Data Structures and Algorithms

1606 Submissions

[6] viXra:1606.0348 [pdf] replaced on 2016-07-06 16:57:41

Kalman Folding 2: Tracking and System Dynamics

Authors: Brian Beckman
Comments: 7 Pages.

In Kalman Folding, Part 1, we present basic, static Kalman filtering as a functional fold, highlighting the unique advantages of this form for deploying test-hardened code verbatim in harsh, mission-critical environments. The examples in that paper are all static, meaning that the states of the model do not depend on the independent variable, often physical time. Here, we present a dynamic Kalman filter in the same, functional form. This filter can handle many dynamic, time-evolving applications including some tracking and navigation problems, and is easilly extended to nonlinear and non-Gaussian forms, the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) respectively. Those are subjects of other papers in this Kalman-folding series. Here, we reproduce a tracking example from a well known reference, but in functional form, highlighting the advantages of that form.
Category: Data Structures and Algorithms

[5] viXra:1606.0328 [pdf] submitted on 2016-06-29 14:21:33

Kalman Folding, Part 1 (Review Draft)

Authors: Brian Beckman
Comments: 19 Pages.

Kalman filtering is commonplace in engineering, but less familiar to software developers. It is the central tool for estimating states of a model, one observation at a time. It runs fast in constant memory. It is the mainstay of tracking and navigation, but it is equally applicable to econometrics, recommendations, control: any application where we update models over time. By writing a Kalman filter as a functional fold, we can test code in friendly environments and then deploy identical code with confidence in unfriendly environments. In friendly environments, data are deterministic, static, and present in memory. In unfriendly, real-world environments, data are unpredictable, dynamic, and arrive asynchronously. The flexibility to deploy exactly the code that was tested is especially important for numerical code like filters. Detecting, diagnosing and correcting numerical issues without repeatable data sequences is impractical. Once code is hardened, it can be critical to deploy exactly the same code, to the binary level, in production, because of numerical brittleness. Functional form makes it easy to test and deploy exactly the same code because it minimizes the coupling between code and environment.
Category: Data Structures and Algorithms

[4] viXra:1606.0182 [pdf] submitted on 2016-06-17 22:40:41

Universal Natural Memory Embedding -3 (DSAA)

Authors: Ramesh Chandra Bagadi
Comments: 18 Pages.

In this research investigation, the author has presented a theory of ‘Universal Relative Metric That Generates A Field Super-Set To The Fields Generated By Various Distinct Relative Metrics’.
Category: Data Structures and Algorithms

[3] viXra:1606.0157 [pdf] submitted on 2016-06-15 07:29:20

Universal Natural Memory Embedding - 2

Authors: Ramesh Chandra Bagadi
Comments: 14 Pages.

In this research investigation, the author has presented a theory of ‘The Universal Irreducible Any Field Generating Metric’.
Category: Data Structures and Algorithms

[2] viXra:1606.0156 [pdf] submitted on 2016-06-15 07:30:06

Universal Natural Memory Embedding - Two

Authors: Ramesh Chandra Bagadi
Comments: 14 Pages.

In this research investigation, the author has presented a theory of ‘The Universal Irreducible Any Field Generating Metric’.
Category: Data Structures and Algorithms

[1] viXra:1606.0147 [pdf] submitted on 2016-06-15 00:16:12

Universal Natural Memory Embedding

Authors: Ramesh Chandra Bagadi
Comments: 14 Pages.

In this research investigation, the author has presented a theory of ‘Universal Natural Memory Embedding’.
Category: Data Structures and Algorithms