Reviewerly/exHarmony huggingface dataset:
Data Mining Management System Optimization using Swarm Intelligence

Because of a phenomenon known as the “curs e of dimensionality,” standard machine learning algorithms have difficulty dealing with high-dimensional data. There are more possible examples in the data space as the number of dimensions increases; however, as the number of dimensions increases, the amount of data that can be accessed decreases. There are a greater number of potential instances in the data space when there are more dimensions. The amount of data required by machine learning algorithms to address problems with such a high dimension increases exponentially with the number of problem-related characteristics. In this paper, we examine the suggested algorithms' methods for selecting features and their relationship to the data representation.
Super Resolution Image Acquisition for Object Detection in the Military Industry

Automatic object detection is important in the military industry. Since these objects are small and camouflaged, that is, they are not clear, it becomes even more important that they appear clear and large. Therefore, in order to facilitate object detection algorithms in the field of the military industry, we present a model that obtains high-resolution and high-dimensional images from low-resolution and low-dimensional images. The presented model is a combination of fast super-resolution convolutional neural networks and the VGG16 model, which is widely used in the literature. Due to the limited data in the field of the military industry, the dataset was collected manually from the internet. Our dataset, which has 900 images in total, has been reproduced with certain data augmentation techniques. For model training, low-dimensional images were obtained from the collected high-dimensional images by the bicubic interpolation method. After model training, a BRISQUE score of 47.81 was obtained.
Time-varying formation tracking with distributed multi-sensor multi-target filtering

Formation tracking is used widely in targets enclosing, monitoring, and striking; however, in practical scenes, the targets are always uncooperative. The problem of time-varying formation tracking for multi-agent with multi-target, which states are unknown is studied in this paper. In order to obtain the accurate state estimations of targets, a distributed multi-sensor multi-target filtering algorithm based on the cubature Kalman filter scheme and multiple heterogeneous sensors is proposed. Then the state estimations obtained by the filtering algorithm are adopted to design a time-varying formation tracking protocol for multi-agent, enabling multi-agent to form a time-varying formation to track the convex combination of targets. Finally, the effectiveness of this proposed algorithm is illustrated by numerical example.
Portioning Algorithm Using the Bisection Method for Slicing Food

Food is an essential part of human life and plays a crucial role in maintaining good health and well-being. In various industries, such as food processing and packaging, it is essential to ensure that raw materials are divided equally to optimize the production process and reduce waste. However, traditional methods of food processing and packaging can be time-consuming and prone to errors. Hence, we are interested in developing a method for accurately portion materials into equal sizes using the Intel RealSense D435i 3D camera to capture point cloud images of object, which are then processed using Python code, running on a Raspberry Pi 4, to generate cutting planes. In the experiment on object size variations, three sizes of plasticine weighing 50 g, 150 g, and 250 g. resulting in errors of 10.2%, 8.8%, and 7.3%, respectively. In the experiment on the number of cutting plane variations, keeping the object weight fixed at 150 g at 150 g, and divided into 2, 3, 4, and 5 pieces. The resulting errors were 1.3%, 8.8%, 10.7%, and 18.2%, respectively, according to the number of pieces. Our algorithm can generate precise cutting planes to partition the volume of an object. The primary cause of errors is the shape resolution of the object's point cloud that the camera can collect and the use of human hands for cutting the object.
Safety Procedure Using Path Planning Methods for Tilt-Wing Unmanned Aerial Vehicles

This paper presents a safety procedure for tilt-wing unmanned aerial vehicles (UAV) considering flight dynamic constraints, geofences, and static obstacles. The proposed safety procedure ensures that the UAV remains in a safe flight state or is transferred into such a state, if the initial flight path cannot be flown as planned. This is accomplished by planning an emergency flight path to one of the predefined safety points periodically during normal operation using existing path planning algorithms. This emergency flight path is planned even before an error has occurred and is automatically activated in case of errors due to external influences, system failure, or defects. Once the safety point is reached, the flight system can remain in hover state or enter a transition to landing. Within this paper, the procedure is implemented for a tilt-wing UAV and offers a safety feature that is executed at regular intervals during flight.
Spectral Vector-valued Image Restoration using a Hyperbolic Partial Differential Equation-based Filter

Aneffective denoising and restoration framework that removes successfully theadditive white Gaussian noise (AWGN) from the spectral images is introduced inthis paper. Since the multispectral images can be described as vector-valuedfunctions, the proposed technique uses a novel partial differential equation(PDE)-based model created for this type of image functions. So, we consider anonlinear vector-valued hyperbolic PDE filtering model for the restorationtask. It is equivalent to a system of second-order hyperbolic equationsevolving simultaneously and sharing some coupling terms that model theinter-channel correlation. A finite difference-based fast-convergingdiscretization algorithm which solves numerically the PDE-based model ispresented next. It has been applied successfully in the MSI restorationexperiments that are also described here.
Mixed Reality Human-Robot Interface to Generate and Visualize 6DoF Trajectories: Application to Omnidirectional Aerial Vehicles

Omnidirectional aerial vehicles are an attractive tool for automated inspection tasks. Planning suitable trajectories in industrial environments is not trivial though and often requires human input. Existing trajectory planning tools generally rely on prior and accurate models of both the environment and the vehicle. Furthermore, their common 2D visualization for human operators is generally unsuitable for intuitive understanding of motions in SE(3). In this work, we exploit Mixed Reality to improve and simplify mission planning, by allowing the user to generate and perceive a trajectory directly in the real environment. The operator can precisely and intuitively plan a dynamically-feasible 6DoF trajectory by adding and modifying waypoints. Each waypoint is visualized as a holographic representation of the physical robot including the camera frustum for visual inspection tasks. Dynamic and static holograms corresponding to spatial and temporal information of the resulting trajectory are also overlaid onto the real world, allowing an operator to quickly assess potential collisions and inspection coverage. We experimentally demonstrate the effectiveness of the developed application and indicate its efficiency based on related work. The encouraging results motivate future quantitative evaluations in the form of user studies.
SpaccX's Mission to Mars: Leveraging Service-Oriented Architecture for a Successful Journey

SpaceX, founded by entrepreneur Elon Musk, has set an ambitious goal of colonizing Mars and making humanity an interplanetary species. To achieve this monumental task, SpaceX requires a robust and scalable architecture that can support the complex and diverse needs of a mission to Mars. In this paper, we propose the use of Service-Oriented Architecture (SOA) as a framework for designing and implementing the technological infrastructure for SpaceX's mission to Mars. SOA is a software architectural approach that promotes loose coupling, scalability, and reusability of software services, making it well-suited for the challenges of a Mars mission. This paper outlines the key concepts of SOA and demonstrates how SpaceX can leverage SOA principles and best practices to ensure a successful journey to Mars.
Wind-Aware Path Optimization for an Aerobot in the Atmosphere of Venus Using Genetic Algorithms

This paper presents a path optimization solution for an autonomous aerial robot (aerobot) in the windy atmosphere of Venus. The aircraft is required to travel from its current position to a goal position by following minimum energy paths. The approach proposed in this paper uses genetic algorithms, a heuristic search that, based on a population of initially feasible paths and a set of biologically inspired operations, finds a low-cost path. The proposed cost function accounts for energy expenditure, such as thrust or drag, and also energy accumulation, such as charging with solar panels and gains from potential energy (e.g., due to upward directional winds). Path feasibility is assured by computing local reachability regions based on the wind velocity and the maximum speed of the aerobot. The method is illustrated through a series of simulations that show our results as a function of the number of iterations and path population sizes. A comparison with a previous algorithm is also made.
Software architecture for controlling in real time aerial prototypes

Nowadays, there exists several platforms or experimental prototypes of aerial vehicles to control algorithm validation, most of them being specific for certain applications. The implementation of the control laws in commercial platforms is often restricted to certain criteria and pre-established conditions defined by the commercial system (or the builder). In this paper, a new generic software architecture operating under Linux, MATLAB®, ROS and Ardupilot is introduced for analyzing, evaluating and improving control algorithms for aerial robotics. The tedious programming code is not necessary because the code is generated by MATLAB Simulink®. The proposed architecture is composed of a ground station (GS) and a robot with an embedded system. This platform is validated with a new aerial prototype with tilting four rotors. Experimental results illustrate the good performance of the software architecture even when different maneuvers are demanded to the aerial prototype.
A Load Compensation Controller for Off-the-shelf Unmanned Aerial Vehicles

This paper addresses the problem of controlling a system composed of a unmanned aerial vehicle and a payload attached to it via a cable, while compensating for the payload effects on the drone. This work considers that many off-the-shelf UAVs have their own low-level controller which the user cannot access, and attempts to merge the classical approach of modeling a drone-mass system with controllers previously developed for use with drones equipped with low-level controllers. In order to test such an approach the robot Anafi, from Parrot Drones SAS, was used with a mass attached to it. The experiments show a consistent reduction on navigation errors, which helps validate the proposed method.
Toward improving tracking precision in motion capture systems

Motion capture systems are a must-have part of a modern research laboratory. The system has a wide area of applications such as movie, sport, medicine or experiments validations. The system provides accurate pose (both position and orientation) data and allows tracking several objects at the same time. The calibration process is a crucial part of using the system, which determines the further accuracy of tracking. This paper presents research struggling with this problem and describes a new approach to the camera placement process for the OptiTrack system, using the dedicated application programming interface and its features. The document discusses the general concept, shows the benefits of using it and confirms its usefulness. It helps to improve camera placement to ensure detection and reduction or elimination of "low tracking zones". The working tool is also presented.
UAV Path Planning Employing MPC-Reinforcement Learning Method Considering Collision Avoidance

In this paper, we tackle the problem of Unmanned Aerial (UAV) path planning in complex and uncertain environments by designing a Model Predictive Control (MPC), based on a Long-Short-Term Memory (LSTM) network integrated into the Deep Deterministic Policy Gradient algorithm. In the proposed solution, LSTM-MPC operates as a deterministic policy within the DDPG network, and it leverages a predicting pool to store predicted future states and actions for improved robustness and efficiency. The use of the predicting pool also enables the initialization of the critic network, leading to improved convergence speed and reduced failure rate compared to traditional reinforcement learning and deep reinforcement learning methods. The effectiveness of the proposed solution is evaluated by numerical simulations.
Quadcopter Capable of Autonomously Chasing Micro-Aircraft with Real-Time Visual Tracker

It is difficult for unmanned aerial vehicles to chase another micro-aircraft (MA) due to the small size and its fast maneuverability. Thus, this study developed a fast and accurate visual tracker for real-time inference. Then, a quadcopter was controlled to chase a target MA by considering the result of the visual tracker. Specifically, the pitch, throttle, and yaw of the quadcopter were determined by the PD controller based on the position, and the size of the MA in the image. The newly developed visual tracker comprises an adaptive search region (SR) and a fully convolutional neural network. The size and the location of the SR were constantly adjusted over image frames by considering the tracking result of the MA in previous frames. Furthermore, if the size and the location of the SR are not precise enough, the SR was updated to minimize the tracking failure. Performance of the SR was improved by using the Kalman filter. In real flight experiments, the quadcopter equipped with the proposed model successfully chased the MA which randomly moved at approximately 5 m/s.
Literature Review On Metaheuristics Techniques In The Health Care Industry

In recent times, machine learning has provided increasingly satisfying results in the field of medicine, providing results with very high accuracy while helping to reduce costs and diagnose the disease in real time. To achieve this, it is necessary to develop different deep machine learning techniques. Some of these are metaheuristic techniques that offer practical solutions for different types of chronic diseases. These types of algorithms have received the most attention in solving optimization problems. Therefore, this paper presents a wide review of the literature for solving the problems of feature selection using metaheuristic algorithms and selecting those that have had the highest performance compared to the results given by other algorithms. In this paper, a study of 71 articles from a research database was carried out, from which metaheuristic algorithms were analyzed and evidenced on the optimization and selection of features for the prediction of chronic diseases using numerical, binary, or even imaging data. The efficiency of the algorithms is measured based on the accuracy results, error rate, F-means, or other parameters or graphical representations found in this study. This work will help researchers to improve any of the methods, hybridize them, or even build applications for predicting diseases in the future. Gaps in this field have also been identified, and future studies should be conducted.
UAV Embedded Real-Time Object Detection by a DCNN Model Trained on Synthetic Dataset

The utilization of unmanned aerial vehicles (UAVs) in civilian and military applications has significantly increased in recent years. A common task associated with these applications is detecting objects of interest in images captured by onboard UAV cameras. The ongoing development of advanced deep convolutional neural network (DCNN) algorithms has substantially improved the accuracy of general image segmentation and classification. However, applying these techniques to images obtained from UAVs requires a representative dataset for enhanced performance. This paper presents a method for DCNN-based object detection, utilizing resources embedded in a 1.5kg quadrotor-type UAV. To address the lack of representative datasets for our target scope, we employed a DCNN model trained on a self-generated synthetic dataset. The proposed method has been validated through real experiments, and the results demonstrate this approach’s feasibility for real-time surveillance with fully onboard processing. Furthermore, this offers a stand-alone, portable, and cost-effective solution for surveillance tasks using a small UAV.
Drug Target Interaction Prediction Using Convolutional Neural Network (CNN)

The goal of this research is to construct an artificial intelligence-based machine learning and deep learning system for predicting drug-target interactions. In this study, we categorize drug-target interactions across various drug combinations using a convolutional neural network (CNN) model. Deep learning-based CNN achieves state-of-the-art performance on the DDI-Corpus dataset, with an F1-score of 0.82 0.012 for the single model and 0.81 0.015 for the ensemble model, and a validation accuracy of 96.72 %, Our findings are intriguing, but it's important to remember that various types of scientific journals are essentially identical. When clinical reports and empirical data are compared, the discrepancies become increasingly evident. Furthermore, each category contains numerous outliers. Additionally, unusual pharmacological routes or DDIs are feasible. The inner workings of such a temporal system are beyond the scope of the current qualitative investigation but will be the focus of a future qualitative investigation. Future research aims to identify informational gaps in the existing literature that may lead to the discovery of concealed discovery indicators (DDIs).
Comparison of Otsu's and Rosin's Methods for Threshold Determination

Real time edge detection of objects in images implemented in FPGA circuits is often based on gradient calculation using appropriate convolutional masks. The resulting edge map is obtained by thresholding the gradient image. The key issue is determining the threshold value. In practice, Otsu's and Rosin's methods are most often used to determine the threshold value. Using images from three databases, we compared these two methods and the results are presented in our paper.
New Method of Hash Functions Analysis

This paper will illustrate a new class of analysis off hash functions. The method will be demonstrated on the algorithm SHA-256. The idea behind the attack is to represent the algorithm as Boolean equations and solve them using tree notation. The new attack will help to speed up the process of finding vulnerabilities in hash functions, which may help to create more secure hash functions in the future. Despite the fact that the article will describe the approbation of the method only on SHA-256, these results can also be extrapolated to other hashing algorithms, since the idea, presented in this method, does not depend on an algorithm specification.
Autonomous Navigation and Control of a Quadrotor Using Deep Reinforcement Learning

A deep reinforcement learning-based control framework has been proposed in this paper to achieve autonomous navigation and control of a quadrotor. Cascaded reinforcement learning agents form the control framework. First, a path following (PF) agent controls the quadrotor’s tracking behavior by directly mapping environment states into motor commands. The second agent modifies the desired path to avoid any detected obstacles along the path. The obstacle avoidance (OA) agent achieves this task by adding an offset distance deflection to the tracking error before sending it to the path-following agent. Generalization of the obstacle avoidance behavior in three-dimensional space was achieved by the usage of frame transformation. The two agents were trained using the "Twin Delayed Deep Deterministic Policy Gradient" (TD3) algorithm, and the developed framework succeeded in avoiding multiple obstacles of different sizes and configurations in simulation.
A Modified Artificial Potential Field for UAV Collision Avoidance

As UAV applications in the civilian airspace increases, securely operating them in congested environment becomes more challenging. A Cauchy Artificial Potential Field (CAPF) method is presented in this research to make UAV navigation practical and secure in a cluttered dynamic environment. The CAPF approach enables the UAVs to avoid collision with obstacles that could either be static or dynamic (Another UAV) commanding mostly non-aggressive maneuvers. The approach presented in the research has been verified through simulations and testing. We compare the results of CAPF with MAPF and the proposed approach has shown improvement in terms of total acceleration and in distance traveled by vehicles while providing safer margins at higher speeds.
Uncertainty Aware Deep Learning Model for Secure and Trustworthy Channel Estimation in 5G Networks

With the rise of intelligent applications, such as self-driving cars and augmented reality, the security and reliability of wireless communication systems have become increasingly crucial. One of the most critical components of ensuring a high-quality experience is channel estimation, which is fundamental for efficient transmission and interference management in wire-less networks. However, using deep neural networks (DNNs) in channel estimation raises security and trust concerns due to their complexity and the need for more transparency in decision-making. This paper proposes a Monte Carlo Dropout (MCDO)-based approach for secure and trustworthy channel estimation in 5G networks. Our approach combines the advantages of traditional and deep learning techniques by incorporating conventional pilot-based channel estimation as a prior in the deep learning model. Additionally, we use MCDO to obtain uncertainty-aware predictions, enhancing the model's security and trustworthiness. Our experiments demonstrate that our proposed approach outper-forms traditional and deep learning-based approaches regarding security, trustworthiness, and performance in SG scenarios.