The development and technological advancement of wireless sensor networks in different fields has been a revolution for mankind. To meet the high-end requirements, the support of the cloud that provides the resources for the application is very much essential. This paper presents an architecture called cloud sense to connect cyber and physical spaces for wireless body area networks with varying high-end workflow at different perspectives. The scalability issue in collecting patient data and processing the data is established using ganglia that is a scalable, distributed monitoring system to support high-performance computing in clusters for the set of input events such as electrocardiogram (ECG), blood pressure (BP), saturation of peripheral oxygen (SPO2), temperature, and skin conductance of the kind of human body parameters. Various parameter metrics have been analyzed based on the equivalent creation of instances. The connectivity mechanism behind the proposed cyber-physical system is unique of its kind; it is exhibited through wireless Internet on a small scale of three remote locations; the system works well with specific network parameter metrics; and the results proved that availability and scalability issues were addressed with numerical analysis.

This paper presents a framework for detecting objects in images based on global features and contours. The first step is a shape matching algorithm that uses the background subtraction process. Object detection is accomplished by an examination of the oversegmentation of the image, where the space of the potential boundary of the object is examined to identify boundaries that have a direct resemblance to the prototype of the object type to be detected. Our analysis method removes edges using bilinear interpolation and reestablishes color sensors as lines and retracts background lines from the previous frame. Object contours are generated with clustered lines. The objects detected will then be recognized using the extraction technique. Here, we analyze the color and shape characteristics with which each object is capable of managing occlusion and interference. As an extension of object detection and recognition, F1 car simulation is experimented with simulation using various layers, such as layer drops, convolutionary layers, and boundary elimination, avoiding obstacles in different pathways.

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This research work aims at the recognition of through slot feature from a CAD part model using its data exchange file STEP AP224 of ISO 10303 standards. This is a part of ongoing research work for developing a program for Computer Aided process planning to link CAD and CAM. The feature is identified by developing algorithm or procedure, which can be implemented by a computer program.