An Application-Driven Procedure for Optimal Signal Digitization of Automotive-Grade Ultrasonic Sensors
In this work, a methodology is presented for identifying the optimal digitization parameters for the analog signal of ultrasonic sensors. These digitization parameters are the resolution of the analog to digital conversion and the sampling rate. This is accomplished though the derivation of characteristic curves based on Fano inequality and the calculation of the mutual information content over a given dataset. The mutual information is calculated between the examples in the dataset and the corresponding variation in the feature that needs to be estimated. The optimal parameters are identified in a manner that ensures optimal estimation performance while preventing inefficiency in using unnecessarily powerful analog to digital converters.
[1] Fano, Robert M., and David Hawkins. ”Transmission of information: A
statistical theory of communications.” American Journal of Physics 29
(1961): 793-794.
[2] Fisher, John W., Michael Siracusa, and Kinh Tieu. ”Estimation of
signal information content for classification.” 2009 IEEE 13th Digital
Signal Processing Workshop and 5th IEEE Signal Processing Education
Workshop. IEEE, 2009.
[3] Kraskov, Alexander, Harald St¨ogbauer, and Peter Grassberger.
”Estimating mutual information.” Physical review E 69, no. 6 (2004):
066138.
[4] Moon, Young-I, Balaji Rajagopalan, and Upmanu Lall. ”Estimation of
mutual information using kernel density estimators.” Physical Review E
52, no. 3 (1995): 2318.
[5] Battiti, Roberto. ”Using mutual information for selecting features in
supervised neural net learning.” IEEE Transactions on neural networks
5, no. 4 (1994): 537-550.
[6] Peng, Hanchuan, Fuhui Long, and Chris Ding. ”Feature selection based
on mutual information criteria of max-dependency, max-relevance, and
min-redundancy.” IEEE Transactions on pattern analysis and machine
intelligence 27, no. 8 (2005): 1226-1238.
[7] Fleuret, Franc¸ois. ”Fast binary feature selection with conditional mutual
information.” Journal of Machine learning research 5, no. Nov (2004):
1531-1555.
[8] Liu, Huawen, Jigui Sun, Lei Liu, and Huijie Zhang. ”Feature selection
with dynamic mutual information.” Pattern Recognition 42, no. 7 (2009):
1330-1339.
[9] Vergara, Jorge R., and Pablo A. Est´evez. ”A review of feature
selection methods based on mutual information.” Neural computing and
applications 24, no. 1 (2014): 175-186.
[10] Horng, Ming-Huwi. ”Multi-class support vector machine for
classification of the ultrasonic images of supraspinatus.” Expert
Systems with Applications 36, no. 4 (2009): 8124-8133.
[11] Pereira, Wagner Coelho A., Andr´e V. Alvarenga, Antonio Fernando
C. Infantosi, Leonardo Macrini, and Carlos E. Pedreira. ”A non-linear
morphometric feature selection approach for breast tumor contour from
ultrasonic images.” Computers in Biology and Medicine 40, no. 11-12
(2010): 912-918.
[12] Shekhar, Raj, and Vladimir Zagrodsky. ”Mutual information-based rigid
and nonrigid registration of ultrasound volumes.” IEEE transactions on
medical imaging 21, no. 1 (2002): 9-22.
[13] Prelov, Vyacheslav V., and Edward C. van der Meulen. ”Mutual
information, variation, and Fano’s inequality.” Problems of Information
Transmission 44, no. 3 (2008): 185-197.
[14] Yang, Sheng, and Jun Gu. ”Feature selection based on mutual
information and redundancy-synergy coefficient.” Journal of Zhejiang
University-Science A 5, no. 11 (2004): 1382-1391.
[15] Cooper, Matthew, and Michael Miller. ”Information gain in object
recognition via sensor fusion.” In Proceedings of the International
Conference on Multisource-Multisensor Information Fusion (Fusion’98),
vol. 1, pp. 143-148. 1998.
[16] Poepperli, Maximilian, Raghavendra Gulagundi, Senthil Yogamani,
and Stefan Milz. ”Capsule neural network based height classification
using low-cost automotive ultrasonic sensors.” In 2019 IEEE Intelligent
Vehicles Symposium (IV), pp. 661-666. IEEE, 2019.
[17] Mohamed, Mohamed-Elamir, Heinrich Gotzig, Raoul Zoellner, and
Patrick Maeder. ”A Machine Learning Approach for Detecting Ultrasonic
Echoes in Noisy Environments.” In 2019 IEEE 89th Vehicular Technology
Conference (VTC2019-Spring), pp. 1-6. IEEE, 2019.
[18] Mohamed, Mohamed-Elamir, Heinrich Gotzig, Raoul Zoellner, and
Patrick Maeder. ”A Machine Learning Approach for Ultrasonic Noise
Suppression With Minimal Distortion” In 2019 IEEE International
Ultrasonic Symposium (IUS2019), IEEE, 2019.
[1] Fano, Robert M., and David Hawkins. ”Transmission of information: A
statistical theory of communications.” American Journal of Physics 29
(1961): 793-794.
[2] Fisher, John W., Michael Siracusa, and Kinh Tieu. ”Estimation of
signal information content for classification.” 2009 IEEE 13th Digital
Signal Processing Workshop and 5th IEEE Signal Processing Education
Workshop. IEEE, 2009.
[3] Kraskov, Alexander, Harald St¨ogbauer, and Peter Grassberger.
”Estimating mutual information.” Physical review E 69, no. 6 (2004):
066138.
[4] Moon, Young-I, Balaji Rajagopalan, and Upmanu Lall. ”Estimation of
mutual information using kernel density estimators.” Physical Review E
52, no. 3 (1995): 2318.
[5] Battiti, Roberto. ”Using mutual information for selecting features in
supervised neural net learning.” IEEE Transactions on neural networks
5, no. 4 (1994): 537-550.
[6] Peng, Hanchuan, Fuhui Long, and Chris Ding. ”Feature selection based
on mutual information criteria of max-dependency, max-relevance, and
min-redundancy.” IEEE Transactions on pattern analysis and machine
intelligence 27, no. 8 (2005): 1226-1238.
[7] Fleuret, Franc¸ois. ”Fast binary feature selection with conditional mutual
information.” Journal of Machine learning research 5, no. Nov (2004):
1531-1555.
[8] Liu, Huawen, Jigui Sun, Lei Liu, and Huijie Zhang. ”Feature selection
with dynamic mutual information.” Pattern Recognition 42, no. 7 (2009):
1330-1339.
[9] Vergara, Jorge R., and Pablo A. Est´evez. ”A review of feature
selection methods based on mutual information.” Neural computing and
applications 24, no. 1 (2014): 175-186.
[10] Horng, Ming-Huwi. ”Multi-class support vector machine for
classification of the ultrasonic images of supraspinatus.” Expert
Systems with Applications 36, no. 4 (2009): 8124-8133.
[11] Pereira, Wagner Coelho A., Andr´e V. Alvarenga, Antonio Fernando
C. Infantosi, Leonardo Macrini, and Carlos E. Pedreira. ”A non-linear
morphometric feature selection approach for breast tumor contour from
ultrasonic images.” Computers in Biology and Medicine 40, no. 11-12
(2010): 912-918.
[12] Shekhar, Raj, and Vladimir Zagrodsky. ”Mutual information-based rigid
and nonrigid registration of ultrasound volumes.” IEEE transactions on
medical imaging 21, no. 1 (2002): 9-22.
[13] Prelov, Vyacheslav V., and Edward C. van der Meulen. ”Mutual
information, variation, and Fano’s inequality.” Problems of Information
Transmission 44, no. 3 (2008): 185-197.
[14] Yang, Sheng, and Jun Gu. ”Feature selection based on mutual
information and redundancy-synergy coefficient.” Journal of Zhejiang
University-Science A 5, no. 11 (2004): 1382-1391.
[15] Cooper, Matthew, and Michael Miller. ”Information gain in object
recognition via sensor fusion.” In Proceedings of the International
Conference on Multisource-Multisensor Information Fusion (Fusion’98),
vol. 1, pp. 143-148. 1998.
[16] Poepperli, Maximilian, Raghavendra Gulagundi, Senthil Yogamani,
and Stefan Milz. ”Capsule neural network based height classification
using low-cost automotive ultrasonic sensors.” In 2019 IEEE Intelligent
Vehicles Symposium (IV), pp. 661-666. IEEE, 2019.
[17] Mohamed, Mohamed-Elamir, Heinrich Gotzig, Raoul Zoellner, and
Patrick Maeder. ”A Machine Learning Approach for Detecting Ultrasonic
Echoes in Noisy Environments.” In 2019 IEEE 89th Vehicular Technology
Conference (VTC2019-Spring), pp. 1-6. IEEE, 2019.
[18] Mohamed, Mohamed-Elamir, Heinrich Gotzig, Raoul Zoellner, and
Patrick Maeder. ”A Machine Learning Approach for Ultrasonic Noise
Suppression With Minimal Distortion” In 2019 IEEE International
Ultrasonic Symposium (IUS2019), IEEE, 2019.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:80920", author = "Mohamed Shawki Elamir and Heinrich Gotzig and Raoul Zoellner and Patrick Maeder", title = "An Application-Driven Procedure for Optimal Signal Digitization of Automotive-Grade Ultrasonic Sensors", abstract = "In this work, a methodology is presented for identifying the optimal digitization parameters for the analog signal of ultrasonic sensors. These digitization parameters are the resolution of the analog to digital conversion and the sampling rate. This is accomplished though the derivation of characteristic curves based on Fano inequality and the calculation of the mutual information content over a given dataset. The mutual information is calculated between the examples in the dataset and the corresponding variation in the feature that needs to be estimated. The optimal parameters are identified in a manner that ensures optimal estimation performance while preventing inefficiency in using unnecessarily powerful analog to digital converters.", keywords = "Analog to digital conversion, digitization, sampling
rate, ultrasonic sensors.", volume = "16", number = "8", pages = "202-4", }
