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Article Reference SMACC: A System for Microplastics Automatic Counting and Classification
by Antonio C. Domínguez Brito published Feb 17, 2020
The management of plastic debris is a serious issue due to its durability. Unfortunately, million tons of plastic end up in the sea becoming one of the biggest current environmental problems. One way to monitor the amount of plastic in beaches is to collect samples and visually count and sort the plastic particles present in them. This is a very time-consuming task. In this work, we present a Computer Vision-based system which is able to automatically count and classify microplastic particles (1-5 mm) into five different visual classes. After cleaning a collected sample in the lab, the proposed system makes use of a pair of its images with different characteristics. The procedure includes a segmentation step, which is based on the Sauvola thresholding method, followed by a feature extraction and classification step. Different features and classifiers are evaluated as well as a deep learning approach. The system is tested on 12 different beach samples with a total of 2507 microplastic particles. The particles of each sample were manually counted and sorted by an expert. This data represents the ground truth, which is compared later with the results of the automatic processing proposals to evaluate their accuracy. The difference in the number of particles is 34 (1.4%) and the error in their classification is less than 4% for all types except for the line shapes particles. These results are obtained in less than half of the time needed by the human expert doing the same task manually. This implies that it is possible to process more than twice as many samples using the same time, allowing the biologists to monitor wider areas and more frequently than doing the process manually.
Located in Publicaciones / Publications
Article Reference Multi-frequency and light-avoiding characteristics of deep acoustic layers in the North Atlantic
by Antonio C. Domínguez Brito published Feb 17, 2020
This study aimed to add light-avoidance as a categorizing technique for the study of mesopelagic acoustic layers. Data recorded along the 20° W parallel from 20° N to Iceland showed three types of mesopelagic layers: the non-avoiding non-migrant deep scattering layer (NMDSL), which dropped its intensity toward the north, the avoiding migrating fish layers (MDSL), which were more intense at upwelling areas and toward the north, and a secondary deeper NMDSL at the southern part. Light avoidance was only discernible at 18 kHz within the main NMDSL when this layer was intense, suggesting that migrants are barely seen at 38 kHz when other resonant scatterers occupy these depths. These results highlight the importance of employing the 18 kHz frequency from a vessel borne echosounder or lowered echosounders attached to a probe to study gas-bearing migrants.
Located in Publicaciones / Publications
Article Reference An Approach to Multi-Objective Path Planning Optimization for Underwater Gliders
by Antonio C. Domínguez Brito published Feb 16, 2020
Underwater gliders are energy-efficient vehicles that rely on changes in buoyancy in order to convert up and down movement into forward displacement. These vehicles are conceived as multi-sensor platforms, and can be used to collect ocean data for long periods in wide range areas. This endurance is achieved at the cost of low speed, which requires extensive planning to ensure vehicle safety and mission success, particularly when dealing with strong ocean currents. As gliders are often involved on missions that pursue multiple objectives (track events, reach a target point, avoid obstacles, sample specified areas, save energy), path planning requires a way to deal with several constraints at the same time; this makes glider path planning a multi-objective (MO) optimization problem. In this work, we analyse the usage of the non-dominated sorting genetic algorithm II (NSGA-II) to tackle a MO glider path planning application on a complex environment integrating 3D and time varying ocean currents. Multiple experiments using a glider kinematic simulator coupled with NSGA-II, combining different control parameters were carried out, to find the best parameter configuration that provided suitable paths for the desired mission. Ultimately, the system described in this work was able to optimize multi-objective trajectories, providing non dominated solutions. Such a planning tool could be of great interest in real mission planning, to assist glider pilots in selecting the most convenient paths for the vehicle, taking into account ocean forecasts and particular characteristics of the deployment location.
Located in Publicaciones / Publications
Article Reference A DIY Low-Cost Wireless Wind Data Acquisition System Used to Study an Arid Coastal Foredune
by Antonio C. Domínguez Brito published Feb 16, 2020
Environmental studies on coastal dune systems are faced with a considerable cost barrier due to the cost of the instrumentation and sensory equipment required for data collection. These systems play an important role in coastal areas as a protection against erosion and as providers of stability to coastal sedimentary deposits. The DIY (Do-It-Yourself) approach to data acquisition can reduce the cost of these environmental studies. In this paper, a low-cost DIY wireless wind data acquisition system is presented which reduces the cost barrier inherent to these types of studies. The system is deployed for the analysis of the foredune of Maspalomas, an arid dune field situated on the south coast of Gran Canaria (Canary Islands, Spain), for the specific purpose of studying the dynamics of a dune type (tongue dunes), which is typical of this environment. The results obtained can be of interest for the study of these coastal environments at both the local level, for the management of this particular dune field, and at the general level for other similar dune fields around the world.
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Article Reference ACUSQUAT Project: Acoustic behavioural monitoring of the Angelshark (Squatina squatina) in critical conservation areas
by Antonio C. Domínguez Brito published Jan 20, 2020
Located in Publicaciones / Publications
Article Reference Success history applied to expert system for underwater glider path planning using differential evolution
by Antonio C. Domínguez Brito published Dec 20, 2019
This paper presents an application of a recently well performing evolutionary algorithm for continuous numerical optimization, Success-History Based Adaptive Differential Evolution Algorithm (SHADE) including Linear population size reduction (L-SHADE), to an expert system for underwater glider path planning (UGPP). The proposed algorithm is compared to other similar algorithms and also to results from literature. The motivation of this work is to provide an alternative to the current glider mission control systems, that are based mostly on multidisciplinary human-expert teams from robotic and oceanographic areas. Initially configured as a decision-support expert system, the natural evolution of the tool is targeting higher autonomy levels. To assess the performance of the applied optimizers, the test functions for UGPP are utilized as defined in literature, which simulate real-life oceanic mission scenarios. Based on these test functions, in this paper, the performance of the proposed application of L-SHADE to UGPP is aggregated using statistical analyis. The depicted fitness convergence graphs, final obtained fitness plots, trajectories drawn, and per-scenario analysis show that the new proposed algorithm yields stable and competitive output trajectories. Over the set of benchmark missions, the newly obtained results with a configured L-SHADE outperforms existing literature results in UGPP and ranks best over the compared algorithms. Moreover, some additional previously applied algorithms have been reconfigured to yield improved performance. Thereby, this new application of evolutionary algorithms to UGPP contributes significantly to the capacity of the decision-makers, when they use the improved UGPP expert system yielding better trajectories.
Located in Publicaciones / Publications
Article Reference Deep learning for source camera identification on mobile devices
by Antonio C. Domínguez Brito published Dec 20, 2019
In the present paper, we propose a source camera identification (SCI) method for mobile devices based on deep learning. Recently, convolutional neural networks (CNNs) have shown a remarkable performance on several tasks such as image recognition, video analysis or natural language processing. A CNN consists on a set of layers where each layer is composed by a set of high pass filters which are applied all over the input image. This convolution process provides the unique ability to extract features automatically from data and to learn from those features. Our proposal describes a CNN architecture which is able to infer the noise pattern of mobile camera sensors (also known as camera fingerprint) with the aim at detecting and identifying not only the mobile device used to capture an image (with a 98% of accuracy), but also from which embedded camera the image was captured. More specifically, we provide an extensive analysis on the proposed architecture considering different configurations. The experiment has been carried out using the images captured from different mobile device cameras (MICHE-I Dataset) and the obtained results have proved the robustness of the proposed method.
Located in Publicaciones / Publications
Article Reference A semantic parliamentary multimedia approach for retrieval of video clips with content understanding
by Antonio C. Domínguez Brito published Dec 20, 2019
Digital videos of parliamentary activity play an important role in enhancing transparency and accountability for open e-government. The rapid growth in these videos and the lack of semantic annotations and relationships between video and knowledge resources make it increasingly difficult to find accurate video clips with contextual information for content understanding. To overcome this problem, we highlight the need for building multimedia systems based on a semantic vision. With this aim, we focus on (1) how to address the knowledge representation for automatic extraction of contextual information for video content understanding; (2) how to link the parliamentary knowledge structures within video resources to provide accurate video clips retrieval; and (3) how to perform semantic annotation on video resources. The methodology applied is focused on a systematic approach that uses techniques from ontology engineering. This approach is based on the definition of two models: the semantic model and the reference architecture. The semantic model is composed of a reference ontology and a semantic video annotation framework. The ontology provides the support for video content understanding and the semantic vocabulary for annotating video resources. The video annotation framework is based on an RDF-powered semantic video annotation to effectively relate low- and mid-level visual features, corresponding to speakers’ interventions, to high-level parliamentary concepts. To evaluate the proposed system, a prototype for the Canary Islands Parliament (Spain) has been carried out. The results show how semantic enhancement is a key enabler for improved video retrieval on parliamentary multimedia content.
Located in Publicaciones / Publications
Article Reference ILRA: Novelty Detection in Face-Based Intervener Re-Identification
by Antonio C. Domínguez Brito published Dec 20, 2019
Transparency laws facilitate citizens to monitor the activities of political representatives. In this sense, automatic or manual diarization of parliamentary sessions is required, the latter being time consuming. In the present work, this problem is addressed as a person re-identification problem. Re-identification is defined as the process of matching individuals under different camera views. This paper, in particular, deals with open world person re-identification scenarios, where the captured probe in one camera is not always present in the gallery collected in another one, i.e., determining whether the probe belongs to a novel identity or not. This procedure is mandatory before matching the identity. In most cases, novelty detection is tackled applying a threshold founded in a linear separation of the identities. We propose a threshold-less approach to solve the novelty detection problem, which is based on a one-class classifier and therefore it does not need any user defined threshold. Unlike other approaches that combine audio-visual features, an Isometric LogRatio transformation of a posteriori (ILRA) probabilities is applied to local and deep computed descriptors extracted from the face, which exhibits symmetry and can be exploited in the re-identification process unlike audio streams. These features are used to train the one-class classifier to detect the novelty of the individual. The proposal is evaluated in real parliamentary session recordings that exhibit challenging variations in terms of pose and location of the interveners. The experimental evaluation explores different configuration sets where our system achieves significant improvement on the given scenario, obtaining an average F measure of 71.29% for online analyzed videos. In addition, ILRA performs better than face descriptors used in recent face-based closed world recognition approaches, achieving an average improvement of 1.6% with respect to a deep descriptor.
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Inproceedings Reference Acoustic Detection of Tagged Angelsharks from an Autonomous Sailboat
by Antonio C. Domínguez Brito published Dec 10, 2019 last modified Dec 21, 2019 02:38 PM
Autonomous sailboats are silent surface vehicles which are well suited for acoustic monitoring. The integration of an acoustic receiver in an unmanned surface vehicle has a large potential for population monitoring as it permits to report geo-referenced detections in real time, so that researchers can adapt monitoring strategies as data arrive. In this paper we present preliminary work, done on the framework of ACUSQUAT project, to explore the usage of an acoustic receiver onboard a small (2 m length-over-all) autonomous sailboat in order to detect the presence of tagged adult exemplars of angelshark (Squatina squatina), the target species in ACUSQUAT, in certain areas which have demonstrated that this approach is feasible. Results obtained in simulation and during field trials are presented.
Located in Publicaciones / Publications