Vehicle Logo Recognition by Spatial-SIFT Combined with Logistic Regression

An efficient recognition framework requires both good feature representation and effective classification methods. This paper proposes such a framework based on a spatial Scale Invariant Feature Transform (SIFT) combined with a logistic regression classifier. The performance of the proposed framework is compared to that of state-of-the-art methods based on the Histogram of Orientation Gradients, SIFT features, Support Vector Machine and K-Nearest Neighbours classifiers. By testing with the largest vehicle logo data-set, it is shown that the proposed framework can achieve a classification accuracy of 99.93%, the best among all studied methods. Moreover, the proposed framework shows robustness when noise is added in both training and testing images

Automates: the future of autonomous cars

El futur dels cotxes autònoms sembla brillant, tot i així, personatges com el mateix Elon Musk, entre d'altres, ens porten prometent que serien part de les nostres vides des de fa gairebé deu anys. Tot i això aquí seguim, amb els nostres vehicles que sí, que són genials, però de moment encara no es condueixen sols. Aquestes falses promeses i el concepte de que una màquina condueixi el cotxe per nosaltres encara genera rebuig a la majoria de la població, quan de fet més d'un 90% dels accidents de trànsit avui dia són a causa de l'error humà, i aquestes màquines seran moltes coses, però precisament humanes de moment no són. En aquest projecte s’indaga sobre l’estat actual d’aquests vehicles, que de fet certs serveis de cotxes autònoms ja ronden els carrers d’algunes de les ciutats més grans del món, com ara San Francisco. La clau és descobrir si els vehicles autònoms tenen el potencial real de convertir-se en el servei del futur. Per això, es recorre a les metodologies de Disseny de Futurs, analitzant les tendències del sector i així presentant una sèrie d'Escenaris Futurs. Aquestes metodologies ens permetran entreveure cap on ens porten els desenvolupaments actuals, per així descobrir els passos que hauríem de seguir i els que no per a una correcta i eficient implementació d'aquestes tecnologies en un futur més aviat proper que llunyà.El futuro de los coches autónomos parece brillante, aún así, personajes como el mismísimo Elon Musk, entre otros, nos llevan prometiendo que iban a ser parte de nuestras vidas desde hace ya casi diez años. Sin embargo aquí seguimos, con nuestros vehículos que sí, que son geniales, pero de momento aún no se conducen solos. Estas falsas promesas y el concepto de que una máquina conduzca el coche por nosotros aún genera rechazo en la mayoría de la población, cuando lo cierto es que más de un 90% de los accidentes de tráfico hoy en día son a causa del error humano, y estas máquinas serán muchas cosas pero precisamente humanas no son. En este proyecto se indaga sobre el estado actual de estos vehículos, que de hecho ciertos servicios de coches autónomos ya rondan las calles de algunas de las ciudades más grandes del mundo, como por ejemplo San Francisco. La clave es descubrir si los vehículos autónomos tienen el potencial real de convertirse en el servicio del futuro. Para ello, se recurre a las metodologías de Diseño de Futuros, analizando las tendencias del sector y así presentando una serie de Escenarios Futuros. Estas metodologías nos permitirán vislumbrar hacia dónde nos llevan los desarrollos actuales, para así descubrir los pasos que deberíamos seguir y los que no para una correcta y eficiente implementación de estas tecnologías en un futuro más próximo que lejano.The future of autonomous cars seems bright, even though, famous people like Elon Musk himself, among others, have been making promises around the fact that those cars would be part of our lives for almost ten years, but here we are, with our vehicles that are great, but for now they still don't drive for themselves. These false promises and the concept of a machine driving a car for us still generates rejection in the majority of the population, when the fact is that more than 90% of traffic accidents nowadays are due to human error, and these machines will be sort of things but not humans at all. This project investigates the current state of these vehicles, that in fact these autonomous car services already transit the streets of some of the largest cities in the world, cities like San Francisco. The key is to find out if autonomous vehicles have the real potential to become the service of the future. Therefore, Futures Design methodologies are used, analysing the trends of the sector and thus presenting a series of Future Scenarios. These methodologies will allow us to understand where current developments are leading us, so then we can understand the steps that we should follow as a society and those that we should not for a correct and efficient implementation of these technologies in the near future

Modelagem de abundância com drones : detectabilidade, desenho amostral e revisão automática de imagens em um estudo com cervos-do-pantanal

Entender como a abundância de uma espécie se distribui no espaço e/ou no tempo é uma questão fundamental em ecologia e conservação e ajuda, por exemplo, a elucidar relações entre a heterogeneidade de paisagens e populações ou compreender influência de predação na distribuição de indivíduos. Informações de tamanho populacional também são essenciais para avaliar risco de extinção, monitorar populações ameaçadas e planejar ações de conservação. Modelar a abundância de cervos-do-pantanal (Blastocerus dichotomus), sendo um grande herbívoro da América do Sul, pode ser importante para entender relações da espécie com a variação espacial da produtividade primária, das áreas úmidas que a espécie ocupa e do seu principal predador, a onça- pintada. Além disso, por estar ameaçado de extinção, estimar a abundância de cervos pode contribuir para avaliar populações relictuais da espécie, assim como monitorar populações após grandes eventos, como os incêndios de 2020 no Pantanal. Porém, acessar estimativas de abundância confiáveis de maneira eficiente requer métodos robustos que levem em conta os possíveis erros nas contagens e que forneçam as estimativas em tempo hábil, além de um desenho amostral otimizado para aproveitar os recursos geralmente escassos. Os drones tem aparecido como uma ferramenta versátil e custo-efetiva para amostragem de populações animais e vêm sendo aplicados para várias espécies diferentes nos mais variados contextos ecológicos. Como um método emergente, o uso de drones na ecologia fornece oportunidades para explorar novas possibilidades de amostragem e análise de dados, ao mesmo tempo em que pode apresentar novos desafios. Nesta tese, i) exploro oportunidade e desafios na utilização de drones para modelagem de abundância de animais, abordando questões de erros de detecção, desenho amostral e como lidar com os grandes bancos de imagens gerados; e ii) aplico os métodos desenvolvidos para estudar a variação na abundância de cervo-do- pantanal, assim como estabelecer uma abordagem para monitoramento robusto e efetivo dessa espécie. Assim, no primeiro capítulo, conduzo uma revisão na literatura descrevendo os potenciais erros de detecção que podem enviesar estimativas de abundância com drones, buscando soluções atuais para lidar com esses erros e identificando lacunas que precisam de desenvolvimento. Nessa revisão, destaco o potencial dos modelos hierárquicos para estimar abundância em amostragens com drone. No segundo capítulo, aplico amostragens espaço-temporalmente replicadas com drone, analisadas com modelos hierárquicos N-mixture, para entender o efeito de processos topo-base (distribuição de onças-pintadas) e base-topo (disponibilidade de forragem de qualidade e corpos d’água) na distribuição da abundância de cervos-do- pantanal. Nesse estudo, encontrei que, na época seca, os cervos se concentram em áreas de alta qualidade (maior disponibilidade de forragem e próximas a corpos d’água), mesmo sendo a região em que é esperado maior efeito da predação. No capítulo 3, em um estudo com simulações, avalio o desempenho de modelos N-mixture para estimativas de abundância a partir de amostragens espaço-temporalmente replicadas, explorando otimização de esforço amostral e o impacto de um protocolo com observadores duplos na acurácia das estimativas. No capítulo 4, desenvolvo uma abordagem para estimar abundância com drone usando observadores múltiplos na revisão das imagens, sendo um dos observadores baseado em um processamento semiautomático usando algoritmos de inteligência artificial. Nesse estudo, exploro técnicas de aprendizado profundo de máquina, com redes neurais convolucionais, acessíveis para ecólogos, treinando algoritmos para detectar cervos nas imagens de drone. Além de ajudar a elucidar questões sobre as relações do cervo-do-pantanal com aspectos diferentes da paisagem do Pantanal, as abordagens exploradas e desenvolvidas aqui têm um grande potencial de aplicação, ajudando a estabelecer os drones como uma ferramenta eficiente para modelagem e monitoramento populacional de diversas espécies animais, e particularmente de cervos.Understanding how abundance distributes in space and/or time is a fundamental question in ecology and conservation, and it helps, for example, to elucidate relationships between landscape heterogeneity or predation and populations. Information on the population size also is essential to evaluate extinction risk, monitor threatened species and plan conservation actions. Abundance modeling of marsh deer (Blastocerus dichotomus), as a large herbivore of South America, may be important to understand the relationships of this species with spatial variation in primary productivity, in the availability of wetlands that the species inhabits, and in the distribution of its main predator, the jaguar. Moreover, since marsh deer threatened to extinction, estimating its abundance can be contribute in assessments of relictual populations, as well as in monitoring the species after big events, such as the Pantanal 2020 megafires. However, efficiently assessing reliable abundance estimates require robust methods that account for possible sources of error in counts while providing the estimates timely. An optimized sampling design is also important, in order to make the best use of the usual scarce resources. Drones have raised as a versatile and cost- effective tool for sampling animal populations, and they have been applied for several species in a wide variety of ecological contexts. As being an emergent method, the use of drones in ecology provides opportunities to explore novel possibilities of sampling and analyzing data, while potentially presenting new challenges. In this thesis I: i) explore opportunities and challenges in the use of drones for animal abundance modeling, approaching issues about detection errors, sampling design and how to deal with the huge image sets generated from drone flights; and ii) apply the developed methods to study the spatial variation in marsh deer abundance and to establish an approach to monitor this species robustly and efficiently. Thus, in the first chapter, I carry on a literature review describing potential sources of errors that may bias abundance estimation with drones and the current solutions to address them, identifying gaps that need development. In this review, I highlight the potential of hierarchical models for abundance estimations from drone-based surveys. In the second chapter, I apply spatiotemporally replicated drone surveys, analyzed with N-mixture models, to understand the influence of bottom-up (forage and water) and top-down (jaguar density) variables on the spatial variation of marsh deer local abundance. In such study, I found that, in the dry season, the deer concentrate in high quality areas (high-quality forage available and close to water bodies), even these regions being expected to present higher predation risks. In chapter 3, in a simulation study, I evaluate the performance of N- mixture models for abundance estimation from spatiotemporally replicated surveys, exploring optimization of sampling effort and the impact of a double-observer protocol on estimation accuracy. In chapter 4, I develop a pipeline to estimate abundance from drone-based surveys using a multiple-observer protocol in which one of the observer is a semiautomated procedure based on deep learning algorithms. In such study, I explore deep learning techniques with convolutional neural networks that are accessible for ecologists, and train algorithms to detect marsh deer in drone imagery. Besides helping to elucidate questions about the relationships of marsh deer with landscape variables in Pantanal, the approaches explored and developed here have a great potential of application in order to establish drones as an efficient technique for population modeling and monitoring of several wildlife species, and particularly the marsh deer

Machine Learning Methods for Autonomous Object Recognition and Restoration in Images

Image recognition and image restoration are important tasks in the field of image processing. Image recognition are becoming very popular due to the state-of-the-art deep learning methods. However, these models usually require big datasets and high computational costs, which could be challenging. This thesis proposes an online learning framework that deals with both small and big datasets. For small datasets, a Cauchy prior logistic regression classifier is proposed to provide a quick convergence, and the online weight updating scheme is efficient due to the previously trained weights being reused. For big datasets, convolutional neural network could be implemented. For image recognition, non-parametric classifiers are often used for image recognition such as K-nearest neighbours, however, K-nearest neighbours are vulnerable to noise and high dimensional features. This thesis proposes a non-parametric classifier based on Bayesian compressive sensing; the developed classifier is robust and it does not need a training stage. For image restoration, which is usually performed before image recognition as a preprocessing process. This thesis proposes such a joint framework that performs image recognition and restoration simultaneously. In image restoration, image rotation and occlusion are common problems but convolutional neural networks are not suitable to solve these due to the limitation of the convolutional process and pooling process. This thesis develops a joint framework based on capsule networks. The developed joint capsule framework could achieve a good result on recognition, image de-noising, recovering rotation and removing occlusion. The developed algorithms have been evaluated for vehicle logo restoration and recognition, however, they are transferable to other implementations. This thesis also developed an automatic detection and recognition framework for badger monitoring for the first time. Badger plays a key role in the transmission of bovine tuberculosis, which is described by government as the most pressing animal health problem in the UK. An automatic badger monitoring system could help researcher to understand the transmission mechanisms and thereby to develop methods to deal with the transmission between species

Coaching Imagery to Athletes with Aphantasia

We administered the Plymouth Sensory Imagery Questionnaire (Psi-Q) which tests multi-sensory imagery, to athletes (n=329) from 9 different sports to locate poor/aphantasic (baseline scores <4.2/10) imagers with the aim to subsequently enhance imagery ability. The low imagery sample (n=27) were randomly split into two groups who received the intervention: Functional Imagery Training (FIT), either immediately, or delayed by one month at which point the delayed group were tested again on the Psi-Q. All participants were tested after FIT delivery and six months post intervention. The delayed group showed no significant change between baseline and the start of FIT delivery but both groups imagery score improved significantly (p=0.001) after the intervention which was maintained six months post intervention. This indicates that imagery can be trained, with those who identify as having aphantasia (although one participant did not improve on visual scores), and improvements maintained in poor imagers. Follow up interviews (n=22) on sporting application revealed that the majority now use imagery daily on process goals. Recommendations are given for ways to assess and train imagery in an applied sport setting

Toward a Bio-Inspired System Architecting Framework: Simulation of the Integration of Autonomous Bus Fleets & Alternative Fuel Infrastructures in Closed Sociotechnical Environments

Cities are set to become highly interconnected and coordinated environments composed of emerging technologies meant to alleviate or resolve some of the daunting issues of the 21st century such as rapid urbanization, resource scarcity, and excessive population demand in urban centers. These cybernetically-enabled built environments are expected to solve these complex problems through the use of technologies that incorporate sensors and other data collection means to fuse and understand large sums of data/information generated from other technologies and its human population. Many of these technologies will be pivotal assets in supporting and managing capabilities in various city sectors ranging from energy to healthcare. However, among these sectors, a significant amount of attention within the recent decade has been in the transportation sector due to the flood of new technological growth and cultivation, which is currently seeing extensive research, development, and even implementation of emerging technologies such as autonomous vehicles (AVs), the Internet of Things (IoT), alternative xxxvi fueling sources, clean propulsion technologies, cloud/edge computing, and many other technologies. Within the current body of knowledge, it is fairly well known how many of these emerging technologies will perform in isolation as stand-alone entities, but little is known about their performance when integrated into a transportation system with other emerging technologies and humans within the system organization. This merging of new age technologies and humans can make analyzing next generation transportation systems extremely complex to understand. Additionally, with new and alternative forms of technologies expected to come in the near-future, one can say that the quantity of technologies, especially in the smart city context, will consist of a continuously expanding array of technologies whose capabilities will increase with technological advancements, which can change the performance of a given system architecture. Therefore, the objective of this research is to understand the system architecture implications of integrating different alternative fueling infrastructures with autonomous bus (AB) fleets in the transportation system within a closed sociotechnical environment. By being able to understand the system architecture implications of alternative fueling infrastructures and AB fleets, this could provide performance-based input into a more sophisticated approach or framework which is proposed as a future work of this research

JuxtaLearn D3.2 Performance Framework

This deliverable, D3.2, for Work Package 3 incorporating the pedagogy from WP2 and orchestration factors mapped in D3.1 reviews aspects of performance in the context of participative video making. It reviews literature on curiosity and engagement characteristics of interaction mechanisms for public displays and anticipates requirements for social network analysis of relevant public videos from WP6 task 6.3. Thus, to support JuxtaLearn performance it proposes a reflective performance framework that encompasses the material environment and objects required, the participants, and the knowledge needed

Targeted inhibition of PIK3CA mutations in glioma cell models

Gliomas are the most common and lethal brain tumors in adults, with efficient therapies non-existent. PIK3CA mutations are potential therapeutic targets, described as early constitutive events in glioblastomas (GBM). The goal of this project was to assess the impact of PIK3CA mutations on glioma aggressiveness and evaluate the inhibition of PI3Kα in glioma cell models harboring PIK3CA mutations. Additionally, we intended to characterize the IPOLFG cohort with IDH2 mutational analysis, previously classified according to IDH1 and PIK3CA mutational status and 1p19q co-deletion. Thus, IDH2 mutations were evaluated by Sanger sequencing, and immune cell infiltrates were estimated using the TIMER platform. Functional studies were performed using a GBM cell line – U87MG, transfected to contain E545K and H1047R mutations. The effects of temozolomide and alpelisib treatment on cell viability, death, and PI3K/AKT pathway activation were assessed. Only one out of 279 sequenced samples harbored an IDH2 mutation. PIK3CA mutation frequencies remained unaltered in glioma molecular subgroups. Using TIMER analysis, we observed increased CD8+ T cell infiltration in PIK3CA mutant low-grade gliomas, whilst PIK3CA and STAT5B expressions were positively correlated. Regarding PIK3CA mutations on glioma aggressiveness, no differences were observed in U87MG colony formation, migration, or invasion, regardless of PIK3CA mutational status. Moreover, PIK3CA mutations did not change U87MG cells’ sensitivity to either alpelisib or temozolomide, with alpelisib potentiating PI3K/AKT signaling. Here, we achieved a more refined characterization of the IPOLFG glioma cohort and uncovered a possible, novel link between tumor microenvironment and the prevalence of PIK3CA mutations during glioma progression. Our data shows that PIK3CA mutations neither significantly impact GBM cell aggressiveness nor confer added sensitivity to PI3Kα targeted inhibition. Nevertheless, we found that GBM cells seem to trigger complex, not yet described compensatory mechanisms, which could pave the way for future studies relating pan-PI3K inhibitors and the targeting of other pathways

2016 - The Twenty-first Annual Symposium of Student Scholars

The full program book from the Twenty-first Annual Symposium of Student Scholars, held on April 21, 2016. Includes abstracts from the presentations and posters.https://digitalcommons.kennesaw.edu/sssprograms/1015/thumbnail.jp