Bilgisayar Mühendisliği Bölümü Koleksiyonu
Permanent URI for this collectionhttps://gcris.khas.edu.tr/handle/20.500.12469/45
Browse
Recent Submissions
Conference Object Citation Count: 0Audience Tracking and Cheering Content Control in Sports Events(IEEE, 2020) Yeşilyurt, Gözdenur; Dursun, Sefa; Kumas, Osman; Çakir, Nagehan; Arsan, TanerSwearing cheers encountered in sports competitions do not comply with sports ethics and morals. Even if this kind of cheering is a group, the entire tribune block is penalized in accordance with the current rules. This method is not preventive and individual punishment should be used. The aim of this study is to determine the individuals who cheer with swearing content. In this study, the person detection is made with the multi-task cascaded convolutional neural network. Moreover, facial landmarks representing the facial regions and the regions related to them are determined as a result of this process. The mouth region is also determined by means of these important points removed, and finally the mouth is determined according to the equation. The face recognition is carried out because the person would be in a state of yelling if the mouth opening ratio exceeds the threshold value by determining the rate of opening. Landmarks extracted from the facial regions for the face recognition are transformed into feature vectors by FaceNet, and the model is created by classifying these vectors with classifiers to use in recognition process. When evaluated in terms of industry, face recognition and detection systems find a wide field of study.Article Citation Count: 1A Bayesian Approach To Developing a Strategic Early Warning System for the French Milk Market(Halmstad University, 2017) Bisson, Christophe; Gürpınar, FurkanA new approach is provided in our paper for creating a strategic early warning system allowing the estimation of the future state of the milk market as scenarios. This is in line with the recent call from the EU commission for tools that help to better address such a highly volatile market. We applied different multivariate time series regression and Bayesian networks on a pre-determined map of relations between macro-economic indicators. The evaluation of our findings with root mean square error (RMSE) performance score enhances the robustness of the prediction model constructed. Our model could be used by competitive intelligence teams to obtain sharper scenarios, leading companies and public organisations to better anticipate market changes and make more robust decisions.Book Part Citation Count: 2First Impressions on Social Network Sites: Impact of Self-Disclosure Breadth on Attraction(Academic Conferences and Publishing International Limited, 2017) Baruh, Lemi; Cemacılar, Zeynep; Bisson, Christophe; Chisik, Yoram I.This paper reports the results of two experiments that investigate the relationship between the quantity of information disclosed on an SNS profile and profile viewers' first impressions of the profile owner. Both experiments utilized a 2 (low quantity of information vs. high quantity of information) by 2 (male vs. female profile) design. In the first experiment (n = 1059), the respondents were randomly assigned to the experimental conditions. The results showed that profile viewers were more favorable to profiles of women. Also, both for female and male SNS profiles, higher quantity of information led to more positive ratings of the profile owner. The second experiment expanded the findings from the first experiment in two ways. First, in the second experiment (n = 320), rather than being randomly assigned to the profile gender condition, the respondents could pick the gender of the profile they would review. Second, informed by previous research on face to face interactions which indicate that quantity of self-disclosure can increase interpersonal attraction by reducing the level of uncertainty about relational outcomes, we tested whether uncertainty reduction mediated the relationship between quantity of information presented in an SNS profile and interpersonal attraction. Female profiles were selected more often than male profiles by both female and male respondents; however, there was no difference in interpersonal attraction ratings that male and female profiles received. Higher quantity of information presented in an SNS profile had a significant impact on interpersonal attraction. The results from the second experiment also indicated that while quantity of information positively influenced profile viewers' perceptions regarding the agreeableness of the profile owner, it did not have an impact on viewers' perceptions regarding the dependability of the profile owner. As predicted, the impact of quantity of information on interpersonal attraction was mediated by a reduction in uncertainty levels.Book Part Citation Count: 1Effect of Inter-Block Region on Compressed Sensing Based Channel Estimation in Tds-Ofdm Systems(Institute of Electrical and Electronics Engineers Inc., 2016) Başaran, Mehmet; Erküçük, Serhat; Şenol, Habib; Çırpan, Hakan AliTime domain synchronous orthogonal frequency division multiplexing (TDS-OFDM) is the basis technology for digital television standard (DTV) employed in some countries thanks to its high spectral efficiency when compared to traditional cyclic prefix OFDM. Moreover, it does not require pilot usage in frequency domain channel estimation. Instead of data usage as cyclic prefix, pseudo-noise (PN) sequences are transmitted in guard intervals. Due to interference from the previous OFDM data symbol, the received signal in guard interval can be decomposed into a small-sized signal that contains only PN sequences utilizing the inter-block-interference (IBI)-free region in the convolution matrix. Due to sparsity, multipath fading channel can be obtained by the application of compressed sensing (CS) technique to reconstruct the high-dimensional sparse channel from the decreased-size of received signal through the known PN sequence matrix. In this study, the effect of the size of IBI-free region on CS and Bayesian CS (BCS) based channel estimation is investigated. Accordingly, reconstruction error performances of basis pursuit (BP) and BCS are compared. Simulation results show that the channel estimation can be improved by trading-off the length of the IBI-free region. However, an increase in IBI-free region leads to decreased energy efficiency at both the transmitter and receiver side.Conference Object Citation Count: 1An Analysis for the Use of Compressed Sensing Method in Microwave Imaging(IEEE, 2017) Yiğit, Enes; Tekbaş, Mustafa; Ünal, İlhami; Erdoğan, Sercan; Çalışkan, CaferOne of the most important problems encountered in microwave imaging methods is intensive data processing traffic that occurs when high resolution and real time tracking is desired. Radar signals can be recovered without loss of data with a randomly selected subset of the measurement data by compression sensing (CS) method which has been popular in recent years. For this reason, in this study, the use and capabilities of the CS method were investigated for tracking moving human, and the target information was correctly determined for the data obtained much below the Nyquist sampling criterion. In this study, it was revealed that the CS method can be developed for target detection and trackingConference Object Citation Count: 2The Performance of the Rss-Based Lateration Algorithm for Indoor Localization(Institute of Electrical and Electronics Engineers Inc., 2019) Barodi, Lubana; Dağ, TamerLocalization methods have evolved over the years and led the invention of the Global Positioning System for outdoor localization. In time, the need for indoor localization has also arisen and research on indoor positioning systems has increased significantly. One of the most widely studied indoor localization algorithms is considered to be the Received Signal Strength-based lateration. The mentioned algorithm has a major advantage of being simple. In addition, it is low cost since it uses the existing infrastructure. In this paper, the performance of the RSS-based lateration is evaluated and compared for various scenarios through simulation studies. The accuracy of the algorithm is calculated when the path loss exponent value is known, when the path loss exponent value is estimated or when least-squares approximation is used. In addition, the impact of certain parameters such as the size of the area, the grid size or the noise level are investigated.Conference Object Citation Count: 1Ask me: A Question Answering System via Dynamic Memory Networks(Institute of Electrical and Electronics Engineers Inc., 2019) Yiğit, Gülsüm; Amasyalı, Mehmet FatihMost of the natural language processing problems can be reduced into a question answering problem. Dynamic Memory Networks (DMNs) are one of the solution approaches for question answering problems. Based on the analysis of a question answering system built by DMNs described in [1], this study proposes a model named DMN∗ which contains several improvements on its input and attention modules. DMN∗ architecture is distinguished by a multi-layer bidirectional LSTM (Long Short Term Memory) architecture on input module and several changes in computation of attention score in attention module. Experiments are conducted on Facebook bAbi dataset [2]. We also introduce Turkish bAbi dataset, and produce increased vocabulary sized tasks for each dataset. The experiments are performed on English and Turkish datasets and the accuracy performance results are compared by the work described in [1]. Our evaluation shows that the proposed model DMN∗ obtains improved accuracy performance results on various tasks for both Turkish and English.Conference Object Citation Count: 0Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Datasets Using Deep Learning(Springer, 2020) Karadayı, YıldızTechniques used for spatio-temporal anomaly detection in an unsupervised settings has attracted great attention in recent years. It has extensive use in a wide variety of applications such as: medical diagnosis, sensor events analysis, earth science, fraud detection systems, etc. Most of the real world time series datasets have spatial dimension as additional context such as geographic location. Although many temporal data are spatio-temporal in nature, existing techniques are limited to handle both contextual (spatial and temporal) attributes during anomaly detection process. Taking into account of spatial context in addition to temporal context would help uncovering complex anomaly types and unexpected and interesting knowledge about problem domain. In this paper, a new approach to the problem of unsupervised anomaly detection in a multivariate spatio-temporal dataset is proposed using a hybrid deep learning framework. The proposed approach is composed of a Long Short Term Memory (LSTM) Encoder and Deep Neural Network (DNN) based classifier to extract spatial and temporal contexts. Although the approach has been employed on crime dataset from San Francisco Police Department to detect spatio-temporal anomalies, it can be applied to any spatio-temporal datasets.Conference Object Citation Count: 0Multi-State Video Transmission With Network Coding(IEEE, 2018) Şengel, Öznur; Ekmekçi Flierl, SılaThe goal of this work is to send video packets to all nodes in the network by enveloping Multi-State Video Coding (MSVC) at the same time network coding to maximize the throughput and video quality. This work has two main parts: 1) Multi-State Video Coding and 2) Network Coding. The main purpose of this work is to maximize not only the video quality but also the network throughput. We used Multi-State Video Coding to achieve robustness and we used network coding to increase throughput over the network. After generating the two subsequences using MSVC, we apply network coding to support transmission of packets. In this manner, we aim to increase the throughput as well as robustness and quality of the video transmission.Article Citation Count: 11Channel Estimation for Tds-Ofdm Systems in Rapidly Time-Varying Mobile Channels(IEEE-Inst Electrical Electronics Engineers Inc, 2018) Başaran, Mehmet; Şenol, Habib; Erküçük, Serhat; Çırpan, Hakan AliThis paper explores the performance of time-domain synchronous orthogonal frequency-division multiplexing (TDS-OFDM) systems operated under rapidly time-varying mobile channels. Since a rapidly time-varying channel contains more unknown channel coefficients than the number of observations, the mobile channel can conveniently be modeled with the discrete Legendre polynomial basis expansion model to reduce the number of unknowns. The linear minimum mean square error (LMMSE) estimate can be exploited for channel estimation on inter-block-interference-free received signal samples owing to transmitting pseudo-noise (PN) sequences. In conventional TDS-OFDM systems, the channel estimation performance is limited due to estimating channel responses only from the beginning part of the channel. Therefore, a new system model named "partitioned TDS-OFDM system" is proposed to improve the system performance by inserting multiple PN sequences to the middle and end parts of the channel as well. In addition to providing the reconstruction error performance, Bayesian Cramer-Rao lower hound is derived analytically. Also, the LMMSE-based symbol detection is employed. To alleviate the negative effects of inter-carrier-interference (ICI) occuring in mobile channels, ICI cancellation is applied to enhance the detection performance. The simulation results demonstrate that the proposed TDS-OFDM system is superior to the conventional system and its corresponding performance is able to approach the achievable lower performance bound.Article Citation Count: 3Realistic Channel Estimation of Ieee 802.11af Systems in Tv White Space(Institute of Electrical and Electronics Engineers Inc., 2020) Başaran, Mehmet; Macit, Mustafa Can; Şenol, Habib; Erküçük, SerhatThis work investigates the realistic performance of IEEE 802.11af systems released for the efficient spectrum utilization of TV white space (TVWS). These systems are operated over many contiguous or non-contiguous channels based on the TVWS frequency band availability. Accordingly, we consider realistic channel estimation for TVWS system and analyze the corresponding system performance of linear minimum mean square error (LMMSE) and orthogonal matching pursuit (OMP) algorithms. While LMMSE estimates channel path gains assuming perfectly known tap delay locations, OMP estimates both channel path gains and delays. Owing to the realistic implementation and estimating the full channel state information (CSI) of sparse channels, we mainly assess the OMP performance together with the LMMSE estimation for comparison in terms of channel reconstruction and symbol detection errors. To address the channel estimation performance, Bayesian Cramer-Rao bound is derived theoretically for both perfect and imperfect CSI, and confirmed with the simulations. Simulation results demonstrate that the realistic OMP-based symbol detection performance is found to be only 1-2 dB inferior compared to the near-optimal LMMSE-based estimation with known delays in low and medium signal-to-noise-ratio regions, where communication mainly occurs in practice. In addition, the effects of channel multipath number, channel resolution and operation modes on the system performance are studied for different scenarios. The results of this work are important for the practical implementation of IEEE 802.11af-based systems.Article Citation Count: 2Subchannel Allocation and Power Control for Uplink Femtocell Radio Networks With Imperfect Channel State Information(Springer, 2019) Altabbaa, Mhd Tahssin; Arsan, Taner; Panayırcı, ErdalFemtocell technology is emerging as a key solution for mobile operators for its advantage in coverage and capacity enhancement along with its cost effectiveness. However, densely and randomly deployed femtocells while sharing the frequency spectrum of the macrocell arises a severe interference environment. In femtocells deployment, interference coming from a femtocell user affect other femtocell users and the macrocell users, where maintaining the communication of the users in both tiers is a mandatory. In this paper, a novel power control algorithm is proposed for optimizing the uplink transmission powers of femtocell users in a TDD-OFDM communication model in the presence of a channel estimation error and intra-tier interference. We consider signal to interference and noise ratio as the objective function where the proposed constraints deal with: (1) the aggregated interference coming from femtocell tier and received at the active subchannels by the macrocell tier, and (2) the maximum uplink power a femtocell user equipment is allowed to occupy per admissible subchannel. Based on Lagrangian multipliers, the proposed power control approach grants the priority in subchannel usage for macrocell user, then it allows or prohibits frequency reuse of a subchannel with the femtocell tier. A comparison is then made with a pure isolation method that does not allow femtocell user equipments to occupy the active subchannels at the macrocell tier. The numerical results of the proposed approach show a high total rate of femtocell user equipments and the average uplink power is below the maximum allowable transmission power.Article Citation Count: 18Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data Using Deep Learning: Early Detection of Covid-19 Outbreak in Italy(Ieee-Inst Electrıcal Electronıcs Engıneers Inc, 2020) Karadayı, Yıldız; Aydın, Mehmet Nafiz; Öğrenci, Arif SelçukUnsupervised anomaly detection for spatio-temporal data has extensive use in a wide variety of applications such as earth science, traffic monitoring, fraud and disease outbreak detection. Most real-world time series data have a spatial dimension as an additional context which is often expressed in terms of coordinates of the region of interest (such as latitude - longitude information). However, existing techniques are limited to handle spatial and temporal contextual attributes in an integrated and meaningful way considering both spatial and temporal dependency between observations. In this paper, a hybrid deep learning framework is proposed to solve the unsupervised anomaly detection problem in multivariate spatio-temporal data. The proposed framework works with unlabeled data and no prior knowledge about anomalies are assumed. As a case study, we use the public COVID-19 data provided by the Italian Department of Civil Protection. Northern Italy regions' COVID-19 data are used to train the framework; and then any abnormal trends or upswings in COVID-19 data of central and southern Italian regions are detected. The proposed framework detects early signals of the COVID-19 outbreak in test regions based on the reconstruction error. For performance comparison, we perform a detailed evaluation of 15 algorithms on the COVID-19 Italy dataset including the state-of-the-art deep learning architectures. Experimental results show that our framework shows significant improvement on unsupervised anomaly detection performance even in data scarce and high contamination ratio scenarios (where the ratio of anomalies in the data set is more than 5%). It achieves the earliest detection of COVID-19 outbreak and shows better performance on tracking the peaks of the COVID-19 pandemic in test regions. As the timeliness of detection is quite important in the fight against any outbreak, our framework provides useful insight to suppress the resurgence of local novel coronavirus outbreaks as early as possible.Article Citation Count: 6Novel Application Software for the Semi-Automated Analysis of Infrared Meibography Images(2019) Shehzad, Danish; Gorcuyeva, Sona; Dağ, Tamer; Bozkurt, BanuPurpose: To develop semi-automated application software that quickly analyzes infrared meibography images taken with the CSO Sirius Topographer (CSO, Italy) and to compare them to the manual analysis system on the device (Phoenix software platform). Methods: A total of 52 meibography images verified as high quality were used and analyzed through manual and semi-automated meibomian gland (MG) detector software in this study. For the manual method, an experienced researcher circumscribed the MGs by putting dots around grape-like clusters in a predetermined rectangular area, and Phoenix software measured the MG loss area by percentage, which took around 10 to 15 minutes. MG loss was graded from 1 (<25%) to 4 (severe >75%). For the semi-automated method, 2 blind physicians (I and II) determined the area to be masked by putting 5 to 6 dots on the raw images and measured the MG loss area using the newly developed semi-automated MG detector application software in less than 1 minute. Semi-automated measurements were repeated 3 times on different days, and the results were evaluated using paired-sample t test, Bland-Altman, and kappa κ analysis. Results: The mean MG loss area was 37.24% with the manual analysis and 40.09%, 37.89%, and 40.08% in the first, second, and third runs with the semi-automated analysis (P < 0.05). Manual analysis scores showed a remarkable correlation with the semi-automated analysis performed by 2 operators (r = 0.950 and r = 0.959, respectively) (P < 0.001). According to Bland-Altman analysis, the 95% limits of agreement between manual analysis and semi-automated analysis by operator I were between -10.69% and 5% [concordance correlation coefficient (CCC) = 0.912] and between -9.97% and 4.3% (CCC = 0.923) for operator II. The limit of interoperator agreement in semi-automated analysis was between -4.89% and 4.92% (CCC = 0.973). There was good to very good agreement in grading between manual and semi-automated analysis results (κ 0.76-0.84) and very good interoperator agreement with semi-automated software (κ 0.91) (P < 0.001). Conclusions: For the manual analysis of meibography images, around one hundred dots have to be put around grape-like clusters to determine the MGs, which makes the process too long and prone to errors. The newly developed semi-automated software is a highly reproducible, practical, and faster method to analyze infrared meibography images with excellent correlation with the manual analysis.Article Citation Count: 0Büyük Patlama – Büyük Çöküş Optimizasyon Yöntemi Kullanılarak Bluetooth Tabanlı İç Mekan Konum Belirleme Sisteminin Doğruluğunun İyileştirilmesi(Süleyman Demirel Üniversitesi, 2018) Arsan, TanerDüşük enerjili Bluetooth işaretçi (Bluetooth low energy - BLE beacon) teknolojisi, iç mekan konum belirleme sistemlerinde başarılı ve düşük maliyetli çözümler sunan gelişmekte olan bir teknolojidir. Bu çalışmada, BLE işaretçileri (beacons) kullanan bir iç mekan konum belirleme sistemi geliştirilmiş, kullanılan ilave algoritmalarla standart sensörlerden elde edilen konum değerlerinin doğruluğunun artırılması amaçlanmıştır. Bunun için, deneysel iç mekan konum algılama sisteminden elde edilen konum bilgilerine Büyük Patlama – Büyük Çöküş (Big Bang – Big Crunch (BB-BC)) optimizasyon yöntemi uygulanmış ve konum doğruluğunun geliştirildiği yapılan testlerle kanıtlanmıştır. Test alanı olarak, 9,60 m × 3,90 m boyutundaki 37,44 m2'lik alan seçilmiş ve 2,40 m × 1,30 m boyutundaki oniki tane ızgara alanına ayak izi (fingerprinting) algoritması uygulanmıştır. Test alanına dört tane BLE işaretçi (beacon) yerleştirilmiş, on iki test alanından 150 saniye boyunca toplam 9.000 ölçüm yapılmıştır. Ölçüm sonuçları Büyük Patlama – Büyük Çöküş optimizasyon yöntemi ile Öklid uzaklık eşleştirme yöntemi ve Kalman Filtresi kullanılarak iyileştirilmiş, bu sayede konum doğruluğu %26,62'den %75,69'a arttırılmıştır.Article Citation Count: 0Büyük Patlama Büyük Çöküş Optimizasyon Yöntemi ile Ultra Geniş Band Sensörlerinin İç Mekân Konum Belirleme Doğruluklarının İyileştirilmesi(Pamukkale Üniversitesi, 2018) Arsan, TanerUltra geniş band teknolojisi, birçok iç mekân konum belirleme sisteminde başarılı çözümler sunan, diğer yöntemlere kıyasla daha iyi performans gösteren, gelişmekte olan bir teknolojidir. Bu çalışmada, ultra geniş band (Ultra Wide Band-UWB) sensörler kullanılarak bir iç mekân konum belirleme sistemi geliştirilmiş ve kullanılan ek algoritmalarla, standart donanımların sağladığı doğruluk düzeyi arttırılırken aynı zamanda ortalama hatayı azaltmak hedeflenmiştir. Bu amaçla Büyük Patlama - Büyük Çöküş (Big Bang-Big Crunch veya BB-BC) optimizasyon yöntemi deneysel iç mekân konumlandırma sistemine uygulanmış ve ölçüm doğruluğu üzerindeki olumlu etkisi yapılan testlerle kanıtlanmıştır. Test alanı olarak 7.35 m × 5.41 m boyutlarında 39.76 m2 'lik bir alan seçilmiş ve özel olarak tasarlanmış bir tavan sistemine yerden 2.85 m yüksekliğe üç farklı UWB alıcı yerleştirilmiş ve 182 adet test noktasından 60 sn.süreyle toplam 10.920 ölçüm alınmıştır. Ölçüm sonuçları Büyük Patlama - Büyük Çöküş optimizasyon algoritması ile düzeltilerek, ortalama hatası önceki 20.72 cm değerinden 15.02 cm’ye düşürülmüş, böylelikle ölçüm sonuçlarının doğruluğu arttırılmıştır.Article Citation Count: 9Lsb Image Steganography Based on Blocks Matrix Determinant Method(KSII-KOR SOC Internet Information, 2019) Shehzad, Danish; Dağ, TamerImage steganography is one of the key types of steganography where a message to be sent is hidden inside the cover image. The most commonly used techniques for image steganography rely on LSB steganography. In this paper, a novel image steganography technique based on blocks matrix determinant method is proposed. Under this method, a cover image is divided into blocks of size 2 x 2 pixels and the determinant of each block is calculated. The comparison of the determinant values and corresponding data bits yields a delicate way for the embedment of data bits. The main aim of the proposed technique is to ensure concealment of secret data inside an image without affecting the cover image quality. When the proposed steganography method is compared with other existing LSB steganography methods, it is observed that it not only provides higher PSNR, lower MSE but also guarantees better quality of the stego image.Article Citation Count: 10A Clustering-Based Approach for Improving the Accuracy of Uwb Sensor-Based Indoor Positioning System(Hindawi LTD, 2019) Arsan, Taner; Hameez, Mohammed Muwafaq NooriThere are several methods which can be used to locate an object or people in an indoor location. Ultra-wideband (UWB) is a specifically promising indoor positioning technology because of its high accuracy, resistance to interference, and better penetration. This study aims to improve the accuracy of the UWB sensor-based indoor positioning system. To achieve that, the proposed system is trained by using the K-means algorithm with an additional average silhouette method. This helps us to define the optimal number of clusters to be used by the K-means algorithm based on the value of the silhouette coefficient. Fuzzy c-means and mean shift algorithms are added for comparison purposes. This paper also introduces the impact of the Kalman filter while using the measured UWB test points as an input for the Kalman filter in order to obtain a better estimation of the position. As a result, the average localization error is reduced by 43.26% (from 16.3442 cm to 9.2745 cm) when combining the K-means algorithm with the Kalman filter in which the Kalman-filtered UWB-measured test points are used as an input for the proposed system.Article Citation Count: 4Accurate Indoor Positioning With Ultra-Wide Band Sensors(Tubitak, 2020) Arsan, TanerUltra-wide band is one of the emerging indoor positioning technologies. In the application phase, accuracy and interference are important criteria of indoor positioning systems. Not only the method used in positioning, but also the algorithms used in improving the accuracy is a key factor. In this paper, we tried to eliminate the effects of off-set and noise in the data of the ultra-wide band sensor-based indoor positioning system. For this purpose, optimization algorithms and filters have been applied to the raw data, and the accuracy has been improved. A test bed with the dimensions of 7.35 m x 5.41 m and 50 cm x 50 cm grids has been selected, and a total of 27,000 measurements have been collected from 180 test points. The average positioning error of this test bed is calculated as 16.34 cm. Then, several combinations of algorithms are applied to raw data. The combination of Big Bang-Big Crunch algorithm for optimization, and then the Kalman Filter have yielded the most accurate results. Briefly, the average positioning error has been reduced from 16.34 cm to 7.43 cm.Conference Object Citation Count: 1An Analysis For The Use Of Compressed Sensing Method İn Microwave İmaging [mikrodalga Görüntülemede Sıkıştırılmış Algılama Yönteminin Kullanımına Yönelik Bir Analiz](Institute of Electrical and Electronics Engineers Inc., 2017) Yiğit, Enes; Tekbaş, Mustafa; Ünal, İlhami; Erdogan, Sercan; Çalışkan, CaferOne of the most important problems encountered in microwave imaging methods is intensive data processing traffic that occurs when high resolution and real time tracking is desired. Radar signals can be recovered without loss of data with a randomly selected subset of the measurement data by compression sensing (CS) method which has been popular in recent years. For this reason in this study the use and capabilities of the CS method were investigated for tracking moving human and the target information was correctly determined for the data obtained much below the Nyquist sampling criterion. In this study it was revealed that the CS method can be developed for target detection and tracking. © 2017 IEEE.
