http://idea.library.drexel.edu/handle/1860/721 http://idea.library.drexel.edu/retrieve/15311 http://idea.library.drexel.edu/handle/1860/721 Search the Channel search http://idea.library.drexel.edu/simple-search http://idea.library.drexel.edu/handle/1860/4080 Title: Optimum cavity length for high conversion efficiency quantum well diode lasers <br/> <br/>Authors: Rosen, Arye <br/> <br/>Abstract: The cavity length which maximizes the peak power conversion efficiency is determined for quantum well diode lasers. These calculations are based upon simple models of the diode injection laser's electrical and optical behaviors, including saturation in the quantum well gain current characteristic. Here the influences of the distributed optical cavity loss, electrical resistivity, and facet reflectivity on the optimum cavity length are described. Although a lower facet reflectivity results in increased threshold current, there are advantages to longer devices, as the peak conversion efficiency is not reduced. Since the optimum cavity length is greater for low reflectivity, the diode series resistance is smaller. Furthermore, when operating at the point where conversion efficiency is a maximum, the power output of the device with low facet reflectivity exceeds that of the device with higher facet reflectivity. Therein lies the principle advantage of reduced front-facet reflectivities in high power, high efficiency quantum well diode lasers. Good agreement results when these predictions are applied to a strained InGaAs/AlGaAs single quantum well laser (A = 0.93 f./m). http://idea.library.drexel.edu/handle/1860/4050 Title: Content-Based Music Genre Classification Using Sparse Approximation Techniques <br/> <br/>Authors: Aryafar, Kamelia; Adams, Trevor R.; Shokoufandeh, Ali <br/> <br/>Abstract: In this study we evaluated the performance of genre classification systems using various feature vectors and learning methods. Using a fixed classifier, i.e., the Gaussian mixture models we were able to create a suboptimal feature vector to characterize the audio signals in a low dimensional feature space. We then utilized this modified feature representation to solve the problem of music genre classification. We evaluated the performance of the recent sparsity-eager support vector machines classifier using the proposed feature vector and compared the results to the classic support vector machines and Gaussian mixture models as the baseline classifiers. http://idea.library.drexel.edu/handle/1860/4049 Title: Dominant Color Learning by Subject Extraction <br/> <br/>Authors: Aryafar, Kamelia; Attenberg, Josh; Condon, Fiona <br/> <br/>Abstract: Advances in the digital media industry have resulted in an exponential growth in available image data sets. This exponential growth has in turn spurred great interest in various methods for acquiring, processing, analyzing, and understanding images in order to produce numerical or symbolic information such as color and texture characteristics. Detecting the dominant color of an object in the image without any prior knowledge about the background model, the object characteristics or the scene geometry is a challenging problem. The two major challenges in assigning a dominant color to the image subject are the isolation of the subject by background subtraction and the extraction of dominant color from the approximated subject region. In this work, we combine an estimated subject mask with the image color histogram to detect the dominant image color. http://idea.library.drexel.edu/handle/1860/4048 Title: Automatic Classification of Digital Music by Genre <br/> <br/>Authors: Aryafar, Kamelia; Shokoufandeh, Ali <br/> <br/>Abstract: Over the past two decades, advances in the digital music industry have resulted in an exponential growth in music data sets. This exponential growth has in turn spurred great interest in music information retrieval (MIR) problems, organizing large music collections, and content-based search methods for digital music libraries. Equally important are the related problems in music classification such as genre classification, music mood analysis, and artist identification. Music genre classification is a well-studied problem in the music information retrieval community and has a wide range of applications. In this project we address the problem of genre classification by representing the MFCC feature vectors in an extended semantic space. We combine this audio representation with machine learning techniques to perform genre classification with the goal of obtaining higher classification accuracy. http://idea.library.drexel.edu/handle/1860/4047 Title: Music Genre Classification Using Explicit Semantic Analysis <br/> <br/>Authors: Aryafar, Kamelia; Shokoufandeh, Ali <br/> <br/>Abstract: Music genre classification is the categorization of a piece of music into its corresponding categorical labels created by humans and has been traditionally performed through a manual process. Automatic music genre classification, a fundamental problem in the musical information retrieval community, has been gaining more attention with advances in the development of the digital music industry. Most current genre classification methods tend to be based on the extraction of short-time features in combination with high-level audio features to perform genre classification. However, the representation of short-time features, using time windows, in a semantic space has received little attention. This paper proposes a vector space model of mel-frequency cepstral coefficients (MFCCs) that can, in turn, be used by a supervised learning schema for music genre classification. Inspired by explicit semantic analysis of textual documents using term frequency-inverse document frequency (tf-idf), a semantic space model is proposed to represent music samples. The effectiveness of this representation of audio samples is then demonstrated in music genre classification using various machine learning classification algorithms, including support vector machines (SVMs) and k-nearest neighbor clustering. Our preliminary results suggest that the proposed method is comparable to genre classification methods that use low-level audio features. http://idea.library.drexel.edu/handle/1860/3963 Title: HPDLC Films doped with Carbon Nano-onions to improve its electro optic response <br/> <br/>Authors: Bellingham, Alyssa; Shriyan, Sameet K.; Fontecchio, Adam K. <br/> <br/>Abstract: Holographically-formed Polymer Dispersed Liquid Crystal films (HPDLC) are electro-optical thin films that phase separate upon exposure to an interference pattern to form a Bragg grating composed of alternating layers of liquid crystal droplets and polymer. The Bragg grating allows the film to reflect a preselected wavelength of light. When an electric field is applied to the film , the liquid crystals in the film align in the direction of that field allowing all wavelengths of light to pass through. This switching property makes these films ideal for many applications including displays, biomedical sensors, gas analysis, and Hyperspectral imaging devices. In order to reduce the voltage at which the films switch, materials with high conductivities can be introduced into the polymer regions of the films to slightly increase the overall conductivity of the polymer relative to the liquid crystal. This research focuses on doping the polymer used in the films with carbon nano-onions, which resemble nano-scale buckyballs in structure. They can be functionalized to attach only to the polymer regions during phase separation, which will increase the polymer’s conductivity and lead to a reduction in the voltage needed to switch the samples without hindering the alignment of the liquid crystals. In the mixtures that have been developed so far, there have been aggregates of nano-onions, which reduces the increase in conductivity of the polymer caused by the introduction of nano-onions and decreases transmission through the film. Ideally, the films will retain higher than 80% transmission with the carbon nano-onions, so work is currently being done to reduce the nano-onion aggregates and determine the best ratio of nano-onions to polymer. http://idea.library.drexel.edu/handle/1860/3961 Title: Working Memory Monitoring of Air Traffic Controllers Using Functional Near Infrared Spectroscopy <br/> <br/>Authors: Ayaz, Hasan; Bunce, Scott; Willems, Ben; Hah, Sehchang; Shewokis, Patricia A.; Izzetoglu, Kurtulus; Onaral, Banu <br/> <br/>Abstract: Significant progress has been made over the last decade in understanding the physiological and neural bases of cognitive processes and behavior. The advent of new and improved brain imaging tools, that allow monitoring brain activity in ecologically valid environments, is expected to allow better identification of neurophysiological markers of human performance. Further, deployment of portable neuroimaging technologies to real time settings could help assess cognitive and motivational states of personnel assigned to perform critical tasks. Functional Near-Infrared Spectroscopy (fNIR) is an emerging optical brain imaging technology that relies on optical techniques to detect changes of hemodynamic responses within the prefrontal cortex in response to sensory, motor, or cognitive activation. Teaming with ongoing studies at the Federal Aviation Administration (FAA) William J. Hughes Technical Center’s Research, Development, and Human Factors Laboratory, fNIR has been used to monitor certified controllers as they manage realistic scenarios under typical and emergent conditions. As part of the study, 24 participants performed a standardized n-back task; which is a working memory assessment task with 4 conditions of controlled difficulty level. Repeated measures analysis of variance showed that average oxygenation changes at voxel that is close to AF7 in International 10-20 System, located within left inferior frontal gyrus in the dorsalateral prefrontal cortex, correlates with the task difficulty and increases monotonically with increasing task difficulty (F(3,69)= 4.37, p < 0.05). Post hoc analyses confirmed the differences in oxygenation changes as a function of task difficulty with 3-back is larger than the 0- and1-back tasks. These results are in agreement with recent meta-analysis of fMRI data of n-back studies. http://idea.library.drexel.edu/handle/1860/3866 Title: Happily Ever After? How do Online Daters Define and Discuss Success? <br/> <br/>Authors: Mascaro, Christopher; Magee, Rachel <br/> <br/>Abstract: Online dating has been the focus of numerous studies. Prior research focuses on the characteristics of individuals who use online dating sites (Stephure et al., 2009; Sautter et al., 2010), how individuals represent themselves on different services (Hancock & Toma 2009; Ellison et al., 2009), and the search strategies individuals utilize to find a partner (Fiore et al.,2010; Hitsch et al., 2006). To date, there have been no studies that examine success stories associated with online dating websites. The following study attempts to address this gap by examining publicly available success stories collected through a systematic random sample from online dating success sites affiliated with Match.com (n=544), eHarmony (n=213) and OkCupid (n=61). Our analysis highlights two interesting findings. First, definitions of success differ between online dating websites. eHarmony’s success stories are mostly comprised of married couples (84%), whereas the number of married couples on Match.com (46.7%) and OkCupid (23%)is significantly lower. Additionally, the number of eHarmony and Match.com success profiles increases from Dating through Engaged to Married, whereas OkCupid’s frequency decreases from Dating through Married. Second, both eHarmony and Match.com success stories were found to have a higher frequency of phrases related to more serious aspects of relationships, such as “the rest of my life” and “marry him.” OkCupid success profiles were found to have a higher frequency of phrases dealing with the situational aspects of dating such as “we decided to meet” and “to get to know.” These findings demonstrate that individuals that utilize different dating sites have different definitions of success. The motivation for these different definitions between online dating websites is unknown. The results from this study help to inform future research of the cultural dimensions associated with each site that may lead to the different definitions of success. http://idea.library.drexel.edu/handle/1860/3865 Title: Discourse analysis of the digital reference service of the Internet Public Library <br/> <br/>Authors: Poole, Erik; Li, Jiexun; Park, Jung-ran <br/> <br/>Abstract: The use of interpersonal communications in online settings is becoming more common. Examining why patrons continually engage in dialogue with librarians can help improve the reference service and result in increased usage. In this study, we adopted four types of stylometric features (lexical, structural, sentimental, politeness) to analyze the discourse data from the question-answering service of the Internet Public Library (IPL)’s Online Reference Service (www.ipl.org). We compared librarians’ responses that do not elicit subsequent queries from users with the ones that do elicit subsequent queries. Through the use of a Principle Component Analysis, we found specific sentimental features associated with dialogue containing subsequent queries from patrons. http://idea.library.drexel.edu/handle/1860/3864 Title: Social support in online healthcare social networking <br/> <br/>Authors: Chuang, Katherine; Yang, Christopher C. <br/> <br/>Abstract: Online journals let patients share their thoughts and to easily seek support from friends by overcoming geographic and time boundaries. 3 months of journals and comments previously collected by a web crawler from Medhelp's alcoholism community were examined for social support types. Content analysis using social support defined by related literature shows that most journal posts provided information and sought more emotional support. Comments tend to offer informational support and request very minimal support. This work is a piece of an ongoing project that identifies the communication patterns of patients in online support groups.