GPS Imaging of vertical land motion in California and Nevada: Implications for Sierra Nevada uplift.

PubMed

Hammond, William C; Blewitt, Geoffrey; Kreemer, Corné

2016-10-01

We introduce Global Positioning System (GPS) Imaging, a new technique for robust estimation of the vertical velocity field of the Earth's surface, and apply it to the Sierra Nevada Mountain range in the western United States. Starting with vertical position time series from Global Positioning System (GPS) stations, we first estimate vertical velocities using the MIDAS robust trend estimator, which is insensitive to undocumented steps, outliers, seasonality, and heteroscedasticity. Using the Delaunay triangulation of station locations, we then apply a weighted median spatial filter to remove velocity outliers and enhance signals common to multiple stations. Finally, we interpolate the data using weighted median estimation on a grid. The resulting velocity field is temporally and spatially robust and edges in the field remain sharp. Results from data spanning 5-20 years show that the Sierra Nevada is the most rapid and extensive uplift feature in the western United States, rising up to 2 mm/yr along most of the range. The uplift is juxtaposed against domains of subsidence attributable to groundwater withdrawal in California's Central Valley. The uplift boundary is consistently stationary, although uplift is faster over the 2011-2016 period of drought. Uplift patterns are consistent with groundwater extraction and concomitant elastic bedrock uplift, plus slower background tectonic uplift. A discontinuity in the velocity field across the southeastern edge of the Sierra Nevada reveals a contrast in lithospheric strength, suggesting a relationship between late Cenozoic uplift of the southern Sierra Nevada and evolution of the southern Walker Lane.

Robust Variable Selection with Exponential Squared Loss.

PubMed

Wang, Xueqin; Jiang, Yunlu; Huang, Mian; Zhang, Heping

2013-04-01

Robust variable selection procedures through penalized regression have been gaining increased attention in the literature. They can be used to perform variable selection and are expected to yield robust estimates. However, to the best of our knowledge, the robustness of those penalized regression procedures has not been well characterized. In this paper, we propose a class of penalized robust regression estimators based on exponential squared loss. The motivation for this new procedure is that it enables us to characterize its robustness that has not been done for the existing procedures, while its performance is near optimal and superior to some recently developed methods. Specifically, under defined regularity conditions, our estimators are [Formula: see text] and possess the oracle property. Importantly, we show that our estimators can achieve the highest asymptotic breakdown point of 1/2 and that their influence functions are bounded with respect to the outliers in either the response or the covariate domain. We performed simulation studies to compare our proposed method with some recent methods, using the oracle method as the benchmark. We consider common sources of influential points. Our simulation studies reveal that our proposed method performs similarly to the oracle method in terms of the model error and the positive selection rate even in the presence of influential points. In contrast, other existing procedures have a much lower non-causal selection rate. Furthermore, we re-analyze the Boston Housing Price Dataset and the Plasma Beta-Carotene Level Dataset that are commonly used examples for regression diagnostics of influential points. Our analysis unravels the discrepancies of using our robust method versus the other penalized regression method, underscoring the importance of developing and applying robust penalized regression methods.

Robust Variable Selection with Exponential Squared Loss

PubMed Central

Wang, Xueqin; Jiang, Yunlu; Huang, Mian; Zhang, Heping

2013-01-01

Robust variable selection procedures through penalized regression have been gaining increased attention in the literature. They can be used to perform variable selection and are expected to yield robust estimates. However, to the best of our knowledge, the robustness of those penalized regression procedures has not been well characterized. In this paper, we propose a class of penalized robust regression estimators based on exponential squared loss. The motivation for this new procedure is that it enables us to characterize its robustness that has not been done for the existing procedures, while its performance is near optimal and superior to some recently developed methods. Specifically, under defined regularity conditions, our estimators are n-consistent and possess the oracle property. Importantly, we show that our estimators can achieve the highest asymptotic breakdown point of 1/2 and that their influence functions are bounded with respect to the outliers in either the response or the covariate domain. We performed simulation studies to compare our proposed method with some recent methods, using the oracle method as the benchmark. We consider common sources of influential points. Our simulation studies reveal that our proposed method performs similarly to the oracle method in terms of the model error and the positive selection rate even in the presence of influential points. In contrast, other existing procedures have a much lower non-causal selection rate. Furthermore, we re-analyze the Boston Housing Price Dataset and the Plasma Beta-Carotene Level Dataset that are commonly used examples for regression diagnostics of influential points. Our analysis unravels the discrepancies of using our robust method versus the other penalized regression method, underscoring the importance of developing and applying robust penalized regression methods. PMID:23913996

Design and Analysis of Map Relative Localization for Access to Hazardous Landing Sites on Mars

NASA Technical Reports Server (NTRS)

Johnson, Andrew E.; Aaron, Seth; Cheng, Yang; Montgomery, James; Trawny, Nikolas; Tweddle, Brent; Vaughan, Geoffrey; Zheng, Jason

2016-01-01

Human and robotic planetary lander missions require accurate surface relative position knowledge to land near science targets or next to pre-deployed assets. In the absence of GPS, accurate position estimates can be obtained by automatically matching sensor data collected during descent to an on-board map. The Lander Vision System (LVS) that is being developed for Mars landing applications generates landmark matches in descent imagery and combines these with inertial data to estimate vehicle position, velocity and attitude. This paper describes recent LVS design work focused on making the map relative localization algorithms robust to challenging environmental conditions like bland terrain, appearance differences between the map and image and initial input state errors. Improved results are shown using data from a recent LVS field test campaign. This paper also fills a gap in analysis to date by assessing the performance of the LVS with data sets containing significant vertical motion including a complete data set from the Mars Science Laboratory mission, a Mars landing simulation, and field test data taken over multiple altitudes above the same scene. Accurate and robust performance is achieved for all data sets indicating that vertical motion does not play a significant role in position estimation performance.

Robust tracking of dexterous continuum robots: Fusing FBG shape sensing and stereo vision.

PubMed

Rumei Zhang; Hao Liu; Jianda Han

2017-07-01

Robust and efficient tracking of continuum robots is important for improving patient safety during space-confined minimally invasive surgery, however, it has been a particularly challenging task for researchers. In this paper, we present a novel tracking scheme by fusing fiber Bragg grating (FBG) shape sensing and stereo vision to estimate the position of continuum robots. Previous visual tracking easily suffers from the lack of robustness and leads to failure, while the FBG shape sensor can only reconstruct the local shape with integral cumulative error. The proposed fusion is anticipated to compensate for their shortcomings and improve the tracking accuracy. To verify its effectiveness, the robots' centerline is recognized by morphology operation and reconstructed by stereo matching algorithm. The shape obtained by FBG sensor is transformed into distal tip position with respect to the camera coordinate system through previously calibrated registration matrices. An experimental platform was set up and repeated tracking experiments were carried out. The accuracy estimated by averaging the absolute positioning errors between shape sensing and stereo vision is 0.67±0.65 mm, 0.41±0.25 mm, 0.72±0.43 mm for x, y and z, respectively. Results indicate that the proposed fusion is feasible and can be used for closed-loop control of continuum robots.

Delay-and-sum beamforming for direction of arrival estimation applied to gunshot acoustics

NASA Astrophysics Data System (ADS)

Ramos, António L. L.; Holm, Sverre; Gudvangen, Sigmund; Otterlei, Ragnvald

2011-06-01

Sniper positioning systems described in the literature use a two-step algorithm to estimate the sniper's location. First, the shockwave and the muzzle blast acoustic signatures must be detected and recognized, followed by an estimation of their respective direction-of-arrival (DOA). Second, the actual sniper's position is calculated based on the estimated DOA via an iterative algorithm that varies from system to system. The overall performance of such a system, however, is highly compromised when the first step is not carried out successfully. Currently available systems rely on a simple calculation of differences of time-of-arrival to estimate angles-of-arrival. This approach, however, lacks robustness by not taking full advantage of the array of sensors. This paper shows how the delay-and-sum beamforming technique can be applied to estimate the DOA for both the shockwave and the muzzle blast. The method has the twofold advantage of 1) adding an array gain of 10 logM, i.e., an increased SNR of 6 dB for a 4-microphone array, which is equivalent to doubling the detection range assuming free-field propagation; and 2) offering improved robustness in handling single- and multi-shots events as well as reflections by taking advantage of the spatial filtering capability.

GPS Imaging of Time-Dependent Seasonal Strain in Central California

NASA Astrophysics Data System (ADS)

Kraner, M.; Hammond, W. C.; Kreemer, C.; Borsa, A. A.; Blewitt, G.

2016-12-01

Recently, studies are suggesting that crustal deformation can be time-dependent and nontectonic. Continuous global positioning system (cGPS) measurements are now showing how steady long-term deformation can be influenced by factors such as fluctuations in loading and temperature variations. Here we model the seasonal time-dependent dilatational and shear strain in Central California, specifically surrounding the Parkfield region and try to uncover the sources of these deformation patterns. We use 8 years of cGPS data (2008 - 2016) processed by the Nevada Geodetic Laboratory and carefully select the cGPS stations for our analysis based on the vertical position of cGPS time series during the drought period. In building our strain model, we first detrend the selected station time series using a set of velocities from the robust MIDAS trend estimator. This estimation algorithm is a robust approach that is insensitive to common problems such as step discontinuities, outliers, and seasonality. We use these detrended time series to estimate the median cGPS positions for each month of the 8-year period and filter displacement differences between these monthly median positions using a filtering technique called "GPS Imaging." This technique improves the overall robustness and spatial resolution of the input displacements for the strain model. We then model our dilatational and shear strain field for each month of time series. We also test a variety of a priori constraints, which controls the style of faulting within the strain model. Upon examining our strain maps, we find that a seasonal strain signal exists in Central California. We investigate how this signal compares to thermoelastic, hydrologic, and atmospheric loading models during the 8-year period. We additionally determine whether the drought played a role in influencing the seasonal signal.

Adaptive AOA-aided TOA self-positioning for mobile wireless sensor networks.

PubMed

Wen, Chih-Yu; Chan, Fu-Kai

2010-01-01

Location-awareness is crucial and becoming increasingly important to many applications in wireless sensor networks. This paper presents a network-based positioning system and outlines recent work in which we have developed an efficient principled approach to localize a mobile sensor using time of arrival (TOA) and angle of arrival (AOA) information employing multiple seeds in the line-of-sight scenario. By receiving the periodic broadcasts from the seeds, the mobile target sensors can obtain adequate observations and localize themselves automatically. The proposed positioning scheme performs location estimation in three phases: (I) AOA-aided TOA measurement, (II) Geometrical positioning with particle filter, and (III) Adaptive fuzzy control. Based on the distance measurements and the initial position estimate, adaptive fuzzy control scheme is applied to solve the localization adjustment problem. The simulations show that the proposed approach provides adaptive flexibility and robust improvement in position estimation.

A Multi-Sensor Fusion MAV State Estimation from Long-Range Stereo, IMU, GPS and Barometric Sensors.

PubMed

Song, Yu; Nuske, Stephen; Scherer, Sebastian

2016-12-22

State estimation is the most critical capability for MAV (Micro-Aerial Vehicle) localization, autonomous obstacle avoidance, robust flight control and 3D environmental mapping. There are three main challenges for MAV state estimation: (1) it can deal with aggressive 6 DOF (Degree Of Freedom) motion; (2) it should be robust to intermittent GPS (Global Positioning System) (even GPS-denied) situations; (3) it should work well both for low- and high-altitude flight. In this paper, we present a state estimation technique by fusing long-range stereo visual odometry, GPS, barometric and IMU (Inertial Measurement Unit) measurements. The new estimation system has two main parts, a stochastic cloning EKF (Extended Kalman Filter) estimator that loosely fuses both absolute state measurements (GPS, barometer) and the relative state measurements (IMU, visual odometry), and is derived and discussed in detail. A long-range stereo visual odometry is proposed for high-altitude MAV odometry calculation by using both multi-view stereo triangulation and a multi-view stereo inverse depth filter. The odometry takes the EKF information (IMU integral) for robust camera pose tracking and image feature matching, and the stereo odometry output serves as the relative measurements for the update of the state estimation. Experimental results on a benchmark dataset and our real flight dataset show the effectiveness of the proposed state estimation system, especially for the aggressive, intermittent GPS and high-altitude MAV flight.

Adaptive Robust Output Feedback Control for a Marine Dynamic Positioning System Based on a High-Gain Observer.

PubMed

Du, Jialu; Hu, Xin; Liu, Hongbo; Chen, C L Philip

2015-11-01

This paper develops an adaptive robust output feedback control scheme for dynamically positioned ships with unavailable velocities and unknown dynamic parameters in an unknown time-variant disturbance environment. The controller is designed by incorporating the high-gain observer and radial basis function (RBF) neural networks in vectorial backstepping method. The high-gain observer provides the estimations of the ship position and heading as well as velocities. The RBF neural networks are employed to compensate for the uncertainties of ship dynamics. The adaptive laws incorporating a leakage term are designed to estimate the weights of RBF neural networks and the bounds of unknown time-variant environmental disturbances. In contrast to the existing results of dynamic positioning (DP) controllers, the proposed control scheme relies only on the ship position and heading measurements and does not require a priori knowledge of the ship dynamics and external disturbances. By means of Lyapunov functions, it is theoretically proved that our output feedback controller can control a ship's position and heading to the arbitrarily small neighborhood of the desired target values while guaranteeing that all signals in the closed-loop DP control system are uniformly ultimately bounded. Finally, simulations involving two ships are carried out, and simulation results demonstrate the effectiveness of the proposed control scheme.

Vector Observation-Aided/Attitude-Rate Estimation Using Global Positioning System Signals

NASA Technical Reports Server (NTRS)

Oshman, Yaakov; Markley, F. Landis

1997-01-01

A sequential filtering algorithm is presented for attitude and attitude-rate estimation from Global Positioning System (GPS) differential carrier phase measurements. A third-order, minimal-parameter method for solving the attitude matrix kinematic equation is used to parameterize the filter's state, which renders the resulting estimator computationally efficient. Borrowing from tracking theory concepts, the angular acceleration is modeled as an exponentially autocorrelated stochastic process, thus avoiding the use of the uncertain spacecraft dynamic model. The new formulation facilitates the use of aiding vector observations in a unified filtering algorithm, which can enhance the method's robustness and accuracy. Numerical examples are used to demonstrate the performance of the method.

Robust Huber-based iterated divided difference filtering with application to cooperative localization of autonomous underwater vehicles.

PubMed

Gao, Wei; Liu, Yalong; Xu, Bo

2014-12-19

A new algorithm called Huber-based iterated divided difference filtering (HIDDF) is derived and applied to cooperative localization of autonomous underwater vehicles (AUVs) supported by a single surface leader. The position states are estimated using acoustic range measurements relative to the leader, in which some disadvantages such as weak observability, large initial error and contaminated measurements with outliers are inherent. By integrating both merits of iterated divided difference filtering (IDDF) and Huber's M-estimation methodology, the new filtering method could not only achieve more accurate estimation and faster convergence contrast to standard divided difference filtering (DDF) in conditions of weak observability and large initial error, but also exhibit robustness with respect to outlier measurements, for which the standard IDDF would exhibit severe degradation in estimation accuracy. The correctness as well as validity of the algorithm is demonstrated through experiment results.

Incremental inverse kinematics based vision servo for autonomous robotic capture of non-cooperative space debris

NASA Astrophysics Data System (ADS)

Dong, Gangqi; Zhu, Z. H.

2016-04-01

This paper proposed a new incremental inverse kinematics based vision servo approach for robotic manipulators to capture a non-cooperative target autonomously. The target's pose and motion are estimated by a vision system using integrated photogrammetry and EKF algorithm. Based on the estimated pose and motion of the target, the instantaneous desired position of the end-effector is predicted by inverse kinematics and the robotic manipulator is moved incrementally from its current configuration subject to the joint speed limits. This approach effectively eliminates the multiple solutions in the inverse kinematics and increases the robustness of the control algorithm. The proposed approach is validated by a hardware-in-the-loop simulation, where the pose and motion of the non-cooperative target is estimated by a real vision system. The simulation results demonstrate the effectiveness and robustness of the proposed estimation approach for the target and the incremental control strategy for the robotic manipulator.

Adaptive output feedback control of flexible-joint robots using neural networks: dynamic surface design approach.

PubMed

Yoo, Sung Jin; Park, Jin Bae; Choi, Yoon Ho

2008-10-01

In this paper, we propose a new robust output feedback control approach for flexible-joint electrically driven (FJED) robots via the observer dynamic surface design technique. The proposed method only requires position measurements of the FJED robots. To estimate the link and actuator velocity information of the FJED robots with model uncertainties, we develop an adaptive observer using self-recurrent wavelet neural networks (SRWNNs). The SRWNNs are used to approximate model uncertainties in both robot (link) dynamics and actuator dynamics, and all their weights are trained online. Based on the designed observer, the link position tracking controller using the estimated states is induced from the dynamic surface design procedure. Therefore, the proposed controller can be designed more simply than the observer backstepping controller. From the Lyapunov stability analysis, it is shown that all signals in a closed-loop adaptive system are uniformly ultimately bounded. Finally, the simulation results on a three-link FJED robot are presented to validate the good position tracking performance and robustness of the proposed control system against payload uncertainties and external disturbances.

A Method for Estimating View Transformations from Image Correspondences Based on the Harmony Search Algorithm.

PubMed

Cuevas, Erik; Díaz, Margarita

2015-01-01

In this paper, a new method for robustly estimating multiple view relations from point correspondences is presented. The approach combines the popular random sampling consensus (RANSAC) algorithm and the evolutionary method harmony search (HS). With this combination, the proposed method adopts a different sampling strategy than RANSAC to generate putative solutions. Under the new mechanism, at each iteration, new candidate solutions are built taking into account the quality of the models generated by previous candidate solutions, rather than purely random as it is the case of RANSAC. The rules for the generation of candidate solutions (samples) are motivated by the improvisation process that occurs when a musician searches for a better state of harmony. As a result, the proposed approach can substantially reduce the number of iterations still preserving the robust capabilities of RANSAC. The method is generic and its use is illustrated by the estimation of homographies, considering synthetic and real images. Additionally, in order to demonstrate the performance of the proposed approach within a real engineering application, it is employed to solve the problem of position estimation in a humanoid robot. Experimental results validate the efficiency of the proposed method in terms of accuracy, speed, and robustness.

A Multi-Sensor Fusion MAV State Estimation from Long-Range Stereo, IMU, GPS and Barometric Sensors

PubMed Central

Song, Yu; Nuske, Stephen; Scherer, Sebastian

2016-01-01

State estimation is the most critical capability for MAV (Micro-Aerial Vehicle) localization, autonomous obstacle avoidance, robust flight control and 3D environmental mapping. There are three main challenges for MAV state estimation: (1) it can deal with aggressive 6 DOF (Degree Of Freedom) motion; (2) it should be robust to intermittent GPS (Global Positioning System) (even GPS-denied) situations; (3) it should work well both for low- and high-altitude flight. In this paper, we present a state estimation technique by fusing long-range stereo visual odometry, GPS, barometric and IMU (Inertial Measurement Unit) measurements. The new estimation system has two main parts, a stochastic cloning EKF (Extended Kalman Filter) estimator that loosely fuses both absolute state measurements (GPS, barometer) and the relative state measurements (IMU, visual odometry), and is derived and discussed in detail. A long-range stereo visual odometry is proposed for high-altitude MAV odometry calculation by using both multi-view stereo triangulation and a multi-view stereo inverse depth filter. The odometry takes the EKF information (IMU integral) for robust camera pose tracking and image feature matching, and the stereo odometry output serves as the relative measurements for the update of the state estimation. Experimental results on a benchmark dataset and our real flight dataset show the effectiveness of the proposed state estimation system, especially for the aggressive, intermittent GPS and high-altitude MAV flight. PMID:28025524

A Robust High-Accuracy Ultrasound Indoor Positioning System Based on a Wireless Sensor Network.

PubMed

Qi, Jun; Liu, Guo-Ping

2017-11-06

This paper describes the development and implementation of a robust high-accuracy ultrasonic indoor positioning system (UIPS). The UIPS consists of several wireless ultrasonic beacons in the indoor environment. Each of them has a fixed and known position coordinate and can collect all the transmissions from the target node or emit ultrasonic signals. Every wireless sensor network (WSN) node has two communication modules: one is WiFi, that transmits the data to the server, and the other is the radio frequency (RF) module, which is only used for time synchronization between different nodes, with accuracy up to 1 μ s. The distance between the beacon and the target node is calculated by measuring the time-of-flight (TOF) for the ultrasonic signal, and then the position of the target is computed by some distances and the coordinate of the beacons. TOF estimation is the most important technique in the UIPS. A new time domain method to extract the envelope of the ultrasonic signals is presented in order to estimate the TOF. This method, with the envelope detection filter, estimates the value with the sampled values on both sides based on the least squares method (LSM). The simulation results show that the method can achieve envelope detection with a good filtering effect by means of the LSM. The highest precision and variance can reach 0.61 mm and 0.23 mm, respectively, in pseudo-range measurements with UIPS. A maximum location error of 10.2 mm is achieved in the positioning experiments for a moving robot, when UIPS works on the line-of-sight (LOS) signal.

Hierarchical human action recognition around sleeping using obscured posture information

NASA Astrophysics Data System (ADS)

Kudo, Yuta; Sashida, Takehiko; Aoki, Yoshimitsu

2015-04-01

This paper presents a new approach for human action recognition around sleeping with the human body parts locations and the positional relationship between human and sleeping environment. Body parts are estimated from the depth image obtained by a time-of-flight (TOF) sensor using oriented 3D normal vector. Issues in action recognition of sleeping situation are the demand of availability in darkness, and hiding of the human body by duvets. Therefore, the extraction of image features is difficult since color and edge features are obscured by covers. Thus, first in our method, positions of four parts of the body (head, torso, thigh, and lower leg) are estimated by using the shape model of bodily surface constructed by oriented 3D normal vector. This shape model can represent the surface shape of rough body, and is effective in robust posture estimation of the body hidden with duvets. Then, action descriptor is extracted from the position of each body part. The descriptor includes temporal variation of each part of the body and spatial vector of position of the parts and the bed. Furthermore, this paper proposes hierarchical action classes and classifiers to improve the indistinct action classification. Classifiers are composed of two layers, and recognize human action by using the action descriptor. First layer focuses on spatial descriptor and classifies action roughly. Second layer focuses on temporal descriptor and classifies action finely. This approach achieves a robust recognition of obscured human by using the posture information and the hierarchical action recognition.

Robust and efficient vision system for group of cooperating mobile robots with application to soccer robots.

PubMed

Klancar, Gregor; Kristan, Matej; Kovacic, Stanislav; Orqueda, Omar

2004-07-01

In this paper a global vision scheme for estimation of positions and orientations of mobile robots is presented. It is applied to robot soccer application which is a fast dynamic game and therefore needs an efficient and robust vision system implemented. General applicability of the vision system can be found in other robot applications such as mobile transport robots in production, warehouses, attendant robots, fast vision tracking of targets of interest and entertainment robotics. Basic operation of the vision system is divided into two steps. In the first, the incoming image is scanned and pixels are classified into a finite number of classes. At the same time, a segmentation algorithm is used to find corresponding regions belonging to one of the classes. In the second step, all the regions are examined. Selection of the ones that are a part of the observed object is made by means of simple logic procedures. The novelty is focused on optimization of the processing time needed to finish the estimation of possible object positions. Better results of the vision system are achieved by implementing camera calibration and shading correction algorithm. The former corrects camera lens distortion, while the latter increases robustness to irregular illumination conditions.

Handheld pose tracking using vision-inertial sensors with occlusion handling

NASA Astrophysics Data System (ADS)

Li, Juan; Slembrouck, Maarten; Deboeverie, Francis; Bernardos, Ana M.; Besada, Juan A.; Veelaert, Peter; Aghajan, Hamid; Casar, José R.; Philips, Wilfried

2016-07-01

Tracking of a handheld device's three-dimensional (3-D) position and orientation is fundamental to various application domains, including augmented reality (AR), virtual reality, and interaction in smart spaces. Existing systems still offer limited performance in terms of accuracy, robustness, computational cost, and ease of deployment. We present a low-cost, accurate, and robust system for handheld pose tracking using fused vision and inertial data. The integration of measurements from embedded accelerometers reduces the number of unknown parameters in the six-degree-of-freedom pose calculation. The proposed system requires two light-emitting diode (LED) markers to be attached to the device, which are tracked by external cameras through a robust algorithm against illumination changes. Three data fusion methods have been proposed, including the triangulation-based stereo-vision system, constraint-based stereo-vision system with occlusion handling, and triangulation-based multivision system. Real-time demonstrations of the proposed system applied to AR and 3-D gaming are also included. The accuracy assessment of the proposed system is carried out by comparing with the data generated by the state-of-the-art commercial motion tracking system OptiTrack. Experimental results show that the proposed system has achieved high accuracy of few centimeters in position estimation and few degrees in orientation estimation.

Weighted least squares techniques for improved received signal strength based localization.

PubMed

Tarrío, Paula; Bernardos, Ana M; Casar, José R

2011-01-01

The practical deployment of wireless positioning systems requires minimizing the calibration procedures while improving the location estimation accuracy. Received Signal Strength localization techniques using propagation channel models are the simplest alternative, but they are usually designed under the assumption that the radio propagation model is to be perfectly characterized a priori. In practice, this assumption does not hold and the localization results are affected by the inaccuracies of the theoretical, roughly calibrated or just imperfect channel models used to compute location. In this paper, we propose the use of weighted multilateration techniques to gain robustness with respect to these inaccuracies, reducing the dependency of having an optimal channel model. In particular, we propose two weighted least squares techniques based on the standard hyperbolic and circular positioning algorithms that specifically consider the accuracies of the different measurements to obtain a better estimation of the position. These techniques are compared to the standard hyperbolic and circular positioning techniques through both numerical simulations and an exhaustive set of real experiments on different types of wireless networks (a wireless sensor network, a WiFi network and a Bluetooth network). The algorithms not only produce better localization results with a very limited overhead in terms of computational cost but also achieve a greater robustness to inaccuracies in channel modeling.

Weighted Least Squares Techniques for Improved Received Signal Strength Based Localization

PubMed Central

Tarrío, Paula; Bernardos, Ana M.; Casar, José R.

2011-01-01

The practical deployment of wireless positioning systems requires minimizing the calibration procedures while improving the location estimation accuracy. Received Signal Strength localization techniques using propagation channel models are the simplest alternative, but they are usually designed under the assumption that the radio propagation model is to be perfectly characterized a priori. In practice, this assumption does not hold and the localization results are affected by the inaccuracies of the theoretical, roughly calibrated or just imperfect channel models used to compute location. In this paper, we propose the use of weighted multilateration techniques to gain robustness with respect to these inaccuracies, reducing the dependency of having an optimal channel model. In particular, we propose two weighted least squares techniques based on the standard hyperbolic and circular positioning algorithms that specifically consider the accuracies of the different measurements to obtain a better estimation of the position. These techniques are compared to the standard hyperbolic and circular positioning techniques through both numerical simulations and an exhaustive set of real experiments on different types of wireless networks (a wireless sensor network, a WiFi network and a Bluetooth network). The algorithms not only produce better localization results with a very limited overhead in terms of computational cost but also achieve a greater robustness to inaccuracies in channel modeling. PMID:22164092

Data-Adaptive Bias-Reduced Doubly Robust Estimation.

PubMed

Vermeulen, Karel; Vansteelandt, Stijn

2016-05-01

Doubly robust estimators have now been proposed for a variety of target parameters in the causal inference and missing data literature. These consistently estimate the parameter of interest under a semiparametric model when one of two nuisance working models is correctly specified, regardless of which. The recently proposed bias-reduced doubly robust estimation procedure aims to partially retain this robustness in more realistic settings where both working models are misspecified. These so-called bias-reduced doubly robust estimators make use of special (finite-dimensional) nuisance parameter estimators that are designed to locally minimize the squared asymptotic bias of the doubly robust estimator in certain directions of these finite-dimensional nuisance parameters under misspecification of both parametric working models. In this article, we extend this idea to incorporate the use of data-adaptive estimators (infinite-dimensional nuisance parameters), by exploiting the bias reduction estimation principle in the direction of only one nuisance parameter. We additionally provide an asymptotic linearity theorem which gives the influence function of the proposed doubly robust estimator under correct specification of a parametric nuisance working model for the missingness mechanism/propensity score but a possibly misspecified (finite- or infinite-dimensional) outcome working model. Simulation studies confirm the desirable finite-sample performance of the proposed estimators relative to a variety of other doubly robust estimators.

A Method for Estimating View Transformations from Image Correspondences Based on the Harmony Search Algorithm

PubMed Central

Cuevas, Erik; Díaz, Margarita

2015-01-01

In this paper, a new method for robustly estimating multiple view relations from point correspondences is presented. The approach combines the popular random sampling consensus (RANSAC) algorithm and the evolutionary method harmony search (HS). With this combination, the proposed method adopts a different sampling strategy than RANSAC to generate putative solutions. Under the new mechanism, at each iteration, new candidate solutions are built taking into account the quality of the models generated by previous candidate solutions, rather than purely random as it is the case of RANSAC. The rules for the generation of candidate solutions (samples) are motivated by the improvisation process that occurs when a musician searches for a better state of harmony. As a result, the proposed approach can substantially reduce the number of iterations still preserving the robust capabilities of RANSAC. The method is generic and its use is illustrated by the estimation of homographies, considering synthetic and real images. Additionally, in order to demonstrate the performance of the proposed approach within a real engineering application, it is employed to solve the problem of position estimation in a humanoid robot. Experimental results validate the efficiency of the proposed method in terms of accuracy, speed, and robustness. PMID:26339228

Accurate motion parameter estimation for colonoscopy tracking using a regression method

NASA Astrophysics Data System (ADS)

Liu, Jianfei; Subramanian, Kalpathi R.; Yoo, Terry S.

2010-03-01

Co-located optical and virtual colonoscopy images have the potential to provide important clinical information during routine colonoscopy procedures. In our earlier work, we presented an optical flow based algorithm to compute egomotion from live colonoscopy video, permitting navigation and visualization of the corresponding patient anatomy. In the original algorithm, motion parameters were estimated using the traditional Least Sum of squares(LS) procedure which can be unstable in the context of optical flow vectors with large errors. In the improved algorithm, we use the Least Median of Squares (LMS) method, a robust regression method for motion parameter estimation. Using the LMS method, we iteratively analyze and converge toward the main distribution of the flow vectors, while disregarding outliers. We show through three experiments the improvement in tracking results obtained using the LMS method, in comparison to the LS estimator. The first experiment demonstrates better spatial accuracy in positioning the virtual camera in the sigmoid colon. The second and third experiments demonstrate the robustness of this estimator, resulting in longer tracked sequences: from 300 to 1310 in the ascending colon, and 410 to 1316 in the transverse colon.

Single-lens stereovision system using a prism: position estimation of a multi-ocular prism.

PubMed

Cui, Xiaoyu; Lim, Kah Bin; Zhao, Yue; Kee, Wei Loon

2014-05-01

In this paper, a position estimation method using a prism-based single-lens stereovision system is proposed. A multifaced prism was considered as a single optical system composed of few refractive planes. A transformation matrix which relates the coordinates of an object point to its coordinates on the image plane through the refraction of the prism was derived based on geometrical optics. A mathematical model which is able to denote the position of an arbitrary faces prism with only seven parameters is introduced. This model further extends the application of the single-lens stereovision system using a prism to other areas. Experimentation results are presented to prove the effectiveness and robustness of our proposed model.

Hand Pose Estimation by Fusion of Inertial and Magnetic Sensing Aided by a Permanent Magnet.

PubMed

Kortier, Henk G; Antonsson, Jacob; Schepers, H Martin; Gustafsson, Fredrik; Veltink, Peter H

2015-09-01

Tracking human body motions using inertial sensors has become a well-accepted method in ambulatory applications since the subject is not confined to a lab-bounded volume. However, a major drawback is the inability to estimate relative body positions over time because inertial sensor information only allows position tracking through strapdown integration, but does not provide any information about relative positions. In addition, strapdown integration inherently results in drift of the estimated position over time. We propose a novel method in which a permanent magnet combined with 3-D magnetometers and 3-D inertial sensors are used to estimate the global trunk orientation and relative pose of the hand with respect to the trunk. An Extended Kalman Filter is presented to fuse estimates obtained from inertial sensors with magnetic updates such that the position and orientation between the human hand and trunk as well as the global trunk orientation can be estimated robustly. This has been demonstrated in multiple experiments in which various hand tasks were performed. The most complex task in which simultaneous movements of both trunk and hand were performed resulted in an average rms position difference with an optical reference system of 19.7±2.2 mm whereas the relative trunk-hand and global trunk orientation error was 2.3±0.9 and 8.6±8.7 deg respectively.

Robust Magnetotelluric Impedance Estimation

NASA Astrophysics Data System (ADS)

Sutarno, D.

2010-12-01

Robust magnetotelluric (MT) response function estimators are now in standard use by the induction community. Properly devised and applied, these have ability to reduce the influence of unusual data (outliers). The estimators always yield impedance estimates which are better than the conventional least square (LS) estimation because the `real' MT data almost never satisfy the statistical assumptions of Gaussian distribution and stationary upon which normal spectral analysis is based. This paper discuses the development and application of robust estimation procedures which can be classified as M-estimators to MT data. Starting with the description of the estimators, special attention is addressed to the recent development of a bounded-influence robust estimation, including utilization of the Hilbert Transform (HT) operation on causal MT impedance functions. The resulting robust performances are illustrated using synthetic as well as real MT data.

A Robust High-Accuracy Ultrasound Indoor Positioning System Based on a Wireless Sensor Network

PubMed Central

Qi, Jun; Liu, Guo-Ping

2017-01-01

This paper describes the development and implementation of a robust high-accuracy ultrasonic indoor positioning system (UIPS). The UIPS consists of several wireless ultrasonic beacons in the indoor environment. Each of them has a fixed and known position coordinate and can collect all the transmissions from the target node or emit ultrasonic signals. Every wireless sensor network (WSN) node has two communication modules: one is WiFi, that transmits the data to the server, and the other is the radio frequency (RF) module, which is only used for time synchronization between different nodes, with accuracy up to 1 μs. The distance between the beacon and the target node is calculated by measuring the time-of-flight (TOF) for the ultrasonic signal, and then the position of the target is computed by some distances and the coordinate of the beacons. TOF estimation is the most important technique in the UIPS. A new time domain method to extract the envelope of the ultrasonic signals is presented in order to estimate the TOF. This method, with the envelope detection filter, estimates the value with the sampled values on both sides based on the least squares method (LSM). The simulation results show that the method can achieve envelope detection with a good filtering effect by means of the LSM. The highest precision and variance can reach 0.61 mm and 0.23 mm, respectively, in pseudo-range measurements with UIPS. A maximum location error of 10.2 mm is achieved in the positioning experiments for a moving robot, when UIPS works on the line-of-sight (LOS) signal. PMID:29113126

On the asymptotic standard error of a class of robust estimators of ability in dichotomous item response models.

PubMed

Magis, David

2014-11-01

In item response theory, the classical estimators of ability are highly sensitive to response disturbances and can return strongly biased estimates of the true underlying ability level. Robust methods were introduced to lessen the impact of such aberrant responses on the estimation process. The computation of asymptotic (i.e., large-sample) standard errors (ASE) for these robust estimators, however, has not yet been fully considered. This paper focuses on a broad class of robust ability estimators, defined by an appropriate selection of the weight function and the residual measure, for which the ASE is derived from the theory of estimating equations. The maximum likelihood (ML) and the robust estimators, together with their estimated ASEs, are then compared in a simulation study by generating random guessing disturbances. It is concluded that both the estimators and their ASE perform similarly in the absence of random guessing, while the robust estimator and its estimated ASE are less biased and outperform their ML counterparts in the presence of random guessing with large impact on the item response process. © 2013 The British Psychological Society.

Robust Methods for Moderation Analysis with a Two-Level Regression Model.

PubMed

Yang, Miao; Yuan, Ke-Hai

2016-01-01

Moderation analysis has many applications in social sciences. Most widely used estimation methods for moderation analysis assume that errors are normally distributed and homoscedastic. When these assumptions are not met, the results from a classical moderation analysis can be misleading. For more reliable moderation analysis, this article proposes two robust methods with a two-level regression model when the predictors do not contain measurement error. One method is based on maximum likelihood with Student's t distribution and the other is based on M-estimators with Huber-type weights. An algorithm for obtaining the robust estimators is developed. Consistent estimates of standard errors of the robust estimators are provided. The robust approaches are compared against normal-distribution-based maximum likelihood (NML) with respect to power and accuracy of parameter estimates through a simulation study. Results show that the robust approaches outperform NML under various distributional conditions. Application of the robust methods is illustrated through a real data example. An R program is developed and documented to facilitate the application of the robust methods.

Bias and robustness of uncertainty components estimates in transient climate projections

NASA Astrophysics Data System (ADS)

Hingray, Benoit; Blanchet, Juliette; Jean-Philippe, Vidal

2016-04-01

A critical issue in climate change studies is the estimation of uncertainties in projections along with the contribution of the different uncertainty sources, including scenario uncertainty, the different components of model uncertainty and internal variability. Quantifying the different uncertainty sources faces actually different problems. For instance and for the sake of simplicity, an estimate of model uncertainty is classically obtained from the empirical variance of the climate responses obtained for the different modeling chains. These estimates are however biased. Another difficulty arises from the limited number of members that are classically available for most modeling chains. In this case, the climate response of one given chain and the effect of its internal variability may be actually difficult if not impossible to separate. The estimate of scenario uncertainty, model uncertainty and internal variability components are thus likely to be not really robust. We explore the importance of the bias and the robustness of the estimates for two classical Analysis of Variance (ANOVA) approaches: a Single Time approach (STANOVA), based on the only data available for the considered projection lead time and a time series based approach (QEANOVA), which assumes quasi-ergodicity of climate outputs over the whole available climate simulation period (Hingray and Saïd, 2014). We explore both issues for a simple but classical configuration where uncertainties in projections are composed of two single sources: model uncertainty and internal climate variability. The bias in model uncertainty estimates is explored from theoretical expressions of unbiased estimators developed for both ANOVA approaches. The robustness of uncertainty estimates is explored for multiple synthetic ensembles of time series projections generated with MonteCarlo simulations. For both ANOVA approaches, when the empirical variance of climate responses is used to estimate model uncertainty, the bias is always positive. It can be especially high with STANOVA. In the most critical configurations, when the number of members available for each modeling chain is small (< 3) and when internal variability explains most of total uncertainty variance (75% or more), the overestimation is higher than 100% of the true model uncertainty variance. The bias can be considerably reduced with a time series ANOVA approach, owing to the multiple time steps accounted for. The longer the transient time period used for the analysis, the larger the reduction. When a quasi-ergodic ANOVA approach is applied to decadal data for the whole 1980-2100 period, the bias is reduced by a factor 2.5 to 20 depending on the projection lead time. In all cases, the bias is likely to be not negligible for a large number of climate impact studies resulting in a likely large overestimation of the contribution of model uncertainty to total variance. For both approaches, the robustness of all uncertainty estimates is higher when more members are available, when internal variability is smaller and/or the response-to-uncertainty ratio is higher. QEANOVA estimates are much more robust than STANOVA ones: QEANOVA simulated confidence intervals are roughly 3 to 5 times smaller than STANOVA ones. Excepted for STANOVA when less than 3 members is available, the robustness is rather high for total uncertainty and moderate for internal variability estimates. For model uncertainty or response-to-uncertainty ratio estimates, the robustness is conversely low for QEANOVA to very low for STANOVA. In the most critical configurations (small number of member, large internal variability), large over- or underestimation of uncertainty components is very thus likely. To propose relevant uncertainty analyses and avoid misleading interpretations, estimates of uncertainty components should be therefore bias corrected and ideally come with estimates of their robustness. This work is part of the COMPLEX Project (European Collaborative Project FP7-ENV-2012 number: 308601; http://www.complex.ac.uk/). Hingray, B., Saïd, M., 2014. Partitioning internal variability and model uncertainty components in a multimodel multireplicate ensemble of climate projections. J.Climate. doi:10.1175/JCLI-D-13-00629.1 Hingray, B., Blanchet, J. (revision) Unbiased estimators for uncertainty components in transient climate projections. J. Climate Hingray, B., Blanchet, J., Vidal, J.P. (revision) Robustness of uncertainty components estimates in climate projections. J.Climate

Robust Hinfinity position control synthesis of an electro-hydraulic servo system.

PubMed

Milić, Vladimir; Situm, Zeljko; Essert, Mario

2010-10-01

This paper focuses on the use of the techniques based on linear matrix inequalities for robust H(infinity) position control synthesis of an electro-hydraulic servo system. A nonlinear dynamic model of the hydraulic cylindrical actuator with a proportional valve has been developed. For the purpose of the feedback control an uncertain linearized mathematical model of the system has been derived. The structured (parametric) perturbations in the electro-hydraulic coefficients are taken into account. H(infinity) controller extended with an integral action is proposed. To estimate internal states of the electro-hydraulic servo system an observer is designed. Developed control algorithms have been tested experimentally in the laboratory model of an electro-hydraulic servo system. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

Passive polarimetric imagery-based material classification robust to illumination source position and viewpoint.

PubMed

Thilak Krishna, Thilakam Vimal; Creusere, Charles D; Voelz, David G

2011-01-01

Polarization, a property of light that conveys information about the transverse electric field orientation, complements other attributes of electromagnetic radiation such as intensity and frequency. Using multiple passive polarimetric images, we develop an iterative, model-based approach to estimate the complex index of refraction and apply it to target classification.

Improved and Robust Detection of Cell Nuclei from Four Dimensional Fluorescence Images

PubMed Central

Bashar, Md. Khayrul; Yamagata, Kazuo; Kobayashi, Tetsuya J.

2014-01-01

Segmentation-free direct methods are quite efficient for automated nuclei extraction from high dimensional images. A few such methods do exist but most of them do not ensure algorithmic robustness to parameter and noise variations. In this research, we propose a method based on multiscale adaptive filtering for efficient and robust detection of nuclei centroids from four dimensional (4D) fluorescence images. A temporal feedback mechanism is employed between the enhancement and the initial detection steps of a typical direct method. We estimate the minimum and maximum nuclei diameters from the previous frame and feed back them as filter lengths for multiscale enhancement of the current frame. A radial intensity-gradient function is optimized at positions of initial centroids to estimate all nuclei diameters. This procedure continues for processing subsequent images in the sequence. Above mechanism thus ensures proper enhancement by automated estimation of major parameters. This brings robustness and safeguards the system against additive noises and effects from wrong parameters. Later, the method and its single-scale variant are simplified for further reduction of parameters. The proposed method is then extended for nuclei volume segmentation. The same optimization technique is applied to final centroid positions of the enhanced image and the estimated diameters are projected onto the binary candidate regions to segment nuclei volumes.Our method is finally integrated with a simple sequential tracking approach to establish nuclear trajectories in the 4D space. Experimental evaluations with five image-sequences (each having 271 3D sequential images) corresponding to five different mouse embryos show promising performances of our methods in terms of nuclear detection, segmentation, and tracking. A detail analysis with a sub-sequence of 101 3D images from an embryo reveals that the proposed method can improve the nuclei detection accuracy by 9 over the previous methods, which used inappropriate large valued parameters. Results also confirm that the proposed method and its variants achieve high detection accuracies ( 98 mean F-measure) irrespective of the large variations of filter parameters and noise levels. PMID:25020042

Robust estimation for partially linear models with large-dimensional covariates

PubMed Central

Zhu, LiPing; Li, RunZe; Cui, HengJian

2014-01-01

We are concerned with robust estimation procedures to estimate the parameters in partially linear models with large-dimensional covariates. To enhance the interpretability, we suggest implementing a noncon-cave regularization method in the robust estimation procedure to select important covariates from the linear component. We establish the consistency for both the linear and the nonlinear components when the covariate dimension diverges at the rate of o(n), where n is the sample size. We show that the robust estimate of linear component performs asymptotically as well as its oracle counterpart which assumes the baseline function and the unimportant covariates were known a priori. With a consistent estimator of the linear component, we estimate the nonparametric component by a robust local linear regression. It is proved that the robust estimate of nonlinear component performs asymptotically as well as if the linear component were known in advance. Comprehensive simulation studies are carried out and an application is presented to examine the finite-sample performance of the proposed procedures. PMID:24955087

Robust estimation for partially linear models with large-dimensional covariates.

PubMed

Zhu, LiPing; Li, RunZe; Cui, HengJian

2013-10-01

We are concerned with robust estimation procedures to estimate the parameters in partially linear models with large-dimensional covariates. To enhance the interpretability, we suggest implementing a noncon-cave regularization method in the robust estimation procedure to select important covariates from the linear component. We establish the consistency for both the linear and the nonlinear components when the covariate dimension diverges at the rate of [Formula: see text], where n is the sample size. We show that the robust estimate of linear component performs asymptotically as well as its oracle counterpart which assumes the baseline function and the unimportant covariates were known a priori. With a consistent estimator of the linear component, we estimate the nonparametric component by a robust local linear regression. It is proved that the robust estimate of nonlinear component performs asymptotically as well as if the linear component were known in advance. Comprehensive simulation studies are carried out and an application is presented to examine the finite-sample performance of the proposed procedures.

An enhanced inertial navigation system based on a low-cost IMU and laser scanner

NASA Astrophysics Data System (ADS)

Kim, Hyung-Soon; Baeg, Seung-Ho; Yang, Kwang-Woong; Cho, Kuk; Park, Sangdeok

2012-06-01

This paper describes an enhanced fusion method for an Inertial Navigation System (INS) based on a 3-axis accelerometer sensor, a 3-axis gyroscope sensor and a laser scanner. In GPS-denied environments, indoor or dense forests, a pure INS odometry is available for estimating the trajectory of a human or robot. However it has a critical implementation problem: a drift error of velocity, position and heading angles. Commonly the problem can be solved by fusing visual landmarks, a magnetometer or radio beacons. These methods are not robust in diverse environments: darkness, fog or sunlight, an unstable magnetic field and an environmental obstacle. We propose to overcome the drift problem using an Iterative Closest Point (ICP) scan matching algorithm with a laser scanner. This system consists of three parts. The first is the INS. It estimates attitude, velocity, position based on a 6-axis Inertial Measurement Unit (IMU) with both 'Heuristic Reduction of Gyro Drift' (HRGD) and 'Heuristic Reduction of Velocity Drift' (HRVD) methods. A frame-to-frame ICP matching algorithm for estimating position and attitude by laser scan data is the second. The third is an extended kalman filter method for multi-sensor data fusing: INS and Laser Range Finder (LRF). The proposed method is simple and robust in diverse environments, so we could reduce the drift error efficiently. We confirm the result comparing an odometry of the experimental result with ICP and LRF aided-INS in a long corridor.

Robust Gaussian Graphical Modeling via l1 Penalization

PubMed Central

Sun, Hokeun; Li, Hongzhe

2012-01-01

Summary Gaussian graphical models have been widely used as an effective method for studying the conditional independency structure among genes and for constructing genetic networks. However, gene expression data typically have heavier tails or more outlying observations than the standard Gaussian distribution. Such outliers in gene expression data can lead to wrong inference on the dependency structure among the genes. We propose a l1 penalized estimation procedure for the sparse Gaussian graphical models that is robustified against possible outliers. The likelihood function is weighted according to how the observation is deviated, where the deviation of the observation is measured based on its own likelihood. An efficient computational algorithm based on the coordinate gradient descent method is developed to obtain the minimizer of the negative penalized robustified-likelihood, where nonzero elements of the concentration matrix represents the graphical links among the genes. After the graphical structure is obtained, we re-estimate the positive definite concentration matrix using an iterative proportional fitting algorithm. Through simulations, we demonstrate that the proposed robust method performs much better than the graphical Lasso for the Gaussian graphical models in terms of both graph structure selection and estimation when outliers are present. We apply the robust estimation procedure to an analysis of yeast gene expression data and show that the resulting graph has better biological interpretation than that obtained from the graphical Lasso. PMID:23020775

Robust time and frequency domain estimation methods in adaptive control

NASA Technical Reports Server (NTRS)

Lamaire, Richard Orville

1987-01-01

A robust identification method was developed for use in an adaptive control system. The type of estimator is called the robust estimator, since it is robust to the effects of both unmodeled dynamics and an unmeasurable disturbance. The development of the robust estimator was motivated by a need to provide guarantees in the identification part of an adaptive controller. To enable the design of a robust control system, a nominal model as well as a frequency-domain bounding function on the modeling uncertainty associated with this nominal model must be provided. Two estimation methods are presented for finding parameter estimates, and, hence, a nominal model. One of these methods is based on the well developed field of time-domain parameter estimation. In a second method of finding parameter estimates, a type of weighted least-squares fitting to a frequency-domain estimated model is used. The frequency-domain estimator is shown to perform better, in general, than the time-domain parameter estimator. In addition, a methodology for finding a frequency-domain bounding function on the disturbance is used to compute a frequency-domain bounding function on the additive modeling error due to the effects of the disturbance and the use of finite-length data. The performance of the robust estimator in both open-loop and closed-loop situations is examined through the use of simulations.

Position Estimation and Local Mapping Using Omnidirectional Images and Global Appearance Descriptors

PubMed Central

Berenguer, Yerai; Payá, Luis; Ballesta, Mónica; Reinoso, Oscar

2015-01-01

This work presents some methods to create local maps and to estimate the position of a mobile robot, using the global appearance of omnidirectional images. We use a robot that carries an omnidirectional vision system on it. Every omnidirectional image acquired by the robot is described only with one global appearance descriptor, based on the Radon transform. In the work presented in this paper, two different possibilities have been considered. In the first one, we assume the existence of a map previously built composed of omnidirectional images that have been captured from previously-known positions. The purpose in this case consists of estimating the nearest position of the map to the current position of the robot, making use of the visual information acquired by the robot from its current (unknown) position. In the second one, we assume that we have a model of the environment composed of omnidirectional images, but with no information about the location of where the images were acquired. The purpose in this case consists of building a local map and estimating the position of the robot within this map. Both methods are tested with different databases (including virtual and real images) taking into consideration the changes of the position of different objects in the environment, different lighting conditions and occlusions. The results show the effectiveness and the robustness of both methods. PMID:26501289

Robust Regression Procedures for Predictor Variable Outliers.

DTIC Science & Technology

1982-03-01

space of probability dis- tributions. Then the influence function of the estimator is defined to be the derivative of the functional evaluated at the...measure of the impact of an outlier x0 on the estimator . . . . . .0 10 T(F) is the " influence function " which is defined to be T(F) - lirT(F")-T(F...positive and negative directions. An em- pirical influence function can be defined in a similar fashion simply by replacing F with F in eqn. (3.4).n

Doubly robust nonparametric inference on the average treatment effect.

PubMed

Benkeser, D; Carone, M; Laan, M J Van Der; Gilbert, P B

2017-12-01

Doubly robust estimators are widely used to draw inference about the average effect of a treatment. Such estimators are consistent for the effect of interest if either one of two nuisance parameters is consistently estimated. However, if flexible, data-adaptive estimators of these nuisance parameters are used, double robustness does not readily extend to inference. We present a general theoretical study of the behaviour of doubly robust estimators of an average treatment effect when one of the nuisance parameters is inconsistently estimated. We contrast different methods for constructing such estimators and investigate the extent to which they may be modified to also allow doubly robust inference. We find that while targeted minimum loss-based estimation can be used to solve this problem very naturally, common alternative frameworks appear to be inappropriate for this purpose. We provide a theoretical study and a numerical evaluation of the alternatives considered. Our simulations highlight the need for and usefulness of these approaches in practice, while our theoretical developments have broad implications for the construction of estimators that permit doubly robust inference in other problems.

Robust nonparametric quantification of clustering density of molecules in single-molecule localization microscopy

PubMed Central

Jiang, Shenghang; Park, Seongjin; Challapalli, Sai Divya; Fei, Jingyi; Wang, Yong

2017-01-01

We report a robust nonparametric descriptor, J′(r), for quantifying the density of clustering molecules in single-molecule localization microscopy. J′(r), based on nearest neighbor distribution functions, does not require any parameter as an input for analyzing point patterns. We show that J′(r) displays a valley shape in the presence of clusters of molecules, and the characteristics of the valley reliably report the clustering features in the data. Most importantly, the position of the J′(r) valley (rJm′) depends exclusively on the density of clustering molecules (ρc). Therefore, it is ideal for direct estimation of the clustering density of molecules in single-molecule localization microscopy. As an example, this descriptor was applied to estimate the clustering density of ptsG mRNA in E. coli bacteria. PMID:28636661

Robust geostatistical analysis of spatial data

NASA Astrophysics Data System (ADS)

Papritz, Andreas; Künsch, Hans Rudolf; Schwierz, Cornelia; Stahel, Werner A.

2013-04-01

Most of the geostatistical software tools rely on non-robust algorithms. This is unfortunate, because outlying observations are rather the rule than the exception, in particular in environmental data sets. Outliers affect the modelling of the large-scale spatial trend, the estimation of the spatial dependence of the residual variation and the predictions by kriging. Identifying outliers manually is cumbersome and requires expertise because one needs parameter estimates to decide which observation is a potential outlier. Moreover, inference after the rejection of some observations is problematic. A better approach is to use robust algorithms that prevent automatically that outlying observations have undue influence. Former studies on robust geostatistics focused on robust estimation of the sample variogram and ordinary kriging without external drift. Furthermore, Richardson and Welsh (1995) proposed a robustified version of (restricted) maximum likelihood ([RE]ML) estimation for the variance components of a linear mixed model, which was later used by Marchant and Lark (2007) for robust REML estimation of the variogram. We propose here a novel method for robust REML estimation of the variogram of a Gaussian random field that is possibly contaminated by independent errors from a long-tailed distribution. It is based on robustification of estimating equations for the Gaussian REML estimation (Welsh and Richardson, 1997). Besides robust estimates of the parameters of the external drift and of the variogram, the method also provides standard errors for the estimated parameters, robustified kriging predictions at both sampled and non-sampled locations and kriging variances. Apart from presenting our modelling framework, we shall present selected simulation results by which we explored the properties of the new method. This will be complemented by an analysis a data set on heavy metal contamination of the soil in the vicinity of a metal smelter. Marchant, B.P. and Lark, R.M. 2007. Robust estimation of the variogram by residual maximum likelihood. Geoderma 140: 62-72. Richardson, A.M. and Welsh, A.H. 1995. Robust restricted maximum likelihood in mixed linear models. Biometrics 51: 1429-1439. Welsh, A.H. and Richardson, A.M. 1997. Approaches to the robust estimation of mixed models. In: Handbook of Statistics Vol. 15, Elsevier, pp. 343-384.

A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gaussian filter

NASA Astrophysics Data System (ADS)

Li, Yi; Abdel-Monem, Mohamed; Gopalakrishnan, Rahul; Berecibar, Maitane; Nanini-Maury, Elise; Omar, Noshin; van den Bossche, Peter; Van Mierlo, Joeri

2018-01-01

This paper proposes an advanced state of health (SoH) estimation method for high energy NMC lithium-ion batteries based on the incremental capacity (IC) analysis. IC curves are used due to their ability of detect and quantify battery degradation mechanism. A simple and robust smoothing method is proposed based on Gaussian filter to reduce the noise on IC curves, the signatures associated with battery ageing can therefore be accurately identified. A linear regression relationship is found between the battery capacity with the positions of features of interest (FOIs) on IC curves. Results show that the developed SoH estimation function from one single battery cell is able to evaluate the SoH of other batteries cycled under different cycling depth with less than 2.5% maximum errors, which proves the robustness of the proposed method on SoH estimation. With this technique, partial charging voltage curves can be used for SoH estimation and the testing time can be therefore largely reduced. This method shows great potential to be applied in reality, as it only requires static charging curves and can be easily implemented in battery management system (BMS).

A game-theoretic approach for calibration of low-cost magnetometers under noise uncertainty

NASA Astrophysics Data System (ADS)

Siddharth, S.; Ali, A. S.; El-Sheimy, N.; Goodall, C. L.; Syed, Z. F.

2012-02-01

Pedestrian heading estimation is a fundamental challenge in Global Navigation Satellite System (GNSS)-denied environments. Additionally, the heading observability considerably degrades in low-speed mode of operation (e.g. walking), making this problem even more challenging. The goal of this work is to improve the heading solution when hand-held personal/portable devices, such as cell phones, are used for positioning and to improve the heading estimation in GNSS-denied signal environments. Most smart phones are now equipped with self-contained, low cost, small size and power-efficient sensors, such as magnetometers, gyroscopes and accelerometers. A magnetometer needs calibration before it can be properly employed for navigation purposes. Magnetometers play an important role in absolute heading estimation and are embedded in many smart phones. Before the users navigate with the phone, a calibration is invoked to ensure an improved signal quality. This signal is used later in the heading estimation. In most of the magnetometer-calibration approaches, the motion modes are seldom described to achieve a robust calibration. Also, suitable calibration approaches fail to discuss the stopping criteria for calibration. In this paper, the following three topics are discussed in detail that are important to achieve proper magnetometer-calibration results and in turn the most robust heading solution for the user while taking care of the device misalignment with respect to the user: (a) game-theoretic concepts to attain better filter parameter tuning and robustness in noise uncertainty, (b) best maneuvers with focus on 3D and 2D motion modes and related challenges and (c) investigation of the calibration termination criteria leveraging the calibration robustness and efficiency.

Robust variance estimation with dependent effect sizes: practical considerations including a software tutorial in Stata and spss.

PubMed

Tanner-Smith, Emily E; Tipton, Elizabeth

2014-03-01

Methodologists have recently proposed robust variance estimation as one way to handle dependent effect sizes in meta-analysis. Software macros for robust variance estimation in meta-analysis are currently available for Stata (StataCorp LP, College Station, TX, USA) and spss (IBM, Armonk, NY, USA), yet there is little guidance for authors regarding the practical application and implementation of those macros. This paper provides a brief tutorial on the implementation of the Stata and spss macros and discusses practical issues meta-analysts should consider when estimating meta-regression models with robust variance estimates. Two example databases are used in the tutorial to illustrate the use of meta-analysis with robust variance estimates. Copyright © 2013 John Wiley & Sons, Ltd.

Vision System for Coarsely Estimating Motion Parameters for Unknown Fast Moving Objects in Space

PubMed Central

Chen, Min; Hashimoto, Koichi

2017-01-01

Motivated by biological interests in analyzing navigation behaviors of flying animals, we attempt to build a system measuring their motion states. To do this, in this paper, we build a vision system to detect unknown fast moving objects within a given space, calculating their motion parameters represented by positions and poses. We proposed a novel method to detect reliable interest points from images of moving objects, which can be hardly detected by general purpose interest point detectors. 3D points reconstructed using these interest points are then grouped and maintained for detected objects, according to a careful schedule, considering appearance and perspective changes. In the estimation step, a method is introduced to adapt the robust estimation procedure used for dense point set to the case for sparse set, reducing the potential risk of greatly biased estimation. Experiments are conducted against real scenes, showing the capability of the system of detecting multiple unknown moving objects and estimating their positions and poses. PMID:29206189

A game theory approach to target tracking in sensor networks.

PubMed

Gu, Dongbing

2011-02-01

In this paper, we investigate a moving-target tracking problem with sensor networks. Each sensor node has a sensor to observe the target and a processor to estimate the target position. It also has wireless communication capability but with limited range and can only communicate with neighbors. The moving target is assumed to be an intelligent agent, which is "smart" enough to escape from the detection by maximizing the estimation error. This adversary behavior makes the target tracking problem more difficult. We formulate this target estimation problem as a zero-sum game in this paper and use a minimax filter to estimate the target position. The minimax filter is a robust filter that minimizes the estimation error by considering the worst case noise. Furthermore, we develop a distributed version of the minimax filter for multiple sensor nodes. The distributed computation is implemented via modeling the information received from neighbors as measurements in the minimax filter. The simulation results show that the target tracking algorithm proposed in this paper provides a satisfactory result.

Instantaneous power control of a high speed permanent magnet synchronous generator based on a sliding mode observer and a phase locked loop

NASA Astrophysics Data System (ADS)

Duan, Jiandong; Fan, Shaogui; Wu, Fengjiang; Sun, Li; Wang, Guanglin

2018-06-01

This paper proposes an instantaneous power control method for high speed permanent magnet synchronous generators (PMSG), to realize the decoupled control of active power and reactive power, through vector control based on a sliding mode observer (SMO), and a phase locked loop (PLL). Consequently, the high speed PMSG has a high internal power factor, to ensure efficient operation. Vector control and accurate estimation of the instantaneous power require an accurate estimate of the rotor position. The SMO is able to estimate the back electromotive force (EMF). The rotor position and speed can be obtained using a combination of the PLL technique and the phase compensation method. This method has the advantages of robust operation, and being resistant to noise when estimating the position of the rotor. Using instantaneous power theory, the relationship between the output active power, reactive power, and stator current of the PMSG is deduced, and the power constraint condition is analysed for operation at the unit internal power factor. Finally, the accuracy of the rotor position detection, the instantaneous power detection, and the control methods are verified using simulations and experiments.

Towards Robust Self-Calibration for Handheld 3d Line Laser Scanning

NASA Astrophysics Data System (ADS)

Bleier, M.; Nüchter, A.

2017-11-01

This paper studies self-calibration of a structured light system, which reconstructs 3D information using video from a static consumer camera and a handheld cross line laser projector. Intersections between the individual laser curves and geometric constraints on the relative position of the laser planes are exploited to achieve dense 3D reconstruction. This is possible without any prior knowledge of the movement of the projector. However, inaccurrately extracted laser lines introduce noise in the detected intersection positions and therefore distort the reconstruction result. Furthermore, when scanning objects with specular reflections, such as glossy painted or metalic surfaces, the reflections are often extracted from the camera image as erroneous laser curves. In this paper we investiagte how robust estimates of the parameters of the laser planes can be obtained despite of noisy detections.

Robust estimation approach for blind denoising.

PubMed

Rabie, Tamer

2005-11-01

This work develops a new robust statistical framework for blind image denoising. Robust statistics addresses the problem of estimation when the idealized assumptions about a system are occasionally violated. The contaminating noise in an image is considered as a violation of the assumption of spatial coherence of the image intensities and is treated as an outlier random variable. A denoised image is estimated by fitting a spatially coherent stationary image model to the available noisy data using a robust estimator-based regression method within an optimal-size adaptive window. The robust formulation aims at eliminating the noise outliers while preserving the edge structures in the restored image. Several examples demonstrating the effectiveness of this robust denoising technique are reported and a comparison with other standard denoising filters is presented.

The comparison between several robust ridge regression estimators in the presence of multicollinearity and multiple outliers

NASA Astrophysics Data System (ADS)

Zahari, Siti Meriam; Ramli, Norazan Mohamed; Moktar, Balkiah; Zainol, Mohammad Said

2014-09-01

In the presence of multicollinearity and multiple outliers, statistical inference of linear regression model using ordinary least squares (OLS) estimators would be severely affected and produces misleading results. To overcome this, many approaches have been investigated. These include robust methods which were reported to be less sensitive to the presence of outliers. In addition, ridge regression technique was employed to tackle multicollinearity problem. In order to mitigate both problems, a combination of ridge regression and robust methods was discussed in this study. The superiority of this approach was examined when simultaneous presence of multicollinearity and multiple outliers occurred in multiple linear regression. This study aimed to look at the performance of several well-known robust estimators; M, MM, RIDGE and robust ridge regression estimators, namely Weighted Ridge M-estimator (WRM), Weighted Ridge MM (WRMM), Ridge MM (RMM), in such a situation. Results of the study showed that in the presence of simultaneous multicollinearity and multiple outliers (in both x and y-direction), the RMM and RIDGE are more or less similar in terms of superiority over the other estimators, regardless of the number of observation, level of collinearity and percentage of outliers used. However, when outliers occurred in only single direction (y-direction), the WRMM estimator is the most superior among the robust ridge regression estimators, by producing the least variance. In conclusion, the robust ridge regression is the best alternative as compared to robust and conventional least squares estimators when dealing with simultaneous presence of multicollinearity and outliers.

Development of robust flexible OLED encapsulations using simulated estimations and experimental validations

NASA Astrophysics Data System (ADS)

Lee, Chang-Chun; Shih, Yan-Shin; Wu, Chih-Sheng; Tsai, Chia-Hao; Yeh, Shu-Tang; Peng, Yi-Hao; Chen, Kuang-Jung

2012-07-01

This work analyses the overall stress/strain characteristic of flexible encapsulations with organic light-emitting diode (OLED) devices. A robust methodology composed of a mechanical model of multi-thin film under bending loads and related stress simulations based on nonlinear finite element analysis (FEA) is proposed, and validated to be more reliable compared with related experimental data. With various geometrical combinations of cover plate, stacked thin films and plastic substrate, the position of the neutral axis (NA) plate, which is regarded as a key design parameter to minimize stress impact for the concerned OLED devices, is acquired using the present methodology. The results point out that both the thickness and mechanical properties of the cover plate help in determining the NA location. In addition, several concave and convex radii are applied to examine the reliable mechanical tolerance and to provide an insight into the estimated reliability of foldable OLED encapsulations.

Return on Investment of a Work-Family Intervention: Evidence From the Work, Family, and Health Network.

PubMed

Barbosa, Carolina; Bray, Jeremy W; Dowd, William N; Mills, Michael J; Moen, Phyllis; Wipfli, Brad; Olson, Ryan; Kelly, Erin L

2015-09-01

To estimate the return on investment (ROI) of a workplace initiative to reduce work-family conflict in a group-randomized 18-month field experiment in an information technology firm in the United States. Intervention resources were micro-costed; benefits included medical costs, productivity (presenteeism), and turnover. Regression models were used to estimate the ROI, and cluster-robust bootstrap was used to calculate its confidence interval. For each participant, model-adjusted costs of the intervention were $690 and company savings were $1850 (2011 prices). The ROI was 1.68 (95% confidence interval, -8.85 to 9.47) and was robust in sensitivity analyses. The positive ROI indicates that employers' investment in an intervention to reduce work-family conflict can enhance their business. Although this was the first study to present a confidence interval for the ROI, results are comparable with the literature.

An effective and robust method for tracking multiple fish in video image based on fish head detection.

PubMed

Qian, Zhi-Ming; Wang, Shuo Hong; Cheng, Xi En; Chen, Yan Qiu

2016-06-23

Fish tracking is an important step for video based analysis of fish behavior. Due to severe body deformation and mutual occlusion of multiple swimming fish, accurate and robust fish tracking from video image sequence is a highly challenging problem. The current tracking methods based on motion information are not accurate and robust enough to track the waving body and handle occlusion. In order to better overcome these problems, we propose a multiple fish tracking method based on fish head detection. The shape and gray scale characteristics of the fish image are employed to locate the fish head position. For each detected fish head, we utilize the gray distribution of the head region to estimate the fish head direction. Both the position and direction information from fish detection are then combined to build a cost function of fish swimming. Based on the cost function, global optimization method can be applied to associate the target between consecutive frames. Results show that our method can accurately detect the position and direction information of fish head, and has a good tracking performance for dozens of fish. The proposed method can successfully obtain the motion trajectories for dozens of fish so as to provide more precise data to accommodate systematic analysis of fish behavior.

A frequency-domain estimator for use in adaptive control systems

NASA Technical Reports Server (NTRS)

Lamaire, Richard O.; Valavani, Lena; Athans, Michael; Stein, Gunter

1991-01-01

This paper presents a frequency-domain estimator that can identify both a parametrized nominal model of a plant as well as a frequency-domain bounding function on the modeling error associated with this nominal model. This estimator, which we call a robust estimator, can be used in conjunction with a robust control-law redesign algorithm to form a robust adaptive controller.

A Robust Method for Ego-Motion Estimation in Urban Environment Using Stereo Camera.

PubMed

Ci, Wenyan; Huang, Yingping

2016-10-17

Visual odometry estimates the ego-motion of an agent (e.g., vehicle and robot) using image information and is a key component for autonomous vehicles and robotics. This paper proposes a robust and precise method for estimating the 6-DoF ego-motion, using a stereo rig with optical flow analysis. An objective function fitted with a set of feature points is created by establishing the mathematical relationship between optical flow, depth and camera ego-motion parameters through the camera's 3-dimensional motion and planar imaging model. Accordingly, the six motion parameters are computed by minimizing the objective function, using the iterative Levenberg-Marquard method. One of key points for visual odometry is that the feature points selected for the computation should contain inliers as much as possible. In this work, the feature points and their optical flows are initially detected by using the Kanade-Lucas-Tomasi (KLT) algorithm. A circle matching is followed to remove the outliers caused by the mismatching of the KLT algorithm. A space position constraint is imposed to filter out the moving points from the point set detected by the KLT algorithm. The Random Sample Consensus (RANSAC) algorithm is employed to further refine the feature point set, i.e., to eliminate the effects of outliers. The remaining points are tracked to estimate the ego-motion parameters in the subsequent frames. The approach presented here is tested on real traffic videos and the results prove the robustness and precision of the method.

A Robust Method for Ego-Motion Estimation in Urban Environment Using Stereo Camera

PubMed Central

Ci, Wenyan; Huang, Yingping

2016-01-01

Visual odometry estimates the ego-motion of an agent (e.g., vehicle and robot) using image information and is a key component for autonomous vehicles and robotics. This paper proposes a robust and precise method for estimating the 6-DoF ego-motion, using a stereo rig with optical flow analysis. An objective function fitted with a set of feature points is created by establishing the mathematical relationship between optical flow, depth and camera ego-motion parameters through the camera’s 3-dimensional motion and planar imaging model. Accordingly, the six motion parameters are computed by minimizing the objective function, using the iterative Levenberg–Marquard method. One of key points for visual odometry is that the feature points selected for the computation should contain inliers as much as possible. In this work, the feature points and their optical flows are initially detected by using the Kanade–Lucas–Tomasi (KLT) algorithm. A circle matching is followed to remove the outliers caused by the mismatching of the KLT algorithm. A space position constraint is imposed to filter out the moving points from the point set detected by the KLT algorithm. The Random Sample Consensus (RANSAC) algorithm is employed to further refine the feature point set, i.e., to eliminate the effects of outliers. The remaining points are tracked to estimate the ego-motion parameters in the subsequent frames. The approach presented here is tested on real traffic videos and the results prove the robustness and precision of the method. PMID:27763508

Array processing for RFID tag localization exploiting multi-frequency signals

NASA Astrophysics Data System (ADS)

Zhang, Yimin; Li, Xin; Amin, Moeness G.

2009-05-01

RFID is an increasingly valuable business and technology tool for electronically identifying, locating, and tracking products, assets, and personnel. As a result, precise positioning and tracking of RFID tags and readers have received considerable attention from both academic and industrial communities. Finding the position of RFID tags is considered an important task in various real-time locating systems (RTLS). As such, numerous RFID localization products have been developed for various applications. The majority of RFID positioning systems is based on the fusion of pieces of relevant information, such as the range and the direction-of-arrival (DOA). For example, trilateration can determine the tag position by using the range information of the tag estimated from three or more spatially separated reader antennas. Triangulation is another method to locate RFID tags that use the direction-of-arrival (DOA) information estimated at multiple spatially separated locations. The RFID tag positions can also be determined through hybrid techniques that combine the range and DOA information. The focus of this paper to study the design and performance of the localization of passive RFID tags using array processing techniques in a multipath environment, and exploiting multi-frequency CW signals. The latter are used to decorrelate the coherent multipath signals for effective DOA estimation and for the purpose of accurate range estimation. Accordingly, the spatial and frequency dimensionalities are fully utilized for robust and accurate positioning of RFID tags.

Robust feedback zoom tracking for digital video surveillance.

PubMed

Zou, Tengyue; Tang, Xiaoqi; Song, Bao; Wang, Jin; Chen, Jihong

2012-01-01

Zoom tracking is an important function in video surveillance, particularly in traffic management and security monitoring. It involves keeping an object of interest in focus during the zoom operation. Zoom tracking is typically achieved by moving the zoom and focus motors in lenses following the so-called "trace curve", which shows the in-focus motor positions versus the zoom motor positions for a specific object distance. The main task of a zoom tracking approach is to accurately estimate the trace curve for the specified object. Because a proportional integral derivative (PID) controller has historically been considered to be the best controller in the absence of knowledge of the underlying process and its high-quality performance in motor control, in this paper, we propose a novel feedback zoom tracking (FZT) approach based on the geometric trace curve estimation and PID feedback controller. The performance of this approach is compared with existing zoom tracking methods in digital video surveillance. The real-time implementation results obtained on an actual digital video platform indicate that the developed FZT approach not only solves the traditional one-to-many mapping problem without pre-training but also improves the robustness for tracking moving or switching objects which is the key challenge in video surveillance.

Estimates of cost-effectiveness of prehospital continuous positive airway pressure in the management of acute pulmonary edema.

PubMed

Hubble, Michael W; Richards, Michael E; Wilfong, Denise A

2008-01-01

To estimate the cost-effectiveness of continuous positive airway pressure (CPAP) in managing prehospital acute pulmonary edema in an urban EMS system. Using estimates from published reports on prehospital and emergency department CPAP, a cost-effectiveness model of implementing CPAP in a typical urban EMS system was derived from the societal perspective as well as the perspective of the implementing EMS system. To assess the robustness of the model, a series of univariate and multivariate sensitivity analyses was performed on the input variables. The cost of consumables, equipment, and training yielded a total cost of $89 per CPAP application. The theoretical system would be expected to use CPAP 4 times per 1000 EMS patients and is expected to save 0.75 additional lives per 1000 EMS patients at a cost of $490 per life saved. CPAP is also expected to result in approximately one less intubation per 6 CPAP applications and reduce hospitalization costs by $4075 per year for each CPAP application. Through sensitivity analyses the model was verified to be robust across a wide range of input variable assumptions. Previous studies have demonstrated the clinical effectiveness of CPAP in the management of acute pulmonary edema. Through a theoretical analysis which modeled the costs and clinical benefits of implementing CPAP in an urban EMS system, prehospital CPAP appears to be a cost-effective treatment.

Analysis of Sources of Large Positioning Errors in Deterministic Fingerprinting

PubMed Central

2017-01-01

Wi-Fi fingerprinting is widely used for indoor positioning and indoor navigation due to the ubiquity of wireless networks, high proliferation of Wi-Fi-enabled mobile devices, and its reasonable positioning accuracy. The assumption is that the position can be estimated based on the received signal strength intensity from multiple wireless access points at a given point. The positioning accuracy, within a few meters, enables the use of Wi-Fi fingerprinting in many different applications. However, it has been detected that the positioning error might be very large in a few cases, which might prevent its use in applications with high accuracy positioning requirements. Hybrid methods are the new trend in indoor positioning since they benefit from multiple diverse technologies (Wi-Fi, Bluetooth, and Inertial Sensors, among many others) and, therefore, they can provide a more robust positioning accuracy. In order to have an optimal combination of technologies, it is crucial to identify when large errors occur and prevent the use of extremely bad positioning estimations in hybrid algorithms. This paper investigates why large positioning errors occur in Wi-Fi fingerprinting and how to detect them by using the received signal strength intensities. PMID:29186921

Bio-inspired sensing and control for disturbance rejection and stabilization

NASA Astrophysics Data System (ADS)

Gremillion, Gregory; Humbert, James S.

2015-05-01

The successful operation of small unmanned aircraft systems (sUAS) in dynamic environments demands robust stability in the presence of exogenous disturbances. Flying insects are sensor-rich platforms, with highly redundant arrays of sensors distributed across the insect body that are integrated to extract rich information with diminished noise. This work presents a novel sensing framework in which measurements from an array of accelerometers distributed across a simulated flight vehicle are linearly combined to directly estimate the applied forces and torques with improvements in SNR. In simulation, the estimation performance is quantified as a function of sensor noise level, position estimate error, and sensor quantity.

A Two-Step Method to Identify Positive Deviant Physician Organizations of Accountable Care Organizations with Robust Performance Management Systems.

PubMed

Pimperl, Alexander F; Rodriguez, Hector P; Schmittdiel, Julie A; Shortell, Stephen M

2018-06-01

To identify positive deviant (PD) physician organizations of Accountable Care Organizations (ACOs) with robust performance management systems (PMSYS). Third National Survey of Physician Organizations (NSPO3, n = 1,398). Organizational and external factors from NSPO3 were analyzed. Linear regression estimated the association of internal and contextual factors on PMSYS. Two cutpoints (75th/90th percentiles) identified PDs with the largest residuals and highest PMSYS scores. A total of 65 and 41 PDs were identified using 75th and 90th percentiles cutpoints, respectively. The 90th percentile more strongly differentiated PDs from non-PDs. Having a high proportion of vulnerable patients appears to constrain PMSYS development. Our PD identification method increases the likelihood that PD organizations selected for in-depth inquiry are high-performing organizations that exceed expectations. © Health Research and Educational Trust.

Robust linear discriminant models to solve financial crisis in banking sectors

NASA Astrophysics Data System (ADS)

Lim, Yai-Fung; Yahaya, Sharipah Soaad Syed; Idris, Faoziah; Ali, Hazlina; Omar, Zurni

2014-12-01

Linear discriminant analysis (LDA) is a widely-used technique in patterns classification via an equation which will minimize the probability of misclassifying cases into their respective categories. However, the performance of classical estimators in LDA highly depends on the assumptions of normality and homoscedasticity. Several robust estimators in LDA such as Minimum Covariance Determinant (MCD), S-estimators and Minimum Volume Ellipsoid (MVE) are addressed by many authors to alleviate the problem of non-robustness of the classical estimates. In this paper, we investigate on the financial crisis of the Malaysian banking institutions using robust LDA and classical LDA methods. Our objective is to distinguish the "distress" and "non-distress" banks in Malaysia by using the LDA models. Hit ratio is used to validate the accuracy predictive of LDA models. The performance of LDA is evaluated by estimating the misclassification rate via apparent error rate. The results and comparisons show that the robust estimators provide a better performance than the classical estimators for LDA.

Robust Fault Detection Using Robust Z1 Estimation and Fuzzy Logic

NASA Technical Reports Server (NTRS)

Curry, Tramone; Collins, Emmanuel G., Jr.; Selekwa, Majura; Guo, Ten-Huei (Technical Monitor)

2001-01-01

This research considers the application of robust Z(sub 1), estimation in conjunction with fuzzy logic to robust fault detection for an aircraft fight control system. It begins with the development of robust Z(sub 1) estimators based on multiplier theory and then develops a fixed threshold approach to fault detection (FD). It then considers the use of fuzzy logic for robust residual evaluation and FD. Due to modeling errors and unmeasurable disturbances, it is difficult to distinguish between the effects of an actual fault and those caused by uncertainty and disturbance. Hence, it is the aim of a robust FD system to be sensitive to faults while remaining insensitive to uncertainty and disturbances. While fixed thresholds only allow a decision on whether a fault has or has not occurred, it is more valuable to have the residual evaluation lead to a conclusion related to the degree of, or probability of, a fault. Fuzzy logic is a viable means of determining the degree of a fault and allows the introduction of human observations that may not be incorporated in the rigorous threshold theory. Hence, fuzzy logic can provide a more reliable and informative fault detection process. Using an aircraft flight control system, the results of FD using robust Z(sub 1) estimation with a fixed threshold are demonstrated. FD that combines robust Z(sub 1) estimation and fuzzy logic is also demonstrated. It is seen that combining the robust estimator with fuzzy logic proves to be advantageous in increasing the sensitivity to smaller faults while remaining insensitive to uncertainty and disturbances.

New robust statistical procedures for the polytomous logistic regression models.

PubMed

Castilla, Elena; Ghosh, Abhik; Martin, Nirian; Pardo, Leandro

2018-05-17

This article derives a new family of estimators, namely the minimum density power divergence estimators, as a robust generalization of the maximum likelihood estimator for the polytomous logistic regression model. Based on these estimators, a family of Wald-type test statistics for linear hypotheses is introduced. Robustness properties of both the proposed estimators and the test statistics are theoretically studied through the classical influence function analysis. Appropriate real life examples are presented to justify the requirement of suitable robust statistical procedures in place of the likelihood based inference for the polytomous logistic regression model. The validity of the theoretical results established in the article are further confirmed empirically through suitable simulation studies. Finally, an approach for the data-driven selection of the robustness tuning parameter is proposed with empirical justifications. © 2018, The International Biometric Society.

A statistically robust EEG re-referencing procedure to mitigate reference effect

PubMed Central

Lepage, Kyle Q.; Kramer, Mark A.; Chu, Catherine J.

2014-01-01

Background The electroencephalogram (EEG) remains the primary tool for diagnosis of abnormal brain activity in clinical neurology and for in vivo recordings of human neurophysiology in neuroscience research. In EEG data acquisition, voltage is measured at positions on the scalp with respect to a reference electrode. When this reference electrode responds to electrical activity or artifact all electrodes are affected. Successful analysis of EEG data often involves re-referencing procedures that modify the recorded traces and seek to minimize the impact of reference electrode activity upon functions of the original EEG recordings. New method We provide a novel, statistically robust procedure that adapts a robust maximum-likelihood type estimator to the problem of reference estimation, reduces the influence of neural activity from the re-referencing operation, and maintains good performance in a wide variety of empirical scenarios. Results The performance of the proposed and existing re-referencing procedures are validated in simulation and with examples of EEG recordings. To facilitate this comparison, channel-to-channel correlations are investigated theoretically and in simulation. Comparison with existing methods The proposed procedure avoids using data contaminated by neural signal and remains unbiased in recording scenarios where physical references, the common average reference (CAR) and the reference estimation standardization technique (REST) are not optimal. Conclusion The proposed procedure is simple, fast, and avoids the potential for substantial bias when analyzing low-density EEG data. PMID:24975291

ECG on the road: robust and unobtrusive estimation of heart rate.

PubMed

Wartzek, Tobias; Eilebrecht, Benjamin; Lem, Jeroen; Lindner, Hans-Joachim; Leonhardt, Steffen; Walter, Marian

2011-11-01

Modern automobiles include an increasing number of assistance systems to increase the driver's safety. This feasibility study investigated unobtrusive capacitive ECG measurements in an automotive environment. Electrodes integrated into the driving seat allowed to measure a reliable ECG in 86% of the drivers; when only (light) cotton clothing was worn by the drivers, this value increased to 95%. Results show that an array of sensors is needed that can adapt to the different drivers and sitting positions. Measurements while driving show that traveling on the highway does not distort the signal any more than with the car engine turned OFF, whereas driving in city traffic results in a lowered detection rate due to the driver's heavier movements. To enable robust and reliable estimation of heart rate, an algorithm is presented (based on principal component analysis) to detect and discard time intervals with artifacts. This, then, allows a reliable estimation of heart rate of up to 61% in city traffic and up to 86% on the highway: as a percentage of the total driving period with at least four consecutive QRS complexes.

Robust Alternatives to the Standard Deviation in Processing of Physics Experimental Data

NASA Astrophysics Data System (ADS)

Shulenin, V. P.

2016-10-01

Properties of robust estimations of the scale parameter are studied. It is noted that the median of absolute deviations and the modified estimation of the average Gini differences have asymptotically normal distributions and bounded influence functions, are B-robust estimations, and hence, unlike the estimation of the standard deviation, are protected from the presence of outliers in the sample. Results of comparison of estimations of the scale parameter are given for a Gaussian model with contamination. An adaptive variant of the modified estimation of the average Gini differences is considered.

A signal strength priority based position estimation for mobile platforms

NASA Astrophysics Data System (ADS)

Kalgikar, Bhargav; Akopian, David; Chen, Philip

2010-01-01

Global Positioning System (GPS) products help to navigate while driving, hiking, boating, and flying. GPS uses a combination of orbiting satellites to determine position coordinates. This works great in most outdoor areas, but the satellite signals are not strong enough to penetrate inside most indoor environments. As a result, a new strain of indoor positioning technologies that make use of 802.11 wireless LANs (WLAN) is beginning to appear on the market. In WLAN positioning the system either monitors propagation delays between wireless access points and wireless device users to apply trilateration techniques or it maintains the database of location-specific signal fingerprints which is used to identify the most likely match of incoming signal data with those preliminary surveyed and saved in the database. In this paper we investigate the issue of deploying WLAN positioning software on mobile platforms with typically limited computational resources. We suggest a novel received signal strength rank order based location estimation system to reduce computational loads with a robust performance. The proposed system performance is compared to conventional approaches.

Implementation of a sliding-mode-based position sensorless drive for high-speed micro permanent-magnet synchronous motors.

PubMed

Chi, Wen-Chun; Cheng, Ming-Yang

2014-03-01

Due to issues such as limited space, it is difficult if it is not impossible to employ a position sensor in the drive control of high-speed micro PMSMs. In order to alleviate this problem, this paper analyzes and implements a simple and robust position sensorless field-oriented control method of high-speed micro PMSMs based on the sliding-mode observer. In particular, the angular position and velocity of the rotor of the high-speed micro PMSM are estimated using the sliding-mode observer. This observer is able to accurately estimate rotor position in the low speed region and guarantee fast convergence of the observer in the high speed region. The proposed position sensorless control method is suitable for electric dental handpiece motor drives where a wide speed range operation is essential. The proposed sensorless FOC method is implemented using a cost-effective 16-bit microcontroller and tested in a prototype electric dental handpiece motor. Several experiments are performed to verify the effectiveness of the proposed method. © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Robust location and spread measures for nonparametric probability density function estimation.

PubMed

López-Rubio, Ezequiel

2009-10-01

Robustness against outliers is a desirable property of any unsupervised learning scheme. In particular, probability density estimators benefit from incorporating this feature. A possible strategy to achieve this goal is to substitute the sample mean and the sample covariance matrix by more robust location and spread estimators. Here we use the L1-median to develop a nonparametric probability density function (PDF) estimator. We prove its most relevant properties, and we show its performance in density estimation and classification applications.

Geomagnetic matching navigation algorithm based on robust estimation

NASA Astrophysics Data System (ADS)

Xie, Weinan; Huang, Liping; Qu, Zhenshen; Wang, Zhenhuan

2017-08-01

The outliers in the geomagnetic survey data seriously affect the precision of the geomagnetic matching navigation and badly disrupt its reliability. A novel algorithm which can eliminate the outliers influence is investigated in this paper. First, the weight function is designed and its principle of the robust estimation is introduced. By combining the relation equation between the matching trajectory and the reference trajectory with the Taylor series expansion for geomagnetic information, a mathematical expression of the longitude, latitude and heading errors is acquired. The robust target function is obtained by the weight function and the mathematical expression. Then the geomagnetic matching problem is converted to the solutions of nonlinear equations. Finally, Newton iteration is applied to implement the novel algorithm. Simulation results show that the matching error of the novel algorithm is decreased to 7.75% compared to the conventional mean square difference (MSD) algorithm, and is decreased to 18.39% to the conventional iterative contour matching algorithm when the outlier is 40nT. Meanwhile, the position error of the novel algorithm is 0.017° while the other two algorithms fail to match when the outlier is 400nT.

A cooperative positioning algorithm for DSRC enabled vehicular networks

NASA Astrophysics Data System (ADS)

Efatmaneshnik, M.; Kealy, A.; Alam, N.; Dempster, A. G.

2011-12-01

Many of the safety related applications that can be facilitated by Dedicated Short Range Communications (DSRC), such as vehicle proximity warnings, automated braking (e.g. at level crossings), speed advisories, pedestrian alerts etc., rely on a robust vehicle positioning capability such as that provided by a Global Navigation Satellite System (GNSS). Vehicles in remote areas, entering tunnels, high rise areas or any high multipath/ weak signal environment will challenge the integrity of GNSS position solutions, and ultimately the safety application it underpins. To address this challenge, this paper presents an innovative application of Cooperative Positioning techniques within vehicular networks. CP refers to any method of integrating measurements from different positioning systems and sensors in order to improve the overall quality (accuracy and reliability) of the final position solution. This paper investigates the potential of the DSRC infrastructure itself to provide an inter-vehicular ranging signal that can be used as a measurement within the CP algorithm. In this paper, time-based techniques of ranging are introduced and bandwidth requirements are investigated and presented. The robustness of the CP algorithm to inter-vehicle connection failure as well as GNSS dropouts is also demonstrated using simulation studies. Finally, the performance of the Constrained Kalman Filter used to integrate GNSS measurements with DSRC derived range estimates within a typical VANET is described and evaluated.

Comparison of robustness to outliers between robust poisson models and log-binomial models when estimating relative risks for common binary outcomes: a simulation study.

PubMed

Chen, Wansu; Shi, Jiaxiao; Qian, Lei; Azen, Stanley P

2014-06-26

To estimate relative risks or risk ratios for common binary outcomes, the most popular model-based methods are the robust (also known as modified) Poisson and the log-binomial regression. Of the two methods, it is believed that the log-binomial regression yields more efficient estimators because it is maximum likelihood based, while the robust Poisson model may be less affected by outliers. Evidence to support the robustness of robust Poisson models in comparison with log-binomial models is very limited. In this study a simulation was conducted to evaluate the performance of the two methods in several scenarios where outliers existed. The findings indicate that for data coming from a population where the relationship between the outcome and the covariate was in a simple form (e.g. log-linear), the two models yielded comparable biases and mean square errors. However, if the true relationship contained a higher order term, the robust Poisson models consistently outperformed the log-binomial models even when the level of contamination is low. The robust Poisson models are more robust (or less sensitive) to outliers compared to the log-binomial models when estimating relative risks or risk ratios for common binary outcomes. Users should be aware of the limitations when choosing appropriate models to estimate relative risks or risk ratios.

A simple finite element method for linear hyperbolic problems

DOE PAGES

Mu, Lin; Ye, Xiu

2017-09-14

Here, we introduce a simple finite element method for solving first order hyperbolic equations with easy implementation and analysis. Our new method, with a symmetric, positive definite system, is designed to use discontinuous approximations on finite element partitions consisting of arbitrary shape of polygons/polyhedra. Error estimate is established. Extensive numerical examples are tested that demonstrate the robustness and flexibility of the method.

A simple finite element method for linear hyperbolic problems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mu, Lin; Ye, Xiu

Here, we introduce a simple finite element method for solving first order hyperbolic equations with easy implementation and analysis. Our new method, with a symmetric, positive definite system, is designed to use discontinuous approximations on finite element partitions consisting of arbitrary shape of polygons/polyhedra. Error estimate is established. Extensive numerical examples are tested that demonstrate the robustness and flexibility of the method.

Tissue resistivity estimation in the presence of positional and geometrical uncertainties.

PubMed

Baysal, U; Eyüboğlu, B M

2000-08-01

Geometrical uncertainties (organ boundary variation and electrode position uncertainties) are the biggest sources of error in estimating electrical resistivity of tissues from body surface measurements. In this study, in order to decrease estimation errors, the statistically constrained minimum mean squared error estimation algorithm (MiMSEE) is constrained with a priori knowledge of the geometrical uncertainties in addition to the constraints based on geometry, resistivity range, linearization and instrumentation errors. The MiMSEE calculates an optimum inverse matrix, which maps the surface measurements to the unknown resistivity distribution. The required data are obtained from four-electrode impedance measurements, similar to injected-current electrical impedance tomography (EIT). In this study, the surface measurements are simulated by using a numerical thorax model. The data are perturbed with additive instrumentation noise. Simulated surface measurements are then used to estimate the tissue resistivities by using the proposed algorithm. The results are compared with the results of conventional least squares error estimator (LSEE). Depending on the region, the MiMSEE yields an estimation error between 0.42% and 31.3% compared with 7.12% to 2010% for the LSEE. It is shown that the MiMSEE is quite robust even in the case of geometrical uncertainties.

Robust Portfolio Optimization Using Pseudodistances.

PubMed

Toma, Aida; Leoni-Aubin, Samuela

2015-01-01

The presence of outliers in financial asset returns is a frequently occurring phenomenon which may lead to unreliable mean-variance optimized portfolios. This fact is due to the unbounded influence that outliers can have on the mean returns and covariance estimators that are inputs in the optimization procedure. In this paper we present robust estimators of mean and covariance matrix obtained by minimizing an empirical version of a pseudodistance between the assumed model and the true model underlying the data. We prove and discuss theoretical properties of these estimators, such as affine equivariance, B-robustness, asymptotic normality and asymptotic relative efficiency. These estimators can be easily used in place of the classical estimators, thereby providing robust optimized portfolios. A Monte Carlo simulation study and applications to real data show the advantages of the proposed approach. We study both in-sample and out-of-sample performance of the proposed robust portfolios comparing them with some other portfolios known in literature.

Robust Portfolio Optimization Using Pseudodistances

PubMed Central

2015-01-01

The presence of outliers in financial asset returns is a frequently occurring phenomenon which may lead to unreliable mean-variance optimized portfolios. This fact is due to the unbounded influence that outliers can have on the mean returns and covariance estimators that are inputs in the optimization procedure. In this paper we present robust estimators of mean and covariance matrix obtained by minimizing an empirical version of a pseudodistance between the assumed model and the true model underlying the data. We prove and discuss theoretical properties of these estimators, such as affine equivariance, B-robustness, asymptotic normality and asymptotic relative efficiency. These estimators can be easily used in place of the classical estimators, thereby providing robust optimized portfolios. A Monte Carlo simulation study and applications to real data show the advantages of the proposed approach. We study both in-sample and out-of-sample performance of the proposed robust portfolios comparing them with some other portfolios known in literature. PMID:26468948

Polarization-based index of refraction and reflection angle estimation for remote sensing applications.

PubMed

Thilak, Vimal; Voelz, David G; Creusere, Charles D

2007-10-20

A passive-polarization-based imaging system records the polarization state of light reflected by objects that are illuminated with an unpolarized and generally uncontrolled source. Such systems can be useful in many remote sensing applications including target detection, object segmentation, and material classification. We present a method to jointly estimate the complex index of refraction and the reflection angle (reflected zenith angle) of a target from multiple measurements collected by a passive polarimeter. An expression for the degree of polarization is derived from the microfacet polarimetric bidirectional reflectance model for the case of scattering in the plane of incidence. Using this expression, we develop a nonlinear least-squares estimation algorithm for extracting an apparent index of refraction and the reflection angle from a set of polarization measurements collected from multiple source positions. Computer simulation results show that the estimation accuracy generally improves with an increasing number of source position measurements. Laboratory results indicate that the proposed method is effective for recovering the reflection angle and that the estimated index of refraction provides a feature vector that is robust to the reflection angle.

Polarization-based index of refraction and reflection angle estimation for remote sensing applications

NASA Astrophysics Data System (ADS)

Thilak, Vimal; Voelz, David G.; Creusere, Charles D.

2007-10-01

A passive-polarization-based imaging system records the polarization state of light reflected by objects that are illuminated with an unpolarized and generally uncontrolled source. Such systems can be useful in many remote sensing applications including target detection, object segmentation, and material classification. We present a method to jointly estimate the complex index of refraction and the reflection angle (reflected zenith angle) of a target from multiple measurements collected by a passive polarimeter. An expression for the degree of polarization is derived from the microfacet polarimetric bidirectional reflectance model for the case of scattering in the plane of incidence. Using this expression, we develop a nonlinear least-squares estimation algorithm for extracting an apparent index of refraction and the reflection angle from a set of polarization measurements collected from multiple source positions. Computer simulation results show that the estimation accuracy generally improves with an increasing number of source position measurements. Laboratory results indicate that the proposed method is effective for recovering the reflection angle and that the estimated index of refraction provides a feature vector that is robust to the reflection angle.

Using Visual Odometry to Estimate Position and Attitude

NASA Technical Reports Server (NTRS)

Maimone, Mark; Cheng, Yang; Matthies, Larry; Schoppers, Marcel; Olson, Clark

2007-01-01

A computer program in the guidance system of a mobile robot generates estimates of the position and attitude of the robot, using features of the terrain on which the robot is moving, by processing digitized images acquired by a stereoscopic pair of electronic cameras mounted rigidly on the robot. Developed for use in localizing the Mars Exploration Rover (MER) vehicles on Martian terrain, the program can also be used for similar purposes on terrestrial robots moving in sufficiently visually textured environments: examples include low-flying robotic aircraft and wheeled robots moving on rocky terrain or inside buildings. In simplified terms, the program automatically detects visual features and tracks them across stereoscopic pairs of images acquired by the cameras. The 3D locations of the tracked features are then robustly processed into an estimate of overall vehicle motion. Testing has shown that by use of this software, the error in the estimate of the position of the robot can be limited to no more than 2 percent of the distance traveled, provided that the terrain is sufficiently rich in features. This software has proven extremely useful on the MER vehicles during driving on sandy and highly sloped terrains on Mars.

Method for hyperspectral imagery exploitation and pixel spectral unmixing

NASA Technical Reports Server (NTRS)

Lin, Ching-Fang (Inventor)

2003-01-01

An efficiently hybrid approach to exploit hyperspectral imagery and unmix spectral pixels. This hybrid approach uses a genetic algorithm to solve the abundance vector for the first pixel of a hyperspectral image cube. This abundance vector is used as initial state in a robust filter to derive the abundance estimate for the next pixel. By using Kalman filter, the abundance estimate for a pixel can be obtained in one iteration procedure which is much fast than genetic algorithm. The output of the robust filter is fed to genetic algorithm again to derive accurate abundance estimate for the current pixel. The using of robust filter solution as starting point of the genetic algorithm speeds up the evolution of the genetic algorithm. After obtaining the accurate abundance estimate, the procedure goes to next pixel, and uses the output of genetic algorithm as the previous state estimate to derive abundance estimate for this pixel using robust filter. And again use the genetic algorithm to derive accurate abundance estimate efficiently based on the robust filter solution. This iteration continues until pixels in a hyperspectral image cube end.

Robust Variance Estimation with Dependent Effect Sizes: Practical Considerations Including a Software Tutorial in Stata and SPSS

ERIC Educational Resources Information Center

Tanner-Smith, Emily E.; Tipton, Elizabeth

2014-01-01

Methodologists have recently proposed robust variance estimation as one way to handle dependent effect sizes in meta-analysis. Software macros for robust variance estimation in meta-analysis are currently available for Stata (StataCorp LP, College Station, TX, USA) and SPSS (IBM, Armonk, NY, USA), yet there is little guidance for authors regarding…

Maximum likelihood estimation in calibrating a stereo camera setup.

PubMed

Muijtjens, A M; Roos, J M; Arts, T; Hasman, A

1999-02-01

Motion and deformation of the cardiac wall may be measured by following the positions of implanted radiopaque markers in three dimensions, using two x-ray cameras simultaneously. Regularly, calibration of the position measurement system is obtained by registration of the images of a calibration object, containing 10-20 radiopaque markers at known positions. Unfortunately, an accidental change of the position of a camera after calibration requires complete recalibration. Alternatively, redundant information in the measured image positions of stereo pairs can be used for calibration. Thus, a separate calibration procedure can be avoided. In the current study a model is developed that describes the geometry of the camera setup by five dimensionless parameters. Maximum Likelihood (ML) estimates of these parameters were obtained in an error analysis. It is shown that the ML estimates can be found by application of a nonlinear least squares procedure. Compared to the standard unweighted least squares procedure, the ML method resulted in more accurate estimates without noticeable bias. The accuracy of the ML method was investigated in relation to the object aperture. The reconstruction problem appeared well conditioned as long as the object aperture is larger than 0.1 rad. The angle between the two viewing directions appeared to be the parameter that was most likely to cause major inaccuracies in the reconstruction of the 3-D positions of the markers. Hence, attempts to improve the robustness of the method should primarily focus on reduction of the error in this parameter.

Robust Control Algorithm for a Two Cart System and an Inverted Pendulum

NASA Technical Reports Server (NTRS)

Wilson, Chris L.; Capo-Lugo, Pedro

2011-01-01

The Rectilinear Control System can be used to simulate a launch vehicle during liftoff. Several control schemes have been developed that can control different dynamic models of the rectilinear plant. A robust control algorithm was developed that can control a pendulum to maintain an inverted position. A fluid slosh tank will be attached to the pendulum in order to test robustness in the presence of unknown slosh characteristics. The rectilinear plant consists of a DC motor and three carts mounted in series. Each cart s weight can be adjusted with brass masses and the carts can be coupled with springs. The pendulum is mounted on the first cart and an adjustable air damper can be attached to the third cart if desired. Each cart and the pendulum have a quadrature encoder to determine position. Full state feedback was implemented in order to develop the control algorithm along with a state estimator to determine the velocity states of the system. A MATLAB program was used to convert the state space matrices from continuous time to discrete time. This program also used a desired phase margin and damping ratio to determine the feedback gain matrix that would be used in the LabVIEW program. This experiment will allow engineers to gain a better understanding of liquid propellant slosh dynamics, therefore enabling them to develop more robust control algorithms for launch vehicle systems

Covariate selection with group lasso and doubly robust estimation of causal effects

PubMed Central

Koch, Brandon; Vock, David M.; Wolfson, Julian

2017-01-01

Summary The efficiency of doubly robust estimators of the average causal effect (ACE) of a treatment can be improved by including in the treatment and outcome models only those covariates which are related to both treatment and outcome (i.e., confounders) or related only to the outcome. However, it is often challenging to identify such covariates among the large number that may be measured in a given study. In this paper, we propose GLiDeR (Group Lasso and Doubly Robust Estimation), a novel variable selection technique for identifying confounders and predictors of outcome using an adaptive group lasso approach that simultaneously performs coefficient selection, regularization, and estimation across the treatment and outcome models. The selected variables and corresponding coefficient estimates are used in a standard doubly robust ACE estimator. We provide asymptotic results showing that, for a broad class of data generating mechanisms, GLiDeR yields a consistent estimator of the ACE when either the outcome or treatment model is correctly specified. A comprehensive simulation study shows that GLiDeR is more efficient than doubly robust methods using standard variable selection techniques and has substantial computational advantages over a recently proposed doubly robust Bayesian model averaging method. We illustrate our method by estimating the causal treatment effect of bilateral versus single-lung transplant on forced expiratory volume in one year after transplant using an observational registry. PMID:28636276

Covariate selection with group lasso and doubly robust estimation of causal effects.

PubMed

Koch, Brandon; Vock, David M; Wolfson, Julian

2018-03-01

The efficiency of doubly robust estimators of the average causal effect (ACE) of a treatment can be improved by including in the treatment and outcome models only those covariates which are related to both treatment and outcome (i.e., confounders) or related only to the outcome. However, it is often challenging to identify such covariates among the large number that may be measured in a given study. In this article, we propose GLiDeR (Group Lasso and Doubly Robust Estimation), a novel variable selection technique for identifying confounders and predictors of outcome using an adaptive group lasso approach that simultaneously performs coefficient selection, regularization, and estimation across the treatment and outcome models. The selected variables and corresponding coefficient estimates are used in a standard doubly robust ACE estimator. We provide asymptotic results showing that, for a broad class of data generating mechanisms, GLiDeR yields a consistent estimator of the ACE when either the outcome or treatment model is correctly specified. A comprehensive simulation study shows that GLiDeR is more efficient than doubly robust methods using standard variable selection techniques and has substantial computational advantages over a recently proposed doubly robust Bayesian model averaging method. We illustrate our method by estimating the causal treatment effect of bilateral versus single-lung transplant on forced expiratory volume in one year after transplant using an observational registry. © 2017, The International Biometric Society.

Attitude/attitude-rate estimation from GPS differential phase measurements using integrated-rate parameters

NASA Technical Reports Server (NTRS)

Oshman, Yaakov; Markley, Landis

1998-01-01

A sequential filtering algorithm is presented for attitude and attitude-rate estimation from Global Positioning System (GPS) differential carrier phase measurements. A third-order, minimal-parameter method for solving the attitude matrix kinematic equation is used to parameterize the filter's state, which renders the resulting estimator computationally efficient. Borrowing from tracking theory concepts, the angular acceleration is modeled as an exponentially autocorrelated stochastic process, thus avoiding the use of the uncertain spacecraft dynamic model. The new formulation facilitates the use of aiding vector observations in a unified filtering algorithm, which can enhance the method's robustness and accuracy. Numerical examples are used to demonstrate the performance of the method.

Using Robust Variance Estimation to Combine Multiple Regression Estimates with Meta-Analysis

ERIC Educational Resources Information Center

Williams, Ryan

2013-01-01

The purpose of this study was to explore the use of robust variance estimation for combining commonly specified multiple regression models and for combining sample-dependent focal slope estimates from diversely specified models. The proposed estimator obviates traditionally required information about the covariance structure of the dependent…

Marginal Structural Models with Counterfactual Effect Modifiers.

PubMed

Zheng, Wenjing; Luo, Zhehui; van der Laan, Mark J

2018-06-08

In health and social sciences, research questions often involve systematic assessment of the modification of treatment causal effect by patient characteristics. In longitudinal settings, time-varying or post-intervention effect modifiers are also of interest. In this work, we investigate the robust and efficient estimation of the Counterfactual-History-Adjusted Marginal Structural Model (van der Laan MJ, Petersen M. Statistical learning of origin-specific statically optimal individualized treatment rules. Int J Biostat. 2007;3), which models the conditional intervention-specific mean outcome given a counterfactual modifier history in an ideal experiment. We establish the semiparametric efficiency theory for these models, and present a substitution-based, semiparametric efficient and doubly robust estimator using the targeted maximum likelihood estimation methodology (TMLE, e.g. van der Laan MJ, Rubin DB. Targeted maximum likelihood learning. Int J Biostat. 2006;2, van der Laan MJ, Rose S. Targeted learning: causal inference for observational and experimental data, 1st ed. Springer Series in Statistics. Springer, 2011). To facilitate implementation in applications where the effect modifier is high dimensional, our third contribution is a projected influence function (and the corresponding projected TMLE estimator), which retains most of the robustness of its efficient peer and can be easily implemented in applications where the use of the efficient influence function becomes taxing. We compare the projected TMLE estimator with an Inverse Probability of Treatment Weighted estimator (e.g. Robins JM. Marginal structural models. In: Proceedings of the American Statistical Association. Section on Bayesian Statistical Science, 1-10. 1997a, Hernan MA, Brumback B, Robins JM. Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. 2000;11:561-570), and a non-targeted G-computation estimator (Robins JM. A new approach to causal inference in mortality studies with sustained exposure periods - application to control of the healthy worker survivor effect. Math Modell. 1986;7:1393-1512.). The comparative performance of these estimators is assessed in a simulation study. The use of the projected TMLE estimator is illustrated in a secondary data analysis for the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial where effect modifiers are subject to missing at random.

Graphical Evaluation of the Ridge-Type Robust Regression Estimators in Mixture Experiments

PubMed Central

Erkoc, Ali; Emiroglu, Esra

2014-01-01

In mixture experiments, estimation of the parameters is generally based on ordinary least squares (OLS). However, in the presence of multicollinearity and outliers, OLS can result in very poor estimates. In this case, effects due to the combined outlier-multicollinearity problem can be reduced to certain extent by using alternative approaches. One of these approaches is to use biased-robust regression techniques for the estimation of parameters. In this paper, we evaluate various ridge-type robust estimators in the cases where there are multicollinearity and outliers during the analysis of mixture experiments. Also, for selection of biasing parameter, we use fraction of design space plots for evaluating the effect of the ridge-type robust estimators with respect to the scaled mean squared error of prediction. The suggested graphical approach is illustrated on Hald cement data set. PMID:25202738

Graphical evaluation of the ridge-type robust regression estimators in mixture experiments.

PubMed

Erkoc, Ali; Emiroglu, Esra; Akay, Kadri Ulas

2014-01-01

In mixture experiments, estimation of the parameters is generally based on ordinary least squares (OLS). However, in the presence of multicollinearity and outliers, OLS can result in very poor estimates. In this case, effects due to the combined outlier-multicollinearity problem can be reduced to certain extent by using alternative approaches. One of these approaches is to use biased-robust regression techniques for the estimation of parameters. In this paper, we evaluate various ridge-type robust estimators in the cases where there are multicollinearity and outliers during the analysis of mixture experiments. Also, for selection of biasing parameter, we use fraction of design space plots for evaluating the effect of the ridge-type robust estimators with respect to the scaled mean squared error of prediction. The suggested graphical approach is illustrated on Hald cement data set.

A LEO Satellite Navigation Algorithm Based on GPS and Magnetometer Data

NASA Technical Reports Server (NTRS)

Deutschmann, Julie; Bar-Itzhack, Itzhack; Harman, Rick; Bauer, Frank H. (Technical Monitor)

2000-01-01

The Global Positioning System (GPS) has become a standard method for low cost onboard satellite orbit determination. The use of a GPS receiver as an attitude and rate sensor has also been developed in the recent past. Additionally, focus has been given to attitude and orbit estimation using the magnetometer, a low cost, reliable sensor. Combining measurements from both GPS and a magnetometer can provide a robust navigation system that takes advantage of the estimation qualities of both measurements. Ultimately a low cost, accurate navigation system can result, potentially eliminating the need for more costly sensors, including gyroscopes.

On robust parameter estimation in brain-computer interfacing

NASA Astrophysics Data System (ADS)

Samek, Wojciech; Nakajima, Shinichi; Kawanabe, Motoaki; Müller, Klaus-Robert

2017-12-01

Objective. The reliable estimation of parameters such as mean or covariance matrix from noisy and high-dimensional observations is a prerequisite for successful application of signal processing and machine learning algorithms in brain-computer interfacing (BCI). This challenging task becomes significantly more difficult if the data set contains outliers, e.g. due to subject movements, eye blinks or loose electrodes, as they may heavily bias the estimation and the subsequent statistical analysis. Although various robust estimators have been developed to tackle the outlier problem, they ignore important structural information in the data and thus may not be optimal. Typical structural elements in BCI data are the trials consisting of a few hundred EEG samples and indicating the start and end of a task. Approach. This work discusses the parameter estimation problem in BCI and introduces a novel hierarchical view on robustness which naturally comprises different types of outlierness occurring in structured data. Furthermore, the class of minimum divergence estimators is reviewed and a robust mean and covariance estimator for structured data is derived and evaluated with simulations and on a benchmark data set. Main results. The results show that state-of-the-art BCI algorithms benefit from robustly estimated parameters. Significance. Since parameter estimation is an integral part of various machine learning algorithms, the presented techniques are applicable to many problems beyond BCI.

Robust Eye Center Localization through Face Alignment and Invariant Isocentric Patterns

PubMed Central

Teng, Dongdong; Chen, Dihu; Tan, Hongzhou

2015-01-01

The localization of eye centers is a very useful cue for numerous applications like face recognition, facial expression recognition, and the early screening of neurological pathologies. Several methods relying on available light for accurate eye-center localization have been exploited. However, despite the considerable improvements that eye-center localization systems have undergone in recent years, only few of these developments deal with the challenges posed by the profile (non-frontal face). In this paper, we first use the explicit shape regression method to obtain the rough location of the eye centers. Because this method extracts global information from the human face, it is robust against any changes in the eye region. We exploit this robustness and utilize it as a constraint. To locate the eye centers accurately, we employ isophote curvature features, the accuracy of which has been demonstrated in a previous study. By applying these features, we obtain a series of eye-center locations which are candidates for the actual position of the eye-center. Among these locations, the estimated locations which minimize the reconstruction error between the two methods mentioned above are taken as the closest approximation for the eye centers locations. Therefore, we combine explicit shape regression and isophote curvature feature analysis to achieve robustness and accuracy, respectively. In practical experiments, we use BioID and FERET datasets to test our approach to obtaining an accurate eye-center location while retaining robustness against changes in scale and pose. In addition, we apply our method to non-frontal faces to test its robustness and accuracy, which are essential in gaze estimation but have seldom been mentioned in previous works. Through extensive experimentation, we show that the proposed method can achieve a significant improvement in accuracy and robustness over state-of-the-art techniques, with our method ranking second in terms of accuracy. According to our implementation on a PC with a Xeon 2.5Ghz CPU, the frame rate of the eye tracking process can achieve 38 Hz. PMID:26426929

Robust 3D Position Estimation in Wide and Unconstrained Indoor Environments

PubMed Central

Mossel, Annette

2015-01-01

In this paper, a system for 3D position estimation in wide, unconstrained indoor environments is presented that employs infrared optical outside-in tracking of rigid-body targets with a stereo camera rig. To overcome limitations of state-of-the-art optical tracking systems, a pipeline for robust target identification and 3D point reconstruction has been investigated that enables camera calibration and tracking in environments with poor illumination, static and moving ambient light sources, occlusions and harsh conditions, such as fog. For evaluation, the system has been successfully applied in three different wide and unconstrained indoor environments, (1) user tracking for virtual and augmented reality applications, (2) handheld target tracking for tunneling and (3) machine guidance for mining. The results of each use case are discussed to embed the presented approach into a larger technological and application context. The experimental results demonstrate the system’s capabilities to track targets up to 100 m. Comparing the proposed approach to prior art in optical tracking in terms of range coverage and accuracy, it significantly extends the available tracking range, while only requiring two cameras and providing a relative 3D point accuracy with sub-centimeter deviation up to 30 m and low-centimeter deviation up to 100 m. PMID:26694388

Robust Feedback Zoom Tracking for Digital Video Surveillance

PubMed Central

Zou, Tengyue; Tang, Xiaoqi; Song, Bao; Wang, Jin; Chen, Jihong

2012-01-01

Zoom tracking is an important function in video surveillance, particularly in traffic management and security monitoring. It involves keeping an object of interest in focus during the zoom operation. Zoom tracking is typically achieved by moving the zoom and focus motors in lenses following the so-called “trace curve”, which shows the in-focus motor positions versus the zoom motor positions for a specific object distance. The main task of a zoom tracking approach is to accurately estimate the trace curve for the specified object. Because a proportional integral derivative (PID) controller has historically been considered to be the best controller in the absence of knowledge of the underlying process and its high-quality performance in motor control, in this paper, we propose a novel feedback zoom tracking (FZT) approach based on the geometric trace curve estimation and PID feedback controller. The performance of this approach is compared with existing zoom tracking methods in digital video surveillance. The real-time implementation results obtained on an actual digital video platform indicate that the developed FZT approach not only solves the traditional one-to-many mapping problem without pre-training but also improves the robustness for tracking moving or switching objects which is the key challenge in video surveillance. PMID:22969388

Robust regression for large-scale neuroimaging studies.

PubMed

Fritsch, Virgile; Da Mota, Benoit; Loth, Eva; Varoquaux, Gaël; Banaschewski, Tobias; Barker, Gareth J; Bokde, Arun L W; Brühl, Rüdiger; Butzek, Brigitte; Conrod, Patricia; Flor, Herta; Garavan, Hugh; Lemaitre, Hervé; Mann, Karl; Nees, Frauke; Paus, Tomas; Schad, Daniel J; Schümann, Gunter; Frouin, Vincent; Poline, Jean-Baptiste; Thirion, Bertrand

2015-05-01

Multi-subject datasets used in neuroimaging group studies have a complex structure, as they exhibit non-stationary statistical properties across regions and display various artifacts. While studies with small sample sizes can rarely be shown to deviate from standard hypotheses (such as the normality of the residuals) due to the poor sensitivity of normality tests with low degrees of freedom, large-scale studies (e.g. >100 subjects) exhibit more obvious deviations from these hypotheses and call for more refined models for statistical inference. Here, we demonstrate the benefits of robust regression as a tool for analyzing large neuroimaging cohorts. First, we use an analytic test based on robust parameter estimates; based on simulations, this procedure is shown to provide an accurate statistical control without resorting to permutations. Second, we show that robust regression yields more detections than standard algorithms using as an example an imaging genetics study with 392 subjects. Third, we show that robust regression can avoid false positives in a large-scale analysis of brain-behavior relationships with over 1500 subjects. Finally we embed robust regression in the Randomized Parcellation Based Inference (RPBI) method and demonstrate that this combination further improves the sensitivity of tests carried out across the whole brain. Altogether, our results show that robust procedures provide important advantages in large-scale neuroimaging group studies. Copyright © 2015 Elsevier Inc. All rights reserved.

Robust estimation for ordinary differential equation models.

PubMed

Cao, J; Wang, L; Xu, J

2011-12-01

Applied scientists often like to use ordinary differential equations (ODEs) to model complex dynamic processes that arise in biology, engineering, medicine, and many other areas. It is interesting but challenging to estimate ODE parameters from noisy data, especially when the data have some outliers. We propose a robust method to address this problem. The dynamic process is represented with a nonparametric function, which is a linear combination of basis functions. The nonparametric function is estimated by a robust penalized smoothing method. The penalty term is defined with the parametric ODE model, which controls the roughness of the nonparametric function and maintains the fidelity of the nonparametric function to the ODE model. The basis coefficients and ODE parameters are estimated in two nested levels of optimization. The coefficient estimates are treated as an implicit function of ODE parameters, which enables one to derive the analytic gradients for optimization using the implicit function theorem. Simulation studies show that the robust method gives satisfactory estimates for the ODE parameters from noisy data with outliers. The robust method is demonstrated by estimating a predator-prey ODE model from real ecological data. © 2011, The International Biometric Society.

Robust geostatistical analysis of spatial data

NASA Astrophysics Data System (ADS)

Papritz, A.; Künsch, H. R.; Schwierz, C.; Stahel, W. A.

2012-04-01

Most of the geostatistical software tools rely on non-robust algorithms. This is unfortunate, because outlying observations are rather the rule than the exception, in particular in environmental data sets. Outlying observations may results from errors (e.g. in data transcription) or from local perturbations in the processes that are responsible for a given pattern of spatial variation. As an example, the spatial distribution of some trace metal in the soils of a region may be distorted by emissions of local anthropogenic sources. Outliers affect the modelling of the large-scale spatial variation, the so-called external drift or trend, the estimation of the spatial dependence of the residual variation and the predictions by kriging. Identifying outliers manually is cumbersome and requires expertise because one needs parameter estimates to decide which observation is a potential outlier. Moreover, inference after the rejection of some observations is problematic. A better approach is to use robust algorithms that prevent automatically that outlying observations have undue influence. Former studies on robust geostatistics focused on robust estimation of the sample variogram and ordinary kriging without external drift. Furthermore, Richardson and Welsh (1995) [2] proposed a robustified version of (restricted) maximum likelihood ([RE]ML) estimation for the variance components of a linear mixed model, which was later used by Marchant and Lark (2007) [1] for robust REML estimation of the variogram. We propose here a novel method for robust REML estimation of the variogram of a Gaussian random field that is possibly contaminated by independent errors from a long-tailed distribution. It is based on robustification of estimating equations for the Gaussian REML estimation. Besides robust estimates of the parameters of the external drift and of the variogram, the method also provides standard errors for the estimated parameters, robustified kriging predictions at both sampled and unsampled locations and kriging variances. The method has been implemented in an R package. Apart from presenting our modelling framework, we shall present selected simulation results by which we explored the properties of the new method. This will be complemented by an analysis of the Tarrawarra soil moisture data set [3].

Uncertainty Estimation in Tsunami Initial Condition From Rapid Bayesian Finite Fault Modeling

NASA Astrophysics Data System (ADS)

Benavente, R. F.; Dettmer, J.; Cummins, P. R.; Urrutia, A.; Cienfuegos, R.

2017-12-01

It is well known that kinematic rupture models for a given earthquake can present discrepancies even when similar datasets are employed in the inversion process. While quantifying this variability can be critical when making early estimates of the earthquake and triggered tsunami impact, "most likely models" are normally used for this purpose. In this work, we quantify the uncertainty of the tsunami initial condition for the great Illapel earthquake (Mw = 8.3, 2015, Chile). We focus on utilizing data and inversion methods that are suitable to rapid source characterization yet provide meaningful and robust results. Rupture models from teleseismic body and surface waves as well as W-phase are derived and accompanied by Bayesian uncertainty estimates from linearized inversion under positivity constraints. We show that robust and consistent features about the rupture kinematics appear when working within this probabilistic framework. Moreover, by using static dislocation theory, we translate the probabilistic slip distributions into seafloor deformation which we interpret as a tsunami initial condition. After considering uncertainty, our probabilistic seafloor deformation models obtained from different data types appear consistent with each other providing meaningful results. We also show that selecting just a single "representative" solution from the ensemble of initial conditions for tsunami propagation may lead to overestimating information content in the data. Our results suggest that rapid, probabilistic rupture models can play a significant role during emergency response by providing robust information about the extent of the disaster.

Joint release rate estimation and measurement-by-measurement model correction for atmospheric radionuclide emission in nuclear accidents: An application to wind tunnel experiments.

PubMed

Li, Xinpeng; Li, Hong; Liu, Yun; Xiong, Wei; Fang, Sheng

2018-03-05

The release rate of atmospheric radionuclide emissions is a critical factor in the emergency response to nuclear accidents. However, there are unavoidable biases in radionuclide transport models, leading to inaccurate estimates. In this study, a method that simultaneously corrects these biases and estimates the release rate is developed. Our approach provides a more complete measurement-by-measurement correction of the biases with a coefficient matrix that considers both deterministic and stochastic deviations. This matrix and the release rate are jointly solved by the alternating minimization algorithm. The proposed method is generic because it does not rely on specific features of transport models or scenarios. It is validated against wind tunnel experiments that simulate accidental releases in a heterogonous and densely built nuclear power plant site. The sensitivities to the position, number, and quality of measurements and extendibility of the method are also investigated. The results demonstrate that this method effectively corrects the model biases, and therefore outperforms Tikhonov's method in both release rate estimation and model prediction. The proposed approach is robust to uncertainties and extendible with various center estimators, thus providing a flexible framework for robust source inversion in real accidents, even if large uncertainties exist in multiple factors. Copyright © 2017 Elsevier B.V. All rights reserved.

Robust k-mer frequency estimation using gapped k-mers

PubMed Central

Ghandi, Mahmoud; Mohammad-Noori, Morteza

2013-01-01

Oligomers of fixed length, k, commonly known as k-mers, are often used as fundamental elements in the description of DNA sequence features of diverse biological function, or as intermediate elements in the constuction of more complex descriptors of sequence features such as position weight matrices. k-mers are very useful as general sequence features because they constitute a complete and unbiased feature set, and do not require parameterization based on incomplete knowledge of biological mechanisms. However, a fundamental limitation in the use of k-mers as sequence features is that as k is increased, larger spatial correlations in DNA sequence elements can be described, but the frequency of observing any specific k-mer becomes very small, and rapidly approaches a sparse matrix of binary counts. Thus any statistical learning approach using k-mers will be susceptible to noisy estimation of k-mer frequencies once k becomes large. Because all molecular DNA interactions have limited spatial extent, gapped k-mers often carry the relevant biological signal. Here we use gapped k-mer counts to more robustly estimate the ungapped k-mer frequencies, by deriving an equation for the minimum norm estimate of k-mer frequencies given an observed set of gapped k-mer frequencies. We demonstrate that this approach provides a more accurate estimate of the k-mer frequencies in real biological sequences using a sample of CTCF binding sites in the human genome. PMID:23861010

Robust k-mer frequency estimation using gapped k-mers.

PubMed

Ghandi, Mahmoud; Mohammad-Noori, Morteza; Beer, Michael A

2014-08-01

Oligomers of fixed length, k, commonly known as k-mers, are often used as fundamental elements in the description of DNA sequence features of diverse biological function, or as intermediate elements in the constuction of more complex descriptors of sequence features such as position weight matrices. k-mers are very useful as general sequence features because they constitute a complete and unbiased feature set, and do not require parameterization based on incomplete knowledge of biological mechanisms. However, a fundamental limitation in the use of k-mers as sequence features is that as k is increased, larger spatial correlations in DNA sequence elements can be described, but the frequency of observing any specific k-mer becomes very small, and rapidly approaches a sparse matrix of binary counts. Thus any statistical learning approach using k-mers will be susceptible to noisy estimation of k-mer frequencies once k becomes large. Because all molecular DNA interactions have limited spatial extent, gapped k-mers often carry the relevant biological signal. Here we use gapped k-mer counts to more robustly estimate the ungapped k-mer frequencies, by deriving an equation for the minimum norm estimate of k-mer frequencies given an observed set of gapped k-mer frequencies. We demonstrate that this approach provides a more accurate estimate of the k-mer frequencies in real biological sequences using a sample of CTCF binding sites in the human genome.

Kinematic Localization for Global Navigation Satellite Systems: A Kalman Filtering Approach

NASA Astrophysics Data System (ADS)

Tabatabaee, Mohammad Hadi

Use of the Global Positioning System (GNSS) has expanded significantly in the past decade, especially with advances in embedded systems and the emergence of smartphones and the Internet of Things (IoT). The growing demand has stimulated research on development of GNSS techniques and programming tools. The focus of much of the research efforts have been on high-level algorithms and augmentations. This dissertation focuses on the low-level methods at the heart of GNSS systems and proposes a new methods for GNSS positioning problems based on concepts of distance geometry and the use of Kalman filters. The methods presented in this dissertation provide algebraic solutions to problems that have predominantly been solved using iterative methods. The proposed methods are highly efficient, provide accurate estimates, and exhibit a degree of robustness in the presence of unfavorable satellite geometry. The algorithm operates in two stages; an estimation of the receiver clock bias and removal of the bias from the pseudorange observables, followed by the localization of the GNSS receiver. The use of a Kalman filter in between the two stages allows for an improvement of the clock bias estimate with a noticeable impact on the position estimates. The receiver localization step has also been formulated in a linear manner allowing for the direct application of a Kalman filter without any need for linearization. The methodology has also been extended to double differential observables for high accuracy pseudorange and carrier phase position estimates.

Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter.

PubMed

Alatise, Mary B; Hancke, Gerhard P

2017-09-21

Using a single sensor to determine the pose estimation of a device cannot give accurate results. This paper presents a fusion of an inertial sensor of six degrees of freedom (6-DoF) which comprises the 3-axis of an accelerometer and the 3-axis of a gyroscope, and a vision to determine a low-cost and accurate position for an autonomous mobile robot. For vision, a monocular vision-based object detection algorithm speeded-up robust feature (SURF) and random sample consensus (RANSAC) algorithms were integrated and used to recognize a sample object in several images taken. As against the conventional method that depend on point-tracking, RANSAC uses an iterative method to estimate the parameters of a mathematical model from a set of captured data which contains outliers. With SURF and RANSAC, improved accuracy is certain; this is because of their ability to find interest points (features) under different viewing conditions using a Hessain matrix. This approach is proposed because of its simple implementation, low cost, and improved accuracy. With an extended Kalman filter (EKF), data from inertial sensors and a camera were fused to estimate the position and orientation of the mobile robot. All these sensors were mounted on the mobile robot to obtain an accurate localization. An indoor experiment was carried out to validate and evaluate the performance. Experimental results show that the proposed method is fast in computation, reliable and robust, and can be considered for practical applications. The performance of the experiments was verified by the ground truth data and root mean square errors (RMSEs).

Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter

PubMed Central

Hancke, Gerhard P.

2017-01-01

Using a single sensor to determine the pose estimation of a device cannot give accurate results. This paper presents a fusion of an inertial sensor of six degrees of freedom (6-DoF) which comprises the 3-axis of an accelerometer and the 3-axis of a gyroscope, and a vision to determine a low-cost and accurate position for an autonomous mobile robot. For vision, a monocular vision-based object detection algorithm speeded-up robust feature (SURF) and random sample consensus (RANSAC) algorithms were integrated and used to recognize a sample object in several images taken. As against the conventional method that depend on point-tracking, RANSAC uses an iterative method to estimate the parameters of a mathematical model from a set of captured data which contains outliers. With SURF and RANSAC, improved accuracy is certain; this is because of their ability to find interest points (features) under different viewing conditions using a Hessain matrix. This approach is proposed because of its simple implementation, low cost, and improved accuracy. With an extended Kalman filter (EKF), data from inertial sensors and a camera were fused to estimate the position and orientation of the mobile robot. All these sensors were mounted on the mobile robot to obtain an accurate localization. An indoor experiment was carried out to validate and evaluate the performance. Experimental results show that the proposed method is fast in computation, reliable and robust, and can be considered for practical applications. The performance of the experiments was verified by the ground truth data and root mean square errors (RMSEs). PMID:28934102

Integrated direct/indirect adaptive robust motion trajectory tracking control of pneumatic cylinders

NASA Astrophysics Data System (ADS)

Meng, Deyuan; Tao, Guoliang; Zhu, Xiaocong

2013-09-01

This paper studies the precision motion trajectory tracking control of a pneumatic cylinder driven by a proportional-directional control valve. An integrated direct/indirect adaptive robust controller is proposed. The controller employs a physical model based indirect-type parameter estimation to obtain reliable estimates of unknown model parameters, and utilises a robust control method with dynamic compensation type fast adaptation to attenuate the effects of parameter estimation errors, unmodelled dynamics and disturbances. Due to the use of projection mapping, the robust control law and the parameter adaption algorithm can be designed separately. Since the system model uncertainties are unmatched, the recursive backstepping technology is adopted to design the robust control law. Extensive comparative experimental results are presented to illustrate the effectiveness of the proposed controller and its performance robustness to parameter variations and sudden disturbances.

The effectiveness of robust RMCD control chart as outliers’ detector

NASA Astrophysics Data System (ADS)

Darmanto; Astutik, Suci

2017-12-01

A well-known control chart to monitor a multivariate process is Hotelling’s T 2 which its parameters are estimated classically, very sensitive and also marred by masking and swamping of outliers data effect. To overcome these situation, robust estimators are strongly recommended. One of robust estimators is re-weighted minimum covariance determinant (RMCD) which has robust characteristics as same as MCD. In this paper, the effectiveness term is accuracy of the RMCD control chart in detecting outliers as real outliers. In other word, how effectively this control chart can identify and remove masking and swamping effects of outliers. We assessed the effectiveness the robust control chart based on simulation by considering different scenarios: n sample sizes, proportion of outliers, number of p quality characteristics. We found that in some scenarios, this RMCD robust control chart works effectively.

Fast and Adaptive Auto-focusing Microscope

NASA Astrophysics Data System (ADS)

Obara, Takeshi; Igarashi, Yasunobu; Hashimoto, Koichi

Optical microscopes are widely used in biological and medical researches. By using the microscope, we can observe cellular movements including intracellular ions and molecules tagged with fluorescent dyes at a high magnification. However, a freely motile cell easily escapes from a 3D field of view of the typical microscope. Therefore, we propose a novel auto-focusing algorithm and develop a auto-focusing and tracking microscope. XYZ positions of a microscopic stage are feedback controlled to focus and track the cell automatically. A bright-field image is used to estimate a cellular position. XY centroids are used to estimate XY positions of the tracked cell. To estimate Z position, we use a diffraction pattern around the cell membrane. This estimation method is so-called Depth from Diffraction (DFDi). However, this method is not robust for individual differences between cells because the diffraction pattern depends on each cellular shape. Therefore, in this study, we propose a real-time correction of DFDi by using 2D Laplacian of an intracellular area as a goodness of the focus. To evaluate the performance of our developed algorithm and microscope, we auto-focus and track a freely moving paramecium. In this experimental result, the paramecium is auto-focused and kept inside the scope of the microscope during 45s. The evaluated focal error is within 5µm, while a length and a thickness of the paramecium are about 200µm and 50µm, respectively.

Efficient Robust Regression via Two-Stage Generalized Empirical Likelihood

PubMed Central

Bondell, Howard D.; Stefanski, Leonard A.

2013-01-01

Large- and finite-sample efficiency and resistance to outliers are the key goals of robust statistics. Although often not simultaneously attainable, we develop and study a linear regression estimator that comes close. Efficiency obtains from the estimator’s close connection to generalized empirical likelihood, and its favorable robustness properties are obtained by constraining the associated sum of (weighted) squared residuals. We prove maximum attainable finite-sample replacement breakdown point, and full asymptotic efficiency for normal errors. Simulation evidence shows that compared to existing robust regression estimators, the new estimator has relatively high efficiency for small sample sizes, and comparable outlier resistance. The estimator is further illustrated and compared to existing methods via application to a real data set with purported outliers. PMID:23976805

Precise visual navigation using multi-stereo vision and landmark matching

NASA Astrophysics Data System (ADS)

Zhu, Zhiwei; Oskiper, Taragay; Samarasekera, Supun; Kumar, Rakesh

2007-04-01

Traditional vision-based navigation system often drifts over time during navigation. In this paper, we propose a set of techniques which greatly reduce the long term drift and also improve its robustness to many failure conditions. In our approach, two pairs of stereo cameras are integrated to form a forward/backward multi-stereo camera system. As a result, the Field-Of-View of the system is extended significantly to capture more natural landmarks from the scene. This helps to increase the pose estimation accuracy as well as reduce the failure situations. Secondly, a global landmark matching technique is used to recognize the previously visited locations during navigation. Using the matched landmarks, a pose correction technique is used to eliminate the accumulated navigation drift. Finally, in order to further improve the robustness of the system, measurements from low-cost Inertial Measurement Unit (IMU) and Global Positioning System (GPS) sensors are integrated with the visual odometry in an extended Kalman Filtering framework. Our system is significantly more accurate and robust than previously published techniques (1~5% localization error) over long-distance navigation both indoors and outdoors. Real world experiments on a human worn system show that the location can be estimated within 1 meter over 500 meters (around 0.1% localization error averagely) without the use of GPS information.

A robust background regression based score estimation algorithm for hyperspectral anomaly detection

NASA Astrophysics Data System (ADS)

Zhao, Rui; Du, Bo; Zhang, Liangpei; Zhang, Lefei

2016-12-01

Anomaly detection has become a hot topic in the hyperspectral image analysis and processing fields in recent years. The most important issue for hyperspectral anomaly detection is the background estimation and suppression. Unreasonable or non-robust background estimation usually leads to unsatisfactory anomaly detection results. Furthermore, the inherent nonlinearity of hyperspectral images may cover up the intrinsic data structure in the anomaly detection. In order to implement robust background estimation, as well as to explore the intrinsic data structure of the hyperspectral image, we propose a robust background regression based score estimation algorithm (RBRSE) for hyperspectral anomaly detection. The Robust Background Regression (RBR) is actually a label assignment procedure which segments the hyperspectral data into a robust background dataset and a potential anomaly dataset with an intersection boundary. In the RBR, a kernel expansion technique, which explores the nonlinear structure of the hyperspectral data in a reproducing kernel Hilbert space, is utilized to formulate the data as a density feature representation. A minimum squared loss relationship is constructed between the data density feature and the corresponding assigned labels of the hyperspectral data, to formulate the foundation of the regression. Furthermore, a manifold regularization term which explores the manifold smoothness of the hyperspectral data, and a maximization term of the robust background average density, which suppresses the bias caused by the potential anomalies, are jointly appended in the RBR procedure. After this, a paired-dataset based k-nn score estimation method is undertaken on the robust background and potential anomaly datasets, to implement the detection output. The experimental results show that RBRSE achieves superior ROC curves, AUC values, and background-anomaly separation than some of the other state-of-the-art anomaly detection methods, and is easy to implement in practice.

Smoking, HIV, and risk of pregnancy loss.

PubMed

Westreich, Daniel; Cates, Jordan; Cohen, Mardge; Weber, Kathleen M; Seidman, Dominika; Cropsey, Karen; Wright, Rodney; Milam, Joel; Young, Mary A; Mehta, C Christina; Gustafson, Deborah R; Golub, Elizabeth T; Fischl, Margaret A; Adimora, Adaora A

2017-02-20

Cigarette smoking during pregnancy increases risks of poor pregnancy outcomes including miscarriage and stillbirth (pregnancy loss), but the effect of smoking on pregnancy loss among HIV-infected women has not been explored. Here, investigated the impact of smoking on risk of pregnancy loss among HIV-positive and HIV-negative women, and estimated the potential impact of realistic smoking cessation interventions on risk of pregnancy loss among HIV-positive women. We analyzed pregnancy outcomes in HIV-positive and HIV-negative participants in the Women's Interagency HIV Study between 1994 and 2014. We estimated effects of current smoking at or immediately before pregnancy on pregnancy loss; we controlled for confounding using regression approaches, and estimated potential impact of realistic smoking cessation interventions using a semiparametric g-formula approach. Analysis examined 1033 pregnancies among 659 women. The effect of smoking on pregnancy loss differed dramatically by HIV status: adjusted for confounding, the risk difference comparing current smokers to current nonsmokers was 19.2% (95% confidence limit 10.9-27.5%) in HIV-positive women and 9.7% (95% confidence limit 0.0-19.4%) in HIV-negative women. These results were robust to sensitivity analyses. We estimated that we would need to offer a realistic smoking cessation intervention to 36 women to prevent one pregnancy loss. Smoking is a highly prevalent exposure with important consequences for pregnancy in HIV-positive pregnant women in the United States, even in the presence of potent highly active antiretroviral therapy. This evidence supports greater efforts to promote smoking cessation interventions among HIV-positive women, especially those who desire to become pregnant.

A weak Galerkin least-squares finite element method for div-curl systems

NASA Astrophysics Data System (ADS)

Li, Jichun; Ye, Xiu; Zhang, Shangyou

2018-06-01

In this paper, we introduce a weak Galerkin least-squares method for solving div-curl problem. This finite element method leads to a symmetric positive definite system and has the flexibility to work with general meshes such as hybrid mesh, polytopal mesh and mesh with hanging nodes. Error estimates of the finite element solution are derived. The numerical examples demonstrate the robustness and flexibility of the proposed method.

Estimating national forest carbon stocks and dynamics: combining models and remotely sensed information

NASA Astrophysics Data System (ADS)

Smallman, Thomas Luke; Exbrayat, Jean-François; Bloom, Anthony; Williams, Mathew

2017-04-01

Forests are a critical component of the global carbon cycle, storing significant amounts of carbon, split between living biomass and dead organic matter. The carbon budget of forests is the most uncertain component of the global carbon cycle - it is currently impossible to quantify accurately the carbon source/sink strength of forest biomes due to their heterogeneity and complex dynamics. It has been a major challenge to generate robust carbon budgets across landscapes due to data scarcity. Models have been used for estimating carbon budgets, but outputs have lacked an assessment of uncertainty, making a robust assessment of their reliability and accuracy challenging. Here a Metropolis Hastings - Markov Chain Monte Carlo (MH-MCMC) data assimilation framework has been used to combine remotely sensed leaf area index (MODIS), biomass (where available) and deforestation estimates, in addition to forest planting information from the UK's national forest inventory, an estimate of soil carbon from the Harmonized World Database (HWSD) and plant trait information with a process model (DALEC) to produce a constrained analysis with a robust estimate of uncertainty of the UK forestry carbon budget between 2000 and 2010. Our analysis estimates the mean annual UK forest carbon sink at -3.9 MgC ha-1 yr-1 with a 95 % confidence interval between -4.0 and -3.1 MgC ha-1yr-1. The UK national forest inventory (NFI) estimates the mean UK forest carbon sink to be between -1.4 and -5.5 MgC ha-1 yr-1. The analysis estimate for total forest biomass stock in 2010 is estimated at 229 (177/232) TgC, while the NFI an estimated total forest biomass carbon stock of 216 TgC. Leaf carbon area (LCA) is a key plant trait which we are able to estimate using our analysis. Comparison of median estimates for (LCA) retrieved from the analysis and a UK land cover map show higher and lower values for LCA are estimated areas dominated by needle leaf and broad leaf forests forest respectively, consistent with ecological expectations. Moreover, LCA is positively and negatively correlated with leaf-life span and allocation of photosynthate to foliage respectively, supported by field observations. This emergence of key plant traits and correlations between traits increases our confidence in the robustness of this analysis. Furthermore, this framework also allows us to search for additional emergent properties from the analysis such as spatial variation of retrieved drought tolerance. Finally our analysis is able to identify components of the carbon cycle with the largest uncertainty e.g. allocation of photosynthate to wood and wood residence times, providing targets for future observations (e.g. ESA's BIOMASS mission). Our Bayesian analysis system is ideally suited for assimilation of multiple biomass estimates and their associated uncertainties to reduce both the overall analysis uncertainty and bias in estimates biomass stocks.

Efficacy of attention bias modification using threat and appetitive stimuli: a meta-analytic review.

PubMed

Beard, Courtney; Sawyer, Alice T; Hofmann, Stefan G

2012-12-01

Attention bias modification (ABM) protocols aim to modify attentional biases underlying many forms of pathology. Our objective was to conduct an effect size analysis of ABM across a wide range of samples and psychological problems. We conducted a literature search using PubMed, PsycInfo, and author searches to identify randomized studies that examined the effects of ABM on attention and subjective experiences. We identified 37 studies (41 experiments) totaling 2,135 participants who were randomized to training toward neutral, positive, threat, or appetitive stimuli or to a control condition. The effect size estimate for changes in attentional bias was large for the neutral versus threat comparisons (g=1.06), neutral versus appetitive (g=1.41), and neutral versus control comparisons (g=0.80), and small for positive versus control (g=0.24). The effects of ABM on attention bias were moderated by stimulus type (words vs. pictures) and sample characteristics (healthy vs. high symptomatology). Effect sizes of ABM on subjective experiences ranged from 0.03 to 0.60 for postchallenge outcomes, -0.31 to 0.51 for posttreatment, and were moderated by number of training sessions, stimulus type, and stimulus orientation (top/bottom vs. left/right). Fail-safe N calculations suggested that the effect size estimates were robust for the training effects on attentional biases, but not for the effect on subjective experiences. ABM studies using threat stimuli produced significant effects on attention bias across comparison conditions, whereas appetitive stimuli produced changes in attention only when comparing appetitive versus neutral conditions. ABM has a moderate and robust effect on attention bias when using threat stimuli. Further studies are needed to determine whether these effects are also robust when using appetitive stimuli and for affecting subjective experiences. Copyright © 2012. Published by Elsevier Ltd.

Robust guaranteed-cost adaptive quantum phase estimation

NASA Astrophysics Data System (ADS)

Roy, Shibdas; Berry, Dominic W.; Petersen, Ian R.; Huntington, Elanor H.

2017-05-01

Quantum parameter estimation plays a key role in many fields like quantum computation, communication, and metrology. Optimal estimation allows one to achieve the most precise parameter estimates, but requires accurate knowledge of the model. Any inevitable uncertainty in the model parameters may heavily degrade the quality of the estimate. It is therefore desired to make the estimation process robust to such uncertainties. Robust estimation was previously studied for a varying phase, where the goal was to estimate the phase at some time in the past, using the measurement results from both before and after that time within a fixed time interval up to current time. Here, we consider a robust guaranteed-cost filter yielding robust estimates of a varying phase in real time, where the current phase is estimated using only past measurements. Our filter minimizes the largest (worst-case) variance in the allowable range of the uncertain model parameter(s) and this determines its guaranteed cost. It outperforms in the worst case the optimal Kalman filter designed for the model with no uncertainty, which corresponds to the center of the possible range of the uncertain parameter(s). Moreover, unlike the Kalman filter, our filter in the worst case always performs better than the best achievable variance for heterodyne measurements, which we consider as the tolerable threshold for our system. Furthermore, we consider effective quantum efficiency and effective noise power, and show that our filter provides the best results by these measures in the worst case.

A Robust Approach to Risk Assessment Based on Species Sensitivity Distributions.

PubMed

Monti, Gianna S; Filzmoser, Peter; Deutsch, Roland C

2018-05-03

The guidelines for setting environmental quality standards are increasingly based on probabilistic risk assessment due to a growing general awareness of the need for probabilistic procedures. One of the commonly used tools in probabilistic risk assessment is the species sensitivity distribution (SSD), which represents the proportion of species affected belonging to a biological assemblage as a function of exposure to a specific toxicant. Our focus is on the inverse use of the SSD curve with the aim of estimating the concentration, HCp, of a toxic compound that is hazardous to p% of the biological community under study. Toward this end, we propose the use of robust statistical methods in order to take into account the presence of outliers or apparent skew in the data, which may occur without any ecological basis. A robust approach exploits the full neighborhood of a parametric model, enabling the analyst to account for the typical real-world deviations from ideal models. We examine two classic HCp estimation approaches and consider robust versions of these estimators. In addition, we also use data transformations in conjunction with robust estimation methods in case of heteroscedasticity. Different scenarios using real data sets as well as simulated data are presented in order to illustrate and compare the proposed approaches. These scenarios illustrate that the use of robust estimation methods enhances HCp estimation. © 2018 Society for Risk Analysis.

Robust image modeling techniques with an image restoration application

NASA Astrophysics Data System (ADS)

Kashyap, Rangasami L.; Eom, Kie-Bum

1988-08-01

A robust parameter-estimation algorithm for a nonsymmetric half-plane (NSHP) autoregressive model, where the driving noise is a mixture of a Gaussian and an outlier process, is presented. The convergence of the estimation algorithm is proved. An algorithm to estimate parameters and original image intensity simultaneously from the impulse-noise-corrupted image, where the model governing the image is not available, is also presented. The robustness of the parameter estimates is demonstrated by simulation. Finally, an algorithm to restore realistic images is presented. The entire image generally does not obey a simple image model, but a small portion (e.g., 8 x 8) of the image is assumed to obey an NSHP model. The original image is divided into windows and the robust estimation algorithm is applied for each window. The restoration algorithm is tested by comparing it to traditional methods on several different images.

A robust ridge regression approach in the presence of both multicollinearity and outliers in the data

NASA Astrophysics Data System (ADS)

Shariff, Nurul Sima Mohamad; Ferdaos, Nur Aqilah

2017-08-01

Multicollinearity often leads to inconsistent and unreliable parameter estimates in regression analysis. This situation will be more severe in the presence of outliers it will cause fatter tails in the error distributions than the normal distributions. The well-known procedure that is robust to multicollinearity problem is the ridge regression method. This method however is expected to be affected by the presence of outliers due to some assumptions imposed in the modeling procedure. Thus, the robust version of existing ridge method with some modification in the inverse matrix and the estimated response value is introduced. The performance of the proposed method is discussed and comparisons are made with several existing estimators namely, Ordinary Least Squares (OLS), ridge regression and robust ridge regression based on GM-estimates. The finding of this study is able to produce reliable parameter estimates in the presence of both multicollinearity and outliers in the data.

Towards SI-traceable radio occultation excess phase processing with integrated uncertainty estimation for climate applications

NASA Astrophysics Data System (ADS)

Innerkofler, Josef; Pock, Christian; Kirchengast, Gottfried; Schwaerz, Marc; Jaeggi, Adrian; Schwarz, Jakob

2016-04-01

The GNSS Radio Occultation (RO) measurement technique is highly valuable for climate monitoring of the atmosphere as it provides accurate and precise measurements in the troposphere and stratosphere regions with global coverage, long-term stability, and virtually all-weather capability. The novel Reference Occultation Processing System (rOPS), currently under development at the WEGC at University of Graz aims to process raw RO measurements into essential climate variables, such as temperature, pressure, and tropospheric water vapor, in a way which is SI-traceable to the universal time standard and which includes rigorous uncertainty propagation. As part of this rOPS climate-quality processing system, accurate atmospheric excess phase profiles with new approaches integrating uncertainty propagation are derived from the raw occultation tracking data and orbit data. Regarding the latter, highly accurate orbit positions and velocities of the GNSS transmitter satellites and the RO receiver satellites in low Earth orbit (LEO) need to be determined, in order to enable high accuracy of the excess phase profiles. Using several representative test days of GPS orbit data from the CODE and IGS archives, which are available at accuracies of about 3 cm (position) / 0.03 mm/s (velocity), and employing Bernese 5.2 and Napeos 3.3.1 software packages for the LEO orbit determination of the CHAMP, GRACE, and MetOp RO satellites, we achieved robust SI-traced LEO orbit uncertainty estimates of about 5 cm (position) / 0.05 mm/s (velocity) for the daily orbits, including estimates of systematic uncertainty bounds and of propagated random uncertainties. For COSMIC RO satellites, we found decreased accuracy estimates near 10-15 cm (position) / 0.1-0.15 mm/s (velocity), since the characteristics of the small COSMIC satellite platforms and antennas provide somewhat less favorable orbit determination conditions. We present the setup of how we (I) used the Bernese and Napeos package in mutual cross-check for this purpose, (II) integrated satellite laser-ranging validation of the estimated systematic uncertainty bounds, (III) expanded the Bernese 5.2 software for propagating random uncertainties from the GPS orbit data and LEO navigation tracking data input to the LEO data output. Preliminary excess phase results including propagated uncertainty estimates will also be shown. Except for disturbed space weather conditions, we expect a robust performance at millimeter level for the derived excess phases, which after large-scale processing of the RO data of many years can provide a new SI-traced fundamental climate data record.

Fisheye-Based Method for GPS Localization Improvement in Unknown Semi-Obstructed Areas

PubMed Central

Moreau, Julien; Ambellouis, Sébastien; Ruichek, Yassine

2017-01-01

A precise GNSS (Global Navigation Satellite System) localization is vital for autonomous road vehicles, especially in cluttered or urban environments where satellites are occluded, preventing accurate positioning. We propose to fuse GPS (Global Positioning System) data with fisheye stereovision to face this problem independently to additional data, possibly outdated, unavailable, and needing correlation with reality. Our stereoscope is sky-facing with 360° × 180° fisheye cameras to observe surrounding obstacles. We propose a 3D modelling and plane extraction through following steps: stereoscope self-calibration for decalibration robustness, stereo matching considering neighbours epipolar curves to compute 3D, and robust plane fitting based on generated cartography and Hough transform. We use these 3D data with GPS raw data to estimate NLOS (Non Line Of Sight) reflected signals pseudorange delay. We exploit extracted planes to build a visibility mask for NLOS detection. A simplified 3D canyon model allows to compute reflections pseudorange delays. In the end, GPS positioning is computed considering corrected pseudoranges. With experimentations on real fixed scenes, we show generated 3D models reaching metric accuracy and improvement of horizontal GPS positioning accuracy by more than 50%. The proposed procedure is effective, and the proposed NLOS detection outperforms CN0-based methods (Carrier-to-receiver Noise density). PMID:28106746

Causal inference with missing exposure information: Methods and applications to an obstetric study.

PubMed

Zhang, Zhiwei; Liu, Wei; Zhang, Bo; Tang, Li; Zhang, Jun

2016-10-01

Causal inference in observational studies is frequently challenged by the occurrence of missing data, in addition to confounding. Motivated by the Consortium on Safe Labor, a large observational study of obstetric labor practice and birth outcomes, this article focuses on the problem of missing exposure information in a causal analysis of observational data. This problem can be approached from different angles (i.e. missing covariates and causal inference), and useful methods can be obtained by drawing upon the available techniques and insights in both areas. In this article, we describe and compare a collection of methods based on different modeling assumptions, under standard assumptions for missing data (i.e. missing-at-random and positivity) and for causal inference with complete data (i.e. no unmeasured confounding and another positivity assumption). These methods involve three models: one for treatment assignment, one for the dependence of outcome on treatment and covariates, and one for the missing data mechanism. In general, consistent estimation of causal quantities requires correct specification of at least two of the three models, although there may be some flexibility as to which two models need to be correct. Such flexibility is afforded by doubly robust estimators adapted from the missing covariates literature and the literature on causal inference with complete data, and by a newly developed triply robust estimator that is consistent if any two of the three models are correct. The methods are applied to the Consortium on Safe Labor data and compared in a simulation study mimicking the Consortium on Safe Labor. © The Author(s) 2013.

Robustness enhancement of neurocontroller and state estimator

NASA Technical Reports Server (NTRS)

Troudet, Terry

1993-01-01

The feasibility of enhancing neurocontrol robustness, through training of the neurocontroller and state estimator in the presence of system uncertainties, is investigated on the example of a multivariable aircraft control problem. The performance and robustness of the newly trained neurocontroller are compared to those for an existing neurocontrol design scheme. The newly designed dynamic neurocontroller exhibits a better trade-off between phase and gain stability margins, and it is significantly more robust to degradations of the plant dynamics.

Performance of a high resolution cavity beam position monitor system

NASA Astrophysics Data System (ADS)

Walston, Sean; Boogert, Stewart; Chung, Carl; Fitsos, Pete; Frisch, Joe; Gronberg, Jeff; Hayano, Hitoshi; Honda, Yosuke; Kolomensky, Yury; Lyapin, Alexey; Malton, Stephen; May, Justin; McCormick, Douglas; Meller, Robert; Miller, David; Orimoto, Toyoko; Ross, Marc; Slater, Mark; Smith, Steve; Smith, Tonee; Terunuma, Nobuhiro; Thomson, Mark; Urakawa, Junji; Vogel, Vladimir; Ward, David; White, Glen

2007-07-01

It has been estimated that an RF cavity Beam Position Monitor (BPM) could provide a position measurement resolution of less than 1 nm. We have developed a high resolution cavity BPM and associated electronics. A triplet comprised of these BPMs was installed in the extraction line of the Accelerator Test Facility (ATF) at the High Energy Accelerator Research Organization (KEK) for testing with its ultra-low emittance beam. The three BPMs were each rigidly mounted inside an alignment frame on six variable-length struts which could be used to move the BPMs in position and angle. We have developed novel methods for extracting the position and tilt information from the BPM signals including a robust calibration algorithm which is immune to beam jitter. To date, we have demonstrated a position resolution of 15.6 nm and a tilt resolution of 2.1 μrad over a dynamic range of approximately ±20 μm.

Estimating national forest carbon stocks and dynamics: combining models and remotely sensed information

NASA Astrophysics Data System (ADS)

Smallman, Luke; Williams, Mathew

2016-04-01

Forests are a critical component of the global carbon cycle, storing significant amounts of carbon, split between living biomass and dead organic matter. The carbon budget of forests is the most uncertain component of the global carbon cycle - it is currently impossible to quantify accurately the carbon source/sink strength of forest biomes due to their heterogeneity and complex dynamics. It has been a major challenge to generate robust carbon budgets across landscapes due to data scarcity. Models have been used but outputs have lacked an assessment of uncertainty, making a robust assessment of their reliability and accuracy challenging. Here a Metropolis Hastings - Markov Chain Monte Carlo (MH-MCMC) data assimilation framework has been used to combine remotely sensed leaf area index (MODIS), biomass (where available) and deforestation estimates, in addition to forest planting and clear-felling information from the UK's national forest inventory, an estimate of soil carbon from the Harmonized World Database (HWSD) and plant trait information with a process model (DALEC) to produce a constrained analysis with a robust estimate of uncertainty of the UK forestry carbon budget between 2000 and 2010. Our analysis estimates the mean annual UK forest carbon sink at -3.9 MgC ha-1yr-1 with a 95 % confidence interval between -4.0 and -3.1 MgC ha-1 yr-1. The UK national forest inventory (NFI) estimates the mean UK forest carbon sink to be between -1.4 and -5.5 MgC ha-1 yr-1. The analysis estimate for total forest biomass stock in 2010 is estimated at 229 (177/232) TgC, while the NFI an estimated total forest biomass carbon stock of 216 TgC. Leaf carbon area (LCA) is a key plant trait which we are able to estimate using our analysis. Comparison of median estimates for LCA retrieved from the analysis and a UK land cover map show higher and lower values for LCA are estimated areas dominated by needle leaf and broad leaf forests forest respectively, consistent with ecological expectations. Moreover, the retrieved LCA is positively correlated with leaf-life span and negatively correlated with allocation of photosynthate to foliage, supported by field observations. This emergence of key plant traits and correlations between traits increases our confidence in the robustness of this analysis. Furthermore, this framework also allows us to search for additional emergent properties from the analysis such as spatial variation of retrieved drought tolerance. Finally our analysis is able to identify components of the carbon cycle with the largest uncertainty providing targets for future observations (e.g. remotely sensed biomass). Our Bayesian analysis system is ideally suited for assimilation of multiple biomass estimates and their associated uncertainties to reduce both uncertainty in the state of the system but also process parameters (e.g. wood residence time).

Pairing field methods to improve inference in wildlife surveys while accommodating detection covariance

USGS Publications Warehouse

Clare, John; McKinney, Shawn T.; DePue, John E.; Loftin, Cynthia S.

2017-01-01

It is common to use multiple field sampling methods when implementing wildlife surveys to compare method efficacy or cost efficiency, integrate distinct pieces of information provided by separate methods, or evaluate method-specific biases and misclassification error. Existing models that combine information from multiple field methods or sampling devices permit rigorous comparison of method-specific detection parameters, enable estimation of additional parameters such as false-positive detection probability, and improve occurrence or abundance estimates, but with the assumption that the separate sampling methods produce detections independently of one another. This assumption is tenuous if methods are paired or deployed in close proximity simultaneously, a common practice that reduces the additional effort required to implement multiple methods and reduces the risk that differences between method-specific detection parameters are confounded by other environmental factors. We develop occupancy and spatial capture–recapture models that permit covariance between the detections produced by different methods, use simulation to compare estimator performance of the new models to models assuming independence, and provide an empirical application based on American marten (Martes americana) surveys using paired remote cameras, hair catches, and snow tracking. Simulation results indicate existing models that assume that methods independently detect organisms produce biased parameter estimates and substantially understate estimate uncertainty when this assumption is violated, while our reformulated models are robust to either methodological independence or covariance. Empirical results suggested that remote cameras and snow tracking had comparable probability of detecting present martens, but that snow tracking also produced false-positive marten detections that could potentially substantially bias distribution estimates if not corrected for. Remote cameras detected marten individuals more readily than passive hair catches. Inability to photographically distinguish individual sex did not appear to induce negative bias in camera density estimates; instead, hair catches appeared to produce detection competition between individuals that may have been a source of negative bias. Our model reformulations broaden the range of circumstances in which analyses incorporating multiple sources of information can be robustly used, and our empirical results demonstrate that using multiple field-methods can enhance inferences regarding ecological parameters of interest and improve understanding of how reliably survey methods sample these parameters.

Robust versus consistent variance estimators in marginal structural Cox models.

PubMed

Enders, Dirk; Engel, Susanne; Linder, Roland; Pigeot, Iris

2018-06-11

In survival analyses, inverse-probability-of-treatment (IPT) and inverse-probability-of-censoring (IPC) weighted estimators of parameters in marginal structural Cox models are often used to estimate treatment effects in the presence of time-dependent confounding and censoring. In most applications, a robust variance estimator of the IPT and IPC weighted estimator is calculated leading to conservative confidence intervals. This estimator assumes that the weights are known rather than estimated from the data. Although a consistent estimator of the asymptotic variance of the IPT and IPC weighted estimator is generally available, applications and thus information on the performance of the consistent estimator are lacking. Reasons might be a cumbersome implementation in statistical software, which is further complicated by missing details on the variance formula. In this paper, we therefore provide a detailed derivation of the variance of the asymptotic distribution of the IPT and IPC weighted estimator and explicitly state the necessary terms to calculate a consistent estimator of this variance. We compare the performance of the robust and consistent variance estimators in an application based on routine health care data and in a simulation study. The simulation reveals no substantial differences between the 2 estimators in medium and large data sets with no unmeasured confounding, but the consistent variance estimator performs poorly in small samples or under unmeasured confounding, if the number of confounders is large. We thus conclude that the robust estimator is more appropriate for all practical purposes. Copyright © 2018 John Wiley & Sons, Ltd.

Robust Modal Filtering and Control of the X-56A Model with Simulated Fiber Optic Sensor Failures

NASA Technical Reports Server (NTRS)

Suh, Peter M.; Chin, Alexander W.; Marvis, Dimitri N.

2014-01-01

The X-56A aircraft is a remotely-piloted aircraft with flutter modes intentionally designed into the flight envelope. The X-56A program must demonstrate flight control while suppressing all unstable modes. A previous X-56A model study demonstrated a distributed-sensing-based active shape and active flutter suppression controller. The controller relies on an estimator which is sensitive to bias. This estimator is improved herein, and a real-time robust estimator is derived and demonstrated on 1530 fiber optic sensors. It is shown in simulation that the estimator can simultaneously reject 230 worst-case fiber optic sensor failures automatically. These sensor failures include locations with high leverage (or importance). To reduce the impact of leverage outliers, concentration based on a Mahalanobis trim criterion is introduced. A redescending M-estimator with Tukey bisquare weights is used to improve location and dispersion estimates within each concentration step in the presence of asymmetry (or leverage). A dynamic simulation is used to compare the concentrated robust estimator to a state-of-the-art real-time robust multivariate estimator. The estimators support a previously-derived mu-optimal shape controller. It is found that during the failure scenario, the concentrated modal estimator keeps the system stable.

Robust Modal Filtering and Control of the X-56A Model with Simulated Fiber Optic Sensor Failures

NASA Technical Reports Server (NTRS)

Suh, Peter M.; Chin, Alexander W.; Mavris, Dimitri N.

2016-01-01

The X-56A aircraft is a remotely-piloted aircraft with flutter modes intentionally designed into the flight envelope. The X-56A program must demonstrate flight control while suppressing all unstable modes. A previous X-56A model study demonstrated a distributed-sensing-based active shape and active flutter suppression controller. The controller relies on an estimator which is sensitive to bias. This estimator is improved herein, and a real-time robust estimator is derived and demonstrated on 1530 fiber optic sensors. It is shown in simulation that the estimator can simultaneously reject 230 worst-case fiber optic sensor failures automatically. These sensor failures include locations with high leverage (or importance). To reduce the impact of leverage outliers, concentration based on a Mahalanobis trim criterion is introduced. A redescending M-estimator with Tukey bisquare weights is used to improve location and dispersion estimates within each concentration step in the presence of asymmetry (or leverage). A dynamic simulation is used to compare the concentrated robust estimator to a state-of-the-art real-time robust multivariate estimator. The estimators support a previously-derived mu-optimal shape controller. It is found that during the failure scenario, the concentrated modal estimator keeps the system stable.

The Robustness of LISREL Estimates in Structural Equation Models with Categorical Variables.

ERIC Educational Resources Information Center

Ethington, Corinna A.

1987-01-01

This study examined the effect of type of correlation matrix on the robustness of LISREL maximum likelihood and unweighted least squares structural parameter estimates for models with categorical variables. The analysis of mixed matrices produced estimates that closely approximated the model parameters except where dichotomous variables were…

Optimal threshold estimation for binary classifiers using game theory.

PubMed

Sanchez, Ignacio Enrique

2016-01-01

Many bioinformatics algorithms can be understood as binary classifiers. They are usually compared using the area under the receiver operating characteristic ( ROC ) curve. On the other hand, choosing the best threshold for practical use is a complex task, due to uncertain and context-dependent skews in the abundance of positives in nature and in the yields/costs for correct/incorrect classification. We argue that considering a classifier as a player in a zero-sum game allows us to use the minimax principle from game theory to determine the optimal operating point. The proposed classifier threshold corresponds to the intersection between the ROC curve and the descending diagonal in ROC space and yields a minimax accuracy of 1-FPR. Our proposal can be readily implemented in practice, and reveals that the empirical condition for threshold estimation of "specificity equals sensitivity" maximizes robustness against uncertainties in the abundance of positives in nature and classification costs.

Robust 3D-2D image registration: application to spine interventions and vertebral labeling in the presence of anatomical deformation

NASA Astrophysics Data System (ADS)

Otake, Yoshito; Wang, Adam S.; Webster Stayman, J.; Uneri, Ali; Kleinszig, Gerhard; Vogt, Sebastian; Khanna, A. Jay; Gokaslan, Ziya L.; Siewerdsen, Jeffrey H.

2013-12-01

We present a framework for robustly estimating registration between a 3D volume image and a 2D projection image and evaluate its precision and robustness in spine interventions for vertebral localization in the presence of anatomical deformation. The framework employs a normalized gradient information similarity metric and multi-start covariance matrix adaptation evolution strategy optimization with local-restarts, which provided improved robustness against deformation and content mismatch. The parallelized implementation allowed orders-of-magnitude acceleration in computation time and improved the robustness of registration via multi-start global optimization. Experiments involved a cadaver specimen and two CT datasets (supine and prone) and 36 C-arm fluoroscopy images acquired with the specimen in four positions (supine, prone, supine with lordosis, prone with kyphosis), three regions (thoracic, abdominal, and lumbar), and three levels of geometric magnification (1.7, 2.0, 2.4). Registration accuracy was evaluated in terms of projection distance error (PDE) between the estimated and true target points in the projection image, including 14 400 random trials (200 trials on the 72 registration scenarios) with initialization error up to ±200 mm and ±10°. The resulting median PDE was better than 0.1 mm in all cases, depending somewhat on the resolution of input CT and fluoroscopy images. The cadaver experiments illustrated the tradeoff between robustness and computation time, yielding a success rate of 99.993% in vertebral labeling (with ‘success’ defined as PDE <5 mm) using 1,718 664 ± 96 582 function evaluations computed in 54.0 ± 3.5 s on a mid-range GPU (nVidia, GeForce GTX690). Parameters yielding a faster search (e.g., fewer multi-starts) reduced robustness under conditions of large deformation and poor initialization (99.535% success for the same data registered in 13.1 s), but given good initialization (e.g., ±5 mm, assuming a robust initial run) the same registration could be solved with 99.993% success in 6.3 s. The ability to register CT to fluoroscopy in a manner robust to patient deformation could be valuable in applications such as radiation therapy, interventional radiology, and an assistant to target localization (e.g., vertebral labeling) in image-guided spine surgery.

A Cost Benefit Analysis of an Active Travel Intervention with Health and Carbon Emission Reduction Benefits.

PubMed

Chapman, Ralph; Keall, Michael; Howden-Chapman, Philippa; Grams, Mark; Witten, Karen; Randal, Edward; Woodward, Alistair

2018-05-11

Active travel (walking and cycling) is beneficial for people’s health and has many co-benefits, such as reducing motor vehicle congestion and pollution in urban areas. There have been few robust evaluations of active travel, and very few studies have valued health and emissions outcomes. The ACTIVE before-and-after quasi-experimental study estimated the net benefits of health and other outcomes from New Zealand’s Model Communities Programme using an empirical analysis comparing two intervention cities with two control cities. The Programme funded investment in cycle paths, other walking and cycling facilities, cycle parking, ‘shared spaces’, media campaigns and events, such as ‘Share the Road’, and cycle-skills training. Using the modified Integrated Transport and Health Impacts Model, the Programme’s net economic benefits were estimated from the changes in use of active travel modes. Annual benefits for health in the intervention cities were estimated at 34.4 disability-adjusted life years (DALYs) and two lives saved due to reductions in cardiac disease, diabetes, cancer, and respiratory disease. Reductions in transport-related carbon emissions were also estimated and valued. Using a discount rate of 3.5%, the estimated benefit/cost ratio was 11:1 and was robust to sensitivity testing. It is concluded that when concerted investment is made in active travel in a city, there is likely to be a measurable, positive return on investment.

A Cost Benefit Analysis of an Active Travel Intervention with Health and Carbon Emission Reduction Benefits

PubMed Central

Grams, Mark; Witten, Karen; Woodward, Alistair

2018-01-01

Active travel (walking and cycling) is beneficial for people’s health and has many co-benefits, such as reducing motor vehicle congestion and pollution in urban areas. There have been few robust evaluations of active travel, and very few studies have valued health and emissions outcomes. The ACTIVE before-and-after quasi-experimental study estimated the net benefits of health and other outcomes from New Zealand’s Model Communities Programme using an empirical analysis comparing two intervention cities with two control cities. The Programme funded investment in cycle paths, other walking and cycling facilities, cycle parking, ‘shared spaces’, media campaigns and events, such as ‘Share the Road’, and cycle-skills training. Using the modified Integrated Transport and Health Impacts Model, the Programme’s net economic benefits were estimated from the changes in use of active travel modes. Annual benefits for health in the intervention cities were estimated at 34.4 disability-adjusted life years (DALYs) and two lives saved due to reductions in cardiac disease, diabetes, cancer, and respiratory disease. Reductions in transport-related carbon emissions were also estimated and valued. Using a discount rate of 3.5%, the estimated benefit/cost ratio was 11:1 and was robust to sensitivity testing. It is concluded that when concerted investment is made in active travel in a city, there is likely to be a measurable, positive return on investment. PMID:29751618

A note on variance estimation in random effects meta-regression.

PubMed

Sidik, Kurex; Jonkman, Jeffrey N

2005-01-01

For random effects meta-regression inference, variance estimation for the parameter estimates is discussed. Because estimated weights are used for meta-regression analysis in practice, the assumed or estimated covariance matrix used in meta-regression is not strictly correct, due to possible errors in estimating the weights. Therefore, this note investigates the use of a robust variance estimation approach for obtaining variances of the parameter estimates in random effects meta-regression inference. This method treats the assumed covariance matrix of the effect measure variables as a working covariance matrix. Using an example of meta-analysis data from clinical trials of a vaccine, the robust variance estimation approach is illustrated in comparison with two other methods of variance estimation. A simulation study is presented, comparing the three methods of variance estimation in terms of bias and coverage probability. We find that, despite the seeming suitability of the robust estimator for random effects meta-regression, the improved variance estimator of Knapp and Hartung (2003) yields the best performance among the three estimators, and thus may provide the best protection against errors in the estimated weights.

A Robust Method to Detect Zero Velocity for Improved 3D Personal Navigation Using Inertial Sensors

PubMed Central

Xu, Zhengyi; Wei, Jianming; Zhang, Bo; Yang, Weijun

2015-01-01

This paper proposes a robust zero velocity (ZV) detector algorithm to accurately calculate stationary periods in a gait cycle. The proposed algorithm adopts an effective gait cycle segmentation method and introduces a Bayesian network (BN) model based on the measurements of inertial sensors and kinesiology knowledge to infer the ZV period. During the detected ZV period, an Extended Kalman Filter (EKF) is used to estimate the error states and calibrate the position error. The experiments reveal that the removal rate of ZV false detections by the proposed method increases 80% compared with traditional method at high walking speed. Furthermore, based on the detected ZV, the Personal Inertial Navigation System (PINS) algorithm aided by EKF performs better, especially in the altitude aspect. PMID:25831086

Toward a better understanding of what makes positive psychology interventions work: predicting happiness and depression from the person × intervention fit in a follow-up after 3.5 years.

PubMed

Proyer, René T; Wellenzohn, Sara; Gander, Fabian; Ruch, Willibald

2015-03-01

Robust evidence exists that positive psychology interventions are effective in enhancing well-being and ameliorating depression. Comparatively little is known about the conditions under which they work best. Models describing characteristics that impact the effectiveness of positive interventions typically contain features of the person, of the activity, and the fit between the two. This study focuses on indicators of the person × intervention fit in predicting happiness and depressive symptoms 3.5 years after completion of the intervention. A sample of 165 women completed measures for happiness and depressive symptoms before and about 3.5 years after completion of a positive intervention (random assignment to one out of nine interventions, which were aggregated for the analyses). Four fit indicators were assessed: Preference; continued practice; effort; and early reactivity. Three out of four person × intervention fit indicators were positively related to happiness or negatively related to depression when controlled for the pretest scores. Together, they explained 6 per cent of the variance in happiness, and 10 per cent of the variance of depressive symptoms. Most tested indicators of a person × intervention fit are robust predictors of happiness and depressive symptoms-even after 3.5 years. They might serve for an early estimation of the effectiveness of a positive intervention. © 2014 The International Association of Applied Psychology.

The use of a robust capture-recapture design in small mammal population studies: A field example with Microtus pennsylvanicus

USGS Publications Warehouse

Nichols, James D.; Pollock, Kenneth H.; Hines, James E.

1984-01-01

The robust design of Pollock (1982) was used to estimate parameters of a Maryland M. pennsylvanicus population. Closed model tests provided strong evidence of heterogeneity of capture probability, and model M eta (Otis et al., 1978) was selected as the most appropriate model for estimating population size. The Jolly-Seber model goodness-of-fit test indicated rejection of the model for this data set, and the M eta estimates of population size were all higher than the Jolly-Seber estimates. Both of these results are consistent with the evidence of heterogeneous capture probabilities. The authors thus used M eta estimates of population size, Jolly-Seber estimates of survival rate, and estimates of birth-immigration based on a combination of the population size and survival rate estimates. Advantages of the robust design estimates for certain inference procedures are discussed, and the design is recommended for future small mammal capture-recapture studies directed at estimation.

Sensor-based auto-focusing system using multi-scale feature extraction and phase correlation matching.

PubMed

Jang, Jinbeum; Yoo, Yoonjong; Kim, Jongheon; Paik, Joonki

2015-03-10

This paper presents a novel auto-focusing system based on a CMOS sensor containing pixels with different phases. Robust extraction of features in a severely defocused image is the fundamental problem of a phase-difference auto-focusing system. In order to solve this problem, a multi-resolution feature extraction algorithm is proposed. Given the extracted features, the proposed auto-focusing system can provide the ideal focusing position using phase correlation matching. The proposed auto-focusing (AF) algorithm consists of four steps: (i) acquisition of left and right images using AF points in the region-of-interest; (ii) feature extraction in the left image under low illumination and out-of-focus blur; (iii) the generation of two feature images using the phase difference between the left and right images; and (iv) estimation of the phase shifting vector using phase correlation matching. Since the proposed system accurately estimates the phase difference in the out-of-focus blurred image under low illumination, it can provide faster, more robust auto focusing than existing systems.

Sensor-Based Auto-Focusing System Using Multi-Scale Feature Extraction and Phase Correlation Matching

PubMed Central

Jang, Jinbeum; Yoo, Yoonjong; Kim, Jongheon; Paik, Joonki

2015-01-01

This paper presents a novel auto-focusing system based on a CMOS sensor containing pixels with different phases. Robust extraction of features in a severely defocused image is the fundamental problem of a phase-difference auto-focusing system. In order to solve this problem, a multi-resolution feature extraction algorithm is proposed. Given the extracted features, the proposed auto-focusing system can provide the ideal focusing position using phase correlation matching. The proposed auto-focusing (AF) algorithm consists of four steps: (i) acquisition of left and right images using AF points in the region-of-interest; (ii) feature extraction in the left image under low illumination and out-of-focus blur; (iii) the generation of two feature images using the phase difference between the left and right images; and (iv) estimation of the phase shifting vector using phase correlation matching. Since the proposed system accurately estimates the phase difference in the out-of-focus blurred image under low illumination, it can provide faster, more robust auto focusing than existing systems. PMID:25763645

Robust multiperson tracking from a mobile platform.

PubMed

Ess, Andreas; Leibe, Bastian; Schindler, Konrad; van Gool, Luc

2009-10-01

In this paper, we address the problem of multiperson tracking in busy pedestrian zones using a stereo rig mounted on a mobile platform. The complexity of the problem calls for an integrated solution that extracts as much visual information as possible and combines it through cognitive feedback cycles. We propose such an approach, which jointly estimates camera position, stereo depth, object detection, and tracking. The interplay between those components is represented by a graphical model. Since the model has to incorporate object-object interactions and temporal links to past frames, direct inference is intractable. We, therefore, propose a two-stage procedure: for each frame, we first solve a simplified version of the model (disregarding interactions and temporal continuity) to estimate the scene geometry and an overcomplete set of object detections. Conditioned on these results, we then address object interactions, tracking, and prediction in a second step. The approach is experimentally evaluated on several long and difficult video sequences from busy inner-city locations. Our results show that the proposed integration makes it possible to deliver robust tracking performance in scenes of realistic complexity.

Detailed 3D representations for object recognition and modeling.

PubMed

Zia, M Zeeshan; Stark, Michael; Schiele, Bernt; Schindler, Konrad

2013-11-01

Geometric 3D reasoning at the level of objects has received renewed attention recently in the context of visual scene understanding. The level of geometric detail, however, is typically limited to qualitative representations or coarse boxes. This is linked to the fact that today's object class detectors are tuned toward robust 2D matching rather than accurate 3D geometry, encouraged by bounding-box-based benchmarks such as Pascal VOC. In this paper, we revisit ideas from the early days of computer vision, namely, detailed, 3D geometric object class representations for recognition. These representations can recover geometrically far more accurate object hypotheses than just bounding boxes, including continuous estimates of object pose and 3D wireframes with relative 3D positions of object parts. In combination with robust techniques for shape description and inference, we outperform state-of-the-art results in monocular 3D pose estimation. In a series of experiments, we analyze our approach in detail and demonstrate novel applications enabled by such an object class representation, such as fine-grained categorization of cars and bicycles, according to their 3D geometry, and ultrawide baseline matching.

The effects of resonances on time delay estimation for water leak detection in plastic pipes

NASA Astrophysics Data System (ADS)

Almeida, Fabrício C. L.; Brennan, Michael J.; Joseph, Phillip F.; Gao, Yan; Paschoalini, Amarildo T.

2018-04-01

In the use of acoustic correlation methods for water leak detection, sensors are placed at pipe access points either side of a suspected leak, and the peak in the cross-correlation function of the measured signals gives the time difference (delay) between the arrival times of the leak noise at the sensors. Combining this information with the speed at which the leak noise propagates along the pipe, gives an estimate for the location of the leak with respect to one of the measurement positions. It is possible for the structural dynamics of the pipe system to corrupt the time delay estimate, which results in the leak being incorrectly located. In this paper, data from test-rigs in the United Kingdom and Canada are used to demonstrate this phenomenon, and analytical models of resonators are coupled with a pipe model to replicate the experimental results. The model is then used to investigate which of the two commonly used correlation algorithms, the Basic Cross-Correlation (BCC) function or the Phase Transform (PHAT), is more robust to the undesirable structural dynamics of the pipe system. It is found that time delay estimation is highly sensitive to the frequency bandwidth over which the analysis is conducted. Moreover, it is found that the PHAT is particularly sensitive to the presence of resonances and can give an incorrect time delay estimate, whereas the BCC function is found to be much more robust, giving a consistently accurate time delay estimate for a range of dynamic conditions.

A robust vision-based sensor fusion approach for real-time pose estimation.

PubMed

Assa, Akbar; Janabi-Sharifi, Farrokh

2014-02-01

Object pose estimation is of great importance to many applications, such as augmented reality, localization and mapping, motion capture, and visual servoing. Although many approaches based on a monocular camera have been proposed, only a few works have concentrated on applying multicamera sensor fusion techniques to pose estimation. Higher accuracy and enhanced robustness toward sensor defects or failures are some of the advantages of these schemes. This paper presents a new Kalman-based sensor fusion approach for pose estimation that offers higher accuracy and precision, and is robust to camera motion and image occlusion, compared to its predecessors. Extensive experiments are conducted to validate the superiority of this fusion method over currently employed vision-based pose estimation algorithms.

Robust Coefficients Alpha and Omega and Confidence Intervals With Outlying Observations and Missing Data: Methods and Software.

PubMed

Zhang, Zhiyong; Yuan, Ke-Hai

2016-06-01

Cronbach's coefficient alpha is a widely used reliability measure in social, behavioral, and education sciences. It is reported in nearly every study that involves measuring a construct through multiple items. With non-tau-equivalent items, McDonald's omega has been used as a popular alternative to alpha in the literature. Traditional estimation methods for alpha and omega often implicitly assume that data are complete and normally distributed. This study proposes robust procedures to estimate both alpha and omega as well as corresponding standard errors and confidence intervals from samples that may contain potential outlying observations and missing values. The influence of outlying observations and missing data on the estimates of alpha and omega is investigated through two simulation studies. Results show that the newly developed robust method yields substantially improved alpha and omega estimates as well as better coverage rates of confidence intervals than the conventional nonrobust method. An R package coefficientalpha is developed and demonstrated to obtain robust estimates of alpha and omega.

Robust Coefficients Alpha and Omega and Confidence Intervals With Outlying Observations and Missing Data

PubMed Central

Zhang, Zhiyong; Yuan, Ke-Hai

2015-01-01

Cronbach’s coefficient alpha is a widely used reliability measure in social, behavioral, and education sciences. It is reported in nearly every study that involves measuring a construct through multiple items. With non-tau-equivalent items, McDonald’s omega has been used as a popular alternative to alpha in the literature. Traditional estimation methods for alpha and omega often implicitly assume that data are complete and normally distributed. This study proposes robust procedures to estimate both alpha and omega as well as corresponding standard errors and confidence intervals from samples that may contain potential outlying observations and missing values. The influence of outlying observations and missing data on the estimates of alpha and omega is investigated through two simulation studies. Results show that the newly developed robust method yields substantially improved alpha and omega estimates as well as better coverage rates of confidence intervals than the conventional nonrobust method. An R package coefficientalpha is developed and demonstrated to obtain robust estimates of alpha and omega. PMID:29795870

Robustness of location estimators under t-distributions: a literature review

NASA Astrophysics Data System (ADS)

Sumarni, C.; Sadik, K.; Notodiputro, K. A.; Sartono, B.

2017-03-01

The assumption of normality is commonly used in estimation of parameters in statistical modelling, but this assumption is very sensitive to outliers. The t-distribution is more robust than the normal distribution since the t-distributions have longer tails. The robustness measures of location estimators under t-distributions are reviewed and discussed in this paper. For the purpose of illustration we use the onion yield data which includes outliers as a case study and showed that the t model produces better fit than the normal model.

Robust Regression for Slope Estimation in Curriculum-Based Measurement Progress Monitoring

ERIC Educational Resources Information Center

Mercer, Sterett H.; Lyons, Alina F.; Johnston, Lauren E.; Millhoff, Courtney L.

2015-01-01

Although ordinary least-squares (OLS) regression has been identified as a preferred method to calculate rates of improvement for individual students during curriculum-based measurement (CBM) progress monitoring, OLS slope estimates are sensitive to the presence of extreme values. Robust estimators have been developed that are less biased by…

Design and Experimental Evaluation of a Robust Position Controller for an Electrohydrostatic Actuator Using Adaptive Antiwindup Sliding Mode Scheme

PubMed Central

Lee, Ji Min; Park, Sung Hwan; Kim, Jong Shik

2013-01-01

A robust control scheme is proposed for the position control of the electrohydrostatic actuator (EHA) when considering hardware saturation, load disturbance, and lumped system uncertainties and nonlinearities. To reduce overshoot due to a saturation of electric motor and to realize robustness against load disturbance and lumped system uncertainties such as varying parameters and modeling error, this paper proposes an adaptive antiwindup PID sliding mode scheme as a robust position controller for the EHA system. An optimal PID controller and an optimal anti-windup PID controller are also designed to compare control performance. An EHA prototype is developed, carrying out system modeling and parameter identification in designing the position controller. The simply identified linear model serves as the basis for the design of the position controllers, while the robustness of the control systems is compared by experiments. The adaptive anti-windup PID sliding mode controller has been found to have the desired performance and become robust against hardware saturation, load disturbance, and lumped system uncertainties and nonlinearities. PMID:23983640

Predictive IP controller for robust position control of linear servo system.

PubMed

Lu, Shaowu; Zhou, Fengxing; Ma, Yajie; Tang, Xiaoqi

2016-07-01

Position control is a typical application of linear servo system. In this paper, to reduce the system overshoot, an integral plus proportional (IP) controller is used in the position control implementation. To further improve the control performance, a gain-tuning IP controller based on a generalized predictive control (GPC) law is proposed. Firstly, to represent the dynamics of the position loop, a second-order linear model is used and its model parameters are estimated on-line by using a recursive least squares method. Secondly, based on the GPC law, an optimal control sequence is obtained by using receding horizon, then directly supplies the IP controller with the corresponding control parameters in the real operations. Finally, simulation and experimental results are presented to show the efficiency of proposed scheme. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

ROBUST: an interactive FORTRAN-77 package for exploratory data analysis using parametric, ROBUST and nonparametric location and scale estimates, data transformations, normality tests, and outlier assessment

NASA Astrophysics Data System (ADS)

Rock, N. M. S.

ROBUST calculates 53 statistics, plus significance levels for 6 hypothesis tests, on each of up to 52 variables. These together allow the following properties of the data distribution for each variable to be examined in detail: (1) Location. Three means (arithmetic, geometric, harmonic) are calculated, together with the midrange and 19 high-performance robust L-, M-, and W-estimates of location (combined, adaptive, trimmed estimates, etc.) (2) Scale. The standard deviation is calculated along with the H-spread/2 (≈ semi-interquartile range), the mean and median absolute deviations from both mean and median, and a biweight scale estimator. The 23 location and 6 scale estimators programmed cover all possible degrees of robustness. (3) Normality: Distributions are tested against the null hypothesis that they are normal, using the 3rd (√ h1) and 4th ( b 2) moments, Geary's ratio (mean deviation/standard deviation), Filliben's probability plot correlation coefficient, and a more robust test based on the biweight scale estimator. These statistics collectively are sensitive to most usual departures from normality. (4) Presence of outliers. The maximum and minimum values are assessed individually or jointly using Grubbs' maximum Studentized residuals, Harvey's and Dixon's criteria, and the Studentized range. For a single input variable, outliers can be either winsorized or eliminated and all estimates recalculated iteratively as desired. The following data-transformations also can be applied: linear, log 10, generalized Box Cox power (including log, reciprocal, and square root), exponentiation, and standardization. For more than one variable, all results are tabulated in a single run of ROBUST. Further options are incorporated to assess ratios (of two variables) as well as discrete variables, and be concerned with missing data. Cumulative S-plots (for assessing normality graphically) also can be generated. The mutual consistency or inconsistency of all these measures helps to detect errors in data as well as to assess data-distributions themselves.

Developing a Fundamental Model for an Integrated GPS/INS State Estimation System with Kalman Filtering

NASA Technical Reports Server (NTRS)

Canfield, Stephen

1999-01-01

This work will demonstrate the integration of sensor and system dynamic data and their appropriate models using an optimal filter to create a robust, adaptable, easily reconfigurable state (motion) estimation system. This state estimation system will clearly show the application of fundamental modeling and filtering techniques. These techniques are presented at a general, first principles level, that can easily be adapted to specific applications. An example of such an application is demonstrated through the development of an integrated GPS/INS navigation system. This system acquires both global position data and inertial body data, to provide optimal estimates of current position and attitude states. The optimal states are estimated using a Kalman filter. The state estimation system will include appropriate error models for the measurement hardware. The results of this work will lead to the development of a "black-box" state estimation system that supplies current motion information (position and attitude states) that can be used to carry out guidance and control strategies. This black-box state estimation system is developed independent of the vehicle dynamics and therefore is directly applicable to a variety of vehicles. Issues in system modeling and application of Kalman filtering techniques are investigated and presented. These issues include linearized models of equations of state, models of the measurement sensors, and appropriate application and parameter setting (tuning) of the Kalman filter. The general model and subsequent algorithm is developed in Matlab for numerical testing. The results of this system are demonstrated through application to data from the X-33 Michael's 9A8 mission and are presented in plots and simple animations.

When Can Categorical Variables Be Treated as Continuous? A Comparison of Robust Continuous and Categorical SEM Estimation Methods under Suboptimal Conditions

ERIC Educational Resources Information Center

Rhemtulla, Mijke; Brosseau-Liard, Patricia E.; Savalei, Victoria

2012-01-01

A simulation study compared the performance of robust normal theory maximum likelihood (ML) and robust categorical least squares (cat-LS) methodology for estimating confirmatory factor analysis models with ordinal variables. Data were generated from 2 models with 2-7 categories, 4 sample sizes, 2 latent distributions, and 5 patterns of category…

Software For Least-Squares And Robust Estimation

NASA Technical Reports Server (NTRS)

Jeffreys, William H.; Fitzpatrick, Michael J.; Mcarthur, Barbara E.; Mccartney, James

1990-01-01

GAUSSFIT computer program includes full-featured programming language facilitating creation of mathematical models solving least-squares and robust-estimation problems. Programming language designed to make it easy to specify complex reduction models. Written in 100 percent C language.

Rank-preserving regression: a more robust rank regression model against outliers.

PubMed

Chen, Tian; Kowalski, Jeanne; Chen, Rui; Wu, Pan; Zhang, Hui; Feng, Changyong; Tu, Xin M

2016-08-30

Mean-based semi-parametric regression models such as the popular generalized estimating equations are widely used to improve robustness of inference over parametric models. Unfortunately, such models are quite sensitive to outlying observations. The Wilcoxon-score-based rank regression (RR) provides more robust estimates over generalized estimating equations against outliers. However, the RR and its extensions do not sufficiently address missing data arising in longitudinal studies. In this paper, we propose a new approach to address outliers under a different framework based on the functional response models. This functional-response-model-based alternative not only addresses limitations of the RR and its extensions for longitudinal data, but, with its rank-preserving property, even provides more robust estimates than these alternatives. The proposed approach is illustrated with both real and simulated data. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Aeroservoelastic Uncertainty Model Identification from Flight Data

NASA Technical Reports Server (NTRS)

Brenner, Martin J.

2001-01-01

Uncertainty modeling is a critical element in the estimation of robust stability margins for stability boundary prediction and robust flight control system development. There has been a serious deficiency to date in aeroservoelastic data analysis with attention to uncertainty modeling. Uncertainty can be estimated from flight data using both parametric and nonparametric identification techniques. The model validation problem addressed in this paper is to identify aeroservoelastic models with associated uncertainty structures from a limited amount of controlled excitation inputs over an extensive flight envelope. The challenge to this problem is to update analytical models from flight data estimates while also deriving non-conservative uncertainty descriptions consistent with the flight data. Multisine control surface command inputs and control system feedbacks are used as signals in a wavelet-based modal parameter estimation procedure for model updates. Transfer function estimates are incorporated in a robust minimax estimation scheme to get input-output parameters and error bounds consistent with the data and model structure. Uncertainty estimates derived from the data in this manner provide an appropriate and relevant representation for model development and robust stability analysis. This model-plus-uncertainty identification procedure is applied to aeroservoelastic flight data from the NASA Dryden Flight Research Center F-18 Systems Research Aircraft.

Estimating open population site occupancy from presence-absence data lacking the robust design.

PubMed

Dail, D; Madsen, L

2013-03-01

Many animal monitoring studies seek to estimate the proportion of a study area occupied by a target population. The study area is divided into spatially distinct sites where the detected presence or absence of the population is recorded, and this is repeated in time for multiple seasons. However, when occupied sites are detected with probability p < 1, the lack of a detection does not imply lack of occupancy. MacKenzie et al. (2003, Ecology 84, 2200-2207) developed a multiseason model for estimating seasonal site occupancy (ψt ) while accounting for unknown p. Their model performs well when observations are collected according to the robust design, where multiple sampling occasions occur during each season; the repeated sampling aids in the estimation p. However, their model does not perform as well when the robust design is lacking. In this paper, we propose an alternative likelihood model that yields improved seasonal estimates of p and Ψt in the absence of the robust design. We construct the marginal likelihood of the observed data by conditioning on, and summing out, the latent number of occupied sites during each season. A simulation study shows that in cases without the robust design, the proposed model estimates p with less bias than the MacKenzie et al. model and hence improves the estimates of Ψt . We apply both models to a data set consisting of repeated presence-absence observations of American robins (Turdus migratorius) with yearly survey periods. The two models are compared to a third estimator available when the repeated counts (from the same study) are considered, with the proposed model yielding estimates of Ψt closest to estimates from the point count model. Copyright © 2013, The International Biometric Society.

Spatio-Temporal Fluctuations of the Earthquake Magnitude Distribution: Robust Estimation and Predictive Power

NASA Astrophysics Data System (ADS)

Olsen, S.; Zaliapin, I.

2008-12-01

We establish positive correlation between the local spatio-temporal fluctuations of the earthquake magnitude distribution and the occurrence of regional earthquakes. In order to accomplish this goal, we develop a sequential Bayesian statistical estimation framework for the b-value (slope of the Gutenberg-Richter's exponential approximation to the observed magnitude distribution) and for the ratio a(t) between the earthquake intensities in two non-overlapping magnitude intervals. The time-dependent dynamics of these parameters is analyzed using Markov Chain Models (MCM). The main advantage of this approach over the traditional window-based estimation is its "soft" parameterization, which allows one to obtain stable results with realistically small samples. We furthermore discuss a statistical methodology for establishing lagged correlations between continuous and point processes. The developed methods are applied to the observed seismicity of California, Nevada, and Japan on different temporal and spatial scales. We report an oscillatory dynamics of the estimated parameters, and find that the detected oscillations are positively correlated with the occurrence of large regional earthquakes, as well as with small events with magnitudes as low as 2.5. The reported results have important implications for further development of earthquake prediction and seismic hazard assessment methods.

Linkage disequilibrium interval mapping of quantitative trait loci.

PubMed

Boitard, Simon; Abdallah, Jihad; de Rochambeau, Hubert; Cierco-Ayrolles, Christine; Mangin, Brigitte

2006-03-16

For many years gene mapping studies have been performed through linkage analyses based on pedigree data. Recently, linkage disequilibrium methods based on unrelated individuals have been advocated as powerful tools to refine estimates of gene location. Many strategies have been proposed to deal with simply inherited disease traits. However, locating quantitative trait loci is statistically more challenging and considerable research is needed to provide robust and computationally efficient methods. Under a three-locus Wright-Fisher model, we derived approximate expressions for the expected haplotype frequencies in a population. We considered haplotypes comprising one trait locus and two flanking markers. Using these theoretical expressions, we built a likelihood-maximization method, called HAPim, for estimating the location of a quantitative trait locus. For each postulated position, the method only requires information from the two flanking markers. Over a wide range of simulation scenarios it was found to be more accurate than a two-marker composite likelihood method. It also performed as well as identity by descent methods, whilst being valuable in a wider range of populations. Our method makes efficient use of marker information, and can be valuable for fine mapping purposes. Its performance is increased if multiallelic markers are available. Several improvements can be developed to account for more complex evolution scenarios or provide robust confidence intervals for the location estimates.

Shrinkage covariance matrix approach based on robust trimmed mean in gene sets detection

NASA Astrophysics Data System (ADS)

Karjanto, Suryaefiza; Ramli, Norazan Mohamed; Ghani, Nor Azura Md; Aripin, Rasimah; Yusop, Noorezatty Mohd

2015-02-01

Microarray involves of placing an orderly arrangement of thousands of gene sequences in a grid on a suitable surface. The technology has made a novelty discovery since its development and obtained an increasing attention among researchers. The widespread of microarray technology is largely due to its ability to perform simultaneous analysis of thousands of genes in a massively parallel manner in one experiment. Hence, it provides valuable knowledge on gene interaction and function. The microarray data set typically consists of tens of thousands of genes (variables) from just dozens of samples due to various constraints. Therefore, the sample covariance matrix in Hotelling's T2 statistic is not positive definite and become singular, thus it cannot be inverted. In this research, the Hotelling's T2 statistic is combined with a shrinkage approach as an alternative estimation to estimate the covariance matrix to detect significant gene sets. The use of shrinkage covariance matrix overcomes the singularity problem by converting an unbiased to an improved biased estimator of covariance matrix. Robust trimmed mean is integrated into the shrinkage matrix to reduce the influence of outliers and consequently increases its efficiency. The performance of the proposed method is measured using several simulation designs. The results are expected to outperform existing techniques in many tested conditions.

Relative Navigation for Formation Flying of Spacecraft

NASA Technical Reports Server (NTRS)

Alonso, Roberto; Du, Ju-Young; Hughes, Declan; Junkins, John L.; Crassidis, John L.

2001-01-01

This paper presents a robust and efficient approach for relative navigation and attitude estimation of spacecraft flying in formation. This approach uses measurements from a new optical sensor that provides a line of sight vector from the master spacecraft to the secondary satellite. The overall system provides a novel, reliable, and autonomous relative navigation and attitude determination system, employing relatively simple electronic circuits with modest digital signal processing requirements and is fully independent of any external systems. Experimental calibration results are presented, which are used to achieve accurate line of sight measurements. State estimation for formation flying is achieved through an optimal observer design. Also, because the rotational and translational motions are coupled through the observation vectors, three approaches are suggested to separate both signals just for stability analysis. Simulation and experimental results indicate that the combined sensor/estimator approach provides accurate relative position and attitude estimates.

Men’s Facial Width-to-Height Ratio Predicts Aggression: A Meta-Analysis

PubMed Central

Haselhuhn, Michael P.; Ormiston, Margaret E.; Wong, Elaine M.

2015-01-01

Recent research has identified men’s facial width-to-height ratio (fWHR) as a reliable predictor of aggressive tendencies and behavior. Other research, however, has failed to replicate the fWHR-aggression relationship and has questioned whether previous findings are robust. In the current paper, we synthesize existing work by conducting a meta-analysis to estimate whether and how fWHR predicts aggression. Our results indicate a small, but significant, positive relationship between men’s fWHR and aggression. PMID:25849992

Robust inference in the negative binomial regression model with an application to falls data.

PubMed

Aeberhard, William H; Cantoni, Eva; Heritier, Stephane

2014-12-01

A popular way to model overdispersed count data, such as the number of falls reported during intervention studies, is by means of the negative binomial (NB) distribution. Classical estimating methods are well-known to be sensitive to model misspecifications, taking the form of patients falling much more than expected in such intervention studies where the NB regression model is used. We extend in this article two approaches for building robust M-estimators of the regression parameters in the class of generalized linear models to the NB distribution. The first approach achieves robustness in the response by applying a bounded function on the Pearson residuals arising in the maximum likelihood estimating equations, while the second approach achieves robustness by bounding the unscaled deviance components. For both approaches, we explore different choices for the bounding functions. Through a unified notation, we show how close these approaches may actually be as long as the bounding functions are chosen and tuned appropriately, and provide the asymptotic distributions of the resulting estimators. Moreover, we introduce a robust weighted maximum likelihood estimator for the overdispersion parameter, specific to the NB distribution. Simulations under various settings show that redescending bounding functions yield estimates with smaller biases under contamination while keeping high efficiency at the assumed model, and this for both approaches. We present an application to a recent randomized controlled trial measuring the effectiveness of an exercise program at reducing the number of falls among people suffering from Parkinsons disease to illustrate the diagnostic use of such robust procedures and their need for reliable inference. © 2014, The International Biometric Society.

Integrated Navigation System Design for Micro Planetary Rovers: Comparison of Absolute Heading Estimation Algorithms and Nonlinear Filtering

PubMed Central

Ilyas, Muhammad; Hong, Beomjin; Cho, Kuk; Baeg, Seung-Ho; Park, Sangdeok

2016-01-01

This paper provides algorithms to fuse relative and absolute microelectromechanical systems (MEMS) navigation sensors, suitable for micro planetary rovers, to provide a more accurate estimation of navigation information, specifically, attitude and position. Planetary rovers have extremely slow speed (~1 cm/s) and lack conventional navigation sensors/systems, hence the general methods of terrestrial navigation may not be applicable to these applications. While relative attitude and position can be tracked in a way similar to those for ground robots, absolute navigation information is hard to achieve on a remote celestial body, like Moon or Mars, in contrast to terrestrial applications. In this study, two absolute attitude estimation algorithms were developed and compared for accuracy and robustness. The estimated absolute attitude was fused with the relative attitude sensors in a framework of nonlinear filters. The nonlinear Extended Kalman filter (EKF) and Unscented Kalman filter (UKF) were compared in pursuit of better accuracy and reliability in this nonlinear estimation problem, using only on-board low cost MEMS sensors. Experimental results confirmed the viability of the proposed algorithms and the sensor suite, for low cost and low weight micro planetary rovers. It is demonstrated that integrating the relative and absolute navigation MEMS sensors reduces the navigation errors to the desired level. PMID:27223293

Integrated Navigation System Design for Micro Planetary Rovers: Comparison of Absolute Heading Estimation Algorithms and Nonlinear Filtering.

PubMed

Ilyas, Muhammad; Hong, Beomjin; Cho, Kuk; Baeg, Seung-Ho; Park, Sangdeok

2016-05-23

This paper provides algorithms to fuse relative and absolute microelectromechanical systems (MEMS) navigation sensors, suitable for micro planetary rovers, to provide a more accurate estimation of navigation information, specifically, attitude and position. Planetary rovers have extremely slow speed (~1 cm/s) and lack conventional navigation sensors/systems, hence the general methods of terrestrial navigation may not be applicable to these applications. While relative attitude and position can be tracked in a way similar to those for ground robots, absolute navigation information is hard to achieve on a remote celestial body, like Moon or Mars, in contrast to terrestrial applications. In this study, two absolute attitude estimation algorithms were developed and compared for accuracy and robustness. The estimated absolute attitude was fused with the relative attitude sensors in a framework of nonlinear filters. The nonlinear Extended Kalman filter (EKF) and Unscented Kalman filter (UKF) were compared in pursuit of better accuracy and reliability in this nonlinear estimation problem, using only on-board low cost MEMS sensors. Experimental results confirmed the viability of the proposed algorithms and the sensor suite, for low cost and low weight micro planetary rovers. It is demonstrated that integrating the relative and absolute navigation MEMS sensors reduces the navigation errors to the desired level.

A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance.

PubMed

Zheng, Binqi; Fu, Pengcheng; Li, Baoqing; Yuan, Xiaobing

2018-03-07

The Unscented Kalman filter (UKF) may suffer from performance degradation and even divergence while mismatch between the noise distribution assumed as a priori by users and the actual ones in a real nonlinear system. To resolve this problem, this paper proposes a robust adaptive UKF (RAUKF) to improve the accuracy and robustness of state estimation with uncertain noise covariance. More specifically, at each timestep, a standard UKF will be implemented first to obtain the state estimations using the new acquired measurement data. Then an online fault-detection mechanism is adopted to judge if it is necessary to update current noise covariance. If necessary, innovation-based method and residual-based method are used to calculate the estimations of current noise covariance of process and measurement, respectively. By utilizing a weighting factor, the filter will combine the last noise covariance matrices with the estimations as the new noise covariance matrices. Finally, the state estimations will be corrected according to the new noise covariance matrices and previous state estimations. Compared with the standard UKF and other adaptive UKF algorithms, RAUKF converges faster to the actual noise covariance and thus achieves a better performance in terms of robustness, accuracy, and computation for nonlinear estimation with uncertain noise covariance, which is demonstrated by the simulation results.

A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance

PubMed Central

Zheng, Binqi; Yuan, Xiaobing

2018-01-01

The Unscented Kalman filter (UKF) may suffer from performance degradation and even divergence while mismatch between the noise distribution assumed as a priori by users and the actual ones in a real nonlinear system. To resolve this problem, this paper proposes a robust adaptive UKF (RAUKF) to improve the accuracy and robustness of state estimation with uncertain noise covariance. More specifically, at each timestep, a standard UKF will be implemented first to obtain the state estimations using the new acquired measurement data. Then an online fault-detection mechanism is adopted to judge if it is necessary to update current noise covariance. If necessary, innovation-based method and residual-based method are used to calculate the estimations of current noise covariance of process and measurement, respectively. By utilizing a weighting factor, the filter will combine the last noise covariance matrices with the estimations as the new noise covariance matrices. Finally, the state estimations will be corrected according to the new noise covariance matrices and previous state estimations. Compared with the standard UKF and other adaptive UKF algorithms, RAUKF converges faster to the actual noise covariance and thus achieves a better performance in terms of robustness, accuracy, and computation for nonlinear estimation with uncertain noise covariance, which is demonstrated by the simulation results. PMID:29518960

Data-Driven Robust RVFLNs Modeling of a Blast Furnace Iron-Making Process Using Cauchy Distribution Weighted M-Estimation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, Ping; Lv, Youbin; Wang, Hong

Optimal operation of a practical blast furnace (BF) ironmaking process depends largely on a good measurement of molten iron quality (MIQ) indices. However, measuring the MIQ online is not feasible using the available techniques. In this paper, a novel data-driven robust modeling is proposed for online estimation of MIQ using improved random vector functional-link networks (RVFLNs). Since the output weights of traditional RVFLNs are obtained by the least squares approach, a robustness problem may occur when the training dataset is contaminated with outliers. This affects the modeling accuracy of RVFLNs. To solve this problem, a Cauchy distribution weighted M-estimation basedmore » robust RFVLNs is proposed. Since the weights of different outlier data are properly determined by the Cauchy distribution, their corresponding contribution on modeling can be properly distinguished. Thus robust and better modeling results can be achieved. Moreover, given that the BF is a complex nonlinear system with numerous coupling variables, the data-driven canonical correlation analysis is employed to identify the most influential components from multitudinous factors that affect the MIQ indices to reduce the model dimension. Finally, experiments using industrial data and comparative studies have demonstrated that the obtained model produces a better modeling and estimating accuracy and stronger robustness than other modeling methods.« less

Hybrid Orientation Based Human Limbs Motion Tracking Method

PubMed Central

Glonek, Grzegorz; Wojciechowski, Adam

2017-01-01

One of the key technologies that lays behind the human–machine interaction and human motion diagnosis is the limbs motion tracking. To make the limbs tracking efficient, it must be able to estimate a precise and unambiguous position of each tracked human joint and resulting body part pose. In recent years, body pose estimation became very popular and broadly available for home users because of easy access to cheap tracking devices. Their robustness can be improved by different tracking modes data fusion. The paper defines the novel approach—orientation based data fusion—instead of dominating in literature position based approach, for two classes of tracking devices: depth sensors (i.e., Microsoft Kinect) and inertial measurement units (IMU). The detailed analysis of their working characteristics allowed to elaborate a new method that let fuse more precisely limbs orientation data from both devices and compensates their imprecisions. The paper presents the series of performed experiments that verified the method’s accuracy. This novel approach allowed to outperform the precision of position-based joints tracking, the methods dominating in the literature, of up to 18%. PMID:29232832

A robust statistical estimation (RoSE) algorithm jointly recovers the 3D location and intensity of single molecules accurately and precisely

NASA Astrophysics Data System (ADS)

Mazidi, Hesam; Nehorai, Arye; Lew, Matthew D.

2018-02-01

In single-molecule (SM) super-resolution microscopy, the complexity of a biological structure, high molecular density, and a low signal-to-background ratio (SBR) may lead to imaging artifacts without a robust localization algorithm. Moreover, engineered point spread functions (PSFs) for 3D imaging pose difficulties due to their intricate features. We develop a Robust Statistical Estimation algorithm, called RoSE, that enables joint estimation of the 3D location and photon counts of SMs accurately and precisely using various PSFs under conditions of high molecular density and low SBR.

A Very Simple Method to Calculate the (Positive) Largest Lyapunov Exponent Using Interval Extensions

NASA Astrophysics Data System (ADS)

Mendes, Eduardo M. A. M.; Nepomuceno, Erivelton G.

2016-12-01

In this letter, a very simple method to calculate the positive Largest Lyapunov Exponent (LLE) based on the concept of interval extensions and using the original equations of motion is presented. The exponent is estimated from the slope of the line derived from the lower bound error when considering two interval extensions of the original system. It is shown that the algorithm is robust, fast and easy to implement and can be considered as alternative to other algorithms available in the literature. The method has been successfully tested in five well-known systems: Logistic, Hénon, Lorenz and Rössler equations and the Mackey-Glass system.

Efficient robust doubly adaptive regularized regression with applications.

PubMed

Karunamuni, Rohana J; Kong, Linglong; Tu, Wei

2018-01-01

We consider the problem of estimation and variable selection for general linear regression models. Regularized regression procedures have been widely used for variable selection, but most existing methods perform poorly in the presence of outliers. We construct a new penalized procedure that simultaneously attains full efficiency and maximum robustness. Furthermore, the proposed procedure satisfies the oracle properties. The new procedure is designed to achieve sparse and robust solutions by imposing adaptive weights on both the decision loss and the penalty function. The proposed method of estimation and variable selection attains full efficiency when the model is correct and, at the same time, achieves maximum robustness when outliers are present. We examine the robustness properties using the finite-sample breakdown point and an influence function. We show that the proposed estimator attains the maximum breakdown point. Furthermore, there is no loss in efficiency when there are no outliers or the error distribution is normal. For practical implementation of the proposed method, we present a computational algorithm. We examine the finite-sample and robustness properties using Monte Carlo studies. Two datasets are also analyzed.

An improved method for bivariate meta-analysis when within-study correlations are unknown.

PubMed

Hong, Chuan; D Riley, Richard; Chen, Yong

2018-03-01

Multivariate meta-analysis, which jointly analyzes multiple and possibly correlated outcomes in a single analysis, is becoming increasingly popular in recent years. An attractive feature of the multivariate meta-analysis is its ability to account for the dependence between multiple estimates from the same study. However, standard inference procedures for multivariate meta-analysis require the knowledge of within-study correlations, which are usually unavailable. This limits standard inference approaches in practice. Riley et al proposed a working model and an overall synthesis correlation parameter to account for the marginal correlation between outcomes, where the only data needed are those required for a separate univariate random-effects meta-analysis. As within-study correlations are not required, the Riley method is applicable to a wide variety of evidence synthesis situations. However, the standard variance estimator of the Riley method is not entirely correct under many important settings. As a consequence, the coverage of a function of pooled estimates may not reach the nominal level even when the number of studies in the multivariate meta-analysis is large. In this paper, we improve the Riley method by proposing a robust variance estimator, which is asymptotically correct even when the model is misspecified (ie, when the likelihood function is incorrect). Simulation studies of a bivariate meta-analysis, in a variety of settings, show a function of pooled estimates has improved performance when using the proposed robust variance estimator. In terms of individual pooled estimates themselves, the standard variance estimator and robust variance estimator give similar results to the original method, with appropriate coverage. The proposed robust variance estimator performs well when the number of studies is relatively large. Therefore, we recommend the use of the robust method for meta-analyses with a relatively large number of studies (eg, m≥50). When the sample size is relatively small, we recommend the use of the robust method under the working independence assumption. We illustrate the proposed method through 2 meta-analyses. Copyright © 2017 John Wiley & Sons, Ltd.

Toward Robust Estimation of the Components of Forest Population Change

Treesearch

Francis A. Roesch

2014-01-01

Multiple levels of simulation are used to test the robustness of estimators of the components of change. I first created a variety of spatial-temporal populations based on, but more variable than, an actual forest monitoring data set and then sampled those populations under a variety of sampling error structures. The performance of each of four estimation approaches is...

The Graphical Display of Simulation Results, with Applications to the Comparison of Robust IRT Estimators of Ability.

ERIC Educational Resources Information Center

Thissen, David; Wainer, Howard

Simulation studies of the performance of (potentially) robust statistical estimation produce large quantities of numbers in the form of performance indices of the various estimators under various conditions. This report presents a multivariate graphical display used to aid in the digestion of the plentiful results in a current study of Item…

Sliding mode output feedback control based on tracking error observer with disturbance estimator.

PubMed

Xiao, Lingfei; Zhu, Yue

2014-07-01

For a class of systems who suffers from disturbances, an original output feedback sliding mode control method is presented based on a novel tracking error observer with disturbance estimator. The mathematical models of the systems are not required to be with high accuracy, and the disturbances can be vanishing or nonvanishing, while the bounds of disturbances are unknown. By constructing a differential sliding surface and employing reaching law approach, a sliding mode controller is obtained. On the basis of an extended disturbance estimator, a creative tracking error observer is produced. By using the observation of tracking error and the estimation of disturbance, the sliding mode controller is implementable. It is proved that the disturbance estimation error and tracking observation error are bounded, the sliding surface is reachable and the closed-loop system is robustly stable. The simulations on a servomotor positioning system and a five-degree-of-freedom active magnetic bearings system verify the effect of the proposed method. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Decadal-scale rates of reef erosion following El Niño-related mass coral mortality.

PubMed

Roff, George; Zhao, Jian-Xin; Mumby, Peter J

2015-12-01

As the frequency and intensity of coral mortality events increase under climate change, understanding how declines in coral cover may affect the bioerosion of reef frameworks is of increasing importance. Here, we explore decadal-scale rates of bioerosion of the framework building coral Orbicella annularis by grazing parrotfish following the 1997/1998 El Niño-related mass mortality event at Long Cay, Belize. Using high-precision U-Th dating and CT scan analysis, we quantified in situ rates of external bioerosion over a 13-year period (1998-2011). Based upon the error-weighted average U-Th age of dead O. annularis skeletons, we estimate the average external bioerosion between 1998 and 2011 as 0.92 ± 0.55 cm depth. Empirical observations of herbivore foraging, and a nonlinear numerical response of parrotfish to an increase in food availability, were used to create a model of external bioerosion at Long Cay. Model estimates of external bioerosion were in close agreement with U-Th estimates (0.85 ± 0.09 cm). The model was then used to quantify how rates of external bioerosion changed across a gradient of coral mortality (i.e., from few corals experiencing mortality following coral bleaching to complete mortality). Our results indicate that external bioerosion is remarkably robust to declines in coral cover, with no significant relationship predicted between the rate of external bioerosion and the proportion of O. annularis that died in the 1998 bleaching event. The outcome was robust because the reduction in grazing intensity that follows coral mortality was compensated for by a positive numerical response of parrotfish to an increase in food availability. Our model estimates further indicate that for an O. annularis-dominated reef to maintain a positive state of reef accretion, a necessity for sustained ecosystem function, live cover of O. annularis must not drop below a ~5-10% threshold of cover. © 2015 John Wiley & Sons Ltd.

Scheduling policies of intelligent sensors and sensor/actuators in flexible structures

NASA Astrophysics Data System (ADS)

Demetriou, Michael A.; Potami, Raffaele

2006-03-01

In this note, we revisit the problem of actuator/sensor placement in large civil infrastructures and flexible space structures within the context of spatial robustness. The positioning of these devices becomes more important in systems employing wireless sensor and actuator networks (WSAN) for improved control performance and for rapid failure detection. The ability of the sensing and actuating devices to possess the property of spatial robustness results in reduced control energy and therefore the spatial distribution of disturbances is integrated into the location optimization measures. In our studies, the structure under consideration is a flexible plate clamped at all sides. First, we consider the case of sensor placement and the optimization scheme attempts to produce those locations that minimize the effects of the spatial distribution of disturbances on the state estimation error; thus the sensor locations produce state estimators with minimized disturbance-to-error transfer function norms. A two-stage optimization procedure is employed whereby one first considers the open loop system and the spatial distribution of disturbances is found that produces the maximal effects on the entire open loop state. Once this "worst" spatial distribution of disturbances is found, the optimization scheme subsequently finds the locations that produce state estimators with minimum transfer function norms. In the second part, we consider the collocated actuator/sensor pairs and the optimization scheme produces those locations that result in compensators with the smallest norms of the disturbance-to-state transfer functions. Going a step further, an intelligent control scheme is presented which, at each time interval, activates a subset of the actuator/sensor pairs in order provide robustness against spatiotemporally moving disturbances and minimize power consumption by keeping some sensor/actuators in sleep mode.

Ego-motion based on EM for bionic navigation

NASA Astrophysics Data System (ADS)

Yue, Xiaofeng; Wang, L. J.; Liu, J. G.

2015-12-01

Researches have proved that flying insects such as bees can achieve efficient and robust flight control, and biologists have explored some biomimetic principles regarding how they control flight. Based on those basic studies and principles acquired from the flying insects, this paper proposes a different solution of recovering ego-motion for low level navigation. Firstly, a new type of entropy flow is provided to calculate the motion parameters. Secondly, EKF, which has been used for navigation for some years to correct accumulated error, and estimation-Maximization, which is always used to estimate parameters, are put together to determine the ego-motion estimation of aerial vehicles. Numerical simulation on MATLAB has proved that this navigation system provides more accurate position and smaller mean absolute error than pure optical flow navigation. This paper has done pioneering work in bionic mechanism to space navigation.

A Robust State Estimation Framework Considering Measurement Correlations and Imperfect Synchronization

DOE PAGES

Zhao, Junbo; Wang, Shaobu; Mili, Lamine; ...

2018-01-08

Here, this paper develops a robust power system state estimation framework with the consideration of measurement correlations and imperfect synchronization. In the framework, correlations of SCADA and Phasor Measurements (PMUs) are calculated separately through unscented transformation and a Vector Auto-Regression (VAR) model. In particular, PMU measurements during the waiting period of two SCADA measurement scans are buffered to develop the VAR model with robustly estimated parameters using projection statistics approach. The latter takes into account the temporal and spatial correlations of PMU measurements and provides redundant measurements to suppress bad data and mitigate imperfect synchronization. In case where the SCADAmore » and PMU measurements are not time synchronized, either the forecasted PMU measurements or the prior SCADA measurements from the last estimation run are leveraged to restore system observability. Then, a robust generalized maximum-likelihood (GM)-estimator is extended to integrate measurement error correlations and to handle the outliers in the SCADA and PMU measurements. Simulation results that stem from a comprehensive comparison with other alternatives under various conditions demonstrate the benefits of the proposed framework.« less

A Robust State Estimation Framework Considering Measurement Correlations and Imperfect Synchronization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhao, Junbo; Wang, Shaobu; Mili, Lamine

Here, this paper develops a robust power system state estimation framework with the consideration of measurement correlations and imperfect synchronization. In the framework, correlations of SCADA and Phasor Measurements (PMUs) are calculated separately through unscented transformation and a Vector Auto-Regression (VAR) model. In particular, PMU measurements during the waiting period of two SCADA measurement scans are buffered to develop the VAR model with robustly estimated parameters using projection statistics approach. The latter takes into account the temporal and spatial correlations of PMU measurements and provides redundant measurements to suppress bad data and mitigate imperfect synchronization. In case where the SCADAmore » and PMU measurements are not time synchronized, either the forecasted PMU measurements or the prior SCADA measurements from the last estimation run are leveraged to restore system observability. Then, a robust generalized maximum-likelihood (GM)-estimator is extended to integrate measurement error correlations and to handle the outliers in the SCADA and PMU measurements. Simulation results that stem from a comprehensive comparison with other alternatives under various conditions demonstrate the benefits of the proposed framework.« less

Costs of trastuzumab in combination with chemotherapy for HER2-positive advanced gastric or gastroesophageal junction cancer: an economic evaluation in the Chinese context.

PubMed

Wu, Bin; Ye, Ming; Chen, Huafeng; Shen, Jinfang F

2012-02-01

Adding trastuzumab to a conventional regimen of chemotherapy can improve survival in patients with human epidermal growth factor receptor 2 (HER2)-positive advanced gastric or gastroesophageal junction (GEJ) cancer, but the economic impact of this practice is unknown. The purpose of this cost-effectiveness analysis was to estimate the effects of adding trastuzumab to standard chemotherapy in patients with HER2-positive advanced gastric or GEJ cancer on health and economic outcomes in China. A Markov model was developed to simulate the clinical course of typical patients with HER2-positive advanced gastric or GEJ cancer. Five-year quality-adjusted life-years (QALYs), costs, and incremental cost-effectiveness ratios (ICERs) were estimated. Model inputs were derived from the published literature and government sources. Direct costs were estimated from the perspective of Chinese society. One-way and probabilistic sensitivity analyses were conducted. On baseline analysis, the addition of trastuzumab increased cost and QALY by $56,004.30 (year-2010 US $) and 0.18, respectively, relative to conventional chemotherapy, resulting in an ICER of $251,667.10/QALY gained. Probabilistic sensitivity analyses supported that the addition of trastuzumab was not cost-effective. Budgetary impact analysis estimated that the annual increase in fiscal expenditures would be ~$1 billion. On univariate sensitivity analysis, the median overall survival time for conventional chemotherapy was the most influential factor with respect to the robustness of the model. The findings from the present analysis suggest that the addition of trastuzumab to conventional chemotherapy might not be cost-effective in patients with HER2-positive advanced gastric or GEJ cancer. Copyright © 2012 Elsevier HS Journals, Inc. All rights reserved.

Acceptance test of a commercially available software for automatic image registration of computed tomography (CT), magnetic resonance imaging (MRI) and 99mTc-methoxyisobutylisonitrile (MIBI) single-photon emission computed tomography (SPECT) brain images.

PubMed

Loi, Gianfranco; Dominietto, Marco; Manfredda, Irene; Mones, Eleonora; Carriero, Alessandro; Inglese, Eugenio; Krengli, Marco; Brambilla, Marco

2008-09-01

This note describes a method to characterize the performances of image fusion software (Syntegra) with respect to accuracy and robustness. Computed tomography (CT), magnetic resonance imaging (MRI), and single-photon emission computed tomography (SPECT) studies were acquired from two phantoms and 10 patients. Image registration was performed independently by two couples composed of one radiotherapist and one physicist by means of superposition of anatomic landmarks. Each couple performed jointly and saved the registration. The two solutions were averaged to obtain the gold standard registration. A new set of estimators was defined to identify translation and rotation errors in the coordinate axes, independently from point position in image field of view (FOV). Algorithms evaluated were local correlation (LC) for CT-MRI, normalized mutual information (MI) for CT-MRI, and CT-SPECT registrations. To evaluate accuracy, estimator values were compared to limiting values for the algorithms employed, both in phantoms and in patients. To evaluate robustness, different alignments between images taken from a sample patient were produced and registration errors determined. LC algorithm resulted accurate in CT-MRI registrations in phantoms, but exceeded limiting values in 3 of 10 patients. MI algorithm resulted accurate in CT-MRI and CT-SPECT registrations in phantoms; limiting values were exceeded in one case in CT-MRI and never reached in CT-SPECT registrations. Thus, the evaluation of robustness was restricted to the algorithm of MI both for CT-MRI and CT-SPECT registrations. The algorithm of MI proved to be robust: limiting values were not exceeded with translation perturbations up to 2.5 cm, rotation perturbations up to 10 degrees and roto-translational perturbation up to 3 cm and 5 degrees.

Precipitation, temperature, and teleconnection signals across the combined North American, Monsoon Asia, and Old World Drought Atlases

NASA Astrophysics Data System (ADS)

Smerdon, J. E.; Baek, S. H.; Coats, S.; Williams, P.; Cook, B.; Cook, E. R.; Seager, R.

2017-12-01

The tree-ring-based North American Drought Atlas (NADA), Monsoon Asia Drought Atlas (MADA), and Old World Drought Atlas (OWDA) collectively yield a near-hemispheric gridded reconstruction of hydroclimate variability over the last millennium. To test the robustness of the large-scale representation of hydroclimate variability across the drought atlases, the joint expression of seasonal climate variability and teleconnections in the NADA, MADA, and OWDA are compared against two global, observation-based PDSI products. Predominantly positive (negative) correlations are determined between seasonal precipitation (surface air temperature) and collocated tree-ring-based PDSI, with average Pearson's correlation coefficients increasing in magnitude from boreal winter to summer. For precipitation, these correlations tend to be stronger in the boreal winter and summer when calculated for the observed PDSI record, while remaining similar for temperature. Notwithstanding these differences, the drought atlases robustly express teleconnection patterns associated with the El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), and Atlantic Multidecadal Oscillation (AMO). These expressions exist in the drought atlas estimates of boreal summer PDSI despite the fact that these modes of climate variability are dominant in boreal winter, with the exception of the Atlantic Multidecadal Oscillation. ENSO and NAO teleconnection patterns in the drought atlases are particularly consistent with their well-known dominant expressions in boreal winter and over the OWDA domain, respectively. Collectively, our findings confirm that the joint Northern Hemisphere drought atlases robustly reflect large-scale patterns of hydroclimate variability on seasonal to multidecadal timescales over the 20th century and are likely to provide similarly robust estimates of hydroclimate variability prior to the existence of widespread instrumental data.

Drogue pose estimation for unmanned aerial vehicle autonomous aerial refueling system based on infrared vision sensor

NASA Astrophysics Data System (ADS)

Chen, Shanjun; Duan, Haibin; Deng, Yimin; Li, Cong; Zhao, Guozhi; Xu, Yan

2017-12-01

Autonomous aerial refueling is a significant technology that can significantly extend the endurance of unmanned aerial vehicles. A reliable method that can accurately estimate the position and attitude of the probe relative to the drogue is the key to such a capability. A drogue pose estimation method based on infrared vision sensor is introduced with the general goal of yielding an accurate and reliable drogue state estimate. First, by employing direct least squares ellipse fitting and convex hull in OpenCV, a feature point matching and interference point elimination method is proposed. In addition, considering the conditions that some infrared LEDs are damaged or occluded, a missing point estimation method based on perspective transformation and affine transformation is designed. Finally, an accurate and robust pose estimation algorithm improved by the runner-root algorithm is proposed. The feasibility of the designed visual measurement system is demonstrated by flight test, and the results indicate that our proposed method enables precise and reliable pose estimation of the probe relative to the drogue, even in some poor conditions.

Optimization of seasonal ARIMA models using differential evolution - simulated annealing (DESA) algorithm in forecasting dengue cases in Baguio City

NASA Astrophysics Data System (ADS)

Addawe, Rizavel C.; Addawe, Joel M.; Magadia, Joselito C.

2016-10-01

Accurate forecasting of dengue cases would significantly improve epidemic prevention and control capabilities. This paper attempts to provide useful models in forecasting dengue epidemic specific to the young and adult population of Baguio City. To capture the seasonal variations in dengue incidence, this paper develops a robust modeling approach to identify and estimate seasonal autoregressive integrated moving average (SARIMA) models in the presence of additive outliers. Since the least squares estimators are not robust in the presence of outliers, we suggest a robust estimation based on winsorized and reweighted least squares estimators. A hybrid algorithm, Differential Evolution - Simulated Annealing (DESA), is used to identify and estimate the parameters of the optimal SARIMA model. The method is applied to the monthly reported dengue cases in Baguio City, Philippines.

Neural network-based position synchronised internal force control scheme for cooperative manipulator system

NASA Astrophysics Data System (ADS)

Wang, Jin; Xu, Fan; Lu, GuoDong

2017-09-01

More complex problems of simultaneous position and internal force control occur with cooperative manipulator systems than that of a single one. In the presence of unwanted parametric and modelling uncertainties as well as external disturbances, a decentralised position synchronised force control scheme is proposed. With a feedforward neural network estimating engine, a precise model of the system dynamics is not required. Unlike conventional cooperative or synchronised controllers, virtual position and virtual synchronisation errors are introduced for internal force tracking control and task space position synchronisation. Meanwhile joint space synchronisation and force measurement are unnecessary. Together with simulation studies and analysis, the position and the internal force errors are shown to asymptotically converge to zero. Moreover, the controller exhibits different characteristics with selected synchronisation factors. Under certain settings, it can deal with temporary cooperation by an intelligent retreat mechanism, where less internal force would occur and rigid collision can be avoided. Using a Lyapunov stability approach, the controller is proven to be robust in face of the aforementioned uncertainties.

Adaptive robust motion trajectory tracking control of pneumatic cylinders with LuGre model-based friction compensation

NASA Astrophysics Data System (ADS)

Meng, Deyuan; Tao, Guoliang; Liu, Hao; Zhu, Xiaocong

2014-07-01

Friction compensation is particularly important for motion trajectory tracking control of pneumatic cylinders at low speed movement. However, most of the existing model-based friction compensation schemes use simple classical models, which are not enough to address applications with high-accuracy position requirements. Furthermore, the friction force in the cylinder is time-varying, and there exist rather severe unmodelled dynamics and unknown disturbances in the pneumatic system. To deal with these problems effectively, an adaptive robust controller with LuGre model-based dynamic friction compensation is constructed. The proposed controller employs on-line recursive least squares estimation (RLSE) to reduce the extent of parametric uncertainties, and utilizes the sliding mode control method to attenuate the effects of parameter estimation errors, unmodelled dynamics and disturbances. In addition, in order to realize LuGre model-based friction compensation, the modified dual-observer structure for estimating immeasurable friction internal state is developed. Therefore, a prescribed motion tracking transient performance and final tracking accuracy can be guaranteed. Since the system model uncertainties are unmatched, the recursive backstepping design technology is applied. In order to solve the conflicts between the sliding mode control design and the adaptive control design, the projection mapping is used to condition the RLSE algorithm so that the parameter estimates are kept within a known bounded convex set. Finally, the proposed controller is tested for tracking sinusoidal trajectories and smooth square trajectory under different loads and sudden disturbance. The testing results demonstrate that the achievable performance of the proposed controller is excellent and is much better than most other studies in literature. Especially when a 0.5 Hz sinusoidal trajectory is tracked, the maximum tracking error is 0.96 mm and the average tracking error is 0.45 mm. This paper constructs an adaptive robust controller which can compensate the friction force in the cylinder.

Robust estimation procedure in panel data model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shariff, Nurul Sima Mohamad; Hamzah, Nor Aishah

2014-06-19

The panel data modeling has received a great attention in econometric research recently. This is due to the availability of data sources and the interest to study cross sections of individuals observed over time. However, the problems may arise in modeling the panel in the presence of cross sectional dependence and outliers. Even though there are few methods that take into consideration the presence of cross sectional dependence in the panel, the methods may provide inconsistent parameter estimates and inferences when outliers occur in the panel. As such, an alternative method that is robust to outliers and cross sectional dependencemore » is introduced in this paper. The properties and construction of the confidence interval for the parameter estimates are also considered in this paper. The robustness of the procedure is investigated and comparisons are made to the existing method via simulation studies. Our results have shown that robust approach is able to produce an accurate and reliable parameter estimates under the condition considered.« less

Certified Normal: Alzheimer’s Disease Biomarkers and Normative Estimates of Cognitive Functioning

PubMed Central

Hassenstab, Jason; Chasse, Rachel; Grabow, Perri; Benzinger, Tammie L.S.; Fagan, Anne M.; Xiong, Chengjie; Jasielec, Mateusz; Grant, Elizabeth; Morris, John C.

2016-01-01

Normative samples drawn from older populations may unintentionally include individuals with preclinical Alzheimer’s disease (AD) pathology, resulting in reduced means, increased variability, and overestimation of age-effects on cognitive performance. 264 cognitively normal (CDR=0) older adults were classified as biomarker-negative (“Robust Normal,” n=177) or biomarker-positive (“Preclinical Alzheimer’s Disease” (PCAD), n=87) based on amyloid imaging, cerebrospinal fluid biomarkers, and hippocampal volumes. PCAD participants performed worse than Robust Normals on nearly all cognitive measures. Removing PCAD participants from the normative sample yielded higher means and less variability on episodic memory, visuospatial ability, and executive functioning measures. These results were more pronounced in participants aged 75 and older. Notably, removing PCAD participants from the sample significantly reduced age effects across all cognitive domains. Applying norms from the Robust Normal sample to a separate cohort did not improve CDR classification when using standard deviation cutoff scores. Overall, removing individuals with biomarker evidence of preclinical AD improves normative sample quality and substantially reduces age-effects on cognitive performance, but provides no substantive benefit for diagnostic classifications. PMID:27255812

Robust Arm and Hand Tracking by Unsupervised Context Learning

PubMed Central

Spruyt, Vincent; Ledda, Alessandro; Philips, Wilfried

2014-01-01

Hand tracking in video is an increasingly popular research field due to the rise of novel human-computer interaction methods. However, robust and real-time hand tracking in unconstrained environments remains a challenging task due to the high number of degrees of freedom and the non-rigid character of the human hand. In this paper, we propose an unsupervised method to automatically learn the context in which a hand is embedded. This context includes the arm and any other object that coherently moves along with the hand. We introduce two novel methods to incorporate this context information into a probabilistic tracking framework, and introduce a simple yet effective solution to estimate the position of the arm. Finally, we show that our method greatly increases robustness against occlusion and cluttered background, without degrading tracking performance if no contextual information is available. The proposed real-time algorithm is shown to outperform the current state-of-the-art by evaluating it on three publicly available video datasets. Furthermore, a novel dataset is created and made publicly available for the research community. PMID:25004155

Automatic Detection of Seismocardiogram Sensor Misplacement for Robust Pre-Ejection Period Estimation in Unsupervised Settings.

PubMed

Ashouri, Hazar; Inan, Omer T

2017-06-15

Seismocardiography (SCG), the measurement of the local chest vibrations due to the movements of blood and the heart, is a non-invasive technique for assessing myocardial contractility via the pre-ejection period (PEP). Recently, SCG-based extraction of PEP has been shown to be an effective means of classifying decompensated from compensated heart failure patients, and thus can be potentially used for monitoring such patients at home. Accurate extraction of PEP from SCG signals hinges on lab-based population data (i.e., regression curves) linking particular time-domain features of the SCG signal to corresponding features from reference standard bulky instruments such as impedance cardiography (ICG). Such regression curves, in the case of SCG, have always been estimated based on the "ideal" positioning of the SCG sensor on the chest. However, in settings such as the home where users may position the SCG measurement hardware on the chest without supervision, it is likely that the sensor will not always be placed exactly on this "ideal" location on the sternum, but rather on other positions on the chest as well. In this study, we show for the first time that the regression curve for estimating PEP from SCG signals differs significantly as the position of the sensor changes. We further devise a method to automatically detect when the sensor is placed in any position other than the desired one in order to avoid inaccurate systolic time interval estimation. Our classification algorithm for this purpose resulted in 0.83 precision and 0.82 recall when classifying whether the sensor is placed in the desired position or not. The classifier was tested with heartbeats taken both at rest, and also during exercise recovery to ensure that waveform changes due to positioning could be accurately discriminated from those due to physiological effects.

Estimation of optimal pivot point for remote center of motion alignment in surgery.

PubMed

Rosa, Benoît; Gruijthuijsen, Caspar; Van Cleynenbreugel, Ben; Sloten, Jos Vander; Reynaerts, Dominiek; Poorten, Emmanuel Vander

2015-02-01

The determination of an optimal pivot point ([Formula: see text]) is important for instrument manipulation in minimally invasive surgery. Such knowledge is of particular importance for robotic-assisted surgery where robots need to rotate precisely around a specific point in space in order to minimize trauma to the body wall while maintaining position control. Remote center of motion (RCM) mechanisms are commonly used, where the RCM point is manually and visually aligned. If not positioned appropriately, this misalignment might lead to intolerably high forces on the body wall with increased risk of postoperative complications or instrument damage. An automated method to align the RCM with the [Formula: see text] was developed and tested. Computer vision and a lightweight calibration procedure are used to estimate the optimal pivot point. One or two pre-calibrated cameras viewing the surgical scene are employed. The surgeon is asked to make short pivoting movements, applying as little torque as possible, with an instrument of choice passing through the insertion point while camera images are being recorded. The physical properties of an instrument rotating around a pivot point are exploited in a random sample consensus scheme to robustly estimate the ideal position of the RCM in the image planes. Triangulation is used to estimate the RCM position in 3D. Experiments were performed on a specially designed mockup to test the method. The position of the pivot point is estimated with an average error less than 1.85 mm using two webcams placed from approximately 30 cm to 1 m away from the scene. The entire procedure was completed in a few seconds. In automated method to estimate the ideal position of the RCM was shown to be reliable. The method can be implemented within a visual servoing approach to automatically place the RCM point, or the results can be displayed on a screen to provide guidance to the surgeon. Further work includes the development of an image-guided alignment method and validation with in vivo experiments.

Development and application of a modified dynamic time warping algorithm (DTW-S) to analyses of primate brain expression time series

PubMed Central

2011-01-01

Background Comparing biological time series data across different conditions, or different specimens, is a common but still challenging task. Algorithms aligning two time series represent a valuable tool for such comparisons. While many powerful computation tools for time series alignment have been developed, they do not provide significance estimates for time shift measurements. Results Here, we present an extended version of the original DTW algorithm that allows us to determine the significance of time shift estimates in time series alignments, the DTW-Significance (DTW-S) algorithm. The DTW-S combines important properties of the original algorithm and other published time series alignment tools: DTW-S calculates the optimal alignment for each time point of each gene, it uses interpolated time points for time shift estimation, and it does not require alignment of the time-series end points. As a new feature, we implement a simulation procedure based on parameters estimated from real time series data, on a series-by-series basis, allowing us to determine the false positive rate (FPR) and the significance of the estimated time shift values. We assess the performance of our method using simulation data and real expression time series from two published primate brain expression datasets. Our results show that this method can provide accurate and robust time shift estimates for each time point on a gene-by-gene basis. Using these estimates, we are able to uncover novel features of the biological processes underlying human brain development and maturation. Conclusions The DTW-S provides a convenient tool for calculating accurate and robust time shift estimates at each time point for each gene, based on time series data. The estimates can be used to uncover novel biological features of the system being studied. The DTW-S is freely available as an R package TimeShift at http://www.picb.ac.cn/Comparative/data.html. PMID:21851598

Development and application of a modified dynamic time warping algorithm (DTW-S) to analyses of primate brain expression time series.

PubMed

Yuan, Yuan; Chen, Yi-Ping Phoebe; Ni, Shengyu; Xu, Augix Guohua; Tang, Lin; Vingron, Martin; Somel, Mehmet; Khaitovich, Philipp

2011-08-18

Comparing biological time series data across different conditions, or different specimens, is a common but still challenging task. Algorithms aligning two time series represent a valuable tool for such comparisons. While many powerful computation tools for time series alignment have been developed, they do not provide significance estimates for time shift measurements. Here, we present an extended version of the original DTW algorithm that allows us to determine the significance of time shift estimates in time series alignments, the DTW-Significance (DTW-S) algorithm. The DTW-S combines important properties of the original algorithm and other published time series alignment tools: DTW-S calculates the optimal alignment for each time point of each gene, it uses interpolated time points for time shift estimation, and it does not require alignment of the time-series end points. As a new feature, we implement a simulation procedure based on parameters estimated from real time series data, on a series-by-series basis, allowing us to determine the false positive rate (FPR) and the significance of the estimated time shift values. We assess the performance of our method using simulation data and real expression time series from two published primate brain expression datasets. Our results show that this method can provide accurate and robust time shift estimates for each time point on a gene-by-gene basis. Using these estimates, we are able to uncover novel features of the biological processes underlying human brain development and maturation. The DTW-S provides a convenient tool for calculating accurate and robust time shift estimates at each time point for each gene, based on time series data. The estimates can be used to uncover novel biological features of the system being studied. The DTW-S is freely available as an R package TimeShift at http://www.picb.ac.cn/Comparative/data.html.

Traffic-related air pollution and obesity formation in children: a longitudinal, multilevel analysis.

PubMed

Jerrett, Michael; McConnell, Rob; Wolch, Jennifer; Chang, Roger; Lam, Claudia; Dunton, Genevieve; Gilliland, Frank; Lurmann, Fred; Islam, Talat; Berhane, Kiros

2014-06-09

Biologically plausible mechanisms link traffic-related air pollution to metabolic disorders and potentially to obesity. Here we sought to determine whether traffic density and traffic-related air pollution were positively associated with growth in body mass index (BMI = kg/m2) in children aged 5-11 years. Participants were drawn from a prospective cohort of children who lived in 13 communities across Southern California (N = 4550). Children were enrolled while attending kindergarten and first grade and followed for 4 years, with height and weight measured annually. Dispersion models were used to estimate exposure to traffic-related air pollution. Multilevel models were used to estimate and test traffic density and traffic pollution related to BMI growth. Data were collected between 2002-2010 and analyzed in 2011-12. Traffic pollution was positively associated with growth in BMI and was robust to adjustment for many confounders. The effect size in the adjusted model indicated about a 13.6% increase in annual BMI growth when comparing the lowest to the highest tenth percentile of air pollution exposure, which resulted in an increase of nearly 0.4 BMI units on attained BMI at age 10. Traffic density also had a positive association with BMI growth, but this effect was less robust in multivariate models. Traffic pollution was positively associated with growth in BMI in children aged 5-11 years. Traffic pollution may be controlled via emission restrictions; changes in land use that promote jobs-housing balance and use of public transit and hence reduce vehicle miles traveled; promotion of zero emissions vehicles; transit and car-sharing programs; or by limiting high pollution traffic, such as diesel trucks, from residential areas or places where children play outdoors, such as schools and parks. These measures may have beneficial effects in terms of reduced obesity formation in children.

Traffic-related air pollution and obesity formation in children: a longitudinal, multilevel analysis

PubMed Central

2014-01-01

Background Biologically plausible mechanisms link traffic-related air pollution to metabolic disorders and potentially to obesity. Here we sought to determine whether traffic density and traffic-related air pollution were positively associated with growth in body mass index (BMI = kg/m2) in children aged 5–11 years. Methods Participants were drawn from a prospective cohort of children who lived in 13 communities across Southern California (N = 4550). Children were enrolled while attending kindergarten and first grade and followed for 4 years, with height and weight measured annually. Dispersion models were used to estimate exposure to traffic-related air pollution. Multilevel models were used to estimate and test traffic density and traffic pollution related to BMI growth. Data were collected between 2002–2010 and analyzed in 2011–12. Results Traffic pollution was positively associated with growth in BMI and was robust to adjustment for many confounders. The effect size in the adjusted model indicated about a 13.6% increase in annual BMI growth when comparing the lowest to the highest tenth percentile of air pollution exposure, which resulted in an increase of nearly 0.4 BMI units on attained BMI at age 10. Traffic density also had a positive association with BMI growth, but this effect was less robust in multivariate models. Conclusions Traffic pollution was positively associated with growth in BMI in children aged 5–11 years. Traffic pollution may be controlled via emission restrictions; changes in land use that promote jobs-housing balance and use of public transit and hence reduce vehicle miles traveled; promotion of zero emissions vehicles; transit and car-sharing programs; or by limiting high pollution traffic, such as diesel trucks, from residential areas or places where children play outdoors, such as schools and parks. These measures may have beneficial effects in terms of reduced obesity formation in children. PMID:24913018

Spatial distribution, sampling precision and survey design optimisation with non-normal variables: The case of anchovy (Engraulis encrasicolus) recruitment in Spanish Mediterranean waters

NASA Astrophysics Data System (ADS)

Tugores, M. Pilar; Iglesias, Magdalena; Oñate, Dolores; Miquel, Joan

2016-02-01

In the Mediterranean Sea, the European anchovy (Engraulis encrasicolus) displays a key role in ecological and economical terms. Ensuring stock sustainability requires the provision of crucial information, such as species spatial distribution or unbiased abundance and precision estimates, so that management strategies can be defined (e.g. fishing quotas, temporal closure areas or marine protected areas MPA). Furthermore, the estimation of the precision of global abundance at different sampling intensities can be used for survey design optimisation. Geostatistics provide a priori unbiased estimations of the spatial structure, global abundance and precision for autocorrelated data. However, their application to non-Gaussian data introduces difficulties in the analysis in conjunction with low robustness or unbiasedness. The present study applied intrinsic geostatistics in two dimensions in order to (i) analyse the spatial distribution of anchovy in Spanish Western Mediterranean waters during the species' recruitment season, (ii) produce distribution maps, (iii) estimate global abundance and its precision, (iv) analyse the effect of changing the sampling intensity on the precision of global abundance estimates and, (v) evaluate the effects of several methodological options on the robustness of all the analysed parameters. The results suggested that while the spatial structure was usually non-robust to the tested methodological options when working with the original dataset, it became more robust for the transformed datasets (especially for the log-backtransformed dataset). The global abundance was always highly robust and the global precision was highly or moderately robust to most of the methodological options, except for data transformation.

Pairing field methods to improve inference in wildlife surveys while accommodating detection covariance.

PubMed

Clare, John; McKinney, Shawn T; DePue, John E; Loftin, Cynthia S

2017-10-01

It is common to use multiple field sampling methods when implementing wildlife surveys to compare method efficacy or cost efficiency, integrate distinct pieces of information provided by separate methods, or evaluate method-specific biases and misclassification error. Existing models that combine information from multiple field methods or sampling devices permit rigorous comparison of method-specific detection parameters, enable estimation of additional parameters such as false-positive detection probability, and improve occurrence or abundance estimates, but with the assumption that the separate sampling methods produce detections independently of one another. This assumption is tenuous if methods are paired or deployed in close proximity simultaneously, a common practice that reduces the additional effort required to implement multiple methods and reduces the risk that differences between method-specific detection parameters are confounded by other environmental factors. We develop occupancy and spatial capture-recapture models that permit covariance between the detections produced by different methods, use simulation to compare estimator performance of the new models to models assuming independence, and provide an empirical application based on American marten (Martes americana) surveys using paired remote cameras, hair catches, and snow tracking. Simulation results indicate existing models that assume that methods independently detect organisms produce biased parameter estimates and substantially understate estimate uncertainty when this assumption is violated, while our reformulated models are robust to either methodological independence or covariance. Empirical results suggested that remote cameras and snow tracking had comparable probability of detecting present martens, but that snow tracking also produced false-positive marten detections that could potentially substantially bias distribution estimates if not corrected for. Remote cameras detected marten individuals more readily than passive hair catches. Inability to photographically distinguish individual sex did not appear to induce negative bias in camera density estimates; instead, hair catches appeared to produce detection competition between individuals that may have been a source of negative bias. Our model reformulations broaden the range of circumstances in which analyses incorporating multiple sources of information can be robustly used, and our empirical results demonstrate that using multiple field-methods can enhance inferences regarding ecological parameters of interest and improve understanding of how reliably survey methods sample these parameters. © 2017 by the Ecological Society of America.

Robust detection, isolation and accommodation for sensor failures

NASA Technical Reports Server (NTRS)

Emami-Naeini, A.; Akhter, M. M.; Rock, S. M.

1986-01-01

The objective is to extend the recent advances in robust control system design of multivariable systems to sensor failure detection, isolation, and accommodation (DIA), and estimator design. This effort provides analysis tools to quantify the trade-off between performance robustness and DIA sensitivity, which are to be used to achieve higher levels of performance robustness for given levels of DIA sensitivity. An innovations-based DIA scheme is used. Estimators, which depend upon a model of the process and process inputs and outputs, are used to generate these innovations. Thresholds used to determine failure detection are computed based on bounds on modeling errors, noise properties, and the class of failures. The applicability of the newly developed tools are demonstrated on a multivariable aircraft turbojet engine example. A new concept call the threshold selector was developed. It represents a significant and innovative tool for the analysis and synthesis of DiA algorithms. The estimators were made robust by introduction of an internal model and by frequency shaping. The internal mode provides asymptotically unbiased filter estimates.The incorporation of frequency shaping of the Linear Quadratic Gaussian cost functional modifies the estimator design to make it suitable for sensor failure DIA. The results are compared with previous studies which used thresholds that were selcted empirically. Comparison of these two techniques on a nonlinear dynamic engine simulation shows improved performance of the new method compared to previous techniques

A Robust Step Detection Algorithm and Walking Distance Estimation Based on Daily Wrist Activity Recognition Using a Smart Band.

PubMed

Trong Bui, Duong; Nguyen, Nhan Duc; Jeong, Gu-Min

2018-06-25

Human activity recognition and pedestrian dead reckoning are an interesting field because of their importance utilities in daily life healthcare. Currently, these fields are facing many challenges, one of which is the lack of a robust algorithm with high performance. This paper proposes a new method to implement a robust step detection and adaptive distance estimation algorithm based on the classification of five daily wrist activities during walking at various speeds using a smart band. The key idea is that the non-parametric adaptive distance estimator is performed after two activity classifiers and a robust step detector. In this study, two classifiers perform two phases of recognizing five wrist activities during walking. Then, a robust step detection algorithm, which is integrated with an adaptive threshold, peak and valley correction algorithm, is applied to the classified activities to detect the walking steps. In addition, the misclassification activities are fed back to the previous layer. Finally, three adaptive distance estimators, which are based on a non-parametric model of the average walking speed, calculate the length of each strike. The experimental results show that the average classification accuracy is about 99%, and the accuracy of the step detection is 98.7%. The error of the estimated distance is 2.2⁻4.2% depending on the type of wrist activities.

Adaptive nonsingular fast terminal sliding-mode control for the tracking problem of uncertain dynamical systems.

PubMed

Boukattaya, Mohamed; Mezghani, Neila; Damak, Tarak

2018-06-01

In this paper, robust and adaptive nonsingular fast terminal sliding-mode (NFTSM) control schemes for the trajectory tracking problem are proposed with known or unknown upper bound of the system uncertainty and external disturbances. The developed controllers take the advantage of the NFTSM theory to ensure fast convergence rate, singularity avoidance, and robustness against uncertainties and external disturbances. First, a robust NFTSM controller is proposed which guarantees that sliding surface and equilibrium point can be reached in a short finite-time from any initial state. Then, in order to cope with the unknown upper bound of the system uncertainty which may be occurring in practical applications, a new adaptive NFTSM algorithm is developed. One feature of the proposed control law is their adaptation techniques where the prior knowledge of parameters uncertainty and disturbances is not needed. However, the adaptive tuning law can estimate the upper bound of these uncertainties using only position and velocity measurements. Moreover, the proposed controller eliminates the chattering effect without losing the robustness property and the precision. Stability analysis is performed using the Lyapunov stability theory, and simulation studies are conducted to verify the effectiveness of the developed control schemes. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Robust and fast pedestrian detection method for far-infrared automotive driving assistance systems

NASA Astrophysics Data System (ADS)

Liu, Qiong; Zhuang, Jiajun; Ma, Jun

2013-09-01

Despite considerable effort has been contributed to night-time pedestrian detection for automotive driving assistance systems recent years, robust and real-time pedestrian detection is by no means a trivial task and is still underway due to the moving cameras, uncontrolled outdoor environments, wide range of possible pedestrian presentations and the stringent performance criteria for automotive applications. This paper presents an alternative night-time pedestrian detection method using monocular far-infrared (FIR) camera, which includes two modules (regions of interest (ROIs) generation and pedestrian recognition) in a cascade fashion. Pixel-gradient oriented vertical projection is first proposed to estimate the vertical image stripes that might contain pedestrians, and then local thresholding image segmentation is adopted to generate ROIs more accurately within the estimated vertical stripes. A novel descriptor called PEWHOG (pyramid entropy weighted histograms of oriented gradients) is proposed to represent FIR pedestrians in recognition module. Specifically, PEWHOG is used to capture both the local object shape described by the entropy weighted distribution of oriented gradient histograms and its pyramid spatial layout. Then PEWHOG is fed to a three-branch structured classifier using support vector machines (SVM) with histogram intersection kernel (HIK). An off-line training procedure combining both the bootstrapping and early-stopping strategy is introduced to generate a more robust classifier by exploiting hard negative samples iteratively. Finally, multi-frame validation is utilized to suppress some transient false positives. Experimental results on FIR video sequences from various scenarios demonstrate that the presented method is effective and promising.

Single-camera visual odometry to track a surgical X-ray C-arm base.

PubMed

Esfandiari, Hooman; Lichti, Derek; Anglin, Carolyn

2017-12-01

This study provides a framework for a single-camera odometry system for localizing a surgical C-arm base. An application-specific monocular visual odometry system (a downward-looking consumer-grade camera rigidly attached to the C-arm base) is proposed in this research. The cumulative dead-reckoning estimation of the base is extracted based on frame-to-frame homography estimation. Optical-flow results are utilized to feed the odometry. Online positional and orientation parameters are then reported. Positional accuracy of better than 2% (of the total traveled distance) for most of the cases and 4% for all the cases studied and angular accuracy of better than 2% (of absolute cumulative changes in orientation) were achieved with this method. This study provides a robust and accurate tracking framework that not only can be integrated with the current C-arm joint-tracking system (i.e. TC-arm) but also is capable of being employed for similar applications in other fields (e.g. robotics).

Robust detection of heart beats in multimodal records using slope- and peak-sensitive band-pass filters.

PubMed

Pangerc, Urška; Jager, Franc

2015-08-01

In this work, we present the development, architecture and evaluation of a new and robust heart beat detector in multimodal records. The detector uses electrocardiogram (ECG) signals, and/or pulsatile (P) signals, such as: blood pressure, artery blood pressure and pulmonary artery pressure, if present. The base approach behind the architecture of the detector is collecting signal energy (differentiating and low-pass filtering, squaring, integrating). To calculate the detection and noise functions, simple and fast slope- and peak-sensitive band-pass digital filters were designed. By using morphological smoothing, the detection functions were further improved and noise intervals were estimated. The detector looks for possible pacemaker heart rate patterns and repairs the ECG signals and detection functions. Heart beats are detected in each of the ECG and P signals in two steps: a repetitive learning phase and a follow-up detecting phase. The detected heart beat positions from the ECG signals are merged into a single stream of detected ECG heart beat positions. The merged ECG heart beat positions and detected heart beat positions from the P signals are verified for their regularity regarding the expected heart rate. The detected heart beat positions of a P signal with the best match to the merged ECG heart beat positions are selected for mapping into the noise and no-signal intervals of the record. The overall evaluation scores in terms of average sensitivity and positive predictive values obtained on databases that are freely available on the Physionet website were as follows: the MIT-BIH Arrhythmia database (99.91%), the MGH/MF Waveform database (95.14%), the augmented training set of the follow-up phase of the PhysioNet/Computing in Cardiology Challenge 2014 (97.67%), and the Challenge test set (93.64%).

Statistical Sensor Fusion of a 9-DOF Mems Imu for Indoor Navigation

NASA Astrophysics Data System (ADS)

Chow, J. C. K.

2017-09-01

Sensor fusion of a MEMS IMU with a magnetometer is a popular system design, because such 9-DoF (degrees of freedom) systems are capable of achieving drift-free 3D orientation tracking. However, these systems are often vulnerable to ambient magnetic distortions and lack useful position information; in the absence of external position aiding (e.g. satellite/ultra-wideband positioning systems) the dead-reckoned position accuracy from a 9-DoF MEMS IMU deteriorates rapidly due to unmodelled errors. Positioning information is valuable in many satellite-denied geomatics applications (e.g. indoor navigation, location-based services, etc.). This paper proposes an improved 9-DoF IMU indoor pose tracking method using batch optimization. By adopting a robust in-situ user self-calibration approach to model the systematic errors of the accelerometer, gyroscope, and magnetometer simultaneously in a tightly-coupled post-processed least-squares framework, the accuracy of the estimated trajectory from a 9-DoF MEMS IMU can be improved. Through a combination of relative magnetic measurement updates and a robust weight function, the method is able to tolerate a high level of magnetic distortions. The proposed auto-calibration method was tested in-use under various heterogeneous magnetic field conditions to mimic a person walking with the sensor in their pocket, a person checking their phone, and a person walking with a smartwatch. In these experiments, the presented algorithm improved the in-situ dead-reckoning orientation accuracy by 79.8-89.5 % and the dead-reckoned positioning accuracy by 72.9-92.8 %, thus reducing the relative positioning error from metre-level to decimetre-level after ten seconds of integration, without making assumptions about the user's dynamics.

On the robustness of a Bayes estimate. [in reliability theory

NASA Technical Reports Server (NTRS)

Canavos, G. C.

1974-01-01

This paper examines the robustness of a Bayes estimator with respect to the assigned prior distribution. A Bayesian analysis for a stochastic scale parameter of a Weibull failure model is summarized in which the natural conjugate is assigned as the prior distribution of the random parameter. The sensitivity analysis is carried out by the Monte Carlo method in which, although an inverted gamma is the assigned prior, realizations are generated using distribution functions of varying shape. For several distributional forms and even for some fixed values of the parameter, simulated mean squared errors of Bayes and minimum variance unbiased estimators are determined and compared. Results indicate that the Bayes estimator remains squared-error superior and appears to be largely robust to the form of the assigned prior distribution.

A robust and accurate center-frequency estimation (RACE) algorithm for improving motion estimation performance of SinMod on tagged cardiac MR images without known tagging parameters.

PubMed

Liu, Hong; Wang, Jie; Xu, Xiangyang; Song, Enmin; Wang, Qian; Jin, Renchao; Hung, Chih-Cheng; Fei, Baowei

2014-11-01

A robust and accurate center-frequency (CF) estimation (RACE) algorithm for improving the performance of the local sine-wave modeling (SinMod) method, which is a good motion estimation method for tagged cardiac magnetic resonance (MR) images, is proposed in this study. The RACE algorithm can automatically, effectively and efficiently produce a very appropriate CF estimate for the SinMod method, under the circumstance that the specified tagging parameters are unknown, on account of the following two key techniques: (1) the well-known mean-shift algorithm, which can provide accurate and rapid CF estimation; and (2) an original two-direction-combination strategy, which can further enhance the accuracy and robustness of CF estimation. Some other available CF estimation algorithms are brought out for comparison. Several validation approaches that can work on the real data without ground truths are specially designed. Experimental results on human body in vivo cardiac data demonstrate the significance of accurate CF estimation for SinMod, and validate the effectiveness of RACE in facilitating the motion estimation performance of SinMod. Copyright © 2014 Elsevier Inc. All rights reserved.

Robust Spectral Unmixing of Sparse Multispectral Lidar Waveforms using Gamma Markov Random Fields

DOE PAGES

Altmann, Yoann; Maccarone, Aurora; McCarthy, Aongus; ...

2017-05-10

Here, this paper presents a new Bayesian spectral un-mixing algorithm to analyse remote scenes sensed via sparse multispectral Lidar measurements. To a first approximation, in the presence of a target, each Lidar waveform consists of a main peak, whose position depends on the target distance and whose amplitude depends on the wavelength of the laser source considered (i.e, on the target reflectivity). Besides, these temporal responses are usually assumed to be corrupted by Poisson noise in the low photon count regime. When considering multiple wavelengths, it becomes possible to use spectral information in order to identify and quantify the mainmore » materials in the scene, in addition to estimation of the Lidar-based range profiles. Due to its anomaly detection capability, the proposed hierarchical Bayesian model, coupled with an efficient Markov chain Monte Carlo algorithm, allows robust estimation of depth images together with abundance and outlier maps associated with the observed 3D scene. The proposed methodology is illustrated via experiments conducted with real multispectral Lidar data acquired in a controlled environment. The results demonstrate the possibility to unmix spectral responses constructed from extremely sparse photon counts (less than 10 photons per pixel and band).« less

Enhancing Data Assimilation by Evolutionary Particle Filter and Markov Chain Monte Carlo

NASA Astrophysics Data System (ADS)

Moradkhani, H.; Abbaszadeh, P.; Yan, H.

2016-12-01

Particle Filters (PFs) have received increasing attention by the researchers from different disciplines in hydro-geosciences as an effective method to improve model predictions in nonlinear and non-Gaussian dynamical systems. The implication of dual state and parameter estimation by means of data assimilation in hydrology and geoscience has evolved since 2005 from SIR-PF to PF-MCMC and now to the most effective and robust framework through evolutionary PF approach based on Genetic Algorithm (GA) and Markov Chain Monte Carlo (MCMC), the so-called EPF-MCMC. In this framework, the posterior distribution undergoes an evolutionary process to update an ensemble of prior states that more closely resemble realistic posterior probability distribution. The premise of this approach is that the particles move to optimal position using the GA optimization coupled with MCMC increasing the number of effective particles, hence the particle degeneracy is avoided while the particle diversity is improved. The proposed algorithm is applied on a conceptual and highly nonlinear hydrologic model and the effectiveness, robustness and reliability of the method in jointly estimating the states and parameters and also reducing the uncertainty is demonstrated for few river basins across the United States.

Mapping Quantitative Traits in Unselected Families: Algorithms and Examples

PubMed Central

Dupuis, Josée; Shi, Jianxin; Manning, Alisa K.; Benjamin, Emelia J.; Meigs, James B.; Cupples, L. Adrienne; Siegmund, David

2009-01-01

Linkage analysis has been widely used to identify from family data genetic variants influencing quantitative traits. Common approaches have both strengths and limitations. Likelihood ratio tests typically computed in variance component analysis can accommodate large families but are highly sensitive to departure from normality assumptions. Regression-based approaches are more robust but their use has primarily been restricted to nuclear families. In this paper, we develop methods for mapping quantitative traits in moderately large pedigrees. Our methods are based on the score statistic which in contrast to the likelihood ratio statistic, can use nonparametric estimators of variability to achieve robustness of the false positive rate against departures from the hypothesized phenotypic model. Because the score statistic is easier to calculate than the likelihood ratio statistic, our basic mapping methods utilize relatively simple computer code that performs statistical analysis on output from any program that computes estimates of identity-by-descent. This simplicity also permits development and evaluation of methods to deal with multivariate and ordinal phenotypes, and with gene-gene and gene-environment interaction. We demonstrate our methods on simulated data and on fasting insulin, a quantitative trait measured in the Framingham Heart Study. PMID:19278016

Optimization-Based Sensor Fusion of GNSS and IMU Using a Moving Horizon Approach

PubMed Central

Girrbach, Fabian; Hol, Jeroen D.; Bellusci, Giovanni; Diehl, Moritz

2017-01-01

The rise of autonomous systems operating close to humans imposes new challenges in terms of robustness and precision on the estimation and control algorithms. Approaches based on nonlinear optimization, such as moving horizon estimation, have been shown to improve the accuracy of the estimated solution compared to traditional filter techniques. This paper introduces an optimization-based framework for multi-sensor fusion following a moving horizon scheme. The framework is applied to the often occurring estimation problem of motion tracking by fusing measurements of a global navigation satellite system receiver and an inertial measurement unit. The resulting algorithm is used to estimate position, velocity, and orientation of a maneuvering airplane and is evaluated against an accurate reference trajectory. A detailed study of the influence of the horizon length on the quality of the solution is presented and evaluated against filter-like and batch solutions of the problem. The versatile configuration possibilities of the framework are finally used to analyze the estimated solutions at different evaluation times exposing a nearly linear behavior of the sensor fusion problem. PMID:28534857

Optimization-Based Sensor Fusion of GNSS and IMU Using a Moving Horizon Approach.

PubMed

Girrbach, Fabian; Hol, Jeroen D; Bellusci, Giovanni; Diehl, Moritz

2017-05-19

The rise of autonomous systems operating close to humans imposes new challenges in terms of robustness and precision on the estimation and control algorithms. Approaches based on nonlinear optimization, such as moving horizon estimation, have been shown to improve the accuracy of the estimated solution compared to traditional filter techniques. This paper introduces an optimization-based framework for multi-sensor fusion following a moving horizon scheme. The framework is applied to the often occurring estimation problem of motion tracking by fusing measurements of a global navigation satellite system receiver and an inertial measurement unit. The resulting algorithm is used to estimate position, velocity, and orientation of a maneuvering airplane and is evaluated against an accurate reference trajectory. A detailed study of the influence of the horizon length on the quality of the solution is presented and evaluated against filter-like and batch solutions of the problem. The versatile configuration possibilities of the framework are finally used to analyze the estimated solutions at different evaluation times exposing a nearly linear behavior of the sensor fusion problem.

The 'robust' capture-recapture design allows components of recruitment to be estimated

USGS Publications Warehouse

Pollock, K.H.; Kendall, W.L.; Nichols, J.D.; Lebreton, J.-D.; North, P.M.

1993-01-01

The 'robust' capture-recapture design (Pollock 1982) allows analyses which combine features of closed population model analyses (Otis et aI., 1978, White et aI., 1982) and open population model analyses (Pollock et aI., 1990). Estimators obtained under these analyses are more robust to unequal catch ability than traditional Jolly-Seber estimators (Pollock, 1982; Pollock et al., 1990; Kendall, 1992). The robust design also allows estimation of parameters for population size, survival rate and recruitment numbers for all periods of the study unlike under Jolly-Seber type models. The major advantage of this design that we emphasize in this short review paper is that it allows separate estimation of immigration and in situ recruitment numbers for a two or more age class model (Nichols and Pollock, 1990). This is contrasted with the age-dependent Jolly-Seber model (Pollock, 1981; Stokes, 1984; Pollock et L, 1990) which provides separate estimates for immigration and in situ recruitment for all but the first two age classes where there is at least a three age class model. The ability to achieve this separation of recruitment components can be very important to population modelers and wildlife managers as many species can only be separated into two easily identified age classes in the field.

Estimating the outcome of a pregnancy test: women's judgements in foresight and hindsight.

PubMed

Pennington, D C; Rutter, D R; McKenna, K; Morley, I E

1980-11-01

Previous research on judgement under uncertainty has suggested that, when we know the outcome of some event, we perceive that outcome as more likely than when we do not have outcome knowledge. That is, in comparison with judgements made in foresight, judgements made in hindsight are biased in the direction of the outcome the judge believes to have happened. While the effect appears to be robust in the laboratory, it has very seldom been tested in real life. This experiment therefore went outside the laboratory, and examined women's estimates of the outcome of a pregnacy test. It was predicted that those who knew the result of their test (hindsight) would perceive that outcome as more likely than those asked to make the estimate before they knew the result (foresight). The prediction was supported only for women whose result was positive and, furthermore, the positive group made consistently higher estimates than the negative group, both in hindsight and foresight. The findings were therefore less marked and more complex than in previous laboratory research, and support the argument that experiments and materials must be constructed with salience for the subjects. The findings are interpreted in the light ot Tversky & Kahneman's (1974) work on heuristic rules of thinking.

A random sampling approach for robust estimation of tissue-to-plasma ratio from extremely sparse data.

PubMed

Chu, Hui-May; Ette, Ene I

2005-09-02

his study was performed to develop a new nonparametric approach for the estimation of robust tissue-to-plasma ratio from extremely sparsely sampled paired data (ie, one sample each from plasma and tissue per subject). Tissue-to-plasma ratio was estimated from paired/unpaired experimental data using independent time points approach, area under the curve (AUC) values calculated with the naïve data averaging approach, and AUC values calculated using sampling based approaches (eg, the pseudoprofile-based bootstrap [PpbB] approach and the random sampling approach [our proposed approach]). The random sampling approach involves the use of a 2-phase algorithm. The convergence of the sampling/resampling approaches was investigated, as well as the robustness of the estimates produced by different approaches. To evaluate the latter, new data sets were generated by introducing outlier(s) into the real data set. One to 2 concentration values were inflated by 10% to 40% from their original values to produce the outliers. Tissue-to-plasma ratios computed using the independent time points approach varied between 0 and 50 across time points. The ratio obtained from AUC values acquired using the naive data averaging approach was not associated with any measure of uncertainty or variability. Calculating the ratio without regard to pairing yielded poorer estimates. The random sampling and pseudoprofile-based bootstrap approaches yielded tissue-to-plasma ratios with uncertainty and variability. However, the random sampling approach, because of the 2-phase nature of its algorithm, yielded more robust estimates and required fewer replications. Therefore, a 2-phase random sampling approach is proposed for the robust estimation of tissue-to-plasma ratio from extremely sparsely sampled data.

Genetic Introgression and the Survival of Florida Panther Kittens

PubMed Central

Hostetler, Jeffrey A.; Onorato, David P.; Nichols, James D.; Johnson, Warren E.; Roelke, Melody E.; O’Brien, Stephen J.; Jansen, Deborah; Oli, Madan K.

2010-01-01

Estimates of survival for the young of a species are critical for population models. These models can often be improved by determining the effects of management actions and population abundance on this demographic parameter. We used multiple sources of data collected during 1982-2008 and a live recapture-dead recovery modeling framework to estimate and model survival of Florida panther (Puma concolor coryi) kittens (age 0 – 1 year). Overall, annual survival of Florida panther kittens was 0.323 ± 0.071 (SE), which was lower than estimates used in previous population models. In 1995, female pumas from Texas (P. c. stanleyana) were released into occupied panther range as part of an intentional introgression program to restore genetic variability. We found that kitten survival generally increased with degree of admixture: F1 admixed and backcrossed to Texas kittens survived better than canonical Florida panther and backcrossed to canonical kittens. Average heterozygosity positively influenced kitten and older panther survival, whereas index of panther abundance negatively influenced kitten survival. Our results provide strong evidence for the positive population-level impact of genetic introgression on Florida panthers. Our approach to integrate data from multiple sources was effective at improving robustness as well as precision of estimates of Florida panther kitten survival, and can be useful in estimating vital rates for other elusive species with sparse data. PMID:21113436

Genetic introgression and the survival of Florida panther kittens

USGS Publications Warehouse

Hostetler, Jeffrey A.; Onorato, David P.; Nichols, James D.; Johnson, Warren E.; Roelke, Melody E.; O'Brien, Stephen J.; Jansen, Deborah; Oli, Madan K.

2010-01-01

Estimates of survival for the young of a species are critical for population models. These models can often be improved by determining the effects of management actions and population abundance on this demographic parameter. We used multiple sources of data collected during 1982–2008 and a live-recapture dead-recovery modeling framework to estimate and model survival of Florida panther (Puma concolor coryi) kittens (age 0–1 year). Overall, annual survival of Florida panther kittens was 0.323 ± 0.071 (SE), which was lower than estimates used in previous population models. In 1995, female pumas from Texas (P. c. stanleyana) were released into occupied panther range as part of an intentional introgression program to restore genetic variability. We found that kitten survival generally increased with degree of admixture: F1 admixed and backcrossed to Texas kittens survived better than canonical Florida panther and backcrossed to canonical kittens. Average heterozygosity positively influenced kitten and older panther survival, whereas index of panther abundance negatively influenced kitten survival. Our results provide strong evidence for the positive population-level impact of genetic introgression on Florida panthers. Our approach to integrate data from multiple sources was effective at improving robustness as well as precision of estimates of Florida panther kitten survival, and can be useful in estimating vital rates for other elusive species with sparse data.

Correlation dimension and phase space contraction via extreme value theory

NASA Astrophysics Data System (ADS)

Faranda, Davide; Vaienti, Sandro

2018-04-01

We show how to obtain theoretical and numerical estimates of correlation dimension and phase space contraction by using the extreme value theory. The maxima of suitable observables sampled along the trajectory of a chaotic dynamical system converge asymptotically to classical extreme value laws where: (i) the inverse of the scale parameter gives the correlation dimension and (ii) the extremal index is associated with the rate of phase space contraction for backward iteration, which in dimension 1 and 2, is closely related to the positive Lyapunov exponent and in higher dimensions is related to the metric entropy. We call it the Dynamical Extremal Index. Numerical estimates are straightforward to obtain as they imply just a simple fit to a univariate distribution. Numerical tests range from low dimensional maps, to generalized Henon maps and climate data. The estimates of the indicators are particularly robust even with relatively short time series.

A long-term target detection approach in infrared image sequence

NASA Astrophysics Data System (ADS)

Li, Hang; Zhang, Qi; Wang, Xin; Hu, Chao

2016-10-01

An automatic target detection method used in long term infrared (IR) image sequence from a moving platform is proposed. Firstly, based on POME(the principle of maximum entropy), target candidates are iteratively segmented. Then the real target is captured via two different selection approaches. At the beginning of image sequence, the genuine target with litter texture is discriminated from other candidates by using contrast-based confidence measure. On the other hand, when the target becomes larger, we apply online EM method to estimate and update the distributions of target's size and position based on the prior detection results, and then recognize the genuine one which satisfies both the constraints of size and position. Experimental results demonstrate that the presented method is accurate, robust and efficient.

Position and Orientation Tracking in a Ubiquitous Monitoring System for Parkinson Disease Patients With Freezing of Gait Symptom

PubMed Central

Català, Andreu; Rodríguez Martín, Daniel; van der Aa, Nico; Chen, Wei; Rauterberg, Matthias

2013-01-01

Background Freezing of gait (FoG) is one of the most disturbing and least understood symptoms in Parkinson disease (PD). Although the majority of existing assistive systems assume accurate detections of FoG episodes, the detection itself is still an open problem. The specificity of FoG is its dependency on the context of a patient, such as the current location or activity. Knowing the patient's context might improve FoG detection. One of the main technical challenges that needs to be solved in order to start using contextual information for FoG detection is accurate estimation of the patient's position and orientation toward key elements of his or her indoor environment. Objective The objectives of this paper are to (1) present the concept of the monitoring system, based on wearable and ambient sensors, which is designed to detect FoG using the spatial context of the user, (2) establish a set of requirements for the application of position and orientation tracking in FoG detection, (3) evaluate the accuracy of the position estimation for the tracking system, and (4) evaluate two different methods for human orientation estimation. Methods We developed a prototype system to localize humans and track their orientation, as an important prerequisite for a context-based FoG monitoring system. To setup the system for experiments with real PD patients, the accuracy of the position and orientation tracking was assessed under laboratory conditions in 12 participants. To collect the data, the participants were asked to wear a smartphone, with and without known orientation around the waist, while walking over a predefined path in the marked area captured by two Kinect cameras with non-overlapping fields of view. Results We used the root mean square error (RMSE) as the main performance measure. The vision based position tracking algorithm achieved RMSE = 0.16 m in position estimation for upright standing people. The experimental results for the proposed human orientation estimation methods demonstrated the adaptivity and robustness to changes in the smartphone attachment position, when the fusion of both vision and inertial information was used. Conclusions The system achieves satisfactory accuracy on indoor position tracking for the use in the FoG detection application with spatial context. The combination of inertial and vision information has the potential for correct patient heading estimation even when the inertial wearable sensor device is put into an a priori unknown position. PMID:25098265

Position and orientation tracking in a ubiquitous monitoring system for Parkinson disease patients with freezing of gait symptom.

PubMed

Takač, Boris; Català, Andreu; Rodríguez Martín, Daniel; van der Aa, Nico; Chen, Wei; Rauterberg, Matthias

2013-07-15

Freezing of gait (FoG) is one of the most disturbing and least understood symptoms in Parkinson disease (PD). Although the majority of existing assistive systems assume accurate detections of FoG episodes, the detection itself is still an open problem. The specificity of FoG is its dependency on the context of a patient, such as the current location or activity. Knowing the patient's context might improve FoG detection. One of the main technical challenges that needs to be solved in order to start using contextual information for FoG detection is accurate estimation of the patient's position and orientation toward key elements of his or her indoor environment. The objectives of this paper are to (1) present the concept of the monitoring system, based on wearable and ambient sensors, which is designed to detect FoG using the spatial context of the user, (2) establish a set of requirements for the application of position and orientation tracking in FoG detection, (3) evaluate the accuracy of the position estimation for the tracking system, and (4) evaluate two different methods for human orientation estimation. We developed a prototype system to localize humans and track their orientation, as an important prerequisite for a context-based FoG monitoring system. To setup the system for experiments with real PD patients, the accuracy of the position and orientation tracking was assessed under laboratory conditions in 12 participants. To collect the data, the participants were asked to wear a smartphone, with and without known orientation around the waist, while walking over a predefined path in the marked area captured by two Kinect cameras with non-overlapping fields of view. We used the root mean square error (RMSE) as the main performance measure. The vision based position tracking algorithm achieved RMSE = 0.16 m in position estimation for upright standing people. The experimental results for the proposed human orientation estimation methods demonstrated the adaptivity and robustness to changes in the smartphone attachment position, when the fusion of both vision and inertial information was used. The system achieves satisfactory accuracy on indoor position tracking for the use in the FoG detection application with spatial context. The combination of inertial and vision information has the potential for correct patient heading estimation even when the inertial wearable sensor device is put into an a priori unknown position.

A near-optimal low complexity sensor fusion technique for accurate indoor localization based on ultrasound time of arrival measurements from low-quality sensors

NASA Astrophysics Data System (ADS)

Mitilineos, Stelios A.; Argyreas, Nick D.; Thomopoulos, Stelios C. A.

2009-05-01

A fusion-based localization technique for location-based services in indoor environments is introduced herein, based on ultrasound time-of-arrival measurements from multiple off-the-shelf range estimating sensors which are used in a market-available localization system. In-situ field measurements results indicated that the respective off-the-shelf system was unable to estimate position in most of the cases, while the underlying sensors are of low-quality and yield highly inaccurate range and position estimates. An extensive analysis is performed and a model of the sensor-performance characteristics is established. A low-complexity but accurate sensor fusion and localization technique is then developed, which consists inof evaluating multiple sensor measurements and selecting the one that is considered most-accurate based on the underlying sensor model. Optimality, in the sense of a genie selecting the optimum sensor, is subsequently evaluated and compared to the proposed technique. The experimental results indicate that the proposed fusion method exhibits near-optimal performance and, albeit being theoretically suboptimal, it largely overcomes most flaws of the underlying single-sensor system resulting in a localization system of increased accuracy, robustness and availability.

Predicting the evolution of complex networks via similarity dynamics

NASA Astrophysics Data System (ADS)

Wu, Tao; Chen, Leiting; Zhong, Linfeng; Xian, Xingping

2017-01-01

Almost all real-world networks are subject to constant evolution, and plenty of them have been investigated empirically to uncover the underlying evolution mechanism. However, the evolution prediction of dynamic networks still remains a challenging problem. The crux of this matter is to estimate the future network links of dynamic networks. This paper studies the evolution prediction of dynamic networks with link prediction paradigm. To estimate the likelihood of the existence of links more accurate, an effective and robust similarity index is presented by exploiting network structure adaptively. Moreover, most of the existing link prediction methods do not make a clear distinction between future links and missing links. In order to predict the future links, the networks are regarded as dynamic systems in this paper, and a similarity updating method, spatial-temporal position drift model, is developed to simulate the evolutionary dynamics of node similarity. Then the updated similarities are used as input information for the future links' likelihood estimation. Extensive experiments on real-world networks suggest that the proposed similarity index performs better than baseline methods and the position drift model performs well for evolution prediction in real-world evolving networks.

Proximity Navigation of Highly Constrained Spacecraft

NASA Technical Reports Server (NTRS)

Scarritt, S.; Swartwout, M.

2007-01-01

Bandit is a 3-kg automated spacecraft in development at Washington University in St. Louis. Bandit's primary mission is to demonstrate proximity navigation, including docking, around a 25-kg student-built host spacecraft. However, because of extreme constraints in mass, power and volume, traditional sensing and actuation methods are not available. In particular, Bandit carries only 8 fixed-magnitude cold-gas thrusters to control its 6 DOF motion. Bandit lacks true inertial sensing, and the ability to sense position relative to the host has error bounds that approach the size of the Bandit itself. Some of the navigation problems are addressed through an extremely robust, error-tolerant soft dock. In addition, we have identified a control methodology that performs well in this constrained environment: behavior-based velocity potential functions, which use a minimum-seeking method similar to Lyapunov functions. We have also adapted the discrete Kalman filter for use on Bandit for position estimation and have developed a similar measurement vs. propagation weighting algorithm for attitude estimation. This paper provides an overview of Bandit and describes the control and estimation approach. Results using our 6DOF flight simulator are provided, demonstrating that these methods show promise for flight use.

Doubly Robust Additive Hazards Models to Estimate Effects of a Continuous Exposure on Survival.

PubMed

Wang, Yan; Lee, Mihye; Liu, Pengfei; Shi, Liuhua; Yu, Zhi; Abu Awad, Yara; Zanobetti, Antonella; Schwartz, Joel D

2017-11-01

The effect of an exposure on survival can be biased when the regression model is misspecified. Hazard difference is easier to use in risk assessment than hazard ratio and has a clearer interpretation in the assessment of effect modifications. We proposed two doubly robust additive hazards models to estimate the causal hazard difference of a continuous exposure on survival. The first model is an inverse probability-weighted additive hazards regression. The second model is an extension of the doubly robust estimator for binary exposures by categorizing the continuous exposure. We compared these with the marginal structural model and outcome regression with correct and incorrect model specifications using simulations. We applied doubly robust additive hazard models to the estimation of hazard difference of long-term exposure to PM2.5 (particulate matter with an aerodynamic diameter less than or equal to 2.5 microns) on survival using a large cohort of 13 million older adults residing in seven states of the Southeastern United States. We showed that the proposed approaches are doubly robust. We found that each 1 μg m increase in annual PM2.5 exposure was associated with a causal hazard difference in mortality of 8.0 × 10 (95% confidence interval 7.4 × 10, 8.7 × 10), which was modified by age, medical history, socioeconomic status, and urbanicity. The overall hazard difference translates to approximately 5.5 (5.1, 6.0) thousand deaths per year in the study population. The proposed approaches improve the robustness of the additive hazards model and produce a novel additive causal estimate of PM2.5 on survival and several additive effect modifications, including social inequality.

Robust multiperson detection and tracking for mobile service and social robots.

PubMed

Li, Liyuan; Yan, Shuicheng; Yu, Xinguo; Tan, Yeow Kee; Li, Haizhou

2012-10-01

This paper proposes an efficient system which integrates multiple vision models for robust multiperson detection and tracking for mobile service and social robots in public environments. The core technique is a novel maximum likelihood (ML)-based algorithm which combines the multimodel detections in mean-shift tracking. First, a likelihood probability which integrates detections and similarity to local appearance is defined. Then, an expectation-maximization (EM)-like mean-shift algorithm is derived under the ML framework. In each iteration, the E-step estimates the associations to the detections, and the M-step locates the new position according to the ML criterion. To be robust to the complex crowded scenarios for multiperson tracking, an improved sequential strategy to perform the mean-shift tracking is proposed. Under this strategy, human objects are tracked sequentially according to their priority order. To balance the efficiency and robustness for real-time performance, at each stage, the first two objects from the list of the priority order are tested, and the one with the higher score is selected. The proposed method has been successfully implemented on real-world service and social robots. The vision system integrates stereo-based and histograms-of-oriented-gradients-based human detections, occlusion reasoning, and sequential mean-shift tracking. Various examples to show the advantages and robustness of the proposed system for multiperson tracking from mobile robots are presented. Quantitative evaluations on the performance of multiperson tracking are also performed. Experimental results indicate that significant improvements have been achieved by using the proposed method.

Tools of Robustness for Item Response Theory.

ERIC Educational Resources Information Center

Jones, Douglas H.

This paper briefly demonstrates a few of the possibilities of a systematic application of robustness theory, concentrating on the estimation of ability when the true item response model does and does not fit the data. The definition of the maximum likelihood estimator (MLE) of ability is briefly reviewed. After introducing the notion of…

Model Uncertainty and Robustness: A Computational Framework for Multimodel Analysis

ERIC Educational Resources Information Center

Young, Cristobal; Holsteen, Katherine

2017-01-01

Model uncertainty is pervasive in social science. A key question is how robust empirical results are to sensible changes in model specification. We present a new approach and applied statistical software for computational multimodel analysis. Our approach proceeds in two steps: First, we estimate the modeling distribution of estimates across all…

The Robustness of LISREL Estimates in Structural Equation Models with Categorical Variables.

ERIC Educational Resources Information Center

Ethington, Corinna A.

This study examined the effect of type of correlation matrix on the robustness of LISREL maximum likelihood and unweighted least squares structural parameter estimates for models with categorical manifest variables. Two types of correlation matrices were analyzed; one containing Pearson product-moment correlations and one containing tetrachoric,…

Sliding Mode Approaches for Robust Control, State Estimation, Secure Communication, and Fault Diagnosis in Nuclear Systems

NASA Astrophysics Data System (ADS)

Ablay, Gunyaz

Using traditional control methods for controller design, parameter estimation and fault diagnosis may lead to poor results with nuclear systems in practice because of approximations and uncertainties in the system models used, possibly resulting in unexpected plant unavailability. This experience has led to an interest in development of robust control, estimation and fault diagnosis methods. One particularly robust approach is the sliding mode control methodology. Sliding mode approaches have been of great interest and importance in industry and engineering in the recent decades due to their potential for producing economic, safe and reliable designs. In order to utilize these advantages, sliding mode approaches are implemented for robust control, state estimation, secure communication and fault diagnosis in nuclear plant systems. In addition, a sliding mode output observer is developed for fault diagnosis in dynamical systems. To validate the effectiveness of the methodologies, several nuclear plant system models are considered for applications, including point reactor kinetics, xenon concentration dynamics, an uncertain pressurizer model, a U-tube steam generator model and a coupled nonlinear nuclear reactor model.

Statistical robustness of machine-learning estimates for characterizing a groundwater-surface water system, Southland, New Zealand

NASA Astrophysics Data System (ADS)

Friedel, M. J.; Daughney, C.

2016-12-01

The development of a successful surface-groundwater management strategy depends on the quality of data provided for analysis. This study evaluates the statistical robustness when using a modified self-organizing map (MSOM) technique to estimate missing values for three hypersurface models: synoptic groundwater-surface water hydrochemistry, time-series of groundwater-surface water hydrochemistry, and mixed-survey (combination of groundwater-surface water hydrochemistry and lithologies) hydrostratigraphic unit data. These models of increasing complexity are developed and validated based on observations from the Southland region of New Zealand. In each case, the estimation method is sufficiently robust to cope with groundwater-surface water hydrochemistry vagaries due to sample size and extreme data insufficiency, even when >80% of the data are missing. The estimation of surface water hydrochemistry time series values enabled the evaluation of seasonal variation, and the imputation of lithologies facilitated the evaluation of hydrostratigraphic controls on groundwater-surface water interaction. The robust statistical results for groundwater-surface water models of increasing data complexity provide justification to apply the MSOM technique in other regions of New Zealand and abroad.

Robust mislabel logistic regression without modeling mislabel probabilities.

PubMed

Hung, Hung; Jou, Zhi-Yu; Huang, Su-Yun

2018-03-01

Logistic regression is among the most widely used statistical methods for linear discriminant analysis. In many applications, we only observe possibly mislabeled responses. Fitting a conventional logistic regression can then lead to biased estimation. One common resolution is to fit a mislabel logistic regression model, which takes into consideration of mislabeled responses. Another common method is to adopt a robust M-estimation by down-weighting suspected instances. In this work, we propose a new robust mislabel logistic regression based on γ-divergence. Our proposal possesses two advantageous features: (1) It does not need to model the mislabel probabilities. (2) The minimum γ-divergence estimation leads to a weighted estimating equation without the need to include any bias correction term, that is, it is automatically bias-corrected. These features make the proposed γ-logistic regression more robust in model fitting and more intuitive for model interpretation through a simple weighting scheme. Our method is also easy to implement, and two types of algorithms are included. Simulation studies and the Pima data application are presented to demonstrate the performance of γ-logistic regression. © 2017, The International Biometric Society.

LEAP: Looking beyond pixels with continuous-space EstimAtion of Point sources

NASA Astrophysics Data System (ADS)

Pan, Hanjie; Simeoni, Matthieu; Hurley, Paul; Blu, Thierry; Vetterli, Martin

2017-12-01

Context. Two main classes of imaging algorithms have emerged in radio interferometry: the CLEAN algorithm and its multiple variants, and compressed-sensing inspired methods. They are both discrete in nature, and estimate source locations and intensities on a regular grid. For the traditional CLEAN-based imaging pipeline, the resolution power of the tool is limited by the width of the synthesized beam, which is inversely proportional to the largest baseline. The finite rate of innovation (FRI) framework is a robust method to find the locations of point-sources in a continuum without grid imposition. The continuous formulation makes the FRI recovery performance only dependent on the number of measurements and the number of sources in the sky. FRI can theoretically find sources below the perceived tool resolution. To date, FRI had never been tested in the extreme conditions inherent to radio astronomy: weak signal / high noise, huge data sets, large numbers of sources. Aims: The aims were (i) to adapt FRI to radio astronomy, (ii) verify it can recover sources in radio astronomy conditions with more accurate positioning than CLEAN, and possibly resolve some sources that would otherwise be missed, (iii) show that sources can be found using less data than would otherwise be required to find them, and (iv) show that FRI does not lead to an augmented rate of false positives. Methods: We implemented a continuous domain sparse reconstruction algorithm in Python. The angular resolution performance of the new algorithm was assessed under simulation, and with visibility measurements from the LOFAR telescope. Existing catalogs were used to confirm the existence of sources. Results: We adapted the FRI framework to radio interferometry, and showed that it is possible to determine accurate off-grid point-source locations and their corresponding intensities. In addition, FRI-based sparse reconstruction required less integration time and smaller baselines to reach a comparable reconstruction quality compared to a conventional method. The achieved angular resolution is higher than the perceived instrument resolution, and very close sources can be reliably distinguished. The proposed approach has cubic complexity in the total number (typically around a few thousand) of uniform Fourier data of the sky image estimated from the reconstruction. It is also demonstrated that the method is robust to the presence of extended-sources, and that false-positives can be addressed by choosing an adequate model order to match the noise level.

Quantifying the causal effects of 20mph zones on road casualties in London via doubly robust estimation.

PubMed

Li, Haojie; Graham, Daniel J

2016-08-01

This paper estimates the causal effect of 20mph zones on road casualties in London. Potential confounders in the key relationship of interest are included within outcome regression and propensity score models, and the models are then combined to form a doubly robust estimator. A total of 234 treated zones and 2844 potential control zones are included in the data sample. The propensity score model is used to select a viable control group which has common support in the covariate distributions. We compare the doubly robust estimates with those obtained using three other methods: inverse probability weighting, regression adjustment, and propensity score matching. The results indicate that 20mph zones have had a significant causal impact on road casualty reduction in both absolute and proportional terms. Copyright © 2016 Elsevier Ltd. All rights reserved.

Robust small area estimation of poverty indicators using M-quantile approach (Case study: Sub-district level in Bogor district)

NASA Astrophysics Data System (ADS)

Girinoto, Sadik, Kusman; Indahwati

2017-03-01

The National Socio-Economic Survey samples are designed to produce estimates of parameters of planned domains (provinces and districts). The estimation of unplanned domains (sub-districts and villages) has its limitation to obtain reliable direct estimates. One of the possible solutions to overcome this problem is employing small area estimation techniques. The popular choice of small area estimation is based on linear mixed models. However, such models need strong distributional assumptions and do not easy allow for outlier-robust estimation. As an alternative approach for this purpose, M-quantile regression approach to small area estimation based on modeling specific M-quantile coefficients of conditional distribution of study variable given auxiliary covariates. It obtained outlier-robust estimation from influence function of M-estimator type and also no need strong distributional assumptions. In this paper, the aim of study is to estimate the poverty indicator at sub-district level in Bogor District-West Java using M-quantile models for small area estimation. Using data taken from National Socioeconomic Survey and Villages Potential Statistics, the results provide a detailed description of pattern of incidence and intensity of poverty within Bogor district. We also compare the results with direct estimates. The results showed the framework may be preferable when direct estimate having no incidence of poverty at all in the small area.

Robust power spectral estimation for EEG data

PubMed Central

Melman, Tamar; Victor, Jonathan D.

2016-01-01

Background Typical electroencephalogram (EEG) recordings often contain substantial artifact. These artifacts, often large and intermittent, can interfere with quantification of the EEG via its power spectrum. To reduce the impact of artifact, EEG records are typically cleaned by a preprocessing stage that removes individual segments or components of the recording. However, such preprocessing can introduce bias, discard available signal, and be labor-intensive. With this motivation, we present a method that uses robust statistics to reduce dependence on preprocessing by minimizing the effect of large intermittent outliers on the spectral estimates. New method Using the multitaper method[1] as a starting point, we replaced the final step of the standard power spectrum calculation with a quantile-based estimator, and the Jackknife approach to confidence intervals with a Bayesian approach. The method is implemented in provided MATLAB modules, which extend the widely used Chronux toolbox. Results Using both simulated and human data, we show that in the presence of large intermittent outliers, the robust method produces improved estimates of the power spectrum, and that the Bayesian confidence intervals yield close-to-veridical coverage factors. Comparison to existing method The robust method, as compared to the standard method, is less affected by artifact: inclusion of outliers produces fewer changes in the shape of the power spectrum as well as in the coverage factor. Conclusion In the presence of large intermittent outliers, the robust method can reduce dependence on data preprocessing as compared to standard methods of spectral estimation. PMID:27102041

Robust power spectral estimation for EEG data.

PubMed

Melman, Tamar; Victor, Jonathan D

2016-08-01

Typical electroencephalogram (EEG) recordings often contain substantial artifact. These artifacts, often large and intermittent, can interfere with quantification of the EEG via its power spectrum. To reduce the impact of artifact, EEG records are typically cleaned by a preprocessing stage that removes individual segments or components of the recording. However, such preprocessing can introduce bias, discard available signal, and be labor-intensive. With this motivation, we present a method that uses robust statistics to reduce dependence on preprocessing by minimizing the effect of large intermittent outliers on the spectral estimates. Using the multitaper method (Thomson, 1982) as a starting point, we replaced the final step of the standard power spectrum calculation with a quantile-based estimator, and the Jackknife approach to confidence intervals with a Bayesian approach. The method is implemented in provided MATLAB modules, which extend the widely used Chronux toolbox. Using both simulated and human data, we show that in the presence of large intermittent outliers, the robust method produces improved estimates of the power spectrum, and that the Bayesian confidence intervals yield close-to-veridical coverage factors. The robust method, as compared to the standard method, is less affected by artifact: inclusion of outliers produces fewer changes in the shape of the power spectrum as well as in the coverage factor. In the presence of large intermittent outliers, the robust method can reduce dependence on data preprocessing as compared to standard methods of spectral estimation. Copyright © 2016 Elsevier B.V. All rights reserved.

A Robust and Multi-Weighted Approach to Estimating Topographically Correlated Tropospheric Delays in Radar Interferograms

PubMed Central

Zhu, Bangyan; Li, Jiancheng; Chu, Zhengwei; Tang, Wei; Wang, Bin; Li, Dawei

2016-01-01

Spatial and temporal variations in the vertical stratification of the troposphere introduce significant propagation delays in interferometric synthetic aperture radar (InSAR) observations. Observations of small amplitude surface deformations and regional subsidence rates are plagued by tropospheric delays, and strongly correlated with topographic height variations. Phase-based tropospheric correction techniques assuming a linear relationship between interferometric phase and topography have been exploited and developed, with mixed success. Producing robust estimates of tropospheric phase delay however plays a critical role in increasing the accuracy of InSAR measurements. Meanwhile, few phase-based correction methods account for the spatially variable tropospheric delay over lager study regions. Here, we present a robust and multi-weighted approach to estimate the correlation between phase and topography that is relatively insensitive to confounding processes such as regional subsidence over larger regions as well as under varying tropospheric conditions. An expanded form of robust least squares is introduced to estimate the spatially variable correlation between phase and topography by splitting the interferograms into multiple blocks. Within each block, correlation is robustly estimated from the band-filtered phase and topography. Phase-elevation ratios are multiply- weighted and extrapolated to each persistent scatter (PS) pixel. We applied the proposed method to Envisat ASAR images over the Southern California area, USA, and found that our method mitigated the atmospheric noise better than the conventional phase-based method. The corrected ground surface deformation agreed better with those measured from GPS. PMID:27420066

A Robust and Multi-Weighted Approach to Estimating Topographically Correlated Tropospheric Delays in Radar Interferograms.

PubMed

Zhu, Bangyan; Li, Jiancheng; Chu, Zhengwei; Tang, Wei; Wang, Bin; Li, Dawei

2016-07-12

Spatial and temporal variations in the vertical stratification of the troposphere introduce significant propagation delays in interferometric synthetic aperture radar (InSAR) observations. Observations of small amplitude surface deformations and regional subsidence rates are plagued by tropospheric delays, and strongly correlated with topographic height variations. Phase-based tropospheric correction techniques assuming a linear relationship between interferometric phase and topography have been exploited and developed, with mixed success. Producing robust estimates of tropospheric phase delay however plays a critical role in increasing the accuracy of InSAR measurements. Meanwhile, few phase-based correction methods account for the spatially variable tropospheric delay over lager study regions. Here, we present a robust and multi-weighted approach to estimate the correlation between phase and topography that is relatively insensitive to confounding processes such as regional subsidence over larger regions as well as under varying tropospheric conditions. An expanded form of robust least squares is introduced to estimate the spatially variable correlation between phase and topography by splitting the interferograms into multiple blocks. Within each block, correlation is robustly estimated from the band-filtered phase and topography. Phase-elevation ratios are multiply- weighted and extrapolated to each persistent scatter (PS) pixel. We applied the proposed method to Envisat ASAR images over the Southern California area, USA, and found that our method mitigated the atmospheric noise better than the conventional phase-based method. The corrected ground surface deformation agreed better with those measured from GPS.

Automated framework for estimation of lung tumor locations in kV-CBCT images for tumor-based patient positioning in stereotactic lung body radiotherapy

NASA Astrophysics Data System (ADS)

Yoshidome, Satoshi; Arimura, Hidetaka; Terashima, Koutarou; Hirakawa, Masakazu; Hirose, Taka-aki; Fukunaga, Junichi; Nakamura, Yasuhiko

2017-03-01

Recently, image-guided radiotherapy (IGRT) systems using kilovolt cone-beam computed tomography (kV-CBCT) images have become more common for highly accurate patient positioning in stereotactic lung body radiotherapy (SLBRT). However, current IGRT procedures are based on bone structures and subjective correction. Therefore, the aim of this study was to evaluate the proposed framework for automated estimation of lung tumor locations in kV-CBCT images for tumor-based patient positioning in SLBRT. Twenty clinical cases are considered, involving solid, pure ground-glass opacity (GGO), mixed GGO, solitary, and non-solitary tumor types. The proposed framework consists of four steps: (1) determination of a search region for tumor location detection in a kV-CBCT image; (2) extraction of a tumor template from a planning CT image; (3) preprocessing for tumor region enhancement (edge and tumor enhancement using a Sobel filter and a blob structure enhancement (BSE) filter, respectively); and (4) tumor location estimation based on a template-matching technique. The location errors in the original, edge-, and tumor-enhanced images were found to be 1.2 ± 0.7 mm, 4.2 ± 8.0 mm, and 2.7 ± 4.6 mm, respectively. The location errors in the original images of solid, pure GGO, mixed GGO, solitary, and non-solitary types of tumors were 1.2 ± 0.7 mm, 1.3 ± 0.9 mm, 0.4 ± 0.6 mm, 1.1 ± 0.8 mm and 1.0 ± 0.7 mm, respectively. These results suggest that the proposed framework is robust as regards automatic estimation of several types of tumor locations in kV-CBCT images for tumor-based patient positioning in SLBRT.

Quantitative assessment of hit detection and confirmation in single and duplicate high-throughput screenings.

PubMed

Wu, Zhijin; Liu, Dongmei; Sui, Yunxia

2008-02-01

The process of identifying active targets (hits) in high-throughput screening (HTS) usually involves 2 steps: first, removing or adjusting for systematic variation in the measurement process so that extreme values represent strong biological activity instead of systematic biases such as plate effect or edge effect and, second, choosing a meaningful cutoff on the calculated statistic to declare positive compounds. Both false-positive and false-negative errors are inevitable in this process. Common control or estimation of error rates is often based on an assumption of normal distribution of the noise. The error rates in hit detection, especially false-negative rates, are hard to verify because in most assays, only compounds selected in primary screening are followed up in confirmation experiments. In this article, the authors take advantage of a quantitative HTS experiment in which all compounds are tested 42 times over a wide range of 14 concentrations so true positives can be found through a dose-response curve. Using the activity status defined by dose curve, the authors analyzed the effect of various data-processing procedures on the sensitivity and specificity of hit detection, the control of error rate, and hit confirmation. A new summary score is proposed and demonstrated to perform well in hit detection and useful in confirmation rate estimation. In general, adjusting for positional effects is beneficial, but a robust test can prevent overadjustment. Error rates estimated based on normal assumption do not agree with actual error rates, for the tails of noise distribution deviate from normal distribution. However, false discovery rate based on empirically estimated null distribution is very close to observed false discovery proportion.

Wavelet Filtering to Reduce Conservatism in Aeroservoelastic Robust Stability Margins

NASA Technical Reports Server (NTRS)

Brenner, Marty; Lind, Rick

1998-01-01

Wavelet analysis for filtering and system identification was used to improve the estimation of aeroservoelastic stability margins. The conservatism of the robust stability margins was reduced with parametric and nonparametric time-frequency analysis of flight data in the model validation process. Nonparametric wavelet processing of data was used to reduce the effects of external desirableness and unmodeled dynamics. Parametric estimates of modal stability were also extracted using the wavelet transform. Computation of robust stability margins for stability boundary prediction depends on uncertainty descriptions derived from the data for model validation. F-18 high Alpha Research Vehicle aeroservoelastic flight test data demonstrated improved robust stability prediction by extension of the stability boundary beyond the flight regime.

Response time distributions in rapid chess: a large-scale decision making experiment.

PubMed

Sigman, Mariano; Etchemendy, Pablo; Slezak, Diego Fernández; Cecchi, Guillermo A

2010-01-01

Rapid chess provides an unparalleled laboratory to understand decision making in a natural environment. In a chess game, players choose consecutively around 40 moves in a finite time budget. The goodness of each choice can be determined quantitatively since current chess algorithms estimate precisely the value of a position. Web-based chess produces vast amounts of data, millions of decisions per day, incommensurable with traditional psychological experiments. We generated a database of response times (RTs) and position value in rapid chess games. We measured robust emergent statistical observables: (1) RT distributions are long-tailed and show qualitatively distinct forms at different stages of the game, (2) RT of successive moves are highly correlated both for intra- and inter-player moves. These findings have theoretical implications since they deny two basic assumptions of sequential decision making algorithms: RTs are not stationary and can not be generated by a state-function. Our results also have practical implications. First, we characterized the capacity of blunders and score fluctuations to predict a player strength, which is yet an open problem in chess softwares. Second, we show that the winning likelihood can be reliably estimated from a weighted combination of remaining times and position evaluation.

Response Time Distributions in Rapid Chess: A Large-Scale Decision Making Experiment

PubMed Central

Sigman, Mariano; Etchemendy, Pablo; Slezak, Diego Fernández; Cecchi, Guillermo A.

2010-01-01

Rapid chess provides an unparalleled laboratory to understand decision making in a natural environment. In a chess game, players choose consecutively around 40 moves in a finite time budget. The goodness of each choice can be determined quantitatively since current chess algorithms estimate precisely the value of a position. Web-based chess produces vast amounts of data, millions of decisions per day, incommensurable with traditional psychological experiments. We generated a database of response times (RTs) and position value in rapid chess games. We measured robust emergent statistical observables: (1) RT distributions are long-tailed and show qualitatively distinct forms at different stages of the game, (2) RT of successive moves are highly correlated both for intra- and inter-player moves. These findings have theoretical implications since they deny two basic assumptions of sequential decision making algorithms: RTs are not stationary and can not be generated by a state-function. Our results also have practical implications. First, we characterized the capacity of blunders and score fluctuations to predict a player strength, which is yet an open problem in chess softwares. Second, we show that the winning likelihood can be reliably estimated from a weighted combination of remaining times and position evaluation. PMID:21031032

Reliable femoral frame construction based on MRI dedicated to muscles position follow-up.

PubMed

Dubois, G; Bonneau, D; Lafage, V; Rouch, P; Skalli, W

2015-10-01

In vivo follow-up of muscle shape variation represents a challenge when evaluating muscle development due to disease or treatment. Recent developments in muscles reconstruction techniques indicate MRI as a clinical tool for the follow-up of the thigh muscles. The comparison of 3D muscles shape from two different sequences is not easy because there is no common frame. This study proposes an innovative method for the reconstruction of a reliable femoral frame based on the femoral head and both condyles centers. In order to robustify the definition of condylar spheres, an original method was developed to combine the estimation of diameters of both condyles from the lateral antero-posterior distance and the estimation of the spheres center from an optimization process. The influence of spacing between MR slices and of origin positions was studied. For all axes, the proposed method presented an angular error lower than 1° with spacing between slice of 10 mm and the optimal position of the origin was identified at 56 % of the distance between the femoral head center and the barycenter of both condyles. The high reliability of this method provides a robust frame for clinical follow-up based on MRI .

Robust GNSS and InSAR tomography of neutrospheric refractivity using a Compressive Sensing approach

NASA Astrophysics Data System (ADS)

Heublein, Marion; Alshawaf, Fadwa; Zhu, Xiao Xiang; Hinz, Stefan

2017-04-01

Motivation: An accurate knowledge of the 3D distribution of water vapor in the atmosphere is a key element for weather forecasting and climate research. In addition, a precise determination of water vapor is also required for accurate positioning and deformation monitoring using Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR). Several approaches for 3D tomographic water vapor reconstruction from GNSS-based Slant Wet Delay (SWD) estimates using the least squares (LSQ) adjustment exist. However, the tomographic system is in general ill-conditioned and its solution is unstable. Therefore, additional information or constraints need to be added in order to regularize the system. Goal of this work: In this work, we analyze the potential of Compressive Sensing (CS) for robustly reconstructing neutrospheric refractivity from GNSS SWD estimates. Moreover, the benefit of adding InSAR SWD estimates into the tomographic system is studied. Approach: A sparse representation of the refractivity field is obtained using a dictionary composed of Discrete Cosine Transforms (DCT) in longitude and latitude direction and of an Euler transform in height direction. This sparsity of the signal can be used as a prior for regularization and the CS inversion is solved by minimizing the number of non-zero entries of the sparse solution in the DCT-Euler domain. No other regularization constraints or prior knowledge is applied. The tomographic reconstruction relies on total SWD estimates from GNSS Precise Point Positioning (PPP) and Persistent Scatterer (PS) InSAR. On the one hand, GNSS PPP SWD estimates are included into the system of equations. On the other hand, 2D ZWD maps are obtained by a combination of point-wise estimates of the wet delay using GNSS observations and partial InSAR wet delay maps. These ZWD estimates are aggregated to derive realistic wet delay input data at given points as if corresponding to GNSS sites within the study area. The made-up ZWD values can be mapped into different elevation and azimuth angles. Moreover, using the same observation geometry as in the case of the GNSS and InSAR data, a synthetic set of SWD values was generated based on WRF simulations. Results: The CS approach shows particular strength in the case of a small number of SWD estimates. When compared to LSQ, the sparse reconstruction is much more robust. In the case of a low density of GNSS sites, adding InSAR SWD estimates improves the reconstruction accuracy for both LSQ and CS. Based on a synthetic SWD dataset generated using WRF simulations of wet refractivity, the CS based solution of the tomographic system is validated. In the vertical direction, the refractivity distribution deduced from GNSS and InSAR SWD estimates is compared to a tropospheric humidity data set provided by EUMETSAT consisting of daily mean values of specific humidity given on six pressure levels between 1000 hPa and 200 hPa. Study area: The Upper Rhine Graben (URG) characterized by negligible surface deformations is chosen as study area. A network of seven permanent GNSS receivers is used for this study, and a total number of 17 SAR images, acquired by ENVISAT ASAR is available.

ATMAD: robust image analysis for Automatic Tissue MicroArray De-arraying.

PubMed

Nguyen, Hoai Nam; Paveau, Vincent; Cauchois, Cyril; Kervrann, Charles

2018-04-19

Over the last two decades, an innovative technology called Tissue Microarray (TMA), which combines multi-tissue and DNA microarray concepts, has been widely used in the field of histology. It consists of a collection of several (up to 1000 or more) tissue samples that are assembled onto a single support - typically a glass slide - according to a design grid (array) layout, in order to allow multiplex analysis by treating numerous samples under identical and standardized conditions. However, during the TMA manufacturing process, the sample positions can be highly distorted from the design grid due to the imprecision when assembling tissue samples and the deformation of the embedding waxes. Consequently, these distortions may lead to severe errors of (histological) assay results when the sample identities are mismatched between the design and its manufactured output. The development of a robust method for de-arraying TMA, which localizes and matches TMA samples with their design grid, is therefore crucial to overcome the bottleneck of this prominent technology. In this paper, we propose an Automatic, fast and robust TMA De-arraying (ATMAD) approach dedicated to images acquired with brightfield and fluorescence microscopes (or scanners). First, tissue samples are localized in the large image by applying a locally adaptive thresholding on the isotropic wavelet transform of the input TMA image. To reduce false detections, a parametric shape model is considered for segmenting ellipse-shaped objects at each detected position. Segmented objects that do not meet the size and the roundness criteria are discarded from the list of tissue samples before being matched with the design grid. Sample matching is performed by estimating the TMA grid deformation under the thin-plate model. Finally, thanks to the estimated deformation, the true tissue samples that were preliminary rejected in the early image processing step are recognized by running a second segmentation step. We developed a novel de-arraying approach for TMA analysis. By combining wavelet-based detection, active contour segmentation, and thin-plate spline interpolation, our approach is able to handle TMA images with high dynamic, poor signal-to-noise ratio, complex background and non-linear deformation of TMA grid. In addition, the deformation estimation produces quantitative information to asset the manufacturing quality of TMAs.

Profitability of Contrarian Strategies in the Chinese Stock Market

PubMed Central

Shi, Huai-Long; Jiang, Zhi-Qiang; Zhou, Wei-Xing

2015-01-01

This paper reexamines the profitability of loser, winner and contrarian portfolios in the Chinese stock market using monthly data of all stocks traded on the Shanghai Stock Exchange and Shenzhen Stock Exchange covering the period from January 1997 to December 2012. We find evidence of short-term and long-term contrarian profitability in the whole sample period when the estimation and holding horizons are 1 month or longer than 12 months and the annualized return of contrarian portfolios increases with the estimation and holding horizons. We perform subperiod analysis and find that the long-term contrarian effect is significant in both bullish and bearish states, while the short-term contrarian effect disappears in bullish states. We compare the performance of contrarian portfolios based on different grouping manners in the estimation period and unveil that decile grouping outperforms quintile grouping and tertile grouping, which is more evident and robust in the long run. Generally, loser portfolios and winner portfolios have positive returns and loser portfolios perform much better than winner portfolios. Both loser and winner portfolios in bullish states perform better than those in the whole sample period. In contrast, loser and winner portfolios have smaller returns in bearish states, in which loser portfolio returns are significant only in the long term and winner portfolio returns become insignificant. These results are robust to the one-month skipping between the estimation and holding periods and for the two stock exchanges. Our findings show that the Chinese stock market is not efficient in the weak form. These findings also have obvious practical implications for financial practitioners. PMID:26368537

Profitability of Contrarian Strategies in the Chinese Stock Market.

PubMed

Shi, Huai-Long; Jiang, Zhi-Qiang; Zhou, Wei-Xing

2015-01-01

This paper reexamines the profitability of loser, winner and contrarian portfolios in the Chinese stock market using monthly data of all stocks traded on the Shanghai Stock Exchange and Shenzhen Stock Exchange covering the period from January 1997 to December 2012. We find evidence of short-term and long-term contrarian profitability in the whole sample period when the estimation and holding horizons are 1 month or longer than 12 months and the annualized return of contrarian portfolios increases with the estimation and holding horizons. We perform subperiod analysis and find that the long-term contrarian effect is significant in both bullish and bearish states, while the short-term contrarian effect disappears in bullish states. We compare the performance of contrarian portfolios based on different grouping manners in the estimation period and unveil that decile grouping outperforms quintile grouping and tertile grouping, which is more evident and robust in the long run. Generally, loser portfolios and winner portfolios have positive returns and loser portfolios perform much better than winner portfolios. Both loser and winner portfolios in bullish states perform better than those in the whole sample period. In contrast, loser and winner portfolios have smaller returns in bearish states, in which loser portfolio returns are significant only in the long term and winner portfolio returns become insignificant. These results are robust to the one-month skipping between the estimation and holding periods and for the two stock exchanges. Our findings show that the Chinese stock market is not efficient in the weak form. These findings also have obvious practical implications for financial practitioners.

Application of unscented Kalman filter for robust pose estimation in image-guided surgery

NASA Astrophysics Data System (ADS)

Vaccarella, Alberto; De Momi, Elena; Valenti, Marta; Ferrigno, Giancarlo; Enquobahrie, Andinet

2012-02-01

Image-guided surgery (IGS) allows clinicians to view current, intra-operative scenes superimposed on preoperative images (typically MRI or CT scans). IGS systems use localization systems to track and visualize surgical tools overlaid on top of preoperative images of the patient during surgery. The most commonly used localization systems in the Operating Rooms (OR) are optical tracking systems (OTS) due to their ease of use and cost effectiveness. However, OTS' suffer from the major drawback of line-of-sight requirements. State space approaches based on different implementations of the Kalman filter have recently been investigated in order to compensate for short line-of-sight occlusion. However, the proposed parameterizations for the rigid body orientation suffer from singularities at certain values of rotation angles. The purpose of this work is to develop a quaternion-based Unscented Kalman Filter (UKF) for robust optical tracking of both position and orientation of surgical tools in order to compensate marker occlusion issues. This paper presents preliminary results towards a Kalman-based Sensor Management Engine (SME). The engine will filter and fuse multimodal tracking streams of data. This work was motivated by our experience working in robot-based applications for keyhole neurosurgery (ROBOCAST project). The algorithm was evaluated using real data from NDI Polaris tracker. The results show that our estimation technique is able to compensate for marker occlusion with a maximum error of 2.5° for orientation and 2.36 mm for position. The proposed approach will be useful in over-crowded state-of-the-art ORs where achieving continuous visibility of all tracked objects will be difficult.

Robust multivariate nonparametric tests for detection of two-sample location shift in clinical trials

PubMed Central

Jiang, Xuejun; Guo, Xu; Zhang, Ning; Wang, Bo

2018-01-01

This article presents and investigates performance of a series of robust multivariate nonparametric tests for detection of location shift between two multivariate samples in randomized controlled trials. The tests are built upon robust estimators of distribution locations (medians, Hodges-Lehmann estimators, and an extended U statistic) with both unscaled and scaled versions. The nonparametric tests are robust to outliers and do not assume that the two samples are drawn from multivariate normal distributions. Bootstrap and permutation approaches are introduced for determining the p-values of the proposed test statistics. Simulation studies are conducted and numerical results are reported to examine performance of the proposed statistical tests. The numerical results demonstrate that the robust multivariate nonparametric tests constructed from the Hodges-Lehmann estimators are more efficient than those based on medians and the extended U statistic. The permutation approach can provide a more stringent control of Type I error and is generally more powerful than the bootstrap procedure. The proposed robust nonparametric tests are applied to detect multivariate distributional difference between the intervention and control groups in the Thai Healthy Choices study and examine the intervention effect of a four-session motivational interviewing-based intervention developed in the study to reduce risk behaviors among youth living with HIV. PMID:29672555

The use of resighting data to estimate the rate of population growth of the snail kite in Florida

USGS Publications Warehouse

Dreitz, V.J.; Nichols, J.D.; Hines, J.E.; Bennetts, R.E.; Kitchens, W.M.; DeAngelis, D.L.

2002-01-01

The rate of population growth (lambda) is an important demographic parameter used to assess the viability of a population and to develop management and conservation agendas. We examined the use of resighting data to estimate lambda for the snail kite population in Florida from 1997-2000. The analyses consisted of (1) a robust design approach that derives an estimate of lambda from estimates of population size and (2) the Pradel (1996) temporal symmetry (TSM) approach that directly estimates lambda using an open-population capture-recapture model. Besides resighting data, both approaches required information on the number of unmarked individuals that were sighted during the sampling periods. The point estimates of lambda differed between the robust design and TSM approaches, but the 95% confidence intervals overlapped substantially. We believe the differences may be the result of sparse data and do not indicate the inappropriateness of either modelling technique. We focused on the results of the robust design because this approach provided estimates for all study years. Variation among these estimates was smaller than levels of variation among ad hoc estimates based on previously reported index statistics. We recommend that lambda of snail kites be estimated using capture-resighting methods rather than ad hoc counts.

Local Composite Quantile Regression Smoothing for Harris Recurrent Markov Processes

PubMed Central

Li, Degui; Li, Runze

2016-01-01

In this paper, we study the local polynomial composite quantile regression (CQR) smoothing method for the nonlinear and nonparametric models under the Harris recurrent Markov chain framework. The local polynomial CQR regression method is a robust alternative to the widely-used local polynomial method, and has been well studied in stationary time series. In this paper, we relax the stationarity restriction on the model, and allow that the regressors are generated by a general Harris recurrent Markov process which includes both the stationary (positive recurrent) and nonstationary (null recurrent) cases. Under some mild conditions, we establish the asymptotic theory for the proposed local polynomial CQR estimator of the mean regression function, and show that the convergence rate for the estimator in nonstationary case is slower than that in stationary case. Furthermore, a weighted type local polynomial CQR estimator is provided to improve the estimation efficiency, and a data-driven bandwidth selection is introduced to choose the optimal bandwidth involved in the nonparametric estimators. Finally, we give some numerical studies to examine the finite sample performance of the developed methodology and theory. PMID:27667894

Estimating Latent Variable Interactions With Non-Normal Observed Data: A Comparison of Four Approaches

PubMed Central

Cham, Heining; West, Stephen G.; Ma, Yue; Aiken, Leona S.

2012-01-01

A Monte Carlo simulation was conducted to investigate the robustness of four latent variable interaction modeling approaches (Constrained Product Indicator [CPI], Generalized Appended Product Indicator [GAPI], Unconstrained Product Indicator [UPI], and Latent Moderated Structural Equations [LMS]) under high degrees of non-normality of the observed exogenous variables. Results showed that the CPI and LMS approaches yielded biased estimates of the interaction effect when the exogenous variables were highly non-normal. When the violation of non-normality was not severe (normal; symmetric with excess kurtosis < 1), the LMS approach yielded the most efficient estimates of the latent interaction effect with the highest statistical power. In highly non-normal conditions, the GAPI and UPI approaches with ML estimation yielded unbiased latent interaction effect estimates, with acceptable actual Type-I error rates for both the Wald and likelihood ratio tests of interaction effect at N ≥ 500. An empirical example illustrated the use of the four approaches in testing a latent variable interaction between academic self-efficacy and positive family role models in the prediction of academic performance. PMID:23457417

A Leo Satellite Navigation Algorithm Based on GPS and Magnetometer Data

NASA Technical Reports Server (NTRS)

Deutschmann, Julie; Harman, Rick; Bar-Itzhack, Itzhack

2001-01-01

The Global Positioning System (GPS) has become a standard method for low cost onboard satellite orbit determination. The use of a GPS receiver as an attitude and rate sensor has also been developed in the recent past. Additionally, focus has been given to attitude and orbit estimation using the magnetometer, a low cost, reliable sensor. Combining measurements from both GPS and a magnetometer can provide a robust navigation system that takes advantage of the estimation qualities of both measurements. Ultimately, a low cost, accurate navigation system can result, potentially eliminating the need for more costly sensors, including gyroscopes. This work presents the development of a technique to eliminate numerical differentiation of the GPS phase measurements and also compares the use of one versus two GPS satellites.

Robust Means and Covariance Matrices by the Minimum Volume Ellipsoid (MVE).

ERIC Educational Resources Information Center

Blankmeyer, Eric

P. Rousseeuw and A. Leroy (1987) proposed a very robust alternative to classical estimates of mean vectors and covariance matrices, the Minimum Volume Ellipsoid (MVE). This paper describes the MVE technique and presents a BASIC program to implement it. The MVE is a "high breakdown" estimator, one that can cope with samples in which as…

A robust bayesian estimate of the concordance correlation coefficient.

PubMed

Feng, Dai; Baumgartner, Richard; Svetnik, Vladimir

2015-01-01

A need for assessment of agreement arises in many situations including statistical biomarker qualification or assay or method validation. Concordance correlation coefficient (CCC) is one of the most popular scaled indices reported in evaluation of agreement. Robust methods for CCC estimation currently present an important statistical challenge. Here, we propose a novel Bayesian method of robust estimation of CCC based on multivariate Student's t-distribution and compare it with its alternatives. Furthermore, we extend the method to practically relevant settings, enabling incorporation of confounding covariates and replications. The superiority of the new approach is demonstrated using simulation as well as real datasets from biomarker application in electroencephalography (EEG). This biomarker is relevant in neuroscience for development of treatments for insomnia.

Robust logistic regression to narrow down the winner's curse for rare and recessive susceptibility variants.

PubMed

Kesselmeier, Miriam; Lorenzo Bermejo, Justo

2017-11-01

Logistic regression is the most common technique used for genetic case-control association studies. A disadvantage of standard maximum likelihood estimators of the genotype relative risk (GRR) is their strong dependence on outlier subjects, for example, patients diagnosed at unusually young age. Robust methods are available to constrain outlier influence, but they are scarcely used in genetic studies. This article provides a non-intimidating introduction to robust logistic regression, and investigates its benefits and limitations in genetic association studies. We applied the bounded Huber and extended the R package 'robustbase' with the re-descending Hampel functions to down-weight outlier influence. Computer simulations were carried out to assess the type I error rate, mean squared error (MSE) and statistical power according to major characteristics of the genetic study and investigated markers. Simulations were complemented with the analysis of real data. Both standard and robust estimation controlled type I error rates. Standard logistic regression showed the highest power but standard GRR estimates also showed the largest bias and MSE, in particular for associated rare and recessive variants. For illustration, a recessive variant with a true GRR=6.32 and a minor allele frequency=0.05 investigated in a 1000 case/1000 control study by standard logistic regression resulted in power=0.60 and MSE=16.5. The corresponding figures for Huber-based estimation were power=0.51 and MSE=0.53. Overall, Hampel- and Huber-based GRR estimates did not differ much. Robust logistic regression may represent a valuable alternative to standard maximum likelihood estimation when the focus lies on risk prediction rather than identification of susceptibility variants. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Robust state estimation for uncertain fuzzy bidirectional associative memory networks with time-varying delays

NASA Astrophysics Data System (ADS)

Vadivel, P.; Sakthivel, R.; Mathiyalagan, K.; Arunkumar, A.

2013-09-01

This paper addresses the issue of robust state estimation for a class of fuzzy bidirectional associative memory (BAM) neural networks with time-varying delays and parameter uncertainties. By constructing the Lyapunov-Krasovskii functional, which contains the triple-integral term and using the free-weighting matrix technique, a set of sufficient conditions are derived in terms of linear matrix inequalities (LMIs) to estimate the neuron states through available output measurements such that the dynamics of the estimation error system is robustly asymptotically stable. In particular, we consider a generalized activation function in which the traditional assumptions on the boundedness, monotony and differentiability of the activation functions are removed. More precisely, the design of the state estimator for such BAM neural networks can be obtained by solving some LMIs, which are dependent on the size of the time derivative of the time-varying delays. Finally, a numerical example with simulation result is given to illustrate the obtained theoretical results.

Robust estimation of partially linear models for longitudinal data with dropouts and measurement error.

PubMed

Qin, Guoyou; Zhang, Jiajia; Zhu, Zhongyi; Fung, Wing

2016-12-20

Outliers, measurement error, and missing data are commonly seen in longitudinal data because of its data collection process. However, no method can address all three of these issues simultaneously. This paper focuses on the robust estimation of partially linear models for longitudinal data with dropouts and measurement error. A new robust estimating equation, simultaneously tackling outliers, measurement error, and missingness, is proposed. The asymptotic properties of the proposed estimator are established under some regularity conditions. The proposed method is easy to implement in practice by utilizing the existing standard generalized estimating equations algorithms. The comprehensive simulation studies show the strength of the proposed method in dealing with longitudinal data with all three features. Finally, the proposed method is applied to data from the Lifestyle Education for Activity and Nutrition study and confirms the effectiveness of the intervention in producing weight loss at month 9. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Doubly robust estimation of generalized partial linear models for longitudinal data with dropouts.

PubMed

Lin, Huiming; Fu, Bo; Qin, Guoyou; Zhu, Zhongyi

2017-12-01

We develop a doubly robust estimation of generalized partial linear models for longitudinal data with dropouts. Our method extends the highly efficient aggregate unbiased estimating function approach proposed in Qu et al. (2010) to a doubly robust one in the sense that under missing at random (MAR), our estimator is consistent when either the linear conditional mean condition is satisfied or a model for the dropout process is correctly specified. We begin with a generalized linear model for the marginal mean, and then move forward to a generalized partial linear model, allowing for nonparametric covariate effect by using the regression spline smoothing approximation. We establish the asymptotic theory for the proposed method and use simulation studies to compare its finite sample performance with that of Qu's method, the complete-case generalized estimating equation (GEE) and the inverse-probability weighted GEE. The proposed method is finally illustrated using data from a longitudinal cohort study. © 2017, The International Biometric Society.

Skeletal Correlates for Body Mass Estimation in Modern and Fossil Flying Birds

PubMed Central

Field, Daniel J.; Lynner, Colton; Brown, Christian; Darroch, Simon A. F.

2013-01-01

Scaling relationships between skeletal dimensions and body mass in extant birds are often used to estimate body mass in fossil crown-group birds, as well as in stem-group avialans. However, useful statistical measurements for constraining the precision and accuracy of fossil mass estimates are rarely provided, which prevents the quantification of robust upper and lower bound body mass estimates for fossils. Here, we generate thirteen body mass correlations and associated measures of statistical robustness using a sample of 863 extant flying birds. By providing robust body mass regressions with upper- and lower-bound prediction intervals for individual skeletal elements, we address the longstanding problem of body mass estimation for highly fragmentary fossil birds. We demonstrate that the most precise proxy for estimating body mass in the overall dataset, measured both as coefficient determination of ordinary least squares regression and percent prediction error, is the maximum diameter of the coracoid’s humeral articulation facet (the glenoid). We further demonstrate that this result is consistent among the majority of investigated avian orders (10 out of 18). As a result, we suggest that, in the majority of cases, this proxy may provide the most accurate estimates of body mass for volant fossil birds. Additionally, by presenting statistical measurements of body mass prediction error for thirteen different body mass regressions, this study provides a much-needed quantitative framework for the accurate estimation of body mass and associated ecological correlates in fossil birds. The application of these regressions will enhance the precision and robustness of many mass-based inferences in future paleornithological studies. PMID:24312392

Control algorithms for aerobraking in the Martian atmosphere

NASA Technical Reports Server (NTRS)

Ward, Donald T.; Shipley, Buford W., Jr.

1991-01-01

The Analytic Predictor Corrector (APC) and Energy Controller (EC) atmospheric guidance concepts were adapted to control an interplanetary vehicle aerobraking in the Martian atmosphere. Changes are made to the APC to improve its robustness to density variations. These changes include adaptation of a new exit phase algorithm, an adaptive transition velocity to initiate the exit phase, refinement of the reference dynamic pressure calculation and two improved density estimation techniques. The modified controller with the hybrid density estimation technique is called the Mars Hybrid Predictor Corrector (MHPC), while the modified controller with a polynomial density estimator is called the Mars Predictor Corrector (MPC). A Lyapunov Steepest Descent Controller (LSDC) is adapted to control the vehicle. The LSDC lacked robustness, so a Lyapunov tracking exit phase algorithm is developed to guide the vehicle along a reference trajectory. This algorithm, when using the hybrid density estimation technique to define the reference path, is called the Lyapunov Hybrid Tracking Controller (LHTC). With the polynomial density estimator used to define the reference trajectory, the algorithm is called the Lyapunov Tracking Controller (LTC). These four new controllers are tested using a six degree of freedom computer simulation to evaluate their robustness. The MHPC, MPC, LHTC, and LTC show dramatic improvements in robustness over the APC and EC.

How Accurate and Robust Are the Phylogenetic Estimates of Austronesian Language Relationships?

PubMed Central

Greenhill, Simon J.; Drummond, Alexei J.; Gray, Russell D.

2010-01-01

We recently used computational phylogenetic methods on lexical data to test between two scenarios for the peopling of the Pacific. Our analyses of lexical data supported a pulse-pause scenario of Pacific settlement in which the Austronesian speakers originated in Taiwan around 5,200 years ago and rapidly spread through the Pacific in a series of expansion pulses and settlement pauses. We claimed that there was high congruence between traditional language subgroups and those observed in the language phylogenies, and that the estimated age of the Austronesian expansion at 5,200 years ago was consistent with the archaeological evidence. However, the congruence between the language phylogenies and the evidence from historical linguistics was not quantitatively assessed using tree comparison metrics. The robustness of the divergence time estimates to different calibration points was also not investigated exhaustively. Here we address these limitations by using a systematic tree comparison metric to calculate the similarity between the Bayesian phylogenetic trees and the subgroups proposed by historical linguistics, and by re-estimating the age of the Austronesian expansion using only the most robust calibrations. The results show that the Austronesian language phylogenies are highly congruent with the traditional subgroupings, and the date estimates are robust even when calculated using a restricted set of historical calibrations. PMID:20224774

Artificial neural networks can be effectively used to model changes of intracranial pressure (ICP) during spinal surgery using different non invasive ICP surrogate estimators.

PubMed

Watad, Abdulla; Bragazzi, Nicola L; Bacigaluppi, Susanna; Amital, Howard; Watad, Samaa; Sharif, Kassem; Bisharat, Bishara; Siri, Anna; Mahamid, Ala; Abu Ras, Hakim; Nasr, Ahmed; Bilotta, Federico; Robba, Chiara; Adawi, Mohammad

2018-02-23

Artificial Intelligence (AI) techniques play a major role in anesthesiology, even though their importance is often overlooked. In the extant literature, AI approaches, such as Artificial Neural Networks (ANNs), have been underutilized, mainly being used to model patient's consciousness state, to predict the precise amount of anesthetic gases, the level of analgesia, or the need of anesthesiological blocks, among others. In the field of neurosurgery, ANNs have been effectively applied to the diagnosis and prognosis of cerebral tumors, seizures, low back pain, and also to the monitoring of intracranial pressure (ICP). A MultiLayer Perceptron (MLP), which is a feedforward ANN, with hyperbolic tangent as activation function in the input/hidden layers, softmax as activation function in the output layer, and cross-entropy as error function, was used to model the impact of prone versus supine position and the use of positive end expiratory pressure (PEEP) on ICP in a sample of 30 patients undergoing spinal surgery. Different non invasive surrogate estimations of ICP have been used and compared: namely, mean optic nerve sheath diameter (ONSD), non invasive estimated cerebral perfusion pressure (NCPP), pulsatility index (PI), ICP derived from PI (ICP-PI), and flow velocity diastolic formula (FVDICP). ONSD proved to be a more robust surrogate estimation of ICP, with a predictive power of 75%, whilst the power of NCPP, ICP-PI, PI, and FVDICP were 60.5%, 54.8%, 53.1%, and 47.7%, respectively. Our MLP analysis confirmed our findings previously obtained with regression, correlation, multivariate Receiving Operator Curve (multi-ROC) analyses. ANNs can be successfully used to predict the effects of prone versus supine position and PEEP on ICP in patients undergoing spinal surgery using different non invasive surrogate estimators of ICP.

Certified normal: Alzheimer's disease biomarkers and normative estimates of cognitive functioning.

PubMed

Hassenstab, Jason; Chasse, Rachel; Grabow, Perri; Benzinger, Tammie L S; Fagan, Anne M; Xiong, Chengjie; Jasielec, Mateusz; Grant, Elizabeth; Morris, John C

2016-07-01

Normative samples drawn from older populations may unintentionally include individuals with preclinical Alzheimer's disease (AD) pathology, resulting in reduced means, increased variability, and overestimation of age effects on cognitive performance. A total of 264 cognitively normal (Clinical Dementia Rating = 0) older adults were classified as biomarker negative ("Robust Normal," n = 177) or biomarker positive ("Preclinical Alzheimer's Disease" [PCAD], n = 87) based on amyloid imaging, cerebrospinal fluid biomarkers, and hippocampal volumes. PCAD participants performed worse than robust normals on nearly all cognitive measures. Removing PCAD participants from the normative sample yielded higher means and less variability on episodic memory, visuospatial ability, and executive functioning measures. These results were more pronounced in participants aged 75 years and older. Notably, removing PCAD participants from the sample significantly reduced age effects across all cognitive domains. Applying norms from the robust normal sample to a separate cohort did not improve Clinical Dementia Rating classification when using standard deviation cutoff scores. Overall, removing individuals with biomarker evidence of preclinical AD improves normative sample quality and substantially reduces age effects on cognitive performance but provides no substantive benefit for diagnostic classifications. Copyright © 2016 Elsevier Inc. All rights reserved.

Robust double gain unscented Kalman filter for small satellite attitude estimation

NASA Astrophysics Data System (ADS)

Cao, Lu; Yang, Weiwei; Li, Hengnian; Zhang, Zhidong; Shi, Jianjun

2017-08-01

Limited by the low precision of small satellite sensors, the estimation theories with high performance remains the most popular research topic for the attitude estimation. The Kalman filter (KF) and its extensions have been widely applied in the satellite attitude estimation and achieved plenty of achievements. However, most of the existing methods just take use of the current time-step's priori measurement residuals to complete the measurement update and state estimation, which always ignores the extraction and utilization of the previous time-step's posteriori measurement residuals. In addition, the uncertainty model errors always exist in the attitude dynamic system, which also put forward the higher performance requirements for the classical KF in attitude estimation problem. Therefore, the novel robust double gain unscented Kalman filter (RDG-UKF) is presented in this paper to satisfy the above requirements for the small satellite attitude estimation with the low precision sensors. It is assumed that the system state estimation errors can be exhibited in the measurement residual; therefore, the new method is to derive the second Kalman gain Kk2 for making full use of the previous time-step's measurement residual to improve the utilization efficiency of the measurement data. Moreover, the sequence orthogonal principle and unscented transform (UT) strategy are introduced to robust and enhance the performance of the novel Kalman Filter in order to reduce the influence of existing uncertainty model errors. Numerical simulations show that the proposed RDG-UKF is more effective and robustness in dealing with the model errors and low precision sensors for the attitude estimation of small satellite by comparing with the classical unscented Kalman Filter (UKF).

Robust Flutter Margin Analysis that Incorporates Flight Data

NASA Technical Reports Server (NTRS)

Lind, Rick; Brenner, Martin J.

1998-01-01

An approach for computing worst-case flutter margins has been formulated in a robust stability framework. Uncertainty operators are included with a linear model to describe modeling errors and flight variations. The structured singular value, mu, computes a stability margin that directly accounts for these uncertainties. This approach introduces a new method of computing flutter margins and an associated new parameter for describing these margins. The mu margins are robust margins that indicate worst-case stability estimates with respect to the defined uncertainty. Worst-case flutter margins are computed for the F/A-18 Systems Research Aircraft using uncertainty sets generated by flight data analysis. The robust margins demonstrate flight conditions for flutter may lie closer to the flight envelope than previously estimated by p-k analysis.

Global and system-specific resting-state fMRI fluctuations are uncorrelated: principal component analysis reveals anti-correlated networks.

PubMed

Carbonell, Felix; Bellec, Pierre; Shmuel, Amir

2011-01-01

The influence of the global average signal (GAS) on functional-magnetic resonance imaging (fMRI)-based resting-state functional connectivity is a matter of ongoing debate. The global average fluctuations increase the correlation between functional systems beyond the correlation that reflects their specific functional connectivity. Hence, removal of the GAS is a common practice for facilitating the observation of network-specific functional connectivity. This strategy relies on the implicit assumption of a linear-additive model according to which global fluctuations, irrespective of their origin, and network-specific fluctuations are super-positioned. However, removal of the GAS introduces spurious negative correlations between functional systems, bringing into question the validity of previous findings of negative correlations between fluctuations in the default-mode and the task-positive networks. Here we present an alternative method for estimating global fluctuations, immune to the complications associated with the GAS. Principal components analysis was applied to resting-state fMRI time-series. A global-signal effect estimator was defined as the principal component (PC) that correlated best with the GAS. The mean correlation coefficient between our proposed PC-based global effect estimator and the GAS was 0.97±0.05, demonstrating that our estimator successfully approximated the GAS. In 66 out of 68 runs, the PC that showed the highest correlation with the GAS was the first PC. Since PCs are orthogonal, our method provides an estimator of the global fluctuations, which is uncorrelated to the remaining, network-specific fluctuations. Moreover, unlike the regression of the GAS, the regression of the PC-based global effect estimator does not introduce spurious anti-correlations beyond the decrease in seed-based correlation values allowed by the assumed additive model. After regressing this PC-based estimator out of the original time-series, we observed robust anti-correlations between resting-state fluctuations in the default-mode and the task-positive networks. We conclude that resting-state global fluctuations and network-specific fluctuations are uncorrelated, supporting a Resting-State Linear-Additive Model. In addition, we conclude that the network-specific resting-state fluctuations of the default-mode and task-positive networks show artifact-free anti-correlations.

Magnitude Estimation for the 2011 Tohoku-Oki Earthquake Based on Ground Motion Prediction Equations

NASA Astrophysics Data System (ADS)

Eshaghi, Attieh; Tiampo, Kristy F.; Ghofrani, Hadi; Atkinson, Gail M.

2015-08-01

This study investigates whether real-time strong ground motion data from seismic stations could have been used to provide an accurate estimate of the magnitude of the 2011 Tohoku-Oki earthquake in Japan. Ultimately, such an estimate could be used as input data for a tsunami forecast and would lead to more robust earthquake and tsunami early warning. We collected the strong motion accelerograms recorded by borehole and free-field (surface) Kiban Kyoshin network stations that registered this mega-thrust earthquake in order to perform an off-line test to estimate the magnitude based on ground motion prediction equations (GMPEs). GMPEs for peak ground acceleration and peak ground velocity (PGV) from a previous study by Eshaghi et al. in the Bulletin of the Seismological Society of America 103. (2013) derived using events with moment magnitude ( M) ≥ 5.0, 1998-2010, were used to estimate the magnitude of this event. We developed new GMPEs using a more complete database (1998-2011), which added only 1 year but approximately twice as much data to the initial catalog (including important large events), to improve the determination of attenuation parameters and magnitude scaling. These new GMPEs were used to estimate the magnitude of the Tohoku-Oki event. The estimates obtained were compared with real time magnitude estimates provided by the existing earthquake early warning system in Japan. Unlike the current operational magnitude estimation methods, our method did not saturate and can provide robust estimates of moment magnitude within ~100 s after earthquake onset for both catalogs. It was found that correcting for average shear-wave velocity in the uppermost 30 m () improved the accuracy of magnitude estimates from surface recordings, particularly for magnitude estimates of PGV (Mpgv). The new GMPEs also were used to estimate the magnitude of all earthquakes in the new catalog with at least 20 records. Results show that the magnitude estimate from PGV values using borehole recordings had the smallest standard deviation among the estimated magnitudes and produced more stable and robust magnitude estimates. This suggests that incorporating borehole strong ground-motion records immediately available after the occurrence of large earthquakes can provide robust and accurate magnitude estimation.

Feedback Robust Cubature Kalman Filter for Target Tracking Using an Angle Sensor.

PubMed

Wu, Hao; Chen, Shuxin; Yang, Binfeng; Chen, Kun

2016-05-09

The direction of arrival (DOA) tracking problem based on an angle sensor is an important topic in many fields. In this paper, a nonlinear filter named the feedback M-estimation based robust cubature Kalman filter (FMR-CKF) is proposed to deal with measurement outliers from the angle sensor. The filter designs a new equivalent weight function with the Mahalanobis distance to combine the cubature Kalman filter (CKF) with the M-estimation method. Moreover, by embedding a feedback strategy which consists of a splitting and merging procedure, the proper sub-filter (the standard CKF or the robust CKF) can be chosen in each time index. Hence, the probability of the outliers' misjudgment can be reduced. Numerical experiments show that the FMR-CKF performs better than the CKF and conventional robust filters in terms of accuracy and robustness with good computational efficiency. Additionally, the filter can be extended to the nonlinear applications using other types of sensors.

Robust Mean and Covariance Structure Analysis through Iteratively Reweighted Least Squares.

ERIC Educational Resources Information Center

Yuan, Ke-Hai; Bentler, Peter M.

2000-01-01

Adapts robust schemes to mean and covariance structures, providing an iteratively reweighted least squares approach to robust structural equation modeling. Each case is weighted according to its distance, based on first and second order moments. Test statistics and standard error estimators are given. (SLD)

Toward robust estimation of the components of forest population change: simulation results

Treesearch

Francis A. Roesch

2014-01-01

This report presents the full simulation results of the work described in Roesch (2014), in which multiple levels of simulation were used to test the robustness of estimators for the components of forest change. In that study, a variety of spatial-temporal populations were created based on, but more variable than, an actual forest monitoring dataset, and then those...

Estimating 3D positions and velocities of projectiles from monocular views.

PubMed

Ribnick, Evan; Atev, Stefan; Papanikolopoulos, Nikolaos P

2009-05-01

In this paper, we consider the problem of localizing a projectile in 3D based on its apparent motion in a stationary monocular view. A thorough theoretical analysis is developed, from which we establish the minimum conditions for the existence of a unique solution. The theoretical results obtained have important implications for applications involving projectile motion. A robust, nonlinear optimization-based formulation is proposed, and the use of a local optimization method is justified by detailed examination of the local convexity structure of the cost function. The potential of this approach is validated by experimental results.

Sports and Child Development

PubMed Central

2016-01-01

The role of curricular activities for the formation of education, health and behavioural outcomes has been widely studied. Yet, the role of extra-curricular activities has received little attention. This study analyzes the effect of participation in sports clubs—one of the most popular extra-curricular activities among children. We use alternative datasets and flexible semi-parametric estimation methods with a specific way to use the panel dimension of the data to address selection into sports. We find positive and robust effects on children’s school performance and peer relations. Crowding out of passive leisure activities can partially explain the effects. PMID:27144474

Saccadic interception of a moving visual target after a spatiotemporal perturbation.

PubMed

Fleuriet, Jérome; Goffart, Laurent

2012-01-11

Animals can make saccadic eye movements to intercept a moving object at the right place and time. Such interceptive saccades indicate that, despite variable sensorimotor delays, the brain is able to estimate the current spatiotemporal (hic et nunc) coordinates of a target at saccade end. The present work further tests the robustness of this estimate in the monkey when a change in eye position and a delay are experimentally added before the onset of the saccade and in the absence of visual feedback. These perturbations are induced by brief microstimulation in the deep superior colliculus (dSC). When the microstimulation moves the eyes in the direction opposite to the target motion, a correction saccade brings gaze back on the target path or very near. When it moves the eye in the same direction, the performance is more variable and depends on the stimulated sites. Saccades fall ahead of the target with an error that increases when the stimulation is applied more caudally in the dSC. The numerous cases of compensation indicate that the brain is able to maintain an accurate and robust estimate of the location of the moving target. The inaccuracies observed when stimulating the dSC that encodes the visual field traversed by the target indicate that dSC microstimulation can interfere with signals encoding the target motion path. The results are discussed within the framework of the dual-drive and the remapping hypotheses.

Myocardial strains from 3D displacement encoded magnetic resonance imaging

PubMed Central

2012-01-01

Background The ability to measure and quantify myocardial motion and deformation provides a useful tool to assist in the diagnosis, prognosis and management of heart disease. The recent development of magnetic resonance imaging methods, such as harmonic phase analysis of tagging and displacement encoding with stimulated echoes (DENSE), make detailed non-invasive 3D kinematic analyses of human myocardium possible in the clinic and for research purposes. A robust analysis method is required, however. Methods We propose to estimate strain using a polynomial function which produces local models of the displacement field obtained with DENSE. Given a specific polynomial order, the model is obtained as the least squares fit of the acquired displacement field. These local models are subsequently used to produce estimates of the full strain tensor. Results The proposed method is evaluated on a numerical phantom as well as in vivo on a healthy human heart. The evaluation showed that the proposed method produced accurate results and showed low sensitivity to noise in the numerical phantom. The method was also demonstrated in vivo by assessment of the full strain tensor and to resolve transmural strain variations. Conclusions Strain estimation within a 3D myocardial volume based on polynomial functions yields accurate and robust results when validated on an analytical model. The polynomial field is capable of resolving the measured material positions from the in vivo data, and the obtained in vivo strains values agree with previously reported myocardial strains in normal human hearts. PMID:22533791

Budget impact analysis of everolimus for the treatment of hormone receptor positive, human epidermal growth factor receptor-2 negative (HER2-) advanced breast cancer in the United States.

PubMed

Xie, Jipan; Diener, Melissa; De, Gourab; Yang, Hongbo; Wu, Eric Q; Namjoshi, Madhav

2013-01-01

To estimate the budget impact of everolimus as the first and second treatment option after letrozole or anastrozole (L/A) failure for post-menopausal women with hormone receptor positive (HR+), human epidermal growth factor receptor-2 negative (HER2-) advanced breast cancer (ABC). Pharmacy and medical budget impacts (2011 USD) were estimated over the first year of everolimus use in HR+, HER2- ABC from a US payer perspective. Epidemiology data were used to estimate target population size. Pre-everolimus entry treatment options included exemestane, fulvestrant, and tamoxifen. Pre- and post-everolimus entry market shares were estimated based on market research and assumptions. Drug costs were based on wholesale acquisition cost. Patients were assumed to be on treatment until progression or death. Annual medical costs were calculated as the average of pre- and post-progression medical costs weighted by the time in each period, adjusted for survival. One-way and two-way sensitivity analyses were conducted to assess the model robustness. In a hypothetical 1,000,000 member plan, 72 and 159 patients were expected to be candidates for everolimus treatment as first and second treatment option, respectively, after L/A failure. The total budget impact for the first year post-everolimus entry was $0.044 per member per month [PMPM] (pharmacy budget: $0.058 PMPM; medical budget: -$0.014 PMPM), assuming 10% of the target population would receive everolimus. The total budget impacts for the first and second treatment options after L/A failure were $0.014 PMPM (pharmacy budget: $0.018; medical budget: -$0.004) and $0.030 PMPM (pharmacy budget: $0.040; medical budget: -$0.010), respectively. Results remained robust in sensitivity analyses. Assumptions about some model input parameters were necessary and may impact results. Increased pharmacy costs for HR+, HER2- ABC following everolimus entry are expected to be partially offset by reduced medical service costs. Pharmacy and total budget increases were modest.

Auto Regressive Moving Average (ARMA) Modeling Method for Gyro Random Noise Using a Robust Kalman Filter

PubMed Central

Huang, Lei

2015-01-01

To solve the problem in which the conventional ARMA modeling methods for gyro random noise require a large number of samples and converge slowly, an ARMA modeling method using a robust Kalman filtering is developed. The ARMA model parameters are employed as state arguments. Unknown time-varying estimators of observation noise are used to achieve the estimated mean and variance of the observation noise. Using the robust Kalman filtering, the ARMA model parameters are estimated accurately. The developed ARMA modeling method has the advantages of a rapid convergence and high accuracy. Thus, the required sample size is reduced. It can be applied to modeling applications for gyro random noise in which a fast and accurate ARMA modeling method is required. PMID:26437409

Robust Speech Enhancement Using Two-Stage Filtered Minima Controlled Recursive Averaging

NASA Astrophysics Data System (ADS)

Ghourchian, Negar; Selouani, Sid-Ahmed; O'Shaughnessy, Douglas

In this paper we propose an algorithm for estimating noise in highly non-stationary noisy environments, which is a challenging problem in speech enhancement. This method is based on minima-controlled recursive averaging (MCRA) whereby an accurate, robust and efficient noise power spectrum estimation is demonstrated. We propose a two-stage technique to prevent the appearance of musical noise after enhancement. This algorithm filters the noisy speech to achieve a robust signal with minimum distortion in the first stage. Subsequently, it estimates the residual noise using MCRA and removes it with spectral subtraction. The proposed Filtered MCRA (FMCRA) performance is evaluated using objective tests on the Aurora database under various noisy environments. These measures indicate the higher output SNR and lower output residual noise and distortion.

Robust control of the DC-DC boost converter based on the uncertainty and disturbance estimator

NASA Astrophysics Data System (ADS)

Oucheriah, Said

2017-11-01

In this paper, a robust non-linear controller based on the uncertainty and disturbance estimator (UDE) scheme is successfully developed and implemented for the output voltage regulation of the DC-DC boost converter. System uncertainties, external disturbances and unknown non-linear dynamics are lumped as a signal that is accurately estimated using a low-pass filter and their effects are cancelled by the controller. This methodology forms the basis of the UDE-based controller. A simple procedure is also developed that systematically determines the parameters of the controller to meet certain specifications. Using simulation, the effectiveness of the proposed controller is compared against the sliding-mode control (SMC). Experimental tests also show that the proposed controller is robust to system uncertainties, large input and load perturbations.

Stability Depends on Positive Autoregulation in Boolean Gene Regulatory Networks

PubMed Central

Pinho, Ricardo; Garcia, Victor; Irimia, Manuel; Feldman, Marcus W.

2014-01-01

Network motifs have been identified as building blocks of regulatory networks, including gene regulatory networks (GRNs). The most basic motif, autoregulation, has been associated with bistability (when positive) and with homeostasis and robustness to noise (when negative), but its general importance in network behavior is poorly understood. Moreover, how specific autoregulatory motifs are selected during evolution and how this relates to robustness is largely unknown. Here, we used a class of GRN models, Boolean networks, to investigate the relationship between autoregulation and network stability and robustness under various conditions. We ran evolutionary simulation experiments for different models of selection, including mutation and recombination. Each generation simulated the development of a population of organisms modeled by GRNs. We found that stability and robustness positively correlate with autoregulation; in all investigated scenarios, stable networks had mostly positive autoregulation. Assuming biological networks correspond to stable networks, these results suggest that biological networks should often be dominated by positive autoregulatory loops. This seems to be the case for most studied eukaryotic transcription factor networks, including those in yeast, flies and mammals. PMID:25375153

The Utility of Robust Means in Statistics

ERIC Educational Resources Information Center

Goodwyn, Fara

2012-01-01

Location estimates calculated from heuristic data were examined using traditional and robust statistical methods. The current paper demonstrates the impact outliers have on the sample mean and proposes robust methods to control for outliers in sample data. Traditional methods fail because they rely on the statistical assumptions of normality and…

An efficient sequential approach to tracking multiple objects through crowds for real-time intelligent CCTV systems.

PubMed

Li, Liyuan; Huang, Weimin; Gu, Irene Yu-Hua; Luo, Ruijiang; Tian, Qi

2008-10-01

Efficiency and robustness are the two most important issues for multiobject tracking algorithms in real-time intelligent video surveillance systems. We propose a novel 2.5-D approach to real-time multiobject tracking in crowds, which is formulated as a maximum a posteriori estimation problem and is approximated through an assignment step and a location step. Observing that the occluding object is usually less affected by the occluded objects, sequential solutions for the assignment and the location are derived. A novel dominant color histogram (DCH) is proposed as an efficient object model. The DCH can be regarded as a generalized color histogram, where dominant colors are selected based on a given distance measure. Comparing with conventional color histograms, the DCH only requires a few color components (31 on average). Furthermore, our theoretical analysis and evaluation on real data have shown that DCHs are robust to illumination changes. Using the DCH, efficient implementations of sequential solutions for the assignment and location steps are proposed. The assignment step includes the estimation of the depth order for the objects in a dispersing group, one-by-one assignment, and feature exclusion from the group representation. The location step includes the depth-order estimation for the objects in a new group, the two-phase mean-shift location, and the exclusion of tracked objects from the new position in the group. Multiobject tracking results and evaluation from public data sets are presented. Experiments on image sequences captured from crowded public environments have shown good tracking results, where about 90% of the objects have been successfully tracked with the correct identification numbers by the proposed method. Our results and evaluation have indicated that the method is efficient and robust for tracking multiple objects (>or= 3) in complex occlusion for real-world surveillance scenarios.

Efficacy of robust optimization plan with partial-arc VMAT for photon volumetric-modulated arc therapy: A phantom study.

PubMed

Miura, Hideharu; Ozawa, Shuichi; Nagata, Yasushi

2017-09-01

This study investigated position dependence in planning target volume (PTV)-based and robust optimization plans using full-arc and partial-arc volumetric modulated arc therapy (VMAT). The gantry angles at the periphery, intermediate, and center CTV positions were 181°-180° (full-arc VMAT) and 181°-360° (partial-arc VMAT). A PTV-based optimization plan was defined by 5 mm margin expansion of the CTV to a PTV volume, on which the dose constraints were applied. The robust optimization plan consisted of a directly optimized dose to the CTV under a maximum-uncertainties setup of 5 mm. The prescription dose was normalized to the CTV D 99% (the minimum relative dose that covers 99% of the volume of the CTV) as an original plan. The isocenter was rigidly shifted at 1 mm intervals in the anterior-posterior (A-P), superior-inferior (S-I), and right-left (R-L) directions from the original position to the maximum-uncertainties setup of 5 mm in the original plan, yielding recalculated dose distributions. It was found that for the intermediate and center positions, the uncertainties in the D 99% doses to the CTV for all directions did not significantly differ when comparing the PTV-based and robust optimization plans (P > 0.05). For the periphery position, uncertainties in the D 99% doses to the CTV in the R-L direction for the robust optimization plan were found to be lower than those in the PTV-based optimization plan (P < 0.05). Our study demonstrated that a robust optimization plan's efficacy using partial-arc VMAT depends on the periphery CTV position. © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Exploiting passive polarimetric imagery for remote sensing applications

NASA Astrophysics Data System (ADS)

Vimal Thilak Krishna, Thilakam

Polarization is a property of light or electromagnetic radiation that conveys information about the orientation of the transverse electric and magnetic fields. The polarization of reflected light complements other electromagnetic radiation attributes such as intensity, frequency, or spectral characteristics. A passive polarization based imaging system records the polarization state of light reflected by objects that are illuminated with an unpolarized and generally uncontrolled source. The polarization due to surface reflections from such objects contains information about the targets that can be exploited in remote sensing applications such as target detection, target classification, object recognition and shape extraction/recognition. In recent years, there has been renewed interest in the use of passive polarization information in remote sensing applications. The goal of our research is to design image processing algorithms for remote sensing applications by utilizing physics-based models that describe the polarization imparted by optical scattering from an object. In this dissertation, we present a method to estimate the complex index of refraction and reflection angle from multiple polarization measurements. This method employs a polarimetric bidirectional reflectance distribution function (pBRDF) that accounts for polarization due to specular scattering. The parameters of interest are derived by utilizing a nonlinear least squares estimation algorithm, and computer simulation results show that the estimation accuracy generally improves with an increasing number of source position measurements. Furthermore, laboratory results indicate that the proposed method is effective for recovering the reflection angle and that the estimated index of refraction provides a feature vector that is robust to the reflection angle. We also study the use of extracted index of refraction as a feature vector in designing two important image processing applications, namely image segmentation and material classification so that the resulting systems are largely invariant to illumination source location. This is in contrast to most passive polarization-based image processing algorithms proposed in the literature that employ quantities such as Stokes vectors and the degree of polarization and which are not robust to changes in illumination conditions. The estimated index of refraction, on the other hand, is invariant to illumination conditions and hence can be used as an input to image processing algorithms. The proposed estimation framework also is extended to the case where the position of the observer (camera) moves between measurements while that of the source remains fixed. Finally, we explore briefly the topic of parameter estimation for a generalized model that accounts for both specular and volumetric scattering. A combination of simulation and experimental results are provided to evaluate the effectiveness of the above methods.

Robust estimation for class averaging in cryo-EM Single Particle Reconstruction.

PubMed

Huang, Chenxi; Tagare, Hemant D

2014-01-01

Single Particle Reconstruction (SPR) for Cryogenic Electron Microscopy (cryo-EM) aligns and averages the images extracted from micrographs to improve the Signal-to-Noise ratio (SNR). Outliers compromise the fidelity of the averaging. We propose a robust cross-correlation-like w-estimator for combating the effect of outliers on the average images in cryo-EM. The estimator accounts for the natural variation of signal contrast among the images and eliminates the need for a threshold for outlier rejection. We show that the influence function of our estimator is asymptotically bounded. Evaluations of the estimator on simulated and real cryo-EM images show good performance in the presence of outliers.

Evaluation of the robustness of estimating five components from a skin spectral image

NASA Astrophysics Data System (ADS)

Akaho, Rina; Hirose, Misa; Tsumura, Norimichi

2018-04-01

We evaluated the robustness of a method used to estimate five components (i.e., melanin, oxy-hemoglobin, deoxy-hemoglobin, shading, and surface reflectance) from the spectral reflectance of skin at five wavelengths against noise and a change in epidermis thickness. We also estimated the five components from recorded images of age spots and circles under the eyes using the method. We found that noise in the image must be no more 0.1% to accurately estimate the five components and that the thickness of the epidermis affects the estimation. We acquired the distribution of major causes for age spots and circles under the eyes by applying the method to recorded spectral images.

Progress in using real-time GPS for seismic monitoring of the Cascadia megathrust

NASA Astrophysics Data System (ADS)

Szeliga, W. M.; Melbourne, T. I.; Santillan, V. M.; Scrivner, C.; Webb, F.

2014-12-01

We report on progress in our development of a comprehensive real-time GPS-based seismic monitoring system for the Cascadia subduction zone. This system is based on 1 Hz point position estimates computed in the ITRF08 reference frame. Convergence from phase and range observables to point position estimates is accelerated using a Kalman filter based, on-line stream editor. Positions are estimated using a short-arc approach and algorithms from JPL's GIPSY-OASIS software with satellite clock and orbit products from the International GNSS Service (IGS). The resulting positions show typical RMS scatter of 2.5 cm in the horizontal and 5 cm in the vertical with latencies below 2 seconds. To facilitate the use of these point position streams for applications such as seismic monitoring, we broadcast real-time positions and covariances using custom-built streaming software. This software is capable of buffering 24-hour streams for hundreds of stations and providing them through a REST-ful web interface. To demonstrate the power of this approach, we have developed a Java-based front-end that provides a real-time visual display of time-series, vector displacement, and contoured peak ground displacement. We have also implemented continuous estimation of finite fault slip along the Cascadia megathrust using an NIF approach. The resulting continuous slip distributions are combined with pre-computed tsunami Green's functions to generate real-time tsunami run-up estimates for the entire Cascadia coastal margin. This Java-based front-end is available for download through the PANGA website. We currently analyze 80 PBO and PANGA stations along the Cascadia margin and are gearing up to process all 400+ real-time stations operating in the Pacific Northwest, many of which are currently telemetered in real-time to CWU. These will serve as milestones towards our over-arching goal of extending our processing to include all of the available real-time streams from the Pacific rim. In addition, we are developing methodologies to combine our real-time solutions with those from Scripps Institute of Oceanography's PPP-AR real-time solutions as well as real-time solutions from the USGS. These combined products should improve the robustness and reliability of real-time point-position streams in the near future.

False Positive and False Negative Effects on Network Attacks

NASA Astrophysics Data System (ADS)

Shang, Yilun

2018-01-01

Robustness against attacks serves as evidence for complex network structures and failure mechanisms that lie behind them. Most often, due to detection capability limitation or good disguises, attacks on networks are subject to false positives and false negatives, meaning that functional nodes may be falsely regarded as compromised by the attacker and vice versa. In this work, we initiate a study of false positive/negative effects on network robustness against three fundamental types of attack strategies, namely, random attacks (RA), localized attacks (LA), and targeted attack (TA). By developing a general mathematical framework based upon the percolation model, we investigate analytically and by numerical simulations of attack robustness with false positive/negative rate (FPR/FNR) on three benchmark models including Erdős-Rényi (ER) networks, random regular (RR) networks, and scale-free (SF) networks. We show that ER networks are equivalently robust against RA and LA only when FPR equals zero or the initial network is intact. We find several interesting crossovers in RR and SF networks when FPR is taken into consideration. By defining the cost of attack, we observe diminishing marginal attack efficiency for RA, LA, and TA. Our finding highlights the potential risk of underestimating or ignoring FPR in understanding attack robustness. The results may provide insights into ways of enhancing robustness of network architecture and improve the level of protection of critical infrastructures.

WTA estimates using the method of paired comparison: tests of robustness

Treesearch

Patricia A. Champ; John B. Loomis

1998-01-01

The method of paired comparison is modified to allow choices between two alternative gains so as to estimate willingness to accept (WTA) without loss aversion. The robustness of WTA values for two public goods is tested with respect to sensitivity of theWTA measure to the context of the bundle of goods used in the paired comparison exercise and to the scope (scale) of...

Robust inference under the beta regression model with application to health care studies.

PubMed

Ghosh, Abhik

2017-01-01

Data on rates, percentages, or proportions arise frequently in many different applied disciplines like medical biology, health care, psychology, and several others. In this paper, we develop a robust inference procedure for the beta regression model, which is used to describe such response variables taking values in (0, 1) through some related explanatory variables. In relation to the beta regression model, the issue of robustness has been largely ignored in the literature so far. The existing maximum likelihood-based inference has serious lack of robustness against outliers in data and generate drastically different (erroneous) inference in the presence of data contamination. Here, we develop the robust minimum density power divergence estimator and a class of robust Wald-type tests for the beta regression model along with several applications. We derive their asymptotic properties and describe their robustness theoretically through the influence function analyses. Finite sample performances of the proposed estimators and tests are examined through suitable simulation studies and real data applications in the context of health care and psychology. Although we primarily focus on the beta regression models with a fixed dispersion parameter, some indications are also provided for extension to the variable dispersion beta regression models with an application.

Prevalence of asymptomatic Zika virus infection: a systematic review.

PubMed

Haby, Michelle M; Pinart, Mariona; Elias, Vanessa; Reveiz, Ludovic

2018-06-01

To conduct a systematic review to estimate the prevalence of asymptomatic Zika virus infection in the general population and in specific population groups. We searched PubMed®, Embase® and LILACS online databases from inception to 26 January 2018. We included observational epidemiological studies where laboratory testing was used to confirm positive exposure of participants to Zika virus and in which Zika virus symptom status was also recorded. We excluded studies in which having symptoms of Zika virus was a criterion for inclusion. The main outcome assessed was percentage of all Zika virus-positive participants who were asymptomatic. We used a quality-effects approach and the double arcsine transformation for the meta-analysis. We assessed 753 studies for inclusion, of which 23 were included in the meta-analysis, totalling 11 305 Zika virus-positive participants. The high degree of heterogeneity in the studies ( I 2  = 99%) suggests that the pooled prevalence of asymptomatic Zika virus-positive participants was probably not a robust estimate. Analysis based on subgroups of the population (general population, returned travellers, blood donors, adults with Guillain-Barré syndrome, pregnant women and babies with microcephaly) was not able to explain the heterogeneity. Funnel and Doi plots showed major asymmetry, suggesting selection bias or true heterogeneity. Better-quality research is needed, using standardized methods, to determine the true prevalence of asymptomatic Zika virus and whether it varies between populations or over time.

Cost-effectiveness of pembrolizumab versus docetaxel for the treatment of previously treated PD-L1 positive advanced NSCLC patients in the United States.

PubMed

Huang, Min; Lou, Yanyan; Pellissier, James; Burke, Thomas; Liu, Frank Xiaoqing; Xu, Ruifeng; Velcheti, Vamsidhar

2017-02-01

This analysis aimed to evaluate the cost-effectiveness of pembrolizumab compared with docetaxel in patients with previously treated advanced non-squamous cell lung cancer (NSCLC) with PD-L1 positive tumors (total proportion score [TPS] ≥ 50%). The analysis was conducted from a US third-party payer perspective. A partitioned-survival model was developed using data from patients from the KEYNOTE 010 clinical trial. The model used Kaplan-Meier (KM) estimates of progression-free survival (PFS) and overall survival (OS) from the trial for patients treated with either pembrolizumab 2 mg/kg or docetaxel 75 mg/m 2 with extrapolation based on fitted parametric functions and long-term registry data. Quality-adjusted life years (QALYs) were derived based on EQ-5D data from KEYNOTE 010 using a time to death approach. Costs of drug acquisition/administration, adverse event management, and clinical management of advanced NSCLC were included in the model. The base-case analysis used a time horizon of 20 years. Costs and health outcomes were discounted at a rate of 3% per year. A series of one-way and probabilistic sensitivity analyses were performed to test the robustness of the results. Base case results project for PD-L1 positive (TPS ≥50%) patients treated with pembrolizumab a mean survival of 2.25 years. For docetaxel, a mean survival time of 1.07 years was estimated. Expected QALYs were 1.71 and 0.76 for pembrolizumab and docetaxel, respectively. The incremental cost per QALY gained with pembrolizumab vs docetaxel is $168,619/QALY, which is cost-effective in the US using a threshold of 3-times GDP per capita. Sensitivity analyses showed the results to be robust over plausible values of the majority of inputs. Results were most sensitive to extrapolation of overall survival. Pembrolizumab improves survival, increases QALYs, and can be considered as a cost-effective option compared to docetaxel in PD-L1 positive (TPS ≥50%) pre-treated advanced NSCLC patients in the US.

Robust estimates of environmental effects on population vital rates: an integrated capture–recapture model of seasonal brook trout growth, survival and movement in a stream network

USGS Publications Warehouse

Letcher, Benjamin H.; Schueller, Paul; Bassar, Ronald D.; Nislow, Keith H.; Coombs, Jason A.; Sakrejda, Krzysztof; Morrissey, Michael; Sigourney, Douglas B.; Whiteley, Andrew R.; O'Donnell, Matthew J.; Dubreuil, Todd L.

2015-01-01

Modelling the effects of environmental change on populations is a key challenge for ecologists, particularly as the pace of change increases. Currently, modelling efforts are limited by difficulties in establishing robust relationships between environmental drivers and population responses.We developed an integrated capture–recapture state-space model to estimate the effects of two key environmental drivers (stream flow and temperature) on demographic rates (body growth, movement and survival) using a long-term (11 years), high-resolution (individually tagged, sampled seasonally) data set of brook trout (Salvelinus fontinalis) from four sites in a stream network. Our integrated model provides an effective context within which to estimate environmental driver effects because it takes full advantage of data by estimating (latent) state values for missing observations, because it propagates uncertainty among model components and because it accounts for the major demographic rates and interactions that contribute to annual survival.We found that stream flow and temperature had strong effects on brook trout demography. Some effects, such as reduction in survival associated with low stream flow and high temperature during the summer season, were consistent across sites and age classes, suggesting that they may serve as robust indicators of vulnerability to environmental change. Other survival effects varied across ages, sites and seasons, indicating that flow and temperature may not be the primary drivers of survival in those cases. Flow and temperature also affected body growth rates; these responses were consistent across sites but differed dramatically between age classes and seasons. Finally, we found that tributary and mainstem sites responded differently to variation in flow and temperature.Annual survival (combination of survival and body growth across seasons) was insensitive to body growth and was most sensitive to flow (positive) and temperature (negative) in the summer and fall.These observations, combined with our ability to estimate the occurrence, magnitude and direction of fish movement between these habitat types, indicated that heterogeneity in response may provide a mechanism providing potential resilience to environmental change. Given that the challenges we faced in our study are likely to be common to many intensive data sets, the integrated modelling approach could be generally applicable and useful.

Robust estimates of environmental effects on population vital rates: an integrated capture-recapture model of seasonal brook trout growth, survival and movement in a stream network.

PubMed

Letcher, Benjamin H; Schueller, Paul; Bassar, Ronald D; Nislow, Keith H; Coombs, Jason A; Sakrejda, Krzysztof; Morrissey, Michael; Sigourney, Douglas B; Whiteley, Andrew R; O'Donnell, Matthew J; Dubreuil, Todd L

2015-03-01

Modelling the effects of environmental change on populations is a key challenge for ecologists, particularly as the pace of change increases. Currently, modelling efforts are limited by difficulties in establishing robust relationships between environmental drivers and population responses. We developed an integrated capture-recapture state-space model to estimate the effects of two key environmental drivers (stream flow and temperature) on demographic rates (body growth, movement and survival) using a long-term (11 years), high-resolution (individually tagged, sampled seasonally) data set of brook trout (Salvelinus fontinalis) from four sites in a stream network. Our integrated model provides an effective context within which to estimate environmental driver effects because it takes full advantage of data by estimating (latent) state values for missing observations, because it propagates uncertainty among model components and because it accounts for the major demographic rates and interactions that contribute to annual survival. We found that stream flow and temperature had strong effects on brook trout demography. Some effects, such as reduction in survival associated with low stream flow and high temperature during the summer season, were consistent across sites and age classes, suggesting that they may serve as robust indicators of vulnerability to environmental change. Other survival effects varied across ages, sites and seasons, indicating that flow and temperature may not be the primary drivers of survival in those cases. Flow and temperature also affected body growth rates; these responses were consistent across sites but differed dramatically between age classes and seasons. Finally, we found that tributary and mainstem sites responded differently to variation in flow and temperature. Annual survival (combination of survival and body growth across seasons) was insensitive to body growth and was most sensitive to flow (positive) and temperature (negative) in the summer and fall. These observations, combined with our ability to estimate the occurrence, magnitude and direction of fish movement between these habitat types, indicated that heterogeneity in response may provide a mechanism providing potential resilience to environmental change. Given that the challenges we faced in our study are likely to be common to many intensive data sets, the integrated modelling approach could be generally applicable and useful. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.

Cost-effectiveness of adjuvant docetaxel for node-positive breast cancer patients: results of the PACS 01 economic study.

PubMed

Marino, P; Siani, C; Roché, H; Protière, C; Fumoleau, P; Spielmann, M; Martin, A-L; Viens, P; Le Corroller Soriano, A-G

2010-07-01

Using data from the PACS 01 randomized trial, we evaluated the cost-effectiveness of anthracyclines plus docetaxel (Taxotere; FEC-D) versus anthracyclines alone (FEC100) in patients with node-positive breast cancer. Costs and outcomes were assessed in 1996 patients and the incremental cost-effectiveness ratios (ICERs) were estimated, using quality-adjusted life years (QALYs) as outcome. To deal with uncertainty due to sampling fluctuations, confidence regions around the ICERs were calculated and cost-effectiveness acceptability curves were drawn up. Sensitivity analyses were also carried out to assess the robustness of conclusions. The mean cost of treatment was 33% higher with strategy FEC-D, but this difference decreased to 18% at a 5-year horizon. The ICER of FEC-D versus FEC100 was estimated to be 9665euro per QALY gained (95% confidence interval euro2372-euro55 515). The estimated probability that FEC-D was cost-effective reached >96% for a threshold of euro50 000 per QALY gained. If the price of taxane decreased slightly, the ICER would reach some very reasonable levels and this strategy would therefore be much more cost-effective. The sequential use of FEC100 followed by docetaxel appears to be a cost-effective alternative, even when uncertainty is taken into account.

Music-Elicited Emotion Identification Using Optical Flow Analysis of Human Face

NASA Astrophysics Data System (ADS)

Kniaz, V. V.; Smirnova, Z. N.

2015-05-01

Human emotion identification from image sequences is highly demanded nowadays. The range of possible applications can vary from an automatic smile shutter function of consumer grade digital cameras to Biofied Building technologies, which enables communication between building space and residents. The highly perceptual nature of human emotions leads to the complexity of their classification and identification. The main question arises from the subjective quality of emotional classification of events that elicit human emotions. A variety of methods for formal classification of emotions were developed in musical psychology. This work is focused on identification of human emotions evoked by musical pieces using human face tracking and optical flow analysis. Facial feature tracking algorithm used for facial feature speed and position estimation is presented. Facial features were extracted from each image sequence using human face tracking with local binary patterns (LBP) features. Accurate relative speeds of facial features were estimated using optical flow analysis. Obtained relative positions and speeds were used as the output facial emotion vector. The algorithm was tested using original software and recorded image sequences. The proposed technique proves to give a robust identification of human emotions elicited by musical pieces. The estimated models could be used for human emotion identification from image sequences in such fields as emotion based musical background or mood dependent radio.

Robust H(∞) positional control of 2-DOF robotic arm driven by electro-hydraulic servo system.

PubMed

Guo, Qing; Yu, Tian; Jiang, Dan

2015-11-01

In this paper an H∞ positional feedback controller is developed to improve the robust performance under structural and parametric uncertainty disturbance in electro-hydraulic servo system (EHSS). The robust control model is described as the linear state-space equation by upper linear fractional transformation. According to the solution of H∞ sub-optimal control problem, the robust controller is designed and simplified to lower order linear model which is easily realized in EHSS. The simulation and experimental results can validate the robustness of this proposed method. The comparison result with PI control shows that the robust controller is suitable for this EHSS under the critical condition where the desired system bandwidth is higher and the external load of the hydraulic actuator is closed to its limited capability. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Robust Angle Estimation for MIMO Radar with the Coexistence of Mutual Coupling and Colored Noise.

PubMed

Wang, Junxiang; Wang, Xianpeng; Xu, Dingjie; Bi, Guoan

2018-03-09

This paper deals with joint estimation of direction-of-departure (DOD) and direction-of- arrival (DOA) in bistatic multiple-input multiple-output (MIMO) radar with the coexistence of unknown mutual coupling and spatial colored noise by developing a novel robust covariance tensor-based angle estimation method. In the proposed method, a third-order tensor is firstly formulated for capturing the multidimensional nature of the received data. Then taking advantage of the temporal uncorrelated characteristic of colored noise and the banded complex symmetric Toeplitz structure of the mutual coupling matrices, a novel fourth-order covariance tensor is constructed for eliminating the influence of both spatial colored noise and mutual coupling. After a robust signal subspace estimation is obtained by using the higher-order singular value decomposition (HOSVD) technique, the rotational invariance technique is applied to achieve the DODs and DOAs. Compared with the existing HOSVD-based subspace methods, the proposed method can provide superior angle estimation performance and automatically jointly perform the DODs and DOAs. Results from numerical experiments are presented to verify the effectiveness of the proposed method.

A Secure Trust Establishment Scheme for Wireless Sensor Networks

PubMed Central

Ishmanov, Farruh; Kim, Sung Won; Nam, Seung Yeob

2014-01-01

Trust establishment is an important tool to improve cooperation and enhance security in wireless sensor networks. The core of trust establishment is trust estimation. If a trust estimation method is not robust against attack and misbehavior, the trust values produced will be meaningless, and system performance will be degraded. We present a novel trust estimation method that is robust against on-off attacks and persistent malicious behavior. Moreover, in order to aggregate recommendations securely, we propose using a modified one-step M-estimator scheme. The novelty of the proposed scheme arises from combining past misbehavior with current status in a comprehensive way. Specifically, we introduce an aggregated misbehavior component in trust estimation, which assists in detecting an on-off attack and persistent malicious behavior. In order to determine the current status of the node, we employ previous trust values and current measured misbehavior components. These components are combined to obtain a robust trust value. Theoretical analyses and evaluation results show that our scheme performs better than other trust schemes in terms of detecting an on-off attack and persistent misbehavior. PMID:24451471

Robust Angle Estimation for MIMO Radar with the Coexistence of Mutual Coupling and Colored Noise

PubMed Central

Wang, Junxiang; Wang, Xianpeng; Xu, Dingjie; Bi, Guoan

2018-01-01

This paper deals with joint estimation of direction-of-departure (DOD) and direction-of- arrival (DOA) in bistatic multiple-input multiple-output (MIMO) radar with the coexistence of unknown mutual coupling and spatial colored noise by developing a novel robust covariance tensor-based angle estimation method. In the proposed method, a third-order tensor is firstly formulated for capturing the multidimensional nature of the received data. Then taking advantage of the temporal uncorrelated characteristic of colored noise and the banded complex symmetric Toeplitz structure of the mutual coupling matrices, a novel fourth-order covariance tensor is constructed for eliminating the influence of both spatial colored noise and mutual coupling. After a robust signal subspace estimation is obtained by using the higher-order singular value decomposition (HOSVD) technique, the rotational invariance technique is applied to achieve the DODs and DOAs. Compared with the existing HOSVD-based subspace methods, the proposed method can provide superior angle estimation performance and automatically jointly perform the DODs and DOAs. Results from numerical experiments are presented to verify the effectiveness of the proposed method. PMID:29522499

A subagging regression method for estimating the qualitative and quantitative state of groundwater

NASA Astrophysics Data System (ADS)

Jeong, Jina; Park, Eungyu; Han, Weon Shik; Kim, Kue-Young

2017-08-01

A subsample aggregating (subagging) regression (SBR) method for the analysis of groundwater data pertaining to trend-estimation-associated uncertainty is proposed. The SBR method is validated against synthetic data competitively with other conventional robust and non-robust methods. From the results, it is verified that the estimation accuracies of the SBR method are consistent and superior to those of other methods, and the uncertainties are reasonably estimated; the others have no uncertainty analysis option. To validate further, actual groundwater data are employed and analyzed comparatively with Gaussian process regression (GPR). For all cases, the trend and the associated uncertainties are reasonably estimated by both SBR and GPR regardless of Gaussian or non-Gaussian skewed data. However, it is expected that GPR has a limitation in applications to severely corrupted data by outliers owing to its non-robustness. From the implementations, it is determined that the SBR method has the potential to be further developed as an effective tool of anomaly detection or outlier identification in groundwater state data such as the groundwater level and contaminant concentration.

Reliable spacecraft rendezvous without velocity measurement

NASA Astrophysics Data System (ADS)

He, Shaoming; Lin, Defu

2018-03-01

This paper investigates the problem of finite-time velocity-free autonomous rendezvous for spacecraft in the presence of external disturbances during the terminal phase. First of all, to address the problem of lack of relative velocity measurement, a robust observer is proposed to estimate the unknown relative velocity information in a finite time. It is shown that the effect of external disturbances on the estimation precision can be suppressed to a relatively low level. With the reconstructed velocity information, a finite-time output feedback control law is then formulated to stabilize the rendezvous system. Theoretical analysis and rigorous proof show that the relative position and its rate can converge to a small compacted region in finite time. Numerical simulations are performed to evaluate the performance of the proposed approach in the presence of external disturbances and actuator faults.

Recent Experience with a Hybrid SCADA/PMU On-Line State Estimator

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rizy, D Tom

2009-01-01

PMU devices are expected to grow in number from a few to several hundreds in the next five years. Some relays are already global positioning system-capable and could provide the same type of data as any PMU. This introduces a new paradigm of very fast accurate synchrophasor measurements from across the grid in real-time that augment and parallel existing slower SCADA measurements. Control center applications will benefit from this PMU data; for example, use of PMU data in state estimation is expected to improve accuracy and robustness, which in turn will result in more timely and accurate N-1 security analysis,more » resulting in an overall improvement of grid system reliability and security. This paper describes results from a recent implementation of this technology, the benefits and future work.« less

Multiple robustness in factorized likelihood models.

PubMed

Molina, J; Rotnitzky, A; Sued, M; Robins, J M

2017-09-01

We consider inference under a nonparametric or semiparametric model with likelihood that factorizes as the product of two or more variation-independent factors. We are interested in a finite-dimensional parameter that depends on only one of the likelihood factors and whose estimation requires the auxiliary estimation of one or several nuisance functions. We investigate general structures conducive to the construction of so-called multiply robust estimating functions, whose computation requires postulating several dimension-reducing models but which have mean zero at the true parameter value provided one of these models is correct.

Relations between Brain Structure and Attentional Function in Spina Bifida: Utilization of Robust Statistical Approaches

PubMed Central

Kulesz, Paulina A.; Tian, Siva; Juranek, Jenifer; Fletcher, Jack M.; Francis, David J.

2015-01-01

Objective Weak structure-function relations for brain and behavior may stem from problems in estimating these relations in small clinical samples with frequently occurring outliers. In the current project, we focused on the utility of using alternative statistics to estimate these relations. Method Fifty-four children with spina bifida meningomyelocele performed attention tasks and received MRI of the brain. Using a bootstrap sampling process, the Pearson product moment correlation was compared with four robust correlations: the percentage bend correlation, the Winsorized correlation, the skipped correlation using the Donoho-Gasko median, and the skipped correlation using the minimum volume ellipsoid estimator Results All methods yielded similar estimates of the relations between measures of brain volume and attention performance. The similarity of estimates across correlation methods suggested that the weak structure-function relations previously found in many studies are not readily attributable to the presence of outlying observations and other factors that violate the assumptions behind the Pearson correlation. Conclusions Given the difficulty of assembling large samples for brain-behavior studies, estimating correlations using multiple, robust methods may enhance the statistical conclusion validity of studies yielding small, but often clinically significant, correlations. PMID:25495830

Relations between volumetric measures of brain structure and attentional function in spina bifida: utilization of robust statistical approaches.

PubMed

Kulesz, Paulina A; Tian, Siva; Juranek, Jenifer; Fletcher, Jack M; Francis, David J

2015-03-01

Weak structure-function relations for brain and behavior may stem from problems in estimating these relations in small clinical samples with frequently occurring outliers. In the current project, we focused on the utility of using alternative statistics to estimate these relations. Fifty-four children with spina bifida meningomyelocele performed attention tasks and received MRI of the brain. Using a bootstrap sampling process, the Pearson product-moment correlation was compared with 4 robust correlations: the percentage bend correlation, the Winsorized correlation, the skipped correlation using the Donoho-Gasko median, and the skipped correlation using the minimum volume ellipsoid estimator. All methods yielded similar estimates of the relations between measures of brain volume and attention performance. The similarity of estimates across correlation methods suggested that the weak structure-function relations previously found in many studies are not readily attributable to the presence of outlying observations and other factors that violate the assumptions behind the Pearson correlation. Given the difficulty of assembling large samples for brain-behavior studies, estimating correlations using multiple, robust methods may enhance the statistical conclusion validity of studies yielding small, but often clinically significant, correlations. PsycINFO Database Record (c) 2015 APA, all rights reserved.

Heading Estimation for Pedestrian Dead Reckoning Based on Robust Adaptive Kalman Filtering.

PubMed

Wu, Dongjin; Xia, Linyuan; Geng, Jijun

2018-06-19

Pedestrian dead reckoning (PDR) using smart phone-embedded micro-electro-mechanical system (MEMS) sensors plays a key role in ubiquitous localization indoors and outdoors. However, as a relative localization method, it suffers from the problem of error accumulation which prevents it from long term independent running. Heading estimation error is one of the main location error sources, and therefore, in order to improve the location tracking performance of the PDR method in complex environments, an approach based on robust adaptive Kalman filtering (RAKF) for estimating accurate headings is proposed. In our approach, outputs from gyroscope, accelerometer, and magnetometer sensors are fused using the solution of Kalman filtering (KF) that the heading measurements derived from accelerations and magnetic field data are used to correct the states integrated from angular rates. In order to identify and control measurement outliers, a maximum likelihood-type estimator (M-estimator)-based model is used. Moreover, an adaptive factor is applied to resist the negative effects of state model disturbances. Extensive experiments under static and dynamic conditions were conducted in indoor environments. The experimental results demonstrate the proposed approach provides more accurate heading estimates and supports more robust and dynamic adaptive location tracking, compared with methods based on conventional KF.

A robust method using propensity score stratification for correcting verification bias for binary tests

PubMed Central

He, Hua; McDermott, Michael P.

2012-01-01

Sensitivity and specificity are common measures of the accuracy of a diagnostic test. The usual estimators of these quantities are unbiased if data on the diagnostic test result and the true disease status are obtained from all subjects in an appropriately selected sample. In some studies, verification of the true disease status is performed only for a subset of subjects, possibly depending on the result of the diagnostic test and other characteristics of the subjects. Estimators of sensitivity and specificity based on this subset of subjects are typically biased; this is known as verification bias. Methods have been proposed to correct verification bias under the assumption that the missing data on disease status are missing at random (MAR), that is, the probability of missingness depends on the true (missing) disease status only through the test result and observed covariate information. When some of the covariates are continuous, or the number of covariates is relatively large, the existing methods require parametric models for the probability of disease or the probability of verification (given the test result and covariates), and hence are subject to model misspecification. We propose a new method for correcting verification bias based on the propensity score, defined as the predicted probability of verification given the test result and observed covariates. This is estimated separately for those with positive and negative test results. The new method classifies the verified sample into several subsamples that have homogeneous propensity scores and allows correction for verification bias. Simulation studies demonstrate that the new estimators are more robust to model misspecification than existing methods, but still perform well when the models for the probability of disease and probability of verification are correctly specified. PMID:21856650

Bayesian Inference and Application of Robust Growth Curve Models Using Student's "t" Distribution

ERIC Educational Resources Information Center

Zhang, Zhiyong; Lai, Keke; Lu, Zhenqiu; Tong, Xin

2013-01-01

Despite the widespread popularity of growth curve analysis, few studies have investigated robust growth curve models. In this article, the "t" distribution is applied to model heavy-tailed data and contaminated normal data with outliers for growth curve analysis. The derived robust growth curve models are estimated through Bayesian…

Enhanced echolocation via robust statistics and super-resolution of sonar images

NASA Astrophysics Data System (ADS)

Kim, Kio

Echolocation is a process in which an animal uses acoustic signals to exchange information with environments. In a recent study, Neretti et al. have shown that the use of robust statistics can significantly improve the resiliency of echolocation against noise and enhance its accuracy by suppressing the development of sidelobes in the processing of an echo signal. In this research, the use of robust statistics is extended to problems in underwater explorations. The dissertation consists of two parts. Part I describes how robust statistics can enhance the identification of target objects, which in this case are cylindrical containers filled with four different liquids. Particularly, this work employs a variation of an existing robust estimator called an L-estimator, which was first suggested by Koenker and Bassett. As pointed out by Au et al.; a 'highlight interval' is an important feature, and it is closely related with many other important features that are known to be crucial for dolphin echolocation. A varied L-estimator described in this text is used to enhance the detection of highlight intervals, which eventually leads to a successful classification of echo signals. Part II extends the problem into 2 dimensions. Thanks to the advances in material and computer technology, various sonar imaging modalities are available on the market. By registering acoustic images from such video sequences, one can extract more information on the region of interest. Computer vision and image processing allowed application of robust statistics to the acoustic images produced by forward looking sonar systems, such as Dual-frequency Identification Sonar and ProViewer. The first use of robust statistics for sonar image enhancement in this text is in image registration. Random Sampling Consensus (RANSAC) is widely used for image registration. The registration algorithm using RANSAC is optimized for sonar image registration, and the performance is studied. The second use of robust statistics is in fusing the images. It is shown that the maximum a posteriori fusion method can be formulated in a Kalman filter-like manner, and also that the resulting expression is identical to a W-estimator with a specific weight function.

Leader-follower formation control of underactuated surface vehicles based on sliding mode control and parameter estimation.

PubMed

Sun, Zhijian; Zhang, Guoqing; Lu, Yu; Zhang, Weidong

2018-01-01

This paper studies the leader-follower formation control of underactuated surface vehicles with model uncertainties and environmental disturbances. A parameter estimation and upper bound estimation based sliding mode control scheme is proposed to solve the problem of the unknown plant parameters and environmental disturbances. For each of these leader-follower formation systems, the dynamic equations of position and attitude are analyzed using coordinate transformation with the aid of the backstepping technique. All the variables are guaranteed to be uniformly ultimately bounded stable in the closed-loop system, which is proven by the distribution design Lyapunov function synthesis. The main advantages of this approach are that: first, parameter estimation based sliding mode control can enhance the robustness of the closed-loop system in presence of model uncertainties and environmental disturbances; second, a continuous function is developed to replace the signum function in the design of sliding mode scheme, which devotes to reduce the chattering of the control system. Finally, numerical simulations are given to demonstrate the effectiveness of the proposed method. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Real-time detection of moving objects from moving vehicles using dense stereo and optical flow

NASA Technical Reports Server (NTRS)

Talukder, Ashit; Matthies, Larry

2004-01-01

Dynamic scene perception is very important for autonomous vehicles operating around other moving vehicles and humans. Most work on real-time object tracking from moving platforms has used sparse features or assumed flat scene structures. We have recently extended a real-time, dense stereo system to include realtime, dense optical flow, enabling more comprehensive dynamic scene analysis. We describe algorithms to robustly estimate 6-DOF robot egomotion in the presence of moving objects using dense flow and dense stereo. We then use dense stereo and egomotion estimates to identify & other moving objects while the robot itself is moving. We present results showing accurate egomotion estimation and detection of moving people and vehicles under general 6-DOF motion of the robot and independently moving objects. The system runs at 18.3 Hz on a 1.4 GHz Pentium M laptop, computing 160x120 disparity maps and optical flow fields, egomotion, and moving object segmentation. We believe this is a significant step toward general unconstrained dynamic scene analysis for mobile robots, as well as for improved position estimation where GPS is unavailable.

Real-time detection of moving objects from moving vehicles using dense stereo and optical flow

NASA Technical Reports Server (NTRS)

Talukder, Ashit; Matthies, Larry

2004-01-01

Dynamic scene perception is very important for autonomous vehicles operating around other moving vehicles and humans. Most work on real-time object tracking from moving platforms has used sparse features or assumed flat scene structures. We have recently extended a real-time, dense stereo system to include real-time, dense optical flow, enabling more comprehensive dynamic scene analysis. We describe algorithms to robustly estimate 6-DOF robot egomotion in the presence of moving objects using dense flow and dense stereo. We then use dense stereo and egomotion estimates to identity other moving objects while the robot itself is moving. We present results showing accurate egomotion estimation and detection of moving people and vehicles under general 6-DOF motion of the robot and independently moving objects. The system runs at 18.3 Hz on a 1.4 GHz Pentium M laptop, computing 160x120 disparity maps and optical flow fields, egomotion, and moving object segmentation. We believe this is a significant step toward general unconstrained dynamic scene analysis for mobile robots, as well as for improved position estimation where GPS is unavailable.

Real-time Detection of Moving Objects from Moving Vehicles Using Dense Stereo and Optical Flow

NASA Technical Reports Server (NTRS)

Talukder, Ashit; Matthies, Larry

2004-01-01

Dynamic scene perception is very important for autonomous vehicles operating around other moving vehicles and humans. Most work on real-time object tracking from moving platforms has used sparse features or assumed flat scene structures. We have recently extended a real-time. dense stereo system to include realtime. dense optical flow, enabling more comprehensive dynamic scene analysis. We describe algorithms to robustly estimate 6-DOF robot egomotion in the presence of moving objects using dense flow and dense stereo. We then use dense stereo and egomotion estimates to identify other moving objects while the robot itself is moving. We present results showing accurate egomotion estimation and detection of moving people and vehicles under general 6DOF motion of the robot and independently moving objects. The system runs at 18.3 Hz on a 1.4 GHz Pentium M laptop. computing 160x120 disparity maps and optical flow fields, egomotion, and moving object segmentation. We believe this is a significant step toward general unconstrained dynamic scene analysis for mobile robots, as well as for improved position estimation where GPS is unavailable.

Single-cell entropy for accurate estimation of differentiation potency from a cell's transcriptome

NASA Astrophysics Data System (ADS)

Teschendorff, Andrew E.; Enver, Tariq

2017-06-01

The ability to quantify differentiation potential of single cells is a task of critical importance. Here we demonstrate, using over 7,000 single-cell RNA-Seq profiles, that differentiation potency of a single cell can be approximated by computing the signalling promiscuity, or entropy, of a cell's transcriptome in the context of an interaction network, without the need for feature selection. We show that signalling entropy provides a more accurate and robust potency estimate than other entropy-based measures, driven in part by a subtle positive correlation between the transcriptome and connectome. Signalling entropy identifies known cell subpopulations of varying potency and drug resistant cancer stem-cell phenotypes, including those derived from circulating tumour cells. It further reveals that expression heterogeneity within single-cell populations is regulated. In summary, signalling entropy allows in silico estimation of the differentiation potency and plasticity of single cells and bulk samples, providing a means to identify normal and cancer stem-cell phenotypes.

A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation

PubMed Central

Jiang, Chen; Zhang, Shu-Bi; Zhang, Qiu-Zhao

2016-01-01

The Kalman filter is an optimal estimator with numerous applications in technology, especially in systems with Gaussian distributed noise. Moreover, the adaptive Kalman filtering algorithms, based on the Kalman filter, can control the influence of dynamic model errors. In contrast to the adaptive Kalman filtering algorithms, the H-infinity filter is able to address the interference of the stochastic model by minimization of the worst-case estimation error. In this paper, a novel adaptive H-infinity filtering algorithm, which integrates the adaptive Kalman filter and the H-infinity filter in order to perform a comprehensive filtering algorithm, is presented. In the proposed algorithm, a robust estimation method is employed to control the influence of outliers. In order to verify the proposed algorithm, experiments with real data of the Global Positioning System (GPS) and Inertial Navigation System (INS) integrated navigation, were conducted. The experimental results have shown that the proposed algorithm has multiple advantages compared to the other filtering algorithms. PMID:27999361

A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation.

PubMed

Jiang, Chen; Zhang, Shu-Bi; Zhang, Qiu-Zhao

2016-12-19

The Kalman filter is an optimal estimator with numerous applications in technology, especially in systems with Gaussian distributed noise. Moreover, the adaptive Kalman filtering algorithms, based on the Kalman filter, can control the influence of dynamic model errors. In contrast to the adaptive Kalman filtering algorithms, the H-infinity filter is able to address the interference of the stochastic model by minimization of the worst-case estimation error. In this paper, a novel adaptive H-infinity filtering algorithm, which integrates the adaptive Kalman filter and the H-infinity filter in order to perform a comprehensive filtering algorithm, is presented. In the proposed algorithm, a robust estimation method is employed to control the influence of outliers. In order to verify the proposed algorithm, experiments with real data of the Global Positioning System (GPS) and Inertial Navigation System (INS) integrated navigation, were conducted. The experimental results have shown that the proposed algorithm has multiple advantages compared to the other filtering algorithms.

Inflation in the closed FLRW model and the CMB

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bonga, Béatrice; Gupt, Brajesh; Yokomizo, Nelson, E-mail: bpb165@psu.edu, E-mail: bgupt@gravity.psu.edu, E-mail: yokomizo@gravity.psu.edu

2016-10-01

Recent cosmic microwave background (CMB) observations put strong constraints on the spatial curvature via estimation of the parameter Ω{sub k} assuming an almost scale invariant primordial power spectrum. We study the evolution of the background geometry and gauge-invariant scalar perturbations in an inflationary closed FLRW model and calculate the primordial power spectrum. We find that the inflationary dynamics is modified due to the presence of spatial curvature, leading to corrections to the nearly scale invariant power spectrum at the end of inflation. When evolved to the surface of last scattering, the resulting temperature anisotropy spectrum ( C {sup TT}{sub ℓ})more » shows deficit of power at low multipoles (ℓ < 20). By comparing our results with the recent Planck data we discuss the role of spatial curvature in accounting for CMB anomalies and in the estimation of the parameter Ω{sub k}. Since the curvature effects are limited to low multipoles, the Planck estimation of cosmological parameters remains robust under inclusion of positive spatial curvature.« less

Single-cell entropy for accurate estimation of differentiation potency from a cell's transcriptome

PubMed Central

Teschendorff, Andrew E.; Enver, Tariq

2017-01-01

The ability to quantify differentiation potential of single cells is a task of critical importance. Here we demonstrate, using over 7,000 single-cell RNA-Seq profiles, that differentiation potency of a single cell can be approximated by computing the signalling promiscuity, or entropy, of a cell's transcriptome in the context of an interaction network, without the need for feature selection. We show that signalling entropy provides a more accurate and robust potency estimate than other entropy-based measures, driven in part by a subtle positive correlation between the transcriptome and connectome. Signalling entropy identifies known cell subpopulations of varying potency and drug resistant cancer stem-cell phenotypes, including those derived from circulating tumour cells. It further reveals that expression heterogeneity within single-cell populations is regulated. In summary, signalling entropy allows in silico estimation of the differentiation potency and plasticity of single cells and bulk samples, providing a means to identify normal and cancer stem-cell phenotypes. PMID:28569836

Semiblind channel estimation for MIMO-OFDM systems

NASA Astrophysics Data System (ADS)

Chen, Yi-Sheng; Song, Jyu-Han

2012-12-01

This article proposes a semiblind channel estimation method for multiple-input multiple-output orthogonal frequency-division multiplexing systems based on circular precoding. Relying on the precoding scheme at the transmitters, the autocorrelation matrix of the received data induces a structure relating the outer product of the channel frequency response matrix and precoding coefficients. This structure makes it possible to extract information about channel product matrices, which can be used to form a Hermitian matrix whose positive eigenvalues and corresponding eigenvectors yield the channel impulse response matrix. This article also tests the resistance of the precoding design to finite-sample estimation errors, and explores the effects of the precoding scheme on channel equalization by performing pairwise error probability analysis. The proposed method is immune to channel zero locations, and is reasonably robust to channel order overestimation. The proposed method is applicable to the scenarios in which the number of transmitters exceeds that of the receivers. Simulation results demonstrate the performance of the proposed method and compare it with some existing methods.

Robust Fault Detection for Aircraft Using Mixed Structured Singular Value Theory and Fuzzy Logic

NASA Technical Reports Server (NTRS)

Collins, Emmanuel G.

2000-01-01

The purpose of fault detection is to identify when a fault or failure has occurred in a system such as an aircraft or expendable launch vehicle. The faults may occur in sensors, actuators, structural components, etc. One of the primary approaches to model-based fault detection relies on analytical redundancy. That is the output of a computer-based model (actually a state estimator) is compared with the sensor measurements of the actual system to determine when a fault has occurred. Unfortunately, the state estimator is based on an idealized mathematical description of the underlying plant that is never totally accurate. As a result of these modeling errors, false alarms can occur. This research uses mixed structured singular value theory, a relatively recent and powerful robustness analysis tool, to develop robust estimators and demonstrates the use of these estimators in fault detection. To allow qualitative human experience to be effectively incorporated into the detection process fuzzy logic is used to predict the seriousness of the fault that has occurred.

Filtering Based Adaptive Visual Odometry Sensor Framework Robust to Blurred Images

PubMed Central

Zhao, Haiying; Liu, Yong; Xie, Xiaojia; Liao, Yiyi; Liu, Xixi

2016-01-01

Visual odometry (VO) estimation from blurred image is a challenging problem in practical robot applications, and the blurred images will severely reduce the estimation accuracy of the VO. In this paper, we address the problem of visual odometry estimation from blurred images, and present an adaptive visual odometry estimation framework robust to blurred images. Our approach employs an objective measure of images, named small image gradient distribution (SIGD), to evaluate the blurring degree of the image, then an adaptive blurred image classification algorithm is proposed to recognize the blurred images, finally we propose an anti-blurred key-frame selection algorithm to enable the VO robust to blurred images. We also carried out varied comparable experiments to evaluate the performance of the VO algorithms with our anti-blur framework under varied blurred images, and the experimental results show that our approach can achieve superior performance comparing to the state-of-the-art methods under the condition with blurred images while not increasing too much computation cost to the original VO algorithms. PMID:27399704

Robust Mosaicking of Stereo Digital Elevation Models from the Ames Stereo Pipeline

NASA Technical Reports Server (NTRS)

Kim, Tae Min; Moratto, Zachary M.; Nefian, Ara Victor

2010-01-01

Robust estimation method is proposed to combine multiple observations and create consistent, accurate, dense Digital Elevation Models (DEMs) from lunar orbital imagery. The NASA Ames Intelligent Robotics Group (IRG) aims to produce higher-quality terrain reconstructions of the Moon from Apollo Metric Camera (AMC) data than is currently possible. In particular, IRG makes use of a stereo vision process, the Ames Stereo Pipeline (ASP), to automatically generate DEMs from consecutive AMC image pairs. However, the DEMs currently produced by the ASP often contain errors and inconsistencies due to image noise, shadows, etc. The proposed method addresses this problem by making use of multiple observations and by considering their goodness of fit to improve both the accuracy and robustness of the estimate. The stepwise regression method is applied to estimate the relaxed weight of each observation.

H∞ state estimation for discrete-time memristive recurrent neural networks with stochastic time-delays

NASA Astrophysics Data System (ADS)

Liu, Hongjian; Wang, Zidong; Shen, Bo; Alsaadi, Fuad E.

2016-07-01

This paper deals with the robust H∞ state estimation problem for a class of memristive recurrent neural networks with stochastic time-delays. The stochastic time-delays under consideration are governed by a Bernoulli-distributed stochastic sequence. The purpose of the addressed problem is to design the robust state estimator such that the dynamics of the estimation error is exponentially stable in the mean square, and the prescribed ? performance constraint is met. By utilizing the difference inclusion theory and choosing a proper Lyapunov-Krasovskii functional, the existence condition of the desired estimator is derived. Based on it, the explicit expression of the estimator gain is given in terms of the solution to a linear matrix inequality. Finally, a numerical example is employed to demonstrate the effectiveness and applicability of the proposed estimation approach.

Robust efficient estimation of heart rate pulse from video.

PubMed

Xu, Shuchang; Sun, Lingyun; Rohde, Gustavo Kunde

2014-04-01

We describe a simple but robust algorithm for estimating the heart rate pulse from video sequences containing human skin in real time. Based on a model of light interaction with human skin, we define the change of blood concentration due to arterial pulsation as a pixel quotient in log space, and successfully use the derived signal for computing the pulse heart rate. Various experiments with different cameras, different illumination condition, and different skin locations were conducted to demonstrate the effectiveness and robustness of the proposed algorithm. Examples computed with normal illumination show the algorithm is comparable with pulse oximeter devices both in accuracy and sensitivity.

Robust efficient estimation of heart rate pulse from video

PubMed Central

Xu, Shuchang; Sun, Lingyun; Rohde, Gustavo Kunde

2014-01-01

We describe a simple but robust algorithm for estimating the heart rate pulse from video sequences containing human skin in real time. Based on a model of light interaction with human skin, we define the change of blood concentration due to arterial pulsation as a pixel quotient in log space, and successfully use the derived signal for computing the pulse heart rate. Various experiments with different cameras, different illumination condition, and different skin locations were conducted to demonstrate the effectiveness and robustness of the proposed algorithm. Examples computed with normal illumination show the algorithm is comparable with pulse oximeter devices both in accuracy and sensitivity. PMID:24761294

Automatic SAR/optical cross-matching for GCP monograph generation

NASA Astrophysics Data System (ADS)

Nutricato, Raffaele; Morea, Alberto; Nitti, Davide Oscar; La Mantia, Claudio; Agrimano, Luigi; Samarelli, Sergio; Chiaradia, Maria Teresa

2016-10-01

Ground Control Points (GCP), automatically extracted from Synthetic Aperture Radar (SAR) images through 3D stereo analysis, can be effectively exploited for an automatic orthorectification of optical imagery if they can be robustly located in the basic optical images. The present study outlines a SAR/Optical cross-matching procedure that allows a robust alignment of radar and optical images, and consequently to derive automatically the corresponding sub-pixel position of the GCPs in the optical image in input, expressed as fractional pixel/line image coordinates. The cross-matching in performed in two subsequent steps, in order to gradually gather a better precision. The first step is based on the Mutual Information (MI) maximization between optical and SAR chips while the last one uses the Normalized Cross-Correlation as similarity metric. This work outlines the designed algorithmic solution and discusses the results derived over the urban area of Pisa (Italy), where more than ten COSMO-SkyMed Enhanced Spotlight stereo images with different beams and passes are available. The experimental analysis involves different satellite images, in order to evaluate the performances of the algorithm w.r.t. the optical spatial resolution. An assessment of the performances of the algorithm has been carried out, and errors are computed by measuring the distance between the GCP pixel/line position in the optical image, automatically estimated by the tool, and the "true" position of the GCP, visually identified by an expert user in the optical images.

Explicit construction of quadratic Lyapunov functions for the small gain, positivity, circle, and Popov theorems and their application to robust stability

NASA Technical Reports Server (NTRS)

Haddad, Wassim M.; Bernstein, Dennis S.

1991-01-01

Lyapunov function proofs of sufficient conditions for asymptotic stability are given for feedback interconnections of bounded real and positive real transfer functions. Two cases are considered: (1) a proper bounded real (resp., positive real) transfer function with a bounded real (resp., positive real) time-varying memoryless nonlinearity; and (2) two strictly proper bounded real (resp., positive real) transfer functions. A similar treatment is given for the circle and Popov theorems. Application of these results to robust stability with time-varying bounded real, positive real, and sector-bounded uncertainty is discussed.

Improved ultrasound transducer positioning by fetal heart location estimation during Doppler based heart rate measurements.

PubMed

Hamelmann, Paul; Vullings, Rik; Schmitt, Lars; Kolen, Alexander F; Mischi, Massimo; van Laar, Judith O E H; Bergmans, Jan W M

2017-09-21

Doppler ultrasound (US) is the most commonly applied method to measure the fetal heart rate (fHR). When the fetal heart is not properly located within the ultrasonic beam, fHR measurements often fail. As a consequence, clinical staff need to reposition the US transducer on the maternal abdomen, which can be a time consuming and tedious task. In this article, a method is presented to aid clinicians with the positioning of the US transducer to produce robust fHR measurements. A maximum likelihood estimation (MLE) algorithm is developed, which provides information on fetal heart location using the power of the Doppler signals received in the individual elements of a standard US transducer for fHR recordings. The performance of the algorithm is evaluated with simulations and in vitro experiments performed on a beating-heart setup. Both the experiments and the simulations show that the heart location can be accurately determined with an error of less than 7 mm within the measurement volume of the employed US transducer. The results show that the developed algorithm can be used to provide accurate feedback on fetal heart location for improved positioning of the US transducer, which may lead to improved measurements of the fHR.

A positional estimation technique for an autonomous land vehicle in an unstructured environment

NASA Technical Reports Server (NTRS)

Talluri, Raj; Aggarwal, J. K.

1990-01-01

This paper presents a solution to the positional estimation problem of an autonomous land vehicle navigating in an unstructured mountainous terrain. A Digital Elevation Map (DEM) of the area in which the robot is to navigate is assumed to be given. It is also assumed that the robot is equipped with a camera that can be panned and tilted, and a device to measure the elevation of the robot above the ground surface. No recognizable landmarks are assumed to be present in the environment in which the robot is to navigate. The solution presented makes use of the DEM information, and structures the problem as a heuristic search in the DEM for the possible robot location. The shape and position of the horizon line in the image plane and the known camera geometry of the perspective projection are used as parameters to search the DEM. Various heuristics drawn from the geometric constraints are used to prune the search space significantly. The algorithm is made robust to errors in the imaging process by accounting for the worst care errors. The approach is tested using DEM data of areas in Colorado and Texas. The method is suitable for use in outdoor mobile robots and planetary rovers.

Robust linear discriminant analysis with distance based estimators

NASA Astrophysics Data System (ADS)

Lim, Yai-Fung; Yahaya, Sharipah Soaad Syed; Ali, Hazlina

2017-11-01

Linear discriminant analysis (LDA) is one of the supervised classification techniques concerning relationship between a categorical variable and a set of continuous variables. The main objective of LDA is to create a function to distinguish between populations and allocating future observations to previously defined populations. Under the assumptions of normality and homoscedasticity, the LDA yields optimal linear discriminant rule (LDR) between two or more groups. However, the optimality of LDA highly relies on the sample mean and pooled sample covariance matrix which are known to be sensitive to outliers. To alleviate these conflicts, a new robust LDA using distance based estimators known as minimum variance vector (MVV) has been proposed in this study. The MVV estimators were used to substitute the classical sample mean and classical sample covariance to form a robust linear discriminant rule (RLDR). Simulation and real data study were conducted to examine on the performance of the proposed RLDR measured in terms of misclassification error rates. The computational result showed that the proposed RLDR is better than the classical LDR and was comparable with the existing robust LDR.

Robust ridge regression estimators for nonlinear models with applications to high throughput screening assay data.

PubMed

Lim, Changwon

2015-03-30

Nonlinear regression is often used to evaluate the toxicity of a chemical or a drug by fitting data from a dose-response study. Toxicologists and pharmacologists may draw a conclusion about whether a chemical is toxic by testing the significance of the estimated parameters. However, sometimes the null hypothesis cannot be rejected even though the fit is quite good. One possible reason for such cases is that the estimated standard errors of the parameter estimates are extremely large. In this paper, we propose robust ridge regression estimation procedures for nonlinear models to solve this problem. The asymptotic properties of the proposed estimators are investigated; in particular, their mean squared errors are derived. The performances of the proposed estimators are compared with several standard estimators using simulation studies. The proposed methodology is also illustrated using high throughput screening assay data obtained from the National Toxicology Program. Copyright © 2014 John Wiley & Sons, Ltd.

Trajectory prediction for ballistic missiles based on boost-phase LOS measurements

NASA Astrophysics Data System (ADS)

Yeddanapudi, Murali; Bar-Shalom, Yaakov

1997-10-01

This paper addresses the problem of the estimation of the trajectory of a tactical ballistic missile using line of sight (LOS) measurements from one or more passive sensors (typically satellites). The major difficulties of this problem include: the estimation of the unknown time of launch, incorporation of (inaccurate) target thrust profiles to model the target dynamics during the boost phase and an overall ill-conditioning of the estimation problem due to poor observability of the target motion via the LOS measurements. We present a robust estimation procedure based on the Levenberg-Marquardt algorithm that provides both the target state estimate and error covariance taking into consideration the complications mentioned above. An important consideration in the defense against tactical ballistic missiles is the determination of the target position and error covariance at the acquisition range of a surveillance radar in the vicinity of the impact point. We present a systematic procedure to propagate the target state and covariance to a nominal time, when it is within the detection range of a surveillance radar to obtain a cueing volume. Mont Carlo simulation studies on typical single and two sensor scenarios indicate that the proposed algorithms are accurate in terms of the estimates and the estimator calculated covariances are consistent with the errors.

Robust fractional order sliding mode control of doubly-fed induction generator (DFIG)-based wind turbines.

PubMed

Ebrahimkhani, Sadegh

2016-07-01

Wind power plants have nonlinear dynamics and contain many uncertainties such as unknown nonlinear disturbances and parameter uncertainties. Thus, it is a difficult task to design a robust reliable controller for this system. This paper proposes a novel robust fractional-order sliding mode (FOSM) controller for maximum power point tracking (MPPT) control of doubly fed induction generator (DFIG)-based wind energy conversion system. In order to enhance the robustness of the control system, uncertainties and disturbances are estimated using a fractional order uncertainty estimator. In the proposed method a continuous control strategy is developed to achieve the chattering free fractional order sliding-mode control, and also no knowledge of the uncertainties and disturbances or their bound is assumed. The boundedness and convergence properties of the closed-loop signals are proven using Lyapunov׳s stability theory. Simulation results in the presence of various uncertainties were carried out to evaluate the effectiveness and robustness of the proposed control scheme. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

An anti-disturbing real time pose estimation method and system

NASA Astrophysics Data System (ADS)

Zhou, Jian; Zhang, Xiao-hu

2011-08-01

Pose estimation relating two-dimensional (2D) images to three-dimensional (3D) rigid object need some known features to track. In practice, there are many algorithms which perform this task in high accuracy, but all of these algorithms suffer from features lost. This paper investigated the pose estimation when numbers of known features or even all of them were invisible. Firstly, known features were tracked to calculate pose in the current and the next image. Secondly, some unknown but good features to track were automatically detected in the current and the next image. Thirdly, those unknown features which were on the rigid and could match each other in the two images were retained. Because of the motion characteristic of the rigid object, the 3D information of those unknown features on the rigid could be solved by the rigid object's pose at the two moment and their 2D information in the two images except only two case: the first one was that both camera and object have no relative motion and camera parameter such as focus length, principle point, and etc. have no change at the two moment; the second one was that there was no shared scene or no matched feature in the two image. Finally, because those unknown features at the first time were known now, pose estimation could go on in the followed images in spite of the missing of known features in the beginning by repeating the process mentioned above. The robustness of pose estimation by different features detection algorithms such as Kanade-Lucas-Tomasi (KLT) feature, Scale Invariant Feature Transform (SIFT) and Speed Up Robust Feature (SURF) were compared and the compact of the different relative motion between camera and the rigid object were discussed in this paper. Graphic Processing Unit (GPU) parallel computing was also used to extract and to match hundreds of features for real time pose estimation which was hard to work on Central Processing Unit (CPU). Compared with other pose estimation methods, this new method can estimate pose between camera and object when part even all known features are lost, and has a quick response time benefit from GPU parallel computing. The method present here can be used widely in vision-guide techniques to strengthen its intelligence and generalization, which can also play an important role in autonomous navigation and positioning, robots fields at unknown environment. The results of simulation and experiments demonstrate that proposed method could suppress noise effectively, extracted features robustly, and achieve the real time need. Theory analysis and experiment shows the method is reasonable and efficient.

A modern robust approach to remotely estimate chlorophyll in coastal and inland zones

NASA Astrophysics Data System (ADS)

Shanmugam, Palanisamy; He, Xianqiang; Singh, Rakesh Kumar; Varunan, Theenathayalan

2018-05-01

The chlorophyll concentration of a water body is an important proxy for representing the phytoplankton biomass. Its estimation from multi or hyper-spectral remote sensing data in natural waters is generally achieved by using (i) the waveband ratioing in two or more bands in the blue-green or (ii) by using a combination of the radiance peak position and magnitude in the red-near-infrared (NIR) spectrum. The blue-green ratio algorithms have been extensively used with satellite ocean color data to investigate chlorophyll distributions in open ocean and clear waters and the application of red-NIR algorithms is often restricted to turbid productive water bodies. These issues present the greatest obstacles to our ability to formulate a modern robust method suitable for quantitative assessments of the chlorophyll concentration in a diverse range of water types. The present study is focused to investigate the normalized water-leaving radiance spectra in the visible and NIR region and propose a robust algorithm (Generalized ABI, GABI algorithm) for chlorophyll concentration retrieval based on Algal Bloom index (ABI) which separates phytoplankton signals from other constituents in the water column. The GABI algorithm is validated using independent in-situ data from various regional to global waters and its performance is further evaluated by comparison with the blue-green waveband ratios and red-NIR algorithms. The results revealed that GABI yields significantly more accurate chlorophyll concentrations (with uncertainties less than 13.5%) and remains more stable in different waters types when compared with the blue-green waveband ratios and red-NIR algorithms. The performance of GABI is further demonstrated using HICO images from nearshore turbid productive waters and MERIS and MODIS-Aqua images from coastal and offshore waters of the Arabian Sea, Bay of Bengal and East China Sea.

Experimental design and statistical methods for improved hit detection in high-throughput screening.

PubMed

Malo, Nathalie; Hanley, James A; Carlile, Graeme; Liu, Jing; Pelletier, Jerry; Thomas, David; Nadon, Robert

2010-09-01

Identification of active compounds in high-throughput screening (HTS) contexts can be substantially improved by applying classical experimental design and statistical inference principles to all phases of HTS studies. The authors present both experimental and simulated data to illustrate how true-positive rates can be maximized without increasing false-positive rates by the following analytical process. First, the use of robust data preprocessing methods reduces unwanted variation by removing row, column, and plate biases. Second, replicate measurements allow estimation of the magnitude of the remaining random error and the use of formal statistical models to benchmark putative hits relative to what is expected by chance. Receiver Operating Characteristic (ROC) analyses revealed superior power for data preprocessed by a trimmed-mean polish method combined with the RVM t-test, particularly for small- to moderate-sized biological hits.

A long-term target detection approach in infrared image sequence

NASA Astrophysics Data System (ADS)

Li, Hang; Zhang, Qi; Li, Yuanyuan; Wang, Liqiang

2015-12-01

An automatic target detection method used in long term infrared (IR) image sequence from a moving platform is proposed. Firstly, based on non-linear histogram equalization, target candidates are coarse-to-fine segmented by using two self-adapt thresholds generated in the intensity space. Then the real target is captured via two different selection approaches. At the beginning of image sequence, the genuine target with litter texture is discriminated from other candidates by using contrast-based confidence measure. On the other hand, when the target becomes larger, we apply online EM method to iteratively estimate and update the distributions of target's size and position based on the prior detection results, and then recognize the genuine one which satisfies both the constraints of size and position. Experimental results demonstrate that the presented method is accurate, robust and efficient.

Estimation of the relative sensitivity of the comparative tuberculin skin test in tuberculous cattle herds subjected to depopulation.

PubMed

Karolemeas, Katerina; de la Rua-Domenech, Ricardo; Cooper, Roderick; Goodchild, Anthony V; Clifton-Hadley, Richard S; Conlan, Andrew J K; Mitchell, Andrew P; Hewinson, R Glyn; Donnelly, Christl A; Wood, James L N; McKinley, Trevelyan J

2012-01-01

Bovine tuberculosis (bTB) is one of the most serious economic animal health problems affecting the cattle industry in Great Britain (GB), with incidence in cattle herds increasing since the mid-1980s. The single intradermal comparative cervical tuberculin (SICCT) test is the primary screening test in the bTB surveillance and control programme in GB and Ireland. The sensitivity (ability to detect infected cattle) of this test is central to the efficacy of the current testing regime, but most previous studies that have estimated test sensitivity (relative to the number of slaughtered cattle with visible lesions [VL] and/or positive culture results) lacked post-mortem data for SICCT test-negative cattle. The slaughter of entire herds ("whole herd slaughters" or "depopulations") that are infected by bTB are occasionally conducted in GB as a last-resort control measure to resolve intractable bTB herd breakdowns. These provide additional post-mortem data for SICCT test-negative cattle, allowing a rare opportunity to calculate the animal-level sensitivity of the test relative to the total number of SICCT test-positive and negative VL animals identified post-mortem (rSe). In this study, data were analysed from 16 whole herd slaughters (748 SICCT test-positive and 1031 SICCT test-negative cattle) conducted in GB between 1988 and 2010, using a bayesian hierarchical model. The overall rSe estimate of the SICCT test at the severe interpretation was 85% (95% credible interval [CI]: 78-91%), and at standard interpretation was 81% (95% CI: 70-89%). These estimates are more robust than those previously reported in GB due to inclusion of post-mortem data from SICCT test-negative cattle.

Delayed Monocular SLAM Approach Applied to Unmanned Aerial Vehicles.

PubMed

Munguia, Rodrigo; Urzua, Sarquis; Grau, Antoni

2016-01-01

In recent years, many researchers have addressed the issue of making Unmanned Aerial Vehicles (UAVs) more and more autonomous. In this context, the state estimation of the vehicle position is a fundamental necessity for any application involving autonomy. However, the problem of position estimation could not be solved in some scenarios, even when a GPS signal is available, for instance, an application requiring performing precision manoeuvres in a complex environment. Therefore, some additional sensory information should be integrated into the system in order to improve accuracy and robustness. In this work, a novel vision-based simultaneous localization and mapping (SLAM) method with application to unmanned aerial vehicles is proposed. One of the contributions of this work is to design and develop a novel technique for estimating features depth which is based on a stochastic technique of triangulation. In the proposed method the camera is mounted over a servo-controlled gimbal that counteracts the changes in attitude of the quadcopter. Due to the above assumption, the overall problem is simplified and it is focused on the position estimation of the aerial vehicle. Also, the tracking process of visual features is made easier due to the stabilized video. Another contribution of this work is to demonstrate that the integration of very noisy GPS measurements into the system for an initial short period of time is enough to initialize the metric scale. The performance of this proposed method is validated by means of experiments with real data carried out in unstructured outdoor environments. A comparative study shows that, when compared with related methods, the proposed approach performs better in terms of accuracy and computational time.

On the use of INS to improve Feature Matching

NASA Astrophysics Data System (ADS)

Masiero, A.; Guarnieri, A.; Vettore, A.; Pirotti, F.

2014-11-01

The continuous technological improvement of mobile devices opens the frontiers of Mobile Mapping systems to very compact systems, i.e. a smartphone or a tablet. This motivates the development of efficient 3D reconstruction techniques based on the sensors typically embedded in such devices, i.e. imaging sensors, GPS and Inertial Navigation System (INS). Such methods usually exploits photogrammetry techniques (structure from motion) to provide an estimation of the geometry of the scene. Actually, 3D reconstruction techniques (e.g. structure from motion) rely on use of features properly matched in different images to compute the 3D positions of objects by means of triangulation. Hence, correct feature matching is of fundamental importance to ensure good quality 3D reconstructions. Matching methods are based on the appearance of features, that can change as a consequence of variations of camera position and orientation, and environment illumination. For this reason, several methods have been developed in recent years in order to provide feature descriptors robust (ideally invariant) to such variations, e.g. Scale-Invariant Feature Transform (SIFT), Affine SIFT, Hessian affine and Harris affine detectors, Maximally Stable Extremal Regions (MSER). This work deals with the integration of information provided by the INS in the feature matching procedure: a previously developed navigation algorithm is used to constantly estimate the device position and orientation. Then, such information is exploited to estimate the transformation of feature regions between two camera views. This allows to compare regions from different images but associated to the same feature as seen by the same point of view, hence significantly easing the comparison of feature characteristics and, consequently, improving matching. SIFT-like descriptors are used in order to ensure good matching results in presence of illumination variations and to compensate the approximations related to the estimation process.

Estimation of the Relative Sensitivity of the Comparative Tuberculin Skin Test in Tuberculous Cattle Herds Subjected to Depopulation

PubMed Central

Karolemeas, Katerina; de la Rua-Domenech, Ricardo; Cooper, Roderick; Goodchild, Anthony V.; Clifton-Hadley, Richard S.; Conlan, Andrew J. K.; Mitchell, Andrew P.; Hewinson, R. Glyn; Donnelly, Christl A.; Wood, James L. N.; McKinley, Trevelyan J.

2012-01-01

Bovine tuberculosis (bTB) is one of the most serious economic animal health problems affecting the cattle industry in Great Britain (GB), with incidence in cattle herds increasing since the mid-1980s. The single intradermal comparative cervical tuberculin (SICCT) test is the primary screening test in the bTB surveillance and control programme in GB and Ireland. The sensitivity (ability to detect infected cattle) of this test is central to the efficacy of the current testing regime, but most previous studies that have estimated test sensitivity (relative to the number of slaughtered cattle with visible lesions [VL] and/or positive culture results) lacked post-mortem data for SICCT test-negative cattle. The slaughter of entire herds (“whole herd slaughters” or “depopulations”) that are infected by bTB are occasionally conducted in GB as a last-resort control measure to resolve intractable bTB herd breakdowns. These provide additional post-mortem data for SICCT test-negative cattle, allowing a rare opportunity to calculate the animal-level sensitivity of the test relative to the total number of SICCT test-positive and negative VL animals identified post-mortem (rSe). In this study, data were analysed from 16 whole herd slaughters (748 SICCT test-positive and 1031 SICCT test-negative cattle) conducted in GB between 1988 and 2010, using a Bayesian hierarchical model. The overall rSe estimate of the SICCT test at the severe interpretation was 85% (95% credible interval [CI]: 78–91%), and at standard interpretation was 81% (95% CI: 70–89%). These estimates are more robust than those previously reported in GB due to inclusion of post-mortem data from SICCT test-negative cattle. PMID:22927952

A combined vision-inertial fusion approach for 6-DoF object pose estimation

NASA Astrophysics Data System (ADS)

Li, Juan; Bernardos, Ana M.; Tarrío, Paula; Casar, José R.

2015-02-01

The estimation of the 3D position and orientation of moving objects (`pose' estimation) is a critical process for many applications in robotics, computer vision or mobile services. Although major research efforts have been carried out to design accurate, fast and robust indoor pose estimation systems, it remains as an open challenge to provide a low-cost, easy to deploy and reliable solution. Addressing this issue, this paper describes a hybrid approach for 6 degrees of freedom (6-DoF) pose estimation that fuses acceleration data and stereo vision to overcome the respective weaknesses of single technology approaches. The system relies on COTS technologies (standard webcams, accelerometers) and printable colored markers. It uses a set of infrastructure cameras, located to have the object to be tracked visible most of the operation time; the target object has to include an embedded accelerometer and be tagged with a fiducial marker. This simple marker has been designed for easy detection and segmentation and it may be adapted to different service scenarios (in shape and colors). Experimental results show that the proposed system provides high accuracy, while satisfactorily dealing with the real-time constraints.

Bayesian estimation of the sensitivity and specificity of individual fecal culture and Paralisa to detect Mycobacterium avium subspecies paratuberculosis infection in young farmed deer.

PubMed

Stringer, Lesley A; Jones, Geoff; Jewell, Chris P; Noble, Alasdair D; Heuer, Cord; Wilson, Peter R; Johnson, Wesley O

2013-11-01

A Bayesian latent class model was used to estimate the sensitivity and specificity of an immunoglobulin G1 serum enzyme-linked immunosorbent assay (Paralisa) and individual fecal culture to detect young deer infected with Mycobacterium avium subsp. paratuberculosis. Paired fecal and serum samples were collected, between July 2009 and April 2010, from 20 individual yearling (12-24-month-old) deer in each of 20 South Island and 18 North Island herds in New Zealand and subjected to culture and Paralisa, respectively. Two fecal samples and 16 serum samples from 356 North Island deer, and 55 fecal and 37 serum samples from 401 South Island deer, were positive. The estimate of individual fecal culture sensitivity was 77% (95% credible interval [CI] = 61-92%) with specificity of 99% (95% CI = 98-99.7%). The Paralisa sensitivity estimate was 19% (95% CI = 10-30%), with specificity of 94% (95% CI = 93-96%). All estimates were robust to variation of priors and assumptions tested in a sensitivity analysis. The data informs the use of the tests in determining infection status at the individual and herd level.

Can Selforganizing Maps Accurately Predict Photometric Redshifts?

NASA Technical Reports Server (NTRS)

Way, Michael J.; Klose, Christian

2012-01-01

We present an unsupervised machine-learning approach that can be employed for estimating photometric redshifts. The proposed method is based on a vector quantization called the self-organizing-map (SOM) approach. A variety of photometrically derived input values were utilized from the Sloan Digital Sky Survey's main galaxy sample, luminous red galaxy, and quasar samples, along with the PHAT0 data set from the Photo-z Accuracy Testing project. Regression results obtained with this new approach were evaluated in terms of root-mean-square error (RMSE) to estimate the accuracy of the photometric redshift estimates. The results demonstrate competitive RMSE and outlier percentages when compared with several other popular approaches, such as artificial neural networks and Gaussian process regression. SOM RMSE results (using delta(z) = z(sub phot) - z(sub spec)) are 0.023 for the main galaxy sample, 0.027 for the luminous red galaxy sample, 0.418 for quasars, and 0.022 for PHAT0 synthetic data. The results demonstrate that there are nonunique solutions for estimating SOM RMSEs. Further research is needed in order to find more robust estimation techniques using SOMs, but the results herein are a positive indication of their capabilities when compared with other well-known methods

Dominant root locus in state estimator design for material flow processes: A case study of hot strip rolling.

PubMed

Fišer, Jaromír; Zítek, Pavel; Skopec, Pavel; Knobloch, Jan; Vyhlídal, Tomáš

2017-05-01

The purpose of the paper is to achieve a constrained estimation of process state variables using the anisochronic state observer tuned by the dominant root locus technique. The anisochronic state observer is based on the state-space time delay model of the process. Moreover the process model is identified not only as delayed but also as non-linear. This model is developed to describe a material flow process. The root locus technique combined with the magnitude optimum method is utilized to investigate the estimation process. Resulting dominant roots location serves as a measure of estimation process performance. The higher the dominant (natural) frequency in the leftmost position of the complex plane the more enhanced performance with good robustness is achieved. Also the model based observer control methodology for material flow processes is provided by means of the separation principle. For demonstration purposes, the computer-based anisochronic state observer is applied to the strip temperatures estimation in the hot strip finishing mill composed of seven stands. This application was the original motivation to the presented research. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Partial Deconvolution with Inaccurate Blur Kernel.

PubMed

Ren, Dongwei; Zuo, Wangmeng; Zhang, David; Xu, Jun; Zhang, Lei

2017-10-17

Most non-blind deconvolution methods are developed under the error-free kernel assumption, and are not robust to inaccurate blur kernel. Unfortunately, despite the great progress in blind deconvolution, estimation error remains inevitable during blur kernel estimation. Consequently, severe artifacts such as ringing effects and distortions are likely to be introduced in the non-blind deconvolution stage. In this paper, we tackle this issue by suggesting: (i) a partial map in the Fourier domain for modeling kernel estimation error, and (ii) a partial deconvolution model for robust deblurring with inaccurate blur kernel. The partial map is constructed by detecting the reliable Fourier entries of estimated blur kernel. And partial deconvolution is applied to wavelet-based and learning-based models to suppress the adverse effect of kernel estimation error. Furthermore, an E-M algorithm is developed for estimating the partial map and recovering the latent sharp image alternatively. Experimental results show that our partial deconvolution model is effective in relieving artifacts caused by inaccurate blur kernel, and can achieve favorable deblurring quality on synthetic and real blurry images.Most non-blind deconvolution methods are developed under the error-free kernel assumption, and are not robust to inaccurate blur kernel. Unfortunately, despite the great progress in blind deconvolution, estimation error remains inevitable during blur kernel estimation. Consequently, severe artifacts such as ringing effects and distortions are likely to be introduced in the non-blind deconvolution stage. In this paper, we tackle this issue by suggesting: (i) a partial map in the Fourier domain for modeling kernel estimation error, and (ii) a partial deconvolution model for robust deblurring with inaccurate blur kernel. The partial map is constructed by detecting the reliable Fourier entries of estimated blur kernel. And partial deconvolution is applied to wavelet-based and learning-based models to suppress the adverse effect of kernel estimation error. Furthermore, an E-M algorithm is developed for estimating the partial map and recovering the latent sharp image alternatively. Experimental results show that our partial deconvolution model is effective in relieving artifacts caused by inaccurate blur kernel, and can achieve favorable deblurring quality on synthetic and real blurry images.

Robust all-source positioning of UAVs based on belief propagation

NASA Astrophysics Data System (ADS)

Chen, Xi; Gao, Wenyun; Wang, Jiabo

2013-12-01

For unmanned air vehicles (UAVs) to survive hostile operational environments, it is always preferable to utilize all wireless positioning sources available to fuse a robust position. While belief propagation is a well-established method for all source data fusion, it is not an easy job to handle all the mathematics therein. In this work, a comprehensive mathematical framework for belief propagation-based all-source positioning of UAVs is developed, taking wireless sources including Global Navigation Satellite Systems (GNSS) space vehicles, peer UAVs, ground control stations, and signal of opportunities. Based on the mathematical framework, a positioning algorithm named Belief propagation-based Opportunistic Positioning of UAVs (BOPU) is proposed, with an unscented particle filter for Bayesian approximation. The robustness of the proposed BOPU is evaluated by a fictitious scenario that a group of formation flying UAVs encounter GNSS countermeasures en route. Four different configurations of measurements availability are simulated. The results show that the performance of BOPU varies only slightly with different measurements availability.

Asymptotic solutions for the case of nearly symmetric gravitational lens systems

NASA Astrophysics Data System (ADS)

Wertz, O.; Pelgrims, V.; Surdej, J.

2012-08-01

Gravitational lensing provides a powerful tool to determine the Hubble parameter H0 from the measurement of the time delay Δt between two lensed images of a background variable source. Nevertheless, knowledge of the deflector mass distribution constitutes a hurdle. We propose in the present work interesting solutions for the case of nearly symmetric gravitational lens systems. For the case of a small misalignment between the source, the deflector and the observer, we first consider power-law (ɛ) axially symmetric models for which we derive an analytical relation between the amplification ratio and source position which is independent of the power-law slope ɛ. According to this relation, we deduce an expression for H0 also irrespective of the value ɛ. Secondly, we consider the power-law axially symmetric lens models with an external large-scale gravitational field, the shear γ, resulting in the so-called ɛ-γ models, for which we deduce simple first-order equations linking the model parameters and the lensed image positions, the latter being observable quantities. We also deduce simple relations between H0 and observables quantities only. From these equations, we may estimate the value of the Hubble parameter in a robust way. Nevertheless, comparison between the ɛ-γ and singular isothermal ellipsoid (SIE) models leads to the conclusion that these models remain most often distinct. Therefore, even for the case of a small misalignment, use of the first-order equations and precise astrometric measurements of the positions of the lensed images with respect to the centre of the deflector enables one to discriminate between these two families of models. Finally, we confront the models with numerical simulations to evaluate the intrinsic error of the first-order expressions used when deriving the model parameters under the assumption of a quasi-alignment between the source, the deflector and the observer. From these same simulations, we estimate for the case of the ɛ-γ family of models that the standard deviation affecting H0 is ? which merely reflects the adopted astrometric uncertainties on the relative image positions, typically ? arcsec. In conclusions, we stress the importance of getting very accurate measurements of the relative positions of the multiple lensed images and of the time delays for the case of nearly symmetric gravitational lens systems, in order to derive robust and precise values of the Hubble parameter.

Miniature Dual-Corona Ionizer for Bipolar Charging of Aerosol

PubMed Central

Qi, Chaolong; Kulkarni, Pramod

2015-01-01

A corona-based bipolar charger has been developed for use in compact, field-portable mobility size spectrometers. The charger employs an aerosol flow cavity exposed to two corona ionizers producing ions of opposite polarity. Each corona ionizer houses two electrodes in parallel needle-mesh configuration and is operated at the same magnitude of corona current. Experimental measurement of detailed charge distribution of near-monodisperse particles of different diameter in the submicrometer size range showed that the charger is capable of producing well-defined, consistent bipolar charge distributions for flow rates up to 1.5 L/min and aerosol concentration up to 107 per cm3. For particles with preexisting charge of +1, 0, and −1, the measured charge distributions agreed well with the theoretical distributions within the range of experimental and theoretical uncertainties. The transmission efficiency of the charger was measured to be 80% for 10 nm particles (at 0.3 L/min and 5 μA corona current) and increased with increasing diameter beyond this size. Measurement of uncharged fractions at various combinations of positive and negative corona currents showed the charger performance to be insensitive to fluctuations in corona current. Ion concentrations under positive and negative unipolar operation were estimated to be 8.2 × 107 and 3.37 × 108 cm−3 for positive and negative ions; the n·t product value under positive corona operation was independently estimated to be 8.5 × 105 s/cm3. The ion concentration estimates indicate the charger to be capable of “neutralizing” typical atmospheric and industrial aerosols in most measurement applications. The miniature size, simple and robust operation makes the charger suitable for portable mobility spectrometers. PMID:26512158

Miniature Dual-Corona Ionizer for Bipolar Charging of Aerosol.

PubMed

Qi, Chaolong; Kulkarni, Pramod

2013-01-01

A corona-based bipolar charger has been developed for use in compact, field-portable mobility size spectrometers. The charger employs an aerosol flow cavity exposed to two corona ionizers producing ions of opposite polarity. Each corona ionizer houses two electrodes in parallel needle-mesh configuration and is operated at the same magnitude of corona current. Experimental measurement of detailed charge distribution of near-monodisperse particles of different diameter in the submicrometer size range showed that the charger is capable of producing well-defined, consistent bipolar charge distributions for flow rates up to 1.5 L/min and aerosol concentration up to 10 7 per cm 3 . For particles with preexisting charge of +1, 0, and -1, the measured charge distributions agreed well with the theoretical distributions within the range of experimental and theoretical uncertainties. The transmission efficiency of the charger was measured to be 80% for 10 nm particles (at 0.3 L/min and 5 μ A corona current) and increased with increasing diameter beyond this size. Measurement of uncharged fractions at various combinations of positive and negative corona currents showed the charger performance to be insensitive to fluctuations in corona current. Ion concentrations under positive and negative unipolar operation were estimated to be 8.2 × 10 7 and 3.37 × 10 8 cm -3 for positive and negative ions; the n · t product value under positive corona operation was independently estimated to be 8.5 × 10 5 s/cm 3 . The ion concentration estimates indicate the charger to be capable of "neutralizing" typical atmospheric and industrial aerosols in most measurement applications. The miniature size, simple and robust operation makes the charger suitable for portable mobility spectrometers.

A positive feedback at the cellular level promotes robustness and modulation at the circuit level

PubMed Central

Dethier, Julie; Drion, Guillaume; Franci, Alessio

2015-01-01

This article highlights the role of a positive feedback gating mechanism at the cellular level in the robustness and modulation properties of rhythmic activities at the circuit level. The results are presented in the context of half-center oscillators, which are simple rhythmic circuits composed of two reciprocally connected inhibitory neuronal populations. Specifically, we focus on rhythms that rely on a particular excitability property, the postinhibitory rebound, an intrinsic cellular property that elicits transient membrane depolarization when released from hyperpolarization. Two distinct ionic currents can evoke this transient depolarization: a hyperpolarization-activated cation current and a low-threshold T-type calcium current. The presence of a slow activation is specific to the T-type calcium current and provides a slow positive feedback at the cellular level that is absent in the cation current. We show that this slow positive feedback is required to endow the network rhythm with physiological modulation and robustness properties. This study thereby identifies an essential cellular property to be retained at the network level in modeling network robustness and modulation. PMID:26311181

Progress on the CWU READI Analysis Center

NASA Astrophysics Data System (ADS)

Melbourne, T. I.; Szeliga, W. M.; Santillan, V. M.; Scrivner, C.

2015-12-01

Real-time GPS position streams are desirable for a variety of seismic monitoring and hazard mitigation applications. We report on progress in our development of a comprehensive real-time GPS-based seismic monitoring system for the Cascadia subduction zone. This system is based on 1 Hz point position estimates computed in the ITRF08 reference frame. Convergence from phase and range observables to point position estimates is accelerated using a Kalman filter based, on-line stream editor that produces independent estimations of carrier phase integer biases and other parameters. Positions are then estimated using a short-arc approach and algorithms from JPL's GIPSY-OASIS software with satellite clock and orbit products from the International GNSS Service (IGS). The resulting positions show typical RMS scatter of 2.5 cm in the horizontal and 5 cm in the vertical with latencies below 2 seconds. To facilitate the use of these point position streams for applications such as seismic monitoring, we broadcast real-time positions and covariances using custom-built aggregation-distribution software based on RabbitMQ messaging platform. This software is capable of buffering 24-hour streams for hundreds of stations and providing them through a REST-ful web interface. To demonstrate the power of this approach, we have developed a Java-based front-end that provides a real-time visual display of time-series, displacement vector fields, and map-view, contoured, peak ground displacement. This Java-based front-end is available for download through the PANGA website. We are currently analyzing 80 PBO and PANGA stations along the Cascadia margin and gearing up to process all 400+ real-time stations that are operating in the Pacific Northwest, many of which are currently telemetered in real-time to CWU. These will serve as milestones towards our over-arching goal of extending our processing to include all of the available real-time streams from the Pacific rim. In addition, we have developed a Kalman filter to combine CWU real-time PPP solutions with those from Scripps Institute of Oceanography's PPP-AR real-time solutions as well as real-time solutions from the USGS. These combined products should improve the robustness and reliability of real-time point-position streams in the near future.

Estimating temporary emigration and breeding proportions using capture-recapture data with Pollock's robust design

USGS Publications Warehouse

Kendall, W.L.; Nichols, J.D.; Hines, J.E.

1997-01-01

Statistical inference for capture-recapture studies of open animal populations typically relies on the assumption that all emigration from the studied population is permanent. However, there are many instances in which this assumption is unlikely to be met. We define two general models for the process of temporary emigration, completely random and Markovian. We then consider effects of these two types of temporary emigration on Jolly-Seber (Seber 1982) estimators and on estimators arising from the full-likelihood approach of Kendall et al. (1995) to robust design data. Capture-recapture data arising from Pollock's (1982) robust design provide the basis for obtaining unbiased estimates of demographic parameters in the presence of temporary emigration and for estimating the probability of temporary emigration. We present a likelihood-based approach to dealing with temporary emigration that permits estimation under different models of temporary emigration and yields tests for completely random and Markovian emigration. In addition, we use the relationship between capture probability estimates based on closed and open models under completely random temporary emigration to derive three ad hoc estimators for the probability of temporary emigration, two of which should be especially useful in situations where capture probabilities are heterogeneous among individual animals. Ad hoc and full-likelihood estimators are illustrated for small mammal capture-recapture data sets. We believe that these models and estimators will be useful for testing hypotheses about the process of temporary emigration, for estimating demographic parameters in the presence of temporary emigration, and for estimating probabilities of temporary emigration. These latter estimates are frequently of ecological interest as indicators of animal movement and, in some sampling situations, as direct estimates of breeding probabilities and proportions.

Self-Critical, and Robust, Procedures for the Analysis of Multivariate Normal Data.

DTIC Science & Technology

1982-06-01

Influence Functions The influence function is the most important tt of qual- itative zobustness since many other robustness characteristics of an estimator...may be derived from it. The influence function characterizes the (asymptotic) response of an estimator to an additional observation as a function of...the influence function be bounded. It is also advantageous, in our opinion, if the influence functions are re-descending to zero. The influence function for

Efficient and robust computation of PDF features from diffusion MR signal.

PubMed

Assemlal, Haz-Edine; Tschumperlé, David; Brun, Luc

2009-10-01

We present a method for the estimation of various features of the tissue micro-architecture using the diffusion magnetic resonance imaging. The considered features are designed from the displacement probability density function (PDF). The estimation is based on two steps: first the approximation of the signal by a series expansion made of Gaussian-Laguerre and Spherical Harmonics functions; followed by a projection on a finite dimensional space. Besides, we propose to tackle the problem of the robustness to Rician noise corrupting in-vivo acquisitions. Our feature estimation is expressed as a variational minimization process leading to a variational framework which is robust to noise. This approach is very flexible regarding the number of samples and enables the computation of a large set of various features of the local tissues structure. We demonstrate the effectiveness of the method with results on both synthetic phantom and real MR datasets acquired in a clinical time-frame.

Robust Video Stabilization Using Particle Keypoint Update and l1-Optimized Camera Path

PubMed Central

Jeon, Semi; Yoon, Inhye; Jang, Jinbeum; Yang, Seungji; Kim, Jisung; Paik, Joonki

2017-01-01

Acquisition of stabilized video is an important issue for various type of digital cameras. This paper presents an adaptive camera path estimation method using robust feature detection to remove shaky artifacts in a video. The proposed algorithm consists of three steps: (i) robust feature detection using particle keypoints between adjacent frames; (ii) camera path estimation and smoothing; and (iii) rendering to reconstruct a stabilized video. As a result, the proposed algorithm can estimate the optimal homography by redefining important feature points in the flat region using particle keypoints. In addition, stabilized frames with less holes can be generated from the optimal, adaptive camera path that minimizes a temporal total variation (TV). The proposed video stabilization method is suitable for enhancing the visual quality for various portable cameras and can be applied to robot vision, driving assistant systems, and visual surveillance systems. PMID:28208622

Correlation techniques to determine model form in robust nonlinear system realization/identification

NASA Technical Reports Server (NTRS)

Stry, Greselda I.; Mook, D. Joseph

1991-01-01

The fundamental challenge in identification of nonlinear dynamic systems is determining the appropriate form of the model. A robust technique is presented which essentially eliminates this problem for many applications. The technique is based on the Minimum Model Error (MME) optimal estimation approach. A detailed literature review is included in which fundamental differences between the current approach and previous work is described. The most significant feature is the ability to identify nonlinear dynamic systems without prior assumption regarding the form of the nonlinearities, in contrast to existing nonlinear identification approaches which usually require detailed assumptions of the nonlinearities. Model form is determined via statistical correlation of the MME optimal state estimates with the MME optimal model error estimates. The example illustrations indicate that the method is robust with respect to prior ignorance of the model, and with respect to measurement noise, measurement frequency, and measurement record length.

Correction of contaminated yaw rate signal and estimation of sensor bias for an electric vehicle under normal driving conditions

NASA Astrophysics Data System (ADS)

Zhang, Guoguang; Yu, Zitian; Wang, Junmin

2017-03-01

Yaw rate is a crucial signal for the motion control systems of ground vehicles. Yet it may be contaminated by sensor bias. In order to correct the contaminated yaw rate signal and estimate the sensor bias, a robust gain-scheduling observer is proposed in this paper. First of all, a two-degree-of-freedom (2DOF) vehicle lateral and yaw dynamic model is presented, and then a Luenberger-like observer is proposed. To make the observer more applicable to real vehicle driving operations, a 2DOF vehicle model with uncertainties on the coefficients of tire cornering stiffness is employed. Further, a gain-scheduling approach and a robustness enhancement are introduced, leading to a robust gain-scheduling observer. Sensor bias detection mechanism is also designed. Case studies are conducted using an electric ground vehicle to assess the performance of signal correction and sensor bias estimation under difference scenarios.

Statistical plant set estimation using Schroeder-phased multisinusoidal input design

NASA Technical Reports Server (NTRS)

Bayard, D. S.

1992-01-01

A frequency domain method is developed for plant set estimation. The estimation of a plant 'set' rather than a point estimate is required to support many methods of modern robust control design. The approach here is based on using a Schroeder-phased multisinusoid input design which has the special property of placing input energy only at the discrete frequency points used in the computation. A detailed analysis of the statistical properties of the frequency domain estimator is given, leading to exact expressions for the probability distribution of the estimation error, and many important properties. It is shown that, for any nominal parametric plant estimate, one can use these results to construct an overbound on the additive uncertainty to any prescribed statistical confidence. The 'soft' bound thus obtained can be used to replace 'hard' bounds presently used in many robust control analysis and synthesis methods.

Model-based sphere localization (MBSL) in x-ray projections

NASA Astrophysics Data System (ADS)

Sawall, Stefan; Maier, Joscha; Leinweber, Carsten; Funck, Carsten; Kuntz, Jan; Kachelrieß, Marc

2017-08-01

The detection of spherical markers in x-ray projections is an important task in a variety of applications, e.g. geometric calibration and detector distortion correction. Therein, the projection of the sphere center on the detector is of particular interest as the used spherical beads are no ideal point-like objects. Only few methods have been proposed to estimate this respective position on the detector with sufficient accuracy and surrogate positions, e.g. the center of gravity, are used, impairing the results of subsequent algorithms. We propose to estimate the projection of the sphere center on the detector using a simulation-based method matching an artificial projection to the actual measurement. The proposed algorithm intrinsically corrects for all polychromatic effects included in the measurement and absent in the simulation by a polynomial which is estimated simultaneously. Furthermore, neither the acquisition geometry nor any object properties besides the fact that the object is of spherical shape need to be known to find the center of the bead. It is shown by simulations that the algorithm estimates the center projection with an error of less than 1% of the detector pixel size in case of realistic noise levels and that the method is robust to the sphere material, sphere size, and acquisition parameters. A comparison to three reference methods using simulations and measurements indicates that the proposed method is an order of magnitude more accurate compared to these algorithms. The proposed method is an accurate algorithm to estimate the center of spherical markers in CT projections in the presence of polychromatic effects and noise.

Doping in Two Elite Athletics Competitions Assessed by Randomized-Response Surveys.

PubMed

Ulrich, Rolf; Pope, Harrison G; Cléret, Léa; Petróczi, Andrea; Nepusz, Tamás; Schaffer, Jay; Kanayama, Gen; Comstock, R Dawn; Simon, Perikles

2018-01-01

Doping in sports compromises fair play and endangers health. To deter doping among elite athletes, the World Anti-Doping Agency (WADA) oversees testing of several hundred thousand athletic blood and urine samples annually, of which 1-2% test positive. Measures using the Athlete Biological Passport suggest a higher mean prevalence of about 14% positive tests. Biological testing, however, likely fails to detect many cutting-edge doping techniques, and thus the true prevalence of doping remains unknown. We surveyed 2167 athletes at two sporting events: the 13th International Association of Athletics Federations Word Championships in Athletics (WCA) in Daegu, South Korea in August 2011 and the 12th Quadrennial Pan-Arab Games (PAG) in Doha, Qatar in December 2011. To estimate the prevalence of doping, we utilized a "randomized response technique," which guarantees anonymity for individuals when answering a sensitive question. We also administered a control question at PAG assessing past-year use of supplements. The estimated prevalence of past-year doping was 43.6% (95% confidence interval 39.4-47.9) at WCA and 57.1% (52.4-61.8) at PAG. The estimated prevalence of past-year supplement use at PAG was 70.1% (65.6-74.7%). Sensitivity analyses, assessing the robustness of these estimates under numerous hypothetical scenarios of intentional or unintentional noncompliance by respondents, suggested that we were unlikely to have overestimated the true prevalence of doping. Doping appears remarkably widespread among elite athletes, and remains largely unchecked despite current biological testing. The survey technique presented here will allow future investigators to generate continued reference estimates of the prevalence of doping.

Convex relaxations of spectral sparsity for robust super-resolution and line spectrum estimation

NASA Astrophysics Data System (ADS)

Chi, Yuejie

2017-08-01

We consider recovering the amplitudes and locations of spikes in a point source signal from its low-pass spectrum that may suffer from missing data and arbitrary outliers. We first review and provide a unified view of several recently proposed convex relaxations that characterize and capitalize the spectral sparsity of the point source signal without discretization under the framework of atomic norms. Next we propose a new algorithm when the spikes are known a priori to be positive, motivated by applications such as neural spike sorting and fluorescence microscopy imaging. Numerical experiments are provided to demonstrate the effectiveness of the proposed approach.

Universal distribution of mutational effects on protein stability, uncoupling of protein robustness from sequence evolution and distinct evolutionary modes of prokaryotic and eukaryotic proteins

NASA Astrophysics Data System (ADS)

Faure, Guilhem; Koonin, Eugene V.

2015-05-01

Robustness to destabilizing effects of mutations is thought of as a key factor of protein evolution. The connections between two measures of robustness, the relative core size and the computationally estimated effect of mutations on protein stability (ΔΔG), protein abundance and the selection pressure on protein-coding genes (dN/dS) were analyzed for the organisms with a large number of available protein structures including four eukaryotes, two bacteria and one archaeon. The distribution of the effects of mutations in the core on protein stability is universal and indistinguishable in eukaryotes and bacteria, centered at slightly destabilizing amino acid replacements, and with a heavy tail of more strongly destabilizing replacements. The distribution of mutational effects in the hyperthermophilic archaeon Thermococcus gammatolerans is significantly shifted toward strongly destabilizing replacements which is indicative of stronger constraints that are imposed on proteins in hyperthermophiles. The median effect of mutations is strongly, positively correlated with the relative core size, in evidence of the congruence between the two measures of protein robustness. However, both measures show only limited correlations to the expression level and selection pressure on protein-coding genes. Thus, the degree of robustness reflected in the universal distribution of mutational effects appears to be a fundamental, ancient feature of globular protein folds whereas the observed variations are largely neutral and uncoupled from short term protein evolution. A weak anticorrelation between protein core size and selection pressure is observed only for surface residues in prokaryotes but a stronger anticorrelation is observed for all residues in eukaryotic proteins. This substantial difference between proteins of prokaryotes and eukaryotes is likely to stem from the demonstrable higher compactness of prokaryotic proteins.

Effects of tag loss on direct estimates of population growth rate

USGS Publications Warehouse

Rotella, J.J.; Hines, J.E.

2005-01-01

The temporal symmetry approach of R. Pradel can be used with capture-recapture data to produce retrospective estimates of a population's growth rate, lambda(i), and the relative contributions to lambda(i) from different components of the population. Direct estimation of lambda(i) provides an alternative to using population projection matrices to estimate asymptotic lambda and is seeing increased use. However, the robustness of direct estimates of lambda(1) to violations of several key assumptions has not yet been investigated. Here, we consider tag loss as a possible source of bias for scenarios in which the rate of tag loss is (1) the same for all marked animals in the population and (2) a function of tag age. We computed analytic approximations of the expected values for each of the parameter estimators involved in direct estimation and used those values to calculate bias and precision for each parameter estimator. Estimates of lambda(i) were robust to homogeneous rates of tag loss. When tag loss rates varied by tag age, bias occurred for some of the sampling situations evaluated, especially those with low capture probability, a high rate of tag loss, or both. For situations with low rates of tag loss and high capture probability, bias was low and often negligible. Estimates of contributions of demographic components to lambda(i) were not robust to tag loss. Tag loss reduced the precision of all estimates because tag loss results in fewer marked animals remaining available for estimation. Clearly tag loss should be prevented if possible, and should be considered in analyses of lambda(i), but tag loss does not necessarily preclude unbiased estimation of lambda(i).

A robust sparse-modeling framework for estimating schizophrenia biomarkers from fMRI.

PubMed

Dillon, Keith; Calhoun, Vince; Wang, Yu-Ping

2017-01-30

Our goal is to identify the brain regions most relevant to mental illness using neuroimaging. State of the art machine learning methods commonly suffer from repeatability difficulties in this application, particularly when using large and heterogeneous populations for samples. We revisit both dimensionality reduction and sparse modeling, and recast them in a common optimization-based framework. This allows us to combine the benefits of both types of methods in an approach which we call unambiguous components. We use this to estimate the image component with a constrained variability, which is best correlated with the unknown disease mechanism. We apply the method to the estimation of neuroimaging biomarkers for schizophrenia, using task fMRI data from a large multi-site study. The proposed approach yields an improvement in both robustness of the estimate and classification accuracy. We find that unambiguous components incorporate roughly two thirds of the same brain regions as sparsity-based methods LASSO and elastic net, while roughly one third of the selected regions differ. Further, unambiguous components achieve superior classification accuracy in differentiating cases from controls. Unambiguous components provide a robust way to estimate important regions of imaging data. Copyright © 2016 Elsevier B.V. All rights reserved.

Comparing population size estimators for plethodontid salamanders

USGS Publications Warehouse

Bailey, L.L.; Simons, T.R.; Pollock, K.H.

2004-01-01

Despite concern over amphibian declines, few studies estimate absolute abundances because of logistic and economic constraints and previously poor estimator performance. Two estimation approaches recommended for amphibian studies are mark-recapture and depletion (or removal) sampling. We compared abundance estimation via various mark-recapture and depletion methods, using data from a three-year study of terrestrial salamanders in Great Smoky Mountains National Park. Our results indicate that short-term closed-population, robust design, and depletion methods estimate surface population of salamanders (i.e., those near the surface and available for capture during a given sampling occasion). In longer duration studies, temporary emigration violates assumptions of both open- and closed-population mark-recapture estimation models. However, if the temporary emigration is completely random, these models should yield unbiased estimates of the total population (superpopulation) of salamanders in the sampled area. We recommend using Pollock's robust design in mark-recapture studies because of its flexibility to incorporate variation in capture probabilities and to estimate temporary emigration probabilities.

Robust and efficient estimation with weighted composite quantile regression

NASA Astrophysics Data System (ADS)

Jiang, Xuejun; Li, Jingzhi; Xia, Tian; Yan, Wanfeng

2016-09-01

In this paper we introduce a weighted composite quantile regression (CQR) estimation approach and study its application in nonlinear models such as exponential models and ARCH-type models. The weighted CQR is augmented by using a data-driven weighting scheme. With the error distribution unspecified, the proposed estimators share robustness from quantile regression and achieve nearly the same efficiency as the oracle maximum likelihood estimator (MLE) for a variety of error distributions including the normal, mixed-normal, Student's t, Cauchy distributions, etc. We also suggest an algorithm for the fast implementation of the proposed methodology. Simulations are carried out to compare the performance of different estimators, and the proposed approach is used to analyze the daily S&P 500 Composite index, which verifies the effectiveness and efficiency of our theoretical results.

A cascaded two-step Kalman filter for estimation of human body segment orientation using MEMS-IMU.

PubMed

Zihajehzadeh, S; Loh, D; Lee, M; Hoskinson, R; Park, E J

2014-01-01

Orientation of human body segments is an important quantity in many biomechanical analyses. To get robust and drift-free 3-D orientation, raw data from miniature body worn MEMS-based inertial measurement units (IMU) should be blended in a Kalman filter. Aiming at less computational cost, this work presents a novel cascaded two-step Kalman filter orientation estimation algorithm. Tilt angles are estimated in the first step of the proposed cascaded Kalman filter. The estimated tilt angles are passed to the second step of the filter for yaw angle calculation. The orientation results are benchmarked against the ones from a highly accurate tactical grade IMU. Experimental results reveal that the proposed algorithm provides robust orientation estimation in both kinematically and magnetically disturbed conditions.

Long-Term Outcomes of Adding HPV Vaccine to the Anal Intraepithelial Neoplasia Treatment Regimen in HIV-Positive Men Who Have Sex With Men

PubMed Central

Deshmukh, Ashish A.; Chhatwal, Jagpreet; Chiao, Elizabeth Y.; Nyitray, Alan G.; Das, Prajnan; Cantor, Scott B.

2015-01-01

Background. Recent evidence shows that quadrivalent human papillomavirus (qHPV) vaccination in men who have sex with men (MSM) who have a history of high-grade anal intraepithelial neoplasia (HGAIN) was associated with a 50% reduction in the risk of recurrent HGAIN. We evaluated the long-term clinical and economic outcomes of adding the qHPV vaccine to the treatment regimen for HGAIN in human immunodeficiency virus (HIV)–positive MSM aged ≥27 years. Methods. We constructed a Markov model based on anal histology in HIV-positive MSM comparing qHPV vaccination with no vaccination after treatment for HGAIN, the current practice. The model parameters, including baseline prevalence, disease transitions, costs, and utilities, were either obtained from the literature or calibrated using a natural history model of anal carcinogenesis. The model outputs included lifetime costs, quality-adjusted life years, and lifetime risk of developing anal cancer. We estimated the incremental cost-effectiveness ratio of qHPV vaccination compared to no qHPV vaccination and decrease in lifetime risk of anal cancer. We also conducted deterministic and probabilistic sensitivity analyses to evaluate the robustness of the results. Results. Use of qHPV vaccination after treatment for HGAIN decreased the lifetime risk of anal cancer by 63% compared with no vaccination. The qHPV vaccination strategy was cost saving; it decreased lifetime costs by $419 and increased quality-adjusted life years by 0.16. Results were robust to the sensitivity analysis. Conclusions. Vaccinating HIV-positive MSM aged ≥27 years with qHPV vaccine after treatment for HGAIN is a cost-saving strategy. Therefore, expansion of current vaccination guidelines to include this population should be a high priority. PMID:26223993

On-Line Robust Modal Stability Prediction using Wavelet Processing

NASA Technical Reports Server (NTRS)

Brenner, Martin J.; Lind, Rick

1998-01-01

Wavelet analysis for filtering and system identification has been used to improve the estimation of aeroservoelastic stability margins. The conservatism of the robust stability margins is reduced with parametric and nonparametric time- frequency analysis of flight data in the model validation process. Nonparametric wavelet processing of data is used to reduce the effects of external disturbances and unmodeled dynamics. Parametric estimates of modal stability are also extracted using the wavelet transform. Computation of robust stability margins for stability boundary prediction depends on uncertainty descriptions derived from the data for model validation. The F-18 High Alpha Research Vehicle aeroservoelastic flight test data demonstrates improved robust stability prediction by extension of the stability boundary beyond the flight regime. Guidelines and computation times are presented to show the efficiency and practical aspects of these procedures for on-line implementation. Feasibility of the method is shown for processing flight data from time- varying nonstationary test points.

Multi-Mode Estimation for Small Fixed Wing Unmanned Aerial Vehicle Localization Based on a Linear Matrix Inequality Approach

PubMed Central

Elzoghby, Mostafa; Li, Fu; Arafa, Ibrahim. I.; Arif, Usman

2017-01-01

Information fusion from multiple sensors ensures the accuracy and robustness of a navigation system, especially in the absence of global positioning system (GPS) data which gets degraded in many cases. A way to deal with multi-mode estimation for a small fixed wing unmanned aerial vehicle (UAV) localization framework is proposed, which depends on utilizing a Luenberger observer-based linear matrix inequality (LMI) approach. The proposed estimation technique relies on the interaction between multiple measurement modes and a continuous observer. The state estimation is performed in a switching environment between multiple active sensors to exploit the available information as much as possible, especially in GPS-denied environments. Luenberger observer-based projection is implemented as a continuous observer to optimize the estimation performance. The observer gain might be chosen by solving a Lyapunov equation by means of a LMI algorithm. Convergence is achieved by utilizing the linear matrix inequality (LMI), based on Lyapunov stability which keeps the dynamic estimation error bounded by selecting the observer gain matrix (L). Simulation results are presented for a small UAV fixed wing localization problem. The results obtained using the proposed approach are compared with a single mode Extended Kalman Filter (EKF). Simulation results are presented to demonstrate the viability of the proposed strategy. PMID:28420214

Does more education lead to better health habits? Evidence from the school reforms in Australia.

PubMed

Li, Jinhu; Powdthavee, Nattavudh

2015-02-01

The current study provides new empirical evidence on the causal effect of education on health-related behaviors by exploiting historical changes in the compulsory schooling laws in Australia. Since World War II, Australian states increased the minimum school leaving age from 14 to 15 in different years. Using differences in the laws regarding minimum school leaving age across different cohorts and across different states as a source of exogenous variation in education, we show that more education improves people's diets and their tendency to engage in more regular exercise and drinking moderately, but not necessarily their tendency to avoid smoking and to engage in more preventive health checks. The improvements in health behaviors are also reflected in the estimated positive effect of education on some health outcomes. Our results are robust to alternative measures of education and different estimation methods. Copyright © 2014 Elsevier Ltd. All rights reserved.

ESARR: enhanced situational awareness via road sign recognition

NASA Astrophysics Data System (ADS)

Perlin, V. E.; Johnson, D. B.; Rohde, M. M.; Lupa, R. M.; Fiorani, G.; Mohammad, S.

2010-04-01

The enhanced situational awareness via road sign recognition (ESARR) system provides vehicle position estimates in the absence of GPS signal via automated processing of roadway fiducials (primarily directional road signs). Sign images are detected and extracted from vehicle-mounted camera system, and preprocessed and read via a custom optical character recognition (OCR) system specifically designed to cope with low quality input imagery. Vehicle motion and 3D scene geometry estimation enables efficient and robust sign detection with low false alarm rates. Multi-level text processing coupled with GIS database validation enables effective interpretation even of extremely low resolution low contrast sign images. In this paper, ESARR development progress will be reported on, including the design and architecture, image processing framework, localization methodologies, and results to date. Highlights of the real-time vehicle-based directional road-sign detection and interpretation system will be described along with the challenges and progress in overcoming them.

Sensor Network Localization by Eigenvector Synchronization Over the Euclidean Group

PubMed Central

CUCURINGU, MIHAI; LIPMAN, YARON; SINGER, AMIT

2013-01-01

We present a new approach to localization of sensors from noisy measurements of a subset of their Euclidean distances. Our algorithm starts by finding, embedding, and aligning uniquely realizable subsets of neighboring sensors called patches. In the noise-free case, each patch agrees with its global positioning up to an unknown rigid motion of translation, rotation, and possibly reflection. The reflections and rotations are estimated using the recently developed eigenvector synchronization algorithm, while the translations are estimated by solving an overdetermined linear system. The algorithm is scalable as the number of nodes increases and can be implemented in a distributed fashion. Extensive numerical experiments show that it compares favorably to other existing algorithms in terms of robustness to noise, sparse connectivity, and running time. While our approach is applicable to higher dimensions, in the current article, we focus on the two-dimensional case. PMID:23946700

TSaT-MUSIC: a novel algorithm for rapid and accurate ultrasonic 3D localization

NASA Astrophysics Data System (ADS)

Mizutani, Kyohei; Ito, Toshio; Sugimoto, Masanori; Hashizume, Hiromichi

2011-12-01

We describe a fast and accurate indoor localization technique using the multiple signal classification (MUSIC) algorithm. The MUSIC algorithm is known as a high-resolution method for estimating directions of arrival (DOAs) or propagation delays. A critical problem in using the MUSIC algorithm for localization is its computational complexity. Therefore, we devised a novel algorithm called Time Space additional Temporal-MUSIC, which can rapidly and simultaneously identify DOAs and delays of mul-ticarrier ultrasonic waves from transmitters. Computer simulations have proved that the computation time of the proposed algorithm is almost constant in spite of increasing numbers of incoming waves and is faster than that of existing methods based on the MUSIC algorithm. The robustness of the proposed algorithm is discussed through simulations. Experiments in real environments showed that the standard deviation of position estimations in 3D space is less than 10 mm, which is satisfactory for indoor localization.

Global and System-Specific Resting-State fMRI Fluctuations Are Uncorrelated: Principal Component Analysis Reveals Anti-Correlated Networks

PubMed Central

Carbonell, Felix; Bellec, Pierre

2011-01-01

Abstract The influence of the global average signal (GAS) on functional-magnetic resonance imaging (fMRI)–based resting-state functional connectivity is a matter of ongoing debate. The global average fluctuations increase the correlation between functional systems beyond the correlation that reflects their specific functional connectivity. Hence, removal of the GAS is a common practice for facilitating the observation of network-specific functional connectivity. This strategy relies on the implicit assumption of a linear-additive model according to which global fluctuations, irrespective of their origin, and network-specific fluctuations are super-positioned. However, removal of the GAS introduces spurious negative correlations between functional systems, bringing into question the validity of previous findings of negative correlations between fluctuations in the default-mode and the task-positive networks. Here we present an alternative method for estimating global fluctuations, immune to the complications associated with the GAS. Principal components analysis was applied to resting-state fMRI time-series. A global-signal effect estimator was defined as the principal component (PC) that correlated best with the GAS. The mean correlation coefficient between our proposed PC-based global effect estimator and the GAS was 0.97±0.05, demonstrating that our estimator successfully approximated the GAS. In 66 out of 68 runs, the PC that showed the highest correlation with the GAS was the first PC. Since PCs are orthogonal, our method provides an estimator of the global fluctuations, which is uncorrelated to the remaining, network-specific fluctuations. Moreover, unlike the regression of the GAS, the regression of the PC-based global effect estimator does not introduce spurious anti-correlations beyond the decrease in seed-based correlation values allowed by the assumed additive model. After regressing this PC-based estimator out of the original time-series, we observed robust anti-correlations between resting-state fluctuations in the default-mode and the task-positive networks. We conclude that resting-state global fluctuations and network-specific fluctuations are uncorrelated, supporting a Resting-State Linear-Additive Model. In addition, we conclude that the network-specific resting-state fluctuations of the default-mode and task-positive networks show artifact-free anti-correlations. PMID:22444074

Generating Multivariate Ordinal Data via Entropy Principles.

PubMed

Lee, Yen; Kaplan, David

2018-03-01

When conducting robustness research where the focus of attention is on the impact of non-normality, the marginal skewness and kurtosis are often used to set the degree of non-normality. Monte Carlo methods are commonly applied to conduct this type of research by simulating data from distributions with skewness and kurtosis constrained to pre-specified values. Although several procedures have been proposed to simulate data from distributions with these constraints, no corresponding procedures have been applied for discrete distributions. In this paper, we present two procedures based on the principles of maximum entropy and minimum cross-entropy to estimate the multivariate observed ordinal distributions with constraints on skewness and kurtosis. For these procedures, the correlation matrix of the observed variables is not specified but depends on the relationships between the latent response variables. With the estimated distributions, researchers can study robustness not only focusing on the levels of non-normality but also on the variations in the distribution shapes. A simulation study demonstrates that these procedures yield excellent agreement between specified parameters and those of estimated distributions. A robustness study concerning the effect of distribution shape in the context of confirmatory factor analysis shows that shape can affect the robust [Formula: see text] and robust fit indices, especially when the sample size is small, the data are severely non-normal, and the fitted model is complex.

Robust Criterion for the Existence of Nonhyperbolic Ergodic Measures

NASA Astrophysics Data System (ADS)

Bochi, Jairo; Bonatti, Christian; Díaz, Lorenzo J.

2016-06-01

We give explicit C 1-open conditions that ensure that a diffeomorphism possesses a nonhyperbolic ergodic measure with positive entropy. Actually, our criterion provides the existence of a partially hyperbolic compact set with one-dimensional center and positive topological entropy on which the center Lyapunov exponent vanishes uniformly. The conditions of the criterion are met on a C 1-dense and open subset of the set of diffeomorphisms having a robust cycle. As a corollary, there exists a C 1-open and dense subset of the set of non-Anosov robustly transitive diffeomorphisms consisting of systems with nonhyperbolic ergodic measures with positive entropy. The criterion is based on a notion of a blender defined dynamically in terms of strict invariance of a family of discs.

A reverberation-time-aware DNN approach leveraging spatial information for microphone array dereverberation

NASA Astrophysics Data System (ADS)

Wu, Bo; Yang, Minglei; Li, Kehuang; Huang, Zhen; Siniscalchi, Sabato Marco; Wang, Tong; Lee, Chin-Hui

2017-12-01

A reverberation-time-aware deep-neural-network (DNN)-based multi-channel speech dereverberation framework is proposed to handle a wide range of reverberation times (RT60s). There are three key steps in designing a robust system. First, to accomplish simultaneous speech dereverberation and beamforming, we propose a framework, namely DNNSpatial, by selectively concatenating log-power spectral (LPS) input features of reverberant speech from multiple microphones in an array and map them into the expected output LPS features of anechoic reference speech based on a single deep neural network (DNN). Next, the temporal auto-correlation function of received signals at different RT60s is investigated to show that RT60-dependent temporal-spatial contexts in feature selection are needed in the DNNSpatial training stage in order to optimize the system performance in diverse reverberant environments. Finally, the RT60 is estimated to select the proper temporal and spatial contexts before feeding the log-power spectrum features to the trained DNNs for speech dereverberation. The experimental evidence gathered in this study indicates that the proposed framework outperforms the state-of-the-art signal processing dereverberation algorithm weighted prediction error (WPE) and conventional DNNSpatial systems without taking the reverberation time into account, even for extremely weak and severe reverberant conditions. The proposed technique generalizes well to unseen room size, array geometry and loudspeaker position, and is robust to reverberation time estimation error.

Real-time people counting system using a single video camera

NASA Astrophysics Data System (ADS)

Lefloch, Damien; Cheikh, Faouzi A.; Hardeberg, Jon Y.; Gouton, Pierre; Picot-Clemente, Romain

2008-02-01

There is growing interest in video-based solutions for people monitoring and counting in business and security applications. Compared to classic sensor-based solutions the video-based ones allow for more versatile functionalities, improved performance with lower costs. In this paper, we propose a real-time system for people counting based on single low-end non-calibrated video camera. The two main challenges addressed in this paper are: robust estimation of the scene background and the number of real persons in merge-split scenarios. The latter is likely to occur whenever multiple persons move closely, e.g. in shopping centers. Several persons may be considered to be a single person by automatic segmentation algorithms, due to occlusions or shadows, leading to under-counting. Therefore, to account for noises, illumination and static objects changes, a background substraction is performed using an adaptive background model (updated over time based on motion information) and automatic thresholding. Furthermore, post-processing of the segmentation results is performed, in the HSV color space, to remove shadows. Moving objects are tracked using an adaptive Kalman filter, allowing a robust estimation of the objects future positions even under heavy occlusion. The system is implemented in Matlab, and gives encouraging results even at high frame rates. Experimental results obtained based on the PETS2006 datasets are presented at the end of the paper.

Fractional-order active fault-tolerant force-position controller design for the legged robots using saturated actuator with unknown bias and gain degradation

NASA Astrophysics Data System (ADS)

Farid, Yousef; Majd, Vahid Johari; Ehsani-Seresht, Abbas

2018-05-01

In this paper, a novel fault accommodation strategy is proposed for the legged robots subject to the actuator faults including actuation bias and effective gain degradation as well as the actuator saturation. First, the combined dynamics of two coupled subsystems consisting of the dynamics of the legs subsystem and the body subsystem are developed. Then, the interaction of the robot with the environment is formulated as the contact force optimization problem with equality and inequality constraints. The desired force is obtained by a dynamic model. A robust super twisting fault estimator is proposed to precisely estimate the defective torque amplitude of the faulty actuator in finite time. Defining a novel fractional sliding surface, a fractional nonsingular terminal sliding mode control law is developed. Moreover, by introducing a suitable auxiliary system and using its state vector in the designed controller, the proposed fault-tolerant control (FTC) scheme guarantees the finite-time stability of the closed-loop control system. The robustness and finite-time convergence of the proposed control law is established using the Lyapunov stability theory. Finally, numerical simulations are performed on a quadruped robot to demonstrate the stable walking of the robot with and without actuator faults, and actuator saturation constraints, and the results are compared to results with an integer order fault-tolerant controller.

Robust uniform persistence in discrete and continuous dynamical systems using Lyapunov exponents.

PubMed

Salceanu, Paul L

2011-07-01

This paper extends the work of Salceanu and Smith [12, 13] where Lyapunov exponents were used to obtain conditions for uniform persistence ina class of dissipative discrete-time dynamical systems on the positive orthant of R(m), generated by maps. Here a united approach is taken, for both discrete and continuous time, and the dissipativity assumption is relaxed. Sufficient conditions are given for compact subsets of an invariant part of the boundary of R(m+) to be robust uniform weak repellers. These conditions require Lyapunov exponents be positive on such sets. It is shown how this leads to robust uniform persistence. The results apply to the investigation of robust uniform persistence of the disease in host populations, as shown in an application.

Robust Synchronization Schemes for Dynamic Channel Environments

NASA Technical Reports Server (NTRS)

Xiong, Fugin

2003-01-01

Professor Xiong will investigate robust synchronization schemes for dynamic channel environment. A sliding window will be investigated for symbol timing synchronizer and an open loop carrier estimator for carrier synchronization. Matlab/Simulink will be used for modeling and simulations.

Robust stability of second-order systems

NASA Technical Reports Server (NTRS)

Chuang, C.-H.

1995-01-01

It has been shown recently how virtual passive controllers can be designed for second-order dynamic systems to achieve robust stability. The virtual controllers were visualized as systems made up of spring, mass and damping elements. In this paper, a new approach emphasizing on the notion of positive realness to the same second-order dynamic systems is used. Necessary and sufficient conditions for positive realness are presented for scalar spring-mass-dashpot systems. For multi-input multi-output systems, we show how a mass-spring-dashpot system can be made positive real by properly choosing its output variables. In particular, sufficient conditions are shown for the system without output velocity. Furthermore, if velocity cannot be measured then the system parameters must be precise to keep the system positive real. In practice, system parameters are not always constant and cannot be measured precisely. Therefore, in order to be useful positive real systems must be robust to some degrees. This can be achieved with the design presented in this paper.

Prevalence of asymptomatic Zika virus infection: a systematic review

PubMed Central

Pinart, Mariona; Elias, Vanessa; Reveiz, Ludovic

2018-01-01

Abstract Objective To conduct a systematic review to estimate the prevalence of asymptomatic Zika virus infection in the general population and in specific population groups. Methods We searched PubMed®, Embase® and LILACS online databases from inception to 26 January 2018. We included observational epidemiological studies where laboratory testing was used to confirm positive exposure of participants to Zika virus and in which Zika virus symptom status was also recorded. We excluded studies in which having symptoms of Zika virus was a criterion for inclusion. The main outcome assessed was percentage of all Zika virus-positive participants who were asymptomatic. We used a quality-effects approach and the double arcsine transformation for the meta-analysis. Findings We assessed 753 studies for inclusion, of which 23 were included in the meta-analysis, totalling 11 305 Zika virus-positive participants. The high degree of heterogeneity in the studies (I2 = 99%) suggests that the pooled prevalence of asymptomatic Zika virus-positive participants was probably not a robust estimate. Analysis based on subgroups of the population (general population, returned travellers, blood donors, adults with Guillain–Barré syndrome, pregnant women and babies with microcephaly) was not able to explain the heterogeneity. Funnel and Doi plots showed major asymmetry, suggesting selection bias or true heterogeneity. Conclusion Better-quality research is needed, using standardized methods, to determine the true prevalence of asymptomatic Zika virus and whether it varies between populations or over time. PMID:29904223

Generalized weighted likelihood density estimators with application to finite mixture of exponential family distributions

PubMed Central

Zhan, Tingting; Chevoneva, Inna; Iglewicz, Boris

2010-01-01

The family of weighted likelihood estimators largely overlaps with minimum divergence estimators. They are robust to data contaminations compared to MLE. We define the class of generalized weighted likelihood estimators (GWLE), provide its influence function and discuss the efficiency requirements. We introduce a new truncated cubic-inverse weight, which is both first and second order efficient and more robust than previously reported weights. We also discuss new ways of selecting the smoothing bandwidth and weighted starting values for the iterative algorithm. The advantage of the truncated cubic-inverse weight is illustrated in a simulation study of three-components normal mixtures model with large overlaps and heavy contaminations. A real data example is also provided. PMID:20835375

Endoscopic feature tracking for augmented-reality assisted prosthesis selection in mitral valve repair

NASA Astrophysics Data System (ADS)

Engelhardt, Sandy; Kolb, Silvio; De Simone, Raffaele; Karck, Matthias; Meinzer, Hans-Peter; Wolf, Ivo

2016-03-01

Mitral valve annuloplasty describes a surgical procedure where an artificial prosthesis is sutured onto the anatomical structure of the mitral annulus to re-establish the valve's functionality. Choosing an appropriate commercially available ring size and shape is a difficult decision the surgeon has to make intraoperatively according to his experience. In our augmented-reality framework, digitalized ring models are superimposed onto endoscopic image streams without using any additional hardware. To place the ring model on the proper position within the endoscopic image plane, a pose estimation is performed that depends on the localization of sutures placed by the surgeon around the leaflet origins and punctured through the stiffer structure of the annulus. In this work, the tissue penetration points are tracked by the real-time capable Lucas Kanade optical flow algorithm. The accuracy and robustness of this tracking algorithm is investigated with respect to the question whether outliers influence the subsequent pose estimation. Our results suggest that optical flow is very stable for a variety of different endoscopic scenes and tracking errors do not affect the position of the superimposed virtual objects in the scene, making this approach a viable candidate for annuloplasty augmented reality-enhanced decision support.

Adaptive Estimation of Multiple Fading Factors for GPS/INS Integrated Navigation Systems.

PubMed

Jiang, Chen; Zhang, Shu-Bi; Zhang, Qiu-Zhao

2017-06-01

The Kalman filter has been widely applied in the field of dynamic navigation and positioning. However, its performance will be degraded in the presence of significant model errors and uncertain interferences. In the literature, the fading filter was proposed to control the influences of the model errors, and the H-infinity filter can be adopted to address the uncertainties by minimizing the estimation error in the worst case. In this paper, a new multiple fading factor, suitable for the Global Positioning System (GPS) and the Inertial Navigation System (INS) integrated navigation system, is proposed based on the optimization of the filter, and a comprehensive filtering algorithm is constructed by integrating the advantages of the H-infinity filter and the proposed multiple fading filter. Measurement data of the GPS/INS integrated navigation system are collected under actual conditions. Stability and robustness of the proposed filtering algorithm are tested with various experiments and contrastive analysis are performed with the measurement data. Results demonstrate that both the filter divergence and the influences of outliers are restrained effectively with the proposed filtering algorithm, and precision of the filtering results are improved simultaneously.

Statistics based sampling for controller and estimator design

NASA Astrophysics Data System (ADS)

Tenne, Dirk

The purpose of this research is the development of statistical design tools for robust feed-forward/feedback controllers and nonlinear estimators. This dissertation is threefold and addresses the aforementioned topics nonlinear estimation, target tracking and robust control. To develop statistically robust controllers and nonlinear estimation algorithms, research has been performed to extend existing techniques, which propagate the statistics of the state, to achieve higher order accuracy. The so-called unscented transformation has been extended to capture higher order moments. Furthermore, higher order moment update algorithms based on a truncated power series have been developed. The proposed techniques are tested on various benchmark examples. Furthermore, the unscented transformation has been utilized to develop a three dimensional geometrically constrained target tracker. The proposed planar circular prediction algorithm has been developed in a local coordinate framework, which is amenable to extension of the tracking algorithm to three dimensional space. This tracker combines the predictions of a circular prediction algorithm and a constant velocity filter by utilizing the Covariance Intersection. This combined prediction can be updated with the subsequent measurement using a linear estimator. The proposed technique is illustrated on a 3D benchmark trajectory, which includes coordinated turns and straight line maneuvers. The third part of this dissertation addresses the design of controller which include knowledge of parametric uncertainties and their distributions. The parameter distributions are approximated by a finite set of points which are calculated by the unscented transformation. This set of points is used to design robust controllers which minimize a statistical performance of the plant over the domain of uncertainty consisting of a combination of the mean and variance. The proposed technique is illustrated on three benchmark problems. The first relates to the design of prefilters for a linear and nonlinear spring-mass-dashpot system and the second applies a feedback controller to a hovering helicopter. Lastly, the statistical robust controller design is devoted to a concurrent feed-forward/feedback controller structure for a high-speed low tension tape drive.

Real-Time GPS Monitoring for Earthquake Rapid Assessment in the San Francisco Bay Area

NASA Astrophysics Data System (ADS)

Guillemot, C.; Langbein, J. O.; Murray, J. R.

2012-12-01

The U.S. Geological Survey Earthquake Science Center has deployed a network of eight real-time Global Positioning System (GPS) stations in the San Francisco Bay area and is implementing software applications to continuously evaluate the status of the deformation within the network. Real-time monitoring of the station positions is expected to provide valuable information for rapidly estimating source parameters should a large earthquake occur in the San Francisco Bay area. Because earthquake response applications require robust data access, as a first step we have developed a suite of web-based applications which are now routinely used to monitor the network's operational status and data streaming performance. The web tools provide continuously updated displays of important telemetry parameters such as data latency and receive rates, as well as source voltage and temperature information within each instrument enclosure. Automated software on the backend uses the streaming performance data to mitigate the impact of outages, radio interference and bandwidth congestion on deformation monitoring operations. A separate set of software applications manages the recovery of lost data due to faulty communication links. Displacement estimates are computed in real-time for various combinations of USGS, Plate Boundary Observatory (PBO) and Bay Area Regional Deformation (BARD) network stations. We are currently comparing results from two software packages (one commercial and one open-source) used to process 1-Hz data on the fly and produce estimates of differential positions. The continuous monitoring of telemetry makes it possible to tune the network to minimize the impact of transient interruptions of the data flow, from one or more stations, on the estimated positions. Ongoing work is focused on using data streaming performance history to optimize the quality of the position, reduce drift and outliers by switching to the best set of stations within the network, and automatically select the "next best" station to use as reference. We are also working towards minimizing the loss of streamed data during concurrent data downloads by improving file management on the GPS receivers.

Critical bounds on noise and SNR for robust estimation of real-time brain activity from functional near infra-red spectroscopy.

PubMed

Aqil, Muhammad; Jeong, Myung Yung

2018-04-24

The robust characterization of real-time brain activity carries potential for many applications. However, the contamination of measured signals by various instrumental, environmental, and physiological sources of noise introduces a substantial amount of signal variance and, consequently, challenges real-time estimation of contributions from underlying neuronal sources. Functional near infra-red spectroscopy (fNIRS) is an emerging imaging modality whose real-time potential is yet to be fully explored. The objectives of the current study are to (i) validate a time-dependent linear model of hemodynamic responses in fNIRS, and (ii) test the robustness of this approach against measurement noise (instrumental and physiological) and mis-specification of the hemodynamic response basis functions (amplitude, latency, and duration). We propose a linear hemodynamic model with time-varying parameters, which are estimated (adapted and tracked) using a dynamic recursive least square algorithm. Owing to the linear nature of the activation model, the problem of achieving robust convergence to an accurate estimation of the model parameters is recast as a problem of parameter error stability around the origin. We show that robust convergence of the proposed method is guaranteed in the presence of an acceptable degree of model misspecification and we derive an upper bound on noise under which reliable parameters can still be inferred. We also derived a lower bound on signal-to-noise-ratio over which the reliable parameters can still be inferred from a channel/voxel. Whilst here applied to fNIRS, the proposed methodology is applicable to other hemodynamic-based imaging technologies such as functional magnetic resonance imaging. Copyright © 2018 Elsevier Inc. All rights reserved.

Bayes filter modification for drivability map estimation with observations from stereo vision

NASA Astrophysics Data System (ADS)

Panchenko, Aleksei; Prun, Viktor; Turchenkov, Dmitri

2017-02-01

Reconstruction of a drivability map for a moving vehicle is a well-known research topic in applied robotics. Here creating such a map for an autonomous truck on a generally planar surface containing separate obstacles is considered. The source of measurements for the truck is a calibrated pair of cameras. The stereo system detects and reconstructs several types of objects, such as road borders, other vehicles, pedestrians and general tall objects or highly saturated objects (e.g. road cone). For creating a robust mapping module we use a modification of Bayes filtering, which introduces some novel techniques for occupancy map update step. Specifically, our modified version becomes applicable to the presence of false positive measurement errors, stereo shading and obstacle occlusion. We implemented the technique and achieved real-time 15 FPS computations on an industrial shake proof PC. Our real world experiments show the positive effect of the filtering step.

Robust Maneuvering Envelope Estimation Based on Reachability Analysis in an Optimal Control Formulation

NASA Technical Reports Server (NTRS)

Lombaerts, Thomas; Schuet, Stefan R.; Wheeler, Kevin; Acosta, Diana; Kaneshige, John

2013-01-01

This paper discusses an algorithm for estimating the safe maneuvering envelope of damaged aircraft. The algorithm performs a robust reachability analysis through an optimal control formulation while making use of time scale separation and taking into account uncertainties in the aerodynamic derivatives. Starting with an optimal control formulation, the optimization problem can be rewritten as a Hamilton- Jacobi-Bellman equation. This equation can be solved by level set methods. This approach has been applied on an aircraft example involving structural airframe damage. Monte Carlo validation tests have confirmed that this approach is successful in estimating the safe maneuvering envelope for damaged aircraft.

Fine Particle Sources and Cardiorespiratory Morbidity: An Application of Chemical Mass Balance and Factor Analytical Source-Apportionment Methods

PubMed Central

Sarnat, Jeremy A.; Marmur, Amit; Klein, Mitchel; Kim, Eugene; Russell, Armistead G.; Sarnat, Stefanie E.; Mulholland, James A.; Hopke, Philip K.; Tolbert, Paige E.

2008-01-01

Background Interest in the health effects of particulate matter (PM) has focused on identifying sources of PM, including biomass burning, power plants, and gasoline and diesel emissions that may be associated with adverse health risks. Few epidemiologic studies, however, have included source-apportionment estimates in their examinations of PM health effects. We analyzed a time-series of chemically speciated PM measurements in Atlanta, Georgia, and conducted an epidemiologic analysis using data from three distinct source-apportionment methods. Objective The key objective of this analysis was to compare epidemiologic findings generated using both factor analysis and mass balance source-apportionment methods. Methods We analyzed data collected between November 1998 and December 2002 using positive-matrix factorization (PMF), modified chemical mass balance (CMB-LGO), and a tracer approach. Emergency department (ED) visits for a combined cardiovascular (CVD) and respiratory disease (RD) group were assessed as end points. We estimated the risk ratio (RR) associated with same day PM concentrations using Poisson generalized linear models. Results There were significant, positive associations between same-day PM2.5 (PM with aero-dynamic diameter ≤ 2.5 μm) concentrations attributed to mobile sources (RR range, 1.018–1.025) and biomass combustion, primarily prescribed forest burning and residential wood combustion, (RR range, 1.024–1.033) source categories and CVD-related ED visits. Associations between the source categories and RD visits were not significant for all models except sulfate-rich secondary PM2.5 (RR range, 1.012–1.020). Generally, the epidemiologic results were robust to the selection of source-apportionment method, with strong agreement between the RR estimates from the PMF and CMB-LGO models, as well as with results from models using single-species tracers as surrogates of the source-apportioned PM2.5 values. Conclusions Despite differences among the source-apportionment methods, these findings suggest that modeled source-apportioned data can produce robust estimates of acute health risk. In Atlanta, there were consistent associations across methods between PM2.5 from mobile sources and biomass burning with both cardiovascular and respiratory ED visits, and between sulfate-rich secondary PM2.5 with respiratory visits. PMID:18414627

An Estimation Method of Waiting Time for Health Service at Hospital by Using a Portable RFID and Robust Estimation

NASA Astrophysics Data System (ADS)

Ishigaki, Tsukasa; Yamamoto, Yoshinobu; Nakamura, Yoshiyuki; Akamatsu, Motoyuki

Patients that have an health service by doctor have to wait long time at many hospitals. The long waiting time is the worst factor of patient's dissatisfaction for hospital service according to questionnaire for patients. The present paper describes an estimation method of the waiting time for each patient without an electronic medical chart system. The method applies a portable RFID system to data acquisition and robust estimation of probability distribution of the health service and test time by doctor for high-accurate waiting time estimation. We carried out an health service of data acquisition at a real hospital and verified the efficiency of the proposed method. The proposed system widely can be used as data acquisition system in various fields such as marketing service, entertainment or human behavior measurement.

Reader reaction to "a robust method for estimating optimal treatment regimes" by Zhang et al. (2012).

PubMed

Taylor, Jeremy M G; Cheng, Wenting; Foster, Jared C

2015-03-01

A recent article (Zhang et al., 2012, Biometrics 168, 1010-1018) compares regression based and inverse probability based methods of estimating an optimal treatment regime and shows for a small number of covariates that inverse probability weighted methods are more robust to model misspecification than regression methods. We demonstrate that using models that fit the data better reduces the concern about non-robustness for the regression methods. We extend the simulation study of Zhang et al. (2012, Biometrics 168, 1010-1018), also considering the situation of a larger number of covariates, and show that incorporating random forests into both regression and inverse probability weighted based methods improves their properties. © 2014, The International Biometric Society.

Pixel-level multisensor image fusion based on matrix completion and robust principal component analysis

NASA Astrophysics Data System (ADS)

Wang, Zhuozheng; Deller, J. R.; Fleet, Blair D.

2016-01-01

Acquired digital images are often corrupted by a lack of camera focus, faulty illumination, or missing data. An algorithm is presented for fusion of multiple corrupted images of a scene using the lifting wavelet transform. The method employs adaptive fusion arithmetic based on matrix completion and self-adaptive regional variance estimation. Characteristics of the wavelet coefficients are used to adaptively select fusion rules. Robust principal component analysis is applied to low-frequency image components, and regional variance estimation is applied to high-frequency components. Experiments reveal that the method is effective for multifocus, visible-light, and infrared image fusion. Compared with traditional algorithms, the new algorithm not only increases the amount of preserved information and clarity but also improves robustness.

Investigating Robustness of Item Response Theory Proficiency Estimators to Atypical Response Behaviors under Two-Stage Multistage Testing. ETS GRE® Board Research Report. ETS GRE®-16-03. ETS Research Report No. RR-16-22

ERIC Educational Resources Information Center

Kim, Sooyeon; Moses, Tim

2016-01-01

The purpose of this study is to evaluate the extent to which item response theory (IRT) proficiency estimation methods are robust to the presence of aberrant responses under the "GRE"® General Test multistage adaptive testing (MST) design. To that end, a wide range of atypical response behaviors affecting as much as 10% of the test items…

Improved blood glucose estimation through multi-sensor fusion.

PubMed

Xiong, Feiyu; Hipszer, Brian R; Joseph, Jeffrey; Kam, Moshe

2011-01-01

Continuous glucose monitoring systems are an integral component of diabetes management. Efforts to improve the accuracy and robustness of these systems are at the forefront of diabetes research. Towards this goal, a multi-sensor approach was evaluated in hospitalized patients. In this paper, we report on a multi-sensor fusion algorithm to combine glucose sensor measurements in a retrospective fashion. The results demonstrate the algorithm's ability to improve the accuracy and robustness of the blood glucose estimation with current glucose sensor technology.

Robust Bayesian clustering.

PubMed

Archambeau, Cédric; Verleysen, Michel

2007-01-01

A new variational Bayesian learning algorithm for Student-t mixture models is introduced. This algorithm leads to (i) robust density estimation, (ii) robust clustering and (iii) robust automatic model selection. Gaussian mixture models are learning machines which are based on a divide-and-conquer approach. They are commonly used for density estimation and clustering tasks, but are sensitive to outliers. The Student-t distribution has heavier tails than the Gaussian distribution and is therefore less sensitive to any departure of the empirical distribution from Gaussianity. As a consequence, the Student-t distribution is suitable for constructing robust mixture models. In this work, we formalize the Bayesian Student-t mixture model as a latent variable model in a different way from Svensén and Bishop [Svensén, M., & Bishop, C. M. (2005). Robust Bayesian mixture modelling. Neurocomputing, 64, 235-252]. The main difference resides in the fact that it is not necessary to assume a factorized approximation of the posterior distribution on the latent indicator variables and the latent scale variables in order to obtain a tractable solution. Not neglecting the correlations between these unobserved random variables leads to a Bayesian model having an increased robustness. Furthermore, it is expected that the lower bound on the log-evidence is tighter. Based on this bound, the model complexity, i.e. the number of components in the mixture, can be inferred with a higher confidence.

Fully-automated approach to hippocampus segmentation using a graph-cuts algorithm combined with atlas-based segmentation and morphological opening.

PubMed

Kwak, Kichang; Yoon, Uicheul; Lee, Dong-Kyun; Kim, Geon Ha; Seo, Sang Won; Na, Duk L; Shim, Hack-Joon; Lee, Jong-Min

2013-09-01

The hippocampus has been known to be an important structure as a biomarker for Alzheimer's disease (AD) and other neurological and psychiatric diseases. However, it requires accurate, robust and reproducible delineation of hippocampal structures. In this study, an automated hippocampal segmentation method based on a graph-cuts algorithm combined with atlas-based segmentation and morphological opening was proposed. First of all, the atlas-based segmentation was applied to define initial hippocampal region for a priori information on graph-cuts. The definition of initial seeds was further elaborated by incorporating estimation of partial volume probabilities at each voxel. Finally, morphological opening was applied to reduce false positive of the result processed by graph-cuts. In the experiments with twenty-seven healthy normal subjects, the proposed method showed more reliable results (similarity index=0.81±0.03) than the conventional atlas-based segmentation method (0.72±0.04). Also as for segmentation accuracy which is measured in terms of the ratios of false positive and false negative, the proposed method (precision=0.76±0.04, recall=0.86±0.05) produced lower ratios than the conventional methods (0.73±0.05, 0.72±0.06) demonstrating its plausibility for accurate, robust and reliable segmentation of hippocampus. Copyright © 2013 Elsevier Inc. All rights reserved.

3D statistical shape models incorporating 3D random forest regression voting for robust CT liver segmentation

NASA Astrophysics Data System (ADS)

Norajitra, Tobias; Meinzer, Hans-Peter; Maier-Hein, Klaus H.

2015-03-01

During image segmentation, 3D Statistical Shape Models (SSM) usually conduct a limited search for target landmarks within one-dimensional search profiles perpendicular to the model surface. In addition, landmark appearance is modeled only locally based on linear profiles and weak learners, altogether leading to segmentation errors from landmark ambiguities and limited search coverage. We present a new method for 3D SSM segmentation based on 3D Random Forest Regression Voting. For each surface landmark, a Random Regression Forest is trained that learns a 3D spatial displacement function between the according reference landmark and a set of surrounding sample points, based on an infinite set of non-local randomized 3D Haar-like features. Landmark search is then conducted omni-directionally within 3D search spaces, where voxelwise forest predictions on landmark position contribute to a common voting map which reflects the overall position estimate. Segmentation experiments were conducted on a set of 45 CT volumes of the human liver, of which 40 images were randomly chosen for training and 5 for testing. Without parameter optimization, using a simple candidate selection and a single resolution approach, excellent results were achieved, while faster convergence and better concavity segmentation were observed, altogether underlining the potential of our approach in terms of increased robustness from distinct landmark detection and from better search coverage.

GRACE gravity data help constraining seismic models of the 2004 Sumatran earthquake

NASA Astrophysics Data System (ADS)

Cambiotti, G.; Bordoni, A.; Sabadini, R.; Colli, L.

2011-10-01

The analysis of Gravity Recovery and Climate Experiment (GRACE) Level 2 data time series from the Center for Space Research (CSR) and GeoForschungsZentrum (GFZ) allows us to extract a new estimate of the co-seismic gravity signal due to the 2004 Sumatran earthquake. Owing to compressible self-gravitating Earth models, including sea level feedback in a new self-consistent way and designed to compute gravitational perturbations due to volume changes separately, we are able to prove that the asymmetry in the co-seismic gravity pattern, in which the north-eastern negative anomaly is twice as large as the south-western positive anomaly, is not due to the previously overestimated dilatation in the crust. The overestimate was due to a large dilatation localized at the fault discontinuity, the gravitational effect of which is compensated by an opposite contribution from topography due to the uplifted crust. After this localized dilatation is removed, we instead predict compression in the footwall and dilatation in the hanging wall. The overall anomaly is then mainly due to the additional gravitational effects of the ocean after water is displaced away from the uplifted crust, as first indicated by de Linage et al. (2009). We also detail the differences between compressible and incompressible material properties. By focusing on the most robust estimates from GRACE data, consisting of the peak-to-peak gravity anomaly and an asymmetry coefficient, that is given by the ratio of the negative gravity anomaly over the positive anomaly, we show that they are quite sensitive to seismic source depths and dip angles. This allows us to exploit space gravity data for the first time to help constraining centroid-momentum-tensor (CMT) source analyses of the 2004 Sumatran earthquake and to conclude that the seismic moment has been released mainly in the lower crust rather than the lithospheric mantle. Thus, GRACE data and CMT source analyses, as well as geodetic slip distributions aided by GPS, complement each other for a robust inference of the seismic source of large earthquakes. Particular care is devoted to the spatial filtering of the gravity anomalies estimated both from observations and models to make their comparison significant.

Filtering sensory information with XCSF: improving learning robustness and robot arm control performance.

PubMed

Kneissler, Jan; Stalph, Patrick O; Drugowitsch, Jan; Butz, Martin V

2014-01-01

It has been shown previously that the control of a robot arm can be efficiently learned using the XCSF learning classifier system, which is a nonlinear regression system based on evolutionary computation. So far, however, the predictive knowledge about how actual motor activity changes the state of the arm system has not been exploited. In this paper, we utilize the forward velocity kinematics knowledge of XCSF to alleviate the negative effect of noisy sensors for successful learning and control. We incorporate Kalman filtering for estimating successive arm positions, iteratively combining sensory readings with XCSF-based predictions of hand position changes over time. The filtered arm position is used to improve both trajectory planning and further learning of the forward velocity kinematics. We test the approach on a simulated kinematic robot arm model. The results show that the combination can improve learning and control performance significantly. However, it also shows that variance estimates of XCSF prediction may be underestimated, in which case self-delusional spiraling effects can hinder effective learning. Thus, we introduce a heuristic parameter, which can be motivated by theory, and which limits the influence of XCSF's predictions on its own further learning input. As a result, we obtain drastic improvements in noise tolerance, allowing the system to cope with more than 10 times higher noise levels.

Guess LOD approach: sufficient conditions for robustness.

PubMed

Williamson, J A; Amos, C I

1995-01-01

Analysis of genetic linkage between a disease and a marker locus requires specifying a genetic model describing both the inheritance pattern and the gene frequencies of the marker and trait loci. Misspecification of the genetic model is likely for etiologically complex diseases. In previous work we have shown through analytic studies that misspecifying the genetic model for disease inheritance does not lead to excess false-positive evidence for genetic linkage provided the genetic marker alleles of all pedigree members are known, or can be inferred without bias from the data. Here, under various selection or ascertainment schemes we extend these previous results to situations in which the genetic model for the marker locus may be incorrect. We provide sufficient conditions for the asymptotic unbiased estimation of the recombination fraction under the null hypothesis of no linkage, and also conditions for the limiting distribution of the likelihood ratio test for no linkage to be chi-squared. Through simulation studies we document some situations under which asymptotic bias can result when the genetic model is misspecified. Among those situations under which an excess of false-positive evidence for genetic linkage can be generated, the most common is failure to provide accurate estimates of the marker allele frequencies. We show that in most cases false-positive evidence for genetic linkage is unlikely to result solely from the misspecification of the genetic model for disease or trait inheritance.

Robust, automatic GPS station velocities and velocity time series

NASA Astrophysics Data System (ADS)

Blewitt, G.; Kreemer, C.; Hammond, W. C.

2014-12-01

Automation in GPS coordinate time series analysis makes results more objective and reproducible, but not necessarily as robust as the human eye to detect problems. Moreover, it is not a realistic option to manually scan our current load of >20,000 time series per day. This motivates us to find an automatic way to estimate station velocities that is robust to outliers, discontinuities, seasonality, and noise characteristics (e.g., heteroscedasticity). Here we present a non-parametric method based on the Theil-Sen estimator, defined as the median of velocities vij=(xj-xi)/(tj-ti) computed between all pairs (i, j). Theil-Sen estimators produce statistically identical solutions to ordinary least squares for normally distributed data, but they can tolerate up to 29% of data being problematic. To mitigate seasonality, our proposed estimator only uses pairs approximately separated by an integer number of years (N-δt)<(tj-ti )<(N+δt), where δt is chosen to be small enough to capture seasonality, yet large enough to reduce random error. We fix N=1 to maximally protect against discontinuities. In addition to estimating an overall velocity, we also use these pairs to estimate velocity time series. To test our methods, we process real data sets that have already been used with velocities published in the NA12 reference frame. Accuracy can be tested by the scatter of horizontal velocities in the North American plate interior, which is known to be stable to ~0.3 mm/yr. This presents new opportunities for time series interpretation. For example, the pattern of velocity variations at the interannual scale can help separate tectonic from hydrological processes. Without any step detection, velocity estimates prove to be robust for stations affected by the Mw7.2 2010 El Mayor-Cucapah earthquake, and velocity time series show a clear change after the earthquake, without any of the usual parametric constraints, such as relaxation of postseismic velocities to their preseismic values.

Vector Graph Assisted Pedestrian Dead Reckoning Using an Unconstrained Smartphone

PubMed Central

Qian, Jiuchao; Pei, Ling; Ma, Jiabin; Ying, Rendong; Liu, Peilin

2015-01-01

The paper presents a hybrid indoor positioning solution based on a pedestrian dead reckoning (PDR) approach using built-in sensors on a smartphone. To address the challenges of flexible and complex contexts of carrying a phone while walking, a robust step detection algorithm based on motion-awareness has been proposed. Given the fact that step length is influenced by different motion states, an adaptive step length estimation algorithm based on motion recognition is developed. Heading estimation is carried out by an attitude acquisition algorithm, which contains a two-phase filter to mitigate the distortion of magnetic anomalies. In order to estimate the heading for an unconstrained smartphone, principal component analysis (PCA) of acceleration is applied to determine the offset between the orientation of smartphone and the actual heading of a pedestrian. Moreover, a particle filter with vector graph assisted particle weighting is introduced to correct the deviation in step length and heading estimation. Extensive field tests, including four contexts of carrying a phone, have been conducted in an office building to verify the performance of the proposed algorithm. Test results show that the proposed algorithm can achieve sub-meter mean error in all contexts. PMID:25738763

Evaluation of long-term survival: use of diagnostics and robust estimators with Cox's proportional hazards model.

PubMed

Valsecchi, M G; Silvestri, D; Sasieni, P

1996-12-30

We consider methodological problems in evaluating long-term survival in clinical trials. In particular we examine the use of several methods that extend the basic Cox regression analysis. In the presence of a long term observation, the proportional hazard (PH) assumption may easily be violated and a few long term survivors may have a large effect on parameter estimates. We consider both model selection and robust estimation in a data set of 474 ovarian cancer patients enrolled in a clinical trial and followed for between 7 and 12 years after randomization. Two diagnostic plots for assessing goodness-of-fit are introduced. One shows the variation in time of parameter estimates and is an alternative to PH checking based on time-dependent covariates. The other takes advantage of the martingale residual process in time to represent the lack of fit with a metric of the type 'observed minus expected' number of events. Robust estimation is carried out by maximizing a weighted partial likelihood which downweights the contribution to estimation of influential observations. This type of complementary analysis of long-term results of clinical studies is useful in assessing the soundness of the conclusions on treatment effect. In the example analysed here, the difference in survival between treatments was mostly confined to those individuals who survived at least two years beyond randomization.

Doubly robust matching estimators for high dimensional confounding adjustment.

PubMed

Antonelli, Joseph; Cefalu, Matthew; Palmer, Nathan; Agniel, Denis

2018-05-11

Valid estimation of treatment effects from observational data requires proper control of confounding. If the number of covariates is large relative to the number of observations, then controlling for all available covariates is infeasible. In cases where a sparsity condition holds, variable selection or penalization can reduce the dimension of the covariate space in a manner that allows for valid estimation of treatment effects. In this article, we propose matching on both the estimated propensity score and the estimated prognostic scores when the number of covariates is large relative to the number of observations. We derive asymptotic results for the matching estimator and show that it is doubly robust in the sense that only one of the two score models need be correct to obtain a consistent estimator. We show via simulation its effectiveness in controlling for confounding and highlight its potential to address nonlinear confounding. Finally, we apply the proposed procedure to analyze the effect of gender on prescription opioid use using insurance claims data. © 2018, The International Biometric Society.

Statistical Methods and Sampling Design for Estimating Step Trends in Surface-Water Quality

USGS Publications Warehouse

Hirsch, Robert M.

1988-01-01

This paper addresses two components of the problem of estimating the magnitude of step trends in surface water quality. The first is finding a robust estimator appropriate to the data characteristics expected in water-quality time series. The J. L. Hodges-E. L. Lehmann class of estimators is found to be robust in comparison to other nonparametric and moment-based estimators. A seasonal Hodges-Lehmann estimator is developed and shown to have desirable properties. Second, the effectiveness of various sampling strategies is examined using Monte Carlo simulation coupled with application of this estimator. The simulation is based on a large set of total phosphorus data from the Potomac River. To assure that the simulated records have realistic properties, the data are modeled in a multiplicative fashion incorporating flow, hysteresis, seasonal, and noise components. The results demonstrate the importance of balancing the length of the two sampling periods and balancing the number of data values between the two periods.

A Robust Statistics Approach to Minimum Variance Portfolio Optimization

NASA Astrophysics Data System (ADS)

Yang, Liusha; Couillet, Romain; McKay, Matthew R.

2015-12-01

We study the design of portfolios under a minimum risk criterion. The performance of the optimized portfolio relies on the accuracy of the estimated covariance matrix of the portfolio asset returns. For large portfolios, the number of available market returns is often of similar order to the number of assets, so that the sample covariance matrix performs poorly as a covariance estimator. Additionally, financial market data often contain outliers which, if not correctly handled, may further corrupt the covariance estimation. We address these shortcomings by studying the performance of a hybrid covariance matrix estimator based on Tyler's robust M-estimator and on Ledoit-Wolf's shrinkage estimator while assuming samples with heavy-tailed distribution. Employing recent results from random matrix theory, we develop a consistent estimator of (a scaled version of) the realized portfolio risk, which is minimized by optimizing online the shrinkage intensity. Our portfolio optimization method is shown via simulations to outperform existing methods both for synthetic and real market data.

Evaluation of Ares-I Control System Robustness to Uncertain Aerodynamics and Flex Dynamics

NASA Technical Reports Server (NTRS)

Jang, Jiann-Woei; VanTassel, Chris; Bedrossian, Nazareth; Hall, Charles; Spanos, Pol

2008-01-01

This paper discusses the application of robust control theory to evaluate robustness of the Ares-I control systems. Three techniques for estimating upper and lower bounds of uncertain parameters which yield stable closed-loop response are used here: (1) Monte Carlo analysis, (2) mu analysis, and (3) characteristic frequency response analysis. All three methods are used to evaluate stability envelopes of the Ares-I control systems with uncertain aerodynamics and flex dynamics. The results show that characteristic frequency response analysis is the most effective of these methods for assessing robustness.

Robust Smoothing: Smoothing Parameter Selection and Applications to Fluorescence Spectroscopy∂

PubMed Central

Lee, Jong Soo; Cox, Dennis D.

2009-01-01

Fluorescence spectroscopy has emerged in recent years as an effective way to detect cervical cancer. Investigation of the data preprocessing stage uncovered a need for a robust smoothing to extract the signal from the noise. Various robust smoothing methods for estimating fluorescence emission spectra are compared and data driven methods for the selection of smoothing parameter are suggested. The methods currently implemented in R for smoothing parameter selection proved to be unsatisfactory, and a computationally efficient procedure that approximates robust leave-one-out cross validation is presented. PMID:20729976

Autonomous frequency domain identification: Theory and experiment

NASA Technical Reports Server (NTRS)

Yam, Yeung; Bayard, D. S.; Hadaegh, F. Y.; Mettler, E.; Milman, M. H.; Scheid, R. E.

1989-01-01

The analysis, design, and on-orbit tuning of robust controllers require more information about the plant than simply a nominal estimate of the plant transfer function. Information is also required concerning the uncertainty in the nominal estimate, or more generally, the identification of a model set within which the true plant is known to lie. The identification methodology that was developed and experimentally demonstrated makes use of a simple but useful characterization of the model uncertainty based on the output error. This is a characterization of the additive uncertainty in the plant model, which has found considerable use in many robust control analysis and synthesis techniques. The identification process is initiated by a stochastic input u which is applied to the plant p giving rise to the output. Spectral estimation (h = P sub uy/P sub uu) is used as an estimate of p and the model order is estimated using the produce moment matrix (PMM) method. A parametric model unit direction vector p is then determined by curve fitting the spectral estimate to a rational transfer function. The additive uncertainty delta sub m = p - unit direction vector p is then estimated by the cross spectral estimate delta = P sub ue/P sub uu where e = y - unit direction vectory y is the output error, and unit direction vector y = unit direction vector pu is the computed output of the parametric model subjected to the actual input u. The experimental results demonstrate the curve fitting algorithm produces the reduced-order plant model which minimizes the additive uncertainty. The nominal transfer function estimate unit direction vector p and the estimate delta of the additive uncertainty delta sub m are subsequently available to be used for optimization of robust controller performance and stability.

Regional-scale analysis of extreme precipitation from short and fragmented records

NASA Astrophysics Data System (ADS)

Libertino, Andrea; Allamano, Paola; Laio, Francesco; Claps, Pierluigi

2018-02-01

Rain gauge is the oldest and most accurate instrument for rainfall measurement, able to provide long series of reliable data. However, rain gauge records are often plagued by gaps, spatio-temporal discontinuities and inhomogeneities that could affect their suitability for a statistical assessment of the characteristics of extreme rainfall. Furthermore, the need to discard the shorter series for obtaining robust estimates leads to ignore a significant amount of information which can be essential, especially when large return periods estimates are sought. This work describes a robust statistical framework for dealing with uneven and fragmented rainfall records on a regional spatial domain. The proposed technique, named "patched kriging" allows one to exploit all the information available from the recorded series, independently of their length, to provide extreme rainfall estimates in ungauged areas. The methodology involves the sequential application of the ordinary kriging equations, producing a homogeneous dataset of synthetic series with uniform lengths. In this way, the errors inherent to any regional statistical estimation can be easily represented in the spatial domain and, possibly, corrected. Furthermore, the homogeneity of the obtained series, provides robustness toward local artefacts during the parameter-estimation phase. The application to a case study in the north-western Italy demonstrates the potential of the methodology and provides a significant base for discussing its advantages over previous techniques.

Robust learning for optimal treatment decision with NP-dimensionality

PubMed Central

Shi, Chengchun; Song, Rui; Lu, Wenbin

2016-01-01

In order to identify important variables that are involved in making optimal treatment decision, Lu, Zhang and Zeng (2013) proposed a penalized least squared regression framework for a fixed number of predictors, which is robust against the misspecification of the conditional mean model. Two problems arise: (i) in a world of explosively big data, effective methods are needed to handle ultra-high dimensional data set, for example, with the dimension of predictors is of the non-polynomial (NP) order of the sample size; (ii) both the propensity score and conditional mean models need to be estimated from data under NP dimensionality. In this paper, we propose a robust procedure for estimating the optimal treatment regime under NP dimensionality. In both steps, penalized regressions are employed with the non-concave penalty function, where the conditional mean model of the response given predictors may be misspecified. The asymptotic properties, such as weak oracle properties, selection consistency and oracle distributions, of the proposed estimators are investigated. In addition, we study the limiting distribution of the estimated value function for the obtained optimal treatment regime. The empirical performance of the proposed estimation method is evaluated by simulations and an application to a depression dataset from the STAR*D study. PMID:28781717

Improved Spatial Registration and Target Tracking Method for Sensors on Multiple Missiles.

PubMed

Lu, Xiaodong; Xie, Yuting; Zhou, Jun

2018-05-27

Inspired by the problem that the current spatial registration methods are unsuitable for three-dimensional (3-D) sensor on high-dynamic platform, this paper focuses on the estimation for the registration errors of cooperative missiles and motion states of maneuvering target. There are two types of errors being discussed: sensor measurement biases and attitude biases. Firstly, an improved Kalman Filter on Earth-Centered Earth-Fixed (ECEF-KF) coordinate algorithm is proposed to estimate the deviations mentioned above, from which the outcomes are furtherly compensated to the error terms. Secondly, the Pseudo Linear Kalman Filter (PLKF) and the nonlinear scheme the Unscented Kalman Filter (UKF) with modified inputs are employed for target tracking. The convergence of filtering results are monitored by a position-judgement logic, and a low-pass first order filter is selectively introduced before compensation to inhibit the jitter of estimations. In the simulation, the ECEF-KF enhancement is proven to improve the accuracy and robustness of the space alignment, while the conditional-compensation-based PLKF method is demonstrated to be the optimal performance in target tracking.

Evaluating metrics of local topographic position for multiscale geomorphometric analysis

NASA Astrophysics Data System (ADS)

Newman, D. R.; Lindsay, J. B.; Cockburn, J. M. H.

2018-07-01

The field of geomorphometry has increasingly moved towards the use of multiscale analytical techniques, due to the availability of fine-resolution digital elevation models (DEMs) and the inherent scale-dependency of many DEM-derived attributes such as local topographic position (LTP). LTP is useful for landform and soils mapping and numerous other environmental applications. Multiple LTP metrics have been proposed and applied in the literature; however, elevation percentile (EP) is notable for its robustness to elevation error and applicability to non-Gaussian local elevation distributions, both of which are common characteristics of DEM data sets. Multiscale LTP analysis involves the estimation of spatial patterns using a range of neighborhood sizes, traditionally achieved by applying spatial filtering techniques with varying kernel sizes. While EP can be demonstrated to provide accurate estimates of LTP, the computationally intensive method of its calculation makes it unsuited to multiscale LTP analysis, particularly at large neighborhood sizes or with fine-resolution DEMs. This research assessed the suitability of three LTP metrics for multiscale terrain characterization by quantifying their computational efficiency and by comparing their ability to approximate EP spatial patterns under varying topographic conditions. The tested LTP metrics included: deviation from mean elevation (DEV), percent elevation range (PER), and the novel relative topographic position (RTP) index. The results demonstrated that DEV, calculated using the integral image technique, offers fast and scale-invariant computation. DEV spatial patterns were strongly correlated with EP (r2 range of 0.699 to 0.967) under all tested topographic conditions. RTP was also a strong predictor of EP (r2 range of 0.594 to 0.917). PER was the weakest predictor of EP (r2 range of 0.031 to 0.801) without offering a substantial improvement in computational efficiency over RTP. PER was therefore determined to be unsuitable for most multiscale applications. It was concluded that the scale-invariant property offered by the integral image used by the DEV method counters the minor losses in robustness compared to EP, making DEV the optimal LTP metric for multiscale applications.

The relationship of cranial, orbital and nasal cavity size with the morphology of the supraorbital region in modern Homo sapiens.

PubMed

Nowaczewska, Wioletta; Łapicka, Urszula; Cieślik, Agata; Biecek, Przemysław

2017-09-01

Morphological variation of the supraorbital region (SR) in human crania has been investigated and its potential sources suggested, along with the importance of the size of the facial skeleton, neurocranium, and orbit for the formation of this region. However, previous studies have not indicated whether facial size exhibits a stronger association with SR robusticity than neurocranial size or sex; moreover, the association between orbital volume and SR robusticity has been analysed only in non-human primate skulls. In this study we investigate whether the size of the facial skeleton, neurocranium, two measures of relative orbital size (orbital volume and estimated orbital aperture area), the relative size of the nasal cavity, and the relative estimated area of the anterior nasal cavity opening are related to SR robusticity; we also examine which of these analysed relationships is strongest, as well as independent of the influence of the other traits, in a geographically diverse modern human cranial sample. The results of Spearman's rank and partial rank correlations (encompassing models including or excluding sex and geographic origin) show a relationship between most of the above-mentioned variables and SR robusticity, with the exception of the estimated relative area of the orbital opening (in the case of the results of Spearman's rank correlations) and the traits of the nasal cavity. Of all the analysed traits, sex appears to be the most important for the formation of SR robusticity and, of two measures of cranial size, neurocranial size was the most significant. The strong relationship between SR robusticity and relative orbital volume was observed in models without the geographic origin factor. The results concerning analysed models suggest the influence of this factor on this relationship; however, to explain this influence, further studies are needed.

a Robust Method for Stereo Visual Odometry Based on Multiple Euclidean Distance Constraint and Ransac Algorithm

NASA Astrophysics Data System (ADS)

Zhou, Q.; Tong, X.; Liu, S.; Lu, X.; Liu, S.; Chen, P.; Jin, Y.; Xie, H.

2017-07-01

Visual Odometry (VO) is a critical component for planetary robot navigation and safety. It estimates the ego-motion using stereo images frame by frame. Feature points extraction and matching is one of the key steps for robotic motion estimation which largely influences the precision and robustness. In this work, we choose the Oriented FAST and Rotated BRIEF (ORB) features by considering both accuracy and speed issues. For more robustness in challenging environment e.g., rough terrain or planetary surface, this paper presents a robust outliers elimination method based on Euclidean Distance Constraint (EDC) and Random Sample Consensus (RANSAC) algorithm. In the matching process, a set of ORB feature points are extracted from the current left and right synchronous images and the Brute Force (BF) matcher is used to find the correspondences between the two images for the Space Intersection. Then the EDC and RANSAC algorithms are carried out to eliminate mismatches whose distances are beyond a predefined threshold. Similarly, when the left image of the next time matches the feature points with the current left images, the EDC and RANSAC are iteratively performed. After the above mentioned, there are exceptional remaining mismatched points in some cases, for which the third time RANSAC is applied to eliminate the effects of those outliers in the estimation of the ego-motion parameters (Interior Orientation and Exterior Orientation). The proposed approach has been tested on a real-world vehicle dataset and the result benefits from its high robustness.

Robust back-stepping output feedback trajectory tracking for quadrotors via extended state observer and sigmoid tracking differentiator

NASA Astrophysics Data System (ADS)

Shao, Xingling; Liu, Jun; Wang, Honglun

2018-05-01

In this paper, a robust back-stepping output feedback trajectory tracking controller is proposed for quadrotors subject to parametric uncertainties and external disturbances. Based on the hierarchical control principle, the quadrotor dynamics is decomposed into translational and rotational subsystems to facilitate the back-stepping control design. With given model information incorporated into observer design, a high-order extended state observer (ESO) that relies only on position measurements is developed to estimate the remaining unmeasurable states and the lumped disturbances in rotational subsystem simultaneously. To overcome the problem of "explosion of complexity" in the back-stepping design, the sigmoid tracking differentiator (STD) is introduced to compute the derivative of virtual control laws. The advantage is that the proposed controller via output-feedback scheme not only can ensure good tracking performance using very limited information of quadrotors, but also has the ability of handling the undesired uncertainties. The stability analysis is established using the Lyapunov theory. Simulation results demonstrate the effectiveness of the proposed control scheme in achieving a guaranteed tracking performance with respect to an 8-shaped reference trajectory.

Using high-resolution variant frequencies to empower clinical genome interpretation.

PubMed

Whiffin, Nicola; Minikel, Eric; Walsh, Roddy; O'Donnell-Luria, Anne H; Karczewski, Konrad; Ing, Alexander Y; Barton, Paul J R; Funke, Birgit; Cook, Stuart A; MacArthur, Daniel; Ware, James S

2017-10-01

PurposeWhole-exome and whole-genome sequencing have transformed the discovery of genetic variants that cause human Mendelian disease, but discriminating pathogenic from benign variants remains a daunting challenge. Rarity is recognized as a necessary, although not sufficient, criterion for pathogenicity, but frequency cutoffs used in Mendelian analysis are often arbitrary and overly lenient. Recent very large reference datasets, such as the Exome Aggregation Consortium (ExAC), provide an unprecedented opportunity to obtain robust frequency estimates even for very rare variants.MethodsWe present a statistical framework for the frequency-based filtering of candidate disease-causing variants, accounting for disease prevalence, genetic and allelic heterogeneity, inheritance mode, penetrance, and sampling variance in reference datasets.ResultsUsing the example of cardiomyopathy, we show that our approach reduces by two-thirds the number of candidate variants under consideration in the average exome, without removing true pathogenic variants (false-positive rate<0.001).ConclusionWe outline a statistically robust framework for assessing whether a variant is "too common" to be causative for a Mendelian disorder of interest. We present precomputed allele frequency cutoffs for all variants in the ExAC dataset.

Numerical study of blast characteristics from detonation of homogeneous explosives

NASA Astrophysics Data System (ADS)

Balakrishnan, Kaushik; Genin, Franklin; Nance, Doug V.; Menon, Suresh

2010-04-01

A new robust numerical methodology is used to investigate the propagation of blast waves from homogeneous explosives. The gas-phase governing equations are solved using a hybrid solver that combines a higher-order shock capturing scheme with a low-dissipation central scheme. Explosives of interest include Nitromethane, Trinitrotoluene, and High-Melting Explosive. The shock overpressure and total impulse are estimated at different radial locations and compared for the different explosives. An empirical scaling correlation is presented for the shock overpressure, incident positive phase pressure impulse, and total impulse. The role of hydrodynamic instabilities to the blast effects of explosives is also investigated in three dimensions, and significant mixing between the detonation products and air is observed. This mixing results in afterburn, which is found to augment the impulse characteristics of explosives. Furthermore, the impulse characteristics are also observed to be three-dimensional in the region of the mixing layer. This paper highlights that while some blast features can be successfully predicted from simple one-dimensional studies, the growth of hydrodynamic instabilities and the impulsive loading of homogeneous explosives require robust three-dimensional investigation.

Understanding the science of portion control and the art of downsizing.

PubMed

Hetherington, Marion M; Blundell-Birtill, Pam; Caton, Samantha J; Cecil, Joanne E; Evans, Charlotte E; Rolls, Barbara J; Tang, Tang

2018-05-24

Offering large portions of high-energy-dense (HED) foods increases overall intake in children and adults. This is known as the portion size effect (PSE). It is robust, reliable and enduring. Over time, the PSE may facilitate overeating and ultimately positive energy balance. Therefore, it is important to understand what drives the PSE and what might be done to counter the effects of an environment promoting large portions, especially in children. Explanations for the PSE are many and diverse, ranging from consumer error in estimating portion size to simple heuristics such as cleaning the plate or eating in accordance with consumption norms. However, individual characteristics and hedonic processes influence the PSE, suggesting a more complex explanation than error or heuristics. Here PSE studies are reviewed to identify interventions that can be used to downsize portions of HED foods, with a focus on children who are still learning about social norms for portion size. Although the scientific evidence for the PSE is robust, there is still a need for creative downsizing solutions to facilitate portion control as children and adolescents establish their eating habits.

Track and vertex reconstruction: From classical to adaptive methods

NASA Astrophysics Data System (ADS)

Strandlie, Are; Frühwirth, Rudolf

2010-04-01

This paper reviews classical and adaptive methods of track and vertex reconstruction in particle physics experiments. Adaptive methods have been developed to meet the experimental challenges at high-energy colliders, in particular, the CERN Large Hadron Collider. They can be characterized by the obliteration of the traditional boundaries between pattern recognition and statistical estimation, by the competition between different hypotheses about what constitutes a track or a vertex, and by a high level of flexibility and robustness achieved with a minimum of assumptions about the data. The theoretical background of some of the adaptive methods is described, and it is shown that there is a close connection between the two main branches of adaptive methods: neural networks and deformable templates, on the one hand, and robust stochastic filters with annealing, on the other hand. As both classical and adaptive methods of track and vertex reconstruction presuppose precise knowledge of the positions of the sensitive detector elements, the paper includes an overview of detector alignment methods and a survey of the alignment strategies employed by past and current experiments.

Estimating the association between metabolic risk factors and marijuana use in U.S. adults using data from the continuous National Health and Nutrition Examination Survey.

PubMed

Thompson, Christin Ann; Hay, Joel W

2015-07-01

More research is needed on the health effects of marijuana use. Results of previous studies indicate that marijuana could alleviate certain factors of metabolic syndrome, such as obesity. Data on 6281 persons from National Health and Nutrition Examination Survey from 2005 to 2012 were used to estimate the effect of marijuana use on cardiometabolic risk factors. The reliability of ordinary least squares (OLS) regression models was tested by replacing marijuana use as the risk factor of interest with alcohol and carbohydrate consumption. Instrumental variable methods were used to account for the potential endogeneity of marijuana use. OLS models show lower fasting insulin, insulin resistance, body mass index, and waist circumference in users compared with nonusers. However, when alcohol and carbohydrate intake substitute for marijuana use in OLS models, similar metabolic benefits are estimated. The Durbin-Wu-Hausman tests provide evidence of endogeneity of marijuana use in OLS models, but instrumental variables models do not yield significant estimates for marijuana use. These findings challenge the robustness of OLS estimates of a positive relationship between marijuana use and fasting insulin, insulin resistance, body mass index, and waist circumference. Copyright © 2015 Elsevier Inc. All rights reserved.

Distortion correction of echo planar images applying the concept of finite rate of innovation to point spread function mapping (FRIP).

PubMed

Nunes, Rita G; Hajnal, Joseph V

2018-06-01

Point spread function (PSF) mapping enables estimating the displacement fields required for distortion correction of echo planar images. Recently, a highly accelerated approach was introduced for estimating displacements from the phase slope of under-sampled PSF mapping data. Sampling schemes with varying spacing were proposed requiring stepwise phase unwrapping. To avoid unwrapping errors, an alternative approach applying the concept of finite rate of innovation to PSF mapping (FRIP) is introduced, using a pattern search strategy to locate the PSF peak, and the two methods are compared. Fully sampled PSF data was acquired in six subjects at 3.0 T, and distortion maps were estimated after retrospective under-sampling. The two methods were compared for both previously published and newly optimized sampling patterns. Prospectively under-sampled data were also acquired. Shift maps were estimated and deviations relative to the fully sampled reference map were calculated. The best performance was achieved when using FRIP with a previously proposed sampling scheme. The two methods were comparable for the remaining schemes. The displacement field errors tended to be lower as the number of samples or their spacing increased. A robust method for estimating the position of the PSF peak has been introduced.

Analytical method for the fast time-domain reconstruction of fluorescent inclusions in vitro and in vivo.

PubMed

Han, Sung-Ho; Farshchi-Heydari, Salman; Hall, David J

2010-01-20

A novel time-domain optical method to reconstruct the relative concentration, lifetime, and depth of a fluorescent inclusion is described. We establish an analytical method for the estimations of these parameters for a localized fluorescent object directly from the simple evaluations of continuous wave intensity, exponential decay, and temporal position of the maximum of the fluorescence temporal point-spread function. Since the more complex full inversion process is not involved, this method permits a robust and fast processing in exploring the properties of a fluorescent inclusion. This method is confirmed by in vitro and in vivo experiments. Copyright 2010 Biophysical Society. Published by Elsevier Inc. All rights reserved.

The damped wave equation with unbounded damping

NASA Astrophysics Data System (ADS)

Freitas, Pedro; Siegl, Petr; Tretter, Christiane

2018-06-01

We analyze new phenomena arising in linear damped wave equations on unbounded domains when the damping is allowed to become unbounded at infinity. We prove the generation of a contraction semigroup, study the relation between the spectra of the semigroup generator and the associated quadratic operator function, the convergence of non-real eigenvalues in the asymptotic regime of diverging damping on a subdomain, and we investigate the appearance of essential spectrum on the negative real axis. We further show that the presence of the latter prevents exponential estimates for the semigroup and turns out to be a robust effect that cannot be easily canceled by adding a positive potential. These analytic results are illustrated by examples.

Design, innovation, and rural creative places: Are the arts the cherry on top, or the secret sauce?

PubMed

Wojan, Timothy R; Nichols, Bonnie

2018-01-01

Creative class theory explains the positive relationship between the arts and commercial innovation as the mutual attraction of artists and other creative workers by an unobserved creative milieu. This study explores alternative theories for rural settings, by analyzing establishment-level survey data combined with data on the local arts scene. The study identifies the local contextual factors associated with a strong design orientation, and estimates the impact that a strong design orientation has on the local economy. Data on innovation and design come from a nationally representative sample of establishments in tradable industries. Latent class analysis allows identifying unobserved subpopulations comprised of establishments with different design and innovation orientations. Logistic regression allows estimating the association between an establishment's design orientation and local contextual factors. A quantile instrumental variable regression allows assessing the robustness of the logistic regression results with respect to endogeneity. An estimate of design orientation at the local level derived from the survey is used to examine variation in economic performance during the period of recovery from the Great Recession (2010-2014). Three distinct innovation (substantive, nominal, and non-innovators) and design orientations (design-integrated, "design last finish," and no systematic approach to design) are identified. Innovation- and design-intensive establishments were identified in both rural and urban areas. Rural design-integrated establishments tended to locate in counties with more highly educated workforces and containing at least one performing arts organization. A quantile instrumental variable regression confirmed that the logistic regression result is robust to endogeneity concerns. Finally, rural areas characterized by design-integrated establishments experienced faster growth in wages relative to rural areas characterized by establishments using no systematic approach to design.

Design, innovation, and rural creative places: Are the arts the cherry on top, or the secret sauce?

PubMed Central

Nichols, Bonnie

2018-01-01

Objective Creative class theory explains the positive relationship between the arts and commercial innovation as the mutual attraction of artists and other creative workers by an unobserved creative milieu. This study explores alternative theories for rural settings, by analyzing establishment-level survey data combined with data on the local arts scene. The study identifies the local contextual factors associated with a strong design orientation, and estimates the impact that a strong design orientation has on the local economy. Method Data on innovation and design come from a nationally representative sample of establishments in tradable industries. Latent class analysis allows identifying unobserved subpopulations comprised of establishments with different design and innovation orientations. Logistic regression allows estimating the association between an establishment’s design orientation and local contextual factors. A quantile instrumental variable regression allows assessing the robustness of the logistic regression results with respect to endogeneity. An estimate of design orientation at the local level derived from the survey is used to examine variation in economic performance during the period of recovery from the Great Recession (2010–2014). Results Three distinct innovation (substantive, nominal, and non-innovators) and design orientations (design-integrated, “design last finish,” and no systematic approach to design) are identified. Innovation- and design-intensive establishments were identified in both rural and urban areas. Rural design-integrated establishments tended to locate in counties with more highly educated workforces and containing at least one performing arts organization. A quantile instrumental variable regression confirmed that the logistic regression result is robust to endogeneity concerns. Finally, rural areas characterized by design-integrated establishments experienced faster growth in wages relative to rural areas characterized by establishments using no systematic approach to design. PMID:29489884

A robust trust establishment scheme for wireless sensor networks.

PubMed

Ishmanov, Farruh; Kim, Sung Won; Nam, Seung Yeob

2015-03-23

Security techniques like cryptography and authentication can fail to protect a network once a node is compromised. Hence, trust establishment continuously monitors and evaluates node behavior to detect malicious and compromised nodes. However, just like other security schemes, trust establishment is also vulnerable to attack. Moreover, malicious nodes might misbehave intelligently to trick trust establishment schemes. Unfortunately, attack-resistance and robustness issues with trust establishment schemes have not received much attention from the research community. Considering the vulnerability of trust establishment to different attacks and the unique features of sensor nodes in wireless sensor networks, we propose a lightweight and robust trust establishment scheme. The proposed trust scheme is lightweight thanks to a simple trust estimation method. The comprehensiveness and flexibility of the proposed trust estimation scheme make it robust against different types of attack and misbehavior. Performance evaluation under different types of misbehavior and on-off attacks shows that the detection rate of the proposed trust mechanism is higher and more stable compared to other trust mechanisms.

A robust nonlinear filter for image restoration.

PubMed

Koivunen, V

1995-01-01

A class of nonlinear regression filters based on robust estimation theory is introduced. The goal of the filtering is to recover a high-quality image from degraded observations. Models for desired image structures and contaminating processes are employed, but deviations from strict assumptions are allowed since the assumptions on signal and noise are typically only approximately true. The robustness of filters is usually addressed only in a distributional sense, i.e., the actual error distribution deviates from the nominal one. In this paper, the robustness is considered in a broad sense since the outliers may also be due to inappropriate signal model, or there may be more than one statistical population present in the processing window, causing biased estimates. Two filtering algorithms minimizing a least trimmed squares criterion are provided. The design of the filters is simple since no scale parameters or context-dependent threshold values are required. Experimental results using both real and simulated data are presented. The filters effectively attenuate both impulsive and nonimpulsive noise while recovering the signal structure and preserving interesting details.

A hybrid smartphone indoor positioning solution for mobile LBS.

PubMed

Liu, Jingbin; Chen, Ruizhi; Pei, Ling; Guinness, Robert; Kuusniemi, Heidi

2012-12-12

Smartphone positioning is an enabling technology used to create new business in the navigation and mobile location-based services (LBS) industries. This paper presents a smartphone indoor positioning engine named HIPE that can be easily integrated with mobile LBS. HIPE is a hybrid solution that fuses measurements of smartphone sensors with wireless signals. The smartphone sensors are used to measure the user's motion dynamics information (MDI), which represent the spatial correlation of various locations. Two algorithms based on hidden Markov model (HMM) problems, the grid-based filter and the Viterbi algorithm, are used in this paper as the central processor for data fusion to resolve the position estimates, and these algorithms are applicable for different applications, e.g., real-time navigation and location tracking, respectively. HIPE is more widely applicable for various motion scenarios than solutions proposed in previous studies because it uses no deterministic motion models, which have been commonly used in previous works. The experimental results showed that HIPE can provide adequate positioning accuracy and robustness for different scenarios of MDI combinations. HIPE is a cost-efficient solution, and it can work flexibly with different smartphone platforms, which may have different types of sensors available for the measurement of MDI data. The reliability of the positioning solution was found to increase with increasing precision of the MDI data.

A subagging regression method for estimating the qualitative and quantitative state of groundwater

NASA Astrophysics Data System (ADS)

Jeong, J.; Park, E.; Choi, J.; Han, W. S.; Yun, S. T.

2016-12-01

A subagging regression (SBR) method for the analysis of groundwater data pertaining to the estimation of trend and the associated uncertainty is proposed. The SBR method is validated against synthetic data competitively with other conventional robust and non-robust methods. From the results, it is verified that the estimation accuracies of the SBR method are consistent and superior to those of the other methods and the uncertainties are reasonably estimated where the others have no uncertainty analysis option. To validate further, real quantitative and qualitative data are employed and analyzed comparatively with Gaussian process regression (GPR). For all cases, the trend and the associated uncertainties are reasonably estimated by SBR, whereas the GPR has limitations in representing the variability of non-Gaussian skewed data. From the implementations, it is determined that the SBR method has potential to be further developed as an effective tool of anomaly detection or outlier identification in groundwater state data.

Using open robust design models to estimate temporary emigration from capture-recapture data.

PubMed

Kendall, W L; Bjorkland, R

2001-12-01

Capture-recapture studies are crucial in many circumstances for estimating demographic parameters for wildlife and fish populations. Pollock's robust design, involving multiple sampling occasions per period of interest, provides several advantages over classical approaches. This includes the ability to estimate the probability of being present and available for detection, which in some situations is equivalent to breeding probability. We present a model for estimating availability for detection that relaxes two assumptions required in previous approaches. The first is that the sampled population is closed to additions and deletions across samples within a period of interest. The second is that each member of the population has the same probability of being available for detection in a given period. We apply our model to estimate survival and breeding probability in a study of hawksbill sea turtles (Eretmochelys imbricata), where previous approaches are not appropriate.

Using open robust design models to estimate temporary emigration from capture-recapture data

USGS Publications Warehouse

Kendall, W.L.; Bjorkland, R.

2001-01-01

Capture-recapture studies are crucial in many circumstances for estimating demographic parameters for wildlife and fish populations. Pollock's robust design, involving multiple sampling occasions per period of interest, provides several advantages over classical approaches. This includes the ability to estimate the probability of being present and available for detection, which in some situations is equivalent to breeding probability. We present a model for estimating availability for detection that relaxes two assumptions required in previous approaches. The first is that the sampled population is closed to additions and deletions across samples within a period of interest. The second is that each member of the population has the same probability of being available for detection in a given period. We apply our model to estimate survival and breeding probability in a study of hawksbill sea turtles (Eretmochelys imbricata), where previous approaches are not appropriate.

Design principles for robust oscillatory behavior.

PubMed

Castillo-Hair, Sebastian M; Villota, Elizabeth R; Coronado, Alberto M

2015-09-01

Oscillatory responses are ubiquitous in regulatory networks of living organisms, a fact that has led to extensive efforts to study and replicate the circuits involved. However, to date, design principles that underlie the robustness of natural oscillators are not completely known. Here we study a three-component enzymatic network model in order to determine the topological requirements for robust oscillation. First, by simulating every possible topological arrangement and varying their parameter values, we demonstrate that robust oscillators can be obtained by augmenting the number of both negative feedback loops and positive autoregulations while maintaining an appropriate balance of positive and negative interactions. We then identify network motifs, whose presence in more complex topologies is a necessary condition for obtaining oscillatory responses. Finally, we pinpoint a series of simple architectural patterns that progressively render more robust oscillators. Together, these findings can help in the design of more reliable synthetic biomolecular networks and may also have implications in the understanding of other oscillatory systems.

Robust QRS detection for HRV estimation from compressively sensed ECG measurements for remote health-monitoring systems.

PubMed

Pant, Jeevan K; Krishnan, Sridhar

2018-03-15

To present a new compressive sensing (CS)-based method for the acquisition of ECG signals and for robust estimation of heart-rate variability (HRV) parameters from compressively sensed measurements with high compression ratio. CS is used in the biosensor to compress the ECG signal. Estimation of the locations of QRS segments is carried out by applying two algorithms on the compressed measurements. The first algorithm reconstructs the ECG signal by enforcing a block-sparse structure on the first-order difference of the signal, so the transient QRS segments are significantly emphasized on the first-order difference of the signal. Multiple block-divisions of the signals are carried out with various block lengths, and multiple reconstructed signals are combined to enhance the robustness of the localization of the QRS segments. The second algorithm removes errors in the locations of QRS segments by applying low-pass filtering and morphological operations. The proposed CS-based method is found to be effective for the reconstruction of ECG signals by enforcing transient QRS structures on the first-order difference of the signal. It is demonstrated to be robust not only to high compression ratio but also to various artefacts present in ECG signals acquired by using on-body wireless sensors. HRV parameters computed by using the QRS locations estimated from the signals reconstructed with a compression ratio as high as 90% are comparable with that computed by using QRS locations estimated by using the Pan-Tompkins algorithm. The proposed method is useful for the realization of long-term HRV monitoring systems by using CS-based low-power wireless on-body biosensors.

Technical notes and correspondence: Stochastic robustness of linear time-invariant control systems

NASA Technical Reports Server (NTRS)

Stengel, Robert F.; Ray, Laura R.

1991-01-01

A simple numerical procedure for estimating the stochastic robustness of a linear time-invariant system is described. Monte Carlo evaluations of the system's eigenvalues allows the probability of instability and the related stochastic root locus to be estimated. This analysis approach treats not only Gaussian parameter uncertainties but non-Gaussian cases, including uncertain-but-bounded variation. Confidence intervals for the scalar probability of instability address computational issues inherent in Monte Carlo simulation. Trivial extensions of the procedure admit consideration of alternate discriminants; thus, the probabilities that stipulated degrees of instability will be exceeded or that closed-loop roots will leave desirable regions can also be estimated. Results are particularly amenable to graphical presentation.

Robust Foot Clearance Estimation Based on the Integration of Foot-Mounted IMU Acceleration Data

PubMed Central

Benoussaad, Mourad; Sijobert, Benoît; Mombaur, Katja; Azevedo Coste, Christine

2015-01-01

This paper introduces a method for the robust estimation of foot clearance during walking, using a single inertial measurement unit (IMU) placed on the subject’s foot. The proposed solution is based on double integration and drift cancellation of foot acceleration signals. The method is insensitive to misalignment of IMU axes with respect to foot axes. Details are provided regarding calibration and signal processing procedures. Experimental validation was performed on 10 healthy subjects under three walking conditions: normal, fast and with obstacles. Foot clearance estimation results were compared to measurements from an optical motion capture system. The mean error between them is significantly less than 15% under the various walking conditions. PMID:26703622

A mixture model for robust registration in Kinect sensor

NASA Astrophysics Data System (ADS)

Peng, Li; Zhou, Huabing; Zhu, Shengguo

2018-03-01

The Microsoft Kinect sensor has been widely used in many applications, but it suffers from the drawback of low registration precision between color image and depth image. In this paper, we present a robust method to improve the registration precision by a mixture model that can handle multiply images with the nonparametric model. We impose non-parametric geometrical constraints on the correspondence, as a prior distribution, in a reproducing kernel Hilbert space (RKHS).The estimation is performed by the EM algorithm which by also estimating the variance of the prior model is able to obtain good estimates. We illustrate the proposed method on the public available dataset. The experimental results show that our approach outperforms the baseline methods.

Worst-Case Flutter Margins from F/A-18 Aircraft Aeroelastic Data

NASA Technical Reports Server (NTRS)

Lind, Rick; Brenner, Marty

1997-01-01

An approach for computing worst-case flutter margins has been formulated in a robust stability framework. Uncertainty operators are included with a linear model to describe modeling errors and flight variations. The structured singular value, micron, computes a stability margin which directly accounts for these uncertainties. This approach introduces a new method of computing flutter margins and an associated new parameter for describing these margins. The micron margins are robust margins which indicate worst-case stability estimates with respect to the defined uncertainty. Worst-case flutter margins are computed for the F/A-18 SRA using uncertainty sets generated by flight data analysis. The robust margins demonstrate flight conditions for flutter may lie closer to the flight envelope than previously estimated by p-k analysis.

The Relationship between Organizational Health and Robust School Vision in Elementary Schools

ERIC Educational Resources Information Center

Korkmaz, Mehmet

2006-01-01

Teachers play an important role in developing a robust school vision. This study is aimed to find out the likely relationship between the teachers' perception of school health and a robust school vision. It has been found that there is a significant positive relationship between teachers' perceptions of organizational health and the relative…

Direct adaptive robust tracking control for 6 DOF industrial robot with enhanced accuracy.

PubMed

Yin, Xiuxing; Pan, Li

2018-01-01

A direct adaptive robust tracking control is proposed for trajectory tracking of 6 DOF industrial robot in the presence of parametric uncertainties, external disturbances and uncertain nonlinearities. The controller is designed based on the dynamic characteristics in the working space of the end-effector of the 6 DOF robot. The controller includes robust control term and model compensation term that is developed directly based on the input reference or desired motion trajectory. A projection-type parametric adaptation law is also designed to compensate for parametric estimation errors for the adaptive robust control. The feasibility and effectiveness of the proposed direct adaptive robust control law and the associated projection-type parametric adaptation law have been comparatively evaluated based on two 6 DOF industrial robots. The test results demonstrate that the proposed control can be employed to better maintain the desired trajectory tracking even in the presence of large parametric uncertainties and external disturbances as compared with PD controller and nonlinear controller. The parametric estimates also eventually converge to the real values along with the convergence of tracking errors, which further validate the effectiveness of the proposed parametric adaption law. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Robustness of fit indices to outliers and leverage observations in structural equation modeling.

PubMed

Yuan, Ke-Hai; Zhong, Xiaoling

2013-06-01

Normal-distribution-based maximum likelihood (NML) is the most widely used method in structural equation modeling (SEM), although practical data tend to be nonnormally distributed. The effect of nonnormally distributed data or data contamination on the normal-distribution-based likelihood ratio (LR) statistic is well understood due to many analytical and empirical studies. In SEM, fit indices are used as widely as the LR statistic. In addition to NML, robust procedures have been developed for more efficient and less biased parameter estimates with practical data. This article studies the effect of outliers and leverage observations on fit indices following NML and two robust methods. Analysis and empirical results indicate that good leverage observations following NML and one of the robust methods lead most fit indices to give more support to the substantive model. While outliers tend to make a good model superficially bad according to many fit indices following NML, they have little effect on those following the two robust procedures. Implications of the results to data analysis are discussed, and recommendations are provided regarding the use of estimation methods and interpretation of fit indices. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

Robust Diagnosis Method Based on Parameter Estimation for an Interturn Short-Circuit Fault in Multipole PMSM under High-Speed Operation.

PubMed

Lee, Jewon; Moon, Seokbae; Jeong, Hyeyun; Kim, Sang Woo

2015-11-20

This paper proposes a diagnosis method for a multipole permanent magnet synchronous motor (PMSM) under an interturn short circuit fault. Previous works in this area have suffered from the uncertainties of the PMSM parameters, which can lead to misdiagnosis. The proposed method estimates the q-axis inductance (Lq) of the faulty PMSM to solve this problem. The proposed method also estimates the faulty phase and the value of G, which serves as an index of the severity of the fault. The q-axis current is used to estimate the faulty phase, the values of G and Lq. For this reason, two open-loop observers and an optimization method based on a particle-swarm are implemented. The q-axis current of a healthy PMSM is estimated by the open-loop observer with the parameters of a healthy PMSM. The Lq estimation significantly compensates for the estimation errors in high-speed operation. The experimental results demonstrate that the proposed method can estimate the faulty phase, G, and Lq besides exhibiting robustness against parameter uncertainties.

Distinct molecular cues ensure a robust microtubule-dependent nuclear positioning in the Drosophila oocyte

PubMed Central

Tissot, Nicolas; Lepesant, Jean-Antoine; Bernard, Fred; Legent, Kevin; Bosveld, Floris; Martin, Charlotte; Faklaris, Orestis; Bellaïche, Yohanns; Coppey, Maïté; Guichet, Antoine

2017-01-01

Controlling nucleus localization is crucial for a variety of cellular functions. In the Drosophila oocyte, nuclear asymmetric positioning is essential for the reorganization of the microtubule (MT) network that controls the polarized transport of axis determinants. A combination of quantitative three-dimensional live imaging and laser ablation-mediated force analysis reveal that nuclear positioning is ensured with an unexpected level of robustness. We show that the nucleus is pushed to the oocyte antero-dorsal cortex by MTs and that its migration can proceed through distinct tracks. Centrosome-associated MTs favour one migratory route. In addition, the MT-associated protein Mud/NuMA that is asymmetrically localized in an Asp-dependent manner at the nuclear envelope hemisphere where MT nucleation is higher promotes a separate route. Our results demonstrate that centrosomes do not provide an obligatory driving force for nuclear movement, but together with Mud, contribute to the mechanisms that ensure the robustness of asymmetric nuclear positioning. PMID:28447612

Diagnostics of Robust Growth Curve Modeling Using Student's "t" Distribution

ERIC Educational Resources Information Center

Tong, Xin; Zhang, Zhiyong

2012-01-01

Growth curve models with different types of distributions of random effects and of intraindividual measurement errors for robust analysis are compared. After demonstrating the influence of distribution specification on parameter estimation, 3 methods for diagnosing the distributions for both random effects and intraindividual measurement errors…

Observability and Estimation of Distributed Space Systems via Local Information-Exchange Networks

NASA Technical Reports Server (NTRS)

Fathpour, Nanaz; Hadaegh, Fred Y.; Mesbahi, Mehran; Rahmani, Amirreza

2011-01-01

Spacecraft formation flying involves the coordination of states among multiple spacecraft through relative sensing, inter-spacecraft communication, and control. Most existing formation-flying estimation algorithms can only be supported via highly centralized, all-to-all, static relative sensing. New algorithms are proposed that are scalable, modular, and robust to variations in the topology and link characteristics of the formation exchange network. These distributed algorithms rely on a local information exchange network, relaxing the assumptions on existing algorithms. Distributed space systems rely on a signal transmission network among multiple spacecraft for their operation. Control and coordination among multiple spacecraft in a formation is facilitated via a network of relative sensing and interspacecraft communications. Guidance, navigation, and control rely on the sensing network. This network becomes more complex the more spacecraft are added, or as mission requirements become more complex. The observability of a formation state was observed by a set of local observations from a particular node in the formation. Formation observability can be parameterized in terms of the matrices appearing in the formation dynamics and observation matrices. An agreement protocol was used as a mechanism for observing formation states from local measurements. An agreement protocol is essentially an unforced dynamic system whose trajectory is governed by the interconnection geometry and initial condition of each node, with a goal of reaching a common value of interest. The observability of the interconnected system depends on the geometry of the network, as well as the position of the observer relative to the topology. For the first time, critical GN&C (guidance, navigation, and control estimation) subsystems are synthesized by bringing the contribution of the spacecraft information-exchange network to the forefront of algorithmic analysis and design. The result is a formation estimation algorithm that is modular and robust to variations in the topology and link properties of the underlying formation network.

Vector autoregressive models: A Gini approach

NASA Astrophysics Data System (ADS)

Mussard, Stéphane; Ndiaye, Oumar Hamady

2018-02-01

In this paper, it is proven that the usual VAR models may be performed in the Gini sense, that is, on a ℓ1 metric space. The Gini regression is robust to outliers. As a consequence, when data are contaminated by extreme values, we show that semi-parametric VAR-Gini regressions may be used to obtain robust estimators. The inference about the estimators is made with the ℓ1 norm. Also, impulse response functions and Gini decompositions for prevision errors are introduced. Finally, Granger's causality tests are properly derived based on U-statistics.

Finite time state and disturbance estimation for robust performance of motion control systems using sliding modes

NASA Astrophysics Data System (ADS)

Tamhane, Bhagyashri; Kurode, Shailaja

2018-05-01

In this paper, simultaneous state and disturbance estimation of a drive system composed of motor connected to a load is proposed. Such a system is represented by a two mass model realising in a fourth-order plant. Backlash is introduced as the nonlinear disturbance in gears which is proposed to be estimated and in turn compensated. For this motion control system, a two-stage higher order sliding-mode observer is proposed for state and backlash estimation. The novelty lies in the fact that for this fourth-order system, output is considered from the motor end only, i.e. its angular displacement. The unmeasured states consisting of output derivative, load-side angular displacement and its derivative along with backlash are estimated in finite time. This disturbance due to backlash is unmatched in nature. The estimated states and disturbance are used to devise a robust sliding-mode control. This proposed scheme is validated in simulation and experimentation.

Human Age Estimation Method Robust to Camera Sensor and/or Face Movement

PubMed Central

Nguyen, Dat Tien; Cho, So Ra; Pham, Tuyen Danh; Park, Kang Ryoung

2015-01-01

Human age can be employed in many useful real-life applications, such as customer service systems, automatic vending machines, entertainment, etc. In order to obtain age information, image-based age estimation systems have been developed using information from the human face. However, limitations exist for current age estimation systems because of the various factors of camera motion and optical blurring, facial expressions, gender, etc. Motion blurring can usually be presented on face images by the movement of the camera sensor and/or the movement of the face during image acquisition. Therefore, the facial feature in captured images can be transformed according to the amount of motion, which causes performance degradation of age estimation systems. In this paper, the problem caused by motion blurring is addressed and its solution is proposed in order to make age estimation systems robust to the effects of motion blurring. Experiment results show that our method is more efficient for enhancing age estimation performance compared with systems that do not employ our method. PMID:26334282

Probability based remaining capacity estimation using data-driven and neural network model

NASA Astrophysics Data System (ADS)

Wang, Yujie; Yang, Duo; Zhang, Xu; Chen, Zonghai

2016-05-01

Since large numbers of lithium-ion batteries are composed in pack and the batteries are complex electrochemical devices, their monitoring and safety concerns are key issues for the applications of battery technology. An accurate estimation of battery remaining capacity is crucial for optimization of the vehicle control, preventing battery from over-charging and over-discharging and ensuring the safety during its service life. The remaining capacity estimation of a battery includes the estimation of state-of-charge (SOC) and state-of-energy (SOE). In this work, a probability based adaptive estimator is presented to obtain accurate and reliable estimation results for both SOC and SOE. For the SOC estimation, an n ordered RC equivalent circuit model is employed by combining an electrochemical model to obtain more accurate voltage prediction results. For the SOE estimation, a sliding window neural network model is proposed to investigate the relationship between the terminal voltage and the model inputs. To verify the accuracy and robustness of the proposed model and estimation algorithm, experiments under different dynamic operation current profiles are performed on the commercial 1665130-type lithium-ion batteries. The results illustrate that accurate and robust estimation can be obtained by the proposed method.

Volcanic Surface Deformation in Dominica From GPS Geodesy: Results From the 2007 NSF- REU Site

NASA Astrophysics Data System (ADS)

Murphy, R.; James, S.; Styron, R. H.; Turner, H. L.; Ashlock, A.; Cavness, C.; Collier, X.; Fauria, K.; Feinstein, R.; Staisch, L.; Williams, B.; Mattioli, G. S.; Jansma, P. E.; Cothren, J.

2007-12-01

GPS measurements have been collected on the island of Dominica in the Lesser Antilles between 2001 and 2007, with five month-long campaigns completed in June of each year supported in part by a NSF REU Site award for the past two years. All GPS data were collected using dual-frequency, code-phase receivers and geodetic-quality antenna, primarily choke rings. Three consecutive 24 hr observation days were normally obtained for each site. Precise station positions were estimated with GIPSY-OASISII using an absolute point positioning strategy and final, precise orbits, clocks, earth orientation parameters, and x-files. All position estimates were updated to ITRF05 and a revised Caribbean Euler pole was used to place our observations in a CAR-fixed frame. Time series were created to determine the velocity of each station. Forward and inverse elastic half-space models with planar (i.e. dike) and Mogi (i.e. point) sources were investigated. Inverse modeling was completed using a downhill simplex method of function minimization. Selected site velocities were used to create appropriate models for specific regions of Dominica, which correspond to known centers of pre-historic volcanic or recent shallow, seismic activity. Because of the current distribution of GPS sites with robust velocity estimates, we limit our models to possible magmatic activity in the northern, proximal to the volcanic centers of Morne Diablotins and Morne aux Diables, and southern, proximal to volcanic centers of Soufriere and Morne Plat Pays, regions of the island. Surface deformation data from the northernmost sites may be fit with the development of a several km-long dike trending approximately northeast- southwest. Activity in the southern volcanic centers is best modeled by an expanding point source at approximately 1 km depth.

Neural network uncertainty assessment using Bayesian statistics: a remote sensing application

NASA Technical Reports Server (NTRS)

Aires, F.; Prigent, C.; Rossow, W. B.

2004-01-01

Neural network (NN) techniques have proved successful for many regression problems, in particular for remote sensing; however, uncertainty estimates are rarely provided. In this article, a Bayesian technique to evaluate uncertainties of the NN parameters (i.e., synaptic weights) is first presented. In contrast to more traditional approaches based on point estimation of the NN weights, we assess uncertainties on such estimates to monitor the robustness of the NN model. These theoretical developments are illustrated by applying them to the problem of retrieving surface skin temperature, microwave surface emissivities, and integrated water vapor content from a combined analysis of satellite microwave and infrared observations over land. The weight uncertainty estimates are then used to compute analytically the uncertainties in the network outputs (i.e., error bars and correlation structure of these errors). Such quantities are very important for evaluating any application of an NN model. The uncertainties on the NN Jacobians are then considered in the third part of this article. Used for regression fitting, NN models can be used effectively to represent highly nonlinear, multivariate functions. In this situation, most emphasis is put on estimating the output errors, but almost no attention has been given to errors associated with the internal structure of the regression model. The complex structure of dependency inside the NN is the essence of the model, and assessing its quality, coherency, and physical character makes all the difference between a blackbox model with small output errors and a reliable, robust, and physically coherent model. Such dependency structures are described to the first order by the NN Jacobians: they indicate the sensitivity of one output with respect to the inputs of the model for given input data. We use a Monte Carlo integration procedure to estimate the robustness of the NN Jacobians. A regularization strategy based on principal component analysis is proposed to suppress the multicollinearities in order to make these Jacobians robust and physically meaningful.

A rapid and robust gradient measurement technique using dynamic single-point imaging.

PubMed

Jang, Hyungseok; McMillan, Alan B

2017-09-01

We propose a new gradient measurement technique based on dynamic single-point imaging (SPI), which allows simple, rapid, and robust measurement of k-space trajectory. To enable gradient measurement, we utilize the variable field-of-view (FOV) property of dynamic SPI, which is dependent on gradient shape. First, one-dimensional (1D) dynamic SPI data are acquired from a targeted gradient axis, and then relative FOV scaling factors between 1D images or k-spaces at varying encoding times are found. These relative scaling factors are the relative k-space position that can be used for image reconstruction. The gradient measurement technique also can be used to estimate the gradient impulse response function for reproducible gradient estimation as a linear time invariant system. The proposed measurement technique was used to improve reconstructed image quality in 3D ultrashort echo, 2D spiral, and multi-echo bipolar gradient-echo imaging. In multi-echo bipolar gradient-echo imaging, measurement of the k-space trajectory allowed the use of a ramp-sampled trajectory for improved acquisition speed (approximately 30%) and more accurate quantitative fat and water separation in a phantom. The proposed dynamic SPI-based method allows fast k-space trajectory measurement with a simple implementation and no additional hardware for improved image quality. Magn Reson Med 78:950-962, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Transferability and robustness of real-time freeway crash risk assessment.

PubMed

Shew, Cameron; Pande, Anurag; Nuworsoo, Cornelius

2013-09-01

This study examines the data from single loop detectors on northbound (NB) US-101 in San Jose, California to estimate real-time crash risk assessment models. The classification tree and neural network based crash risk assessment models developed with data from NB US-101 are applied to data from the same freeway, as well as to the data from nearby segments of the SB US-101, NB I-880, and SB I-880 corridors. The performance of crash risk assessment models on these nearby segments is the focus of this research. The model applications show that it is in fact possible to use the same model for multiple freeways, as the underlying relationships between traffic data and crash risk remain similar. The framework provided here may be helpful to authorities for freeway segments with newly installed traffic surveillance apparatuses, since the real-time crash risk assessment models from nearby freeways with existing infrastructure would be able to provide a reasonable estimate of crash risk. The robustness of the model output is also assessed by location, time of day, and day of week. The analysis shows that on some locations the models may require further learning due to higher than expected false positive (e.g., the I-680/I-280 interchange on US-101 NB) or false negative rates. The approach for post-processing the results from the model provides ideas to refine the model prior to or during the implementation. Copyright © 2013 National Safety Council and Elsevier Ltd. All rights reserved.

Inference on the Ranks of the Canonical Correlation Matrices for Elliptically Symmetric Populations.

DTIC Science & Technology

1985-05-01

robust estimates of the covariance matrix, the reader is referred to Devlin, Gnanadesikan and Kettenring (1975) and Maronna (1976). Murihead and...contoured distributions. J. Multivariate Anal. 11, 368-385. 6. DEVLIN, S.J. GNANADESIKAN , R. and KETTENRING, J. (1975). Robust estima- tion and outlier

Robust Low-dose CT Perfusion Deconvolution via Tensor Total-Variation Regularization

PubMed Central

Zhang, Shaoting; Chen, Tsuhan; Sanelli, Pina C.

2016-01-01

Acute brain diseases such as acute strokes and transit ischemic attacks are the leading causes of mortality and morbidity worldwide, responsible for 9% of total death every year. ‘Time is brain’ is a widely accepted concept in acute cerebrovascular disease treatment. Efficient and accurate computational framework for hemodynamic parameters estimation can save critical time for thrombolytic therapy. Meanwhile the high level of accumulated radiation dosage due to continuous image acquisition in CT perfusion (CTP) raised concerns on patient safety and public health. However, low-radiation leads to increased noise and artifacts which require more sophisticated and time-consuming algorithms for robust estimation. In this paper, we focus on developing a robust and efficient framework to accurately estimate the perfusion parameters at low radiation dosage. Specifically, we present a tensor total-variation (TTV) technique which fuses the spatial correlation of the vascular structure and the temporal continuation of the blood signal flow. An efficient algorithm is proposed to find the solution with fast convergence and reduced computational complexity. Extensive evaluations are carried out in terms of sensitivity to noise levels, estimation accuracy, contrast preservation, and performed on digital perfusion phantom estimation, as well as in-vivo clinical subjects. Our framework reduces the necessary radiation dose to only 8% of the original level and outperforms the state-of-art algorithms with peak signal-to-noise ratio improved by 32%. It reduces the oscillation in the residue functions, corrects over-estimation of cerebral blood flow (CBF) and under-estimation of mean transit time (MTT), and maintains the distinction between the deficit and normal regions. PMID:25706579

mBEEF-vdW: Robust fitting of error estimation density functionals

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lundgaard, Keld T.; Wellendorff, Jess; Voss, Johannes

Here, we propose a general-purpose semilocal/nonlocal exchange-correlation functional approximation, named mBEEF-vdW. The exchange is a meta generalized gradient approximation, and the correlation is a semilocal and nonlocal mixture, with the Rutgers-Chalmers approximation for van der Waals (vdW) forces. The functional is fitted within the Bayesian error estimation functional (BEEF) framework. We improve the previously used fitting procedures by introducing a robust MM-estimator based loss function, reducing the sensitivity to outliers in the datasets. To more reliably determine the optimal model complexity, we furthermore introduce a generalization of the bootstrap 0.632 estimator with hierarchical bootstrap sampling and geometric mean estimator overmore » the training datasets. Using this estimator, we show that the robust loss function leads to a 10% improvement in the estimated prediction error over the previously used least-squares loss function. The mBEEF-vdW functional is benchmarked against popular density functional approximations over a wide range of datasets relevant for heterogeneous catalysis, including datasets that were not used for its training. Overall, we find that mBEEF-vdW has a higher general accuracy than competing popular functionals, and it is one of the best performing functionals on chemisorption systems, surface energies, lattice constants, and dispersion. We also show the potential-energy curve of graphene on the nickel(111) surface, where mBEEF-vdW matches the experimental binding length. mBEEF-vdW is currently available in gpaw and other density functional theory codes through Libxc, version 3.0.0.« less

mBEEF-vdW: Robust fitting of error estimation density functionals

DOE PAGES

Lundgaard, Keld T.; Wellendorff, Jess; Voss, Johannes; ...

2016-06-15

Here, we propose a general-purpose semilocal/nonlocal exchange-correlation functional approximation, named mBEEF-vdW. The exchange is a meta generalized gradient approximation, and the correlation is a semilocal and nonlocal mixture, with the Rutgers-Chalmers approximation for van der Waals (vdW) forces. The functional is fitted within the Bayesian error estimation functional (BEEF) framework. We improve the previously used fitting procedures by introducing a robust MM-estimator based loss function, reducing the sensitivity to outliers in the datasets. To more reliably determine the optimal model complexity, we furthermore introduce a generalization of the bootstrap 0.632 estimator with hierarchical bootstrap sampling and geometric mean estimator overmore » the training datasets. Using this estimator, we show that the robust loss function leads to a 10% improvement in the estimated prediction error over the previously used least-squares loss function. The mBEEF-vdW functional is benchmarked against popular density functional approximations over a wide range of datasets relevant for heterogeneous catalysis, including datasets that were not used for its training. Overall, we find that mBEEF-vdW has a higher general accuracy than competing popular functionals, and it is one of the best performing functionals on chemisorption systems, surface energies, lattice constants, and dispersion. We also show the potential-energy curve of graphene on the nickel(111) surface, where mBEEF-vdW matches the experimental binding length. mBEEF-vdW is currently available in gpaw and other density functional theory codes through Libxc, version 3.0.0.« less

Artificial arterial blood pressure artifact models and an evaluation of a robust blood pressure and heart rate estimator

PubMed Central

Li, Qiao; Mark, Roger G; Clifford, Gari D

2009-01-01

Background Within the intensive care unit (ICU), arterial blood pressure (ABP) is typically recorded at different (and sometimes uneven) sampling frequencies, and from different sensors, and is often corrupted by different artifacts and noise which are often non-Gaussian, nonlinear and nonstationary. Extracting robust parameters from such signals, and providing confidences in the estimates is therefore difficult and requires an adaptive filtering approach which accounts for artifact types. Methods Using a large ICU database, and over 6000 hours of simultaneously acquired electrocardiogram (ECG) and ABP waveforms sampled at 125 Hz from a 437 patient subset, we documented six general types of ABP artifact. We describe a new ABP signal quality index (SQI), based upon the combination of two previously reported signal quality measures weighted together. One index measures morphological normality, and the other degradation due to noise. After extracting a 6084-hour subset of clean data using our SQI, we evaluated a new robust tracking algorithm for estimating blood pressure and heart rate (HR) based upon a Kalman Filter (KF) with an update sequence modified by the KF innovation sequence and the value of the SQI. In order to do this, we have created six novel models of different categories of artifacts that we have identified in our ABP waveform data. These artifact models were then injected into clean ABP waveforms in a controlled manner. Clinical blood pressure (systolic, mean and diastolic) estimates were then made from the ABP waveforms for both clean and corrupted data. The mean absolute error for systolic, mean and diastolic blood pressure was then calculated for different levels of artifact pollution to provide estimates of expected errors given a single value of the SQI. Results Our artifact models demonstrate that artifact types have differing effects on systolic, diastolic and mean ABP estimates. We show that, for most artifact types, diastolic ABP estimates are less noise-sensitive than mean ABP estimates, which in turn are more robust than systolic ABP estimates. We also show that our SQI can provide error bounds for both HR and ABP estimates. Conclusion The KF/SQI-fusion method described in this article was shown to provide an accurate estimate of blood pressure and HR derived from the ABP waveform even in the presence of high levels of persistent noise and artifact, and during extreme bradycardia and tachycardia. Differences in error between artifact types, measurement sensors and the quality of the source signal can be factored into physiological estimation using an unbiased adaptive filter, signal innovation and signal quality measures. PMID:19586547

Automatic detection of patient identification and positioning errors in radiation therapy treatment using 3-dimensional setup images.

PubMed

Jani, Shyam S; Low, Daniel A; Lamb, James M

2015-01-01

To develop an automated system that detects patient identification and positioning errors between 3-dimensional computed tomography (CT) and kilovoltage CT planning images. Planning kilovoltage CT images were collected for head and neck (H&N), pelvis, and spine treatments with corresponding 3-dimensional cone beam CT and megavoltage CT setup images from TrueBeam and TomoTherapy units, respectively. Patient identification errors were simulated by registering setup and planning images from different patients. For positioning errors, setup and planning images were misaligned by 1 to 5 cm in the 6 anatomical directions for H&N and pelvis patients. Spinal misalignments were simulated by misaligning to adjacent vertebral bodies. Image pairs were assessed using commonly used image similarity metrics as well as custom-designed metrics. Linear discriminant analysis classification models were trained and tested on the imaging datasets, and misclassification error (MCE), sensitivity, and specificity parameters were estimated using 10-fold cross-validation. For patient identification, our workflow produced MCE estimates of 0.66%, 1.67%, and 0% for H&N, pelvis, and spine TomoTherapy images, respectively. Sensitivity and specificity ranged from 97.5% to 100%. MCEs of 3.5%, 2.3%, and 2.1% were obtained for TrueBeam images of the above sites, respectively, with sensitivity and specificity estimates between 95.4% and 97.7%. MCEs for 1-cm H&N/pelvis misalignments were 1.3%/5.1% and 9.1%/8.6% for TomoTherapy and TrueBeam images, respectively. Two-centimeter MCE estimates were 0.4%/1.6% and 3.1/3.2%, respectively. MCEs for vertebral body misalignments were 4.8% and 3.6% for TomoTherapy and TrueBeam images, respectively. Patient identification and gross misalignment errors can be robustly and automatically detected using 3-dimensional setup images of different energies across 3 commonly treated anatomical sites. Copyright © 2015 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.

Robust and sparse correlation matrix estimation for the analysis of high-dimensional genomics data.

PubMed

Serra, Angela; Coretto, Pietro; Fratello, Michele; Tagliaferri, Roberto; Stegle, Oliver

2018-02-15

Microarray technology can be used to study the expression of thousands of genes across a number of different experimental conditions, usually hundreds. The underlying principle is that genes sharing similar expression patterns, across different samples, can be part of the same co-expression system, or they may share the same biological functions. Groups of genes are usually identified based on cluster analysis. Clustering methods rely on the similarity matrix between genes. A common choice to measure similarity is to compute the sample correlation matrix. Dimensionality reduction is another popular data analysis task which is also based on covariance/correlation matrix estimates. Unfortunately, covariance/correlation matrix estimation suffers from the intrinsic noise present in high-dimensional data. Sources of noise are: sampling variations, presents of outlying sample units, and the fact that in most cases the number of units is much larger than the number of genes. In this paper, we propose a robust correlation matrix estimator that is regularized based on adaptive thresholding. The resulting method jointly tames the effects of the high-dimensionality, and data contamination. Computations are easy to implement and do not require hand tunings. Both simulated and real data are analyzed. A Monte Carlo experiment shows that the proposed method is capable of remarkable performances. Our correlation metric is more robust to outliers compared with the existing alternatives in two gene expression datasets. It is also shown how the regularization allows to automatically detect and filter spurious correlations. The same regularization is also extended to other less robust correlation measures. Finally, we apply the ARACNE algorithm on the SyNTreN gene expression data. Sensitivity and specificity of the reconstructed network is compared with the gold standard. We show that ARACNE performs better when it takes the proposed correlation matrix estimator as input. The R software is available at https://github.com/angy89/RobustSparseCorrelation. aserra@unisa.it or robtag@unisa.it. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

A robust motion estimation system for minimal invasive laparoscopy

NASA Astrophysics Data System (ADS)

Marcinczak, Jan Marek; von Öhsen, Udo; Grigat, Rolf-Rainer

2012-02-01

Laparoscopy is a reliable imaging method to examine the liver. However, due to the limited field of view, a lot of experience is required from the surgeon to interpret the observed anatomy. Reconstruction of organ surfaces provide valuable additional information to the surgeon for a reliable diagnosis. Without an additional external tracking system the structure can be recovered from feature correspondences between different frames. In laparoscopic images blurred frames, specular reflections and inhomogeneous illumination make feature tracking a challenging task. We propose an ego-motion estimation system for minimal invasive laparoscopy that can cope with specular reflection, inhomogeneous illumination and blurred frames. To obtain robust feature correspondence, the approach combines SIFT and specular reflection segmentation with a multi-frame tracking scheme. The calibrated five-point algorithm is used with the MSAC robust estimator to compute the motion of the endoscope from multi-frame correspondence. The algorithm is evaluated using endoscopic videos of a phantom. The small incisions and the rigid endoscope limit the motion in minimal invasive laparoscopy. These limitations are considered in our evaluation and are used to analyze the accuracy of pose estimation that can be achieved by our approach. The endoscope is moved by a robotic system and the ground truth motion is recorded. The evaluation on typical endoscopic motion gives precise results and demonstrates the practicability of the proposed pose estimation system.

Design of parallel transmission radiofrequency pulses robust against respiration in cardiac MRI at 7 Tesla.

PubMed

Schmitter, Sebastian; Wu, Xiaoping; Uğurbil, Kâmil; Van de Moortele, Pierre-François

2015-11-01

Two-spoke parallel transmission (pTX) radiofrequency (RF) pulses have been demonstrated in cardiac MRI at 7T. However, current pulse designs rely on a single set of B1(+)/B0 maps that may not be valid for subsequent scans acquired at another phase of the respiration cycle because of organ displacement. Such mismatches may yield severe excitation profile degradation. B1(+)/B0 maps were obtained, using 16 transmit channels at 7T, at three breath-hold positions: exhale, half-inhale, and inhale. Standard and robust RF pulses were designed using maps obtained at exhale only, and at multiple respiratory positions, respectively. Excitation patterns were analyzed for all positions using Bloch simulations. Flip-angle homogeneity was compared in vivo in cardiac CINE acquisitions. Standard one- and two-spoke pTX RF pulses are sensitive to breath-hold position, primarily due to B1(+) alterations, with high dependency on excitation trajectory for two spokes. In vivo excitation inhomogeneity varied from nRMSE = 8.2% (exhale) up to 32.5% (inhale) with the standard design; much more stable results were obtained with the robust design with nRMSE = 9.1% (exhale) and 10.6% (inhale). A new pTX RF pulse design robust against respiration induced variations of B1(+)/B0 maps is demonstrated and is expected to have a positive impact on cardiac MRI in breath-hold, free-breathing, and real-time acquisitions. © 2014 Wiley Periodicals, Inc.

A novel machine learning-enabled framework for instantaneous heart rate monitoring from motion-artifact-corrupted electrocardiogram signals.

PubMed

Zhang, Qingxue; Zhou, Dian; Zeng, Xuan

2016-11-01

This paper proposes a novel machine learning-enabled framework to robustly monitor the instantaneous heart rate (IHR) from wrist-electrocardiography (ECG) signals continuously and heavily corrupted by random motion artifacts in wearable applications. The framework includes two stages, i.e. heartbeat identification and refinement, respectively. In the first stage, an adaptive threshold-based auto-segmentation approach is proposed to select out heartbeat candidates, including the real heartbeats and large amounts of motion-artifact-induced interferential spikes. Then twenty-six features are extracted for each candidate in time, spatial, frequency and statistical domains, and evaluated by a spare support vector machine (SVM) to select out ten critical features which can effectively reveal residual heartbeat information. Afterwards, an SVM model, created on the training data using the selected feature set, is applied to find high confident heartbeats from a large number of candidates in the testing data. In the second stage, the SVM classification results are further refined by two steps: (1) a rule-based classifier with two attributes named 'continuity check' and 'locality check' for outlier (false positives) removal, and (2) a heartbeat interpolation strategy for missing-heartbeat (false negatives) recovery. The framework is evaluated on a wrist-ECG dataset acquired by a semi-customized platform and also a public dataset. When the signal-to-noise ratio is as low as  -7 dB, the mean absolute error of the estimated IHR is 1.4 beats per minute (BPM) and the root mean square error is 6.5 BPM. The proposed framework greatly outperforms well-established approaches, demonstrating that it can effectively identify the heartbeats from ECG signals continuously corrupted by intense motion artifacts and robustly estimate the IHR. This study is expected to contribute to robust long-term wearable IHR monitoring for pervasive heart health and fitness management.

M-estimation for robust sparse unmixing of hyperspectral images

NASA Astrophysics Data System (ADS)

Toomik, Maria; Lu, Shijian; Nelson, James D. B.

2016-10-01

Hyperspectral unmixing methods often use a conventional least squares based lasso which assumes that the data follows the Gaussian distribution. The normality assumption is an approximation which is generally invalid for real imagery data. We consider a robust (non-Gaussian) approach to sparse spectral unmixing of remotely sensed imagery which reduces the sensitivity of the estimator to outliers and relaxes the linearity assumption. The method consists of several appropriate penalties. We propose to use an lp norm with 0 < p < 1 in the sparse regression problem, which induces more sparsity in the results, but makes the problem non-convex. On the other hand, the problem, though non-convex, can be solved quite straightforwardly with an extensible algorithm based on iteratively reweighted least squares. To deal with the huge size of modern spectral libraries we introduce a library reduction step, similar to the multiple signal classification (MUSIC) array processing algorithm, which not only speeds up unmixing but also yields superior results. In the hyperspectral setting we extend the traditional least squares method to the robust heavy-tailed case and propose a generalised M-lasso solution. M-estimation replaces the Gaussian likelihood with a fixed function ρ(e) that restrains outliers. The M-estimate function reduces the effect of errors with large amplitudes or even assigns the outliers zero weights. Our experimental results on real hyperspectral data show that noise with large amplitudes (outliers) often exists in the data. This ability to mitigate the influence of such outliers can therefore offer greater robustness. Qualitative hyperspectral unmixing results on real hyperspectral image data corroborate the efficacy of the proposed method.