<p>I present an application to visualization of GPS data and Linear Correlations and models. A collection of data for each vehicle is used to compute correlations. Deviating correlations can be indicative of a faulty vehicle.</p><p> The correlation values for each vehicle are computed with use linear regression algorithms using up to 4 signals in the data (with varied time window), and display the model parameters in a window next to the GPS map. Multiple measurements (multiple drive routes and multiple model parameters) are displayed at the same time, allowing tracking over time and comparison of different vehicles.</p><p> </p><p> The whole technique is demonstrated on three data which is set on first frame by user. The results are displayed with a java application and Google Map.</p>

I present an application to visualization of GPS data and Linear Correlations and models. A collection of data for each vehicle is used to compute correlations. Deviating correlations can be indicative of a faulty vehicle. The correlation values for each vehicle are computed with use linear regression algorithms using up to 4 signals in the data (with varied time window), and display the model parameters in a window next to the GPS map. Multiple measurements (multiple drive routes and multiple model parameters) are displayed at the same time, allowing tracking over time and comparison of different vehicles. The whole technique is demonstrated on three data which is set on first frame by user. The results are displayed with a java application and Google Map.

<p>We present a technique to identify deviating systems among a group of systems in a</p><p>self-organized way. A compressed representation of each system is used to compute similarity measures, which are combined in an affinity matrix of all systems. Deviation detection and clustering is then used to identify deviating systems based on this affinity matrix.</p><p>The compressed representation is computed with Principal Component Analysis and</p><p>Kernel Principal Component Analysis. The similarity measure between two compressed</p><p>representations is based on the angle between the spaces spanned by the principal</p><p>components, but other methods of calculating a similarity measure are suggested as</p><p>well. The subsequent deviation detection is carried out by computing the probability of</p><p>each system to be observed given all the other systems. Clustering of the systems is</p><p>done with hierarchical clustering and spectral clustering. The whole technique is demonstrated on four data sets of mechanical systems, two of a simulated cooling system and two of human gait. The results show its applicability on these mechanical systems.</p>

We present a technique to identify deviating systems among a group of systems in a self-organized way. A compressed representation of each system is used to compute similarity measures, which are combined in an affinity matrix of all systems. Deviation detection and clustering is then used to identify deviating systems based on this affinity matrix. The compressed representation is computed with Principal Component Analysis and Kernel Principal Component Analysis. The similarity measure between two compressed representations is based on the angle between the spaces spanned by the principal components, but other methods of calculating a similarity measure are suggested as well. The subsequent deviation detection is carried out by computing the probability of each system to be observed given all the other systems. Clustering of the systems is done with hierarchical clustering and spectral clustering. The whole technique is demonstrated on four data sets of mechanical systems, two of a simulated cooling system and two of human gait. The results show its applicability on these mechanical systems.

<p>A technique to detect deviations in sets of systems in a self-organized way is described in this work. System features are extracted to allow compact representation of the system. Distances between systems are calculated by computing distances between the features. The distances are then stored in an affinity matrix. Deviating systems are detected by assuming a statistical model for the affinities. The key idea is to extract features and and identify deviating systems in a self-organized way, using nonlinear techniques for the feature extraction. The results are compared with those achieved with linear techniques, (principal component analysis).</p><p>The features are computed with principal curves and an isometric feature mapping. In the case of principal curves the feature is the curve itself. In the case of isometric feature mapping is the feature a set of curves in the embedding space. The similarity measure between two representations is either the Hausdorff distance, or the Frechet distance. The deviation detection is performed by computing the probability of each system to be observed given all the other systems. To perform reliable inference the Bootstrapping technique was used.</p><p>The technique is demonstrated on simulated and on-road vehicle cooling system data. The results show the applicability and comparison with linear techniques.</p>

<p>The main idea of our work is the calibration parameters for the Heston stochastic volatility model. We make this procedure by using the OMXS30 index from the NASDAQ OMX Nordic Exchange Market. We separate our data into the stable period and high-volatility period on this Nordic Market. Deviation detection problem are solved using the Bayesian analysis of change-points. We estimate parameters of the Heston model for each of periods and make some conclusions.</p>

The main idea of our work is the calibration parameters for the Heston stochastic volatility model. We make this procedure by using the OMXS30 index from the NASDAQ OMX Nordic Exchange Market. We separate our data into the stable period and high-volatility period on this Nordic Market. Deviation detection problem are solved using the Bayesian analysis of change-points. We estimate parameters of the Heston model for each of periods and make some conclusions.

A technique to detect deviations in sets of systems in a self-organized way is described in this work. System features are extracted to allow compact representation of the system. Distances between systems are calculated by computing distances between the features. The distances are then stored in an affinity matrix. Deviating systems are detected by assuming a statistical model for the affinities. The key idea is to extract features and and identify deviating systems in a self-organized way, using nonlinear techniques for the feature extraction. The results are compared with those achieved with linear techniques, (principal component analysis). The features are computed with principal curves and an isometric feature mapping. In the case of principal curves the feature is the curve itself. In the case of isometric feature mapping is the feature a set of curves in the embedding space. The similarity measure between two representations is either the Hausdorff distance, or the Frechet distance. The deviation detection is performed by computing the probability of each system to be observed given all the other systems. To perform reliable inference the Bootstrapping technique was used. The technique is demonstrated on simulated and on-road vehicle cooling system data. The results show the applicability and comparison with linear techniques.

No / Human observers can simultaneously track up to five targets in motion (Z. W. Pylyshyn & R. W. Storm, 1988). We examined the precision for detecting deviations in linear trajectories by measuring deviation thresholds as a function of the number of trajectories (T ). When all trajectories in the stimulus undergo the same deviation, thresholds are uninfluenced by T for T <= 10. When only one of the trajectories undergoes a deviation, thresholds rise steeply as T is increased [e.g., 3.3º (T = 1), 12.3º (T = 2), 32.9º (T = 4) for one observer]; observers are unable to simultaneously process more than one trajectory in our threshold-measuring paradigm. When the deviating trajectory is cued (e.g., using a different color), varying T has little influence on deviation threshold. The use of a different color for each trajectory does not facilitate deviation detection. Our current data suggest that for deviations that have low discriminability (i.e., close to threshold) the number of trajectories that can be monitored effectively is close to one. In contrast, when the stimuli containing highly discriminable (i.e., substantially suprathreshold) deviations are used, as many as three or four trajectories can be simultaneously monitored (S. P. Tripathy, 2003). Our results highlight a severe loss of positional information when attempting to track multiple objects, particularly in a threshold paradigm.

No / Amblyopia results in a severe loss of positional information and in the ability to accurately enumerate objects (V. Sharma, D. M. Levi, & S. A. Klein, 2000). In this study, we asked whether amblyopia also disrupts the ability to track a near-threshold change in the trajectory of a single target amongst multiple similar potential targets. In the first experiment, we examined the precision for detecting a deviation in the linear motion trajectory of a dot by measuring deviation thresholds as a function of the number of moving trajectories (T). As in normal observers, we found that in both eyes of amblyopes, threshold increases steeply as T increases from 1 to 4. Surprisingly, for T = 1-4, thresholds were essentially identical in both eyes of the amblyopes and were similar to those of normal observers. In a second experiment, we measured the precision for detecting a deviation in the orientation of a static, bilinear "trajectory" by again measuring deviation thresholds (i.e., angle discrimination) as a function of the number of oriented line "trajectories" (T). Relative to the nonamblyopic eye, amblyopes show a marked threshold elevation for a static target when T = 1. However, thresholds increased with T with approximately the same slope as in their preferred eye and in the eyes of the normal controls. We conclude that while amblyopia disrupts static angle discrimination, amblyopic dynamic deviation detection thresholds are normal or very nearly so.