Influence of Digital Signage Usage on Product Sale among Leading Supermarkets in Kenya

In the changing business environment, retailers are always searching new strategies to attract and hold customers. Digital signage is a very attractive medium for advertising and general communication in open spaces. It has been adopted by many business sectors that have benefited from the advantages it offers. This study sought to establish the effect digital signage has on the sale of products among leading supermarkets in Kenya. The objective of this study was to find out the influence of digital signage on product sales among leading supermarkets in Kenya. This study adopted a descriptive research design and respondents were drawn from Tuskys, Uchumi and Nakumatt supermarkets. They included managers, assistant managers, supervisors and merchandisers.  A questionnaire was used for data collection.  Data collected was analyzed using Statistical Package for Social Scientists (SPSS). The data was presented in Tables and Figures using frequencies and percentages. The findings show that digital signage does influence the sales of products. It was also found out that digital signage was perceived to be helpful by informing the respondents of the products and influencing their purchase decisions. The location of digital signage was found to be critical to the success of the advertisement. However, positioning of the screens and advertisement content for relevance and completeness were the recommended remedies

Subjective and objective quality assessment of ancient degraded documents

Archiving, restoration and analysis of damaged manuscripts have been largely increased in recent decades. Usually, these documents are physically degraded because of aging and improper handing. They also cannot be processed manually because a massive volume of these documents exist in libraries and archives around the world. Therefore, automatic methodologies are needed to preserve and to process their content. These documents are usually processed through their images. Degraded document image processing is a difficult task mainly because of the existing physical degradations. While it can be very difficult to accurately locate and remove such distortions, analyzing the severity and type(s) of these distortions is feasible. This analysis provides useful information on the type and severity of degradations with a number of applications. The main contributions of this thesis are to propose models for objectively assessing the physical condition of document images and to classify their degradations. In this thesis, three datasets of degraded document images along with the subjective ratings for each image are developed. In addition, three no-reference document image quality assessment (NR-DIQA) metrics are proposed for historical and medieval document images. It should be mentioned that degraded medieval document images are a subset of the historical document images and may contain both graphical and textual content. Finally, we propose a degradation classification model in order to identify common distortion types in old document images. Essentially, existing no reference image quality assessment (NR-IQA) metrics are not designed to assess physical document distortions. In the first contribution, we propose the first dataset of degraded document images along with the human opinion scores for each document image. This dataset is introduced to evaluate the quality of historical document images. We also propose an objective NR-DIQA metric based on the statistics of the mean subtracted contrast normalized (MSCN) coefficients computed from segmented layers of each document image. The segmentation into four layers of foreground and background is done based on an analysis of the log-Gabor filters. This segmentation is based on the assumption that the sensitivity of the human visual system (HVS) is different at the locations of text and non-text. Experimental results show that the proposed metric has comparable or better performance than the state-of-the-art metrics, while it has a moderate complexity. Degradation identification and quality assessment can complement each other to provide information on both type and severity of degradations in document images. Therefore, we introduced, in the second contribution, a multi-distortion historical document image database that can be used for the research on quality assessment of degraded documents as well as degradation classification. The developed dataset contains historical document images which are classified into four categories based on their distortion types, namely, paper translucency, stain, readers’ annotations, and worn holes. An efficient NR-DIQA metric is then proposed based on three sets of spatial and frequency image features extracted from two layers of text and non-text. In addition, these features are used to estimate the probability of the four aforementioned physical distortions for the first time in the literature. Both proposed quality assessment and degradation classification models deliver a very promising performance. Finally, we develop in the third contribution a dataset and a quality assessment metric for degraded medieval document (DMD) images. This type of degraded images contains both textual and pictorial information. The introduced DMD dataset is the first dataset in its category that also provides human ratings. Also, we propose a new no-reference metric in order to evaluate the quality of DMD images in the developed dataset. The proposed metric is based on the extraction of several statistical features from three layers of text, non-text, and graphics. The segmentation is based on color saliency with assumption that pictorial parts are colorful. It also follows HVS that gives different weights to each layer. The experimental results validate the effectiveness of the proposed NR-DIQA strategy for DMD images

Vehicular Instrumentation and Data Processing for the Study of Driver Intent

The primary goal of this thesis is to provide processed experimental data needed to determine whether driver intentionality and driving-related actions can be predicted from quantitative and qualitative analysis of driver behaviour. Towards this end, an instrumented experimental vehicle capable of recording several synchronized streams of data from the surroundings of the vehicle, the driver gaze with head pose and the vehicle state in a naturalistic driving environment was designed and developed. Several driving data sequences in both urban and rural environments were recorded with the instrumented vehicle. These sequences were automatically annotated for relevant artifacts such as lanes, vehicles and safely driveable areas within road lanes. A framework and associated algorithms required for cross-calibrating the gaze tracking system with the world coordinate system mounted on the outdoor stereo system was also designed and implemented, allowing the mapping of the driver gaze with the surrounding environment. This instrumentation is currently being used for the study of driver intent, geared towards the development of driver maneuver prediction models

Reconhecimento de expressões faciais em neonatos

Orientador: Profa Dra Olga R. P. BellonDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 30/10/2019Inclui referências: p. 46-51Área de concentração: Ciência da ComputaçãoResumo: A avaliação de dor é uma tarefa difícil e complexa, que é particularmente importante para recém-nascidos, que não conseguem verbaliza-la de maneira adequada e são vulneráveis a danos cerebrais decorrentes do não tratamento da dor. As ferramentas utilizadas no ambiente clínico para auxiliar na avaliação de dor requerem treinamento dos profissionais de saúde que irão utilizá-las, e seu uso é afetado pelo viés no reconhecimento da dor de cada indivíduo. Por essa razão, esforços tem sido colocados em automatizar essa tarefa, e uma das maneiras de fazê-lo é analisando a expressão facial do neonato, uma vez que esta é comprovadamente correlacionada à dor. Nessa dissertação, as diferenças entre os principais trabalhos em reconhecimento automático de expressão facial de neonatos são apresentadas, examinando os métodos utilizados, bases de dados e performances dos sistemas. Com isso em mente, testamos os principais métodos utilizados com objetivo de comparar suas performances mais a fundo. Esse estudo também avança o entendimento da base de dados COPE, a única base de dados de expressão facial de neonatos publicamente disponível. Conduzimos testes com métodos off the shelf para detecção de face, e em 54% das imagens nenhuma face foi detectada, reforçando a necessidade do desenvolvimento de sistemas específicos para recém-nascidos ou mais robustos à mudanças de público. Desde a publicação da base COPE em 2005, avanços significativos foram alcançados na área de processamento de imagens, e por essa razão comparamos métodos clássicos de extração de características em processamento de imagens com características provenientes de redes neurais convolucionais (CNNs), que são consideradas estado da arte para a maioria das aplicações de visão computacional. Um delta de 19% foi observado entre os filtros de gabor (melhor dos métodos clássicos) e características da ResNet50 (melhor das CNNs). Também testamos a robustez dos métodos a ruído, um fator importante em problemas de visão computacional onde devem ser considerados cenários da vida real. Para os métodos clássicos, foi observado um delta menor na performance entre cenários limpos e ruidosos, mas de maneira geral a performance foi pior que das CNNs. Em adição, estressando a performance das CNNs, testamos quais camadas produziriam melhor performance, na tentativa de verificar se camadas mais rasas poderiam ter desempenho igual ou melhor que camadas mais profundas, o que significaria menor custo computacional. Os resultados mostraram melhores resultados utilizando as camadas mais profundas. De maneira geral, estudando a literatura da área notamos uma tendência na utilização de métricas enviesadas, como acurácia, em um campo onde uma visão mais completa de performance de modelos deveria ser utilizada, por se tratar de um público tão vulnerável. Por fim, também observamos uma dificuldade no acesso as bases da literatura. Nossos esforços reforçam o potencial da utilização de métodos de visão computacional, porém fora limitados à base de dados utilizada. Palavras-chave: Expressões faciais, avaliação de dor, visão computacionalAbstract: Pain evaluation is a difficult and complex task, that is particularly important for newborns, who cannot verbalize it properly and are vulnerable to cerebral damage due to untreated pain. The current pain assessment tools used in clinical settings require extensive training for the caregivers and can be affected by each individual's bias towards pain recognition. For this reason, efforts have been made to automate this task, and one of the ways to do so is analyzing the newborn's facial expression, that has been proved to correlate with pain. In this dissertation, the differences among the most prominent works in automatic neonatal facial expression recognition were outlined, examining methods used, databases and final performance. With this in mind, we tested main methods used to compare their performances more in depth. This study also advances the understanding of the COPE database, the only publicly available newborn facial expression database. We conducted a test with off the shelf methods for face detection, and found that in 54% of the images, no face was found, reinforcing the need to develop either tailored applications or more robust ones. Since the COPE database was published, in 2005, significant advances in image processing have been made, and for this reason, we compared classical image processing feature extraction methods with Convolutional Neural Networks (CNNs), that are considered to be state of the art for most computer vision problems. We saw a difference of 19% in recall when using gabor filters (best of classical methods) and then the ResNet50 features (best of CNNs). We also tested the methods in regards to robustness to image noise, an important factor for computer vision problems when real world scenarios are considered. We found that image processing methods had a smaller delta in performance from clean to noisy scenarios, but had overall poor performance. In addition, stressing the CNNs performance, we also studied which layers yielded best performance in order to verify if shallow layers could produce the same results as deeper ones for this application, meaning less computational cost, but our test showed superior performance in deeper layers. Overall, studying the literature we noticed a tendency to use biased metrics, such as accuracy, in a field where a more complete view of model performance should be used. Moreover, we also found it very difficult to access data for this field. Our findings reinforce the potential of more complex computer vision methods, but are limited to the dataset that was used. Keywords: Facial expression, pain assessment, computer visio

Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring

Artificially intelligent perception is increasingly present in the lives of every one of us. Vehicles are no exception, (...) In the near future, pattern recognition will have an even stronger role in vehicles, as self-driving cars will require automated ways to understand what is happening around (and within) them and act accordingly. (...) This doctoral work focused on advancing in-vehicle sensing through the research of novel computer vision and pattern recognition methodologies for both biometrics and wellbeing monitoring. The main focus has been on electrocardiogram (ECG) biometrics, a trait well-known for its potential for seamless driver monitoring. Major efforts were devoted to achieving improved performance in identification and identity verification in off-the-person scenarios, well-known for increased noise and variability. Here, end-to-end deep learning ECG biometric solutions were proposed and important topics were addressed such as cross-database and long-term performance, waveform relevance through explainability, and interlead conversion. Face biometrics, a natural complement to the ECG in seamless unconstrained scenarios, was also studied in this work. The open challenges of masked face recognition and interpretability in biometrics were tackled in an effort to evolve towards algorithms that are more transparent, trustworthy, and robust to significant occlusions. Within the topic of wellbeing monitoring, improved solutions to multimodal emotion recognition in groups of people and activity/violence recognition in in-vehicle scenarios were proposed. At last, we also proposed a novel way to learn template security within end-to-end models, dismissing additional separate encryption processes, and a self-supervised learning approach tailored to sequential data, in order to ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022 to the University of Port

High-speed surface profilometry based on an adaptive microscope with axial chromatic encoding

An adaptive microscope with axial chromatic encoding is designed and developed, namely the AdaScope. With the ability to confocally address any locations within the measurement volume, the AdaScope provides the hardware foundation for a cascade measurement strategy to be developed, dramatically accelerating the speed of 3D confocal microscopy

Knowledge and Management Models for Sustainable Growth

In the last years sustainability has become a topic of global concern and a key issue in the strategic agenda of both business organizations and public authorities and organisations. Significant changes in business landscape, the emergence of new technology, including social media, the pressure of new social concerns, have called into question established conceptualizations of competitiveness, wealth creation and growth. New and unaddressed set of issues regarding how private and public organisations manage and invest their resources to create sustainable value have brought to light. In particular the increasing focus on environmental and social themes has suggested new dimensions to be taken into account in the value creation dynamics, both at organisations and communities level. For companies the need of integrating corporate social and environmental responsibility issues into strategy and daily business operations, pose profound challenges, which, in turn, involve numerous processes and complex decisions influenced by many stakeholders. Facing these challenges calls for the creation, use and exploitation of new knowledge as well as the development of proper management models, approaches and tools aimed to contribute to the development and realization of environmentally and socially sustainable business strategies and practices

The Modelling of Biological Growth: a Pattern Theoretic Approach

Mathematical and statistical modeling and analysis of biological growth using images collected over time are important for understanding of normal and abnormal development. In computational anatomy, changes in the shape of a growing anatomical structure have been modeled by means of diffeomorphic transformations in the background coordinate space. Various image and landmark matching algorithms have been developed for inference of large transformations that perform image registration consistent with the material properties of brain anatomy under study. However, from a biological perspective, it is not material constants that regulate growth, it is the genetic control system. A pattern theoretic model called the Growth as Random Iterated Diffeomorphisims (GRID) introduced by Ulf Grenander (Brown University) constructs growth-induced transformations according to fundamental biological principles of growth. They are governed by an underlying genetic control that is expressed in terms of probability laws governing the spatial-temporal patterns of elementary cell decisions (e.g., cell division/death). This thesis addresses computational and stochastic aspects of the GRID model and develops its application to image analysis of growth. The first part of the thesis introduces the original GRID view of growth-induced deformation on a fine time scale as a composition of several, elementary, local deformations each resulting from a random cell decision, a highly localized event in space-time called a seed. A formalization of the proposed model using theory of stochastic processes is presented, namely, an approximation of the GRID model by the diffusion process and the Fokker-Planck equation describing the evolution of the probability density of seed trajectories in space-time. Its time-dependent and stationary numerical solutions reveal bimodal distribution of a random seed trajectory in space-time. The second part of the thesis considers the growth pattern on a coarse time scale which underlies visible shape changes seen in images. It is shown that such a "macroscopic" growth pattern is a solution to a deterministic integro-differential equation in the form of a diffeomorphic flow dependent on the GRID growth variables such as the probability density of cell decisions and the rate of contraction/expansion. Since the GRID variables are unobserved, they have to be estimated from image data. Using the GRID macroscopic growth equation such an estimation problem is formulated as an optimal control problem. The estimated GRID variables are optimal controls that force the image of an initial organism to be continuously transformed into the image of a grown organism. The GRID-based inference method is implemented for inference of growth properties of the Drosophila wing disc directly from confocal micrographs of Wingless gene expression patterns

La Guerra Civil en el País Vasco en la prensa local norteamericana (1936-1939)

838 p.El objeto de esta tesis doctoral es analizar cómo la prensa local norteamericana informó a sus lectores sobre la Guerra Civil en el País Vasco entre 1936 y 1939. La mayor parte de los estudios sobre la opinión pública y la Guerra Civil se centran en los grandes periódicos o en las revistas de opinión. Por el contrario, aquí estudiamos un buen número de pequeños periódicos locales de Estados Unidos, pero que sumados tuvieron gran incidencia en la conformación de la opinión sobre la guerra de España. El carácter específico del conflicto en el País Vasco permite analizar hasta qué punto esos diarios reflejaron aspectos como la actitud del Partido Nacionalista Vasco ante la guerra, la particularidad de la cuestión religiosa o la implantación del denominado ¿oasis vasco¿.De 1936 ¿ 1937, se presta especial atención a sucesos clave como el bombardeo de Guernica ( 26 de abril de 1937), las evacuaciones infantiles, el incendio de Irún o la toma de San Sebastián y Bilbao por los franquistas. Analizamos, asimismo, las fuentes de las noticias de esos pequeños diarios locales ( las grandes agencias de prensa de la época: United Press, Associated Press, etc). El estudio permite concluir que la opinión pública norteamericana fué consciente de la especificidad de la Guerra Civil en Euskadi