Assignment on Context-aware merging of middle-of-life data

Introduction of Context-aware merging of middle of life data

Data analytics requires dedicated tools that are very complex. To processing the big data as compare to effective semantic processing of middle-of-life data(MoLD). There is a problem with big MoLD with unique characteristics and it is efficiently not yet and addressed through the commercialized by methods and tools. Molds need the quasi-real life handling due to their feedback relationships and their nature where the process of operations along with the environment products developed. The fewer efforts which are created to exploit the MoLD as well as knowledge and value creation also based on the type of data. The new information along with the technologies like the sensors are conveyed through a MoLD and the phases of the product are also used and identified ( Eddahab, 2020).

Implementation of MATLAB code

Example 1 of Context-aware merging of middle of life data:

According to the requirement, we take the examples of the 6 axis and the 9 axis fusion algorithm which is also completed the orientation. The sensor fusion, as well as tracking toolbox, involves the different algorithms which are computed the orientation for the inertial measurements along with the magnetic angular rate gravity units. The type of sensors for orientation estimation is three which is commonly used, magnetometer, accelerometers, and the gyroscope. The accelerometer measured the proper acceleration of the gyroscope and the angular velocity. By the local magnetic field, magnetometer measures the various algorithms which are also used to fuse the various combinations of sensors and estimate orientations.

To track the various maneuvering targets which are also used the different tracking filters. SO the below 2nd examples have presented the use of single motion along with various multiple motion models

Results of Context-aware merging of middle of life data

The result of example 1 is shown below; the algorithm is presented here which is properly tuned and the estimation of orientation along with the robust of against environmental noise sources. It is also significant to suppose the various situations where the sensor is used and tuned the filter accordingly.

Conclusion of Context-aware merging of middle of life data

Summing up all discussion it is concluded that sensor data streams in MoLD are very complex for use. So, in this research, we analyze the two examples with the sensor data streams. The Matlab Code implementation is also presented along with the result.