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OCEANS'17 Aberdeen

 

                                 

This 40th Student Poster Program of the OCEANS Conferences was held at OCEANS’17 MTS/IEEE Aberdeen, at the Aberdeen Exhibition and Conference Centre, from June 20 to June 22. The program was organized by Faye Campbell (HGF Limited) as local coordinator and Philippe Courmontagne, SPC Chair, from IEEE OES.
     This student Poster Program has been initiated by Norman Miller in 1989 and became an integral part of the OCEANS conferences in 1991. For this edition, more than 100 abstracts were received and 20 were selected for this contest, not without difficulty given the high quality of the received abstracts. Students came from universities and industries around the globe (Canada, China, France, Germany, India, Japan, Korea, Norway, Scotland, Spain and the United States). All participants were awarded with complimentary conference registration, reimbursement for their travel fees, and were provided with free accommodation at the University of Aberdeen’s Halls of Residence at Hillhead. The program was supported by funding from the US Navy Office of Naval Research Global and from the two Aberdeen’s universities (University of Aberdeen and Robert Gordon University), which enabled the students to attend the conference.
     The posters were on display in the Exhibition Hall, allowing the students to exchange and describe their research work to the community.
     The posters were judged by a team organized by IEEE OES and the local chair. The roster of students and their schools are (in order of appearance of the Program Book):

  • Well explained!
    Jialei Zhang, Huazhong University of Science and Technology
  • Yadpiroon Onmek, LIRMM Laboratory, University of ­Montpellier
  • Jincy Johny, Robert Gordon University
  • Javier Busquets-Mataix, Norwegian University of Science and Technology
  • Cesar Galarza, Universitad Politecnica de Catalunya
  • Habib Mirhedayati Roudsari, Dalhousie University
  • Jonghyun Ahn, Kyushu Institute of Technology
  • Minsung Sung, Pohang University of Science and Technology
  • Zhi Li, Memorial University of Newfoundland
  • Zonghua Liu, University of Aberdeen
  • Gregory Murad Reis, Florida International University
  • Farheen Fauziya, IIT Delhi
  • Eduard Vidal, University of Girona
  • Puneet Chhabra, Heriot Watt University
  • Bilal Wehbe, DFKI—Robotics Innovation Center
  • Björn Barz, Friedrich Schiller University of Jena
  • Xinlong Liu, Memorial University of Newfoundland
  • Jianghui Li, University of York
  • Klemen Istenic, University of Girona
  • Felix Schneider, Friedrich Schiller University of Jena
Who will be the winner …

     The judging was completed on Thursday morning and the winners were announced in front of the delegates during a congratulatory speech given as part of the Closing Plenaries by Phillipe Courmontagne (Student Poster Chair, IEEE OES).
     Philippe Courmontagne presented, with Faye Campbell, each student with a Certificate of Participation in the OCEANS’17 MTS/IEEE Aberdeen. Then, Philippe Courmontagne presented the third place winner to Habib Mirhedayati Roudsari, from Canada. Then, he presented the second price to Bilal Wehbe, from Germany. The first price, the “Norman Miller’s Price”, has been presented to Klemen Istenic, from the University of Girona (Spain), for his poster entitled “Mission-time 3D Reconstruction with Quality Estimation”. As with previous years, monetary prizes were awarded for the posters collectively ranked 1st, 2nd and 3rd place by the judges ($3000, $2000 and $1000 respectively).
     The audience gave the students a big hand following the awards presentations. The session ended with a photograph session.
     The roster of students and their poster titles are given below with an abstract of their paper.

                                      
The awards ceremony.

Jialei Zhang, Huazhong University of Science and Technology
Automatic inspection of subsea optical cable by an autonomous underwater vehicle
The changes of the seafloor environment caused by natural disasters and human activities greatly shorten the life span of the subsea optical cable. It is urgent to carry out routine inspection and maintenance for subsea cables. In this paper, an automatic inspection system to address these problems is proposed where the localization and inspection mission is conducted by an autonomous underwater vehicle (AUV) carrying with a tri-axial electromagnetic sensor. Firstly, the framework of the inspection system is presented, and the function of each subsystem is introduced briefly. Secondly, the inspection algorithms are designed which include localization algorithm, online path planning and path following control algorithms. A dedicated particle swarm optimization (PSO) algorithm is adopted to localize the subsea optical cable. In addition, the swath path is planned online so that the AUV can detect the cable in the repeatedly crossing manner. With the planned of swath path, the cable detection is elaborately constructed as a classic path following control problem, such that the AUV can track the planned path and inspect the optical cable automatically. Finally, the numerical simulation results are provided to validate the effectiveness and feasibility of the automatic inspection system.

Yadpiroon Onmek, LIRMM Laboratory, University of Montpellier
3D Underwater Reconstruction of Archeology object with a mono camera camcorder: the case study at Gourneyras lake
This work presents the 3D reconstruction of the archeology objects in underwater environment. The videos and images are obtained from a calibrated camera system. The features of interest between image pairs are selected by the FAST detector and FLANN descriptor. Subsequently, the RANSAC method is applied to reject outlier points. The putative inliers are matched by triangulation to produce sparse point clouds in 3D space, using a pinhole camera model and Euclidean distance estimation. The 3D model textrue is achieved through a commond 3D Delaunay triangulation procedure. Moreover, evaluation of the quality of the underwater 3D reconstruction model to find its accuracy is done by measurement, comparing it with a 3D industrial scan model as well as a real object.

 

 

 

Jincy Johny, Robert Gordon University
Design of optical fibre based highly sensitive acoustic sensor for underwater applications
Fibre optic sensing is a key technology for a variety of underwater sensing and monitoring applications. Fibre optic acoustic sensors are mainly based on interferometric detection approach where the acoustic pressure-induced phase shift of light has been used as sensing principle. Recently, fibre optic acoustic sensors based on speciality fibres like Photonic Crystal Fibre (PCF) were reported. However, interferometry based detection approaches amongst all these fibre optics sensors are intensity based and therefore susceptible to light power fluctuations and require a complex instrumentation related to signal detection. Besides, wavelength based detection approach using FBG (Fibre Bragg Grating) offers significant advantages over the conventional approach. FBG sensors were reported to have higher performance for underwater acoustic sensing applications. This paper reports a novel design of an underwater acoustic pressure sensor using a combination of PCF and FBG to provide high sensitivity. Theoretical investigations were carried out on the PCF-FBG sensor to study the effect of applied pressure and induced strain on the FBG inscribed in the core of PCF. Effect of light confinement in PCF was studied for different geometrical parameters and 4-ring PCF structure was reported. Further, sensitivity enhancement was proposed utilizing air hole structure of the PCF to enhance the impact of acoustic pressure on the induced strain in FBG.

Javier Busquets-Mataix, Norwegian University of Science and Technology
Hybrid Glider Alba14 with Laser-Acoustic Data Transfer as a Low-Cost Independent Instrumentation Data-Mule
The management of sensor nodes on the sea floor represents an important challenge due to the high difficulties associated to the deployment and recovering of untethered instruments on the seafloor. These difficulties are not only related to the management of the instruments themselves but also related to the valuable information that can be lost. Ambient sound and interferences, maritime traffic, marine mammals behavior, oceanographic data are all among this critical information suitable to be registered. The fact of part of these information being missed, represent a void gap in the spatio-temporal identification phenomena analysis. For reducing the risk of losing information and with the aim of providing a feasible and low cost systems for recovering information from seafloor, a hybrid glider vehicle equipped with acoustic and laser optical communications is proposed. Preliminary studies as well as experimental results of optical/acoustic communications integrated in the hybrid glider Alba14 as data mule are presented.

Cesar Galarza, Universitad Politecnica de Catalunya
Design a vectorial propulsion system for Guanay II AUV *
The autonomous underwater vehicle (AUV) Guanay II was designed to navigate on the surface of the sea, and to realize a vertical immersion in specific points. This vehicle has three thrusters located on stern, oriented to provide propulsion and yaw control on the horizontal plane. On the other hand, the immersion system used in the Guanay II is based in the change of the buoyancy of the vehicle, by using a piston system. Therefore, the vehicle does not have the ability to navigate in immersion, due to its design, which the inclination of the vehicle (pitch angle) cannot be controlled. In this work, we show the design a vector propulsion system for the vehicle Guanay II, which will allow to control the pitch of the AUV in immersion. For this purpose, we have provided to the two laterals thruster the possibility of varying their propulsion angle on the vertical plane, using two servomotors. Next, we will show the design and the results obtained.

Habib Mirhedayati Roudsari, Dalhousie University
Channel Model for Wideband Time-Varying Underwater Acoustic Systems
In this paper a wideband underwater acoustic (UWA) channel simulator is developed based on the geometry of the system deployment and by considering the statistics of the random amplitude variation of the channel. This channel simulator is capable of modeling any relative motion between the transmitter and receiver. The delays of multipath arrivals are calculated based on the geometrical and physical parameters of the deployment. The time-varying fractional delay line (TVFDL) is utilized as a flexible and low-complexity software tool to model time-scaling observed on individual paths. The fading characteristics of the channel which is extracted from the measurements is utilized to model the time-varying amplitudes of paths. Also, an orthogonal frequency division multiplexing (OFDM) system is tested throughout a sea trial. The geometrical and statistical parameters of the sea trial are utilized to test the OFDM system using the proposed channel simulator. The bit error rate (BER) of the system is calculated in both
measurements and simulations and it will be shown that the assessment of the communication performance realized using simulations is very close to that of the measured performance.

Jonghyun Ahn, Kyushu Institute of Technology
Sea-Floor Image Transmission System for AUV
Autonomous Underwater Vehicle (AUV) has become one of the promising tool for ocean exploration during the last few decades, and, in particular, is the solution for the spatial-temporal investigations in wide areas for a long period. One of the next mission expected from AUV is deep sea specimen sampling, which is currently performed by Remotely Operated Vehicle (ROV) or Human Occupied Vehicle (HOV) where the sampling targets are selected by scientists on-line. In order to establish the similar on-line investigation with AUV system, the sea-floor images have to be transmitted to the scientists on the support vessel by acoustic communication. However, the speed of the acoustic communication is low compared with that of radio communication, and the data can be lost because of the directionality of acoustic modem, the positional relationship between the AUV and the support vessel, attenuation and so on. The robust image transmission system is necessary with acoustic communication for in-situ decision making for sampling by AUV with many tasks. In this paper, we propose a sea-floor image transmission system with image compression, and evaluated by sea trials in Suruga-bay. The image compression method is based on a set of color palettes, where the colors of a color palette are assigned as a set of main colors obtained from the minimum variance quantization, to represents a typical sea-floor image. The colors of the obtained images are replaced by the most similar colors in the color palette. The images compressed by a 16-colors color palette are evaluated by Structural SIMilarity (SSIM) method, and these compressed images have shown the SSIM index of 88.5%. The duration of one image transmission is about 40 seconds in the sea trials and the transmission success rate is 75%.

Minsung Sung, Pohang University of Science and Technology
Vision based Real-time Fish Detection Using Convolutional Neural Network
Underwater vision has specific characteristics such as high attenuation of lights, severe noise and haze in the images. For real-time fish detection using underwater vision, this paper proposes convolutional neural network based techniques based on You Only Look Once algorithm. Actual fish video images were used to evaluate the reliability and accuracy of the proposed method. As a result, the network recorded 93% classification accuracy, 0.634 intersection over union between predicted bounding box and ground truth, and 16.7 frames per second of fish detection. It also outperforms another fish detector using sliding window algorithm and classifier trained with histogram of oriented gradient features and support vector machine.

 

 

 

 

Zhi Li, Memorial University of Newfoundland
Vector Field Path Following Control for Unmanned Surface Vehicles
Many ocean survey missions require an Unmanned Surface Vehicle (USV) to accurately follow predefined paths, and thus, an efficient and robust path following control algorithm is essential for many applications. The Vector Field Method (VF) has been widely employed in the Unmanned Aerial Vehicle (UAV) community, and evaluating this well-accepted method for USVs will be of great interest for USV practitioners. In this paper, we will adapt and apply this algorithm on the USV path following problem. We provide a comprehensive study of the VF algorithm for tracking straight and circular paths, which includes searching the parameter space, doing simulation tests and carrying out field trials. Finally, a mock ocean survey task has been planned and the successful results prove the robustness and accuracy of the introduced VF algorithm.


Zonghua Liu, University of Aberdeen
Efficient Affine-invariant Fourier Descriptors for Identification of Marine Plankton
A study of population and distribution of plankton in the sea can be a good indicator of the health of the marine environment. Many digital images of marine plankton have been recorded. Image extraction and plankton identification can aid research of oceanic plankton. In this paper, we present a method to compute affine-invariant Fourier Descriptors (FDs) for marine plankton image retrieval. This method computes FDs of a shape boundary through the quasi-continuous Fourier transform. The experimental results show that the proposed FDs capture more information of the shape boundary than the the same number of traditional discrete FDs. Before calculation of FDs, each plankton image is pre-processed and the plankton shape is compacted into the boundary polygon. We have developed a set of approaches to quickly extract the exact and compact boundary polygon of an object, including methods of edge detection, boundary tracing, coordinate compensation of the boundary points and break-point detection. An affine-invariant curve normalization method then is adopted to reduce the geometrical deformations or distortions from the polygonal boundary curves possibly caused by changes of the camera angle. The experimental implementation shows that this curve normalization method is robust and can successfully eliminate transformations of translation, scaling, non-uniform scaling and shearing from two affine-transformrelated curves. Lastly, the ability of the proposed FDs to identify plankton images with deformations is tested on an artificial image dataset. The experiment shows that the proposed FDs have better performance than the traditional FDs in terms of retrieval efficiency.

Gregory Murad Reis, Florida International University
Increasing Persistent Navigation Capabilities for Underwater Vehicles with Augmented Terrain-Based Navigation
Accurate and energy-efficient navigation and localization methods for autonomous underwater vehicles continues to be an active area of research. As interesting as they are important, ocean processes are spatiotemporally dynamic and their study requires vehicles that can maneuver and sample intelligently while underwater for extended durations. In this paper, we present a new technique for augmenting terrain-based navigation with physical water data to enhance the utility of traditional methods for navigation and localization. We examine the construct of this augmentation method over a range of deployment regions, e.g., ocean and freshwater lake. Data from field trials are presented and analyzed for multiple deployments of an autonomous underwater vehicle.

 

 

Farheen Fauziya, IIT Delhi
AoA based Analysis of Vector Sensor Receiver for Underwater Acoustic Communications
In this paper, we analyze a compact vector sensor receiver using angle of arrival (AoA) framework which was recently proposed by Fauziya et. al. We demonstrate that this receiver performs better than a scalar receiver at no extra computational cost. The receiver exploits the inherent capability of a vector sensor to provide spatial diversity without the use of a sensor array. The paper also discuss a compressive sensing based channel estimator that performs better than a least squares estimator. Channel estimation is performed using a training sequence and the simulation clearly bring out the superior performance of the compressive sensing based channel estimator and that of the vector sensor based compact receiver.

 

 

 

Eduard Vidal, University of Girona
AUV Online Mission Replanning for Gap Filling and Target Inspection
In most of the current operational autonomous underwater vehicles (AUVs), a survey mission is generally composed of two main stages. The first one conducts an exhaustive coverage over an area of interest, while gathering data of the sea bottom. Then, and after processing the collected data, a second mission is programmed to obtain more detailed information of potential targets, and to cover the gaps that resulted from the first exploration. However, this two-survey strategy can be inefficient, since it requires establishing a communication link between the AUV and its operator for retrieving the data and reprogramming the second mission. To cope with this situation, we present a mission planner that endows an AUV with the capability of extending its missions online. With our approach, the vehicle is also required to conduct an initial and predefined survey of an area of interest, but it processes the gathered data onboard to plan 3D feasible paths to complement the initial exploration. To validate our approach, we present real-world results with the AsterX AUV.

Puneet Chhabra, Heriot Watt University
Target Classification in SAS Imagery using Orthogonal Basis Selection
This work proposes an approach that finds efficient representations for training and classification of different mine like objects (MLOs) in underwater imagery, e.g. side scan sonar and synthetic aperture sonar (SAS). The focus is on the design and selection of a compact, optimal and a non linear subspace, a dictionary, based on the gradient and curvature models in 2D images. Here, the traditional sparse approximation formulation is decoupled and modified by an additional discriminating objective function and a corresponding selection strategy is proposed. During training, using a set of labelled sonar images, a single optimized discriminatory dictionary is learnt which can then be used to represent MLOs. During classification, this dictionary together with optimized coefficient vectors is used to label scene entities. Evaluation of our approach has resulted in classification accuracies of 95% and 94% on realistic synthetic side-scan images and real CMRE SAS imagery, respectively.

 

 

Bilal Wehbe, DFKI—Robotics Innovation Center
Learning Coupled Dynamic Models of Underwater Vehicles using Support Vector Regression
This work addresses a data driven approach which employs a machine learning technique known as Support Vector Regression (SVR), to identify the coupled dynamical model of an autonomous underwater vehicle. To train the regressor, we use a dataset collected from the robot’s on-board navigation sensors and actuators. To achieve a better fit to the experimental data, a variant of a radial-basis-function kernel is used in combination with the SVR which accounts for the different complexities of each of the contributing input features of the model. We compare our method to other explicit hydrodynamic damping models that were identified using the total least squares method and with less complex SVR methods. To analyze the transferability, we clearly separate training and testing data obtained in real-world experiments. Our presented method shows much better results especially compared to classical approaches.

Björn Barz, Friedrich Schiller University of Jena
Maximally Divergent Intervals for Extreme Weather Event Detection
We approach the task of detecting anomalous or extreme events in multivariate spatio-temporal climate data using an unsupervised machine learning algorithm for detection of anomalous intervals in time-series. In contrast to many existing algorithms for outlier and anomaly detection, our method does not search for point-wise anomalies, but for contiguous anomalous intervals. We demonstrate the suitability of our approach through numerous experiments on climate data, including detection of hurricanes, North Sea storms, and low-pressure fields.

 

 

 

 

 

 

Xinlong Liu, Memorial University of Newfoundland
Wind Speed Determination From X-Band Nautical Radar Images
A new method is presented for estimation of wind speed from X-band nautical radar sea surface images. Ensemble empirical mode decomposition (EEMD) is first applied to the radar data. A normalization scheme is then used to obtain the amplitude modulation (AM) part of the first intrinsic mode function (IMF). Wind speed is determined from a combination of the first IMF and the residual using a logarithmic relationship. The method can be applied to both rain-free and rain-contaminated radar images. Radar and anemometer data collected in a sea trial off the east coast of Canada are employed for the test. Compared to the spectral-analysis-based method, the proposed method improves the wind speed result with an increase of about 0.06 in the correlation coefficient and a decrease of about 0.28 m/s in the root-mean-square (RMS) difference with respect to the reference.

 

 

 

Jianghui Li, University of York
DOA tracking in time-varying underwater acoustic communication channels
Underwater acoustic (UWA) communication channels are characterized by spreading of received signals in directions of arrival (DOAs). The DOAs often vary rapidly within small angular intervals, which are usually produced for the most part by moving ocean surface/internal waves and platforms. In this paper, the time-varying UWA communication channels are investigated. Based on the investigation, a beamforming technique that tracks DOAs is proposed, and used for UWA communications with guard-free orthogonal frequency-division multiplexing (OFDM) signal transmission. This beamforming is compared with a beamforming without DOA tracking, and the results show that the receiver with this beamforming outperforms that without DOA tracking. The comparison is based on data from a 14-element non-uniform vertical linear array in a simulation at a distance of 80 km, and in two sea trials at distances of 30 km and 105 km.

 

 

Klemen Istenic, University of Girona
Mission-time 3D Reconstruction with Quality Estimation

Accurate and detailed 3-dimensional (3D) models of the underwater environment are becoming increasingly important in modern marine surveys, since they convey immense information that can be easily interpreted. Techniques such as bundle adjustment (BA) and structure from motion (SfM), which jointly estimate sparse 3D points of the scene and camera poses, have gained popularity in underwater mapping applications. However, for large-area surveys these methods are computationally expensive and not intended for online application. This paper proposes an SfM pipeline based on solving the BA problem in an incremental and efficient way. Furthermore, the new system can provide not only the solution of the optimization (camera trajectory along time and the 3D points of the environment), but also the estimate of the uncertainty associated with the 3D reconstruction. This system is able to produce results in mission-time, i.e. while the robot is in the water or very shortly afterwards. Such quick availability is of great importance during survey operations as it allows data quality assessment in-situ, and eventual replanning of missions in case of need.

 

Felix Schneider, Friedrich Schiller University of Jena
Modelling Ocean Parameters Through Graphical Models
Ocean parameter modelling is an important task for many fields. While using simulations and simple statistical models may not yield desired results in reasonable time, using graphical models like Bayesian networks can address this problem. In this paper, we show the application of Bayesian networks to the tasks of estimating and predicting significant wave heights in the North Sea. Additionally we present the K2 IO algorithm, a modification to the K2 algorithm developed for the prediction task. Experiments show the possibilities and problems of estimation and prediction with Bayesian networks. They also show that the K2 IO algorithm produces a structure that is suitable for prediction in a shorter time than the K2 algorithm.


   

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