Novel algorithm for tilt measurements using MEMS accelerometers

Abstract

A novel algorithm for determining pitch and roll under static or quasi-static conditions is presented. It is based on application of a triaxial micromachined accelerometer for the necessary measurements. In order to obtain a possibly high accuracy, a simple data fusion is applied. Depending on the nominal value of pitch/roll, appropriate mathematical formula is selected. Additionally, the algorithm makes it possible to check if the operating conditions are dynamic, as indications of accelerometers are then not reliable. Implementation of the algorithm makes it possible to determine pitch and roll with accuracy of ca. 0.2° over 360° or better.

Implementation of explainable artificial intelligence in commercial communication systems using micro systems

“…From Figure 5, it is detected that percentage of interpretability for proposed method is raised to 68% whereas the existing method 7,13,14 is at risk at initial phase of implementation whereas after certain data movements it is maximized to 51%. Even though low interpretability is achieved in existing method the difference in percentage as compared with proposed method is much higher and for large data set the intended distance will again enlarge.…”

“…Equation ( 6) denotes the quality with respect to resonant frequencies and it is possible to provide extension bandwidth using a step up noise parameter. The maximum step characteristics of MEMS can be determined using Equation (7) as follows, (7) where e i denotes the change in distance measurements.…”

“…From Figure 2 and Table 2, it can be observed that the respite time is varied from 10 to 200 and corresponding critical points are measured and compared with existing methods. 7,13,14 The major objective in this scenario is to reduce the number of critical points but MEMS is conniving all measured values from several directions. Thus the average value from three axes is considered as measurement point to have at least constant critical nodes.…”

“…From Figure 4, it is evident that the projected design on XAI provides low impairment percentage of 6 whereas the existing method has lost many data within a small information gain platform on an average of 44%. The most important reason for failure cases in existing method 7,13,14 is that XAI is not integrated with MEMS as only standard sensors which perceives information and passes it to the receiver is utilized. This type of process can provide quick results on evaluated parametric values but again the process will fail at insignificant gain regions.…”

“…Figure 5 is plotted based on the three axis extraction and classification mechanism where percentage of transparency is much higher for proposed method in case of high resolution images. But due to low interpretability the existing method 7,13,14 fails to provide a transparent operation as the depth of images are not clear. In all BFS application the extraction process should be transparent to the users thus high safety transaction can be guaranteed.…”