Miracle-dz/UniFAR-FTUs huggingface dataset:
A Wireless Power Transfer System Powering Multiple Gate Drivers in a Modular Multilevel Converter

In this paper, a novel wireless power transfer system (WPT) is designed for powering the multiple gate drivers in a modular multilevel converter. Considering that the gate drivers are usually distributed over a long distance, the repeater units are designed, which are placed coaxially along the power transfer route. Then the magnetic field can be enhanced by these repeater units and high system efficiency is ensured even with long power transfer distance. Each repeater unit corresponds to a load and consists of two repeater coils that are both bipolar. Moreover, the two bipolar coils in the same repeater unit are placed perpendicularly to remove the magnetic coupling between them. A suitable compensation topology is designed for the proposed WPT system, so that the constant voltage (CV) characteristic can be achieved for each load when neglecting the coil's parasitic resistance. Thus, the load power can be regulated independently without affecting each other, which greatly simplifies the power control design. Experimental results are provided to validate the proposed WPT system.
A Study On Various Cyber Attacks And A Proposed Intelligent System For Monitoring Such Attacks

World is moving rapidly towards the digital transformation. The internet across the world is growing rapidly which gives rise to many opportunities in every field including entertainment, finance, education, sports etc. As every coin has two aspects, the internet also has many advantages and disadvantages. We know about the advantages, but the only major disadvantage that everyone should be aware of is the increase in cyber-attacks. It is nothing, but the illegal activity committed on the internet. Many government websites and systems were hacked in the past few years and it has caused a huge loss to the nations like India, USA and China. These governments have already taken various steps to counter these crimes & attacks. But still, the attackers are coming up with new ways of attacking every time. There is need for some concrete solution which is not in the reach of the attackers. For developing such systems and tools we have done a deep analysis of various types of cybercrime and attacks happened in past along with existing solutions proposed by many researchers. Therefore, this paper presents a study on various cyber-attacks that were triggered in India and other countries in the past few years. The various prevention methods proposed in past to deal with these kinds of attacks. These prevention methods are based on machine learning algorithms like a random forest, k-means clustering, support vector machine and artificial neural network applied in order to prevent cyber-attacks. Also, in this paper, we have reported proposed intelligent system which is based supervised and unsupervised learning techniques to avoid these cyber-attacks. This proposed system might provide high efficiency with a minimum human intervention which can be implemented and used as a universal solution to most of the common cyber-attacks.
Detection of Cardiomegaly from Chest X-rays using Otsu Algorithm and Convolutional Neural Network

This paper proposes an automated classification method of chest x-rays and combines image processing and deep learning to do the same. A convolutional neural network has been used for the purpose of image classification. The experimental results are computed by using the NIH chest x-rays dataset from Kaggle. The results, when compared to benchmark algorithms, indicate that the 8-layered convolutional neural network considerably increases the accuracy in general. Image processing makes the chest x-ray images binary, making the classification much more efficient.
3d H∞ Controller Design for an Experimental Scanning Tunneling Microscope Device

In this paper, a MIMO control strategy in terms of an H∞ controller is proposed to control an experimental Scanning-Tunneling-Microscope device. Control issues as stability, hysteresis, creep, cross-coupling, structural vibration, robustness against noise and model uncertainties are overcome by the proposed strategy for such a device which requires precision for subnanometric displacements.Real-time results are provided to support the performances of the chosen control strategy.
A Deep Learning Approach Replacing the Finite Difference Method for In Situ Stress Prediction

In the domain of geotechnical engineering analysis, fast Lagrangian analysis based on the finite difference method is the most commonly used numerical analysis method. It is a classical numerical algorithm applied to calculate the in situ stress. In this paper, we propose a deep learning (DL) architecture called the enhance-and-split feature capsule network embedded in fully convolutional neural networks (ES-Caps-FCN) to predict the in situ stress for a strain-softening model when using the finite difference method for the numerical computation. Experiments indicate that this novel approach is stable and convergent. Compared with some classical prediction methods including linear regression analysis and a deep neural network, the mean squared error of our proposed algorithm is as low as 0.059866%, which is lower than the 0.616676% of the deep neural network prediction algorithm and the 0.978495% of the conventional machine learning algorithm. Additionally, the calculation efficiency of fully trained deep learning models is superior to that of the conventional finite difference method. Therefore, DL is a feasible and promising fast and accurate surrogate for the finite difference method for solving the in situ stress.
Large Scale Image Retrieval with Locality Sensitive Hashing

With the ever growing size of image databases today and the increasing demand for search and recommendations from within those databases, it is becoming more and more critical to find efficient image search techniques. This paper proposes a fast approach for extracting the most similar image to a given query image from a database such that the extracted images are most similar semantically. This involves reducing the dimensions of the images by encoding them uniquely to lower dimensional vector using CNN and then applying locality sensitive hashing along with euclidean distance similarity to find the most similar images.
Semi-implicit Discretization of the Uniform Robust Exact Differentiator

This paper studies the discretization of the uniform robust exact differentiator. The commonly used Euler forward method is not suitable to discretize fixed-time algorithms, as global stability can not be guaranteed. Hence a semi-implicit discretization approach is proposed which yields a globally stable discrete system and suppresses any discretization chattering in the unperturbed case. Global stability properties are derived and a method to find suitable sampling times is presented.
Sampling-Based Stability Evaluation with Second-Order Margins for Unknown Systems with Gaussian Processes*

This paper proposes a method of guaranteeing stability of unknown systems that are identified by Gaussian process (GP) regressions. Stability conditions of the GPs, which are inequalities for an infinite number of states in the state space, are relaxed as inequalities for a finite number of sampled states. This relaxation invokes margins in the inequalities, degrading the accuracy when evaluating the stability. This paper derives novel margins whose sizes are second-order to the intervals between the sampled states. By shortening the intervals, the second-order margins become small compared to first-order margins given by existing approaches.
Automated Segmentation of Substantia Nigra and Red Nucleus in Quantitative Susceptibility Mapping Images

Substantia nigra (SN) and red nucleus (RN) located in midbrain are integral in the study of brain disease such as Parkinson's disease (PD). The automatic segmentation of SN and RN in high-resolution quantitative susceptibility mapping (QSM) images can aid in PD characterization and progression. However, only a few methods have been proposed to segment them, owing to the recent development of high quality imaging. Therefore, we describe a novel method for the segmentation of SN and RN in QSM images using contrast enhancement, level set method, wavelet transform and watershed transform. The segmentation performance is evaluated in 20 subjects containing both healthy and PD patients. The results of the proposed segmentation method were closer to the manual segmentation performed by the radiologist than the popular level set methods. The Dice coefficient of the left SN and right SN were 0.77 ± 0.09 and 0.78 ± 0.07 respectively while the Dice for the left RN and right RN were 0.80 ± 0.08 and 0.77 ± 0.08 respectively.
Compact Wideband-Folded Ridge Substrate-Integrated Waveguide Filter

In this letter, a novel folded ridge substrate-integrated waveguide (FRSIW) is proposed by folding a classic ridge substrate-integrated waveguide (RSIW). The FRSIW demonstrates a size nearly half of its RSIW counterpart while keeping the main propagation properties of the RSIW. A prototype of the wideband FRSIW filter with compact size has been designed and tested. The measurements show that the proposed wideband FRSIW filter has the advantages of low loss, compact size, and easy fabrication and integration with other electronic systems.
DREM-based Adaptive Observer for Induction Motors *

In this paper we address the problems of observer design for induction motors (IM). The rotor resistance and load torque are supposed to be unknown. The only measured signals are stator current and control voltage. The approach is based on a new way of applying Dynamic Regressor Extension and Mixing method (DREM) to regression models with non-stationary parameters. Firstly, using partial changes of coordinates and filtering technique, IM model is represented in regression-like form. Applying DREM yields relations, which are used to construct flux observer and rotor resistance estimator. On the next step proposed method is applied again to obtain speed and load torque estimates. The proposed speed observer design can be also applied to the permanent magnet synchronous motors.
A Framework of Virtual War Room and Matrix Sketch-Based Streaming Anomaly Detection for Microservice Systems

Recently, microservice has been a popular architecture to construct cloud-native systems. This novel architecture brings agility and accelerates the software development process significantly. However, it is not easy to manage and operate microservice systems due to their scale and complexity. Many approaches are proposed to automatically operate microservice systems such as anomaly detection. Nevertheless, those methods cannot be sufficiently validated and compared due to a lack of real microservice systems, which leads to the slow process of intelligent operation. These challenges inspire us to build a system named “VWR”, a framework of Virtual War Room for operating microservice applications which allows users to simulate their microservice architectures with low overhead and inject multiple types of faults into the microservice system with chaos engineering. VWR can mimic user requests and record the end-to-end tracing data (i.e., service call chains) for each request in a way consistent with OpenTracing. With easily designed tests and the produced streaming tracing data, the users can validate the performance of their intelligent operation algorithms and improve the algorithms as needed. Besides, based on the streaming tracing data generated by VWR, we introduce a novel unsupervised anomaly detection algorithm based on Matrix Sketch and set it as a default intelligent operation algorithm in VWR. This algorithm can detect anomalies by analyzing high-dimensional performance data collected from a microservice system in a streaming manner. The experimental result in VWR shows that the matrix sketch based method can precisely detect anomalies in microservice systems and outperform some widely used anomaly detection methods such as isolation forest in some scenario. We believe more approaches on the intelligent operation of microservice systems can be constructed based on VWR.
Outage Analysis based Channel Allocation for Underlay D2D Communication in Fading Scenarios

Device-to-Device (D2D) communication has been proved to be a promising technique to efficiently utilize the radio spectrum thereby enhancing the capacity and meeting the huge data rate requirements of the future wireless networks. However, in underlay D2D communications, channel allocation (CA) is a challenging problem so as to limit the cross-tier interferences below a certain threshold. Most of the existing works on CA are based on maintaining minimum quality-of-service (QoS) requirements only for cellular users (CUs), or the D2DQoS constraints in a non-fading scenario. Moreover, most of them assume that the central controlling entity has all the prior information about the incoming D2D users, which appears unfeasible. In this work, we consider a more realistic underlay D2D communication scenario, and introduce an outage probability-based QoS constraint on the D2D links for enhanced reliability; in the presence of the combined effect of path-loss and fading. The channels are allocated online by maximizing the sum-outage-capacity of the full-duplex D2D network. The efficacy of the CA algorithm has been evaluated through rigorous simulations and the results have been numerically analyzed.
A 3-Dimensional Bioprinted Scaffold With Human Umbilical Cord Blood–Mesenchymal Stem Cells Improves Regeneration of Chronic Full-Thickness Rotator Cuff Tear in a Rabbit Model

Background: Chronic full-thickness rotator cuff tears (FTRCTs) represent a major clinical concern because they show highly compromised healing capacity. Purpose: To evaluate the efficacy of using a 3-dimensional (3D) bioprinted scaffold with human umbilical cord blood (hUCB)–mesenchymal stem cells (MSCs) for regeneration of chronic FTRCTs in a rabbit model. Study Design: Controlled laboratory study. Methods: A total of 32 rabbits were randomly assigned to 4 treatment groups (n = 8 per group) at 6 weeks after a 5-mm FTRCT was created on the supraspinatus tendon. Group 1 (G1-SAL) was transplanted with normal saline. Group 2 (G2-MSC) was transplanted with hUCB-MSCs (0.2 mL, 1 × 106) into FTRCTs. Group 3 (G3-3D) was transplanted with a 3D bioprinted construct without MSCs, and group 4 (G4-3D+MSC) was transplanted with a 3D bioprinted construct containing hUCB-MSCs (0.2 mL, 1 × 106 cells) into FTRCTs. All 32 rabbits were euthanized at 4 weeks after treatment. Examination of gross morphologic changes and histologic results was performed on all rabbits after sacrifice. Motion analysis was also performed before and after treatment. Results: In G4-3D+MSC, newly regenerated collagen type 1 fibers, walking distance, fast walking time, and mean walking speed were greater than those in G2-MSC based on histochemical and motion analyses. In addition, when compared with G3-3D, G4-3D+MSC showed more prominent regenerated tendon fibers and better parameters of motion analysis. However, there was no significant difference in gross tear size among G2-MSC, G3-3D, and G4-3D+MSC, although these groups showed significant decreases in tear size as compared with the control group (G1-SAL). Conclusion: Findings of this study show that a tissue engineering strategy based on a 3D bioprinted scaffold filled with hUCB-MSCs can improve the microenvironment for regenerative processes of FTRCT without any surgical repair. Clinical Relevance: In the case of rotator cuff tear, the cell loss of the external MSCs can be increased by exposure to synovial fluid. Therefore, a 3D bioprinted scaffold in combination with MSCs without surgical repair may be effective in increasing cell retention in FTRCT.
Decision Variance in Risk-Averse Online Learning

Online learning has traditionally focused on the expected rewards. In this paper, a risk-averse online learning problem under the performance measure of the mean-variance of the rewards is studied. Both the bandit and full information settings are considered. The performance of several existing policies is analyzed, and new fundamental limitations on risk-averse learning is established. In particular, it is shown that although a logarithmic distribution-dependent regret in time T is achievable (similar to the risk-neutral problem), the worst-case (i.e. minimax) regret is lower bounded by Ω(T) (in contrast to the $\Omega \left( {\sqrt T } \right)$ lower bound in the risk-neutral problem). This sharp difference from the risk-neutral counterpart is caused by the the variance in the player’s decisions, which, while absent in the regret under the expected reward criterion, contributes to excess mean-variance due to the non-linearity of this risk measure. The role of the decision variance in regret performance reflects a risk-averse player’s desire for robust decisions and outcomes.
Saliency Detection using Gaussian Distribution

The methodology used to detect prominent regions of an image is known as Salient Image Detection. Saliency detection has become a major area of research, due to its application in computer vision, autonomous robots, medical imagery and also in data transmission. In this paper, we have put forth a method which capitalizes on the fact that salient regions of an image rarely touch the image boundaries. Our method computes the absolute difference between the mean boundary pixels and all the image pixels, followed by application of Gaussian distribution. We have performed experiments, after which it was concluded that our approach has a better quantitative and qualitative output than other popular saliency detection algorithms.
Antenna Configuration Restrictions for MIMO Radar Influenced by Ground Reflections

In this paper we consider the issue of applying virtual antenna array conception for MIMO (Multiple-Input Multiple-Output) radar influenced by ground reflection. Two virtual antenna array criteria are formulated and theoretically analyzed. Based on these criteria antenna configuration restrictions are obtained and proved. Also two rules for transmitting and receiving elements location are presented.
Circuit Partitioning optimization using Parallel refinement algorithm

Circuit partitioning is a major step in workload distribution for parallel computing system and CMOS VLSI circuit design. Circuit partitioning involves graph partitioning for effective utilization of available CPU power in case of higher number of cores per processor. It improves the degree of partition and amount of parallelism requirement of the partition. In this paper, we propose a shared memory parallel double way method of refinement of current partitioning which can break the local minima. The proposed method shows superiority in comparison to exiting serial refinement methods and achieves speed up of 1.2× to 24.5×24threads while revealing only . 37% higher edge cuts in comparison to serial run.
An Iterative Horizon-Splitting Method for Model Predictive Control*

This paper presents a simple iterative method for linear model predictive control (MPC). Approximate value functions requiring only first-order derivatives and incorporating fixed second-order information are used, which leads to a method that splits the MPC problem into subproblems along the prediction horizon, and only the states and costates (Lagrange multipliers corresponding to the state equations) are exchanged between consecutive subproblems during iteration. The convergence is guaranteed under the framework of the majorization-minimization principle. The performance of the proposed method was assessed against both first- and secondorder methods with two numerical experiments. The results indicate that the proposed method can obtain a moderately accurate solution with a small number of inexpensive iterations.
Impact of Early Life Trauma Trauma and PTSD Among Youths in Outpatient Treatment for Alcohol Use Disorders

This article examines the relationships among trauma histories and diagnoses of partial or full posttraumatic stress disorder (PTSD) among 106 youths in outpatient treatment for alcohol use disorders (AUD). Subjects were 13 to 18 years old and were recruited from treatment facilities, schools, and juvenile corrections programs. Chi-square (c2) tests and one-way analyses of variance examined prevalence estimates of exposure to traumatic stressors, PTSD status, and related characteristics. Pearson’s correlation coefficients, K-Cluster procedure, and logistic regression examined correlates of PTSD status. Almost 90% reported a trauma history, and 13% met criteria for a PTSD diagnosis. Logistic regression analyses indicated that sexual abuse history, comorbid psychiatric symptom severity, and suicide ideation were associated with PTSD. These findings underscore the importance of addressing trauma with youths in AUD treatment.
NTU ROSE Lab at TRECVID 2018: Ad-hoc Video Search and Video to Text

This paper describes our participation in the ad-hoc video search and video to text tasks of TRECVID 2018. In ad-hoc video search, we adapted an image-based visual semantic embedding approach and trained our model on combined MS COCO and Flicker30k datasets. We extracted multiple keyframes from each shot and performed similarity search using the computed embeddings. In video to text, description generation task, we trained a video captioning model with multiple features using a reinforcement learning method on the combination of MSR-VTT and MSVD video captioning datasets. For the matching and ranking subtask, we trained two types of image-based ranking models on the MS COCO dataset.
KU-ISPL TRECVID 2018 VTT Model

KU-ISPL model for TRECVID 2018 Video-to-Text (VTT) is presented in this paper. A stack of two LSTM with attention mechanism is structured in the VTT architecture. We employ a sequence-to-sequence model to deal with sequential input and output. The encoder in our model encodes video frames into visual representations and the decoder decodes the visual representations into textual words. Attention mechanism is exploited for best use of contextually pertinent frames in input video. The model pays attention to the hidden states of 2 nd LSTM in the encoder to obtain efficient hidden states in the decoder. Visual feature, acoustic feature and detection result of videos are extracted from deep learning models and the resulting features are subsequently concatenated into one. It is used for an input descriptor of the model. The stacked LSTM and attention weights are jointly trained and the whole model is an end-to-end trainable network. We proceed by making 4 runs for our model by combining various types of features to explore how the information impacts the performance of sentence generation. The sentence matching method is based on the fusion score of Meteor and Bleu. Because the TRECVID VTT task is open domain, the sentence generation and sentence matching system are trained by various database such as MSVD, MVAD, and MSR-VTT. Experimental results show that the proposed model performs better than the model without attention mechanism.