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Published Research Abstracts

Robust Resource Allocation in a Massive Multiplayer Online Gaming Environment

Abstract

The environment considered in this research is a massive multiplayer online gaming (MMOG) environment. Each user controls an avatar (an image that represents and is manipulated by a user) in a virtual world and interacts with other users. An important aspect of MMOG is maintaining a fair environment among users (i.e., not give an unfair advantage to users with faster connections or more powerful computers). The experience (either positive or negative) the user has with the MMOG environment is dependent on how quickly the game world responds to the user’s actions. This study focuses on scaling the system based on demand, while maintaining an environment that guarantees fairness. Consider an environment where there is a main server (MS) that controls the state of the virtual world. If the performance falls below acceptable standards, the MS can off-load calculations to secondary servers (SSs). An SS is a user’s computer that is converted into a server. Four heuristics are proposed for determining the number of SSs, which users are converted to SSs, and how users are assigned to the SSs and the MS. The goal of the heuristics is to provide a “fair” environment for all the users, and to be “robust” against the uncertainty of the number of new players that may join a given system configuration. The heuristics are evaluated and compared by simulation.

Resource Allocation in a Client/Server Hybrid Network for Virtual World Environments

Abstract

The creation of a virtual world environment (VWE) has significant costs, such as maintenance of server rooms, server administration, and customer service. The initial development cost is not the only factor that needs to be considered; factors such as the popularity of a VWE and unexpected technical problems during and after the launch can affect the final cost and success of a VWE. The capacity of servers in a client/server VWE is hard to scale and cannot adjust quickly to peaks in demand while maintaining the required response time. To handle these peaks in demand, we propose to employ users’ computers as secondary servers. The introduction of users’ computers as secondary servers allows the performance of the VWE to support an increase in users. In this study, we develop and implement five static heuristics to implement a secondary server scheme that reduces the time taken to compute the state of the VWE. The number of heterogeneous secondary servers, conversion of a player to a secondary server, and assignment of players to secondary servers are determined by the heuristics implemented in this study. A lower bound of the performance is derived to evaluate the results of the heuristics.

DIMENSIONALITY REDUCTION USING NEURAL NETWORKS

Abstract

A multi-layer neural network with multiple hidden layers was trained as an autoencoder using steepest descent, scaled conjugate gradient and alopex algorithms. These algorithms were used in different combinations with steepest descent and alopex used as pretraining algorithms followed by training using scaled conjugate gradient. All the algorithms were also used to train the autoencoders without any pretraining. Three datasets: USPS digits, MNIST digits, and Olivetti faces were used for training. The results were compared with those of Hinton et al. (Hinton and Salakhutdinov, 2006) for MNIST and Olivetti face dataset. Results indicate that while we were able to prove that pretraining is important for obtaining good results, the pretraining approach used by Hinton et al. obtains lower RMSE than other methods. However, scaled conjugate gradient turned out to be the fastest, computationally.

MS Thesis (Abstract)

Electroencephalogram (EEG) is the measurement of the electrical activity of the brain measured by placing electrodes on the scalp. These EEG signals give the micro-voltage difference between different parts of the brain in a non-invasive manner. The brain activity measured in this way is being currently analyzed for a possible diagnosis of physiological and psychiatric diseases. These signals have also found a way into cognitive research. At Colorado State University we are trying to investigate the use of EEG as computer input. CSU brain computer interface.

In this particular research our goal is to classify two mental tasks. A subject is asked to think about a mental task and the EEG signals measured using six electrodes on his scalp. In order to differentiate between two different tasks, the EEG signals produced by each task needs to be classified. For a better classification and analysis of the brain activity associated with each mental task we need to reduce the dimensionality of these data sets. For dimensionality reduction we use multilayer neural network with a middle bottleneck layer. The network is trained using a fast convergence algorithm which is basically a modified Levenberg-Marquardt algorithm. After training the network the output at the bottleneck layer is fed to a classifier. We used a Linear Discriminant Analysis (LDA) and a Quadratic Discriminant Analysis (QDA) classifier for the mental task classification. In other experiments we are also training our neural network using scaled conjugate gradient algorithm. We are also working on using support vector machines for classifying the bottleneck output and to further understand the EEG data. I defended my thesis on July 5, 2007. Here is my final thesis.

Group Membership

Student Member IEEE.

Student Member IEEE EMBS.

Member AI, EEG, and Vision groups @CSU.