An Improved Color Video Super-Resolution Using Kernel Regression and Fuzzy Enhancement

An improved color video super-resolution technique using kernel regression and fuzzy enhancement is presented in this paper. A high resolution frame is computed from a set of low resolution video frames by kernel regression using an adaptive Gaussian kernel. A fuzzy smoothing filter is proposed to enhance the regression output. The proposed technique is a low cost software solution to resolution enhancement of color video in multimedia applications. The performance of the proposed technique is evaluated using several color videos and it is found to be better than other techniques in producing high quality high resolution color videos

Content Based Video Retrieval using SURF Descriptor

This paper presents a Robust Content Based Video Retrieval (CBVR) system. This system retrieves similar videos based on a local feature descriptor called SURF (Speeded Up Robust Feature). The higher dimensionality of SURF like feature descriptors causes huge storage consumption during indexing of video information. To achieve a dimensionality reduction on the SURF feature descriptor, this system employs a stochastic dimensionality reduction method and thus provides a model data for the videos. On retrieval, the model data of the test clip is classified to its similar videos using a minimum distance classifier. The performance of this system is evaluated using two different minimum distance classifiers during the retrieval stage. The experimental analyses performed on the system shows that the system has a retrieval performance of 78%. This system also analyses the performance efficiency of the low dimensional SURF descriptor.

Reinforcement Learning solution for Unit Commitment Problem through pursuit method

Unit commitment is an optimization task in electric power generation control sector. It involves scheduling the ON/OFF status of the generating units to meet the load demand with minimum generation cost satisfying the different constraints existing in the system. Numerical solutions developed are limited for small systems and heuristic methodologies find difficulty in handling stochastic cost functions associated with practical systems. This paper models Unit Commitment as a multi stage decision task and Reinforcement Learning solution is formulated through one efficient exploration strategy: Pursuit method. The correctness and efficiency of the developed solutions are verified for standard test systems

Genetic algorithm based indicator sequence method for exon prediction

Considerable research effort has been devoted in predicting the exon regions of genes. The binary indicator (BI), Electron ion interaction pseudo potential (EIIP), Filter method are some of the methods. All these methods make use of the period three behavior of the exon region. Even though the method suggested in this paper is similar to above mentioned methods , it introduces a set of sequences for mapping the nucleotides selected by applying genetic algorithm and found to be more promising

Evolution of Better Wavelet Coefficients for Fingerprint Image Compression using cropped images

This paper explains the Genetic Algorithm (GA) evolution of optimized wavelet that surpass the cdf9/7 wavelet for fingerprint compression and reconstruction. Optimized wavelets have already been evolved in previous works in the literature, but they are highly computationally complex and time consuming. Therefore, in this work, a simple approach is made to reduce the computational complexity of the evolution algorithm. A training image set comprised of three 32x32 size cropped images performed much better than the reported coefficients in literature. An average improvement of 1.0059 dB in PSNR above the classical cdf9/7 wavelet over the 80 fingerprint images was achieved. In addition, the computational speed was increased by 90.18 %. The evolved coefficients for compression ratio (CR) 16:1 yielded better average PSNR for other CRs also. Improvement in average PSNR was experienced for degraded and noisy images as well

Thermo- Optic and Nonlinear-Optical studies on CdSe Quantum Dots for Photonic Applications

In general, linear- optic, thermo- optic and nonlinear- optical studies on CdSe QDs based nano uids and their special applications in solar cells and random lasers have been studied in this thesis. Photo acous- tic and thermal lens studies are the two characterization methods used for thermo- optic studies whereas Z- scan method is used for nonlinear- optical charecterization. In all these cases we have selected CdSe QDs based nano uid as potential photonic material and studied the e ect of metal NPs on its properties. Linear optical studies on these materials have been done using vari- ous characterization methods and photo induced studies is one of them. Thermal lens studies on these materials give information about heat transport properties of these materials and their suitability for applica- tions such as coolant and insulators. Photo acoustic studies shows the e ect of light on the absorption energy levels of the materials. We have also observed that these materials can be used as optical limiters in the eld of nonlinear optics. Special applications of these materials have been studied in the eld of solar cell such as QDSSCs, where CdSe QDs act as the sensitizing materials for light harvesting. Random lasers have many applications in the eld of laser technology, in which CdSe QDs act as scattering media for the gain.

Folien zur Vorlesung Sortierverfahren

Die hier frei verfügbare Foliensammlung stammt aus der gleichnamigen Master-Veranstaltung im Sommersemester 2014. Das zugehörige Skript (in Englisch) kann als PDF heruntergeladen werden von der Universität Turku (Lutz M. Wegner, Sorting – The Turku Lectures, Lecture Notes in Computing 2014, Univ. of Turku, Finland, http://tucs.fi/publications/attachment.php?fname=bWegner_LutzMx14a.full.pdf). Das überarbeitete Material geht auf eine dort im Jahre 1987 gehaltene Gastvorlesung zurück. Betrachtet werden Varianten von Quicksort und Heapsort, speziell bei Quicksort solche für Multimengen und Vorsortierung, sowohl für verkettete Listen als auch für Datenstrukturen mit Direktzugriff. Neben dem Standardheapsort werden die Floydverbesserung und Dijkstras Smoothsort vorgestellt. Eine Netzwerkvariante und zwei Externspeicherverfahren auf Basis von Quicksort und Heapsort werden untersucht. Der Stand der Technik bei den stabilen, in-situ Verfahren wird skizziert. Der Stoff eignet sich für eine Vorlesung mit angegliederten Projektübungen, in denen Studierende selbstständig Algorithmen, die als Pseudo-Code vorgegeben sind, in effizienten, lauffähigen Java-Code umsetzen und in einer Laufzeitbibliothek mit vorgegebenen Testdaten messen.

Gone with the internet - How the old education was lost

Despite its young history, Computer Science Education has seen a number of "revolutions". Being a veteran in the field, the author reflects on the many changes he has seen in computing and its teaching. The intent of this personal collection is to point out that most revolutions came unforeseen and that many of the new learning initiatives, despite high financial input, ultimately failed. The author then considers the current revolution (MOOC, inverted lectures, peer instruction, game design) and, based on the lessons learned earlier, argues why video recording is so successful. Given the fact that this is the decade we lost print (papers, printed books, book shops, libraries), the author then conjectures that the impact of the Internet will make this revolution different from previous ones in that most of the changes are irreversible. As a consequence he warns against storming ahead blindly and suggests to conserve - while it is still possible - valuable components of what might soon be called the antebellum age of education.

Global Depth Perception from Familiar Scene Structure

In the absence of cues for absolute depth measurements as binocular disparity, motion, or defocus, the absolute distance between the observer and a scene cannot be measured. The interpretation of shading, edges and junctions may provide a 3D model of the scene but it will not inform about the actual "size" of the space. One possible source of information for absolute depth estimation is the image size of known objects. However, this is computationally complex due to the difficulty of the object recognition process. Here we propose a source of information for absolute depth estimation that does not rely on specific objects: we introduce a procedure for absolute depth estimation based on the recognition of the whole scene. The shape of the space of the scene and the structures present in the scene are strongly related to the scale of observation. We demonstrate that, by recognizing the properties of the structures present in the image, we can infer the scale of the scene, and therefore its absolute mean depth. We illustrate the interest in computing the mean depth of the scene with application to scene recognition and object detection.

Big Data or Right Data?

Abstract Big data nowadays is a fashionable topic, independently of what people mean when they use this term. But being big is just a matter of volume, although there is no clear agreement in the size threshold. On the other hand, it is easy to capture large amounts of data using a brute force approach. So the real goal should not be big data but to ask ourselves, for a given problem, what is the right data and how much of it is needed. For some problems this would imply big data, but for the majority of the problems much less data will and is needed. In this talk we explore the trade-offs involved and the main problems that come with big data using the Web as case study: scalability, redundancy, bias, noise, spam, and privacy. Speaker Biography Ricardo Baeza-Yates Ricardo Baeza-Yates is VP of Research for Yahoo Labs leading teams in United States, Europe and Latin America since 2006 and based in Sunnyvale, California, since August 2014. During this time he has lead the labs in Barcelona and Santiago de Chile. Between 2008 and 2012 he also oversaw the Haifa lab. He is also part time Professor at the Dept. of Information and Communication Technologies of the Universitat Pompeu Fabra, in Barcelona, Spain. During 2005 he was an ICREA research professor at the same university. Until 2004 he was Professor and before founder and Director of the Center for Web Research at the Dept. of Computing Science of the University of Chile (in leave of absence until today). He obtained a Ph.D. in CS from the University of Waterloo, Canada, in 1989. Before he obtained two masters (M.Sc. CS & M.Eng. EE) and the electronics engineer degree from the University of Chile in Santiago. He is co-author of the best-seller Modern Information Retrieval textbook, published in 1999 by Addison-Wesley with a second enlarged edition in 2011, that won the ASIST 2012 Book of the Year award. He is also co-author of the 2nd edition of the Handbook of Algorithms and Data Structures, Addison-Wesley, 1991; and co-editor of Information Retrieval: Algorithms and Data Structures, Prentice-Hall, 1992, among more than 500 other publications. From 2002 to 2004 he was elected to the board of governors of the IEEE Computer Society and in 2012 he was elected for the ACM Council. He has received the Organization of American States award for young researchers in exact sciences (1993), the Graham Medal for innovation in computing given by the University of Waterloo to distinguished ex-alumni (2007), the CLEI Latin American distinction for contributions to CS in the region (2009), and the National Award of the Chilean Association of Engineers (2010), among other distinctions. In 2003 he was the first computer scientist to be elected to the Chilean Academy of Sciences and since 2010 is a founding member of the Chilean Academy of Engineering. In 2009 he was named ACM Fellow and in 2011 IEEE Fellow.

Process scheduling by output considerations

In multi-tasking systems when it is not possible to guarantee completion of all activities by specified times, the scheduling problem is not straightforward. Examples of this situation in real-time programming include the occurrence of alarm conditions and the buffering of output to peripherals in on-line facilities. The latter case is studied here with the hope of indicating one solution to the general problem.

A wavenumber independent boundary element method for an acoustic scattering problem

In this paper we consider the impedance boundary value problem for the Helmholtz equation in a half-plane with piecewise constant boundary data, a problem which models, for example, outdoor sound propagation over inhomogeneous. at terrain. To achieve good approximation at high frequencies with a relatively low number of degrees of freedom, we propose a novel Galerkin boundary element method, using a graded mesh with smaller elements adjacent to discontinuities in impedance and a special set of basis functions so that, on each element, the approximation space contains polynomials ( of degree.) multiplied by traces of plane waves on the boundary. We prove stability and convergence and show that the error in computing the total acoustic field is O( N-(v+1) log(1/2) N), where the number of degrees of freedom is proportional to N logN. This error estimate is independent of the wavenumber, and thus the number of degrees of freedom required to achieve a prescribed level of accuracy does not increase as the wavenumber tends to infinity.

A high-wavenumber boundary-element method for an acoustic scattering problem

In this paper we show stability and convergence for a novel Galerkin boundary element method approach to the impedance boundary value problem for the Helmholtz equation in a half-plane with piecewise constant boundary data. This problem models, for example, outdoor sound propagation over inhomogeneous flat terrain. To achieve a good approximation with a relatively low number of degrees of freedom we employ a graded mesh with smaller elements adjacent to discontinuities in impedance, and a special set of basis functions for the Galerkin method so that, on each element, the approximation space consists of polynomials (of degree $\nu$) multiplied by traces of plane waves on the boundary. In the case where the impedance is constant outside an interval $[a,b]$, which only requires the discretization of $[a,b]$, we show theoretically and experimentally that the $L_2$ error in computing the acoustic field on $[a,b]$ is ${\cal O}(\log^{\nu+3/2}|k(b-a)| M^{-(\nu+1)})$, where $M$ is the number of degrees of freedom and $k$ is the wavenumber. This indicates that the proposed method is especially commendable for large intervals or a high wavenumber. In a final section we sketch how the same methodology extends to more general scattering problems.

Bringing the target to the cursor: proxy targets for older adults

Studies in the literature have proposed techniques to facilitate pointing in graphical user interfaces through the use of proxy targets. Proxy targets effectively bring the target to the cursor, thereby reducing the distance that the cursor must travel. This paper describes a study which aims to provide an initial understanding of how older adults respond to proxy targets, and compares older with younger users. We found that users in both age groups adjusted to the proxy targets without difficulty, and there was no indication in the cursor trajectories that users were confused about which target, i.e. the original versus the proxy, was to be selected. In terms of times, preliminary results show that for younger users, proxies did not provide any benefits over direct selection, while for older users, times were increased with proxy targets. A full analysis of the movement times, error rates, throughput and subjective feedback is currently underway.