Browsing by Subject "Computational costs"
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Item Open Access A comparative study of map building techniques by processing sonar arc-maps(2005) Kurt, Arda; Barshan, BillurIn this study, four signal processing schemes regarding sonar sensor based map-building applications were compared. The newly proposed method, Directional Maximum is found to be successful in terms of reducing the innate angular ambiguity of the sonar sensors. With respect to several works presented earlier in the same field and specifically map-building related studies, the new method is successful both in terms of mean absolute error and computational cost.Item Open Access Compressive sensing based flame detection in infrared videos(IEEE, 2013) Günay, Osman; Çetin, A. EnisIn this paper, a Compressive Sensing based feature extraction algorithm is proposed for flame detection using infrared cameras. First, bright and moving regions in videos are detected. Then the videos are divided into spatio-temporal blocks and spatial and temporal feature vectors are exctracted from these blocks. Compressive Sensing is used to exctract spatial feature vectors. Compressed measurements are obtained by multiplying the pixels in the block with the sensing matrix. A new method is also developed to generate the sensing matrix. A random vector generated according to standard Gaussian distribution is passed through a wavelet transform and the resulting matrix is used as the sensing matrix. Temporal features are obtained from the vector that is formed from the difference of mean intensity values of the frames in two neighboring blocks. Spatial feature vectors are classified using Adaboost. Temporal feature vectors are classified using hidden Markov models. To reduce the computational cost only moving and bright regions are classified and classification is performed at specified intervals instead of every frame. © 2013 IEEE.Item Open Access A computational homogenization framework for soft elastohydrodynamic lubrication(Springer, 2012) Budt, M.; Temizer, İlker; Wriggers, P.The interaction between microscopically rough surfaces and hydrodynamic thin film lubrication is investigated under the assumption of finite deformations. Within a coupled micro-macro analysis setting, the influence of roughness onto the macroscopic scale is determined using FE 2-type homogenization techniques to reduce the overall computational cost. Exact to within a separation of scales assumption, a computationally efficient two-phase micromechanical test is proposed to identify the macroscopic interface fluid flux from a lubrication analysis performed on the deformed configuration of a representative surface element. Parameter studies show a strong influence of both roughness and surface deformation on the macroscopic response for isotropic and anisotropic surfacial microstructures.Item Open Access Exact diffraction calculation from fields specified over arbitrary curved surfaces(Elsevier, 2011-07-30) Esmer, G. B.; Onural, L.; Özaktaş, Haldun M.Calculation of the scalar diffraction field over the entire space from a given field over a surface is an important problem in computer generated holography. A straightforward approach to compute the diffraction field from field samples given on a surface is to superpose the emanated fields from each such sample. In this approach, possible mutual interactions between the fields at these samples are omitted and the calculated field may be significantly in error. In the proposed diffraction calculation algorithm, mutual interactions are taken into consideration, and thus the exact diffraction field can be calculated. The algorithm is based on posing the problem as the inverse of a problem whose formulation is straightforward. The problem is then solved by a signal decomposition approach. The computational cost of the proposed method is high, but it yields the exact scalar diffraction field over the entire space from the data on a surface.Item Open Access A five-level static cache architecture for web search engines(Elsevier Ltd, 2012) Ozcan, R.; Altingovde, I. S.; Cambazoglu, B. B.; Junqueira, F. P.; Ulusoy, ÖzgürCaching is a crucial performance component of large-scale web search engines, as it greatly helps reducing average query response times and query processing workloads on backend search clusters. In this paper, we describe a multi-level static cache architecture that stores five different item types: query results, precomputed scores, posting lists, precomputed intersections of posting lists, and documents. Moreover, we propose a greedy heuristic to prioritize items for caching, based on gains computed by using items' past access frequencies, estimated computational costs, and storage overheads. This heuristic takes into account the inter-dependency between individual items when making its caching decisions, i.e.; after a particular item is cached, gains of all items that are affected by this decision are updated. Our simulations under realistic assumptions reveal that the proposed heuristic performs better than dividing the entire cache space among particular item types at fixed proportions. © 2010 Elsevier Ltd. All rights reserved.Item Open Access Human activity recognition using inertial/magnetic sensor units(Springer, Berlin, Heidelberg, 2010) Altun, Kerem; Barshan, BillurThis paper provides a comparative study on the different techniques of classifying human activities that are performed using body-worn miniature inertial and magnetic sensors. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), the least-squares method (LSM), the k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN). Daily and sports activities are classified using five sensor units worn by eight subjects on the chest, the arms, and the legs. Each sensor unit comprises a triaxial gyroscope, a triaxial accelerometer, and a triaxial magnetometer. Principal component analysis (PCA) and sequential forward feature selection (SFFS) methods are employed for feature reduction. For a small number of features, SFFS demonstrates better performance and should be preferable especially in real-time applications. The classifiers are validated using different cross-validation techniques. Among the different classifiers we have considered, BDM results in the highest correct classification rate with relatively small computational cost. © 2010 Springer-Verlag Berlin Heidelberg.Item Open Access Man-made object classification in SAR images using 2-D cepstrum(IEEE, 2009-05) Eryildirim, A.; Çetin, A. EnisIn this paper, a novel descriptive feature parameter extraction method from Synthetic Aperture Radar (SAR) images is proposed. The new method is based on the two-dimensional (2-D) real cepstrum. This novel 2-D cepstrum method is compared with principal component analysis (PCA) method by testing over the MSTAR image database. The extracted features are classified using Support Vector Machine (SVM). We demonstrate that discrimination of natural background (clutter) and man-made objects (metal objects) in SAR imagery is possible using the 2-D cepstrum feature parameters. In addition, the computational cost of the cepstrum method is lower than the PCA method. Experimental results are presented. ©2009 IEEE.Item Open Access PO-MLFMA hybrid technique for the solution of electromagnetic scattering problems involving complex targets(Institution of Engineering and Technology, 2007) Gürel, Levent; Manyas, Alp; Ergül, ÖzgürThe multilevel fast multipole algorithm (MLFMA) is a powerful tool for efficient and accurate solutions of electromagnetic scattering problems involving large and complicated structures. On the other hand, it is still desirable to increase the efficiency of the solutions further by combining the MLFMA implementations with the high- frequency techniques such as the physical optics (PO). In this paper, we present our efforts in order to reduce the computational cost of the MLFMA solutions by introducing PO currents appropriately on the scatterer. Since PO is valid only on smooth and large surfaces that are illuminated strongly by the incident fields, accurate solutions require careful choices of the PO and MLFMA regions. Our hybrid technique is useful especially when multiple solutions are required for different frequencies, illuminations, and scenarios, so that the direct solutions with MLFMA become expensive. For these problems, we easily accelerate the MLFMA solutions by systematically introducing the PO currents and reducing the matrix dimensions without sacrificing the accuracy.Item Open Access Region covariance descriptors calculated over the salient points for target tracking(IEEE, 2012) Çakir, S.; Aytaç, T.; Yildirim, A.; Beheshti, S.; Gerek Ö.N.; Çetin, A. EnisFeatures extracted at salient points in the image are used to construct region covariance descriptor (RCD) for target tracking purposes. In the classical approach, the RCD is computed by using the features at each pixel location and thus, increases the computational cost in the scenarios where large targets are tracked. The approach in which the features at each pixel location are used, is redundant in cases where image statistics do not change significantly between neighboring pixels. Furthermore, this may decrease the tracking accuracy while tracking large targets which have background dominating structures. In the proposed approach, the salient points are extracted via the Shi and Tomasi's minimum eigenvalue method and a descriptor based target tracking structure is constructed based on the features extracted only at these salient points. Experimental results indicate that the proposed method provides comparable and in some cases even better tracking results compared to the classical method while providing a computationally more efficient structure. © 2012 IEEE.Item Open Access Scene classification with random forests and object and color distributions(IEEE, 2013) İşcen, Ahmet; Gölge, Eren; Armağan, Anıl; Duygulu, PınarWe propose a method to recognize the scene of an image by finding the objects and the colors it contains. We approach this problem by creating a binary vector of detected objects and a histogram of the colors that the image contains. We then use these features to train a random forest classifier in order to determine the scene of each image. For class-based classifiers, our method gives comparable results with the state of art methods, such as Object Bank method, for the indoor scene dataset that we used. Additionally, while well-known methods are computationally expensive, our method has a low computational cost. © 2013 IEEE.Item Open Access Target detection in SAR images using codifference and directional filters(SPIE, 2010) Duman, Kaan; Çetin, A. EnisTarget detection in SAR images using region covariance (RC) and codifference methods is shown to be accurate despite the high computational cost. The proposed method uses directional filters in order to decrease the search space. As a result the computational cost of the RC based algorithm significantly decreases. Images in MSTAR SAR database are first classified into several categories using directional filters (DFs). Target and clutter image features are extracted using RC and codifference methods in each class. The RC and codifference matrix features are compared using l 1 norm distance metric. Support vector machines which are trained using these matrices are also used in decision making. Simulation results are presented. © 2010 Copyright SPIE - The International Society for Optical Engineering.