MIUS News: Maps and Imagery User Services @ FIU Green Library: Vol. 1, Issue 1 2007

Fall 2007 Newsletter for FIU's Maps and Imagery User Services department.

MUIS News: Maps and User Imagery Services @ FIU Green Library Vol. 2, Issue 1 2008

Florida International University's Fall 2008 Map and User Imagery Services Newsletter.

MIUS News: Maps and Imagery User Services @ FIU Green Library: Vol. 2, Issue 2 Spring 2009

Florida International University's Spring 2009 Map and User Imagery Services Newsletter.

MIUS News: Maps and Imagery User Services @ FIU Green Library: Vol. 3, Issue 1 Fall 2009

Florida International University's Fall 2009 Map and User Imagery Services Newsletter.

MIUS News: Maps and Imagery User Services @ FIU Green Library: Vol. 3, Issue 2 Fall 2009

Florida International University's Fall 2009 Map and User Imagery Services Newsletter; Vol. 3, issue 2.

MIUS News: Maps and Imagery User Services @ FIU Green Library: Vol. 3, Issue 2, Spring 2010

Florida International University's Spring 2010 Map and User Imagery Services Newsletter.

MIUS News: Maps and Imagery User Services @ FIU Green Library: Vol. 4, Issue 1 Fall 2012

Florida International University's Fall 2012 Map and User Imagery Services Newsletter.

MIUS News: Maps and Imagery User Services @ FIU Green Library: Vol. 4, Issue 2 Spring/Summer 2013

Florida International University's Spring/Summer 2013 Map and User Imagery Services Newsletter.

MIUS News : Maps and Imagery User Services @ FIU Green Library: Vol. 5 Issue 1 Fall 2013

Florida International University's Fall 2013 Map and User Imagery Services Newsletter

MIUS News: Maps and Imagery User Services @ FIU Green Library: Volume 6, Issue 1 Spring 2015

Florida International University's Spring 2015 Maps and User Imagery Services Newsletter

Mitigating inconsistencies by coupling data cleaning, filtering, and contextual data validation in wireless sensor networks

With the advent of peer to peer networks, and more importantly sensor networks, the desire to extract useful information from continuous and unbounded streams of data has become more prominent. For example, in tele-health applications, sensor based data streaming systems are used to continuously and accurately monitor Alzheimer's patients and their surrounding environment. Typically, the requirements of such applications necessitate the cleaning and filtering of continuous, corrupted and incomplete data streams gathered wirelessly in dynamically varying conditions. Yet, existing data stream cleaning and filtering schemes are incapable of capturing the dynamics of the environment while simultaneously suppressing the losses and corruption introduced by uncertain environmental, hardware, and network conditions. Consequently, existing data cleaning and filtering paradigms are being challenged. This dissertation develops novel schemes for cleaning data streams received from a wireless sensor network operating under non-linear and dynamically varying conditions. The study establishes a paradigm for validating spatio-temporal associations among data sources to enhance data cleaning. To simplify the complexity of the validation process, the developed solution maps the requirements of the application on a geometrical space and identifies the potential sensor nodes of interest. Additionally, this dissertation models a wireless sensor network data reduction system by ascertaining that segregating data adaptation and prediction processes will augment the data reduction rates. The schemes presented in this study are evaluated using simulation and information theory concepts. The results demonstrate that dynamic conditions of the environment are better managed when validation is used for data cleaning. They also show that when a fast convergent adaptation process is deployed, data reduction rates are significantly improved. Targeted applications of the developed methodology include machine health monitoring, tele-health, environment and habitat monitoring, intermodal transportation and homeland security.

Does core strength training influence kinetic efficiency, lower extremity stability, and 5000m performance in runners?

Context: Core strength training (CST) has been popular in the fitness industry for a decade. Although strong core muscles are believed to enhance athletic performance, only few scientific studies have been conducted to identify the effectiveness of CST on improving athletic performance. Objective: Identify the effects of a 6-wk CST on running kinetics, lower extremity stability, and running performance in recreational and competitive runners. Design and Setting: A test-retest, randomized control design was used to assess the effect of CST and no CST on ground reaction force (GRF), lower extremity stability scores, and running performance. Participants: Twenty-eight healthy adults (age, 36.9+9.4yrs, height, 168.4+9.6cm, mass, 70.1+15.3kg) were recruited and randomly divided into two groups. Main outcome Measures: GRF was determined by calculating peak impact vertical GRF (vGRF), peak active vGRF, duration of the breaking or horizontal GRF (hGRF), and duration of the propulsive hGRF as measured while running across a force plate. Lower extremity stability in three directions (anterior, posterior, lateral) was assessed using the Star Excursion Balance Test (SEBT). Running performance was determined by 5000 meter run measured on selected outdoor tracks. Six 2 (time) X 2 (condition) mixed-design ANOVA were used to determine if CST influences on each dependent variable, p < .05. Results: No significant interactions were found for any kinetic variables and SEBT score, p>.05. But 5000m run time showed significant interaction, p < .05. SEBT scores improved in both groups, but more in the experimental group. Conclusion: CST did not significantly influence kinetic efficiency and lower extremity stability, but did influence running performance.

The relationship between the secondary mathematics curriculum, college persistence, and success at an urban community college

According to Venezia, Kirst, and Antonio (2003) and Barth’s 2002 Thinking K16 Ticket to Nowhere report, the disconnect between K-12 and postsecondary education was a contributing factor to high attrition rates. Since mathematics emerged as a primary concern for college readiness, Barth (2002) called for improving student transitions from K-12 to postsecondary institutions through the use of state or local data. The purpose of the present study was to analyze mathematics course-taking patterns of secondary students in a local context and to evaluate high school characteristics in order to explore their relationships with Associate degree attainment or continuous enrollment at an urban community college. Also, this study extended a national study conducted by Clifford Adelman (The Toolbox Revisited, 2006) as it specifically focused on community college students that were not included his study. Furthermore, this study used the theoretical framework that human capital, social capital, and cultural capital influence habitus—an individual’s or a group’s learned inclination to behave within the parameters of the imposed prevailing culture and norms. Specifically, the school embedded culture as it relates to tracking worked as a reproduction tool of ultimate benefit for the privileged group (Oakes, 1994). ^ Using multilevel analysis, this ex post facto study examined non-causal relationships between math course-taking patterns and college persistence of public high school graduates who enrolled at the local community college for up to 6 years. One school-level variable (percent of racial/ethnic minorities) and 7 student-level variables (community college math proportion, remedial math attempts, race, gender, first-year credits earned, socioeconomic status, and summer credits earned) emerged as predictors for college persistence. Study results indicated that students who enter higher education at the community college may have had lower opportunities to learn and therefore needed higher levels of remediation, which was shown to detract students from degree completion. Community college leaders are called to partner with local high schools with high percentages of racial/ethnic minorities to design academic programs aimed at improving the academic preparation of high school students in mathematics and promote student engagement during the first year and summers of college. ^

Mitigating Inconsistencies by Coupling Data Cleaning, Filtering, and Contextual Data Validation in Wireless Sensor Networks

With the advent of peer to peer networks, and more importantly sensor networks, the desire to extract useful information from continuous and unbounded streams of data has become more prominent. For example, in tele-health applications, sensor based data streaming systems are used to continuously and accurately monitor Alzheimer's patients and their surrounding environment. Typically, the requirements of such applications necessitate the cleaning and filtering of continuous, corrupted and incomplete data streams gathered wirelessly in dynamically varying conditions. Yet, existing data stream cleaning and filtering schemes are incapable of capturing the dynamics of the environment while simultaneously suppressing the losses and corruption introduced by uncertain environmental, hardware, and network conditions. Consequently, existing data cleaning and filtering paradigms are being challenged. This dissertation develops novel schemes for cleaning data streams received from a wireless sensor network operating under non-linear and dynamically varying conditions. The study establishes a paradigm for validating spatio-temporal associations among data sources to enhance data cleaning. To simplify the complexity of the validation process, the developed solution maps the requirements of the application on a geometrical space and identifies the potential sensor nodes of interest. Additionally, this dissertation models a wireless sensor network data reduction system by ascertaining that segregating data adaptation and prediction processes will augment the data reduction rates. The schemes presented in this study are evaluated using simulation and information theory concepts. The results demonstrate that dynamic conditions of the environment are better managed when validation is used for data cleaning. They also show that when a fast convergent adaptation process is deployed, data reduction rates are significantly improved. Targeted applications of the developed methodology include machine health monitoring, tele-health, environment and habitat monitoring, intermodal transportation and homeland security.