Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Reconfigurable Aperture-Coupled Filtering Antenna With Circular Polarization Diversity

A novel reconfigurable aperture-coupled filtering antenna with circular polarization diversity using two PIN diodes is presented in this paper. This antenna has a simple structure, which consists of a Jerusalem-cross microstrip radiator, a novel aperture-coupled feeding structure and a conducting reflector. A square ring slot has been adopted to implement the coupling mechanism. By introducing some simple modifications of the square ring, the circular polarization can be achieved. Two PIN diodes are used to reconfigure the polarization: Right-hand circular polarization (RHCP) or left-hand circular polarization (LHCP). Besides, the aperture-coupled feeding network and Jerusalem-cross microstrip radiator help to realize a 2nd-order bandpass filtering performance with embedded filtering circuits. Details of the antenna design are shown, and the simulated results exhibited and discussed.
Data Assimilation Method for the Ocean Circulation Model NEMO and Its Application for the Calculation of Ocean Characteristics in the Arctic Zone of Russia

Based on the GKF (Generalized Kalman Filter) data assimilation method, which we have proposed earlier, jointly with the NEMO (Nucleus for European Modelling of the Ocean) model of ocean circulation, the spatial-temporal variability of several model characteristics, in particular, the ocean level field and the water temperature field in the Arctic Zone of Russia, is studied in numerical experiments. The ocean level data taken from the AVISO (Archiving, Validating and Interpolation Satellite Observation) archive are assimilated into the NEMO model. The ocean level and temperature are calculated both with and without data assimilation (the control run). The results of calculations are analyzed and it is shown that the main spatial variability of the characteristics after data assimilation is in a good agreement with the localization of currents in the Northern Atlantics and the Arctic Zone of Russia. The authors have performed the installation and adaptation of the NEMO software package on the K-60 high-performance computer (HPC) in the Keldysh Institute of Applied Mathematics of the Russian Academy of Sciences (Moscow, Russia). The qualitative estimate of this variability is presented and it is shown at what time interval the dependence of the calculated characteristics on the observation data manifests itself.
A comparative study of the state-of-the-art algorithms on multi-objective problems using performance metrics

Most real-world optimization problems have several objectives which can require to satisfy simultaneously. As the complexity of the problems increases, the problems become more challenging to solve for the algorithms. Over the last few years, many state-of-the-art multi-objective optimization algorithms (MOOAs) have been developed but, to the best knowledge of the authors, there has been no comprehensive comparative study in the literature together with these algorithms. Moreover, there are many parameters that affect the performances of algorithms, such as the population size, iteration number, and initial population. These values may also differ from one study to another, making comparisons more difficult to perform fairly. In this study, four MOOAs which were not previously used together for the comparisons of performance are applied to find optimal solutions of various mathematical optimization problems with the same initial conditions. The performances of the algorithms are evaluated by using two different performance metrics. In addition, Pareto fronts found by the algorithms are given comparatively. Experimental results give an overview for the performance of each algorithm on the different type of the problems by making fair comparison.
The Contactless Permanent Magnet Energy Harvester using Gyration from Roller Conveyor

Recent years, the progress of Internet of Things (IoT) has attracted attention, and its introduction is being considered in various fields of industry. Many companies seek to improve services by using IoT to improve the intelligence of factories or logistics systems. When installing IoT devices in factories and logistics systems, there are several problems such as installation of power supply costs and wiring costs. Therefore, the authors have proposed one of the unique energy harvesting technology solutions that can supply power from the factory's conductive roller conveyor without physical contact. The proposed generator is driven by eddy currents from a rotating conductive roller conveyor. The controller behind the generator performs maximum power point tracking and output energy management. If this energy harvesting system drives the ZigBee IoT device, this study validates generator design, measurement, and controller simulation. If this system is realized in factories or distribution centers around the world, companies may be able to reduce costs and provide a high level of service.
An Efficient High-Precision Data Detection for Massive MU-MIMO Systems

Data detection is among the most crucial process task for massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems. In this letter, we propose a novel efficient high precision soft-output data detection algorithm, which iteratively generates a signal vector and reduces its complexity meanwhile. This algorithm guarantees its significant error-rate performance and reduced complexity by combing an optimization method, ADMM, and modified neighborhood search algorithm. Simulation results demonstrate that the proposed detection achieves superior performance over the existing methods at low computational complexity.
A Low-profile Wideband Single-Layer Antenna Array for 28-GHz Applications

A novel low-profile printed $4\times 4$ linearly polarized (LP) antenna array is proposed for 28-GHz fifth generation (5G) communication applications. The antenna array adopts a modified magneto-electric (ME) dipole antenna as the antenna element whose magnetic dipole is modified to be C-shaped cavities which reduces the thickness to only 0.787mm. The proposed antenna element is fed through a parallel coplanar waveguide (CPW) feeding network. The antenna elements and the feeding network share the same substrate, making the antenna array single-layered. The antenna array has a bandwidth of 35.7% ($\text{SWR} < 2$) from 21.5 to 32 GHz, which covers the potential 5G frequency bands at 28-GHz millimeter-wave range. Good unidirectional radiation characteristics are also achieved.
Performance Measure on Efficient Wireless Data Transfer in Highly Conductive Medium

Efficient radio waves propagation with associated data transfer in conductive seawater is yet a challenge due to high attenuation as compared to air. The domain needs room for improvement for larger data transfer distance (>1m). If improved, the applications include marine activities and offshore oil and gas industries will be in a greater benefit. This paper presents an experimental design for data transfer link using conduction current method. The gain and the filter are well-calibrated for the frequencies ranging from 10kHz to 1 MHz with varying baud rates. Performance analysis of the experimental design using electrodes based transmitter system enabled authentic data collection with enhanced configurability. Evaluating Bit Error Rate (BER) while varying data speed and varying transmission distance revealed that at the constant carrier frequency and circuit tuning BER increases up to 88% upon increasing the data rate (10 KHz). Similarly, on increasing electrodes separation, the acceptable higher data rates are received with slightly less efficiency than the received signal at lower data rates.
Reconfigurable Metal-Rimmed Antenna for 4G/5G Smartphones

In this paper, multiband antennas based on a single ring slot are proposed for 4G/5G smartphone applications. Here, a reconfigurable 4G antenna (820–960 MHz and 1710–2690 MHz) is initially devised by loading multiple grounded stubs into the upper section of the ring slot. To further cover the sub-6 GHz spectrum (3400–3600 MHz) for future 5G communications, a 4-element MIMO (multi-input-multi-output) slot antennas configuration is designed by utilizing the lower section of the ring slot. Due to the advantages such as multiband operation, MIMO configuration for 5G communications, high isolation and compact structure, the proposed antenna design is attractive for 4G/5G smartphones.
Design and Simulation of FinFET based Temperature Sensor

In this VLSI era, integrated circuit designing has attained new trends. With the scaling technology the feature size have reached below 40nm in implementation. With such a huge component density over a small die heat dissipation analysis is important. There is a need to develop and monitor temperature for all the components over IC. The paper represents the design and simulation of FinFET based temperature sensor. The sensor is designed in Symica EDA tool using FinFET on 32nm feature and uses 1.2 volts as a supply voltage. This circuit is used for sensing the temperature and it has one PNP transistor and two FinFET and generates the complementary to absolute temperature (CTAT) voltage simulated over temperature range from −20°c to + 120°c.
Drought Monitoring Using the Sentinel-3-Based Multiyear Vegetation Temperature Condition Index in the Guanzhong Plain, China

The vegetation temperature condition index (VTCI) has been shown to perform well for drought monitoring using multiyear advanced very high-resolution radiometer (AVHRR) and moderate resolution imaging spectroradiometer (MODIS) data. Compared to single-year VTCI, the VTCI calculated by multiyear data can quantitatively reflect the degree of drought and precipitation in a region. Sentinel-3 is a recently launched remote sensing satellite with high temporal resolution and is similar to the satellites carrying AVHRR and MODIS sensors. One year of Sentinel-3 data is available for calculating the VTCI, and given the need for developing quantitatively drought monitoring capabilities, the aim of this study is to investigate the methods used to calculate the potential multiyear Sentinel-3 VTCI for quantitative drought monitoring. This is based on a comparison with multiyear Terra MODIS VTCI and a correlation analysis with cumulative precipitation data. The analysis results indicate that the potential multiyear Sentinel-3 VTCI can be accurately calculated from the single-year Sentinel-3 VTCI based on the linear correlation between the single-year VTCI and multiyear VTCI derived from Terra MODIS, which do not exhibit obvious systematic deviations from the multiyear Terra MODIS VTCI (the absolute value of the average bias <0.04). The multiyear Sentinel-3 VTCIs can quantitatively reflect the drought levels, which are significantly correlated with the recent cumulative precipitation throughout the study period (R2 = 0.731, P < 0.001). Therefore, it is proposed that Sentinel-3 can successfully inherit the VTCI-based drought monitoring tasks from MODIS.
Simulation Study on Thermal Characteristics and Temperature Distribution of Lithium-Ion Battery Pack in Electric Trike

The lithium-ion batteries (LIB) frequently being used in the electric vehicle due to high energy density, high power density, and fairly long-life cycles. In this paper, LIB applied to electric trike which becoming very popular recently. Performance and life cycles of LIB sensitive to temperature, so it is necessary to maintain the temperature condition in the range of -20oC to 40oC. Temperature significantly affects the performance of the LIB battery and also limits the applications of the LIB battery. To be able to maintain the temperature of LIB at optimum temperature, the temperature distribution inside the LIB pack should be investigated first. This paper study the temperature distribution of the LIB pack used in the electric trike. Pack geometry, as well as the surrounding condition, first build in Solidworks. The geometry model then imported to Comsol Multiphysics to study phenomena of mass transport and heat transfer within the LIB pack and surrounding area. The main objective of this study is to lay the foundation to design the battery thermal management system (BTMS) in the LIB pack for the electric trike.
Study on Protection Principle of Novel Filtering Transformer Based on Coupled Inductance

In view of the increasingly severe power quality problems, this paper studies a novel filtering transformer with four windings. It has a specially designed filtering winding and has a good multi-port harmonic suppression function. Based on the coupled inductance parameters and the transformer model, this paper reveals the special connection of the coupling inductor of the novel filtering transformer. By establishing the voltage-current conversion relationship equation of the novel filtering transformer, the protection principle applicable to the novel transformer is expounded. Furthermore, the protection action equation of the novel filtering transformer is derived, and then the internal fault identification criterion is proposed. The protection principle is based on the corresponding relationship between the internal structure of the transformer and the coupled inductance parameters. Combined with the characteristics of the coupled inductor, it can identify internal faults and at the same time avoid the external fault of the transformer. Finally, the correctness and reliability of the protection principle are verified by building a MATLAB simulation model.
Synchronization in Networks of Linear Systems With Three Time Scales

In this paper, we study the synchronization for networks with three-time-scale linear systems. This article uses a fixed undirected graph to describe the topology of the network. Under mild assumptions, we perform a two-stage reduction on the singularly perturbed systems and get approximate systems. The analysis of the reduced systems shows the form of the state feedback controller that implements the asymptotic synchronization of the approximation system. The synchronization of the network is changed into achieve simultaneous stabilization of state using variable transformation. According to the reduced approximate system’s relevance to the original singularly perturbed system, we design the state feedback controller which can achieve the asymptotic synchronization of the original networks. Our results are illustrated by a numerical examples.
Microfabricated bioelectronic systems for prevention, diagnostics and treatment of neurological disorders

Neurological disorders are a major cause of disability and mortality worldwide. Today, new devices and techniques allow clinicians and scientists to assess the living brain, monitor and alter the nervous system activity in real time. An essential approach relies on direct, physical interfacing of electrical probes with the neural tissue. Currently approved clinical interfaces have limited precision for both recording and stimulation, in large part because of their formfactor and low number of electrical channels communicating with the neural tissue. We report on current progress and remaining challenges in miniaturized, biomimetic and integrated implantable neural systems that will lead novel diagnosis, treatments and cures for people suffering and living with neurological disorders.
A High-Sensitivity Antenna Sensor for Two-Dimensional Strain Monitoring

Sensitivity is one of the important parameters to assess the performance of strain sensors. In order to obtain high sensitivity, a novel sensing mechanism made use of circularly-polarized (CP) patch antenna is investigated for strain monitoring. Two-dimensional strain sensing is achieved by a proof-of-concept prototype, namely, a microstrip-line-fed nearly square patch antenna sensor is initiated in this paper. Very encouraging results of high sensitivity were obtained: $+74.2\ \text{ppm}/\mu\varepsilon$ and $-67.5 \text{ppm}/\mu\varepsilon$ for two orthogonal directions whereas the sign indicates the strain direction. The proposed sensor is at least 33 times more sensitive than commercial resistive metal foil strain gages.
Multi Kernel Fusion Convolutional Neural Network for Wind Turbine Fault Diagnosis

To accurately diagnose the type of failure, make full use of computing resources and automatically identify different health conditions of wind turbine (WT), a new multi-kernel fusion convolutional neural network (MKFCNN) is proposed in this paper. The proposed method is based on a one-dimensional convolutional neural network (1-D CNN). Convolution kernels of different sizes are used in each layer of the network to extract features of different scales of data, which is inspired by the inception vl model. Compared with ordinary CNN, its unique network design reduces a lot of network parameters, reduces the risk of network overfitting, and saves a lot of computing resources. The superiority of the proposed method is verified on a generic WT benchmark simulation model and compares with support vector machine (SVM), decision tree, random forest and CNN.
Image-Based Crack Detection Using Sub-image Technique

Automated detection of asphalt pavement distresses is a very popular computer vision and image processing problem. In recent years, automated detection is an essential part of every pavement management system, since it allows very fast detection of distresses on the road. This is important because timely detection can prevent many road accidents, and hence it has potential to save lives. In this paper, we presented a new unsupervised image processing method for segmentation of the most common road distresses-pavement cracks. The method first performs slicing of an image into M×N sub-images, and then removes sub-images without cracks based on empirically defined threshold. Analysis is then carried out only on a small number of sub-images, which significantly reduces computation time. Then, a series of images processing tasks are performed to select only pixels with pavement cracks. The method is suitable as a pre-processing step in a number of computer vision tasks, and can provide rough estimation of damaged area in an image.
A Dynamic Inductive Power Transfer System

This study is done in the area of in-motion inductive power transfer systems, commonly known as dynamic wireless power transfer, with application for charging electric vehicles' batteries. First, a co-simulation module of the overall system is developed, which integrates the finite element analysis model of magnetically coupled coils with models of the power electronics and other required external circuitry subsystems for wireless charging. The coils model is designed using the ANSYS Maxwell™ software and the system co-simulation is performed in ANSYS Simplorer™. Second, the dynamic inductive power transfer system is experimentally investigated on a scaled-down testbed using dSPACE DS1104 controller board.
Reconfigurable Tri-band Circularly Polarized Antenna for GNSS Applications

In order to satisfy dual mode switching between the Global Navigation Satellite System (GLONASS) and Global Positioning System (GPS)/BeiDou Navigation Satellite System (BDS), a Trip-band miniaturized circularly polarized (CP) reconfigurable antenna is presented. It is improved from a single-fed miniaturized printed antenna through inserting inter-digital structures with PIN diodes in the shorting strips. Frequency reconfigurable can be achieved by changing the state of the pin-diode. In this letter, the equivalent circuit of the inserting inter-digital structure is discussed and frequency reconfigurable principle is given in details. Then the key parameters of the proposed antenna are optimized. Finally, the antenna sample was fabricated and measured. The results show that the proposed CP antenna has the advantages of frequency reconfigurable, miniaturization, easy integration and good performance. The antenna will reveal a good potential in Navigation applications.
A Lightweight Neural Network Framework for Cross-Domain Road Matching

Matching aerial images against pre-stored road landmarks (a.k.a. Aerial-Road Matching) is a critical technology to enhance UAS (Unmanned Aircraft System) navigation in GPS-denied urban environments. Current matching approaches typically consist of two stages: extracting roads from aerial images, and performing road matching based on handcrafted features. The above two-stage matching approaches are computationally inefficient. To handle the above challenge, we for the first time study the problem of end-to-end Aerial-Road matching. Considering that UAS typically have limited computation capacity and storage space, we propose a novel lightweight convolutional neural network architecture for cross-domain Aerial-Road matching. Our framework first map input Aerial-OSM pair into a common feature embedding space using an asymmetric two-branch neural network model. We then feed two feature embeddings into a correlation layer to produce a correlation map in order to handle the misalignments between Aerial-Road input pairs. The correlation map is finally input into a fully connected layer followed by a sigmoid layer to produce the likelihood of the input pair to be a true match. We conduct extensive experiments to evaluate the performance of our approach. Experimental results demonstrate the remarkable accuracy of our proposed lightweight neural network architecture in Aerial-Road matching.
Edge Computing-Aided Framework of Fault Detection for Traction Control Systems in High-Speed Trains

Long-time operation of traction systems under severe working environment easily induces various faults in high-speed trains, and thereby puts passengers in an unsafe position. Thanks to widely equipped sensors in high-speed trains and the technical development of advanced data analysis techniques, real-time fault detection (FD) using only online data is an inevitable trend to improve safety and reliability of high-speed trains. In this paper, an edge computing-aided FD framework for traction systems in high-speed trains is proposed; the superior advantages include: 1) its implementation can be easily carried out without system information such as accurate mathematical models of traction systems; 2) it has the well-deserved expansibility without being redesigned for the control structure of traction systems; 3) it nearly does not occupy resource of traction control units, and it does not overburden the information transmission systems. This highly intelligent FD framework using edge computing is firstly speculated in details, and its core theory is then applied to a dSPACE platform of high-speed trains for verification.
Performance Analysis of Non Cooperative Spectrum Sensing Schemes in 5G Cognitive Radio Networks

Cognitive Radio Networks (CRN) and the Fifth Generation of wireless cellular standard (5G) is considered as the vital solution for the increasing usage of cellular and internet services. 5G Architecture consists of interconnection of different wireless networks, including LAN, WAN, World Wide Web. The usage of smart antennas, CDMA technologies and CRN will lead to the development of the ultra-high speed and efficient 5G network. The unoccupiedwireless channels in the spectrumband can be effectively utilized by using an intelligent radio called "Cognitive Radio",therby eliminating the user interference and blockage. Cognitive radio network monitors the nearby radio links and tracks information regarding the availability of the spectrum. Collected information and dataare used to provide the access to the cognitive radio users while guaranteeing the normal operation of the licensed users. In this context, spectrum awareness and spectrum exploitation techniques are the major mechanisms of cognitive radio technology. Among these, spectrum sensing comes under the spectrum awareness technique. It plays a major role in any CRN for a better selection of the radio link from the available pool of spectrum. This paper exploits various non cooperative spectrum sensing methodssuch as Energy Detector, Matched Filter, Covariance Detector and Eigen value based detector. These spectrum sensing algorithms are simulated and the simulation results are analyzed and compared.