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

Single network adaptive critic aided dynamic inversion for optimal regulation and command tracking with online adaptation for enhanced robustness

To combine the advantages of both stability and optimality-based designs, a single network adaptive critic (SNAC) aided nonlinear dynamic inversion approach is presented in this paper. Here, the gains of a dynamic inversion controller are selected in such a way that the resulting controller behaves very close to a pre-synthesized SNAC controller in the output regulation sense. Because SNAC is based on optimal control theory, it makes the dynamic inversion controller operate nearly optimal. More important, it retains the two major benefits of dynamic inversion, namely (i) a closed-form expression of the controller and (ii) easy scalability to command tracking applications without knowing the reference commands a priori. An extended architecture is also presented in this paper that adapts online to system modeling and inversion errors, as well as reduced control effectiveness, thereby leading to enhanced robustness. The strengths of this hybrid method of applying SNAC to optimize an nonlinear dynamic inversion controller is demonstrated by considering a benchmark problem in robotics, that is, a two-link robotic manipulator system. Copyright (C) 2013 John Wiley & Sons, Ltd.

MULTI-PITCH TRACKING USING GAUSSIAN MIXTURE MODEL WITH TIME VARYING PARAMETERS AND GRATING COMPRESSION TRANSFORM

Grating Compression Transform (GCT) is a two-dimensional analysis of speech signal which has been shown to be effective in multi-pitch tracking in speech mixtures. Multi-pitch tracking methods using GCT apply Kalman filter framework to obtain pitch tracks which requires training of the filter parameters using true pitch tracks. We propose an unsupervised method for obtaining multiple pitch tracks. In the proposed method, multiple pitch tracks are modeled using time-varying means of a Gaussian mixture model (GMM), referred to as TVGMM. The TVGMM parameters are estimated using multiple pitch values at each frame in a given utterance obtained from different patches of the spectrogram using GCT. We evaluate the performance of the proposed method on all voiced speech mixtures as well as random speech mixtures having well separated and close pitch tracks. TVGMM achieves multi-pitch tracking with 51% and 53% multi-pitch estimates having error <= 20% for random mixtures and all-voiced mixtures respectively. TVGMM also results in lower root mean squared error in pitch track estimation compared to that by Kalman filtering.

VISUAL OBJECT TRACKING VIA RANDOM FERNS BASED CLASSIFICATION

Designing a robust algorithm for visual object tracking has been a challenging task since many years. There are trackers in the literature that are reasonably accurate for many tracking scenarios but most of them are computationally expensive. This narrows down their applicability as many tracking applications demand real time response. In this paper, we present a tracker based on random ferns. Tracking is posed as a classification problem and classification is done using ferns. We used ferns as they rely on binary features and are extremely fast at both training and classification as compared to other classification algorithms. Our experiments show that the proposed tracker performs well on some of the most challenging tracking datasets and executes much faster than one of the state-of-the-art trackers, without much difference in tracking accuracy.

Robust tracking with interest points: A sparse representation approach

Visual tracking is an important task in various computer vision applications including visual surveillance, human computer interaction, event detection, video indexing and retrieval. Recent state of the art sparse representation (SR) based trackers show better robustness than many of the other existing trackers. One of the issues with these SR trackers is low execution speed. The particle filter framework is one of the major aspects responsible for slow execution, and is common to most of the existing SR trackers. In this paper,(1) we propose a robust interest point based tracker in l(1) minimization framework that runs at real-time with performance comparable to the state of the art trackers. In the proposed tracker, the target dictionary is obtained from the patches around target interest points. Next, the interest points from the candidate window of the current frame are obtained. The correspondence between target and candidate points is obtained via solving the proposed l(1) minimization problem. In order to prune the noisy matches, a robust matching criterion is proposed, where only the reliable candidate points that mutually match with target and candidate dictionary elements are considered for tracking. The object is localized by measuring the displacement of these interest points. The reliable candidate patches are used for updating the target dictionary. The performance and accuracy of the proposed tracker is benchmarked with several complex video sequences. The tracker is found to be considerably fast as compared to the reported state of the art trackers. The proposed tracker is further evaluated for various local patch sizes, number of interest points and regularization parameters. The performance of the tracker for various challenges including illumination change, occlusion, and background clutter has been quantified with a benchmark dataset containing 50 videos. (C) 2014 Elsevier B.V. All rights reserved.

Boundary Tracking using a Hybrid Cyclic Pursuit Scheme

There has been a lot of work in the literature, related to the mapping of boundaries of regions, using multiple agents. Most of these are based on optimization techniques or rely on potential fields to drive the agents towards the boundary and then retain them there while they space out evenly along the perimeter or surface (in two-dimensional and three-dimensional cases, respectively). In this paper an algorithm to track the boundary of a region in space is provided based on the cyclic pursuit scheme. This enables the agents to constantly move along the perimeter in a cluster, thereby tracking a dynamically changing boundary. The trajectories of the agents provide a sketch of the boundary. The use of multiple agents may facilitate minimization of tracking error by providing accurate estimates of points on the boundary, besides providing redundancy. Simulation results are provided to highlight the performance of the proposed scheme.

All Digital Energy Sensing for Minimum Energy Tracking

Minimizing energy consumption is of utmost importance in an energy starved system with relaxed performance requirements. This brief presents a digital energy sensing method that requires neither a constant voltage reference nor a time reference. An energy minimizing loop uses this to find the minimum energy point and sets the supply voltage between 0.2 and 0.5 V. Energy savings up to 1275% over existing minimum energy tracking techniques in the literature is achieved.

Timing Recovery Algorithms and Architectures for 2-D Magnetic Recording Systems

We investigate the problem of timing recovery for 2-D magnetic recording (TDMR) channels. We develop a timing error model for TDMR channel considering the phase and frequency offsets with noise. We propose a 2-D data-aided phase-locked loop (PLL) architecture for tracking variations in the position and movement of the read head in the down-track and cross-track directions and analyze the convergence of the algorithm under non-separable timing errors. We further develop a 2-D interpolation-based timing recovery scheme that works in conjunction with the 2-D PLL. We quantify the efficiency of our proposed algorithms by simulations over a 2-D magnetic recording channel with timing errors.

A zero charge-pump mismatch current tracking loop for reference spur reduction in PLLs

The charge-pump (CP) mismatch current is a dominant source of static phase error and reference spur in the nano-meter CMOS PLL implementations due to its worsened channel length modulation effect. This paper presents a charge-pump (CP) mismatch current reduction technique utilizing an adaptive body bias tuning of CP transistors and a zero CP mismatch current tracking PLL architecture for reference spur suppression. A chip prototype of the proposed circuit was implemented in 0.13 mu m CMOS technology. The frequency synthesizer consumes 8.2 mA current from a 13 V supply voltage and achieves a phase noise of -96.01 dBc/Hz @ 1 MHz offset from a 2.4 GHz RF carrier. The charge-pump measurements using the proposed calibration technique exhibited a mismatch current of less than 0.3 mu A (0.55%) over the VCO control voltage range of 0.3-1.0 V. The closed loop measurements show a minimized static phase error of within +/- 70 ps and a similar or equal to 9 dB reduction in reference spur level across the PLL output frequency range 2.4-2.5 GHz. The presented CP calibration technique compensates for the DC current mismatch and the mismatch due to channel length modulation effect and therefore improves the performance of CP-PLLs in nano-meter CMOS implementations. (C) 2015 Elsevier Ltd. All rights reserved.

Multiple Spectral Peak Tracking for Heart Rate Monitoring from Photoplethysmography Signal During Intensive Physical Exercise

We propose a multiple initialization based spectral peak tracking (MISPT) technique for heart rate monitoring from photoplethysmography (PPG) signal. MISPT is applied on the PPG signal after removing the motion artifact using an adaptive noise cancellation filter. MISPT yields several estimates of the heart rate trajectory from the spectrogram of the denoised PPG signal which are finally combined using a novel measure called trajectory strength. Multiple initializations help in correcting erroneous heart rate trajectories unlike the typical SPT which uses only single initialization. Experiments on the PPG data from 12 subjects recorded during intensive physical exercise show that the MISPT based heart rate monitoring indeed yields a better heart rate estimate compared to the SPT with single initialization. On the 12 datasets MISPT results in an average absolute error of 1.11 BPM which is lower than 1.28 BPM obtained by the state-of-the-art online heart rate monitoring algorithm.

A flame particle tracking analysis of turbulence-chemistry interaction in hydrogen-air premixed flames

Interactions of turbulence, molecular transport, and energy transport, coupled with chemistry play a crucial role in the evolution of flame surface geometry, propagation, annihilation, and local extinction/re-ignition characteristics of intensely turbulent premixed flames. This study seeks to understand how these interactions affect flame surface annihilation of lean hydrogen-air premixed turbulent flames. Direct numerical simulations (DNSs) are conducted at different parametric conditions with a detailed reaction mechanism and transport properties for hydrogen-air flames. Flame particle tracking (FPT) technique is used to follow specific flame surface segments. An analytical expression for the local displacement flame speed (S-d) of a temperature isosurface is considered, and the contributions of transport, chemistry, and kinematics on the displacement flame speed at different turbulence-flame interaction conditions are identified. In general, the displacement flame speed for the flame particles is found to increase with time for all conditions considered. This is because, eventually all flame surfaces and their resident flame particles approach annihilation by reactant island formation at the end of stretching and folding processes induced by turbulence. Statistics of principal curvature evolving in time, obtained using FPT, suggest that these islands are ellipsoidal on average enclosing fresh reactants. Further examinations show that the increase in S-d is caused by the increased negative curvature of the flame surface and eventual homogenization of temperature gradients as these reactant islands shrink due to flame propagation and turbulent mixing. Finally, the evolution of the normalized, averaged, displacement flame speed vs. stretch Karlovitz number are found to collapse on a narrow band, suggesting that a unified description of flame speed dependence on stretch rate may be possible in the Lagrangian description. (C) 2015 The Combustion Institute. Published by Elsevier Inc. All rights reserved.

Inhibition of p300 lysine acetyltransferase activity by luteolin reduces tumor growth in head and neck squamous cell carcinoma (HNSCC) xenograft mouse model

Chromatin acetylation is attributed with distinct functional relevance with respect to gene expression in normal and diseased conditions thereby leading to a topical interest in the concept of epigenetic modulators and therapy. We report here the identification and characterization of the acetylation inhibitory potential of an important dietary flavonoid, luteolin. Luteolin was found to inhibit p300 acetyltransferase with competitive binding to the acetyl CoA binding site. Luteolin treatment in a xenografted tumor model of head and neck squamous cell carcinoma (HNSCC), led to a dramatic reduction in tumor growth within 4 weeks corresponding to a decrease in histone acetylation. Cells treated with luteolin exhibit cell cycle arrest and decreased cell migration. Luteolin treatment led to an alteration in gene expression and miRNA profile including up-regulation of p53 induced miR-195/215, let7C; potentially translating into a tumor suppressor function. It also led to down regulation of oncomiRNAs such as miR-135a, thereby reflecting global changes in the microRNA network. Furthermore, a direct correlation between the inhibition of histone acetylation and gene expression was established using chromatin immunoprecipitation on promoters of differentially expressed genes. A network of dysregulated genes and miRNAs was mapped along with the gene ontology categories, and the effects of luteolin were observed to be potentially at multiple levels: at the level of gene expression, miRNA expression and miRNA processing.

Tracking the migration of the Indian continent using the carbonate clumped isotope technique on Phanerozoic soil carbonates

Approximately 140 million years ago, the Indian plate separated from Gondwana and migrated by almost 90 degrees latitude to its current location, forming the Himalayan-Tibetan system. Large discrepancies exist in the rate of migration of Indian plate during Phanerozoic. Here we describe a new approach to paleo-latitudinal reconstruction based on simultaneous determination of carbonate formation temperature and delta O-18 of soil carbonates, constrained by the abundances of C-13-O-18 bonds in palaeosol carbonates. Assuming that the palaeosol carbonates have a strong relationship with the composition of the meteoric water, delta O-18 carbonate of palaeosol can constrain paleo-latitudinal position. Weighted mean annual rainfall delta O-18 water values measured at several stations across the southern latitudes are used to derive a polynomial equation: delta(18)Ow = -0.006 x (LAT)(2) - 0.294 x (LAT) - 5.29 which is used for latitudinal reconstruction. We use this approach to show the northward migration of the Indian plate from 46.8 +/- 5.8 degrees S during the Permian (269 M. y.) to 30 +/- 11 degrees S during the Triassic (248 M. y.), 14.7 +/- 8.7 degrees S during the early Cretaceous (135 M. y.), and 28 +/- 8.8 degrees S during the late Cretaceous ( 68 M. y.). Soil carbonate delta O-18 provides an alternative method for tracing the latitudinal position of Indian plate in the past and the estimates are consistent with the paleo-magnetic records which document the position of Indian plate prior to 135 +/- 3 M. y.

A quality-guided displacement tracking algorithm for ultrasonic elasticity imaging.

Displacement estimation is a key step in the evaluation of tissue elasticity by quasistatic strain imaging. An efficient approach may incorporate a tracking strategy whereby each estimate is initially obtained from its neighbours' displacements and then refined through a localized search. This increases the accuracy and reduces the computational expense compared with exhaustive search. However, simple tracking strategies fail when the target displacement map exhibits complex structure. For example, there may be discontinuities and regions of indeterminate displacement caused by decorrelation between the pre- and post-deformation radio frequency (RF) echo signals. This paper introduces a novel displacement tracking algorithm, with a search strategy guided by a data quality indicator. Comparisons with existing methods show that the proposed algorithm is more robust when the displacement distribution is challenging.

Models and algorithms for detection and tracking of coordinated groups

In this paper, we describe models and algorithms for detection and tracking of group and individual targets. We develop two novel group dynamical models, within a continuous time setting, that aim to mimic behavioural properties of groups. We also describe two possible ways of modeling interactions between closely using Markov Random Field (MRF) and repulsive forces. These can be combined together with a group structure transition model to create realistic evolving group models. We use a Markov Chain Monte Carlo (MCMC)-Particles Algorithm to perform sequential inference. Computer simulations demonstrate the ability of the algorithm to detect and track targets within groups, as well as infer the correct group structure over time. ©2008 IEEE.