image processing

Due to the drastic change in aerodynamic parameters, the problem of controlling the landing mode will be much more complicated than the high altitude flight. On the other hand, the presence of obstacles in the way of the bird's landing is another condition that should be considered in the design of the controller. Thus, according to the above, it is necessary to use the predictive controller to solve the problem. This controller inherently has a high resistance to model change. Also, if this controller is used in a restricted manner, it can be used to bypass obstacles in the path of movement during landing. The aim of this paper is to control the system for automatic landing by means of model-based pre-interlinear control. The reason for using the pre-interlinear controller is the presence of obstacles in the path of movement and the fulfillment of the constraints in the environment to remove the obstacles. As a final result, this controller is designed in such a way that the effect of external disturbances on the bird is minimized and the stability of the system is not jeopardized by the emergence of model uncertainties. Also, in this method, the effect caused by the delay of the external navigation system is taken into account in the closed loop system and the stability of the system is guaranteed. Finally, the proposed controller design is calculated for a real bird model and its performance simulation in the presence of obstacles, lateral and longitudinal wind is presented.

Quadcopter flying robots are powerful and technological devices for flight operations, and many studies have been carried out to control and make them intelligent. But most of these studies have focused on their motion control and less attention has been paid to their application for moving object detection in a dynamic environment such as the street. In this article, we develop a hybrid method based on Viola-Jones algorithm and deep neural networks so that can be intelligently used by quadcopter to identify moving objects in the real environment. In this method, deep neural network training is done using Viola-Jones classifier cascades, by adding objects as video sequences, with a real-world background. By experimentally implementing this Viola-Jones cascade optimized algorithm on a quadcopter robot, we show that in detecting frames containing moving objects received from the camera installed on the robot, it has high performance rate, low false alarm (negative-error, positive-error) and low error rate. Algorithm performance in this test was 89% positive correct diagnosis and 13% error in diagnosis. Also, the accuracy increases when the Gaussian blur filter is used.

In this paper, an alternative approach in operational modal analysis is presented, utilizing image processing technique and transmissibility functions. Imaging sensors do not impose additional mass on the structure due to their non-contact nature, while transmissibility functions, independent of excitation type, can directly extract mode shapes. The innovation of this research lies in combining these two techniques to record dynamic responses and identify modal properties. To capture the temporal response history from video signals, the block-matching method with sub-pixel accuracy was employed. Validation was conducted by recording the response of the tip of a cantilevered steel beam subjected to impact excitation, using a high-speed camera and a laser vibrometer, simultaneously. The RMSE plots in the time domain and the PSD in the frequency domain indicate high accuracy of this method. Using this approach, the displacement time histories of various points on the structure were extracted from the video signals, and the modal properties, including natural frequencies, damping ratios, and mode shapes, were identified using the transmissibility matrix method. The results obtained from the proposed method were compared with the stochastic subspace identification (SSI) method and analytical solutions. The findings reveal the accuracy of the modal identification approach introduced in this article. The highest relative error in estimating the natural frequencies of the first and second modes, compared to the values from the laser method, are 0.19% and 0.13%, respectively, and in comparison to the analytical values, they are 0.34% and 1.5%, respectively.

The analysis of heat transfer in the channel in many types of heat exchangers, such as electric cooling equipment, solar collectors, heat exchanger systems, high-performance boilers, gas turbine blade coolers, etc., is the basis of the design, construction, and optimization. Controlling heat transfer to increase the rate of heat transfer in such systems by improving the cooling method is an effective energy engineering from the point of saving energy. Increasing the heat transfer performance in the scales of macro and microchannels is crucial. The use of expanded surfaces in the channel is a practical method to increase the heat transfer coefficient. In the upcoming article, the smart design of a two-dimensional nanofluid heat exchanger has been studied numerically in order to achieve optimal performance conditions in terms of heat transfer rate, the amount of deposition of nanoparticles in the structure of the exchanger, as well as the fluid pressure drop while passing through it. It can be seen that the geometric structure optimized by the combination of genetic algorithm and computational fluid dynamics of this channel causes an increase of 1.14% in the enthalpy of the passing nanofluid, a decrease of 11.21% in the pressure drop of the passing nanofluid, and a reduction of 8.44% percentage in the deposition of nanoparticles inside the channel and a total increase of 24.82% in the fitting function defined in terms of these three variables, compared to the channel designed in previous studies. Therefore, this optimal channel has a higher heat transfer rate with a pressure drop and a lower amount of nanoparticle deposition compared to the previous channel, which proves the ability of the genetic algorithm with computational fluid dynamics in the optimal design of all types of heat exchangers.

The Charpy impact test is an experimental method for determination of materials dynamic properties at different temperatures to investigate the ductile to brittle transition behavior of tested materials. The percentages of ductile and brittle fractures can be evaluated based on fracture area of Charpy specimen (according to API E23 standard) by visual techniques which do not provide exact percentages of these fractures. In this study, a method is proposed to calculate the exact percentage of ductile fractures using image processing, which makes it possible to quantitatively examine different parts of the fracture surface with high accuracy. All steps of image processing are described for eleven Charpy standard specimens of API X70 steel, tested at temperatures between +20 to -80 °C with a temperature increment of 10 °C. In this research, converting a qualitative image of fracture surface to a quantitative matrix is described for the first time. Prediction of the shape of ductile and brittle parts of the fracture surface at temperatures between +20 and -80 °C is one of the results of this study. The percentages of ductile fractures using image processing for temperatures of +20, 0, -20, -40, -40, -60 and -80 °C were obtained as 100, 100, 86, 53, 36 and 0, respectively. The transition temperature was -45 °C for this steel, corresponding of 50% ductile fracture.

In this paper, a simple and low weight gadget, without the need for a nurse, is designed in a low cost, taking into safety, flexibility and mobility for the user. This paper presents a new system that the controller system is designed and implemented based on the combination of acceleration sensor data and image processing system with the aim of controlling the movement of the smart wheelchair. This design, the user can take control of the smart wheelchair without using hands. This gadget can be a good solution for disabled people to make their life easy.The results of the smart wheelchair control testing with hybrid controller indicate that the accuracy and speed of the wheelchair response high. The experimental tests suggest that the proposed design of controller gadget is efficient and low cost as well as allowing disabled people to more easily control their wheelchairs and to lead independent lives.

Studying vegetation status is one of the most important environmental issues. Using contemporary and field methods in this regard will cost a lot of time and money. Remote sensing technology and satellite image processing is a solution that can facilitate such difficult processes. In this study, remote sensing technology will be integrated with web geographic services to provide users with adequate analysis of the growth of the plants and their changes based on vegetation indices in the shortest possible time. Important technologies used for this purpose are ENVI IDL, Google Map API, JavaScript, and ASP.NET. The service was successfully implemented and was employed for Landsat satellite images. In order to evaluate the performance of the service at different points and with a different number of images, the timeliness of the NDVI vegetation index was prepared. The processing time evaluation also showed that by increasing the number of satellite images the processing time will increase linearly.