Gamal M. Aly

Digital design of variable structure control systems

Reachability conditions are developed for discrete single-input-single-output variable structure control systems described by linear mathematical models in general state-space form to reach a switching function from anywhere in state space. Stability conditions of a sliding mode are investigated. A modified algorithm is proposed to simplify the design procedure. Practical application to a thermal process has been achieved by using the modified algorithm to show the potential for development and practical results are compared with those using the classical PID controller design.

JPEG encoder for low-cost FPGAs

This paper presents the implementation of a JPEG encoder that exploits minimal usage of FPGA resources. The encoder compresses an image as a stream of 8times8 blocks with each element of the block applied and processed individually. The zigzag unit typically found in implementations of JPEG encoders is eliminated. The division operation of the quantization step is replaced by a combination of multiplication and shift operations. The encoder is implemented on Xilinx Spartan-3 FPGA and is benchmarked against two software implementations on four test images. It is demonstrated that it yields performance of similar quality while requiring very limited FPGA resources. A co-emulation technique is applied to reduce development time and to test and verify the encoder design.

Performance analysis of a Principal Component Analysis ensemble classifier for Emotiv headset P300 spellers

The current trend to use Brain-Computer Interfaces (BCIs) with mobile devices mandates the development of efficient EEG data processing methods. In this paper, we demonstrate the performance of a Principal Component Analysis (PCA) ensemble classifier for P300-based spellers. We recorded EEG data from multiple subjects using the Emotiv neuroheadset in the context of a classical oddball P300 speller paradigm. We compare the performance of the proposed ensemble classifier to the performance of traditional feature extraction and classifier methods. Our results demonstrate the capability of the PCA ensemble classifier to classify P300 data recorded using the Emotiv neuroheadset with an average accuracy of 86.29% on cross-validation data. In addition, offline testing of the recorded data reveals an average classification accuracy of 73.3% that is significantly higher than that achieved using traditional methods. Finally, we demonstrate the effect of the parameters of the P300 speller paradigm on the performance of the method.

Multi-model control of MIMO systems: location and control algorithms

This paper presents a multi-model control scheme that depends on the multiple representation of a process using linear models. A dynamic system can be represented by several models, each of which is different in either the simplifications involved, the reductions involved, or the dynamic characteristics. A new tracking multi-model control algorithm for deterministic systems is proposed. An auxiliary input called the ‘state correction’ is calculated and applied to the models so as to minimize a performance index which is a function of the difference between the process outputs and the model outputs. A simulation study is given to show the potential of the proposed algorithm.

The computation of optimal singular control

An algorithm for the solution of optimal control problems with singular subarcs is presented. The modified quasilinearization is extended to the solution of those problems where the initial conditions are updated successively. A major advantage of the quasilinearization method is its rapidity of convergence despite its simplicity in programming. It is not necessary to introduce penalty functions for the treatment of boundary conditions. An illustrative example is presented.

A principal component analysis ensemble classifier for P300 speller applications

Recent advances in developing Brain-Computer Interfaces (BCIs) have opened up a new realm for designing efficient systems that could enable disabled people to communicate. The P300 speller is one important BCI application that allows the selection of characters on a virtual keyboard by analyzing recorded electroencephalography (EEG) activity. In this work, we propose an ensemble classifier that uses Principal Component Analysis (PCA) features to identify evoked P300 signals from EEG recordings. We examine the performance of the proposed method, using different linear classifiers, on the datasets provided by the BCI competition III. Results demonstrate a classification accuracy of 91% using the proposed method. In addition, our results indicate a significant improvement in classification accuracy compared to traditional feature extraction and classification approaches. The proposed method results in low across-subjects variability compared to other methods with minimal parameter tuning required which could be useful in mobile platform P300 applications.

New fuzzy-based indoor positioning scheme using ZigBee wireless protocol

This paper proposes new fuzzy-based scheme providing position information vital for smart home applications. The scheme runs efficiently on the economical wireless ZigBee nodes and uses the link quality indicator (LQI) measured without the need for any additional hardware. It uses fuzzy logic to represent measure noise and surrounding environmental impacts. Position estimation is based upon the fuzzy information provided by all available ZigBee nodes and the surrounding environment is modeled by assembling a set of representative fuzzy vectors. Fuzzy levels are determined by applying the K-means algorithm given the LQI distribution as input. The scheme performance is compared to two popular schemes, the I3BM and the Environment Adaptive. The three are implemented using the Jennic JN5148 ZigBee PRO kit. It is demonstrated that for regular motion the proposed scheme significantly outperforms the other two without high computational cost, slow response, or large memory requirement.

RTOS Modeling Using SystemC

In this paper we present our methodology for modeling a priority-based preemptive real time operating system (RTOS) kernel in SystemC. We use the current modeling constructs of SystemC 2.0 and throughout our development of the kernel’s system calls we propose new constructs that can be used in RTOS modeling. We show then the interaction of the embedded software module consisting of the RTOS kernel and the running application tasks with a hardware module representing a bus functional model (BFM) of a generic microcontroller.

Accurate floating-point operation using controlled floating-point precision

Rounding and accumulation of errors when using floating point numbers are important factors in computer arithmetic. Many applications suffer from these problems. The underlying machine architecture and representation of floating point numbers play the major role in the level and value of errors in this type of calculations. A quantitative measure of a system error level is the machine epsilon. In the current representation of floating point numbers, the machine epsilon can be as small as 9.63E-35 in the 128 bit version of IEEE standard floating point representation system. In this work a novel solution that guarantees achieving the desired minimum error regardless of the machine architecture is presented. The proposed model can archive a machine epsilon of about 4.94E-324. A new representation model is given and a complete arithmetic system with basic operations is presented. The accuracy of the proposed method is verified by inverting a high order, Hilbert matrix, an ill-conditioned matrix that cannot be solved in the traditional floating point standard. Finally some comparisons are given.

MindEdit: A P300-based text editor for mobile devices

Practical application of Brain-Computer Interfaces (BCIs) requires that the whole BCI system be portable. The mobility of BCI systems involves two aspects: making the electroencephalography (EEG) recording devices portable, and developing software applications with low computational complexity to be able to run on low computational-power devices such as tablets and smartphones. This paper addresses the development of MindEdit; a P300-based text editor for Android-based devices. Given the limited resources of mobile devices and their limited computational power, a novel ensemble classifier is utilized that uses Principal Component Analysis (PCA) features to identify P300 evoked potentials from EEG recordings. PCA computations in the proposed method are channel-based as opposed to concatenating all channels as in traditional feature extraction methods; thus, this method has less computational complexity compared to traditional P300 detection methods. The performance of the method is demonstrated on data recorded from MindEdit on an Android tablet using the Emotiv wireless neuroheadset. Results demonstrate the capability of the introduced PCA ensemble classifier to classify P300 data with maximum average accuracy of 78.37±16.09% for cross-validation data and 77.5±19.69% for online test data using only 10 trials per symbol and a 33-character training dataset. Our analysis indicates that the introduced method outperforms traditional feature extraction methods. For a faster operation of MindEdit, a variable number of trials scheme is introduced that resulted in an online average accuracy of 64.17±19.6% and a maximum bitrate of 6.25bit/min. These results demonstrate the efficacy of using the developed BCI application with mobile devices.