Fathi El-Samie

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.

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.

Homomorphic image watermarking with a singular value decomposition algorithm

Information embedding and retrieval in digital images.Digital Watermarking.Image processing. In this paper, a new homomorphic image watermarking method implementing the Singular Value Decomposition (SVD) algorithm is presented. The idea of the proposed method is based on embedding the watermark with the SVD algorithm in the reflectance component after applying the homomorphic transform. The reflectance component contains most of the image features but with low energy, and hence watermarks embedded in this component will be invisible. A block-by-block implementation of the proposed method is also introduced. The watermark embedding on a block-by-block basis makes the watermark more robust to attacks. A comparison study between the proposed method and the traditional SVD watermarking method is presented in the presence of attacks. The proposed method is more robust to various attacks. The embedding of chaotic encrypted watermarks is also investigated in this paper to increase the level of security.

SC-FDMA for Mobile Communications

SC-FDMA for Mobile Communications examines Single-Carrier Frequency Division Multiple Access (SC-FDMA). Explaining this rapidly evolving system for mobile communications, it describes its advantages and limitations and outlines possible solutions for addressing its current limitations.The book explores the emerging trend of cooperative communication with SC-FDMA and how it can improve the physical layer security. It considers the design of distributed coding schemes and protocols for wireless relay networks where users cooperate to send their data to the destination.Supplying you with the required foundation in cooperative communication and cooperative diversity, it presents an improved Discrete Cosine Transform (DCT)-based SC-FDMA system. It introduces a distributed spacetime coding scheme and evaluates its performance and studies distributed SFC for broadband relay channels. Presents relay selection schemes for improving the physical layer Introduces a new transceiver scheme for the SC-FDMA system Describes spacetime/frequency coding schemes for SC-FDMA Includes MATLAB codes for all simulation experiments The book investigates Carrier Frequency Offsets (CFO) for the Single-Input Single-Output (SISO) SC-FDMA system, and Multiple-Input Multiple-Output (MIMO) SC-FDMA system simulation software. Covering the design of cooperative diversity schemes for the SC-FDMA system in the uplink direction, it also introduces and studies a new transceiver scheme for the SC-FDMA system.

Efficient implementation of chaotic image encryption in transform domains

Investigation of transform domain encryption.IWT encryption study.Study of chaotic Baker map permutation. The primary goal of this paper is security management in data image transmission and storage. Because of the increased use of images in industrial operations, it is necessary to protect the secret data of the image against unauthorized access. In this paper, we introduce a novel approach for image encryption based on employing a cyclic shift and the 2-D chaotic Baker map in different transform domains. The Integer Wavelet Transform (IWT), the Discrete Wavelet Transform (DWT), and the Discrete Cosine Transform (DCT) are exploited in the proposed encryption approach. The characteristics of the transform domains are studied and used to carry out the chaotic encryption. A comparison study between the transform-domain encryption approaches in the presence of attacks shows the superiority of encryption in the DWT domain.

Robust and secure fractional wavelet image watermarking

This paper presents an efficient fractional wavelet transform (FWT) image watermarking technique based on combining the discrete wavelet transform (DWT) and the fractional Fourier transform (FRFT). In the proposed technique, the host image is wavelet transformed with two resolution levels, and then, the middle frequency sub-bands are FRFT transformed. The watermark is hidden by altering the selected FRFT coefficients of the middle frequency sub-bands of the 2-level DWT-transformed host image. Two pseudo-random noise (PN) sequences are used to modulate the selected FRFT coefficients with the watermark pixels, and inverse transforms are finally applied to get the watermarked image. In watermark extraction, we just need the same two PN sequences used in the embedding process and the watermark size. The correlation factor is used to determine whether the extracted pixel is one or zero. The proposed fractional wavelet transform (FWT) image watermarking method is tested with different image processing attacks and under composite attacks to verify its robustness. Experimental results demonstrated improved robustness and security.

Compressive sensing applied to radar systems: an overview

Modern radar systems tend to utilize high bandwidth, which requires high sampling rate, and in many cases, these systems involve phased array configurations with a large number of transmit–receive elements. In contrast, the ultimate goal of a radar system is often to estimate only a limited number of target parameters. Thus, there is a pursuit to find better means to perform the radar signal acquisition as well as processing with much reduced amount of data and power requirement. Recently, there has been a great interest to consider compressive sensing (CS) for radar system design; CS is a novel technique which offers the framework for sparse signal detection and estimation for optimized data handling. In radars, CS enables the achievement of better range-Doppler resolution in comparison with the traditional techniques. However, CS requires the selection of suitable (sparse) signal model, the design of measurement system as well as the implementation of appropriate signal recovery method. This work attempts to present an overview of these CS aspects, particularly when CS is applied in monostatic pulse-Doppler and MIMO type of radars. Some of the associated challenges, e.g., grid mismatch and detector design issues, are also discussed.

Seizure detection with common spatial pattern and Support Vector Machines

This paper extends the use of the Common Spatial Pattern (CSP) algorithm for epileptic Electroencephalography (EEG) seizure detection. The CSP algorithm is applied on EEG signal derivative, which contains reinforced details of the signal. The main idea of the proposed approach is to apply a differentiator on the multi-channel EEG signal, and hence the signal is segmented into overlapping segments. Each segment is projected on a CSP projection matrix to extract the training and testing features. In selecting the training period, a leave-one-hour-out cross validation strategy is adopted. A Support Vector Machine (SVM) classifier is then trained with the training features to classify inter-ictal and ictal segments. Two variants of the CSP are presented and tested in this paper; the original CSP and the Diagonal Loading CSP.

Block-based SVD image watermarking in spatial and transform domains

The idea of this paper is to implement an efficient block-by-block singular value (SV) decomposition digital image watermarking algorithm, which is implemented in both the spatial and transforms domains. The discrete wavelet transform (DWT), the discrete cosine transform and the discrete Fourier transform are exploited for this purpose. The original image or one of its transforms is segmented into non-overlapping blocks, and consequently the image to be inserted as a watermark is embedded in the SVs of these blocks. Embedding the watermark on a block-by-block manner ensures security and robustness to attacks such like Gaussian noise, cropping and compression. The proposed algorithm can also be used for colour image watermarking. A comparison study between the proposed block-based watermarking algorithm and the method of Liu is performed for watermarking in all domains. Simulation results ensure that the proposed algorithm is more effective than the traditional method of Liu, especially when the watermarking is performed in the DWT domain.

Adaptive Resource Allocation Algorithms for Multi-user MIMO-OFDM Systems

Radio resource allocation to the system users is the key challenge issue in multi-user orthogonal frequency division multiplexing (MU-OFDM) systems. In this paper, an efficient and low-complexity proportional rate-adaptive radio resource (sub-carrier and power) allocation algorithm for MU-OFDM is proposed to maximize the sum-rate capacity of the system and achieve acceptable users’ rates fairness. Three-dimensional (spatial, frequency, and multi-user) greedy power allocation (GPA) algorithms for the MU multi-input multi-output OFDM (MU MIMO-OFDM) systems are proposed. The proposed algorithms start with spatial sub-carrier allocation followed by an optimal two-dimensional spatial-frequency GPA (SFGPA) step, which exploits both the spatial and frequency diversities. Therefore, spatial, frequency, and multi-user diversities are exploited by the proposed three-dimensional GPA algorithms. Several experiments are carried out to test the performance of the proposed three-dimensional GPA algorithms in terms of sum-rate capacity. The optimal solution is achieved by the three-dimensional dynamic sub-carrier-SFGPA with average gain (DS-SFGPA-AG) algorithm, which achieves the best sum-rate capacity performance compared with the other power allocation algorithms. The performance of the DS-SFGPA-AG algorithm is also analyzed and compared for the four OFDM-based systems; single-user (SU) OFDM, SU MIMO-OFDM, MU OFDM, and MU MIMO-OFDM. The MU MIMO-OFDM system with adaptive sub-carrier and power allocation outperforms the other systems.