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Haddad, Wassim M.

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    Set-Valued Protocols for Almost Consensus of Multiagent Systems with Uncertain Interagent Communication
    (Georgia Institute of Technology, 2014-06) Sadikhov, Teymur ; Haddad, Wassim M. ; Goebel, Rafal ; Egerstedt, Magnus B.
    One of the main challenges in robotics applications is dealing with inaccurate sensor data. Specifically, for a group of mobile robots the measurement of the exact location of the other robots relative to a particular robot is often inaccurate due to sensor uncertainty or detrimental environmental conditions. In this paper, we address the consensus problem for a group of agent robots with uncertain interagent communication. Measurement uncertainty is characterized by balls of radius r centered at the neighboring agents exact locations. We show that the agents reach an almost consensus state and converge to a time-varying ball of radius r and include an analysis approach to the problem based on set-valued analysis. Finally, several illustrative numerical examples are provided to demonstrate the efficacy of the proposed set-valued consensus protocol framework.
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    Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging
    (Georgia Institute of Technology, 2010-06) Gholami, Behnood ; Haddad, Wassim M. ; Tannenbaum, Allen R.
    Pain assessment in patients who are unable to verbally communicate is a challenging problem. The fundamental limitations in pain assessment in neonates stem from subjective assessment criteria, rather than quantifiable andmeasurable data. This often results in poor quality and inconsistent treatment of patient pain management. Recent advancements in pattern recognition techniques using relevance vector machine (RVM) learning techniques can assist medical staff in assessing pain by constantly monitoring the patient and providing the clinician with quantifiable data for pain management. The RVMclassification technique is a Bayesian extension of the support vector machine (SVM) algorithm, which achieves comparable performance to SVMwhile providing posterior probabilities for class memberships and a sparser model. If classes represent “pure” facial expressions (i.e., extreme expressions that an observer can identify with a high degree of confidence), then the posterior probability of the membership of some intermediate facial expression to a class can provide an estimate of the intensity of such an expression. In this paper, we use the RVM classification technique to distinguish pain from nonpain in neonates as well as assess their pain intensity levels. We also correlate our results with the pain intensity assessed by expert and nonexpert human examiners.
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    Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning
    (Georgia Institute of Technology, 2010-02-14) Gao, Yi ; Gholami, Behnood ; MacLeod, Robert S. ; Blauer, Joshua ; Haddad, Wassim M. ; Tannenbaum, Allen R.
    Atrial fibrillation, a cardiac arrhythmia characterized by unsynchronized electrical activity in the atrial chambers of the heart, is a rapidly growing problem in modern societies. One treatment, referred to as catheter ablation, targets specific parts of the left atrium for radio frequency ablation using an intracardiac catheter. Magnetic resonance imaging has been used for both pre- and and post-ablation assessment of the atrial wall. Magnetic resonance imaging can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this paper we use shape learning and shape-based image segmentation to identify the endocardial wall of the left atrium in the delayed-enhancement magnetic resonance images.
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    An Unsupervised Learning Approach for Facial Expression Recognition using Semi-Definite Programming and Generalized Principal Component Analysis
    (Georgia Institute of Technology, 2010-01-08) Gholami, Behnood ; Haddad, Wassim M. ; Tannenbaum, Allen R.
    In this paper, we consider facial expression recognition using an unsupervised learning framework. Specifically, given a data set composed of a number of facial images of the same subject with different facial expressions, the algorithm segments the data set into groups corresponding to different facial expressions. Each facial image can be regarded as a point in a high-dimensional space, and the collection of images of the same subject resides on a manifold within this space. We show that different facial expressions reside on distinct subspaces if the manifold is unfolded. In particular, semi-definite embedding is used to reduce the dimensionality and unfold the manifold of facial images. Next, generalized principal component analysis is used to fit a series of subspaces to the data points and associate each data point to a subspace. Data points that belong to the same subspace are shown to belong to the same facial expression.