This research focuses on developing novel approaches in load balancing and restoration problems in electrical power distribution systems. The first approach introduces an inter-connected network topology, referred to as (n, k)-star topology. While power distribution systems can be constructed in different communication network topologies, the performance and fault assessment of the networked systems can be challenging to analyze. The (n, k)-star topologies have well defined performance and stability analysis metrics. Typically, these metrics are defined based on: i) degree, ii) diameter, and iii) conditional diagnosability of a faulty node. These parameters could be evaluated and assessed before a physical (n, k)-star topology power distribution system is constructed. Moreover, in the second approach, we evaluate load balancing problems by using a decentralized algorithm, i.e., the Multi-Agent System (MAS) based consensus algorithm on an (n, k)-star power topology. With aforementioned research approaches, an (n, k)-star power distribution system can be assessed with proposed metrics and assessed with encouraging results compared to other topology networked systems. Other encouraging results are found in efficiency and performance enhancement during information exchange using the decentralized algorithm. It has been proven that a load balance solution is convergent and asymptotically stable with a simple gain controller. The analysis can be achieved without constructing a physical network to help evaluate the design. Using the (n, k)-star topology and MAS, the load balancing/restoration problems can be solved much more quickly and accurately compared to other approaches shown in the literature. / Electrical and Computer Engineering

In this thesis, we focus on the design of the fault-tolerant routing algorithm for the (n, k)-star graph. We apply the idea of collecting the limited global information used for routing on the n-star graph to the (n, k)-star graph. First, we build the probabilistic safety vector (PSV) with modified cycle patterns. Then, our routing algorithm decides the fault-free routing path with the help of PSV. In order to improve the routing performance with more faulty nodes, we dynamically assign the threshold for our routing algorithm. The performance is judged by the average length of routing paths. Compared with distance first search and safety level, we get the best performance in the simulations.

Parkinson's disease (PD) is a degenerative illness whose cardinal symptoms include rigidity, tremor, and slowness of movement. In addition to its widely recognized effects PD can have a profound effect on speech and voice.The speech symptoms most commonly demonstrated by patients with PD are reduced vocal loudness, monopitch, disruptions of voice quality, and abnormally fast rate of speech. This cluster of speech symptoms is often termed Hypokinetic Dysarthria.The disease can be difficult to diagnose accurately, especially in its early stages, due to this reason, automatic techniques based on Artificial Intelligence should increase the diagnosing accuracy and to help the doctors make better decisions. The aim of the thesis work is to predict the PD based on the audio files collected from various patients.Audio files are preprocessed in order to attain the features.The preprocessed data contains 23 attributes and 195 instances. On an average there are six voice recordings per person, By using data compression technique such as Discrete Cosine Transform (DCT) number of instances can be minimized, after data compression, attribute selection is done using several WEKA build in methods such as ChiSquared, GainRatio, Infogain after identifying the important attributes, we evaluate attributes one by one by using stepwise regression.Based on the selected attributes we process in WEKA by using cost sensitive classifier with various algorithms like MultiPass LVQ, Logistic Model Tree(LMT), K-Star.The classified results shows on an average 80%.By using this features 95% approximate classification of PD is acheived.This shows that using the audio dataset, PD could be predicted with a higher level of accuracy.