Autism spectrum disorder (ASD) is associated with emotion processing difficulties, including limitations in understanding the emotional states of others and processing one's own internal experiences. The nature of these difficulties remains largely unknown. This is due, in part, to challenges in acquiring reliable self-reports of emotional experiences from this population. Automatically characterizing emotional states with the use of physiological signals is a potential means of overcoming this problem, as physiological signals can provide an objective and nonverbal method for assessing affective states. However, this approach has not been well considered with ASD to date. To this end, we investigated detection of autonomic responses to positive and negative stimuli in children with ASD using four physiological measurements. Electrocardiograms, respiration, skin conductance and temperature were measured while 15 children with ASD viewed standard images known to evoke varying levels of valence (positive and negative) and arousal (low and high intensity). Using an ensemble of classifiers, affective states induced by stimuli of positive and negative valence or high and low arousal was differentiated at average accuracies approaching or exceeding 80 percent. These results suggest the feasibility of discerning affective states in individuals with ASD objectively using physiological signals.

Indoor positioning is an enabling technology for delivery of location-based services in mobile computing environments. This paper proposes a positioning solution using received signal strength in indoor wireless local area networks. In this application, an explicit measurement equation and the corresponding noise statistics are unknown because of the complexity of the indoor propagation channel. To address these challenges, we introduce a new state-space Bayesian filter: the nonparametric information (NI) filter. This filter effectively tracks motion in situations where the Kalman filter and its variants are inapplicable, while maintaining a computational complexity comparable to that of the Kalman filter. To deal with the noisy nature of the indoor propagation environment, the NI filter is used in the design of an intelligent dynamic WLAN tracking system. The system anticipates future position values and adapts its sensing and estimation parameters accordingly. Our experimental results conducted on measurements from a real office environment indicate that the combination of the intelligent design and the NI filter results in significant improvements over the Kalman and particle filters.

An important parameter in analysis of physiological tremor is the diagnosis and study of neurological disorders. The instantaneous tremor frequency (ITF) is an important parameter in tremor analysis. This paper proposes a novel stochastic filter, the multiple extended Kalman filter (M-EKF), for tracking of ITF from neural microelectrode recordings. The M-EKF mitigates degradations in filter performance resulting from a mismatch between assumed initial conditions and those of a particular realization of a stochastic system. Specifically, the M-EKF is comprised of a bank of extended Kalman filters (EKF), each initialized with different conditions, selected according to the unscented transform. The final estimate is a weighted average of the individual estimates provided by each EKF where the weights reflect how closely the assumed EKF initial conditions match those of the true system. The M-EKF is applied to a synthetic tremor model to display its superior performance to that of the EKF and the unscented Kalman filter.

Positioning in wireless networks has gained significant ground as an enabling technology for various applications such as event detection and context awareness. Since these positioning systems rely on radio features to locate a mobile, they are susceptible to non-cryptographic attacks resulting from malicious alteration of the propagation environment. This paper proposes a sensor selection scheme for increasing the resilience of fingerprinting-based positioning systems to RSS-based attacks in the context of wireless local area networks (WLAN). A distributed positioning scheme is proposed whereby an estimate is obtained from each WLAN access point (AP). Sensor selection is performed based on a nonparametric estimate of the Fisher information. Experimental results indicate superior performance compared to existing methods and graceful performance degradation in presence of RSS attacks.

The recent proliferation of Location-Based Services (LBSs) has necessitated the development of effective indoor positioning solutions. In such a context, Wireless Local Area Network (WLAN) positioning is a particularly viable solution in terms of hardware and installation costs due to the ubiquity of WLAN infrastructures. This paper examines three aspects of the problem of indoor WLAN positioning using received signal strength (RSS). First, we show that, due to the variability of RSS features over space, a spatially localized positioning method leads to improved positioning results. Second, we explore the problem of access point (AP) selection for positioning and demonstrate the need for further research in this area. Third, we present a kernelized distance calculation algorithm for comparing RSS observations to RSS training records. Experimental results indicate that the proposed system leads to a 17 percent (0.56 m) improvement over the widely used K-nearest neighbor and histogram-based methods.

This paper proposes a dynamic MMSE estimator for tracking mobile users in indoor wireless local area networks (WLAN) based on received signal strength (RSS). The method uses a training-based static estimate obtained by an adaptive kernel density estimator as the input into a Kalman Filter. Predictions from the filter are used during the next iteration to adaptively select a subset of training data, contained in a radio map, for the static estimator. Experimental results show that the combination of the Kalman filter and the adaptive radio map technique results in nearly 0.5 m (20%) improvement in root mean square location accuracy when compared to static localization

A practical component to an upper year electrical and computer engineering course in information engineering or multimedia systems is presented. Developed to introduce students to image indexing and retrieval, the experiment uses a Microsoft Windows binary of the freely available MPEG-7 Experimentation Model from ISO for image description. The experiment is broken into three interdependent stages spanning a three week period. Each stage demonstrates various concepts associated with image indexing and retrieval. Students require C/C++ programming skills to parse binary encoded MPEG-7 descriptions, output ASCII description data, and perform retrievals on indexed information. The laboratory is distributed in nature, and each participating student is made to work on a small, mutually exclusive image set. The experiment drives home the urgency of the media indexing problem in light of the rapidly growing market need for multimedia management applications resulting from the huge influx of multimedia data. Through feedback, student views indicate that despite being long and time consuming, the experiment provides an interesting, original and stimulating experience.

The use of low-level visual features such as colour and shape in content-based image retrieval system leads to several ambiguities. Specifically, due to the many-to-many mapping between the low-level feature space and high-level user concepts, a conceptual user query may not be modelled as a single point in the feature space. Furthermore, conceptually similar images may not fall close to each other in the low-level feature space. This work addresses these issues by proposing an interactive technique, query feedback. This method employs user input to represent a high-level conceptual query as various points in the low-level space. Thus, similarity to a given conceptual query is obtained as the fusion of several low-level representations in the feature space.

An open peer-to-peer architecture for performing distributed image indexing and retrieval is proposed. The system employs a sociological model of human acquaintance networks (small world theory) and concepts derived from the nature of the World Wide Web. Retrieval is performed using agents, and a node hopping algorithm is employed that exploits node referrals established from descriptor data stored locally by each node. A general framework for this small world image miner (SWIM) is presented along with a realization using MPEG-7 color structure descriptor data for 2400 images. Results related to search agent path length and network node degree are presented.

From a perceptual standpoint, the subjectivity inherent in understanding and interpreting visual content in multimedia indexing and retrieval motivates the need for online interactive learning. Since efficiency and speed are important factors in interactive visual content retrieval, most of the current approaches impose restrictive assumptions on similarity calculation and learning algorithms. Specifically, content-based image retrieval techniques generally assume that perceptually similar images are situated close to each other within a connected region of a given space of visual features. This paper proposes a novel method for interactive image retrieval using query feedback. Query feedback learns the user query as well as the correspondence between high-level user concepts and their low-level machine representation by performing retrievals according to multiple queries supplied by the user during the course of a retrieval session. The results presented in this paper demonstrate that this algorithm provides accurate retrieval results with acceptable interaction speed compared to existing methods.

The large volumes of artistic visual data available to museums, art galleries, and online collections motivate the need for effective means to retrieve relevant information from such repositories. This paper proposes a decision making framework for content-based retrieval of art images based on a combination of low-level features. Traditionally, the similarity among two images has been calculated as a weighted distance between two feature vectors. This approach, however, may not be mathematically and computationally appropriate and does not provide enough flexibility in modeling user queries. This paper proposes a framework that generalizes a wide set of previous approaches to similarity calculation including the weighted distance approach. In this framework, image similarities are obtained through a decision making process based on low-level feature distances using fuzzy theory. The analysis and results of this paper indicate that the aggregation technique presented here provides an effective, general, and flexible tool for similarity calculation based on the combination of individual descriptors and features.

The obstacle of generating hybrid queries within the context of content-based image retrieval is still very real. In attempts to overcome this, fuzzy aggregation can be used to combine single, simple index queries into larger, more complex ones. The paper outlines the use of a fuzzy aggregation technique for hybrid querying which has the ability to adjust its behavior according to operator-controlled parameters. The resulting aggregator can be viewed as a feature-adaptive overall similarity measure. We limit the scope of the aggregator to queries involving color content, color coverage, and horizontal/vertical trends, and apply it to a media database comprised of Corel images of fixed size. Preliminary results show promise and illustrate that hybrid queries using the fuzzy aggregator are effective in their ability to retrieve relevant images while suppressing erroneous retrievals when compared to simple, single-feature queries. In addition, the results obtained are at a minimum comparable to multiple-feature queries generated using a weighted mean approach, but exhibiting scalability and greater flexibility in parameter adjustment.