Miracle-dz/UniFAR-FTUs huggingface dataset:
100 kW, 10 kHz Medium-Frequency Transformer Design Optimization and Experimental Verification

This paper describes a novel, model-based, and design optimization methodology for high-power medium-frequency transformers (MFTs) for medium-voltage high-power electronic applications, namely emerging solid-state transformers. Presented procedure enables the designer to interactively select the most optimal design in a simple and intuitive way, while inherently offering flexibility in terms of various design alternatives, depending on the component availability. The core of the design algorithm is explained in detail together with all of the relevant modeling and technical challenges associated with realization of a prototype. A <inline-formula><tex-math notation="LaTeX">$\text{100 kW}$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$\text{10 kHz}$</tex-math></inline-formula> MFT prototype has been designed, according to the presented algorithm, practically realized and thoroughly tested in order to verify the theoretical design.
Multi-MTTR Asynchronous-Asymmetric Channel-Hopping Sequences for Scalable Cognitive Radio Networks

In this paper, a family of channel-hopping (CH) sequences, so-called multi-maximum time-to-rendezvous (MTTR) asynchronous-asymmetric prime sequences (MAAPSs), for cognitive radio networks (CRNs) is designed and analyzed. They have known and enlarged cardinality (for the benefit of user identification), full degree of rendezvous, and pairwise shift invariant. For the first time, the MAAPSs can be partitioned into multiple subsets of CH sequences with adjustable MTTR. This unique property supports a trade-off between cardinality and MTTR in scalable CRNs. With a channel-contention-resolution mechanism in use, the rendezvous-success (RS) rate and variance of the MAAPSs in a practical CRN are analyzed. With short MTTR and smaller RS-rate variance, the MAAPSs can support quicker, more stable and uniform rendezvous processes than their counterparts. Finally, the analytical models of two combined metrics of TTR and RS rate, so-called maximum and average time-to-RS, are formulated and validated with computer simulation.
A Modular Multilevel Converter with a Zigzag Transformer for Bipolar MVDC Distribution Systems

This letter presents a modular multilevel converter (MMC) for bipolar medium-voltage direct-current (MVDC)) distribution systems. By employing a zigzag transformer as a grid interface transformer with a dedicated operation method, the operation of two dc poles of an MMC can be fully decoupled. Consequently, the MMC can provide full bipolar-operation capability. No additional component is required, and no penalty of increased power rating is imposed on the grid interface transformer. The scheme based on the proposed topology is competitive and cost-effective for emerging MVDC distribution systems. Feasibility of the proposed scheme is verified by both simulations and down-scale experiments.
A 60-GHz Transceiver and Baseband With Polarization MIMO in 28-nm CMOS

Polarization (pol) selectivity has been widely adopted in modern RF transceivers to enable the channel diversity. This paper utilizes orthogonal pol modes to support simultaneous independent data streams on the same frequency channel, thereby doubling the effective channel capacity. The energy-efficient 60-GHz transceiver prototype uses two transmit/receive (TX/RX) chains with a single printed circuit board (PCB) antenna to demonstrate over-the-air (OTA) 2 <inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 13.9 Gb/s 16 quadrature-amplitude modulation (QAM) dual-pol (DP) multiplexing-based multi-input and multi-output (MIMO), thus achieving 2<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> higher communicated bits/symbol than a single-pol transceiver. At these multi-Gb/s rates, multi-path ISI and cross-pol effects limit the feasibility of DP–MIMO. To mitigate these channel effects, a mixed-signal 384-coefficient feed-forward equalizer (FFE)- and 2400-coefficient decision-feedback equalizer (DFE)-based area- and energy-efficient RX baseband is also presented. The baseband supports DP–MIMO up to 40 Gb/s for line-of-sight (LOS) 16QAM and 14 Gb/s non-line-of-sight (NLOS) QPSK, at 3<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> to 38<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> better energy efficiency per coefficient than state of the art.
Characterization of Human Body Shadowing in Measured Millimeter-wave Indoor Channels

In this paper, the human shadowing effect is studied at the millimeter-wave band centered at 28 GHz. The analysis takes a holistic view of the opportunities and challenges that are brought by the presence of a human in the millimeter-wave channel. The impact of human body shadowing on the antenna array performance is investigated, where the impairments due to the non-stationarity over the antenna array caused by human-induced shadowing are presented. The spatial-delay information is obtained using a frequency-invariant beamforming algorithm. The measured channel in a free space scenario is compared with the channel when a human subject and a metallic cylinder of similar size to the human subject are present in the channel. The results show that despite a high shadowing attenuation when the human blocks the propagation paths, there are opportunities presented by the presence of the human which can enhance the spatial diversity of the channel at some spatial locations.
A DSE-Based SMC Method of Sensorless DFIG Wind Turbines Connected to Power Grids for Energy Extraction and Power Quality Enhancement

Extracting the maximum possible kinetic energy from the ambient environment and enhancing the output power quality are two main concerning issues on the topic of integrating renewable energy resources with power grids. For this purpose, we propose a dynamic state estimation (DSE)-based sliding mode control (SMC) strategy to achieve such control purposes for doubly fed induction generator (DFIG) wind turbines connected to multi-machine power grids. The newly devised method leverages cutting-edge smart-grid technology for the design of dynamic state estimator, and the acquired states of sensorless DFIG systems are then utilized in the construction of the SMC scheme. A dechattering method is also proposed to alleviate unnecessary switchings that intrinsically exist in the original SMC scheme. Compared with the conventional control method, when operating under high wind speeds, the newly designed SMC strategy of DFIGs is capable of producing a faster response time, a higher mechanical energy extraction percentage, and a better power quality. Owing to the modern smart-grid technology and simplicity in implementing the SMC strategy, the proposed control method infers its potential of practical applications in industrial vision.
CREAM: CNN-REgularized ADMM Framework for Compressive-Sensed Image Reconstruction

Compressive sensing (CS) has drawn an enormous amount of attention in recent years due to its sub-Nyquist sampling rate and low-complexity requirement at the encoder. However, it turns out that the decoder in lieu of the encoder suffers from heavy computation in order to decently recover the signal from its CS measurements. With the aim of developing a fast yet accurate algorithm, in this paper, we propose to leverage a deep convolutional neural network (CNN) prior model to the constrained CS reconstruction formulation and solve it via the alternating direction method of multipliers (ADMM). The proposed CNN-REgularized ADMM framework, dubbed CREAM, is able to recover image signals from CS measurements effectively and efficiently. On the one hand, the developed constrained CS formulation by CREAM enables fewer regularization parameters and less computational complexity compared with traditional unconstrained CS formulation by ADMM. On the other hand, rather than training a neural network from scratch, an off-the-shelf CNN model is directly incorporated into CREAM even without the effort of fine tuning, in which CNN has exhibited its desirable reconstruction performance and low computational complexity. Hereby, powerful GPU can be utilized to speed up the reconstruction. Experiments demonstrate that our proposed method for CS reconstruction of natural images surpasses state-of-the-art CS models by a significant margin in speed and performance.
Gait Analysis Using RGBD Sensors

Human gait analysis, the study of human locomotion, is possible with low-cost RGBD sensors such as the Kinect sensor. However, due to the inherent depth sensing accuracy limitations of these sensors as the distance from the sensor increases, the distance range of gait analysis too becomes small and inefficient for clinical use. We present a system that uses two independent Kinects in a data fusion framework that increases the distance range of gait analysis from 2.5 m to 4 m with three gait cycles. Our gait parameters are reasonably accurate and comparable with existing systems with 4% error in length measurements and 5° error in flexion measurements. The system is extensible to have several Kinects.
Back to Basics in Ultrasound Velocimetry: Tracking Speckles by Using a Standard Piv Algorithm

In this IUS proceeding, we describe a classical block-matching approach that we used during the 2018 SA-VFI (Synthetic Aperture 2-D Vector Flow imaging) challenge. To estimate frame-to-frame displacements, we used blockwise FFT-based ensemble cross-correlations. Subpixel displacements were obtained by parabolic peak fitting. We opted for a coarse-to-fine multiscale scheme to increase the resolution and precision. Robustness and accuracy were improved by including a robust unsupervised smoother in the estimation process. 2-D velocity vector fields were computed in several flow phantoms (from both simulations and experiments) provided by the organizers of the SAV-FI challenge. Our results showed that the standard block-matching approach provided reliable 2-D velocity vector fields, in terms of magnitude and angle. Complex flow patterns, like those occurring in the carotid bifurcation, were also estimated accurately. In summary, the long-standing block-matching technique by normalized cross-correlation is effective for flow estimation by ultrasound imaging. The final estimates returned by the method will be uploaded on the SA-VFI platform, and the results will be compared with those obtained by all the competitors during the challenge session at IEEE IUS 2018 in Kobe (Japan).
The Obstacle-restriction Method for Tele-operation of Unmanned Aerial Vehicles with Restricted Motion

This paper presents a collision avoidance method for tele-operated unmanned aerial vehicles (UAVs). The method is designed to assist the operator at all times, such that the operator can focus solely on the main objectives instead of avoiding obstacles. We restrict the altitude to be fixed in a three dimensional environment to simplify the control and operation of the UAV. The method contributes a number of desired properties not found in other collision avoidance systems for tele-operated UAVs. Our method i) can handle situations where there is no input from the user by actively stopping and proceeding to avoid obstacles, ii) allows the operator to slide between prioritizing staying away from objects and getting close to them in a safe way when so required, and iii) provides for intuitive control by not deviating too far from the control input of the operator. We demonstrate the effectiveness of the method in real world experiments with a physical hexacopter in different indoor scenarios. We also present simulation results where we compare controlling the UAV with and without our method activated.
Learning to Run with Potential-Based Reward Shaping and Demonstrations from Video Data

Learning to produce efficient movement behaviour for humanoid robots from scratch is a hard problem, as has been illustrated by the “Learning to run” competition at NIPS 2017. The goal of this competition was to train a two-legged model of a humanoid body to run in a simulated race course with maximum speed. All submissions took a tabula rasa approach to reinforcement learning (RL) and were able to produce relatively fast, but not optimal running behaviour. In this paper, we demonstrate how data from videos of human running (e.g. taken from YouTube) can be used to shape the reward of the humanoid learning agent to speed up the learning and produce a better result. Specifically, we are using the positions of key body parts at regular time intervals to define a potential function for potential-based reward shaping (PBRS). Since PBRS does not change the optimal policy, this approach allows the RL agent to overcome sub-optimalities in the human movements that are shown in the videos. We present experiments in which we combine selected techniques from the top ten approaches from the NIPS competition with further optimizations to create an high-performing agent as a baseline. We then demonstrate how video-based reward shaping improves the performance further, resulting in an RL agent that runs twice as fast as the baseline in 12 hours of training. We furthermore show that our approach can overcome sub-optimal running behaviour in videos, with the learned policy significantly outperforming that of the running agent from the video.
A Markov Detection Tree-Based Centralized Scheme to Automatically Identify Malicious Webpages on Cloud Platforms

The effective detection of malicious webpages plays a paramount role in ensuring the Web security on the Internet. However, the detection results of current methods are poor and their efficiency is low, and thus, it is important and challenging to design an efficient detection scheme that can improve the accuracy of classification of malicious webpages. To overcome this challenge, a Markov detection tree scheme is proposed in this paper to automatically identify and classify malicious webpages, where the link relations of unified resource locators, the information gain ratio, and Markov decision process as well as decision tree are used to analyze malicious webpages simultaneously. To increase the detection accuracy for malicious webpages, two methods of filling missing values are presented to process the null attribute values of webpages. We compare the performance of our algorithms when the different methods are applied in terms of the information gain ratio, classification accuracy, and detection efficiency. Our experimental results show that the proposed methods can improve the accuracy and efficiency in the classification of malicious webpage detections.
Quadrotors' Low-cost Vision-based Autonomous Landing Architecture on a Moving Platform

In this paper, a low-cost vision-based autonomous landing architecture for an unmanned aerial vehicle (UAV) on a moving platform is presented. First, a novel landing pad was designed for a monocular camera to robustly detect the pad in both high and low altitudes. In order to solve mirror effect and occasional misidentification, a 3D points cluster algorithm for relative position estimation is presented. Second, the dynamics of the quadrotor is simplified for this lading task and a PD controller is designed based on the estimated relative position. Finally, the low-cost system architecture of the quadrotor and the experiment results are both presented to show the effectiveness of the proposed method.
A 60-GHz 144-Element Phased-Array Transceiver for Backhaul Application

A 144-element phased array transceiver is realized using a modular tiled approach that supports 802.11ad, MCS12 single carrier 16-quadratic-amplitude modulation (QAM) 4.6 Gbps, in the 60-GHz band. It consists of a system-on-a chip (SOC) (MAC/PHY/BB to IF) in 28-nm CMOS, and one IF-to-60-GHz transceiver master chip driving twelve 60-GHz phased array transceiver slave chips fabricated in a 40-nm CMOS. Using the master-slave configuration, the 60-GHz transceiver with 12 phase-controlled TX/RX slices is expanded to 144 phase-controlled slices. Each final TX/RX slice is then connected to two patch antennas on LTCC substrate. A tiled approach is used to create the 288 patch antenna array out of six identical tiles each with two slave 60-GHz transceivers connected to a 48-element antenna array. The single tile phased array with 48 antennas has a measured beam steering scan angle of 60° in azimuth and 10° in elevation. The full phased array transceiver with 288 antennas has a measured over the air (OTA) max effective isotropic radiated power (EIRP) of 51 dBm at saturated power (PSAT), and EIRP of 44.8 dBm with −22 dB EVM for MCS12 (16QAM-4.6 Gbps) at broadside. It has an OTA measured sensitivity of −87.3 and −80.4 dBm for MCS9 (QPSK-2.5 Gbps) and MCS12, respectively, at broadside. A packet error rate of 10−5 was measured for MCS9 and MCS12 with an OTA input power of −85 and −77.5 dBm, respectively, for the full phased array transceiver at broadside.
Modulation-Constrained Clustering Approach to Blind Modulation Classification for MIMO Systems

Blind modulation classification is a fundamental step before signal detection in cognitive radio networks where the knowledge of modulation scheme is not completely known. In this paper, a modulation-constrained (MC) clustering classifier is proposed for recognizing the modulation scheme with unknown channel matrix and noise variance for MIMO systems. By recognizing the fact that the received signals within an observation interval form into clusters and exploiting the intrinsic relationships between different digital modulation schemes, the modulation classification is transformed into a number of clustering problems, one for each modulation scheme, with the final classification decision based on the maximum likelihood criterion. To improve the learning efficiency, centroid reconstruction is proposed to recover all cluster centroids through a limited number of parameters by exploiting the structural relationships in constellation diagrams. Furthermore, a method to initialize the cluster centroids is also proposed. The proposed MC classifier together with centroid reconstruction and initialization methods not only reduce the number of parameters to be estimated, but also help to initialize the clustering algorithm for the enhanced convergence performance. Simulation results show that our algorithm can perform excellently even at low SNR and with very short observation interval length.
Multi-Scale YOLOv2 for Hand Detection in Complex Scenes

This paper presents a model named Multi-Scale YOLOv2 (MS-YOLOv2) for hand detection in complex scenes. The proposed MS-YOLOv2 is implemented by introducing three modules to YOLOv2, including a Multi-Scale Feature Refinement Module to acquire fine-grained features, a Channel Importance Evaluation Module to recalibrate feature channels and a Hard Example Punishment Module to get rid of hand interference areas. Experiment results show that the proposed MS-YOLOv2 makes much performance improvement to YOLOv2, but with little computational complexity gain. On our dataset, the proposed MS-YOLOv2 can achieve 98.2% of AP and 97.9% of AR. Moreover, on the VIVA challenge, the proposed MS-YOLOv2 achieves AP/AR of 85.1%/45.8% at Level-1 and 80.1%/45.9% at Level-2.
New Metrics for Disk Failure Prediction That Go Beyond Prediction Accuracy

Prediction accuracy (true positives, false positives, and so on) is the usual way for evaluating disk-failure prediction models. Realistically however, we aim not only to correctly predict failures, but also to protect data against failure, i.e., we need to take appropriate action after a failure prediction. In the context of storage systems, protecting data requires that we migrate at-risk data, but this consumes network and disk bandwidth, which is particularly problematic for large-scale and cloud systems. This paper consolidates and builds on Li et al. (2016), where we propose using two new metrics, migration rate (MR) and mismigration rate (MMR), to measure the quality of disk failure prediction: MR measures how much at-risk data is migrated (and therefore protected) as a result of correct failure predictions, while MMR measures how much data is migrated needlessly as a result of incorrect failure predictions. In this paper, we additionally propose measuring quality in terms of migration time and mismigration time, which measure the time spent migrating at-risk disks, and the time spent mismigrating healthy disks caused by false alarms, respectively. To demonstrate these metrics’ usefulness, we use them to compare disk-failure prediction methods: we compare: 1) a classification tree (CT) model against a state-of-the-art recurrent neural network (RNN) model and 2) a gradient-boosted regression tree (GBRT) model (which predicts residual life) against RNN. We observe that while RNN performs best in the prediction accuracy experiments, the CT and GBRT models sometimes outperform RNN in the resource-dependent migration-rate experiments. We conclude that prediction accuracy is sometimes misleading: correct predictions do not necessarily imply protected data. We additionally present an improved GBRT model (GBRT+), which offers a practical improvement in disk residual-life prediction accordingly to the newly proposed metrics.
Joint Power Allocation and MCS Selection in Downlink NOMA System

In this paper, we propose joint power allocation and modulation and coding scheme (MCS) selection for downlink non-orthogonal multiple access system considering max-min user fairness. The proposed scheme determines the power allocation factor according to the MCS level of the far user. It allocates the maximum fraction of power to the near user while guaranteeing the decoding of the far user, so that it can improve data rate of both users and achieve user fairness. Also, since there is a finite number of MCS levels in the practical system, the complexity of the proposed scheme is greatly reduced. Finally, the simulation results show that the proposed scheme achieves the performance close to the maximum of minimum user rate within a small number of iterations.
Coordinated Control Strategy for Transient Performance Improvement of LCC Based HVDC Transmission System with STATCOM Under Weak AC Grid

LCC based HVDC transmission system linked to the weak AC grid suffers from severe operational problems like continuous occurrence of commutation failure and longer fault recovery time during various fault conditions. In order to cope with these operational problems different FACTS devices are used. However, to get better results there is a need of proper coordinated control strategy (CCS) between FACTS devices and HVDC system. This paper presents a novel CCS between LCC based HVDC transmission system and STATCOM. The configured CCS is based on employing extinction angle of the LCC-HVDC system, implementation of drop rate limiter and reactive power control mode of STATCOM. The CCS is developed in PSCAD/EMTDC and various transient simulations are performed in order to validate its effectiveness on transient performance of the study system. The analysis shows that CCS can improve the transient performance of HVDC system by enhancing the commutation failure immunity index (CFII), reducing the risk of occurrence of commutation failure in inverter station and significantly improve the fault recovery performance during single and three phase AC faults.