Abstract. 

Block Island Wind Farm (BIWF) with five wind turbines was the first offshore wind farm in the USA. Construction of the Block Island wind farm involved driving piles using impact hammers to pin the jacket type structures into the seabed. Pile driving noise and acoustic particle velocity were measured on a 3-axis gimbaled geophone were used in this study. The location of the geophone is known but its orientation in the horizontal plane is unknown. The particle velocity components in three mutually perpendicular directions and the acoustic pressure from the geophone were used to calculate the direction-of-arrival (DOA) of the pile driving noise. The orientation of the geophone is fixed based on the estimated and actual DOA.

Abstract. 

In the maritime environment, bearings-only tracking (BOT) has been holding the attention of researchers from many decades. The purpose of BOT is to estimate the kinematics of the target which is often cited as Target Motion Analysis (TMA). The highly nonlinear nature of the BOT problem requires the use of Bayesian framework algorithms for state estimation. Monte Carlo simulation for the performance evaluation of these algorithms based on the angle on target bow (ATB) is carried out in this paper using Matlab. The scenarios are also designed mathematically to analyse the algorithms using simulator before applying them in real time.

Abstract. 

A novel wideband beamforming method is proposed where the beamforming is achieved by using Modified Farrow Structure FIR Filtering method. This method provides efficient time delay compensation since it considers only the fractional part of delay unlike other time domain FIR filtering methods. The simulation results shows the magnitude response of Modified Farrow Structure FIR Filter is almost unity up to half of the nyquist frequency which is most desired in performing time delay compensation for wideband beamforming in sonar applications.

Abstract. 

Degradation of Signal to Noise Ratio (SNR) in an acoustic receiver due to circuit elements is studied. Lumped parameter equivalent circuit of a hydrophone and noise equivalent circuit of a preamplifier is used to model the receiver system as an electronic circuit utilising the potential of electrical circuit theory and analysed the acoustic receiver noise performance. As the signal passes through the receiver chain, the thermal noise of hydrophone and various noise sources of the preamplifier get added to the signal, degrading the SNR. An expression for SNR at various locations of the receiver system is developed in terms of the circuit parameters. The effect of equivalent input voltage noise, equivalent input current noise and bias resistor on the total system noise is studied in detail and the results are presented in this paper. The method presented here provides a tool for analysing the acoustic receiver in the electrical domain and to optimize the system performance.

Abstract. 

A prototype implementation of an acoustic spatial filtering based on the mathematical model of a far-field plane wave incident on a sensor array is described in this paper. A 32-element equally spaced linear sensor array is considered for the simulation which is accomplished using Delay and Sum method that involves combining delayed signals from each sensors of the array. The implemented array works in the frequency band from 300 Hz to 3400 Hz. The inter element spacing between the array elements is chosen as half the wave-length corresponding to the maximum frequency to avoid grating lobes. Tracking is done based on steered beamformer and smoothen by Kalman filter. All simulations are done in MATLAB. Each sensor responses are recorded by MP 32 microphone preamplifier and ORION32 HD 64- channel audio interface. Parameters, Signal to noise ratio and signal to interference noise ratio are compared by varying number of sensors as well as angular separation between desired signal and interference signal. Results plotted are the beam patterns obtained at various source angle, the frequency spectrum of the original and beamformed signal at the beam steered angle. Also plotted tracking of the source at different time span using linear sensor array with and without Kalman filter.

Abstract. 

This article explains first attempt on implementation of Indian Regional Navigation Satellite System (IRNSS) based NavIC satellite position acquisition receiver module in Lagrangian drifting buoy. Presently, most of Ocean observation systems developed by international community are built using US satellite based Global Positioning System (GPS) receiver modules for acquiring Geo-position and time synchronization applications. The National Institute of Ocean Technology (NIOT) has attempted and built INSAT based drifting buoys interfaced with Indian made NavIC satellite receiver modules works under IRNSS satellite constellations and carried out extensive laboratory and field validations. This IRNSS based satellite receiver technology is fully planned, developed, established and controlled by the Indian Space Research Organization (ISRO), Department of Space. It is designed to provide accurate position information service to users in India as well as the region extending up to 1500 km from Indian terrestrial boundary. NavIC L5 signal ensures to obtain a single point positional accuracy of ±2.5 m. The concept implementation is to replace the usage of existing USA based GPS module with India made IRNSS-NavIC module, thus to make drifting buoy a complete indigenous product. The Drifting buoys are deployed to measure sea surface temperature and ocean current. A unique attempt with NavIC satellite receiver module to have near real time data at every hour is achieved and results of recent field deployment carried out in Bay of Bengal is presented in this paper.

Abstract. 

This paper focuses on the design and development of controller for control of depth of autonomous underwater vehicle (AUV). The dynamics of Remus underwater vehicle is considered in this work. Controller is designed based on the liberalized equations of motion. State space model is developed for AUV in diving plane by taking few assumptions. A full order observer is implemented and controller is designed on new states that are estimated by full order observer. The angle of stern planes is considered as input and depth as output. The state space model is implemented for MATLAB simulation. Optimal values of controller gain matrix and observer gain matrix are calculated by using pole placement method. All simulations have carried out on MATLAB and desired response is achieved by taking optimum values of gain matrices.

Abstract. 

In this paper, an underwater wireless optical communication (UWOC) link has been established to investigate the variation in received optical power at different modulating frequencies for different water channels namely pure water channel, turbulent water channel and saline water channel. Furthermore, the beam divergence is analysed for all aforementioned water channels.

Abstract. 

Unmanned Aquatic Vehicles (UAVs) are becoming the most important types of aircraft that are capable of travelling in underwater. However, the design implemented was mostly the multi-rotor configuration which are not much efficient in terms of maneuvering achievability, lifetime etc. Therefore, there arises the need for an UAV with high lifetime, higher efficiency in design, secure flight and low maintenance cost. In this paper a unique UAV is proposed which are capable of operating efficiently in underwater. Maneuverability of such UAV can be greatly achieved by doing the accurate research on the hydrodynamic effect. Therefore, the aim of this study is to analyze the hydrodynamic behavior of the flow over a UAV, to get application domain details. The sketch of such a UAV is ultimately a challenging one, since it implies significant propulsive and structural design tradeoffs for mission in underwater. The challenging aspect in the design has been overcome by using advanced engineering applications like CFD simulations. ANSYS-FLUENT 16.2 software is used as a CFD solver, which is used for predicting the flow properties on the fish detecting environment.

Abstract. 

The undersea acoustic communication channels are severely band-limited resulting in relatively low data rates compared to those achievable in RF communications. As the ocean noise being complex and non-Gaussian, which arise due to numerous mechanisms including weather, surface wave action, biologics, shipping and industrial noises near the coastline, spread spectrum communication technique is an ideal choice for improving the bit error rate performance of undersea communication systems. The spreading of the spectral bandwidth also results in accomplishing secure communications thus, increasing the resistance to interference, and noise rejection. In view of the behaving of the undersea channels for the propagation of acoustic signals a study has been carried out to simulate the effects of underwater channel using BPSK based direct sequence spread spectrum systems with improved performance incorporating the undersea channel impulse responses, under varying environmental conditions.

Abstract. 

This paper is about the functioning of an IoT based wave detecting system which completely works on the basis of solar energy. With the help of this wave detecting system, we can easily trace out the frequency of the waves and can receive an awareness about the danger coming to strike us. This wave detecting system is having multiple applications and placing this system in the sea helps us to check for pollution, temperature variation, pH variation of sea water, turbidity etc. The entire working of this system is based on the waves and its frequency variations. With the IoT enabled feature of this wave detecting system, individuals could easily receive up to date information from the ocean. Being GPS attached, it is easy to spot the system in the wide water surface.

Abstract. 

Underwater vehicle captured images can be in degradation quality due to the underwater environment. To overcome this, image enhancement method such as Empirical Mode Decomposition (EMD) and Xtended Central Symmetric Local Binary Pattern (XCS-LBP) are applied on different shades of Side Scan Sonar (SSS) to extract the texture feature. These features can be evaluated using a statistical measure technique called Gray Level Co-occurrence Matrix (GLCM). Finally, it is concluded that the recommended EMD with XCS-LBP has shown superior results compared to other algorithms.

Abstract. 

The presence of sediments, lighting inconsistencies, colour variations and dissolved particles give hazy effect to the underwater images. To overcome this, enhancement techniques are required. In this paper, the model based dehazing algorithms are analysed. The effectiveness and limitations of various algorithms are analysed both in terms of subjective and objective measures. The Underwater Hazeline Prior (UHP) algorithm is contrast adjusted to form the Modified Colour Restoration (ColRM). This ColRM achieves a metric improvement of 2.5% in Underwater Color Image Quality Enhancement (UCIQE) than the UHP algorithm. Further contrast improvement strategies for underwater images are also discussed.

Abstract. 

Enhancement of the underwater images is essential because of the poor illumination, dispersion and scattering losses of the environment. This paper proposes a luminosity conserving and contrast enhancing method for enhancement of the underwater images. In the proposed method, initially, the images are subjected to white balance in order to remove the unwanted colour cast. A modified approach adopted from Gray World algorithm is used for colour correction. The processed image is subsequently subjected to unsharp masking and contrast limited histogram equalization to ensure the enhancement of edges and contrast respectively. The experimental results demonstrate that the proposed method can enhance the images.

Abstract. 

Contrast degradation due to wavelength-based light attenuation compromise quality of sub-aquatic images. In this paper, L-CLAHE Intensification Filter (L-CIF) algorithm is proposed to improve the quality of underwater images and to enhance underwater images in pre-processing stages. The proposed algorithm consists of three modules, named L-CLAHE, intensification, and filtering. The input image is converted to L*a*b color space. CLAHE algorithm is used to process the extracted L* channel. The output of L-CLAHE module is processed with gamma correction, histogram equalization and bilateral filter in intensification and filter stages. The output image is contrast improved and color corrected.

Abstract. 

This paper proposes an algorithm for tracking cables from underwater images. The proposed algorithm goes through a pre-processing stage which aims at enhancing the image for facilitating the line detection process. Then the image is segmented using a clustering algorithm and the edges are detected using the Sobel operator. The detected edges are then passed through Hough Transform algorithm for getting the lines. The algorithm has been tested on underwater images having cables with different orientations and is seen that the cable gets tracked, whatever its position be.

Abstract. 

This paper describes a method for classifying different textures of Side Scan Sonar (SSS) images. The many applications in the field of oceanography requires a high reoslution images. In order to enhance the texture feature of the SSS images Super Resolution (SR) techniques are applied. Then the texture features of the SR images are extracted using Local Binary Pattern (LBP),Local Directional Pattern(LDP) and Local Ternary Pattern (LTD). The classification is carried for the SSS image database by using Support Vector Machine(SVM). The LDP with SR shows higher accuracy rate of 83.4 % compared to other methods.

Abstract. 

Among the different models investigated for SONAR image denoising, the analysis considers two noise models, the signal independent additive noise model and the multiplicative noise model. For the signal-independent additive model, in the transform domain for the denoising a mutiresolution analysis method and the sparsity of the natural sonar images are exploited. For the same model in the spatial domain a side scan sonar image is denoised by Kalman filter-based estimation method. For the multiplicative noise model, in the transform domain a mixed noise removal based on probabilistic patch-based processing is employed and in the spatial domain, the fractional integral mask based method and an unscented Kalman filter based estimation method is adopted.

Abstract. 

This paper discusses the design and analysis of 6000 meters depth rated pressure casing made of 17- 4 PH Stainless Steel grade H900. Analytical calculation and finite element analysis are carried out to predict the buckling behavior and strength of the pressure casing. The results of numerical analysis shows reduction in buckling pressure while considering geometric and material nonlinearities. At critical equilibrium state, a dimensionless number ϕt of a structure is used. From the stress-strain curve of a material, a four parameter formula that contains ϕt is established. This formula is used for calculating the inelastic buckling of deep sea water pressure casing. The results from analysis shows that the selected material and design are acceptable for pressure casing at 600 bar hydrostatic pressure.

Abstract. 

For the past few years, underwater exploration has increased exponentially. Currently available instruments for data collections (Side Scan Sonar, Multi Beam echo sounder, sub bottom profiler and Remotely Operated Vehicle) in underwater research and observation not only provide the data on objects and species, but also provide the sea surface data. In this regard, selecting suitable features is a huge task. Due to limited datasets in Underwater, it is difficult to classify the objects/features from underwater images. In order to overcome this, machine learning based Bag of Features model is adopted in this paper. The dataset is obtained from shallow water using ROV. Since the underwater optical images have low light intensity, making the classification of features a difficult task; SURF (Speeded-Up Robust Features) and SVM (Support Vector Machines) algorithms are implemented in Bag of Features model to attain maximum accuracy. The performance evaluation of training and testing datasets gives better performances.

Abstract. 

The purpose of our work is to detect and estimate the size of underwater coral reef from image frames using vision based technique. Deep learning architecture is proposed to detect the coral reefs. To extract the frames and build data set, real time underwater video is used. The size of reef is estimated using distance-based algorithm with detected coral information. A nonlinear function is formulated and optimized to get accurate reef size as pixels taken by it in image changes with distance. This algorithm can also be used for real-time analysis of the underwater environment. It is evident from the analysis results and comparison with actual data that the proposed method is accurate in estimating the area occupied by underwater coral reefs.

Abstract. 

Underwater object detection is very essential for pipeline detection, tracking and safe navigation. This paper presents methods to detect pipeline by using Support Vector Machine and Random Forest classifiers with 5 features - Histogram of Oriented Gradients, Scale Invariant Feature Transform, Speed-UP Robust Features, Shi-Tomasi Corner Detector and Harris Corner Detector from the images taken by Remotely Operated Vehicle. The better result is given by Random Forest classifier with Histogram of Oriented Gradients with an accuracy of 98.81%.

Abstract. 

In this paper, machine learning is introduced for acoustic source localization in an indoor environment. Source localization is regarded as a supervised learning problem and is solved by random forest regressor. Experiments were conducted to collect narrow band audio signals from an acoustic source placed at different angles using a linear array of 8 microphones. The signals captured by the microphone array are used to train the model. Finally, the performance of the model on an independent test set was evaluated and results show that the random forest regressor model can be effectively used for indoor acoustic source localization.