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Raafat ElFouly

Cairo University, Computer Engineering Department, Faculty Member

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Annett Ungethüm

Technische Universität Dresden

Daniel McFarlin

Dawit Olika Terfassa

Norwegian School of Theology

Skku

Indian Institute Of Technology, Roorkee

Alistair Rendell

The Australian National University

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Papers by Raafat ElFouly

Creating a Compiler Optimized Inlineable Implementation of Intel Svml Simd Intrinsics

International Journal of Engineering, Jul 28, 2018

Single Input Multiple Data (SIMD) provides data parallelism execution via implemented SIMD instru... more Single Input Multiple Data (SIMD) provides data parallelism execution via implemented SIMD instructions and registers. Most mainstream computers architectures have been enhanced to provide data parallelism though SIMD extensions. These advances in parallel hardware have not been accompanied by the necessary software libraries granting programmers a set of functions that could work across all major compilers adding a 4x, 8x or 16x performance increase compared to standard SISD. Intel's SVML library offers SIMD implementation of Math and Scientific functions that have never been ported to work outside of Intel's own compiler. An Open Source inlineable implementation would increase performance and portability. This paper illustrates the development of an alternative compiler neutral implementation of Intel's SVML library.

Hybrid Scheduling Algorithm for Periodic Tasks in Real-Time Systems

by AHMED ALSHEIKHY and Raafat ElFouly

مجلة جامعة الملك عبدالعزيز-العلوم الهندسية

New Monitoring Architectures for underwater oil/Gas Pipeline using Hyper sensors

In this paper we propose new real time architectures for monitoring underwater oil and gas pipeli... more In this paper we propose new real time architectures for monitoring underwater oil and gas pipelines by using underwater wireless sensor network (UWSN). New monitoring architectures for underwater oil/gas pipeline inspection system combine a real time UWSN with nondestructive In Line Inspection (ILI) technology. These architecture will help in reducing or detecting the pipeline’s defects such as cracks, corrosions, welds, pipeline’s wall thickness ...etc by improving data transfer from the pipeline to the processor to extract useful information and deliver it to the onshore main station. Hence, decreasing delays in default detection.

Hybrid Scheduling Algorithm for Periodic Tasks in Real-Time Systems

مجلة جامعة الملك عبدالعزيز-العلوم الهندسية

Weighted Record Sample for Underwater Seismic Monitoring Application

Underwater acoustic sensor networks have been developed as a new technology for real-time underwa... more Underwater acoustic sensor networks have been developed as a new technology for real-time underwater applications, including seismic monitoring, disaster prevention, and oil well inspection. Unfortunately, this new technology is constrained to data sensing, large-volume transmission, and forwarding. As a result, the transmission of large volumes of data is costly in terms of both time and power. We thus focused our research activities on the development of embedded underwater computing systems. In this advanced technology, information extraction is performed underwater using data mining techniques or compression algorithms. We previously presented a new set of real-time underwater embedded system architectures that can manage multiple network configurations. In this study, we extend our research to develop information extraction for seismic monitoring underwater application to meet real-time constraints. The system performance is measured in terms of the minimum end-to-end delay and...

An efficient dynamic scheduling algorithm for periodic tasks in real-time systems using dynamic average estimation

2016 IEEE Symposium on Computers and Communication (ISCC), 2016

—Real-time embedded systems have become widely used in many fields such as control, monitoring an... more —Real-time embedded systems have become widely used in many fields such as control, monitoring and aviation. They perform several tasks under strict time constraints. In such systems, deadline miss may lead to catastrophic results so that all jobs need to be scheduled appropriately to ensure that they meet their deadline times. This paper presents an efficient dynamic scheduling algorithm during run-time to schedule periodic tasks in multiprocessor environments and uniprocessor as well using a dynamic average estimation. Dynamic average estimation refers to changing in different probability distributions when a task is added or removed from them. It is not always available a value of Worst-Case Execution Time (WCET) in many real-time applications such as multimedia where data has a great variation. The proposed approach selects which task or a set of tasks must be picked up for execution. A simulation system was developed to show validation of the proposed approach.

Fast and Compact ASIC Implementation of SFlash New Signature Scheme

The idea of using multivariate polynomials as public keys has attracted several cryptographers, S... more The idea of using multivariate polynomials as public keys has attracted several cryptographers, SFlash signature scheme is a variant of the Matsumoto and Imai multivariate public Key cryptosystem and selected by NESSIE Consortium. In this paper we describe a hardware implementation of SFlash based on bit-parallel architectures to achieve high speed circuits for operations on Finite Fields which can be efficiently used as an authentication unit in wireless devices, smart cards and RFID networks. We have proposed a new generalization to Karatsuba-Ofman multiplier as the core of the design. An ASIC chip can be realized with 78K gates counts and 2.8 2 mm die size with 0.35 m m CMOS technology, with a maximum clock frequency 140 MHZ, which takes about 21.5 s m to sign 259-Bits data.

Heurstic approaches for underwater sensing and processing deployment

2015 11th International Computer Engineering Conference (ICENCO), 2015

An improved dynamic Round Robin scheduling algorithm based on a variant quantum time

2015 11th International Computer Engineering Conference (ICENCO), 2015

A New Approach of Ultrasonic Beam-Edge Tracking and Strip Generation Technique for Ultrasonic Inspection Systems

A General Purpose Fuzzy Logic Based Computer System

A new multidimensional penalized likelihood regression method

2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2008

Penalized likelihood regression is a concept whereby the log-likelihood of the observations is co... more Penalized likelihood regression is a concept whereby the log-likelihood of the observations is combined with a term measuring the smoothness of the fit, and the resulting expression is then optimized. This concept vies for achieving a compromise between goodness of fit (as typified by the likelihood function) and smoothness of the data. Penalized likelihood regression, which has been developed in the statistics literature since the seventies, has focused mostly on the onedimensional case. Attempts to consider the general multidimensional case have been limited. In this paper we propose a new multidimensional penalized likelihood regression method. The approach is based on proposing a roughness term based on the discrepancy between the function values among the Knearest-neighbors. The proposed formulation yields a simple solution in terms of a system of linear equations. We also derive an iterative solution to the problem that sheds light on its basic functionality. The iteration consists of repeatedly taking the weighted average of the target output value and the estimated function values of the K-nearest-neighbors. We show that the proposed model is fairly versatile in that it exhibits nice features in handling user-defined function constraints and data imperfections. Experimental results confirm that it is competitive with the Gaussian process regression method (one of the best methods out there), and exhibits significant speed advantage.

Biometric Fusion Using Enhanced SVM Classification

2008 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2008

Support Vector Machines or SVM is one of the most successful and powerful statistical learning cl... more Support Vector Machines or SVM is one of the most successful and powerful statistical learning classification techniques. It has been also implemented in the biometric field. In this paper we propose the use of SVM as a fusion tool. We propose a system that fuses the classification obtained from the iris biometric and the fingerprint biometric. In addition, we show how score normalization can have a dramatic effect on performance (and the speed). The proposed model leads to considerable improvement in accuracy. In fact, the new fusion model improved the classification accuracy from around 96% for the best single biometric (the iris in this case) to over 99.8%. We believe that fingerprint and iris are a good combination to fuse and hope that this merits further research.

Stock Technical Analysis using Fuzzy Logic

Indian International Conference on Artificial Intelligence, 2005

This paper proposes a fuzzy logic based DSS for stock market. This system will help investors of ... more This paper proposes a fuzzy logic based DSS for stock market. This system will help investors of the stock market to take the correct buy/sell/hold decisions. The results obtained from the proposed fuzzy logic model were satisfactory. A fuzzy tuning methodology was introduced to enhance the accuracy of the decisions. The tuning methodology which uses genetic algorithms is presented also in this paper. Experimental simulation using actual price data from NASDAQ index is carried out to demonstrate the power of the proposed model.

Prediction of inner and outer diameter flaws using ultrasonic pipe inspection system

Extended Kalman filtering and Interacting Multiple Model for tracking maneuvering targets in sensor netwotrks

Intelligent solutions in …, 2009

This paper consider the nonlinear state estimate problem for tracking maneuvering targets. Two me... more This paper consider the nonlinear state estimate problem for tracking maneuvering targets. Two methods are introduced to overcome the difficulty of non-linear model. The first method uses Interacting Multiple Model (IMM) which includes 2, 3, 4 and 10 models. These models are linear, each model stands for an operation point of the nonlinear model. Two model sets are designed using Equal-Distance Model-Set Design for each. The effect of increasing the number of models, separation between them and noise effect on the accuracy is introduced. The second method uses Second order Extended Kalman Filter (EKF2) which is a single nonlinear filter. Both methods are evaluated by simulation using two scenarios. A comparison between them is evaluated by computing their accuracy, change of operation range and computational complexity (computational time) at different measurement noise. Based on this study for small range of variation of nonlinear parameter, and low noise the EKF2 introduced quick and accurate tracking. For a large range of nonlinearity and good separation between models of IMM, at minimum noise large and small numbers of models of IMM introduced best accuracy but as the noise increase large number keeps higher accuracy until the large numbers and small numbers of IMM introduced bad accuracy. At high noise optimizing number of models and separation between model sets, IMM introduces better accuracy.

Real time localization algorithm for maneuvering targets in non-uniform sensor networks

Intelligent solutions in …, 2009

Wireless ad-hoc sensor networks have recently emerged as a premier research topic. They have grea... more

Stock Technical Analysis using Multi Agent and Fuzzy Logic

Proceedings of the World Congress …, 2007

this paper proposes a multi agent and fuzzy logic based DSS for stock market. This system will he... more this paper proposes a multi agent and fuzzy logic based DSS for stock market. This system will help investors of the stock market to take the correct buy/sell/hold decisions. The results obtained from the proposed fuzzy logic model were satisfactory but not accurate. A fuzzy tuning methodology was introduced to enhance the accuracy of the decisions. The tuning methodology which uses genetic algorithms is presented also in this paper. A multi agent framework is proposed for the implementation of the system. Experimental simulation using actual price data form NASDAQ index is carried out to demonstrate the power of the proposed model.

Reversed Message Authentication Code Chain Broadcast (RMCB) Protocol

Proceeding (565) Wireless …, 2007

REVERSED MESSAGE AUTHENTICATION CODE CHAIN BROADCAST (RMCB) PROTOCOLArafat E. Hegazy, Ahmed M. Da... more

Fast and Compact ASIC Implementation of SFlash New Signature Scheme

sersc.org

The idea of using multivariate polynomials as public keys has attracted several cryptographers, S... more The idea of using multivariate polynomials as public keys has attracted several cryptographers, SFlash signature scheme is a variant of the Matsumoto and Imai multivariate public Key cryptosystem and selected by NESSIE Consortium. In this paper we describe a hardware implementation of SFlash based on bit-parallel architectures to achieve high speed circuits for operations on Finite Fields which can be efficiently used as an authentication unit in wireless devices, smart cards and RFID networks. We have proposed a new generalization to Karatsuba-Ofman multiplier as the core of the design. An ASIC chip can be realized with 78K gates counts and 2.8 2 mm die size with 0.35 m m CMOS technology, with a maximum clock frequency 140 MHZ, which takes about 21.5 s m to sign 259-Bits data.

Creating a Compiler Optimized Inlineable Implementation of Intel Svml Simd Intrinsics

International Journal of Engineering, Jul 28, 2018

Single Input Multiple Data (SIMD) provides data parallelism execution via implemented SIMD instru... more Single Input Multiple Data (SIMD) provides data parallelism execution via implemented SIMD instructions and registers. Most mainstream computers architectures have been enhanced to provide data parallelism though SIMD extensions. These advances in parallel hardware have not been accompanied by the necessary software libraries granting programmers a set of functions that could work across all major compilers adding a 4x, 8x or 16x performance increase compared to standard SISD. Intel's SVML library offers SIMD implementation of Math and Scientific functions that have never been ported to work outside of Intel's own compiler. An Open Source inlineable implementation would increase performance and portability. This paper illustrates the development of an alternative compiler neutral implementation of Intel's SVML library.

Hybrid Scheduling Algorithm for Periodic Tasks in Real-Time Systems

by AHMED ALSHEIKHY and Raafat ElFouly

مجلة جامعة الملك عبدالعزيز-العلوم الهندسية

New Monitoring Architectures for underwater oil/Gas Pipeline using Hyper sensors

In this paper we propose new real time architectures for monitoring underwater oil and gas pipeli... more In this paper we propose new real time architectures for monitoring underwater oil and gas pipelines by using underwater wireless sensor network (UWSN). New monitoring architectures for underwater oil/gas pipeline inspection system combine a real time UWSN with nondestructive In Line Inspection (ILI) technology. These architecture will help in reducing or detecting the pipeline’s defects such as cracks, corrosions, welds, pipeline’s wall thickness ...etc by improving data transfer from the pipeline to the processor to extract useful information and deliver it to the onshore main station. Hence, decreasing delays in default detection.

Hybrid Scheduling Algorithm for Periodic Tasks in Real-Time Systems

مجلة جامعة الملك عبدالعزيز-العلوم الهندسية

Weighted Record Sample for Underwater Seismic Monitoring Application

Underwater acoustic sensor networks have been developed as a new technology for real-time underwa... more Underwater acoustic sensor networks have been developed as a new technology for real-time underwater applications, including seismic monitoring, disaster prevention, and oil well inspection. Unfortunately, this new technology is constrained to data sensing, large-volume transmission, and forwarding. As a result, the transmission of large volumes of data is costly in terms of both time and power. We thus focused our research activities on the development of embedded underwater computing systems. In this advanced technology, information extraction is performed underwater using data mining techniques or compression algorithms. We previously presented a new set of real-time underwater embedded system architectures that can manage multiple network configurations. In this study, we extend our research to develop information extraction for seismic monitoring underwater application to meet real-time constraints. The system performance is measured in terms of the minimum end-to-end delay and...

An efficient dynamic scheduling algorithm for periodic tasks in real-time systems using dynamic average estimation

2016 IEEE Symposium on Computers and Communication (ISCC), 2016

—Real-time embedded systems have become widely used in many fields such as control, monitoring an... more —Real-time embedded systems have become widely used in many fields such as control, monitoring and aviation. They perform several tasks under strict time constraints. In such systems, deadline miss may lead to catastrophic results so that all jobs need to be scheduled appropriately to ensure that they meet their deadline times. This paper presents an efficient dynamic scheduling algorithm during run-time to schedule periodic tasks in multiprocessor environments and uniprocessor as well using a dynamic average estimation. Dynamic average estimation refers to changing in different probability distributions when a task is added or removed from them. It is not always available a value of Worst-Case Execution Time (WCET) in many real-time applications such as multimedia where data has a great variation. The proposed approach selects which task or a set of tasks must be picked up for execution. A simulation system was developed to show validation of the proposed approach.

Fast and Compact ASIC Implementation of SFlash New Signature Scheme

The idea of using multivariate polynomials as public keys has attracted several cryptographers, S... more The idea of using multivariate polynomials as public keys has attracted several cryptographers, SFlash signature scheme is a variant of the Matsumoto and Imai multivariate public Key cryptosystem and selected by NESSIE Consortium. In this paper we describe a hardware implementation of SFlash based on bit-parallel architectures to achieve high speed circuits for operations on Finite Fields which can be efficiently used as an authentication unit in wireless devices, smart cards and RFID networks. We have proposed a new generalization to Karatsuba-Ofman multiplier as the core of the design. An ASIC chip can be realized with 78K gates counts and 2.8 2 mm die size with 0.35 m m CMOS technology, with a maximum clock frequency 140 MHZ, which takes about 21.5 s m to sign 259-Bits data.

Heurstic approaches for underwater sensing and processing deployment

2015 11th International Computer Engineering Conference (ICENCO), 2015

An improved dynamic Round Robin scheduling algorithm based on a variant quantum time

2015 11th International Computer Engineering Conference (ICENCO), 2015

A New Approach of Ultrasonic Beam-Edge Tracking and Strip Generation Technique for Ultrasonic Inspection Systems

A General Purpose Fuzzy Logic Based Computer System

A new multidimensional penalized likelihood regression method

2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2008

Penalized likelihood regression is a concept whereby the log-likelihood of the observations is co... more Penalized likelihood regression is a concept whereby the log-likelihood of the observations is combined with a term measuring the smoothness of the fit, and the resulting expression is then optimized. This concept vies for achieving a compromise between goodness of fit (as typified by the likelihood function) and smoothness of the data. Penalized likelihood regression, which has been developed in the statistics literature since the seventies, has focused mostly on the onedimensional case. Attempts to consider the general multidimensional case have been limited. In this paper we propose a new multidimensional penalized likelihood regression method. The approach is based on proposing a roughness term based on the discrepancy between the function values among the Knearest-neighbors. The proposed formulation yields a simple solution in terms of a system of linear equations. We also derive an iterative solution to the problem that sheds light on its basic functionality. The iteration consists of repeatedly taking the weighted average of the target output value and the estimated function values of the K-nearest-neighbors. We show that the proposed model is fairly versatile in that it exhibits nice features in handling user-defined function constraints and data imperfections. Experimental results confirm that it is competitive with the Gaussian process regression method (one of the best methods out there), and exhibits significant speed advantage.

Biometric Fusion Using Enhanced SVM Classification

2008 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2008

Support Vector Machines or SVM is one of the most successful and powerful statistical learning cl... more Support Vector Machines or SVM is one of the most successful and powerful statistical learning classification techniques. It has been also implemented in the biometric field. In this paper we propose the use of SVM as a fusion tool. We propose a system that fuses the classification obtained from the iris biometric and the fingerprint biometric. In addition, we show how score normalization can have a dramatic effect on performance (and the speed). The proposed model leads to considerable improvement in accuracy. In fact, the new fusion model improved the classification accuracy from around 96% for the best single biometric (the iris in this case) to over 99.8%. We believe that fingerprint and iris are a good combination to fuse and hope that this merits further research.

Stock Technical Analysis using Fuzzy Logic

Indian International Conference on Artificial Intelligence, 2005

This paper proposes a fuzzy logic based DSS for stock market. This system will help investors of ... more This paper proposes a fuzzy logic based DSS for stock market. This system will help investors of the stock market to take the correct buy/sell/hold decisions. The results obtained from the proposed fuzzy logic model were satisfactory. A fuzzy tuning methodology was introduced to enhance the accuracy of the decisions. The tuning methodology which uses genetic algorithms is presented also in this paper. Experimental simulation using actual price data from NASDAQ index is carried out to demonstrate the power of the proposed model.

Prediction of inner and outer diameter flaws using ultrasonic pipe inspection system

Extended Kalman filtering and Interacting Multiple Model for tracking maneuvering targets in sensor netwotrks

Intelligent solutions in …, 2009

This paper consider the nonlinear state estimate problem for tracking maneuvering targets. Two me... more This paper consider the nonlinear state estimate problem for tracking maneuvering targets. Two methods are introduced to overcome the difficulty of non-linear model. The first method uses Interacting Multiple Model (IMM) which includes 2, 3, 4 and 10 models. These models are linear, each model stands for an operation point of the nonlinear model. Two model sets are designed using Equal-Distance Model-Set Design for each. The effect of increasing the number of models, separation between them and noise effect on the accuracy is introduced. The second method uses Second order Extended Kalman Filter (EKF2) which is a single nonlinear filter. Both methods are evaluated by simulation using two scenarios. A comparison between them is evaluated by computing their accuracy, change of operation range and computational complexity (computational time) at different measurement noise. Based on this study for small range of variation of nonlinear parameter, and low noise the EKF2 introduced quick and accurate tracking. For a large range of nonlinearity and good separation between models of IMM, at minimum noise large and small numbers of models of IMM introduced best accuracy but as the noise increase large number keeps higher accuracy until the large numbers and small numbers of IMM introduced bad accuracy. At high noise optimizing number of models and separation between model sets, IMM introduces better accuracy.

Real time localization algorithm for maneuvering targets in non-uniform sensor networks

Intelligent solutions in …, 2009

Wireless ad-hoc sensor networks have recently emerged as a premier research topic. They have grea... more

Stock Technical Analysis using Multi Agent and Fuzzy Logic

Proceedings of the World Congress …, 2007

this paper proposes a multi agent and fuzzy logic based DSS for stock market. This system will he... more this paper proposes a multi agent and fuzzy logic based DSS for stock market. This system will help investors of the stock market to take the correct buy/sell/hold decisions. The results obtained from the proposed fuzzy logic model were satisfactory but not accurate. A fuzzy tuning methodology was introduced to enhance the accuracy of the decisions. The tuning methodology which uses genetic algorithms is presented also in this paper. A multi agent framework is proposed for the implementation of the system. Experimental simulation using actual price data form NASDAQ index is carried out to demonstrate the power of the proposed model.

Reversed Message Authentication Code Chain Broadcast (RMCB) Protocol

Proceeding (565) Wireless …, 2007

REVERSED MESSAGE AUTHENTICATION CODE CHAIN BROADCAST (RMCB) PROTOCOLArafat E. Hegazy, Ahmed M. Da... more

Fast and Compact ASIC Implementation of SFlash New Signature Scheme

sersc.org

The idea of using multivariate polynomials as public keys has attracted several cryptographers, S... more The idea of using multivariate polynomials as public keys has attracted several cryptographers, SFlash signature scheme is a variant of the Matsumoto and Imai multivariate public Key cryptosystem and selected by NESSIE Consortium. In this paper we describe a hardware implementation of SFlash based on bit-parallel architectures to achieve high speed circuits for operations on Finite Fields which can be efficiently used as an authentication unit in wireless devices, smart cards and RFID networks. We have proposed a new generalization to Karatsuba-Ofman multiplier as the core of the design. An ASIC chip can be realized with 78K gates counts and 2.8 2 mm die size with 0.35 m m CMOS technology, with a maximum clock frequency 140 MHZ, which takes about 21.5 s m to sign 259-Bits data.