none

none

To have a voice link with other diving partners or surface personnel, divers need to put on a communication mask. The second stage regulator or mouthpiece is equipped with a circuit to pick up the voice of the diver. Then the voice is frequency-modulates into ultrasonic signal to be transmitted into water. A receiver on the other side picks up the ultrasonic signal and demodulates it back to voice, and plays back in diver's earphone set. This technology is mature but not widely adopted for its price. Most divers still use their favorite way to communicate with each other, i.e. DSL (divers' sign language.) As more and more intelligent machines or robots are built to help divers for their underwater task, divers not only need to exchange messages with their human partners but also machines. However, it seems that there are not many input devices available other than push buttons or joysticks. We know that divers¡¦hands are always busy with holding tools or gauges. Additional input devices will further complicate their movement, also distract their attention for safety measures. With this consideration, this paper intends to develop an algorithm to read the DSL as input commands for computer-aided diving system. To simplify the image processing part of the problem, we attach an LED at the tip of each finger. The gesture or the hand sign is then captured by a CCD camera. After thresholding, there will only five or less than five bright spots left in the image. The remaining part of the task is to design a classifier that can identify if the unknown sign is one from the pool. Furthermore, a constraint imposed is that the algorithm should work without knowing all of the signs in advance. This is an analogy to that human can recognize a face is someone known seen before or a stranger. We modify the concept of eigenfaces developed by Turk and Pentland into eigenhands. The idea is to choose geometrical properties of the bright spots (finger tips), like distance from fingertips to the centroid or the total area of the polygon with fingertips as its vertices as the features of the corresponding hand sign. All these features are quantitative, so we can put several features together to construct a vector to represent a specific hand sign. These vectors are treated as the raw data of the hand signs, and an essential subset or subspace can be spanned by the eigen vectors of the first few large corresponding values. It is less than the total number of hand signed involved. The projection of the raw vector along these eigen vectors are called the principal components of the hand sign. Principal components are abstract but they can serve as keys to match the candidate from a larger pool. With these types of simple geometrical features, the success rate of cross identification among 30 different subjects' 16 gestures varies to 91.04% .

Knowledge of the implementation practices of successful elementary schools will be beneficial to other elementary principals who seek to improve student success in reading. This study examined perceptions of principals from elementary schools in Texas whose schools received the Gold Performance Acknowledgement (GPA) from the Texas Education Agency (TEA) for Continuous Improvement in Reading (CIR) on the Texas Assessment of Academic Skills (TAAS) in 2002. The study had two purposes: (1) to identify the principal??s perception of the levels of implementation and impact of selected reading strategies used by selected elementary schools in Texas to improve student success in reading and (2) to determine the principal??s perception of the extent to which Accelerated Reader (AR) and AR-like recommend practices were used in selected elementary schools in Texas. The research design for this study was descriptive. Parameters, which are descriptive measures of a population, were used since all 721 members of the population were mailed questionnaires. Research was conducted during the winter of2004. Two hundred fifty-two principals responded. A questionnaire using a Likerttype scale for the principals?? responses was used to collect the data. Principals?? perceptions were measured to determine the degree of implementation and impact of AR and other selected reading strategies. Data were analyzed for all 252 respondents for selected reading strategies and by the categories of AR and non-AR schools for AR recommended reading strategies and AR-like recommended reading strategies, respectively. This study identified the characteristics of a successful reading program in Texas elementary schools. To maximize their budgets while improving student success in reading, principals should provide their teachers with professional development, implement student/teacher conferences to direct reading practice, allow students to self-select books on their independent reading level for independent reading practice, consider use of literature circles, classroom libraries and reading textbooks, review the use of rewards and posting of goals to determine if these practices increase students?? success in reading, assess computer reading programs to determine if there are less costly options available, and in schools using the AR program, review implementation practices for greater impact.

Thesis (Ph.D)--Computing, Georgia Institute of Technology, 2009. / Committee Chair: Santosh Vempala; Committee Member: Adam Kalai; Committee Member: Haesun Park; Committee Member: Ravi Kannan; Committee Member: Vladimir Koltchinskii. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Thesis (Ed. D.)--University of Missouri-Columbia, 2002. / Typescript. Vita. Includes bibliographical references (leaves 96-104). Also available on the Internet.

Thesis (Ed. D.)--University of Missouri-Columbia, 2002. / Typescript. Vita. Includes bibliographical references (leaves 96-104). Also available on the Internet.

In recent years, massive data sets are generated in many areas of science and business, and are gathered by using advanced data acquisition techniques. New approaches are therefore required to facilitate effective data management and data analysis in this big data era, especially to analyze multidimensional data for real-time applications. This thesis aims at developing generic and effective algorithms for compressing and recovering online multidimensional data, and applying such algorithms in image processing and other related areas. Since multidimensional data are usually represented by tensors, this research uses multilinear algebra as the mathematical foundation to facilitate development. After reviewing the techniques of singular value decomposition (SVD), principal component analysis (PCA) and tensor decomposition, this thesis deduces an effective multilinear principal component analysis (MPCA) method to process such data by seeking optimal orthogonal basis functions that map the original tensor space to a tensor subspace with minimal reconstruction error. Two real examples, 3D data compression for positron emission tomography (PET) and offline fabric defect detection, are used to illustrate the tensor decomposition method and the deduced MPCA method, respectively. Based on the deduced MPCA method, this research develops an incremental MPCA (IMPCA) algorithm which targets at compressing and recovering online tensor objects. To reduce computational complexity of the IMPCA algorithm, this research investigates the low-rank updates of singular values in the matrix and tensor domains, which leads to the development of a sequential low-rank update scheme similar to the sequential Karhunen-Loeve algorithm (SKL) for incremental matrix singular value decomposition, a sequential low-rank update scheme for incremental tensor decomposition, and a quick subspace tracking (QST) algorithm to further enhance the low-rank updates of singular values if the matrix is positive-symmetric definite. Although QST is slightly inferior to the SKL algorithm in terms of accuracy in estimating eigenvector and eigenvalue, the algorithm has lower computational complexity. Two fast incremental MPCA (IMPCA) algorithms are then developed by incorporating the SKL algorithm and the QST algorithm separately into the IMPCA algorithm. Results obtained from applying the developed IMPCA algorithms to detect anomalies from online multidimensional data in a number of numerical experiments, and to track and reconstruct the global surface temperature anomalies over the past several decades clearly confirm the excellent performance of the algorithms. This research also applies the developed IMPCA algorithms to solve an online fabric defect inspection problem. Unlike existing pixel-wise detection schemes, the developed algorithms employ a scanning window to extract tensor objects from fabric images, and to detect the occurrence of anomalies. The proposed method is unsupervised because no pre-training is needed. Two image processing techniques, selective local Gabor binary patterns (SLGBP) and multi-channel feature combination, are developed to accomplish the feature extraction of textile patterns and represent the features as tensor objects. Results of experiments conducted by using a real textile dataset confirm that the developed algorithms are comparable to existing supervised methods in terms of accuracy and computational complexity. A cost-effective parallel implementation scheme is developed to solve the problem in real-time. / published_or_final_version / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / Geography and Geology / Master / Master of Philosophy

Principals and instructional coaches from four high-performing and equitable-performing elementary schools stratified by socioeconomic level were interviewed. The main concern of participants was meeting student performance challenges set by accountability systems and measured by mandated tests. This concern was manifested in a focus on the needs and educational progress of individual children. Other participant concerns included investing in teachers, analyzing test data collaboratively, intervening on behalf of struggling students, dealing with the current accountability environment, building productive learning environments, achieving educational equity, and maintaining identity as a leader. Participants demonstrated a rather low knowledge of accountability system mechanics and ambivalence about accountability system features; however, they fully embraced the spirit of "leaving no child left behind." They also emphasized many elementes of leadership that predate the current accountability environment, including instructional leadership, professional learning communities, and high expectations for students. Although categories were consistent among the schools, other factors co-vary with school size (performance unit of analysis) or school socioeconomic status (future vs. present orientation). A statistical measure for evaluating the educational equity of schools is introduced.