AN ILLUSTRATION OF THE USE OF COGNITIVE INTERACTION IN NANOROBOTICS AN AREA OF NANOTECHNOLOGY FOR MEDICAL DEVELOPMENT

ABSTRACT For many decades, nanotechnology has been developed with cooperation from researchers in several fields of studies including physics, chemistry, biology, material science, engineering, and computer science and it presents a wide range of problems and opportunities not just diverse issues, but issues of different kinds. In this project, we explore the nanotechnology development community in the area of Nano robotics and its functionality in both the science and medical world hoping is that this may lead to the realization of our visions [1].
A computational approach with the application of medical Nano robotics will be used to illustrate the Nano robot integrated architecture and layout. This should provide a suitable choice to establish a practical medical Nano robotics platform for the health sector [2].

TABLE OF CONTENT CERTIFICATION DEDICATION ACKNOWLEDGEMENT ABSTRACT TABLE OF CONTENT
CHAPTER ONE INTRODUCTION 1.0 BACKGROUND OF STUDY 1.1 STATEMENT OF PROBLEM 1.2 PURPOSE OF STUDY 1.3 MOTIVATION OF STUDY 1.4 SIGNIFICANCE OF STUDY 1.5 SCOPE OF STUDY 1.6 LIMITATION OF STUDY 1.7 DEFINITION OF TERMS
CHAPTER TWO LITERATURE REVIEW 2.1 BRIEF OVERVIEW OF NANOROBOTICS 2.2 IDEAS OF NANOROBOTICS 2.2.1  VIEWS OF NANOROBOTICS 2.3 PAST, PRESENT, AND FUTURE RESEARCHES OF NANOROBOTICS IN MEDICINE 2.4 PHYSIOLOGY OF THE HUMAN CIRCULATORY SYSTEM (THE BLOOD, ITS VESSELS, TRANSPORT PHENOMENON AND BLOOD FLOW) 2.5 TECHNOLOGY AND TOOLS APPLIED
CHAPTER 3 3.0 INTRODUCTION 3.1 PROPERTIES OF SWARM INTELLIGENCE SYSTEMS 3.2 SWARM INTELLIGENCE ALGORITHMS 3.2.1  ANT COLONY OPTIMIZATION 3.2.2  PARTICLE SWARM OPTIMIZATION 3.3 COMPARISON OF SWARM INTELLIGENCE MODELS (ANT COLONY OPTIMIZATION AND PARTICLE SWARM OPTIMIZATION). 3.3.1  COMMUNICATION MECHANISM 3.3.2  PROBLEM TYPES 3.3.3  PROBLEM REPRESENTATION 3.3.4  APPLICABILITY OF ALGORITHM 3.3.5  ALGORITHM’S OBJECTIVES 3.4 ANALYSIS OF SIMULATOR 3.4.1  WORLD CLASS 3.4.1.1     SWARMWORLD CLASS 3.4.2  ACTOR CLASSES 3.4.3  ADVANTAGES OF ACE 3.4.4  DISADVANTAGES OF ACE 3.5 METHODOLOGY 3.5.1  WATERFALL MODEL 3.6 SOFTWARE STRUCTURAL REPRESENTATION 3.6.1  MOTION CONTROLLER DIAGRAM 3.6.2  CLASS DIAGRAM 3.6.3  SYSTEM FLOW CHART 3.7 FLOWCHART SYMBOLS
CHAPTER FOUR 4.1 INTRODUCTION 4.2 DEVELOPING A GOOD SIMULATION 4.3 SYSTEM IMPLEMENTATION 4.4 INPUT SPECIFICATION 4.5 OUPUT SPECIFICATION 4.6 HARDWARE REQUIREMENTS 4.7 SOFTWARE REQUIREMENTS 4.8 RUNNING THE PROGRAM 4.8.1  QUITTING THE PROGRAM 4.9 CHOICE OF PROGRAMMING LANGUAGE 4.10     PROGRAM IMPLEMENTATION 4.11     PROGRAM DOCUMENTATION
CHAPTER FIVE 5.1 SUMMARY 5.2 CONCLUSION 5.3 RECOMMENDATION
APPENDIX A
LIST OF FIGURES Figure 1: Sizes of various elements Figure 2: Drexler's Self-replicating system Figure 3: Block Diagram of a Nanorobot Figure 4: Human Circulatory system Figure 5: Greenfoot Environment Figure 6: Java code section of Greenfoot Figure 7: Generic MVC Structure, courtesy of Sun (above) Figure 8: Models of collective behaviors (Grosan et al., 2006) Figure 9: Ant food hunt strategy (1) Figure 10: Ant food hunt strategy (2) Figure 11: Trait behavior in bird swarms. Figure 12: Water fall model Figure 13: Motion controller Diagram Figure 14: UML Class diagram Figure 15: Flow chart Symbols Figure 16: Simulation flow chart Figure 17: Inputs in source code Figure 18: Inputs in source code (1) Figure 19: Output of Simulation Move 1 Figure 20: Output of Simulation Move 2 Figure 21: Output of Simulation Move 3 Figure 22: Output of Simulation Move 4 Figure 23: Output of Simulation Move 5 Figure 24: Output of Simulation Move 6 Figure 25: Output of Simulation Move 7 Figure 26: Output of Simulation Move 8 Figure 27: Output of Simulation Move 9 Figure 28: Output of Simulation Move 10