On the capacity of mobile ad hoc networks with delay constraints

Previous work on ad hoc network capacity has focused primarily on source-destination throughput requirements for different models and transmission scenarios, with an emphasis on delay tolerant applications. In such problems, network capacity enhancement is achieved as a tradeoff with transmission delay. In this paper, the capacity of ad hoc networks supporting delay sensitive traffic is studied. First, a general framework is . proposed for characterizing the interactions between the physical and the network layer in an ad hoc network. Then, CDMA ad hoc networks, in which advanced signal processing techniques such as multiuser detection are relied upon to enhance the user capacity, are analyzed. The network capacity is characterized using a combination of geometric arguments and large scale analysis, for several network scenarios employing matched filters, decorrelators and minimum-mean-square-error receivers. Insight into the network performance for finite systems is also provided by means of simulations. Both analysis and simulations show a significant network capacity gain for ad hoc networks employing multiuser detectors, compared with those using matched filter receivers, as well as very good performance even under tight delay and transmission power requirements.     

Jointly optimal power and admission control for delay sensitive traffic in CDMA networks with LMMSE receivers

Quality-of-service (QoS) guarantees for multiclass code division multiple access networks are provided by means of cross-layer optimization across the physical and network layers. At the physical layer, the QoS requirements are specified in terms of a target signal-to-interference ratio (SIR) requirement, and optimal target powers are dynamically adjusted according to the current number of users in the system. At the network layer, the QoS requirements are the blocking probabilities and the call connection delays. The network layer guarantees that both physical layer and network layer QoS are met by employing admission control. An optimal admission control policy is proposed based on a semi-Markov decision process formulation. The tradeoff between blocking and delay is discussed for various buffer configurations. The advantage of advanced signal processing receivers is established using a comparative capacity analysis and simulation with the classical scenario in which the system uses matched filter receivers.     

Visual Tracking by Continuous Density Propagation in Sequential Bayesian Filtering Framework

Particle filtering is frequently used for visual tracking problems since it provides a general framework for estimating and propagating probability density functions for nonlinear and non-Gaussian dynamic systems. However, this algorithm is based on a Monte Carlo approach and the cost of sampling and measurement is a problematic issue, especially for high-dimensional problems. We describe an alternative to the classical particle filter in which the underlying density function has an analytic representation for better approximation and effective propagation. The techniques of density interpolation and density approximation are introduced to represent the likelihood and the posterior densities with Gaussian mixtures, where all relevant parameters are automatically determined. The proposed analytic approach is shown to perform more efficiently in sampling in high-dimensional space. We apply the algorithm to real-time tracking problems and demonstrate its performance on real video sequences as well as synthetic examples.     

Mean shift: a robust approach toward feature space analysis

A general non-parametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure: the mean shift. For discrete data, we prove the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and, thus, its utility in detecting the modes of the density. The relation of the mean shift procedure to the Nadaraya-Watson estimator from kernel regression and the robust M-estimators; of location is also established. Algorithms for two low-level vision tasks discontinuity-preserving smoothing and image segmentation - are described as applications. In these algorithms, the only user-set parameter is the resolution of the analysis, and either gray-level or color images are accepted as input. Extensive experimental results illustrate their excellent performance     

Wireless access to the World Wide Web in an integrated CDMA system

We propose a wireless access mechanism for web traffic packets in an integrated wireless code-division multiple-access system that has both voice and Web traffic. The proposed scheme is a medium-access control layer/link layer (MAC/LL) scheduling algorithm that consists of a two level control: admission control and packet scheduling. The admission control restricts the number of users in the system such that quality-of-service requirements [target signal-to-interference ratio (SIR) and delay] for both voice and Web traffic can be met. The packet scheduling balances the system interference on a slot-by-slot basis such that the target SIRs can be achieved for all users (voice and Web browsing sessions) with a higher scheduling priority for voice. Designing admission control for Web users based on the average offered rate per session is difficult due to the high variations in the offered load generated by heavy tailed distributions for Web traffic. To overcome this problem, we propose an admission control algorithm that adaptively estimates the aggregate average load based on load measurements using a sliding observation window.     

Call admission control in wireless multimedia networks

Call admission control (CAC) is a mechanism used in networks to administer quality of service (QoS). Whereas the CAC problem in time-division multiple access (TDMA)-based cellular networks is simply related to the number of physical channels available in the network, it is strongly related to the physical layer performance in code-division multiple access (CDMA) networks since the multi-access interference in them is a function of the number of users and is a limiting factor in ensuring QoS. In this article, the CAC issues in multimedia DS-CDMA systems are reviewed by illustrating the basic principles underlying various schemes that have been proposed progressively from the simplest to the complex. The article also introduces SIR as a measure of QoS and describes the relatively simple schemes to administer CAC. The expression for SIR resulting from linear minimum mean-squared error processing is also presented. This article illustrates how CAC for multiple class service can be casted into an optimality framework and then discuss the recent work addressing self-similar multiple access interference.     

Automatic detection and segmentation of lymph nodes from CT data

Lymph nodes are assessed routinely in clinical practice and their size is followed throughout radiation or chemotherapy to monitor the effectiveness of cancer treatment. This paper presents a robust learning-based method for automatic detection and segmentation of solid lymph nodes from CT data, with the following contributions. First, it presents a learning based approach to solid lymph node detection that relies on marginal space learning to achieve great speedup with virtually no loss in accuracy. Second, it presents a computationally efficient segmentation method for solid lymph nodes (LN). Third, it introduces two new sets of features that are effective for LN detection, one that self-aligns to high gradients and another set obtained from the segmentation result. The method is evaluated for axillary LN detection on 131 volumes containing 371 LN, yielding a 83.0% detection rate with 1.0 false positive per volume. It is further evaluated for pelvic and abdominal LN detection on 54 volumes containing 569 LN, yielding a 80.0% detection rate with 3.2 false positives per volume. The running time is 5-20 s per volume for axillary areas and 15-40 s for pelvic. An added benefit of the method is the capability to detect and segment conglomerated lymph nodes.     

Delta modulation based prediction for access control in integratedvoice/data CDMA systems

In this paper, we propose access control protocols for integrated voice/data code division multiple access (CDMA) systems that are based on estimating the residual capacity available for data users. The data model considered in the analysis is suitable for non-transparent services (e.g., e-mail, file transfer, store-and-forward facsimile, etc.). The residual capacity is derived from the feasibility condition for power control using an adaptive prediction technique based on delta modulation. The analysis is extended for the case of imperfect power control. Two access control protocols are considered: modified delta modulation with scheduled access (MDM-S) and modified delta modulation with random access (MDM-R). Comparisons with earlier proposed protocols reveal a better performance in terms of average throughput for data for a given system capacity and outage probability     

Prediction based collaborative trackers (PCT): a robust and accurate approach toward 3D medical object tracking

Robust and fast 3D tracking of deformable objects, such as heart, is a challenging task because of the relatively low image contrast and speed requirement. Many existing 2D algorithms might not be directly applied on the 3D tracking problem. The 3D tracking performance is limited due to dramatically increased data size, landmarks ambiguity, signal drop-out or complex nonrigid deformation. In this paper, we present a robust, fast, and accurate 3D tracking algorithm: prediction based collaborative trackers (PCT). A novel one-step forward prediction is introduced to generate the motion prior using motion manifold learning. Collaborative trackers are introduced to achieve both temporal consistency and failure recovery. Compared with tracking by detection and 3D optical flow, PCT provides the best results. The new tracking algorithm is completely automatic and computationally efficient. It requires less than 1.5 s to process a 3D volume which contains millions of voxels. In order to demonstrate the generality of PCT, the tracker is fully tested on three large clinical datasets for three 3D heart tracking problems with two different imaging modalities: endocardium tracking of the left ventricle (67 sequences, 1134 3D volumetric echocardiography data), dense tracking in the myocardial regions between the epicardium and endocardium of the left ventricle (503 sequences, roughly 9000 3D volumetric echocardiography data), and whole heart four chambers tracking (20 sequences, 200 cardiac 3D volumetric CT data). Our datasets are much larger than most studies reported in the literature and we achieve very accurate tracking results compared with human experts' annotations and recent literature.     

Detection and measurement of fetal anatomies from ultrasound images using a constrained probabilistic boosting tree

We propose a novel method for the automatic detection and measurement of fetal anatomical structures in ultrasound images. This problem offers a myriad of challenges, including: difficulty of modeling the appearance variations of the visual object of interest, robustness to speckle noise and signal dropout, and large search space of the detection procedure. Previous solutions typically rely on the explicit encoding of prior knowledge and formulation of the problem as a perceptual grouping task solved through clustering or variational approaches. These methods are constrained by the validity of the underlying assumptions and usually are not enough to capture the complex appearances of fetal anatomies. We propose a novel system for fast automatic detection and measurement of fetal anatomies that directly exploits a large database of expert annotated fetal anatomical structures in ultrasound images. Our method learns automatically to distinguish between the appearance of the object of interest and background by training a constrained probabilistic boosting tree classifier. This system is able to produce the automatic segmentation of several fetal anatomies using the same basic detection algorithm. We show results on fully automatic measurement of biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), femur length (FL), humerus length (HL), and crown rump length (CRL). Notice that our approach is the first in the literature to deal with the HL and CRL measurements. Extensive experiments (with clinical validation) show that our system is, on average, close to the accuracy of experts in terms of segmentation and obstetric measurements. Finally, this system runs under half second on a standard dual-core PC computer.