Advances in Brain-computer Interface (BCI) technology have opened the door to assisting millions of people worldwide with disabilities. In this work, we focus on assessing brain attention state that could be used to selectively run an application on a hand-held device. We examine different classification techniques to assess brain attention state. Spectral analysis of the recorded EEG activity was performed to compute the Alpha band power for different subjects during attentive and non-attentive tasks. The estimated power values were used to train a number of classical classifiers to

This paper presents a novel automatic multiple moving target detection and tracking framework that executes in real-time with enhanced accuracy and is suitable for UAV imagery. The framework is deployed for on-board processing and tested over datasets collected by our UAV system. The framework is based on image feature processing and projective geometry and is carried out on the following stages. First, FAST corners are detected and matched, and then outlier features are computed with least median square estimation. Moving targets are subsequently detected by using a density-based spatial

Misfeasors (or insiders) are considered among the most difficult intruders to detect due to their knowledge and authorization within the organization. Machine learning techniques have been widely used for intrusion detection but only little work has addressed the use of machine learning for detecting and classifying different types of insiders. The aim of this study is to exploit different recognition models for misfeasors detection by adding the Mac address as a feature in classification. Three different recognition models (a Rule Based Model, a Hierarchical Classification Model and a

Object localization has become a necessary module in many radiofrequency identification (RFID) systems that require tracking features besides the conventional identification feature. A number of techniques exists in literature that uses the RFID signal information to locate the tagged objects, i.e. objects wearing RFID tags. Nevertheless, in many applications, it is required to track objects that do not carry a tag (whether intentionally or unintentionally). In this work, we propose a technique for tag-less object localization. The technique is based on reconstructing the attenuation map of

Brain-Computer Interfaces (BCI) is a one kind of communication system that enables control of devices or communication with others only through brain signal activities without using motor activities. The main application for BCI is to provide an alternative channel for helping disabled persons, hereafter mentioned as subjects, to communicate with the external world. This paper tries to demonstrate the performance of different machine learning algorithms based on classification accuracy. Performance has been evaluated on dataset II from BCI Competition III for the year 2004 for two subjects 'A'

The main idea to correct sight disorders using lasers is to modify corneal curvature by applying laser to specific layers of the cornea. Intrastromal Photorefractive keratectomy is a laser technique used to correct sight disorders by evaporating corneal tissue, which results in small cavities that may coincide to form a larger cavity that will collapse to deform the curvature of the cornea. In this work, a 3D finite element model of the cornea was designed with typical parameters to simulate the procedure. The model outcome was compared with an earlier 2D model used for the same purpose, so as

Soft labels allow a pattern to belong to multiple classes with different degrees. In many real world applications the association of a pattern to multiple classes is more realistic; to describe overlap and uncertainties in class belongingness. The objective of this work is to develop a fuzzy Gaussian process model for classification of soft labeled data. Gaussian process models have gained popularity in the recent years in classification and regression problems and are example of a flexible, probabilistic, non-parametric model with uncertainty predictions. Here we derive a fuzzy Gaussian model

Mobile Ad hoc Networks (MANETs) have lots of applications. Due to the features of open medium, absence of infrastructure, dynamic changing network topology, cooperative algorithms, lack of centralized monitoring and management point, resource constraints and lack of a clear line of defense, these networks are vulnerable to attacks. A vital problem that must be solved in order to realize these applications is that concerning the security aspects of such networks. Solving these problems combined with the widespread availability of devices such as PDAs, laptops, small fixtures on buildings and

Brain-Computer Interfaces (BCI) is a one kind of communication system that enables control of devices or communication with others only through brain signal activities without using motor activities. P300 Speller is a BCI paradigm that helps disabled subjects to spell words by means of their brain signal activities. This paper tries to demonstrate the performance of different machine learning algorithms based on classification accuracy. Performance has been evaluated on the data sets acquired using BC12000's P300 Speller Paradigm provided by BCI competitions II (2003) & III (2004) organizers

Purpose: This paper aims to present an integrated framework for hotel revenue room maximization. The revenue management (RM) model presented in this work treats the shortcomings in existing systems. In particular, it extends existing optimization techniques for hotel revenue management to address group reservations and uses "forecasted demand" arrivals generated from the real data. Design/methodology/approach: The proposed forecasting module attempts to model the hotel reservation process from first principles. In particular, it models hotel arrivals as an interrelated process of stochastic