Mohd Zuki Yusoff

A compact multiband hybrid meander-Koch Fractal antenna for WLAN USB dongle

There is a current upsurge for the high data rate and wide bandwidth in the communication technologies. This has posed several challenges for the researchers. These challenges include but are not limited to size reduction, high efficiency, and low-cost. The prevailing fashion in the communication system is to integrate several WLAN standards in small user equipment. The emergence of the USB dongle for WLAN has attracted the antenna design engineers to design multi band antennas. The antenna size is restricted to the small size of the USB dongle. The main challenge is to reduce the antenna size without compromising on the performance related characteristics such as; wide bandwidth, low VSWR, high gain and high radiation efficiency. In this paper a novel Koch-meander Fractal antenna has been proposed for the application of WLAN USB dongle. The proposed compact antenna is simulated in CST microwave studio. The simulation results depicts that the antenna has a −10 dB return loss bandwidth of 2.2909–2.553 GHz and 5.1406–5.8737 GHz. The minimum return loss of −28.9 dB is achieved at the lower band of 2.41 GHz. While the upper band depicts a return loss of −20.8 dB at 5.36 GHz. The proposed antenna is capable of covering the whole band required for the WLAN 802.11 a/b/g standards.

Single-Trial Subspace-Based Approach for VEP Extraction

A signal subspace approach for extracting visual evoked potentials (VEPs) from the background electroencephalogram (EEG) colored noise without the need for a prewhitening stage is proposed. Linear estimation of the clean signal is performed by minimizing signal distortion while maintaining the residual noise energy below some given threshold. The generalized eigendecomposition of the covariance matrices of a VEP signal and brain background EEG noise is used to transform them jointly to diagonal matrices. The generalized subspace is then decomposed into signal subspace and noise subspace. Enhancement is performed by nulling the components in the noise subspace and retaining the components in the signal subspace. The performance of the proposed algorithm is tested with simulated and real data, and compared with the recently proposed signal subspace techniques. With the simulated data, the algorithms are used to estimate the latencies of P100, P 200, and P300 of VEP signals corrupted by additive colored noise at different values of SNR. With the real data, the VEP signals are collected at Selayang Hospital, Kuala Lumpur, Malaysia, and the capability of the proposed algorithm in detecting the latency of P100 is obtained and compared with other subspace techniques. The ensemble averaging technique is used as a baseline for this comparison. The results indicated significant improvement by the proposed technique in terms of better accuracy and less failure rate.

Automatic eye-blink artifact removal method based on EMD-CCA

This research proposes a new hybrid algorithm for automatic removal of eye blink artifact from EEG data based on empirical mode decomposition (EMD) and canonical correlation analysis (CCA). The validity and efficiency of the proposed algorithm is evaluated using correlation coefficient and signal-to-artifact ratio (SAR) and the proposed algorithm is also compared with other popular eye blink artifact removal techniques (CCA, ICA, EMD-ICA) on simulated EEG data of two channels. From the simulation results, the average correlation coefficients for the EEG channels are obtained as 0.908 and 0.864 respectively. The SAR of the EEG signal also improved from 2.2 dB to 6.0 dB after correction using our proposed method. Compared to other eye blink artifact removal techniques, our proposed method has two benefits. Firstly, no visual inspection is required to detect the eye blink artifact components. Secondly, computational assessment of corrected EEG waveforms reveals that the proposed algorithm retrieves the EEG data by removing the eye blink artifacts reliably.

A method for automatic removal of eye blink artifacts from EEG based on EMD-ICA

The electroencephalography (EEG) recordings are mostly contaminated by eye blink artifacts. It is very difficult to analyze and interpret the EEG signal due to frequent occurrence of the eye blink artifact. In this paper, a new hybrid algorithm that automatically removes the eye blink artifact from the EEG, based on Empirical Mode Decomposition (EMD) and Independent Component Analysis (ICA) is proposed. The proposed algorithm is evaluated on simulated EEG to calculate correlation coefficient and signal-to-artifact ratio (SAR). A non-corrected EEG was simulated to have a SAR of -19.1673 dB. From the simulation results, the highest average correlation coefficient and SAR of corrected EEG from non-corrected EEG are obtained as 0.871094 and 2.71645 dB respectively by applying proposed algorithm. The results demonstrate that proposed method recovers the EEG data by removing the eye blink artifacts reliably. In addition, the proposed method is applied on real spontaneous EEG data with eye blink artifact.

Road sign detection and recognition system for real-time embedded applications

In this paper, we propose a road sign detection and recognition algorithm for an embedded application, which requires computationally simple but accurate algorithms. The algorithm is developed by using the Hue Saturation Intensity (HSI) color space to segment the road signs color (red, yellow, blue and white) and the regions of interest (ROI) in order to locate and determine the shape of the road sign (diamond, square, hexagonal and circular) in real street-view images. The shape is then used to classify road signs into four categories, i.e. warning, mandatory, prohibitory and informational signs. The characteristics of the shapes such as area and perimeter variables are used to identify the symbols in the road signs. These variables are compared with the template library which we have developed. The proposed algorithm is tested on the Malaysias road signs which are captured on the roadside in their real environment. Experimental results on the accuracy of the road sign detection are reported in this paper. The proposed algorithm will be implemented in a real time embedded system using Xilinx Video Starter Kit Board- Spartan-3a DSP 3400A Edition.

Current issues and challenges of MIMO antenna designs

The demand for high data rate and channel bandwidth is always the primary area of concern in modern wireless communication systems. All the modern wireless communication system applications are rapidly shifting towards multiple input multiple output (MIMO) from single input and single output (SISO) and single input multiple output (SIMO) systems, increasing the demand for the integration of multiple antennas in the users equipment. The development of MIMO antennas and its performance evaluation for the portable wireless communication terminals have attracted many researchers to improve channel capacity, bandwidth, gain, polarization diversity and reduce coupling between inter elements. Further these systems also demands that the multiple element antenna (MEA) should be minimized so that they can fit in the compact and robust user equipment, and support multiband operation for the equipment reusability in the different regions of the world. Antenna designers have ample vacuum in the development of novel antenna systems for the new generation networks by enhancing the impedance bandwidth and multiband resonance. In addition improving radiation pattern diversity reduces the correlation coefficient and increases MIMO antenna performance. This review paper is focused on the related work in the field of MIMO antenna designs. Researchers have proposed various different techniques to design antennas at a range of microwave bands of interest, where the key concern of the research is to enhance the channel capacity by reducing the correlation coefficient and increasing the isolation between multi element antennas.

Shape-based road sign detection and recognition for embedded application using MATLAB

In this paper, we propose a road sign detection and recognition algorithm for an embedded application. The algorithm is based on the Hough transform method to detect lines in order to identify and determine the shape of the road sign. Shape measurements are currently employed to identify the road sign ratios of area and perimeter. The variables are compared with the template library which we have developed. In order to evaluate the accuracy, the proposed algorithm was tested on most of the Malaysian road signs, and the results are reported in this paper. Successful detection and recognition rate is about 83.67%.

A generalized subspace approach for estimating visual evoked potentials

A “single-trial” signal subspace approach for extracting visual evoked potential (VEP) from the ongoing “colored” electroencephalogram (EEG) noise is proposed. The algorithm applies the generalized eigendecomposition on the covariance matrices of the VEP and noise to transform them jointly into diagonal matrices in order to avoid a pre-whitening stage. The proposed generalized subspace approach (GSA) decomposes the corrupted VEP space into a signal subspace and noise subspace. Enhancement is achieved by removing the noise subspace and estimating the clean VEPs only from the signal subspace. The validity and effectiveness of the proposed GSA scheme in estimating the latencies of P100s (used in objective assessment of visual pathways) are evaluated using real data collected from Selayang Hospital in Kuala Lumpur. The performance of GSA is compared with the recently proposed single-trial technique called the Third Order Correlation (TOC).

An electrically small meander line antenna for wireless applications

The meander line antenna (MLA) is conventionally an electrically small antenna. Electrically small antennas pose several performance related issues such as narrow bandwidth, high VSWR, low gain and high cross polarization levels. In this paper a modified two element MLA is proposed. The modified MLA possesses novelty in terms of its geometrical shape, which is achieved with the unification of the two MLA elements. This altered geometry helped in better input impedance matching with the MLA elements and consequently achieving the dual band operation. The proposed antenna is designed to cater the ISM (Industrial, Scientific, Medical) bands of 2.45 and 5 GHz. The antenna performance parameters are optimized to achieve reasonably wide impedance bandwidth, low VSWR, high gain and an omnidirectional radiation pattern. Moreover, the current distribution and the effect of the ground plane size on the altered MLA are discussed in this paper.

Estimation of visual evoked potentials using a signal subspace approach

Extraction of visual evoked potentials (VEPs) from the human brain is generally very difficult due to its poor signal-to-noise ratio (SNR) property. A signal subspace technique is presented to estimate VEPs hidden inside highly colored electro-encephalogram (EEG) noise. This method is borrowed and modified from signal subspace techniques originally used for enhancing speech corrupted by colored noise. The signal subspace is estimated by applying eigenvalue decomposition on the approximated signal covariance matrix. The signal subspace-based algorithm is able to satisfactorily extract the P100, P200 and P300 peak latencies from artificially generated noisy VEPs. The simulation results show that the estimator maintains an average success rate of 87% with an average percentage error of less than 9%, when subjected to SNR from 0 dB to -10 dB.