3 resultados para Mean Field Analysis

em Digital Commons at Florida International University


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The purpose of this study was to evaluate the effect of cooperative learning strategies on students' attitudes toward science and achievement in BSC 1005L, a non-science majors' general biology laboratory course at an urban community college. Data were gathered on the participants' attitudes toward science and cognitive biology level pre and post treatment in BSC 1005L. Elements of the Learning Together model developed by Johnson and Johnson and the Student Team-Achievement Divisions model created by Slavin were incorporated into the experimental sections of BSC 1005L.^ Four sections of BSC 1005L participated in this study. Participants were enrolled in the 1998 spring (January) term. Students met weekly in a two hour laboratory session. The treatment was administered to the experimental group over a ten week period. A quasi-experimental pretest-posttest control group design was used. Students in the cooperative learning group (n$\sb1$ = 27) were administered the Test of Science-Related Attitudes (TOSRA) and the cognitive biology test at the same time as the control group (n$\sb2$ = 19) (at the beginning and end of the term).^ Statistical analyses confirmed that both groups were equivalent regarding ethnicity, gender, college grade point average and number of absences. Independent sample t-tests performed on pretest mean scores indicated no significant differences in the TOSRA scale two or biology knowledge between the cooperative learning group and the control group. The scores of TOSRA scales: one, three, four, five, six, and seven were significantly lower in the cooperative learning group. Independent sample t-tests of the mean score differences did not show any significant differences in posttest attitudes toward science or biology knowledge between the two groups. Paired t-tests did not indicate any significant differences on the TOSRA or biology knowledge within the cooperative learning group. Paired t-tests did show significant differences within the control group on TOSRA scale two and biology knowledge. ANCOVAs did not indicate any significant differences on the post mean scores of the TOSRA or biology knowledge adjusted by differences in the pretest mean scores. Analysis of the research data did not show any significant correlation between attitudes toward science and biology knowledge. ^

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This study was an evaluation of a Field Project Model Curriculum and its impact on achievement, attitude toward science, attitude toward the environment, self-concept, and academic self-concept with at-risk eleventh and twelfth grade students. One hundred eight students were pretested and posttested on the Piers-Harris Children's Self-Concept Scale, PHCSC (1985); the Self-Concept as a Learner Scale, SCAL (1978); the Marine Science Test, MST (1987); the Science Attitude Inventory, SAI (1970); and the Environmental Attitude Scale, EAS (1972). Using a stratified random design, three groups of students were randomly assigned according to sex and stanine level, to three treatment groups. Group one received the field project method, group two received the field study method, and group three received the field trip method. All three groups followed the marine biology course content as specified by Florida Student Performance Objectives and Frameworks. The intervention occurred for ten months with each group participating in outside-of-classroom activities on a trimonthly basis. Analysis of covariance procedures were used to determine treatment effects. F-ratios, p-levels and t-tests at p $<$.0062 (.05/8) indicated that a significant difference existed among the three treatment groups. Findings indicated that groups one and two were significantly different from group three with group one displaying significantly higher results than group two. There were no significant differences between males and females in performance on the five dependent variables. The tenets underlying environmental education are congruent with the recommendations toward the reform of science education. These include a value analysis approach, inquiry methods, and critical thinking strategies that are applied to environmental issues. ^

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The move from Standard Definition (SD) to High Definition (HD) represents a six times increases in data, which needs to be processed. With expanding resolutions and evolving compression, there is a need for high performance with flexible architectures to allow for quick upgrade ability. The technology advances in image display resolutions, advanced compression techniques, and video intelligence. Software implementation of these systems can attain accuracy with tradeoffs among processing performance (to achieve specified frame rates, working on large image data sets), power and cost constraints. There is a need for new architectures to be in pace with the fast innovations in video and imaging. It contains dedicated hardware implementation of the pixel and frame rate processes on Field Programmable Gate Array (FPGA) to achieve the real-time performance. ^ The following outlines the contributions of the dissertation. (1) We develop a target detection system by applying a novel running average mean threshold (RAMT) approach to globalize the threshold required for background subtraction. This approach adapts the threshold automatically to different environments (indoor and outdoor) and different targets (humans and vehicles). For low power consumption and better performance, we design the complete system on FPGA. (2) We introduce a safe distance factor and develop an algorithm for occlusion occurrence detection during target tracking. A novel mean-threshold is calculated by motion-position analysis. (3) A new strategy for gesture recognition is developed using Combinational Neural Networks (CNN) based on a tree structure. Analysis of the method is done on American Sign Language (ASL) gestures. We introduce novel point of interests approach to reduce the feature vector size and gradient threshold approach for accurate classification. (4) We design a gesture recognition system using a hardware/ software co-simulation neural network for high speed and low memory storage requirements provided by the FPGA. We develop an innovative maximum distant algorithm which uses only 0.39% of the image as the feature vector to train and test the system design. Database set gestures involved in different applications may vary. Therefore, it is highly essential to keep the feature vector as low as possible while maintaining the same accuracy and performance^