Miracle-dz/UniFAR-FTUs huggingface dataset:
Millimeter-wave Automotive Radar using Extrapolation for Improved Angular Resolution

Automotive engineers are repeatedly faced with the issue of choosing radars with the best angular resolution while having reduced processing times. In this paper, a novel extrapolation method is presented to estimate the amplitudes and phases for virtual antenna elements at the receiver periphery. This method has shown to resolve two targets where the (real) baseline receiver antenna cannot. Furthermore, a comparison study is provided which considers different extrapolation methods that can be applied to the system. Together with other techniques for improving resolution, such as Multiple-Input Multiple-Output (MIMO) radars, beamsteering for increased power distribution, and compressed sensing for optimising sampling requirements, a radar system based on the fusion of these techniques can provide a powerful new platform which can fast process and accurately detect targets.
Realistic SAR Implementation for Automotive Applications

$A$ synthetic aperture radar (SAR) set-up is presented which has been implemented at 77 GHz keeping the automotive requirements in mind. The time-domain back-projection algorithm has been realised in real-time which can deliver ready SAR images of the target scenario within milliseconds of having been measured with the radar. With the presented set-up, it is also possible to measure the trajectory of the test vehicle very precisely without the use of external systems, for example GPS. The travelled trajectory can be computed with a maximum error of approximately 2%. The SAR solution is therefore entirely self-sustaining and proven to deliver accurate results.
Towards an Efficient Tomato Harvesting Robot: 3D Perception, Manipulation, and End-Effector

Fruit and vegetable harvesting robots have been widely studied and developed in recent years. However, despite extensive research commercial tomato harvesting robots still remain a challenge. In this paper, we propose an efficient tomato harvesting robot that combines the principle of 3D perception, Manipulation, and an End-effector. For this robot, tomatoes are detected based on deep learning, after which 3D coordinates of the target crop are extracted and motion control of the manipulator based on 3D coordination. In addition, a suction pad featuring the kirigami pattern, which is a part of the suction gripper, was developed to grip individual tomatoes in clusters. A scissor-shaped cutting module with an assist unit, which is used to overcome structural limitations and implement effective cutting, was also desinged and tested. The proposed tomato harvesting robot was validated and evaluated on a laboratory testbed basd on the performance of each component. Therefore, in this study, we propose and verify a new robot design for the effective harvesting of tomatoes.
Rail Surface Inspection System Using Differential Topographic Images

In this paper a surface inspection system for rails is presented. Rails must meet the strict requirements of inter-national quality standards, however there are few commercial surface inspection systems for rails and also, a lack of publications describing the design and configuration of inspection systems in detail. Therefore, manufacturers must develop their own systems or buy one of the few commercial ones available. These systems also need a long, cumbersome and expensive con-figuration process the manufacturer cannot perform without the assistance of the inspection system provider. The system proposed in this paper needs a set of samples and the requirements of the international standards to carry out an automatic configuration process avoiding the cost of manual configuration. The system uses four profilometers to acquire the surface of the rail. The acquired data is compared to a mathematical model of the rail to generate differential topographic images of the surface of the rail. Then a computer vision algorithm is used to detect defects based on the tolerances established in the international quality standards. The system has been tested and validated using a set of rails and a rail pattern from ArcelorMittal, with better results than the other two systems installed in a factory.
A Fully Differential Ultra-Compact and High Isolation Transformer-Based Wilkinson Power Divider

An ultra-compact transformer-based differential power divider with low insertion loss and high isolation is presented in this paper. To achieve the impedance transforming and power combining, two differential transformers are connected in parallel in the proposed Wilkinson power divider. Shunt capacitors are added between the output ports for matching and isolation enhancement. Designed in 40nm CMOS technology, the proposed power divider occupies a core chip area of $396 \mu \times 156\mu$. Post-layout simulation results show that the power divider achieves an insertion loss (S21) of 1.37dB, an input return loss (S11) of 14dB, an output return loss (S22) of 21dB and a port-to-port isolation of 32dB at 28.5GHz.
A Broadband 60-GHz Low Noise Amplifier with 3.2 dB Noise Figure and 24 dB Gain

This paper presents a broadband mm-wave low noise amplifier (LNA). The amplifier consists of two cascode stages and is realized in a 130-nm SiGe BiCMOS technology over a total chip area of 0.41 mm2. For maximizing the bandwidth, the LNA employs dual-band inter-stage and output matching networks. As a result, a 3-dB bandwidth (BW) of 23 GHz, from 54 to 77 GHz, is achieved. The amplifier demonstrates a maximum gain of 23.8 dB, while consuming a dc power of only 8 mW. Measurement results show that the LNA has an average noise figure (NF) of 3.2 dB, which is - to the best knowledge of the authors - the lowest reported NF for amplifiers operating in this band. Compared to state of the art, this LNA provides the best figure of merit considering noise, gain, BW, large signal performance and power consumption.
Road Traffic Risk Safety Prediction Based on BP Neural Network

In the field of smart transportation, the prediction of road traffic accident is an essential part. It analyzes the collected traffic information and predict the occurrence of traffic accidents in order to achieve effectively early warning and reduce ultimately traffic accidents. Generally, the traffic information includes speed, weather, light and road conditions. These data are usually not quantified. Thus, in this paper, those data will be quantified before they are able to be used as input parameters for the prediction. Based on data of historical traffic accidents and their statistical results in the UK, an accident severity model is established by regression analysis. A BP neural network optimized by genetic algorithm is utilized for predicting the accident severity under the current conditions. Via comparing both results, a road traffic safety risk prediction model is established. In order to improve the prediction accuracy of the BP neural network, three parameters for testing are presented and analyzed. Finally, the results show that the prediction of road traffic risk with optimized BP neural network is reasonable. It provides an effective safe driving for route planning.
Early Image Termination Technique During STDP Training of Spiking Neural Network

Spiking Neural Network (SNN) is a breed of neural networks that seek to achieve low energy and power by more closely mimicking biological brains. SNNs are often trained using lightweight unsupervised learning such as Spike Time Dependent Plasticity (STDP). However, STDP is prone to redundant time steps during training since STDP cannot determine current image needs further training or not. To reduce redundant time steps and lower energy costs during STDP training, we propose a novel technique that terminates training upon an image preemptively. The proposed technique reduces time steps by 44% with accuracy drop of 0.91% on MNIST.
Single-Feed, Highly-Directive, Higher-Order-Mode Cavity Antenna and Its Beam Tilting Realization

Fast-speed, mast-capacity, and low-cost communications are highly desired for future wireless systems. Single-feed overmoded slot-based rectangular cavity antennas are developed to meet this demand. A TE(10)(11)(0) mode is excited in the cavity with a rectangular waveguide. A total of 110 slots appropriately etched in its top surface yields a system that radiates its beam into the broadside direction with a gain of 26.6 dBi. An engineered phased patch surface is then introduced to facilitate tilted-beam pattern for high-order-mode cavity antennas. The realized cavity antenna augmented with an appropriately-shaped phased patch surface attained a tilted beam at 30° with respect to the broadside direction. An antenna prototype was fabricated, and measured results agree well with the simulated ones.
Broadband High-Gain Aperture-Coupled Patch Antenna Array Based on SIW for 60 GHz Applications

In this paper, a wideband high-gain aperture-coupled antenna (ACPA) array based on substrate integrated waveguide (SIW), and microstrip line feeding is described at the 60 GHz unlicensed frequency spectrum. From the simulated results, the bandwidth and gain enhanced by four-elements microstrip patch antenna is utilized as a radiating element, which shows impedance bandwidth (|S11| < −10 dB) and maximum gain of 33.8% and 13.1 dBi from 50 GHz to 72 GHz, respectively. Besides, a 4×4 array designed with 16-way T-junction by SIW power divider feeding network achieves a bandwidth of 29% and peak gain of 25 dBi.
Mobility-Aware Optimal Task Offloading in Distributed Edge Computing

To cope with limited capabilities of mobile devices, task offloading in distributed edge computing (DEC) environments is perceived as a promising solution. However, the mobility of devices makes the task offloading a more challenging issue. In this paper, we investigate mobility-awareness for optimal task offloading in DEC environments. To this end, we formulate an optimization problem to minimize the response time of offloaded tasks. Simulation results demonstrate that the mobility-aware task offloading scheme can reduce the response time by 14% $\sim 21$% compared with the conventional task offloading schemes without any mobility-awareness.
Statistically Efficient Simultaneous Amplitude Measurement of Multiple Linear Chirp Signals

In this article, the amplitude measurement of multiple and simultaneously received linear chirp signals is investigated. The achievable scalability, that is, the maximum number of simultaneously transmitted superimposed signals that a receiver can characterize with a given target accuracy, is explored using simulations. The effect of chirp duration and bandwidth on the estimation accuracy and on the scalability is investigated. The effect of inaccuracies affecting the realized chirp duration and bandwidth is also discussed. It is shown that the scalability of the proposed approach is competitive with respect to multiple access methods, such as frequency-division multiple access or code-division multiple access. It is also shown that accurate amplitude measurements are achievable even when the collected signal is the superimposition of up to 20 chirp signals with amplitudes differing by three orders of magnitude.
Fane-KG: A Semantic Knowledge Graph for Context-Based Fake News Detection on Social Media

Fake news detection on social media has been very challenging, with diverse techniques already implemented based on content of social media data. However, there is a growing need for use of social data context as well for detection techniques. Leveraging semantic technologies capabilities, this research focused on contextual modelling for social media data, with Twitter data utilised as case study. The raw data is aggregated, processed and transformed into a semantic knowledge graph based on RDF data which is subsequently stored within a graph database. With the tweets initially classified as either fake or real using Fakenewsnet application, the knowledge graph facilitates advanced data analytics and potential extension to the social context modelling developed. Furthermore, the modelled data, alongside ensuing inferential data based on class relationships within the knowledge graph constitute a vital input for data analytics with machine learning towards subsequent classification of other news articles as either fake or not.
Multivariate Time Series Forecast in Industrial Process Based on XGBoost and GRU

In this paper, a time series prediction model that merges eXtreme Gradient Boosting (XGBoost) and Gate Recurrent Unit (GRU), XGB-GRU model, is proposed for multivariate time series prediction in industry. The XGB-GRU model uses XGBoost’s strong feature extraction capabilities to extract the hidden information of multiple control variables in industrial data. Next, the model uses GRU’s unique gating unit to extract the timing information in the industrial data. Finally, the importance of XGBoost output variables to guide actual production and solve the problem of inexplicability of neural networks. Predicting the temperature of the heating furnace verifies that the proposed XGB-GRU is better than a single XGBoost and GRU model. And the model has a good fit to the predicted value.
Enhancing the Out-of-Band Response of Hybrid Wide-Band Filters in Rectangular Waveguide

The objective of this paper is to describe a number of different techniques to enhance the out-of-band performance of wide-band rectangular waveguide filters. In particular, we discuss the use of mixed inductive and capacitive (hybrid) couplings, in conjunction with a staircase configuration and E-plane T-junctions, to yield a significant improvement both in the out-of-band response and in the filter selectivity. In addition to theory, we also discuss the performance of several filter prototypes, thereby fully validating the new filter topology.
Dendrimers based on ruthenium(II) and osmium(II) polypyridine complexes and the approach of using complexes as ligands and complexes as metals

The use of the “complexes as ligands and complexes as metals” synthetic strategy for the preparation of luminescent and redox-active Ru(II) and Os(II) dendrimers, the dominant synthetic approach for this novel class of compounds, is reviewed. A few comments on the photophysical and redox properties of the metal dendrimers are provided and an overview of alternative synthetic approaches is also presented.
New physical analysis capability for counterfeit electronics and reverse engineering

a new cross-section workflow for improved access to buried structures within electronic devices has been developed. A focused-ion beam scanning-electron microscope (FIB-SEM) system has been integrated by attaching a femtosecond laser to the loadlock chamber, enabling rapid site-specific preparation of internal device details. Evidence is provided that compared with traditional techniques, this new workflow enabled by the Crossbeam laser FIB-SEM offers improved speed and productivity, while simultaneously delivering more precise dimensional sampling and reduced damage to the surrounding area.
Tracking Characteristics and Surface Erosion Condition of Epoxy Resin for Dry-type Reactor

Aiming at the deterioration of epoxy resin cast insulation in outdoor dry reactor under the action of long term electrical indentation partial discharge. The electrical indentation experiment platform of insulating material under inclined plate method was set up. The characteristics of electrical indentation discharge and the corrosion loss of insulating materi-al were measured and analyzed. The characteristics of partial discharge (PD), corrosion loss, hydrophobicity and surface roughness were measured experimentally. The surface morphology, 3D morphology and Fourier transform infrared (FTIR) images of epoxy resin insulating materials at different stages after discharge erosion were analyzed. Different contents were studied. The results show that the electric in-dentation process of epoxy resin insulation material has experienced initial, stable, developing and explosive phases, and the flashover discharge. With the change of the surface morphology and the formation of the surface products, the surface roughness of the materials showed an overall upward trend, and the surface roughness increased fastest during the discharge burst. The changes of surface morphology and 3D morphology of material constitute the basis for judging the insulating surface condition and corrosion damage development condition.
Analysis of Power Grid Voltage Stability with High Penetration of Solar PV Systems

Grid integration of solar photovoltaic (PV) systems has been escalating in recent years, with two main motivations: reducing greenhouse gas emission and minimizing energy cost. However, the intermittent nature of solar PV generated power can significantly affect the grid voltage stability. Therefore, intermittent solar PV power generation and uncertainties associated with load demand are required to be accounted to gain a holistic understanding on power grid voltage stability with high penetration of PV energy sources. This paper presents a framework for power grid voltage stability analysis considering uncertainties associated with PV power generation and load demand using Monte Carlo simulation. Commonly used voltage stability indicators such as critical eigen value, line loss, reactive power have been considered in the proposed framework. The proposed methodology has been verified by analysing voltage stability of IEEE 14 bus test system, with high penetration of PV energy sources and considering uncertainties associated with load demand. The results provide a clear insight to voltage stability of power grid with different penetration level of PV energy sources into the power grid.
Proximal Gradient Algorithm for Optimal formation Control of Continuous-Time Multi-Agent Systems

A distributed proximal gradient algorithm is proposed in this paper to solve the linear quadratic formation control problem of continuous-time multi-agent systems. Similar to [1], an auxiliary variable is introduced to describe the reference point of formation, which is one of the optimization objectives, and a nonseparable multi-objective optimization problem is formulated. Applying the alternating proximal gradient method of multipliers, the solution to optimal formation control problem is turned into dealing with one simple optimal tracking control problem and two matrix algebraic operation steps alternatively, which saves a lot of computation resources. Numerical examples of homogenous systems, heterogeneous systems and wheeled vehicles are provided to demonstrate the efficiency of the proposed algorithm.
Tactile Paving Detection by Dynamic Thresholding Based on HSV Space Analysis for Developing a Walking Support System

In this research, we present a system that enables independent walking in individuals with visual impairment by recognizing tactile paving. The system warns the user about dangers posed by deviations from the walking path or possible collisions. Therefore, the tactile paving itself and obstacles on it must be detected. This paper discusses problems related to detecting tactile paving. To address these problems, a new camera-based method is introduced, which detects tactile paving automatically. The method relies on a dynamic threshold approach in the HSV color space, making the image processing more robust against varying lighting conditions, environments, and differences in color. Such an approach is not possible using the methods based on fixed thresholds reported in the literature. The results of our experiments confirm that high rates of tactile paving detection were achieved under various conditions.
Far-field pattern Analysis of multimode circular patch antenna on SISL platform using CMA

A multimode circular patch antenna is studied using characteristic mode analysis on SISL platform. Four modes with directive far field patterns that resonate close to each other are obtained. With the physical insight provided by the CMA, a feeding structure is designed to excite two modes with wide band characteristics. More than two modes can be utilized by properly adjusting the feeding system and other feeding systems can be used as well. This method is applicable to antenna design with different properties for different applications.
Machine Learning Based Device Simulation Using Multi-variable Non-linear Regression to Assess the Impact of Device Parameter Variability on Threshold Voltage of Double Gate-All-Around (DGAA) MOSFET

For the first time, the machine learning approach is proposed for the analysis of device parameter variability impact on the threshold voltage of silicon-nanotube-based double gate-all-around (DGAA) MOSFET using multi-variable non-linear regression with five input variables. Interior-point algorithm is implemented in MATLAB and used for training the hypothesis. Algorithm is supplied with 2000 random initial guesses to ensure global minima in optimization for healthier accuracy at comfortable simulation time. The worst-case accuracy of 96.33 percent is achieved in prediction with an average percentage prediction error of 0.83 percent. Study revealed that, with proper training and optimization of the hypothesis (fitness) function it is possible to predict the threshold voltage of the device by keeping good trade-off between computation time and accuracy.
Development of graphene-carbon nanotube-coated magnetic nanocomposite as an efficient sorbent for HPLC determination of organophosphorus pesticides in environmental water samples

ABSTRACT A magnetic solid-phase extraction (MSPE) method coupled to high performance liquid chromatography with UV (HPLC-UV) was proposed for the determination of organophosphorus pesticides (OPPs) at trace levels in environmental water samples. The ternary nanocomposite of graphene-carbon nanotube-Fe3O4 (G-CNT-Fe3O4) has been synthesised via a simple solvothermal process and the resultant material was characterised by powder X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM) and Fourier transform infrared (FTIR) spectroscopy. Significant factors that affect the extraction efficiency, such as amount of magnetic nanocomposite, extraction time, ionic strength, solution pH and desorption conditions were carefully investigated. The results demonstrated that the proposed method had a wide dynamic linear range (0.005–200 ng mL−1), good linearity (R2 = 0.9955–0.9996) and low detection limits (1.4–11 pg mL−1). High enrichment factors were achieved ranging from 930 to 1510. The results show that the developed method is suitable for trace level monitoring of OPPs in environmental water samples.
Angle estimation of coherent targets via Toeplitz induced compressed matrix method for the Bistatic MIMO radar

A computationally efficient preprocessing technique for the estimation of coherent angles of targets for the bistatic MIMO radar centered on Toeplitz based Matrix compression algorithm is presented. The proposed scheme first resolves the coherent source problem by exploiting the diagonalization structure of the received data and reconstructing the output to formulate a Toeplitz matrix. By applying a Nyström transformation, a compressed matrix is derived to enhance speedy computation. The proposed method, aside from being robust to coherent target signals with a much lower estimation error, presents a faster computational time than its competitors.