Machine-learning based detection of marine mammal vocalizations in snapping-shrimp dominated ambient noise.

Passive acoustics is an effective method for monitoring marine mammals, facilitating both detection and population estimation. In warm tropical waters, this technique encounters challenges due to the high persistent level of ambient impulsive noise originating from the snapping shrimp present throughout this region. This study presents the development and application of a neural-network based detector for marine-mammal vocalizations in long term acoustic data recorded by us at ten locations in Singapore waters. The detector's performance is observed to be impeded by the high shrimp noise activity. To counteract this, we investigate several techniques to improve detection capabilities in shrimp noise including the use of simple nonlinear denoisers and a machine-learning based denoiser. These are shown to enhance the detection performance significantly. Finally, we discuss some of the vocalizations detected over three years of our acoustic recorder deployments using the robust detectors developed.

Depth-dependence of the underwater noise emission from melting glacier ice.

Submarine-melting of ice at the glacier-ocean interface accounts for a large portion of the ice-loss at tidewater glaciers and produces sound via bubble-release. The sound production is dominant in the sub-surface region near the glacier-ocean interface. This depth-dependence of the sound is studied by melting ice blocks in a glacial bay at various depths up to 20 m and recording their acoustics over a large frequency range. The acoustic energy decreases with depth in line with expectations from the physics of the phenomenon and is fit to an exponentially decaying curve. The estimated variation will be useful for interpreting the sound in marine-terminating glaciers bays in terms of the submarine-melting activity.

Multi-USV Adaptive Exploration Using Kernel Information and Residual Variance.

Using a team of robots for estimating scalar environmental fields is an emerging approach. The aim of such an approach is to reduce the mission time for collecting informative data as compared to a single robot. However, increasing the number of robots requires coordination and efficient use of the mission time to provide a good approximation of the scalar field. We suggest an online multi-robot framework m-AdaPP to handle this coordination. We test our framework for estimating a scalar environmental field with no prior information and benchmark the performance via field experiments against conventional approaches such as lawn mower patterns. We demonstrated that our framework is capable of handling a team of robots for estimating a scalar field and outperforms conventional approaches used for approximating water quality parameters. The suggested framework can be used for estimating other scalar functions such as air temperature or vegetative index using land or aerial robots as well. Finally, we show an example use case of our adaptive algorithm in a scientific study for understanding micro-level interactions.

Vertical directionality and spatial coherence of the sound field in glacial bays in Hornsund Fjord.

Arctic glacial bays are among the loudest natural environments in the ocean, owing to heavy submarine melting, calving, freshwater discharge, and ice-wave interactions. Understanding the coherence and vertical directionality of the ambient sound there can provide insights about the mechanisms behind the ice loss in these regions. It can also provide key information for operating technologies such as sonar, communication, and navigation systems. To study the unexplored sound coherence and vertical directionality in glacial bays, a vertical hydrophone array was deployed, and acoustic measurements were made at four glacier termini in Hornsund Fjord, Spitsbergen, in June and July 2019. The measurements show that the sound generated by melting glacier ice is more dominant in the upper portion of the water column near the glacier terminus. The melt water from the submarine melting and the freshwater discharge from the glacier create a glacially modified water duct near the sea surface. This disrupts the inter-sensor vertical coherence in the channel. However, some coherence across the duct is preserved for sound arising from spatially localized events at low frequencies. Overall, the observations in this study can help improve the understanding of the submarine melting phenomenon in glacial bays.

Adaptive behaviors in multi-agent source localization using passive sensing.

In this paper, the role of adaptive group cohesion in a cooperative multi-agent source localization problem is investigated. A distributed source localization algorithm is presented for a homogeneous team of simple agents. An agent uses a single sensor to sense the gradient and two sensors to sense its neighbors. The algorithm is a set of individualistic and social behaviors where the individualistic behavior is as simple as an agent keeping its previous heading and is not self-sufficient in localizing the source. Source localization is achieved as an emergent property through agent's adaptive interactions with the neighbors and the environment. Given a single agent is incapable of localizing the source, maintaining team connectivity at all times is crucial. Two simple temporal sampling behaviors, intensity-based-adaptation and connectivity-based-adaptation, ensure an efficient localization strategy with minimal agent breakaways. The agent behaviors are simultaneously optimized using a two phase evolutionary optimization process. The optimized behaviors are estimated with analytical models and the resulting collective behavior is validated against the agent's sensor and actuator noise, strong multi-path interference due to environment variability, initialization distance sensitivity and loss of source signal.

Ambient noise imaging in warm shallow waters; robust statistical algorithms and range estimation.

The high frequency ambient noise in warm shallow waters is dominated by snapping shrimp. The loud snapping noises they produce are impulsive and broadband. As the noise propagates through the water, it interacts with the seabed, sea surface, and submerged objects. An array of acoustic pressure sensors can produce images of the submerged objects using this noise as the source of acoustic "illumination." This concept is called ambient noise imaging (ANI) and was demonstrated using ADONIS, an ANI camera developed at the Scripps Institution of Oceanography. To overcome some of the limitations of ADONIS, a second generation ANI camera (ROMANIS) was developed at the National University of Singapore. The acoustic time series recordings made by ROMANIS during field experiments in Singapore show that the ambient noise is well modeled by a symmetric α-stable (SαS) distribution. As high-order moments of SαS distributions generally do not converge, ANI algorithms based on low-order moments and fractiles are developed and demonstrated. By localizing nearby snaps and identifying the echoes from an object, the range to the object can be passively estimated. This technique is also demonstrated using the data collected with ROMANIS.

Spectrogram denoising and automated extraction of the fundamental frequency variation of dolphin whistles.

Marine mammal vocalizations are often analyzed using time-frequency representations (TFRs) which highlight their nonstationarities. One commonly used TFR is the spectrogram. The characteristic spectrogram time-frequency (TF) contours of marine mammal vocalizations play a significant role in whistle classification and individual or group identification. A major hurdle in the robust automated extraction of TF contours from spectrograms is underwater noise. An image-based algorithm has been developed for denoising and extraction of TF contours from noisy underwater recordings. An objective procedure for measuring the accuracy of extracted spectrogram contours is also proposed. This method is shown to perform well when dealing with the challenging problem of denoising broadband transients commonly encountered in warm shallow waters inhabited by snapping shrimp. Furthermore, it would also be useful with other types of broadband transient noise.

A high-frequency warm shallow water acoustic communications channel model and measurements.

Underwater acoustic communication is a core enabling technology with applications in ocean monitoring using remote sensors and autonomous underwater vehicles. One of the more challenging underwater acoustic communication channels is the medium-range very shallow warm-water channel, common in tropical coastal regions. This channel exhibits two key features-extensive time-varying multipath and high levels of non-Gaussian ambient noise due to snapping shrimp-both of which limit the performance of traditional communication techniques. A good understanding of the communications channel is key to the design of communication systems. It aids in the development of signal processing techniques as well as in the testing of the techniques via simulation. In this article, a physics-based channel model for the very shallow warm-water acoustic channel at high frequencies is developed, which are of interest to medium-range communication system developers. The model is based on ray acoustics and includes time-varying statistical effects as well as non-Gaussian ambient noise statistics observed during channel studies. The model is calibrated and its accuracy validated using measurements made at sea.

Grid cellware: the first grid-enabled tool for modelling and simulating cellular processes.

Modelling and simulation of complex cellular transactions involve development of platforms that understand diverse mathematical representations and are capable of handling large backend computations. Grid Cellware, an integrated modelling and simulation tool, has been developed to precisely address these niche requirements of the modelling community. Grid Cellware implements various pathway simulation algorithms along with adaptive Swarm algorithm for parameter estimation. For enchanced computational productivity Grid Cellware uses grid technology with Globus as the middleware.

Cellware--a multi-algorithmic software for computational systems biology.

UNLABELLED: The intracellular environment of a cell hosts a wide variety of enzymatic reactions, diffusion events, molecular binding, polymerization and metabolic channeling. To transform these biological events into a computational framework, distinct modeling strategies are required. While currently no tool is capable of capturing all these events, progress is being made to create an integrated environment for the modeling community. To address this niche requirement, Cellware has been developed to offer a multi-algorithmic environment for modeling and simulating both deterministic and stochastic events in the cell. AVAILABILITY: The software is available for free and can be downloaded from http://www.bii.a-star.edu.sg/sbg/cellware