Optimum steering input determination and path-tracking of all-wheel steer vehicles on uneven terrains based on constrained optimization

In this paper we propose a framework for optimum steering input determination of all-wheel steer vehicles (AWSV) on rough terrains. The framework computes the steering input which minimizes the tracking error for a given trajectory. Unlike previous methodologies of computing steering inputs of car-like vehicles, the proposed methodology depends explicitly on the vehicle dynamics and can be extended to vehicle having arbitrary number of steering inputs. A fully generic framework has been used to derive the vehicle dynamics and a non-linear programming based constrained optimization approach has been used to compute the steering input considering the instantaneous vehicle dynamics, no-slip and contact constraints of the vehicle. All Wheel steer Vehicles have a special parallel steering ability where the instantaneous centre of rotation (ICR) is at infinity. The proposed framework automatically enables the vehicle to choose between parallel steer and normal operation depending on the error with respect to the desired trajectory. The efficacy of the proposed framework is proved by extensive uneven terrain simulations, for trajectories with continuous or discontinuous velocity profile.

A Diffusion-Based Processor Reallocation Strategy for Tracking Multiple Dynamically Varying Weather Phenomena

Many meteorological phenomena occur at different locations simultaneously. These phenomena vary temporally and spatially. It is essential to track these multiple phenomena for accurate weather prediction. Efficient analysis require high-resolution simulations which can be conducted by introducing finer resolution nested simulations, nests at the locations of these phenomena. Simultaneous tracking of these multiple weather phenomena requires simultaneous execution of the nests on different subsets of the maximum number of processors for the main weather simulation. Dynamic variation in the number of these nests require efficient processor reallocation strategies. In this paper, we have developed strategies for efficient partitioning and repartitioning of the nests among the processors. As a case study, we consider an application of tracking multiple organized cloud clusters in tropical weather systems. We first present a parallel data analysis algorithm to detect such clouds. We have developed a tree-based hierarchical diffusion method which reallocates processors for the nests such that the redistribution cost is less. We achieve this by a novel tree reorganization approach. We show that our approach exhibits up to 25% lower redistribution cost and 53% lesser hop-bytes than the processor reallocation strategy that does not consider the existing processor allocation.

A fractional order proportional integral controller for path following and trajectory tracking of miniature air vehicles

In this paper, a fractional order proportional-integral controller is developed for a miniature air vehicle for rectilinear path following and trajectory tracking. The controller is implemented by constructing a vector field surrounding the path to be followed, which is then used to generate course commands for the miniature air vehicle. The fractional order proportional-integral controller is simulated using the fundamentals of fractional calculus, and the results for this controller are compared with those obtained for a proportional controller and a proportional integral controller. In order to analyze the performance of the controllers, four performance metrics, namely (maximum) overshoot, control effort, settling time and integral of the timed absolute error cost, have been selected. A comparison of the nominal as well as the robust performances of these controllers indicates that the fractional order proportional-integral controller exhibits the best performance in terms of ITAE while showing comparable performances in all other aspects.

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.

Economic tradeoffs in mitigation, due to different atmospheric lifetimes of CO2 and black carbon

Tradeoffs are examined between mitigating black carbon (BC) and carbon dioxide (CO2) for limiting peak global mean warming, using the following set of methods. A two-box climate model is used to simulate temperatures of the atmosphere and ocean for different rates of mitigation. Mitigation rates for BC and CO2 are characterized by respective timescales for e-folding reduction in emissions intensity of gross global product. There are respective emissions models that force the box model. Lastly there is a simple economics model, with cost of mitigation varying inversely with emission intensity. Constant mitigation timescale corresponds to mitigation at a constant annual rate, for example an e-folding timescale of 40 years corresponds to 2.5% reduction each year. Discounted present cost depends only on respective mitigation timescale and respective mitigation cost at present levels of emission intensity. Least-cost mitigation is posed as choosing respective e-folding timescales, to minimize total mitigation cost under a temperature constraint (e.g. within 2 degrees C above preindustrial). Peak warming is more sensitive to mitigation timescale for CO2 than for BC. Therefore rapid mitigation of CO2 emission intensity is essential to limiting peak warming, but simultaneous mitigation of BC can reduce total mitigation expenditure. (c) 2015 Elsevier B.V. All rights reserved.

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.

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.