
Resonant Frequency Tuning System for Repeater Resonator of Resonant Inductive Coupling Wireless Power Transfer

The resonant inductive coupling wireless power transfer (RIC-WPT) is a promising technique of the wireless power transfer with comparatively large power and high efficiency. However, the performance of RIC-WPT system is generally highly dependent on the fine adjustment of the resonant frequency between the transmitter, receiver, and repeater resonators, although the natural tolerance of the capacitor and inductance of the coil may easily lead to the significant detuning of the resonant frequency. Particularly, the repeater resonator is the key item that can improve the power transfer to a distantly located receiver resonator. However, the repeater resonator tends to have a high quality-factor; and therefore, its performance can be easily deteriorated by the detuning of the resonant frequency. The purpose of this paper is to propose a simple automatic resonant frequency tuning system for the repeater resonator as a remedy for the performance deterioration due to the resonant frequency detuning. The operation of the automatic resonant frequency tuning system was experimentally tested using the prototype wireless power transfer system. The result exhibited robust power transfer capability regardless of the natural tolerance of the resonant frequency.
Evaluation of Performance Metrics for Users of Brain Computer Interfaces during Motor Imagery

It is well-known that proper training is indispensable for users of Brain-Computer Interfaces (BCI) to acquire the required skills to control the system, particularly for BCI based on motor imagery (MI-BCI). However, in order to assess the effectivity of the training procedure, it is necessary to evaluate separately the classification algorithm and the BCI user skills. Recently, new performance metrics to quantify MIBCI skills regardless the classification algorithm, by defining class distinctiveness metrics and trial stability metrics based on Riemannian distance, was proposed. In this study, we calculated such metrics for two balanced datasets containing EEG recorded with slightly different protocols from 30 age-, gender- and education-matched subjects during MI of right-hand and left-hand movements. The protocols revealed almost no difference regarding the class distinctiveness metrics, but showed great difference when it comes to the trial stability metrics. The results of this analysis can guide the improvement of protocols for BCI based on the motor imagery paradigm.
Temperature determination of SiC MPS diodes during surge current event with measurement and simulation

The surge current ruggedness is an important factor for a diode in power electronic applications. The diodes will face very high temperatures during a surge current event. In this work, the temperature was determined during surge current event of SiC MPS diodes under different pulse lengths using measurement and numerical simulation. Even though SiC itself can withstand very high temperatures, the maximum operating temperature of the diode is limited by the anode metallization in TO-247 during the surge current event. The temperature distribution during the surge current event was analysed and explained in detail with help of numerical simulation of the diode model in ANSYS.
Cooperative Charging of Supercapacitor Trams with Current Ripple Suppression

Supercapacitor trams has been an emerging green and intelligent urban rail transportation system. Due to the high power demand of onboard supercapacitors, multiple charging modules are typically connected in parallel to charge the trams. However, The parallel connection of charging modules typically leads to the current imbalance and significant current ripples, which will degrade the reliability of the charging system. In this paper, we propose a cooperative charging approach for supercapacitor trams with ripple current suppression. In the proposed method, charging modules communicate with their neighboring nodes to synchronize the output current and achieve a constant phase difference. Then, a distributed phase-shift control method is proposed to diminish the ripple of the charging current. Experimental results show that the current imbalance can be nearly eliminated and the current ripple is reduced by 80.37% compared to the existing methods.
Three-Dimensional Integrated GaN-based DC-DC Converter with an Inductor Substrate

Generally, the improvement of power density is achieved by increasing switching frequency and using three dimensional (3D) integration technology. The emergence of gallium nitride (GaN) devices can increase switching frequency dramatically and facilitate the realization of 3D integration. By using the 3D integration technology, the structure of power converter can be reconfigured to realize a high space filling rate, and thus to improve the power density. However, the space reconfiguration also changes the magnetic flux distribution and has an impact on the thermal performance of power converter. This paper takes one of the 3D integrated structures as the example for demonstration, which uses a planar inductor as the substrate for the rest parts of power converter. By using finite element analysis (FEA) tool, in-depth study on parasitic inductance and thermal performance of 3D integrated GaN-based power converter was conducted, and experimental prototype was built for verification.
Overcoming Challenges of Validation Automated Driving and Identification of Critical Scenarios

Automated driving has arisen more and more attention both in traditional automobile industries and in IT companies. Rapid development and significant results have been achieved. However, there are few effective and satisfying methods to prove that Highly Automated Driving (HAD) is currently at least as safe as a human driver. In this paper, a novel validation method named Virtual Assessment of Automation in Field Operation (VAAFO) is introduced in detail. In VAAFO, HAD system has no access to actuators but runs in parallel while human driver drives the vehicle. Behavior comparison between human-driven vehicle and HAD in same scenario is a main new feature in VAAFO to validate automated driving and find out potential critical scenarios. Combined with correction of world model and criticality metrics, potential critical scenarios are filtered further to identify real critical scenarios. Different modules of VAAFO are explained elaborately based on these working principles. In principle, the proposed method could accelerate testing and validation of HAD a lot without bringing additional risks. Moreover, critical scenarios will be identified as well.
Review of Milan M. Jovanović’s work and impact on the power electronics industry

Milan M. Jovanović (1952-2018) is the most cited industry-affiliated power electronics researcher. This paper is a review of Milan’s work based on his top 100 cited papers in the field of power electronics (according to Google Scholar), as well as his most recently published papers. His primary area of interest was the development and performance optimization of power electronics technologies for modern server, telecom, and portable data-processing equipment, as well as for on-board electric vehicle applications. Power conversion techniques developed by research teams led by Milan have been employed in hundreds of millions of power supplies made by Delta Electronics and other manufacturers, enabling further miniaturization of electronics equipment and resulting in enormous cumulative energy savings and electronic-material waste reduction.
An FPGA-based Switch-mode Power Amplifier using Boundary Control to achieve High System Bandwidth

Power amplifiers are critical components for Power Hardware-in-the-Loop (PHIL) applications with the requirement of low cost, high bandwidth and fast dynamic response. This paper proposes an innovative combination of a GaN device and boundary controller implemented in an FPGA to realize a high bandwidth power amplifier providing fast dynamic responses. The transient time and system bandwidth have been discussed, analyzed and verified by experiment results. In this paper, the control strategy, the implementation of a physical power amplifier and the experimental results are presented. The Power Amplifier achieves a 27 kHz average switching frequency with an output power of 100W and amplifier gain of 50. The results show the dynamic response of the system is close to the theoretical best performance under certain parameters and it can track the changes of the reference in tens of microseconds. The system reaches the highest potential system bandwidth.
Single-Bacteria Isolation and Selective Extraction Based on Microfluidic Emulsion and Sequential Micro-Sieves

A high proportion of the human microbiota remains unknown, largely due to the large bacterial diversity in microbiome. Key technical challenges include the single-bacteria isolation in order to extract individual bacterial strains from the microbial extracts. Here, a single-bacteria isolation and device is developed based on microfluidic emulsion for generating microdroplets to encapsulate single bacterium, followed by selectively extract the microdroplets for any downstream bacterial culture or analysis. The microfluidic device consists of T-junction and micro-sieve array was fabricated by soft lithography and replica molding. The droplet generated by T-junction could encapsulate bacteria in single bacteria level with high efficiency (~90%). The highest isolation rate for droplet capturing by sequential micro-sieve was over 95%. The medium with the mixture bacteria was injected into the device for the specific bacteria isolation. The target type of bacteria could be sorted with high accuracy (92%). These results indicate that the single-bacteria isolation and cultivation platform is proposed for human skin microbiome studies to evaluate which biological properties of the microbiome and host will yield important new insights in understanding human health and disease related to bacterial infections and general bacteriology.
Fine-Scale Spatial and Spectral Clustering of UAV-Acquired Digital Aerial Photogrammetric (DAP) Point Clouds for Individual Tree Crown Detection and Segmentation

The field of remote sensing is undergoing rapid changes through the utilization of unmanned aerial vehicle (UAV) technology. The rise of this new technology and the corresponding growth in the application of digital aerial photogrammetric point clouds (DAP) require renewed investigation into individual tree detection (ITD) routines; most of which have been developed for airborne laser scanning (ALS) and traditional aerial imagery. This article analyzes the application of a well-known ALS-ITD routine to UAV-acquired DAP. In particular, specific modifications are proposed and evaluated aimed at improving its applicability to DAP, with particular emphasis on the incorporation of spectral information through subcrown scale k-means clustering. The new routine, which utilizes point-level spectral information in the clustering process, improved overall true positive detection by ∼6.3%, with the most significant improvements in true positive detection found in lower canopy class stems. The new routine was also tested without the inclusion of spectral information and was shown to produce poorer results by ∼4.1%, indicating that the inclusion of these data is beneficial for ITD approaches. This new routine represents an advancement of processing approaches incorporating both structural and spectral components for ITD in the era of UAV remote sensing in forested environments.
Evaluation of an indoor positioning system for added-value services in a mall

We present a Wi-Fi smartphone active shop-level Indoor Location System stable over time, which allows the development of a business model for an IT company. The proposed system covers all the steps in the deployment of the solution, from the digital map creation to the evaluation of the performance. To avoid unnecessary battery drain, only RSSI (Received Signal Strength Indicator) levels of already deployed Access Point are used in the location algorithm. The system has been deeply tested against several smartphones with a suitable accuracy in a medium-size mall, independently of the occupancy level and the smartphone model employed in the validation routes.
Fast and Scalable Distributed Tensor Decompositions

Tensor decomposition is a prominent technique for analyzing multi-attribute data and is being increasingly used for data analysis in different application areas. Tensor decomposition methods are computationally intense and often involve irregular memory accesses over large-scale sparse data. Hence it becomes critical to optimize the execution of such data intensive computations and associated data movement to reduce the eventual time-to-solution in data analysis applications. With the prevalence of using advanced high-performance computing (HPC) systems for data analysis applications, it is becoming increasingly important to provide fast and scalable implementation of tensor decompositions and execute them efficiently on modern and advanced HPC systems. In this paper, we present distributed tensor decomposition methods that achieve faster, memory-efficient, and communication-reduced execution on HPC systems. We demonstrate that our techniques reduce the overall communication and execution time of tensor decomposition methods when they are used for analyzing datasets of varied size from real application. We illustrate our results on HPE Superdome Flex server, a high-end modular system offering large-scale in-memory computing, and on a distributed cluster of Intel Xeon multi-core nodes.
The Generalized Bode Criterion: application to the dc voltage control of a three-phase photovoltaic grid-tied inverter

As renewable energies are becoming more important in the electrical generation system, power electronic converters are facing new design issues related not only to their components but also to their control loops. In this context, the Generalized Bode Criterion (GBC) appears as a good tool to correctly determine stability and to help the controller design. In order to show the potential of the GBC and how it can be applied, this paper studies a dc voltage regulation with compensation of the photovoltaic power in a three-phase photovoltaic grid-tied inverter.
Model Predictive Pulse Pattern Control with Integrated Balancing of the Neutral Point Potential

A neutral-point-clamped inverter operated with optimized pulse patterns is considered with an induction machine. For this setup, a control method is proposed that achieves high-bandwidth control of the electromagnetic torque and machine magnetization through stator flux trajectory tracking while balancing the inverter's neutral point (NP) potential. More specifically, a model predictive pulse pattern control problem is formulated and solved that addresses stator flux control and balancing of the NP potential in one single control loop. The proposed controller achieves minimal current distortions per switching frequency and is thus well-suited to medium-voltage power converters operating at low pulse numbers.
Deep End-to-end 3D Person Detection from Camera and Lidar

We present a method for 3D person detection from camera images and lidar point clouds in automotive scenes. The method comprises a deep neural network which estimates the 3D location and extent of persons present in the scene. 3D anchor proposals are refined in two stages: a region proposal network and a subsequent detection network.For both input modalities high-level feature representations are learned from raw sensor data instead of being manually designed. To that end, we use Voxel Feature Encoders [1] to obtain point cloud features instead of widely used projection-based point cloud representations, thus allowing the network to learn to predict the location and extent of persons in an end-to-end manner.Experiments on the validation set of the KITTI 3D object detection benchmark [2] show that the proposed method outperforms state-of-the-art methods with an average precision (AP) of 47.06% on moderate difficulty.