The influences of basic physical properties of clayey silt and silty sand on its laboratory electrical resistivity value in loose and dense conditions

Non-destructive test which refers to electrical resistivity method is recently popular in engineering, environmental, archaeological and mining studies. Based on the previous studies, the results on electrical resistivity interpretation were often debated due to lack of clarification and evidences in quantitative perspective. Traditionally, most of the previous result interpretations were depending on qualitative point of view which is risky to produce unreliable outcomes. In order to minimise those problems, this study has performed a laboratory experiment on soil box electrical resistivity test which was supported by an additional basic physical properties of soil test like particle size distribution test (d), moisture content test (w), density test (ρbulk) and Atterberg limit test (LL, PL and PI). The test was performed to establish a series of electrical resistivity value (ERV) with different quantity of water content for clayey silt and silty sand in loose and dense condition. Apparently, the soil resistivity value was different under loose (L) and dense (C) conditions with moisture content and density variations (silty SAND = ERVLoose: 600 - 7300 Ωm & ERVDense: 490 - 7900 Ωm while Clayey SILT = ERVLoose: 13 - 7700 Ωm & ERVDense: 14 - 8400 Ωm) due to several factors. Moreover, correlation of moisture content (w) and density (ρbulk) due to the ERV was established as follows; Silty SAND: w(L) = 638.8ρ-0.418, w(D) = 1397.1ρ-0.574, ρBulk(L) = 2.6188e-6E-05ρ, ρBulk(D) = 4.099ρ-0.07 while Clayey SILT: w(L) = 109.98ρ-0.268, w(D) = 121.88ρ-0.363, ρBulk(L) = -0.111ln(ρ) + 1.7605, ρBulk(D) = 2.5991ρ-0.037 with determination coefficients, R2 that varied from 0.5643 – 0.8927. This study was successfully demonstrated that the consistency of ERV was greatly influenced by the variation of soil basic physical properties (d, w, ρBulk, LL, PL and PI). Finally, the reliability of the ERV result interpretation can be enhanced due to its ability to produce a meaningful outcome based on supported data from basic geotechnical properties

The influences of basic physical properties of clayey silt and silty sand on its laboratory electrical resistivity value in loose and dense conditions

Non-destructive test which refers to electrical resistivity method is recently popular in engineering, environmental, archaeological and mining studies. Based on the previous studies, the results on electrical resistivity interpretation were often debated due to lack of clarification and evidences in quantitative perspective. Traditionally, most of the previous result interpretations were depending on qualitative point of view which is risky to produce unreliable outcomes. In order to minimise those problems, this study has performed a laboratory experiment on soil box electrical resistivity test which was supported by an additional basic physical properties of soil test like particle size distribution test (d), moisture content test (w), density test (ρbulk) and Atterberg limit test (LL, PL and PI). The test was performed to establish a series of electrical resistivity value (ERV) with different quantity of water content for clayey silt and silty sand in loose and dense condition. Apparently, the soil resistivity value was different under loose (L) and dense (C) conditions with moisture content and density variations (silty SAND = ERVLoose: 600 - 7300 Ωm & ERVDense: 490 - 7900 Ωm while Clayey SILT = ERVLoose: 13 - 7700 Ωm & ERVDense: 14 - 8400 Ωm) due to several factors. Moreover, correlation of moisture content (w) and density (ρbulk) due to the ERV was established as follows; Silty SAND: w(L) = 638.8ρ-0.418, w(D) = 1397.1ρ-0.574, ρBulk(L) = 2.6188e-6E-05ρ, ρBulk(D) = 4.099ρ-0.07 while Clayey SILT: w(L) = 109.98ρ-0.268, w(D) = 121.88ρ-0.363, ρBulk(L) = -0.111ln(ρ) + 1.7605, ρBulk(D) = 2.5991ρ-0.037 with determination coefficients, R2 that varied from 0.5643 – 0.8927. This study was successfully demonstrated that the consistency of ERV was greatly influenced by the variation of soil basic physical properties (d, w, ρBulk, LL, PL and PI). Finally, the reliability of the ERV result interpretation can be enhanced due to its ability to produce a meaningful outcome based on supported data from basic geotechnical properties

Vision-based discrimination of tuna individuals in grow-out cages through a fish bending model

This paper proposes a robust deformable adaptive 2D model, based on computer vision methods, that automatically fits the body (ventral silhouette) of Bluefin tuna while swimming. Our model (without human intervention) adjusts to fish shape and size, obtaining fish orientation, bending to fit their flexion motion and has proved robust enough to overcome possible segmentation inaccuracies. Once the model has been successfully fitted to the fish it can ensure that the detected object is a tuna and not parts of fish or other objects. Automatic requirements of the fishing industry like biometric measurement, specimen counting or catch biomass estimation could then be addressed using a stereoscopic system and meaningful information extracted from our model. We also introduce a fitting procedure based on a fitting parameter - Fitting Error Index (FEI) - which permits us to know the quality of the results. In the experiments our model has achieved very high success rates (up to 90%) discriminating individuals in highly complex images acquired for us in real conditions in the Mediterranean Sea. Conclusions and future improvements to the proposed model are also discussed.This work was partially supported by the EU Commission [2013/410/EU] (BIACOP project). We acknowledge funding of ACUSTUNA project ref. CTM2015-70446-R (MINECO/FEDER, UE).Atienza-Vanacloig, V.; Andreu García, G.; López García, F.; Valiente González, JM.; Puig Pons, V. (2016). Vision-based discrimination of tuna individuals in grow-out cages through a fish bending model. Computers and Electronics in Agriculture. 130:142-150. https://doi.org/10.1016/j.compag.2016.10.009S14215013

Automatic Bluefin Tuna sizing using a stereoscopic vision system

[EN] This article presents a non-invasive fully automatic procedure for Bluefin Tuna sizing, based on a stereoscopic vision system and a deformable model of the fish ventral silhouette. An image processing procedure is performed on each video frame to extract individual fish, followed by a fitting proce- dure to adjust the fish model to the extracted targets, adapting it to the bending movements of the fish. The proposed system is able to give accu- rate measurements of tuna snout fork length (SFL) and widths at five predefined silhouette points without manual intervention. In this work, the system is used to study size evolution in adult Atlantic Bluefin Tuna (Thunnus Thynnus) over time in a growing farm. The dataset is composed of 12 pairs of videos, which were acquired once a month in 2015, between July and October, in three grow-out cages of tuna aquaculture facilities on the west Mediterranean coast. Each grow out cage contains between 300 and 650 fish on an approximate volume of 20 000 m3.Measurements were au- tomatically obtained for the 4 consecutive months after caging and suggest a fattening process: SFL shows an increase of just a few centimetres (2%) while themaximum width (A1)shows arelative increaseofmorethan20%,mostlyinthe first 2months in farm. Moreover, a linear relation (with co- efficient of determination R2> 0.98) between SFL and widths for each month is deduced, and a fattening factor (F) is introduced. The validity of the measurements is proved by comparing 15 780 SFL measurements, obtained with our automatic system in the last month, versus ground truth data of a high percentage of the stock under study (1143 out of 1579), obtaining no statistically significant difference. This procedure could be extended to other species to assess the size distribution of stocks, as discussed in the article.This work was supported by funding from ACUSTUNA project ref. CTM2015-70446-R (MINECO/ERDF, EU). This project has been possible thanks to the collaboration of IEO (Spanish Oceanographic Institute).Muñoz-Benavent, P.; Andreu García, G.; Valiente González, JM.; Atienza-Vanacloig, V.; Puig Pons, V.; Espinosa Roselló, V. (2018). Automatic Bluefin Tuna sizing using a stereoscopic vision system. ICES Journal of Marine Science. 75(1):390-401. https://doi.org/10.1093/icesjms/fsx151S39040175

Enhanced fish bending model for automatic tuna sizing using computer vision

[EN] This paper presents a non-invasive fully automatic procedure to obtain highly accurate fish length estimation in adult Bluefin Tuna, based on a stereoscopic vision system and a deformable model of the fish ventral silhouette. The present work takes a geometric tuna model, which was previously developed by the same authors to discriminate fish in 2D images, and proposes new models to enhance the capabilities of the automatic procedure, from fish discrimination to accurate 3D length estimation. Fish length information is an important indicator of the health of wild fish stocks and for predicting biomass using length-weight relations. The proposal pays special attention to parts of the fish silhouette that have special relevance for accurate length estimation. The models have been designed to best fit the rear part of the fish, in particular the caudal peduncle, and a width parameter has been added to better fit the silhouette. Moreover, algorithms have been developed to extract snout tip and caudal peduncle features, allowing better initialization of model parameters. Snout Fork Length (SFL) measurements using the different models are extracted from images recorded with a stereoscopic vision system in a sea cage containing 312 adult Atlantic Bluefin Tuna. The automatic measurements are compared with two ground truths: one configured with semiautomatic measurements of favourable selected samples and one with real SFL measurements of the tuna stock collected at harvesting. Comparison with the semiautomatic measurements demonstrates that the combination of improved geometric models and feature extraction algorithms delivers good results in terms of fish length estimation error (up to 90% of the samples bounded in a 3% error margin) and number of automatic measurements (up to 950 samples out of 1000). When compared with real SFL measurements of the tuna stock, the system provides a high number of automatic detections (up to 6706 in a video of 135¿min duration, i.e., 50 automatic measurements per minute of recording) and highly accurate length measurements, obtaining no statistically significant difference between automatic and real SFL frequency distributions. This procedure could be extended to other species to assess the size distribution of stocks, as discussed in the paper.This work was supported by funding from ACUSTUNA project ref. CTM2015-70446-R (MINECO/ERDF, EU). This project has been possible thanks to the collaboration of IEO (Spanish Oceanographic Institute). We acknowledge the assistance provided by the Spanish company Grup Balfego S.L. in supplying boats and divers to acquire underwater video in the Mediterranean Sea.Muñoz-Benavent, P.; Andreu García, G.; Valiente González, JM.; Atienza-Vanacloig, V.; Puig Pons, V.; Espinosa Roselló, V. (2018). Enhanced fish bending model for automatic tuna sizing using computer vision. Computers and Electronics in Agriculture. 150:52-61. https://doi.org/10.1016/j.compag.2018.04.005S526115

Automatic Bluefin Tuna Sizing with a Combined Acoustic and Optical Sensor

[EN] A proposal is described for an underwater sensor combining an acoustic device with an optical one to automatically size juvenile bluefin tuna from a ventral perspective. Acoustic and optical information is acquired when the tuna are swimming freely and the fish cross our combined sensor's field of view. Image processing techniques are used to identify and classify fish traces in acoustic data (echogram), while the video frames are processed by fitting a deformable model of the fishes' ventral silhouette. Finally, the fish are sized combining the processed acoustic and optical data, once the correspondence between the two kinds of data is verified. The proposed system is able to automatically give accurate measurements of the tuna's Snout-Fork Length (SFL) and width. In comparison with our previously validated automatic sizing procedure with stereoscopic vision, this proposal improves the samples per hour of computing time by 7.2 times in a tank with 77 juveniles of Atlantic bluefin tuna (Thunnus thynnus), without compromising the accuracy of the measurements. This work validates the procedure for combining acoustic and optical data for fish sizing and is the first step towards an embedded sensor, whose electronics and processing capabilities should be optimized to be autonomous in terms of the power supply and to enable real-time processing.This work was supported by funding from ACUSTUNA project ref. CTM2015-70446-R (MINECO/ERDF, EU) and PAID-10-19 (UPV).Muñoz-Benavent, P.; Puig Pons, V.; Andreu García, G.; Espinosa Roselló, V.; Atienza-Vanacloig, V.; Pérez Arjona, I. (2020). Automatic Bluefin Tuna Sizing with a Combined Acoustic and Optical Sensor. Sensors. 20(18):1-17. https://doi.org/10.3390/s20185294S117201

Proyecto estimacion automatica del peso y longitud de peces: grupo de investigacion TECNIO - Informe Final Pasantia

Las características de los peces como su longitud y el peso son datos esenciales para el proceso de control del desarrollo en entornos orientados a la cría. Sin embargo, obtener estos datos tradicionalmente implica la extracción del hábitat de los individuos. Esto los somete a un conjunto de factores de riesgo que puede causar estrés, heridas o incluso su muerte si las infecciones no son controladas. Adicionalmente, la información de talla y peso es necesaria para determinar el momento adecuado para su venta o para determinar la cantidad adecuada de alimento que se debe suministrar en cada etapa del proceso de cría. Este trabajo se enmarca dentro del proyecto de investigación titulado “Estimación automática de peso y longitud de peces mediante técnicas de visión por computador”, el cual se orientó al diseño e implementación de un modelo basado en técnicas de visión por computadora y procesamiento de imágenes direccionado a mitigar el impacto de la adquisición de estas características. Este trabajo inicia con la modificación del método de pre-procesamiento de la imagen propuesto en el marco del proyecto general. La modificación consistió en la inclusión de un filtro guiado para la eliminación de ruido como etapa final del pre-procesamiento. La modificación fue necesaria para mejorar el resultado de las etapas posteriores del proceso. En particular, la etapa de segmentación. En la etapa de segmentación, se revisó la literatura de métodos relacionados con segmentación de peces en imágenes y se propuso un método hibrido. Se utilizó una combinación de filtros, operaciones morfológicas y mapas de salencia. La correcta segmentación permite, fácilmente, verificar los ejes principales de la morfología del pez y estimar la longitud en unidades del sistema métrico internacional. Posterior a la segmentación, se construyó un procedimiento para la estimación de la longitud del pez segmentado. El procedimiento consistió en interpolar los puntos medios de la morfología del pez a través de un método de regresión polinomial de grado tres y estimar su longitud en pixeles. Finalmente, una relación pixel – centímetros se estimó de acuerdo a la distancia de adquisición. Debido a la fuerte relación longitud-peso encontrada en los peces, las mediciones de estos también permiten estimar su peso. Se construyó una base de datos con individuos reales, se relacionaron imágenes, pesos y longitudes. Debido a que el alcance limita el tratamiento de una sola especie de peces, los datos reales de talla y peso se utilizaron para construir un modelo del comportamiento de la relación longitud-peso mediante regresión polinomial de tercer grado. Es decir, un predictor del peso basado en la longitud del pez para la especie seleccionada. El predictor construido se usó para calcular el peso de nuevos individuos, el cual se usó una vez segmentada la imagen y estimada su longitud. Las pruebas experimentales muestran un comportamiento de error al estimar el peso cercano a 11%

Análisis de imágenes microscópicas para la determinación de la cantidad y el tamaño de larvas de concha de abanico

La concha de abanico es una especie que está siendo muy requerida por el mercado nacional y principalmente internacional. Una de las etapas más importantes en su producción es el abastecimiento de semillas de conchas de abanico. Existen dos métodos para obtenerlas: una es mediante la captación natural y otra es a través de su producción en laboratorios especializados. Debido a los bajos volúmenes obtenidos a través de la captación natural, el Fondo Nacional de Desarrollo Pesquero (FONDEPES) ha implementado un centro para la producción de conchas de abanico en el Centro de Acuicultura La Arena, Playa El Basurero, Distrito Comandante Noel, Provincia de Casma, Departamento de Ancash. Es ahí donde se realiza la producción de semillas de conchas de abanico las cuales son alimentadas con microalgas, también producidas en el laboratorio a partir de cepas seleccionadas. Las larvas son monitoreadas diariamente por personal especializado, empleando un microscopio, para determinar su estado y volumen debido a que esta especie presenta una alta mortandad llegando en algunos casos a eliminar todo el volumen si es que no ha logrado el crecimiento adecuado debido a que son muy sensibles a varios factores como luz, temperatura, alimento, entre otros.Tesi

Diseño y desarrollo de un algoritmo que permita estimar el tamaño de peces, aplicando visión por computadora, y propuesta para realizar la selección adecuada de dichos peces

Se plantea el desarrollo de un algoritmo que permita estimar el tamaño de los peces sin la necesidad de que haya contacto físico entre el hombre y los animales aplicando, para ello, técnicas de visión por computadora. Para realizar el planteamiento se realizó estudios de las diferentes técnicas empleadas en visión por computadora y la necesidad de contar con imágenes tomadas por cámaras seleccionadas para el posterior procesamiento con los métodos estudiados.Tesi

Estágio de investigação aplicada no LINE.IPT/TAGUSVALLEY- Tecnopolo do Vale do Tejo

No âmbito da unidade curricular de Estágio, do 2º ano do curso de Mestrado em Engenharia Eletrotécnica – Especialização em Controlo e Eletrónica Industrial, foi efetuado um estágio curricular, com a duração de 1458 horas, correspondendo a aproximadamente 9 meses. O estágio foi realizado no parque tecnológico do Vale do Tejo, TAGUSVALLEY - Abrantes, no departamento do LINE.IPT – Laboratório de Inovação Industrial e Empresarial. Este centro de investigação desenvolve produtos, soluções e tecnologias essencialmente para as empresas, com especial foco para projetos na área da eletrotecnia, mecânica, informática e tecnologias de informação e comunicação. No presente relatório, expõe-se o trabalho desenvolvido durante o período de estágio, bem como os fundamentos teóricos e a descrição detalhada de cada um dos projetos desenvolvidos nesse âmbito. Os projetos desenvolvidos relacionam-se sobretudo com o processamento de imagem ou visão por computador, no entanto foi ainda desenvolvido algum trabalho no âmbito da programação eletrónica de microcontroladores e microprocessadores. O início de cada um dos projetos foi procedido por uma fase de pesquisa em processamento de imagem e visão por computador, com o objetivo de definir métodos e ferramentas a utilizar, e escolha do equipamento fotográfico e de iluminação. Tendo em vista a complexidade e especificidade técnica do trabalho a executar, foi necessário prever e frequentar um conjunto de formações em visão artificial e métodos avançados de visão artificial. As tarefas desenvolvidas nas áreas da eletrotecnia e informática, o trabalho em equipa, a colaboração com várias entidades assim como o contacto com vários projetos de investigação e desenvolvimento, foram uma experiência bastante enriquecedora, que contribuíram positivamente para a aquisição de novas competências e para a integração no mercado de trabalho e no mundo empresarial na área da eletrotecnia