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Nonlinear Estimation Toolbox 2.0 released!

This is a major toolbox release for cleaning up its API (more consistent method naming and removal of rather unnecessaryfunctionalities) to allow for a better API understanding and better implementation of new features. This release alsoincludes new estimators.

Updated Getting Started to reflect all API changes.

Also improved documentation for Estimators and Probability Distributions. Caught in my soch mac os.

• Filters now only handle the case of a single measurement. For example, Kalman filters now only accept a measurement vector, not a matrix of multiple measurements. However, processing multiple measurements can still be done by stacking measurements to a vector.

• Measurement models can now only generate a single measurement with the simulate() method.

• Likelihood-based filters now accept any type of measurement data, not only real-valued measurement vectors as it is the case for Kalman filters. That is, you can pass, for example, a cell array of data that is required by the likelihood function.

• Various class methods are now sealed to prevent unintended overwriting.

• Added new subfolders for a better structuring toolbox files. You may have to update your MATLAB PATH accordingly.

• Renamed getPointEstimate() to getStateMeanAndCov().

• In addition to getPointEstimate(), getStateMeanAndCov() now also returns the square root of the system state covariance matrix (the lower Cholesky decomposition) analogous to the getMeanAndCov() methods of probability distributions.

• Added setStateMeanAndCov() method for fastly setting the system state without creating a temporary Gaussian distribution, e.g., in order to assign the Gaussian state estimate of a filter to another one. Note that this method does not perform input validation like setState()!

• • Users can now specify custom state estimate post processing via the {set,get}PredictionPostProcessing() and {set,get}setUpdatePostProcessing() methods, e.g., to implement constrained state estimation. The set post processing methods are then executed after each state prediction and measurement update, respectively.

• Added several recursive update filter (RUF) variants

  • ERUF: extended recursive update filter (the originally proposed RUF).

  • CRUF: the fifth-degree cubature recursive update filter.

  • GHRUF: the Gauss-Hermite recursive update filter.

  • RURUF: the randomized unscented recursive update filter.

  • S2RUF: the smart sampling recursive update filter.

  • URUF: the unscented recursive update filter.

• • Dropped the analytic moment computation models used by Kalman filters and moved the analytic moment computation for the linear cases directly into LinearMeasurementModel and LinearSystemModel by introducing the getAnalyticMoments() methods.

  • Users can now specify a custom convergence check via the {set,get}ConvergenceCheck() methods, e.g., based on the Kullback-Leibler divergence (KLD) using the new Utils.getGaussianKLD() method.

  • Added getNumIterations().

  • Renamed setMeasValidationThreshold() to setMeasGatingThreshold().

  • Renamed getMeasValidationThreshold() to getMeasGatingThreshold().

  • Removed getLastUpdateData().

  • Removed the now obsolete AnalyticKF filter.

  • The implementation of a sample-based Kalman filter now handles the special case of equally weighted samples efficiently. That is, there is no need for a specialized implementation, e.g., for the S2KF or the UKF with equally weighted samples. This also holds for the new sample-based recursive update filters.

  • • Renamed setNumIterations() to setNumSamplesFactors.

    • Renamed getNumIterations() to getNumSamplesFactors.

  • • The used LCD-based Gaussian sampling technique can now be configured completely independently for state prediction and measurement update.

    • Added getNumSamplesConfigPrediction().

    • Added getNumSamplesConfigUpdate().

    • Renamed setNumSamplesByFactor() to setNumSamplesByFactors().

    • Removed online computation mode including the setOnlineMode() method.

    • Removed asymmetric LCD-based sampling mode including the setSymmetricMode() method.

  • • Renamed setSampleScaling() to setSampleScalings().

    • Renamed getSampleScaling() to getSampleScalings().

• • Changed default name of SIRPF from 'SIR-PF' to 'SIRPF'.

  • Changed default name of ASIRPF from 'Auxiliary SIR-PF' to 'ASIRPF'.

  • Merged the PF interface into the SIRPF.

• • The used LCD-based Gaussian sampling technique can now be configured completely independently for state prediction and measurement update.

  • Added getNumSamplesConfigPrediction().

  • Added getNumSamplesConfigUpdate().

  • Renamed setNumSamplesByFactor() to setNumSamplesByFactors().

  • Renamed getLastUpdateData() to getNumProgSteps().

• All probability distributions now have a set() method. That is, a distribution can be modified after its creation.

• All default constructors now return an uninitialized distribution.

• Renamed getDimension() to getDim(). Hands of fate mac os.

• Renamed getMeanAndCovariance() to getMeanAndCov().

• Removed the JointDistribution.

• • Added check for invalid covariance matrix when trying to compute its square root in getMeanAndCov().

• The getStdNormalSamples() and getSamples() methods of all Gaussian sampling techniques now return a single scalar weight in case of equally weighted samples in order to efficiently handle the computation of sample-based moments, e.g., means or covariance matrices in Kalman filtering.

• • Now uses a default number of 1,000 particles instead of only 100.

• • Added getSymmetricMode().

  • Added getNumSamplesConfig().

  • Removed online computation mode including the setOnlineMode() method.

• • Renamed getPointEstimates() to getStatesMeanAndCov().

  • In addition to getPointEstimates(), getStatesMeanAndCov() now also returns the square roots of the system state covariance matrices (the lower Cholesky decomposition) analogous to the Filter's getStateMeanAndCov() method.

  • Added setStatesMeanAndCov() method for fastly setting the system state of all filters without creating a temporary Gaussian distribution, e.g., in order to assign the Gaussian state estimate of a filter to another one. Note that this method does not perform input validation like setStates()!

• • Utils.getMeanAndCov() now handles the case of a single scalar weight in case of equally weighted samples (in order to smoothly work with the changed weights of the Gaussian sampling techniques).

  • Utils.kalmanUpdate() now additionally returns the squared Mahalanobis distance of the measurement vector.

  • Added Utils.getGMMeanAndCov() for computing mean and covariance matrix of a Gaussian mixture distribution.

  • Added Utils.getGaussianKLD() for computing the Gaussian Kullback-Leibler divergence (KLD).

  • Added Utils.getGaussianL2Distance() for computing the Gaussian L2 distance.

  • Removed Utils.baseBlockDiag().

  • Removed Utils.getMeanCovAndCrossCov().

  • Removed Utils.getStateSamples().

  • Removed Utils.getStateNoiseSamples().

• Updated Eigen linear algebra library to version 3.3.4.

Invasion Of The Squares From The Fifth Dimension Mac Os X

• Added LinearGaussianFilter.

• Added FirstOrderTaylorLinearGaussianFilter.

• Added SecondOrderTaylorLinearGaussianFilter.

• Added SampleBasedLinearGaussianFilter.

• Added CubatureLinearGaussianFilter.

• Added GaussHermiteLinearGaussianFilter.

• Added RandomizedUnscentedLinearGaussianFilter.

• Added SmartSamplingLinearGaussianFilter.

• Added UnscentedLinearGaussianFilter.

• Added IterativeKalmanFilter.

• Bang bang bang! (itch) mac os. Added RecursiveUpdateFilter.

• Added SampleBasedIterativeKalmanFilter.

• Added SampleBasedRecursiveUpdateFilter.

• Renamed BasePF to ParticleFilter.

• Removed AnalyticMeasurementModel.

• Removed AnalyticSystemModel.

• Removed FOTaylorBasedJointlyGaussianPrediction.

• Removed SOTaylorBasedJointlyGaussianPrediction.

• Removed SampleBasedJointlyGaussianPrediction.

• Removed KF.

• Removed LRKF.

• Free online 3d sketch pad. Updated all 'getting started' examples to the new API, changed the used nonlinear system model, and introduced more detailed evaluation plots in the 'complete estimation example'.

• Added examples for all probability distributions.

• Added examples for all measurement models.

• Added examples for all system models.

• Added unit tests for all Gaussian sampling techniques.

• Added unit tests for nearly all existing filters.

• Added unit tests for all new recursive update filter variants.

• Added unit tests for all probability distributions.

• The general structure for filter unit tests was overhauled.

Apple's latest updates to both iOS and OS X have largely banished skeuomorphism—design elements that imitate real-world counterparts. (The leather textures in Mountain Lion's Calendar, Contacts, and Notes applications are the most familiar examples.) Much like iOS 7, OS X Mavericks strips out those gaudier elements of Apple's past designs and flattens faux-3D textures. Here are some of the visual changes you will likely notice in the new Mac OS.

Leather be gone

According to legend, Steve Jobs so admired the leather texture of the seats in his private jet that he demanded Apple's designers incorporate such a texture into the Calendar, Contacts, and Notes applications (complete with stitching). As much as we love Jobs's vision for most things, though, his obsession with rich Corinthian leather is one we're happy to see fade away in OS X Mavericks.

Not only has the leather border disappeared from each of the above-mentioned programs, but you'll also no longer find faux binding stitches holding your address book together in Contacts. Without all the skeuomorphic elements, the application now has room for a title bar, which displays the number of contacts within the selected group. And Notes loses its torn-paper border at the top of each note as well as the small hieroglyphics-like trash icon at the bottom of each note—because presumably we all understand the function of the Mac's Delete key. https://hereffile705.weebly.com/toy-car-simulator-mac-os.html.

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Clean linen

Though the Corinthian leather was perhaps the most prominent texture in Lion and Mountain Lion, another was lurking about: dark linen.

That subtle pattern of white and gray threads appeared in the background of Notification Center, Mission Control, and even OS X's Accounts window. But no more: It too has been given the heave-ho. Where once you saw linen, now you have dark gray.

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Not content to strip out merely the leather and linen, Apple's designers also went after some less noticeable textures. If you compare Mavericks's DVD Player to the one found in Mountain Lion, for instance, you'll find that controls such as Video Zoom, Video Color, and Audio Equalizer are less transparent than their Mountain Lion counterparts. The Dashboard background was once littered with Lego-like dots; it's now a smooth gray grid. And certain icons in System Preferences are flatter, losing their metal texture of old.

Not dead yet

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While Apple has made some substantial moves away from skeuomorphic design in Mavericks, it hasn't banished the look entirely. Visit the Applications folder, and you'll still find application icons that parrot their purpose: A tabbed address book still represents Contacts, Image Capture sports a point-and-shoot camera, Reminders still resembles a checklist, and TextEdit hasn't lost its pen or its inspirational (and marketable) words from John Appleseed.

More-blatant examples also remain. Launch Game Center, and—whoa!—the polished wood and green felt textures are still prominently on view. And if you're interested in how our ancestors in the early 2000s rendered wood, grass, marble, metal, and fur (complete with reflections, in most cases), you need only launch the venerable Chess application.