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- Title
- AUTOMATIC PARTICLE COUNTING USING AN ACOUSTIC TRANSDUCER.
- Creator
-
Haddad, George, Chen, Ruey-Hung, University of Central Florida
- Abstract / Description
-
Aerosol particle detection and determination finds important applications in the commercial, military and aerospace sectors. Monitoring of clean room environments, and spacecraft integration and check out facilities are some of the most important aplications. In the early days test filters were examined with a microscope to determine the number and size of particles that were being removed from air. Today, most of the commercially available clean room airborne particle counters work on a...
Show moreAerosol particle detection and determination finds important applications in the commercial, military and aerospace sectors. Monitoring of clean room environments, and spacecraft integration and check out facilities are some of the most important aplications. In the early days test filters were examined with a microscope to determine the number and size of particles that were being removed from air. Today, most of the commercially available clean room airborne particle counters work on a light scattering principle. They are referred to as Optical Particle Counter or OPC. Essentially, they utilize a very bright laser light source to illuminate the particles. The burst of light energy is converted into a pulse of electrical energy. By measuring the height of the signal and counting the number of pulses the sizes and quantities of particles could thus be determined. The microscope and the OPC techniques have their limitations. The microscope technique is a post contamination assessment technique and the OPC is costly, hard to maintain, lack in counting efficiency and is not mobile. This experimental study demonstrates a novel and inexpensive particle detection technique which is based on the acoustic signature of airborne particles as they are accelerated through an acoustic transducer. The transducer consists of an inlet converging nozzle, a capillary tube and an expansion section. If the air is laden with particles, as the flow accelerates through the inlet, the particles cannot follow the large change in velocity due to their inertia. Vortices are generated as air flows over the particles prior to entering the capillary. These vortices are believed to generate sound, which is amplified by the transducer acting as an organ pipe. This sound emission if measured contains frequencies that are harmonics of the natural frequency of the transducer's air column. Results show how the frequency content of the acoustic signature relates to the fundamental frequency of the transducer's air column. The transducer is able to detect micron sized particles ( 5 to 50 micron) and the sound intensity is a function of the flowrate but not of particle size. This study also shows the ability of the transducer to determine particle concentration as low as few parts per liter (ppl) and compare the data with that obtained from a commercially available aerodynamic particle sizer.
Show less - Date Issued
- 2005
- Identifier
- CFE0000367, ucf:46331
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000367
- Title
- Integration of Multidimensional Signal Detection Theory with Fuzzy Signal Detection Theory.
- Creator
-
O'Connell, Maureen, Szalma, James, Hancock, Peter, Bohil, Corey, Reinerman, Lauren, University of Central Florida
- Abstract / Description
-
Signal detection theory (SDT) has proven to be a robust and useful statistical model for analyzing human performance in detection and decision making tasks. As with many models extensions have been proposed in order capture and represent the real world to a greater degree. Multidimensional Signal Detection Theory (MSDT) has had success in describing and modeling complex signals, signals that are comprised by more than one identifiable component dimension. Fuzzy Signal Detection Theory (FSDT)...
Show moreSignal detection theory (SDT) has proven to be a robust and useful statistical model for analyzing human performance in detection and decision making tasks. As with many models extensions have been proposed in order capture and represent the real world to a greater degree. Multidimensional Signal Detection Theory (MSDT) has had success in describing and modeling complex signals, signals that are comprised by more than one identifiable component dimension. Fuzzy Signal Detection Theory (FSDT) has had success in modeling and measuring human performance in cases where there exist ambiguity in the signal or response dimension characteristics, through the application of fuzzy set theory to the definition of the performance outcome categories. Multidimensional Fuzzy Signal Detection Theory (MFSDT) was developed to accommodate simultaneously both the multidimensionality of a signal and the fuzzification of outcome categories in order to integrate the two extensions. A series of three studies were performed to develop and test the theory. One study's purpose was to develop and derive multidimensional mapping functions, the aspect of MFSDT where MSDT and FSDT were integrated. Two receiver operating characteristic (ROC) studies were performed, one simulated and one empirical. The results from both ROC analysis indicated that for perceptually separable and perceptually integral complex stimuli that MFDST is a viable methodological approach to analyzing performance of signal detection tasks where there are complex signals with ambiguous signal characteristics.
Show less - Date Issued
- 2015
- Identifier
- CFE0005983, ucf:50763
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005983
- Title
- INFRARED THERMOGRAPHY AND HIGH ACCURACY GPS FOR AUTOMATED ASPHALT CRACK DETECTION.
- Creator
-
Abdel-Monem, Tarek, Oloufa, Amr, University of Central Florida
- Abstract / Description
-
Roads are major public assets. The USA spends billions of dollars each year on road construction and maintenance. To keep these roads in a healthy condition and for better planning and allocation of maintenance budgets, knowledge of distressed locations is needed. Roads develop cracks when they are subjected to stresses that exceed their designed criteria or their materials properties. Early detection and repair of cracks has proven to be the most cost-effective strategy in limiting the...
Show moreRoads are major public assets. The USA spends billions of dollars each year on road construction and maintenance. To keep these roads in a healthy condition and for better planning and allocation of maintenance budgets, knowledge of distressed locations is needed. Roads develop cracks when they are subjected to stresses that exceed their designed criteria or their materials properties. Early detection and repair of cracks has proven to be the most cost-effective strategy in limiting the damage to roads and reducing expenditures. Various methodologies of crack detection were developed and significant techniques were made in the last few years. One of the most important recent technologies is the infrared thermography, which allows the use of infrared waves for crack detection. Another important technology is the global navigation satellite system (GNSS) which currently includes the GPS and GLONASS constellations. With the help of these systems, accurate location coordinates (longitude, latitude and altitude) up to a few centimeters were located. The objective of this research is to test the combined use of GNSS and infrared thermography in an automated system for the detection of asphalt cracks and their locations. To achieve this goal, two tests have been conducted. The first one, regarding the location tagging, was done using two pairs of GPS receivers which can detect signals from both GPS and GLONASS navigation systems in single and dual frequencies (L1 and L2). Different modes have been set to the receiver and comparison graphs were developed to compare accuracies against modes. The second test involves an infrared camera mounted on a car and moving in speeds approaching highway speed limit. The images obtained from the camera were processed using cracks detection software to analyze cracks properties (length, width, density and severity). It was found that the images that were taken by a moving infrared camera were recognized by crack detection software for moving speeds up to 50 mph. At speeds higher than 50 mph, images were blurred. As for location test, The GLONASS combined by GPS receivers got slightly better results than GPS only in both dual and single frequencies. The GLONASS satellites are not always available in view and when they are there, the number of satellites that can be detected by receiver range from one to three satellites at the most and for only a short period of time. It is recommended that future research be conducted to investigate the effect of using different camera lenses on the clarity of the images obtained as well as the effect of raising the camera level above the pavement surface in such a way that the whole lane width (12 ft.) would be covered in one image. Also the total reliance on GPS only receivers in determining cracks location has proven to be enough for this application.
Show less - Date Issued
- 2005
- Identifier
- CFE0000668, ucf:46534
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000668
- Title
- DETECTION AND APPROXIMATION OF FUNCTION OF TWO VARIABLES IN HIGH DIMENSIONS.
- Creator
-
pan, minzhe, LI, XIN, University of Central Florida
- Abstract / Description
-
This thesis originates from the deterministic algorithm of DeVore, Petrova, and Wojtaszcsyk for the detection and approximation of functions of one variable in high dimensions. We propose a deterministic algorithm for the detection and approximation of function of two variables in high dimensions.
- Date Issued
- 2010
- Identifier
- CFE0003467, ucf:48933
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0003467
- Title
- SESSION-BASED INTRUSION DETECTION SYSTEM TO MAP ANOMALOUS NETWORK TRAFFIC.
- Creator
-
Caulkins, Bruce, Wang, Morgan, University of Central Florida
- Abstract / Description
-
Computer crime is a large problem (CSI, 2004; Kabay, 2001a; Kabay, 2001b). Security managers have a variety of tools at their disposal – firewalls, Intrusion Detection Systems (IDSs), encryption, authentication, and other hardware and software solutions to combat computer crime. Many IDS variants exist which allow security managers and engineers to identify attack network packets primarily through the use of signature detection; i.e., the IDS recognizes attack packets due to their well...
Show moreComputer crime is a large problem (CSI, 2004; Kabay, 2001a; Kabay, 2001b). Security managers have a variety of tools at their disposal – firewalls, Intrusion Detection Systems (IDSs), encryption, authentication, and other hardware and software solutions to combat computer crime. Many IDS variants exist which allow security managers and engineers to identify attack network packets primarily through the use of signature detection; i.e., the IDS recognizes attack packets due to their well-known "fingerprints" or signatures as those packets cross the network's gateway threshold. On the other hand, anomaly-based ID systems determine what is normal traffic within a network and reports abnormal traffic behavior. This paper will describe a methodology towards developing a more-robust Intrusion Detection System through the use of data-mining techniques and anomaly detection. These data-mining techniques will dynamically model what a normal network should look like and reduce the false positive and false negative alarm rates in the process. We will use classification-tree techniques to accurately predict probable attack sessions. Overall, our goal is to model network traffic into network sessions and identify those network sessions that have a high-probability of being an attack and can be labeled as a "suspect session." Subsequently, we will use these techniques inclusive of signature detection methods, as they will be used in concert with known signatures and patterns in order to present a better model for detection and protection of networks and systems.
Show less - Date Issued
- 2005
- Identifier
- CFE0000906, ucf:46762
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000906
- Title
- INTRUSION DETECTION IN WIRELESS SENSOR NETWORKS.
- Creator
-
NGUYEN, HONG NHUNG, Turgut, Damla, University of Central Florida
- Abstract / Description
-
There are several applications that use sensor motes and researchers continue to explore additional applications. For this particular application of detecting the movement of humans through the sensor field, a set of Berkley mica2 motes on TinyOS operating system is used. Different sensors such as pressure, light, and so on can be used to identify the presence of an intruder in the field. In our case, the light sensor is chosen for the detection. When an intruder crosses the monitored...
Show moreThere are several applications that use sensor motes and researchers continue to explore additional applications. For this particular application of detecting the movement of humans through the sensor field, a set of Berkley mica2 motes on TinyOS operating system is used. Different sensors such as pressure, light, and so on can be used to identify the presence of an intruder in the field. In our case, the light sensor is chosen for the detection. When an intruder crosses the monitored environment, the system detects the changes of the light values, and any significant change meaning that a change greater than a pre-defined threshold. This indicates the presence of an intruder. An integrated web cam is used to take snapshot of the intruder and transmit the picture through the network to a remote station. The basic motivation of this thesis is that a sensor web system can be used to monitor and detect any intruder in a specific area from a remote location.
Show less - Date Issued
- 2006
- Identifier
- CFE0001027, ucf:46793
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0001027
- Title
- SPLIT PROBE DETECTION OF THE INFLUENZA A VIRUS FOR IMPROVED DIAGNOSTICS IN A POINT OF CARE SYSTEM.
- Creator
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Yishay, Tamar, Gerasimova, Yulia, Harper, James, University of Central Florida
- Abstract / Description
-
A group of Influenza viruses, RNA containing viruses of the Orthomyxoviridae family, consists of Influenza virus types A-D and has been known to cause the Flu, a respiratory illness associated with numerous detrimental symptoms that can lead to death. Influenza A virus (IAV) is constantly changing and is capable of causing pandemics. Currently used diagnostic methods include virus culturing, immunoassays including rapid influenza detection tests (RIDTs), and molecular assays including those...
Show moreA group of Influenza viruses, RNA containing viruses of the Orthomyxoviridae family, consists of Influenza virus types A-D and has been known to cause the Flu, a respiratory illness associated with numerous detrimental symptoms that can lead to death. Influenza A virus (IAV) is constantly changing and is capable of causing pandemics. Currently used diagnostic methods include virus culturing, immunoassays including rapid influenza detection tests (RIDTs), and molecular assays including those based on RT-PCR. Most of the methods can be only performed in the certified diagnostic laboratories equipped with sophisticated instrumentation and/or special biosafety facilities. The results using these methods are not available on a timely basis. RIDTs provide response within 15 minutes but are unable to differentiate between the IAV subtypes. New diagnostic technique, which allows reliable detection of the influenza virus infection and virus genotyping at point-of-care setting, are needed to prevent the spread of the virus and the occurrence of a pandemic. In this project, we propose to use split G-quadruplex (G4) peroxidase probes targeting a fragment of the IAV genome amplified using an isothermal RNA amplification reaction for the detection of IAV infection and virus genotyping. The probes selectively report the virus RNA target with a color change, which can be read by the naked eye. They are capable of differentiating the targets containing as little as a single-nucleotide variation in their sequences. This study aims to optimize the probes, test their selectivity, and calculate the detection limit.
Show less - Date Issued
- 2019
- Identifier
- CFH2000533, ucf:45639
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFH2000533
- Title
- INVESTIGATION OF VISUAL REQUIREMENTS FOR CHANGE DETECTION.
- Creator
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Niederman, Elisabeth, Hancock, Peter, University of Central Florida
- Abstract / Description
-
In this study, participants performed a change detection task. Specifically we examined whether participants had to fixate on a difference between two images before they could detect it. Thirty-six participants performed a change detection task in either a 3 minute or a 1.5 minute condition. We found a significant interaction between task duration and fixation type (whether the participant had fixated on the difference in both, one, or neither image). Participants found a greater number of...
Show moreIn this study, participants performed a change detection task. Specifically we examined whether participants had to fixate on a difference between two images before they could detect it. Thirty-six participants performed a change detection task in either a 3 minute or a 1.5 minute condition. We found a significant interaction between task duration and fixation type (whether the participant had fixated on the difference in both, one, or neither image). Participants found a greater number of differences given more time only when they fixated on the difference in both images. The number of differences which were detected by participants with a fixation on only one image or on neither image did not increase with a corresponding increase in time, indicating that some mechanical error may be involved. This suggests that participants need to fixate on a difference before being able to detect it.
Show less - Date Issued
- 2013
- Identifier
- CFH0004500, ucf:45152
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFH0004500
- Title
- DETECTING CURVED OBJECTS AGAINST CLUTTERED BACKGROUNDS.
- Creator
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Prokaj, Jan, Lobo, Niels, University of Central Florida
- Abstract / Description
-
Detecting curved objects against cluttered backgrounds is a hard problem in computer vision. We present new low-level and mid-level features to function in these environments. The low-level features are fast to compute, because they employ an integral image approach, which makes them especially useful in real-time applications. The mid-level features are built from low-level features, and are optimized for curved object detection. The usefulness of these features is tested by designing an...
Show moreDetecting curved objects against cluttered backgrounds is a hard problem in computer vision. We present new low-level and mid-level features to function in these environments. The low-level features are fast to compute, because they employ an integral image approach, which makes them especially useful in real-time applications. The mid-level features are built from low-level features, and are optimized for curved object detection. The usefulness of these features is tested by designing an object detection algorithm using these features. Object detection is accomplished by transforming the mid-level features into weak classifiers, which then produce a strong classifier using AdaBoost. The resulting strong classifier is then tested on the problem of detecting heads with shoulders. On a database of over 500 images of people, cropped to contain head and shoulders, and with a diverse set of backgrounds, the detection rate is 90% while the false positive rate on a database of 500 negative images is less than 2%.
Show less - Date Issued
- 2008
- Identifier
- CFE0002102, ucf:47535
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0002102
- Title
- EFFECTS OF POLARIZATION AND COHERENCE ON THE PROPAGATION AND THE DETECTION OF STOCHASTIC ELECTROMAGNETIC BEAMS.
- Creator
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Salem, Mohamed, Rolland, Jannick, University of Central Florida
- Abstract / Description
-
Most of the physically realizable optical sources are radiating in a random manner given the random nature of the radiation of a large number of atoms that constitute the source. Besides, a lot of natural and synthetic materials are fluctuating randomly. Hence, the optical fields that one encounters, in most of the applications are fluctuating and must be treated using random or stochastic functions. Within the framework of the scalar-coherence theory, one can describe changes of the...
Show moreMost of the physically realizable optical sources are radiating in a random manner given the random nature of the radiation of a large number of atoms that constitute the source. Besides, a lot of natural and synthetic materials are fluctuating randomly. Hence, the optical fields that one encounters, in most of the applications are fluctuating and must be treated using random or stochastic functions. Within the framework of the scalar-coherence theory, one can describe changes of the properties of any stochastic field such as the spectral density and the spectral degree of coherence on propagation in any linear medium, deterministic or random. One of the frequently encountered random media is the atmospheric turbulence, where the fluctuating refractive index of such medium severely degrades any signal propagating through it; especially it causes intensity fades of the signal. The usage of stochastic beams at the transmitter instead of deterministic ones has been suggested sometime ago to suppress the severe effects of intensity fluctuations caused by the atmospheric turbulence. In this dissertation, we study the usage of partially coherent beams in long path propagation schemes through turbulent atmosphere such as one frequently encounters in remote sensing, in the use of communication systems, and in guiding. Also the used detection scheme at the receiver is important to quantify the received signal efficiently, hence we compare the performance of incoherent (direct) detection versus coherent (heterodyne) detection upon the use of either one of them at the receiver of the communication system of beams propagating in turbulent atmosphere and namely we evaluate the signal-to-noise-ratio (SNR) for each case. The scalar-coherence theory ignored the vector nature of stochastic fields, which should be taken into account for some applications such as the ones that depend on the change of the polarization of the field. Recently generalization for the scalar-coherence theory including the vector aspects of the stochastic beams has been formulated and it is well-known as the unified theory of coherence and polarization of stochastic beams. The use of the unified theory of coherence and polarization makes it possible to study both the coherence properties and the polarization properties of stochastic electromagnetic beams on propagation in any linear media. The central quantity in this theory is a 2 × 2 matrix that describes the statistical ensemble of any stochastic electromagnetic beam in the space-frequency domain or its Fourier transform in the space-time domain. In this dissertation we derive the conditions that the cross-spectral density matrix of a so-called planar, secondary, electromagnetic Gaussian Schell-model source has to satisfy in order to generate a beam propagating in vacuum. Also based on the unified-theory of coherence and polarization we investigate the subtle relationship between coherence and polarization under general circumstances. Besides we show the effects of turbulent atmosphere on the degree of polarization and the polarization state of a partially coherent electromagnetic beam, which propagates through it and we compare with the propagation in vacuum. The detection of the optical signals is important; hence it affects the fidelity of the communication system. In this dissertation we present a general analysis for the optical heterodyne detection of stochastic electromagnetic beams. We derive an expression for the SNR when two stochastic electromagnetic beams are mixed coherently on a detector surface in terms of the space-time domain representation of the beams, the beam coherence polarization matrices. We evaluate also the heterodyne efficiency of a heterodyne detection system for stochastic beams propagating in vacuum and we discuss the dependence of the heterodyne efficiency of the detection process on the changes in the beam parameters as the beam propagates in free space.
Show less - Date Issued
- 2007
- Identifier
- CFE0001932, ucf:47445
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0001932
- Title
- BACKGROUND STABILIZATION AND MOTION DETECTION IN LAUNCH PAD VIDEO MONITORING.
- Creator
-
Gopalan, Kaushik, Kasparis, Takis, University of Central Florida
- Abstract / Description
-
Automatic detection of moving objects in video sequences is a widely researched topic with application in surveillance operations. Methods based on background cancellation by frame differencing are extremely common. However this process becomes much more complicated when the background is not completely stable due to camera motion. This thesis considers a space application where surveillance cameras around a shuttle launch site are used to detect any debris from the shuttle. The ground shake...
Show moreAutomatic detection of moving objects in video sequences is a widely researched topic with application in surveillance operations. Methods based on background cancellation by frame differencing are extremely common. However this process becomes much more complicated when the background is not completely stable due to camera motion. This thesis considers a space application where surveillance cameras around a shuttle launch site are used to detect any debris from the shuttle. The ground shake due to the impact of the launch causes the background to be shaky. We stabilize the background by translation of each frame, the optimum translation being determined by minimizing the energy difference between consecutive frames. This process is optimized by using a sub-image instead of the whole frame, the sub-image being chosen by taking an edge detection plot of the background and choosing the area with greatest density of edges as the sub-image of interest. The stabilized sequence is then processed by taking the difference between consecutive frames and marking areas with high intensity as the areas where motion is taking place. The residual noise from the background stabilization part is filtered out by masking the areas where the background has edges, as these areas have the highest probability of false alarms due to background motion.
Show less - Date Issued
- 2005
- Identifier
- CFE0000801, ucf:46683
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000801
- Title
- Sinkhole detection and quantification using LiDAR data.
- Creator
-
Rajabi, Amirarsalan, Nam, Boo Hyun, Wang, Dingbao, Singh, Arvind, University of Central Florida
- Abstract / Description
-
The state of Florida is highly prone to sinkhole incident and formation, mainly because of the soluble carbonate bedrock which is susceptible to dissolution and groundwater recharge that causes internal soil erosions. Numerous sinkholes, particularly in Central Florida, have occurred. Florida Subsidence Incident Report (FSIR) database contains verified sinkholes with Global Positioning System (GPS) information. In addition to existing detection methods such as subsurface exploration and...
Show moreThe state of Florida is highly prone to sinkhole incident and formation, mainly because of the soluble carbonate bedrock which is susceptible to dissolution and groundwater recharge that causes internal soil erosions. Numerous sinkholes, particularly in Central Florida, have occurred. Florida Subsidence Incident Report (FSIR) database contains verified sinkholes with Global Positioning System (GPS) information. In addition to existing detection methods such as subsurface exploration and geophysical methods, a remote sensing method can be an alternative and efficient means to detect and characterize sinkholes with a wide coverage. the first part of this study is aimed at developing a method to detect sinkholes in Missouri by using Light Detection and Ranging (LiDAR) data. Morphometrical parameters such as TPI (Topographic Position Index), CI (Convergence Index), SI (Slope Index), and DEM (Digital Elevation Model) have a high potential to help detect sinkholes, based on local ground conditions and study area. The GLM (General Linear Model) built in R software is used to obtain morphometrical indices of the study terrain to be trained and build a logistic regression model to detect sinkholes. In the second part of the study, a semi-automated model in ArcMap is then developed to detect sinkholes and also to estimate geometric characteristics of sinkholes (e.g. depth, length, circularity, area, and volume). This remote sensing technique has a potential to detect unreported sinkholes in rural and/or inaccessible areas.
Show less - Date Issued
- 2018
- Identifier
- CFE0007084, ucf:51992
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007084
- Title
- THERMAL DETECTION OF BIOMARKERS USING PHASE CHANGE NANOPARTICLES.
- Creator
-
Wang, Chaoming, Su, Ming, University of Central Florida
- Abstract / Description
-
Most of existing techniques cannot be used to detect molecular biomarkers (i.e., protein and DNA) contained in complex body fluids due to issues such as enzyme inhibition or signal interference. This thesis describes a nanoparticle-based thermal detection method for the highly sensitive detections of multiple DNA biomarkers or proteins contained in different type of fluids such as buffer solution, cell lysate and milk by using solid-liquid phase change nanoparticles as thermal barcodes....
Show moreMost of existing techniques cannot be used to detect molecular biomarkers (i.e., protein and DNA) contained in complex body fluids due to issues such as enzyme inhibition or signal interference. This thesis describes a nanoparticle-based thermal detection method for the highly sensitive detections of multiple DNA biomarkers or proteins contained in different type of fluids such as buffer solution, cell lysate and milk by using solid-liquid phase change nanoparticles as thermal barcodes. Besides, this method has also been applied for thrombin detection by using RNA aptamer-functionalized phase change nanoparticles as thermal probes. Furthermore, using nanostructured Si surface that have higher specific area can enhance the detection sensitivity by four times compared to use flat aluminum surfaces. The detection is based on the principle that the temperature of solid will not rise above its melting temperature unless all solid is molten, thus nanoparticles will have sharp melting peak during a linear thermal scan process. A one-to-one correspondence can be created between one type of nanoparticles and one type of biomarker, and multiple biomarkers can be detected simultaneously using different type nanoparticles. The melting temperature and the heat flow reflect the type and the concentration of biomarker, respectively. The melting temperatures of nanoparticles are designed to be over 100°C to avoid interference from species contained in fluids. The use of thermal nanoparticles allows detection of multiple low concentration DNAs or proteins in a complex fluid such as cell lysate regardless of the color, salt concentration, and conductivity of the sample.
Show less - Date Issued
- 2010
- Identifier
- CFE0003330, ucf:48473
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0003330
- Title
- Applications of Compressive Sensing To Surveillance Problems.
- Creator
-
Huff, Christopher, Mohapatra, Ram, Sun, Qiyu, Han, Deguang, University of Central Florida
- Abstract / Description
-
In many surveillance scenarios, one concern that arises is how to construct an imager that is capable of capturing the scene with high fidelity. This could be problematic for two reasons: first, the optics and electronics in the camera may have difficulty in dealing with so much information; secondly, bandwidth constraints, may pose difficulty in transmitting information from the imager to the user efficiently for reconstruction or realization. In this thesis, we will discuss a mathematical...
Show moreIn many surveillance scenarios, one concern that arises is how to construct an imager that is capable of capturing the scene with high fidelity. This could be problematic for two reasons: first, the optics and electronics in the camera may have difficulty in dealing with so much information; secondly, bandwidth constraints, may pose difficulty in transmitting information from the imager to the user efficiently for reconstruction or realization. In this thesis, we will discuss a mathematical framework that is capable of skirting the two aforementioned issues. This framework is rooted in a technique commonly referred to as compressive sensing. We will explore two of the seminal works in compressive sensing and will present the key theorems and definitions from these two papers. We will then survey three different surveillance scenarios and their respective compressive sensing solutions. The original contribution of this thesis is the development of a distributed compressive sensing model.
Show less - Date Issued
- 2012
- Identifier
- CFE0004317, ucf:49473
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004317
- Title
- DETECTING MALICIOUS SOFTWARE BY DYNAMICEXECUTION.
- Creator
-
Dai, Jianyong, Guha, Ratan, University of Central Florida
- Abstract / Description
-
Traditional way to detect malicious software is based on signature matching. However, signature matching only detects known malicious software. In order to detect unknown malicious software, it is necessary to analyze the software for its impact on the system when the software is executed. In one approach, the software code can be statically analyzed for any malicious patterns. Another approach is to execute the program and determine the nature of the program dynamically. Since the execution...
Show moreTraditional way to detect malicious software is based on signature matching. However, signature matching only detects known malicious software. In order to detect unknown malicious software, it is necessary to analyze the software for its impact on the system when the software is executed. In one approach, the software code can be statically analyzed for any malicious patterns. Another approach is to execute the program and determine the nature of the program dynamically. Since the execution of malicious code may have negative impact on the system, the code must be executed in a controlled environment. For that purpose, we have developed a sandbox to protect the system. Potential malicious behavior is intercepted by hooking Win32 system calls. Using the developed sandbox, we detect unknown virus using dynamic instruction sequences mining techniques. By collecting runtime instruction sequences in basic blocks, we extract instruction sequence patterns based on instruction associations. We build classification models with these patterns. By applying this classification model, we predict the nature of an unknown program. We compare our approach with several other approaches such as simple heuristics, NGram and static instruction sequences. We have also developed a method to identify a family of malicious software utilizing the system call trace. We construct a structural system call diagram from captured dynamic system call traces. We generate smart system call signature using profile hidden Markov model (PHMM) based on modularized system call block. Smart system call signature weakly identifies a family of malicious software.
Show less - Date Issued
- 2009
- Identifier
- CFE0002798, ucf:48141
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0002798
- Title
- Impact of wireless channel uncertainty upon M-ary distributed detection systems.
- Creator
-
Hajibabaei Najafabadi, Zahra, Vosoughi, Azadeh, Rahnavard, Nazanin, Atia, George, University of Central Florida
- Abstract / Description
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We consider a wireless sensor network (WSN), consisting of several sensors and a fusion center (FC), which is tasked with solving an $M$-ary hypothesis testing problem. Sensors make $M$-ary decisions and transmit their digitally modulated decisions over orthogonal channels, which are subject to Rayleigh fading and noise, to the FC. Adopting Bayesian optimality criterion, we consider training and non-training based distributed detection systems and investigate the effect of imperfect channel...
Show moreWe consider a wireless sensor network (WSN), consisting of several sensors and a fusion center (FC), which is tasked with solving an $M$-ary hypothesis testing problem. Sensors make $M$-ary decisions and transmit their digitally modulated decisions over orthogonal channels, which are subject to Rayleigh fading and noise, to the FC. Adopting Bayesian optimality criterion, we consider training and non-training based distributed detection systems and investigate the effect of imperfect channel state information (CSI) on the optimal maximum a posteriori probability (MAP) fusion rules and detection performance, when the sum of training and data symbol transmit powers is fixed. Our results show that for Rayleigh fading channel, when sensors employ $M$-FSK or binary FSK (BFSK) modulation, the error probability is minimized when training symbol transmit power is zero (regardless of the reception mode at the FC). However, for coherent reception, $M$-PSK and binary PSK (BPSK) modulation the error probability is minimized when half of transmit power is allocated for training symbol. If the channel is Rician fading, regardless of the modulation, the error probability is minimized when training transmit power is zero.
Show less - Date Issued
- 2016
- Identifier
- CFE0006111, ucf:51209
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006111
- Title
- Liquid Crystal-Based Biosensors for the Detection of Bile Acids.
- Creator
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He, Sihui, Wu, Shintson, Kuebler, Stephen, Kik, Pieter, Fang, Jiyu, University of Central Florida
- Abstract / Description
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Bile acids are physiologically important metabolites, which are synthesized in liver as the end products of cholesterol metabolism and then secreted into intestine. They are amphiphilic molecules which play a critical role in the digestion and absorption of fats and fat-soluble vitamins through emulsification. The concentration of bile acids is an indicator for liver function. Individual suffering from liver diseases has a sharp increase in bile acid concentrations. Hence, the concentration...
Show moreBile acids are physiologically important metabolites, which are synthesized in liver as the end products of cholesterol metabolism and then secreted into intestine. They are amphiphilic molecules which play a critical role in the digestion and absorption of fats and fat-soluble vitamins through emulsification. The concentration of bile acids is an indicator for liver function. Individual suffering from liver diseases has a sharp increase in bile acid concentrations. Hence, the concentration level of bile acids has long been used as a biomarker for the early diagnosis of intestinal and liver diseases.Conventional methods of bile acid detection such as chromatography-mass spectrometry and enzymatic reactions are complex and expensive. It is highly desired to have a simple, fast, and low-cost detection of bile acids that is available for self-testing or point-of-care testing. To achieve this goal, we develop a liquid crystal-based biosensor for the detection of bile acids. The sensor platform is based on the anchoring transition of liquid crystals (LCs) at the sodium dodecyl sulfate (SDS)-laden LC/aqueous interface for the detection of bile acids in aqueous solution. The first part of this dissertation focuses on the detection mechanism of bile acids. Our studies show that the displacement of SDS from the LC/aqueous interface by the competitive adsorption of bile acids induces a homeotropic-to-planar anchoring transition of the LC at the interface, providing an optical signature for the simple and rapid detection of bile acids. The adsorption of bile acids on the interface was found to follow Langmuir-Freundlich isotherm. The adsorption kinetics of different bile acids is compared. We find that both the number and position of hydroxyl groups of bile acids affect their adsorption kinetics. The different optical patterns of LC films formed by the adsorption of bile acids are also discussed. The second part of this dissertation studies the effect of solution conditions, surfactants, and liquid crystals on the detection limit of the LC-based biosensor for bile acids. Low pH and high ionic strength in the aqueous solution can reduce the electrostatic interaction between SDS and bile acids, which leads to a decreased detection limit. Surfactants with smaller headgroup and lower packing density also help to reduce the detection limit. To further reduce the detection limit, we investigate the effect of LC structures and find that LCs with a shorter chain length give lower detection limits. Also, by substituting a phenyl ring with a cyclohexane ring, we find that the detection limit is further reduced due to the decrease of the interaction between the phenyl rings of LCs. By mixing different LCs together, the detection limit can be linearly tuned from 160 (&)#181;M to 1.5 (&)#181;M, which is comparable to the traditional methods. But the LC-based biosensors have much simpler design and manufacture process.The third part of this dissertation is to apply this LC-based biosensor to the detection of urinary bile acids. We test the influence of several potential interfering species such as urea, creatinine, uric acid and ascorbic acid by conducting experiments in synthetic urine. By adjusting the concentration of SDS, we are able to eliminate the impact of those interfering species, and demonstrate that the LC-based biosensors can selectively detect urinary bile acids in human urine, suggesting its potential for screening liver dysfunctions. The final part of this dissertation is to investigate the application of LC-based biosensors in detecting the lipolysis process by porcine pancreatic lipase (PPL). It has been a long-standing argument over the role of bile salts on the activity of PPL. Thus, we study the time course of the hydrolysis of phospholipid L-dipalmitoylphosphatidylcholine (L-DPPC) by PPL at LC/aqueous interface. The hydrolysis of L-DPPC leads to a homeotropic-to-tilted anchoring transition of the LC at the interface, which allows the hydrolysis process to be monitored by a polarizing optical microscope. The microscopy image analysis reveals a lag-burst kinetics where a lag phase is followed by a burst phase. The effect of bile acids on these two phases is studied. We find that the activity of PPL both in the presence and absence of colipase can be improved by increasing the concentration of bile acids. The improvement becomes more distinct in the presence of colipase.
Show less - Date Issued
- 2015
- Identifier
- CFE0005804, ucf:50028
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005804
- Title
- Applications of Computer Vision Technologies of Automated Crack Detection and Quantification for the Inspection of Civil Infrastructure Systems.
- Creator
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Wu, Liuliu, Yun, Hae-Bum, Nam, Boo Hyun, Catbas, Necati, Foroosh, Hassan, University of Central Florida
- Abstract / Description
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Many components of existing civil infrastructure systems, such as road pavement, bridges, and buildings, are suffered from rapid aging, which require enormous nation's resources from federal and state agencies to inspect and maintain them. Crack is one of important material and structural defects, which must be inspected not only for good maintenance of civil infrastructure with a high quality of safety and serviceability, but also for the opportunity to provide early warning against failure....
Show moreMany components of existing civil infrastructure systems, such as road pavement, bridges, and buildings, are suffered from rapid aging, which require enormous nation's resources from federal and state agencies to inspect and maintain them. Crack is one of important material and structural defects, which must be inspected not only for good maintenance of civil infrastructure with a high quality of safety and serviceability, but also for the opportunity to provide early warning against failure. Conventional human visual inspection is still considered as the primary inspection method. However, it is well established that human visual inspection is subjective and often inaccurate. In order to improve current manual visual inspection for crack detection and evaluation of civil infrastructure, this study explores the application of computer vision techniques as a non-destructive evaluation and testing (NDE(&)T) method for automated crack detection and quantification for different civil infrastructures. In this study, computer vision-based algorithms were developed and evaluated to deal with different situations of field inspection that inspectors could face with in crack detection and quantification. The depth, the distance between camera and object, is a necessary extrinsic parameter that has to be measured to quantify crack size since other parameters, such as focal length, resolution, and camera sensor size are intrinsic, which are usually known by camera manufacturers. Thus, computer vision techniques were evaluated with different crack inspection applications with constant and variable depths. For the fixed-depth applications, computer vision techniques were applied to two field studies, including 1) automated crack detection and quantification for road pavement using the Laser Road Imaging System (LRIS), and 2) automated crack detection on bridge cables surfaces, using a cable inspection robot. For the various-depth applications, two field studies were conducted, including 3) automated crack recognition and width measurement of concrete bridges' cracks using a high-magnification telescopic lens, and 4) automated crack quantification and depth estimation using wearable glasses with stereovision cameras.From the realistic field applications of computer vision techniques, a novel self-adaptive image-processing algorithm was developed using a series of morphological transformations to connect fragmented crack pixels in digital images. The crack-defragmentation algorithm was evaluated with road pavement images. The results showed that the accuracy of automated crack detection, associated with artificial neural network classifier, was significantly improved by reducing both false positive and false negative. Using up to six crack features, including area, length, orientation, texture, intensity, and wheel-path location, crack detection accuracy was evaluated to find the optimal sets of crack features. Lab and field test results of different inspection applications show that proposed compute vision-based crack detection and quantification algorithms can detect and quantify cracks from different structures' surface and depth. Some guidelines of applying computer vision techniques are also suggested for each crack inspection application.
Show less - Date Issued
- 2015
- Identifier
- CFE0005743, ucf:50089
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005743
- Title
- INVESTIGATION OF DAMAGE DETECTION METHODOLOGIES FOR STRUCTURAL HEALTH MONITORING.
- Creator
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Gul, Mustafa, Catbas, F. Necati, University of Central Florida
- Abstract / Description
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Structural Health Monitoring (SHM) is employed to track and evaluate damage and deterioration during regular operation as well as after extreme events for aerospace, mechanical and civil structures. A complete SHM system incorporates performance metrics, sensing, signal processing, data analysis, transmission and management for decision-making purposes. Damage detection in the context of SHM can be successful by employing a collection of robust and practical damage detection methodologies...
Show moreStructural Health Monitoring (SHM) is employed to track and evaluate damage and deterioration during regular operation as well as after extreme events for aerospace, mechanical and civil structures. A complete SHM system incorporates performance metrics, sensing, signal processing, data analysis, transmission and management for decision-making purposes. Damage detection in the context of SHM can be successful by employing a collection of robust and practical damage detection methodologies that can be used to identify, locate and quantify damage or, in general terms, changes in observable behavior. In this study, different damage detection methods are investigated for global condition assessment of structures. First, different parametric and non-parametric approaches are re-visited and further improved for damage detection using vibration data. Modal flexibility, modal curvature and un-scaled flexibility based on the dynamic properties that are obtained using Complex Mode Indicator Function (CMIF) are used as parametric damage features. Second, statistical pattern recognition approaches using time series modeling in conjunction with outlier detection are investigated as a non-parametric damage detection technique. Third, a novel methodology using ARX models (Auto-Regressive models with eXogenous output) is proposed for damage identification. By using this new methodology, it is shown that damage can be detected, located and quantified without the need of external loading information. Next, laboratory studies are conducted on different test structures with a number of different damage scenarios for the evaluation of the techniques in a comparative fashion. Finally, application of the methodologies to real life data is also presented along with the capabilities and limitations of each approach in light of analysis results of the laboratory and real life data.
Show less - Date Issued
- 2009
- Identifier
- CFE0002830, ucf:48069
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0002830
- Title
- Color-Ratio Based Strawberry Plant Localization and Nutrition Deficiency Detection.
- Creator
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Kong, Xiangling, Xu, Yunjun, Elgohary, Tarek, Fu, Qiushi, Wu, Dazhong, Wang, Liqiang, University of Central Florida
- Abstract / Description
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In recent years, precision agriculture has become popular anticipating to partially meet the needs of an ever-growing population with limited resources. Plant localization and nutrient de?ciency detection are two important tasks in precision agriculture. In this dissertation, these two tasks are studied by using a new color-ratio(C-R) index technique. Firstly, a low cost and light scene invariant approach is proposed to detect green and yellow leaves based on the color-ratio (C-R) indices. A...
Show moreIn recent years, precision agriculture has become popular anticipating to partially meet the needs of an ever-growing population with limited resources. Plant localization and nutrient de?ciency detection are two important tasks in precision agriculture. In this dissertation, these two tasks are studied by using a new color-ratio(C-R) index technique. Firstly, a low cost and light scene invariant approach is proposed to detect green and yellow leaves based on the color-ratio (C-R) indices. A plant localization approach is then developed using the relative pixel relationships of adjacent plants. Secondly, the Sobel operator and morphology techniques are applied to segment the target strawberry leaf from a ?eld image. The characterized color for a speci?c nutrient de?ciency is detected by the C-R indices. The pattern of the detected color on the leaf is then examined to determine the speci?c nutrient de?ciency. The proposed approaches are validated in a commercial strawberry farm.
Show less - Date Issued
- 2019
- Identifier
- CFE0007666, ucf:52482
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007666
