University of Maryland, Baltimore County
Not a member yet
17643 research outputs found
Sort by
Second Order Statistics Targets-Specified Virtual Dimensionality
No full textHyperspectral imaging has emerged as a productive and useful technique in remote sensing. With its high spectral resolution the materials in a scene can be detected, discriminated, and identified. This has come at the cost of challenges in the storage, transmission, and processing of the data to retrieve the information of interest. Since the number of spectral bands in a datacube is typically much larger than the true dimensionality of the data approaches have been sought to reduce the dimensionality of the data to allieviate the processing requirements. To enable this effectively it is necessary to know the dimensionality of this subspace. The primary focus of this dissertation is the development of unsupervised approaches to determine this subspace. In this work the focus is on using least-squares techniques to identify the dimensionality and subspace basis. We are specifically interested in a target-specified approach, that is in identifying a subspace of minimum dimension whose basis vectors are actual signatures present in the image. To distinguish this from a subspace basis of pure signatures we call these basis vectors virtual endmembers. This work develops a theory of second-order statistics target-specified virtual dimensionality. Virtual dimensionality is defined as the number of spectrally distinct signatures in hyperspectral data. Unfortunately there is no universal definition of spectrally distinct. In developing the theory of second-order statistics targets-specified virtual dimensionality spectrally distinct is defined in the second-order statistics sense, thus the number of virtual endmember basis vector is the appropriate virtual dimensionality
Progressive Band Processing for Hyperspectral Imaging
No full textHyperspectral imaging has emerged as an image processing technique in many applications. The reason that hyperspectral data is called hyperspectral is mainly because the massive amount of information provided by the hundreds of spectral bands that can be used for data analysis. However, due to very high band-to-band correlation much information may be also redundant. Consequently, how to effectively and best utilize such rich spectral information becomes very challenging. One general approach is data dimensionality reduction which can be performed by data compression techniques, such as data transforms, and data reduction techniques, such as band selection. This dissertation presents a new area in hyperspectral imaging, to be called progressive hyperspectral imaging, which has not been explored in the past. Specifically, it derives a new theory, called Progressive Band Processing (PBP) of hyperspectral data that can significantly reduce computing time and can also be realized in real-time. It is particularly suited for application areas such as hyperspectral data communications and transmission where data can be communicated and transmitted progressively through spectral or satellite channels with limited data storage. Most importantly, PBP allows users to screen preliminary results before deciding to continue with processing the complete data set. These advantages benefit users of hyperspectral data by reducing processing time and increasing the timeliness of crucial decisions made based on the data such as identifying key intelligence information when a required response time is short
Executive Privilege and the Spirit of Republicanism in the Washington Administration
No full textExecutive privilege is the term commonly associated with the authority of the President of the United States to withhold information from Congress or the courts. President George Washington first exercised the power of executive privilege and his precedent is regularly cited as support for the legitimacy and scope of the privilege. Washington is generally considered by modern scholars as having used executive privilege in a limited way and only in the best interests of the public. However, a closer examination of the political history of the Federalist Era and the context in which the President exercised the privilege suggests that Washington may have also used executive privilege to achieve domestic political goals that primarily served the best interests of the President and his administration
Visualizing Sequential Patterns in Large Datasets Using Levels of Abstraction
No full textStudent retention and success are important topics in all academic fields and institutions. Faculty members seek to understand which topics, theories, or skills defeat students or require strengthening to promote success. Programs seek to understand how to better sequence courses to ensure students are prepared for requisite future courses. Institutions seek to understand how to intervene to promote retention and improve graduation rates. Unfortunately, most statistics gathered by Institutional Research efforts are limited to failure rates, enrollment rates, and graduation rates and do not often explore individual student performance or enrollment patterns. While these are often further analyzed by various student demographic attributes such as race and gender, these statistical methods alone are insufficient to understand student performance over time and sequential patterns of enrollment or success and failure. This research presents a method using multiple levels of abstraction to visualize performance patterns over time. To visualize student enrollment and performance patterns, several issues must be addressed including sequential versus concurrent enrollment, spatial layout of course events, and performance over time. Another challenge addressed by this work is that of presenting sequences within the context of the entire program. To address these challenges, multiple simultaneous visualizations are used to illustrate performance within context. The aggregated view represents the lowest level of abstraction: student enrollment and performance are aggregated into a graph structure, presenting patterns of movement throughout the program at the individual course level. The clustered view represents mined sequential patterns of enrollment and performance, illustrating common sequences. The directed view represents the highest level of abstraction and uses two visual elements, heat maps and a vector field, to illustrate overall performance in individual events and movement through the program. Results from multiple cohorts can then be superimposed on the same visualization to enable easy comparisons between patterns. Together, these abstractions provide a focus+context view of student performance, retaining outliers and emphasizing common patterns to illuminate dominant and unique patterns between cohorts of students. This work makes substantial contributions in the fields of data mining, visualization, and education, addressing the challenges of visualizing sequential patterns. In data mining, new techniques are introduced to visualize mined patterns of sequential, concurrent, and cyclic items; enabling comparison of these patterns based on their subsequences, support, and item values. Within visualization, new techniques are introduced to simultaneously visualize patterns with sequential, concurrent, and cyclic events and performance values associated with these patterns. In education, new methods are introduced for visualizing students' grade-based performance over time, supporting a stronger understanding of enrollment and performance patterns throughout an academic program. Utility of these techniques is accomplished through the application to the University of Maryland, Baltimore County's computer science program with the goal of improving instruction and retention
An Ethnography of Traditional Rural Folk Funeral Practice in Northwestern China
No full textThis ethnographic study will analyze data collected through field-based observations, primary ritual texts, and locally conducted interviews of the yin-yang practitioners in the three small villages of Fanmagou, Qijiazhuang, and Wangdazhuang in northwestern China. The practice referred to as yin-yang in this region is part of an archaic folk religious system that can be traced back to at least the Qing dynasty (1644-1911). Despite its deep cultural roots, it is becoming endangered due to the impact of national policies (governing religion and culture) and the general adaptation to modernity in China. Due to the localized nature of this cultural system, the main research method used will be qualitative ethnographic description, with a Geertzian thick description approach to interpretive analysis. The collected data is roughly divided into three categories: (1) transcriptions of interviews with yin-yang practitioners and other local villagers; (2) video tapes, photographs, and field notes of local religious rituals, specifically memorial and burial rites that are led by the yin-yang practitioners, and (3) my own translations of yin-yang scriptural texts that are used in leading the rituals themselves, as well as for the teaching and training of young yin-yang apprentices. The interpretive ethnography that is produced from these rich primary sources will also be considered for its curriculum applications in two primary higher education contexts: 1) As a rich primary source for courses in Chinese culture and language - conducted in either Chinese or English language context, and 2) As a source of engaging and culturally relevant texts for courses in content-based ESOL for Chinese students (in China presumably)
Contextual Information Fusion for the Detection of Cyber-Attacks
No full textResearch in cyber-security has demonstrated that dealing with cyber-attacks is by no means an easy task. One particular limitation of existing research comes from the uncertainty of information gathered and used to discover attacks. Part of this uncertainty is related to lack of attack prediction models that take advantage of contextual information to analyze activities that target computer networks. A major challenge of the existing attack detection approaches is the identification of relevant information to a particular situation, and the use of such information to perform multi-evidence intrusion detection. Addressing such limitations require combining several aspects of context to better predict, avoid and respond to attacks so that several consistent evidence contribute to the decisions about the relevancy of attacks that target the network. A promising path along this direction is to elevate contextual information as a first class object in collecting and analyzing cyber security data. Yet again, the quality and adequacy of contextual information is important to decrease uncertainty in correctly identifying potential cyber-attacks. This dissertation introduces a novel framework that extracts and uses contextual information to discover cyber-attacks. A systematic methodology has been used to identify contextual dimensions that need to be considered to consequently improve the effectiveness of cyber-attack detection process. A methodology which combines graph, probability, and information theories along with domain knowledge is utilized to create several context-based attack prediction models that analyze data at a high- and low-level. This context-based framework identifies not only known, but also unknown attacks which an Intrusion Detection System (IDS) is not aware-of. The outlined framework can be mainly applied in conjunction with existing intrusion detection techniques to improve attack detection rate. In addition to showing the theoretical properties of the generated prediction models, several types of experiments have been conducted to evaluate the prediction models of known and unknown attacks. A comparison with other methodologies shows that a multi-layer fusion of contextual information in the process of attack discovery leads to superior results in terms of better attack detection and fewer false positive rates
Involvement Beliefs and Behaviors of Parents Enrolled in a Community-Based Educational Program
No full textThe educational involvement practices of many Black parents are often overlooked by researchers and practitioners. In addition, the factors that lead to increased involvement among Black parents may be different than those of their non-Black peers. Thus, the goal of the two interrelated studies was to explore factors impacting educational involvement beliefs and behaviors among a population of primarily Black parents. Study 1, a quantitative study that extended the work on the Hoover-Dempsey and Sandler (2005) model of parent involvement, examined the extent to which contextual factors (parent time and energy and parent knowledge) moderate the relation between motivational (parental role construction and self-efficacy) and school-based (parental perception of school outreach) factors and involvement at school and home. The study was also designed to determine if the constructs of the model remained predictive among a racially homogeneous sample. Study 2 was a qualitative exploration of factors that influenced parent involvement behaviors among a sample of parents enrolled in a community-based educational program for the first time. In Study 1, data from 88 parents from a Maryland school district were analyzed to estimate hypothesized Hoover-Dempsey and Sandler model relations. In Study 2, data from 12 parents and program staff were analyzed using frameworks grounded in the work of Hoover-Dempsey and Sandler and Epstein (2009) to identify parent involvement themes present in their experiences. Consistent with expectations, knowledge moderated the relation between self-efficacy and home involvement and time and energy moderated the relation between school outreach and school involvement. Although the full Hoover-Dempsey and Sandler model predicted both home and school involvement, several novel relations were evidenced. Study 2 parents engaged in involvement activities often overlooked by educators such as communicating high expectations, making sacrifices to support their children, and teaching the value of education. These findings emphasize the importance of developing more inclusive, culturally-relevant conceptualizations of parent involvement than those currently employed by researchers and educators. The study also provides evidence that parental involvement efforts will be more successful if schools attend to three critical attributes: information provided to parents, educator knowledge, and educator attitudes
A Study of the Optical Properties of a ZnCdSe/ZnCdMgSe Quantum Well Infrared Photodetector (QWIP)
No full textThe use of II-VI materials has shown great promise in the fabrication of quantum cascade (QC) devices. This project investigates the optical properties of a molecular beam epitaxy (MBE) grown II-VI ZnCdSe/ZnCdMgSe quantum well infrared photodetector (QWIP). Through temperature dependent photoluminescence measurements, we are able to determine that the QWIP has a strong photoluminescence signal and was grown in optimal conditions. This project also attempts to measure the carrier lifetime of this particular QWIP using blue (400-nm) 100-femtosecond (fs) light pulses
A Low Power On-board Processor for a Tongue Assistive Device
No full textIn biomedical wearable devices, patient's convenience and accuracy are the main priorities. To fulfill the patient's convenience requirement, the power consumption, which directly translates to the battery lifetime and size, must be kept as low as possible. Meanwhile, adopted improvements should not impact the accuracy. Therefore, focus on reducing the energy consumption within these devices has already been the subject of a significant amount of research in the past few years. In most wearable devices, all raw data is transmitted to a computer to carry out the required processing. This vast amount of communication leads to a considerable amount of power consumption and the need for a bulky battery, which hinders the device's practicality and patient's convenience. Tongue Drive System (TDS) is a new unobtrusive, wireless, and wearable assistive device that allows for real time tracking of the voluntary tongue motion in the oral space for communication, control, and navigation applications. The intraoral TDS clasps to the upper teeth and resists sensor misplacement. However, the iTDS has more restrictions on its dimensions, limiting the battery size and consequently requiring a considerable reduction in its power consumption to operate over an extended period of two days on a single charge. In this thesis, we propose an ultra low power local processor for the TDS that performs all signals processing on the transmitter side, following the sensors. Implementing the computational engine reduces the data volume that needs to be wirelessly transmitted to a PC or smartphone by a factor of 30x, from 12 kbps to ~400 bps. The proposed design is implemented on an ultra low power IGLOO nano FPGA and is tested on AGLN250 prototype board. According to our post place and route results, implementing the engine on the FPGA significantly drops the required data transmission, while an ASIC implementation in 65 nm CMOS results in 0.128 mW power consumption and occupies a 0.02 〖mm〗^2 footprint. To explore a different architecture, we mapped our proposed TDS processor on the EEHPC many-core. The many-core has a flexible and time saving design procedure. As a result of having a local processor, the power consumption and size of the iTDS will be significantly reduced through the use of a much smaller rechargeable battery. Moreover, the system can operate longer following every recharge, improving the iTDS usability
DEVELOPMENT OF ULTRA-MICRO ELECTRODE BASED BIOSENSORS
No full textHere I present a novel method for fabrication. characterization and optimization of micro scale, electrochemical aptamer-based sensors with the aim of developing sensors capable of quantitatively monitoring small molecule release from single cells in complex media. As a proof of concept, I have selected adenosine triphosphate (ATP) as a target molecule, since it is implicated in astrocyte communication in the central nervous system. Electrochemical methods provide powerful tools for the detection of molecular messenger release, but they are limited to molecules that are electrochemically active in a reasonable potential window. As such, current electrochemical methods for neuronal studies cannot detect ATP, since it is not electrochemically active in the preferred window for analysis. While the goal is to develop ATP sensors, the sensors developed can be generalized to any target molecule. The development of these sensors will enable study of gliotransmission with unprecedented spatiotemporal resolution and chemical specificity for analysis