The performance of a photovoltaic panel is affected by its orientation and angular inclination with the horizontal plane. This occurs because these two parameters alter the amount of solar energy received by the surface of the photovoltaic panel. There are also environmental factors that affect energy production, one example is the dust. Dust particles accumulated on the surface of the panel reduce the arrival of light to the solar modules, reducing the amount of generated energy. The cleaning or mitigation of the modules is important and, to optimize these processes, constant monitoring and evaluation must be carried out. In order to increase the efficiency of photovoltaic panels, the use of image processing methods can be considered for the detection of dust. Therefore, the creation of a document that gathers and analyzes the results of different works developed to solve this problem facilitates access to information, allowing a better understanding of what has already been done and how it can be improved. The objective of this article is to review researches that uses image processing techniques to detect dust on solar panels, in order to compile information to assist research in the area and provide inspiration for future studies.

Macular holes are a blinding condition that occur due to overuse of the fovea, in which a hole alters the natural retinal structure. Optical Coherence Tomography (OCT) is a way of mapping and shaping retinal sections without physical contact and has become a powerful tool for diagnosing pathologies. This paper deals with a review of automated segmentation of macular holes in OCT images, detailing its varied possibilities. It may be considered something new, no reviews were made about the topic. The purpose of this review is to show the latest trends, through the approaches in preprocessing and segmentation. Recent studies were used to validate the research, 2011 onwards, from the Science Direct, IEEE, PubMed and Google scholar bases. The objectives, methodology, tools, database, advantages, disadvantages, validation metrics and results of the selected material are analyzed and mentioned. Based on this, techniques and their results are compared. From this, future outlook scenarios of automated segmentation of macular holes in OCT images are mentioned.

The retina is a part of the ocular system responsible for vision. In the central region of the retina is the macula, that enables detailed view. There is a distinct macular disease called Macular Hole (MH). It causes a condition of low vision related to the weakening of the fovea, high myopia, eye trauma and severe exposure to the sun. A surgery depends of the size and shape of the MH. A macular hole can be identified in Optical Coherence Tomography (OCT) images through the top boundaries of the Internal Limiting Membrane (ILM) and the Retinal Pigment Epithelium (RPE). Manual segmentation of OCT images is time consuming whereas automatic segmentation is fast and has a low computational cost, and consequently of interest to specialists. Thus, the main objective of this work is to develop an algorithm that automatically segments the ILM boundary layer and the area of the MH in OCT images. Another objective that was also pursued included the automatic acquisition of MH measurements. The segmentation was performed through a set of techniques involving shortest distance from a point to a curve (Euclidean Distance), Flood Fill and Border Following algorithms. The proposed method reached satisfactory results for all applications made. The automatic segmentation of MH and the extraction of its measures is a significant contribution to aid the medical diagnosis of the macular hole pathology.

Inspections in areas of difficult access or hostile to the human, pattern recognition, surveillance and monitoring, are some of the many applications in with Unmanned Aerial Vehicles (UAV), can be a solution, opening up new perspectives for the use of this technology. The navigation and the position of the UAVs can be made by autonomous method through the computational vision, which is a technology of construction of artificial systems capable of read information from images or any multidimensional data and making decisions. This work presents a review of the use of computer vision systems by UAVs, with a focus on its many applications. The main objective is to analyze the latest technologies used for the development of computer vision in UAVs, through the tools of data search, information storage and, mainly, processing and analysis of data. The researches encompasses a publication of recent works, 2011 onwards, from the Science Direct portal. For each work were analyzed the objectives, methodology and results. Based in this analysis, was made a comparison between the techniques and their challenges. From this, future outlook scenarios of UAVs using computational vision are mentioned.

The idea of ​​working with a robotic manipulator came from the availability of doing a retrofit of the drive and command system for the RD5 robot, a manipulator that is part of the IFCE's Energy Processing Laboratory. The challenges of working with this robot is the fact this manipulator has not been used for some time, needing adjustments such as tightening its joints, redo connections and add potentiometers instead of damaged ones. The objectives of this work are the development of forward kinematics (FK) and the construction of its drive and command system. The expected positions are obtained from FK using the Denavit-Hatenberg (DH) method. The expected positioning of the manipulator in each situation was compared with that obtained through the drive and command system and thus it was possible to perform the acquisition of position error estimation data. The results were then discussed.