Miracle-dz/UniFAR-FTUs huggingface dataset:
Radionuclide Identification in Open Environment Based on CsI(Na) Scintillation Detector Using Machine Learning Methods

The country’s border areas are vulnerable areas to be affected by neighbouring countries, such as natural resource abuses, smuggling, and environmental pollution. One of the environmental pollution threats is coming from radionuclide sources, both natural and artificial, such as nuclear facility technologies. To safeguard against those threats, PRTDRAN BRIN has planned to provide the technology of radiation and environment monitoring stations at several national border areas. Currently, these stations can only measure gross radiation levels, lacking the capacity for radionuclide identification. This deficiency leads to potentially misleading readings in the presence of anomalies, often attributable to Naturally Occurring Radioactive Material (NORM) or Technologically Enhanced Naturally Occurring Radioactive Material (TNORM). Hence, a device with radionuclide identification capability was built using machine learning methods based on CsI(Na) Scintillation Detector. Datasets collection was conducted in the laboratory with varying counting times from 1 upto 120 minutes and the source-detector distance from 50 up to 150 cm. The Histogram of Oriented Gradients (HOG) method was selected as the feature extraction method with several methods of division regions according to radionuclide peaks. The classification methods determined to be compared are Linear Regression (LR), Linear Discrimination Analysis (LDA), K-Nearest Neighbor (KNN), Decision Tree, Naïve Bayes, Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP) of Neural Network. The test result showed that combining the classification method using SVM and segmenting the spectrum into 3 regions as a feature extraction method is the best, with an accuracy score of 97.845% and training time of 0.997 ms.
A Comparative Study of Visions, Objectives, and Strategies of the Latest Comprehensive Transportation Plans for Metropolitan Areas of International Citis

In China,quick motorization in Chinese cities has expedited urban spatial expansion.Construction of regional transportation system influences the land use pattern,housing,as well as employment distribution in a metropolis.And accessibility also produces impacts on the mode of growth and the level of economic activities. This paper reviews the latest comprehensive transportation plans of 35 international metropolitan areas.Based on an analysis of current urban transportation development in China,the paper proposed ways to improve comprehensive transportation plans of big Chinese cities in following aspects:transportation safety planning; environmental protection;establishment of smart traffic congestion management system;integration of transportation planning institutions,and optimization of traffic system management and operation.
Sparse Array Synthesis of Duplex Dual Circularly Polarized Horn

In this paper, a circularly polarized (CP) horn antenna is designed and a sparse array containing receiving antennas, transmitting antennas and duplex antennas is formed. The horn antenna generates left-hand and right-hand CP (LHCP and RHCP) by different excitation directions as the transmitting and receiving antenna respectively. Genetic algorithm (GA) is used to optimize the placement of sparse arrays and suppress side lobe. The directionality is improved compared to the array consisting of only duplex antennas, and the number of duplexers is reduced.
A Dual-polarized Ultra-wideband Wide-angle Phased Array with FSS Superstate

This paper presents a dual-polarized ultra-wideband wide-angle phased array with FSS superstate. Based on dual-polarized tight-coupled dipole arrays, a wide-angle impedance matching layer loaded with frequency selective surface(FSS) is designed. The effective electric permittivity of FSS is calculated by using the S-parameter retrieval method, and the FSS-WAIM is analyzed and optimized to improve the scanning performance of the antenna. The bandwidth of the phased array is 3.8-16.2GHz (4.26:1) at broadside, and the scanning angle ranges up to 60° in E-plane while 50° in H-plane(VSWR<3).
Real-time Vehicle Pose Estimation Based on Nonlinear Optimization and Probability Interpolation

Parking AGV is more and more widely used in efficient automatic parking, and one of the serious challenges that parking AGV faces is vehicle pose estimation. The existing algorithms for vehicle pose estimation rely on the contour information of the vehicle body, and sensors are usually installed at a higher position such as the top of the vehicle and the vehicle body. However, as the parking AGV only grabs the wheels and the sensor is always installed at a lower position, we can only obtain wheel information and limited body information. In this paper, a new pose estimation method based on probability residual model of wheel point cloud is proposed. Firstly, we use RANSAC to remove the ground points, use the z-axis dichotomy to segment the vehicle body points and wheel points, and get the minimum circumscribed rectangle from the wheel point cloud to obtain a rough estimation of vehicle pose parameters. Secondly, we use gaussian distribution model to build our own wheel probability model. Then, we use trilinear interpolation and residual model established by ourselves to calculate the score and gradient of probability on the wheel probability model, and take vehicle pose parameters roughly estimated by the minimum circumscribed rectangle as the initial value, accurately estimating the vehicle pose parameters through the nonlinear optimization gradient descent method. At last, we compare our method with $\text{FPFH}+\text{ICP}$ method in terms of registration accuracy and registration time. Our method reach 100% recall around RMSE $=0.0132$, and the average cost of time to pose estimation for each frame is 12ms, 1/27 average cost time of method $\text{FPFH}+\text{ICP}$, performing robustly and efficiently in vehicle pose estimation.
A 5.5-6.5GHz Multi-Function Receiver with 6bit Phase Shifter and 31.5dB Gain Control for Phased Array in 180nm CMOS

This paper presents a 5.5-6.5GHz multi-function circuit for phased array transceiver. It mainly consists a 6bit phase shifter(PS), a passive variable gain amplifiers(VGA), an active variable gain amplifier and three driver amplifiers for compensating the loss of PS and passive VGA. The receiver is implemented in 180nm CMOS process for low cost consideration. The circuit achieves root mean square(RMS) phase error of 0.4-1.6° over 5.5-6.5GHz, and 0.4-0.8° over 5-7GHz. The RMS gain error is < 0.2dB with 31.5dB control range in 0.5dB step. The 3dB bandwidth of the channel is 5.4-6.5GHz. The core area is only 500*600um, and the total DC power consumption is 160mw.
Yolact-based Approach for Real-Time Fetal Heart Segmentation

The detection and identification of anomalies in fetal hearts require extensive expertise, clinical experience, and significant time. In this context, deep learning (DL) technology based on instance segmentation has become increasingly popular and effective in accelerating the detection of fetal cardiac anomalies. This research aims to develop a DL model based on instance segmentation using the YOLACT approach to detect fetal cardiac anomalies. Specifically, it focuses on anomalies in the septal wall of the fetal heart using ultrasound (USG) images. Fetal cardiac USG images are used as the dataset to train the DL model. The test results demonstrate that the proposed DL model based on the YOLACT approach is capable of detecting defects in the septal wall of the fetal heart with a mean average precision (mAP) of 93.44% and an intersection over union (IoU) of 82.02%. In this regard, the YOLACT technique enables the identification of abnormalities at the pixel level, providing detailed visualization of the affected areas. This research holds the potential to be a breakthrough in the field of fetal cardiac anomaly detection, as it can reduce dependence on experts and expedite the diagnostic process. By utilizing deep learning technology and the YOLACT approach, it is expected that there will be improvements in accuracy and efficiency in identifying fetal cardiac anomalies, thereby enabling early and better medical interventions for patients.
Wideband Dual-Polarized Metasurface Antenna for 5G Millimeter-Wave Applications Using Characteristic Mode Analysis

In this article, we introduce a wideband metasurface antenna with dual-polarized (DP) operations. Inspired by the resonant property of high impedance surface (HIS), a multi-resonance metasurface is firstly configurated by combining 3 × 3 HIS cells with distinct geometric sizes. The feeding structures with two orthogonal U-shaped microstrips are designed to excite the multiple resonances of the metasurface and realize DP operations. Based on the predicted modal behaviors of multi-resonance metasurface using characteristic mode analysis (CMA), a metasurface antenna example is designed for the fifth-generation (5G) millimeter-wave (mmW) bands, showing a broad bandwidth of 44.7% and 46.2% for the horizontal and vertical polarization, respectively. The proposed metasurface antenna features the advantages of broad bandwidth, DP operations, high efficiency, high isolation, and compact size, which is suitable for wireless terminal devices in 5G mmW communications.
Chinese Emergency Event Extraction Based on Contrastive Learning with Event Semantic Features

Extracting emergency events from a large amount of unstructured information is essential for improving early warning and emergency response. Existing event extraction methods for specialist fields often rely on well-designed syntactic dependencies or external knowledge graphs, which can provide high accuracy in their specialist fields. However, these approaches may have poor generalization capabilities and are difficult to apply directly to the emergency field. In addition, most existing methods ignore the interdependencies between event triggers and event arguments, which may be crucial semantic features for event extraction. To solve these problems, we propose a Chinese emergent event extraction method based on contrastive learning with event semantic features(CNEmergEE). Firstly, the proposed approach automatically expands the pre-trained model’s vocabulary on a domain corpus. Then, continuing learning on the same corpus allows the model to complete domain adaptation. Finally, we use contrast learning to enable models to learn semantic representations of events. Experimental results on the CEC (Chinese Emergency Corpus) dataset show that our approach achieves better improvement.
Nanoscale CMOS Ring Oscillators for Statistical Characterization of Random Telegraph Noise

The Random Telegraph Noise (RTN) is one of the major reliability concerns in nanoscale CMOS technologies. In this paper, we discuss the characterization of RTN in 40 nm CMOS technology using Ring Oscillators (ROSCs). We used two different types of ROSCs to study the temporal and spectral characteristics of the RTN. With this work, we analyse the suitability of the two types of ROSC for on chip statistical analysis of the RTN amplitude strength and its frequency characteristics. Already the preliminary results, without complete statistical coverage show $\triangle$f/$f_{max}$ variations of up to 7.18% which maximize RTN observability. Further, the observed broad variations in normalized phase noise under varying power supply levels in the range of 0.5 V to 1 V indicate that also the bias dependence of RTN centers can be investigated using the ROSC technique.
An Article Similarity-Based Approach for Planning Conference Sessions

Many conferences are held every year. The determination of the sessions for the accepted articles at these conferences is done manually by the organizers. As the number of articles increases, it becomes difficult to bring together related articles. In this study, an approach to the creation of conference sessions is presented. Sessions consist of articles on a common topic. To determine similar articles, semantic closeness between articles has been calculated using SBERT, SPECTER and SciBERT models. Clustering has been performed according to similarity scores, and the most similar articles have been grouped. Sessions are generally created with an equal number of articles. Therefore, unlike traditionally clustering methods, an approach that ensures that each cluster has equal data is proposed. When the session similarities of the real program and the proposed program are compared, an improvement of 9% in real program is achieved.
Application of Grey Prediction Model Based on Python in Carbon Emission Prediction and Low-Carbon Economic Development Analysis

The impact of carbon emissions on climate change is increasingly prominent and caused great concern around the world, in order to help save energy and reduce emissions, it is necessary to conduct research on the factors affecting carbon emissions and the forecasting model, in this paper, using the GM (1,1) forecasting model, Selecting the carbon emission data of a city in Jilin Province from 2017 to 2021, using Python program code to make short-term prediction of carbon emissions of a city, and predicting the increase value of carbon emissions based on the grey prediction model, correlation analysis of the data to predict the increase value of carbon emissions. Through comparative analyses, it is suggested that efficient ways to reduce industrial carbon emissions in a city are to prevent excessive growth of industrial output, optimise energy structure and reduce energy consumption intensity.
Design of Low Loss Dual-Mode Bandpass Filter Based on Printed Ridge Gap Waveguide

A dual-mode filter based on PRGW is proposed in this paper using stepped impedance resonator, which has a compact circuit size and strong design feasibility. In order to simplify the procedure of this design on PRGW, we separate the MS from the lower layer and verify the effectiveness of it, a transition from MS to PRGW is designed which also improves the stability of the air layer. By adjusting the size appropriately and introducing a suitable feeder, we increase the selectivity of the filter. The center frequency of the proposed filter is 32.625GHz and the relative bandwidth is 9%. It has low insertion loss(less than 0.3dB) due to the air layer.
A Compact Vivaldi Antenna for S-band Phased Array Applications

This work presents a 3D all-planar Vivaldi antenna which is used as an element in a S-band phased array. The proposed antenna is composed of exponential radiation patches, impedance matching plates, supporting plates and a ground plate, all of which are planar structures with a thickness of 1 mm. To validate the design, the antenna is measured in an anechoic chamber and a reverberation chamber (RC). Measurement results indicate that the antenna has a bandwidth of 3.8 GHz which covers the S-band completely, and the total efficiency is higher than 90%. The radiation pattern shows a wide beam property which is beneficial to realize a large scanning angle. An array with 8×8 Vivaldi elements is also constructed.
Design of directional coupler based on micro-coaxial transmission line

In this paper, a new type of directional coupler is designed by using the advanced micro-coaxial technology to solve the problem of insufficient power capacity of the feed network. Compared with the traditional microstrip line coupler, this coupler can realize dual-circular polarization feed, and has the advantages of lower loss and greater power capacity. Through design and simulation, it has good performance index. In the range of 12.4~15.9GHz, the reflection coefficient of each port is less than - 15dB, the amplitude difference of the two output ports is controlled within ± 0.5dB, and the phase difference is also kept around 90 °, which meets the design requirements of the branch line directional coupler. While realizing the dual-circular polarization feed, its loss is lower and its power capacity is larger, which has important reference significance for the application of the feed network.
Stochastic Event-based Single Sensor Quickest Change Detection

Quickest change detection has been applied in many aspects and has shown good results in sensor networks. A change point detection problem based on stochastic event-triggered schedule is designed to address the issue of quickest change point detection with a single sensor, while also considering decreasing sensor energy. The stochastic event-triggered communication is determined by the Gaussian function and the values of uniformly distributed random variables. Under this model, mathematical expressions of three indexes were obtained, namely the average communication rate, the average detection delay, and the average communication rate. The simulation results prove that this method is effective.
Sleep Apnea Detection Based on Respiratory Movement Using Harris Corner and Recurrent Neural Networks

Sleep Apnea is a temporary breathing stop during sleep, which can be detected through video observations. The principle is to make precise and efficient head and chest segments, take consistent reference points, and recognize vertical deflection patterns in time sequences. This study used the Fast Fourier Transform for filters other than the head and chest. Then, Harris Corner detects the head and chest in every frame. Then Haar Cascade got good results as feature assistance from faces and tracking with the Kanade Lucas Tomasi Algorithm. The RNN-GRU classifies the vertical pattern of each frame into three classes: No Apnea, Obstructive Sleep Apnea, and Central Sleep Apnea. The results showed that the accuracy used in the RNN-GRU is 85.18% with the Adamax technique.
Ovenized Dual-mode Piezoelectric MEMS Resonator for bovine serum albumin (BSA) sensing

Piezoelectric MEMS resonators have been increasingly employed in various biosensing applications owing to their low cost, compactness, and ultra-high sensitivity. In this article, we demonstrate the use of an ovenized dual-mode solidly mounted resonator (SMR) for sensing bovine serum albumin (BSA). The ovenized SMR is capable of achieving fixed, elevated temperatures using a micro-heater embedded beneath the structure. Therefore, these ovenized resonators can be used for obtaining thermally modulated device response resulting in enhanced sensitivity. This enables high-sensitivity detection of BSA as the adsorption of BSA increases at elevated temperatures due to thermally-induced conformational changes of molecules. In this study, the SMR-based biosensor exhibited a sensitivity of -62 ppm/μg/mL for a BSA concentration of 50 μg/mL without ovenization (at room temperature of 25 °C). Whereas, in the ovenized device where an elevated temperature of ~36.2 °C was achieved by supplying the micro-heater 200 mW the sensitivity observed was -192 ppm/μg/mL for the same BSA concentration. This indicates that the ovenized devices exhibit approximately 3x improved sensitivity due to higher adsorption of BSA at elevated temperatures.
AGV Path Tracking for Intelligent Parking Garages

Through analyzing and transforming the AGV (Automated Guided Vehicle) motion formula, this paper obtains the predictive model of the incremental linear MPC algorithm. Upon the foundation, by designing the cost function and constraints for the predictive model to complete the configuration of the incremental linear MPC algorithm for AGV, this paper proposes an AGV path tracking controller based on the incremental linear MPC algorithm with a deadbond limitation of angle. The simulation results show that the controller can accurately control the AGV to realize the path tracking of curves and straight lines, and also can control the AGV to track the path accurately in the simulation experiment that simulates the practical environment.
A design method for asymmetric coupled line impedance transformer with high transforming ratio

This paper presents a design method for asymmetric coupled line impedance transformers. The initial geometric value determined by this method is very closely to the optimal value. Based on the extraction algorithm of per unit length resistance, inductance, conductance, and capacitance of transmission lines, characteristic parameters of the four-port asymmetric coupled line circuit are re-derived and calculated. Image impedances are adopted for the ideal matching design of two-port devices. An asymmetric coupled line impedance transformer with a high transforming ratio of 100 (50 Ω to 0.5 Ω) is designed, fabricated, and measured. The excellent agreement between simulation and measurement results proves the effectiveness of the proposed accurate design method and unique superiorities of asymmetric coupled lines with better initial values.
Enzyme-based fluorometric image-sensing of transdermal gases for monitoring metabolisms

Transdermal volatile organic compounds (VOCs) are a candidate biomolecule for real-time monitoring of metabolisms. While transdermal VOCs are affected by local skin conditions, research tools to study their effects are lacking. In this study, we developed a gas imaging system for investigating the relationship between local skin conditions and transdermal ethanol emission. In the system, transdermal ethanol concentration was transduced to the fluorescence intensity of nicotinamide adenine dinucleotide (NADH, ex. 340 nm, em. 490 nm) through a catalytic reaction of alcohol dehydrogenase (ADH) on 90 mm by 90 mm of an ADH-immobilized mesh. The developed fluorescence imaging system captured the distribution of fluorescence intensity that reflected the concentration distribution of gaseous ethanol. First, we evaluated the quantitative characteristics of the system by standard gaseous ethanol samples with a gas application method mimicking transdermal gas emissions. Then, the feasibility of alcohol metabolism monitoring with the developed system was tested by human subject experiments. As a result, the system allowed the measuring of 10 ppb to 100 ppm of gaseous ethanol with high selectivity. Also, spatiotemporal imaging of transdermal ethanol emitted from the palm and wrist after drinking alcoholic beverages was possible. The maximum concentrations of transdermal ethanol calculated from fluorescence images were 404.6 ppb and 61.4 ppb for the palm and the wrist respectively, which was in good agreement with previous reports. In the future, we would like to elucidate the relationship between local skin characteristics and transdermal ethanol emissions by superimposing analysis with other systems and the developed gas-imaging system.
An Optimal Approach for The Voltage Deviation Minimization in Grid Connected SPV and BESS Based System

The article shows the optimal controlling of battery energy storage system (BESS) for the DC microgrid. The solar photovoltaic (SPV) cell is also connected in parallel with BESS. Both these sources are integrated with the DC microgrid. The major challenge with the integrated system is to reduce the voltage deviation. Such shortcoming has been overcome with the implementation of a fuzzy logic controller (FLC). It begins with the maximum power extraction from SPV using the perturb and observe (P&O) method and P&O-based fuzzy logic controller. The output of BESS has been connected to the DC microgrid along with the Cuk converter. The controlling of the Cuk converter has been assessed with vector control and fuzzy logic controller. Performance parameters like voltage deviation and accuracy have been measured for the various state of charge (SOC) levels. It is observed that minimum voltage deviation at the DC microgrid has been achieved with FLC in comparison to vector control for various SOC levels. In addition to this, the accuracy of the output voltage has been also improved with FLC over vector control with different SOC levels. MATLAB/Simulink has been used to calculate the performance parameters of the proposed model.