Continuous property measurement techniques and physics based mathematical model for frost growth control

The development of a new set of frost property measurement techniques to be used in the control of frost growth and defrosting processes in refrigeration systems was investigated. Holographic interferometry and infrared thermometry were used to measure the temperature of the frost-air interface, while a beam element load sensor was used to obtain the weight of a deposited frost layer. The proposed measurement techniques were tested for the cases of natural and forced convection, and the characteristic charts were obtained for a set of operational conditions. ^ An improvement of existing frost growth mathematical models was also investigated. The early stage of frost nucleation was commonly not considered in these models and instead an initial value of layer thickness and porosity was regularly assumed. A nucleation model to obtain the droplet diameter and surface porosity at the end of the early frosting period was developed. The drop-wise early condensation in a cold flat plate under natural convection to a hot (room temperature) and humid air was modeled. A nucleation rate was found, and the relation of heat to mass transfer (Lewis number) was obtained. It was found that the Lewis number was much smaller than unity, which is the standard value usually assumed for most frosting numerical models. The nucleation model was validated against available experimental data for the early nucleation and full growth stages of the frosting process. ^ The combination of frost top temperature and weight variation signals can now be used to control the defrosting timing and the developed early nucleation model can now be used to simulate the entire process of frost growth in any surface material. ^

Expanding guitar production techniques: Building the Guitar Application Toolkit (GATK)

The purpose of this thesis was to build the Guitar Application ToolKit (GATK), a series of applications used to expand the sonic capabilities of the acoustic/electric stereo guitar. Furthermore, the goal of the GATK was to extend improvisational capabilities and the compositional techniques generated by this innovative instrument. ^ During the GATK creation process, the current production guitar techniques and overall sonic result were enhanced by planning and implementing a personalized electro-acoustic performance set up, designing custom-made performance interfaces, creating interactive compositional strategies, crafting non-standardized sounds, and controlling various music parameters in real-time using the Max/MSP programming environment. ^ This was the fast thesis project of its kind. It is expected that this thesis will be useful as a reference paper for electronic musicians and music technology students; as a product demonstration for companies that manufacture the relevant software; and as a personal portfolio for future technology related jobs. ^

Quantifying nitrogen cycling rates in freshwater marshes of the southern Everglades using nitrogen-15 tracer techniques

This dissertation research project addressed the question of how hydrologic restoration of the Everglades is impacting the nutrient dynamics of marsh ecosystems in the southern Everglades. These effects were analyzed by quantifying nitrogen (N) cycle dynamics in the region. I utilized stable isotope tracer techniques to investigate nitrogen uptake and cycling between the major ecosystem components of the freshwater marsh system. I recorded the natural isotopic signatures (δ15N and δ 13C) for major ecosystem components from the three major watersheds of the Everglades: Shark River Slough, Taylor Slough, and C-111 basin. Analysis of δ15 N and δ13C natural abundance data were used to demonstrate the spatial extent to which nitrogen from anthropogenic or naturally enriched sources is entering the marshes of the Everglades. In addition, I measured the fluxes on N between various ecosystem components at both near-canal and estuarine ecotone locations. Lastly, I investigated the effect of three phosphorus load treatments (0.00 mg P m-2, 6.66 mg P m-2, and 66.6 mg P m-2) on the rate and magnitude of ecosystem N-uptake and N-cycling. The δ15N and δ13C natural abundance data supported the hypothesis that ecosystem components from near-canal sites have heavier, more enriched δ 15N isotopic signatures than downstream sites. The natural abundance data also showed that the marshes of the southern Everglades are acting as a sink for isotopically heavier, canal-borne dissolved inorganic nitrogen (DIN) and a source for "new" marsh derived dissolved organic nitrogen (DON). In addition, the 15N mesocosm data showed the rapid assimilation of the 15N tracer by the periphyton component and the delayed N uptake by soil and macrophyte components in the southern Everglades.

Development of prediction models for freeway incident durations using data mining techniques

The nation's freeway systems are becoming increasingly congested. A major contribution to traffic congestion on freeways is due to traffic incidents. Traffic incidents are non-recurring events such as accidents or stranded vehicles that cause a temporary roadway capacity reduction, and they can account for as much as 60 percent of all traffic congestion on freeways. One major freeway incident management strategy involves diverting traffic to avoid incident locations by relaying timely information through Intelligent Transportation Systems (ITS) devices such as dynamic message signs or real-time traveler information systems. The decision to divert traffic depends foremost on the expected duration of an incident, which is difficult to predict. In addition, the duration of an incident is affected by many contributing factors. Determining and understanding these factors can help the process of identifying and developing better strategies to reduce incident durations and alleviate traffic congestion. A number of research studies have attempted to develop models to predict incident durations, yet with limited success. ^ This dissertation research attempts to improve on this previous effort by applying data mining techniques to a comprehensive incident database maintained by the District 4 ITS Office of the Florida Department of Transportation (FDOT). Two categories of incident duration prediction models were developed: "offline" models designed for use in the performance evaluation of incident management programs, and "online" models for real-time prediction of incident duration to aid in the decision making of traffic diversion in the event of an ongoing incident. Multiple data mining analysis techniques were applied and evaluated in the research. The multiple linear regression analysis and decision tree based method were applied to develop the offline models, and the rule-based method and a tree algorithm called M5P were used to develop the online models. ^ The results show that the models in general can achieve high prediction accuracy within acceptable time intervals of the actual durations. The research also identifies some new contributing factors that have not been examined in past studies. As part of the research effort, software code was developed to implement the models in the existing software system of District 4 FDOT for actual applications. ^

A method for enhancing digital information displayed to computer users with visual refractive errors via spatial and spectral processing

This research pursued the conceptualization, implementation, and verification of a system that enhances digital information displayed on an LCD panel to users with visual refractive errors. The target user groups for this system are individuals who have moderate to severe visual aberrations for which conventional means of compensation, such as glasses or contact lenses, does not improve their vision. This research is based on a priori knowledge of the user's visual aberration, as measured by a wavefront analyzer. With this information it is possible to generate images that, when displayed to this user, will counteract his/her visual aberration. The method described in this dissertation advances the development of techniques for providing such compensation by integrating spatial information in the image as a means to eliminate some of the shortcomings inherent in using display devices such as monitors or LCD panels. Additionally, physiological considerations are discussed and integrated into the method for providing said compensation. In order to provide a realistic sense of the performance of the methods described, they were tested by mathematical simulation in software, as well as by using a single-lens high resolution CCD camera that models an aberrated eye, and finally with human subjects having various forms of visual aberrations. Experiments were conducted on these systems and the data collected from these experiments was evaluated using statistical analysis. The experimental results revealed that the pre-compensation method resulted in a statistically significant improvement in vision for all of the systems. Although significant, the improvement was not as large as expected for the human subject tests. Further analysis suggest that even under the controlled conditions employed for testing with human subjects, the characterization of the eye may be changing. This would require real-time monitoring of relevant variables (e.g. pupil diameter) and continuous adjustment in the pre-compensation process to yield maximum viewing enhancement.

Enabling adaptability in service aggregates using transparent shaping techniques

Distributed applications are exposed as reusable components that are dynamically discovered and integrated to create new applications. These new applications, in the form of aggregate services, are vulnerable to failure due to the autonomous and distributed nature of their integrated components. This vulnerability creates the need for adaptability in aggregate services. The need for adaptation is accentuated for complex long-running applications as is found in scientific Grid computing, where distributed computing nodes may participate to solve computation and data-intensive problems. Such applications integrate services for coordinated problem solving in areas such as Bioinformatics. For such applications, when a constituent service fails, the application fails, even though there are other nodes that can substitute for the failed service. This concern is not addressed in the specification of high-level composition languages such as that of the Business Process Execution Language (BPEL). We propose an approach to transparently autonomizing existing BPEL processes in order to make them modifiable at runtime and more resilient to the failures in their execution environment. By transparent introduction of adaptive behavior, adaptation preserves the original business logic of the aggregate service and does not tangle the code for adaptive behavior with that of the aggregate service. The major contributions of this dissertation are: first, we assessed the effectiveness of BPEL language support in developing adaptive mechanisms. As a result, we identified the strengths and limitations of BPEL and came up with strategies to address those limitations. Second, we developed a technique to enhance existing BPEL processes transparently in order to support dynamic adaptation. We proposed a framework which uses transparent shaping and generative programming to make BPEL processes adaptive. Third, we developed a technique to dynamically discover and bind to substitute services. Our technique was evaluated and the result showed that dynamic utilization of components improves the flexibility of adaptive BPEL processes. Fourth, we developed an extensible policy-based technique to specify how to handle exceptional behavior. We developed a generic component that introduces adaptive behavior for multiple BPEL processes. Fifth, we identify ways to apply our work to facilitate adaptability in composite Grid services.

A framework for automatic feature extraction from airborne light detection and ranging data

Recent advances in airborne Light Detection and Ranging (LIDAR) technology allow rapid and inexpensive measurements of topography over large areas. Airborne LIDAR systems usually return a 3-dimensional cloud of point measurements from reflective objects scanned by the laser beneath the flight path. This technology is becoming a primary method for extracting information of different kinds of geometrical objects, such as high-resolution digital terrain models (DTMs), buildings and trees, etc. In the past decade, LIDAR gets more and more interest from researchers in the field of remote sensing and GIS. Compared to the traditional data sources, such as aerial photography and satellite images, LIDAR measurements are not influenced by sun shadow and relief displacement. However, voluminous data pose a new challenge for automated extraction the geometrical information from LIDAR measurements because many raster image processing techniques cannot be directly applied to irregularly spaced LIDAR points. ^ In this dissertation, a framework is proposed to filter out information about different kinds of geometrical objects, such as terrain and buildings from LIDAR automatically. They are essential to numerous applications such as flood modeling, landslide prediction and hurricane animation. The framework consists of several intuitive algorithms. Firstly, a progressive morphological filter was developed to detect non-ground LIDAR measurements. By gradually increasing the window size and elevation difference threshold of the filter, the measurements of vehicles, vegetation, and buildings are removed, while ground data are preserved. Then, building measurements are identified from no-ground measurements using a region growing algorithm based on the plane-fitting technique. Raw footprints for segmented building measurements are derived by connecting boundary points and are further simplified and adjusted by several proposed operations to remove noise, which is caused by irregularly spaced LIDAR measurements. To reconstruct 3D building models, the raw 2D topology of each building is first extracted and then further adjusted. Since the adjusting operations for simple building models do not work well on 2D topology, 2D snake algorithm is proposed to adjust 2D topology. The 2D snake algorithm consists of newly defined energy functions for topology adjusting and a linear algorithm to find the minimal energy value of 2D snake problems. Data sets from urbanized areas including large institutional, commercial, and small residential buildings were employed to test the proposed framework. The results demonstrated that the proposed framework achieves a very good performance. ^

Foundational forensic techniques for cellular and ad hoc multi-hop networks

The Internet has become an integral part of our nation’s critical socio-economic infrastructure. With its heightened use and growing complexity however, organizations are at greater risk of cyber crimes. To aid in the investigation of crimes committed on or via the Internet, a network forensics analysis tool pulls together needed digital evidence. It provides a platform for performing deep network analysis by capturing, recording and analyzing network events to find out the source of a security attack or other information security incidents. Existing network forensics work has been mostly focused on the Internet and fixed networks. But the exponential growth and use of wireless technologies, coupled with their unprecedented characteristics, necessitates the development of new network forensic analysis tools. This dissertation fostered the emergence of a new research field in cellular and ad-hoc network forensics. It was one of the first works to identify this problem and offer fundamental techniques and tools that laid the groundwork for future research. In particular, it introduced novel methods to record network incidents and report logged incidents. For recording incidents, location is considered essential to documenting network incidents. However, in network topology spaces, location cannot be measured due to absence of a ‘distance metric’. Therefore, a novel solution was proposed to label locations of nodes within network topology spaces, and then to authenticate the identity of nodes in ad hoc environments. For reporting logged incidents, a novel technique based on Distributed Hash Tables (DHT) was adopted. Although the direct use of DHTs for reporting logged incidents would result in an uncontrollably recursive traffic, a new mechanism was introduced that overcome this recursive process. These logging and reporting techniques aided forensics over cellular and ad-hoc networks, which in turn increased their ability to track and trace attacks to their source. These techniques were a starting point for further research and development that would result in equipping future ad hoc networks with forensic components to complement existing security mechanisms.

The use of hyphenated spectrometric techniques for the environmental forensic assessment of non-traditional pollutants and degradates in the greater Florida Everglades

A comprehensive investigation of sensitive ecosystems in South Florida with the main goal of determining the identity, spatial distribution, and sources of both organic biocides and trace elements in different environmental compartments is reported. This study presents the development and validation of a fractionation and isolation method of twelve polar acidic herbicides commonly applied in the vicinity of the study areas, including e.g. 2,4-D, MCPA, dichlorprop, mecroprop, picloram in surface water. Solid phase extraction (SPE) was used to isolate the analytes from abiotic matrices containing large amounts of dissolved organic material. Atmospheric-pressure ionization (API) with electrospray ionization in negative mode (ESP-) in a Quadrupole Ion Trap mass spectrometer was used to perform the characterization of the herbicides of interest. ^ The application of Laser Ablation-ICP-MS methodology in the analysis of soils and sediments is reported in this study. The analytical performance of the method was evaluated on certified standards and real soil and sediment samples. Residential soils were analyzed to evaluate feasibility of using the powerful technique as a routine and rapid method to monitor potential contaminated sites. Forty eight sediments were also collected from semi pristine areas in South Florida to conduct screening of baseline levels of bioavailable elements in support of risk evaluation. The LA-ICP-MS data were used to perform a statistical evaluation of the elemental composition as a tool for environmental forensics. ^ A LA-ICP-MS protocol was also developed and optimized for the elemental analysis of a wide range of elements in polymeric filters containing atmospheric dust. A quantitative strategy based on internal and external standards allowed for a rapid determination of airborne trace elements in filters containing both contemporary African dust and local dust emissions. These distributions were used to qualitative and quantitative assess differences of composition and to establish provenance and fluxes to protected regional ecosystems such as coral reefs and national parks. ^

Orthogonal frequency division multiplexing based air interfaces and multiple input multiple output techniques in cooperative satellite communications for 4th generation mobile systems

Recently, wireless network technology has grown at such a pace that scientific research has become a practical reality in a very short time span. Mobile wireless communications have witnessed the adoption of several generations, each of them complementing and improving the former. One mobile system that features high data rates and open network architecture is 4G. Currently, the research community and industry, in the field of wireless networks, are working on possible choices for solutions in the 4G system. 4G is a collection of technologies and standards that will allow a range of ubiquitous computing and wireless communication architectures. The researcher considers one of the most important characteristics of future 4G mobile systems the ability to guarantee reliable communications from 100 Mbps, in high mobility links, to as high as 1 Gbps for low mobility users, in addition to high efficiency in the spectrum usage. On mobile wireless communications networks, one important factor is the coverage of large geographical areas. In 4G systems, a hybrid satellite/terrestrial network is crucial to providing users with coverage wherever needed. Subscribers thus require a reliable satellite link to access their services when they are in remote locations, where a terrestrial infrastructure is unavailable. Thus, they must rely upon satellite coverage. Good modulation and access technique are also required in order to transmit high data rates over satellite links to mobile users. This technique must adapt to the characteristics of the satellite channel and also be efficient in the use of allocated bandwidth. Satellite links are fading channels, when used by mobile users. Some measures designed to approach these fading environments make use of: (1) spatial diversity (two receive antenna configuration); (2) time diversity (channel interleaver/spreading techniques); and (3) upper layer FEC. The author proposes the use of OFDM (Orthogonal Frequency Multiple Access) for the satellite link by increasing the time diversity. This technique will allow for an increase of the data rate, as primarily required by multimedia applications, and will also optimally use the available bandwidth. In addition, this dissertation approaches the use of Cooperative Satellite Communications for hybrid satellite/terrestrial networks. By using this technique, the satellite coverage can be extended to areas where there is no direct link to the satellite. For this purpose, a good channel model is necessary.

Document understanding using data mining and machine learning techniques

With the explosive growth of the volume and complexity of document data (e.g., news, blogs, web pages), it has become a necessity to semantically understand documents and deliver meaningful information to users. Areas dealing with these problems are crossing data mining, information retrieval, and machine learning. For example, document clustering and summarization are two fundamental techniques for understanding document data and have attracted much attention in recent years. Given a collection of documents, document clustering aims to partition them into different groups to provide efficient document browsing and navigation mechanisms. One unrevealed area in document clustering is that how to generate meaningful interpretation for the each document cluster resulted from the clustering process. Document summarization is another effective technique for document understanding, which generates a summary by selecting sentences that deliver the major or topic-relevant information in the original documents. How to improve the automatic summarization performance and apply it to newly emerging problems are two valuable research directions. To assist people to capture the semantics of documents effectively and efficiently, the dissertation focuses on developing effective data mining and machine learning algorithms and systems for (1) integrating document clustering and summarization to obtain meaningful document clusters with summarized interpretation, (2) improving document summarization performance and building document understanding systems to solve real-world applications, and (3) summarizing the differences and evolution of multiple document sources.

Inspection of parts with complex geometry and welds with structural health monitoring techniques

Structural Health Monitoring (SHM) systems were developed to evaluate the integrity of a system during operation, and to quickly identify the maintenance problems. They will be used in future aerospace vehicles to improve safety, reduce cost and minimize the maintenance time of a system. Many SHM systems were already developed to evaluate the integrity of plates and used in marine structures. Their implementation in manufacturing processes is still expected. The application of SHM methods for complex geometries and welds are two important challenges in this area of research. This research work started by studying the characteristics of piezoelectric actuators, and a small energy harvester was designed. The output voltages at different frequencies of vibration were acquired to determine the nonlinear characteristics of the piezoelectric stripe actuators. The frequency response was evaluated experimentally. AA battery size energy harvesting devices were developed by using these actuators. When the round and square cross section devices were excited at 50 Hz frequency, they generated 16 V and 25 V respectively. The Surface Response to Excitation (SuRE) and Lamb wave methods were used to estimate the condition of parts with complex geometries. Cutting tools and welded plates were considered. Both approaches used piezoelectric elements that were attached to the surfaces of considered parts. The variation of the magnitude of the frequency response was evaluated when the SuRE method was used. The sum of the square of the differences was calculated. The envelope of the received signal was used for the analysis of wave propagation. Bi-orthogonal wavelet (Binlet) analysis was also used for the evaluation of the data obtained during Lamb wave technique. Both the Lamb wave and SuRE approaches along with the three methods for data analysis worked effectively to detect increasing tool wear. Similarly, they detected defects on the plate, on the weld, and on a separate plate without any sensor as long as it was welded to the test plate.

Determination of signature volatile odor chemicals emanating from novel biological specimens by non-invasive analytical techniques for the potential use in forensic identifications

Human scent, or the volatile organic compounds (VOCs) produced by an individual, has been recognized as a biometric measurement because of the distinct variations in both the presence and abundance of these VOCs between individuals. In forensic science, human scent has been used as a form of associative evidence by linking a suspect to a scene/object through the use of human scent discriminating canines. The scent most often collected and used with these specially trained canines is from the hands because a majority of the evidence collected is likely to have been handled by the suspect. However, the scents from other biological specimens, especially those that are likely to be present at scenes of violent crimes, have yet to be explored. Hair, fingernails and saliva are examples of these types of specimens. ^ In this work, a headspace solid phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) technique was used for the identification of VOCs from hand odor, hair, fingernails and saliva. Sixty individuals were sampled and the profiles of the extracted VOCs were evaluated to assess whether they could be used for distinguishing individuals. Preliminary analysis of the biological specimens collected from an individual (intra-subject) showed that, though these materials have some VOCs in common, their overall chemical profile is different for each specimen type. Pair-wise comparisons, using Spearman Rank correlations, were made between the chemical profiles obtained from each subject, per a specimen type. Greater than 98.8% of the collected samples were distinguished from the subjects for all of the specimen types, demonstrating that these specimens can be used for distinguishing individuals. ^ Additionally, field trials were performed to determine the utility of these specimens as scent sources for human scent discriminating canines. Three trials were conducted to evaluate hair, fingernails and saliva in comparison to hand odor, which was considered the standard source of human odor. It was revealed that canines perform similarly to these alternative human scent sources as they do to hand odor implying that, though there are differences in the chemical profiles released by these specimens, they can still be used for the discrimination of individuals by trained canines.^

Menu Analysis: A Review of Techniques and Approaches

This review discusses menu analysis models in depth to identify the models strengths and weaknesses in attempt to discover opportunities to enhance existing models and evolve menu analysis toward a comprehensive analytical model.

Interpersonal Techniques Boost your "Hospitality Batting Average"

The ability to listen and comprehend the intrinsic meaning behind the words people are saying is an important skill for those in the hospitality industry. The author provides some prescriptions for "winning friends and influencing people.”