Yasar Kemal Alp

Bio: Yasar Kemal Alp is an academic researcher from ASELSAN. The author has contributed to research in topics: Radar & Convex optimization. The author has an hindex of 6, co-authored 44 publications receiving 129 citations. Previous affiliations of Yasar Kemal Alp include Bilkent University.

A New Method for Specific Emitter Identification With Results on Real Radar Measurements

Abstract: 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.

Consideration of environmental and functional factors in calibration of antenna integrated active phased array transmitters

Abstract: When dealing with direct calibration in phased array systems, in which power amplifier (PA) output for each antenna element is measured with respect to all phase and amplitude levels, several issues complicate the calibration algorithm and extend calibration time, such as wide frequency band, large element number, temperature changes, driving power of PAs. In this work, all realizable aspects effecting direct calibration of multi-octave band phased array system with 100+ elements are examined and a robust and consistent algorithm is developed and applied.

FIR Filter Design by Convex Optimization Using Directed Iterative Rank Refinement Algorithm

Abstract: The advances in convex optimization techniques have offered new formulations of design with improved control over the performance of FIR filters. By using lifting techniques, the design of a length- $L$ FIR filter can be formulated as a convex semidefinite program (SDP) in terms of an $L\times L$ matrix that must be rank-1. Although this formulation provides means for introducing highly flexible design constraints on the magnitude and phase responses of the filter, convex solvers implementing interior point methods almost never provide a rank-1 solution matrix. To obtain a rank-1 solution, we propose a novel Directed Iterative Rank Refinement (DIRR) algorithm, where at each iteration a matrix is obtained by solving a convex optimization problem. The semidefinite cost function of that convex optimization problem favors a solution matrix whose dominant singular vector is on a direction determined in the previous iterations. Analytically it is shown that the DIRR iterations provide monotonic improvement, and the global optimum is a fixed point of the iterations. Over a set of design examples it is illustrated that the DIRR requires only a few iterations to converge to an approximately rank-1 solution matrix. The effectiveness of the proposed method and its flexibility are also demonstrated for the cases where in addition to the magnitude constraints, the constraints on the phase and group delay of filter are placed on the designed filter.

Response to FCC 98-208 notice of inquiry in the matter of revision of part 15 of the commission's rules regarding ultra-wideband transmission systems

Abstract: In general, Micropower Impulse Radar (MIR) depends on Ultra-Wideband (UWB) transmission systems. UWB technology can supply innovative new systems and products that have an obvious value for radar and communications uses. Important applications include bridge-deck inspection systems, ground penetrating radar, mine detection, and precise distance resolution for such things as liquid level measurement. Most of these UWB inspection and measurement methods have some unique qualities, which need to be pursued. Therefore, in considering changes to Part 15 the FCC needs to take into account the unique features of UWB technology. MIR is applicable to two general types of UWB systems: radar systems and communications systems. Currently LLNL and its licensees are focusing on radar or radar type systems. LLNL is evaluating MIR for specialized communication systems. MIR is a relatively low power technology. Therefore, MIR systems seem to have a low potential for causing harmful interference to other users of the spectrum since the transmitted signal is spread over a wide bandwidth, which results in a relatively low spectral power density.

A New Unconditionally Stable Scheme for FDTD Method Using Associated Hermite Orthogonal Functions

Abstract: An unconditionally stable solution using associated Hermite (AH) functions is proposed for the finite-difference time-domain (FDTD) method. The electromagnetic fields and their time derivatives in time-do- main Maxwell's equations are expanded by these orthonormal basis func- tions. By applying Galerkin temporal testing procedure to these expanded equations the time variable can be eliminated from the calculations. A set of implicit equations is derived to calculate the magnetic filed expansion coefficients of all orders of AH functions for the temporal variable. And the electrical field coefficients can be obtained respectively. With the ap- propriate translation and scale parameters, we can find a minimum-order basis functions subspace to approach a particular electromagnetic field. The numerical results have shown that the proposed method can reduce the CPU time to 0.59% of the traditional FDTD method while maintaining good accuracy.

Neural Decoding of EEG Signals with Machine Learning: A Systematic Review.

Abstract: Electroencephalography (EEG) is a non-invasive technique used to record the brain’s evoked and induced electrical activity from the scalp. Artificial intelligence, particularly machine learning (ML) and deep learning (DL) algorithms, are increasingly being applied to EEG data for pattern analysis, group membership classification, and brain-computer interface purposes. This study aimed to systematically review recent advances in ML and DL supervised models for decoding and classifying EEG signals. Moreover, this article provides a comprehensive review of the state-of-the-art techniques used for EEG signal preprocessing and feature extraction. To this end, several academic databases were searched to explore relevant studies from the year 2000 to the present. Our results showed that the application of ML and DL in both mental workload and motor imagery tasks has received substantial attention in recent years. A total of 75% of DL studies applied convolutional neural networks with various learning algorithms, and 36% of ML studies achieved competitive accuracy by using a support vector machine algorithm. Wavelet transform was found to be the most common feature extraction method used for all types of tasks. We further examined the specific feature extraction methods and end classifier recommendations discovered in this systematic review.

Drowsiness Detection Using Adaptive Hermite Decomposition and Extreme Learning Machine for Electroencephalogram Signals

Abstract: Automatic drowsiness detection system plays a vital role to prevent the road accidents caused by drowsiness In this regard, the electroencephalogram (EEG) signal provides valuable information of brain physiology for detection of drowsiness EEG signals exhibit non-stationary nature which is tough to explore by prior defined and fixed number of basis functions In this paper, an adaptive Hermite decomposition is proposed for the detection of drowsiness EEG signals In the proposed decomposition, Hermite functions are used as basis functions, which are adaptively selected for each EEG signal by evolutionary optimization algorithms (EOAs) The mean square error of decomposition is proposed as an objective function to EOAs The minimum decomposition error provided artificial bee colony EOA is considered to Hermite coefficients-based feature extraction for EEG signals The extracted features are tested with the extreme learning machine (ELM), decision tree, $k$ -nearest neighbor, least squares support vector machine, artificial neural network, and naive Bayes for detection of alertness and drowsiness EEG signals The proposed method with ELM classifier obtained 9545% and 8792% detection rates for alertness and drowsiness states, respectively, which are better as compared to other existing methods