Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhanced Estimation of Significant Wave Height with Dual-Polarization Sentinel-1 SAR Imagery

Sentinel-1 synthetic aperture radar (SAR) is one of the most advanced open-access satellite systems available, benefitting from its capability for earth observation under all-weather conditions. In this study, more than 280 Sentinel-1 SAR images are used to derive significant wave heights (Hs) of the sea surface using a polarization-enhanced methodology. Two study areas are selected: one is located near Hawai’i in a deep water region, and the other is in transitional water off the U.S. west coast, where the U.S. National Oceanic and Atmospheric Administration (NOAA) buoy data are available for validations. The enhanced Hs retrieval methodology utilizes dual-polarization SAR image data with strong non-Bragg radar backscattering, resulting in a better estimate of the cut-off wavelength than from those using single-polarization SAR data. The new method to derive Hs is applied to SAR images from 2017 taken from both deep water (near Hawai’i) and coastal water locations (off the U.S. West coast). The assessments of the retrieved Hs from SAR images suggest that the dual-polarization methodology can reduce the estimated Hs RMSE by 24.6% as compared to a single-polarization approach. Long-term reliability of the SAR image-derived Hs products based on the new methodology is also consolidated by large amount of in-situ buoy observations for both the coastal and deep waters.
Interactive and Scalable Layout Synthesis with Design Templates

The design of virtual and real spaces is a complex task, as is evidenced by the large number of professionals offering their services. Researchers proposed multiple computational methods that aim to alleviate such complexity. Unfortunately, most methods for layout synthesis are not directly applicable to non-professional consumers, because of usability challenges in terms of computation, user input, and scalability. Hence, we propose a novel layout synthesis system based on design templates. Design templates define geometrical rules for creating rooms, according to the room type and furniture function. With such templates, our system allows a customizable user experience, and is computationally fast while remaining scalable. We demonstrate our method with several example layouts, focusing on both small and large rooms.
Performance Evaluation of an Ensemble Method for Diagnosis of Chronic Kidney Disease with Feature Selection Technique

Chronic Kidney Disease (CKD) is a public health issue which is seen as a significant threat to human life due to abnormal functioning of kidney over a period of months or years which, if left untreated, may damage vital organs in the body leading to an increased rate in cardiovascular mortality which may result in sudden death if not early detected. Data mining techniques are employed in several clinical diagnoses for making intelligent diagnostics decisions that can be applied in disease prediction. The performances of these techniques are very promising in the management of different ailments to reduce the high numbers of people that die yearly due to inaccurate diagnosis of numerous disease conditions. This study evaluates the performance of a bagging ensemble technique on CKD dataset with an effective feature selection technique to yield a reliable and accurate predictive model capable of correctly classifying diseased from non-diseased patients. The study was investigated on a real patient dataset obtained from the UCI machine learning repository consisting of 400 instances with 24 conditional attributes and a decisional class. Radom forest algorithm was used as a measure to select the best subset of features for the predictive models. Naive Bayes, k-Nearest Neighbor, and Decision Tree algorithms serve as the base classifiers whose performance were aggregated using the bagging ensemble approach to improve base learners' performances. Results obtained from the study showed the effect of feature selection and ensemble technique in improving the accuracy of data mining classification algorithms. The model's optimal result is achieved using 7 best-selected features on the ensemble classifier with 100% accuracy of CKD diagnosis compared to 98.3% accuracy without feature selection. Hence, making the model suitable for efficient diagnosis of CKD.
Path Planning based on Clothoid for Autonomous Valet Parking

The problem of path planning of the autonomous valet parking can be explained as the planning of a path from the entrance of the parking lot to the parking space. Firstly, by collecting the location information of key nodes in the parking Iot, the topological map of the parking lot is defined, which is composed of parking space information and road topological network. Secondly, the reasonable parking space searching path and parking path are planned, and their curvature discontinuity is smoothed. The parking path adopts the C-shaped method, the path of searching for parking spaces adopts the line-arc-line splicing method, and use the clothoid curve to smooth the path. Finally, Stanley path tracking algorithm is used to follow the planned path based on MATLAB to verify the effectiveness of the path planning algorithm. The simulation results show that the proposed algorithm can realize the seamless splicing between the searching path and the parking path, and the planned path can be tracked better.
Network Traffic Prediction using Quantile Regression with linear, Tree, and Deep Learning Models

Machine Learning research has progressed tremendously in recent years. Major fields that machine learning pushed its frontier were prediction and data modeling. In this work we evaluate the applicability of a handpicked prediction models on predicting inter-day aggregate network traffic. We chose models that work best with multi-variate feature space. They represent linear, decision trees, and neural network models. Over the years, predicting network traffic has resorted to predicting point values. This approach is not descriptive enough and naively gives a shallow conclusion about the data. We propose using a quantile loss function that predicts boundaries or prediction intervals. Our results show that linear models fared well compared to their simplicity while Long Short-Term Memory Neural Networks gave best results across all experiments.
OPTIMIZATION OF LIPASE-CATALYZED SYNTHESIS OF N-trans-FERULOYLTYRAMINE USING RESPONSE SURFACE METHODOLOGY (RSM)

Enzymatic synthesis of N-trans-feruloyltyramine amide was optimized by response surface methodology (RSM) using 4-hydroxy-3-methoxycinnamic acid and tyramine hydrochloride in a one-step lipase catalyzed reaction using Lipozyme TL IM. Response surface methodology (RSM) based on five-level, four-variable central composite rotatable design (CCRD) was used to evaluate the interaction of synthesis, reaction time (24–96 h), temperature (30°–50°C), amount of enzyme (50–500 mg, 12.5–125.0 IUN), and substrate molar ratio (cinnamic acid:tyramine HCl) 1:1–8:1 mmol on the percentage yield of N-trans-feruloyltyramine amide. The optimum conditions derived via RSM were: reaction time 52 h, temperature 43°C, amount of enzyme 260 mg (65.0 IUN), and substrate molar ratio (cinnamic acid:tyramine HCl) 6.2:1. The actual experimental yield was 96.3% under optimum conditions, which compared well to the maximum predicted value of 97.2%.
Enhancing the management of aircraft taxiing phase by allocating efficient and conflict-free routes

Congestion in ground operations is considered to be among the most complicated issues in air traffic management; The thing that represents a real handicap in the way of the huge increase in air traffic flows without corresponding improvement in many airports’ infrastructure. This matter leads to increased workloads for air traffic controllers, stakeholders and other airport operations. Our work has focused on aircrafts’ taxiing time in airports’ maneuvering area with the objective of minimizing the departure taxiing time, which can be affected by several factors such as routing, taxiing speed, stopping and resuming taxiing . etc., by using a decision support system that assigns a non-stop route and predicts taxiing time more accurately in order to minimize fuel consumption and gas emissions without braking between the stands and the runway.
An Improved Performance Variable Speed Wind Turbine Driving a Doubly Fed Induction Generator Using Sliding Mode Strategy

This paper presents the modeling and the control of the Double-Fed Induction Generator (DFIG) for 10 KW DFIG application incorporated into a Wind Energy Conversion System (WECS). The objective is to design and compare two separate strategies for independently controlling the active and the reactive power generated by a DFIG, in order to conclude on their performances. The PI controller and the sliding mode strategy with a maximum power point tracking (MPPT) used during the energy conversion to extract the maximum of the power. The simulations results show significant improvements in the performances of the proposed sliding mode control (SMC) over the Proportional-Integral controller (PIC), in terms of the dynamic response, static error, Set-point tracking, precision, THD of the Stator Current, and robustness against parametrical variations. The performance of the systems was tested and compared with Matlab/Simulink Software.
FoG Computing based IoT in Healthcare Application

In today's technological world the Internet plays a significant role. Internet of Things (IoT) consists of a large number of wireless sensors and wearable sensors known as Things. These sensors are connected devices in a network and generate a vast amount of data. These sensors have limited storage and computing facility for elderly patient applications. Recently, the Internet of Things (IoT) has drawn considerable interest among the research community. Fog computing has several benefits for elderly healthcare applications such as security, efficient load distribution, and low-latency. Fog computing layer provides several advantages as improved doctor-patient relationships, reduction in medical treatment cost, and customized treatment for elderly patients. The main contribution of this article is to present a practical solution for elderly patients by taking advantage of fog computing for IoT based health systems. The system helps attendants and doctors by providing various healthcare vital parameters for preventive and corrective measures promptly.
Friend Recommendation Decision Systems via Multiple Social Network Alignment

Today, almost all internet users have more than one social network account on different social networks for interaction with friends and other users. Gathering data from various networks to combined into a single node can be used for increasing the success rate of recommendation systems. In this study, data related to thousands of users in nine different social networks are used for successful recommendations to the users. The anchor method is used for topological alignment, and the relationship between nodes is taken into account for calculation. Also, the node similarity method is used to increase the success rate. In this method, the number of successful node matching is increased thanks to the feature selection criteria. An original node alignment and node similarity methods are proposed in the study. Because of combine both node alignment and node similarity method, the proposed method is very successful for the friend recommendation.
Tree Species Classification of Forest Stands Using Multisource Remote Sensing Data

The spatial distribution of forest stands is one of the fundamental properties of forests. Timely and accurately obtained stand distribution can help people better understand, manage, and utilize forests. The development of remote sensing technology has made it possible to map the distribution of tree species in a timely and accurate manner. At present, a large amount of remote sensing data have been accumulated, including high-spatial-resolution images, time-series images, light detection and ranging (LiDAR) data, etc. However, these data have not been fully utilized. To accurately identify the tree species of forest stands, various and complementary data need to be synthesized for classification. A curve matching based method called the fusion of spectral image and point data (FSP) algorithm was developed to fuse high-spatial-resolution images, time-series images, and LiDAR data for forest stand classification. In this method, the multispectral Sentinel-2 image and high-spatial-resolution aerial images were first fused. Then, the fused images were segmented to derive forest stands, which are the basic unit for classification. To extract features from forest stands, the gray histogram of each band was extracted from the aerial images. The average reflectance in each stand was calculated and stacked for the time-series images. The profile curve of forest structure was generated from the LiDAR data. Finally, the features of forest stands were compared with training samples using curve matching methods to derive the tree species. The developed method was tested in a forest farm to classify 11 tree species. The average accuracy of the FSP method for ten performances was between 0.900 and 0.913, and the maximum accuracy was 0.945. The experiments demonstrate that the FSP method is more accurate and stable than traditional machine learning classification methods.
A real-time application to detect human voice disorders

This paper proposes an automatic method to differentiate healthy and pathological human voices in real-time to construct more accurate medical decisions and allow patients with disorders to seek early medical assistance. The main problem encountered in voice recognition is to identify the appropriate features and a classifier. To address a solution to this problem, this paper introduced a fast and accurate voice recognition method by approaching the MFCC audio processing features with three machine learning algorithms; Decision Tree (DT), Support Vector Machine (SVM), and Artificial Neural Network (ANN). As a result, ANN classifier performed the highest classification accuracy of 87.5%, whilst DT and SVM performed 62.5% and 50%, respectively.
A Non-intrusive Approach for Driver's Drowsiness Detection

Fatigueness of the driver is considered a major cause that account to a large numbers of death worldwide. Thus it becomes necessary to develop a system that can help reduce such accidents. During drowsy state, significant differences in the facial features of the driver are observed in comparison to the normal state. The system proposed in the paper is focused on detection as well as alarming the driver after recording the physiological state of the driver. We made use of the non-intrusive approach which monitors the subject in real-time, wherein the blinking of eyes as well as the mouth shape (yawn) of the operator are observed, and if the operator's eyes are shut for more than the threshold value, or the operator is yawning, or if both of them are detected at the same instance then the driver's state is concluded for precautions. The proposed system is designed using Python Language, and OpenCV application is used for image processing employing the use of Viola - Jones Algorithm for the detection of facial features.
Design and Analysis of a Closed-Loop Temperature Engineering Control System using MikroC and Proteus

Throughout the years, automation has gotten better and better. Many industries have shifted to automated systems to make various corporate processes much more efficient. On a fundamental level, automation is a control system that varies in implementation. Automation can allow devices to operate by themselves without the need for human intervention. There are many different applications to control systems, but this paper focuses on the design and analysis of a closed-loop control system-based temperature controller. The group will write a script using mikroC PRO, this script will then be programmed to a PIC18F45K22 microcontroller using Proteus, where the circuit will also be designed and simulated. The group will test whether the design is functional and whether it can control the temperature based not only on the user's desired temperature, but also feedback from sensors indicating the ambient room temperature. This paper can serve as a basis for future design revisions with different styles of implementation and application.
Hole-Free Coprime Array for DOA Estimation: Augmented Uniform Co-Array

The coprime array owns a sparse array structure, which can effectively alleviate mutual coupling, while the holes existing in the difference co-array(DCA) greatly reduce the number of uniform degrees of freedom(uDOFs). In this letter, we propose a new coprime array structure called hole-free coprime array (HFCA) by carefully assembling the subarrays, and the resulting HFCA can generate a hole-free DCA. Furthermore, we derive the optimal HFCA that can produce the largest hole-free DCA with a given number of sensors, which contributes to an increase of uDOFs compared with existing coprime array structures. In addition, the simulations are provided to demonstrate the superiority of HFCA in terms of uDOFs, direction of arrival estimation performance, and spatial resolution.
Classification of Moroccan Decorative Patterns Based on Machine Learning Algorithms

Moroccan craftsmanship has been booming in recent years. The decorations mark refined traces of patterns and forms rich in colors. Despite recent studies, the machine still finds problems recognizing patterns and shapes. In this paper, we propose a new method of classifying decor patterns based on the analysis of the texture and color of the images. Texture information describes the cross section of the pattern while color describes the distribution of colors in the image. In the majority of cases, the Moroccan decoration is composed of five basic elements: ceramic, plaster, fabric, glass, and wood. In a first step, we based ourselves on the techniques of extraction of the characteristics of texture and color. The techniques we used for the description of the images are the Gray-Level Co-occurrence Matrix (GLCM) and Local Binary Patterns (LBP) combined with the Color Histogram (CH). In a second step, we proposed a machine learning classification model based on K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM). The results obtained for the classification of set images exceed an accuracy of 86%.
Detection of Strategic Targets of Interest in Satellite Images using YOLO

This paper details about training a convolutional neural network for object detection and classification of a custom generated dataset from google earth satellite images. The objects of interest in satellite images are strategic targets such as nuclear power plants and oil refineries. The deep learning network is YOLO version 3 which has shown significant improvement in detecting smaller objects. YOLO v3 is three times faster than SSD and its AP metric for COCO dataset is on par with SSD. Hence YOLO v3 is a faster detector in comparison to SSD. But AP0.75 is low when compared with RetinaNet due to higher localization error. On the other hand, the simpler network of YOLO, Tiny YOLO v3 takes lesser detection time. The objective of experimentation is to evaluate the performance of YOLO v3 and Tiny YOLO v3 for objects from satellite imagery. Google earth satellite images consumes less time and provides cost effective solution rather than acquiring the overhead images through unmanned aerial vehicles (UAVs) and drones. The objects size varies from 120 pixels to 1250 pixels. The experimental results demonstrate its detection capability, metrics results. GeForce RTX GPU was used for training the network.
Corporate Governance and the Insolvency Risk: Evidence from Bahrain

The purpose of this study is to examine the impact of corporate governance (CG) mechanisms on the likelihood of firms having high, low, or moderate insolvency risk. Due to the trichotomous categorical nature of the dependent variable, the multinomial logistic regression analysis is used to determine the impact of CG mechanisms on the firms' insolvency risk. Board structure and audit committee structure are used as attributes of CG, while the Altman Z-Score is used as a measure of insolvency risk. The results show that CG mechanisms, namely, the board size, audit committee size, and audit committee meetings have a significant impact on the firms' insolvency risk. However, board meetings, board independence, and audit committee independence do not have any significant impact on insolvency risk. The findings of this study could provide awareness to managers on the relationship between the CG of the firm and the insolvency risk, with respect to Bahraini firms, which could assist them in assessing the probability of insolvency. This study could also be useful from the policy perspective for firms to take necessary measures and precautions to avoid financial distress and extend the continuity of the company by constructing long-term strategies to enhance the CG structure.
Contact Space Computation of Two-Finger Gravity Based Caging Grasps Security Measure

Gravity based caging describes robotic grasps where the robot hand forms a basket-shape supporting an object against gravity. Basket grasps allow secure object transport with simple hands that use a small number of fingers. A basket grasp can be described in the grasped object configuration space as a cavity formed by the supporting finger c-obstacles and the grasped object gravitational energy. A puncture point exists from which the object c-space trajectory escapes out of the cavity with minimal increase in its gravitational energy, which defines the basket grasp depth. This letter describes an algorithm that computes the depth of basket grasps formed by two-finger robotic hands in 2-D gravitational environments. In the basket depth problem, contact space parametrizes all single and two-finger contacts on the supported object boundary as well as the object orientation on the supporting fingers. Starting from a candidate basket grasp, the algorithm incrementally constructs and searches a basket graph embedded in the low-dimensional contact space. The graph edges represent physical movements of the object in contact with the supporting fingers and the object’s minimal-height escape path on the graph determines the basket grasp security measure. The algorithm is fully implemented and demonstrated on representative examples ranging from simplified to realistic objects.
Frequent Itemsets Mining Algorithm based On Differential Privacy and FP-Tree

Mining algorithm for frequent patterns is applies in many fields. Itemsets may be contain user sensitive information. And attacker with strong knowledge can steal user privacy information by analyzing mined itemsets. So, in this paper, a novel algorithm is designed to obtain $K$ itemsets whose support are more than the other itemsets and guarantee security of user privacy in itemsets. The novel algorithm firstly applies exponential mechanism to truncate original transaction dataset. And then the algorithm constructs FP-tree for truncated dataset, and utilizes Laplace mechanism to obtain noise support of itemsets. Finally, according to noise support, the algorithm can obtain $K$ itemsets whose support are more than the other itemsets. In the end, two public datasets are applied to evaluate utility of the algorithm. The experimental result show that the utility of the algorithm is better than the other frequent itemsets mining based on differential privacy.