Miracle-dz/UniFAR-FTUs huggingface dataset:
Study on Power Capacities of Three Types of Wideband Tapered Baluns

Tapered balun is the most commonly used feed structure in ultrawideband tightly coupled dipole antenna array (TCDA). The tapered balun used in TCDA is usually manufactured by printed circuit board technology. When the power fed into the balun exceeds its power capacity, the printed circuit board may suffer thermal failure, which will affect the performance of TCDA. In this paper, the power capacities of the linear tapered balun, exponential tapered balun, and Doph-Tchebycheff tapered balun are analyzed and compared using the multi-physical simulation and prototyping test. The simulation and experimental results indicate that, the linear tapered balun has lower temperature rise and higher power capacity than the other two kinds of balun under natural heat dissipation conditions.
A Fatigue Driving Recognition Method Based on WOA-Attention-GRU

This academic paper addresses the challenges related to the limited accuracy and practicality of laboratory simulation data in driving tests. The main goal is to investigate driving behavior under real-world vehicle conditions. To achieve this, the paper proposes a dual time window approach that effectively captures time series lengths. Moreover, it extracts the intersection of operational behaviors and employs the verification recursive feature elimination method to identify the optimal fatigue feature indices. These indices are then combined with whale optimization and gate-cycle control neural network techniques to detect fatigued driving more effectively. To enhance recognition accuracy, an attention mechanism is introduced. Experimental results using vehicle data demonstrate that the proposed method achieves an 89.84% accuracy rate in detecting three-level fatigued driving, with a false negative rate of 10.99%.
Effect of Rain Attenuation on the Availability of LEO Satellite Communication System

The Low Earth Orbit (LEO) satellite communication system has become an important trend of next-generation communication services. As it is known, rain attenuation affects the communication between satellite and ground, and it is significant to evaluate the effect of rain attenuation on the availability of the LEO satellite communication system. In this paper, a link availability evaluation model is built with the consideration of rain attenuation for the LEO satellite communication system. On this basis, a service availability evaluation algorithm is proposed using Monte Carlo method. Finally, a case is used to find the effect of rain attenuation on the service availability of the LEO satellite communication system.
Multi-robot Cooperative Search for Radioactive Sources Based on Improved Particle Swarm Optimization

Effective management and monitoring of radioactive sources are critical to ensuring nuclear safety and minimizing risks to human health and the environment. This paper proposes a multi-robot collaborative radioactive source search algorithm based on improved particle swarm optimization (PSO). The algorithm fuses the latest observations into the particle sampling, enabling particles to converge toward high-likelihood regions and mitigating particle degradation. In addition, the multi-robot gradually narrows the search range using particle filtering algorithm to improve the accuracy of source state estimation. Finally, all robots combine DWA dynamic window obstacle avoidance with Infotaxis to realize distributed collaborative decisions. Experimental results show that when the algorithm adopts the improved particle swarm optimization algorithm, the search accuracy and average search time under different parameter combinations are significantly improved.
Context-Aware Invasive Plant Instance Synthesis for UAV-Based Crop Field Image Augmentation

This paper introduces a new image augmentation technique called Context-Aware Invasive Plant Instance Synthesis (CAIPIS) to enhance deep learning-based invasive plant detection models in aerial imagery, particularly when dealing with limited datasets. CAIPIS generates diverse and realistic training samples by adding synthesized invasive plant instances into UAV crop field images using a combination of color-based filtering, interval sampling, guided selection, resizing, and Poisson blending. Our testing demonstrated that model trained on the CAIPIS-augmented dataset outperform the one trained with traditional image augmentation techniques, improving the generalization capabilities for invasive plant detection in UAV-based crop field imagery and contributing to precision agriculture and environmental management.
Forward-Backward Inverse Reinforcement Learning for Load Balancing in SDN

Software Defined Network (SDN) is a kind of networking technology that dynamically establishes the network for improving network performance. The SDN architecture is composed of three layers which includes application layer, control layer, infrastructure layer. Each layer is subjected to several challenges in terms of improper controller placement, poor scalability, performance degradation, frequent load imbalance among nodes, weak network connectivity, and many more. In this paper a novel Forward Backward Inverse Reinforcement Learning (FBIRL)-based load balancing framework is defined for SDN domain. The load balancing policies are formulated immediately and the possibility of picking the best action at every step is very high. The performance of the proposed FBIRL is tested over Mininet virtual network. The results are found be promising towards the performance metrics like reduced respond time and blocking probability. Simultaneously increase in the success rate achieved in the client tasks completion.
Ultra-Wideband Array with Multi-Layer Frequency Selective Surface Resistive-card and 51.4:1 Bandwidth

In this conference, a novel ultra-wideband (UWB) array is presented that covers the UHF to Ku-bands (0.35 – 18 GHz). The array incorporates two frequency-selective surface (fss) resistive-card (R-card) in the substrate to suppress resonances within the operational band. A highly improved impedance matching and wide-angle scanning with a fss superstrate is employed. Notably, this arrays achieves a 51.4:1 impedance bandwidth at broadside with VSWR < 3 across most of the operational band. The proposed design and innovation are significant in improving and expanding the technology of array antennas, and opening up new possibilities for future applications.
Flooded Area Segmentation on Remote Sensing Image from Unmanned Aerial Vehicles (UAV) using DeepLabV3 and EfficientNet-B4 Model

Climate change caused by global warming results in increased rainfall and has the potential to cause flooding. Floods are natural disasters that often occur in Indonesia and can cause significant losses. In handling floods, effective monitoring of flood-affected areas is needed. Flood monitoring using remote sensing technology, such as the Unmanned Aerial Vehicle (UAV), is compelling. However, developing an accurate semantic segmentation method to map flood areas is still necessary. This research aims to perform semantic segmentation using DeepLabv3 with various pre-trained backbone models such as ResNet50, EfficientNet-B4, and MobileNet on the FloodNet dataset of 398 data. The measurement of segmentation performance will be evaluated using miou, accuracy, precision, recall metrics, and f1-score. The best evaluation results were obtained using the EfficientNet-B4 model with a miou score of 0.481 and an accuracy of 0.90.
A Cylindrical Conformal Electronically Scanning Conical Beam Antenna Array With Fixed Frequency

A cylindrical conformal electronically scanning conical beam array with fixed frequency is proposed which is realized by using edge-loaded varactor diodes. The array consists of eight antenna elements with equal spacing in the circumferential direction, which can produce a large beam flare angle and small circumferential ripple. By changing the capacitance of the varactor diode, a conical beam flare angle scanning range of 42° - 62° can be achieved. The simulation results show that the gain is better than 5.3 dBi at the frequency of 9 GHz, the circumferential ripple is less than 1.5 dBi. This kind of conical beam scanning antenna with fixed frequency has a promising application in the field of missile fuzing system.
Blur Removal and Image Merging for Image Enhancement on Python

Image Fusion is important to retrieve useful information from a set of images that are given by the user and merge/fuse these images to generate a single output image which is very informative and useful when compared to the input images. In the present work, the blurred images were fused to make clear and informative images. Humans and machines will have good use of the resulting processed image for understanding the details of the captured images through image processing or image fusion methods. In this paper, the image fusion techniques were developed and tested on the Python platform. The image fusion techniques were carried out by suitable algorithms for blur detection and image correction. Another work was carried out to merge images with user-defined parameters to have enhanced image parameters. Image fusion is used in artificial intelligence (AI), intelligent robots, stereo camera fusion, medical imaging, manufacturing process monitoring, electronic circuit design and inspection etc.
Comparative Analysis of Random Forest and CNN for Surgery Mortality Prediction

Accurate surgical outcome forecasting is essential for patient safety, healthcare resource allocation, and healthcare professional's capacity to make well-informed choices. To address this, a paper has been initiated to create a machine learning (ML) and deep learning (DL) model that can effectively determine the likelihood of a patient surviving a surgery. The main goal of this paper is to compare the performance of both models and determine which one yields superior overall results and develop something that can help the healthcare industry in making judgements. To conduct this research, a comprehensive dataset comprising approximately 50,000 surgical cases from various hospitals has been acquired. The paper successfully developed and evaluated various models. The best-performing model was “Random Forest”, which achieved an accuracy of 92.51%. The deep learning models also showed useful results, with the best model achieving an accuracy of 91.3%. The process of fine-tuning the DL model has yielded a significant improvement in accuracy, increasing it from 91.3% to 93.4%.
Improved Indoor Localization Algorithm Combining K-means Clustering Algorithm And Wasserstein Generative Adversarial Network Algorithm

The large number of samples required in the fingerprint database creation phase leads to excessive time and labor costs. And too many sampling points in the fingerprint database will lead to too much computation when matching localization. To address this problem, this paper proposes an improved localization algorithm combining K-means clustering and Wasserstein Generative Adversarial Network(WGAN). Firstly, K-means clustering is performed on the reference points of the initial fingerprint feature library to divide them into different clusters, and then the signal characteristics of the channel state information of the point to be located are collected to determine the cluster in which the point is located, and the fingerprint features are expanded by WGAN to the cluster in which the point is located to form an expanded fingerprint library, and finally the expanded fingerprint library is used to perform online location matching by K-Nearest Neighbor (KNN) algorithm. Finally, the expanded fingerprint library is used for online location matching by KNN algorithm. The experimental results show that for the initial fingerprint library with only 100 sampling points, the average localization accuracy is improved by 18.58% after the expansion. The average improvement of localization accuracy is 11.64% when the initial fingerprint library with 400 samples is expanded and then localized.
A Class-E Power Amplifier with Transformer-Based Load Network in 110-nm CMOS

This paper presents a method for Class-E power amplifier (PA) design. An area-efficient output load network of only one doubly tuned (DT) transformer and a pair of capacitors is implemented thanks to DT transformer’s double peak characteristic of Zin. A design method is presented and it can be used for the design of the output load network of Class-E PA. As a proof of the method, a 12 GHz Class-E PA is designed using GlobalFoundries (GF) 110-nm CMOS process. The circuit achieves a peak power-added-efficiency (PAE) of 32.9%, a maximum saturated output power (Psat) of 17 dBm and a peak power gain of 20.8 dB. The core area of the circuit is 970 μm × 430 μm.
Telemedicine Acceptance in Malaysia's Healthcare System: Systems Quality and Users Behaviour Matter

Telemedicine has offered a new way for a remote medical consultation between patients and doctors. While it has been acknowledged that telemedicine has contributed positively towards the quality of global healthcare systems, in some regions users have been reluctant in adapting to remote medical consultancy. Studies have shown telemedicine acceptance has been poor as a result of rejection among the users which has taken its toll on its great potential to healthcare system. This study seeks to establish factors contributing to patients' acceptance towards telemedicine. An acceptance model was developed with a set of hypotheses using the variables identified from the study of literatures. A survey was conducted among Malaysian public who have patronized both private and public medical care to determine their attitudes towards telemedicine and their willingness to embrace this new approach in seeking medical care. The results of the survey have demonstrated that system's quality and users' behaviour play important roles in the acceptance of telemedicine.
A W-band All-Dielectric Lens Antenna with Flat-Top Radiation Pattern

This paper proposes a W-band all-dielectric lens antenna with the characteristic flat-top beam. The lens loaded on the horn is composed of 10 concentric elliptical rings and a tapered matching layer. Its geometries are optimized by the non-dominated sorting genetic algorithm II (NSGA-II). The lens antenna designed at 92-97 GHz achieves a beamwidth of 40° with ripple level less than 1 dB in E-plane and H-plane. In addition, the proposed antenna can be prototyped easily using 3D-printing technology with low cost.
Wideband Filtering Spoof Surface Plasmon Polaritons Power Divider With Wide Stopband

This manuscript introduces a power divider design that utilizes spoof surface plasmon polaritons (SSPPs) to achieve wideband filtering capabilities and a broad stopband. The dispersion characteristics of the proposed SSPPs are analyzed, and grating slots are etched on the top layer of the substrate to realize the proposed filtering power divider structure using substrate integrated waveguide (SIW). By leveraging the high-pass trait of SIW and low-pass trait of SSPPs, the bandwidth of the filtering power divider can be extensively customized by adjusting their dimensions. Finally, a power divider with a frequency band range of 7-11.2 GHz and filtering characteristics is designed.
Analysis of Automotive Security Risk using Cyber Security

Cyber Security is a field in today”s highly connected world with commercially available 5G networks. Automotive security is essential since cars are now at the centre of V2X communication that connects everything around them. Future innovations like electrification. autonomous driving, sharing and networking will have an impact on the automotive industry. These trends alter annually. Revolution in the automotive industry electronics cars are evolved. Due to the convergence of automotive industry with electronics information and communication technology (ICT) plays a vital role in vehicle security. In spite of that, cyberattacks that happen in the current ICT ecosystem are replicated in a 5G network environment. This proposed work elaborates the different threat models and how risk is analyzed.
Design of a Nearly Isotropic Luneburg Lens Antenna

In this paper, a nearly isotropic Luneburg lens antenna is proposed, in which the lens is composed of cells with variable size produced by 3D printing technology, and the feed is a circularly polarized butterfly cross dipole antenna. The antenna operates at 10GHz with a maximum gain of 20dBi, and omni-directional beam scanning can be achieved by moving the feed. Because of the good isotropy of the designed cell, the lens is applicable to feeds of various polarization modes, and the beams in all directions can achieve almost uniform radiation. The antenna has potential application value in radar, sensing, long-distance communication and other fields.
Hybrid Approach for Retail Store Auditing Using CRNN

In today's world, On-Shelf Availability (OSA) is critical in the field of selling and marketing. It is an important measure of performance for the retail sector that has a significant impact on profit and customer retention. Out-of-Stock causes sales to decline and customer retention to dwindle. Because of the firm's fierce competition, vendors and providers place attractive offers to please customers and keep them returning to their shops. In this work, a hybrid framework has been proposed to prevent out-of-stock problems by utilizing IoT and deep learning-based models for product label detection and recognition. To extract objects and their features a recurrent convolutional neural network has been utilized. IoT-based sensors help identify minimum loads on the shelf. The proposed approach is linked to a camera apparatus which detects inventory levels on the racks and generates signals to the store manager if items are removed from the rack or misaligned. The experimental results show that by using the proposed approach, it is feasible to recognize front-facing items and empty content on the rack with an accuracy ranging from 83%-99%.
Visual-Inertial SLAM Algorithm for Low-Texture Subterranean Environments

In order to solve the problem of poor performance of traditional visual-inertial SLAM algorithms in low-texture subterranean environments, a SLAM algorithm for low-texture subterranean environments was proposed. The algorithm adopts the visual-inertial SLAM scheme, and added the representation, detection, and matching of line features based on point features. The algorithm increased the available visual features, enhanced the robustness of the visual-inertial SLAM scheme, and solved the problem of positioning accuracy degradation caused by insufficient features. Experiments on the EuRoC and DARPA SubT datasets show that the proposed algorithm has stronger robustness and accuracy compared with VINS-Fusion in low-texture subterranean environments.
Dual-Frequency Phase Shifter Design with Large Frequency Ratio

This paper presents a structure and design method of a dual-frequency phase shifter (DFPS) with large frequency ratio. The DFPS is a reflection-type phase shifter that utilizes four symmetrical stepped-impedance transformers to realize a dual-frequency 3 dB hybrid coupler. Two dual-frequency susceptance blocks (DFSBs) realized by stubs are loaded onto two ports of the hybrid coupler to achieve the required phase shifts at the two frequencies. The phase shifts can be independently controlled by setting the susceptances of the DFSBs. A 90° DFPS with frequencies f1=0.9 GHz and f2=4.68 GHz is designed, fabricated and measured to validate the design theory, and good agreement between the simulated and measured results is obtained. Different simulated phase shifts are achieved by varying the parameters of the loaded stubs.
Holdout Stacking Regression Model for Estimating the Population of Rice Brown Plant Hopper Pest

Diseases affecting rice can greatly reduce the yield of rice. Mainly these diseases are caused by pathogens and insects. Pests cause 30% production loss in India. Brown plant hopper (BPH) is one of the pests that cause major damage to rice yield. The proper prediction of pest disease is important. A rice pest prediction model using ensemble technique is proposed in this work. Environmental parameters with the Brown plant hopper pest count are analyzed. Prediction models were developed using multiple-linear, ridge, Decision tree, support vector, K-nearest neighbor and random forest algorithms. A holdout stacking regression model was then developed by combining these regression models. The performance of the all the developed models is evaluated using the evaluation metrics R-Squared score, adjusted R-Squared, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) to find the best model.
A Compact Wideband Circularly Polarized Magnetoelectric Dipole Antenna With Wide Beamwidth

A circularly polarized (CP) magnetoelectric (ME) dipole antenna with wide bandwidth, wide beamwidth, and compact size is proposed. To achieve a wide half-power beamwidth (HPBW), the dipole is designed to be a folded bowtie, and four rotationally symmetric vertical metal plates are placed on the square ground plane. A circular parasitic patch is used to improve the impedance bandwidth. By using a 3-dB coupler, the proposed antenna achieves wideband CP performance. The simulated results show that an impedance bandwidth for VSWR ≤ 2 and a 3-dB axial-ratio bandwidth (ARBW) are from 0.76 to 2.1 GHz (93.7%). Moreover, a bandwidth of HPBW ≥ 115° is from 0.76 to 1.8 GHz (81.2%) and a bandwidth of 3-dB axial-ratio beamwidth (ARBW) ≥ 100° is from 0.76 to 1.46 GHz (63%).
Flexible Piezoresistive Pressure Sensor Based on Graphene Nano Plateletes

Flexible pressure sensors have gained significant attention within the research community due to their exceptional capabilities across numerous applications. Among them, piezoresistive pressure sensors have demonstrated excellent performance, thanks to their simple working mechanism, low-cost sensor fabrication methods, and the extensive range of materials available. In this study, we demonstrate a flexible piezoresistive pressure sensor by following a low-cost fabrication approach that allows us to realize both the sensing layer and substrate. We utilized a sandpaper template-assisted method to achieve a micropatterned sensing substrate, while an efficient liquid-based transfer technique was used to fabricate the sensing layer. By repeating the transfer deposition method, we were able to achieve stable conductivity of the sensing layer, resulting in pressure sensitivity of 0.0002 kPa-1 in the low-pressure range. The moderate sensitivity of the sensor was capable of detecting tactile pressure, as demonstrated by generating Morse code.
Comparison of the Keras-LSTM Algorithms for Classifying Autism Spectrum Disorder Using Facial Images

Autism Spectrum Disorder (ASD) is a neurodevel-opmental condition that affects early childhood communication abilities, leading to depression and suicidal ideation. The CDC reported that one in forty-four children in the US, totaling five million, were diagnosed with autism in 2018. Conventional methods for diagnosing autism involve questionnaires that several researchers have done. However, machine learning techniques for diagnosing autism using the Keras library (VGG16, VGG19, and MobileNet) have shown superior accuracy. The long-short-term memory (LSTM) algorithm has been widely used in various fields, but the direct LSTM algorithm for autism detection has been limited. This study presents a proposed Keras-LSTM architecture, i.e., VGG16-LSTM, VGG19-LSTM, and MobileNet-LSTM, for transfer learning and conducts a comparative analysis. The proposed architectures are simple and robust and can achieve high accuracy with minimal modifications. The dataset for this research contained 2940 facial images of children aged two to fourteen. The results showed that the VGG19-LSTM model had the highest overall accuracy at 75.85%, outperforming the other two models in non-autistic precision, average precision, and all F1-scores. However, VGG16-LSTM demonstrated superior performance in autistic precision (90.27%) and non-autistic recall (96.26%), while MobileNet-LSTM achieved the highest autistic class recall (96.60%).