Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparing phase-locked and non-phase-locked architectures for dual-channel DVB-S passive radar

This paper addresses the possibility to obtain low-cost passive radar that exploits DVB-S satellite transmissions as signals of opportunity using Commercial - Off The Shelf (COTS) hardware. In particular, low-noise down-conversion blocks (LNB) are typically available on the market, which include the Local Oscillator. Their use does not guarantee the fully coherent acquisition of surveillance and reference signal, required for passive radar operation. However, we estimate the frequency drift between the two channels, from the collected signals, and operate effectively for short-range surveillance applications. Using an ad-hoc experimental setup with “less commercial” LNBs that can be locked to a reference, we compare two different types of receiver architectures: the non-phase-locked and a phase-locked architecture where the two LNBs are locked to the same 10 MHz oscillator. It is found that for small coherent integration times, the two schemes have very similar performance, whereas the phase-locked architecture is required to allow longer integration times and thus greater correlation gains. However, the lower-cost prototype is shown to be effective to detect and follow cars at least in the short ranges of typical parking-lots,
Vehicle Counting: Survey and Experiments

Traffic management needs information about traffic to control the flow of transports. With millions of traffic video cameras acting as sensors around the world, collecting the information about traffic flow in real-time is quite easy, but using that information to process and control the traffic flow is a challenge. For detecting vehicles, old methods like inductive loop detectors (ILD), infrared detectors (IRDs), laser sensors, etc. have problems with high cost, efficiency, difficulty, etc. The methods we use in this paper are detection-based counting, regression-based counting. The authors propose a new method that is the combination of two methods above to achieve better results. We also evaluate the viability of using Deep Learning pre-trained models include Faster R-CNN, SSD, YOLO for detection-based. We experiment on 2018 AI CITY CHALLENGE datasets and Vehicles Nepal datasets. Our results show the effectiveness of the combining method in accuracy compares to using each of the methods separately.
On Performance of Deep Reinforcement Learning-based Listen-Before-Talk (LBT) Scheme

Licensed assisted access (LAA) is a promising system to overcome the limited radio resource by sharing the unlicensed band with wireless local area networks (WLANs), and the listen-before-talk (LBT) scheme is a key technology for providing fairness between LAA and WLAN. Recently, deep reinforcement learning (DRL) has been investigated to improve the performance of LBT; however, such approaches assume that there is no processing delay and thus the optimal decision can be immediately done. In this paper, we evaluate the performance of the DRL-based LBT (DRL-LBT) scheme when different processing delays are considered for DRL. Evaluation results demonstrate that the throughput fairness index and the total throughput of DRL-LBT with the processing delay can be degraded up to by 9.4% and 10.0%, respectively, compared with an ideal case without any processing delay.
A Novel Fault Detection of Igbt Open – Circuit Failure In Five-Phase Open-End Winding PMSM Drive System

In order to improve the reliability of motor drive systems, the device faults in the voltage-source inverters should be detected immediately. Aiming at this problem, this paper presents a novel open-circuit fault detection method for five-phase open-end winding permanent magnet synchronous motor (PMSM) drive system. In this paper, the characteristics of zero sequence current in faulty condition are analyzed. Compared with neutral connected PMSM, the zero sequence current will change differently according to the different faulty conditions. Thus the zero sequence current is used to detect the fault. By comparing zero sequence current with phase currents, the faulty phase and condition can be detected. The confirmation time is adopted to improve the reliability. The effectiveness of the proposed method is verified by simulation.
A Novel Miniature Rectenna Integrating Rectifier in Antenna Element for Large-Scale Rectenna Arrays

In this paper, a novel miniature rectenna for large-scale rectenna arrays is proposed, and its characteristics are evaluated by simulation and measurement. As the proposed rectenna integrates a rectifier in a microstrip antenna element, miniaturization is achieved with a simple structure. The antenna element used in the proposed rectenna provides the similar antenna gain with conventional rectangular microstrip antennas. The conversion efficiency of 28.1% was obtained under the condition of $0.68-W/m^{2}$ power density. The proposed miniature rectenna can easily realize large-scale rectenna arrays by simply connecting the ground conductor on the back.
A Maritime Harmonic Radar Search and Rescue System Using Passive and Active Tags

This article introduces a new maritime search and rescue system based on S-band illumination harmonic radar (HR). Passive and active tags have been developed and tested attached to life jackets and a rescue boat. This system was able to detect and range the active tags up to a range of 5800 $m$ in tests on the Baltic Sea with an antenna input power of only 100 W. All electronic GHz components of the system, excluding the S-band power amplifier, were custom developed for this purpose. Special attention is given to the performance and conceptual differences between passive and active tags used in the system and integration with a maritime X-band navigation radar is demonstrated.
A Switchable 256 Elements Ka band Circularly polarized Phased Array Using 45 Degree Linearly Polarized Element

This paper presents a scalable 256-elelment (16 x 16) Ka-band switchable circularly polarized phased array. The proposed phased array antenna can generate LHCP and RHCP, which can be selected by setting the phased shifters in the beamformer chips with 6-bit phase control in SiGe BiCMOS. This phased array uses 64 silicon quad-core receive chips with 4 channels to feed 2x2 quad antennas. Those silicon core-chip chips are assembled on a 12-layer printed circuit board (PCB) together with a Wilkinson divider network and SIW cavity backed patch antennas. The low-cost design uses Taconic TSM-DS3 based multilayer printed circuit board (MPCB), and surface mount technology (SMT). The phased-array can scan to ± 60° in all planes with low sidelobes, a 3-dB beamwidth of 12.6° and 21.2 dBi gain at broadside in 8 x 8 array. The 3-dB axial ratio (AR) in the mainlobe region is lower than 4.8 dB when scanning. It is scalable without grating lobes to allow economical construction of larger phased arrays. The compact size makes it suitable for a low-cost Ka-band SATCOM terminal.
Reduced Sensor PMBLDC Motor Drive with Power Factor Correction and Speed Control

A reduced sensor permanent magnet brushless DC motor drive (PMBLDCMD) is presented to provide a low cost and rugged drive for constant torque, variable speed applications in few kW power rating. This drive is fed from single phase AC mains through diode bridge rectifier (DBR). It has three main sections namely a single ended primary inductance converter (SEPIC) as DC-DC converter operated in discontinuous conduction mode (DCM) for power factor correction (PFC) control; a voltage controlled three phase voltage source inverter (VSI) and a speed controlled PMBLDC motor operated without Hall Sensors. The DCM operation for PFC, voltage control and position sensorless control result in reduction of various sensors used in PMBLDCMD with improvement in power quality at AC mains and desired speed control at load. The proposed drive uses one DC link voltage sensor and one back EMF sensing circuit at motor terminals to execute complete control of PMBLDCMD. The proposed PMBLDCMD is designed, modelled and simulated in MATLAB/SIMULINK. The simulated results of the proposed drive are validated on a hardware prototype using TI 2812 as a controller.
A Secured Smart National Identity Card Management Design using Blockchain

Importance of data security differs from sector to sector. However, data of sensitive nature are increasing day by day and thereby, it requires utmost attention towards the protection of data. Organizations from all over the world are getting involved to deploy the best cyber defense mechanism. Primary concerns for the organizations includes three common factors: people, processes, and technology. Smart national identity cards (smart cards) are immensely sensitive in nature. In Bangladesh, smart cards are being used for 22 services, which include banking, passports, driving licenses, etc. In addition, smart cards also hold bio-metric details for the cardholder such as retina scan, ten finger impressions, picture, and so on. Most of them are confidential data and prone to privacy breach. To address this issue, this paper propose a model for smart card management system using blockchain technology. In recent years, blockchain earned popularity among researchers to protect data from unlawful altercation. Adaptation of our proposed model will help the government of Bangladesh to secure citizens’ private information and bring transparency on their information management. Furthermore, it will provide access control capability to citizens in case of third-party information access requests.
Design of Ku-Band Multiplexer with High Power Dielectric Resonator Filters Using Neural Networks

In this paper, the recently developed modeling technique for high power dielectric resonator (DR) filters based on neural network modeling of simplified generalized scattering matrix (GSM) of filter junctions is applied to the design of a Ku-band multiplexer with DR channel filters. As demonstrated, the modeling technique is highly efficient for multiplexer design, and requires significantly less time than conventional EM-based method. The reflection coefficient and transmission coefficient of the model show a good match to EM simulation results in both near-band and wide-band frequency ranges. The physical dimensions obtained from the developed model also have comparable accuracy to EM results.
Combined Variable Step Size LMS for DVR with Error Regulator Gain Tuning Through Ant-Lion Optimization

In this paper, the dynamic voltage restorer (DVR) performance is enhanced with the combined variable step-size least mean square (CVSSLMS) and optimized PI (Proportional-Integral) regulators-based control algorithm. Multiple voltage disturbances in distribution network like voltage sag, swell, voltage imbalance, and distortions are taken into consideration for compensation using DVR. The CVSSLMS has the combined features of both Normalized LMS (NLMS) and LMS algorithms such as effective transient response and reduced steady state error, respectively. In internal structure of algorithm, PI controllers are used as error regulator to regulate dc and ac bus voltages through error minimization technique. The PI controller gains are tuned through an optimization algorithm named as ant-lion optimization (ALO) algorithm. The added feature of ALO is the avoidance of local optimum. With this control algorithm, the performance of three phase DVR is simulated for the aforesaid disturbances using MATLAB/Simulink model. It is also experimentally tested using DSP (Micro Lab Box, made by Free scale QorlQ P5020) in three phase distribution networks. It is observed that, in both cases, the performance has met the requirement according to the IEEE-519-2014 standard.
Gradient Optimization on Third Order Bandpasses for a 24 GHz Metasurface Lens

In this paper a method for metamaterial realization by incorporating coupling effects of higher order frequency selective surfaces is described. For this, a primary design, generated by equivalent circuit methods, is systematically adjusted and evaluated by full wave simulations. The used optimizer-simulator combination allows solving the inverse problem of metamaterial design without the sophisticated modelling of interlayer coupling. Generated metamaterial bandpasses are finally used to create thin lenses (λ/8) based on common printed circuit board material.
A Soft-Switching Transformer-less Step-down Converter based on Resonant Current Balance Module

In this paper, a soft-switching transformer-less step-down convertor based on resonant current balance module is proposed. Here a LED driver is taken as an example to analyze. The converter includes a stacked half bridge, resonant current balance modules, pairs of LED strings. The resonant current balance modules make the proposed convertor realize automatic current sharing, zero current switching (ZCS) and extending to a 1/N convertor. Based on the ampere-second balance principle, the proposed convertor can realize automatic current balance. The switching frequency is slightly larger than the resonant frequency, so it achieves ZCS. A 15.8W experimental prototype with 100V input voltage and 25V output voltage is established in the laboratory, verifying the feasibility of the oft-switching transformer-less step-down converter.
Early Termination of STDP Learning with Spike Counts in Spiking Neural Networks

Spiking neural network (SNN) is considered as one of the most promising candidates for designing neuromorphic hardware due to its low power computing capability. Since SNNs are made from imitating features of the human brain, bio-plausible spike-timing-dependent plasticity (STDP) learning rule can be adjusted to perform unsupervised learning of SNN. In this paper, we present a spike count based early termination technique for STDP learning in SNN. To reduce redundant timesteps and calculations, spike counts of output neurons can be used to terminate the training process beforehand, thus latency and energy can be decreased. The proposed scheme reduces 50.7% of timesteps and 51.1% of total weight update during training with 0.35% accuracy drop in MNIST application.
Dynamic mapping mechanism between edge capability and service in Internet of things under edge-end synergy

With the rapid popularization of the Internet of everything era and the continuous development of communication technology, more and more terminal devices have entered people’s life and become an important part of changing People’s Daily life. However, these terminals, while bringing convenience, put forward higher requirements for the processing capacity of edge network How to use algorithms to efficiently utilize the resources in edge network to meet the service needs has become a hot issue that people pay attention to. This paper proposes a new network topology structure in which edge nodes and terminal devices work together, quantifying a series of capabilities such as computing resources and storage resources of edge nodes and terminal devices, and then making unified scheduling and allocation to meet the needs of different services. This paper analyzes the relationship between various physical quantities in the model, and proposes a service mapping mechanism based on the minimization of interactive energy consumption and resource occupation cost. The purpose is to minimize interactive energy consumption and resource occupation cost as much as possible on the premise of satisfying service delay and resource request. Heuristic algorithms tend to assign service priority to edge nodes, followed by terminal devices with computing or storage capacity. It mainly considers the difference in energy consumption and resource occupation cost caused by placing the service on a single terminal device and multiple terminal devices, so it tends to place the service on the terminal device with a large amount of resources, so as to reduce transmission energy consumption and the occupation cost of bandwidth and input and output resources. Finally, a simulation experiment is designed to verify the effectiveness of the heuristic algorithm. The results show that the heuristic algorithm has better performance in the case of large service tasks, and can reduce the interaction energy consumption and delay as much as possible, so as to ensure that the cost of resource occupation is not too high.
Sequential Compression Using Efficient LUT Correlation for Display Defect Compensation

In this paper, we propose a novel sequential compression method for the coefficient look-up table (LUT) for compensation of display panel defects. First, the regression LUT coefficient data for defect compensation is rearranged as the display ratio. It is split and compressed into sub-blocks to utilize the correlation of the rearranged data. Then, to further increase the compression ratio, the initially compressed data is compressed again through the differential coding-based compression. In the performance evaluation, the proposed method has the same loss as the existing compression method, while increasing the compression ratio of up to 46.9%.
Chaotic True Random Number Generator for Secure Communication Applications

This work is proposed chaotic true random number generator (TRNG) based on chaotic PWM sequence generator. The chaos CMOS circuit presents here is the chaotic sequence generator. The main components of the chaotic PWM circuit is chaotic sequence generator for chaotic triangular waveforms. The chaotic TRNG was designed by using 0. 18μm CMOS process. The full system is prose to be a implementation of IoT secure data communication system using chaotic security.
How Does Managerial Experience Predict the Internationalization Type of a Young Firm?

This study aims to find out how useful managers’ past general and export experience is in predicting whether young manufacturing firms become fast internationalizers. Extant literature about the role of managerial experience in determining young firms’ internationalization type is scant. This paper fills this gap by providing systematic evidence on which kinds of general and export experience can be used for accurate predictions of two firm types: born globals and general fast internationalizers. Our dataset encompasses information about managerial experience of the whole population of young Estonian manufacturing firms. Based on using four different prediction methods (logistic regression, rough sets, decision tree, neural networks) and a large variety of variables reflecting managers’ past experience, the results indicate that in prediction models, export experience variables are more valuable than general experience variables. Born globals can be predicted with an accuracy of at least 90% in case of all applied machine learning methods, while the precision is lower in case of general fast internationalizers. The study leads to important implications for international business theory and practice.
Analysis of Scientist Collaboration Network Based on the Journal of “Statistical Research”

The theories, thoughts and methods of complex network bring vitality and vigor to the science of science. The collaboration network is one of significant subjects. In recent years, some researchers have established huge collaboration networks to analysis network property. However, in addition to huge collaboration networks, analysis one specific journal is also of great importance. In this paper, we establish a collaboration network based on the data of famous Chinese journal of “Statis-tical Research”. The paper is focus on the scientist influence, collaboration patterns and community structure. Our study found that Xianchun Xu is one of the most famous and influential statisticians. Besides, teacher-student collaboration pattern has gradually become popular increasingly. More importantly, the division of scientist community structure is mainly based on the geographic location of universities. The study is of great significance for the evaluation of scientist influence and the promotion of scientific collaboration in the field of statistics.
Unsupervised Balanced Covariance Learning for Visual-Inertial Sensor Fusion

Incorporating multi-sensor, in filter-based as well as graph-based simultaneous localization and mapping (SLAM), relies on the uncertainties involved in each measurement. Proper covariance estimation is thus critical to balance confidence levels among sensors. Despite its importance, traditional covariance approximation mostly relied on first order derivative or fixed measurement covariance and therefore tended to be error-prone, and even heuristic. Recently, deep learning for uncertainty estimation yielded meaningful performance, but applied to a single sensor in a supervised manner. Unlike the traditional supervised manner, we introduce an unsupervised loss for uncertainty modeling, to learn uncertainty without needing ground truth covariance as a label. Most important, we overcome the limitation of learning a single sensor's uncertainty by introducing a way of balancing uncertainty between different sensor modalities. In doing so, we alleviate the uncertainty balancing issue between sensors that has often been encountered in the multi-sensor SLAM application. Targeting covariance learning for visual odometry, particularly with regard to the integration of inertial sensors, the proposed uncertainty learning method was validated in a visual-inertial odometry application over the public datasets under artificial visual and inertial degradations to mimic harsh environment.
An Assessment of Organizational Capabilities for ERP Implementation in SMEs: A Governance Model for IT Success using a Resource-based Approach

One of the most coveted technological innovations is the increasing use of Integrated Management Software (ERP) since the early 1990s. ERP is considered a powerful reengineering tool that profoundly transforms a company's business processes and changes the way to conduct reengineering projects and implement new software. The significant number of failures as reported in the literature on ERP emphasizes the fact that some companies may not realize the expected benefits. This result becomes particularly significant in the case of SMEs which have their own contingencies in addition to the scarcity of resources which may, in turn, lead to the failure of ERP implementation. This leads initially to ask, in one hand, about the variables of success of this innovation i.e. the determinants of the techno-organizational innovation; and on the other hand, about the existence of a model of dependences analysis between these determinants and their success as perceived by the management. The current empirical research is carried out in 92 companies having adopted a whole or a part of their IS with an ERP system. Having analyzed the data collected via a questionnaire, and applying the method of structural equation (MSE), results prove the existence of one "general fit" between the data and the supposed relations of causality.
A Novel Delay-Product-Type Functional Method to Extended Dissipativity Analysis for Markovian Jump Neural Networks

This paper studies the problem of extended dissipativity analysis for Markovian jump neural networks (MJNNs) with time-varying delay. Combining Wirtinger-based double integral inequality and S-procedure lemma, a novel double integral-based delay-product-type (DIDPT) Lyapunov functional is constructed in this paper, which avoids the incomplete components in the existing works. Then, based on parameter-dependent reciprocally convex inequality (PDRCI) and the novel DIDPT, a new extended dissipativity condition is obtained for MJNNs. A numerical example is employed to illustrate the advantages of the proposed method.