This paper presents a MATLAB-based application to teach the guidance, navigation, and control concepts of quadrotor undergraduate students, using graphical user interface (GUI) 3-D animations. The Simulink model is controlled by proportional integral derivative controller linear quadratic regulator controller. GUI layout's many components can be easily programmed perform various experiments considering simulation as plant; it incorporates systems (CS) fundamentals such time domain response,...

In this study, the authors propose a real‐time multiple licence plate (LP) detection algorithm for dense traffic conditions which is of vital importance in modern era due to increased congestion. The chromatic component YDbDr colour space proposed detect blue regions, whereas simple yet effective method used identify yellow LP regions. low‐intensity pixel values are eliminated as pre‐processing step enhance regions and Otsu obtain binary image. candidate acquired by using connected analysis....

Wetlands are essential ecosystems providing critical ecological services, yet they face significant threats from human activities and climate change. Monitoring mapping these areas accurately is fundamental to formulating effective conservation restoration strategies. Remote sensing, combined with advanced deep learning techniques, offers a scalable efficient solution for wetland classification monitoring. However, the application of technologies often constrained by regional variations in...

This study explores the impact of climatic variability on generalization capabilities a deep learning model for pixel-level crop classification using multi-temporal Sentinel-1 SAR data in Denmark. With agriculture accounting 61% Denmark’s land area, accurate and timely mapping is essential providing insights into distribution, offering valuable information to advisors authorities support large-scale agricultural management, address challenges posed by changing conditions.Our...

This study presents a novel deep-learning approach for estimating Soil Water Content (SWC) with high spatial resolution across multiple soil depths. Additionally, the identifies critical field points based on their drying-out times analyzed by SWC estimations over extended periods. Understanding potential regarding allows operators of heavy agricultural equipment to gain insight into field's traits and prevent excessive compaction. this information can support more strategic efficient...

Drone technology is being used for military, agriculture, aerial photography, surveillance, remote sensing and many more purposes. In this paper, drone plane proposed monitoring targeting the street crime criminals based on real time image processing techniques. Operations of controlled with two units, 1st unit implementation techniques 2nd will handle rest controlling, operations. monitor circular area 5 kilometers it automatically perform all operations can be by operator. Shape detection...

Inversion of large-scale time-domain transient electromagnetic (TEM) surveys is computationally expensive and time-consuming. The calculation partial derivatives for the Jacobian matrix by far most intensive task, as this requires a significant number forward responses. We propose to accelerate inversion process predicting using an artificial neural network. Network training data resistivity models broad range geological settings are generated computing symmetric differences between two...

Airborne time-domain electromagnetic surveys produce extremely large data sets with thousands of line kilometers and millions possible models to explain the data. Inversion such obtain resistivity structures subsurface is computationally intensive involves calculation a significant number forward derivative responses for solving least-squares inverse problem. The flight altitude airborne system needs be included in modeling, which adds further complexity. We propose integrate neural networks...

Abstract. Deep learning (DL) algorithms have shown incredible potential in many applications. The success of these data-hungry methods is largely associated with the availability large-scale datasets, as millions observations are often required to achieve acceptable performance levels. Recently, there has been an increased interest applying deep geophysical applications where electromagnetic used map subsurface geology by observing variations electrical resistivity materials. To date, no...

Feature extraction techniques are extensively being used in satellite imagery and getting impressive attention for remote sensing applications. The state-of-the-art feature methods appropriate according to the categories structures of objects be detected. Based on distinctive computations each method, different types images selected evaluate performance methods, such as binary robust invariant scalable keypoints (BRISK), scale-invariant transform, speeded-up features (SURF), from accelerated...

Geophysical modelling and data inversion are important tools for interpreting the physical properties of Earth's subsurface. Solving inverse problem involves several computational steps is generally a time consuming task. Artificial neural networks have potential to speed up large computations. Such provide means model relationship between inputs outputs without needing know underlying problem. There two main aspects that affect performance networks: optimization network architecture...

Modern transient electromagnetic (TEM) surveys, either ground-based or airborne, may yield thousands of line kilometers data. Parts these data, especially in areas with dense infrastructure, are often disturbed by couplings due to e.g., power cables and fences. In most cases particular when working a hydro-geological context, such coupled data must be culled before inversion. The process identifying culling is manual task, requiring specialists examine the detail. Manual processing...

Abstract. Deep learning algorithms have shown incredible potential in many applications. The success of these data-hungry methods is largely associated with the availability large-scale data sets, as millions observations are often required to achieve acceptable performance levels. Recently, there has been an increased interest applying deep geophysical applications where electromagnetic used map subsurface geology by observing variations electrical resistivity materials. To date, no...

A complex domain‐based approach of active contour model has been proposed for image segmentation which deforms iteratively to partition an into various useful regions. new region‐based pressure force function designed gives shape the dynamic using forces, and efficiently control propagation moving interface. This makes level set binary, uses Gaussian smoothing kernel regulate avoid re‐initialisation procedure. The working scheme is as: real data converted by iota ( i ) times average...

License plate recognition system (LPR) plays a vital role in intelligent transport systems to build up smart environments. Numerous country specific methods have been proposed successfully for an LPR system, but there is need find generalized solution that independent of license layout. The architecture comprised two important stages: (i) character segmentation (LPCS) and (ii) (LPCR). A foreground polarity detection model by using Red-Green-Blue (RGB) channel-based color map order segment...

Region proposals are very important for several perfections of remote sensing applications such as vehicle detection, traffic surveillance and intelligent transport system. In this paper, an efficient region proposal approach has been proposed. The framework is organized into two key steps. first step based on extracting using Cascade second the classification extracted which performed by transfer learning Convolutional Neural Networks (CNN) AlexNet architecture utilized learning. aim...

In order to achieve superior performance, various projection space pairs (PSPs) in neighbor embedding (NE) algorithms are introduced aiming satisfy manifold assumption better. However, the comparison of theses PSPs has not been given much importance previous researches, which could be a guiding factor for choosing better before executing whole process. Besides, evaluation criterions final results like Peak Signal Noise Ratio (PSNR) cannot represent exact performance non-linear due back To...

The most common noise in geophysical data is probably the interference from powerlines. This manifests itself as a sinusoidal signal oscillating at fundamental 50 Hz or 60 frequency of power grid and harmonic components integer multiples. Many different mitigation strategies, tailored for specific method, have been developed to target powerline noise. One method that applies fully sampled model-based subtraction, where model fitted noisy set subsequently subtracted. In cases, this leads...

In-Network processing has been proven as one of the most energy efficient query paradigm for wireless sensor networks, where is done inside network close to source data generation. This paper studies techniques used manage and process queries in networks using The have divided into two sub categories Aggregation based Approximation techniques.