Application of the Complex Moments for Selection of an Optimal Sensor
In the first time we apply the statistics of the complex moments for selection of an optimal pressure sensor (from the available set of sensors) based on their statistical/correlation characteristics. The complex moments contain additional source of information and, therefore, they can realize the c...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/24/8242 |
| _version_ | 1797500842065002496 |
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| author | Raoul R. Nigmatullin Vadim S. Alexandrov |
| author_facet | Raoul R. Nigmatullin Vadim S. Alexandrov |
| author_sort | Raoul R. Nigmatullin |
| collection | DOAJ |
| description | In the first time we apply the statistics of the complex moments for selection of an optimal pressure sensor (from the available set of sensors) based on their statistical/correlation characteristics. The complex moments contain additional source of information and, therefore, they can realize the comparison of random sequences registered for almost identical devices or gadgets. The proposed general algorithm allows to calculate 12 key correlation parameters in the significance space. These correlation parameters allow to realize the desired comparison. New algorithm is rather general and can be applied for a set of other data if they are presented in the form of rectangle matrices. Each matrix contains <i>N</i> data points and <i>M</i> columns that are connected with repetitious cycle of measurements. In addition, we want to underline that the value of correlations evaluated with the help of Pearson correlation coefficient (PCC) has a <i>relative</i> character. One can introduce also <i>external</i> correlations based on the statistics of the fractional/complex moments that form a complete picture of correlations. To the PCC value of internal correlations one can add at least 7 additional external correlators evaluated in the space of fractional and complex moments in order to realize the justified choice. We do suppose that the proposed algorithm (containing an additional source of information in the complex space) can find a wide application in treatment of different data, where it is necessary to select the “best sensors/chips” based on their measured data, presented usually in the form of random rectangle matrices. |
| first_indexed | 2024-03-10T03:09:40Z |
| format | Article |
| id | doaj.art-656a7ca4768a4a9594c83f70b9013cc3 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T03:09:40Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-656a7ca4768a4a9594c83f70b9013cc32023-11-23T10:28:40ZengMDPI AGSensors1424-82202021-12-012124824210.3390/s21248242Application of the Complex Moments for Selection of an Optimal SensorRaoul R. Nigmatullin0Vadim S. Alexandrov1Radioelectronics and Informative-Measurement Technics Department, Kazan National Research Technical University Named after by A.N., Tupolev (KNRTU-KAI), K. Marx Str., 10, 420111 Kazan, Tatarstan, RussiaRadioelectronics and Informative-Measurement Technics Department, Kazan National Research Technical University Named after by A.N., Tupolev (KNRTU-KAI), K. Marx Str., 10, 420111 Kazan, Tatarstan, RussiaIn the first time we apply the statistics of the complex moments for selection of an optimal pressure sensor (from the available set of sensors) based on their statistical/correlation characteristics. The complex moments contain additional source of information and, therefore, they can realize the comparison of random sequences registered for almost identical devices or gadgets. The proposed general algorithm allows to calculate 12 key correlation parameters in the significance space. These correlation parameters allow to realize the desired comparison. New algorithm is rather general and can be applied for a set of other data if they are presented in the form of rectangle matrices. Each matrix contains <i>N</i> data points and <i>M</i> columns that are connected with repetitious cycle of measurements. In addition, we want to underline that the value of correlations evaluated with the help of Pearson correlation coefficient (PCC) has a <i>relative</i> character. One can introduce also <i>external</i> correlations based on the statistics of the fractional/complex moments that form a complete picture of correlations. To the PCC value of internal correlations one can add at least 7 additional external correlators evaluated in the space of fractional and complex moments in order to realize the justified choice. We do suppose that the proposed algorithm (containing an additional source of information in the complex space) can find a wide application in treatment of different data, where it is necessary to select the “best sensors/chips” based on their measured data, presented usually in the form of random rectangle matrices.https://www.mdpi.com/1424-8220/21/24/8242complex momentspressure sensorsmultiple correlations |
| spellingShingle | Raoul R. Nigmatullin Vadim S. Alexandrov Application of the Complex Moments for Selection of an Optimal Sensor Sensors complex moments pressure sensors multiple correlations |
| title | Application of the Complex Moments for Selection of an Optimal Sensor |
| title_full | Application of the Complex Moments for Selection of an Optimal Sensor |
| title_fullStr | Application of the Complex Moments for Selection of an Optimal Sensor |
| title_full_unstemmed | Application of the Complex Moments for Selection of an Optimal Sensor |
| title_short | Application of the Complex Moments for Selection of an Optimal Sensor |
| title_sort | application of the complex moments for selection of an optimal sensor |
| topic | complex moments pressure sensors multiple correlations |
| url | https://www.mdpi.com/1424-8220/21/24/8242 |
| work_keys_str_mv | AT raoulrnigmatullin applicationofthecomplexmomentsforselectionofanoptimalsensor AT vadimsalexandrov applicationofthecomplexmomentsforselectionofanoptimalsensor |