The resonant frequencies of Euler-Bernoulli beams whose resonant behavior is determined by the flexural stiffness are proportional to ��n2. For a double-clamped beam, the ��n values for n = 1, 2, 3, n > 3 are 4.7300, 7.8532, 10.9956, (2n + 1)��/2, respectively. In contrast, the resonant frequency of a string is defined by the tensile pre-stress and the resonant frequencies of higher bending modes are a multiple of the first mode [11]. The string nature of the double-clamped silicon nitride micro beams has been ve
Fingerprint recognition is the most widespread biometric authentication technology used in personal identification systems. The application of fingerprint recognition has been expanding owing to its uniqueness and security.
Most available systems for fingerprint recognition use matching based on minutiae or local features of the fingerprint images [1]. It is well-known that these systems are very sensitive to noise or to quality degradation since the algorithms�� performance in terms of feature extraction and minutiae extraction generally relies on the quality of fingerprint images.Bad-quality images mostly result in spurious and missing features that then degrade the performance of such systems. For many application systems, it is preferable to eliminate low-quality images and to replace them with acceptable higher-quality images to achieve better performance, rather than to attempt to enhance the first inputs.
There have been studies on developing appropriate measures to assess the quality of fingerprint images for two types of fingerprint capture sensors �C optical Drug_discovery and capacitive sensors [2].
Representative quality assessment measures for images from various types of capture sensors have been known to vary due to the physical differences between the sensors. The optical sensor Dacomitinib and capacitive sensor are easily affected by an unclean surface, and the residue fingerprint images can easily affect fingerprint quality. On the other hand, the thermal sensor is easily affected by the temperature and the coarse fingerprint image. Alonso-Fernandez et al. [2] investigated the relationship between sensor types and quality measures.
They reveal that an excellent measure for optical sensor images could be the worst for those of capacitive sensors. Therefore, adaptive measures are required for images captured by various types of sensors, and this may be a major disadvantage in designing a general high-performance fingerprint recognition system.In this paper, we develop an effective quality estimation system that can be used as a general quality estimation system for images from various types of sensors.