An efficient pursuit automata approach for estimating stable all‐pairs shortest paths in stochastic network environments

This paper presents a new solution to the dynamic all‐pairs shortest‐path routing problem using a fast‐converging pursuit automata learning approach. The particular instance of the problem that we have investigated concerns finding the all‐pairs shortest paths in a stochastic graph, where there are continuous probabilistically based updates in edge‐weights. We present the details of the algorithm with an illustrative example. The algorithm can be used to find the all‐pairs shortest paths for the ‘statistical’ average graph, and the solution converges irrespective of whether there are new changes in edge‐weights or not. On the other hand, the existing popular algorithms will fail to exhibit such a behavior and would recalculate the affected all‐pairs shortest paths after each edge‐weight update. There are two important contributions of the proposed algorithm. The first contribution is that not all the edges in a stochastic graph are probed and, even if they are, they are not all probed equally often. Indeed, the algorithm attempts to almost always probe only those edges that will be included in the final list involving all pairs of nodes in the graph, while probing the other edges minimally. This increases the performance of the proposed algorithm. The second contribution is designing a data structure, the elements of which represent the probability that a particular edge in the graph lies in the shortest path between a pair of nodes in the graph. All the algorithms were tested in environments where edge‐weights change stochastically, and where the graph topologies undergo multiple simultaneous edge‐weight updates. Its superiority in terms of the average number of processed nodes, scanned edges and the time per update operation, when compared with the existing algorithms, was experimentally established. Copyright © 2008 John Wiley & Sons, Ltd.     

Tractography gone wild: probabilistic fibre tracking using the wild bootstrap with diffusion tensor MRI

Diffusion tensor magnetic resonance imaging (DT-MRI) permits the noninvasive assessment of tissue microstructure and, with fibre-tracking algorithms, allows for the 3-D trajectories of white matter fasciculi to be reconstructed noninvasively. Probabilistic algorithms allow one to assign a "confidence" to a given reconstructed pathway--but often rely on a priori assumptions about sources of uncertainty in the data. Bootstrap methods have been proposed as a way of circumventing this problem, deriving the uncertainty from the data themselves--but acquisition times for data amenable to precise and robust bootstrapping are clinically prohibitive. By combining the wild bootstrap, recently introduced to the DT-MRI literature, with tractography, we show how confidence can be assigned to reconstructed trajectories using data collected in a fraction of the time required for regular bootstrapping. We compare in vivo wild bootstrap tracking results with regular tracking results and show that results are comparable. This approach therefore allows users who have collected data sets for use with deterministic tracking algorithms, rather than those specifically designed for bootstrapping, to be able to apply bootstrap analyses and retrospectively assign confidence to their reconstructed trajectories with minimum additional effort.     

Approximate distributed Bellman-Ford algorithms

Routing algorithms based on the distributed Bellman-Ford algorithm (DBF) suffer from exponential message complexity in some scenarios. We propose two modifications to the algorithm which result in a polynomial message complexity without adversely affecting the response time of the algorithm. However, the new algorithms may not compute the shortest path. Instead, the paths computed can be worse than the shortest path by at most a constant factor (<3). We call these algorithms approximate DBF algorithms. The modifications proposed to the original algorithm are very simple and easy to implement. The message complexity of the first algorithm, called the multiplicative approximate DBF, is O(nmlog(nΔ)) where Δ is the maximum length over all edges of an n-nodes m-edges network. The message complexity of the second algorithm, called the additive approximate DBF, is O(δ/Δ nm) where δ is the minimum length over all edges in the network     

Analytical Extraction of Extrinsic and Intrinsic FET Parameters

The least squares solution for the entire small-signal equivalent circuit is itself a formidable task. In this paper, a systematic approach comprising the total and conventional least squares method in analytically obtaining the small-signal field-effect transistor (FET) parameters values is introduced. The proposed method eliminates the conventional “cold FET” and “hot FET” modeling constraints and allows an ease in inline process tracking.      

Artifact Trapping During Time Reversal Photoacoustic Imaging for Acoustically Heterogeneous Media

Several different reconstruction algorithms have been proposed for photoacoustic tomography, most of which presuppose that the acoustic properties of the medium are constant and homogeneous. In practice, there are often unknown spatial variations in the acoustic properties, and these algorithms give, at best, only approximate estimates of the true image. The question as to which approach is the most robust in these circumstances is therefore one of practical importance. Image reconstruction by “time reversal”—using a numerical propagation model with a time-varying boundary condition corresponding to the measured data in reversed temporal order—has been shown to be less restrictive in its assumptions than most, and therefore a good candidate for a general and practically useful algorithm. Here, it is shown that such reconstruction algorithms can “trap” time reversed scattered waves, leading to artifacts within the image region. Two ways to mitigate this effect are proposed.      

Robust Reduced-Rank Adaptive Algorithm Based on Parallel Subgradient Projection and Krylov Subspace

In this paper, we propose a novel reduced-rank adaptive filtering algorithm exploiting the Krylov subspace associated with estimates of certain statistics of input and output signals. We point out that, when the estimated statistics are erroneous (e.g., due to sudden changes of environments), the existing Krylov-subspace-based reduced-rank methods compute the point that minimizes a “wrong” mean-square error (MSE) in the subspace. The proposed algorithm exploits the set-theoretic adaptive filtering framework for tracking efficiently the optimal point in the sense of minimizing the “true” MSE in the subspace. Therefore, compared with the existing methods, the proposed algorithm is more suited to adaptive filtering applications. A convergence analysis of the algorithm is performed by extending the adaptive projected subgradient method (APSM). Numerical examples demonstrate that the proposed algorithm enjoys better tracking performance than the existing methods for system identification problems.      

Trickle-based interference cancellation schemes for CDMA systems

In this paper, we introduce a novel approach to interference cancellation (IC) for code division multiple access (CDMA) uplink transmission. Several models combining principles of serial (SIC) and parallel (PIC) interference cancellation are discussed. The proposed scheme is derived from the analysis of these hybrid models and applies a user configuration algorithm (termed “trickle”) in order to provide improved bit-error-rate (BER) performance. The algorithm utilizes an adaptive matrix to compute the required configuration to be used for the subsequent interference cancellation stage. We demonstrate that significant performance improvements can be achieved over various hybrid schemes. A reduced-complexity version of the trickle algorithm is also introduced where the processing delay is greatly reduced while maintaining similar performance. We present several numerical examples through which we demonstrate the efficacy of the proposed algorithms relative to existing interference cancellation algorithms.     

Pascal’s triangle-based range-free localization for anisotropic wireless networks

This paper introduces a Pascal’s triangle model to draw the potential locations and their probabilities for a normal node given the hop counts to the anchors according to the extent of detour of the shortest paths. Based on our proposed model, a Pascal’s triangle-based localization (PTL) algorithm using local connectivity information is presented for anisotropic wireless networks with a small number of anchors. The superiority of the PTL algorithm has been validated over the state-of-the-art algorithms through MATLAB simulations. We have shown that compared to the other algorithms, the PTL algorithm achieves higher localization accuracy with even fewer anchors. We have also validated the performance of the PTL algorithm in a real environment.     

An Erbium Fiber Fabricated by the “Core-Suction” Technique

An erbium doped fiber preform having a lead germanate erbium glass core with silica cladding was fabricated by a newly developed technique named “Core-Suction”. This preform was then drawn into fiber and fiber cross-section, loss spectrum and refractive index profile measured. A 30 cm piece of the manufactured fiber was spliced to a standard silica fiber using a commercial available splicer. This spliced fiber was then used to setup an erbium doped fiber amplifier (EDFA) and gain spectrum of the amplifier measured. Distributed gain of the manufactured erbium fiber was measured using an optical frequency domain reflectometry (OFDR) based optical backscatter reflectometer (OBR). It is demonstrated that the “Core-Suction” technique can be used to make a high-gain amplifier that is compatible with conventional silica fibers.      

基于贝叶斯约束统计框架的DT-MRI脑白质纤维追踪成像

弥散张量磁共振成像(DT-MRI)的脑白质纤维追踪成像利用脑白质水分子弥散构成的弥散张量信息追踪脑白质纤维束并无创重建其3维结构图像。针对现有追踪方法一般以局部体素的弥散张量为主要追踪依据,缺乏对纤维结构、弥散度等人体解剖结构和生理机能的综合考量的缺陷,该文基于贝叶斯理论框架综合分析追踪路径与各体素弥散张量方向和纤维束几何结构相关性,并使用弥散度和追踪纤维角度对两者进行约束,获得各步追踪方向的概率密度分布,通过Markov Chain Monte Carlo采样确定其追踪方向进行追踪成像,通过多次追踪获得具有统计意义的3维结果。最后利用文中方法在合成弥散张量数据上进行了成像仿真,在真实脑部DT-MRI数据上进行了成像实验。仿真和实验结果表明,该方法能实现预期的脑白质纤维追踪成像,比现有追踪成像方法结果更可靠,可重复性更强。     

Adaptive wavelength routing in all-optical networks

We consider routing and wavelength assignment in wavelength-routed all-optical networks (WAN) with circuit switching. The conventional approaches to address this issue consider the two aspects of the problem disjointly by first finding a route from a predetermined set of candidate paths and then searching for an appropriate wavelength assignment. We adopt a more general approach in which we consider all paths between a source-destination (s-d) pair and incorporate network state information into the routing decision. This approach performs routing and wavelength assignment jointly and adaptively, and outperforms fixed routing techniques. We present adaptive routing and wavelength assignment algorithms and evaluate their blocking performance. We obtain an analytical technique to compute approximate blocking probabilities for networks employing fixed and alternate routing. The analysis can also accommodate networks with multiple fibers per link. The blocking performance of the proposed adaptive routing algorithms are compared along with their computational complexity     

Single quantum dot tracking based on perceptual grouping using minimal paths in a spatiotemporal volume.

Semiconductor quantum dots (QDs) are new fluorescent probes with great promise for ultrasensitive biological imaging. When detected at the single-molecule level, QD-tagged molecules can be observed and tracked in the membrane of live cells over unprecedented durations. The motion of these individual molecules, recorded in sequences of fluorescence images, can reveal aspects of the dynamics of cellular processes that remain hidden in conventional ensemble imaging. Due to QD complex optical properties, such as fluorescence intermittency, the quantitative analysis of these sequences is, however, challenging and requires advanced algorithms. We present here a novel approach, which, instead of a frame by frame analysis, is based on perceptual grouping in a spatiotemporal volume. By applying a detection process based on an image fluorescence model, we first obtain an unstructured set of points. Individual molecular trajectories are then considered as minimal paths in a Riemannian metric derived from the fluorescence image stack. These paths are computed with a variant of the fast marching method and few parameters are required. We demonstrate the ability of our algorithm to track intermittent objects both in sequences of synthetic data and in experimental measurements obtained with individual QD-tagged receptors in the membrane of live neurons. While developed for tracking QDs, this method can, however, be used with any fluorescent probes.     

Adaptive quality of service‐based routing approaches: development of neuro‐dynamic state‐dependent reinforcement learning algorithms

In this paper, we propose two adaptive routing algorithms based on reinforcement learning. In the first algorithm, we have used a neural network to approximate the reinforcement signal, allowing the learner to take into account various parameters such as local queue size, for distance estimation. Moreover, each router uses an online learning module to optimize the path in terms of average packet delivery time, by taking into account the waiting queue states of neighbouring routers. In the second algorithm, the exploration of paths is limited to N‐best non‐loop paths in terms of hops number (number of routers in a path), leading to a substantial reduction of convergence time. The performances of the proposed algorithms are evaluated experimentally with OPNET simulator for different levels of traffic's load and compared with standard shortest‐path and Q‐routing algorithms. Our approach proves superior to classical algorithms and is able to route efficiently even when the network load varies in an irregular manner. We also tested our approach on a large network topology to proof its scalability and adaptability. Copyright © 2006 John Wiley & Sons, Ltd.     

A Nonlinear Filter Coupled With Hospitability and Synthetic Inclination Maps for In-Surveillance and Out-of-Surveillance Tracking

This paper presents an algorithm for in-surveillance and out-of-surveillance mobile ground target tracking. In this respect, a nonlinear Bayesian estimation filter is presented. Then, an adaptive algorithm is derived to reduce remarkably the computational burden of this filter, while not degrading the accuracy of the state estimates. Additionally, an algorithm employing the concepts of hospitability and synthetic inclination maps is introduced and is coupled with the nonlinear Bayesian filter to track the mobile ground target once it goes out-of-surveillance. Loosely speaking, the hospitability map can be viewed as a terrain-based map defining a likelihood or a “weight” for each point on the earth's surface proportional to the ability of the target to move and maneuver at that location. On the other hand, the synthetic inclination map describes how the target favors certain regions within the search area, hence being “synthetically” inclined to move toward them.      

Hybrid Wireless-Optical Broadband Access Network (WOBAN): Network Planning Using Lagrangean Relaxation

The concept of a hybrid wireless-optical broadband access network (WOBAN) is a very attractive one. This is because it may be costly in several situations to run fiber to every home (or equivalent end-user premises) from the telecom central office (CO); also, providing wireless access from the CO to every end user may not be possible because of limited spectrum. Thus, running fiber as far as possible from the CO toward the end user and then having wireless access technologies take over may be an excellent compromise. How far should fiber penetrate before wireless takes over is an interesting engineering design and optimization problem, which we address in this paper. We propose and investigate the characteristics of an analytical model for network planning, namely optimum placements of base stations (BSs) and optical network units (ONUs) in a WOBAN (called the primal model, or PM). We develop several constraints to be satisfied: BS and ONU installation constraints, user assignment constraints, channel assignment constraints, capacity constraints, and signal-quality and interference constraints. To solve this PM with reasonable accuracy, we use “Lagrangean relaxation” to obtain the corresponding “Lagrangean dual” model. We solve this dual problem to obtain a lower bound (LB) of the primal problem. We also develop an algorithm (called the primal algorithm) to solve the PM to obtain an upper bound (UB). Via simulation, we compare this PM to a placement heuristic (called the cellular heuristic) and verify that the placement problem is quite sensitive to a set of chosen metrics.      

An Active Contour Model for Segmenting and Measuring Retinal Vessels

This paper presents an algorithm for segmenting and measuring retinal vessels, by growing a “Ribbon of Twins” active contour model, which uses two pairs of contours to capture each vessel edge, while maintaining width consistency. The algorithm is initialized using a generalized morphological order filter to identify approximate vessels centerlines. Once the vessel segments are identified the network topology is determined using an implicit neural cost function to resolve junction configurations. The algorithm is robust, and can accurately locate vessel edges under difficult conditions, including noisy blurred edges, closely parallel vessels, light reflex phenomena, and very fine vessels. It yields precise vessel width measurements, with subpixel average width errors. We compare the algorithm with several benchmarks from the literature, demonstrating higher segmentation sensitivity and more accurate width measurement.      

Benefits of the use of impairment constraint routing in optical networks

In transparent optical networks, the optical signal accumulates the effects of all physical impairments present along the path it traverses. The conventional selection of signal paths based on e.g. shortest path routing without considering the signal quality and its association with the physical impairments does not always provide the optimum solution in terms of network performance such as blocking and resource utilization. This paper proposes an impairment constraint based routing algorithm to achieve an optimal combination of physical and networking performance taking into account all physical linear impairments including noise, chromatic and polarization mode dispersion, crosstalk and filter concatenation effects in an integrated approach. The performance of a typical metropolitan area network is examined and the improvement achieved when using the proposed approach compared to the conventional shortest path routing is demonstrated.     

Mean-Shift Object Tracking with Discrete and Real AdaBoost Techniques

An online mean-shift object tracking algorithm, which consists of a learning stage and an estimation stage, is proposed in this work. The learning stage selects the features for tracking, and the estimation stage composes a likelihood image and applies the mean shift algorithm to it to track an object. The tracking performance depends on the quality of the likelihood image. We propose two schemes to generate and integrate likelihood images: one based on the discrete AdaBoost (DAB) and the other based on the real AdaBoost (RAB). The DAB scheme uses tuned feature values, whereas RAB estimates class probabilities, to select the features and generate the likelihood images. Experiment results show that the proposed algorithm provides more accurate and reliable tracking results than the conventional mean shift tracking algorithms.     

Applications of 40-Gb/s Chirp-Managed Laser in Access and Metro Networks

We investigate 40-Gb/s cost-efficient transmitter for access and metro networks. This 40-Gb/s transmitter comprises a standard directly modulated distributed-feedback (DFB) laser and a subsequent optical filter. Large dispersion tolerance of this transmitter is realized by chirp control through the phase correlation between adjacent bits for the destructive interference in order to erase the power of “0” bits while enhancing the extinction ratio. The chirp model of the DFB laser and the optimum parameters of the optical filter have been numerically analyzed. The chirp-managed 42.8-Gb/s transmission over 20-km standard single mode fiber (SSMF or SMF-28) without dispersion compensation and a centralized lightwave WDM-PON system are experimentally demonstrated. We have also realized the transmission over 100-m graded index plastic optical fiber (GI-POF). Moreover, the application in the metro network over 240-km SSMF or SMF-28 has also been investigated in this paper.      

Computing blocking probabilities in multiclass wavelength-routingnetworks with multicast calls

We present an approximate analytical method to compute efficiently the call-blocking probabilities in wavelength-routing networks with multiple classes of both unicast and multicast calls. Our approach involves the following steps. We start with an approximate solution to a linear single-class unicast network which we developed earlier. Next, all classes of calls on a particular route are aggregated to give an equivalent single-class model. We then extend the path decomposition algorithms that we have developed for single-class networks to handle mesh networks with multiple classes of calls. We show how to use these path decomposition algorithms to decompose large networks with multicast paths into smaller subsystems with only linear paths, which, in turn, are solved by the product-form approximation algorithm. We also consider a state-dependent Poisson arrival process for multicast calls which is more accurate in capturing the behavior of these calls