Browsing by Subject "Error detection"

Now showing 1 - 14 of 14

Open Access
Birleşik sezim ve kestirim sistemlerinin gürültü ile geliştirilmesi
(IEEE, 2014-04) Akbay, Abdullah Başar; Gezici, Sinan
Belirli koşullar altında, optimal olmayan bazı sezici ve kestiricilerin performansını girdilerine gürültü ekleyerek geliştirmek mümkündür. Bu çalışmada, birleşik bir sezim ve kestirim sisteminin gürültü eklenerek geliştirilmesi incelenmektedir. Sistem performansının maksimizasyonu bir optimizasyon problemi olarak tanımlanmaktadır. Optimal toplanır gürültü dağılımının istatiksel özellikleri belirlenmektedir. Sistem performansının gürültü ile iyileştirilemeyeceği bir koşul elde edilmektedir.Önerilen optimizasyon probleminin, bir doğrusal programlama (DP) problemi olarak yaklaşımı sunulmaktadır. Bir sayısal örnek üzerinde, kuramsal bulguları desteklemek amacıyla, gürültü eklenmiş sistem ile orijinal sistemin performansları karşılaştırılmaktadır.
Open Access
Comparison of terahertz technologies for detection and identification of explosives
(SPIE, 2014-05) Beigang, R.; Biedron, S. G.; Dyjak, S.; Ellrich, F.; Haakestad, M.W.; Hübsch, D.; Kartaloglu, Tolga; Özbay, Ekmel; Ospald, F.; Palka, N.; Puc, U.; Czerwiñska, E.; Sahin, A. B.; Sešek, A.; Trontelj, J.; Švigelj, A.; Altan, H.; Van Rheenen, A.D.; Walczakowski, M.
We present results on the comparison of different THz technologies for the detection and identification of a variety of explosives from our laboratory tests that were carried out in the framework of NATO SET-193 THz technology for stand-off detection of explosives: from laboratory spectroscopy to detection in the field under the same controlled conditions. Several laser-pumped pulsed broadband THz time-domain spectroscopy (TDS) systems as well as one electronic frequency-modulated continuous wave (FMCW) device recorded THz spectra in transmission and/or reflection. © 2014 SPIE.
Open Access
Convexity properties of detection probability under additive Gaussian noise: optimal signaling and jamming strategies
(IEEE, 2013) Dulek, B.; Gezici, Sinan; Arıkan, Orhan
In this correspondence, we study the convexity properties for the problem of detecting the presence of a signal emitted from a power constrained transmitter in the presence of additive Gaussian noise under the Neyman-Pearson (NP) framework. It is proved that the detection probability corresponding to the α-level likelihood ratio test (LRT) is either strictly concave or has two inflection points such that the function is strictly concave, strictly convex, and finally strictly concave with respect to increasing values of the signal power. In addition, the analysis is extended from scalar observations to multidimensional colored Gaussian noise corrupted signals. Based on the convexity results, optimal and near-optimal time sharing strategies are proposed for average/peak power constrained transmitters and jammers. Numerical methods with global convergence are also provided to obtain the parameters for the proposed strategies.
Open Access
Directional processing of ultrasonic arc maps and its comparison with existing techniques
(IEEE, 2007) Barshan, Billur; Altun, Kerem
Directional maximum (DM) technique for processing ultrasonic arc maps (UAMs) is proposed and compared to existing techniques. It employs directional processing in extracting the map of the environment from UAMs. DM aims at overcoming the intrinsic angular uncertainty of ultrasonic sensors in map building, as well as eliminating noise and cross-talk related misreadings. The comparison is based on experiments with a mobile robot which ac-quired ultrasonic range measurements through wall following. Three complementary performance criteria are used. The DM technique offers a very good compromise between mean absolute error and correct detection rate, with a processing time less than one tenth of a second. It is also superior in range accuracy and in eliminating artifacts, resulting in the best overall performance. The results indicate several trade-offs in the choice of UAM processing techniques.
Open Access
An efficient algorithm to extract components of a composite signal
(IEEE, 2000) Özdemir, A. Kemal; Arıkan, Orhan
An efficient algorithm is proposed to extract components of a composite signal. The proposed approach has two stages of processing in which the time-frequency supports of the individual signal components are identified and then the individual components are estimated by performing a simple time-frequency domain incision on the identified support of the component. The use of a recently proposed time-frequency representation [1] significantly improves the performance of the proposed approach by providing very accurate description on the auto-Wigner terms of the composite signal. Then, simple fractional Fourier domain incision provides reliable estimates for each of the signal components in O(N log N) complexity for a composite signal of duration N.
Open Access
An empirical eigenvalue-threshold test for sparsity level estimation from compressed measurements
(IEEE, 2014) Lavrenko, A.; Römer, F.; Del Galdo, G.; Thoma, R.; Arıkan, Orhan
Compressed sensing allows for a significant reduction of the number of measurements when the signal of interest is of a sparse nature. Most computationally efficient algorithms for signal recovery rely on some knowledge of the sparsity level, i.e., the number of non-zero elements. However, the sparsity level is often not known a priori and can even vary with time. In this contribution we show that it is possible to estimate the sparsity level directly in the compressed domain, provided that multiple independent observations are available. In fact, one can use classical model order selection algorithms for this purpose. Nevertheless, due to the influence of the measurement process they may not perform satisfactorily in the compressed sensing setup. To overcome this drawback, we propose an approach which exploits the empirical distributions of the noise eigenvalues. We demonstrate its superior performance compared to state-of-the-art model order estimation algorithms numerically.
Open Access
Error resilient layered stereoscopic video streaming
(IEEE, 2007) Tan, Ahmet Serdar; Aksay, A.; Bilen, Ç.; Bozdağı-Akar, G.; Arıkan, Erdal
In this paper, error resilient stereoscopic video streaming problem is addressed. Two different Forward Error Correction (FEC) codes namely Systematic LT and RS codes are utilized to protect the stereoscopic video data against transmission errors. Initially, the stereoscopic video is categorized in 3 layers with different priorities. Then, a packetization scheme is used to increase the efficiency of error protection. A comparative analysis of RS and LT codes are provided via simulations to observe the optimum packetization and UEP strategies.
Open Access
Eye tracking using markov models
(IEEE, 2004) Bağcı, A. M.; Ansari, R.; Khokhar, A.; Çetin, A. Enis
We propose an eye detection and tracking method based on color and geometrical features of the human face using a monocular camera. In this method a decision is made on whether the eyes are closed or not and, using a Markov chain framework to model temporal evolution, the subject's gaze is determined. The method can successfully track facial features even while the head assumes various poses, so long as the nostrils are visible to the camera. We compare our method with recently proposed techniques and results show that it provides more accurate tracking and robustness to variations in view of the face. A procedure for detecting tracking errors is employed to recover the loss of feature points in case of occlusion or very fast head movement. The method may be used in monitoring a driver's alertness and detecting drowsiness, and also in applications requiring non-contact human computer interaction.
Open Access
A low-complexity time-domain MMSE channel estimator for space-time/frequency block-coded OFDM systems
(Hindawi Publishing Corporation, 2006) Şenol, H.; Çırpan, H. A.; Panayırcı, E.; Çevik, M.
Focusing on transmit diversity orthogonal frequency-division multiplexing (OFDM) transmission through frequency-selective channels, this paper pursues a channel estimation approach in time domain for both space-frequency OFDM (SF-OFDM) and space-time OFDM(ST-OFDM) systems based on AR channel modelling. The paper proposes a computationally efficient, pilot-aided linear minimum mean-square-error (MMSE) time-domain channel estimation algorithm for OFDM systems with transmitter diversity in unknown wireless fading channels. The proposed approach employs a convenient representation of the channel impulse responses based on the Karhunen-Loeve (KL) orthogonal expansion and finds MMSE estimates of the uncorrelated KL series expansion coefficients. Based on such an expansion, no matrix inversion is required in the proposed MMSE estimator. Subsequently, optimal rank reduction is applied to obtain significant taps resulting in a smaller computational load on the proposed estimation algorithm. The performance of the proposed approach is studied through the analytical results and computer simulations. In order to explore the performance, the closed-form expression for the average symbol error rate (SER)probability is derived for the maximum ratio receive combiner(MRRC). We then consider the stochastic Cramer-Rao lower bound(CRLB) and derive the closed-form expression for the random KL coefficients, and consequently exploit the performance of the MMSE channel estimator based on the evaluation of minimum Bayesian MSE. We also analyze the effect of a modelling mismatch on the estimator performance. Simulation results confirm our theoretical analysis and illustrate that the proposed algorithms are capable of tracking fast fading and improving overall performance. Copyright © 2006 Hindawi Publishing Corporation. All rights reserved.
Open Access
Optimal detector randomization for multiuser communications systems
(IEEE, 2013) Tutay, M. E.; Gezici, Sinan; Arıkan, Orhan
Optimal detector randomization is studied for the downlink of a multiuser communications system, in which users can perform time-sharing among multiple detectors. A formulation is provided to obtain optimal signal amplitudes, detectors, and detector randomization factors. It is shown that the solution of this joint optimization problem can be calculated in two steps, resulting in significant reduction in computational complexity. It is proved that the optimal solution is achieved via randomization among at most min{K, Nd} detector sets, where K is the number of users and Nd is the number of detectors at each receiver. Lower and upper bounds are derived on the performance of optimal detector randomization, and it is proved that the optimal detector randomization approach can reduce the worst-case average probability of error of the optimal approach that employs a single detector for each user by up to K times. Various sufficient conditions are obtained for the improvability and nonimprovability via detector randomization. In the special case of equal crosscorrelations and noise powers, a simple solution is developed for the optimal detector randomization problem, and necessary and sufficient conditions are presented for the uniqueness of that solution. Numerical examples are provided to illustrate the improvements achieved via detector randomization.
Open Access
Optimal stochastic signal design and detector randomization in the Neyman-Pearson framework
(IEEE, 2012-03) Dülek, Berkan; Gezici, Sinan
Power constrained on-off keying communications systems are investigated in the presence of stochastic signaling and detector randomization. The joint optimal design of decision rules, stochastic signals, and detector randomization factors is performed. It is shown that the solution to the most generic optimization problem that employs both stochastic signaling and detector randomization can be obtained as the randomization among no more than three Neyman-Pearson (NP) decision rules corresponding to three deterministic signal vectors. Numerical examples are also presented. © 2012 IEEE.
Open Access
QR-RLS algorithm for error diffusion of color images
(SPIE, 2000) Unal, G. B.; Yardimci, Y.; Arıkan, Orhan; Çetin, A. Enis
Printing color images on color printers and displaying them on computer monitors requires a significant reduction of physically distinct colors, which causes degradation in image quality. An efficient method to improve the display quality of a quantized image is error diffusion, which works by distributing the previous quantization errors to neighboring pixels, exploiting the eye's averaging of colors in the neighborhood of the point of interest. This creates the illusion of more colors. A new error diffusion method is presented in which the adaptive recursive least-squares (RLS) algorithm is used. This algorithm provides local optimization of the error diffusion filter along with smoothing of the filter coefficients in a neighborhood. To improve the performance, a diagonal scan is used in processing the image.
Open Access
Spectrum sensing via restricted neyman-pearson approach in the presence of non-Gaussian noise
(IEEE, 2013) Turgut, Esma; Gezici, Sinan
In this paper, spectrum sensing in cognitive radio systems is studied for non-Gaussian channels in the presence of prior distribution uncertainty. In most practical cases, some amount of prior information about signals of primary users is available to secondary users but that information is never perfect. In order to design optimal spectrum sensing algorithms in such cases, we propose to employ the restricted Neyman-Pearson (NP) approach, which maximizes the average detection probability under constraints on the worst-case detection and false-alarm probabilities. We derive a restricted NP based spectrum sensing algorithm for additive Gaussian mixture noise channels, and compare its performance against the generalized likelihood ratio test (GLRT) and the energy detector. Simulation results show that the proposed spectrum sensing algorithm provides improvements over the other approaches in terms of minimum (worst-case) and/or average detection probabilities. © 2013 IEEE.
Open Access
What is usual in unusual videos? trajectory snippet histograms for discovering unusualness
(IEEE, 2014-06) İşcen, Ahmet; Armağan, Anıl; Duygulu, Pınar
Unusual events are important as being possible indicators of undesired consequences. Moreover, unusualness in everyday life activities may also be amusing to watch as proven by the popularity of such videos shared in social media. Discovery of unusual events in videos is generally attacked as a problem of finding usual patterns, and then separating the ones that do not resemble them. In this study, we address the problem from a different perspective, and try to answer what type of patterns are shared among unusual videos that make them resemble to each other regardless of the ongoing event. With this challenging problem at hand, we propose a novel descriptor to encode the rapid motions in videos utilizing densely extracted trajectories. The proposed descriptor, which is referred to as trajectory snipped histograms, is used to distinguish unusual videos from usual videos, and further exploited to discover snapshots in which unusualness happen. Experiments on domain specific people falling videos and unrestricted funny videos show the effectiveness of our method in capturing unusualness. © 2014 IEEE.