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Amin Jarrah

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University of Cologne

Prof. Mona Nasr

Helwan University

Ibrahim A. Hameed

Norwegian University of Science and Technology

Mustafa Hussain

East West University

National Institute of Technology, Calicut

Noor Azwan Shairi

Universiti Teknikal Malaysia Melaka

Tayfun Nesimoglu

Middle East Technical University

Institute of Space Technology

Euro Asia International Journals

Universidad de los Andes (Colombia)

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Papers by Amin Jarrah

Accelerating FCM Algorithm Using High-Speed FPGA Reconfigurable Computing Architecture

Journal of Electrical Engineering & Technology

Automotive engine idle speed controller: Nonlinear model predictive control utilizing the firefly algorithm

Computers and Electrical Engineering

The optimisation of travelling salesman problem based on parallel ant colony algorithm

International Journal of Computer Applications in Technology

Accurate Reader Identification for the Arabic Holy Quran Recitations Based on an Enhanced VQ Algorithm

Revue d'Intelligence Artificielle

The Speaker identification process is not a new trend; however, for the Arabic Holy Quran recitat... more The Speaker identification process is not a new trend; however, for the Arabic Holy Quran recitation, there are still quite improvements that can make this process more accurate and reliable. This paper collected the input data from 14 native Arabic reciters, consisting of “Surah Al-Kawthar” speech signals from the Holy Quran. Moreover, this paper discusses the accuracy rates for 8 and 16 features. Indeed, a modified Vector Quantization (VQ) technique will be presented, in addition to realistically matching the centroids of the various codebooks and measuring systems’ effectiveness. Note that the VQ technique will be utilized to generate the codebooks by clustering these features into a finite number of centroids. The proposed system’s software was built and executed using MATLAB®. The proposed system’s total accuracy rate was 97.92% and 98.51% for 8 and 16 centroids codebooks, respectively. However, this study discussed two validation tactics to ensure that the outcomes are reliabl...

A New Approach of Combining Optical Mapping Algorithm with Adaptive Kalman Filter to Achieve Fast and Early Detection of Cardiac Arrests: A Parallel Implementation

Traitement du Signal

This research aims to propose a new approach by combining the Optical Mapping Algorithm (OMA) wit... more This research aims to propose a new approach by combining the Optical Mapping Algorithm (OMA) with the Adaptive Kalman Filter (AKF) to improve the early detection of cardiac arrest. To improve the overall performance of the proposed approach and reduce the execution time significantly, a parallel implementation is suggested using the open-source computer vision (OpenCV) library tool and optimized for heterogeneous multi-core systems. The OpenCV library incorporates many image processing functions that are used to extract the distinctive features of the digital image to indicate the presence or absence of cardiac arrest in order to save the largest possible number of human lives. Experimental results confirm the efficiency of the proposed implementation; as for a loop with a sequence of operations repeated 64 times, the achieved speedup is 5X with an accuracy improvement of 96%. This is due to the exploitation of several optimization techniques, such as pipelining and loop unrolling. The accuracy of dark spot detection of blood vessels by combining the AKF with OMA was improved from 50% to 83.3% compared with the AKF approach alone. Thus, the proposed approach and its parallel implementation are very useful in early cardiac arrest detection to preserve human lives.

High-Performance Implementation of Power Components on FPGA Platform

Journal of Electrical Engineering & Technology

High Performance Changeable Dynamic Gentle Random Early Detection (CDGRED) for Congestion Control at Router Buffer

International Journal of Grid and High Performance Computing

The internet is spreading fast and the diversity of its components affects the performance unpred... more The internet is spreading fast and the diversity of its components affects the performance unpredictably. This leads to the continuous examination of internet hardware structure for the purpose of user experience improvement. Network congestion is one of the challenges that affects network performance, which mostly occurs when the arriving packets exceed available network resources. When this occurs, incoming packets face unpredicted losses or delay. Thus, congestion has an impact on worsening the network performance due to an increase in packet loss. Therefore, a high performance approach called CDGRED was proposed to overcome these constraints using adaptive techniques. An optimized implementation with a suitable parameter tuning for CDGRED method was proposed with results showing clearly enhanced outputs. The CDGRED approach performance is empirically tested and compared with existing methods such as GRED, DGRED, and FLRED. Experimental results prove that the proposed approach ha...

Optimised implementation of AVR system using particle swarm optimisation

International Journal of Computational Science and Engineering

Optimized implementation of an improved KNN classification algorithm using Intel FPGA platform: Covid-19 case study

Journal of King Saud University - Computer and Information Sciences

The improved k-nearest neighbor (KNN) algorithm based on class contribution and feature weighting... more The improved k-nearest neighbor (KNN) algorithm based on class contribution and feature weighting (DCT-KNN) is a highly accurate approach. However, it requires complex computational steps which consumes much time for the classification process. A field programmable gate array (FPGA) can be used to solve this drawback. However, using traditional hardware description language (HDL) to implement FPGA-based accelerators requires a high design time. Fortunately, the open computing language (OpenCL) high level parallel programming tool allows rapid and effective design on FPGA-based hardware accelerators. In this study, OpenCL has been used to examine speeding up the DCT-KNN algorithm on the FPGA parallel computing platform through applying numerous parallelization and optimization techniques. The optimized approach of the improved KNN could be used in various engineering problems that require a high speed of classification process. Classification of the COVID-19 disease is the case study used to examine this work. The experimental results show that implementing the DCT-KNN algorithm on the FPGA platform (Intel De5a-net Arria-10 device was used) gives an extremely high performance when compared to the traditional single-core-CPU based implementation. The execution time for our optimized design on the FPGA accelerator is 44 times faster than the conventional design implemented on the regular CPU-based computational platform.

Weavesim: A Scalable and Reusable Cloud Simulation Framework Leveraging Aspect-Oriented Programming

Jordanian Journal of Computers and Information Technology, 2020

Cloud computing service-oriented simulation frameworks are very important tools for modeling and ... more Cloud computing service-oriented simulation frameworks are very important tools for modeling and simulating the dynamic behavior of cloud-based software systems. However, the existing service-oriented simulation frameworks lack the ability to measure and control the rapidly changing (adaptive) requirements that span over many modules in cloud-based software systems, such as security, logging, monitoring, etc. To address these limitations, this paper presents an efficient framework for reducing the complexity of modeling and simulating the custom and dynamic behavior of cloud-based applications, called WeaveSim. WeaveSim utilizes the aspectoriented programming (AOP) to encapsulate the complexity of developing the dynamic behavior of cloud-based applications by adding another abstract layer called Context-Aware Aspect Layer (CAAL). CAAL reduces the complexity of using CloudSim to simulate cloud-based software systems. Examples of cross-cutting concerns are data encryption, logging and monitoring. Since implementing a cross-cutting concern on a cloud-based simulator, such as CloudSim, requires modifications, from developers, to many core modules of that simulator. However, using WeaveSim, implementing cross-cutting concerns would be an easy task for developers, since they only need to reuse pre-defined joinpoints and pointcuts without modifying the underlying core modules of the simulator. We evaluated WeaveSim on an academically-scaled system. The results of our experimental evaluations show the benefit of WeaveSim in reducing the complexity of implementing cross-cutting concerns on cloud-based software systems. Hence, the reusability, scalability and maintainability of the cloud-based software systems are increased.

Development of parallel architectures for radar/ video signal processing applications

.................................................................................................... more

Enhancing the Parallelization of Backpropagation Neural Network Algorithm for Implementation on FPGA Platform

NAECON 2018 - IEEE National Aerospace and Electronics Conference, 2018

Backpropagation Neural Network Algorithm is a long-standing supervised algorithm, with applicatio... more Backpropagation Neural Network Algorithm is a long-standing supervised algorithm, with applications in many domains. This method is useful when there is a predefined set of input and output vectors, and aiming to produce the output for the given input. However, to reduce the computational time latency, many optimization techniques that are available in Vivado HLS tool were applied to generate a downable code for FPGA platform to enhance the performance of the algorithm. The overall latency has been reduced from 3.44ms without optimization to 2.42ms with optimization. Therefore, the latency reduced by about 1.4 times. This paper also implemented and paralyzed the same algorithm on CPU and GPU for comparison purposes. The result shows the FPGA implementation gives better results than CPU and GPU on the comparison of performance due to the applied optimization techniques in the implementation.

An OpenCL-based parallel acceleration of aSobel edge detection algorithm Using IntelFPGA technology

South African Computer Journal, 2020

This paper examines the feasibility of using commercial out-of-the-box Reconfigurable Field Progr... more This paper examines the feasibility of using commercial out-of-the-box Reconfigurable Field Programmable Gate Array (FPGA) technology and the OpenCL framework to create efficient Sobel edge-detection implementation, which is considered a fundamental part in the field of image and video processing. The revised proposed approach was created at a high level of abstraction and executed on high commodity Intel FPGA platform. This was performed in a manner that was designed to allow the high-level compiler/synthesis tool to manipulate a task a parallelism model. The most promising FPGA and the naive implementations were compared to their single-core CPU software equivalents while manipulating local-memory, pipelining, loop unrolling, vectorization, internal channels mechanisms and memory coalescing to provide a much more effective hardware design. The run-time and the power consumption attributes were estimated for each implementation. The proposed FPGA based implementations were found to...

High-Speed FPGA-based of the Particle Swarm Optimization using HLS Tool

International Journal of Advanced Computer Science and Applications, 2019

The Particle Swarm Optimization (PSO) is a heuristic search method inspired by different biologic... more The Particle Swarm Optimization (PSO) is a heuristic search method inspired by different biological populations on their swarming or collaborative behavior. This novel work has implemented PSO for the Travelling Salesman Problem (TSP) in high-level synthesis to reduce the computational time latency. The high-level synthesis design generates an estimation of the hardware resources needed to implement the PSO algorithm for TSP on FPGA. The targeted FPGA of this algorithm is the Xilinx Zynq family. The algorithm has been implemented for getting the best route between 5 given cities with given distances. The research has used 7 number of particles for a different number of iterations for generating the best route between those 5 cities. The overall latency has been reduced due to the applied optimization techniques. This paper also implemented and parallelized the same algorithm on CPU Intel I7 Processor; the result shows the FPGA implementation gives better results than CPU on the comparison of performance.

Discovering regulatory motifs of genetic networks using the indexing-tree based algorithm: a parallel implementation

Engineering Computations, 2020

PurposeThe problem of motif discovery has become a significant challenge in the era of big data w... more PurposeThe problem of motif discovery has become a significant challenge in the era of big data where there are hundreds of genomes requiring annotations. The importance of motifs has led many researchers to develop different tools and algorithms for finding them. The purpose of this paper is to propose a new algorithm to increase the speed and accuracy of the motif discovering process, which is the main drawback of motif discovery algorithms.Design/methodology/approachAll motifs are sorted in a tree-based indexing structure where each motif is created from a combination of nucleotides: ‘A’, ‘C’, ‘T’ and ‘G’. The full motif can be discovered by extending the search around 4-mer nucleotides in both directions, left and right. Resultant motifs would be identical or degenerated with various lengths.FindingsThe developed implementation discovers conserved string motifs in DNA without having prior information about the motifs. Even for a large data set that contains millions of nucleotid...

FPGA based architecture of Extensive Cancellation Algorithm (ECA) for Passive Bistatic Radar (PBR)

Microprocessors and Microsystems, 2016

Passive Bistatic Radar (PBR) exploits existing signals of opportunity from different sources such... more Passive Bistatic Radar (PBR) exploits existing signals of opportunity from different sources such as Radio and TV signals. Extensive Cancellation Algorithm (ECA) has been proven to be a very effective way to mitigate the effects of direct signal, multipath and clutter echoes in PBR. Also, it is able to detect a moving target accurately when it comes to strong-clutter environment and long-range detection providing evidence for its robustness. However, ECA is a computationally intensive algorithm and will benefit from parallel processing and modern computational platforms such as Field Programmable Gate Arrays (FPGAs). This work involves transformation of ECA by exploring opportunities for parallel processing and elimination of any unnecessary computations and storages. ECA algorithm has been also implemented on FPGAs for high speed computation by exploiting parallel and pipelining approaches. A new software tool called Radar Signal Processing Tool (RSPT) has been developed. It allows the designer to auto-generate fully optimized VHDL representation of ECA by specifying many user input parameters through GUI. The produced VHDL code is independent of FPGA part. It is also appropriate for use with any future high performance FPGAs or ASICs to further cut down computation time. Moreover, it provides the designer a feedback on various performance parameters. This offers the designer an ability to make any adjustments to the ECA component until the desired performance of the overall System on Chip (SoC) is achieved. The computation time of our transformed/optimized algorithm has improved by a factor of 3.8. Its FPGA implementation offers a speed up of 18 over CPU.

Parralelization of non-linear & non-Gaussian Bayesian state estimators (Particle filters)

2015 23rd European Signal Processing Conference (EUSIPCO), 2015

Particle filter has been proven to be a very effective method for identifying targets in non-line... more Particle filter has been proven to be a very effective method for identifying targets in non-linear and non-Gaussian environment. However, particle filter is computationally intensive and may not achieve the real time requirements. So, it's desirable to implement it on parallel platforms by exploiting parallel and pipelining architecture to achieve its real time requirements. In this work, an efficient implementation of particle filter in both FPGA and GPU is proposed. Particle filter has also been implemented using MATLAB Parallel Computing Toolbox (PCT). Experimental results show that FPGA and GPU architectures can significantly outperform an equivalent sequential implementation. The results also show that FPGA implementation provides better performance than the GPU implementation. The achieved execution time on dual core and quad core Dell PC using PCT were higher than FPGAs and GPUs as was expected.

High performance implementation of Neural Networks by networks on chip with 5-port 2-virtual channels

Proceedings of 2010 IEEE International Symposium on Circuits and Systems, 2010

Hardware implementation of Artificial Neural Network (ANN) is proposed by using Networks on Chip ... more Hardware implementation of Artificial Neural Network (ANN) is proposed by using Networks on Chip (NoC) with 5-port 2-virtual channels router, aiming at higher performance and low latency. Experimental results by NIRGAM NoC simulator show that this proposed system has higher Connection-Per-Second (CPS), higher Connection-Per-Second-Per-Weight (CP-SPW), lower communication load. Furthermore this NoC implementation system is reconfigurable and expandable, so that it can be applied to various applications.

Submitted Waiting for Acception

Sensitivity Analysis for Optimal Parameters for Marine Radar Data Processing

A bird and bat monitoring system has been developed that uses marine radar, IR camera and acousti... more A bird and bat monitoring system has been developed that uses marine radar, IR camera and acoustic recorders for wind farm applications. IR video recording is used to mon itor birds and bats activity wh ich will be useful for wildlife biologists in developing mit igation techniques to minimize impact of wind turbines on birds and bats. The goal is to quantify birds and bats activity near wind turbines. Radar will provide z-coordinate (alt itude) and IR camera will provide (x, y) coord inates of birds/bats. Acous tic monitoring is used to identify birds and bats at their species level. This paper deals with the use of marine radar for determining altitudes, direction and quantity (passage rates) of birds/bats. Data from the marine radar is dig itized and processed with open source radR software. Since the data is unknown tracking and quantification can be very challenging. Th is paper deals with the sensitivity analysis and effects of various parameters used in the tracking algorith ...

Accelerating FCM Algorithm Using High-Speed FPGA Reconfigurable Computing Architecture

Journal of Electrical Engineering & Technology

Automotive engine idle speed controller: Nonlinear model predictive control utilizing the firefly algorithm

Computers and Electrical Engineering

The optimisation of travelling salesman problem based on parallel ant colony algorithm

International Journal of Computer Applications in Technology

Accurate Reader Identification for the Arabic Holy Quran Recitations Based on an Enhanced VQ Algorithm

Revue d'Intelligence Artificielle

The Speaker identification process is not a new trend; however, for the Arabic Holy Quran recitat... more The Speaker identification process is not a new trend; however, for the Arabic Holy Quran recitation, there are still quite improvements that can make this process more accurate and reliable. This paper collected the input data from 14 native Arabic reciters, consisting of “Surah Al-Kawthar” speech signals from the Holy Quran. Moreover, this paper discusses the accuracy rates for 8 and 16 features. Indeed, a modified Vector Quantization (VQ) technique will be presented, in addition to realistically matching the centroids of the various codebooks and measuring systems’ effectiveness. Note that the VQ technique will be utilized to generate the codebooks by clustering these features into a finite number of centroids. The proposed system’s software was built and executed using MATLAB®. The proposed system’s total accuracy rate was 97.92% and 98.51% for 8 and 16 centroids codebooks, respectively. However, this study discussed two validation tactics to ensure that the outcomes are reliabl...

A New Approach of Combining Optical Mapping Algorithm with Adaptive Kalman Filter to Achieve Fast and Early Detection of Cardiac Arrests: A Parallel Implementation

Traitement du Signal

This research aims to propose a new approach by combining the Optical Mapping Algorithm (OMA) wit... more This research aims to propose a new approach by combining the Optical Mapping Algorithm (OMA) with the Adaptive Kalman Filter (AKF) to improve the early detection of cardiac arrest. To improve the overall performance of the proposed approach and reduce the execution time significantly, a parallel implementation is suggested using the open-source computer vision (OpenCV) library tool and optimized for heterogeneous multi-core systems. The OpenCV library incorporates many image processing functions that are used to extract the distinctive features of the digital image to indicate the presence or absence of cardiac arrest in order to save the largest possible number of human lives. Experimental results confirm the efficiency of the proposed implementation; as for a loop with a sequence of operations repeated 64 times, the achieved speedup is 5X with an accuracy improvement of 96%. This is due to the exploitation of several optimization techniques, such as pipelining and loop unrolling. The accuracy of dark spot detection of blood vessels by combining the AKF with OMA was improved from 50% to 83.3% compared with the AKF approach alone. Thus, the proposed approach and its parallel implementation are very useful in early cardiac arrest detection to preserve human lives.

High-Performance Implementation of Power Components on FPGA Platform

Journal of Electrical Engineering & Technology

High Performance Changeable Dynamic Gentle Random Early Detection (CDGRED) for Congestion Control at Router Buffer

International Journal of Grid and High Performance Computing

The internet is spreading fast and the diversity of its components affects the performance unpred... more The internet is spreading fast and the diversity of its components affects the performance unpredictably. This leads to the continuous examination of internet hardware structure for the purpose of user experience improvement. Network congestion is one of the challenges that affects network performance, which mostly occurs when the arriving packets exceed available network resources. When this occurs, incoming packets face unpredicted losses or delay. Thus, congestion has an impact on worsening the network performance due to an increase in packet loss. Therefore, a high performance approach called CDGRED was proposed to overcome these constraints using adaptive techniques. An optimized implementation with a suitable parameter tuning for CDGRED method was proposed with results showing clearly enhanced outputs. The CDGRED approach performance is empirically tested and compared with existing methods such as GRED, DGRED, and FLRED. Experimental results prove that the proposed approach ha...

Optimised implementation of AVR system using particle swarm optimisation

International Journal of Computational Science and Engineering

Optimized implementation of an improved KNN classification algorithm using Intel FPGA platform: Covid-19 case study

Journal of King Saud University - Computer and Information Sciences

The improved k-nearest neighbor (KNN) algorithm based on class contribution and feature weighting... more The improved k-nearest neighbor (KNN) algorithm based on class contribution and feature weighting (DCT-KNN) is a highly accurate approach. However, it requires complex computational steps which consumes much time for the classification process. A field programmable gate array (FPGA) can be used to solve this drawback. However, using traditional hardware description language (HDL) to implement FPGA-based accelerators requires a high design time. Fortunately, the open computing language (OpenCL) high level parallel programming tool allows rapid and effective design on FPGA-based hardware accelerators. In this study, OpenCL has been used to examine speeding up the DCT-KNN algorithm on the FPGA parallel computing platform through applying numerous parallelization and optimization techniques. The optimized approach of the improved KNN could be used in various engineering problems that require a high speed of classification process. Classification of the COVID-19 disease is the case study used to examine this work. The experimental results show that implementing the DCT-KNN algorithm on the FPGA platform (Intel De5a-net Arria-10 device was used) gives an extremely high performance when compared to the traditional single-core-CPU based implementation. The execution time for our optimized design on the FPGA accelerator is 44 times faster than the conventional design implemented on the regular CPU-based computational platform.

Weavesim: A Scalable and Reusable Cloud Simulation Framework Leveraging Aspect-Oriented Programming

Jordanian Journal of Computers and Information Technology, 2020

Cloud computing service-oriented simulation frameworks are very important tools for modeling and ... more Cloud computing service-oriented simulation frameworks are very important tools for modeling and simulating the dynamic behavior of cloud-based software systems. However, the existing service-oriented simulation frameworks lack the ability to measure and control the rapidly changing (adaptive) requirements that span over many modules in cloud-based software systems, such as security, logging, monitoring, etc. To address these limitations, this paper presents an efficient framework for reducing the complexity of modeling and simulating the custom and dynamic behavior of cloud-based applications, called WeaveSim. WeaveSim utilizes the aspectoriented programming (AOP) to encapsulate the complexity of developing the dynamic behavior of cloud-based applications by adding another abstract layer called Context-Aware Aspect Layer (CAAL). CAAL reduces the complexity of using CloudSim to simulate cloud-based software systems. Examples of cross-cutting concerns are data encryption, logging and monitoring. Since implementing a cross-cutting concern on a cloud-based simulator, such as CloudSim, requires modifications, from developers, to many core modules of that simulator. However, using WeaveSim, implementing cross-cutting concerns would be an easy task for developers, since they only need to reuse pre-defined joinpoints and pointcuts without modifying the underlying core modules of the simulator. We evaluated WeaveSim on an academically-scaled system. The results of our experimental evaluations show the benefit of WeaveSim in reducing the complexity of implementing cross-cutting concerns on cloud-based software systems. Hence, the reusability, scalability and maintainability of the cloud-based software systems are increased.

Development of parallel architectures for radar/ video signal processing applications

.................................................................................................... more

Enhancing the Parallelization of Backpropagation Neural Network Algorithm for Implementation on FPGA Platform

NAECON 2018 - IEEE National Aerospace and Electronics Conference, 2018

Backpropagation Neural Network Algorithm is a long-standing supervised algorithm, with applicatio... more Backpropagation Neural Network Algorithm is a long-standing supervised algorithm, with applications in many domains. This method is useful when there is a predefined set of input and output vectors, and aiming to produce the output for the given input. However, to reduce the computational time latency, many optimization techniques that are available in Vivado HLS tool were applied to generate a downable code for FPGA platform to enhance the performance of the algorithm. The overall latency has been reduced from 3.44ms without optimization to 2.42ms with optimization. Therefore, the latency reduced by about 1.4 times. This paper also implemented and paralyzed the same algorithm on CPU and GPU for comparison purposes. The result shows the FPGA implementation gives better results than CPU and GPU on the comparison of performance due to the applied optimization techniques in the implementation.

An OpenCL-based parallel acceleration of aSobel edge detection algorithm Using IntelFPGA technology

South African Computer Journal, 2020

This paper examines the feasibility of using commercial out-of-the-box Reconfigurable Field Progr... more This paper examines the feasibility of using commercial out-of-the-box Reconfigurable Field Programmable Gate Array (FPGA) technology and the OpenCL framework to create efficient Sobel edge-detection implementation, which is considered a fundamental part in the field of image and video processing. The revised proposed approach was created at a high level of abstraction and executed on high commodity Intel FPGA platform. This was performed in a manner that was designed to allow the high-level compiler/synthesis tool to manipulate a task a parallelism model. The most promising FPGA and the naive implementations were compared to their single-core CPU software equivalents while manipulating local-memory, pipelining, loop unrolling, vectorization, internal channels mechanisms and memory coalescing to provide a much more effective hardware design. The run-time and the power consumption attributes were estimated for each implementation. The proposed FPGA based implementations were found to...

High-Speed FPGA-based of the Particle Swarm Optimization using HLS Tool

International Journal of Advanced Computer Science and Applications, 2019

The Particle Swarm Optimization (PSO) is a heuristic search method inspired by different biologic... more The Particle Swarm Optimization (PSO) is a heuristic search method inspired by different biological populations on their swarming or collaborative behavior. This novel work has implemented PSO for the Travelling Salesman Problem (TSP) in high-level synthesis to reduce the computational time latency. The high-level synthesis design generates an estimation of the hardware resources needed to implement the PSO algorithm for TSP on FPGA. The targeted FPGA of this algorithm is the Xilinx Zynq family. The algorithm has been implemented for getting the best route between 5 given cities with given distances. The research has used 7 number of particles for a different number of iterations for generating the best route between those 5 cities. The overall latency has been reduced due to the applied optimization techniques. This paper also implemented and parallelized the same algorithm on CPU Intel I7 Processor; the result shows the FPGA implementation gives better results than CPU on the comparison of performance.

Discovering regulatory motifs of genetic networks using the indexing-tree based algorithm: a parallel implementation

Engineering Computations, 2020

PurposeThe problem of motif discovery has become a significant challenge in the era of big data w... more PurposeThe problem of motif discovery has become a significant challenge in the era of big data where there are hundreds of genomes requiring annotations. The importance of motifs has led many researchers to develop different tools and algorithms for finding them. The purpose of this paper is to propose a new algorithm to increase the speed and accuracy of the motif discovering process, which is the main drawback of motif discovery algorithms.Design/methodology/approachAll motifs are sorted in a tree-based indexing structure where each motif is created from a combination of nucleotides: ‘A’, ‘C’, ‘T’ and ‘G’. The full motif can be discovered by extending the search around 4-mer nucleotides in both directions, left and right. Resultant motifs would be identical or degenerated with various lengths.FindingsThe developed implementation discovers conserved string motifs in DNA without having prior information about the motifs. Even for a large data set that contains millions of nucleotid...

FPGA based architecture of Extensive Cancellation Algorithm (ECA) for Passive Bistatic Radar (PBR)

Microprocessors and Microsystems, 2016

Passive Bistatic Radar (PBR) exploits existing signals of opportunity from different sources such... more Passive Bistatic Radar (PBR) exploits existing signals of opportunity from different sources such as Radio and TV signals. Extensive Cancellation Algorithm (ECA) has been proven to be a very effective way to mitigate the effects of direct signal, multipath and clutter echoes in PBR. Also, it is able to detect a moving target accurately when it comes to strong-clutter environment and long-range detection providing evidence for its robustness. However, ECA is a computationally intensive algorithm and will benefit from parallel processing and modern computational platforms such as Field Programmable Gate Arrays (FPGAs). This work involves transformation of ECA by exploring opportunities for parallel processing and elimination of any unnecessary computations and storages. ECA algorithm has been also implemented on FPGAs for high speed computation by exploiting parallel and pipelining approaches. A new software tool called Radar Signal Processing Tool (RSPT) has been developed. It allows the designer to auto-generate fully optimized VHDL representation of ECA by specifying many user input parameters through GUI. The produced VHDL code is independent of FPGA part. It is also appropriate for use with any future high performance FPGAs or ASICs to further cut down computation time. Moreover, it provides the designer a feedback on various performance parameters. This offers the designer an ability to make any adjustments to the ECA component until the desired performance of the overall System on Chip (SoC) is achieved. The computation time of our transformed/optimized algorithm has improved by a factor of 3.8. Its FPGA implementation offers a speed up of 18 over CPU.

Parralelization of non-linear & non-Gaussian Bayesian state estimators (Particle filters)

2015 23rd European Signal Processing Conference (EUSIPCO), 2015

Particle filter has been proven to be a very effective method for identifying targets in non-line... more Particle filter has been proven to be a very effective method for identifying targets in non-linear and non-Gaussian environment. However, particle filter is computationally intensive and may not achieve the real time requirements. So, it's desirable to implement it on parallel platforms by exploiting parallel and pipelining architecture to achieve its real time requirements. In this work, an efficient implementation of particle filter in both FPGA and GPU is proposed. Particle filter has also been implemented using MATLAB Parallel Computing Toolbox (PCT). Experimental results show that FPGA and GPU architectures can significantly outperform an equivalent sequential implementation. The results also show that FPGA implementation provides better performance than the GPU implementation. The achieved execution time on dual core and quad core Dell PC using PCT were higher than FPGAs and GPUs as was expected.

High performance implementation of Neural Networks by networks on chip with 5-port 2-virtual channels

Proceedings of 2010 IEEE International Symposium on Circuits and Systems, 2010

Hardware implementation of Artificial Neural Network (ANN) is proposed by using Networks on Chip ... more Hardware implementation of Artificial Neural Network (ANN) is proposed by using Networks on Chip (NoC) with 5-port 2-virtual channels router, aiming at higher performance and low latency. Experimental results by NIRGAM NoC simulator show that this proposed system has higher Connection-Per-Second (CPS), higher Connection-Per-Second-Per-Weight (CP-SPW), lower communication load. Furthermore this NoC implementation system is reconfigurable and expandable, so that it can be applied to various applications.

Submitted Waiting for Acception

Sensitivity Analysis for Optimal Parameters for Marine Radar Data Processing

A bird and bat monitoring system has been developed that uses marine radar, IR camera and acousti... more A bird and bat monitoring system has been developed that uses marine radar, IR camera and acoustic recorders for wind farm applications. IR video recording is used to mon itor birds and bats activity wh ich will be useful for wildlife biologists in developing mit igation techniques to minimize impact of wind turbines on birds and bats. The goal is to quantify birds and bats activity near wind turbines. Radar will provide z-coordinate (alt itude) and IR camera will provide (x, y) coord inates of birds/bats. Acous tic monitoring is used to identify birds and bats at their species level. This paper deals with the use of marine radar for determining altitudes, direction and quantity (passage rates) of birds/bats. Data from the marine radar is dig itized and processed with open source radR software. Since the data is unknown tracking and quantification can be very challenging. Th is paper deals with the sensitivity analysis and effects of various parameters used in the tracking algorith ...