Miracle-dz/UniFAR-FTUs huggingface dataset:
A Stepwise Rate-Compatible LDPC and Parity Management in NAND Flash Memory-Based Storage Devices

The storage capacity of the NAND flash memory has increased rapidly, and accordingly, the error rate for data writing and reading to the flash memory cell has also escalated. Error-correcting code (ECC) modules, such as low-density parity-check (LDPC), have been applied to flash controllers for error recovery. However, since the error rate increases rapidly, compared to the aging factor and program/erase (P/E) cycle, fixed ECCs and parities are inappropriate methods for resolving this proliferating error, according to the P/E cycle. Therefore, the design of a dynamic ECC scheme and a proper ECC parity management system to increase the lifespan of flash memory storage devices remains in great demand. Herein, an LDPC encoding and decoding scheme is designed to obtain a step-by-step code rate according to the P/E cycle by applying a stepwise rate-compatible LDPC. In addition, an ECC parity management scheme for the increasingly excessive stage-wise ECC is proposed to reduce management and read/write operational overheads. The ECC management scheme also includes the ECC cache system. The proposed LDPC, as well as its management system, will improve the recovery ability of the NAND flash storage device according to the P/E cycle, while it can reduce system read and write overheads due to additional parity data growth.
Multimodal tracking framework for visual odometry in challenging illumination conditions

Research on visual odometry and localisation is largely dominated by solutions developed in the visible spectrum, where illumination is a critical factor. Other parts of the electromagnetic spectrum are currently being investigated to generate solutions dealing with extreme illumination conditions. Multispectral setups are particularly interesting as they provide information from different parts of the spectrum at once. However, the main challenge of such camera setups is the lack of similarity between the images produced, which makes conventional stereo matching techniques obsolete.This work investigates a new way of concurrently processing images from different spectra for application to visual odometry. It particularly focuses on the visible and Long Wave InfraRed (LWIR) spectral bands where dissimilarity between pixel intensities is maximal. A new Multimodal Monocular Visual Odometry solution (MMS-VO) is presented. With this novel approach, features are tracked simultaneously, but only the camera providing the best tracking quality is used to estimate motion. Visual odometry is performed within a windowed bundle adjustment framework, by alternating between the cameras as the nature of the scene changes. Furthermore, the motion estimation process is robustified by selecting adequate keyframes based on parallax.The algorithm was tested on a series of visible-thermal datasets, acquired from a car with real driving conditions. It is shown that feature tracking could be performed in both modalities with the same set of parameters. Additionally, the MMS-VO provides a superior visual odometry trajectory as one camera can compensate when the other is not working.
RoboMag: A Magnetic Actuation System Based on Mobile Electromagnetic Coils With Tunable Working Space

Magnetic field is regarded as a safe and efficient method for controlling minimally invasive medical robots remotely, and magnetic actuation system based on mobile electromagnetic coils are preferred due to the enlarged working space and fast response. Enhanced flexibility on coils' positions will further enable the system to deal with possible constraints inside the workspace, while design and control of such flexible systems remain challenging. In this work, a magnetic actuation system based on three mobile electromagnetic coils with decoupled movements is designed and the performance is demonstrated. The system consists of three 4-axis robotic arms that can adjust the relative positions of three mobile coils independently. The prototype of RoboMag is designed and able to program magnetic field at a required point inside a hemispherical space with a diameter of 152 mm. A method to drive the mobile-electromagnetic-coil-based systems with flexible movements is proposed. To begin with, a model of the robotic platform is built to place the coils to demanded position. A numerical model of a single coil is constructed and average error of magnetic field is reduced to 3.30% after calibration. Afterwards, a method based on coordinate transformation is proposed for the entire system to produce demanded magnetic torque and force at a required position. Experiments are conducted to demonstrate the system's capability on generating magnetic torque and force to actuate medical device analog.
Optimized Vector Control Strategy for Dual-Rotor Axial Flux Permanent Magnet Synchronous Motor for in-Wheel Electric Drive Applications

This paper analyzes the performance of dual-rotor axial flux permanent magnet synchronous motor for its application in electric motor drive. Dual-rotor single-stator topology is employed for analysis due to its superior features compared to other topologies of the motor for electric drive application. Constant torque angle control strategy with hysteresis current controller for inverter switching is implemented with the axial flux motor and is analyzed for validation. To enhance the robustness of the control strategy, the coefficients of proportional-integral controller are optimized with particle swarm optimization algorithm using Matlab/Simulink software to minimize the torque and speed ripples obtained from the conventional setting of proportional-integral controller. The performance analysis of the motor drive with optimized controller coefficients is carried out using Ansys co-simulation with Maxwell and Simplorer softwares. The simulation analysis of the motor with optimized constant torque angle strategy shows good motor performance and robustness which is inferred from results.
Opportunistic Meta-Learning: A Case Study for Quality Assurance in Industry 4.0 Environments

In the context of self-adaptive and self-organising systems, the adaptation of spontaneously available knowledge is a key to improve a system’s learning quality in detecting novelties or improving the accuracy in general. We introduce an opportunistic meta-learning architecture capable of evaluating and ranking knowledge sources at runtime and to adapt to the most promising one. Furthermore, we present experimental simulations in the domain of quality assurance and show that adaptive knowledge can stabilise the learning procedure.
One-Shot Multi-Path Planning for Robotic Applications Using Fully Convolutional Networks

Path planning is important for robot action execution, since a path or a motion trajectory for a particular action has to be defined first before the action can be executed. Most of the current approaches are iterative methods where the trajectory is generated by predicting the next state based on the current state. Here we propose a novel method by utilising a fully convolutional neural network, which allows generation of complete paths even for several agents with one network prediction iteration. We demonstrate that our method is able to successfully generate optimal or close to optimal paths (less than 10% longer) in more than 99% of the cases for single path predictions in 2D and 3D environments. Furthermore, we show that the network is — without specific training on such cases — able to create (close to) optimal paths in 96% of the cases for two and in 84% of the cases for three simultaneously generated paths.
Scalable Concurrent Pools Based on Diffracting Trees

Multithreading synchronization is one of the most essential problems parallel programming. Concurrent pool is one of the most common and demanded data structures in scalable applications. The promising way to implement concurrent pool is using diffracting (diffraction) tree as an auxiliary data structure to increase the scalability. In this work, we try to optimize diffracting-tree based pool and propose our implementations which outperforms the existing ones. We designed concurrent based on diffracting trees which optimize access of threads to global variables for maximization of the efficiency (throughput) of data structure. We performed experimental modeling to evaluate the efficiency of concurrent pools. We give the evidence that our pools have higher scalability compared with the existing pool’s implementations based on diffracting trees. We discuss the experimental results and provide the guidance for using them.
Personality Classification System using Data Mining

Personality is one feature that determines how people interact with the outside world. Personality can be defined as a necessary element of a person’s behavior. The way people interact with other people determines their personality. This paper covers the topic of Automated Personality Classification – a system that analyses the personality of a user based on certain features using Data Mining Algorithms. In this paper, a system is proposed which analyses the personality of an applicant. This system will be helpful for organizations as well as other agencies who would be recruiting applicants based on their personality rather than their technical knowledge. The personality prediction results are based on Big Five Personality traits and the classification is done using Naïve Bayes Algorithm and Support Vector Machine.
Depth Vision Guided Human Activity Recognition in Surgical Procedure using Wearable Multisensor

The increasing complexity of modern surgery rooms brings many challenges. Human activity recognition (HAR) plays a significant part in healthcare, telemedicine, long-term treatment, and even surgery by using wearable inertial sensors or depth cameras. Although the development of artificial intelligence techniques provide various machine learning (ML) methods to identify activities, it is a time-consuming implementation and high work burden to collect and label the large data set. To fascinate efficient data collection and labeling, we propose a novel depth vision-guided HAR architecture to obtain the labels of the collected raw data from the inertial measurement unit (IMU) sensors using depth data automatically. Experimental results show that the novel depth vision-guided interface can be utilized for identifying activities without labeling the data in advance.
Heteromagnetic Swinging Inductor and Its Application for Power Factor Correction Converters

A variable inductor concept based on a magnetic structure consisting of composite of magnetic materials is introduced to meet the designed inductance function of current. A design of the inductor, termed heteromagnetic swinging inductor (HMSI), was fabricated using a toroid core with a gap that was filled by a custom-developed low-permeability magnetic powder + polymer material. Having the gap filled with the magnetic material allowed for greater flexibility in L–I design with added benefit of low fringe field. The analytical model of HMSI was derived and verified by simulation. And the step-by-step design of arbitrary L–I curve HMSI was posted. We applied this swinging inductor to a critical conduction mode boost power factor correction converter to reduce its switching frequency variation range. First, the required L–I curve of the HMSI needed for narrow range of switching frequency was calculated. Then, the HMSI core structure consisting of two magnetic materials was designed. Finally, a prototype of the HMSI was fabricated and tested. Experimental result showed that for a 110-Vrms input, the converter with the HMSI had a switching frequency variation factor, defined as Δf/fmin, six times less than that of the converter using a linear inductor.
Deep Learning Based Human-Robot Co-Manipulation for a Mobile Manipulator

In this paper, a deep learning based human-robot co-manipulation technique using a mobile manipulator is proposed, which employs a CNN-LSTM network to generate robot motions through the short-term human motions. To learn a human motion, the CNN-LSTM network is trained on a library of motions, which is produced from human-robot demonstrations based on the positions of the human arm and the robot joint actions. To make the robot precisely following the generated motions under uncertain dynamics, an adaptive neural network controller is designed to ensure that the robot outputs track the generated trajectory within a small neighborhood of zero. The effectiveness of the proposed approach is demonstrated by performing co-manipulation tasks with a mobile manipulator.
Navigation Route Mapping for Harvesting Robots in Vineyards Using UAV-based Remote Sensing

This work deals with the problem of extracting the route map that needs a harvesting robot in order to navigate between the crop rows in a vineyard. The navigation route mapping is computed by algorithmic processing the RGB imagery acquired by an unmanned aerial vehicle (UAV). The proposed methodology applies simple machine vision methods and it is able to deliver the robot’s navigation route map in real-time with high tolerance to missing plants/weeds as well as with high accuracy.
A Novel Calibration Method between a Camera and a 3D LiDAR with Infrared Images

Fusions of LiDARs (light detection and ranging) and cameras have been effectively and widely employed in the communities of autonomous vehicles, virtual reality and mobile mapping systems (MMS) for different purposes, such as localization, high definition map or simultaneous location and mapping. However, the extrinsic calibration between a camera and a 3D LiDAR is a fundamental prerequisite to guarantee its performance. Some previous methods are inaccurate, have calibration error that is several times the beam divergence, and often require special calibration objects, thereby limiting their ubiquitous use for calibration. To overcome these shortcomings, we propose a novel and high-accuracy method for the extrinsic calibration between a camera and a 3D LiDAR. Our approach relies on the infrared images from a camera with an infrared filter, and the 2D-3D corresponding points in a scene with the corners of a wall can be extracted to calculate the six extrinsic parameters. Experiments using the Velodyne VLP-16 sensor show that the method can achieve an extrinsic accuracy at the level of the beam divergence, which is fully analyzed and validated from two different aspects. Therefore, the calibration method in this paper is highly accurate, effective and does not require special complicated calibration objects; thus, it meets the requirements of practical applications.
Improved Glaucoma Diagnosis Using Deep Learning

Glaucoma is termed as one of the top leading causes of vision loss and in many cases is irreversible [1]. It is a condition that damages the optic nerve and it goes unnoticed in early stages as the symptoms are not prominent in the early stages. Recent approaches have been made to automate the detection of glaucoma based on available datasets. World Health Organization also looks at eye defects to be critical as a result of the health evaluation conducted globally on health challenges. Survey points to the fact that it can become one of the primary concerns in 2020 which might affect around 75-80 million people. We have automated the process of diagnosis of glaucoma using deep learning approaches. Image processing has gained a lot of attraction and can be used for this problem in forming a computer-aided diagnosis for diseases. In the end, we have compared our results with previous approaches, which shows that our method has a better accuracy score.
Log Files Analysis For Network Intrusion Detection

Information security remains an unsolved challenge for organizations, because every day brings new and sophisticated cyber-attacks. The implementation of network security operation systems is a new trend in the companies, to permanently monitor the logs and, in case of any anomalous traffic, to detect and hopefully prevent any security incident. These systems generate a huge amount of logs per second to be handled, leading to the need of automated ways to identify the cyber-attacks.In this paper we propose an effective Log-based Intrusion Detection System (LIDS), to predict an attack or not, based on carefully selected features. The logs from various sources are aggregated into one dashboard and the most discriminative features are first determined. For the attack prediction, a few machine learning techniques were comparatively tested, with the Decision tree being the winner. The proposed system is illustrated with the largest publicly available labelled log file dataset KDD Cup 1999.
Partial Convolutional LSTM for Spatiotemporal Prediction of Incomplete Data

Advanced data analysis techniques facilitate data-driven spatiotemporal prediction in various fields. However, in real-world data, missing values are inevitable, which causes the data incomplete and makes predictions more challenging. Although we can train complex spatiotemporal correlations with deep learning techniques, most deep learning networks require data without any missing values. In this paper, we propose a novel deep learning framework that manages missing values in the grid-based data structure. We design a partial convolutional long-short-term-memory (PConvLSTM) by combining partial convolution for inpainting and convolutional long-short-term-memory (ConvLSTM) for spatiotemporal prediction. We treat incomplete spatiotemporal data with the partial convolution and train spatiotemporal dependencies with the ConvLSTM structure. The trained PConvLSTM can predict continuous spatial data with missing regions in incomplete input data. We also train the network using incomplete spatiotemporal data without ground truth to enhance practicality. Existing deep learning networks to interpolate missing data are mostly trained by applying ground truth data without missing regions. We show that PConvLSTM achieves higher prediction accuracies compared to ConvLSTM for incomplete data without ground truth.
Ultra-High-Accuracy Visual Marker for Indoor Precise Positioning

Indoor positioning technology is essential for indoor mobile robots and drones. However, there has never been a general-purpose technology or infrastructure that enables indoor positioning with an accuracy of less than 10 cm. We have developed an attitude measurement method using multiple dynamic moires with a lenticular lens and developed an ultra- high-accuracy visual marker with an attitude estimation error of less than 0.1°. We also developed a calculation method that minimizes the marker position error by reminimizing reprojection error using its good attitude accuracy. We proved that accurate local positioning with a position error of about 1 cm in a marker coordinate system is possible even when a marker is shot from a distance of 10 m. In addition, a demonstration test was performed in a public space, and it was shown that high-accuracy global positioning with a position error of about 10 cm is possible.
Multi-Frame Depth Super-Resolution for ToF Sensor With Total Variation Regularized L1 Function

In this paper, we propose a multi-frame depth super-resolution (SR) method based on <inline-formula> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> data fidelity with the total variation regularization (TV-<inline-formula> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula>) model. The majority of time-of-flight (ToF) sensors exhibit limited spatial resolution compared to RGB sensors and the improvement of the depth image resolution is an inherently ill-posed problem. To overcome this under-determined problem, the solution space is limited by the regularization term through prior knowledge and the data fidelity term using statistical information of the noise. Firstly, the statistical characteristics of ToF depth images are analyzed to specify the appropriate observation model. Thereafter, the objective function for multi-frame depth SR based on the TV-<inline-formula> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> model is designed by considering the prior knowledge of the depth images. This approach enables the sharpness of the edges to be preserved and the noise amplification to be suppressed simultaneously. Furthermore, an efficient solver based on half-quadratic splitting is proposed. The algorithm minimizes the objective function for the multi-frame SR problem consisting of the TV regularization term and <inline-formula> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> data fidelity term. The proposed method is verified on a synthetic dataset and real-world data acquired from a ToF sensor. The experimental results demonstrate that the proposed method can substantially reconstruct high-resolution depth images in terms of preserving sharp depth discontinuities, without any obvious artifacts, and can increase robustness to noise.
Supporting SMEs in the Lake Constance Region in the Implementation of Cyber-Physical-Systems: Framework and Demonstrator

With the emergence of the recent Industry 4.0 movement, data integration is now also being driven along the production line, made possible primarily by the use of established concepts of intelligent supply chains, such as the digital avatars. Digital avatars - sometimes also called Digital Twins or more broadly Cyber-Physical Systems (CPS) - are already successfully used in holistic systems for intelligent transport ecosystems, similar to the use of Big Data and artificial intelligence technologies interwoven with modern production and supply chains. The goal of this paper is to describe how data from interwoven, autonomous and intelligent supply chains can be integrated into the diverse data ecosystems of the Industry 4.0, influenced by a multitude of data exchange formats and varied data schemas. In this paper, we describe how a framework for supporting SMEs was established in the Lake Constance region and describe a demonstrator sprung from the framework. The demonstrator project's goal is to exhibit and compare two different approaches towards optimisation of manufacturing lines. The first approach is based upon static optimisation of production demand, i.e. exact or heuristic algorithms are used to plan and optimise the assignment of orders to individual machines. In the second scenario, we use real-time situational awareness - implemented as digital avatar - to assign local intelligence to jobs and raw materials in order to compare the results to the traditional planning methods of scenario one. The results are generated using event-discrete simulation and are compared to common (heuristic) job scheduling algorithms.