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Publication Metadata only Activity Uncrashing Heuristic with Noncritical Activity Rescheduling Method for the Discrete Time-Cost Trade-Off Problem(ASCE-AMER SOC CIVIL ENGINEERS, 2020) Sonmez, Rifat; Aminbakhsh, Saman; Atan, Tankut; Middle East Technical University; Atilim University; Bahcesehir UniversityDespite intensive research efforts that have been devoted to discrete time-cost optimization of construction projects, the current methods have very limited capabilities for solving the problem for real-life-sized projects. This study presents a new activity uncrashing heuristic with noncritical activity rescheduling method to narrow the gap between the research and practice for time-cost optimization. The uncrashing heuristic searches for new solutions by uncrashing the critical activities with the highest cost-slope. This novel feature of the proposed heuristic enables identification and elimination of the dominated solutions during the search procedure. Hence, the heuristic can determine new high-quality solutions based on the nondominated solutions. Furthermore, the proposed noncritical activity rescheduling method of the heuristic decreases the amount of scheduling calculations, and high-quality solutions are achieved within a short CPU time. Results of the computational experiments reveal that the new heuristic outperforms state-of-the-art methods significantly for large-scale single-objective cost minimization and Pareto front optimization problems. Hence, the primary contribution of the paper is a new heuristic method that can successfully achieve high-quality solutions for large-scale discrete time-cost optimization problems.Publication Metadata only SENTIMENT ANALYSIS IN TURKISH FILM COMMENTS(IEEE, 2019) Uslu, Abdullah; Tekin, Sefa; Aytekin, Tevfik; Bahcesehir UniversityAnalysing the sentiments in text files, movie website's user comment is an important research fields both for companies and the academics due to the challenges and opportunities it possesses. In this study, we applied lexicon based method and Machine learning algorithms that are support vector machine, naive bayes, logistic regression and decision tree methods to various sized Turkish datasets. As a result, we observed that the Logistic Regression algorithm gave the best result with an accuracy of %77.35Publication Metadata only Classification of the colonic polyps in CT-colonography using region covariance as descriptor features of suspicious regions(2010) Kılıç, Niyazi; Kursun, Olcay; Uçan, Osman Nuri; Kılıç, Niyazi, Department of Electrical and Electronics Engineering, Istanbul Üniversitesi, Istanbul, Turkey; Kursun, Olcay, Department of Electrical and Electronics Engineering, Istanbul Üniversitesi, Istanbul, Turkey; Uçan, Osman Nuri, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyWe present an algorithm to classify polyps in CT colonography images utilizing covariance matrices as object descriptors. Since these descriptors do not lie on a vector space, they cannot simply be fed to traditional machine learning tools such as support vector machines (SVMs) or artificial neural networks (ANNs). To benefit from the simple yet one of the most powerful nonparametric machine learning approach k-nearest neighbor classifier, it suffices to compute the pairwise distances among the covariance descriptors using a distance metric involving their generalized eigenvalues, which also follows from the Lie group structure of positive definite matrices. This approach is fast and discriminates polyps from non-polyps with high accuracy using only a small size descriptor, which consists of 36 unique features per image region extracted from the suspicious regions that we have obtained by combined cellular neural network (CNN) and template matching detection method. These suspicious regions are, in average, 15 × 17 = 255 pixels in our experiments. © Springer Science + Business Media, LLC 2008. © 2010 Elsevier B.V., All rights reserved., MEDLINE® is the source for the MeSH terms of this document.Publication Metadata only Exploiting the power of GPUs for multi-gigabit wireless baseband processing(2010) Kocak, Taskin; Hinitt, Nicholas; Kocak, Taskin, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Hinitt, Nicholas, Department of Electrical and Electronic Engineering, University of Bristol, Bristol, United KingdomIn this paper, we explore the feasibility of achieving gigabit baseband throughput using the vast computational power offered by the graphics processors (GPUs). One of the most computationally intensive functions commonly used in baseband communications, the Fast Fourier Transform (FFT) algorithm, is implemented on an NVIDIA GPU using their general-purpose computing platform called the Compute Unified Device Architecture (CUDA). The paper, first, investigates the implementation of an FFT algorithm using the GPU hardware and exploiting the computational capability available. It then outlines the limitations discovered and the methods used to overcome these challenges. Finally a new algorithm to compute FFT is proposed, which reduces interprocessor communication, and it is further optimized by improving memory access, enabling the processing rate to exceed 4 Gbps, achieving a processing time of a 512-point FFT in less than 200 ns. © 2010 IEEE. © 2010 Elsevier B.V., All rights reserved.Publication Metadata only Stereo depth estimation using synchronous optimization with segment based regularization(2010) Aydin, Tarkan; Akgül, Yusuf Sinan; Aydin, Tarkan, Department of Computer Engineering, Gebze Teknik Üniversitesi, Gebze, Turkey, Bahçeşehir Üniversitesi, Istanbul, Turkey; Akgül, Yusuf Sinan, Department of Computer Engineering, Gebze Teknik Üniversitesi, Gebze, TurkeyStereo correspondence is inherently an ill-posed problem, which is addressed by regularization methods. This paper introduces a novel stereo correspondence method that uses two synchronous interdependent optimizations. The regularization of the correspondence problem is done adaptively by considering the image segments and the intermediate disparity maps of the two optimizations. Our adaptive regularization allows inter-segment diffusion at the beginning of the optimizations to be robust against local minima. When the two optimizations start producing similar disparity maps, our regularization prevents inter-segment diffusion to recover the depth discontinuities. Our experimental results showed that the proposed algorithm can handle sharp discontinuities well and provides disparity maps with accuracy comparable to the state of the art stereo methods. © 2010 Elsevier B.V. All rights reserved. © 2011 Elsevier B.V., All rights reserved.Publication Metadata only Defect-aware nanocrossbar logic mapping using Bipartite Subgraph Isomorphism & canonization(2010) Gören, Sezer; Uǧurdaǧ, Hasan Fatih; Palaz, Okan; Gören, Sezer, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Uǧurdaǧ, Hasan Fatih, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Palaz, Okan, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyThis paper addresses the NP-complete problem of mapping a logic function on to a nanocrossbar with a known defect map. We first show that this problem can be transformed into a Bipartite SubGraph Isomorphism (BSGI) problem. Then we present our proposed KNS-2DS algorithm, which canonizes both graphs in N 2 time (N being the number of nodes) and then matches them in N 3 time in the worst case. KNS-2DS uses a K-Neighbor Sort (KNS) to initialize our main contribution 2D-Sort (2DS). 2DS is an iterative rough canonizer that lets a straight forward matching algorithm complete the job. Our algorithm offers very short run-times (due to canonization) compared to previous work and has success on all benchmarks. KNS-2DS is also novel from the perspective of the BSGI problem in the sense that it is based on canonization but not on a search tree with backtracking. © 2010 IEEE. © 2010 Elsevier B.V., All rights reserved.Publication Metadata only Coupled nonparametric shape and moment-based intershape pose priors for multiple basal ganglia structure segmentation(2010) Uzunbaş, Mustafa Gökhan; Soldea, Octavian; Ünay, Devrim; Çetin, Müjdat; Unal, Gozde Bozkurt; Erçil, Aytül; Ekin, Ahmet; Uzunbaş, Mustafa Gökhan, Faculty of Engineering and Natural Sciences, Sabancı Üniversitesi, Tuzla, Turkey, Department of Computer Science, Piscataway, United States; Soldea, Octavian, Faculty of Engineering and Natural Sciences, Sabancı Üniversitesi, Tuzla, Turkey; Ünay, Devrim, Department of Electrical and Electronic Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Çetin, Müjdat, Faculty of Engineering and Natural Sciences, Sabancı Üniversitesi, Tuzla, Turkey; Unal, Gozde Bozkurt, Faculty of Engineering and Natural Sciences, Sabancı Üniversitesi, Tuzla, Turkey; Erçil, Aytül, Faculty of Engineering and Natural Sciences, Sabancı Üniversitesi, Tuzla, Turkey; Ekin, Ahmet, Video Processing and Analysis Group, Philips Research, Eindhoven, NetherlandsThis paper presents a new active contour-based, statistical method for simultaneous volumetric segmentation of multiple subcortical structures in the brain. In biological tissues, such as the human brain, neighboring structures exhibit co-dependencies which can aid in segmentation, if properly analyzed and modeled. Motivated by this observation, we formulate the segmentation problem as a maximum a posteriori estimation problem, in which we incorporate statistical prior models on the shapes and intershape (relative) poses of the structures of interest. This provides a principled mechanism to bring high level information about the shapes and the relationships of anatomical structures into the segmentation problem. For learning the prior densities we use a nonparametric multivariate kernel density estimation framework. We combine these priors with data in a variational framework and develop an active contour-based iterative segmentation algorithm. We test our method on the problem of volumetric segmentation of basal ganglia structures in magnetic resonance images. We present a set of 2-D and 3-D experiments as well as a quantitative performance analysis. In addition, we perform a comparison to several existent segmentation methods and demonstrate the improvements provided by our approach in terms of segmentation accuracy. © 2010 IEEE. © 2010 Elsevier B.V., All rights reserved.Publication Metadata only Denoising embolic Doppler ultrasound signals using Dual Tree Complex Discrete Wavelet Transform(2010) Serbes, Görkem; Aydın, Nizamettin; Serbes, Görkem, Department of Electrical Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Aydın, Nizamettin, Department of Computer Engineering, Yıldız Teknik Üniversitesi, Istanbul, TurkeyEarly and accurate detection of asymptomatic emboli is important for monitoring of preventive therapy in stroke-prone patients. One of the problems in detection of emboli is the identification of an embolic signal caused by very small emboli. The amplitude of the embolic signal may be so small that advanced processing methods are required to distinguish these signals from Doppler signals arising from red blood cells. In this study instead of conventional discrete wavelet transform, the Dual Tree Complex Discrete Wavelet Transform was used for denoising embolic signals. Performances of both approaches were compared. Unlike the conventional discrete wavelet transform discrete complex wavelet transform is a shift invariant transform with limited redundancy. Results demonstrate that the Dual Tree Complex Discrete Wavelet Transform based denoising outperforms conventional discrete wavelet denoising. Approximately 8 dB improvement is obtained by using the Dual Tree Complex Discrete Wavelet Transform compared to the improvement provided by the conventional Discrete Wavelet Transform (less than 5 dB). © 2010 IEEE. © 2011 Elsevier B.V., All rights reserved.Publication Metadata only Design of optical devices using frequency domain solvers(2010) Ŝimŝek, Ergün; Liu, Qing Huo; Ŝimŝek, Ergün, Department of Electrical and Electronic Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Liu, Qing Huo, Pratt School of Engineering, Durham, United StatesThis work deals with efficient frequency domain solvers specifically developed to design optical and plasmonic devices. Homogeneous and inhomogeneous objects embedded in multilayered media are analyzed using Method of Moment (MoM) and hybrid MoM-Finite Element Method (FEM), respectively. The capability of working with materials of complex permittivity makes these algorithms valid and useful for both microwave and optical regimes. Based on the good match between numerical results obtained with these algorithms and the ones found in the literature, we propose an optical antenna optimum for a semiconductor laser diode operating at a wavelength of 830 nm and an infrared sensor compatible with present silicon technology based optical devices. © 2010 IEEE. © 2011 Elsevier B.V., All rights reserved.Publication Metadata only A wavelet packet adaptive filtering algorithm for enhancing manatee vocalizations(2011) Gür, M. Berke; Niezrecki, Christopher; Gür, M. Berke, Department of Mechanical Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Niezrecki, Christopher, Francis College of Engineering, Lowell, United StatesApproximately a quarter of all West Indian manatee (Trichechus manatus latirostris) mortalities are attributed to collisions with watercraft. A boater warning system based on the passive acoustic detection of manatee vocalizations is one possible solution to reduce manatee-watercraft collisions. The success of such a warning system depends on effective enhancement of the vocalization signals in the presence of high levels of background noise, in particular, noise emitted from watercraft. Recent research has indicated that wavelet domain pre-processing of the noisy vocalizations is capable of significantly improving the detection ranges of passive acoustic vocalization detectors. In this paper, an adaptive denoising procedure, implemented on the wavelet packet transform coefficients obtained from the noisy vocalization signals, is investigated. The proposed denoising algorithm is shown to improve the manatee detection ranges by a factor ranging from two (minimum) to sixteen (maximum) compared to high-pass filtering alone, when evaluated using real manatee vocalization and background noise signals of varying signal-to-noise ratios (SNR). Furthermore, the proposed method is also shown to outperform a previously suggested feedback adaptive line enhancer (FALE) filter on average 3.4 dB in terms of noise suppression and 0.6 dB in terms of waveform preservation. © 2011 Acoustical Society of America. © 2011 Elsevier B.V., All rights reserved., MEDLINE® is the source for the MeSH terms of this document.