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We consider the problem of quantizer design in a distributed estimation system with communication constraints in the case where only a training sequence is available. Our approach is based on a generalization of regression trees. The look-ahead method that we also propose improves significantly the performance. The final system performs similarly to the one that assumes known statistics. Index Terms--Distributed estimation, nonlinear estimation, quantization, trees.

Recent technological advances in mobile computing and wireless communication have made portable devices, such as PDA, laptops, and wireless modems to be very compact and affordable. On the other hand, wireless networks have gained such wide popularity that new network infrastructure is continually introduced. It is thus likely that many of the future portable devices will be equipped with multiple wireless modems such as Bluetooth and 802.11 WLAN, in order to increase device inter-operability. The availability of multiple modems can leverage the performance of the communication traffic generated by the applications, for example Internet access. We envision a tool for managing the device connection through these modems. At the core of this tool is an optimization engine that splits packet traffic across a subset of the available transports so that user's performance metrics are maximized. This paper describes a mathematical model for such an optimization problem considering its applicability to small portable devices. Relevant quality of service (QoS) parameters such as bandwidth, average delay, and energy consumption are covered in the model. The mathematical formulation is validated using a simulated environment. The experimental results have demonstrated the effectiveness of our model and captured the inter-relationship among the quality parameters. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: Service discovery protocols are extremely important for developing distributed applications in ad-hoc environments. However to perform Service Discovery in mobile ad-hoc networks requires the design and development of extremely complex and dynamic systems. This requires a paradigm shift in the methodologies employed to design and evaluate these systems so as to ensure quality and correctness. Currently there exists no generic method for formally specifying these systems. Due to this it is extremely difficult to formally analyze protocols in this domain to study and observe the behavior. The same issue also hinders comparison of different protocols against a fixed set of metrics. In this work we propose a framework for the formal specification of service discovery protocols in mobile ad-hoc networks. The purpose of this framework is to provide a formal basis for performance evaluation and behavioral study of these protocols. This allows different service discovery architectures to be evaluated against a given set of metrics, a generic way to specify protocols and an automated process for performance evaluation and behavioral study. [Copyright &y& Elsevier]

We develop efficient parallel algorithms for the r-dominating set and the p-center problems on trees. On a concurrent-read exclusive-write PRAM, our algorithm for the r-dominating set problem runs in O(log n log log n) time with n processors. The algorithm for the p-center problem runs in O(log n log log n) time with n processors. [ABSTRACT FROM AUTHOR]

As stereoscopic display devices become common, their image quality assessment evaluation becomes increasingly important. Most studies conducted on 3D displays are based on psychophysics experiments with humans rating their experience based on detection tasks. The physical measurements do not map to effects on signal detection performance. Additionally, human observer study results are often subjective and difficult to generalize. We designed a computational stereoscopic observer approach inspired by the mechanisms of stereopsis in human vision for task-based image assessment that makes binary decisions based on a set of image pairs. The stereo-observer is constrained to a left and a right image generated using a visualization operator to render voxel datasets. We analyze white noise and lumpy backgrounds using volume rendering techniques. Our simulation framework generalizes many different types of model observers including existing 2D and 3D observers as well as providing flexibility to formulate a stereo model observer approach following the principles of stereoscopic viewing. This methodology has the potential to replace human observer studies when exploring issues with stereo display devices to be used in medical imaging. We show results quantifying the changes in performance when varying stereo angle as measured by an ideal linear stereoscopic observer. Our findings indicate that there is an increase in performance of about 13-18% for white noise and 20-46% for lumpy backgrounds, where the stereo angle is varied from 0 to 30. The applicability of this observer extends to stereoscopic displays used for in the areas of medical and entertainment imaging applications.