Optimization of the image acquisition procedure in low-field MRI for non-destructive analysis of loin using predictive models

The use of low-field magnetic resonance imaging (LF-MRI) scanners has increased in recent years. The low economic cost in comparison to high-field (HF-MRI) scanners and the ease of maintenance make this type of scanner the best choice for nonmedical purposes. However, LF-MRI scanners produce low-quality images, which encourages the identification of optimization procedures to generate the best possible images. In this paper, optimization of the image acquisition procedure for an LF-MRI scanner is presented, and predictive models are developed. The MRI acquisition procedure was optimized to determine the physicochemical characteristics of pork loin in a nondestructive way using MRI, feature extraction algorithms and data processing methods. The most critical parameters (relaxation times, repetition time, and echo time) of the LF-MRI scanner were optimized, presenting a procedure that could be easily reproduced in other environments or for other purposes. In addition, two feature extraction algorithms (gray level co-occurrence matrix (GLCM) and one point fractal texture algorithm (OPFTA)) were evaluated. The optimization procedure was validated by using several evaluation metrics, achieving reliable and accurate results (r > 0.85; weighted absolute percentage error (WAPE) lower than 0.1%; root mean square error of prediction (RMSEP) lower than 0.1%; true standard deviation (TSTD) lower than 2; and mean absolute error (MAE) lower than 2). These results support the high degree of feasibility and accuracy of the optimized procedure of LF-MRI acquisition. No other papers present a procedure to optimize the image acquisition process in LF-MRI. Eventually, the optimization procedure could be applied to other LF-MRI systems

Evaluation of live growing pigs of different genotypes and sexes using computed tomography

Conocer la composición corporal en animales vivos y la deposición de los diferentes tejidos durante su crecimiento es de vital importancia para, entre otras cosas, caracterizar el efecto de una genética, la condición sexual, el manejo e incluso analizar la eficiencia alimentaria y adecuar la dieta a cada estadio de crecimiento, ya que la composición en tejido graso y muscular del cuerpo de los animal está influenciada tanto por factores intrínsecos como extrínsecos. La aplicación de la tomografía computerizada (CT) en animales vivos permite analizar, de manera no invasiva, la evolución de la composición corporal de un mismo animal a lo largo del período de crecimiento. Esto permite modelizar el crecimiento y desarrollo de los diferentes tejidos del cuerpo sin necesidad alguna de sacrificar el animal. Esta tesis propone conocer la evolución de la composición corporal de cerdos de distintas genéticas (experimento 1) o sexos (experimento 2). El experimento 1 estaba formado por 90 animales de tres genéticas distintas (todas ellas comerciales y altamente utilizadas en el sector), y el experimento 2 estaba formado por 92 animales de cuatro condiciones sexuales distintas (hembras (FE), machos enteros (EM), machos castrados quirúrgicamente (CM) y machos inmunocastrados (IM)). Los animales se evaluaron con el CT a 30, 70, 100 i 120 kg de peso objetivo. Una submuestra de animales de cada genética y sexo (n=5/genética y n=4/sexo) se sacrificaron a los diferentes pesos objetivo y se disecaron total o parcialmente. El resto de animales (animales de seguimiento), se evaluaron con el CT a cada peso objetivo y, al llegar a 120 kg, se sacrificaron. A partir de los animales sacrificados y disecados, se obtuvieron ecuaciones de predicción, de la composición corporal y de las diferentes piezas, que se usaron en el resto de animales de seguimiento. Las predicciones se hicieron para cada genética independientemente (Chapter 4) o bien generalizadas para todas las genéticas y sexos (Chapter 6). Ambas ecuaciones fueron adecuadas para la predicción de la composición corporal (Chapter 8). En este sentido, presentar las predicciones individuales según la genética reduce el error (RMSE entre 0.011 y 0.886). No obstante, la ecuacion global permite generalizar las predicciones para un mayor número de animales, así pues, es preferible usarla cuando la población está mezclada o cuando el parámetro estimado no requiere un alto valor de precisión. Cuando esta precisión se requiere, como es el caso de compañías genéticas, es preferible utilizar las ecuaciones individuales, específicamente desarrolladas para cada genética. Los resultados muestran que los tejidos crecen de manera diferente según la genética s y el sexo (Chapter 5 y 7). El tejido que mostró el mayor coeficiente alométrico fue la grasa, indicando el índice de deposición más rápido de este tejido. De entre las distintas genéticas, LA fue quien mostró la deposición de grasa más rápida (Chapter 5), mientras que respecto a los sexos, los CM e IM fueron los que tuvieron un índice de deposición de grasa más elevado y más lento en los EM y FE (Chapter 7). El comportamiento de la deposición de magro fue inverso al de la grasa. Añadir que, los IM y CM tuvieron un comportamiento muy similar respecto a la velocidad de deposición de grasa y magro, a pesar que los IM se comportaron como los EM hasta que recibieron la segunda dosis de la vacuna de immunocastración. Finalmente, en las condiciones de realización de este trabajo se puede concluir que el CT puede ser muy útil para la industria cárnica, porque los parámetros de calidad y composición de la canal se pueden conocer a pesos muy tempranos. Como resultado, el uso de esta información puede aportar beneficios económicos para todos los integrantes de la cadena alimenticia.Knowledge of the composition of animal bodies and animal tissue growth is very important for the characterization of the effect of a genotype, the sexual condition, the management or to analyze the feed efficiency and adjust the diet to growth states, because fat and lean composition are dependent on intrinsic and extrinsic factors. The application of computed tomography (CT) to living animals allows analyzing non-invasively the evolution of the body composition of a single animal through its growing period. Subsequently, growth and development of the different body tissues can be modeled, without the necessity to slaughter the animal. The PhD Thesis at hand investigates the evolution of the composition of pig bodies from different genetic types (experiment 1) and sexual conditions (experiment 2). Experiment 1 was performed on 90 pigs of three genetic types (all of them were commercial and very used in the swine industry), while experiment 2 was performed on 92 animals with four different sexual conditions (females, entire males, castrated males and immunocastrated males). The animals were scanned by CT at 30, 70, 100 and 120 kg live weigh. One subsample for each genetic type and sexual condition (n=5/genetic type and n=4/sexual condition) were slaughtered at the different target weights and were fully or partially dissected. The rest of the animals (animals of the study) were evaluated with the CT at each target weight and, once they reached 120 kg, they were slaughtered. Knowledge gained from slaughtered and dissected animals was used to formulate prediction equations for body and pieces composition. They were then applied to the animals of the study. Predictions were performed independently for each genetic type (Chapter 4) or generalized for all the genetic types and sexual conditions of this work (Chapter 6). Both equations produce good results for the prediction of body composition (Chapter 8). Presenting the individual predictions depending on the genetic type reduces the error (RMSE between 0.011 and 0.886). However, the global equation allows generalizing the predictions for a bigger number of animals, thus, it has preference if the population is mixed or if high level of accuracy is not required. If high accuracy is needed, for instance for genetic companies, individual equations specifically developed for each genetic type are prefered. Results show that tissues grow different depending on the genetic type and sexual condition (Chapter 5 and 7). Tissue that shows the highest allometric coefficient was the fat, corresponding to the fastest deposition. From the different genotypes, LA was the one that shows the fastest deposition of fat (Chapter 5). With respect to the sexual condition, CM and IM exert the highest deposition value for the fat, with EM and FE showing the lowest (Chapter 7). Lean tissue behaves in the opposite way as fat. The IM and CM had a very similar behavior with respect to the deposition speed of fat and lean, even IM behave as EM until the study animals received the second dose of the immunocastration vaccine. In conclusion, CT can be very useful for the meat industry due to its ability to predict quality parameters, as well as carcass composition, at early growth stage. This technique can thus bring economic benefits for all the livestock and food chain industry

The Moving Page

This paper investigates transitional states of spaces between images, moving images, and the use of sketchbook/page works through a questioning and auto-ethnographic approach to research and practice. Viewing illustration as a refexive space, the investigations demonstrate exchangesbetween authorship, interaction, narrative, time, and space. Valuing the ‘in-between’ states that exist between the unfnished and fnished, the research questions notions of in-fux, moving, nebulous states. Through alternative publishing forms, the research concerns dissemination through emerging digital platforms

The Moving Page

This paper investigates transitional states of spaces between images, moving images, and the use of sketchbook/page works through a questioning and auto-ethnographic approach to research and practice. Viewing illustration as a refexive space, the investigations demonstrate exchangesbetween authorship, interaction, narrative, time, and space. Valuing the ‘in-between’ states that exist between the unfnished and fnished, the research questions notions of in-fux, moving, nebulous states. Through alternative publishing forms, the research concerns dissemination through emerging digital platforms

Influence de la phénologie foliaire automnale de forêts tempérées sur la segmentation d’espèces d’arbres à partir d’imagerie de drone et d’apprentissage profond

La télédétection des forêts est devenue de plus en plus accessible grâce à l'utilisation de véhicules aériens inoccupés (UAV) et à l'apprentissage profond, ce qui permet d'obtenir des images répétées à haute résolution et d’observer les changements phénologiques à des échelles spatiales et temporelles plus importantes. Dans les forêts tempérées, à l'automne, la sénescence des feuilles se produit lorsque les feuilles changent de couleur et tombent. Cependant, l'influence de la sénescence foliaire sur la segmentation des espèces d'arbres à l'aide d'un réseau neuronal convolutif (CNN) n'a pas encore été évaluée. Nous avons acquis de l’imagerie haute résolution par UAV au-dessus d’une forêt tempérée au Québec à sept reprises entre mai et octobre 2021. Nous avons segmenté et identifié 23 000 couronnes d'arbres de 14 classes différentes pour entraîner et valider un CNN pour chaque acquisition d'imagerie. La meilleure segmentation (F1-score le plus élevé) était au début de la coloration des feuilles (début septembre) et le F1-score le plus bas au pic de la coloration automnale (début octobre). La chronologie de la sénescence varie considérablement d’une espèce à l’autre et au sein d’une même espèce, ce qui entraîne une grande variabilité du signal télédétecté. Les espèces d'arbres à feuilles caduques et à feuilles persistantes qui présentaient des traits distinctifs et moins variables dans le temps entre les individus ont été mieux classées. Bien que la segmentation des arbres dans une forêt hétérogène demeure un défi, l'imagerie UAV et l'apprentissage profond démontrent un grand potentiel pour la cartographie des espèces d'arbres. Les résultats obtenus dans une forêt tempérée où la couleur des feuilles change fortement pendant la sénescence automnale montrent que la meilleure performance pour la segmentation des espèces d'arbres se produit au début de ce changement de couleur.Remote sensing of forests has become increasingly accessible with the use of unoccupied aerial vehicles (UAV), along with deep learning, allowing for repeated high-resolution imagery and the capturing of phenological changes at larger spatial and temporal scales. In temperate forests during autumn, leaf senescence occurs when leaves change colour and drop. However, the influence of leaf senescence in temperate forests on tree species segmentation using a Convolutional Neural Network (CNN) has not yet been evaluated. Here, we acquired high-resolution UAV imagery over a temperate forest in Quebec, Canada on seven occasions between May and October 2021. We segmented and labelled 23,000 tree crowns from 14 different classes to train and validate a CNN for each imagery acquisition. The CNN-based segmentation showed the highest F1-score (0.72) at the start of leaf colouring in early September and the lowest F1-score (0.61) at peak fall colouring in early October. The timing of the events occurring during senescence, such as leaf colouring and leaf fall, varied substantially between and within species and according to environmental conditions, leading to higher variability in the remotely sensed signal. Deciduous and evergreen tree species that presented distinctive and less temporally-variable traits between individuals were better classified. While tree segmentation in a heterogenous forest remains challenging, UAV imagery and deep learning show high potential in mapping tree species. Our results from a temperate forest with strong leaf colour changes during autumn senescence show that the best performance for tree species segmentation occurs at the onset of this colour change

Towards a GIS-based Multiscale Visibility Assessment Method for Solar Urban Planning

Urban areas are facing a growing deployment of solar photovoltaic and thermal tech-nologies on building envelopes, both on roofs and on façades, essential for the realization of the Swiss Energy Strategy 2050. This process often occurs regardless of the desirable archi-tectural integration quality in a given urban context, which depends on socio-cultural sensitivi-ty and on the visibility of the solar modules from the public space. Visibility and visual impact are recurrent decisional factors in spatial planning processes, with practical implications in-cluding touristic and real estate promotion, outdoor human comfort, way finding, public feeling of security and advertisement. In this thesis, the definition of visibility under a geometrical, physical and psycho-physiological perspective is explored, several quantitative indicators being described and test-ed. The objective is to provide a scale-dependent methodology to assess the visibility of build-ing envelope surfaces exposed to solar radiation, which could host solar modules, in urban areas. A visibility index is determined for inclusion as a variable in a multi criteria method, cover-ing areas from the strategic broad territorial scale to the district level, including neighborhoods and clusters of buildings. Accomplished research includes the estimation of public visual inter-est on the basis of crowd-sourced photographic databases, complementing geometry-based parameters such as cumulative viewsheds and solid angles. At each scale, the visibility index is systematically overlapped on an urban sensitivity layer issued from land use and on a spatial representation of the solar energy generation potential, at an appropriate level of detail. Results indicate that stakeholders can reasonably expect to harvest a serious amount of solar energy by means of building integrated solar systems without crucially affecting public perception. In the study area located in the city of Geneva (Switzerland), more than 50 m2 / building of non-visible envelope surface receiving sufficient solar radiation for an economically viable solar re-furbishment is available over half of the buildings. Solar thermal collectors or PV panels in-stalled on scarcely visible surfaces, mainly situated in courtyards, far from the streets or in deep urban canyons, could cover about 10% of the annual heating demand or alternatively, the same share of electricity needs on a district basis. At the same time, plenty of highly visible areas remain available for high-end solar deployments, which could also serve pilot and demonstration purposes