PyGrapherConnect

The evolving landscape of backend computational systems especially in biomedical research involving heavy data operations which have a gap of not being used properly. It is due to the lack of communication standard between the frontend and backend. This gap presents a problem to researchers who need to use the frontend for visualizing and manipulating their data but also want to do complex analysis. CAPRI a python-based backend system specializing in analyzing Evidential Reasoning data also has the same issue. This project offers a solution PyGrapherConnect module acting as a data conversion layer between CAPRI and PyGrapher, its frontend interface. It translates graph data generated by PyGrapher into a txt format readable by CAPRI for further analytical processing. It makes it easier for researchers working with Evidential Reasoning (ER) models, based on the Dempster-Shafer theory to perform the final belief assessment. PyGrapherConnect acting as bridge between frontend and backend, provides a solution to researchers for representing and manipulating these ER models as graph structures, helping researchers to make intricated analytical deductions

Adaptable Safe Path Planning Under Uncertainty Constraints

Path Planning in robotics is an open fundamental problem in fully autonomous or manipulator systems. Many standardized algorithms have been proposed taking a different aspect of the problem. Following a path generated by a path planner is not accurate because of sensor noise or malfunctioning, inaccuracy of control actions and commands and so on. This causes inaccuracy in the localization of the robot in the environment, which may lead to a collision if path planning is done assuming accurate motion. This uncertainty constraint induced during the mid-fight of the robot should be added with other design constraints so a collision-free path can be generated. As other constraints, this uncertainty constraint modifies the configuration space during mid-fight and since localization error can lead a configuration space to "bloat", this might lead to isolating the initial and goal configurations separated by critical regions. Fortunately, these constraints are not inherited by design and can be made flexible for planning a path by avoiding constraints wherever necessary. Therefore, this work focuses on maintaining two configuration spaces and provides an intelligent method to make the uncertainty constraints flexible by toggling between two spaces wherever necessary. The simulations considering design constraints of Computer Assisted Surgical Trainer (CAST) are presented as proof-of-concept which provides support to the objective of the thesis and demonstrates that the algorithm extends the functionality of a common path planning algorithm called Probabilistic Roadmap Planners, where by algorithm design it is proved that algorithm is adaptable to PRM in the worst case

Prediction for Stock Marketing Using Machine Learning

Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on an exchange. The successful prediction of a stock's future price could yield significant profit. This paper will showcase how to perform stock prediction using Machine Learning algorithms: Linear Regression, Random Forest and Multilayer Perceptron

Experimental demonstration of 25 GHz wideband chaos in symmetric dual port EDFRL

We study dynamics of chaos in dual port erbium-doped fiber ring laser (EDFRL). The laser consists of two erbium-doped fibers, intracavity filters at 1549.30 nm, isolators, and couplers. At both ports, the laser transitions into the chaotic regime for pump currents greater than 100 mA via period doubling route. We calculate the Lyapunov exponents using Rosenstein’s algorithm. We obtain positive values for the largest Lyapunov exponent (≈0.2) for embedding dimensions 5, 7, 9 and 11 indicating chaos. We compute the power spectrum of the photocurrents at the output ports of the laser. We observe a bandwidth of ≈ 25 GHz at both ports. This ultra wideband nature of chaos obtained has potential applications in high speed random number generation and communication