Graphical User Interface (GUI) Development for an Optical Communication Simulator

Modeling and simulation tools have been an integral part of engineering world for a long time. Various Electronic Design Automation (EDA) tools have been extensively used in various industries and research to evaluate the performance of electronic systems. The advancement of such design tools also has influenced the optical communication sector such that there has been a continuous progress on the Photonic Design Automation (PDA) tools. Currently, many software for simulating optical communications are available. However, they are very expensive and conceal the information on how components are modeled. To avoid these constraints, we developed our own PDA software for optical communication. This thesis delves into the development of Graphical User Interface (GUI) of our software. The studied GUI software conforms to the feature of standard simulation software and assists the users to perform a system-level simulation of fiber optic communication. The developed GUI allows the users to design their layout, run the simulation and view the results in the form of data or plot. The GUI is explained with respect to the graphical layout and the interactive features of the components. The detailed structure is described along with the Java library used to build them. The interactive aspects of GUI are investigated, for adding the hierarchical feature to our GUI software. In addition, a plotting tool is created for the GUI. The thesis provides comprehensive information on working principle of GUI for simulation software and describes the addition of plotting tool and hierarchical design in detail

Time Series Management Systems:A Survey

The collection of time series data increases as more monitoring and automation are being deployed. These deployments range in scale from an Internet of things (IoT) device located in a household to enormous distributed Cyber-Physical Systems (CPSs) producing large volumes of data at high velocity. To store and analyze these vast amounts of data, specialized Time Series Management Systems (TSMSs) have been developed to overcome the limitations of general purpose Database Management Systems (DBMSs) for times series management. In this paper, we present a thorough analysis and classification of TSMSs developed through academic or industrial research and documented through publications. Our classification is organized into categories based on the architectures observed during our analysis. In addition, we provide an overview of each system with a focus on the motivational use case that drove the development of the system, the functionality for storage and querying of time series a system implements, the components the system is composed of, and the capabilities of each system with regard to Stream Processing and Approximate Query Processing (AQP). Last, we provide a summary of research directions proposed by other researchers in the field and present our vision for a next generation TSMS.Comment: 20 Pages, 15 Figures, 2 Tables, Accepted for publication in IEEE TKD

Information fusion architectures for security and resource management in cyber physical systems

Data acquisition through sensors is very crucial in determining the operability of the observed physical entity. Cyber Physical Systems (CPSs) are an example of distributed systems where sensors embedded into the physical system are used in sensing and data acquisition. CPSs are a collaboration between the physical and the computational cyber components. The control decisions sent back to the actuators on the physical components from the computational cyber components closes the feedback loop of the CPS. Since, this feedback is solely based on the data collected through the embedded sensors, information acquisition from the data plays an extremely vital role in determining the operational stability of the CPS. Data collection process may be hindered by disturbances such as system faults, noise and security attacks. Hence, simple data acquisition techniques will not suffice as accurate system representation cannot be obtained. Therefore, more powerful methods of inferring information from collected data such as Information Fusion have to be used. Information fusion is analogous to the cognitive process used by humans to integrate data continuously from their senses to make inferences about their environment. Data from the sensors is combined using techniques drawn from several disciplines such as Adaptive Filtering, Machine Learning and Pattern Recognition. Decisions made from such combination of data form the crux of information fusion and differentiates it from a flat structured data aggregation. In this dissertation, multi-layered information fusion models are used to develop automated decision making architectures to service security and resource management requirements in Cyber Physical Systems --Abstract, page iv