Browsing by Subject "Detection"

Now showing 1 - 20 of 32

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 for noncoherent detection of a modulated sinusoidal carrier
(Institute of Electrical and Electronics Engineers, 2018) Öztürk, Cüneyd; Dülek, B.; Gezici, Sinan
In this correspondence paper, the problem of noncoherent detection of a sinusoidal carrier is considered in the presence of Gaussian noise. The convexity properties of the detection probability are characterized with respect to the signal-To-noise ratio (SNR). It is proved that the detection probability is a strictly concave function of SNR when the false alarm probability α satisfies α > e-2, and it is first a strictly convex function and then a strictly concave function of SNR for α < e-2. In addition, optimal power allocation strategies are derived under average and peak power constraints. It is shown that on-off signaling can be optimal for α < e-2 depending on the power constraints, whereas transmission at a constant power level that is equal to the average power limit is optimal in all other cases.
Open Access
Cost constrained sensor selection and design for binary hypothesis testing
(2020-01) Oymak, Berkay
We consider a sensor selection problem for binary hypothesis testing with costconstrained measurements. Random observations related to a parameter vector of interest are assumed to be generated by a linear system corrupted with Gaussian noise. The aim is to decide on the state of the parameter vector based on a set of measurements collected by a limited number of sensors. The cost of each sensor measurement is determined by the number of amplitude levels that can reliably be distinguished. By imposing constraints on the total cost and the maximum number of sensors that can be employed, a sensor selection problem is formulated in order to maximize the detection performance for binary hypothesis testing. By characterizing the form of the solution corresponding to a relaxed version of the optimization problem, a computationally eﬃcient algorithm with near optimal performance is proposed. In addition to the case of ﬁxed sensor measurement costs, we also consider the case where they are subject to design. In particular, the problem of allocating the total cost budget to a limited number of sensors is addressed by designing the measurement accuracy (i.e., the noise variance) of each sensor to be employed in the detection procedure. The optimal solution is obtained in closed form. Numerical examples are presented to corroborate the proposed methods.
Open Access
Deep learning for radar signal detection in electronic warfare systems
(IEEE, 2020) Nuhoglu, M. A.; Alp, Y. K.; Akyön, Fatih Çağatay
Detection of radar signals is the initial step for passive systems. Since these systems do not have prior information about received signal, application of matched filter and general likelihood ratio tests are infeasible. In this paper, we propose a new method for detecting received pulses automatically with no restriction of having intentional modulation or pulse on pulse situation. Our method utilizes a cognitive detector incorporating bidirectional long-short term memory based deep denoising autoencoders. Moreover, a novel loss function for detection is developed. Performance of the proposed method is compared to two well known detectors, namely: energy detector and time-frequency domain detector. Qualitative experiments show that the proposed method is able to detect presence of a signal with low probability of false alarm and it outperforms the other methods in all signal-to-noise ratio cases.
Open Access
Detection of underdeveloped hazelnuts from fully developed nuts by impact acoustics
(American Society of Agricultural and Biological Engineers, 2006) Onaran, I.; Pearson, T. C.; Yardimci, Y.; Çetin, A. Enis
Shell-to-kernel weight ratio is a vital measurement of quality in hazelnuts as it helps to identify nuts that have underdeveloped kernels. Nuts containing underdeveloped kernels may contain mycotoxin-producing molds, which are linked to cancer and are heavily regulated in international trade. A prototype system was set up to detect underdeveloped hazelnuts by dropping them onto a steel plate and recording the acoustic signal that was generated when a kernel hit the plate. A feature vector comprising line spectral frequencies and time-domain maxima that describes both the time and frequency nature of the impact sound was extracted from each sound signal and used to classify each nut by a support-vector machine. Experimental studies demonstrated accuracies as high as 97% in classifying hazelnuts with underdeveloped kernels.
Open Access
Distributed MIMO radar signal processing
(2022-07) Ercan, Mahmut Kemal
Radar systems are remote sensing tools that generate electromagnetic waves and extract information by receiving altered versions of these waves. Nowadays, many radar types are being used in speciﬁc areas such as weather prediction, automobiles, and the military. One type of radar employed in military applica-tions is called the multistatic radar system. Multistatic radar systems consist of multiple transmitters and receivers widely separated from each other. Although multistatic radar systems have not been invented recently, one type of multistatic radar has recently taken the attention of the literature, the multiple input mul-tiple output (MIMO) radar system. In this thesis, we analyze the performance of some techniques presented in the MIMO radar literature, make improvements, and propose new methods. First, we review the literature for MIMO radar waveform generation. Then, we propose a parameter estimation technique for multiple target cases using the polyphased-piecewise linear frequency modulated (PPLFM) waveform. Secondly, we propose a detection algorithm in which each receiver preprocesses received sig-nals and extracts bistatic range and Doppler for each transmitter. A grid of points in the region of interest (ROI) is generated, and by using a weighting function, a weight for each plot is calculated, and detection is performed via thresholding. Third, we propose a policy-iteration-based position and velocity estimation algo-rithm. We deﬁne a cost function using bistatic range and Doppler measurements for the proposed estimation algorithm. To perform estimation in the presence of multiple targets, we conduct data association by weighting the bistatic measure-ments. Fourth, a tracking algorithm that uses the Generalized Multi Bernoulli Filter is proposed. Lastly, we investigate the alternative MIMO antenna struc-tures and analyze the detection and tracking performance of the Electromagnetic Vector Sensor (EMVS). At the end of the thesis, it is demonstrated that the performance of the proposed algorithms is promising. Additionally, we show that the detection and tracking performance of the EMVS-based MIMO radar system is better than the performance of the MIMO radar system with dipole antennas.
Open Access
The effect of context-dependent lightness on contrast detection and identification, and its neural correlates
(2017-10) Karatok, Zahide Pamir
Perceived contrast of a grating varies with its background (or mean) luminance: of the two gratings with the same photometric contrast the one on higher luminance background appears to have higher contrast. On the other hand, context often causes a large perceived difference between equiluminant regions (e.g., simultaneous brightness contrast). Does perceived contrast also vary with contextdependent background lightness even when the luminance remains constant? In this study, the effect of context-dependent lightness on contrast perception was investigated using psychophysical and functional magnetic resonance imaging (fMRI) methods. First, we measured appearance judgments of participants and demonstrated that context-dependent lightness of background in uences the perceived contrast of rectified gratings. Perceived contrast of gratings superimposed on equiluminant but perceptually lighter background is higher compared to ones on perceptually darker backgrounds. However, this pattern is valid only for incremental, not for decremental contrast. Literature indicates a significant difference between visual processing near and above threshold. Also, behaviorally it has been shown that appearance and threshold tasks are mediated by different mechanisms. Therefore, here, we also measured the effect of context-dependent lightness on contrast detection and discrimination thresholds using a 2-IFC procedure. Results indicate that both detection and discrimination thresholds are lower for the gratings superimposed on perceptually lighter backgrounds. Differently from the appearance results, the effect was observed both for incremental and decremental contrast. In an fMRI study, we investigated whether activity in any brain region correlates with background-lightness-dependent contrast perception. Although our stimulus was physically identical, we observed difference in BOLD response within pre-defined region of interests (ROIs) in different visual areas. Both for incremental and decremental contrast, activation, especially in V1, was greater when the grating was superimposed on lighter background for all the contrast levels tested. Variation in V1 activity with varying contrast links better with the detection and discrimination thresholds than the appearance results. Therefore, this study might offer a neural evidence for dissociation between the mechanisms underlying detection (threshold) and identification (appearance) measures. However, the relationship between the threshold and fMRI data does not really agree with the previous findings in literature. These results indicate that the neural activation caused by the detection mechanism may change depending on the absolute or perceived value of the contrast.
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
Ghostware and rootkit detection techniques for windows
(2006) Bozağaç, Cumhur Doruk
Spyware is a significant problem for most computer users. In public, the term spyware is used with the same meaning as adware, a kind of malicious software used for showing advertisements to the user against his will. Spyware programs are also known for their tendency to hide their presence, but advanced stealth techniques used to be either nonexistent or relatively primitive in terms of effectiveness. In other words, most of the spyware programs were efficient at spying but not very efficient at hiding. This made spyware easily detectable with simple file-scanning and registry-scanning techniques. New spyware programs have merged with rootkits and gained stealth abilities, forming spyware with advanced stealth techniques. In this work we focus on this important subclass of spyware, namely ghostware. Ghostware programs hide their resources from the Operating System Application Programming Interfaces that were designed to query and enumerate them. The resources may include files, Windows Registry entries, processes, and loaded modules and files. In this work, we enumerated these hiding techniques and studied the stealth detection methodologies. We also investigated the effectiveness of the hiding techniques against popular anti-virus programs and anti-spyware programs together with publicly available ghostware detection and rootkit detection tools. The results show that, anti-virus programs or anti-spyware programs are not effective for detecting or removing ghostware applications. Hidden object detection or rootkit detection tools can be useful, however, these tools can only work after the computer is infected and they do not provide any means for removing the ghostware. As a result, our work shows the need for understanding the potential dangers and applications of ghostware and implementing new detection and prevention tools.
Open Access
Joint detection and decoding in the presence of prior information with uncertainty
(Institute of Electrical and Electronics Engineers Inc., 2016) Bayram, S.; Dulek, B.; Gezici, Sinan
An optimal decision framework is proposed for joint detection and decoding when the prior information is available with some uncertainty. The proposed framework provides tradeoffs between the average inclusive error probability (computed using estimated prior probabilities) and the worst case inclusive error probability according to the amount of uncertainty while satisfying constraints on the probability of false alarm and the maximum probability of miss-detection. Theoretical results that characterize the structure of the optimal decision rule according to the proposed criterion are obtained. The proposed decision rule reduces to some well-known detectors in the case of perfect prior information or when the constraints on the probabilities of miss-detection and false alarm are relaxed. Numerical examples are provided to illustrate the theoretical results. © 2016 IEEE.
Open Access
Noise benefits in joint detection and estimation problems
(Elsevier, 2016) Akbay, A. B.; Gezici, Sinan
Adding noise to inputs of some suboptimal detectors or estimators can improve their performance under certain conditions. In the literature, noise benefits have been studied for detection and estimation systems separately. In this study, noise benefits are investigated for joint detection and estimation systems. The analysis is performed under the Neyman-Pearson (NP) and Bayesian detection frameworks and according to the Bayesian estimation criterion. The maximization of the system performance is formulated as an optimization problem. The optimal additive noise is shown to have a specific form, which is derived under both NP and Bayesian detection frameworks. In addition, the proposed optimization problem is approximated as a linear programming (LP) problem, and conditions under which the performance of the system can or cannot be improved via additive noise are obtained. With an illustrative numerical example, performance comparison between the noise enhanced system and the original system is presented to support the theoretical analysis.
Open Access
Noise benefits in joint detection and estimation systems = Birlikte sezim ve kestirim sistemlerinde gürültünün faydaları
(2014) Akbay, Abdullah Başar
Adding noise to inputs of some suboptimal detectors or estimators can improve their performance under certain conditions. In the literature, noise benefits have been studied for detection and estimation systems separately. In this thesis, noise benefits are investigated for joint detection and estimation systems. The analysis is performed under the Neyman-Pearson (NP) and Bayesian detection frameworks and the Bayesian estimation framework. The maximization of the system performance is formulated as an optimization problem. The optimal additive noise is shown to have a specific form, which is derived under both NP and Bayesian detection frameworks. In addition, the proposed optimization problem is approximated as a linear programming (LP) problem, and conditions under which the performance of the system cannot be improved via additive noise are obtained. With an illustrative numerical example, performance comparison between the noise enhanced system and the original system is presented to support the theoretical analysis.
Open Access
Noise enhanced detection in restricted Neyman-Pearson framework
(2013) Gültekin, Şan
Hypothesis tests frequently arise in many different engineering problems. Among the most frequently used tests are Bayesian, minimax, and Neyman-Pearson. Even though these tests are capable of addressing many real-life problems, they can be insufficient in certain scenarios. For this reason, developing new hypothesis tests is an important objective. One such developed test is the restricted NeymanPearson test, where one tries to maximize the average detection probability while keeping the worst-case detection and false-alarm probabilities bounded. Finding the best hypothesis testing approach for a problem-at-hand is an important point. Another important one is to employ a detector with an acceptable performance. In particular, if the employed detector is suboptimal, it is crucial that it meets the performance requirements. Previous research has proven that performance of some suboptimal detectors can be improved by adding noise to their inputs, which is known as noise enhancement. In this thesis we investigate noise enhancement according to the restricted Neyman-Pearson framework. To that aim, we formulate an optimization problem for optimal additive noise. Then, generic improvability and nonimprovability conditions are derived, which specify if additive noise can result in performance improvements. We then analyze the special case in which the parameter space is discrete and finite, and show that the optimal noise probability density function is discrete with a certain number of point masses. The improvability results are also extended and more precise conditions are derived. Finally, a numerical example is provided which illustrates the theoretical results and shows the benefits of applying noise enhancement to a suboptimal detector.
Open Access
Noise enhanced detection in the restricted Bayesian framework
(IEEE, 2010) Bayram, Suat; Gezici, Sinan; Poor H.V.
Effects of additive independent noise are investigated for sub-optimal detectors according to the restricted Bayes criterion. The statistics of optimal additive noise are characterized. Also, sufficient conditions for improvability or nonimprovability of detection via additive noise are obtained. A detection example is presented to study the theoretical results. ©2010 IEEE.
Open Access
Noise enhanced hypothesis-testing according to restricted Neyman-Pearson criterion
(Academic Press, 2014) Bayram, S.; Gultekin, S.; Gezici, Sinan
Noise enhanced hypothesis-testing is studied according to the restricted Neyman-Pearson (NP) criterion. First, a problem formulation is presented for obtaining the optimal probability distribution of additive noise in the restricted NP framework. Then, sufficient conditions for improvability and nonimprovability are derived in order to specify if additive noise can or cannot improve detection performance over scenarios in which no additive noise is employed. Also, for the special case of a finite number of possible parameter values under each hypothesis, it is shown that the optimal additive noise can be represented by a discrete random variable with a certain number of point masses. In addition, particular improvability conditions are derived for that special case. Finally, theoretical results are provided for a numerical example and improvements via additive noise are illustrated. © 2013 Elsevier Inc.
Open Access
Noise enhanced M-ary composite hypothesis-testing in the presence of partial prior information
(IEEE, 2010-12-06) Bayram, S.; Gezici, Sinan
In this correspondence, noise enhanced detection is studied for M-ary composite hypothesis-testing problems in the presence of partial prior information. Optimal additive noise is obtained according to two criteria, which assume a uniform distribution (Criterion 1) or the least-favorable distribution (Criterion 2) for the unknown priors. The statistical characterization of the optimal noise is obtained for each criterion. Specifically, it is shown that the optimal noise can be represented by a constant signal level or by a randomization of a finite number of signal levels according to Criterion 1 and Criterion 2, respectively. In addition, the cases of unknown parameter distributions under some composite hypotheses are considered, and upper bounds on the risks are obtained. Finally, a detection example is provided in order to investigate the theoretical results.
Open Access
Noise-enhanced M-ary hypothesis-testing in the minimax framework
(IEEE, 2009-09) Bayram, Suat; Gezici, Sinan
In this study, the effects of adding independent noise to observations of a suboptimal detector are studied for M-ary hypothesis-testing problems according to the minimax criterion. It is shown that the optimal additional noise can be represented by a randomization of at most M signal values under certain conditions. In addition, a convex relaxation approach is proposed to obtain an accurate approximation to the noise probability distribution in polynomial time. Furthermore, sufficient conditions are presented to determine when additional noise can or cannot improve the performance of a given detector. Finally, a numerical example is presented. © 2009 IEEE.
Open Access
On the improvability and nonimprovability of detection via additional independent noise
(IEEE, 2009-07-28) Bayram, S.; Gezici, Sinan
Addition of independent noise to measurements can improve performance of some suboptimal detectors under certain conditions. In this letter, sufficient conditions under which the performance of a suboptimal detector cannot be enhanced by additional independent noise are derived according to the Neyman–Pearson criterion. Also, sufficient conditions are obtained to specify when the detector performance can be improved. In addition to a generic condition, various explicit sufficient conditions are proposed for easy evaluation of improvability. Finally, a numerical example is presented and the practicality of the proposed conditions is discussed.
Open Access
On the optimality of likelihood ratio test for prospect theory-based binary hypothesis testing
(Institute of Electrical and Electronics Engineers, 2018) Gezici, Sinan; Varshney, P. K.
In this letter, the optimality of the likelihood ratio test (LRT) is investigated for binary hypothesis testing problems in the presence of a behavioral decision-maker. By utilizing prospect theory, a behavioral decision-maker is modeled to cognitively distort probabilities and costs based on some weight and value functions, respectively. It is proved that the LRT may or may not be an optimal decision rule for prospect theory-based binary hypothesis testing, and conditions are derived to specify different scenarios. In addition, it is shown that when the LRT is an optimal decision rule, it corresponds to a randomized decision rule in some cases; i.e., nonrandomized LRTs may not be optimal. This is unlike Bayesian binary hypothesis testing, in which the optimal decision rule can always be expressed in the form of a nonrandomized LRT. Finally, it is proved that the optimal decision rule for prospect theory-based binary hypothesis testing can always be represented by a decision rule that randomizes at most two LRTs. Two examples are presented to corroborate the theoretical results.