Yasar Kemal Alp

Machine-based classification of ADHD and nonADHD participants using time/frequency features of event-related neuroelectric activity

OBJECTIVE Attention-deficit/hyperactivity disorder (ADHD) is the most frequent diagnosis among children who are referred to psychiatry departments. Although ADHD was discovered at the beginning of the 20th century, its diagnosis is still confronted with many problems. METHOD A novel classification approach that discriminates ADHD and nonADHD groups over the time-frequency domain features of event-related potential (ERP) recordings that are taken during Stroop task is presented. Time-Frequency Hermite-Atomizer (TFHA) technique is used for the extraction of high resolution time-frequency domain features that are highly localized in time-frequency domain. Based on an extensive investigation, Support Vector Machine-Recursive Feature Elimination (SVM-RFE) was used to obtain the best discriminating features. RESULTS When the best three features were used, the classification accuracy for the training dataset reached 98%, and the use of five features further improved the accuracy to 99.5%. The accuracy was 100% for the testing dataset. Based on extensive experiments, the delta band emerged as the most contributing frequency band and statistical parameters emerged as the most contributing feature group. CONCLUSION The classification performance of this study suggests that TFHA can be employed as an auxiliary component of the diagnostic and prognostic procedures for ADHD. SIGNIFICANCE The features obtained in this study can potentially contribute to the neuroelectrical understanding and clinical diagnosis of ADHD.

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

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×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.

Online calibration of Modulated Wideband Converter

In this work, we propose a new method for online calibration of recently proposed Modulated Wideband Converter (MWC), which digitizes wideband sparse signals below the Nyquist limit without loss of information by using compressive sensing techniques. Our method requires a single frequency synthesizer card, which can generate clean tones along the operation band of the system, rather than much expensive measurement instruments such as network analyser or vector spectrum analyser, which are not appropriate for online calibration. Moreover, low computational complexity of the proposed method enables its implementation on FPGA so that it can be embedded into the system. Hence, on each power on, the system can utilize self calibration without requiring any additional measurement instruments.

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

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.

ADMM based mainlobe power constrained phase-only sidelobe supression

A novel sidelobe suppression technique is proposed for phased arrays, where only the phases of the array elements are adjusted to suppress the gain in the direction of interest while keeping the mainlobe power at a certain level. Mainlobe power constrained sidelobe suppression is formulated as a convex RSDP (Relaxed Semidefinite Program). Solution to resultant RSDP is obtained by ADMM (Alternating Direction Method of Multipliers) technique, which can handle designs for arrays with number of elements is significantly larger than that can be handled by other convex solvers such as CVX. In addition, although the available convex solvers can not provide a rank-1 solution matrix, a rank-1 solution matrix is obtained by modifying the ADMM iterations. In the conducted experiments, it is observed that proposed ADMM based method can achieve more than 10dB improvement in sidelobe levels compared to alternative techniques.

Machine-based learning system: Classification of ADHD and non-ADHD participants

Karakas, Sirel (Dogus Author) -- Dogutepe, Elvin (Dogus Author) -- Conference full title : 25th Signal Processing and Communications Applications Conference (SIU); 15 May 2017 through 18 May 2017. Antalya; TurkeyAttention-deficit/hyperactivity disorder (ADHD) is the most frequent diagnosis among children who are referred to psychiatry departments. Although ADHD was discovered at the beginning of the 20th century, its diagnosis is confronted with many problems. In this paper, a novel classification approach that discriminates ADHD and non-ADHD groups over the time-frequency domain features of ERP recordings is presented. Support Vector Machine-Recursive Feature Elimination (SVM-RFE) was used to obtain best discriminating features. When only three of these features were used the accuracy of classification reached to 98%, and use of six features further improved classification accuracy to 99.5%. The proposed scheme was tested with a new experimental setup and 100% accuracy is obtained. The results were obtained using RCV. The classification performance of this study suggests that TFHA can be employed as a core component of the diagnostic and prognostic procedures of various psychiatric illnesses.

Sub-band equalization of modulated wideband converter for improved dynamic range performance

In this work, we propose a new method to improve the dynamic range performance of the Modulated Wideband Converter (MWC), which is multi-channel sampling system for digitizing wideband sparse signals below the Nyquist limit without loss of information by using compressive sensing techniques. MWC achieves high dynamic range assuming that subband frequency responses of the system are identical. However, in hardware implementations of MWC, the resulting sub-band frequency responses are not identical and dynamic range performance of the system drops significantly which makes it unusable in practical applications. Proposed method iteratively designs FIR filters for equalizing frequency responses of the all sub-bands. Obtained results from the extensive computer simulations of the MWC system show that proposed method improves the dynamic range performance of the MWC system significantly.

Radar fingerprint extraction via variational mode decomposition

In iMs paper, a novel method for extracting radar fingerprint using the unintentional modulation on radar signals is proposed. Proposed technique decomposes the unintentional modulations into its components using Variational Mode Decomposition (VMD) technique. Then, features that characterize each component are calculated. Simulations using real radar data show that proposed technique can classify radars in the dataset with high performance.

SNR improvement in electronic support measures systems via pulse integration

In ESM (Electronic Support Measures) systems, detection of intentional or unintentional modulation on pulses requires high SNR. By integrating the collected pulses emitted from the radar, SNR can be increased. For utilizing pulse integration, all the pulses should be aligned in time very accurately. In this work, we propose a new method, which estimates the time shifts between the pulses with very high accuracy and resolution. Experiments on both synthetic and real data sets show that proposed method aligns the radar pulses very successfully.

Sub-band equalization filter design for improving dynamic range performance of modulated wideband converter

In this work, we propose an iterative method to improve the dynamic range performance of the Modulated Wideband Converter (MWC), which is multi-channel sampling system for digitizing wideband sparse signals below the Nyquist limit without loss of information by using compressive sensing techniques. Our method jointly designs FIR filters for each sub-band to equalize the frequency response characteristics of the all sub-bands of the MWC. Obtained results from the extensive computer simulations of the MWC system show that the proposed method improves the dynamic range performance of the MWC system significantly.