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opencc-by-4.0Jun 2011View details →

Figure 3. Photosynthetic activity in different wavelengths of light radiation. 5.-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System

The greenhouse protects the plants from the extreme weather conditions. However, if the period of daylight prevents the photosynthetic activity, the plants do not grow. Horticultural lighting allows the grower to extend the growing season. It enables a year-round producing of plants or makes it possible for the grower to start sowing in early spring and continue season till the first frost. Plants need about 10-12 hours light to improve growth. When the plants are producing flowers or fruits the supplemental need of light per day increases up to 16 hours. Figure 3 shows the photosynthetic activity in different wavelengths of light radiation [16].

Figure 9. Trajectory Algorithm Simulation-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

We have presented the system for a fully autonomous navigation of an UAV based on Omni directional vision system and image processing. we explain vision system configuration ,image processing and feature extraction methods and finaly suggest an algorithm based on potential field for navigation of an UAV.

Figure 8. Potential at every point; it is highest in the obstacles and lowest at the goal-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

The numerical potential field path planner is guaranteed to produce a path even if the start or goal is placed in an obstacle. If there is no possible way to get from the start to the goal without passing through an obstacle then the path planner will generate a path through the obstacle, although if there is any alternative then the path will do that instead. For this reason, it is important to make sure that there is some possible path, although there are ways around this restriction such as returning an error if the potential at the start point is too high. The path is found by moving to the neighboring square with the lowest potential, starting at any point in the space and stopping when the goal is reached.

Figure 7. Obstacle force (repulsive potential) and goal force obstacle force-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

Figure 6. Goal force-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

Since the motion trajectory of UAV is divided into several median points that the UAV should reach them one by one in a sequence the output obtained after the execution of AI will be a set of position and velocity vectors. So the task of the trajectory will be to guide the UAV through the obstacles to reach the destination. The routine used for this purpose is the potential field method (also an alternative new method is in progress which models the UAV motion through opponents same as the owing of a bulk of water through obstacles) [5]. In this method, different electrical charges are assigned to UAV, obstacles, and the destination. Then by calculating the potential field of this system of charges a path will be suggested for the UAV. At a higher level, predictions can be used to anticipate the position of the obstacles and make better decisions in order to reach the desired vector. In our path- planning algorithm, an articial potential field is set up in the space; that is, each point in the space is assigned a scalar value. The value at the goal point is set to be 0 and the value of the potential at all other points is positive.

Figure 5. Images binarized by SVM.-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

In instance, the image in Fig. 4 was binarized manually (however, the person in charge of color adjustment is professional) and by using SVM resulting in Fig.5

Figure 4. Images binarized by hand.-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

In instance, the image in Fig. 4 was binarized manually (however, the person in charge of color adjustment is professional) and by using SVM resulting in Fig.5

Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System-Figure 3. The principle of SVM

Manual segmentation training was hard and very time consuming and rely to operator's accuracy, so we developed a color calibration algorithm using SVM(Support Vector Machine). In this subsection, we present a color recognition algorithm using the support vector machine (SVM).SVM is one of the classification algorithms which it has high generality since it can calculate a super plane that maximizes the margin of classes, Fig.3.[8] In our algorithm, the SVM is trained by the H'SY values of the classes and the mean of the obtained image H'SY values. After training, the obtained image is binarized by setting the maximum and minimum value in the distribution of each class as a threshold.

Figure 2. Catadioptric projection modelled by the unit sphere-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

Since the beginning of UAV, the map building was one of the most addressed problems by researchers. Several researchers used Omni directional vision for robot navigation and map building. Because of the wide field of view in Omni directional sensors, the robot does not need to look around using moving parts (cameras or mirrors) or turning the moving parts. The global view offered by Omni directional vision is especially suitable for highly dynamic environments. The Omni directional vision system consists of a hyperbolic mirror, a USB color digital camera (Logitech C905) and a regulation device.

Figure 1. The use of visual servo control for helicopter stabilization-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

Visual servoing is an approach to control motion of a helicopter using information feedback from a camera mounted on it. For their tremendous potential applications in various areas including environmental monitoring and anti-terrorism, unmanned small helicopters are being extensively studied in robotics and control in recent years. However, the research advance in dynamic control of small helicopters is limited due to highly coupled non- linear dynamics and the existence of various uncertain- ties. Many people studied controller design based on a Publisher Item Identifier. linearized or simplified model, but the controllers developed under linearized models cannot guarantee dynamic stability rigorously. Another effort is application of modern non-linear control theory to helicopter control because small helicopter are good test beds for sophisticated control techniques for their small size and highly coupled dynamics [4].

opencc-zeroJun 2024View details →

Dataset: Cadence Design Systems, Inc. (CDNS) Stock Performance

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

Figure 2 in Design and optimization of an experimental maintenance system for yellow clam broodstock Amarilladesma mactroides (Reeve, 1854)

Figure 2. Yellow clams (Amarilladesma mactroides) numbered with a black permanent ink and a red nail polish covered with a bicomponent adhesive based on epoxy resin (Araldite) on the day that it was first marked (a and b) and after 10 days buried in the experimental maintenance system (c).

Figure 1. Experimental unit model built with a 150 in Design and optimization of an experimental maintenance system for yellow clam broodstock Amarilladesma mactroides (Reeve, 1854)

Figure 1. Experimental unit model built with a 150 mm PVC pipe and coupling, used in the maintenance of yellow clam (Amarilladesma mactroides) broodstock in the laboratory.

Figure 4 in Design and optimization of an experimental maintenance system for yellow clam broodstock Amarilladesma mactroides (Reeve, 1854)

Figure 4. Histological sections of the tissues of yellow clam (Amarilladesma mactroides) sampled at the beginning and at the end of trial II (a, b) and trial III (c, d): (a) gonad of female clam with residual and immature oocytes in stage 6 (totally spawned) at the beginning of trial II; and (b) gonad of female clam in stage 5 (partially spawned) at the end of trial II, where it is possible to distinguish immature peduncular oocytes attached to the wall (IO), some free mature polygonal oocytes (MPO) and residual oocytes (RO); (c) gonad of female clam in stage 3 with immature (IO) and mature polygonal oocytes (MPO); (d) gonad of female clam stage 4, where several mature polygonal oocytes (MPO) may be observed.

Figure 3 in Design and optimization of an experimental maintenance system for yellow clam broodstock Amarilladesma mactroides (Reeve, 1854)

Figure 3. Mean daily consumption of microalgae (mixture of Isochrysis galbana and Chaetoceros muelleri on trial I or only C. muelleri on trial II and III) estimated for each individual (cells.mL−1. clam−1) of yellow clam (Amarilladesma mactroides) in trials I, II and III.

opencc-by-4.0Jun 2012View details →

Figure 2. Some screenshots from the software system-Design and Development of a Software System for Swarm Intelligence Based Research Studies

All of the mentioned operations can be performed easily by using the provided controls over the related interfaces – windows of each algorithm. It is also important that each algorithm interface is supported by visual controls to view obtained results with typical iteration-based graphics or problem oriented visual elements. For instance, resulting graph structures are automatically shown by the algorithm interfaces after solving some specific, popular problems like Travelling Salesman Problem (TSP), Vehicle Routing Problem (VCP)…etc. Visually improved using features and functions of the software system are critical aspects to provide more effective and useful platform to perform SI based research studies better. Related to the designed and developed software system, some screenshots from the software system [interfaces of two algorithms (IWDs and ABC)] are represented in Fig. 2.

opencc-by-4.0Jan 2016View details →

Figure 1. The Statechart for the Player movement and the Navigation System-Modeling, Designing, and Implementing an Avatar-based Interactive Map

The next section describes the Unified Modelling Language (UML) diagrams designed for the project, which are a state diagrams (also known as statecharts) for the Player movement, the Navigation system (Figure 1). In addition, we used a class diagram for the Player and Camera movement (Figure 2). When the avatar-based game starts, the state of the Player is Idle, i.e., Player_IDLE. When the user selects the building, it enables the navigation path towards the destination. If the user selects any arrow keys (Right, Left & Up) the state of the player will change to running (i.e., Player_Running). Also, the path will diminish along with the player movement; hence, the state of navigation path will change to Changing_Path.

opencc-by-4.0Jul 2017View details →

BRAIN Journal-Cursor Movement – a Valuable Indicator in Intelligent System Design-Figure 5. Emotional flow after 3 hours of graphical editing

To resume, we started with a product interface, found a way to determine two opposite states, than used that way to map user interaction with the product and determine the emotional answer to that interface. The results can then be used to improve product design, to elicit certain emotional responses, etc. For example, in this case, due to the rapid movement (anger) patterns in the aligning phase, a layout that minimizes this can be developed, using a shortcut menu or dynamically appearing guidelines. Furthermore, the user interface can be imagined to be able to learn working patterns and shift the shortcut menu from an aligning menu, if it detects anger patterns, into a color/shape picking menu, if it detects relaxation patterns, therefore assessing the emotional impact and improving the design of the interface in the same time.

opencc-by-4.0Jul 2017View details →

BRAIN Journal-Cursor Movement – a Valuable Indicator in Intelligent System Design-Figure 2. Detailed view of the individual response

The application that measures the way the user interacts with the PC mouse simulates a simple strategy game. The goal is to choose between different objects in random pattern a particular one. The learning curve of the game is designed to be very fast in order for the subject to quickly understand all the principles and thus the results collected during the test to not be influenced by accommodation to the mechanics. Each 77 individual response were evaluated using the mouse acceleration (reaction) and the number of mouse clicks as indicators for one’s emotional state. Simply plotting acceleration over time provides little information; reaction seems to be very chaotic, and somewhat similar between the two states (Figure 2).