A thorough derivation of the Expectation Maximization (EM) algorithm, which is an iterative numer... more A thorough derivation of the Expectation Maximization (EM) algorithm, which is an iterative numerical method of Maximum Likelihood (ML) estimation, is pre sented for the case of estimating direction of arrivals of unknown deterministic wide-band signals incident from different directions onto a passive array. For the rec^uired signal estimation, alternative regularized least squares estimation tech niques are proposed with significant improvement over the standard least squares techniques. Also, for the angle of arrival estimation of a large number of signals, a tree structured EM algorithm is proposed and compared with the conventional EM approach. Extensive simulation results are presented for comparison of the proposed algorithms with the current high-resolution methods of wide-band di rection finding. In order to handle efficiently the case of available parametric prior models on the received waveforms, the required modifications are also given.
A novel algorithm is proposed for efficiently smoothing the slices of the Wigner distribution by ... more A novel algorithm is proposed for efficiently smoothing the slices of the Wigner distribution by exploiting the recently developed relation between the Radon transform of the ambiguity function and the fractional Fourier transformation [ I ]. The main advantage of the new algorithm is its ability to suppress cross-term interference on chirp-like autocomponents without any detrimental effect to the autocomponents. For a signal with N samples, the computational complexity of the algorithm is O (N log N) flops for each smoothed slice of the Wigner distribution.
Detection and classification of radars based on pulses they transmit is an important application ... more Detection and classification of radars based on pulses they transmit is an important application in electronic warfare systems. In this work, we propose a novel deep-learning based technique that automatically recognizes intra-pulse modulation types of radar signals. Reassigned spectrogram of measured radar signal and detected outliers of its instantaneous phases filtered by a special function are used for training multiple convolutional neural networks. Automatically extracted features from the networks are fused to distinguish frequency and phase modulated signals. Simulation results show that the proposed FF-CNN (Feature Fusion based Convolutional Neural Network) technique outperforms the current state-of-the-art alternatives and is easily scalable among broad range of modulation types.
The expectation maximization (EM) algorithm is presented for the case of estimating direction of ... more The expectation maximization (EM) algorithm is presented for the case of estimating direction of arrivals of unknown deterministic wideband signals. Alternative regularized least squares estimation techniques for the required signal estimation and a tree structure for the data mapping in the EM algorithm are proposed. Extensive simulation results are presented for comparison of the proposed algorithms with the conventional EM approach and the current high-resolution methods of wideband direction finding.
IEEE Transactions on Signal Processing, Dec 15, 2017
In this paper, optimal power allocation strategies are investigated for jammer nodes in a wireles... more In this paper, optimal power allocation strategies are investigated for jammer nodes in a wireless localization system. Building upon the concept of the restricted Bayesian approach, a generalized optimization strategy, called the restricted scheme, is proposed for power allocation of jammer nodes, and its theoretical properties are characterized. In the restricted scheme, the aim is to maximize the average Cramér-Rao lower bound (CRLB) of target nodes while keeping their minimum CRLB above a predefined level in the presence of average (total) and peak power constraints. It is proved that the average CRLB achieved by the restricted scheme is a strictly decreasing and concave function of the constraint on the minimum CRLB level. A closed-form solution is obtained for the restricted scheme when the tradeoff parameter and the total power limit are below certain thresholds. In addition, it is shown that the optimal solution of the restricted scheme corresponds to the use of at most N T jammer nodes, where N T is the number of target nodes, and that the optimal solution of the minimum CRLB maximization scheme is determined by at most N J target nodes, where N J is the number of jammer nodes. Extensions of the restricted scheme and an alternative scheme that aims to maximize the number of disabled target nodes (whose CRLBs are above a preset level) are considered, and the corresponding optimal strategies for jammer power allocation are identified. Numerical examples are provided to verify the theoretical derivations for various network configurations.
IEEE Transactions on Information Forensics and Security, 2020
Specific Emitter Identification (SEI) is the process of specifically identifying mobile transmitt... more Specific Emitter Identification (SEI) is the process of specifically identifying mobile transmitters by extracting unique features from the precise measurements of their emitted signals. A novel signal processing scheme with two stages is proposed for the identification of specific radar emitters. In the first stage, the received radar pulses are accurately time aligned and coherently integrated in order to increase the Signal-to-Noise (SNR) ratio. Using this technique, measurements with SNR improvements of more than 25 dB are obtained, enabling detection of subtle differences between different emitters. In the second step, Variational Mode Decomposition (VMD) is used to decompose both the envelope and the instantaneous frequency of the received signal into a set of modes. Then, these mod signals are characterized by using a group of features for identification. We demonstrate highly successful identification performance with the proposed method on real radar datasets.
Semantic signal processing and communications are poised to play a central part in developing the... more Semantic signal processing and communications are poised to play a central part in developing the next generation of sensor devices and networks. A crucial component of a semantic system is the extraction of semantic signals from the raw input signals, which has become increasingly tractable with the recent advances in machine learning (ML) and artificial intelligence (AI) techniques. The accurate extraction of semantic signals using the aforementioned ML and AI methods, and the detection of semantic innovation for scheduling transmission and/or storage events are critical tasks for reliable semantic signal processing and communications. In this work, we propose a reliable semantic information extraction framework based on our previous work on semantic signal representations in a hierarchical graph-based structure. The proposed framework includes a time integration method to increase fidelity of ML outputs in a class-aware manner, a graph-edit-distance based metric to detect innovation events at the graph-level and filter out sporadic errors, and a Hidden Markov Model (HMM) to produce smooth and reliable graph signals. The proposed methods within the framework are demonstrated individually and collectively through simulations and case studies based on real-world computer vision examples.
Multi-antenna radars exhibit positively correlated detection performance with the number of eleme... more Multi-antenna radars exhibit positively correlated detection performance with the number of elements utilized. The feasibility of refining antenna arrays to reduce cost of operation with only marginal loss of performance has attracted significant attention as utilizing a large number of elements may be prohibitively costly in terms of computation and power. Under cognitive radar paradigm, the goal is to choose an optimal or near optimal subset of elements from an antenna array of pre-specified geometry while meeting certain performance and cost criteria. In this work, we present optimization based selection methods for certain array geometries to select the best K element sub-array in terms of Cramér-Rao lower bound (CRB) on direction-ofarrival (DoA) estimations. Our results indicate that it is possible to reduce K up to a certain point without significant reduction in DoA estimation performance. The maximum possible reduction in K depends on the operating signal-to-noise ratio (SNR) and how much performance loss is tolerated. Thus, once the operating SNR is known, it is possible to utilize fewer array elements with slight decrease in performance.
2020 28th European Signal Processing Conference (EUSIPCO), 2021
Design of measurement matrices is an important aspect of compressed sensing (CS) based direction ... more Design of measurement matrices is an important aspect of compressed sensing (CS) based direction of arrival (DoA) applications that enables reduction in the analog channels to be processed in sparse target environments. Here, a novel measurement matrix design methodology for CS based DoA estimation is proposed and its superior performance over alternative measurement matrix design methodologies is demonstrated. The proposed method uses prior probability distribution of the targets to improve performance. Compared to the state-of-the-art techniques, it is quantitatively demonstrated that the proposed measurement matrix design approach enables significant reduction in the number of analog channels to be processed and adapts to a priori information on the target scene.
Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181)
ш т е і ш ш м в^ш > м ж^х г ш з а с ш ю м a r f í i s s M E S P Îêîa S 3*üjT3iEE-la^Г 'Г ρ-. .f i... more ш т е і ш ш м в^ш > м ж^х г ш з а с ш ю м a r f í i s s M E S P Îêîa S 3*üjT3iEE-la^Г 'Г ρ-. .f iirr-i' B íJB M rm sD ІЮ TJ33¡ jij>m Ί-ш ш в т ю ш-с^ .¿ .ж. ■ OF ш г т т а з ж
2014 22nd European Signal Processing Conference (EUSIPCO), 2014
Compressed sensing allows for a significant reduction of the number of measurements when the sign... more 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.
2009 International Conference on Electrical and Electronics Engineering - ELECO 2009, 2009
The power consumption of a standard SRAM during read/write operations is dependent on the address... more The power consumption of a standard SRAM during read/write operations is dependent on the address applied, the data accessed, and the type of access (read/write). The power analysis resistant SRAM structure developed during the Project ¿SCARD¿ (Side Channel Analysis Resistant Design Flow) of the European Community 6. Framework Program reduces the dependency of power consumption on data and address compared to standard SRAM at the expense of higher power and silicon area consumption. In this work a new SRAM primitive cell structure is proposed to reduce the power consumption and its dependency to data to be written.
2018 26th Signal Processing and Communications Applications Conference (SIU), May 1, 2018
Özetçe-Sıkıştırılmış Algılama (SA) tabanlı teknikler genellikle sinyal uzayını ayrıklaştırarak bi... more Özetçe-Sıkıştırılmış Algılama (SA) tabanlı teknikler genellikle sinyal uzayını ayrıklaştırarak bir ızgara üzerinde ifade ederler. Sinyalin sadece ızgara noktaları üzerinde ve seyrek oldugu varsayılır. Sürekli yapılarından dolayı sinyalleri bir ızgara üzerinde ifade etmek ızgara dışılık problemine sebep olur. Bunun yanında kalibrasyon da performans kaybına sebep olan bir diger etkendir. Bu çalışmada, ızgara dışı çoklu sinyaller için SA'ya dayalı bir kör sensör kalibrasyon yöntemi önerilmiştir. Önerilen yöntem ızgara dışı sinyal parametrelerini, genlik ve faz hatalarını aynı anda çözebilmektedir. Simülasyon çalışmaları yapılmış ve yorumlar verilmiştir. Hesaplanan metrikler ile alınan sonuçlar önerilen yöntemin daha iyi sonuçlar verdigini göstermektedir. Anahtar Kelimeler-Sıkıştırılmış Algılama, kör kalibrasyon, ızgara dışı. Özet-Compressive Sensing (CS) based techniques generally discretize the signal space and assume that the signal is sparse and has support only on the discretized grid points. Due to continuous nature of the signals, representing the signal on a discretized grid results in the off-grid problem. Improper calibration is also another issue which can cause performance degradation. In this paper, a CS based blind calibration method is proposed for the multiple off-grid signal case. Proposed method is capable of estimating the off-grid signal parameters and correcting the gain and the phase errors simultaneously. Simulation analysis is performed and comments are drawn. Results show that the proposed method have superior performance in terms of the calculated metrics.
During this time, he was involved in the inverse problems and fusion of multiple modality measure... more During this time, he was involved in the inverse problems and fusion of multiple modality measurements. In 1993, he joined the
Visible light positioning (VLP) systems based on light emitting diodes can facilitate high accura... more Visible light positioning (VLP) systems based on light emitting diodes can facilitate high accuracy localization services for indoor scenarios. In this paper, direct and twostep positioning approaches are investigated for both synchronous and asynchronous VLP systems. First, the Cramér-Rao lower bound (CRLB) and the direct positioning-based maximum likelihood estimator are derived for 3-D localization of a visible light communication receiver in a synchronous scenario by utilizing information from both time delay parameters and channel attenuation factors. Then, a two-step position estimator is designed for synchronous VLP systems by exploiting the asymptotic properties of time-of-arrival and received signal strength estimates. The proposed two-step estimator is shown to be asymptotically optimal, i.e., converges to the direct estimator at high signal-to-noise ratios. In addition, the CRLB and the direct and two-step estimators are obtained for positioning in asynchronous VLP systems. It is proved that the two-step position estimation is optimal in asynchronous VLP systems for practical pulse shapes. Various numerical examples are provided to illustrate the improved performance of the proposed estimators with respect to the current state-of-the-art and to investigate their robustness against model uncertainties in VLP systems.
2016 IEEE Wireless Communications and Networking Conference, 2016
In this study, optimal channel switching (time sharing) strategies are investigated under average... more In this study, optimal channel switching (time sharing) strategies are investigated under average power and cost constraints in order to maximize the average number of correctly received symbols between a transmitter and a receiver that are connected via multiple additive Gaussian noise channels. The optimal strategy is shown to perform channel switching either among at most three channels with full channel utilization (i.e., no idle periods), or between at most two channels with partial channel utilization. In addition, it is stated that the optimal solution must operate at the maximum average power and the maximum average cost, which facilitates low-complexity approaches for calculating the optimal strategy. For two-channel strategies, an upper bound in terms of the noise standard deviations of the employed channels is provided for the ratio between the optimal power levels. Furthermore, a simple condition depending solely on the systems parameters is derived, under which partial channel utilization cannot be optimal. Numerical examples are presented to demonstrate the validity of the theoretical results. Index Terms-Time sharing, channel switching, Gaussian channel, probability of correct decision, partial transmission.
In this study, optimal channel switching (time sharing) strategies are investigated under average... more In this study, optimal channel switching (time sharing) strategies are investigated under average power and cost constraints for maximizing the average number of correctly received symbols between a transmitter and a receiver that are connected via multiple flat-fading channels with additive Gaussian noise. The optimal strategy is shown to correspond to channel switching either among at most three different channels with full channel utilization (i.e., no idle periods), or between at most two different channels with partial channel utilization. Also, it is stated that the optimal solution must operate at the maximum average power and the maximum average cost, which facilitates low-complexity approaches for obtaining the optimal strategy. For two-channel strategies, an upper bound is derived, in terms of the parameters of the employed channels, on the ratio between the optimal power levels. In addition, theoretical results are derived for characterizing the optimal solution for channel switching between two channels, and for comparing performance of single channel strategies. Sufficient conditions that depend solely on the systems parameters are obtained for specifying when partial channel utilization cannot be optimal. Furthermore, the proposed optimal channel switching problem is investigated for logarithmic cost functions, and various theoretical results are obtained related to the optimal strategy. Numerical examples are presented to illustrate the validity of the theoretical results.
2015 49th Asilomar Conference on Signals, Systems and Computers, 2015
In this work we consider the problem of measurement matrix design for compressed 3-D Direction of... more In this work we consider the problem of measurement matrix design for compressed 3-D Direction of Arrival (DoA) estimation using a sensor array with analog combiner. Since generic measurement matrix designs often do not yield optimal estimation performance, we propose a novel design technique based on the minimization of the Crameŕ-Rao Lower Bound (CRLB). We develop specific approaches for adaptive measurement design for two applications: detection of the newly appearing targets and tracking of the previously detected targets. Numerical results suggest that the developed designs allow to provide the near optimal performance in terms of the CRLB.
2015 23rd European Signal Processing Conference (EUSIPCO), 2015
Efficient recovery of sparse signals from few linear projections is a primary goal in a number of... more Efficient recovery of sparse signals from few linear projections is a primary goal in a number of applications, most notably in a recently-emerged area of compressed sensing. The multiple measurement vector (MMV) joint sparse recovery is an extension of the single vector sparse recovery problem to the case when a set of consequent measurements share the same support. In this contribution we consider a modification of the MMV problem where the signal support can change from one block of data to another and the moment of change is not known in advance. We propose an approach for the support change detection based on the sequential rank estimation of a windowed block of the measurement data. We show that under certain conditions it allows for an unambiguous determination of the moment of change, provided that the consequent data vectors are incoherent to each other.
A thorough derivation of the Expectation Maximization (EM) algorithm, which is an iterative numer... more A thorough derivation of the Expectation Maximization (EM) algorithm, which is an iterative numerical method of Maximum Likelihood (ML) estimation, is pre sented for the case of estimating direction of arrivals of unknown deterministic wide-band signals incident from different directions onto a passive array. For the rec^uired signal estimation, alternative regularized least squares estimation tech niques are proposed with significant improvement over the standard least squares techniques. Also, for the angle of arrival estimation of a large number of signals, a tree structured EM algorithm is proposed and compared with the conventional EM approach. Extensive simulation results are presented for comparison of the proposed algorithms with the current high-resolution methods of wide-band di rection finding. In order to handle efficiently the case of available parametric prior models on the received waveforms, the required modifications are also given.
A novel algorithm is proposed for efficiently smoothing the slices of the Wigner distribution by ... more A novel algorithm is proposed for efficiently smoothing the slices of the Wigner distribution by exploiting the recently developed relation between the Radon transform of the ambiguity function and the fractional Fourier transformation [ I ]. The main advantage of the new algorithm is its ability to suppress cross-term interference on chirp-like autocomponents without any detrimental effect to the autocomponents. For a signal with N samples, the computational complexity of the algorithm is O (N log N) flops for each smoothed slice of the Wigner distribution.
Detection and classification of radars based on pulses they transmit is an important application ... more Detection and classification of radars based on pulses they transmit is an important application in electronic warfare systems. In this work, we propose a novel deep-learning based technique that automatically recognizes intra-pulse modulation types of radar signals. Reassigned spectrogram of measured radar signal and detected outliers of its instantaneous phases filtered by a special function are used for training multiple convolutional neural networks. Automatically extracted features from the networks are fused to distinguish frequency and phase modulated signals. Simulation results show that the proposed FF-CNN (Feature Fusion based Convolutional Neural Network) technique outperforms the current state-of-the-art alternatives and is easily scalable among broad range of modulation types.
The expectation maximization (EM) algorithm is presented for the case of estimating direction of ... more The expectation maximization (EM) algorithm is presented for the case of estimating direction of arrivals of unknown deterministic wideband signals. Alternative regularized least squares estimation techniques for the required signal estimation and a tree structure for the data mapping in the EM algorithm are proposed. Extensive simulation results are presented for comparison of the proposed algorithms with the conventional EM approach and the current high-resolution methods of wideband direction finding.
IEEE Transactions on Signal Processing, Dec 15, 2017
In this paper, optimal power allocation strategies are investigated for jammer nodes in a wireles... more In this paper, optimal power allocation strategies are investigated for jammer nodes in a wireless localization system. Building upon the concept of the restricted Bayesian approach, a generalized optimization strategy, called the restricted scheme, is proposed for power allocation of jammer nodes, and its theoretical properties are characterized. In the restricted scheme, the aim is to maximize the average Cramér-Rao lower bound (CRLB) of target nodes while keeping their minimum CRLB above a predefined level in the presence of average (total) and peak power constraints. It is proved that the average CRLB achieved by the restricted scheme is a strictly decreasing and concave function of the constraint on the minimum CRLB level. A closed-form solution is obtained for the restricted scheme when the tradeoff parameter and the total power limit are below certain thresholds. In addition, it is shown that the optimal solution of the restricted scheme corresponds to the use of at most N T jammer nodes, where N T is the number of target nodes, and that the optimal solution of the minimum CRLB maximization scheme is determined by at most N J target nodes, where N J is the number of jammer nodes. Extensions of the restricted scheme and an alternative scheme that aims to maximize the number of disabled target nodes (whose CRLBs are above a preset level) are considered, and the corresponding optimal strategies for jammer power allocation are identified. Numerical examples are provided to verify the theoretical derivations for various network configurations.
IEEE Transactions on Information Forensics and Security, 2020
Specific Emitter Identification (SEI) is the process of specifically identifying mobile transmitt... more Specific Emitter Identification (SEI) is the process of specifically identifying mobile transmitters by extracting unique features from the precise measurements of their emitted signals. A novel signal processing scheme with two stages is proposed for the identification of specific radar emitters. In the first stage, the received radar pulses are accurately time aligned and coherently integrated in order to increase the Signal-to-Noise (SNR) ratio. Using this technique, measurements with SNR improvements of more than 25 dB are obtained, enabling detection of subtle differences between different emitters. In the second step, Variational Mode Decomposition (VMD) is used to decompose both the envelope and the instantaneous frequency of the received signal into a set of modes. Then, these mod signals are characterized by using a group of features for identification. We demonstrate highly successful identification performance with the proposed method on real radar datasets.
Semantic signal processing and communications are poised to play a central part in developing the... more Semantic signal processing and communications are poised to play a central part in developing the next generation of sensor devices and networks. A crucial component of a semantic system is the extraction of semantic signals from the raw input signals, which has become increasingly tractable with the recent advances in machine learning (ML) and artificial intelligence (AI) techniques. The accurate extraction of semantic signals using the aforementioned ML and AI methods, and the detection of semantic innovation for scheduling transmission and/or storage events are critical tasks for reliable semantic signal processing and communications. In this work, we propose a reliable semantic information extraction framework based on our previous work on semantic signal representations in a hierarchical graph-based structure. The proposed framework includes a time integration method to increase fidelity of ML outputs in a class-aware manner, a graph-edit-distance based metric to detect innovation events at the graph-level and filter out sporadic errors, and a Hidden Markov Model (HMM) to produce smooth and reliable graph signals. The proposed methods within the framework are demonstrated individually and collectively through simulations and case studies based on real-world computer vision examples.
Multi-antenna radars exhibit positively correlated detection performance with the number of eleme... more Multi-antenna radars exhibit positively correlated detection performance with the number of elements utilized. The feasibility of refining antenna arrays to reduce cost of operation with only marginal loss of performance has attracted significant attention as utilizing a large number of elements may be prohibitively costly in terms of computation and power. Under cognitive radar paradigm, the goal is to choose an optimal or near optimal subset of elements from an antenna array of pre-specified geometry while meeting certain performance and cost criteria. In this work, we present optimization based selection methods for certain array geometries to select the best K element sub-array in terms of Cramér-Rao lower bound (CRB) on direction-ofarrival (DoA) estimations. Our results indicate that it is possible to reduce K up to a certain point without significant reduction in DoA estimation performance. The maximum possible reduction in K depends on the operating signal-to-noise ratio (SNR) and how much performance loss is tolerated. Thus, once the operating SNR is known, it is possible to utilize fewer array elements with slight decrease in performance.
2020 28th European Signal Processing Conference (EUSIPCO), 2021
Design of measurement matrices is an important aspect of compressed sensing (CS) based direction ... more Design of measurement matrices is an important aspect of compressed sensing (CS) based direction of arrival (DoA) applications that enables reduction in the analog channels to be processed in sparse target environments. Here, a novel measurement matrix design methodology for CS based DoA estimation is proposed and its superior performance over alternative measurement matrix design methodologies is demonstrated. The proposed method uses prior probability distribution of the targets to improve performance. Compared to the state-of-the-art techniques, it is quantitatively demonstrated that the proposed measurement matrix design approach enables significant reduction in the number of analog channels to be processed and adapts to a priori information on the target scene.
Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181)
ш т е і ш ш м в^ш > м ж^х г ш з а с ш ю м a r f í i s s M E S P Îêîa S 3*üjT3iEE-la^Г 'Г ρ-. .f i... more ш т е і ш ш м в^ш > м ж^х г ш з а с ш ю м a r f í i s s M E S P Îêîa S 3*üjT3iEE-la^Г 'Г ρ-. .f iirr-i' B íJB M rm sD ІЮ TJ33¡ jij>m Ί-ш ш в т ю ш-с^ .¿ .ж. ■ OF ш г т т а з ж
2014 22nd European Signal Processing Conference (EUSIPCO), 2014
Compressed sensing allows for a significant reduction of the number of measurements when the sign... more 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.
2009 International Conference on Electrical and Electronics Engineering - ELECO 2009, 2009
The power consumption of a standard SRAM during read/write operations is dependent on the address... more The power consumption of a standard SRAM during read/write operations is dependent on the address applied, the data accessed, and the type of access (read/write). The power analysis resistant SRAM structure developed during the Project ¿SCARD¿ (Side Channel Analysis Resistant Design Flow) of the European Community 6. Framework Program reduces the dependency of power consumption on data and address compared to standard SRAM at the expense of higher power and silicon area consumption. In this work a new SRAM primitive cell structure is proposed to reduce the power consumption and its dependency to data to be written.
2018 26th Signal Processing and Communications Applications Conference (SIU), May 1, 2018
Özetçe-Sıkıştırılmış Algılama (SA) tabanlı teknikler genellikle sinyal uzayını ayrıklaştırarak bi... more Özetçe-Sıkıştırılmış Algılama (SA) tabanlı teknikler genellikle sinyal uzayını ayrıklaştırarak bir ızgara üzerinde ifade ederler. Sinyalin sadece ızgara noktaları üzerinde ve seyrek oldugu varsayılır. Sürekli yapılarından dolayı sinyalleri bir ızgara üzerinde ifade etmek ızgara dışılık problemine sebep olur. Bunun yanında kalibrasyon da performans kaybına sebep olan bir diger etkendir. Bu çalışmada, ızgara dışı çoklu sinyaller için SA'ya dayalı bir kör sensör kalibrasyon yöntemi önerilmiştir. Önerilen yöntem ızgara dışı sinyal parametrelerini, genlik ve faz hatalarını aynı anda çözebilmektedir. Simülasyon çalışmaları yapılmış ve yorumlar verilmiştir. Hesaplanan metrikler ile alınan sonuçlar önerilen yöntemin daha iyi sonuçlar verdigini göstermektedir. Anahtar Kelimeler-Sıkıştırılmış Algılama, kör kalibrasyon, ızgara dışı. Özet-Compressive Sensing (CS) based techniques generally discretize the signal space and assume that the signal is sparse and has support only on the discretized grid points. Due to continuous nature of the signals, representing the signal on a discretized grid results in the off-grid problem. Improper calibration is also another issue which can cause performance degradation. In this paper, a CS based blind calibration method is proposed for the multiple off-grid signal case. Proposed method is capable of estimating the off-grid signal parameters and correcting the gain and the phase errors simultaneously. Simulation analysis is performed and comments are drawn. Results show that the proposed method have superior performance in terms of the calculated metrics.
During this time, he was involved in the inverse problems and fusion of multiple modality measure... more During this time, he was involved in the inverse problems and fusion of multiple modality measurements. In 1993, he joined the
Visible light positioning (VLP) systems based on light emitting diodes can facilitate high accura... more Visible light positioning (VLP) systems based on light emitting diodes can facilitate high accuracy localization services for indoor scenarios. In this paper, direct and twostep positioning approaches are investigated for both synchronous and asynchronous VLP systems. First, the Cramér-Rao lower bound (CRLB) and the direct positioning-based maximum likelihood estimator are derived for 3-D localization of a visible light communication receiver in a synchronous scenario by utilizing information from both time delay parameters and channel attenuation factors. Then, a two-step position estimator is designed for synchronous VLP systems by exploiting the asymptotic properties of time-of-arrival and received signal strength estimates. The proposed two-step estimator is shown to be asymptotically optimal, i.e., converges to the direct estimator at high signal-to-noise ratios. In addition, the CRLB and the direct and two-step estimators are obtained for positioning in asynchronous VLP systems. It is proved that the two-step position estimation is optimal in asynchronous VLP systems for practical pulse shapes. Various numerical examples are provided to illustrate the improved performance of the proposed estimators with respect to the current state-of-the-art and to investigate their robustness against model uncertainties in VLP systems.
2016 IEEE Wireless Communications and Networking Conference, 2016
In this study, optimal channel switching (time sharing) strategies are investigated under average... more In this study, optimal channel switching (time sharing) strategies are investigated under average power and cost constraints in order to maximize the average number of correctly received symbols between a transmitter and a receiver that are connected via multiple additive Gaussian noise channels. The optimal strategy is shown to perform channel switching either among at most three channels with full channel utilization (i.e., no idle periods), or between at most two channels with partial channel utilization. In addition, it is stated that the optimal solution must operate at the maximum average power and the maximum average cost, which facilitates low-complexity approaches for calculating the optimal strategy. For two-channel strategies, an upper bound in terms of the noise standard deviations of the employed channels is provided for the ratio between the optimal power levels. Furthermore, a simple condition depending solely on the systems parameters is derived, under which partial channel utilization cannot be optimal. Numerical examples are presented to demonstrate the validity of the theoretical results. Index Terms-Time sharing, channel switching, Gaussian channel, probability of correct decision, partial transmission.
In this study, optimal channel switching (time sharing) strategies are investigated under average... more In this study, optimal channel switching (time sharing) strategies are investigated under average power and cost constraints for maximizing the average number of correctly received symbols between a transmitter and a receiver that are connected via multiple flat-fading channels with additive Gaussian noise. The optimal strategy is shown to correspond to channel switching either among at most three different channels with full channel utilization (i.e., no idle periods), or between at most two different channels with partial channel utilization. Also, it is stated that the optimal solution must operate at the maximum average power and the maximum average cost, which facilitates low-complexity approaches for obtaining the optimal strategy. For two-channel strategies, an upper bound is derived, in terms of the parameters of the employed channels, on the ratio between the optimal power levels. In addition, theoretical results are derived for characterizing the optimal solution for channel switching between two channels, and for comparing performance of single channel strategies. Sufficient conditions that depend solely on the systems parameters are obtained for specifying when partial channel utilization cannot be optimal. Furthermore, the proposed optimal channel switching problem is investigated for logarithmic cost functions, and various theoretical results are obtained related to the optimal strategy. Numerical examples are presented to illustrate the validity of the theoretical results.
2015 49th Asilomar Conference on Signals, Systems and Computers, 2015
In this work we consider the problem of measurement matrix design for compressed 3-D Direction of... more In this work we consider the problem of measurement matrix design for compressed 3-D Direction of Arrival (DoA) estimation using a sensor array with analog combiner. Since generic measurement matrix designs often do not yield optimal estimation performance, we propose a novel design technique based on the minimization of the Crameŕ-Rao Lower Bound (CRLB). We develop specific approaches for adaptive measurement design for two applications: detection of the newly appearing targets and tracking of the previously detected targets. Numerical results suggest that the developed designs allow to provide the near optimal performance in terms of the CRLB.
2015 23rd European Signal Processing Conference (EUSIPCO), 2015
Efficient recovery of sparse signals from few linear projections is a primary goal in a number of... more Efficient recovery of sparse signals from few linear projections is a primary goal in a number of applications, most notably in a recently-emerged area of compressed sensing. The multiple measurement vector (MMV) joint sparse recovery is an extension of the single vector sparse recovery problem to the case when a set of consequent measurements share the same support. In this contribution we consider a modification of the MMV problem where the signal support can change from one block of data to another and the moment of change is not known in advance. We propose an approach for the support change detection based on the sequential rank estimation of a windowed block of the measurement data. We show that under certain conditions it allows for an unambiguous determination of the moment of change, provided that the consequent data vectors are incoherent to each other.
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Papers by Orhan Arikan