Sharif Digital Repository

In this research, a new approach for TWS (Track While Scan) systems is introduced. Conventional TWS systems usually use target’s position (in Cartesian or polar coordinate systems), its velocity, and sometimes its acceleration (in main directions of coordinate system which is being used) as the elements of target’s state vector but the suggested algorithms use target’s velocity, course of motion and their rates of change as main tracking quantities and target’s position will be calculated after updating its velocity and course of motion. Hough transform is used as a powerful tool in detecting patterns which are identified with finite parameters (like lines, circles, ellipses, etc) to... 

In this research non-contact ultrasonic based method is used for liquid level measurement in a container. A system has been designed and developed to determine the liquid level and show the results on a personal computer. The system contains three main parts, which are mechanical and electronic parts and the programming. The setup can measure the liquid level with the accuracy of 1cm. An ATMEGA8 microprocessor commands to relays and controls the pumps. The pumps are used to change the height of the liquid in the containers to accurately calibrate the sensor and test its operation. The ultrasonic sensor, which is mounted above the vessel, transfers the data to an ATMEGA64 microprocessor for... 

Distributed stream processing frameworks (DSPFs) are used for real-time processing of big data. Apache Storm is one of the most popular stream processing systems in industry today. By increasing data generation rate we need new methods to overcome processing requirements of DSPFs like Apache Storm. In this thesis we investigate the feasibility of incorporation FPGA acceleration into Apache Storm. Using FPGAs as co-processors in powerful servers can improve performance and accelerate processing of streaming data by increasing parallelism, decreasing processing time of each processing units and decreasing communication delay between these units. Our design includes a hardware part that... 

In recent years, big data processing plays an important role in the era of information technology. The exponential growth of big data volume increases the need for data centers and infrastructures with more processing power. Due to dark silicon and scalability limitations in deep-submicron, the increasing trend of server performance slows down. Therefore, hardware accelerators such as FPGA and GPU are become increasingly popular for improvement of data center processing power. There are two types of big data processing based on the application: stream processing and batch processing. With the widespread use of social networks, online control systems and internet of things services, the... 

One of the most important categories of big data processing is stream processing. In stream processing, processing of data is performed simultaneously with the production of data. one of the most well-known frameworks used for stream processing is Apache Storm. By default, Storm uses a round-robin scheduler to allocate tasks to physical machines. This scheduler randomly performs scheduling and assignment of tasks to physical machines without considering the processing power of physical machines and processing tasks, which makes it impossible to properly utilize the processing resources. In this paper, a scheduling algorithm and resource allocation have been proposed based on the processing... 

Today, due to the increasing use of fossil fuel-powered vehicles, attention has been paid to advanced societies by the amount of pollutants emitted by these vehicles and the need to control and reduce them. As the number of cars increases, the volume of data for processing increases, which increases the need for data centers and processing infrastructure with more computing power. Therefore, in this research, we intend to implement a program that continuously categorizes cars according to specific criteria in terms of emission levels. For this purpose we used Apache Storm, a data stream processing framework, to implement the program.In this research, we develop a simulation for cars with the... 

Today, the pace of information generation, fast processing and instant decision-making is increasing. In this regard, one of the main needs in the field of data management and processing is stream data processing. Today's world needs new methods to deal with and analyze these data. Two of the most challenging aspects of data streams are (i) concept drift, i.e. evolution of data stream over time, which requires the ability to make timely decisions against the high speed of receiving new data; (ii) limited memory storage and the impracticality of using memory due to the large amount of data. Clustering is one of the common methods for processing data streams, without having basic knowledge...