Multiple mechanized ocean vessels, including both surface ships and submerged vehicles, can be simultaneously monitored over instantaneous continental-shelf scale regions >10,000 km 2 via passive ocean acoustic waveguide remote sensing. A large-aperture densely-sampled coherent hydrophone array system is employed in the Norwegian Sea in Spring 2014 to provide directional sensing in 360 degree horizontal azimuth and to significantly enhance the signal-to-noise ratio (SNR) of ship-radiated underwater sound, which improves ship detection ranges by roughly two orders of magnitude over that of a single hydrophone. Here, 30 mechanized ocean vessels spanning ranges from nearby to over 150 km from the coherent hydrophone array, are detected, localized and classified. The vessels are comprised of 20 identified commercial ships and 10 unidentified vehicles present in 8 h/day of Passive Ocean Acoustic Waveguide Remote Sensing (POAWRS) observation for two days. The underwater sounds from each of these ocean vessels received by the coherent hydrophone array are dominated by narrowband signals that are either constant frequency tonals or have frequencies that waver or oscillate slightly in time. The estimated bearing-time trajectory of a sequence of detections obtained from coherent beamforming are employed to determine the horizontal location of each vessel using the Moving Array Triangulation (MAT) technique. For commercial ships present in the region, the estimated horizontal positions obtained from passive acoustic sensing are verified by Global Positioning System (GPS) measurements of the ship locations found in a historical Automatic Identification System (AIS) database. We provide time-frequency characterizations of the underwater sounds radiated from the commercial ships and the unidentified vessels. The time-frequency features along with the bearing-time trajectory of the detected signals are applied to simultaneously track and distinguish these vessels.
more »
« less
A Pilot Study to Assess the Reliability of Sensing Joint Acoustic Emissions of the Wrist
Joint acoustic emission (JAE) sensing has recently proven to be a viable technique for non-invasive quantification indicating knee joint health. In this work, we adapt the acoustic emission sensing method to measure the JAEs of the wrist—another joint commonly affected by injury and degenerative disease. JAEs of seven healthy volunteers were recorded during wrist flexion-extension and rotation with sensitive uniaxial accelerometers placed at eight locations around the wrist. The acoustic data were bandpass filtered (150 Hz–20 kHz). The signal-to-noise ratio (SNR) was used to quantify the strength of the JAE signals in each recording. Then, nine audio features were extracted, and the intraclass correlation coefficient (ICC) (model 3,k), coefficients of variability (CVs), and Jensen–Shannon (JS) divergence were calculated to evaluate the interrater repeatability of the signals. We found that SNR ranged from 4.1 to 9.8 dB, intrasession and intersession ICC values ranged from 0.629 to 0.886, CVs ranged from 0.099 to 0.241, and JS divergence ranged from 0.18 to 0.20, demonstrating high JAE repeatability and signal strength at three locations. The volunteer sample size is not large enough to represent JAE analysis of a larger population, but this work will lay a foundation for future work in using wrist JAEs to aid in diagnosis and treatment tracking of musculoskeletal pathologies and injury in wearable systems.
more »
« less
- Award ID(s):
- 1749677
- NSF-PAR ID:
- 10209172
- Date Published:
- Journal Name:
- Sensors
- Volume:
- 20
- Issue:
- 15
- ISSN:
- 1424-8220
- Page Range / eLocation ID:
- 4240
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
An eight-element oil-filled hydrophone array is used to measure the acoustic field in littoral waters. This prototype array was deployed during an experiment between Jeffrey’s Ledge and the Stellwagen Bank region off the coast of Rockport, Massachusetts USA. During the experiment, several humpback whale vocalizations, distant ship tonals and high frequency conventional echosounder pings were recorded. Visual confirmation of humpback moving in bearing relative to the array verifies the directional sensing from array beamforming. During deployment, the array is towed at speeds varying from 4-7 kts in water depths of roughly 100 m with conditions at sea state 2 to 3. This array system consists of a portable winch with array, tow cable and 3 water-resistant boxes housing electronics. This system is deployed and operated by 2 crew members onboard a 13 m commercial fishing vessel during the experiment. Non-acoustic sensor (NAS) information is obtained to provide depth, temperature, and heading data using commercial off the shelf (COTS) components utilizing RS485/232 data communications. Acoustic data sampling was performed at 8 kHz, 30 kHz and 100 kHz with near real-time processing of data and enhanced Signal to Noise Ratio (SNR) from beamforming. The electrical system components are deployed with 3 stacked electronics boxes housing power, data acquisition and data processing components in water resistant compartments. A laptop computer with 8 TB of external storage and an independent Global Positioning System (GPS) antenna is used to run Passive Ocean Acoustic Waveguide Remote Sensing (POAWRS) software providing beamformed spectrogram data and live NAS data with capability of capturing several days of data. The acquisition system consists of Surface Mount Device (SMD) pre-amplifiers with filter to an analog differential pair shipboard COTS acquisition system. Pre-amplifiers are constructed using SMD technology where components are pressure tolerant and potting is not necessary. Potting of connectors, electronics and hydrophones via 3D printed molding techniques will be discussed. Array internal components are manufactured with Thermoplastic Polyurethane (TPU) 3D printed material to dampen array vibrations with forward and aft vibration isolation modules (VIM). Polyurethane foam (PUF) used to scatter breathing waves and dampen contact from wires inside the array without attenuating high frequencies and allowing for significant noise reduction. A single Tygon array section with a length of 7.5 m and diameter of 38 mm contains 8 transducer elements with a spacing of 75 cm (1 kHz design frequency). Pre- amplifiers and NAS modules are affixed using Vectran and steel wire rope positioned by swaged stops along the strength member. The tow cable length is 100 m with a diameter of 22 mm that is potted to a hose adapter to break out 12 braided copper wire twisted pair conductors and terminates the tow cable Vectran braid. This array in its current state of development is a low-cost alternative to obtain quality acoustic data from a towed array system. Used here for observation of whale vocalizations, this type of array also has many applications in military sonar and seismic surveying. Maintenance on the array can be performed without the use of special facilities or equipment for dehosing and conveniently uses castor oil as an environmentally safe pressure compensating and coupling fluid. Array development including selection of transducers, NAS modules, acoustic acquisition system, array materials and method of construction with results from several deployments will be discussed. We also present beamformed spectrograms containing humpback whale downsweep moans and underwater blowing (bubbles) sounds associated with feeding on sand lance (Ammodytes dubius).more » « less
-
null (Ed.)Purpose Miniaturization of digital technologies has created new opportunities for remote health care and neuroscientific fieldwork. The current study assesses comparisons between in-home auditory brainstem response (ABR) recordings and recordings obtained in a traditional lab setting. Method Click-evoked and speech-evoked ABRs were recorded in 12 normal-hearing, young adult participants over three test sessions in (a) a shielded sound booth within a research lab, (b) a simulated home environment, and (c) the research lab once more. The same single-family house was used for all home testing. Results Analyses of ABR latencies, a common clinical metric, showed high repeatability between the home and lab environments across both the click-evoked and speech-evoked ABRs. Like ABR latencies, response consistency and signal-to-noise ratio (SNR) were robust both in the lab and in the home and did not show significant differences between locations, although variability between the home and lab was higher than latencies, with two participants influencing this lower repeatability between locations. Response consistency and SNR also patterned together, with a trend for higher SNRs to pair with more consistent responses in both the home and lab environments. Conclusions Our findings demonstrate the feasibility of obtaining high-quality ABR recordings within a simulated home environment that closely approximate those recorded in a more traditional recording environment. This line of work may open doors to greater accessibility to underserved clinical and research populations.more » « less
-
Robotic exoskeletons can assist humans with walking by providing supplemental torque in proportion to the user's joint torque. Electromyographic (EMG) control algorithms can estimate a user's joint torque directly using real-time EMG recordings from the muscles that generate the torque. However, EMG signals change as a result of supplemental torque from an exoskeleton, resulting in unreliable estimates of the user's joint torque during active exoskeleton assistance. Here, we present an EMG control framework for robotic exoskeletons that provides consistent joint torque predictions across varying levels of assistance. Experiments with three healthy human participants showed that using diverse training data (from different levels of assistance) enables robust torque predictions, and that a convolutional neural network (CNN), but not a Kalman filter (KF), can capture the non-linear transformations in EMG due to exoskeleton assistance. With diverse training, the CNN could reliably predict joint torque from EMG during zero, low, medium, and high levels of exoskeleton assistance [root mean squared error (RMSE) below 0.096 N-m/kg]. In contrast, without diverse training, RMSE of the CNN ranged from 0.106 to 0.144 N-m/kg. RMSE of the KF ranged from 0.137 to 0.182 N-m/kg without diverse training, and did not improve with diverse training. When participant time is limited, training data should emphasize the highest levels of assistance first and utilize at least 35 full gait cycles for the CNN. The results presented here constitute an important step toward adaptive and robust human augmentation via robotic exoskeletons. This work also highlights the non-linear reorganization of locomotor output when using assistive exoskeletons; significant reductions in EMG activity were observed for the soleus and gastrocnemius, and a significant increase in EMG activity was observed for the erector spinae. Control algorithms that can accommodate spatiotemporal changes in muscle activity have broad implications for exoskeleton-based assistance and rehabilitation following neuromuscular injury.more » « less
-
more » « less
Abstract Real-time thermal monitoring and regulation are critical to the mitigation of thermal runaways and device failures in two-phase cooling systems. Compared to conventional approaches that rely on the Joule effect, thermal gradient or transverse thermoelectric effect, acoustic emission (AE)-based remote sensing is more promising for robust and non-intrusive thermal monitoring. Nevertheless, due to the high stochasticity and noise of acoustic signals, existing implementations of AE in thermal systems have been limited to qualitative state monitoring. In this paper, we present a technology for real-time heat flux quantification during two-phase cooling by coupling acoustic sensing using hydrophones and condenser microphones and regression-based machine learning frameworks. These frameworks integrate a fast Fourier transform feature extraction algorithm with regressors, i.e., Gaussian process regressor and multilayer perceptron regressor for heat flux predictions. The acoustic signals and heat fluxes are collected from pool boiling tests under transient heat loads. It is shown that both hydrophone and condenser microphone signals are successful in predicting heat flux. Multiple models are trained and compared some using only one form of acoustic data while others combine both acoustic types (i.e., hydrophone and microphone) in fusion ML models (i.e., early, joint, late). The models using only hydrophone data are shown to perform better than the models using only microphone data. Also, some forms of fusion are shown to have better performance than either of the single input data type models. This AE-ML technology is demonstrated for accurate heatflux quantification. As such, this work will not only lead to a light, low-cost, and non-contact thermal measurement technology but also a new perspective for the physical explanation of bubble dynamics during boiling.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/s20154240
