View All Items
(1 - 15 of 15)
- Title
- Nutritionally Focused Drive-Thru Menus and the Impact on Consumer Preferences: A Study of the Restaurant Industry.
- Creator
-
Davis, Meschelle Davis, Parsa, Haragopal, Severt, Denver, Singh, Dipendra, University of Central Florida
- Abstract / Description
-
More than one-third of the U.S. citizens (over 70 million people) and 16% of children are classified as obese and are at risk of many diseases including heart disease. Research indicates that 65% of Americans over the age of twenty years old are considered overweight. To address this public health issue, the U.S. Food (&) Drug Administration has proposed new nutritional guidelines for restaurant menus. Thus, the current study investigated the preferences of quick service restaurant (QSR)...
Show moreMore than one-third of the U.S. citizens (over 70 million people) and 16% of children are classified as obese and are at risk of many diseases including heart disease. Research indicates that 65% of Americans over the age of twenty years old are considered overweight. To address this public health issue, the U.S. Food (&) Drug Administration has proposed new nutritional guidelines for restaurant menus. Thus, the current study investigated the preferences of quick service restaurant (QSR) industry consumers with reference to the newly proposed U.S. Food and Drug Administration regulations. This study includes development and redesigning of drive thru menus to comply with the FDA guidelines. A 3x2 factorial design experiment was conducted using real drive thru menus from three major national restaurant chains. The control group consisted of normal drive thru menus obtained from national restaurant chains, and the experimental group was comprised of two sets of pre-tested experimental menus complying with the FDA guidelines. The first set of experimental menus includes presentation of calorie information for all menu items offered. The second set of experimental menus includes color coded calorie specific menu categories (low, regular and high). A set of research hypotheses were developed and data was collected from heavy users of QSR units using Qualtrics software. The collected data were analyzed using SPSS. The obtained results indicated that the QSR menus designed to comply with the FDA's guidelines do not result in loss of revenues as commonly feared by the restaurant industry. But interestingly the second set of experiment menus with color coded nutritional categories (low, regular, high) have led to increased consumer patronage and consumers' willingness to pay. In addition, color coded nutritional menus were preferred over FDA suggested menus designs. The results from the current study are of significant importance to the QSR industry as they strive to comply with the new nutrition guidelines of FDA for drive thru menus.
Show less - Date Issued
- 2012
- Identifier
- CFE0004367, ucf:49441
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004367
- Title
- VIEWS OF REALITY: PERCEPTIONS OF POLICE RESPONSES TO MENTALLY ILL PEOPLE.
- Creator
-
Gonzalez Cruz, Kiara L, Huff-Corzine, Lin, Reckdenwald, Amy, University of Central Florida
- Abstract / Description
-
Society's views about mental illness can influence their views regarding police-response strategies used with the mentally ill. The purpose of this study is to analyze the question: does mental illness impact perceptions of delinquent behavior and police responses? It is important to understand the effects of these interactions to better assist those affected by mental illness and avoid uncertain risks/injuries to the police and citizens involved in an incident. Labeling theory suggests that...
Show moreSociety's views about mental illness can influence their views regarding police-response strategies used with the mentally ill. The purpose of this study is to analyze the question: does mental illness impact perceptions of delinquent behavior and police responses? It is important to understand the effects of these interactions to better assist those affected by mental illness and avoid uncertain risks/injuries to the police and citizens involved in an incident. Labeling theory suggests that people may come to identify and act in ways that reflect how others label them as well as come to define mentally ill individuals in accordance with the label. My interest in understanding how police label mentally ill individuals as either deviant (out-of-the-norm) or criminal because of their condition motivated me to explore what other people thought about this. This study used survey analysis to collect data from 349 Facebook participants. Participants were randomly assigned to 1 of 2 scenarios (excerpt A and excerpt B). The only difference between these two scenarios is that excerpt B directly relates to mental illness while excerpt A does not mention mental illness. In relation to labeling theory, I predict mental illness will impact the perception people have about how police may respond to situations involving the mentally ill. Further studies should expand this research to examine this connection more thoroughly. The broader implications of this research is that it could create awareness as to ways in which to improve police training tactics that could in turn result in better support between mental health services and law enforcement.
Show less - Date Issued
- 2017
- Identifier
- CFH2000180, ucf:45958
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFH2000180
- Title
- AMERICAN AGRIBUSINESS AND BIOTECHNOLOGY: A NEW ERA OF FARMING.
- Creator
-
Ryan, Nicole M, Sadri, Houman, University of Central Florida
- Abstract / Description
-
In the past fifty years there has been an incredible amount of change made to the agrarian system of the United States. New discoveries in the realm of biotechnology led to the adoption of genetically modified organisms (GMOs) in agriculture, and transformed the industry. Due to regulatory policies set during the nineteen-eighties this technology was able to benefit from widespread commercialization. Today, we see the effects of this approach and are entering into a highly volatile political...
Show moreIn the past fifty years there has been an incredible amount of change made to the agrarian system of the United States. New discoveries in the realm of biotechnology led to the adoption of genetically modified organisms (GMOs) in agriculture, and transformed the industry. Due to regulatory policies set during the nineteen-eighties this technology was able to benefit from widespread commercialization. Today, we see the effects of this approach and are entering into a highly volatile political climate in regard to GMOs. This paper aims to provide an analysis of the regulatory system in place and the discrepancies that exist in US policy. The factors evaluated through this thesis include the current US regulatory approach, advancements in biotechnology, and a comparative perspective on US and EU systems. In each of these reviews it is also relevant to mention consumer opinion on GMOs and the role of interest groups. It is important for every American consumer to understand the politics and technology behind their meals. Through the analysis of recent judicial decisions and the enactment of new laws this thesis explains how the use of GMOs in agriculture is causing an unprecedented change to the political structures in place.
Show less - Date Issued
- 2016
- Identifier
- CFH2000035, ucf:45586
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFH2000035
- Title
- ATTACKS ON DIFFICULT INSTANCES OF GRAPH ISOMORPHISM: SEQUENTIAL AND PARALLEL ALGORITHMS.
- Creator
-
Tener, Greg, Deo, Narsingh, University of Central Florida
- Abstract / Description
-
The graph isomorphism problem has received a great deal of attention on both theoretical and practical fronts. However, a polynomial algorithm for the problem has yet to be found. Even so, the best of the existing algorithms perform well in practice; so well that it is challenging to find hard instances for them. The most efficient algorithms, for determining if a pair of graphs are isomorphic, are based on the individualization-refinement paradigm, pioneered by Brendan McKay in 1981 with his...
Show moreThe graph isomorphism problem has received a great deal of attention on both theoretical and practical fronts. However, a polynomial algorithm for the problem has yet to be found. Even so, the best of the existing algorithms perform well in practice; so well that it is challenging to find hard instances for them. The most efficient algorithms, for determining if a pair of graphs are isomorphic, are based on the individualization-refinement paradigm, pioneered by Brendan McKay in 1981 with his algorithm nauty. Nauty and various improved descendants of nauty, such as bliss and saucy, solve the graph isomorphism problem by determining a canonical representative for each of the graphs. The graphs are isomorphic if and only if their canonical representatives are identical. These algorithms also detect the symmetries in a graph which are used to speed up the search for the canonical representative--an approach that performs well in practice. Yet, several families of graphs have been shown to exist which are hard for nauty-like algorithms. This dissertation investigates why these graph families pose difficulty for individualization-refinement algorithms and proposes several techniques for circumventing these limitations. The first technique we propose addresses a fundamental problem pointed out by Miyazaki in 1993. He constructed a family of colored graphs which require exponential time for nauty (and nauty's improved descendants). We analyze Miyazaki's construction to determine the source of difficulty and identify a solution. We modify the base individualization-refinement algorithm by exploiting the symmetries discovered in a graph to guide the search for its canonical representative. This is accomplished with the help of a novel data structure called a guide tree. As a consequence, colored Miyazaki graphs are processed in polynomial time--thus obviating the only known exponential upper-bound on individualization-refinement algorithms (which has stood for the last 16 years). The preceding technique can only help if a graph has enough symmetry to exploit. It cannot be used for another family of hard graphs that have a high degree of regularity, but possess few actual symmetries. To handle these instances, we introduce an adaptive refinement method which utilizes the guide-tree data structure of the preceding technique to use a stronger vertex-invariant, but only when needed. We show that adaptive refinement is very effective, and it can result in dramatic speedups. We then present a third technique ideally suited for large graphs with a preponderance of sparse symmetries. A method was devised by Darga et al. for dealing with these large and highly symmetric graphs, which can reduce runtime by an order of magnitude. We explain the method and show how to incorporate it into our algorithm. Finally, we develop and implement a parallel algorithm for detecting the symmetries in, and finding a canonical representative of a graph. Our novel parallel algorithm divides the search for the symmetries and canonical representative among each processor, allowing for a high degree of scalability. The parallel algorithm is benchmarked on the hardest problem instances, and shown to be effective in subdividing the search space.
Show less - Date Issued
- 2009
- Identifier
- CFE0002894, ucf:48018
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0002894
- Title
- Acute Effects of Placebo and Open-Label Placebo Treatments on Muscle Strength, Voluntary Activation, and Neuromuscular Fatigue.
- Creator
-
Swafford, Alina, Stout, Jeffrey, Fukuda, David, University of Central Florida
- Abstract / Description
-
Placebo treatments have long been used to study the psychological effects of expectancy and conditioning on an inert intervention. Interestingly, open-label placebo treatments (i.e., directly telling subjects they are receiving an inactive intervention) have recently shown promise in minimizing pain in clinical patient populations. We utilized a repeated measures design to examine the acute effects of placebo, open-label placebo, and control treatments on muscle strength and voluntary...
Show morePlacebo treatments have long been used to study the psychological effects of expectancy and conditioning on an inert intervention. Interestingly, open-label placebo treatments (i.e., directly telling subjects they are receiving an inactive intervention) have recently shown promise in minimizing pain in clinical patient populations. We utilized a repeated measures design to examine the acute effects of placebo, open-label placebo, and control treatments on muscle strength and voluntary activation (Experiment #1), as well as neuromuscular fatigue (Experiment #2). Twenty-one untrained males (n=11) and females (n=10) visited the laboratory on three occasions to receive each treatment in a randomized, counter-balanced manner. All visits involved a pretest, 15-minute intervention period, and posttest. In Experiment #1, knee extensor maximal voluntary isometric contraction (MVIC) peak torque and percent voluntary activation were evaluated. In Experiment #2, subjects performed 20, six-second MVICs while surface electromyographic signals were detected from the vastus lateralis. Subjective assessments of energy and perceived exertion were also examined. In Experiment #1, no differences among interventions were demonstrated for peak torque or voluntary activation, but a main effect revealed that energy levels increased following each treatment (p = .016, ?2 = .257). Experiment #2 demonstrated that placebo and open-label placebo treatments had no influence on neuromuscular fatigue, but there were main effects for declines in absolute (p = .001, ?2 = .675) and normalized peak torque (p = .001, ?2 = .765), electromyographic mean frequency (p = .001, ?2 = .565), neuromuscular efficiency (p = .001, ?2 = .585), and energy levels (p = .006, ?2 = .317). Collectively, placebo and open-label placebo treatments had minimal influence on strength, voluntary activation, and fatigue resistance in untrained subjects. We speculate that our subject population and study design intricacies that are unique to placebo trials may explain our findings.
Show less - Date Issued
- 2018
- Identifier
- CFE0007254, ucf:52204
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007254
- Title
- Weakly Labeled Action Recognition and Detection.
- Creator
-
Sultani, Waqas, Shah, Mubarak, Bagci, Ulas, Qi, GuoJun, Yun, Hae-Bum, University of Central Florida
- Abstract / Description
-
Research in human action recognition strives to develop increasingly generalized methods thatare robust to intra-class variability and inter-class ambiguity. Recent years have seen tremendousstrides in improving recognition accuracy on ever larger and complex benchmark datasets, comprisingrealistic actions (")in the wild(") videos. Unfortunately, the all-encompassing, dense, globalrepresentations that bring about such improvements often benefit from the inherent characteristics,specific to...
Show moreResearch in human action recognition strives to develop increasingly generalized methods thatare robust to intra-class variability and inter-class ambiguity. Recent years have seen tremendousstrides in improving recognition accuracy on ever larger and complex benchmark datasets, comprisingrealistic actions (")in the wild(") videos. Unfortunately, the all-encompassing, dense, globalrepresentations that bring about such improvements often benefit from the inherent characteristics,specific to datasets and classes, that do not necessarily reflect knowledge about the entity to berecognized. This results in specific models that perform well within datasets but generalize poorly.Furthermore, training of supervised action recognition and detection methods need several precisespatio-temporal manual annotations to achieve good recognition and detection accuracy. For instance,current deep learning architectures require millions of accurately annotated videos to learnrobust action classifiers. However, these annotations are quite difficult to achieve.In the first part of this dissertation, we explore the reasons for poor classifier performance whentested on novel datasets, and quantify the effect of scene backgrounds on action representationsand recognition. We attempt to address the problem of recognizing human actions while trainingand testing on distinct datasets when test videos are neither labeled nor available during training. Inthis scenario, learning of a joint vocabulary, or domain transfer techniques are not applicable. Weperform different types of partitioning of the GIST feature space for several datasets and computemeasures of background scene complexity, as well as, for the extent to which scenes are helpfulin action classification. We then propose a new process to obtain a measure of confidence in eachpixel of the video being a foreground region using motion, appearance, and saliency together in a3D-Markov Random Field (MRF) based framework. We also propose multiple ways to exploit theforeground confidence: to improve bag-of-words vocabulary, histogram representation of a video,and a novel histogram decomposition based representation and kernel.iiiThe above-mentioned work provides probability of each pixel being belonging to the actor, however,it does not give the precise spatio-temporal location of the actor. Furthermore, above frameworkwould require precise spatio-temporal manual annotations to train an action detector. However,manual annotations in videos are laborious, require several annotators and contain humanbiases. Therefore, in the second part of this dissertation, we propose a weakly labeled approachto automatically obtain spatio-temporal annotations of actors in action videos. We first obtain alarge number of action proposals in each video. To capture a few most representative action proposalsin each video and evade processing thousands of them, we rank them using optical flow andsaliency in a 3D-MRF based framework and select a few proposals using MAP based proposal subsetselection method. We demonstrate that this ranking preserves the high-quality action proposals.Several such proposals are generated for each video of the same action. Our next challenge is toiteratively select one proposal from each video so that all proposals are globally consistent. Weformulate this as Generalized Maximum Clique Graph problem (GMCP) using shape, global andfine-grained similarity of proposals across the videos. The output of our method is the most actionrepresentative proposals from each video. Using our method can also annotate multiple instancesof the same action in a video can also be annotated. Moreover, action detection experiments usingannotations obtained by our method and several baselines demonstrate the superiority of ourapproach.The above-mentioned annotation method uses multiple videos of the same action. Therefore, inthe third part of this dissertation, we tackle the problem of spatio-temporal action localization in avideo, without assuming the availability of multiple videos or any prior annotations. The action islocalized by employing images downloaded from the Internet using action label. Given web images,we first dampen image noise using random walk and evade distracting backgrounds withinimages using image action proposals. Then, given a video, we generate multiple spatio-temporalaction proposals. We suppress camera and background generated proposals by exploiting opticalivflow gradients within proposals. To obtain the most action representative proposals, we propose toreconstruct action proposals in the video by leveraging the action proposals in images. Moreover,we preserve the temporal smoothness of the video and reconstruct all proposal bounding boxesjointly using the constraints that push the coefficients for each bounding box toward a commonconsensus, thus enforcing the coefficient similarity across multiple frames. We solve this optimizationproblem using the variant of two-metric projection algorithm. Finally, the video proposalthat has the lowest reconstruction cost and is motion salient is used to localize the action. Ourmethod is not only applicable to the trimmed videos, but it can also be used for action localizationin untrimmed videos, which is a very challenging problem.Finally, in the third part of this dissertation, we propose a novel approach to generate a few properlyranked action proposals from a large number of noisy proposals. The proposed approach beginswith dividing each proposal into sub-proposals. We assume that the quality of proposal remainsthe same within each sub-proposal. We, then employ a graph optimization method to recombinethe sub-proposals in all action proposals in a single video in order to optimally build new actionproposals and rank them by the combined node and edge scores. For an untrimmed video, we firstdivide the video into shots and then make the above-mentioned graph within each shot. Our methodgenerates a few ranked proposals that can be better than all the existing underlying proposals. Ourexperimental results validated that the properly ranked action proposals can significantly boostaction detection results.Our extensive experimental results on different challenging and realistic action datasets, comparisonswith several competitive baselines and detailed analysis of each step of proposed methodsvalidate the proposed ideas and frameworks.
Show less - Date Issued
- 2017
- Identifier
- CFE0006801, ucf:51809
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006801
- Title
- PERCEPTION OF FACIAL EXPRESSIONS IN SOCIAL ANXIETY AND GAZE ANXIETY.
- Creator
-
Necaise, Aaron, Neer, Sandra, University of Central Florida
- Abstract / Description
-
This study explored the relationship between gaze anxiety and the perception of facial expressions. The literature suggests that individuals experiencing Social Anxiety Disorder (SAD) might have a fear of making direct eye contact, and that these individuals also demonstrate a hypervigilance towards the eye region. It was thought that this increased anxiety concerning eye contact might be related to the tendency of socially anxious individuals to mislabel emotion in the faces of onlookers. A...
Show moreThis study explored the relationship between gaze anxiety and the perception of facial expressions. The literature suggests that individuals experiencing Social Anxiety Disorder (SAD) might have a fear of making direct eye contact, and that these individuals also demonstrate a hypervigilance towards the eye region. It was thought that this increased anxiety concerning eye contact might be related to the tendency of socially anxious individuals to mislabel emotion in the faces of onlookers. A better understanding of the cognitive biases common to SAD could lead to more efficient intervention and assessment methods. In the present study, the Depression Anxiety Stress Scale-21 (DASS-21) and the Social Phobia and Anxiety Inventory-23 (SPAI-23) were used to measure social anxiety, depression, and overall distress. These forms allowed us to separate participants who reported high socially anxious and depressive traits from those in the normal range. We then compared anxiety concerning mutual eye contact as measured by the Gaze Anxiety Rating Scale (GARS) to performance on a facial recognition task. Performance was measured as recognition accuracy and average perceived intensity of onlooker expression on a scale of 1-5. A linear regression analysis revealed that higher GARS scores were related to higher perceived intensity of emotion by socially anxious individuals. An exploratory correlation analysis also revealed that higher gaze anxiety was related to lower accuracy at identifying neutral emotions and higher accuracy at identifying angry emotions. While past research has demonstrated these same biases by socially anxious individuals, gaze anxiety had not been explored extensively. Future research should investigate gaze anxiety�s role as a moderating variable.
Show less - Date Issued
- 2016
- Identifier
- CFH2000039, ucf:45554
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFH2000039
- Title
- PREVENTING CHILDHOOD OBESITY IN SCHOOL-AGED CHILDREN: RELATIONSHIPS BETWEEN READING NUTRITION LABELS AND HEALTHY DIETARY BEHAVIORS.
- Creator
-
Bogers, Kimberly S, Quelly, Susan, University of Central Florida
- Abstract / Description
-
Childhood obesity is a prevalent problem in the United States. Obesity increases the risk for many diseases. Obese children are likely to become obese adults with additional comorbidities. Studies have reported mixed findings regarding associations between reading nutrition labels and improved dietary behaviors/healthy weight status. The purpose of this study is to determine whether the frequency of children reading nutrition labels is related to frequency of performing 12 dietary behaviors....
Show moreChildhood obesity is a prevalent problem in the United States. Obesity increases the risk for many diseases. Obese children are likely to become obese adults with additional comorbidities. Studies have reported mixed findings regarding associations between reading nutrition labels and improved dietary behaviors/healthy weight status. The purpose of this study is to determine whether the frequency of children reading nutrition labels is related to frequency of performing 12 dietary behaviors. De-identified baseline data from a previous quasiexperimental pilot study were analyzed. Data were collected from 4th and 5th graders (n = 42) at an after-school program. An adapted paper survey was administered to the children to measure the number of days (0�7) they read nutrition labels and performed 12 dietary behaviors over the preceding week. Due to non-normal distribution of data, non-parametric Spearman rho correlations were conducted to determine relationships between frequency of reading nutrition labels and dietary behaviors. Positive correlations were found between frequency of reading nutrition labels and eating fruit for breakfast; eating vegetables at lunch/dinner; eating whole grain/multigrain bread (p less than .05); eating fruit for a snack; eating vegetables for a snack (p less than .01). Frequency of reading nutrition labels was inversely related to drinking soda/sugar-sweetened beverages (p less than .05). Significant relationships were found between frequency of reading nutrition labels and several dietary behaviors associated with childhood obesity prevention. Findings are promising and support the need for further intervention research to determine potential direct influences of children reading nutrition labels on dietary behaviors.
Show less - Date Issued
- 2018
- Identifier
- CFH2000281, ucf:45722
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFH2000281
- Title
- LABELED SAMPLING CONSENSUS: A NOVEL ALGORITHM FOR ROBUSTLY FITTING MULTIPLE STRUCTURES USING COMPRESSED SAMPLING.
- Creator
-
Messina, Carl, Foroosh, Hassan, University of Central Florida
- Abstract / Description
-
The ability to robustly fit structures in datasets that contain outliers is a very important task in Image Processing, Pattern Recognition and Computer Vision. Random Sampling Consensus or RANSAC is a very popular method for this task, due to its ability to handle over 50% outliers. The problem with RANSAC is that it is only capable of finding a single structure. Therefore, if a dataset contains multiple structures, they must be found sequentially by finding the best fit, removing the points,...
Show moreThe ability to robustly fit structures in datasets that contain outliers is a very important task in Image Processing, Pattern Recognition and Computer Vision. Random Sampling Consensus or RANSAC is a very popular method for this task, due to its ability to handle over 50% outliers. The problem with RANSAC is that it is only capable of finding a single structure. Therefore, if a dataset contains multiple structures, they must be found sequentially by finding the best fit, removing the points, and repeating the process. However, removing incorrect points from the dataset could prove disastrous. This thesis offers a novel approach to sampling consensus that extends its ability to discover multiple structures in a single iteration through the dataset. The process introduced is an unsupervised method, requiring no previous knowledge to the distribution of the input data. It uniquely assigns labels to different instances of similar structures. The algorithm is thus called Labeled Sampling Consensus or L-SAC. These unique instances will tend to cluster around one another allowing the individual structures to be extracted using simple clustering techniques. Since divisions instead of modes are analyzed, only a single instance of a structure need be recovered. This ability of L-SAC allows a novel sampling procedure to be presented "compressing" the required samples needed compared to traditional sampling schemes while ensuring all structures have been found. L-SAC is a flexible framework that can be applied to many problem domains.
Show less - Date Issued
- 2011
- Identifier
- CFE0003893, ucf:48727
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0003893
- Title
- Improving Efficiency in Deep Learning for Large Scale Visual Recognition.
- Creator
-
Liu, Baoyuan, Foroosh, Hassan, Qi, GuoJun, Welch, Gregory, Sukthankar, Rahul, Pensky, Marianna, University of Central Florida
- Abstract / Description
-
The emerging recent large scale visual recognition methods, and in particular the deep Convolutional Neural Networks (CNN), are promising to revolutionize many computer vision based artificial intelligent applications, such as autonomous driving and online image retrieval systems. One of the main challenges in large scale visual recognition is the complexity of the corresponding algorithms. This is further exacerbated by the fact that in most real-world scenarios they need to run in real time...
Show moreThe emerging recent large scale visual recognition methods, and in particular the deep Convolutional Neural Networks (CNN), are promising to revolutionize many computer vision based artificial intelligent applications, such as autonomous driving and online image retrieval systems. One of the main challenges in large scale visual recognition is the complexity of the corresponding algorithms. This is further exacerbated by the fact that in most real-world scenarios they need to run in real time and on platforms that have limited computational resources. This dissertation focuses on improving the efficiency of such large scale visual recognition algorithms from several perspectives. First, to reduce the complexity of large scale classification to sub-linear with the number of classes, a probabilistic label tree framework is proposed. A test sample is classified by traversing the label tree from the root node. Each node in the tree is associated with a probabilistic estimation of all the labels. The tree is learned recursively with iterative maximum likelihood optimization. Comparing to the hard label partition proposed previously, the probabilistic framework performs classification more accurately with similar efficiency. Second, we explore the redundancy of parameters in Convolutional Neural Networks (CNN) and employ sparse decomposition to significantly reduce both the amount of parameters and computational complexity. Both inter-channel and inner-channel redundancy is exploit to achieve more than 90\% sparsity with approximately 1\% drop of classification accuracy. We also propose a CPU based efficient sparse matrix multiplication algorithm to reduce the actual running time of CNN models with sparse convolutional kernels. Third, we propose a multi-stage framework based on CNN to achieve better efficiency than a single traditional CNN model. With a combination of cascade model and the label tree framework, the proposed method divides the input images in both the image space and the label space, and processes each image with CNN models that are most suitable and efficient. The average complexity of the framework is significantly reduced, while the overall accuracy remains the same as in the single complex model.
Show less - Date Issued
- 2016
- Identifier
- CFE0006472, ucf:51436
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006472
- Title
- Reliable Spectrum Hole Detection in Spectrum-Heterogeneous Mobile Cognitive Radio Networks via Sequential Bayesian Non-parametric Clustering.
- Creator
-
Zaeemzadeh, Alireza, Rahnavard, Nazanin, Vosoughi, Azadeh, Qi, GuoJun, University of Central Florida
- Abstract / Description
-
In this work, the problem of detecting radio spectrum opportunities in spectrum-heterogeneous cognitive radio networks is addressed. Spectrum opportunities are the frequency channels that are underutilized by the primary licensed users. Thus, by enabling the unlicensed users to detect and utilize them, we can improve the efficiency, reliability, and the flexibility of the radio spectrum usage. The main objective of this work is to discover the spectrum opportunities in time, space, and...
Show moreIn this work, the problem of detecting radio spectrum opportunities in spectrum-heterogeneous cognitive radio networks is addressed. Spectrum opportunities are the frequency channels that are underutilized by the primary licensed users. Thus, by enabling the unlicensed users to detect and utilize them, we can improve the efficiency, reliability, and the flexibility of the radio spectrum usage. The main objective of this work is to discover the spectrum opportunities in time, space, and frequency domains, by proposing a low-cost and practical framework. Spectrum-heterogeneous networks are the networks in which different sensors experience different spectrum opportunities. Thus, the sensing data from sensors cannot be combined to reach consensus and to detect the spectrum opportunities. Moreover, unreliable data, caused by noise or malicious attacks, will deteriorate the performance of the decision-making process. The problem becomes even more challenging when the locations of the sensors are unknown. In this work, a probabilistic model is proposed to cluster the sensors based on their readings, not requiring any knowledge of location of the sensors. The complexity of the model, which is the number of clusters, is automatically inferred from the sensing data. The processing node, also referred to as the base station or the fusion center, infers the probability distributions of cluster memberships, channel availabilities, and devices' reliability in an online manner. After receiving each chunk of sensing data, the probability distributions are updated, without requiring to repeat the computations on previous sensing data. All the update rules are derived mathematically, by employing Bayesian data analysis techniques and variational inference.Furthermore, the inferred probability distributions are employed to assign unique spectrum opportunities to each of the sensors. To avoid interference among the sensors, physically adjacent devices should not utilize the same channels. However, since the location of the devices is not known, cluster membership information is used as a measure of adjacency. This is based on the assumption that the measurements of the devices are spatially correlated. Thus, adjacent devices, which experience similar spectrum opportunities, belong to the same cluster. Then, the problem is mapped into a energy minimization problem and solved via graph cuts. The goal of the proposed graph-theory-based method is to assign each device an available channel, while avoiding interference among neighboring devices. The numerical simulations illustrates the effectiveness of the proposed methods, compared to the existing frameworks.
Show less - Date Issued
- 2017
- Identifier
- CFE0006963, ucf:51639
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006963
- Title
- MAMMY.
- Date Created
- 1930s
- Identifier
- DP0015356
- Format
- Image (JPEG)
- PURL
- http://purl.flvc.org/ucf/fd/DP0015356
- Title
- Aunty.
- Date Created
- 1940s
- Identifier
- DP0015368
- Format
- Image (JPEG)
- PURL
- http://purl.flvc.org/ucf/fd/DP0015368
- Title
- Snow Ball.
- Date Created
- 1930s
- Identifier
- DP0015357
- Format
- Image (JPEG)
- PURL
- http://purl.flvc.org/ucf/fd/DP0015357
- Title
- Dixieland brand christian and cockrill.
- Date Created
- 1940s
- Identifier
- DP0015353
- Format
- Image (JPEG)
- PURL
- http://purl.flvc.org/ucf/fd/DP0015353
