Vision-Based Autonomous Human Tracking Mobile Robot

Tracking moving objects is one of the most important but problematic features of motion analysis and understanding. In order to effectively interact robots with people in close proximity, the systems must first be able to detect, track, and follow people. Following a human with a mobile robot arises in many different service robotic applications. This paper proposes to build an autonomous human tracking mobile robot which can solve the occlusion problem during tracking. The robot can make human tracking efficiently by analysing the information obtained from a camera which is equipped on the top of the robot. The system performs human detection by using Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM) algorithms and then uses HSV (Hue Saturation Value) color system for detecting stranger. If the detected human is stranger, robot will make tracking. During the process, the robot needs to track the stranger without missing. So, Kalman filter is used to solve this problem. Kalman filter can estimate the target human when the human is occluded with walls or something. This paper describes the processing results and experimental results of a mobile robot which will track unmarked human efficiently and handle the occlusion using vision sensor and Kalman filter

Cost-Based Decision Model for House Interior Design

Decision Support System is an interactive computer-based systems that intended to help decision makers compile useful information from raw data, documents, personal knowledge, and/or business models to identify and solve problems and make decisions. The proposed system supports to make decision for interior design of the buildings. This system develops an information model to support cost-based decision making in the interior design phase. An interior design object library is developed by collecting interior information attributes and designer’s sample designs together with their respective prices. It can reduce the complex calculation of cost analysis for the whole building. It can support easy to choose the interior design (floor, wall, ceiling, window, door and lighting) by the client who has no design idea. Using this system can easily analyze the design and cost of the whole interior parts of the building

Clustering Patient Records using Fuzzy C-Means and Hard C-Means Algorithm

Data clustering is the process of dividing data elements into classes or clusters so that items in the same class are as similar as possible, and items in different classes are as dissimilar as possible. Most clustering algorithms, assign each data to exactly one cluster, thus forming a crisp (hard) partition of the given data, but fuzzy (soft) partition allows for degrees of membership, to which data belongs to different clusters. In hard clustering, data is divided into distinct clusters, where each data element belongs to exactly one cluster. In fuzzy clustering, data elements can belong to more than one cluster, and associated with each element is a set of assigning these membership levels, and then using them to assign data elements to one or more cluster. This system is implemented clustering data by using Fuzzy C-Means (FCM) and Hard C-Means (HCM) clustering algorithms