Current Search: Li, Jun (x)
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Title
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REFRACTIVE INDICES OF LIQUID CRYSTALS AND THEIR APPLICATIONS IN DISPLAY AND PHOTONIC DEVICES.
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Creator
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Li, Jun, Wu, Shin-Tson, University of Central Florida
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Abstract / Description
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Liquid crystals (LCs) are important materials for flat panel display and photonic devices. Most LC devices use electrical field-, magnetic field-, or temperature-induced refractive index change to modulate the incident light. Molecular constituents, wavelength, and temperature are the three primary factors determining the liquid crystal refractive indices: ne and no for the extraordinary and ordinary rays, respectively. In this dissertation, we derive several physical models for describing...
Show moreLiquid crystals (LCs) are important materials for flat panel display and photonic devices. Most LC devices use electrical field-, magnetic field-, or temperature-induced refractive index change to modulate the incident light. Molecular constituents, wavelength, and temperature are the three primary factors determining the liquid crystal refractive indices: ne and no for the extraordinary and ordinary rays, respectively. In this dissertation, we derive several physical models for describing the wavelength and temperature effects on liquid crystal refractive indices, average refractive index, and birefringence. Based on these models, we develop some high temperature gradient refractive index LC mixtures for photonic applications, such as thermal tunable liquid crystal photonic crystal fibers and thermal solitons. Liquid crystal refractive indices decrease as the wavelength increase. Both ne and no saturate in the infrared region. Wavelength effect on LC refractive indices is important for the design of direct-view displays. In Chapter 2, we derive the extended Cauchy models for describing the wavelength effect on liquid crystal refractive indices in the visible and infrared spectral regions based on the three-band model. The three-coefficient Cauchy model could be used for describing the refractive indices of liquid crystals with low, medium, and high birefringence, whereas the two-coefficient Cauchy model is more suitable for low birefringence liquid crystals. The critical value of the birefringence is deltan~0.12. Temperature is another important factor affecting the LC refractive indices. The thermal effect originated from the lamp of projection display would affect the performance of the employed liquid crystal. In Chapter 3, we derive the four-parameter and three-parameter parabolic models for describing the temperature effect on the LC refractive indices based on Vuks model and Haller equation. We validate the empirical Haller equation quantitatively. We also validate that the average refractive index of liquid crystal decreases linearly as the temperature increases. Liquid crystals exhibit a large thermal nonlinearity which is attractive for new photonic applications using photonic crystal fibers. We derive the physical models for describing the temperature gradient of the LC refractive indices, ne and no, based on the four-parameter model. We find that LC exhibits a crossover temperature To at which dno/dT is equal to zero. The physical models of the temperature gradient indicate that ne, the extraordinary refractive index, always decreases as the temperature increases since dne/dT is always negative, whereas no, the ordinary refractive index, decreases as the temperature increases when the temperature is lower than the crossover temperature (dno/dT<0 when the temperature is lower than To) and increases as the temperature increases when the temperature is higher than the crossover temperature (dno/dT>0 when the temperature is higher than To ). Measurements of LC refractive indices play an important role for validating the physical models and the device design. Liquid crystal is anisotropic and the incident linearly polarized light encounters two different refractive indices when the polarization is parallel or perpendicular to the optic axis. The measurement is more complicated than that for an isotropic medium. In Chapter 4, we use a multi-wavelength Abbe refractometer to measure the LC refractive indices in the visible light region. We measured the LC refractive indices at six wavelengths, lamda=450, 486, 546, 589, 633 and 656 nm by changing the filters. We use a circulating constant temperature bath to control the temperature of the sample. The temperature range is from 10 to 55 oC. The refractive index data measured include five low-birefringence liquid crystals, MLC-9200-000, MLC-9200-100, MLC-6608 (delta_epsilon=-4.2), MLC-6241-000, and UCF-280 (delta_epsilon=-4); four middle-birefringence liquid crystals, 5CB, 5PCH, E7, E48 and BL003; four high-birefringence liquid crystals, BL006, BL038, E44 and UCF-35, and two liquid crystals with high dno/dT at room temperature, UCF-1 and UCF-2. The refractive indices of E7 at two infrared wavelengths lamda=1.55 and 10.6 um are measured by the wedged-cell refractometer method. The UV absorption spectra of several liquid crystals, MLC-9200-000, MLC-9200-100, MLC-6608 and TL-216 are measured, too. In section 6.5, we also measure the refractive index of cured optical films of NOA65 and NOA81 using the multi-wavelength Abbe refractometer. In Chapter 5, we use the experimental data measured in Chapter 4 to validate the physical models we derived, the extended three-coefficient and two-coefficient Cauchy models, the four-parameter and three-parameter parabolic models. For the first time, we validate the Vuks model using the experimental data of liquid crystals directly. We also validate the empirical Haller equation for the LC birefringence delta_n and the linear equation for the LC average refractive index . The study of the LC refractive indices explores several new photonic applications for liquid crystals such as high temperature gradient liquid crystals, high thermal tunable liquid crystal photonic crystal fibers, the laser induced 2D+1 thermal solitons in nematic crystals, determination for the infrared refractive indices of liquid crystals, comparative study for refractive index between liquid crystals and photopolymers for polymer dispersed liquid crystal (PDLC) applications, and so on. In Chapter 6, we introduce these applications one by one. First, we formulate two novel liquid crystals, UCF-1 and UCF-2, with high dno/dT at room temperature. The dno/dT of UCF-1 is about 4X higher than that of 5CB at room temperature. Second, we infiltrate UCF-1 into the micro holes around the silica core of a section of three-rod core PCF and set up a highly thermal tunable liquid crystal photonic crystal fiber. The guided mode has an effective area of 440 Ým2 with an insertion loss of less than 0.5dB. The loss is mainly attributed to coupling losses between the index-guided section and the bandgap-guided section. The thermal tuning sensitivity of the spectral position of the bandgap was measured to be 27 nm/degree around room temperature, which is 4.6 times higher than that using the commercial E7 LC mixture operated at a temperature above 50 degree C. Third, the novel liquid crystals UCF-1 and UCF-2 are preferred to trigger the laser-induced thermal solitons in nematic liquid crystal confined in a capillary because of the high positive temperature gradient at room temperature. Fourth, we extrapolate the refractive index data measured at the visible light region to the near and far infrared region basing on the extended Cauchy model and four-parameter model. The extrapolation method is validated by the experimental data measured at the visible light and infrared light regions. Knowing the LC refractive indices at the infrared region is important for some photonic devices operated in this light region. Finally, we make a completely comparative study for refractive index between two photocurable polymers (NOA65 and NOA81) and two series of Merck liquid crystals, E-series (E44, E48, and E7) and BL-series (BL038, BL003 and BL006) in order to optimize the performance of polymer dispersed liquid crystals (PDLC). Among the LC materials we studied, BL038 and E48 are good candidates for making PDLC system incorporating NOA65. The BL038 PDLC cell shows a higher contrast ratio than the E48 cell because BL038 has a better matched ordinary refractive index, higher birefringence, and similar miscibility as compared to E48. Liquid crystals having a good miscibility with polymer, matched ordinary refractive index, and higher birefringence help to improve the PDLC contrast ratio for display applications. In Chapter 7, we give a general summary for the dissertation.
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Date Issued
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2005
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Identifier
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CFE0000808, ucf:46677
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Format
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Document (PDF)
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PURL
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http://purl.flvc.org/ucf/fd/CFE0000808
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Title
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Taming Wild Faces: Web-Scale, Open-Universe Face Identification in Still and Video Imagery.
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Creator
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Ortiz, Enrique, Shah, Mubarak, Sukthankar, Rahul, Da Vitoria Lobo, Niels, Wang, Jun, Li, Xin, University of Central Florida
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Abstract / Description
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With the increasing pervasiveness of digital cameras, the Internet, and social networking, there is a growing need to catalog and analyze large collections of photos and videos. In this dissertation, we explore unconstrained still-image and video-based face recognition in real-world scenarios, e.g. social photo sharing and movie trailers, where people of interest are recognized and all others are ignored. In such a scenario, we must obtain high precision in recognizing the known identities,...
Show moreWith the increasing pervasiveness of digital cameras, the Internet, and social networking, there is a growing need to catalog and analyze large collections of photos and videos. In this dissertation, we explore unconstrained still-image and video-based face recognition in real-world scenarios, e.g. social photo sharing and movie trailers, where people of interest are recognized and all others are ignored. In such a scenario, we must obtain high precision in recognizing the known identities, while accurately rejecting those of no interest.Recent advancements in face recognition research has seen Sparse Representation-based Classification (SRC) advance to the forefront of competing methods. However, its drawbacks, slow speed and sensitivity to variations in pose, illumination, and occlusion, have hindered its wide-spread applicability. The contributions of this dissertation are three-fold: 1. For still-image data, we propose a novel Linearly Approximated Sparse Representation-based Classification (LASRC) algorithm that uses linear regression to perform sample selection for l1-minimization, thus harnessing the speed of least-squares and the robustness of SRC. On our large dataset collected from Facebook, LASRC performs equally to standard SRC with a speedup of 100-250x.2. For video, applying the popular l1-minimization for face recognition on a frame-by-frame basis is prohibitively expensive computationally, so we propose a new algorithm Mean Sequence SRC (MSSRC) that performs video face recognition using a joint optimization leveraging all of the available video data and employing the knowledge that the face track frames belong to the same individual. Employing MSSRC results in a speedup of 5x on average over SRC on a frame-by-frame basis.3. Finally, we make the observation that MSSRC sometimes assigns inconsistent identities to the same individual in a scene that could be corrected based on their visual similarity. Therefore, we construct a probabilistic affinity graph combining appearance and co-occurrence similarities to model the relationship between face tracks in a video. Using this relationship graph, we employ random walk analysis to propagate strong class predictions among similar face tracks, while dampening weak predictions. Our method results in a performance gain of 15.8% in average precision over using MSSRC alone.
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Date Issued
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2014
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Identifier
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CFE0005536, ucf:50313
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Format
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Document (PDF)
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PURL
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http://purl.flvc.org/ucf/fd/CFE0005536
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Title
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Hashing for Multimedia Similarity Modeling and Large-Scale Retrieval.
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Creator
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Li, Kai, Hua, Kien, Qi, GuoJun, Hu, Haiyan, Wang, Chung-Ching, University of Central Florida
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Abstract / Description
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In recent years, the amount of multimedia data such as images, texts, and videos have been growing rapidly on the Internet. Motivated by such trends, this thesis is dedicated to exploiting hashing-based solutions to reveal multimedia data correlations and support intra-media and inter-media similarity search among huge volumes of multimedia data.We start by investigating a hashing-based solution for audio-visual similarity modeling and apply it to the audio-visual sound source localization...
Show moreIn recent years, the amount of multimedia data such as images, texts, and videos have been growing rapidly on the Internet. Motivated by such trends, this thesis is dedicated to exploiting hashing-based solutions to reveal multimedia data correlations and support intra-media and inter-media similarity search among huge volumes of multimedia data.We start by investigating a hashing-based solution for audio-visual similarity modeling and apply it to the audio-visual sound source localization problem. We show that synchronized signals in audio and visual modalities demonstrate similar temporal changing patterns in certain feature spaces. We propose to use a permutation-based random hashing technique to capture the temporal order dynamics of audio and visual features by hashing them along the temporal axis into a common Hamming space. In this way, the audio-visual correlation problem is transformed into a similarity search problem in the Hamming space. Our hashing-based audio-visual similarity modeling has shown superior performances in the localization and segmentation of sounding objects in videos.The success of the permutation-based hashing method motivates us to generalize and formally define the supervised ranking-based hashing problem, and study its application to large-scale image retrieval. Specifically, we propose an effective supervised learning procedure to learn optimized ranking-based hash functions that can be used for large-scale similarity search. Compared with the randomized version, the optimized ranking-based hash codes are much more compact and discriminative. Moreover, it can be easily extended to kernel space to discover more complex ranking structures that cannot be revealed in linear subspaces. Experiments on large image datasets demonstrate the effectiveness of the proposed method for image retrieval.We further studied the ranking-based hashing method for the cross-media similarity search problem. Specifically, we propose two optimization methods to jointly learn two groups of linear subspaces, one for each media type, so that features' ranking orders in different linear subspaces maximally preserve the cross-media similarities. Additionally, we develop this ranking-based hashing method in the cross-media context into a flexible hashing framework with a more general solution. We have demonstrated through extensive experiments on several real-world datasets that the proposed cross-media hashing method can achieve superior cross-media retrieval performances against several state-of-the-art algorithms.Lastly, to make better use of the supervisory label information, as well as to further improve the efficiency and accuracy of supervised hashing, we propose a novel multimedia discrete hashing framework that optimizes an instance-wise loss objective, as compared to the pairwise losses, using an efficient discrete optimization method. In addition, the proposed method decouples the binary codes learning and hash function learning into two separate stages, thus making the proposed method equally applicable for both single-media and cross-media search. Extensive experiments on both single-media and cross-media retrieval tasks demonstrate the effectiveness of the proposed method.
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Date Issued
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2017
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Identifier
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CFE0006759, ucf:51840
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Format
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Document (PDF)
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PURL
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http://purl.flvc.org/ucf/fd/CFE0006759
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Title
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Transcriptional and Post-transcriptional Regulation of Gene Expression.
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Creator
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Ding, Jun, Hu, Haiyan, Li, Xiaoman, Zhang, Shaojie, Jin, Yier, University of Central Florida
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Abstract / Description
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Regulation of gene expression includes a variety of mechanisms to increase or decrease specific gene products. Gene expression can be regulated at any stage from transcription to post-transcription and it's essential to almost all living organisms, as it increases the versatility and adaptability by allowing the cell to express the needed proteins. In this dissertation, we comprehensively studied the gene regulation from both transcriptional and post-transcriptional points of view....
Show moreRegulation of gene expression includes a variety of mechanisms to increase or decrease specific gene products. Gene expression can be regulated at any stage from transcription to post-transcription and it's essential to almost all living organisms, as it increases the versatility and adaptability by allowing the cell to express the needed proteins. In this dissertation, we comprehensively studied the gene regulation from both transcriptional and post-transcriptional points of view. Transcriptional regulation is by which cells regulate the transcription from DNA to RNA, thereby directing gene activity. Transcriptional factors (TFs) play a very important role in transcriptional regulation and they are proteins that bind to specific DNA sequences (regulatory elements) to regulate the gene expression. Current studies on TF binding are still very limited and thus, it leaves much to be improved on understanding the TF binding mechanism. To fill this gap, we proposed a variety of computational methods for predicting TF binding elements, which have been proved to be more efficient and accurate compared with other existing tools such as DREME and RSAT peaks-motif. On the other hand, studying only the transcriptional gene regulation is not enough for a comprehensive understanding. Therefore, we also studied the gene regulation at the post-transcriptional level. MicroRNAs (miRNAs) are believed to post-transcriptionally regulate the expression of thousands of target mRNAs, yet the miRNA binding mechanism is still not well understood. In this dissertation, we explored both the traditional and novel features of miRNA-binding and proposed several computational models for miRNA target prediction. The developed tools outperformed the traditional microRNA target prediction methods (.e.g miRanda and TargetScan) in terms of prediction accuracy (precision, recall) and time efficiency.
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Date Issued
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2016
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Identifier
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CFE0006098, ucf:51197
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Format
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Document (PDF)
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PURL
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http://purl.flvc.org/ucf/fd/CFE0006098