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Emotions have a direct influence on an individual's physical and cognitive behaviour, as well as their performance, a student with a positive emotional state will learn and perform better. This paper presents an agent framework that addresses the relationship between user's state of emotion during learning and the modification of learning pace and feedback-type in a virtual environment for learning...
Object tracking is one of the most important components in numerous applications of computer vision. In this paper, the target is represented by a series of binary patterns, where each binary pattern consists of several rectangle pairs in variable size and location. As complementary to traditional binary descriptors, these patterns are extracted in both the intensity domain and the gradient domain...
The optimal jammer placement problem is proposed for a wireless localization network, where the aim is to degrade the accuracy of locating target nodes as much as possible. In particular, the optimal location of a jammer node is obtained in order to maximize the minimum of the Cramér-Rao lower bounds for a number of target nodes under location related constraints for the jammer node. Theoretical results...
In this study, optimal jamming of wireless localization systems is investigated. Two optimal power allocation schemes are proposed for jammer nodes in the presence of total and peak power constraints. In the first scheme, power is allocated to jammer nodes in order to maximize the average Cramér-Rao lower bound (CRLB) of target nodes whereas in the second scheme the power allocation is performed for...
Crowd sensing is an effective zero-cost method to map physical spatial fields by exploiting sensors already embedded in smartphones. The potentially huge amount of generated data and random measurement positions represent serious challenges to be addressed. In this paper we propose a combined Gaussian process (GP)-State space method for crowd mapping whose complexity and memory requirements for field...
This paper presents a spatial-temporal aware analytical framework to solve the truth finding problem in social sensing applications. Social sensing has emerged as a new big data application paradigm of collecting observations about the physical environment from social sensors (e.g., humans) or devices on their behalf. The collected observations may be true or false, and hence are viewed as binary...
Cognitive radio (CR) techniques promise to significantly increase the available spectrum thus wireless bandwidth. With the increase of spectrum allowed for CR, it is critical and challenging to perform efficient wideband sensing. We propose an integrated sequential wideband sensing framework which concurrently exploits sequential detection and compressed sensing (CS) techniques for more accurate and...
In this paper, we propose a three-dimensional gesture interactive system design of home automation for physically handicapped people. In order to provide a convenient and comfortable environment, we design a finger and hand gesture user interface for physically handicapped people based on stereo cameras to achieve remote control and gesture recognition system. We use stereo camera to capture stereo...
This paper presents a novel range-free geo-localization algorithm in wireless networks. The algorithm does not require ranging devices. It uses node connectivity to estimate the location of unknown (location unaware) nodes based on two or more anchor (location aware) nodes. The algorithm works in two steps. In the first step, the True Intersection Points (TIPs) that constitute the vertices of the...
This study presents an assessment of multiple approaches to determine the home and/or other important locations to a Twitter user. In this study, we present a unique approach to the problem of geotagged data sparsity in social media when performing geoinferencing tasks. Given the sparsity of explicitly geotagged Twitter data, the ability to perform accurate and reliable user geolocation from a limited...
Entity resolution is the basic operation of data quality management, and the key step to find the value of data. The parallel data processing framework based on MapReduce can deal with the challenge brought by big data. However, there exist two important issues, avoiding redundant pairs led by the multi-pass blocking method and optimizing candidate pairs based on the transitive relations of similarity...
Gait recognition, a manner to measure a person walk, has emerged as a new biometric technology due to the imperative need for efficient security infrastructure. There are several uniqueness associated with this technology: non-invasive, hard to conceal, and perceivable at a low-resolution. Unfortunately, gait exhibits large variations due to changes in view angles, clothing, footwear, carrying conditions,...
In this paper we investigate the role of different temporal windows in classification of functional near-infrared spectroscopy (fNIRS) signals corresponding to mental arithmetic and mental counting for development of a brain-computer interface. Signals are acquired from the prefrontal cortex of four healthy subjects during mental arithmetic and mental counting tasks using a continuous-wave fNIRS system,...
This study presents a small part of the major study, involved in categorizing EEG calmness. The kNN classifier was used to classify EEG features named as asymmetry index (AsI) which was extracted during relaxed state and non-relaxed state. Results from the previous study showed that the EEG behaviour during both states appear to have more than two groups. The group of four EEG behaviours and three...
In this paper, an epileptic seizure event detection algorithm utilizing five features namely singular values, total average power, delta band average power, variance and mean, is proposed. Using CHB-MIT Scalp EEG Database, the calculations of the features are performed over a sliding window of one second. The algorithm was evaluated in terms of accuracy, sensitivity, specificity and failure rate....
Increasing safety and environmental requirements of modern cities transport infrastructure rely on progress in the area of intelligent transportation systems and presume the widespread use of V2V and V2I communication to implement the user-oriented cloud services like persistent Internet access, traffic accidents information and routing capabilities. Because of the noticeable time delay in vehicles...
It is important to use cache efficiently for the content deployment in the aspect of reducing load of server and latency, especially in content-oriented networks such as ICN (Information Centric Networking). Since the capacity of cache on each network node is limited, numbers of cache replacement algorithm have been proposed. However, because of previous methods do not consider the rapid fluctuation...
Segmentation and classification of cells in biological data are important problems in bio-medical image analysis. This paper outlines a novel probabilistic approach to simultaneously classify and segment multiple cells of different classes in a multi-variate setting. Superpixels are extracted from the input vector-valued image, and a 2D hidden Markov model (HMM) is set up on the superpixel graph....
Extremely large graphs, such as those representing the Web or online social networks, require prohibitively large computational resources for an analysis of any of their complex properties. In this paper, we investigate an algorithmic approach to overcoming this difficulty by inferring key properties of the full graph using a strategic sample of small subgraphs of the graph. We focus, in particular,...
The current benchmark speech-based depression detection techniques rely on acoustic speech parameters collected from large sets of representative speech recordings. This study for the first time investigates depression detection based on the higher order influence model (HOIM) coefficients and emotional transition parameters derived from a relatively small set of conversational speech recordings representing...
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