Convergence and tracking analysis of a variable normalised LMF (XE-NLMF) algorithm

The least-mean-fourth (LMF) algorithm is known for its fast convergence and lower steady state error, especially in sub-Gaussian noise environments. Recent work on normalised versions of the LMF algorithm has further enhanced its stability and performance in both Gaussian and sub-Gaussian noise environments. For example, the recently developed normalised LMF (XE-NLMF) algorithm is normalised by the mixed signal and error powers, and weighted by a fixed mixed-power parameter. Unfortunately, this algorithm depends on the selection of this mixing parameter. In this work, a time-varying mixed-power parameter technique is introduced to overcome this dependency. A convergence analysis, transient analysis, and steady-state behaviour of the proposed algorithm are derived and verified through simulations. An enhancement in performance is obtained through the use of this technique in two different scenarios. Moreover, the tracking analysis of the proposed algorithm is carried out in the presence of two sources of nonstationarities: (1) carrier frequency offset between transmitter and receiver and (2) random variations in the environment. Close agreement between analysis and simulation results is obtained. The results show that, unlike in the stationary case, the steady-state excess mean-square error is not a monotonically increasing function of the step size. (c) 2007 Elsevier B.V. All rights reserved.

Analysis of Dependence Tracking Algorithms for Task Dataflow Execution

Processor architectures has taken a turn towards many-core processors, which integrate multiple processing cores on a single chip to increase overall performance, and there are no signs that this trend will stop in the near future. Many-core processors are harder to program than multi-core and single-core processors due to the need of writing parallel or concurrent programs with high degrees of parallelism. Moreover, many-cores have to operate in a mode of strong scaling because of memory bandwidth constraints. In strong scaling increasingly finer-grain parallelism must be extracted in order to keep all processing cores busy.

Task dataflow programming models have a high potential to simplify parallel program- ming because they alleviate the programmer from identifying precisely all inter-task de- pendences when writing programs. Instead, the task dataflow runtime system detects and enforces inter-task dependences during execution based on the description of memory each task accesses. The runtime constructs a task dataflow graph that captures all tasks and their dependences. Tasks are scheduled to execute in parallel taking into account dependences specified in the task graph.

Several papers report important overheads for task dataflow systems, which severely limits the scalability and usability of such systems. In this paper we study efficient schemes to manage task graphs and analyze their scalability. We assume a programming model that supports input, output and in/out annotations on task arguments, as well as commutative in/out and reductions. We analyze the structure of task graphs and identify versions and generations as key concepts for efficient management of task graphs. Then, we present three schemes to manage task graphs building on graph representations, hypergraphs and lists. We also consider a fourth edge-less scheme that synchronizes tasks using integers. Analysis using micro-benchmarks shows that the graph representation is not always scalable and that the edge-less scheme introduces least overhead in nearly all situations.

Exploring eye movement analysis as a measure of selective visual attention in brain injured individuals

Primary Objective: To investigate the utility of using a new method of assessment for deficits in selective visual attention (SVA). Methods and Procedures: An independent groups design compared six participants with brain injuries with six participants from a non-brain injured control group. The Sensomotoric Instruments Eye Movement system with remote eye-tracking device (eye camera), and 2 sets of eight stimuli were employed to determine if the camera would be a sensitive discriminator of SVA in these groups. Main Outcomes and Results: The attention profile displayed by the brain injured group showed that they were slower, made more errors, were less accurate, and more indecisive than the control group. Conclusions: The utility of eye movement analysis as an assessment method was established, with implications for rehabilitation requiring further development. Key words: selective visual attention, eye movement analysis, brain injury

Discovery of Spatial Periodicities in a Coronal Loop Using Automated Edge-Tracking Algorithms

A new method for automated coronal loop tracking, in both spatial and temporal domains, is presented. Applying this technique to TRACE data, obtained using the 171 angstrom filter on 1998 July 14, we detect a coronal loop undergoing a 270 s kink-mode oscillation, as previously found by Aschwanden et al. However, we also detect flare-induced, and previously unnoticed, spatial periodicities on a scale of 3500 km, which occur along the coronal loop edge. Furthermore, we establish a reduction in oscillatory power for these spatial periodicities of 45% over a 222 s interval. We relate the reduction in detected oscillatory power to the physical damping of these loop-top oscillations.

Target Detection and Tracking With Heterogeneous Sensors

We present a multimodal detection and tracking algorithm for sensors composed of a camera mounted between two microphones. Target localization is performed on color-based change detection in the video modality and on time difference of arrival (TDOA) estimation between the two microphones in the audio modality. The TDOA is computed by multiband generalized cross correlation (GCC) analysis. The estimated directions of arrival are then postprocessed using a Riccati Kalman filter. The visual and audio estimates are finally integrated, at the likelihood level, into a particle filter (PF) that uses a zero-order motion model, and a weighted probabilistic data association (WPDA) scheme. We demonstrate that the Kalman filtering (KF) improves the accuracy of the audio source localization and that the WPDA helps to enhance the tracking performance of sensor fusion in reverberant scenarios. The combination of multiband GCC, KF, and WPDA within the particle filtering framework improves the performance of the algorithm in noisy scenarios. We also show how the proposed audiovisual tracker summarizes the observed scene by generating metadata that can be transmitted to other network nodes instead of transmitting the raw images and can be used for very low bit rate communication. Moreover, the generated metadata can also be used to detect and monitor events of interest.

Unidirectional hybridization at a species' range boundary: implications for habitat tracking

Aim Introgressive hybridization between a locally rare species and a more abundant congener can drive population extinction via genetic assimilation, or the replacement of the rare species gene pool with that of the common species. To date, however, few studies have assessed the effects of such processes at the limits of species' distribution ranges. In this study, we have examined the potential for hybridization between range-edge populations of the wintergreen Pyrola minor and sympatric populations of Pyrola grandiflora. Location Qeqertarsuaq, Greenland and Churchill, Manitoba, Canada. Methods Genetic analysis of samples from Greenland and Canada was carried out using a combination of nuclear and chloroplast single nucleotide polymorphisms (SNPs). Results Analysis of nuclear SNPs confirmed hybridization in populations of morphologically intermediate individuals, as well as revealing the existence of cryptic hybrids in ostensibly morphologically pure P. minor populations. Analysis of chloroplast SNPs revealed that this hybridization is unidirectional and suggests that hybrids originate via pollen swamping of P. minor by the more common P. grandiflora. Main conclusions Extensive unidirectional hybridization may lead to the extinction of peripheral populations of P. minor where the two species grow sympatrically. Extinction could occur as a result of genetic assimilation where F1s are fertile, or via the removal of unidirectionally pollinated sterile F1s, or by a combination of these processes. This could compromise the ability of species to respond to climate change via habitat tracking, although the final outcome of these processes may ultimately depend on the rate of global climate change and its effect on the species' distributions. © 2009 Blackwell Publishing Ltd.

Quantification of nanoparticle uptake by cells using microscopical and analytical techniques

Quantification of nanoparticles in biological systems (i.e., cells, tissues and organs) is becoming a vital part of nanotoxicological and nanomedical fields. Dose is a key parameter when assessing behavior and any potential risk of nanomaterials. Various techniques for nanoparticle quantification in cells and tissues already exist but will need further development in order to make measurements reliable, reproducible and intercomparable between different techniques. Microscopy allows detection and location of nanoparticles in cells and has been used extensively in recent years to characterize nanoparticles and their pathways in living systems. Besides microscopical techniques (light microscopy and electron microscopy mainly), analytical techniques such as mass spectrometry, an established technique in trace element analysis, have been used in nanoparticle research. Other techniques require 'labeled particles, fluorescently, radioactively or magnetically. However, these techniques lack spatial resolution and subcellular localization is not possible. To date, only electron microscopy offers the resolving power to determine accumulation of nanoparticles in cells due to its ability to image particles individually. So-called super-resolution light microscopy techniques are emerging to provide sufficient resolution on the light microscopy level to image or 'see particles as individual particles. Nevertheless, all microscopy techniques require statistically sound sampling strategies in order to provide quantitative results. Stereology is a well-known sampling technique in various areas and, in combination with electron microscopy, proves highly successful with regard to quantification of nanoparticle uptake by cells. © 2010 Future Medicine Ltd.

Wildlife tracking data management: a new vision

To date, the processing of wildlife location data has relied on a diversity of software and file formats. Data management and the following spatial and statistical analyses were undertaken in multiple steps, involving many time-consuming importing/exporting phases. Recent technological advancements in tracking systems have made large, continuous, high-frequency datasets of wildlife behavioral data available, such as those derived from the global positioning system (GPS) and other animal-attached sensor devices. These data can be further complemented by a wide range of other information about the animals’ environment. Management of these large and diverse datasets for modelling animal behaviour and ecology can prove challenging, slowing down analysis and increasing the probability of mistakes in data handling. We address these issues by critically evaluating the requirements for good management of GPS data for wildlife biology. We highlight that dedicated data management tools and expertise are needed. We explore current research in wildlife data management. We suggest a general direction of development, based on a modular software architecture with a spatial database at its core, where interoperability, data model design and integration with remote-sensing data sources play an important role in successful GPS data handling.

Live-Cell Tracking Using SIFT Features in DIC Microscopic Videos

In this paper, a novel motion-tracking scheme using scale-invariant features is proposed for automatic cell motility analysis in gray-scale microscopic videos, particularly for the live-cell tracking in low-contrast differential interference contrast (DIC) microscopy. In the proposed approach, scale-invariant feature transform (SIFT) points around live cells in the microscopic image are detected, and a structure locality preservation (SLP) scheme using Laplacian Eigenmap is proposed to track the SIFT feature points along successive frames of low-contrast DIC videos. Experiments on low-contrast DIC microscopic videos of various live-cell lines shows that in comparison with principal component analysis (PCA) based SIFT tracking, the proposed Laplacian-SIFT can significantly reduce the error rate of SIFT feature tracking. With this enhancement, further experimental results demonstrate that the proposed scheme is a robust and accurate approach to tackling the challenge of live-cell tracking in DIC microscopy.