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Title: INTERACTIVITY AND USER-HETEROGENEITY IN ON DEMAND BROADCAST VIDEO.
Creator: Tantaoui El Araki, Mounir, Hua, Kien A., University of Central Florida
Abstract / Description: Video-On-Demand (VOD) has appeared as an important technology for many multimedia applications such as news on demand, digital libraries, home entertainment, and distance learning. In its simplest form, delivery of a video stream requires a dedicated channel for each video session. This scheme is very expensive and non-scalable. To preserve server bandwidth, many users can share a channel using multicast. Two types of multicast have been considered. In a non-periodic multicast setting, users...
Show moreVideo-On-Demand (VOD) has appeared as an important technology for many multimedia applications such as news on demand, digital libraries, home entertainment, and distance learning. In its simplest form, delivery of a video stream requires a dedicated channel for each video session. This scheme is very expensive and non-scalable. To preserve server bandwidth, many users can share a channel using multicast. Two types of multicast have been considered. In a non-periodic multicast setting, users make video requests to the server; and it serves them according to some scheduling policy. In a periodic broadcast environment, the server does not wait for service requests. It broadcasts a video cyclically, e.g., a new stream of the same video is started every t seconds. Although, this type of approach does not guarantee true VOD, the worst service latency experienced by any client is less than t seconds. A distinct advantage of this approach is that it can serve a very large community of users using minimal server bandwidth. In VOD System it is desirable to provide the user with the video-cassette-recorder-like (VCR) capabilities such as fast-forwarding a video or jumping to a specific frame. This issue in the broadcast framework is addressed, where each video and its interactive version are broadcast repeatedly on the network. Existing techniques rely on data prefetching as the mechanism to provide this functionality. This approach provides limited usability since the prefetching rate cannot keep up with typical fast-forward speeds. In the same environment, end users might have access to different bandwidth capabilities at different times. Current periodic broadcast schemes, do not take advantage of high-bandwidth capabilities, nor do they adapt to the low-bandwidth limitation of the receivers. A heterogeneous technique is presented that can adapt to a range of receiving bandwidth capability. Given a server bandwidth and a range of different client bandwidths, users employing the proposed technique will choose either to use their full reception bandwidth capability and therefore accessing the video at a very short time, or using part or enough reception bandwidth at the expense of a longer access latency.
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Date Issued: 2004
Identifier: CFE0000085, ucf:46129
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000085

Title: CAMERA SYSTEM SUPPORT FOR HIGHWAY TRANSPORTATION USING MOBILE DEVICES.
Creator: Minh, Le, Hua, Kien A., University of Central Florida
Abstract / Description: With the very fast growing technology in wireless, advancement in hardware and the dramatically falling cost of mobile computing devices such as PDA, handheld device, People nowadays can have a personal device that fits in their hand but has computing power as a desktop did few years ago. The same device now is able to communicate over a wireless network and view office document at the same time. The combination of size, power and flexibility makes the personal devices increasingly appear in...
Show moreWith the very fast growing technology in wireless, advancement in hardware and the dramatically falling cost of mobile computing devices such as PDA, handheld device, People nowadays can have a personal device that fits in their hand but has computing power as a desktop did few years ago. The same device now is able to communicate over a wireless network and view office document at the same time. The combination of size, power and flexibility makes the personal devices increasingly appear in many aspects of life.In this proposal, we focus on a simple yet useful application of mobile devices and wireless capabilities. The application can help commuters in traffic system to find an optimal route based on video camera surveillance information. This surveillance information is made available to the user through his/her handheld devices. As an example, suppose we have installed several cameras along the expressway. If commuters can access to these cameras, they can observe the situation currently happening along the way, and decide which path would be the most effective to avoid the traffic congestion. This application will eventually improve the effectiveness of current traffic system since it will help to reduce traffic congestions.
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Date Issued: 2004
Identifier: CFE0000094, ucf:46090
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000094

Title: SENSOR-BASED COMPUTING TECHNIQUES FOR REAL-TIME TRAFFIC EVACUATION MANAGEMENT.
Creator: Hamza-Lup, Georgiana, Hua, Kien, University of Central Florida
Abstract / Description: The threat of terrorist incidents is higher than ever before and devastating acts, such as the terrorist attacks on the World Trade Center and the Pentagon, have left many concerns about the possibility of future incidents and their potential impact. Unlike some natural disasters that can be anticipated, terrorist attacks are sudden and unexpected. Even if sometimes we do have partial information about a possible attack, it is generally not known exactly where, when, or how an attack will...
Show moreThe threat of terrorist incidents is higher than ever before and devastating acts, such as the terrorist attacks on the World Trade Center and the Pentagon, have left many concerns about the possibility of future incidents and their potential impact. Unlike some natural disasters that can be anticipated, terrorist attacks are sudden and unexpected. Even if sometimes we do have partial information about a possible attack, it is generally not known exactly where, when, or how an attack will occur. This lack of information posses great challenges on those responsible for security, specifically, on their ability to respond fast, whenever necessary with flexibility and coordination. The surface transportation system plays a critical role in responding to terrorist attacks or other unpredictable human-caused disasters. In particular, existing Intelligent Transportation Systems (ITS) can be enhanced to improve the ability of the surface transportation system to efficiently respond to emergencies and recover from disasters. This research proposes the development of new information technologies to enhance today's ITS with capabilities to improve the crisis response capabilities of the surface transportation system. The objective of this research is to develop a Smart Traffic Evacuation Management System (STEMS) that responds rapidly and effectively to terrorist threats or other unpredictable disasters, by creating dynamic evacuation plans adaptable to continuously changing traffic conditions based on real-time information. The intellectual merit of this research is that the proposed STEMS will possess capabilities to support both the unexpected and unpredictable aspects of a terrorist attack and the dynamic aspect of the traffic network environment. Studies of related work indicate that STEMS is the first system that automatically generates evacuation plans, given the location and scope of an incident and the current traffic network conditions, and dynamically adjusts the plans based on real-time information received from sensors and other surveillance technologies. Refining the plans to keep them consistent with the current conditions significantly improves evacuation effectiveness. The changes that STEMS can handle range from slow, steady variations in traffic conditions, to more sudden variations caused by secondary accidents or other stochastic factors (e.g., high visibility events that determine a sudden increase in the density of the traffic). Being especially designed to handle evacuation in case of terrorist-caused disasters, STEMS can also handle multiple coordinated attacks targeting some strategic area over a short time frame. These are frequently encountered in terrorist acts as they are intended to create panic and terror. Due to the nature of the proposed work, an important component of this project is the development of a simulation environment to support the design and test of STEMS. Developing analytical patterns for modeling traffic dynamics has been explored in the literature at different levels of resolution and realism. Most of the proposed approaches are either too limited in representing reality, or too complex for handling large networks. The contribution of this work consists of investigating and developing traffic models and evacuation algorithms that overcome both of the above limitations. Two of the greatest impacts of this research in terms of science are as follows. First, the new simulation environment developed for this project provides a test bed to facilitate future work on traffic evacuation systems. Secondly, although the models and algorithms developed for STEMS are targeted towards traffic environments and evacuation, their applicability can be extended to other environments (e.g., building evacuation) and other traffic related problems (e.g., real-time route diversion in case of accidents). One of the broader impacts of this research would be the deployment of STEMS in a real environment. This research provides a fundamental tool for handling emergency evacuation for a full range of unpredictable incidents, regardless of cause, origin and scope. Wider and swifter deployment of STEMS will support Homeland Security in general, and will also enhance the surface transportation system on which so many Homeland Security stakeholders depend.
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Date Issued: 2006
Identifier: CFE0001248, ucf:46919
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001248

Title: AUTOMATIC ANNOTATION OF DATABASE IMAGES FOR QUERY-BY-CONCEPT.
Creator: Hiransakolwong, Nualsawat, Hua, kien A., University of Central Florida
Abstract / Description: As digital images become ubiquitous in many applications, the need for efficient and effective retrieval techniques is more demanding than ever. Query by Example (QBE) and Query by Concept (QBC) are among the most popular query models. The former model accepts example images as queries and searches for similar ones based on low-level features such as colors and textures. The latter model allows queries to be expressed in the form of high-level semantics or concept words, such as "boat" or ...
Show moreAs digital images become ubiquitous in many applications, the need for efficient and effective retrieval techniques is more demanding than ever. Query by Example (QBE) and Query by Concept (QBC) are among the most popular query models. The former model accepts example images as queries and searches for similar ones based on low-level features such as colors and textures. The latter model allows queries to be expressed in the form of high-level semantics or concept words, such as "boat" or "car," and finds images that match the specified concepts. Recent research has focused on the connections between these two models and attempts to close the semantic-gap between them. This research involves finding the best method that maps a set of low-level features into high-level concepts. Automatic annotation techniques are investigated in this dissertation to facilitate QBC. In this approach, sets of training images are used to discover the relationship between low-level features and predetermined high-level concepts. The best mapping with respect to the training sets is proposed and used to analyze images, annotating them with the matched concept words. One principal difference between QBE and QBC is that, while similarity matching in QBE must be done at the query time, QBC performs concept exploration off-line. This difference allows QBC techniques to shift the time-consuming task of determining similarity away from the query time, thus facilitating the additional processing time required for increasingly accurate matching. Consequently, QBC's primary design objective is to achieve accurate annotation within a reasonable processing time. This objective is the guiding principle in the design of the following proposed methods which facilitate image annotation: 1.A novel dynamic similarity function. This technique allows users to query with multiple examples: relevant, irrelevant or neutral. It uses the range distance in each group to automatically determine weights in the distance function. Among the advantages of this technique are higher precision and recall rates with fast matching time. 2.Object recognition based on skeletal graphs. The topologies of objects' skeletal graphs are captured and compared at the node level. Such graph representation allows preservation of the skeletal graph's coherence without sacrificing the flexibility of matching similar portions of graphs across different levels. The technique is robust to translation, scaling, and rotation invariants at object level. This technique achieves high precision and recall rates with reasonable matching time and storage space. 3.ASIA (Automatic Sampling-based Image Annotation) is a technique based on a new sampling-based matching framework allowing users to identify their area of interest. ASIA eliminates noise, or irrelevant areas of the image. ASIA is robust to translation, scaling, and rotation invariants at the object level. This technique also achieves high precision and recall rates. While the above techniques may not be the fastest when contrasted with some other recent QBE techniques, they very effectively perform image annotation. The results of applying these processes are accurately annotated database images to which QBC may then be applied. The results of extensive experiments are presented to substantiate the performance advantages of the proposed techniques and allow them to be compared with other recent high-performance techniques. Additionally, a discussion on merging the proposed techniques into a highly effective annotation system is also detailed.
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Date Issued: 2004
Identifier: CFE0000262, ucf:46239
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000262

Title: COLLABORATION ENFORCEMENT IN MOBILE AD HOC NETWORKS.
Creator: Jiang, Ning, Hua, Kien, University of Central Florida
Abstract / Description: Mobile Ad hoc NETworks (MANETs) have attracted great research interest in recent years. Among many issues, lack of motivation for participating nodes to collaborate forms a major obstacle to the adoption of MANETs. Many contemporary collaboration enforcement techniques employ reputation mechanisms for nodes to avoid and penalize malicious participants. Reputation information is propagated among participants and updated based on complicated trust relationships to thwart false accusation of...
Show moreMobile Ad hoc NETworks (MANETs) have attracted great research interest in recent years. Among many issues, lack of motivation for participating nodes to collaborate forms a major obstacle to the adoption of MANETs. Many contemporary collaboration enforcement techniques employ reputation mechanisms for nodes to avoid and penalize malicious participants. Reputation information is propagated among participants and updated based on complicated trust relationships to thwart false accusation of benign nodes. The aforementioned strategy suffers from low scalability and is likely to be exploited by adversaries. To address these problems, we first propose a finite state model. With this technique, no reputation information is propagated in the network and malicious nodes cannot cause false penalty to benign hosts. Misbehaving node detection is performed on-demand; and malicious node punishment and avoidance are accomplished by only maintaining reputation information within neighboring nodes. This scheme, however, requires that each node equip with a tamper-proof hardware. In the second technique, no such restriction applies. Participating nodes classify their one-hop neighbors through direct observation and misbehaving nodes are penalized within their localities. Data packets are dynamically rerouted to circumvent selfish nodes. In both schemes, overall network performance is greatly enhanced. Our approach significantly simplifies the collaboration enforcement process, incurs low overhead, and is robust against various malicious behaviors. Simulation results based on different system configurations indicate that the proposed technique can significantly improve network performance with very low communication cost.
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Date Issued: 2006
Identifier: CFE0001047, ucf:46820
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001047

Title: LEARNING TECHNIQUES FOR INFORMATION RETRIEVAL AND MINING IN HIGH-DIMENSIONAL DATABASES.
Creator: Cheng, Hao, Hua, Kien A., University of Central Florida
Abstract / Description: The main focus of my research is to design effective learning techniques for information retrieval and mining in high-dimensional databases. There are two main aspects in the retrieval and mining research: accuracy and efficiency. The accuracy problem is how to return results which can better match the ground truth, and the efficiency problem is how to evaluate users' requests and execute learning algorithms as fast as possible. However, these problems are non-trivial because of the...
Show moreThe main focus of my research is to design effective learning techniques for information retrieval and mining in high-dimensional databases. There are two main aspects in the retrieval and mining research: accuracy and efficiency. The accuracy problem is how to return results which can better match the ground truth, and the efficiency problem is how to evaluate users' requests and execute learning algorithms as fast as possible. However, these problems are non-trivial because of the complexity of the high-level semantic concepts, the heterogeneous natures of the feature space, the high dimensionality of data representations and the size of the databases. My dissertation is dedicated to addressing these issues. Specifically, my work has five main contributions as follows. The first contribution is a novel manifold learning algorithm, Local and Global Structures Preserving Projection (LGSPP), which defines salient low-dimensional representations for the high-dimensional data. A small number of projection directions are sought in order to properly preserve the local and global structures for the original data. Specifically, two groups of points are extracted for each individual point in the dataset: the first group contains the nearest neighbors of the point, and the other set are a few sampled points far away from the point. These two point sets respectively characterize the local and global structures with regard to the data point. The objective of the embedding is to minimize the distances of the points in each local neighborhood and also to disperse the points far away from their respective remote points in the original space. In this way, the relationships between the data in the original space are well preserved with little distortions. The second contribution is a new constrained clustering algorithm. Conventionally, clustering is an unsupervised learning problem, which systematically partitions a dataset into a small set of clusters such that data in each cluster appear similar to each other compared with those in other clusters. In the proposal, the partial human knowledge is exploited to find better clustering results. Two kinds of constraints are integrated into the clustering algorithm. One is the must-link constraint, indicating that the involved two points belong to the same cluster. On the other hand, the cannot-link constraint denotes that two points are not within the same cluster. Given the input constraints, data points are arranged into small groups and a graph is constructed to preserve the semantic relations between these groups. The assignment procedure makes a best effort to assign each group to a feasible cluster without violating the constraints. The theoretical analysis reveals that the probability of data points being assigned to the true clusters is much higher by the new proposal, compared to conventional methods. In general, the new scheme can produce clusters which can better match the ground truth and respect the semantic relations between points inferred from the constraints. The third contribution is a unified framework for partition-based dimension reduction techniques, which allows efficient similarity retrieval in the high-dimensional data space. Recent similarity search techniques, such as Piecewise Aggregate Approximation (PAA), Segmented Means (SMEAN) and Mean-Standard deviation (MS), prove to be very effective in reducing data dimensionality by partitioning dimensions into subsets and extracting aggregate values from each dimension subset. These partition-based techniques have many advantages including very efficient multi-phased pruning while being simple to implement. They, however, are not adaptive to different characteristics of data in diverse applications. In this study, a unified framework for these partition-based techniques is proposed and the issue of dimension partitions is examined in this framework. An investigation of the relationships of query selectivity and the dimension partition schemes discovers indicators which can predict the performance of a partitioning setting. Accordingly, a greedy algorithm is designed to effectively determine a good partitioning of data dimensions so that the performance of the reduction technique is robust with regard to different datasets. The fourth contribution is an effective similarity search technique in the database of point sets. In the conventional model, an object corresponds to a single vector. In the proposed study, an object is represented by a set of points. In general, this new representation can be used in many real-world applications and carries much more local information, but the retrieval and learning problems become very challenging. The Hausdorff distance is the common distance function to measure the similarity between two point sets, however, this metric is sensitive to outliers in the data. To address this issue, a novel similarity function is defined to better capture the proximity of two objects, in which a one-to-one mapping is established between vectors of the two objects. The optimal mapping minimizes the sum of distances between each paired points. The overall distance of the optimal matching is robust and has high retrieval accuracy. The computation of the new distance function is formulated into the classical assignment problem. The lower-bounding techniques and early-stop mechanism are also proposed to significantly accelerate the expensive similarity search process. The classification problem over the point-set data is called Multiple Instance Learning (MIL) in the machine learning community in which a vector is an instance and an object is a bag of instances. The fifth contribution is to convert the MIL problem into a standard supervised learning in the conventional vector space. Specially, feature vectors of bags are grouped into clusters. Each object is then denoted as a bag of cluster labels, and common patterns of each category are discovered, each of which is further reconstructed into a bag of features. Accordingly, a bag is effectively mapped into a feature space defined by the distances from this bag to all the derived patterns. The standard supervised learning algorithms can be applied to classify objects into pre-defined categories. The results demonstrate that the proposal has better classification accuracy compared to other state-of-the-art techniques. In the future, I will continue to explore my research in large-scale data analysis algorithms, applications and system developments. Especially, I am interested in applications to analyze the massive volume of online data.
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Date Issued: 2009
Identifier: CFE0002882, ucf:48022
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002882

Title: Efficient techniques for management and delivery of video data.
Creator: Oh, Junghwan, Hua, Kien A., Engineering and Computer Science
Abstract / Description: University of Central Florida College of Engineering Thesis; The rapid advances in electronic imaging, storage, data compression telecommunications, and networking technology have resulted in a vast creation and use of digital videos in many important applications such as digital libraries, distance learning, public information systems, electronic commerce, movie on demand, etc. This brings about the need for management as well as delivery of video data. Organizing and managing video data,...
Show moreUniversity of Central Florida College of Engineering Thesis; The rapid advances in electronic imaging, storage, data compression telecommunications, and networking technology have resulted in a vast creation and use of digital videos in many important applications such as digital libraries, distance learning, public information systems, electronic commerce, movie on demand, etc. This brings about the need for management as well as delivery of video data. Organizing and managing video data, however, is much more complex than managing conventional text data due to their semantically rich and unstructured contents. Also, the enormous size of video files requires high communication bandwidth for data delivery. In this dissertation, I present the following techniques for video data management and delivery. Decomposing video into meaningful pieces (i.e., shots) is a very fundamental step to handling the complicated contents of video data. Content-based video parsing techniques are presented and analyzed. In order to reduce the computation cost substantially, a non-sequential approach to shot boundary detection is investigated. Efficient browsing and indexing of video data are essential for video data management. Non-linear browsing and cost-effective indexing schemes for video data based on their contents are described and evaluated. In order to satisfy various user requests, delivering long videos through the limited capacity of bandwidth is challenging work. To reduce the demand on this bandwidth, a hybrid of two effective approaches, periodic broadcast and scheduled multicast, is discussed and simulated. The current techniques related to the above works are discussed thoroughly to explain their advantages and disadvantages, and to make the new improved schemes. The substantial amount of experiments and simulations as well as the concepts are provided to compare the introduced techniques with the other existing ones. The results indicate that they outperform recent techniques by a significant margin. I conclude the dissertation with a discussing of future research directions.
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Date Issued: 2000
Identifier: CFR0001719, ucf:52918
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFR0001719

Title: EXPLORING TECHNIQUES FOR MEASUREMENT AND IMPROVEMENT OF DATA QUALITY WITH APPLICATION TO DETERMINATION OF THE LAST KNOWN POSITION (LKP) IN SEARCH AND RESCUE (SAR) DATA.
Creator: Wakchaure, Abhijit, Hua, Kien, University of Central Florida
Abstract / Description: There is a tremendous volume of data being generated in today's world. As organizations around the globe realize the increased importance of their data as being a valuable asset in gaining a competitive edge in a fast-paced and a dynamic business world, more and more attention is being paid to the quality of the data. Advances in the fields of data mining, predictive modeling, text mining, web mining, business intelligence, health care analytics, etc. all depend on clean, accurate data. That...
Show moreThere is a tremendous volume of data being generated in today's world. As organizations around the globe realize the increased importance of their data as being a valuable asset in gaining a competitive edge in a fast-paced and a dynamic business world, more and more attention is being paid to the quality of the data. Advances in the fields of data mining, predictive modeling, text mining, web mining, business intelligence, health care analytics, etc. all depend on clean, accurate data. That one cannot effectively mine data, which is dirty, comes as no surprise. This research is an exploratory study of different domain data sets, addressing the data quality issues specific to each domain, identifying the challenges faced and arriving at techniques or methodologies for measuring and improving the data quality. The primary focus of the research is on the SAR or Search and Rescue dataset, identifying key issues related to data quality therein and developing an algorithm for improving the data quality. SAR missions which are routinely conducted all over the world show a trend of increasing mission costs. Retrospective studies of historic SAR data not only allow for a detailed analysis and understanding of SAR incidents and patterns, but also form the basis for generating probability maps, analytical data models, etc., which allow for an efficient use of valuable SAR resources and their distribution. One of the challenges with regards to the SAR dataset is that the collection process is not perfect. Often, the LKP or the Last Known Position is not known or cannot be arrived at. The goal is to fully or partially geocode the LKP for as many data points as possible, identify those data points where the LKP cannot be geocoded at all, and further highlight the underlying data quality issues. The SAR Algorithm has been developed, which makes use of partial or incomplete information, cleans and validates the data, and further extracts address information from relevant fields to successfully geocode the data. The algorithm improves the geocoding accuracy and has been validated by a set of approaches.
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Date Issued: 2011
Identifier: CFE0004050, ucf:49142
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004050

Title: QUERY PROCESSING IN LOCATION-BASED SERVICES.
Creator: Liu, Fuyu, Hua, Kien, University of Central Florida
Abstract / Description: With the advances in wireless communication technology and advanced positioning systems, a variety of Location-Based Services (LBS) become available to the public. Mobile users can issue location-based queries to probe their surrounding environments. One important type of query in LBS is moving monitoring queries over mobile objects. Due to the high frequency in location updates and the expensive cost of continuous query processing, server computation capacity and wireless communication...
Show moreWith the advances in wireless communication technology and advanced positioning systems, a variety of Location-Based Services (LBS) become available to the public. Mobile users can issue location-based queries to probe their surrounding environments. One important type of query in LBS is moving monitoring queries over mobile objects. Due to the high frequency in location updates and the expensive cost of continuous query processing, server computation capacity and wireless communication bandwidth are the two limiting factors for large-scale deployment of moving object database systems. To address both of the scalability factors, distributed computing has been considered. These schemes enable moving objects to participate as a peer in query processing to substantially reduce the demand on server computation, and wireless communications associated with location updates. In the first part of this dissertation, we propose a distributed framework to process moving monitoring queries over moving objects in a spatial network environment. In the second part of this dissertation, in order to reduce the communication cost, we leverage both on-demand data access and periodic broadcast to design a new hybrid distributed solution for moving monitoring queries in an open space environment. Location-based services make our daily life more convenient. However, to receive the services, one has to reveal his/her location and query information when issuing location-based queries. This could lead to privacy breach if these personal information are possessed by some untrusted parties. In the third part of this dissertation, we introduce a new privacy protection measure called query l-diversity, and provide two cloaking algorithms to achieve both location k-anonymity and query l-diversity to better protect user privacy. In the fourth part of this dissertation, we design a hybrid three-tier architecture to help reduce privacy exposure. In the fifth part of this dissertation, we propose to use Road Network Embedding technique to process privacy protected queries.
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Date Issued: 2010
Identifier: CFE0003487, ucf:48949
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003487

Title: LIVE VIDEO DATABASE MANAGEMENT SYSTEMS.
Creator: Peng, Rui, Hua, Kien, University of Central Florida
Abstract / Description: With the proliferation of inexpensive cameras and the availability of high-speed wired and wireless networks, networks of distributed cameras are becoming an enabling technology for a broad range of interdisciplinary applications in domains such as public safety and security, manufacturing, transportation, and healthcare. TodayÃ‚Â's live video processing systems on networks of distributed cameras, however, are designed for specific classes of applications. To provide a...
Show moreWith the proliferation of inexpensive cameras and the availability of high-speed wired and wireless networks, networks of distributed cameras are becoming an enabling technology for a broad range of interdisciplinary applications in domains such as public safety and security, manufacturing, transportation, and healthcare. TodayÃ‚Â's live video processing systems on networks of distributed cameras, however, are designed for specific classes of applications. To provide a generic query processing platform for applications of distributed camera networks, we designed and implemented a new class of general purpose database management systems, the live video database management system (LVDBMS). We view networked video cameras as a special class of interconnected storage devices, and allow the user to formulate ad hoc queries over real-time live video feeds. In the first part of this dissertation, an Internet scale framework for sharing and dissemination of general sensor data is presented. This framework provides a platform for general sensor data to be published, searched, shared, and delivered across the Internet. The second part is the design and development of a Live Video Database Management System. LVDBMS allows users to easily focus on events of interest from a multitude of distributed video cameras by posing continuous queries on the live video streams. In the third part, a distributed in-memory database approach is proposed to enhance the LVDBMS with an important capability of tracking objects across cameras.
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Date Issued: 2010
Identifier: CFE0003453, ucf:48419
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003453

Title: EFFICIENT TECHNIQUES FOR RELEVANCE FEEDBACK PROCESSING IN CONTENT-BASED IMAGE RETRIEVAL.
Creator: Liu, Danzhou, Hua, Kien, University of Central Florida
Abstract / Description: In content-based image retrieval (CBIR) systems, there are two general types of search: target search and category search. Unlike queries in traditional database systems, users in most cases cannot specify an ideal query to retrieve the desired results for either target search or category search in multimedia database systems, and have to rely on iterative feedback to refine their query. Efficient evaluation of such iterative queries can be a challenge, especially when the multimedia database...
Show moreIn content-based image retrieval (CBIR) systems, there are two general types of search: target search and category search. Unlike queries in traditional database systems, users in most cases cannot specify an ideal query to retrieve the desired results for either target search or category search in multimedia database systems, and have to rely on iterative feedback to refine their query. Efficient evaluation of such iterative queries can be a challenge, especially when the multimedia database contains a large number of entries, and the search needs many iterations, and when the underlying distance measure is computationally expensive. The overall processing costs, including CPU and disk I/O, are further emphasized if there are numerous concurrent accesses. To address these limitations involved in relevance feedback processing, we propose a generic framework, including a query model, index structures, and query optimization techniques. Specifically, this thesis has five main contributions as follows. The first contribution is an efficient target search technique. We propose four target search methods: naive random scan (NRS), local neighboring movement (LNM), neighboring divide-and-conquer (NDC), and global divide-and-conquer (GDC) methods. All these methods are built around a common strategy: they do not retrieve checked images (i.e., shrink the search space). Furthermore, NDC and GDC exploit Voronoi diagrams to aggressively prune the search space and move towards target images. We theoretically and experimentally prove that the convergence speeds of GDC and NDC are much faster than those of NRS and recent methods. The second contribution is a method to reduce the number of expensive distance computation when answering k-NN queries with non-metric distance measures. We propose an efficient distance mapping function that transfers non-metric measures into metric, and still preserves the original distance orderings. Then existing metric index structures (e.g., M-tree) can be used to reduce the computational cost by exploiting the triangular inequality property. The third contribution is an incremental query processing technique for Support Vector Machines (SVMs). SVMs have been widely used in multimedia retrieval to learn a concept in order to find the best matches. SVMs, however, suffer from the scalability problem associated with larger database sizes. To address this limitation, we propose an efficient query evaluation technique by employing incremental update. The proposed technique also takes advantage of a tuned index structure to efficiently prune irrelevant data. As a result, only a small portion of the data set needs to be accessed for query processing. This index structure also provides an inexpensive means to process the set of candidates to evaluate the final query result. This technique can work with different kernel functions and kernel parameters. The fourth contribution is a method to avoid local optimum traps. Existing CBIR systems, designed around query refinement based on relevance feedback, suffer from local optimum traps that may severely impair the overall retrieval performance. We therefore propose a simulated annealing-based approach to address this important issue. When a stuck-at-a-local-optimum occurs, we employ a neighborhood search technique (i.e., simulated annealing) to continue the search for additional matching images, thus escaping from the local optimum. We also propose an index structure to speed up such neighborhood search. Finally, the fifth contribution is a generic framework to support concurrent accesses. We develop new storage and query processing techniques to exploit sequential access and leverage inter-query concurrency to share computation. Our experimental results, based on the Corel dataset, indicate that the proposed optimization can significantly reduce average response time while achieving better precision and recall, and is scalable to support a large user community. This latter performance characteristic is largely neglected in existing systems making them less suitable for large-scale deployment. With the growing interest in Internet-scale image search applications, our framework offers an effective solution to the scalability problem.
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Date Issued: 2009
Identifier: CFE0002728, ucf:48162
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002728

Title: CONNECTIONLESS APPROACH – A LOCALIZED SCHEME TO MOBILE AD HOC NETWORKS.
Creator: Ho, Yao, Hua, Kien, University of Central Florida
Abstract / Description: According to a Gartner Group (www.gartner.com) report in September 2008, the worldwide telecommunications market is on pace to reach $2 trillion in 2008. Gartner predicts that by 2012, the ratio of mobile to fixed connections will exceed 4-to-1. The North American mobile data market grew to 141.1 million connections in 2007, with a compound annual growth rate of 41.7 percent. It is believed that a large portion will be ad hoc and multi-hop connections, which will open many opportunities for...
Show moreAccording to a Gartner Group (www.gartner.com) report in September 2008, the worldwide telecommunications market is on pace to reach $2 trillion in 2008. Gartner predicts that by 2012, the ratio of mobile to fixed connections will exceed 4-to-1. The North American mobile data market grew to 141.1 million connections in 2007, with a compound annual growth rate of 41.7 percent. It is believed that a large portion will be ad hoc and multi-hop connections, which will open many opportunities for Mobile Ad hoc NETwork (MANET) applications and Wireless Mesh Network (WMN) applications. A MANET is a self-organizing multi-hop wireless network where all nodes participate in the routing and data forwarding process. Such a network can be easily deployed in situations where no base station is available, and a network must be build spontaneously. In applications such as battlefield communications, national crises, disaster recovery, and sensor deployment, a wired network is not available and ad hoc networks provide the only feasible means of communications and information access. Ad hoc networks have also become commonplace for gaming, conferencing, electronic classrooms, and particularly vehicle-to-vehicle communications. A Wireless mash network (WMN) is collection of mesh clients and mesh nodes (routers), with mesh nodes forming the backbone of the network and providing connection to the Internet and other network. Their rapid deployment and ease of maintenance are suitable for on-demand network such as disaster recovery, homeland security, convention centers, hard-to-wire buildings and unfriendly terrains. One important problem with MANET is the routing protocol that needs to work well not just with a small network, but also sustain efficiency and scalability as the network gets expanded and the application transmits data in greater volume. In such an environment, mobility, channel error, and congestion are the main causes for packet loss. Due to mobility of mobile hosts, addressing frequent and unpredictable topology changes is fundamental to MANET research. Two general approaches have been considered: connection-oriented approach and connectionless-oriented approach. In the former, the emphasis is on how to reconnect quickly with low overhead when a broken link occurs. Examples of this approach includes , , , , , , , , , and . In contrast, connectionless-oriented approach focuses on minimizing the occurrence of broken links. We proposed one such scheme called Connectionless Approach (CLA) and . In CLA, the network area is divided into non-overlapping grid cells, each serving as a virtual router. Any physical router (i.e., mobile host), currently inside a virtual router, can help forward the data packet to the next virtual router along the virtual link. This process is repeated until the packet reaches its final destination. Since a virtual link is based on virtual routers which do not move, it is much more robust than physical links used in the connection-oriented techniques. Simulation results in our previous works and , based on GloMoSim , indicate that CLA performs significantly better than connection-oriented techniques (i.e., AODV, DSR, LAR, GRID, TMNR, and GPSR). The contribution of this work consists of investigating and developing new Connectionless-Oriented Approach for Mobile Ad Hoc Network. Two of the greatest impacts of this research are as follows. First, the new approach is targeted towards robustly support high mobility and large scale environment which has been adapted for vehicle-to-vehicle environment in . Second, the detailed simulations which compare eight representative routing protocols, namely AODV, DSR, LAR, GRID, TMNR, GPSR, CBF, and CLA, under high-mobility environments. As many important emergent applications of the technology involved high-mobility nodes, very little is known about the existing routing methods perform relative to each other in high-mobility environments. The simulation results provide insight into ad hoc routing protocols and offer guidelines for mobile ad hoc network applications. Next, we enhanced and extend the connectionless-oriented approach. The current connectionless-oriented approach, however, may suffer from packet drops since traffic congestion is not considered in the packet forwarding policy. We address this weakness by considering the connectionless-oriented approach with a collision avoidance routing technique. After that, we investigate techniques to enforce collaboration among mobile devices in supporting the virtual router functionality. Many works have been published to combat such problem - misbehaving nodes are detected and a routing algorithm is employed to avoid and penalize misbehaving nodes. These techniques, however, cannot be applied to the connectionless-oriented approach since any node in the general direction towards the destination node can potentially help forward the data packets. To address the security and cooperation issues for connectionless-oriented approach, we introduce a cooperation enforcement technique called 3CE (3-Counter Enforcement). In addition, wireless mesh networks have become increasingly popular in recent years. Wireless mash network (WMNs) are collection of mesh clients and mesh nodes (routers), with mesh nodes forming the backbone of the network and providing connection to the Internet and other network. We propose a paradigm that combines virtual routers and mesh nodes to create a hybrid network call VR-Mesh Network. This hybrid network can reduce number of mesh node needed without decrease the performance of the network.
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Date Issued: 2009
Identifier: CFE0002742, ucf:48146
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002742

Title: SEARCH AND DELIVERY TECHNIQUES IN PEER-TO-PEER NETWORKS.
Creator: Do, Tai, Hua, Kien, University of Central Florida
Abstract / Description: The presence of millions of interconnected personal computing devices has given rise to a new class of decentralized networking applications, which are loosely labeled as peer-to-peer (P2P) applications. These P2P applications leverage resources such as processing cycles, storage, content, and network bandwidth available to the user devices, which are also known as peers. A number of current systems - SETI@home, Napster, BitTorrent, and Pastry - are examples of these emerging P2P systems. To...
Show moreThe presence of millions of interconnected personal computing devices has given rise to a new class of decentralized networking applications, which are loosely labeled as peer-to-peer (P2P) applications. These P2P applications leverage resources such as processing cycles, storage, content, and network bandwidth available to the user devices, which are also known as peers. A number of current systems - SETI@home, Napster, BitTorrent, and Pastry - are examples of these emerging P2P systems. To fully realize the potential of the peer-to-peer technology, there is a need to define and provide a set of core competencies, serving as the basic services upon which various peer-to-peer applications can be built on. Among these core competencies, this dissertation focuses on two fundamental services, which are search and delivery. In the first part of the dissertation, delivery techniques to support video-on-demand services in wireline and wireless P2P networks are investigated. Video services are considered due to two reasons. First, video services are the pivotal basis for many other multimedia applications. Second, it is challenging to provide on-demand video services due to asynchronous playback progresses at peers. The proposed techniques enable efficient video sharing between peers with asynchronous playback progresses, and maximize peer bandwidth utilization. In the second part of the dissertation, the problem of supporting continuous moving range queries in wireless mobile peer-to-peer networks is studied. Continuous moving range queries have a number of applications when a moving object wants to monitor its surrounding environment for a period of time. When a fixed network infrastructure is not available, wireless mobile peer-to-peer networks become a viable option to support the continuous query system. The proposed distributed solution ensures the accuracy of the query results under realistic assumptions, and incurs much less overhead than alternative solutions.
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Date Issued: 2009
Identifier: CFE0002753, ucf:48111
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002753

Title: A ROBUST WIRELESS MESH ACCESS ENVIRONMENT FOR MOBILE VIDEO USERS.
Creator: Xie, Fei, Hua, Kien, University of Central Florida
Abstract / Description: The rapid advances in networking technology have enabled large-scale deployments of online video streaming services in todayÃ‚Â's Internet. In particular, wireless Internet access technology has been one of the most transforming and empowering technologies in recent years. We have witnessed a dramatic increase in the number of mobile users who access online video services through wireless access networks, such as wireless mesh networks and 3G cellular networks. Unlike in...
Show moreThe rapid advances in networking technology have enabled large-scale deployments of online video streaming services in todayÃ‚Â's Internet. In particular, wireless Internet access technology has been one of the most transforming and empowering technologies in recent years. We have witnessed a dramatic increase in the number of mobile users who access online video services through wireless access networks, such as wireless mesh networks and 3G cellular networks. Unlike in wired environment, using a dedicated stream for each video service request is very expensive for wireless networks. This simple strategy also has limited scalability when popular content is demanded by a large number of users. It is desirable to have a robust wireless access environment that can sustain a sudden spurt of interest for certain videos due to, say a current event. Moreover, due to the mobility of the video users, smooth streaming performance during the handoff is a key requirement to the robustness of the wireless access networks for mobile video users.　In this dissertation, the author focuses on the robustness of the wireless mesh access (WMA) environment for mobile video users. Novel video sharing techniques are proposed to reduce the burden of video streaming in different WMA environments. The author proposes a cross-layer framework for scalable Video-on-Demand (VOD) service in multi-hop WiMax mesh networks. The author also studies the optimization problems for video multicast in a general wireless mesh networks. The WMA environment is modeled as a connected graph with a video source in one of the nodes and the video requests randomly generated from other nodes in the graph. The optimal video multicast problem in such environment is formulated as two sub-problems. The proposed solutions of the sub-problems are justified using simulation and numerical study. In the case of online video streaming, online video server does not cooperate with the access networks. In this case, the centralized data sharing technique fails since they assume the cooperation between the video server and the network. To tackle this problem, a novel distributed video sharing technique called Dynamic Stream Merging (DSM) is proposed. DSM improves the robustness of the WMA environment without the cooperation from the online video server. It optimizes the per link sharing performance with small time complexity and message complexity. The performance of DSM has been studied using simulations in Network Simulator 2 (NS2) as well as real experiments in a wireless mesh testbed. The Mobile YouTube website (http://m.youtube.com) is used as the online video website in the experiment. Last but not the least; a cross-layer scheme is proposed to avoid the degradation on the video quality during the handoff in the WMA environment. Novel video quality related triggers and the routing metrics at the mesh routers are utilized in the handoff decision making process. A redirection scheme is also proposed to eliminate packet loss caused by the handoff.
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Date Issued: 2010
Identifier: CFE0003241, ucf:48541
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003241

Title: UTILIZING EDGE IN IOT AND VIDEO STREAMING APPLICATIONS TO REDUCE BOTTLENECKS IN INTERNET TRAFFIC.
Creator: Akpinar, Kutalmis, Hua, Kien, Zou, Changchun, Turgut, Damla, Wang, Jun, University of Central Florida
Abstract / Description: There is a large increase in the surge of data over Internet due to the increasing demand on multimedia content. It is estimated that 80% of Internet traffic will be video by 2022, according to a recent study. At the same time, IoT devices on Internet will double the human population. While infrastructure standards on IoT are still nonexistent, enterprise solutions tend to encourage cloud-based solutions, causing an additional surge of data over the Internet. This study proposes solutions to...
Show moreThere is a large increase in the surge of data over Internet due to the increasing demand on multimedia content. It is estimated that 80% of Internet traffic will be video by 2022, according to a recent study. At the same time, IoT devices on Internet will double the human population. While infrastructure standards on IoT are still nonexistent, enterprise solutions tend to encourage cloud-based solutions, causing an additional surge of data over the Internet. This study proposes solutions to bring video traffic and IoT computation back to the edges of the network, so that costly Internet infrastructure upgrades are not necessary. An efficient way to prevent the Internet surge over the network for IoT is to push the application specific computation to the edge of the network, close to where the data is generated, so that large data can be eliminated before being delivered to the cloud. In this study, an event query language and processing environment is provided to process events from various devices. The query processing environment brings the application developers, sensor infrastructure providers and end users together. It uses boolean events as the streaming and processing units. This addresses the device heterogeneity and pushes the data-intense tasks to the edge of network.The second focus of the study is Video-on-Demand applications. A characteristic of VoD traffic is its high redundancy. Due to the demand on popular content, the same video traffic flows through Internet Service Provider's network as overlapping but separate streams. In previous studies on redundancy elimination, overlapping streams are merged into each other in link-level by receiving the packet only for the first stream, and re-using it for the subsequent duplicated streams. In this study, we significantly improve these techniques by introducing a merger-aware routing method.Our final focus is increasing utilization of Content Delivery Network (CDN) servers on the edge of network to reduce the long-distance traffic. The proposed system uses Software Defined Networks (SDN) to route adaptive video streaming clients to the best available CDN servers in terms of network availability. While performing the network assistance, the system does not reveal the video request information to the network provider, thus enabling privacy protection for encrypted streams. The request routing is performed in segment level for adaptive streaming. This enables to re-route the client to the best available CDN without an interruption if network conditions change during the stream.
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Date Issued: 2019
Identifier: CFE0007882, ucf:52774
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007882

Title: Action Recognition, Temporal Localization and Detection in Trimmed and Untrimmed Video.
Creator: Hou, Rui, Shah, Mubarak, Mahalanobis, Abhijit, Hua, Kien, Sukthankar, Rahul, University of Central Florida
Abstract / Description: Automatic understanding of videos is one of the most active areas of computer vision research. It has applications in video surveillance, human computer interaction, video sports analysis, virtual and augmented reality, video retrieval etc. In this dissertation, we address four important tasks in video understanding, namely action recognition, temporal action localization, spatial-temporal action detection and video object/action segmentation. This dissertation makes contributions to above...
Show moreAutomatic understanding of videos is one of the most active areas of computer vision research. It has applications in video surveillance, human computer interaction, video sports analysis, virtual and augmented reality, video retrieval etc. In this dissertation, we address four important tasks in video understanding, namely action recognition, temporal action localization, spatial-temporal action detection and video object/action segmentation. This dissertation makes contributions to above tasks by proposing. First, for video action recognition, we propose a category level feature learning method. Our proposed method automatically identifies such pairs of categories using a criterion of mutual pairwise proximity in the (kernelized) feature space, and a category-level similarity matrix where each entry corresponds to the one-vs-one SVM margin for pairs of categories. Second, for temporal action localization, we propose to exploit the temporal structure of actions by modeling an action as a sequence of sub-actions and present a computationally efficient approach. Third, we propose 3D Tube Convolutional Neural Network (TCNN) based pipeline for action detection. The proposed architecture is a unified deep network that is able to recognize and localize action based on 3D convolution features. It generalizes the popular faster R-CNN framework from images to videos. Last, an end-to-end encoder-decoder based 3D convolutional neural network pipeline is proposed, which is able to segment out the foreground objects from the background. Moreover, the action label can be obtained as well by passing the foreground object into an action classifier. Extensive experiments on several video datasets demonstrate the superior performance of the proposed approach for video understanding compared to the state-of-the-art.
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Date Issued: 2019
Identifier: CFE0007655, ucf:52502
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007655

Title: Real-Time Open Source Traffic Control Software for the Advance Traffic Controller.
Creator: Key, Justin, Radwan, Ahmed, Hua, Kien, Kincaid, John, Leonessa, Alexander, University of Central Florida
Abstract / Description: Under the initiative of Department of Transportation (DOT) a safety-critical, dual redundant, open source traffic signal control application is currently being developed. The system named SCOPE, for Signal Control Program Environment, currently implements standard 8-phase NEMA logic and the National Cooperative Highway Research Program 3-66 preemption logic. SCOPE is designed to be part of the Advanced Traffic Controller (ATC), making use of API standard 2.06b to integrate with the hardware....
Show moreUnder the initiative of Department of Transportation (DOT) a safety-critical, dual redundant, open source traffic signal control application is currently being developed. The system named SCOPE, for Signal Control Program Environment, currently implements standard 8-phase NEMA logic and the National Cooperative Highway Research Program 3-66 preemption logic. SCOPE is designed to be part of the Advanced Traffic Controller (ATC), making use of API standard 2.06b to integrate with the hardware. Safety-critical status is achieved through redundancy of application logic that constantly compares expected signal phase information. From baseline requirements, engineers independently program application code, one using Ada95 and the other using C++.The Traffic EXperimental Analytical Simulation Model, a microscopic single-intersection vehicular simulation, was used for initial validation and testing of the functionality of the system. The second demonstration of the SCOPE, used actuated detector data collected from a recording of a live intersection. Actuator calls were placed on SCOPE at the same times the vehicles triggered the detectors in the video (assuming the vehicles were not in-queue). Using SCOPE the real-world traffic was not only right-of-way safely yielded, but the traffic flow state time average time in-queue reduced. The final phase of testing will occur when the DOT performs Formal Qualification Testing, which is scheduled for 2013.Upon validation and subsequent release to the open source community SCOPE will provide users the ability to replace the proprietary application software residing in ATC cabinets. Transparency will be provided into another aspect of the traffic control signal thus taking the initiative of ATC one step further.
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Date Issued: 2012
Identifier: CFE0004562, ucf:49254
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004562

Title: Harmony Oriented Architecture.
Creator: Martin, Kyle, Hua, Kien, Wu, Annie, Heinrich, Mark, University of Central Florida
Abstract / Description: This thesis presents Harmony Oriented Architecture: a novel architectural paradigm that applies the principles of Harmony Oriented Programming to the architecture of scalable and evolvable distributed systems. It is motivated by research on Ultra Large Scale systems that has revealed inherent limitations in human ability to design large-scale software systems that can only be overcome through radical alternatives to traditional object-oriented software engineering practice that simplifies the...
Show moreThis thesis presents Harmony Oriented Architecture: a novel architectural paradigm that applies the principles of Harmony Oriented Programming to the architecture of scalable and evolvable distributed systems. It is motivated by research on Ultra Large Scale systems that has revealed inherent limitations in human ability to design large-scale software systems that can only be overcome through radical alternatives to traditional object-oriented software engineering practice that simplifies the construction of highly scalable and evolvable system.HOP eschews encapsulation and information hiding, the core principles of object- oriented design, in favor of exposure and information sharing through a spatial abstraction. This helps to avoid the brittle interface dependencies that impede the evolution of object-oriented software. HOA extends these concepts to distributed systems resulting in an architecture in which application components are represented by objects in a spatial database and executed in strict isolation using an embedded application server. Application components store their state entirely in the database and interact solely by diffusing data into a space for proximate components to observe. This architecture provides a high degree of decoupling, isolation, and state exposure allowing highly scalable and evolvable applications to be built.A proof-of-concept prototype of a non-distributed HOA middleware platform supporting JavaScript application components is implemented and evaluated. Results show remarkably good performance considering that little effort was made to optimize the implementation.
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Date Issued: 2011
Identifier: CFE0004480, ucf:49298
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004480

Title: Computational Methods for Comparative Non-coding RNA Analysis: From Structural Motif Identification to Genome-wide Functional Classification.
Creator: Zhong, Cuncong, Zhang, Shaojie, Hu, Haiyan, Hua, Kien, Li, Xiaoman, University of Central Florida
Abstract / Description: Non-coding RNA (ncRNA) plays critical functional roles such as regulation, catalysis, and modification etc. in the biological system. Non-coding RNAs exert their functions based on their specific structures, which makes the thorough understanding of their structures a key step towards their complete functional annotation. In this dissertation, we will cover a suite of computational methods for the comparison of ncRNA secondary and 3D structures, and their applications to ncRNA molecular...
Show moreNon-coding RNA (ncRNA) plays critical functional roles such as regulation, catalysis, and modification etc. in the biological system. Non-coding RNAs exert their functions based on their specific structures, which makes the thorough understanding of their structures a key step towards their complete functional annotation. In this dissertation, we will cover a suite of computational methods for the comparison of ncRNA secondary and 3D structures, and their applications to ncRNA molecular structural annotation and their genome-wide functional survey.Specifically, we have contributed the following five computational methods. First, we have developed an alignment algorithm to compare RNA structural motifs, which are recurrent RNA 3D structural fragments. Second, we have improved upon the previous alignment algorithm by incorporating base-stacking information and devise a new branch-and-bond algorithm. Third, we have developed a clustering pipeline for RNA structural motif classification using the above alignment methods. Fourth, we have generalized the clustering pipeline to a genome-wide analysis of RNA secondary structures. Finally, we have devised an ultra-fast alignment algorithm for RNA secondary structure by using the sparse dynamic programming technique.A large number of novel RNA structural motif instances and ncRNA elements have been discovered throughout these studies. We anticipate that these computational methods will significantly facilitate the analysis of ncRNA structures in the future.
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Date Issued: 2013
Identifier: CFE0004966, ucf:49580
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004966

Title: SPS: an SMS-based Push Service for Energy Saving in Smartphone's Idle State.
Creator: Dondyk, Erich, Zou, Changchun, Chatterjee, Mainak, Hua, Kien, University of Central Florida
Abstract / Description: Despite of all the advances in smartphone technology in recent years, smartphones still remain limited by their battery life. Unlike other power hungry components in the smartphone, the cellular data and Wi-Fi interfaces often continue to be used even while the phone is in the idle state to accommodate unnecessary data traffic produced by some applications. In addition, bad reception has been proven to greatly increase energy consumed by the radio, which happens quite often when smartphone...
Show moreDespite of all the advances in smartphone technology in recent years, smartphones still remain limited by their battery life. Unlike other power hungry components in the smartphone, the cellular data and Wi-Fi interfaces often continue to be used even while the phone is in the idle state to accommodate unnecessary data traffic produced by some applications. In addition, bad reception has been proven to greatly increase energy consumed by the radio, which happens quite often when smartphone users are inside buildings. In this paper, we present a Short message service Push based Service (SPS) to save unnecessary power consumption when smartphones are in idle state, especially in bad reception areas. First, SPS disables a smartphone's data interfaces whenever the phone is in idle state. Second, to preserve the real-time notification functionality required by some apps, such as new email arrivals and social media updates, when a notification is needed, a wakeup text message will be received by the phone, and then SPS enables the phone's data interfaces to connect to the corresponding server to retrieve notification data via the normal data network. Once the notification data has been retrieved, SPS will disable the data interfaces again if the phone is still in idle state. We have developed a complete prototype for Android smartphones. Our experiments show that SPS consumes less energy than the current approach. In areas with bad reception, the SPS prototype can double the battery life of a smartphone.
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Date Issued: 2014
Identifier: CFE0005157, ucf:50718
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005157

Internet (2)	+ -
Mobile Ad Hoc Network (2)	+ -
3D Spatial Relationship (1)	+ -
ATC (1)	+ -
Academic (1)	+ -

Academic -- Engineering (1)	+ -
Adaptive Video Streaming (1)	+ -
Collaboration Enforcement (1)	+ -
Communication (1)	+ -
Communication Connection (1)	+ -
Complex Event Processing (1)	+ -
Computer Vision (1)	+ -
Computerised (1)	+ -
Connectionless Approach (1)	+ -
Consensus Folding (1)	+ -
Content Delivery Networks (1)	+ -
Cooperation Enforcement (1)	+ -
Data quality (1)	+ -
Database management (1)	+ -
Directional search (1)	+ -
Dissertations (1)	+ -
Dynamic Rerouting (1)	+ -
Energy saving (1)	+ -
Engineering -- Dissertations (1)	+ -
Evaluation (1)	+ -
Finite State Model (1)	+ -
First-Take-All (1)	+ -
Human Action Recognition (1)	+ -
Human Activity Recognition (1)	+ -
Image Processing (1)	+ -

University of Central Florida (27)	+ -
Hua, Kien (23)	+ -
Zou, Changchun (5)	+ -
Foroosh, Hassan (4)	+ -
Hua, Kien A. (4)	+ -

Hu, Haiyan (3)	+ -
Hughes, Charles (2)	+ -
Li, Xiaoman (2)	+ -
Wang, Chung-Ching (2)	+ -
Zhang, Shaojie (2)	+ -
Akpinar, Kutalmis (1)	+ -
Aved, Alexander (1)	+ -
Chatterjee, Mainak (1)	+ -
Cheng, Hao (1)	+ -
Do, Tai (1)	+ -
Dondyk, Erich (1)	+ -
Dutton, Ronald (1)	+ -
Engineering and Computer Science (1)	+ -
Guha, Ratan (1)	+ -
Hamza-Lup, Georgiana (1)	+ -
Heinrich, Mark (1)	+ -
Hiransakolwong, Nualsawat (1)	+ -
Ho, Ai (1)	+ -
Ho, Yao (1)	+ -
Hou, Rui (1)	+ -
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