Miracle-dz/UniFAR-FTUs huggingface dataset:
Data-driven Leakage Inductance Modeling of Common Mode Chokes

The accurate leakage inductance modeling of common mode chokes (CMCs) can reduce trial-and-error filter debugging efforts and help avoid the saturation issue caused by the leakage flux. However, the analytical leakage inductance model is difficult to derive due to the complex leakage flux distribution. This paper presents a data-driven approach to model the CMC leakage inductance. A large amount of the leakage inductance data is collected by sweeping the selected input variables for the 3D finite element method (FEM) simulation. Then the data is trained by Artificial Neutral Network (ANN) to regress the nonlinear relationship between the leakage inductance and selected input variables. To verify the proposed method, core ZW4310TC is selected to model the relationship between the leakage inductance and winding parameters. Three CMCs with core ZW4310TC were built with different winding parameters and their leakage inductances were measured to verify the model. Compared with a previous analytical model, the error was reduced from 42.9% to 1.3% at the case where the previous model has the worst accuracy.
Semiconductor-On-Polymer the Evolution of thin IC Packaging

IC packages are getting thinner to facilitate thinner systems. Labels and tags are getting smarter. Electronics are starting to bend, and reliability is in question. Semiconductor-on-Polymer (SoP) Chip Scale Packaging (CSP) is an enabling technology that can reliably integrate packaged ICs in flexible electronics. SoP™ is being used to produce the leading edge of high performance ultra-thin flexible hybrid electronics and sensors today. Chip Scale Packaging (CSP) defines the logical end of IC package scaling in 2D surface area as package area and IC size converge. Scaling thickness is a key metric in further packaging evolution. SoP CSP provides a path to the end of IC package scaling in 3D volume by reducing package thicknesses to less than what is generally regarded as ultra-thin for bare die. The result is feasibility for packaged ICs to be utilized in direct chip attach (DCA) applications. Die thickness is a key contributor to DCA and FC-BGA IC package thickness. SoP replacement of bare die in conventional IC packaging is envisioned to facilitate package thickness reduction and improved reliability for FC-BGA packages, 3D and heterogeneous integration. This presentation introduces SoP technology and describes SoP CSP direct interconnect (DI) assembly that has progressed from 24-pin attachment to System-on-Chip assembly of DI pitch at ≤100um in flexible hybrid electronics. The presentation also shows the technology roadmap for SoP application to IC packaging and how SoP may fit into current IC packaging roadmaps.
Automatic Parallelization to Asynchronous Task-Based Runtimes Through a Generic Runtime Layer

With the end of Moore’s law, asynchronous task-based parallelism has seen growing support as a parallel programming paradigm, with the runtime system offering such advantages as dynamic load balancing, locality, and scalability. However, there has been a proliferation of such programming systems in recent years, each of which presents different performance tradeoffs and runtime semantics. Developing applications on top of these systems thus requires not only application expertise but also deep familiarity with the runtime, exacerbating the perennial problems of programmability and portability.This work makes three main contributions to this growing landscape. First, we extend a polyhedral optimizing compiler with techniques to extract task-based parallelism and data management for a broad class of asynchronous task-based runtimes. Second, we introduce a generic runtime layer for asynchronous task-based systems with representations of data and tasks that are sparse and tiled by default, which serves as an abstract target for the compiler backend. Finally, we implement this generic layer using OpenMP and Legion, demonstrating the flexibility and viability of the generic layer and delivering an end-to-end path for automatic parallelization to asynchronous task-based runtimes. Using a wide range of applications from deep learning to scientific kernels, we obtain geometric mean speedups of 23.0* (OpenMP) and 9.5* (Legion) using 64 threads.
A New Channel Selection Method using Autoencoder for Motor Imagery based Brain Computer Interface

To improve the spatial resolution of the electroen-cephalogram (EEG) signal, it is conventional to use a large number of scalp electrode while recording oscillatory rhythms in motor imagery based brain-computer interface(MI-BCI). However, this increases the dimension of data and might fail to generalize and thus over-fit. Therefore, it is required to reduce the dimension of input data in an optimal way. In this paper, we propose a method using an artificial neural network to reduce the dimension of EEG for MI-BCI. We train an under-complete sparse autoencoder neural network for each subject separately to encode the EEG data optimally. The optimally encoded EEG trials are then used by the Filter Bank Common Spatial Pattern (FBCSP) method to decode the imagined motor movement. In similar lines, autoencoder was trained on subject independent data. We achieved improved motor imagery classification accuracy when the dimension of the data was almost reduced by half compared to the state-of art FBCSP. The performance of the proposed method is also compared with Sparse Common Spatial Pattern (SCSP) based channel selection method. The average classification accuracy obtained for 10 subjects is 74.3±8.06 % with only 13 encoded channels. Also, for the autoencoder trained to be subject independent we obtained an average classification accuracy of 66.64±3.93% with only 11 encoded channels after crossvalidation. The study extends the use of autoencoder neural networks in motor imagery based brain-computer interface and shows significant improvement in performance with reduced data dimension.
Simulation Study on ASCMP Protocol in Utility Tunnel WSN

Utility tunnels are an important part of smart cities. This research focuses on the application of wireless sensors networks (WSNs) in utility tunnels. The utility tunnel space and Chinese utility tunnel building standard are considered in the design process. An adaptive subunit clustering multi-hop protocol (ASCMP) is proposed. In this protocol, some improvements are applied to increase the WSN’s performance, such as cluster head (CH) election, network transmission, and node energy setting. Finally, ASCMP is compared with three other classic WSNs, namely, LEACH, SEP, and M-LEACH. Simulation results demonstrate that ASCMP’s data transfer rate and lifetime increase by 71.76% and 200%, respectively, as shown by MATLAB 2012a simulations.
Model Predictive Control for Flexible Reduction of Active Power Oscillation in Grid-tied Multilevel Inverters under Unbalanced and Distorted Microgrid Conditions

The unbalanced and distorted grid phenomena is expected to be a critical factor for future microgrids due to extensive use of unbalanced and nonlinear loads. These microgrid conditions keep changing depending on the load profile. The DC-link voltage fluctuation, which happens due to polluted grid conditions, not only leads to instability of the system but also compromises the lifetime of the batteries and DC-link capacitors. Active power oscillation can only be reduced by compromising with the harmonic distortion of the grid currents. Therefore, to cope with various expected unbalanced and distorted microgrid conditions, a trade-off between active power oscillation and total harmonic distortion of grid current is required. This paper proposes a novel flexible reference current generation technique by using a tuning parameter to reduce active power oscillation flexibly. The generated reference current comprises not only the positive and negative sequence currents but also lower order harmonic components. The peak values of all the components present in the reference current are decided in a stationary reference frame. Since model predictive controller tracks multifrequency reference currents without any additional requirement and with good transient performance, it has been used to control the neutral point clamped grid-tied three-level inverter. The concept is validated through simulation and experimental results.
Containers vs Virtual Machines: Choosing the Right Virtualization Technology for Mobile Edge Cloud

Mobile Edge Cloud (MEC) is paving the way for 5G networks with respect to latency and computation offloading. To support user mobility, a MEC application should be migrated from one MEC server to another MEC server, which is in close proximity to users. Providing low migration time and low downtime is one of the critical challenges in MEC. Prior studies mainly focused on the service migration in data centers where only the downtime is taken into account. Meanwhile, the total migration time in MEC significantly influences the service latency due to the user's mobility. In this paper, we present a comparative measurement study on service migration in MEC. The evaluation results, which are obtained from commercial edge servers, show the infeasibility of the investigated migration approaches. Even though containerization is undoubtedly a lightweight virtualization technology, it is not mature enough to support the service migration in MEC. Specifically, both VM migration and container migration introduce high migration time, and subsequently result in service degradation for latency-sensitive applications in MEC.
A speed and current cascade Continuous Control Set Model Predictive Control architecture for synchronous motor drives

Model predictive control represents a quite mature technology for current control of electric drives. Continuous control set MPC has been widely investigated for the current loop of permanent magnet synchronous motor. Standard PI controllers are often preferred for the speed loop. An innovative speed and current cascade model predictive control architecture for synchronous motor drives is proposed in this paper, to comply with the different requirements of the two loops. Parameter uncertainties represent a critical issue in the implementation of predictive controllers. Considering continuous control set MPC, these mismatches induce bias errors in the desired reference tracking. On one hand, a MPC with intrinsic integral action is adopted in the current loop. The integral action avoids the error induced by electric parameters uncertainties. On the other hand, a separate continuous control set MPC is adopted for the speed loop. A moving horizon estimator supports the latter MPC algorithm, compensating mismatches in the mechanical model of the system. This estimator is used, for instance, to compensate external loading torques. The proposed architecture is validated through experiments on an interior permanent magnet motor. Performances of the proposed solution are compared to the ones obtained using a cascade of PI controllers. Robustness of the architecture against mechanical and electric model uncertainties is investigated too.
The Impact of EV Charging Schemes on the Nordic Energy System

With the expected penetration of Electric Vehicles (EV) along with ambitious initiatives for variable renewable energy development in the Nordics, energy systems will have to adapt to the increased demand and higher share of variable supply. The study investigates EV integration pathways with different degrees of flexibility provided by the charging schemes passive charging (PC), Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) in the energy system model Balmorel. While PC yields in the largest annualized system cost, V2G can achieve considerable savings. Smart charging schemes reduces electricity price variability considerably compared to PC. Further, investments in additional battery storage systems are delayed using G2V until 2050 whereas PC have significantly larger requirements already decades before. The flexible charging schemes have subsequently positive effects on emission reduction. Allowing the model to use the foresight mode and taking into account future developments earlier reduces the system cost of all scenarios. While V2G still is the cheapest option, it however, is considerably small in the improvements which shows a certain robustness whereas PC gains relatively more by being able to adjusting earlier for the substantial peak demand requirements.
An Improved Dynamic Control-based Hardware-in-the-Loop Laboratory Testing for Permanent Magnet Synchronous Motors

This paper presents a new improved dynamic control (IDC) for the speed and currents of a surface-mounted permanent magnet synchronous motor (SMPMSM). In industry applications, standard proportional-integral (PI) controller is commonly used. Nonetheless, the load regulation capability and the tracking performance should be compromised in the designing process of the controller. The proposed IDC controller parameters are calculated based on the mechanical dynamic model and the predefined drive system performance. When the IDC parameters are obtained, the distinct speed performance can be accomplished at different operating conditions. A 850-W SMPMSM drive test setup, connected load, and the feedback sensors of the current and speed are emulated using the Typhoon Hardware-in-the-loop (HIL 402) tool with the dSPACE MicroLabBox. The performance of the proposed IDC scheme is evaluated through the verification results under rated speed reversal and load torque disturbance conditions. A comparative study between the proposed IDC and conventional PI controller is carried to confirm its simplicity, good performance, and robustness.
Robust 3D Skeleton Tracking based on OpenPose and a Probabilistic Tracking Framework

For human skeleton tracking, conventional methods may directly rely on the Kinect built-in function to extract skeleton joints and realize the tracking function. However, the left-right confusion problem and the self-occlusion problem would make the Kinect joints unstable and incorrect. In this paper, we aim to solve these crucial issues and realize a reliable 3D skeleton tracking system via multiple Kinect cameras. First, the proposed method corrected the Kinect skeleton by referring to the OpenPose-extracted joints. Since OpenPose extracts the joints by analyzing image content, we can differentiate the front side and the back side and thus can correct the left-right confusion. Second, to alleviate the selfocclusion problem, we back-project the 2D OpenPose joints from multiple cameras and form an alternative 3D skeleton. The OpenPose reconstructed joints are treated as robust 3D anchors for multiple skeleton fusion. Last but not least, we introduce the inter-joint constraints into our skeleton tracking framework so that we can trace all joints simultaneously, make sure the skeleton movement consistent, and well maintain the length between neighboring joints. We evaluate our method with challenging actions. A practical system is also built for demonstration. The experimental results show the system can track skeleton stably without error propagation and vibration. The average localization error is also smaller than conventional methods.
Automotive Safety: a Neural Network Approach for Lane Departure Detection using Real World Driving Data

Lane departures, where the vehicle leaves the lane due to driver inattention, drowsiness, or incorrect situation assessment, are one of the most serious accident and fatality prone scenarios. To further improve traffic safety, we are asking the question: How much can a neural network approach improve the reliability of lane departure predictions compared to traditional model-based methods? Our results show a relative improvement in reliability of 7% in terms of true positive rate and 22% reduction of the false positive rate with respect to a constant velocity model method. The key contributions of this work are the introduction of sparse sampling in the input data, a thorough comparison with a baseline solution, and the evaluation on real world driving data.
Atomistic Modeling of Friction Force Dependence on Contact Area of Metallic Nanoparticles on Graphene

We compare the calculation of the contact area between the palladium and aluminum nanoparticles and graphene substrate by an ellipse and by the distance between atoms. Linear approximations of shear stress depending on the contact area of different metals were compared. It has been observed that aluminum nanoparticles are spherical and palladium nanoparticles have an ellipsoid shape, because the metal-metal interaction in palladium is stronger than that of aluminum.
High-Efficiency Power Amplifier for 1.8 MHz: The Development of a Class-E PA With Components for High Efficiency

High efficiency is especially important for portable equipment and other applications where low power consumption is required. Consequently, power efficiency has been thoroughly discussed and investigated over the past few decades [7]. From this research, a number of different approaches and technologies have evolved for power amplifiers (PAs) and used in many applications that require high RF power, including those involving radar, medicine, space systems, wireless power transfer systems, and ham radio. Class-E and class-F amplifiers, like class-D and Doherty amplifiers, for example, are often chosen to provide high efficiency with relatively few components.
A simplified discrete-time implementation of FCS-MPC applied to an IM drive

Model predictive control (MPC) has drawn significant attention from the power electronics research community in the last decade. Regarding the application of MPC in motor control, several studies have been conducted that include design and implementation of various predictive torque control techniques. In this study, MPC of an induction motor is implemented via TMDXIDDK379D, a motor drive development platform produced by Texas Instruments (TI). The main motivation is to show the engineers and researchers a way of simple, fast and low cost experimentation on MPC. By using an off the shelf motor drive development platform and modifying the existing macros and libraries for motor control, the implementation time and difficulty can be reduced considerably. Extensive experimental data on the drive performance is also provided.
Control Flow Errors in an Industry 4.0 Setup: a Preliminary Study

Today’s industry is rapidly evolving towards Industry 4.0 in which the Internet of Things is transforming machines into highly-interactive cyber-physical systems. At its core, these cyber-physical systems are driven by industrial embedded systems. However, the trend of many interacting systems creates a harsher working environment for these embedded systems to operate in, considering among other things electromagnetic interference. This working environment can introduce bit flips in the embedded system’s hardware which cause control flow and data flow errors in its control software. This paper analyzes the effects of such control flow errors on an Industry 4.0 setup. We also apply a software implemented control flow error detection method to determine its gain in reliability. Finally, this paper demonstrates a basic but effective automated recovery method for our Industry 4.0 setup.
InertialNet: Toward Robust SLAM via Visual Inertial Measurement

SLAM (simultaneous localization and mapping) is commonly considered as a crucial component to achieving autonomous robot navigation. Currently, most of the existing visual 3D SLAM systems are still not robust enough. Image blur, variation of illumination, and low-texture scenes may lead to registration failures. To let the visual odometry (VO) deal with these problems, the workflow of traditional approaches becomes bulky and complicated. On the other hand, the advancement of deep learning brings new opportunities. In this paper, we use a deep network model to predict complex camera motion. It is different from previous supervised learning VO researches and requires no camera trajectories which are diﬃcult to obtain. Using the image input and IMU output as an end-to-end training pair makes data collection cost-effective. The optical flow structure also makes the system independent of the appearance of training sets. The experimental results show that the proposed architecture has faster training convergence, and the model parameters are also significantly reduced. Our method is able to remain certain robustness under image blur, illumination changes, and low-texture scenes. It can correctly predict the new EuRoC dataset, which is more challenging than the KITTI dataset.
Automated Test Generation for IEC 61131-3 ST Programs via Dynamic Symbolic Execution

A programmable logic controller (PLC) is essentially a computer dedicated to industrial control which is widely used in the field of global automation control. However, PLC software bugs can result in economic losses and even personal safety issues. PLC software must be thoroughly tested regarding function, structure, safety, and other aspects to avoid accidents. Existing PLC tools are mainly based on the manual setting of input data, which is not only unable to be well automated but also cannot provide information about code coverage. This paper presents an automated test case generation approach for a Structured Text (ST) language to reduce the cost of testing, using dynamic symbolic execution. We apply this method to implement the coverage-based automated test case generation tool STAutoTester. We have evaluated STAutoTester on 21 programs. The experimental results show that STAutoTester can effectively handle these programs. For 11 ST programs, STAutoTester reduces, on average, 87.5% of generated test cases compared to SYMPLC.
Naturalistic Driving Study for Older Drivers based on the DriveSafe App

Elderly population is increasing year after year in the developed countries. However, the knowledge of actual mobility needs of senior drivers is scarce. In this paper, we present a naturalistic driving study (NDS) focused on older drivers through smartphone technology and using our DriveSafe app. Our system automatically generates a driving analysis report based on objective indicators. The proposal supposes an improvement over the traditional surveys and observers, and represents an advance over the current NDSs by using smartphones instead of complex instrumented vehicles. Our method avoids the problems of manual annotation by using an automatic method for data reduction information. Furthermore, a comparison between traditional questionnaires and information provided by our system is carried out and conclusions are presented.