Miracle-dz/UniFAR-FTUs huggingface dataset:
Terahertz Scale Equivalent Far-Field Test Technique

The need for measurements of the radar cross section for electrically large targets has recently grown. Target radar cross section measurements can be made using the far-field test, compact-field test, or near-field test, respectively. The scattering characteristics can be measured or predicted using these three approaches for common targets, but not for electrically large objects. Hence, in this research, a terahertz scaled equivalent far-field test technique is investigated to realize the equivalent far-field test by lowering the far-field condition. Also, the scale-model of the trihedral corner reflector is measured using the terahertz frequency modulated continuous wave (FMCW) test system. A good agreement is discovered between the trihedral corner reflector measurement findings and modeling results. The method’s viability and accuracy are confirmed.
High-isolation MIMO Antenna Array for 5G Sub-6-GHz Applications

A 2×2 broadband multiple-input-multiple-output (MIMO) antenna array with high isolation for 5G sub-6-GHz frequencies is presented. The MIMO array is composed of four elements fed by a microstrip. A gain of 3.95 dB with a working frequency of 1.15 GHz (from 3.15 to 4.30 GHz) is obtained in the array element. Several slots were set on the ground plane of the array to improve the isolation between different elements from 10 dB to more than 20 dB. The MIMO antenna array achieved a gain of 8.91 dB with the isolation between different elements being more than 20 dB in the working frequency. The proposed array has the merits of small size, wide bandwidth, high isolation, and good symmetry of radiation pattern, which are required for 5G sub-6-GHz applications.
Machine Learning-Based Wave Downscaling Using Transformer Model, Case Study in Jakarta Bay

A more local approach is needed for wave forecasting in more complex sea areas, such as coastal areas. This approach is made so that the information about ocean wave conditions can be obtained in a high resolution and more accurate. Accurate wave forecasting information can prevent accidents when carrying out marine activities highly dependent on wave conditions, such as shipping, ocean engineering, and other maritime operations. Traditional local approach for local wave forecasting is usually obtained by performing simulation from global grid wave information into a high-resolution local grid using numerical simulation. This approach demands high computational cost. We propose a machine-learning approach for downscaling modeling that will use global wave height data and turn it into high-resolution local wave height data using the Transformer model. We use the local wave data from the result of Simulating Wave Nearshore (SWAN) numerical model simulation of the global dataset from ERA5to train the model. We chose Jakarta Bay as a case study. Moreover, we also compare the results of the Transformer model with Bidirectional Long Short-Term Memory. Results of the experiments show promising results; we found that the Transformer has overall good performance with a CC of 0.971, RMSE of 0.043, and MAPE of 11.763, and we also find that the training process for Transformer is faster 5 times faster than Bi-LSTM.
Linear Quadratic Integrator Control Design for Battery-Supercapacitor Hybrid Energy Storage System

Battery-supercapacitor is one of the hybrid energy storage systems (HESS) that recently develops for environmental-friendly energy systems. In its design, a control system is required to generate the desired current for the load and manage the use between the battery and the supercapacitor to supply the load. This research aims to design a current control system for the HESS battery-supercapacitor using the Linear Quadratic Integrator (LQI) control approach. This controller is used due to its ability to optimize state variables and the control signal by adjusting the weighting matrices Q and R. We use the Bryson method to get the matrices Q and R to minimize the states and the control signal. The LQI control then generates a duty cycle signal to control the active topology DC/DC boost converter. The simulation results demonstrate the successful operation of the LQI control and achieving optimal performance. Battery-supercapacitor current can supply the load according to the load current demand. The resulting IAE value is less than 0.8 and the duty cycle is less than 0.6 for the load change testing scenario.
An analysis method of small-signal model for packaged GaN HEMT

This paper proposes a method for modeling the small-signal model of GaN HEMT with package. With the combination of step-by-step extraction method and the full electromagnetic simulation of the passive part, this method can avoid the large amount of data optimization efforts in the traditional method. The simulation results of the model obtained by this paper are in good agreement with the commercial model, which validates the proposed modeling method. The GaN HEMT model CGH55015F2 of Wolfspeed, Inc. is selected as the modeling object.
A Pattern Reconfigurable Antenna Featuring Large Tilted Angle for 5G Applications

A novel pattern reconfigurable antenna based on quasi-Yagi principle is proposed in this paper. The proposed antenna consists of four arc-shaped radiators, a parasitic patch and a pair of reconfigurable feeding networks. By switching the operating states of four PIN diodes in the feeding network, the surface current distribution of four arc-shaped radiators can be controlled respectively. In such way, the pattern reconfigurable characteristic is obtained. To further improve the antenna gain, a parasitic patch is added. Moreover, based on the Yagi operating principle, the parasitic patch acts as the director, which greatly increases the antenna tilted angle. The simulation results show that the antenna gain is 3.77dBi-4.29dBi and the tilted angle is increased from 50° to around 70°. Moreover, eight radiation modes with different beam directions and good impedance matching from 2.4 GHz to 2.5 GHz are achieved, which is suitable for 5G indoor applications.
Point Cloud Semantic Segmentation Based on the Mapping from the Local Geometric Descriptor to Local Features

Local feature extraction is crucial for point cloud semantic segmentation. Since the local features are essentially derived from the local geometric structure of the point cloud, the relationship between the local structure and the local features of points plays an important role. We propose a semantic segmentation network by learning the mapping from the local spatial structure to the local features of points. In the proposed network, the mapping-based set abstraction (MBSA) block is designed to extract local features based on the mapping from the local geometric descriptor to local features. Specifically, the proposed MBSA block learns a mapping through the local features of the point cloud and then combines the mapping matrix with the local geometric descriptor to obtain the output features. The local geometric descriptor represents the local spatial structure of the point cloud. To enhance the learned features, we propose the feature interaction module to enrich the local information. Experiments prove that our proposed method of implicitly learning the mapping relationship between the local geometric descriptor and local features performs well in point cloud semantic segmentation.
Space-Time Modulated Metasurface Antennas for Near-Field Focusing Applications

This paper introduces the design of a novel near-field focused metasurface antenna utilizing space-time modulation technology. The antenna is capable of concentrating the radiated electromagnetic wave into a specific focal spot in the near-field region, making it ideal for use in a range of applications, such as sensing, imaging, and wireless power transfer. The holographic approach is utilized to synthesize the required amplitude envelope along the wave-guiding structure. Additionally, the space-time modulated (STM) metasurface antenna can cyclically on-off switch its meta-atoms to form an equivalent synthetic envelope along the length of the metasurface antenna aperture. The proposed STM metasurface antenna is validated through measurement results, which demonstrate its near-field focused capability.
Micro-Expression Recognition Based on Dual Branch Neural Network

Micro-expression refers to uncontrollable muscle movements that appear on people's faces when they try to hide or suppress their true emotions. Micro-expression has the characteristics of short duration and small amplitude of action, which limits the research on micro-expression recognition. A micro-expression recognition method based on Swin Transformer and ConvNeXt dual branch neural network (STCN) is proposed to address the localization of micro-expression actions and the loss of facial spatial structure information, using Transformer and convolutional neural network(CNN) as parallel branches. This neural network takes apex-onset optical flow features as input, integrates the local perception of convolutional neural networks and the long-distance relationship capture ability of Transformers, and can obtain quality representations of both detailed and macroscopic information to improve the accuracy of micro-expression recognition. The experiment was conducted on the CASME series datasets and SMIC datasets, and the results showed that the STCN network can effectively improve the accuracy of micro-expression recognition.
Design of a 2-via EBG structure for reducing the strip-line coupling in array applications

This paper presents a 2-via electromagnetic bandgap (EBG) structure for decoupling array antenna feedline. The proposed structure has bandstop characteristics and can suppress the wave propagation between feedline effectively by placing the 2-via EBG structure under the dual magnetoelectric dipole array antenna feedline periodically as an example. Simulation results indicate that the maximum mutual coupling decreases by more than 15 dB compared with 1-via EBG structure, and the isolation improvement of 10 dB covers a bandwidth of 3 GHz. At the same time, the unit size is reduced by about 40%, and the overall structure is more compact. The dual magnetoelectric dipole antenna combined with the proposed 2-via EBG, which the part of antenna is from reference [3], exhibits wide bandwidth and more compact structure and high isolation performance.
Short-term Subway Passenger Flow Prediction based on GCN-LSTM-MHA

The short-term prediction of subway passenger flow is of great reference value for the operation and management of the subway system. To improve the accuracy of the prediction results, this paper designs a combined model based on Graph Convolutional Neural Network and Long Short-Term Memory Network with Multi-Head Attention mechanism. Moreover, the Tree-Structured Parzen Estimator is used to optimize the relatively important hyperparameters in the model. Experimental results demonstrate that the GCN-LSTM-MHA model effectively captures the spatiotemporal correlation of subway passenger flow data and outperforms other baseline models in terms of prediction accuracy.
Évaluation de la fonctionnalité des corridors écologiques dans le bassin minier lensois par une approche multi-taxons

La fragmentation cause le declin de nombreuses especes, en milieu urbain notamment. Les corridors en tant qu’outil de conservation pour pallier cette perte de biodiversite ont souvent ete remis en cause. Le projet CorBaM (Corridors biologiques dans le Bassin Minier) a entre autres pour but de valider la fonctionnalite de corridors definis theoriquement en amont par cartographie. Pour les definir, la methode des chemins de moindre cout a ete employee. La pertinence des corridors etant variable selon les especes, il faut evaluer la fonctionnalite pour le maximum. Les modeles choisis ici sont l’avifaune, les lepidopteres heteroceres et le Herisson d’Europe (Erinaceus europaeus). Les methodes utilisees pour connaitre leurs deplacements sont respectivement la repasse, Capture-Marquage-Recapture et le radiotracking. Pour les oiseaux, les resultats montrent une affinite particuliere pour les corridors dans leurs deplacements dans le paysage urbain. Ils repondent davantage en corridor et ils s’y deplacent plus rapidement. Les mouvements des papillons de nuit se font eux aussi preferentiellement via les corridors. Leur taux de recapture est plus important en zone corridor qu’en matrice. L’analyse cartographique des domaines vitaux des herissons montre a priori une correlation entre surface du domaine vital et importance du reseau de corridors. A terme, ce projet permettra de fournir un outil cartographique predictif de la fonctionnalite des corridors ecologiques en milieu urbain. Il apporte aux acteurs locaux donc un outil d’aide a la decision pour l’amenagement en faveur de la conservation de la biodiversite.
MoS2 Functionalized Paper Sensor for Quantification of Glucose: Experimental Observation and Ab-Initio Calculations

A paper-based electrochemical sensor has been developed for quantitative estimation of glucose concentration in spiked serum samples. Molybdenum disulphide (MoS2) has been hydrothermally grown over the paper substrate to detect glucose non-enzymatically. The functionalized material shows an enhanced biocatalytic activity towards the target analyte molecule; thus, an excellent electrochemical response is observed. Furthermore, the differential pulse voltammetric responses of the developed sensor show a very good recovery rate of 95.84% for spiked serum samples. The amperometric responses also confirm the specificity of the sensing platform. Moreover, a first principle analysis has been carried out to promote the suitability of MoS2 for detection of glucose.
Wideband Low-Profile Dual-Polarized Metasurface Antenna

A wideband low-profile dual-polarized metasurface patch antenna is proposed in this paper. To realize dual polarizations, the metasurface antenna consists of patch cells and is fed by a cross-shaped slot. A straight microstrip line and a fork-shaped feedline are employed for the two ports. Centering at 5 GHz, a wide overlapping -10-dB impedance bandwidth between the two ports of 19.4% (4.52–5.49 GHz) is obtained in the simulation. The port isolation is higher than 25.55 dB within the operating frequency. A low profile of 0. 07λ makes the antenna promising for wireless communications.
FPGA-based Radar Altimeter Pulse Compression And Coherent Accumulation Implementation

Coherent accumulation is an important technique to improve the detection capability of pulsed radar altimeter targets when range-walk and velocity ambiguity do not occur. This paper focuses on the pulse compression technique and the FPGA implementation of the coherent accumulation technique. As the radar signal requires high precision for processing data, and the precision of floating-point numbers is higher than that of fixed-point numbers in the process of hardware implementation. Therefore, the data types handled in this paper are all in the form of IEEE 754 standard single-precision floating-point numbers. On this basis, a complex multiplication model for floating-point numbers is designed and improved in this paper, and the proposed complex multiplication module achieves a lower LUT resources and better Static Timing compared with the traditional complex multiplication operation.
Beam Steering Patch Antenna Array Using Reconfigurable Patch

In this letter, reconfigurable parasitical patches are used to design the beam steering patch antenna array. The designed beam-steerable antenna array is composed of two fixed patches and two reconfigurable parasitic patches. The reconfigurable parasitic patch is made up of two rectangular patches and a varactor diode. By changing the capacitance value of the varactor diode, the impedance of the parasitic patch is changed, which affects the parasitic patch's current phase and antenna beam direction. Stable impedance matching under different capacitance values can be achieved without additional structure. An antenna array is designed and fabricated to verify the proposed method. The beam-steerable angles of the simulated and measured results are ±30° and ±27°, respectively.
Electrohysterogram System with Remote Assistance for Pregnant Women

The World Health Organisation (WHO) states, preterm birth (PTB) as the leading cause of death for children under the age of five. An estimated 15 million babies are born preterm every year, and this number is still rising. Infants born prior to the 37th week of gestation are considered to be delivered prematurely. This project aims to Design and Develop an Integrated EHG System with Remote Assistance. This EHG system primarily comprises of a signal acquisition system, wireless transmission of signal (MQTT) to a remote receiver and a Graphical User Interface for the integrated system. It acquires EHG signals using a dedicated hardware, stores the signal values in a CSV file format and publishes the contents of the CSV file to a cloud broker that can be accessed by multiple subscribers. A GUI is created to display patient details and the signal acquired, making the system user-friendly. Women during pregnancy may experience physical discomfort, complicated medical conditions, limitations to transport facilities which may restrict their access to necessary healthcare facilities. Remote Assistance acts as a valuable tool to support pregnant women under such circumstances. A standalone signal acquisition system is made available with the patient, thereby contributing to one end of the wireless communication network. While the other end comprises of a GUI accessed by the doctors. This makes remote monitoring of the patient's health almost in real time possible. EHG is a promising technique for monitoring pregnancy and has the potential to provide valuable information for improving the management of labor and delivery.
A Novel Approach to Diabetes Risk Assessment Using Advanced Deep Neural Networks and LSTM Networks

This research article presents a novel approach to diabetes risk assessment using advanced deep neural networks and Long Short-Term Memory (LSTM) networks. By harnessing the power of deep learning techniques, our approach aims to model complex relationships between risk factors and the development of diabetes. The incorporation of LSTM networks enables the analysis of longitudinal data, facilitating the identification of temporal patterns and trends indicative of diabetes risk. Leveraging a large and diverse dataset, our model is trained to learn intricate associations between risk factors and diabetes onset, outperforming traditional risk assessment models in terms of accuracy and predictive capabilities. With a test loss of 0.417 and a test accuracy of approximately 82.9%, the proposed approach has the potential to revolutionize diabetes risk prediction by enabling early detection and proactive interventions, leading to improved patient outcomes and more personalized healthcare strategies. This research contributes to the field of diabetes risk assessment by introducing an innovative avenue that integrates advanced deep neural networks and LSTM networks, paving the way for enhanced diabetes management and alleviating the burden on healthcare systems.
Detection of the Colorectal Cancer TP53 p.R248W Mutation on a Lab-on-Chip ISFET Platform

This paper describes a method of detection for the TP53 mutation that leads to the p.R248W substitution, commonly found in colorectal cancer cases. This is achieved through the design and implementation of a pH-sensitive and mutation specific Loop-mediated isothermal amplification (pH-LAMP) method and the experimental validation using a Lab-on-Chip (LoC) platform, with an integrated array of Ion-Sensitive Field Effect Transistors (ISFETs) that can detect pH changes. Synthetic TP53 DNA was detected in pH-LAMP reactions, on both a qPCR platform and the LoC, using primers that are specific to each allele (wild-type or mutant). Assays successfully detected the target mutation within 20minutes, demonstrating the potential of the method for multi-target testing. This technology, when coupled with a sample preparation method, could allow for a Point-of-Care genetic testing platform for colorectal cancer to be realised, aiding patient stratification and treatment decisions.
77GHz High Gain Antenna Based on Microstrip Coupling Excitation

In this paper, a 77GHz high-gain antenna that can be applied to automotive angle radar is proposed. The antenna is composed of two layers of dielectric substrates with the same thickness and the same dielectric constant. The lower layer forms a spatial electromagnetic field by feeding the microstrip line, then, the electromagnetic coupling between the microstrip line and the upper metal patch is used to form an H-plane wide beam. In order to increase the surface current on the metal patch, the gain is improved by connecting the patch and the microstrip line by metallized vias in the upper medium. Finally, the antenna achieves |S11|<-10dB within 77~78.43GHz. The 3dB beam width of the H-plane is greater than 81.5°, and the gain is greater than 16.1dBi in the bandwidth.
Prediction of Diabetes Mellitus in the Upcoming Year using SMOTE and Random Forest

Diabetes Mellitus (DM) is one of the six diseases most people suffer from, which costs money for treatment. Patients with this disease increased by 2.4% of the total population in Indonesia between 2013 and 2018. This is undoubtedly very dangerous if serious precautions are not taken. DM prevention is critical to avoid increasing incidence in the upcoming years. One of the preventive measures is a prediction based on data from the results of a medical examination (medical or general checkup), which is taken for periodic examination once a year for three years (2019-2021). The data used in this study came from the Telkom Health Foundation (Yakes) Bandung. Predictions can be made using machine learning models, such as Random Forest, Naive Bayes, Decision Tree, Logistic Regression, AdaBoost, XGBoost, and SVM. This study aims to predict DM for the upcoming year with a preprocessing approach to general checkup data because the general checkup data has problems such as missing values and imbalanced data. This study uses the mean technique for handling missing values and the SMOTE technique for handling imbalanced. The prediction results show that the random forest prediction model is superior to other prediction models, with an accuracy of 98.2%, precision of 82.6%, recall of 66.4%, and f1-score of 71.4%.
DOA/RSS Joint Estimation Method Based on Iterative Maximally Sparse Convex Optimization

Electromagnetic interference (EMI) source localization is an important topic in electromagnetic compatibility (EMC). Received signal strength (RSS) and direction of arrival (DOA) information are often used for localization. In order to overcome the difficulty of multi-sources localization when DOA or RSS is used alone, a DOA/RSS joint estimation method is proposed based on sparse recovery framework and iterative maximally sparse convex (IMSC) optimization. The proposed method maximizes the non-convexity of the penalty function while ensuring that the objective function is convex, which makes the RSS estimation more accurate. The simulation results reveal that the proposed method can accurately estimate the DOA and RSS for multi-sources.