A Novel MEMS Based Piezoresistive Vector Hydrophone for Low Frequency Detection

Chen, Shang; Chen, Xue; Zhang, Binzhen; Xie, Bin; Qiao, Hui

doi:10.1109/icma.2007.4303830

Cited by 11 publications

(12 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The vector hydrophone measures the direction of sound vibration and sound intensity, so it presents advantages in the detection of underwater acoustic targets emitting low and ultra-low frequency signals [23] such as fish sounds. These technologies are relatively inexpensive and possess remote monitoring capabilities for long-term data acquisition.…”

Section: Introductionmentioning

confidence: 99%

Automatic Taxonomic Classification of Fish Based on Their Acoustic Signals

2016

View full text Add to dashboard Cite

Fish as well as birds, mammals, insects and other animals are capable of emitting sounds for diverse purposes, which can be recorded through microphone sensors. Although fish vocalizations have been known for a long time, they have been poorly studied and applied in their taxonomic classification. This work presents a novel approach for automatic remote acoustic identification of fish through their acoustic signals by applying pattern recognition techniques. The sound signals are preprocessed and automatically segmented to extract each call from the background noise. Then, the calls are parameterized using Linear and Mel Frequency Cepstral Coefficients (LFCC and MFCC), Shannon Entropy (SE) and Syllable Length (SL), yielding useful information for the classification phase. In our experiments, 102 different fish species have been successfully identified with three widely used machine learning algorithms: K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Machine (SVM). Experimental results show an average classification accuracy of 95.24%, 93.56% and 95.58%, respectively.

show abstract

Section: Introductionmentioning

confidence: 99%

Automatic Taxonomic Classification of Fish Based on Their Acoustic Signals

2016

View full text Add to dashboard Cite

show abstract

“…A Wheatstone bridge-like circuitry is used to measure resistance change and deduce acceleration. For more than three decades, piezoresistive accelerometers (e.g., [49,52,53,[55][56][57][58][59][60][61][62][63][64]) have shown progressive improvements making them a viable option for various applications including microgravity and low frequency applications [59,63].…”

Section: Piezoresistivementioning

confidence: 99%

“…Piezoresistive accelerometers have been designed to work in ranges as small as 1 g [55] or as large as 250 g [61]. In average, they work within 10-50 g [49,52,53,[56][57][58][59][60][62][63][64]. This operation range is two orders of magnitude larger than desired operation range (4 ng-80 mg).…”

Section: Piezoresistivementioning

confidence: 99%

“…(ii) Bandwidth. Piezoresistive accelerometer designs [53,55,[58][59][60][61][62][63] statistically tend to have a median bandwidth of 1 KHz. The upper measurement limit typically varies between 100 Hz and 2 KHz, whereas the lower limit is conventionally nonzero (5-100 Hz) [46,55,59,[63][64][65], except for the case of employing nanowires [66] that are able to respond to 0 Hz acceleration (static acceleration).…”

Section: Piezoresistivementioning

confidence: 99%

See 1 more Smart Citation

Accelerometer Sensor Specifications to Predict Hydrocarbon Using Passive Seismic Technique

2016

View full text Add to dashboard Cite

The ambient seismic ground noise has been investigated in several surveys worldwide in the last 10 years to verify the correlation between observed seismic energy anomalies at the surface and the presence of hydrocarbon reserves beneath. This is due to the premise that anomalies provide information about the geology and potential presence of hydrocarbon. However a technology gap manifested in nonoptimal detection of seismic signals of interest is observed. This is due to the fact that available sensors are not designed on the basis of passive seismic signal attributes and mainly in terms of amplitude and bandwidth. This is because of that fact that passive seismic acquisition requires greater instrumentation sensitivity, noise immunity, and bandwidth, with active seismic acquisition, where vibratory or impulsive sources were utilized to receive reflections through geophones. Therefore, in the case of passive seismic acquisition, it is necessary to select the best monitoring equipment for its success or failure. Hence, concerning sensors performance, this paper highlights the technological gap and motivates developing dedicated sensors for optimal solution at lower frequencies. Thus, the improved passive seismic recording helps in oil and gas industry to perform better fracture mapping and identify more appropriate stratigraphy at low frequencies.

show abstract

“…Large scale array is necessary for locating the target when the underwater target acoustic source is at low frequency because the array size must compare with the acoustic wavelength [1]. So the vector hydrophone (VH) is attracting more and more interest due to its small package and small-scale array [2].…”

Section: Introductionmentioning

confidence: 99%

Ultra thin fiber laser vector hydrophone

Zhang

et al. 2011

SPIE Proceedings

View full text Add to dashboard Cite

This paper presents a two-axis fiber laser vector hydrophone which uses a V-shaped flexed beam to enhance the sensitivity and reduce the dimensions. Theoretical analyses of the sensitivity and frequent response are given. The key parameters that determine the sensitivity and resonant frequency are discussed. The experimental results show an acceleration sensitivity of 39.2 pm/g and 53.2 pm/g at the x, y axis respectively, a resonant frequency of about 310 Hz, and a directivity resolution larger than 20 dB.

show abstract

A Novel MEMS Based Piezoresistive Vector Hydrophone for Low Frequency Detection

Cited by 11 publications

References 9 publications

Automatic Taxonomic Classification of Fish Based on Their Acoustic Signals

Automatic Taxonomic Classification of Fish Based on Their Acoustic Signals

Accelerometer Sensor Specifications to Predict Hydrocarbon Using Passive Seismic Technique

Ultra thin fiber laser vector hydrophone

Contact Info

Product

Resources

About