The current trend to use Brain-Computer Interfaces (BCIs) with mobile devices mandates the development of efficient EEG data processing methods. In this paper, we demonstrate the performance of a Principal Component Analysis (PCA) ensemble classifier for P300-based spellers. We recorded EEG data from multiple subjects using the Emotiv neuroheadset in the context of a classical oddball P300 speller paradigm. We compare the performance of the proposed ensemble classifier to the performance of traditional feature extraction and classifier methods. Our results demonstrate the capability of the PCA ensemble classifier to classify P300 data recorded using the Emotiv neuroheadset with an average accuracy of 86.29% on cross-validation data. In addition, offline testing of the recorded data reveals an average classification accuracy of 73.3% that is significantly higher than that achieved using traditional methods. Finally, we demonstrate the effect of the parameters of the P300 speller paradigm on the performance of the method.

Face recognition has received attention from research communities recently. Law enforcement agencies are using facial recognition software as a crime-fighting tool. The quick increasing of Mobile Devices usage and the explosive growth of the mobile applications Mobile face recognition is one of important application. In the same time the mobile devices are facing many challenges in their resources as low computing power, battery life, limited bandwidth and storage. Mobile Cloud Computing (MCC) has been introduced to be a potential technology for mobile services and to solve the mobile resources problem by moving the processing and the storage of data out from mobile devices to the cloud. MCC offers abundant computing power that can be tapped easily. This paper gives explanation of MCC definition, Building private cloud with Open Source cloud OS, Face recognition as MCC Application with presenting the face detection and face recognition algorithms. Our results demonstrate that the proposed architecture is promising for real-time Mobile cloud computing by reducing the overall processing time.

Most application developers are willing to give up some performance and chip utilization in exchange of productivity. High-level tools for developing reconfigurable computing applications trade performance with ease-of-use. However, it is hard to know in a general sense how much performance and utilization one is giving up and how much ease-of-use he/she is gaining. More importantly, given the lack of standards and the uncertainty generated by sales literature, it is very hard to know the real differences that exist among different high-level programming paradigms. In order to do so, one needs a formal methodology and/or a framework that uses a common set of metrics and common experiments over a number of representative tools. In this work, we consider three representative high-level tools, Impulse-C, Mitrion-C, and DSPLogic in the Cray XD1 environment. These tools were selected to represent imperative programming, functional programming and graphical programming, and thereby demonstrate the applicability of our methodology. It will be shown that in spite of the disparity in concepts behind those tools, our methodology will be able to formally uncover the basic differences among them and analytically assess their comparative performance, utilization, and ease-of-use.

Clouds have a critical role in many studies, e.g. weather- and climate-related studies. However, they represent a source of errors in many applications, and the presence of cloud contamination can hinder the use of satellite data. This requires a cloud detection process to mask out cloudy pixels from further processing. The trend for remote sensing satellite missions has always been towards smaller size, lower cost, more flexibility, and higher computational power. Reconfigurable computers (RCs) combine the flexibility of traditional microprocessors with the power of field programmable gate arrays (FPGAs). Therefore, RCs are a promising candidate for on-board preprocessing. This paper presents the design and implementation of an RC-based real-time cloud detection system. We investigate the potential of using RCs for on-board preprocessing by prototyping the Landsat 7 ETM+ ACCA algorithm on one of the state-of-the art reconfigurable platforms, SRC-6E. Although a reasonable amount of investigations of the ACCA cloud detection algorithm using FPGAs has been reported in the literature, very few details/results were provided and/or limited contributions were accomplished. Our work has been proven to provide higher performance and higher detection accuracy

Utilizing brain activity to interact with the external environment is no longer impossible thanks to recent advances in developing Brain-Computer Interfaces (BCIs). This paper proposes a novel web browser application that utilizes user's Steady-State Visual Evoked Potentials (SSVEPs) from electroencephalography (EEG) to surf the internet. Development of a BCI web browsing application is challenging as it has to provide a comfortable web navigation using an easy-to-use interface, with the highest accuracy of command detection and correct execution. In addition, the response time needs to be as small as possible. The developed web browser was examined on three healthy subjects using the Emotiv EPOC headset to record EEG signals, and demonstrated to be fully functional in web navigation tasks with an average accuracy across subjects of 86.08 ± 15.46%.