Abstract: In this paper, a scalable augmented reality framework for handheld devices is presented. The presented framework is enabled by using a server-client data communication structure, in which the search for tracking targets among a database of images is performed on the server-side while pixel-wise 3D tracking is performed on the client-side, which, in this case, is a handheld mobile device. Image search on the server-side adopts a residual-enhanced image descriptors representation that gives the framework a scalability property. The tracking algorithm on the client-side is based on a gravity-aligned feature descriptor which takes the advantage of a sensor-equipped mobile device and an optimized intensity-based image alignment approach that ensures the accuracy of 3D tracking. Automatic content streaming is achieved by using a key-frame selection algorithm, client working phase monitoring and standardized rules for content communication between the server and client. The recognition accuracy test performed on a standard dataset shows that the method adopted in the presented framework outperforms the Bag-of-Words (BoW) method that has been used in some of the previous systems. Experimental test conducted on a set of video sequences indicated the real-time performance of the tracking system with a frame rate at 15-30 frames per second. The presented framework is exposed to be functional in practical situations with a demonstration application on a campus walk-around.
Abstract: In most of the popular implementation of Parallel GAs
the whole population is divided into a set of subpopulations, each
subpopulation executes GA independently and some individuals are
migrated at fixed intervals on a ring topology. In these studies,
the migrations usually occur 'synchronously' among subpopulations.
Therefore, CPUs are not used efficiently and the communication
do not occur efficiently either. A few studies tried asynchronous
migration but it is hard to implement and setting proper parameter
values is difficult.
The aim of our research is to develop a migration method which is
easy to implement, which is easy to set parameter values, and which
reduces communication traffic. In this paper, we propose a traffic
reduction method for the Asynchronous Parallel Distributed GA by
migration of elites only. This is a Server-Client model. Every client
executes GA on a subpopulation and sends an elite information to the
server. The server manages the elite information of each client and
the migrations occur according to the evolution of sub-population in
a client. This facilitates the reduction in communication traffic.
To evaluate our proposed model, we apply it to many function optimization
problems. We confirm that our proposed method performs
as well as current methods, the communication traffic is less, and
setting of the parameters are much easier.