Physical design of USB1.1

In earlier days, interfacing peripheral devices to host computer has a big problematic. There existed so many different kinds’ ports like serial port, parallel port, PS/2 etc. And their use restricts many situations, Such as no hot-pluggability and involuntary configuration. There are very less number of methods to connect the peripheral devices to host computer. The main reason that Universal Serial Bus was implemented to provide an additional benefits compared to earlier interfacing ports. USB is designed to allow many peripheral be connecting using single standardize interface. It provides an expandable fast, cost effective, hot-pluggable plug and play serial hardware interface that makes life of computer user easier allowing them to plug different devices to into USB port and have them configured automatically. In this thesis demonstrated the USB v1.1 architecture part in briefly and generated gate level net list form RTL code by applying the different constraints like timing, area and power. By applying the various types design constraints so that the performance was improved by 30%. And then it implemented in physically by using SoC encounter EDI system, estimation of chip size, power analysis and routing the clock signal to all flip-flops presented in the design. To reduce the clock switching power implemented register clustering algorithm (DBSCAN). In this design implementation TSMC 180nm technology library is used

An Effective Approach to Predicting Large Dataset in Spatial Data Mining Area

Due to enormous quantities of spatial satellite images, telecommunication images, health related tools etc., it is often impractical for users to have detailed and thorough examination of spatial data (S). Large dataset is very common and pervasive in a number of application areas. Discovering or predicting patterns from these datasets is very vital. This research focused on developing new methods, models and techniques for accomplishing advanced spatial data mining (ASDM) tasks. The algorithms were designed to challenge state-of-the-art data technologies and they are tested with randomly generated and actual real-world data. Two main approaches were adopted to achieve the objectives (1) identifying the actual data types (DTs), data structures and spatial content of a given dataset (to make our model versatile and robust) and (2) integrating these data types into an appropriate database management system (DBMS) framework, for easy management and manipulation. These two approaches helped to discover the general and varying types of patterns that exist within any given dataset non-spatial, spatial or even temporal (because spatial data are always influenced by temporal agents) datasets. An iterative method was adopted for system development methodology in this study. The method was adopted as a strategy to combat the irregularity that often exists within spatial datasets. In the course of this study, some of the challenges we encountered which also doubled as current challenges facing spatial data mining includes: (a) time complexity in availing useful data for analysis, (b) time complexity in loading data to storage and (c) difficulties in discovering spatial, non-spatial and temporal correlations between different data objects. However, despite the above challenges, there are some opportunities that spatial data can benefit from including: Cloud computing, Spark technology, Parallelisation, and Bulk-loading methods. Techniques and application areas of spatial data mining (SDM) were identified and their strength and limitations were equally documented. Finally, new methods and algorithms for mining very large data of spatial/non-spatial bias were created. The proposed models/systems are documented in the sections as follows: (a) Development of a new technique for parallel indexing of large dataset (PaX-DBSCAN), (b) Development of new techniques for clustering (X-DBSCAN) in a learning process, (c) Development of a new technique for detecting human skin in an image, (d) Development of a new technique for finding face in an image, (e) Development of a novel technique for management of large spatial and non-spatial datasets (aX-tree). The most prominent among our methods is the new structure used in (c) above -- packed maintained k-dimensional tree (Pmkd-tree), for fast spatial indexing and querying. The structure is a combination system that combines all the proposed algorithms to produce one solid, standard, useful and quality system. The intention of the new final algorithm (system) is to combine the entire initial proposed algorithms to come up with one strong generic effective tool for predicting large dataset SDM area, which it is capable of finding patterns that exist among spatial or non-spatial objects in a DBMS. In addition to Pmkd-tree, we also implemented a novel spatial structure, packed quad-tree (Pquad-Tree), to balance and speed up the performance of the regular quad-tree. Our systems so far have shown a manifestation of efficiency in terms of performance, storage and speed. The final Systems (Pmkd-tree and Pquad-Tree) are generic systems that are flexible, robust, light and stable. They are explicit spatial models for analysing any given problem and for predicting objects as spatially distributed events, using basic SDM algorithms. They can be applied to pattern matching, image processing, computer vision, bioinformatics, information retrieval, machine learning (classification and clustering) and many other computational tasks

Machine learning applications for personalised automated radiotherapy planning

Automated radiotherapy planning is characterised by reduction in manual planning due to an increase in computerised planning. Current methods can produce plans suitable for clinical use. However, every case is unique and manual intervention is often needed. The goal of this work was to determine whether it is feasible to develop a fully automated planning system producing clinically optimal plans, and if so, to begin developing it. This work explored relationships between automated planning parameters and anatomical features with respect to dosimetric outcomes. A rules-based automated planning technique was used, an algorithm requiring calibration of input parameters prior to use. This calibration determines the target objectives the algorithm will optimise to. Existing calibration methods use a single set of calibrated parameters per treatment site and are applied to all patients. This approach is considered sufficient to meet clinical goals but may not be sufficient for development of optimal personalised planning due to anatomical variance between patients. Using a validated rules-based planning methodology and obtaining patient bespoke expert-driven calibrated parameters as the optimal gold standard and validation benchmark, two machine learning techniques were explored for apriori configuration of parameters for the delivery of personalised treatment planning. The main objective was to train models to predict gold standard parameters hence generating expert planning automatically. A secondary objective was to determine dosimetric differences between plans generated via machine learned parameters and a traditional single set of parameters applied to all cases. Preliminary studies were carried out to define what will be considered gold standard and to identify anatomical features for inclusion in the main study as well as their relationships to calibrated parameters. The research presented here was applied to three sites: prostate, rectum and lung. Findings are also expected to provide heuristics for research to be carried out on other treatment sites