TY  - JOUR
AB  - Optical detection of sound, using opto-mechanical micromachined ultrasound sensors (OMUS), is a promising detection technology for optoacoustic (OptA) imaging because it achieves a small active detection area, in the few tens of micrometers size, without loss of sensitivity as a function of area size. It also has potential to be produced as array configurations at low cost. However, while OMUS sensitivity has been reported in terms of noise equivalent pressure density (NEPD), there has been no comparison to conventional piezoelectric transducers under identical conditions. We differentially compared a highly sensitive ring-resonator-based OMUS and a single element focused piezoelectric ultrasound transducer (FPUT), under the same experimental conditions. The comparison considered the detectors’ signal-to-noise ratio (SNR), impulse response, axial point-spread-function and their spatial sensitivity. Our results show that OMUS attained lower SNR to FPUT, when operating at the same working distance, but similar performance when placed close to the sample interrogated, for example as it relates to optoacoustic microscopy. Advantageously, OMUS uniquely offers the spatial behavior of a point-like acoustic detector which reduces the sensitivity to ultrasound interference effects occurring on the large detection area of FPUTs. We discuss the implications of the two detection approaches in the design of OptA systems.
AU  - Prebeck, A.
AU  - Keulemans, G.*
AU  - Stahl, U.
AU  - Jans, H.*
AU  - Rottenberg, X.*
AU  - Ntziachristos, V.
C1  - 75486
C2  - 58082
SP  - 34459 - 34467
TI  - Comparison of bulk piezoelectric and opto-mechanical micromachined detectors for optoacoustic and ultrasound sensing.
JO  - IEEE Sens. J.
VL  - 25
IS  - 18
PY  - 2025
SN  - 1530-437X
ER  - 

TY  - JOUR
AB  - The recently proposed hierarchical temporal memory (HTM) paradigm of soft computing is applied to the detection and classification of foreign materials in a conveyor belt carrying tobacco leaves in a cigarette manufacturing industry. The HTM has been exposed to hyperspectral imaging data from 10 types of unwanted materials intermingled with tobacco leaves. The impact of the HTM architecture and the configuration of internal parameters on its classification performance have been explored. Classification results match or surpass those attained with other methods, such as Artificial Neural Networks (ANNs), with the advantage that HTM are able to handle raw spectral data and no preprocessing, spectral compression, or reflectance correction is required. It is also demonstrated that an optimized configuration of the HTM architecture and internal values can be derived from the statistical properties of the hyperspectral data, allowing the extension of the approach to other classification problems.
AU  - Rodriguez-Cobo, L.*
AU  - Garcia-Allende, P.
AU  - Cobo, A.*
AU  - Lopez-Higuera, J.M.*
AU  - Conde, O.M.*
C1  - 8424
C2  - 30166
SP  - 2767-2775
TI  - Raw material classification by means of hyperspectral imaging and hierarchical temporal memories.
JO  - IEEE Sens. J.
VL  - 12
IS  - 9
PB  - IEEE - Inst Electrical Electronics Engineers Inc
PY  - 2012
SN  - 1530-437X
ER  -