Miracle-dz/UniFAR-FTUs huggingface dataset:
Sweetpotato Whitefly Natural Enemies: Parasite Surveys in Urban Areas and Cotton Fields and Identification of a New Predator

Surveys for adult sweetpotato whitefly parasites on ornamentals in urban areas and in cultivated cotton fields show high parasite activity in urban areas vs. activity in cultivated cotton fields. A previously unreported Drapetis spp. fly was identified and found to occur in cotton fields in several areas in the state.
Situational awareness in the automotive domain - Effects on driving performance and driver's behavior

Situational awareness has a significant role in human decision making in dynamic environments. It represents the state of knowledge required to make effective decisions and take appropriate actions. Situational awareness observation was first introduced in aviation to evaluate pilot performance in aircraft operations. Such an assessment provided much information about the pilot's perception and understanding of the situation, as well as his or her ability to adapt to the situation or even predict its state in the (near) future. This type of insight was soon recognized as beneficial in other fields dealing with dynamic environments and human-machine interaction, including automotive research and industry. This paper outlines why appropriate situational awareness is necessary for safe driving and how it can affect driving performance, especially in automated vehicles. It also discusses how it can affect driving behavior, especially with the introduction and increase of vehicle automation. Finally, available methods for assessing situational awareness are presented and examples of their application in the available literature are given.
Smart Aero Generator Monitoring System using IoT

Wind turbine plays a major role in energy production. From small wind turbines to ocean wind turbines, wind turbine industry introduces the latest version with bigger flexible blades, high towers, good efficiency and low-cost repairs. Small wind turbines malfunctioning will lead to a reduction in total energy. Faults like increasing in temperature may lead to fire accidents. The excessive vibration of the turbines may cause the turbine to sustain damage. The current shock will be passed to the people from the turbine if they try to contact it. IoT sensors can help us to overcome these obstacles. The proposed system will monitor the temperature, measures the speed, smoke and motion near the windmills. If the temperature exceeds the threshold then an alert will be generated and also water will be splashed in order to overcome the fire accident. If the speed of the wind is more than the threshold then the windmill be turned off to avoid damage in the turbines. Also, if an unnecessary movement is detected after a particular distance then the gate will be closed in the proposed system. It also measures the smoke for the gases released and generates an alert to avoid further damage to human lives and the windmill. The experimental results prove that the proposed system will improve the security of the windmill and also reduces the damage that occurs in the environment. The alert will be generated within 15s of an incident which proves that this is an efficient system when compared to the existing ones.
Adaptive disturbance rejection using AI

The main objective presented in this paper is to develop and implement the cascade control structure for the separation process and the enrichments of the 13C isotope, being a time-consuming industrial process. This classical control structure is compared with an adaptive control method obtained using artificial intelligence methods. The classical cascade control strategy consists of a PI controller in the internal loop tuned using the modulus criterion and a PID controller in the external loop. This structure can reject disturbances that occur both on the input of monoethanolamine and on the isotope concentration itself, by implementing two control elements consisting of a PD controller with filter, each. The separation process’s settling time with enrichment of the 13C isotope is reduced by implementing the adaptive cascade control structure: for this purpose, all the four controllers are discretized using an interval of independent sampling times for each corresponding case, resulting in a range of coefficients for each controller; these coefficients are learnt using neural networks, facilitating the conversion back to the continuous time, leading so to the possibility to minimize the process settling time.
Trimmed Flight States of an Over-Actuated Tilt-Wing Aircraft for Linear Controller Synthesis

This paper presents an approach for determination of trimmed flight states of an over-actuated tilt-wing aircraft for linear controller synthesis. Tilt-wing aircraft combine efficient forward flight with vertical takeoff and landing capabilities and thus offer many advantages for unmanned aerial vehicle applications. The ability to cover a wide range of airspeeds comes along with significant changes in aerodynamics and consequently flight mechanics, which makes flight state control a challenging task. Proven gain-scheduling concepts for flight state control rely on pre-determined trimmed flight states. A new tilt-wing aircraft design for even more efficiency in forward flight features a tiltable wing and a tiltable tailplane. This results in over-actuation of the longitudinal motion and hence the determination of control device commands for trimmed flight becomes a multi-dimensional problem. This paper presents an approach to provide a distinct set of control device commands for each trimmed flight state, w.r. t. the specific tilt-wing configuration and user-defined constraints. The trimmed flight states and the corresponding control device commands are the basis for linear controller synthesis, which is applied for the entire flight envelope. The proposed approach has been successfully implemented for an unmanned tilt-wing demonstrator and evaluated in flight tests.
A Fog Computing Monitoring System For Fused Visible And Infrared Imagery with an application to building preservation

With the spread of modern IoT architectures for industry 4.0, cloud and edge computing technologies have become the main enablers of new a concept of interoperability based on the seamless integration of the frontend sensors, with the backend network and processing systems. While precise architectural references and standardized approaches are still under development, in this paper the concept of edge/fog computing is applied referring to modern fused visible-thermal imaging systems\ currently available on several commercial cameras. In such context, the research proposed aims at developing a monitoring system based on fog technology for gathering and analyzing fused RGB-Thermal Images, and implementing a specific algorithms for extracting relevant information for centralized decision making systems. Based on the architecture proposed a case study is presented referred to the preservation of buildings and civil infrastructures.
Fault cause Identification of Distribution System Based on Association Rule

Identifying the fault cause in the distribution system is very challenging. In the face of massive fault information, accurately identifying the cause of faults is the key to accurate fault warning. Aiming at five fault causes in the distribution systems, this paper analyzes the correlation between different fault causes with text features and electrical features. FP-Growth algorithm is used to mine association rules, and a comprehensive database containing all historical fault events and a knowledge base containing effective strong association rules are constructed. The Jaccard similarity coefficient is used to identify fault cause. Finally, the accuracy of the proposed algorithm is verified by field measured data.
An Enhanced Deep Learning Algorithms for Image Recognition and Plant Leaf Disease Detection

Object recognition in photographs has only been possible with the advent of modern deep learning (DL) techniques. Following its success in other fields, DL techniques are increasingly being used to a broad variety of agricultural operations. Due to its intrinsic complexity, image-based plant disease detection is difficult to solve. Targeted management relies on more than just detection and requires accurate identification of each species. An automated disease detection system aids the farmer by providing a speedy and accurate diagnosis of plant illness. Accelerating agricultural diagnostics requires the development of an automated system for identifying plant leaf diseases. Using machine learning and image processing, the authors of this study outline a strategy for detecting leaf blight. A photo of a leaf may be fed into this framework. In a first processing stage, noise is eliminated from the leaf images. The mean filter may be used to get rid of noise. Histogram equalisation is a method for enhancing image quality. Segmentation is a technique used in photography to break a large image down into more manageable sections. This helps to establish the picture's limits. The image is broken up using the BIRCH (Balance Iterative Reducing and Clustering utilising Hierarchies) technique. For this purpose of feature extraction, fine grained classification model, a convolutional neural network is used. After that, the pictures are categorized using a variety of methods, one of which is RCNN (Region based Convolutional neural network). Using this approach, this study discovered the split ratio, computational time, and as a result RCNN has resulted in a high accuracy of 0.98 when detecting the leaf compared to other algorithms.
A Miniaturized All-Metal Dual-Polarized Antenna Array with Heat Dissipation Enhancement

In this paper, a miniaturized all-metal dualpolarized antenna array with heat dissipation enhancement is proposed for base station application. The size of the antenna array is 3x3 with its vertical spacing 77 mm and horizontal spacing 53 mm. The radiator is bent vertically to realize the miniaturization of the antenna without sacrificing original electromagnetic performance. The antenna operates in the band of 2496 MHz to 2690 MHz with an electrical down-tilting of 6°±1°. Due to the advantage of the all-metal design of the antenna, it has stronger heat dissipation performance in the natural thermal simulation environment compared with the ordinary dipole antenna array printed on the dielectric plate.
Breast Cancer Identification and Classification Using Contextual Deep Learning Technique in Histopathological Images

Breast aggressive ductal carcinoma (IDC) are both forms of ductal carcinoma, and the pathological distinction between the two is crucial for selecting the most effective course of treatment and predicting the most favourable clinical results. Since there is a finite number of pathologists who can use microscopes for conventional diagnosis, it is imperative that novel approaches be developed that can promptly and precisely identify huge statistics of histopathological specimens. It would be tremendously helpful for routine pathological diagnosis if computational pathology tools existed to aid pathologists in the detection and classification whole slide images (WSIs). In this study, we contemporary a deep and broad contextual convolutional neural network (CCNN) for histopathology image categorization, which outperforms state-of-the-art deep networks. By efficiently utilising local spatio-spectral correlations of adjacent separate vectors, the suggested CCNN can discover previously undiscovered local contextual interactions. One step in the proposed CNN pipeline is a filter bank, which allows for the simultaneous utilisation of data. Once the multi-scale filter bank's spatial and spectral feature maps have been created, they are integrated to create a joint spatio-spectral feature map. When a fully convolutional network is fed the joint feature map containing the breast cancer image's rich spectral and spatial features, it may make predictions about the labels that should be assigned to each individual pixel vector. The suggested method is assessed using dataset, and a comparison of its performance to that of state-of-the-art techniques is provided.
Deep Learning Approaches for Object Detection in Short-Range GBSAR Images

This paper describes the developed system for detecting objects on low-resolution radar images obtained using Ground-Based Synthetic Aperture Radar (GBSAR). The focus of the work is on the developed methods and algorithms for the object localization, and building and selecting a classifier for use in the system with a limited size dataset and low radar image resolution. In the first step object localization detects and extracts the image parts corresponding to objects and then processes them for use in the classification algorithms. Different classifier algorithms were tested and the results of the algorithm executions are presented and performance metrics on different datasets compared. Based on the results it is shown that even with a very small dataset and a low-resolution system a successful detection can be efficiently achieved.
Design of a Miniaturised Wide-Stopband SIW Filter

In this paper, we propose a miniaturized wide-stopband SIW dual-band filter. SIW limits the propagation of electromagnetic waves and thus enables the miniaturization of the filter. A passband below the waveguide cutoff frequency is generated by etching the CSRR structure on the surface of the SIW, while a modified CSRR-DGS structure is etched on the backside of the SIW structure, and the other passband is generated by coupling the CSRR etched on the surface of the SIW to the ground. In addition, the CSRR-DGS structure broadens the stopband without increasing the filter size. Through the circuit equivalent analysis and parameter optimization of the filter structure, the final simulation results are as follows: the filter has a passband of 3.5 GHz (WIMAX) and 5.2 GHz (WLAN), the 3dB bandwidths of the two bands are 130MHz and 180MHz respectively, the insertion loss is 0.5 dB and 0.65 dB, and the size is $0.18\lambda_{\mathrm{g}}\times 0.27\lambda_{\mathrm{g}}$. Overall, it achieves high performance and also has a wide range of applications.
On-board Parameter Identification for Reusable Launchers during Descent via Compressed Sparse Regression

This article presents the application of an on-board parameter identification approach to a reusable launch vehicle benchmark during its descent phase. The identification framework is based upon data-driven sparse regression techniques in conjunction with compressed sensing, and allows for the identification of linear or nonlinear equations from measurement data alone. The results show that the proposed approach is able to identify the system behaviour for different guidance/control architectures as well as for nominal and dispersed scenarios.
Efficient and Lightweight Human Pose Estimation Algorithm

This study examines a straightforward and effective human posture estimation technique based on a SimpleBaseline network. A simple and effective network model is created, and a multi-feature fusion module is implemented on the foundation of SimpleBaseline to fuse high-resolution feature maps and low-resolution feature maps, in an effort to address the issue that the present human pose estimation task model is too complex. The effective Shuffle module's functions include replacing the original network's basic module, reducing unnecessary parameters, and enhancing model inference. A coordinate attention mechanism is presented to embed location information into channel attention to increase the accuracy of the lightweight network and make the network more lightweight and efficient in order to address the issue of prediction accuracy decline after model light weighting.
A Survey on Diabetes Detection Based on Machine Learning Classifiers

Computer-assisted sickness diagnosis is less costly, time-saving, accurate and it eliminates the need for extra employees in medical decision-making. According to many nutrition studies, nearly a quarter of the world's population suffers from chronic illnesses such as diabetes. As a result, an effective machine learning regressor capable of correctly diagnosing diabetes is urgently needed. The aim is to determine whether individual classifiers or a set of classifier combinations provide the best diabetes detection accuracy. The concentration of glucose in human blood is known as the blood glucose level. Diabetes is a long-term condition marked by a lack of glucose. To maintain blood sugar levels within the suggested target range, insulin therapy is necessary. As per the statistic from the WHO, diabetes affects almost 400 million individuals worldwide. The need for regular blood glucose monitoring in the therapy phase cannot be overstated. Diabetes can be detected in a variety of ways. ML models are utilized to determine whether or not the patient has diabetes to confirm the sickness. This paper attempts to survey various recently published literature that have proposed different approaches to detecting Diabetes Mellitus using machine learning techniques. Some of the surveyed techniques utilise comparative analysis of ML as well as DL classifiers, while others techniques use medical records to identify the risk predictor.
Health State Estimation of Lithium-ion Batteries Based on APDE-Bi-GRU-Attention Model

To maintain safe and dependable battery operation and lower battery system maintenance costs, state of health (SOH) estimate, one of the primary BMS tasks for electric vehicles, is essential. In order to improve the SOH prediction accuracy of lithium battery, a SOH prediction method based on the improved differential evolution algorithm (APDE) and the bi-directional gated recurrent unit network (Bi-GRU-attention) combined with the attention mechanism is proposed. Firstly, the feature factors related to battery capacity are extracted from battery charging data and correlation analysis is performed, and the four with high correlation are selected as inputs of model. Then, the Attention mechanism is applied to the Bi-GRU network to form the Bi-GRU-attention network, and the key parameters of the network parameters are optimally searched by the improved differential evolution algorithm (APDE) to build the final prediction model. Experiments are conducted on NASA dataset, and the results show that the APDE-Bi-GRU-attention method has higher prediction accuracy and fit, which verifies the feasibility of the prediction method.
VHF Propagation Modelling in Forest Environment Using Parabolic Equation Method

The parabolic equation method is used to simulate the radio wave propagation in forest environment. The random distribution model of forest is established by Monte Carlo method, and the electric field distribution in forest environment is analyzed. The path loss distributions are observed by changing the density of tree distribution. A forest model with different densities is constructed by Monte Carlo method, and the path propagation loss of VHF band is simulated.
ANN-Based Scalable Video Encoding Method for Crime Surveillance-Intelligence of Things Applications

The proliferation of high-quality, networked CCTV cameras in public areas has raised the volume of constantly generated video data significantly in recent years. The need for scalable, distributed video processing infrastructure is growing as a result. In order to make unstructured videos supplied to the platform searchable and comprehensible by both humans and machines, an intelligent video analytics platform runs them through a series of trans-disciplinary algorithms. The use of video analytics extends well beyond just surveillance to include things like video asset management. Many different commercial and academic approaches exist here. Most current systems for face and object recognition, however, have a time-honored client-server architecture that leaves out support for increasingly intricate use cases. Furthermore, distributed computing is seldom used to manage such frameworks at a scale. Furthermore, no current works provide any kind of assistance with low-level networked video processing APIs. Not only did they ignore the expanding needs of customers, academics, and developers, but they also neglected to address a comprehensive service-oriented environment. In this work, we offer an Artificial Neural Network based Scalable Video Coding (ANN-SVC) framework for intelligent video surveillance as a means of resolving these problems. The suggested system can handle both live video feeds and video analytics in batches. Similarly, the batch processing data for each realtime stream is available. This work therefore has a connection to the idea of symmetry. We also provide Spark's first distributed video processing library. To guarantee scalability, efficacy, and fault-tolerance, SIAT takes use of cutting-edge distributed computing technology. Finally, in an effort to back up our assertions, we implement and test our suggested framework. The simulation shows better results in throughput, gooodput and PSNR.
Vehicular Connectivity Modeling and Simulation using Modified NP-CSMA

The objective of this work is to investigate through mathematical modeling using modified Non-Persistent Carrier Sensing Multiple Access Model (CSMA-NP), effect of human response (driver), sensors and electronics processing time, and communication time on vehicular connectivity. The work combined both time ratio function and CSMA-NP function, in order to enable such analysis, both analytically and numerically, using MATLAB. The obtained analytical (model based) and numerical (MATLAB simulation) results of effect of the considered time parameter. Analysis carried out in this work covering throughput, collisions, and slot probability. The research proved that the considered time parameters have modulating effect on the original data traffic-throughput response, with decreased values as the time duration increases. The work also shows that collisions decrease as a function of increasing time duration, due to reduction of traffic as it is spread over a longer time period. It is also shown in this work that as slot probability decreases, so does throughput and collisions due to lower expectancy of vehicular communication per slot. The introduction of simulation supported mathematical model that includes communication, processing, and human time parameters, should greatly help in the design process of both vehicular electronics and communication networks.
Response of NMOS to Combined Ionization and EMP Environments

Electronic systems in space or nuclear devices are exposed to both ionizing radiation and electromagnetic radiation environments. Experimental results have indicated that the semiconductor devices may suffer severe damage in the combined environment of ionizing radiation and electromagnetic radiation. In this work, a total ionizing dose (TID) effects and electromagnetic pulses (EMP) combined effect model of NMOS device is established. Simulation results show that the NMOS will exhibit more severe leakage current increase and sub-threshold characteristics degradation in the combined environments.
Detection and Recognition of Traditional Chinese Medicine Slice Based on YOLOv8

Traditional Chinese medicine (TCM) herbal pieces are a type of TCM preparation with complex production processes and a wide variety of species. It is a challenging task for ordinary people to identify TCM herbal pieces. To solve this problem, this paper proposes an object detection method for TCM herbal slice based on YOLOv8. The YOLOv8 model is trained on a Chinese herbal slice (CHS) dataset that is collected and labeled from the internet, and with the Mosaic data augmentation, the average precision of YOLOv8 model on all categories can reach 95.8%, with a model size of 5.94MB, and can achieve $30\sim 45$ FPS on the MX150 graphics card environment. Compared with image classification mode, the object detection mode of YOLOv8 can better perform efficient and accurate detection and classification of CHS, which can effectively improve the recognition rate and popularization of CHS, and enable more people to benefit from the culture of TCM.