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

Visual Sensor Networks (VSNs) enable enhanced three-dimensional sensing of spaces and objects, and facilitate collaborative reasoning to open up a new realm of vision-based distributed smart applications including security/surveillance, healthcare delivery, traffic monitoring, just to name a few. However, such applications require sensor nodes that can efficiently process large volumes of visual

Pervasive healthcare environments provide an effective solution for monitoring the wellbeing of the elderly where the general trend of an increasingly ageing population has placed significant burdens on current healthcare systems. An important pervasive healthcare system functionality is patient motion analysis where gait information can be used to detect walking behavior abnormalities that may

Strain measurement is a quantity used for assessing the regional function of the left ventricular (LV) of the heart. They are computed by tracking the motion of the non-invasive, virtual tags in the cardiac muscle with time. Tracking these tags gives information for each region of the cardiac muscle by quantifying its deformation during contraction (systolic period) and relaxation (diastolic

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

Due to their diverse applications, ad hoc networks are appealing for use in many domains. However, their 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, they are vulnerable to many attacks. Therefore, there is a major concern

In the field of pattern recognition multiple classifier systems based on the combination of outputs from different classifiers have been proposed as a method of high performance classification systems. The objective of this work is to develop a fuzzy Gaussian classifier for combining multiple learners, we use a fuzzy Gaussian model to combine the outputs obtained from K-nearest neighbor classifier

The V-turn is an access management technique for at-grade intersection treatments; reducing conflicts and improving safety along arterial roads. The V-turn is mainly used as an alternative to direct left turns at intersections by replacing the full median opening with a directional median opening; this solution is called restricted lefts u-turn intersection treatment (Restricted Lefts V-turn). A

An efficient fat suppression method is presented for MR tagging with complementary spatial modulation of magnetization (CSPAMM). In this method, the complementary modulation is applied to the water content of the tissues, while in-phase modulation is applied to the fat content. Therefore, during image reconstruction, the subtraction of the acquired images increases the tagging contrast of the

In this study, we propose a novel data acquisition and image reconstruction method for parallel magnetic resonance imaging (MRI). The proposed method improves the GRAPPA algorithm by simultaneously collecting data using the body coil in addition to localized surface coils. The body coil data is included in the GRAPPA reconstruction as an additional coil. The reconstructed body coil image shows