Miracle-dz/UniFAR-FTUs huggingface dataset:
Functional Model of a Self-Driving Car Control System

The article describes a structural and functional model of a self-driving car control system, which generates a wide class of mathematical problems. Currently, control systems for self-driving cars are considered at several levels of abstraction and implementation: Mechanics, electronics, perception, scene recognition, control, security, integration of all subsystems into a solid system. Modern research often considers particular problems to be solved for each of the levels separately. In this paper, a parameterized model of the integration of individual components into a complex control system for a self-driving car is considered. Such a model simplifies the design and development of self-driving control systems with configurable automation tools, taking into account the specifics of the solving problem. The parameterized model can be used for CAD design in the field of self-driving car development. A full cycle of development of a control system for a self-driving truck was implemented, which was rub in the “Robocross 2021” competition. The software solution was tested on more than 40 launches of a self-driving truck. Parameterization made it possible to speed up the development of the control system, expressed in man-hours, by 1.5 times compared to the experience of the authors of the article who participated in the same competition in 2018 and 2019. The proposed parameterization was used in the development of individual CAD elements described in this article. Additionally, the implementation of specific modules and functions is a field for experimental research.
Comparison of message filtering characteristics between closed-loop and open-loop feedback schemes

We numerically study the message filtering characteristics of semiconductor laser as a receiver in optical chaos communication. The transmitter is an external-cavity laser subjecting to optical feedback that operates in a chaotic regime. The receiver can operate at a chaotic regime either similar to the transmitter (closed-loop scheme) or without optical feedback (open-loop scheme). We study the effects of frequency detuning and parameters mismatch between transmitter and receiver on quality of the recovered signal in both open and closed loop schemes. We find that the closed-loop scheme has, in general, a higher quality of recovered signal compared with the open-loop. We also study the effects of message frequency on quality of the recovered signal in the two types of schemes. The results demonstrate that the filtering effect of semiconductor laser receiver is larger for low frequency message and decreases as the message frequency approaches the relaxation oscillation frequency of semiconductor laser. The SNR of the recovered signal of the open-loop scheme remains higher than that of the closed-loop scheme when the message frequency is in high frequency region.
A Multi-Level Inverter for Ultra High Speed PM Motor Control Application

The general concepts of multi-level technology involve utilizing a higher number of semiconductor devices to achieve higher power conversion, eliminate harmonics and reduce the switching loss. This paper is concerned with applying multi-level techniques to a PM motor to achieve ultra high speed features with better quality waveforms and less dv/dt for an aerospace application. A three-level diode clamped inverter is used to control a low inductance, ultra high speed PM motor with an integrated designed load compressor. Results from a 15kW experimental prototype are presented to validate the reliability of the inverter, the stability of the DC-link voltage balance and the practicality of vector control in this application. Streszczenie. W artykule opisano zastosowanie wielopoziomowego przeksztaltnika do sterowania silnikiem z magnesami trwalymi. Osiągnieto bardzo dobra jakośc ksztaltu sygnalu napieciowego I male dV/dt. Dzieki temu uzyskano ultra duza szybkośc do zastosowan lotniczych. (Wielopoziomowy przeksztaltnik w zastosowaniu do sterowania silnikami PM o duzych predkościach obrotowych)
Subband auto and cross-correlation estimation by recursive filtering

The auto and cross-correlation functions have proven to be valuable tools for many digital signal processing applications. Here, we extend these two functions and define the subband auto and cross-correlation equivalents. The new functions provide precious insight on the time-varying structure of non-stationary signals. For an efficient implementation recursive filters arranged in a filter bank structure are employed.
A RoboStack Tutorial: Using the Robot Operating System Alongside the Conda and Jupyter Data Science Ecosystems

The Robot Operating System (ROS) has become the de facto standard middleware in the robotics community [1]. ROS bundles everything, from low-level drivers to tools that transform among coordinate systems, to state-of-the-art perception and control algorithms. One of ROS’s key merits is the rich ecosystem of standardized tools to build and distribute ROS-based software.
Transformer-Less Single-Phase Four-Level Inverter for PV System Applications

A new inverter topology for single-phase photovoltaic (PV) systems is proposed in this study. The proposed inverter offers a four-level voltage in its output terminals. This feature results in easier filtering in comparison with other conventional two-level or three-level inverters. In addition, the proposed four-level inverter (PFLI) has a transformer-less topology, which decreases the size, weight, and cost of the entire system and increases the overall efficiency of the system. Although the inverter is transformer-less, it produces a negligible leakage ground current (LGC), which makes this inverter suitable for PV grid-connected applications. The performance of the proposed inverter is compared with that of a four-level neutral point clamped inverter (FLNPCI). Theoretical analysis and computer simulations verify that the PFLI topology is superior to FLNPCI in terms of efficiency and suitability for use in PV transformer-less systems.
ACTIONABLE FRAMEWORK FOR CITY DIGITAL TWIN-ENABLED PREDICTIVE MAINTENANCE AND SECURITY MANAGEMENT SYSTEMS

The growing potential offered by the use of ICT (Information Communication Technology)-based approaches for process management in the built environment is increasingly used to configure effective digital twins of products and processes. Focusing on Smart Cities, the realization of integrated three-dimensional BIM (Building Information Modeling) and GIS (Geographic Information Systems) provides microscopic and macroscopic geometric databases containing static, dynamic, geometric and semantic data representing the information centre for an efficient management of the lifecycle in vertical and horizontal systems. Therefore, processing data and information about the lifecycle of city assets through a Digital Twin Model ensures considerable support to the process management of built environments. Through the integration of these models with Artificial Intelligence (AI) systems it becomes possible to increase the optimization and progressive functional automation of the activities interconnected to the assets’ lifecycle. The proposed research presents a full-digital actionable framework based on pervasive and ubiquitous systems for security and facility management based on City Digital Twins. An integrated digital system for security management is introduced through the use of self-learning systems for unmanned security based on image recognition and AI. Finally, even maintenance operations can benefit from the configuration of predictive maintenance systems aimed at reducing costs and failures through a digital information management system combined with analytics based on data flows from sensors and historical data.
Flight Delay Prediction Using a Hybrid Deep Learning Method

The operational effectiveness of airports and airlines greatly relies on punctuality. Many conventional machine learning and deep learning algorithms are applied in the analysis of air traffic data. However, the hybrid deep learning (HDL) model demonstrates great success with superior results in many complex problems, e.g. image classification and behaviour detection based on video data. Interestingly, no previous attempts have been made to apply the concept of HDL in analysing structured air traffic data before. Hence, this research investigates the effectiveness of the HDL in the departure delays severity prediction (i.e. on-time, delay and extremely delay) for 10 major airports in the U.S. that experience high ground and air congestion. The proposed HDL model is a combination of a feed-forward artificial neural network model with three hidden layers and a conventional gradient boosted tree model (XGBoost). Utilising the passenger flight on-time performance data from the U.S. Department of Transportation, the proposed HDL model achieves a sharp rise of 22.95% in accuracy when compared to a pure neural network model. However, with current data used in this research, a pure machine learning model achieves the best prediction accuracy.
Material Classification Based on SVM for Civil Aircraft

Inventory control plays a crucial role for civil aircraft in reducing production cost to improve the competitiveness. Aiming at the special requirements of aircraft manufacturing industry, we propose a solution to classify the material instead of ABC inventory control. In order to balance economy and reliability, we build a new criterion system to classify the aircraft material into nine categories. We investigate the Support Vector Machine (SVM) method to classify the material for it has been proved very powerful and effective in establishing classification model with small sample, nonlinearity, high dimension and local minima. The choice of corresponding parameters, the choice of the training set, and the choice of the SVM kernel highly affect the performance of the classification. The study was done to determine the appropriate kernel function and to optimize SVM’s parameters for the classification of civil aircraft materials. Research results prove the validity of the classification model by the software implementation.
Optimum Noise Measure Terminations for Microwave Transistor Amplifiers (Short Paper)

The noise performance of the individual stages in a multistage low-noise amplifier can be quantified by means of the noise measure as proposed by Haus and Adler [4]. The minimization of the noise measure of a given active two-port device is of direct interest to the designer of such amplifiers. A new means of obtaining circles of constant noise measure in the source reflection coefficient plane is presented here. These circles can be used to determine the value of the minimum noise measure for a given active device and the associated source termination. The validity of the new method has been verified by comparison with results obtained using existing equations for the admittance plane and also by experiment.
All-optical NRZ-DPSK Clock Recovery Using Chromatic Dispersion Induced Clock Tone

All-optical clock recovery (CR) from 10 Gbps non-return-to-zero differential phase-shift-keying (NRZ-DPSK) signal is demonstrated experimentally, with the chromatic dispersion induced clock tone and semiconductor optical amplifier (SOA)-based mode-locked fiber ring laser. The good performance of our proposed configuration is fulfilled with a 20 km standard single mode fiber to regenerate clock tone of the NRZ-DPSK signal. The recovered clock signal with the extinction ratio over 17 dB and the root-mean-square timing jitter of 718 fs is achieved under 231-1 pseudorandom binary sequence NRZ-DPSK signals measurement.
Virtual Social Presence for Effective Collaborative E-Learning

Collaborative learning is not well supported by current elearning environments. Effective group collaboration in elearning efforts is very difficult by the absence of visual identification of the participants and a shared reference space. This paper argues that virtual worlds possess the necessary aspects to foster effective group collaboration in e-learning efforts. The use of virtual representations (avatars) of the participants, the support of various forms of real time communications and the creative capabilities provided in virtual worlds are suggested as the key factors that promote effective group learning.
A Technical Approach to Characterizing Perceived Walking Comfort of Carpet

A long-term, comprehensive investigation of carpet comfort is described, and its relationship to measured maximum decelerations recorded by a free-fall missile in strument. Comfort is measured by collecting and averaging scores provided by test panels of 25-51 pedestrians. Maximum decelerations are measured with an impact instrument specially modified for this application. Both comfort and impact data are collected as functions of pedestrian traffic, extending to several hundred thousand passages. The results are for the most part quite consistent and permit comfort ranking of five carpets with attached polyurethane cushions and one noncushion control, as well as the establishment of a preliminary correlation between subjective comfort and maximum decelerations obtained with the impact instrument. Relationships of the latter sort may provide a convenient means for rating and tracking the decline in walking comfort of floor covering systems.
PENGUJIAN MODEL DE LONE DAN MC LEAN DALAM PENGEMBANGAN SISTEM INFORMASI IFCA

The purpose of this study was to analyze the positive effect of information quality and system quality on user satisfaction and intensity of system use, user satisfaction positively influences the intensity of the use of the system, the positive influence of user satisfaction and intensity of use of the system to the individual impact, positive influence on the individual impact of organizational impact. This study uses a model of De Lone and Mc Lean (1992) in the development of information systems IFCA in Indonesia. Sample based on the results of the survey questionnaires to managers and supervisors in four of five business units. The results showed that the quality of information has no effect on user satisfaction and system usage intensity. The quality system has positive influence on user satisfaction, but do not affect the intensity of use of the system. User satisfaction has no effect on the intensity of use of the system. Furthermore, the intensity of use of the system has positive influence on individual impact, yet has no effect on user satisfaction of individual impact. The impact of individual positive influence on organizational impact.
Analytical Framework for the Choice of Dispute Resolution Methods in International Construction Projects Based on Risk Factors

International construction projects provide opportunities for developing countries to advance in the global economy and for international construction and design firms to increase their profit and market share. Despite the attractive opportunities that international construction offers, international contractors are faced with many challenges and difficulties when moving into international markets. Many risks are associated with international construction, whether external or project-specific risks. Those risks affect how contract clauses are written, including the dispute resolution clause. This paper discusses the different dispute resolution methods employed in international construction contracts and develops an analytical framework (DRM-Risk matrix) suggesting the use of specific dispute resolution methods depending on the risks expected in the project. The matrix may eventually help international contractors in the selection of the appropriate dispute resolution method during contract formation depe.
Impact of CMOS technology scaling on the atmospheric neutron soft error rate

We investigated scaling of the atmospheric neutron soft error rate (SER) which affects reliability of CMOS circuits at ground level and airplane flight altitudes. We considered CMOS circuits manufactured in a bulk process with a lightly-doped p-type wafer. One method, based on the empirical model, predicts a linear decrease of SER per bit with decreasing feature size L/sub G/. A different method, based on the MBGR model, predicts even faster decrease of SER per bit than linear. If the increasing number of bits is taken into account, then the SER per chip is not expected to increase faster than linearly with decreasing L/sub G/.
Advanced HSPICE Macromodel for Magnetic Tunnel Junction

Magnetoresistive random access memory (MRAM) using a magnetic tunnel junction (MTJ) memory cell has the potential to revolutionize both high-density and high-speed memory applications with devices exhibiting non-volatility and good write endurance. This work presents a macromodel for an MTJ that is applicable to an HSPICE circuit simulator. The macromodel is realized as a five-terminal subcircuit that reproduces the characteristics of an MTJ including hysteresis and asteroid curves with thermal variation, and the R–V characteristics.
Using coexpression to explore cell-type diversity with the fcoex package

Here, we present the fcoex package, which infers coexpression from scRNA-seq data and yields multiple, overlapping classes of cells based on coexpression modules. The tool extends the current scRNA-seq toolbox, providing a multi-hierarchy view on cell functionality and enabling the development of more complete cell atlases. Single-cell RNA sequencing (scRNA-seq) captures details of the cellular landscape, basing a fine-grained view on biological processes. Current pipelines, however, are restricted to single-label perspectives, missing details of the classification landscape. In the pbmc3k blood cell dataset, fcoex detects known classes, like antigen-presenting cells and a new theoretical group of cells, marked by the expression of FCGR3A (CD16). Fcoex extends the current scRNA-seq toolbox, providing a multi-hierarchy view on cell functions as a tool to develop complete cell type atlases. Availability and Implementation Fcoex is written in R and openly available in Bioconductor (https://bioconductor.org/packages/fcoex/). Supplementary information Supplementary data is available at the end of the manuscript. Source code for analysis is available at https://github.com/csbl-inovausp/fcoex_analysis;
Integrative Molecular Analyses of an Individual Transcription Factor-Based Genomic Model for Lung Cancer Prognosis

Objective Precision medicine with molecular profiles has revolutionized the management of lung cancer contributing to improved prognosis. Herein, we aimed to uncover the gene expression profiling of transcription factors (TFs) in lung cancer as well as to develop a TF-based genomic model. Methods We retrospectively curated lung cancer patients from public databases. Through comparing mRNA expression profiling between lung cancer and normal specimens, specific TFs were determined. Thereafter, a TF genomic model was developed with univariate Cox regression and stepwise multivariable Cox analyses, which was verified through the GSE72094 dataset. Gene set enrichment analyses (GSEA) were presented. Downstream targets of TFs were predicted with ChEA, JASPAR, and MotifMap projects, and their biological significance was investigated through the clusterProfiler algorithm. Results In the TCGA cohort, we proposed a TF-based genomic model, comprised of SATB2, HLF, and NPAS2. Lung cancer individuals were remarkably stratified into high- and low-risk groups. Survival analyses uncovered that high-risk populations presented unfavorable survival outcomes. ROCs confirmed the excellent predictive potency in patients' prognosis. Additionally, this model was an independent prognostic indicator in accordance with multivariate analyses. The clinical implication of the model was well verified in an independent dataset. High risk score was in relation to carcinogenic pathways. Downstream targets were characterized by immune and carcinogenic activation. Conclusion The proposed TF genomic model acts as a promising marker for estimation of lung cancer patients' outcomes. Prospective research is required for testing the clinical utility of the model in individualized management of lung cancer.
The balancing principle for parameter choice in distance-regularized domain adaptation

We address the unsolved algorithm design problem of choosing a justified regularization parameter in unsupervised domain adaptation. This problem is intriguing as no labels are available in the target domain. Our approach starts with the observation that the widely-used method of minimizing the source error, penalized by a distance measure between source and target feature representations, shares characteristics with regularized ill-posed inverse problems. Regularization parameters in inverse problems are optimally chosen by the fundamental principle of balancing approximation and sampling errors. We use this principle to balance learning errors and domain distance in a target error bound. As a result, we obtain a theoretically justified rule for the choice of the regularization parameter. In contrast to the state of the art, our approach allows source and target distributions with disjoint supports. An empirical comparative study on benchmark datasets underpins the performance of our approach.
A Separation Technology for Biological Radar Signals of Respiration and Heartbeat

This research aimed to separate the signals of respiration and heartbeat effectively from the biological radar echo signals of vital signs detecting platform. Therefore the characteristics parameters of the respiration and heartbeat could be extracted to provide the basis for clinical diagnosis and disease prevention. This paper put forward a separation algorithm of heartbeat and respiration based on LMS adaptive harmonic cancellation, the combined harmonics of the breathing signal was regarded as a reference input signal of the adaptive filter to improve the algorithm. Related experiments were designed to conduct filter processing. Results show that the algorithm proposed proved to be more effective in the signals separation of respiration and heartbeat.
Exploring the synthesis of MoS2-based composite nanoelectrode materials for hydrogen production from electrolysis of water

Focusing on the global energy crisis and facing major national needs, scientists explore the field of hydrogen production materials by electrolysis of water. As a new catalyst material, MoS2 shows high catalytic efficiency in the electrolysis of water for hydrogen production. It, therefore, has the potential to replace Pt precious metals as a catalytic material in hydrogen precipitation reactions[1]. MoS2 is based on nanomaterials and its catalytic activity and intrinsic properties can be improved by modulation of the morphological structure. The process of preparing MoS2 in nanospheres, amorphous, bread-shaped and flower-shaped MoS2 is discussed in this study. The effect of adding surfactants or other solvents to the synthesis on the morphological structure is investigated in comparison with the efficiency of hydrogen precipitation. Finally, ideas for improving the catalytic efficiency are presented and future developments of MoS2 materials are foreseen. Studies have found that the morphology and structure of MoS2 can be changed by solvents and surfactants. The catalytic activity of nano-microsphere and flower-like is higher than that of bread-like MoS2.
Printed circularly polarised antenna with ultra-wide axial-ratio bandwidth

A circularly polarised printed dipole-like antenna employing asymmetrical arms and an orthogonal slit in the ground plane is presented. It is fed by a stepped microstrip line which connects to the shorter arm. By utilising surface currents on the asymmetrical arms and the orthogonal feedline structure, circular polarisation is realised. Experimental and numerical data are in agreement and the measured results show a fractional impedance bandwidth of 41.3% (1.77-2.69 GHz) and a wide axial-ratio bandwidth of 38.4% (1.81-2.67 GHz).