This dissertation gives an aesthetic analysis on the selected woodblock prints of the Okinawan artist Gima Hiroshi concurrent with an exploration of the identity politics of the Okinawans. Deconstructing the historical circumstances of the archipelago, the contradictions and predicaments that the islanders have been struggling with from the trade era and annexation period, to the wartime, the U.S. occupation and the reversion to the Japanese state are portrayed in the war prints. With the constitution of a multi-vocal identity, a regionalist identity has been articulated. This regionalism is manifested in the artist’s prints about traditions, customs and everyday life in terms of folk dance, drum playing, craftsmanship, festivities, daily activities, agriculture, residential space and the practices related to nature. Following a thorough discussion of the visual texts is the elucidation of essentialism in contemporary Okinawan studies that identity politics is itself delimiting and institutionalizing in representation. Essentialist representations reinforce the dichotomy of the self/other structure that they can be more detrimental than explicit performative discourses. As a concluding argument, this essay finishes with a proposed alternative to essentialist literature – visual representations. The interpretative potentiality and transformative powers of art serve as a stepping stone for the third party to experience the experience of the Other, which challenges the presumptions imposed by the self/other narrative. In this process, the marginalized can be made visible. / published_or_final_version / Literary and Cultural Studies / Master / Master of Arts

Wooden railway sleeper inspections in Sweden are currently performed manually by a human operator; such inspections are based on visual analysis. Machine vision based approach has been done to emulate the visual abilities of human operator to enable automation of the process. Through this process bad sleepers are identified, and a spot is marked on it with specific color (blue in the current case) on the rail so that the maintenance operators are able to identify the spot and replace the sleeper. The motive of this thesis is to help the operators to identify those sleepers which are marked by color (spots), using an “Intelligent Vehicle” which is capable of running on the track. Capturing video while running on the track and segmenting the object of interest (spot) through this vehicle; we can automate this work and minimize the human intuitions. The video acquisition process depends on camera position and source light to obtain fine brightness in acquisition, we have tested 4 different types of combinations (camera position and source light) here to record the video and test the validity of proposed method. A sequence of real time rail frames are extracted from these videos and further processing (depending upon the data acquisition process) is done to identify the spots. After identification of spot each frame is divided in to 9 regions to know the particular region where the spot lies to avoid overlapping with noise, and so on. The proposed method will generate the information regarding in which region the spot lies, based on nine regions in each frame. From the generated results we have made some classification regarding data collection techniques, efficiency, time and speed. In this report, extensive experiments using image sequences from particular camera are reported and the experiments were done using intelligent vehicle as well as test vehicle and the results shows that we have achieved 95% success in identifying the spots when we use video as it is, in other method were we can skip some frames in pre-processing to increase the speed of video but the segmentation results we reduced to 85% and the time was very less compared to previous one. This shows the validity of proposed method in identification of spots lying on wooden railway sleepers where we can compromise between time and efficiency to get the desired result.

Conventional image sensors achieve color imaging using absorptive organic dye filters. These face considerable challenges however in the trend toward ever higher pixel densities and advanced imaging methods such as multispectral imaging and polarization-resolved imaging. In this dissertation, we investigate the optical properties of vertical silicon nanowires with the goal of image sensor applications. First, we demonstrate a multispectral imaging system that uses a novel filter that consists of vertical silicon nanowires embedded in a transparent medium. Second, we demonstrate pixels consisting of vertical silicon nanowires with integrated photodetectors. We show that their spectral sensitivities are governed by nanowire radius, and perform color imaging. In addition, we demonstrate polarization-resolving photodetectors consisting of silicon nanowires with elliptical cross sections. Finally, we discuss a dual detector device. Each pixel consists of vertical silicon nanowires (incorporating photodetectors) formed above a silicon substrate (that also incorporates a photodetector). Our method is very practical from a manufacturing standpoint because all filter functions are defined at the same time through a single lithography step. In addition, our approach is conceptually different from current filter-based methods, as absorbed light in our device is converted to photocurrent, rather than discarded. This ultimately presents the opportunity for very high photon efficiency. / Engineering and Applied Sciences

The achievement gap is still an epidemic in America and many schools struggle with identifying the reasons that their students of color are not achieving at the same rates as their White peers. This review of the literature uncovers some of these reasons and gives counselors ideas for how they can help these students achieve appropriately. Throughout the literature the disproportionate ratio between the overwhelming majority of White school counselors and educators and the growing number of students of color is clear, meaning that Whites in educator roles are over-represented compared to the demographics of the student body. Also, it is evident that the education that counselors have received in the past, and are still receiving, has been lacking in multicultural competency standards. These are just a few of the reasons for the emotional disconnect that students of color feel with the education system and counselors in particular, which makes the job of the counselor difficult when trying to help all children be successful, as is mandated by the American School Counselor Association and NCLB. The literature suggests that White school counselors can become multiculturally competent and can improve school climates to embrace diverse cultures, which in turn increases the chances of student success. Counselors are important change agents in schools and can help to close the achievement gap. / text

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This study described how suburban public elementary school principals and teachers perceived the principals' social justice leadership as shifting demographic diversity increased in racial and ethnic minority students, decreased in white students, increased in child poverty, and threatened schools academic achievement status. The two Arizona high performance suburban public elementary schools (SPES) were located in two different suburban districts on opposite sides of a metropolitan city. A multiple embedded replication case study involved principals and six K-5 grade teachers at each school and included participant semi-structured interviews, school observations, and document analysis. The data showed how principals' leadership was perceived and practiced in educating students with social and educational inequalities while simultaneously trying to maintain high performance schools. Findings revealed that principals' different and similar practices were not motivated from a social justice disposition. Nevertheless, I discovered that principals' leadership practices imperceptibly included tenets of social justice. The teachers perceived that principals made concerted efforts beyond contemporary leadership practices that addressed children's inequalities owing to poverty and lack of academic preparation. The principals and teachers cared for the students and pushed for additional resources. The educators expressed being underprepared professionally for the tensions brought about by students' shifting demographics. An unexpected finding was that child poverty trumped the children's race and ethnicity as the foremost issue challenging the principals and teachers. As a result of the findings, part of my proposition supported the premise that principals would perceive the educational inequalities experienced by students. Conversely, part of the premise stating that principals' perceptions of students' educational inequalities would influence them to use social justice leadership was weakly supported because principals did not perceive or attribute their practices with teachers as driven by a belief in social justice. Two themes emerged from the analysis of patterns across cases: (1) Principals did not have a social justice consciousness driving their leadership practices, and (2) Principals' contemporary leadership practices imperceptibly combined social justice leadership tenets to influence teachers and promote equality of educational opportunity for all students.

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Traditional appearance based face recognition (FR) systems use gray scale images, however recently attention has been drawn to the use of color images. Color inputs have a larger dimensionality, which increases the computational cost, and makes the small sample size (SSS) problem in supervised FR systems more challenging. It is therefore important to determine the scenarios in which usage of color information helps the FR system. In this thesis, it was found that inclusion of chromatic information in FR systems is shown to be particularly advantageous in poor illumination conditions. In supervised systems, a color input of optimal dimensionality would improve the FR performance under SSS conditions. A fusion of decisions from individual spectral planes also helps in the SSS scenario. Finally, chromatic information is integrated into a supervised ensemble learner to address pose and illumination variations. This framework significantly boosts FR performance under a range of learning scenarios.

Image matching is an important and necessary process in Content-Based Image Retrieval (CBIR). We propose three new methods for image matching: the first one is based on the Local Triplet Pattern (LTP) histograms; the second one is based on the Gaussian Mixture Models (GMMs) estimated by using the Extended Mass-constraint (EMass) algorithm; and the third one is called the DCT2KL algorithm. First, the LTP histograms are proposed to capture spatial relationships between color levels of neighboring pixels. An LTP level is extracted from each 3x3 pixel block, which is a unique number describing the color level relationship between a pixel and its neighboring pixels. Second, we consider how to represent and compare multi-dimensional color features using GMMs. GMMs are alternative methods to histograms for representing data distributions. GMMs address the high-dimensional problems from which histograms usually suffer inefficiency. In order to avoid local maxima problems in most GMM estimation algorithms, we apply the deterministic annealing method to estimate GMMs. Third, motivated by image compression algorithms, the DCT2KL method addresses the high dimensional data by using the Discrete Cosine Transform (DCT) coefficients in the YCbCr color space. The DCT coefficients are restored by partially decoding JPEG images. Assume that each DCT coefficient sequence is emitted from a memoryless source, and all these sources are independent of each other. For each target image we form a hypothesis that its DCT coefficient sequences are emitted from the same sources as the corresponding sequences in the query image. Testing these hypotheses by measuring the log-likelihoods leads to a simple yet efficient scheme that ranks each target image according to the Kullback-Leibler (KL) divergence between the empirical distribution of the DCT coefficient sequences in the query image and that in the target image. Finally we present a scheme to combine different features and methods to boost the performance of image retrieval. Experimental results on different image data sets show that these three methods proposed above outperform the related works in literature, and the combination scheme further improves the retrieval performance.

Common bean is a nutritionally and economically important food crop and a major source of dietary protein in many developing countries throughout the world. Seed coat colour and size in this crop are the main factors determining its marketability in different parts of the world. Flavonoid compounds that are responsible for seed coat colour in beans have been shown to have anti-oxidant, anti-proliferative, anti-tumor, anti-inflammatory, and pro-apoptotic activities. They also may enhance the resistance of beans to pest and disease. A better understanding of the relationships between seed coat colour and flavonoid metabolism in the seed coat may help breeders to select for more nutritionally-beneficial bean varieties. The objective of this research was to test the hypothesis that the genes determining colour in beans are structural and regulatory genes of the phenylpropanoid pathway. The map positions of phenylpropanoid genes were determined in two recombinant inbred populations. Segregation patterns of 18 phenylpropanoid pathway genes in the BAT93 × Jalo EEP 558 RIL population and five phenylpropanoid pathway genes in OAC Rex × SVM Taylor were used to place them on the linkage maps for these populations. Five out of 18 genes were mapped within 2-17 cM of colour gene loci in the BAT93 × Jalo EEP 558 RIL population. The sequences of central genes of the phenylpropanoid pathway were determined by sequencing 6 BAC clones selected with probes for two PAL genes, two CHS genes, DFR, and Myb. The functional annotations of the BAC clones were determined and the similarities between bean phenylpropanoid genes and their corresponding orthologs in other plant species were investigated. A recently developed approach of whole genome sequence comparison was utilized to compare the microsynteny of the sequenced BAC clones with regions of the soybean genome. The physical locations of BAC clones were verified on the bean genome and their counterpart locations on the soybean genome were confirmed. The results agreed with previous studies that indicated that bean genome segments have two homologous segments in soybean and confirmed the high degree of microsynteny that is shared between bean and soybean.