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Passive localization and tracking of a mobile emitter, and joint learning of its reverberant three-dimensional (3D) acoustic environment, where critical structural features are unknown, is a key open problem. Unaccounted-for occluders are potentially present, so that the emitter can lose line-of-sight to the receivers, and can only be observed through its reflected raypaths. The locations of reflective boundaries must therefore be jointly estimated with the emitter's position. A multistage global optimization and tracking architecture is developed to solve this problem with a relatively unconstrained model. Each stage of this architecture establishes domain knowledge such as synchronization and initial environment estimation, which are inputs for the following stages of more refined algorithms. This approach is generalizable to different physical scales and modalities and improves on methods that do not exploit the motion of the emitter. In one stage of this architecture, particle swarm optimization is used to simultaneously estimate the environment and the emitter location. In another stage, a Hough transform-inspired boundary localization algorithm is extended to 3D settings, to establish an initial estimate of the environment. The performance of this holistic approach is analyzed and its reliability is demonstrated in a reverberant watertank testbed, which models the shallow-water underwater acoustic setting.

Polymetallic nodules (PMN) are potato-sized concretions containing metals, such as manganese, copper, nickel, cobalt, and rare earth elements, and are a potential valuable resource of minerals. They occur in high abundance and are unevenly distributed across the Clarion Clipperton Fracture Zone. Existing PMN abundance estimation methods using box corers, and manual assessment through seabed photographs are labor and time intensive, and can only survey small sections of seabed at a time. Compared to an underwater camera, acoustic sensors are able to survey the PMN abundance across larger tracts of seabed at a time. In this article, we present a method for PMN abundance assessment using heterogeneous acoustic data, which is a combination of bathymetry information and sidescan sonar measurements of seabed backscatter. We achieve this using an artificial neural network model that classifies a given region into a low or high PMN density region using these features. Our model will enable faster estimation of PMN abundance for future deep seabed site surveys without the need for underwater cameras. To date, our proposed method yields an average accuracy of 85.36% on a testing data set, demonstrating our method's effectiveness in estimating PMN abundance.

Pulse-to-pulse coherent Doppler sonars are widely used to explore boundary layer characteristics of oceans. Multifrequency pulse-to-pulse coherent Doppler sonars have been proposed in the literature to tackle the velocity ambiguity problem faced by single-frequency systems. A robust algorithm for resolving the velocity ambiguity is crucial in such systems. This paper proposes a method based on the robust Chinese remainder theorem to resolve the velocity ambiguity for multifrequency pulse-to-pulse coherent Doppler sonars. We evaluate the proposed method for resolving the velocity ambiguity in terms of the trial fail rate. The simulations show that the proposed method achieves 1/8 trial failure rate as the reference method. A theoretical criterion is also derived to support the observation that in most cases our proposed method is more robust to estimation errors than the reference method.

Broadband acoustic Doppler current profilers (BBADCPs) are instruments that are widely used for measuring ocean currents. The ambiguity velocity of the conventional method used in BBADCPs must accommodate all possible measurement velocities. Unfortunately, allowing a high-ambiguity velocity results in a high measurement deviation in conventional BBADCPs. We propose a method to break through the limitation imposed by the ambiguity velocity to improve BBADCPs. Our proposed method involves designing an orthogonal coprime signal to replace the conventional transmit signal in BBADCPs. The proposed orthogonal coprime signal consists of two orthogonal subsignals, whose ambiguity velocities are designed to be low and coprime. Utilizing the coprime property, we then employ the robust Chinese remainder theorem to resolve the velocity ambiguity from the two independent measurements made via the two orthogonal subsignals. Simulations show that our proposed method decreases the standard deviation of measurement velocity by nearly three times, compared to the conventional method used in BBADCPs. Our simulations also show that the proposed method can yield a 12-dB improvement of signal-to-noise ratio over the conventional method. This can help increase the profiling range significantly.

Passive localization and tracking of a mobile emitter, and joint learning of its reverberant 3D environment, is a challenging task in various application domains. In underwater acoustic monitoring with a receiver array, for example, a submarine may need to be tracked in a setting with natural and man-made obstacles, such as seamounts or piers. If such obstacles occlude the line of sight from this vessel to the receivers, then the non-line of sight reflected arrivals from the reverberant environment must be leveraged for localization. Hence, we need to precisely map these reflective features in order to deliver robust performance. We propose a multi-stage global optimization and tracking architecture to approach this problem. Each stage of this architecture establishes domain knowledge such as synchronization and occluder mapping, which are inputs for the following stages of more refined algorithms. This approach is generalizable to different physical scales, and improves on methods that do not exploit emitter motion. We further introduce a robust neural network-based reflector estimation method that outperforms its alternatives in realistic application settings. The performance of this holistic approach is analyzed and its reliability is demonstrated both in simulation and in a real-life reverberant watertank, which models shallow-water acoustic environments.

In underwater acoustic localization via matched-field-processing, given a propagation model and a suitable environmental parameterization, one searches for the location (of the transmitter or receiver) whose replica field is closest to the observed one. The high computational complexity of such non-gradient-based optimization methods renders them infeasible for many real-time scenarios, especially when an accurate solution is desired due to resolution of the search grid required, or as the search dimensionality increases (e.g., when it is necessary to optimize over uncertain environmental parameters such as sound speed or bathymetry). In this talk, we propose a ray-based, differentiable model for acoustic propagation that can be exploited in a gradient-based optimization for localization. For localization applications in which accurate times of arrival might not be available (e.g., due to the signal's relatively small bandwidth), the proposed method does not directly rely upon times of arrivals. Rather, it seeks the location (and possibly environmental parameters) that minimize the squared-error between the observed signal and its estimation via the differentiable model. We leverage the PyTorch optimization and auto-differentiation tools for the implementation and demonstrate successful localization on synthetic data in a dense multipath environment.

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.

Underwater acoustic localization has traditionally been challenging due to the presence of unknown environmental structure and dynamic conditions. The problem is richer still when such structure includes occlusion, which causes the loss of line-of-sight (LOS) between the acoustic source and the receivers, on which many of the existing localization algorithms rely. We develop a semi-blind passive localization method capable of accurately estimating the source's position even in the possible absence of LOS between the source and all receivers. Based on typically-available prior knowledge of the water surface and bottom, we derive a closed-form expression for the optimal estimator under a multi-ray propagation model, which is suitable for shallow-water environments and high-frequency signals. By exploiting a computationally efficient form of this estimator, our methodology makes comparatively high-resolution localization feasible. We also derive the Cram\'er-Rao bound for this model, which can be used to guide the placement of collections of receivers so as to optimize localization accuracy. The method improves a balance of accuracy and robustness to environmental model mismatch, relative to existing localization methods that are useful in similar settings. The method is validated with simulations and water tank experiments.

Sound plays an important role in animal communication. Sesarmid crabs (family Sesarmidae) are semi-terrestrial crabs found in tropical and temperate regions, and are known to communicate through acoustic signals. The violet vinegar crab Episesarma versicolor (Tweedie, 1940) is a tree-climber known to possess sound-producing structures. The significance of sounds produced by E. versicolor during intraspecific contests, however, remains unknown. We investigated the context in which sound production occurs by staging 27 trials using male crabs. Three main types of acoustic behaviours were documented: rapping, leg stamping, and vibration. With the exception of vibration, these behaviours were also observed in female crabs. The air or the wood substrate was utilised to transmit acoustic signals, with each sound having unique spectral and temporal features serving different purposes. Rapping and leg stamping may be an advertisement of the sender’s resource-holding potential for territorial defence and asserting dominance during fights. Vibration is likely a victory display that discourages the contest loser as well as individuals in the vicinity from engaging the victor in another fight. Episesarma versicolor can therefore produce acoustic signals that may contain information about the sender, minimising costs associated with direct conflicts.

Climate-change induced melting is leading to accelerating ice loss at tidewater glaciers worldwide. A significant component of the freshwater flux from these glaciers arises from submarine melting at the glacier-ocean interface. This melting causes a distinct acoustic signature due to the release of pressurized bubbles underwater, opening up the possibility of monitoring this phenomenon on a large scale using passive acoustic systems. To evaluate the use of sound in monitoring submarine glacier melting, we made acoustic measurements using vertical hydrophone arrays in four glacial bays in Svalbard in 2019. As the recording array was moved away from the glacier, the variation in the recorded acoustic level due to melting ice did not follow a uniformly decreasing trend as one might expect. Moreover, the acoustic intensities at different glaciers were clustered at different levels. These observations indicate that the geometry of the glacier-ocean interface, thermohaline structure of the underwater channel and presence of floating ice in the bay played a role in determining the acoustic field. Most of this variation can be explained through propagation modeling. Moving forward, this model-based interpretation of the field will play an integral part in inverting the sound to estimate the submarine melt rate.

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.

Dense clouds of bubbles—ranging in scale from 10s of micrometers to cm—are generated by breaking waves on wind-driven seas. Wave-induced bubbles increase ocean albedo and play an important role in air-sea exchange processes by enhancing the transfer of gases and generating sea spray aerosol. Because bubbles both scatter sound efficiently and generate pulses of sound on formation, they create important acoustical effects, including the generation of ambient sound, the attenuation and scattering of sound forward scattered from the sea surface, and enhanced surface backscattering at low grazing angles. Examples of these effects will be illustrated along with the treatment of a bubble cloud acoustical scattering regime characterized by low absorption but high phase distortion relevant to high-frequency, coherent acoustic communications. [Work supported by ONR Ocean Acoustics.]

Ice sheets are expected to be major contributors to sea level rise in the coming decades and centuries. Ocean-forced melting at marine terminating glaciers has the potential to be an important driver of mass loss from these ice sheets, but is difficult to measure directly using conventional techniques, and thus remains poorly understood. The air pressure in bubbles trapped in glacier ice is typically greater than the hydrostatic pressure near the surface of the water. This pressure difference causes the bubbles to expand rapidly and radiate sound when they are released as the ice melts. This sound presents a promising signal which could be used to make passive acoustic measurements of submarine melting at glacier termini, if it can be characterized sufficiently. Prior work has demonstrated that there is considerable variability in the acoustic energy radiated by the release of individual bubbles. Here we show that the total acoustic energy radiated by small blocks of glacier ice as they melt is correlated with the total potential energy stored by the compressed air bubbles in the ice. We find that the conversion efficiency is highest for the most energetic blocks of ice, and present some modelling efforts to explain this trend.

In this paper, we address the problem of detecting a known signal in snapping shrimp noise. The latter dominates the ambient soundscape at medium-to-high frequencies in warm shallow waters. The noise process is impulsive, exhibits memory and is modeled effectively by stationary α-sub-Gaussian noise with memory order m (αSGN(m)), which is essentially an impulsive Markov process. Robust detectors have long been used to mitigate the impact of impulsive noise on the performance of digital systems. However, conventional notions of robustness do not assume memory within the noise process. The αSGN(m) model offers a mathematical model to develop robust detectors that also exploit the mutual information between noise samples. Recent works in this area highlight substantial improvement in detection performance over traditional robust methods in snapping shrimp noise. However, implementing such detectors is a challenge as they are parametric and computationally taxing. To achieve more realizable detectors, we derive the locally optimal detector for αSGN(m). From it, we introduce the generalized memory-based sign correlator and its variants, all of which offer near-optimal performance in αSGN(m). The proposed detectors offer excellent performance in snapping shrimp noise and low computational complexity. These properties make them attractive for use in underwater acoustical systems operating in snapping shrimp noise.

Estimation of the modulation frequency of a source is important in various sonar signal processing applications. The conventional estimator of modulating frequency has been formulated under the assumption that the source and noise are Gaussian distributed. This is often inaccurate in several applications such as underwater acoustics where the signal or noise can be impulsive. We formulate novel modulation frequency estimators that are robust to impulses in the data, and outperform the conventional estimator in environments where the observed data are contaminated by impulses. We demonstrate their performance using recorded data. We characterize the performance of the methods in terms of their accuracy, harmonic distortion, and robustness to knowledge of noise statistics. We also derive the Cramer–Rao lower bound for the modulation frequency estimation problem in impulsive data.

The abundance of deep-sea polymetallic nodules (PMN) can be assessed effectively using acoustic backscatter data. Controlled experiments can help us understand this capability in greater detail enabling us to optimize the sonar operating parameters for better performance. However, it is difficult to obtain PMN from deep-sea regions in sufficient quantities to study them in controlled conditions. We explore the possibility of replacing these PMN with artificial nodules by comparing the acoustic backscatter intensities of artificial nodules and PMN from the Indian Ocean Basin in a laboratory experiment. Results from our laboratory measurements reveal that the backscatter intensity from a bentonite sediment layer covered with PMN/artificial nodules, is significantly higher than returns from bentonite with no nodule coverage. Furthermore the intensity of acoustic backscatter from artificial nodules matches that of actual PMN. This indicates that for future studies, we can use these artificial nodules as proxies in place of real PMN to perform initial acoustic testing.

The echolocation signals emitted by odontocetes can be roughly classified into three broad categories: broadband high-frequency echolocation signals, narrowband high-frequency echolocation signals, and frequency modulated clicks. Previous measurements of broadband echolocation signals propagation in the bottlenose dolphin (Tursiops truncatus) did not find any evidence of focusing as the signals travel from the near to far-field. Finite element analysis (FEA) of high-resolution CT scan data was used to examine signal propagation of broadband echolocation signals of dolphins and narrowband echolocation signals of porpoises. The FEA results were used to simulate the propagation of clicks from phonic lips, traveling through the forehead, and finally transmission into the seawater. Biosonar beam formation in the near-field and far-field including the amplitude contours for the two species was determined. The finite element model was validated by finding the simulated amplitude contour in the horizontal plane was consistent with prior direct measurement results for Tursiops. Furthermore, the simulated far-field transmission beam patterns in both vertical and horizontal planes were also qualitatively consistent with results measured from live animals. This study shows that there is no evidence of convergence for either Tursiops or Phocoena as the sound propagates from near-field to far-field.

Coating is an essential method to improve surface properties such as corrosion resistance, lubrication of material used. The simulated laboratory tests is a more feasible alternative to experimental or real-time testing. A study conducted under controlled parameters of load, surface coating, and temperature showed the changes in the tribological characteristics with and without functionally graded cermet coatings and their influence on stainless-steel piston rings. Altogether simulations for six cycles were taken into consideration for each of the test cases - with and without coating. The obtained data from the microstructure imaging in optical microscope following the investigation conducted employing tribometer showed that the resistance of the coated rings was comparatively more than the uncoated rings, hence proving the coating to be beneficial for real-time use.

Marine-terminating glaciers worldwide are melting rapidly in response to climate shifts, resulting in the delivery of freshwater into the oceans. Submarine melting at the glacier-water interface accounts for a significant component of the freshwater delivery from the glacier. This melting produces a distinct acoustic signature, providing a potentially viable modality to monitor glacial ice melting on a large scale via acoustic sensing. In order to evaluate the utility of ambient noise oceanography as a tool to quantify glacial ice-melt, in June 2019 we deployed a vertical hydrophone array and made acoustic measurements at some glacier termini in Hornsund Fjord, Spitsbergen. Quantification via array processing proves to be challenging due to (1) the space- and time-varying sound-speed profile in the underwater channel, and the way it refracts sound in an unknown manner, (2) limited vertical resolution of the array due to its aperture, and (3) interference from other noise sources such as melting icebergs, for example, contaminating the melt sound recordings. We present preliminary results from the processing which reveal different acoustic levels arising from submarine melting at different glaciers. The sound from the melt seems to be more dominant in the upper layers of the water at the glacier-sea interface.

Melting glaciers and ice sheets are important contributors to sea level rise, but the rates at which they are losing mass are difficult to measure precisely. Using the acoustic signals produced by the rapid release of pressurized air from bubbles contained in the ice is a promising avenue for obtaining quantitative estimates of the rate of submarine melting at the termini of tidewater glaciers. The amplitude and character of the observed average signal generated by the bubble release events have been found to depend strongly upon the hydrostatic pressure of the water the bubbles are released into, and therefore on the depth at which said events occur; this dependence must be better understood before the goal of measuring melting rates can be achieved. Data from field experiments performed at the Hornsund fjord in Svalbard, Norway, in which the acoustic melting signal from blocks of glacier ice was recorded at varying depths, will be presented, and compared to the output of an idealized model for the acoustic signal produced by the release of bubbles from melting ice.

Constant-modulus signals such as m-sequences are known to have good autocorrelation properties, as well as good peak-to-average power ratio that allows for full utilization of the transmitter’s power. However, the broadband ambiguity surface for such signals exhibit high sidelobe levels that are undesirable in applications where the signal is subject to broadband Doppler. We formulate an optimization problem to minimize the maximum sidelobe levels of such signals over a set of delay-Doppler values. This problem is non-convex and difficult to solve. We explore a convex regularization of the problem that can readily be solved using semi-definite programming, and show that optimal or near-optimal signals can be designed using this method. We further explore some heuristic methods to reduce computational and memory complexity of the solution, to enable us to design longer signals. We demonstrate the advantage of our signal design over conventional unimodular signals for target detection in strong clutter in a continuous active sonar application.

Multi-frequency pulse-to-pulse coherent Doppler sonars are able to measure velocities beyond the ambiguity limit of conventional single-frequency systems. Resolving this velocity ambiguity is essential for such multi-frequency pulse-to-pulse coherent sonars. This paper proposes to employ the robust Chinese remainder theorem to resolve velocity ambiguity in such a system. The simulations show that our proposed method is able to reduce the trial fail rate of velocity estimation by one order of magnitude, as compared to the method available in the literature.

We compare pulse compression waveforms of different categories in terms of their ambiguity function characteristics and performance in detection and range estimation. How well these characteristics translate into sonar performance in shallow waters is studied via simulations. We select three operating scenarios to compare the performance of these waveforms, namely, short range, long range and detection of multiple targets that are close to each other. Our comparison indicates the relative strengths and weaknesses of these waveforms. The results indicate that some aspects of the relative sonar performance of these waveforms can be gauged from their ambiguity function characteristics, though not always in a straightforward manner. Thus, this gives us a methodology to select suitable waveforms for sonar operation without the need to perform computationally intensive Monte Carlo simulations.

Broadband acoustic Doppler current profilers (ADCPs) are instruments that are widely used in underwater observation. However, conventional broadband ADCPs face the problem of being limited in accuracy due to velocity ambiguity. This paper proposes a method based on using orthogonal coprime pulse pairs and the robust Chinese remainder theorem to mitigate this limitation. The proposed method breaks through the limit of velocity ambiguity of conventional broadband ADCP and improves the performance significantly. The simulations show that our proposed method can decrease the standard deviation of current velocity measurement by nearly three time, when compared to the conventional method.

In biological sciences, herbal plants play a vital role in medicinal purpose. Identifying the leaf with unaffected leaves is more important nowadays. The leaves get affected due to many diseases like Phragmidium meuronatum, Cercospora jasmicola, Oidium tingitanium, Cephaleuros virescens. This paper focuses on the study and identification of unaffected leaves based on automated leaf recognition system. The proposed system uses image processing technique for image segmentation and feature extraction. The dataset includes 100 leaves of 10 different species. The leaves are compared with the database and the based on the threshold value the affected leaves are determined. The texture and color parameters are taken for disease authentication using image processing algorithm. Then it is fed into the real time processor Raspberry pi processor for which anywhere it can be used. The main use of Raspberry Pi is to make the system more user friendly. It uses four USB port and a real time camera which captures the image and feeds in the system. This work is more applicable in the field of agriculture and pharmaceutical industries. Debjyoti Guha | Hari Vishnu | Mr. L. Jegan Antony Marcilin | Mrs. T. Vijayashree "Real time Processor for the Authentication of Affected Herbal Medicinal Leaves Using Image Processing Techniques- Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Active Galaxy , June 2018, URL: https://www.ijtsrd.com/papers/ijtsrd14545.pdf Paper URL: http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/14545/real-time-processor-for-the-authentication-of-affected-herbal-medicinal-leaves-using-image-processing-techniques--review/debjyoti-guha

There is a high abundance of polymetallic nodules (PMN) scattered across the vast Clarion and Clipperton Fracture Zone (CCFZ) in the Pacific Ocean. These nodules possess high economic potential as they are rich in minerals such as manganese, nickel, copper and rare earth elements. Quantification of nodule coverage is important for economic feasibility studies and planning of effective exploitation strategies. Traditional methods for nodule quantification are highly labour and time intensive as they rely on freefall box corer measurements and/or image processing of seabed photographs. Using sidescan sonar data and geotagged photographs collected from an autonomous underwater vehicle (AUV) in our region of interest at CCFZ, we propose a novel technique based on artificial neural network (ANN) to estimate PMN abundance using texture variations from sidescan sonar data. Compared to an optical camera, the sidescan sonar provides a much larger area of coverage, which in effect can drastically increase the area surveyed by an AUV in a given amount of time. Till date, this is the first known published work to elaborate on a data-driven approach in estimating PMN abundance using sidescan sonar backscatter data. Our network yielded a test accuracy of 84%, which shows that it can be used as an effective tool in estimating nodule abundance from sidescan sonar. This approach allows faster evaluation of nodule abundance for future exploration without the need for an underwater camera.

In a number of scenarios, detecting the presence or absence of a known signal may be of practical interest. One such example lies in a communication setting, where packet detection is a vital first step to decode transmitted data. The detection problem can be formulated as a binary hypothesis test within the Neyman-Pearson (NP) framework. Our scenario of interest is warm shallow waters, where the sea floor is inhabited by colonies of snapping shrimp. The ambient noise in such a case is impulsive and exhibits memory. We investigate the performance of optimal detectors corresponding to four additive noise models in snapping shrimp noise. In the literature, proposed detectors typically take only the amplitude statistics of the noise process into account. By also considering the memory, we show that there is substantial improvement in detection performance. The detector in the latter case is based on the recently introduced stationary α-sub-Gaussian noise with memory order m (αSGN(m)) model, which effectively characterizes the temporal amplitude statistics of the snapping shrimp noise process.