Miracle-dz/UniFAR-FTUs huggingface dataset:
A Systematic Literature Review on Plant Disease Detection: Motivations, Classification Techniques, Datasets, Challenges, and Future Trends

Plant pests and diseases are a significant threat to almost all major types of plants and global food security. Traditional inspection across different plant fields is time-consuming and impractical for a wider plantation size, thus reducing crop production. Therefore, many smart agricultural practices are deployed to control plant diseases and pests. Most of these approaches, for example, use vision-based artificial intelligence (AI), machine learning (ML), or deep learning (DL) methods and models to provide disease detection solutions. However, existing open issues must be considered and addressed before AI methods can be used. In this study, we conduct a systematic literature review (SLR) and present a detailed survey of the studies employing data collection techniques and publicly available datasets. To begin the review, 1349 papers were chosen from five major academic databases, namely Springer, IEEE Xplore, Scopus, Google Scholar, and ACM library. After deploying a comprehensive screening process, the review considered 176 final studies based on the importance of the method. Several crops, including grapes, rice, apples, cucumbers, maize, tomatoes, wheat, and potatoes, have tested mainly on the hyperspectral imagery and vision-centered approaches. Support Vector Machines (SVMs) and Logistic regression (LR) classifiers demonstrated an increased accuracy in experiments compared to traditional classifiers. Besides the image taxonomy, disease localization is depicted in these approaches as a bottle neck to disease detection. Cognitive CNNs with attention mechanisms and transfer learning are showing an increasing trend. There is no standard model performance assessment though the majority use accuracy, recall, precision, F1 Score, and confusion matrix. The available 11 datasets are laboratory and in-field based, and 9 are publicly available. Some laboratory-based datasets are considerably small, making them impractical in experiments. Finally, there is a need to avail models with fewer parameters, implementable on small devices and large datasets accommodating several crops and diseases to have robust models.
Likelihood tests of hypothesized durations: determining and accommodating biasing factors

Abstract Paleobiologists frequently hypothesize that a taxon's duration (i.e., the true span from origination to extinction) exceeds its stratigraphic range (i.e., the span from first appearance to last appearance in the fossil record). One can test hypothesized duration by assessing the plausibility of the implicitly hypothesized gaps between origination and first appearance and/or between last appearance and extinction. Several tests assess the probability of not finding a taxon over some stratigraphic gap. Because the likelihood of a hypothesis given data reflects the probability of the data given that hypothesis, these probabilities also give the likelihood of a hypothesized duration. However, many probability/likelihood tests require simplifying assumptions about unknown sampling parameters such as the consistency of sampling over time, sampling intensities for unknown ancestors, and actual sampling intensities themselves. This paper examines the effects of sampling parameters on probability/likelihood tests and presents methods for testing hypotheses about these unknowns while testing hypotheses about true durations. Two data sets are used here as examples. One analysis tests the origination times among Paleozoic gastropods implied by phylogenetic inferences. The other analysis tests the extinction times among Maastrichtian ammonites implied by different numbers of extinction events. In both cases, hypotheses positing many gaps in the fossil record become more likely after accommodating uncertainty about sampling. However, the increased likelihoods are insufficient to prevent these hypotheses from being rejected in favor of hypotheses positing fewer gaps. In both cases, the conclusions are identical to those derived by simple methods using simple models for unknown sampling parameters. Although numerous factors can exaggerate the implausibility of gaps, making these factors parts of testable hypotheses is possible. Thus, excessive assumptions about sampling parameters need not hinder empirical testing of hypothesized durations.
A Histogram-Based Digital Background Calibration Technique for Pipelined A/D Converters

This paper presents a digital background calibration technique for pipelined analog-to-digital converters (ADCs) to correct the gain error due to the capacitors mismatch and finite dc gain and nonlinearity error owing to the residue amplifiers. The proposed calibration scheme corrects these errors by using the histogram-based method. To calculate the linear and nonlinear coefficients, the threshold level of sub-ADC is changed and based on specifications of residue characteristic and output histogram, the first and third order coefficients are extracted. This method does not require any calibration signal or additional analog hardware and relaxes the performance requirements of the analog building circuits. Circuit level simulation results of a 12-bit 100 MS/s pipelined ADC in a 65 nm CMOS technology show that the proposed calibration scheme improves signal-to-noise and distortion (SNDR) and spurious free dynamic range (SFDR) from 30.4 dB and 31.8 dB to 69.3 dB and 81.2 dB, respectively.
A Reinforcement Learning (RL)-Based Hybrid Search Method for Hidden Object Discovery using GPR

Ground Penetrating Radar (GPR) systems are effective tools for discovering various types of buried objects (such as military mines, metal objects, pieces of underground infrastructures etc.). One challenge is to find an efficient search procedure that would reduce the operation time and optimize resource utilization, such as minimizing the power consumption for battery operated UAVs or fuel consumption of land vehicles. Current approaches are based on pre-defined search patterns which, for large and sparse areas, could mean unnecessary waste of time and resources. In this paper we report on a Reinforcement Learning (RL) based search method for training the guidance logic of possible platforms hosting the GPR, so that they navigate more efficiently. We illustrate the applicability of the method using a high-fidelity simulation environment and a newly implemented RL framework. Initial results suggests that combination of a coarse navigation scheme and an RL based training procedure based on GPR scan patterns, can lead to a more efficient target discovery procedure for host platforms.
An Application of a Set-Valued State Estimator Based on Constrained Zonotopes in GNSS-INS Integration

In this paper, the problem of guaranteed state estimation and fault detection for integrated navigation systems in aerospace vehicles is considered. Global Navigation Satellite Systems (GNSS) aided Inertial Navigation System (INS) via sensor fusion is used to estimate the states of a vehicle with high accuracy and at a high sampling rate. Kalman filtering is one of the most popular techniques for state estimation of such systems in a stochastic way. However, poor initialization or parameterization, unknown-but-bounded disturbances, unavailability of statistical information about the sensors and abnormal measurements due to sensor failures is a major drawback. In more practical applications, the covariances are usually approximated by heuristic approaches which may lead to situations where the filter may not perform in an optimal or desired way. In this paper, a set-membership based approach is used to bound the vehicle states by a poly topic set representation known as Constrained Zonotopes. A minimum-variance criterion is used for optimal state estimation and the bounds can be used for integrity monitoring and fault detection for the system since these bounds are guaranteed by construction. An example of GNSS-INS integration for a Quadrotor UAV trajectory is considered and the results are compared with the Extended Kalman filter.
Application of FFT Algorithm in Digital Protection System of Building

Ancient architecture is the essence of Chinese traditional culture and is the star in the long history. Effective protection of ancient buildings is a tribute to traditional culture. In the conservation of ancient buildings, traditional measurement and conservation methods can hardly match the needs of the ever-changing times. The application of digital technology can effectively solve the problem of accurate measurement and preservation of information data, so that the relevant information is more accurate, safer and more comprehensive records. With the popularization of computer technology, many intelligent optimization algorithms have gradually penetrated into the study of ancient building preservation and renewal. The pressure on the conservation and restoration of ancient buildings is increasing, and people are eagerly seeking new algorithms that are more robust, fast, efficient, and general. In this paper, we focus on improving the traditional genetic algorithm and combine the ideas of FFT (Fast Fourier Transform) algorithm to form a unique and improved algorithm, which is applicable to most of the ancient buildings. The experimental results of this paper show that the data collection efficiency is improved by 69% and the data processing efficiency is improved by at least 67% after the optimization of FFT algorithm, and the accuracy of database construction is at least 14% higher than that of the traditional method. It can be seen that the digitization method combined with the FFT algorithm has greater advantages than the traditional method, and will be more accurate and efficient for the conservation of ancient buildings.
Analysis of High Gain Wideband 2 × 2 Printed Slot Array With AMC Surface by Presenting Equivalent Transmission Line Model for C and X-Band Applications

A low-profile broadband $2\times 2$ printed slot array with V-shaped slots by loading broadband artificial magnetic conductor (AMC) is demonstrated. A proposed wideband printed array with tapered V-shaped slots with the feeding of coplanar-waveguide (CPW) is measured in 8.24-11.13 GHz. Also, the proposed wideband planar AMC surface is embedded into the printed array to attain enhanced radiation properties. The printed slot array with the $7\times 7$ AMC surface covers -10 dB measured bandwidth from 4.47 to 13.32 GHz (100%). The design loaded by AMC compared to the design without AMC introduces a reduced size of 109.4%, a bandwidth improvement of 70%, and good impedance matching. Moreover, by adding an AMC surface into the printed array, an enhanced gain of 12.3 dBi with uni-directional radiation patterns is attained. The measured gain over the wide bandwidth shows acceptable stability with an average gain of almost 11 dBi. The proposed AMC unit cell is proposed to resonate at 10.10 GHz with a bandwidth of 7.85-12.24 GHz (43.7%) for X-band applications. In addition, the proposed equivalent transmission line model of the antenna with AMC is presented with acceptable output results. This model forecasts the input impedance of the printed array with AMC at the frequency band of 4.50-13.30 GHz as well.
An Attention Mechanism Approach for Natural Color Retrieval for Plant Monitoring

Even though agriculture practices have been continuously developed with the support of modern technologies, many more improvements can be made to enhance agricultural technologies and businesses. One such technology is the use of specific light color combinations to optimize the growth rate of plants. One obvious drawback is that plants’ color will change according to the light color combinations. The light color can fool human eyes and may cause errors when monitoring for plant anomalies. Color correction methods should be applied to help restore the natural plant color with the white light source from the unnaturally colored plant images. Our color correction method uses an application of self-dot-product attention, multi-head attention, and channel attention combined with a U-Net-based model. This proposed method performs the color correction with the input image in the RGB color space in two steps. First, a global transformation network provides the global function that maps the input RGB color vectors from every pixel and produces the corrected RGB color vectors. The global mapping function is the same for all pixels in the image. Next, a local transformation network attempts to correct the local color distortions such as light the flickering of LED light due to the AC power supplier.
An Intelligent Data-Plane with a Quantized ML Model for Traffic Management

Offloading some of the traffic management decision-making functionalities to intelligent data-planes (IDPs) can significantly enhance the accuracy and adaptation speed of network services. An IDP executes, at line-speed, one or more machine learning (ML) models for real-time inference and decision making. Unfortunately, existing IDP deployments either realize only a limited set of ML models such as decision trees or require substantial modifications in the switch hardware. These limitations can be attributed to the inherent scarcity of both the computational and memory resources and the strict high-speed per-packet processing demands. To address the aforementioned limitations, we propose a novel ML-based management framework, the in-network quantized ML architecture (INQ-MLA). First, INQ-MLA delegates the task of training and continuously optimizing the IDP ML model to the control-plane. The latter adopts a tailored quantization-aware training process to compensate for the effect of precision loss due to quantization. Second, INQ-MLA employs an efficient quantization mechanism to transform the trained ML model parameters (e.g., weights and activation functions outputs) from floating-point representations to smaller low precision fixed integer values that can be easily processed and stored in the data-plane. Finally, INQ-MLA ensures that the deployed ML model is integrated into the IDP pipeline by limiting all its execution operations to simplified arithmetic operations that are available in most switches. We developed a proof-of-concept implementation of our proposed architecture using P4-based switches. Experimental results demonstrate that INQ-MLA can achieve a high-level of accuracy at runtime.
Digital Map Generation Workflow Demonstrated on ZalaZONE Automotive Proving Ground Elements

One major goal of digital twin technology applied in the Architecture, Engineering, and Construction (AEC) Industry is the mapping of roads and road environments with their associated information. Such digital twins can support cost-effective testing, simulations, and the production of mapping backgrounds for automated vehicles and generally for Cooperative, Connected and Automated Mobility (CCAM) developments. The efficient workflow that leads from data acquisition to finished products depends on a number of requirements and knowledge sets. The main contribution of the paper is a general methodology of creating HD-map from point cloud and image data set. Accordingly, the most widely used survey techniques are presented and the requirements for the objects to be derived are summarized. The paper then outlines a general implementation procedure as an expert knowledge through a real-life example, where the results obtained for the ZalaZONE Automotive Proving Ground are demonstrated.
Research on the Application of Trade Gravity Model in the National Bilateral Export Trade

Bilateral trade refers to the trade in which two countries maintain a balance of imports and exports with each other. With the deepening of economic globalization, export trade has gradually become a major strategic issue related to the future of opening up. Therefore, the analysis of the bilateral export trade volume of a country in economic activities has important practical significance. The research improves the trade gravity model through quantile regression, and realizes parameter estimation through EM algorithm based on considering the influence of hidden variables. The results show that the minimum residuals of the parameters are 0.1489, 0.0057, 0.0049, and 0.0066, respectively, which all appear under the Logistic distribution and follow the Logistic distribution. In the analysis of the bilateral export trade volume of a country, the changes in the four quantiles 0.215, 0.515, 0.765, and 0.965 are consistent with the direction of parameter values. The economic scale and distance of the importing and exporting countries have a positive and negative impact on the bilateral export trade volume, respectively, consistent with international trade theory. The above results verify the effectiveness of the proposed model and provide a methodological reference for the dynamic analysis of bilateral export trade volume of countries.
Hybrid Genetic Algorithm Random Forest algorithm (HGARF) for improving the missing value Imputation in Hepatitis Medical Dataset

It has always been difficult to make an exact prediction when there are many missing numbers in the data collection. To solve this problem, the majority of hybrid models either removed the absent cases from the data collection or used a default method to fill in the blanks. In the study described in this article, an improved genetic algorithm metaheuristics-based forecasting model is suggested to forecast the number of confirmed instances of hepatitis. The Random Forest's characteristics are chosen using the improved genetic method, which also improves the efficiency and accuracy of the prediction model overall. On standard medical data sets for Hepatitis from the UCI Repository of Machine Learning, the effectiveness of the suggested model as a predictive categorization system is examined. In addition to accuracy, sensitivity, specificity; and the area under ROC are also computed. The proposed method was contrasted with various current state-of-the-art approaches that were examined in the same setting and using the same datasets. Based on the outcomes of the simulations and the comparative analysis that was done, it was found that the suggested hybrid method outperformed more complex ones with an accuracy of 93.91% and that it could be applied to other prediction models.
Disentangling Firms' Drivers on Big Data Strategy Adoption: The Case of Thai Firms

In the recent world, the fully-integrated manufacturing firms' facilities and information system architecture such as big data can enable them to be more competitive in the uncertain market. However, the research on firms motivating drivers of big data adoption in the Thailand context is in its infancy and inconclusive. This study thus investigates the driving force of contributing factors influencing Thai firms to adopt big data. The research data was obtained from the Thailand Business R&D and Innovation Survey conducted by the National Science and Technology Development Agency (NSTDA) in 2018. Over 3,931 manufacturing firms have been included in this study to validate four hypotheses by logistic regression with the significance level of 0.05. The results show that firm size (p-value < 0.001), firm openness (p-value < 0.001), and acquisition of expert technology (p-value < 0.001) are associated with big data adoption propensity. Research results shed a new laser light on Thai firms' drivers on their proneness to adopt innovative technology in industry 4.0, which provides implications for both academics and practitioners at large.
Gridless DOA Estimation for Automotive Millimeter-Wave Radar with A Novel Space-Time Network

Direction of arrival (DOA) estimation of radio wave is required in many practical applications, and is a key technology in target detection of millimeter wave radar. In this paper, a new space-time network (STNet) is proposed, which models DOA estimation as a regression task to solve the off-grid problem and achieve the effect of gridless estimation. We design a space feature processing module (SFPM) and a time feature processing module (TFPM), which extract features from the covariance matrix of the received signal and the original received signal respectively. Through such processing, we retain sufficient information, obtain more features for the regression task, and ensure the estimation effect under small snapshots. The experimental results are presented to demonstrate the performance of STNet.
Simulation-Based Switching Performance of Self-Heating Effect on SiC-based Power-Electronic Circuits

The high-frequency power-switch-driven self-heating effect on the conduction and switching performance of the 4H- Silicon Carbide (SiC) DMOSFET-based general-purpose inverter stack (GPIS) circuit has been investigated through an integrated device and circuit simulation approach. This study presents the empirical significance of the blocking switch with an embedded body diode in the H-bridge converter circuit on switching and conduction loss with instantaneous temperature rise. It has been explored that an insignificant self-heating effect on the switching performance and non-negligible post-switching body-diode carried conduction loss rise with the real-time increase of device temperature during circuit operation.
A Two-Stage, Soft-Detector Integer-Forcing Receiver Using the Slowest-Descent Method for IEEE 802.16e

This paper proposes an integer-forcing (IF) receiver for a multiple-input multiple-output (MIMO) system. Two low-complexity, soft demapping methods are introduced, which can provide approximate log-likelihood ratios (LLRs) for quadrature amplitude modulation (QAM) mapping in IF detectors. We compared the packet error rate (PER) performance and channel noise of the IF detector with those of zero-forcing (ZF) and minimum min square error (MMSE) detectors, as well as with a list sphere decoding, depth-first search (LSD-DFS) detector, in a 4×4 IID (independent identically distributed) MIMO channel with a 16-QAM modulation testbed. The required precision of the IF detector is reported in the conclusion.
Proposed CNN Model for Classification of Brain Tumor Disease

A brain tumor is a group of abnormal cells within the brain or surrounding tissues. Several variables, including family history, radiation exposure, and some genetic disorders, might increase the likelihood of developing a brain tumor. The typical method for detecting brain tumors is to perform MRI scans, which a medical specialist then examines for diagnosis. While time-consuming, this process is fraught with the possibility of human error, especially when the tumor is in its early stages. As a result, brain tumor diagnosis must be made properly and as soon as possible. With quick and accurate brain tumor identification, this work aims to prevent premature death, provide health in resource-constrained conditions, and promote patients' healthy lifestyles. A CNN model is created in this study to detect brain cancers, and the dataset contains 251 scans. Because datasets are limited in availability, data augmentation is employed to expand the dataset's coverage. The suggested CNN model's outputs were evaluated using the metrics Accuracy, F1-Score, Precision, and Recall. In aggregate, the model has an accuracy of 85%. As a result, deep-learning CNN models have been demonstrated to detect brain tumors while spending no time or resources effectively.
3D Spatial Sensing and Analysis Model of Closed Space CO2 Concentration

We propose a 3-dimensional(3D) CO2 concentration analyzing system to monitor the ventilation of the closed space. this system employs a 3D scan to reduce the labor associated with creating a 3D space model. This research uses multiple CO2 sensors and considers the model to map point CO2 concentration data to the spatial CO2 distribution. We experimented with evaluating the accuracy of the model in the actual bus space.