Signal tracking approach for phase estimation in digital holographic interferometry

In this research work, we introduce a novel approach for phase estimation from noisy reconstructed interference fields in digital holographic interferometry using an unscented Kalman filter. Unlike conventionally used unwrapping algorithms and piecewise polynomial approximation approaches, this paper proposes, for the first time to the best of our knowledge, a signal tracking approach for phase estimation. The state space model derived in this approach is inspired from the Taylor series expansion of the phase function as the process model, and polar to Cartesian conversion as the measurement model. We have characterized our approach by simulations and validated the performance on experimental data (holograms) recorded under various practical conditions. Our study reveals that the proposed approach, when compared with various phase estimation methods available in the literature, outperforms at lower SNR values (i.e., especially in the range 0-20 dB). It is demonstrated with experimental data as well that the proposed approach is a better choice for estimating rapidly varying phase with high dynamic range and noise. (C) 2014 Optical Society of America

Imaging and tracking single fluorescent nanobeads in solution

In single-particle tracking (SPT), fluorescence video microscopy is used to record the motion images of single particle or single molecule. Here, by using a total-internal-reflection microscope equipped with an argon ion laser and a charge-coupled device (CCD) camera with high-speed and high-sensitivity, video images of single nanobeads in solutions were obtained. From the trajectories, the diffusion coefficient of individual nanobead was determined by the mean square displacements as a function of time. The sizes of nanobeads were calculated by Stokes-Einstein equation, and the results were compared with the actual values.

Enhancement of Construction Equipment Detection in Video Frames by Combining with Tracking

Vision-based object detection has been introduced in construction for recognizing and locating construction entities in on-site camera views. It can provide spatial locations of a large number of entities, which is beneficial in large-scale, congested construction sites. However, even a few false detections prevent its practical applications. In resolving this issue, this paper presents a novel hybrid method for locating construction equipment that fuses the function of detection and tracking algorithms. This method detects construction equipment in the video view by taking advantage of entities' motion, shape, and color distribution. Background subtraction, Haar-like features, and eigen-images are used for motion, shape, and color information, respectively. A tracking algorithm steps in the process to make up for the false detections. False detections are identified by catching drastic changes in object size and appearance. The identified false detections are replaced with tracking results. Preliminary experiments show that the combination with tracking has the potential to enhance the detection performance.

An adaptive controller for a class of nonlinear system using direction basis function

For a class of nonlinear dynamical systems, the adaptive controllers are investigated using direction basis function (DBF) in this paper. Based on the criterion of Lyapunov' stability, DBF is designed which guarantees that the output of the controlled system asymptotically tracks the reference signals. Finally, the simulation shows the good tracking effectiveness of the adaptive controller.

Limits of ventricular function: from athlete's heart to a failing heart

The interest in the study of ventricular function has grown considerably in the last decades. In this review, we analyse the extreme values of ventricular function as obtained with Doppler echocardiography. We mainly focus on the parameters that have been used throughout the history of Doppler echocardiography to assess left ventricular (LV) systolic and diastolic function. The ‘athlete's heart’ would be the highest expression of ventricular function whereas its lowest expression is represented by the failing heart, independently from the original aetiology leading to this condition. There are, however, morphological similarities (dilation and hypertrophy) between the athlete's and the failing heart, which emerge as physiological and pathophysiological adaptations, respectively. The introduction of new assessment techniques, specifically speckle tracking, may provide new insight into the properties that determine ventricular filling, specifically left ventricular twisting. The concept of ventricular function must be always considered, although it may not be always possible to distinguish the normal heart of sedentary individuals from that of highly trained hearts based solely on echocardiographic or basic studies.

Inferring Body Pose without Tracking Body Parts

A novel approach for estimating articulated body posture and motion from monocular video sequences is proposed. Human pose is defined as the instantaneous two dimensional configuration (i.e., the projection onto the image plane) of a single articulated body in terms of the position of a predetermined set of joints. First, statistical segmentation of the human bodies from the background is performed and low-level visual features are found given the segmented body shape. The goal is to be able to map these, generally low level, visual features to body configurations. The system estimates different mappings, each one with a specific cluster in the visual feature space. Given a set of body motion sequences for training, unsupervised clustering is obtained via the Expectation Maximation algorithm. Then, for each of the clusters, a function is estimated to build the mapping between low-level features to 3D pose. Currently this mapping is modeled by a neural network. Given new visual features, a mapping from each cluster is performed to yield a set of possible poses. From this set, the system selects the most likely pose given the learned probability distribution and the visual feature similarity between hypothesis and input. Performance of the proposed approach is characterized using a new set of known body postures, showing promising results.

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.

An efficient particle filter for color-based tracking in complex scenes

In this paper, we introduce an efficient method for particle selection in tracking objects in complex scenes. Firstly, we improve the proposal distribution function of the tracking algorithm, including current observation, reducing the cost of evaluating particles with a very low likelihood. In addition, we use a partitioned sampling approach to decompose the dynamic state in several stages. It enables to deal with high-dimensional states without an excessive computational cost. To represent the color distribution, the appearance of the tracked object is modelled by sampled pixels. Based on this representation, the probability of any observation is estimated using non-parametric techniques in color space. As a result, we obtain a Probability color Density Image (PDI) where each pixel points its membership to the target color model. In this way, the evaluation of all particles is accelerated by computing the likelihood p(z|x) using the Integral Image of the PDI.

Tracking the Links to Social Functioning: Asperger Syndrome and Gaze Patterns

The application of Eye Tracking (ET) to the study of social functioning in Asperger Syndrome (AS) provides a unique perspective into social attention and cognition in this atypical neurodevelopmental group. Research in this area has shown how ET can capture social attention atypicalities within this group, such as diminished fixations to the eye region when viewing still images and movie clips; increased fixation to the mouth region; reduced face gaze. Issues exist, however, within the literature, where the type (static/dynamic) and the content (ecological validity) of stimuli used appear to affect the nature of the gaze patterns reported. Objectives: Our research aims were: using the same group of adolescents with AS, to compare their viewing patterns to age and IQ matched typically developing (TD) adolescents using stimuli considered to represent a hierarchy of ecological validity, building from static facial images; through a non-verbal movie clip; through verbal footage from real-life conversation; to eye tracking during real-life conversation. Methods: Eleven participants with AS were compared to 11 TD adolescents, matched for age and IQ. In Study 1, participants were shown 2 sets of static facial images (emotion faces, still images taken from the dynamic clips). In Study 2, three dynamic clips were presented (1 non-verbal movie clip, 2 verbal footage from real-life conversation). Study 3 was an exploratory study of eye tracking during a real-life conversation. Eye movements were recorded via a HiSpeeed (240Hz) SMI eye tracker fitted with chin and forehead rests. Various methods of analysis were used, including a paradigm for temporal analysis of the eye movement data. Results: Results from these studies confirmed that the atypical nature of social attention in AS was successfully captured by this paradigm. While results differed across stimulus sets,
collectively they demonstrated how individuals with AS failed to focus on the most socially relevant aspects of the various stimuli presented. There was also evidence that the eye movements of the AS group were atypically affected by the presence of motion and verbal information. Discriminant Function Analysis demonstrated that the ecological validity of stimuli was an important factor in identifying atypicalities associated with AS, with more accurate classifications of AS and TD groups occurring for more naturalistic stimuli (dynamic rather than static). Graphical analysis of temporal sequences of eye movements revealed the atypical manner in which AS participants followed interactions within the dynamic stimuli. Taken together with data on the order of gaze patterns, more subtle atypicalities were detected in the gaze behaviour of AS individuals towards more socially pertinent regions of the dynamic stimuli. Conclusions: These results have potentially important implications for our understanding of deficits in Asperger Syndrome, as they show that, with more naturalistic stimuli, subtle differences in social attention can be detected that

Identification and output tracking control of Hammerstein systems through wireless networks

his paper investigates the identification and output tracking control of a class of Hammerstein systems through a wireless network within an integrated framework and the statistic characteristics of the wireless network are modelled using the inverse Gaussian cumulative distribution function. In the proposed framework, a new networked identification algorithm is proposed to compensate for the influence of the wireless network delays so as to acquire the more precise Hammerstein system model. Then, the identified model together with the model-based approach is used to design an output tracking controller. Mean square stability conditions are given using linear matrix inequalities (LMIs) and the optimal controller gains can be obtained by solving the corresponding optimization problem expressed using LMIs. Illustrative numerical simulation examples are given to demonstrate the effectiveness of our proposed method.

Inhibition of protease-ENaC signaling improves mucociliary function in cystic fibrosis airways

Rationale: In cystic fibrosis (CF) a reduction in airway surface liquid (ASL) height
compromises mucociliary clearance, favoring mucus plugging and chronic bacterial infection. Inhibitors of ENaC have therapeutic potential in CF airways to reduce the hyperstimulated sodium and fluid absorption to levels which can restore airways hydration.

Objectives: To determine whether a novel compound (QUB-TL1) designed to inhibit protease/ENaC signaling in CF airways restores ASL volume and mucociliary function.

Methods: Protease activity was measured using fluorogenic activity assays. Differentiated primary airway epithelial cell cultures (F508del homozygotes) were used to determined ENaC activity (Ussing chamber recordings), ASL height (confocal microscopy) and mucociliary function (by tracking the surface flow of apically applied microbeads). Cell toxicity was measured by LDH assay.

Measurements and Results: QUB-TL1 inhibits extracellularly-located CAPs, including prostasin, matriptase and furin, the activities of which are observed at excessive levels at the apical surface of CF airway epithelial cells (AECs). QUB-TL1-mediated CAPs inhibition results in diminished ENaC-mediated Na+ absorption in CF AECs due to internalization of a prominent pool of cleaved (active) ENaCγ from the cell surface. Importantly, diminished ENaC activity correlates with improved airway hydration status and mucociliary clearance. We further demonstrate QUB-TL1-mediated furin inhibition, which is in contrast to other serine protease inhibitors (camostat mesylate and aprotinin), affords protection against neutrophil elastase-mediated ENaC activation and Pseudomonas aeruginosa exotoxin A induced cell death.

Conclusions: QUB-TL1 corrects aberrant CAP activities providing a mechanism to delay or prevent the development of CF lung disease in a manner independent of CFTR mutation.

Estudo exploratório da excitabilidade axonal com a "técnica threshold tracking" : controlos saudáveis e doentes com polineuropatia amilóide familiar

Tese de mestrado, Neurociências, Faculdade de Medicina, Universidade de Lisboa, 2015

Formation Control Driven by Cooperative Object Tracking

In this paper we introduce a formation control loop that maximizes the performance of the cooperative perception of a tracked target by a team of mobile robots, while maintaining the team in formation, with a dynamically adjustable geometry which is a function of the quality of the target perception by the team. In the formation control loop, the controller module is a distributed non-linear model predictive controller and the estimator module fuses local estimates of the target state, obtained by a particle filter at each robot. The two modules and their integration are described in detail, including a real-time database associated to a wireless communication protocol that facilitates the exchange of state data while reducing collisions among team members. Simulation and real robot results for indoor and outdoor teams of different robots are presented. The results highlight how our method successfully enables a team of homogeneous robots to minimize the total uncertainty of the tracked target cooperative estimate while complying with performance criteria such as keeping a pre-set distance between the teammates and the target, avoiding collisions with teammates and/or surrounding obstacles.

Characterizing Dynamic Optimization Benchmarks for the Comparison of Multi-Modal Tracking Algorithms

Population-based metaheuristics, such as particle swarm optimization (PSO), have been employed to solve many real-world optimization problems. Although it is of- ten sufficient to find a single solution to these problems, there does exist those cases where identifying multiple, diverse solutions can be beneficial or even required. Some of these problems are further complicated by a change in their objective function over time. This type of optimization is referred to as dynamic, multi-modal optimization. Algorithms which exploit multiple optima in a search space are identified as niching algorithms. Although numerous dynamic, niching algorithms have been developed, their performance is often measured solely on their ability to find a single, global optimum. Furthermore, the comparisons often use synthetic benchmarks whose landscape characteristics are generally limited and unknown. This thesis provides a landscape analysis of the dynamic benchmark functions commonly developed for multi-modal optimization. The benchmark analysis results reveal that the mechanisms responsible for dynamism in the current dynamic bench- marks do not significantly affect landscape features, thus suggesting a lack of representation for problems whose landscape features vary over time. This analysis is used in a comparison of current niching algorithms to identify the effects that specific landscape features have on niching performance. Two performance metrics are proposed to measure both the scalability and accuracy of the niching algorithms. The algorithm comparison results demonstrate the algorithms best suited for a variety of dynamic environments. This comparison also examines each of the algorithms in terms of their niching behaviours and analyzing the range and trade-off between scalability and accuracy when tuning the algorithms respective parameters. These results contribute to the understanding of current niching techniques as well as the problem features that ultimately dictate their success.

Evaluación de la mecánica ventricular izquierda mediante speckle tracking en pacientes sanos en la Clínica Shaio

Introducción: La ecocardiografía es actualmente la técnica de imagen diagnóstica más utilizada para la evaluación de la anatomía y la función cardiovascular. En la actualidad se está utilizando la ecocardiografía por speckle tracking la cual permite una evaluación mas objetiva y confiable de la función ventricular, sin embargo se requieren valores de referencia que hagan que los valores obtenidos sean válidos y útiles para determinar en forma mas oportuna conductas previas al deterioro de su función. Objetivo general: Determinar los valores de referencia para mecánica ventricular izquierda mediante ecocardiografía bidimensional por speckle tracking con equipo Toshiba Artida con transductor multifrecuencia de 3 megahertzios en pacientes sin patología cardiaca conocida en la Fundación Clínica Shaio en el año 2014. Metodología: Análisis de una cohorte prospectiva de todos los pacientes que ingresaron a la Fundación Clínica Shaio para evaluación ecocardiográfica sin patología cardiaca conocida entre los meses Agosto y Diciembre del 2014. Resultados: Se presenta este estudio de la evaluación de la mecánica ventricular izquierda en adultos sanos, los resultados son similares a los obtenidos en estudios de referencia, sin embargo se consideran de gran importancia ya que de acuerdo a la guía actual de evaluación de la mecánica ventricular por strain rate es importante que cada equipo se encuentre estandarizado con el fin de tener resultados válidos de acuerdo a las diferentes patologías en las que se puede aplicar y a nuestra población.