Automated artemia length measurement using U-shaped fully convolutional networks and second-order anisotropic Gaussian kernels

The brine shrimp Artemia, a small crustacean zooplankton organism, is universally used as live prey for larval fish and shrimps in aquaculture. In Artemia studies, it would be highly desired to have access to automated techniques to obtain the length information from Anemia images. However, this problem has so far not been addressed in literature. Moreover, conventional image-based length measurement approaches cannot be readily transferred to measure the Artemia length, due to the distortion of non-rigid bodies, the variation over growth stages and the interference from the antennae and other appendages. To address this problem, we compile a dataset containing 250 images as well as the corresponding label maps of length measuring lines. We propose an automated Anemia length measurement method using U-shaped fully convolutional networks (UNet) and second-order anisotropic Gaussian kernels. For a given Artemia image, the designed UNet model is used to extract a length measuring line structure, and, subsequently, the second-order Gaussian kernels are employed to transform the length measuring line structure into a thin measuring line. For comparison, we also follow conventional fish length measurement approaches and develop a non-learning-based method using mathematical morphology and polynomial curve fitting. We evaluate the proposed method and the competing methods on 100 test images taken from the dataset compiled. Experimental results show that the proposed method can accurately measure the length of Artemia objects in images, obtaining a mean absolute percentage error of 1.16%

Multimodal sentiment analysis in real-life videos

This thesis extends the emerging field of multimodal sentiment analysis of real-life videos, taking two components into consideration: the emotion and the emotion's target. The emotion component of media is traditionally represented as a segment-based intensity model of emotion classes. This representation is replaced here by a value- and time-continuous view. Adjacent research fields, such as affective computing, have largely neglected the linguistic information available from automatic transcripts of audio-video material. As is demonstrated here, this text modality is well-suited for time- and value-continuous prediction. Moreover, source-specific problems, such as trustworthiness, have been largely unexplored so far. This work examines perceived trustworthiness of the source, and its quantification, in user-generated video data and presents a possible modelling path. Furthermore, the transfer between the continuous and discrete emotion representations is explored in order to summarise the emotional context at a segment level. The other component deals with the target of the emotion, for example, the topic the speaker is addressing. Emotion targets in a video dataset can, as is shown here, be coherently extracted based on automatic transcripts without limiting a priori parameters, such as the expected number of targets. Furthermore, alternatives to purely linguistic investigation in predicting targets, such as knowledge-bases and multimodal systems, are investigated. A new dataset is designed for this investigation, and, in conjunction with proposed novel deep neural networks, extensive experiments are conducted to explore the components described above. The developed systems show robust prediction results and demonstrate strengths of the respective modalities, feature sets, and modelling techniques. Finally, foundations are laid for cross-modal information prediction systems with applications to the correction of corrupted in-the-wild signals from real-life videos

The impact of one-decade ecological disturbance on genetic changes : a study on the brine shrimp Artemia urmiana from Urmia Lake, Iran

Urmia Lake, the largest natural habitat of the brine shrimp Artemia urmiana, has progressively desiccated over the last two decades, resulting in a loss of 80% of its surface area and producing thousands of hectares of arid salty land. This ecological crisis has seriously affected the lake's native biodiversity. Artemia urmiana has lost more than 90% of its population during the decade from 1994 (rainy period) to 2004 (drought period) due to salinity increasing to saturation levels (similar to 300 g/l). We studied the influence of this ecological crisis on the genetic diversity of A. urmiana in Urmia Lake, based on one cyst collections in 1994 and 2004. AMOVA analysis on ISSR data demonstrated a 21% genetic variation and there was a 5.5% reduction of polymorphic loci between samples. PCoA showed that 77.42% and 68.75% of specimens clustered separately in 1994 and 2004, respectively. Our analyses of four marker genes revealed different genetic diversity patterns with a decrease of diversity at ITS1 and an increase for Na+/K+ ATPase. There was no notable difference in genetic variation detected for CO/ and 16S genes between the two periods. However, they represented distinctly different haplotypes. ITS1 and COI followed a population expansion model, whereas Na+/K+ ATPase and 16S were under demographic equilibrium without selective pressure in the 1994 samples. Neutrality tests confirmed the excess of rare historical and recent mutations present in COI and ITS1 in both samples. It is evident that a short-term ecological disturbance has impacted the genetic diversity and structure of A. urmiana

Pore-scale characterization and modelling of CO2 flow in tight sandstones using X-ray micro-CT; Knorringfjellet formation of the Longyearbyen CO2 lab, Svalbard

Rocks of the Knorringfjellet Formation in Central Spitsbergen form a potential storage reservoir for CO2 below Longyearbyen. They are characterised by a moderate porosity and low permeability. However, water injection tests have shown positive results and fractures are considered to facilitate fluid flow. Therefore, hard data on fracture parameters and pore characteristics schould be analysed to better understand flow characteristics. Consequently, sandstone and conglomerate samples from the Knorringfjellet Formation were sampled and characterised with High Resolution X-ray Computed Tomography (HRXCT) at the Centre for X-ray Tomography at Ghent University, Belgium (UGCT). The dataset includes samples taken from drillholes in the vicinity of Longyearbyen, drilled during the pilot phase at the Longyearbyen CO2 project, as well as from the Knorringfjellet Formation outcrops at Konusdalen and Criocerasdalen. This was done in order to compare micro-fracture and pore parameters in both settings. With HRXCT, the samples were analysed at pore scale and quantitative information of the pore network and fractures were extracted. Pore networks were used for the modelling of CO2 flow in specific samples and information on fracture aperture was obtained at a micrometre scale. The acquired dataset can be directly used for a better understanding of flow in the aquifer

Dynamic micro-CT analysis of fracture formation in rock specimens subjected to multi-phase fluid flow

In this study, fracture formation in rocks is being studied at the pore-scale through the combination of high-resolution X-ray CT scanning with custom-made add-on modules. The Deben CT5000 system, an in-situ load cell, was used at the scanners at the Centre for X-ray Tomography at Ghent University (UGCT), providing information on mechanical properties of the tested rocks. Micro-CT scans made at the High Energy CT system Optimised for Research (HECTOR) allowed the visualisation of the fracturesk and their formation as well as the analysis of porosity changes in the material, related to the changes in stress