Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Comparison of Learning Methods for Soil Parameter Estimation using Hyperspectral Data

Estimation of soil parameters using hyperspectral imaging data is an important problem in many applications. In this work, we present a comparative performance study of three machine learning-based algorithms using linear regression, convolutional neural networks (CNN) and multilayer perceptron architectures, for the estimation problem at hand. The four parameters to be estimated include potassium, phosphorous pentoxide, magnesium and pH values. Using the dataset that was provided as a grand challenge at ICIP 2022, we show that the CNN model offers the best performance among all the other considered architectures, in terms of the Score performance metric. Moreover, all the considered three architectures outperform the baseline.
Detection of different Crops Types using RapidEye Imagery over Fresno, California

Monitoring the crops periodically and implementing sustainable agricultural practices are the considerable conditions for implementing efficient methodologies for Cropland Mapping. Detection of crop type, acreage calculation, and yield detection are the key research points that are under consideration of the research in today’s era. In this research work, the authors have focused on exploring the ability of Rapid-Eye data satellite images for crop identification/classification. A hybrid crop classification model was proposed for temporal and multispectral RapidEye imagery over the area of Fresno county of California state, United States. The studied area is enriched in a specific crop such as grapes, almonds, alfalfa, and dbl crop winwht/corn that covers 47.34%, 26.93%, 8.79%, and 3.51% of the area respectively. The ground truth data can be downloaded from the United States Department of Agriculture, CropScape-Cropland Data Layer by specifying a few parameters such as year, and area of interest (based on region, state, and county). The proposed classification model was designed by introducing the combination of a convolutional 1-D layer and two Gated Recurrent Unit layers and is aimed to obtain the best performance in terms of quantitative parameters. The authors introduced these layers to predict class probabilities based on a temporal sequence of observations. The proposed model outperforms for time-series data and resulted in a noticeable improvement in an overall accuracy of 96.31% and a kappa coefficient of 0.95.
Communication Performance of OAM Based FSO System in Weak Turbulence Environment

The communication performance of orbital angular momentum (OAM) based free space communication in a weak atmospheric turbulence environment is experimentally studied in this paper. We use spatial light modulator (SLM) to generate +10th and +20th OAM beams, respectively, and highspeed communication with the communication rate of 28Gbit/s in each link is demonstrated. The turbulence channel used is based on the hot plate thermal convection principle, a weak, controllable turbulence environment. The experiment also compares the communication performance of traditional Gaussian beams and +10th and +20th OAM beams. The experimental results show that in a weak turbulent environment, compared with Gaussian beam-based system, high order OAM beam-based system can reduce the scintillation index by 33% to be 0.27 and improve the detection sensitivity by 2.94dB to -21.93dBm.
Performance comparison of visualization-based malware detection and classification techniques

Cybercriminals use malware or malicious software to cause harm to the victim. Malware is a continuous source of concern for security teams. Malware analysis techniques, including static, dynamic, hybrid, and memory analysis, are used to comprehend the behavior and its impact. The aforementioned malware analysis techniques require domain knowledge to extract the artifacts from suspicious files, which is not always possible. A visualization approach, in which malware files are transformed into images, is one of the recently used techniques by researchers for malware detection and classification. In this paper, we apply four widely used techniques based on the visualization using a new dataset of memory dump files of malware families and benign classes. These visualization techniques include a histogram of oriented gradients (HOG) with multilayer perceptron (MLP), convolutional neural network (CNN) with pretrained weight of visual geometry group 16 (VGG), Transfer learning of VGG16 with support vector machine (SVM), and integration of global image descriptor (GIST) and HOG with SVM. Among the selected techniques, CNN with a pretrained weight of VGG16 outperformed the other techniques in terms of accuracy, precision, recall, and f1-score. Apart from the performance metrics, the results of selected techniques are also analyzed in terms of computational cost and memory utilization.
Time delay Independent of Bilateral Tele-Robotic System using Wave Variable Approach

The controlled motion of a robotic manipulator is investigated in this work in the face of time delays in their communication channel. It causes the system to become unstable due to communication delays. To construct the controller, a passivity-based method was used. The stability of Teleoperation without and under the influence of time delays has been investigated. The stability study and simulation results in Teleoperation without time delays demonstrate instability, i.e., the slave does not track the master’s desired motion, causing instability and a decrease in system performance. A wave variable-based strategy is employed to ensure the system’s stability to compensate for time delays and increase the system’s performance. Stability analysis has been done under the influence of time delays and the same with simulation with different values of controller gain for optimal result. Number of iteration used for optimal value of controller gain. In this paper, after incorporating the wave variable approach passivity/stability deteriorate if gain values is used different from the optimal values.
Modern Approaches towards Object Detection of Complex Engineering Drawings

Due to the rapid advances in object detection methods, object recognition in the engineering drawings has gained researchers' attention in recent years. Usually, engineering drawings are analysed using traditional object detection methods. However, complex structures and their connections sometimes make it challenging for the traditional object detection architectures to process the engineering drawings. With the rapid development in deep learning models, more powerful methods- which are able to interpret high-level and complex features- are introduced to address the challenges exhibited by the traditional methods. These models have different training strategies and thus portray varied behaviors. In this paper, we present a comparative analysis of the deep learning models for object recognition in engineering drawings. Furthermore, real-life scenario on how to process and analyses information from specific engineering drawings, namely, piping and instrumentation diagrams (P&IDs) is discussed in details. Finally, future directions are presented which can serve as research guidelines to work in this domain.
Joint Loss-Based Multi-decoder Network for OCT Fluid Segmentation

Optical coherence tomography (OCT) is a popular and clinically viable tool for diagnosing ocular lesions in ophthalmology. In clinical practice, however, since Macular Edema (ME) segmentation of ocular OCT images is subjective, labor-intensive, and prone to error, it is essential to adopt computer-aided systems to help ophthalmologists perform ME segmentation. In this paper, we propose a novel Joint Loss-Based Multi-Decoder Network, namely MDNet, for OCT Fluid Segmentation. MDNet mainly consists of an encoder and three decoder modules, which are used as segmentation branch for label images, contour branch for edge label images, and diffusion branch for distance maps, respectively. A new loss function corresponding to such three modules is also devised for training. Experiments with a publicly available dataset are conducted to validate the effectiveness of MDNet, and results compared with the existing state-of-the-art methods demonstrate that MDNet is advantagous in achiving better segmentation results.
Intelligent Farming with NDVI Integrated Agriculture Solution

This research creates a farm that can self maintain itself by applying intelligent farming through the IoT concept. The problems that it tackles are trying to establish a farm with agriculture 4.0 concept and enable the farm to have a sense of intelligence by setting up edge devices to manage farm data. While designing and developing the solution, an automated and closed loop intelligent concepts are applied with demonstration on effective sensing and maintaining resources. These approach comes with establishing a real-time data monitoring plants’ health and growth status with NDVI. In addition, the proposed work provides an alternative farming method with deployed multiple sensors devices and software programming codes to retrieve the farm data and uploaded to the server for analysis. Results show the temperature is at lowest point (i.e., $\lt 24^{\circ}\mathrm{C})$ corresponding to lowest NDVI (0.125), with highest moisture and humidity in average, on the 9th day. For the day time analysis, collected NVDI values on plant leaves achieved higher range, i.e., $\gt 0.30$ with higher surface area during the noon time.
Suction Grasping Detection for Items Sorting in Warehouse Logistics using Deep Convolutional Neural Networks

Items sorting in warehouse logistics is a labor-intensive and time-consuming work. Combined with computer vision and real-time motion planning technologies, industrial robots have been ideal substitutes for human beings in that cases. But picking and placing a large quantity of object categories including known and novel objects in heavily cluttered environments is really a challenging task. This paper proposes a pipeline to address suction grasping detection for isolated objects. Firstly, a two-dimensional suction configuring is proposed. Secondly, we establish a dataset including depth images, color images and suction labels for logistics warehouse scenario. Thirdly, a lightweight network named Generative Grasp Convolutional Neural Network (GG-CNN) intended for planar antipodal grasp is adapted for predicting spatial suction affordance in pixel. Finally, we get a accuracy of 91.45% on test data sets. Primary contributions of our work are: (1) a practical annotation method and dataset collecting from retail industry, (2) an innovative application of GG-CNN.
Tomato Leaf Disease Classification Using Supervised Learning Techniques: Contrasting Analysis

Presently hydroponic methods of growing different varieties of crops are acquiring acceptance in different parts of the world. It is a technique of growing plants with the help of water. Traditional methods of farming are facing different types of threats like lack of farming lands, expansion of urban areas, change in climatic conditions and extensive use of chemical fertilizers and toxicants which is exhausting the fertility of the soil and transforming cultivable lands into barren lands. With the tremendous growth of population nowadays it is quite difficult to supply all the human race with an adequate amount of food by growing them on the basis of traditional methods. The farming land is also changing in residential areas to meet the demand of the growing population in order to provide them a place for living. As a result, different methods of farming need to be adopted. In this study, hydroponic methods are explained with the help of machine learning algorithms. This system works on the principle of KNN and Lasso Regression algorithm to automatically provide them with nutrients dissolved in water with the help of roots. With the help of this technique, we can overcome the issues of food scarcity by growing an ample number of crops of different types, all year round. Hydroponics is a technique for the cultivation of plants without using soil. Earlier this method was practiced manually by keeping an eye on the nutritional needs of plants by maintaining a balance of various factors such as acidity or basic (pH), electrical conductivity (EC), temperature and the value of total dissolved solids(TDS). In this research Machine, Learning Methods are used that help us to measure pH, TDS, and nutrient temperature value in the nutrient film technique (NFT) by using some sensors. In this research, tomato is an object of an experiment, and Comparison is done using Machine Learning Algorithms to speculate the categorization of nutrient conditions for it.
Cropland Mapping Using Single Date Sentinel-2 Imagery Using Machine Learning Classifiers

Crop classification is an important task that finds application in many fields, such as crop health management, crop yield estimation, and crop monitoring. Sentinel-2 images, which are readily available and have a good temporal and spatial resolution, have a lot of potential for crop classification. The primary objective of this study is to map the cropland areas using spectral signatures derived from single-date Sentinel-2 imagery. The post-classification accuracy metrics (overall accuracy (OA), and kappa score (k)) were assessed using the confusion matrix derived from the testing data samples. For Random Forest (RF) and Support vector machine (SVM) models, the Accuracy Metrics achieved are (OA = 87.33%, k = 0.851) and (OA = 88.6%, k = 0.866) respectively. The results of RF and SVM classified data show that mapped cropland areas are 1560.96 km2 and 1594.89 km2, respectively.
An Adaptive Common Spatial Pattern for EEG Signal Classification in Different Channel

This In the experiment of brain-computer interface motor imagery, CSP is often used for feature extraction. CSP is a supervised learning algorithm that requires a large amount of sample data for training. However, the EEG signal is non- stationary, and the fixed CSP spatial filter does not have an ideal classification effect when there are few training samples. To solve this problem, this paper proposes an adaptive co-space mode. It can continuously update the spatial filter according to the data changes, and then perform feature extraction. This paper takes the data of the third brain-computer interface competition Iva as the processing object. Considering the influence of different EEG regions on classification, we have selected 4 different regions to compare and explore the classification accuracy of CSP and ACSP respectively. In the results, the average classification accuracy of CSP and ACSP is higher than that of CSP in four areas. It shows that the classification performance of ACSP is better than that of traditional CSP
Simulation Analysis of Discrete Admittance Control of Manipulator

For interaction operations by manipulator such as assembly and grinding, admittance control is a commonly used compliance control strategy. Before the actual operation, the simulation is need to analyse the control effect and adjust the control parameters, so the simulation model should be close to the actual system. However, the influence of control period and sampling period in the actual system is not considered in most compliance control simulation. Aiming at this problem, in this paper, firstly the mathematical model of the discrete admittance control is established accounting for the influence of control period and sampling period in an actual manipulator system. Then, the simulation model based on the discrete admittance control is constructed in Simulink to evaluate the performance of the discrete admittance control on an actual manipulator. Finally, through this model, the compliance control effect of the manipulator can be observed under different admittance parameters, sensor sampling periods and manipulator control periods, which helps to the design of manipulator controller.
Channel Estimation For Extra-Large Massive MIMO Systems With Non-Stationary Channels

We consider the problem of uplink channel estimation for an extra large-scale massive multiple-input-multiple-output (XL-mMIMO) system, wherein the base station consists of multiple sub-arrays of antennas. We propose a sub-arraybased variational Bayesian learning (S-VBL) channel estimation algorithm which exploits sparsity caused due to inherent channel non-stationarity in XL-mMIMO systems. We design a Gaussian mixture prior which captures this sparsity. We show that the proposed S-VBL algorithm, by exploiting the XL-mMIMO channel sparsity, vastly outperforms the existing algorithms in terms of the normalized mean squared error and spectral efficiency.
Downlink Spectral Efficiency of RIS-Assisted Cell-Free Massive MIMO-NOMA Systems With CSI Errors

We consider a reconfigurable intelligent surface (RIS) assisted cell-free massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) system, where each access point (AP) serves its users with the aid of the RIS. We practically model the system by assuming imperfect channel state information (CSI) and employing imperfect successive interference cancellation (SIC) at the users’ end. We first obtain the channel estimates using linear minimum mean square error (LMMSE) approach considering the spatial correlation at the RIS and then derive a closed-form downlink spectral efficiency (SE) expression using the statistical CSI. We numerically show that i) the RIS-assisted link is more advantageous at lower transmit power region where the direct link between AP and user is weak, ii) SE with RIS significantly increases with the increase in the number of APs and RIS elements, and iii) NOMA outperforms OMA in terms of SE for the RIS-assisted cell-free system.
Numerical Simulations of Bow Echo Formation Following a Squall Line–Supercell Merger

AbstractOutput from idealized numerical simulations is used to investigate the storm-scale processes responsible for squall-line evolution following a merger with an isolated supercell. A simulation including a squall line–supercell merger is compared to one using the same initial squall line and background environment without the merger. These simulations reveal that while bow echo formation is favored by the strongly sheared background environment, the merger produces a more compact bowing structure owing to a locally enhanced rear-inflow jet. The merger also represents a favored location for severe weather production relative to other portions of the squall line, with surface winds, vertical vorticity, and rainfall all being maximized in the vicinity of the merger.An analysis of storm-scale processes reveals that the premerger squall line weakens as it encounters outflow from the preline supercell, and the supercell becomes the leading edge of the merged system. Subsequent localized strengthening of th.
Structure and dynamics of liquid water on rutile TiO2(110)

Water on TiO2(110) is the most widely studied water-oxide interface, yet questions about water dissociation and hydrogen bonding are controversial. Here we report density-functional theory simulations which show that water does not dissociate at the coverages examined. The aqueous film is layered, with slow moving molecules in the contact layer and fast moving molecules in a second layer, revealing strongly inhomogeneous dynamics of the interfacial water. Hydrogen bonding between the first and second layers is observed as is the exchange of water molecules. These results help to resolve a number of controversies pertaining to the molecular scale behavior of water on TiO2 and provide insight in to the structure and dynamics of water-solid interfaces by, e. g. , demonstrating that water dynamics can vary on the Angstrom length scale and that the presence of second layer water molecules can cause those in the first layer to reorient.
Classification of Fundus Images Captured using D-Eye Smartphone Retinal Imaging System

Due to the wide range in the accessibility of eye care services, their affordability, and the population’s eye care literacy, cataracts, age-related macular degeneration, glaucoma, diabetic retinopathy, etc., are among the top causes of vision impairment worldwide. To increase the standards of eye examination and to detect the most common vision impairment causes early in a hassle-free manner, real time and sophisticated dataset and efficient classification model is essential. Therefore, this paper emphasizes on the portable smartphone-based fundus image capturing system, D-EYE which acts like a visual digital window. The D-EYE system enables routine eye screening, provides information on visible eye illnesses, and captures images for additional analysis of certain medical situations. With the help of the D-EYE device, an efficient dataset of fundus images of glaucoma is generated and subsequently image enhancement is performed through multiple image pre-processing techniques like image sharpening and masking techniques. The fundus images of glaucoma are cropped to look like high resolution fundus images of the eye using the Super-Resolution Generative Adversarial Network (SRGAN). To categorize fundus images as normal or abnormal, or as glaucoma affected or not, a binary classification model was finally developed. The outcomes show that, with an accuracy of 85%, the classification model has performed at its peak level.
Receiving Power of Oblique Laser Links of Optical Mobile Communication System

Laser communication links can provide large capacity and thus have potential in mobile communications, namely optical mobile communications (OMC). In order to communicate with fast moving nodes, tracking system is developed to realize stable laser communication links. However, traditional Gaussian beam model can not be directly applied in the optical communication system with fast moving target due to the irregular shape of the spot. In this paper, we derive the received power in an OMC system with oblique links. An approximate closed-form expression is derived to simplify the calculation of the receiving power gathered in a circular region.
Semantic-based Dynamic Object Separation Algorithm for Visual SLAM in Dynamic Environment

Environment perception and understanding for mobile robots are of great significance for the performance of visual SLAM-based autonomous navigation. However, traditional front-end technologies of visual SLAM is difficult to guarantee the real-time positioning accuracy of mobile robots in dynamic environment due to the lack of processing mechanism of dynamic objects. To address this problem, this paper proposed a semantic-based dynamic object separation algorithm for visual SLAM in dynamic environment. Firstly, a convolutional neural network is rebuilt for image segmentation to obtain image semantic information. And then an optimization algorithm combining semantic information and geometrical optical flow method is designed to remove the dynamic objects in the image. Finally, the tracking and mapping only based on the pure static part of the image are realized, which improves the stability and reliability of the visual SLAM system in the dynamic environment. The effectiveness of the proposed method is verified in indoor and outdoor environments.