Saleh A. Alshebeili

A New Low SAR Antenna Structure for Wireless Handset Applications

This paper proposes a new mobile handset antenna structure to reduce the value of the speciflc absorption rate (SAR). The antenna is based on the PIFA structure and operates at dual-bands of 0.9GHz and 1.8GHz. The chassis current is reduced using a metallic shim-layer inserted between the patch and chassis. This shim-layer is connected to the handset chassis through posts whose number and positions are determined using optimization techniques. Sidewalls are attached to increase the gain of the antenna and reduce the radiation towards human head. Simulations in the cheek mode show that the SAR reduction factor (SRF) of the proposed structure averaged over 10-g is more than 75% at 0.9GHz and 46% at 1.8GHz. The SRF values obtained using simulations and measurements are found to be better than 51% and 76% at 0.9GHz and 1.8GHz, respectively.

EEG seizure detection and prediction algorithms: a survey

Epilepsy patients experience challenges in daily life due to precautions they have to take in order to cope with this condition. When a seizure occurs, it might cause injuries or endanger the life of the patients or others, especially when they are using heavy machinery, e.g., deriving cars. Studies of epilepsy often rely on electroencephalogram (EEG) signals in order to analyze the behavior of the brain during seizures. Locating the seizure period in EEG recordings manually is difficult and time consuming; one often needs to skim through tens or even hundreds of hours of EEG recordings. Therefore, automatic detection of such an activity is of great importance. Another potential usage of EEG signal analysis is in the prediction of epileptic activities before they occur, as this will enable the patients (and caregivers) to take appropriate precautions. In this paper, we first present an overview of seizure detection and prediction problem and provide insights on the challenges in this area. Second, we cover some of the state-of-the-art seizure detection and prediction algorithms and provide comparison between these algorithms. Finally, we conclude with future research directions and open problems in this topic.

Cumulant based identification approaches for nonminimum phase FIR systems

Recursive and least squares methods for identification of non-minimum-phase linear time-invariant (NMP-LTI) FIR systems are developed. The methods utilize the second- and third-order cumulants of the output of the FIR system whose input is an independent, identically distributed (i.i.d.) non-Gaussian process. Since knowledge of the system order is of utmost importance to many system identification algorithms, new procedures for determining the order of an FIR system using only the output cumulants are also presented. To illustrate the effectiveness of the methods, various simulation examples are presented. >

An overview of feature-based methods for digital modulation classification

This paper presents an overview of feature-based (FB) methods developed for Automatic classification of digital modulations. Only the most well-known features and classifiers are considered, categorized, and defined. The features include instantaneous time domain (ITD) parameters, Fourier transform (FT), wavelet transform (WT), higher order moments (HOM) to name a few. The classifiers are artificial neural networks (ANN), support vector machines (SVMs), and decision tree (DT). We also highlight the advantages and disadvantages of each technique in classifying a certain modulation scheme. The objective of this work is to assist newcomers to the field to choose suitable algorithms for intended applications. Furthermore, this work is expected to help in determining the limitations associated with the available FB automatic modulation classification (AMC) methods.

A review of channel selection algorithms for EEG signal processing

Digital processing of electroencephalography (EEG) signals has now been popularly used in a wide variety of applications such as seizure detection/prediction, motor imagery classification, mental task classification, emotion classification, sleep state classification, and drug effects diagnosis. With the large number of EEG channels acquired, it has become apparent that efficient channel selection algorithms are needed with varying importance from one application to another. The main purpose of the channel selection process is threefold: (i) to reduce the computational complexity of any processing task performed on EEG signals by selecting the relevant channels and hence extracting the features of major importance, (ii) to reduce the amount of overfitting that may arise due to the utilization of unnecessary channels, for the purpose of improving the performance, and (iii) to reduce the setup time in some applications. Signal processing tools such as time-domain analysis, power spectral estimation, and wavelet transform have been used for feature extraction and hence for channel selection in most of channel selection algorithms. In addition, different evaluation approaches such as filtering, wrapper, embedded, hybrid, and human-based techniques have been widely used for the evaluation of the selected subset of channels. In this paper, we survey the recent developments in the field of EEG channel selection methods along with their applications and classify these methods according to the evaluation approach.

Optical Image Encryption Based on Chaotic Baker Map and Double Random Phase Encoding

This paper presents a new technique for optical image encryption based on chaotic Baker map and Double Random Phase Encoding (DRPE). This technique is implemented in two layers to enhance the security level of the classical DRPE. The first layer is a pre-processing layer, which is performed with the chaotic Baker map on the original image. In the second layer, the classical DRPE is utilized. Matlab simulation experiments show that the proposed technique enhances the security level of the DRPE, and at the same time has a better immunity to noise.

Speech enhancement with an adaptive Wiener filter

This paper proposes an adaptive Wiener filtering method for speech enhancement. This method depends on the adaptation of the filter transfer function from sample to sample based on the speech signal statistics; the local mean and the local variance. It is implemented in the time domain rather than in the frequency domain to accommodate for the time-varying nature of the speech signals. The proposed method is compared to the traditional frequency-domain Wiener filtering, spectral subtraction and wavelet denoising methods using different speech quality metrics. The simulation results reveal the superiority of the proposed Wiener filtering method in the case of Additive White Gaussian Noise (AWGN) as well as colored noise.

Dense Dielectric Patch Array Antenna With Improved Radiation Characteristics Using EBG Ground Structure and Dielectric Superstrate for Future 5G Cellular Networks

In this paper, a new dense dielectric (DD) patch array antenna prototype operating at 28 GHz for future fifth generation (5G) cellular networks is presented. This array antenna is proposed and designed with a standard printed circuit board process to be suitable for integration with radio frequency/microwave circuitry. The proposed structure employs four circular-shaped DD patch radiator antenna elements fed by a 1-to-4 Wilkinson power divider. To improve the array radiation characteristics, a ground structure based on a compact uniplanar electromagnetic bandgap unit cell has been used. The DD patch shows better radiation and total efficiencies compared with the metallic patch radiator. For further gain improvement, a dielectric layer of a superstrate is applied above the array antenna. The measured impedance bandwidth of the proposed array antenna ranges from 27 to beyond 32 GHz for a reflection coefficient (S11) of less than -10 dB. The proposed design exhibits stable radiation patterns over the whole frequency band of interest, with a total realized gain more than 16 dBi. Due to the remarkable performance of the proposed array, it can be considered as a good candidate for 5G communication applications.

A Monte Carlo simulation for two novel automatic censoring techniques of radar interfering targets in log-normal clutter

In this paper, we present two novel algorithms for automatic censoring of radar interfering targets in log-normal clutter. The proposed algorithms consist of two steps: removing the corrupted reference cells (censoring) and the actual detection. Both steps are performed dynamically by using a suitable set of ranked cells to estimate the unknown background level and set the adaptive thresholds accordingly. The proposed detectors do not require any prior information about the clutter parameters nor do they require the number of interfering targets. The effectiveness of the proposed algorithms is assessed by computing, using Monte Carlo simulations, the probability of censoring and the probability of detection in different background environments.

A GACS modeling approach for MPEG broadcast video

Accurate MPEG source models are needed to support high speed networks such as ATM and Internet. In this paper, we propose a video model called Gaussian auto-regressive and chi-square processes (GACS) for MPEG coded video traffic. The GACS models the sizes of MPEG I, P, and B frames according to the MPEG syntax I-frame>P-frame>B-frame. This is done by decomposing the process of each frame size into a weighted sum of a number of chi-square sequences. Each chi-square sequence is then obtained by squaring a Gaussian process, which is efficiently generated by using an auto-regressive (AR) model whose parameters are determined from an estimated covariance matrix. We evaluate the effectiveness of our model by conducting a series of experiments using a wide variety of long empirical video sequences. The results show that the proposed model leads to excellent data fit and accurate prediction of queuing performance.