Distributed computing thesis

Distributed computing thesis

UAV Based Distributed Automatic Target Detection Algorithm under Realistic Simulated Environmental Effects Shanshan Gong A Thesis submitted to the College of Engineering and Mineral Resources at West Virginia University in partial fulfillment of the requirements for the degree of Master of Science in Electrical Engineering Natalia A. Schmid, blogger.com, Chair Matthew Distributed Computing A distributed system is de ned as a collection of individual processing elements that commu-nicate through a network. The concept of using a distributed system to solve computational problems is known as distributed computing. In distributed computing, a problem is rst broken up into smaller tasks Distributed computing is a field of computer science that studies distributed systems. A distributed system is a system whose components are located on different networked computers, which communicate and coordinate their actions by passing messages to one another from any system. The components interact with one another in order to achieve a common goal

How Distributed Systems are used today sample essay | MyPapersOwl

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Download Free PDF. UAV based distributed automatic target detection algorithm under realistic simulated environmental effects.

Natalia Schmid. Download PDF Download Full PDF Package This paper. A short summary of this paper. UAV Based Distributed Automatic Target Detection Algorithm under Realistic Simulated Environmental Effects Shanshan Gong A Thesis submitted to the College of Engineering and Mineral Resources at West Virginia University in partial fulfillment of the requirements for the degree of Master of Science in Electrical Engineering Natalia A.

Schmid, D. Valenti, Ph. Xin Li, Ph. There is an increasing trend towards fielding swarms of UAVs operating as large-scale sensor networks in the air[1]. Such systems tend to be used primarily for the purpose of acquiring sen- sory data with the goal of automatic detection, identification, and tracking objects of interest. Furthermore, swarmed UAV systems must operate under severe constraints on environmental conditions and sensor limitations. In this work, distributed computing thesis, we investigate the effects of environmental conditions on target detection performance in a UAV network, distributed computing thesis.

We assume that each UAV is equipped with an optical camera, and use a realistic computer simulation to gener- ate synthetic images.

The automatic target detector is a cascade of classifiers based on Haar-like features. In order to improve automatic target detection ATD per- formance in a swarmed UAV system, we propose and design several fusion techniques both at the image and score level and analyze both the case of a single observation and the case of multiple observations of the same target, distributed computing thesis.

Acknowledgements First, I would like to thank Dr. Natalia Schmid for being such a patient and un- derstanding thesis advisor. Her foresight, intuition, distributed computing thesis, and care were instrumental in shaping this work. I have learned so much from her since I joined the Statistical Signal Processing Lab at West Virginia University. I also would like to thank my graduate committee members Dr.

Xin Li and Dr, distributed computing thesis. Matthew Valenti for their expert advice and support to my study and thesis. I must thank Xiaohan for her seemingly infinite supply of ideas distributed computing thesis support for this work.

I also thank Jinyu, Nathan and Francesco for their support and discussion which helped me so much on my study and research. Lastly, I thank my parents and my boyfriend Lei for always supporting my choice. If I may, I would also like to take this moment to thank many great teachers, mentors and friends that I have had the pleasure to interact with over the past two years.

The sum of the pixels which lie within the white rectangles are subtracted from the sum of pixels in the black rectangles. The purpose of automatic target detection ATD is to find regions of interest ROI where a target may be located. By locating ROIs, we can filter out a large amount of background clutter from the terrain scene, making object recognition feasible for large data sets, distributed computing thesis.

The ROIs are then passed to a recognition algorithm that identifies targets [6]. Automatic target detection is one of the most critical steps in the ATR problem, since the results of postprocessing depend critically on this step. Sensors positioned on the ground, installed on airplanes, helicopters, ground vehicles, etc acquire sensory data, distributed computing thesis.

Then the data then have to be processed using automatic detection and recognition algorithms. One of the most secure especially during military mission means of acquiring sensory data involves remotely operated vehicles.

Remotely operated vehicles can be broadly divided into two categories: unmanned aerial vehicles UAVs and unmanned ground vehicles UGVs, distributed computing thesis. In this thesis, distributed computing thesis, we focus on a distributed network of air-borne UAVs used to detect and recognize ground targets.

In developing the next generation of UAVs, one of the ideas is to utilize reactive agents and the associated swarming behavior as part of the command and control sys- tem for distributed computing thesis group distributed computing thesis UAVs functioning cooperatively and independently from ground control, distributed computing thesis.

In a swarmed system, multiple mobile entities are directed to converge on a single point of interest, disperse and regroup again. In this thesis, we explore the possibility of distributed optical camera-based ATD in a swarmed UAVs system.

An ideal detector must be able to differentiate any instantiation of the target class from ev- erything else in the world. with respect to color, texture, pose, scale, and illumination and at the same time be highly specific to avoid confusion with complex background clutter. A more general difficulty is the geometric ambiguity which arises from projecting three dimensions of the world onto two dimensions in the image.

Designers of UAV-based automatic target detection ATD systems face numer- ous challenges. UAVs must operate under severe communication constraints, varying environmental conditions and sensor limitations. Targets can present an infinite va- riety of appearances due to changes in pose and differences in illumination distributed computing thesis visual systems and thermodynamic state in infrared systems.

Non-ideal sensor effects further complicate matters, distributed computing thesis, such as the noise and blurring present in optical systems. In this thesis, we focus on the case where the acquired sensory data are in the form of optical images. Traditionally, optical cameras are low in cost and small in size, which makes them a high preference imagery sensors for a variety of military and civilian applications. The major limitation of optical cameras is their inability to deal with environmental conditions and imperfect camera setup which lowers fidelity of the results in the ATD task, distributed computing thesis.

In a distributed ATD system, images are first processed locally on board each UAV. Rather, images are fused across time and space by using not only multiple images from a single UAV, but the images from multiple UAVs across a portion or sometimes the entire network.

The UAVs share measurements of mutual information which requires a minimal amount of data to be transferred among the UAVs. The information that each UAV possesses about a potential target is further fused with information received from other UAVs. Thus, besides observation noise, environmental effects, and background clutter which prevent the system from being entirely reliable, the biggest factor distributed computing thesis determining reliability is the data fusion strategy which eventually affects the detection performance of the whole system.

First, we will give a general overview of swarmed UAV networks. We will further present a summary of automatic target detection and data fusion algorithms which are applied to optical data.

Stigmergy is a reinforcement learning mechanism that enables ants to indirectly communicate with each other about their environment using a chemical substance called pheromone. For example, while search- ing for food ants start from their nest and leave behind a pheromone trail along the path they traverse. The path leading from the nest to the food receives the highest amount of pheromone.

Pheromone provides positive reinforcement to future ants, distributed computing thesis, and, ants searching for the food later on use the trail as a positive reinforcement to reach the location of the food. This mechanism can be applied to artificial systems as well. In a swarmed UAVs system, a swarm of UAVs controlled by reactive agents is employed to achieve auto- matic target recognition, distributed computing thesis.

The ideology behind the swarm concept is that a system of many simple, expendable units can attain the performance level of a small number of complex aircrafts at a lower cost. Following the pheromone concept, each agent carries in its memory a map that stores levels of digital pheromone in the environment, and the agents can exchange map information when they pass close to each other [10].

Maintaining the digital pheromone map involves increasing the numeric value at the units current location and slowly decreasing levels across the entire map. Pheromone decay ensures that areas are revisited over time so that any changes can be detected. Besides mapping visited areas, reactive agents may also be given a scenario map that outlines specific areas that are of higher interest than others.

Similar to a digital pheromone map, the area of interest map contains constant numeric values based on what type of region is represented [10]. Priority search areas contain values that tend distributed computing thesis attract units to those locations, while no-fly zones or known threats will cause a repellant distributed computing thesis on nearby UAVs. A target detection system knows how to differentiate distributed computing thesis from everything else, distributed computing thesis, while distributed computing thesis target recognition system knows the difference between target A and target B, distributed computing thesis.

A typical detection-style algorithm scans the input image using a subwindow at all positions and scales by classifying each possible subwindow independently. It then reports the number, positions and sizes of found targets. Automatic target detection approaches can be classified into three major cate- gories: feature invariant approach, template matching approach and learning-based approach.

In feature invariant approach, the algorithms aim to find structural fea- tures such as edges [13], textures [14], etc. that exist even when the pose, viewpoint, or lighting conditions vary, and then use the features to locate targets.

The second category consists of algorithms that attempt to match pre-defined template to different parts of the image in order to find a fit. Distributed computing thesis kinds of methods are difficult to extend to more complex objects such as people, since they involve a significant amount of prior information and domain knowledge.

The final object detection approach is characterized by its learning-based algo- rithms. These algorithms learn the salient features of a class from sets of labeled positive and negative examples. There are two essential issues to build such de- tection algorithms. First, features are extracted from the image and the object of interest is encoded using those features. Such feature selection techniques include wavelets [17], Principal Component Analysis Distributed computing thesis [18], etc.

Second, a classifier is learned using these features. Popular techniques for building classifiers include Sup- port Vector Machines SVM [19], neural networks [20] and boosting [21]. One of the successful systems in the area is the pedestrian detection system of Papageorigious et al.

Their system detects the full body of a person. Haar wavelets are used to represent the images and Support Vector Machines SVM classifiers are used to classify the patterns. The system has been improved in [23], detecting pedestrians through the detection of four components of the human body. Another successful example is the face detection system from Rowley et al. Similar object detection system have been developed by others Vaillan et al.

Public defence of Doctoral thesis in Computer and System Science with Luca Beltramelli

, time: 45:50

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