Acoustic Performance Study of Fiber-Optic Acoustic Sensors Based on Fabry–Pérot Etalons with Different Q Factors

The ideal development direction of the fiber-optic acoustic sensor (FOAS) is toward broadband, a high sensitivity and a large dynamic range. In order to further promote the acoustic detection potential of the Fabry–Pérot etalon (FPE)-based FOAS, it is of great significance to study the acoustic performance of the FOAS with the quality (Q) factor of FPE as the research objective. This is because the Q factor represents the storage capability and loss characteristic of the FPE. The three FOASs with different Q factors all achieve a broadband response from 20 Hz to 70 kHz with a flatness of ±2 dB, which is consistent with the theory that the frequency response of the FOAS is not affected by the Q factor. Moreover, the sensitivity of the FOAS is proportional to the Q factor. When the Q factor is 1.04×106, the sensitivity of the FOAS is as high as 526.8 mV/Pa. Meanwhile, the minimum detectable sound pressure of 347.33 μPa/Hz1/2 is achieved. Furthermore, with a Q factor of 0.27×106, the maximum detectable sound pressure and dynamic range are 152.32 dB and 107.2 dB, respectively, which is greatly improved compared with two other FOASs. Separately, the FOASs with different Q factors exhibit an excellent acoustic performance in weak sound detection and high sound pressure detection. Therefore, different acoustic detection requirements can be met by selecting the appropriate Q factor, which further broadens the application range and detection potential of FOASs.

Non-contact ultrasonic inspection by Gas-Coupled Laser Acoustic Detection (GCLAD)

Gas-Coupled Laser Acoustic Detection (GCLAD) is an ultrasonic, non-contact detection technique that has been recently proven to be applicable to the inspection of mechanical components. GCLAD response raises as the intersection length between the probe laser beam and the acoustic wavefront propagating in the air increases; such feature differentiates the GCLAD device from other optical detection instruments, making it a line detection system rather than a point detector. During the inspection of structures mainly extending in two dimensions, the capability to evidence presence of defects in whichever point over a line would enable moving the emitter and the detector along a single direction: this translates in the possibility to decrease the overall required time for interrogation of components compared to point detectors, as well as generating simpler automated monitoring layouts. Based on this assumption, the present study highlights the possibility of employing the GCLAD device as a line inspection tool. To this end, preliminary concepts are provided allowing maximization of the GCLAD response for the non-destructive testing of components which predominantly extend in two dimensions. Afterwards, the GCLAD device is employed in pulse-echo mode for the detection of artificial defects machined on a 12 mm-thick steel plate: the GCLAD probe laser beam is inclined to be perpendicular to the propagation direction of the airborne ultrasound, generated by surface acoustic waves (SAWs) in the solid which are first reflected by the defect flanks and subsequently refracted in the air. Numerical results are provided highlighting the SAW reflection patterns, originated by 3 mm deep surface and subsurface defects, that the GCLAD should interpret. The subsequent experimental campaign highlights that the GCLAD device can identify echoes associated with surface and subsurface defects, located in eight different positions on the plate. B-scan of the component ultimately demonstrates the GCLAD performance in accomplishing the inspection task.

An Optical Acoustic Detection System Based on Fabry Pérot Etalon Stability Structure

The optical acoustic detection system based on the Fabry Pérot Etalon (FPE) with high quality–factor (High Q) and stability structure is described and tested. The FPE contains two high–reflectivity Plano–Concave lenses, achieving high fineness and stability. The protective structure of the confocal stabilized FPE is composed of an invar tube, copper sheath, Bakelite sheath and aluminum housing to protect the sensor from the effects of ambient temperature and vibration. The audio signal is injected into the cavity through the sound hole located in the center of the cavity. Acoustic waves induce the vibration of the medium in the cavity, which leads to a simultaneous change in the FPE optical path and a shift of the interference spectrum. The acoustic detection system is built, and the frequency of the laser is locked on the resonant frequency points of the FPE by using phase modulation technology, so as to detect acoustic signals of different frequencies and amplitudes. In addition, the sensitivity of the proposed sensor exceeds 34.49 mV/Pa in the range of 20 Hz–20 kHz. A Signal-to-Noise Ratio (SNR) of 37 dB can be achieved at 20 Hz. Acoustic signal detection technology based on the FPE stability model is used to test the theoretical feasibility of the future high sensitivity Fabry Pérot Interferometric (FPI) acoustic sensors.

Real-Time Remote Sensing of the Lobesia botrana Moth Using a Wireless Acoustic Detection Sensor

This article presents a wireless sensor for pest detection, specifically the Lobesia botrana moth or vineyard moth. The wireless sensor consists of an acoustic-based detection of the sound generated by a flying Lobesia botrana moth. Once a Lobesia botrana moth is detected, the information about the time, geographical location of the sensor and the number of detection events is sent to a server that gathers the detection statistics in real-time. To detect the Lobesia botrana, its acoustic signal was previously characterized in a controlled environment, obtaining its power spectral density for the acoustic filter design. The sensor is tested in a controlled laboratory environment where the detection of the flying moths is successfully achieved in the presence of all types of environmental noises. Finally, the sensor is installed on a vineyard in a region where the moth has already been detected. The device is able to detect flying Lobesia botrana moths during its flying period, giving results that agree with traditional field traps.

Early Detection of the Wood-boring Insect Semanotus Bifasciatus Using Acoustic Detection Technology

BACKGROUND: Semanotus bifasciatus Motschulsky (Coleoptera: Cerambycidae) is one of the most destructive wood-boring pests of Platycladus trees in East Asia, threatening the protection of ancient cypress species and urban ecological safety. Acoustic detection technology has the advantages of high sensitivity, single wood diagnosis and anti-interference, which can be useful for early identification of cryptic wood boring damage. However, there has been limited research on detection time window and acoustics features that suitable for early detection of forest wood borers. METHODS: In this study, we carried out a manipulated insect infestation experiment by inoculating S. bifasciatus into fresh logs, and the feeding sound signals of S. bifasciatus larvae were recorded in timeseries. Then, nine feature variables were selected to characterize the sounds of larval feeding activity. The best time window for acoustic detection during a single day and the whole larval growth stage was determined. And the optimal models for predicting larval instar and population were established using the stepwise regression (SR) and partial least squares regression (PLSR) approach.RESULTS: (1) The single pulse duration of S. bifasciatus was less than 15 ms, and the peak frequency was approximately 8 kHz; (2) Within a 24-hour day, the feeding sound signals were strongest during 13:00 and 20:00; (3) The feeding activity of larvae was greatest during the 1st to the 3rd instar, declined from the 4th instar, and was lowest at the 5th instar; (4) Weak correlations were found between larval instar and feature variables, r ranging from 0.3 to 0.6. By contrast, the larval population has a strong linear correlation with all variables (r>0.7). Except for Average pulse duration and Peak frequency, there indicated high or severe multicollinearity among other variables (the variance inflation factor, VIF >10); (5) The SR model was optimal for predicting larval instar; its prediction accuracy was R2 = 0.71, RMSEp = 0.42, and RPD = 3.38. Average entropy, Peak frequency, and Average pulse duration had the largest influence on the model. (6) The optimal model for predicting population was the PLSR model, and its prediction accuracy was R2 = 0.97, RMSEp = 61.96, and RPD = 28.87. Except for Peak Freq, the other eight variables had a great impact on the model. CONCLUSION: This study highlighted the suitable detection time window and acoustic feature variables for early identification of S. bifasciatus larvae, and optimal models for predicting its larval instar and population were provided. This work will promote further improvements in the efficiency and accuracy of acoustic detection technology for practical applications, providing a reference for evaluating the early damage of wood-boring pest.

Passive Acoustic Monitoring and Automatic Detection of Diel Patterns and Acoustic Structure of Howler Monkey Roars

Nighttime studies are underrepresented in ecological research. Even well-known behaviors, such as the loud call of howler monkeys, are rarely studied at night. Our goal was to help fill this knowledge gap by studying the 24 h vocal behavior of the Guianan red howler monkey (Alouatta macconnelli) and to compare the acoustic structures of howling bouts made during the day to those made at night. We used passive acoustic monitoring coupled with automatic acoustic detection to study three groups of howlers over three months in the Viruá National Park, Roraima, Brazil. The automatic classifier we built detected 171 howling bouts with a 42% recall rate and 100% precision. Though primarily diurnal, howlers vocalized mainly at night. Greater vocal activity before nautical twilight might be associated with territorial and resource defense behaviors, with howlers calling from roosting sites before starting their daily routines. We also found that during the day, howling bouts were longer and had lower harmonic-to-noise ratios, lower frequencies, and more symmetric energy distributions than bouts at night. Our study adds to growing evidence that passive acoustic monitoring and automatic acoustic detection can be used to study primates and improve our understanding of their vocal behavior.