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Prof. Hazem Abbas
Queen's University

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0 Deep Learning
0 Statistical Learning and Modeling
0 Image and Signal Processing
0 machine learning
0 computer vision

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Deep Learning
computer vision

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Conference paper
Published: 23 March 2021 in Communications in Computer and Information Science
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Live migration is an essential feature in virtual infrastructure and cloud computing datacenters. Using live migration, virtual machines can be online migrated from a physical machine to another with negligible service interruption. Load balance, power saving, dynamic resource allocation, and high availability algorithms in virtual data-centers and cloud computing environments are dependent on live migration. Live migration process has six phases that result in live migration overhead. Currently, virtual datacenters admins run live migrations without an idea about the migration cost prediction and without recommendations about the optimal timing for initiating a VM live migration especially for large memory VMs or for concurrently multiple VMs migration. Without cost prediction and timing optimization, live migration might face longer duration, network bottlenecks and migration failure in some cases. The previously proposed timing optimization approach is based on using machine learning for live migration cost prediction and the network utilization predict ion of the cluster. In this paper, we show how to integrate our machine learning based timing optimization algorithm with VMware vSphere. This integration deployment proves the practicality of the proposed algorithm by presenting the building blocks of the tools and backend scripts that should run to implement this timing optimization feature. The paper shows also how the IT admins can make use of this novel cost prediction and timing optimization option as an integrated plug-in within VMware vSphere UI to be notified with the optimal timing recommendation in case of a having live migration request.

ACS Style

Mohamed Esam Elsaid; Mohamed Sameh; Hazem M. Abbas; Christoph Meinel. Live Migration Timing Optimization Integration with VMware Environments. Communications in Computer and Information Science 2021, 1399, 133 -152.

AMA Style

Mohamed Esam Elsaid, Mohamed Sameh, Hazem M. Abbas, Christoph Meinel. Live Migration Timing Optimization Integration with VMware Environments. Communications in Computer and Information Science. 2021; 1399 ():133-152.

Chicago/Turabian Style

Mohamed Esam Elsaid; Mohamed Sameh; Hazem M. Abbas; Christoph Meinel. 2021. "Live Migration Timing Optimization Integration with VMware Environments." Communications in Computer and Information Science 1399, no. : 133-152.

Journal article
Published: 23 November 2020 in Journal of Sensor and Actuator Networks
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Mobile network traffic is increasing in an unprecedented manner, resulting in growing demand from network operators to deploy more base stations able to serve more devices while maintaining a satisfactory level of service quality. Base stations are considered the leading energy consumer in network infrastructure; consequently, increasing the number of base stations will increase power consumption. By predicting the traffic load on base stations, network optimization techniques can be applied to decrease energy consumption. This research explores different machine learning and statistical methods capable of predicting traffic load on base stations. These methods are examined on a public dataset that provides records of traffic loads of several base stations over the span of one week. Because of the limited number of records in the dataset for each base station, different base stations are grouped while building the prediction model. Due to the different behavior of the base stations, forecasting the traffic load of multiple base stations together becomes challenging. The proposed solution involves clustering the base stations according to their behavior and forecasting the load on the base stations in each cluster individually. Clustering the time series data according to their behavior mitigates the dissimilar behavior problem of the time series when they are trained together. Our findings demonstrate that predictions based on deep recurrent neural networks perform better than other forecasting techniques.

ACS Style

Basma Mahdy; Hazem Abbas; Hossam S. Hassanein; Aboelmagd Noureldin; Hatem Abou-Zeid. A Clustering-Driven Approach to Predict the Traffic Load of Mobile Networks for the Analysis of Base Stations Deployment. Journal of Sensor and Actuator Networks 2020, 9, 53 .

AMA Style

Basma Mahdy, Hazem Abbas, Hossam S. Hassanein, Aboelmagd Noureldin, Hatem Abou-Zeid. A Clustering-Driven Approach to Predict the Traffic Load of Mobile Networks for the Analysis of Base Stations Deployment. Journal of Sensor and Actuator Networks. 2020; 9 (4):53.

Chicago/Turabian Style

Basma Mahdy; Hazem Abbas; Hossam S. Hassanein; Aboelmagd Noureldin; Hatem Abou-Zeid. 2020. "A Clustering-Driven Approach to Predict the Traffic Load of Mobile Networks for the Analysis of Base Stations Deployment." Journal of Sensor and Actuator Networks 9, no. 4: 53.

Conference paper
Published: 02 September 2020 in Transactions on Petri Nets and Other Models of Concurrency XV
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Speech synthesis is the artificial production of human speech. A typical text-to-speech system converts a language text into a waveform. There exist many English TTS systems that produce mature, natural, and human-like speech synthesizers. In contrast, other languages, including Arabic, have not been considered until recently. Existing Arabic speech synthesis solutions are slow, of low quality, and the naturalness of synthesized speech is inferior to the English synthesizers. They also lack essential speech key factors such as intonation, stress, and rhythm. Different works were proposed to solve those issues, including the use of concatenative methods such as unit selection or parametric methods. However, they required a lot of laborious work and domain expertise. Another reason for such poor performance of Arabic speech synthesizers is the lack of speech corpora, unlike English that has many publicly available corpora (LjSpeech, https://keithito.com/LJ-Speech-Dataset/., Blizzard 2012, http://www.cstr.ed.ac.uk/projects/blizzard/2012/phase_one/.) and audiobooks. This work describes how to generate high quality, natural, and human-like Arabic speech using an end-to-end neural deep network architecture. This work uses just \(\langle \)text, audio\(\rangle \) pairs with a relatively small amount of recorded audio samples with a total of 2.41 h. It illustrates how to use English character embedding despite using diacritic Arabic characters as input and how to preprocess these audio samples to achieve the best results.

ACS Style

Fady K. Fahmy; Mahmoud I. Khalil; Hazem M. Abbas. A Transfer Learning End-to-End Arabic Text-To-Speech (TTS) Deep Architecture. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 266 -277.

AMA Style

Fady K. Fahmy, Mahmoud I. Khalil, Hazem M. Abbas. A Transfer Learning End-to-End Arabic Text-To-Speech (TTS) Deep Architecture. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():266-277.

Chicago/Turabian Style

Fady K. Fahmy; Mahmoud I. Khalil; Hazem M. Abbas. 2020. "A Transfer Learning End-to-End Arabic Text-To-Speech (TTS) Deep Architecture." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 266-277.

Preprint content
Published: 10 January 2020
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Viral reads identification is one of the important steps in metagenomic data analysis. It shows up the diversity of the microbial communities and the functional characteristics of microorganisms. There are various tools that can identify viral reads in mixed metagenomic data using similarity and statistical tools. However, the lack of available genome diversity is a serious limitation to the existing techniques. In this work, we applied natural language processing approaches for document classification in analyzing metagenomic sequences. Text featurization is presented by treating DNA similar to natural language. These techniques reveal the importance of using the text feature extraction pipeline in sequence identification by transforming DNA base pairs into a set of characters with a term frequency and inverse document frequency techniques. Various machine learning classification algorithms are applied to viral identification tasks such as logistic regression and multi-layer perceptron. Moreover, we compared classical machine learning algorithms with VirFinder and VirNet, our deep attention model for viral reads identification on generated fragments of viruses and bacteria for benchmarking viral reads identification tools. Then, as a verification of our tool, It was applied to a simulated microbiome and virome data for tool verification and real metagenomic data of Roche 454 and Illumina for a case study.

ACS Style

Aly O. Abdelkareem; Mahmoud I. Khalil; Ali H. A. ElBehery; Hazem M. Abbas. Viral Sequence Identification in Metagenomes using Natural Language Processing Techniques. 2020, 1 .

AMA Style

Aly O. Abdelkareem, Mahmoud I. Khalil, Ali H. A. ElBehery, Hazem M. Abbas. Viral Sequence Identification in Metagenomes using Natural Language Processing Techniques. . 2020; ():1.

Chicago/Turabian Style

Aly O. Abdelkareem; Mahmoud I. Khalil; Ali H. A. ElBehery; Hazem M. Abbas. 2020. "Viral Sequence Identification in Metagenomes using Natural Language Processing Techniques." , no. : 1.

Preprint
Published: 13 December 2019
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Semantic image segmentation plays a pivotal role in many vision applications including autonomous driving and medical image analysis. Most of the former approaches move towards enhancing the performance in terms of accuracy with a little awareness of computational efficiency. In this paper, we introduce LiteSeg, a lightweight architecture for semantic image segmentation. In this work, we explore a new deeper version of Atrous Spatial Pyramid Pooling module (ASPP) and apply short and long residual connections, and depthwise separable convolution, resulting in a faster and efficient model. LiteSeg architecture is introduced and tested with multiple backbone networks as Darknet19, MobileNet, and ShuffleNet to provide multiple trade-offs between accuracy and computational cost. The proposed model LiteSeg, with MobileNetV2 as a backbone network, achieves an accuracy of 67.81% mean intersection over union at 161 frames per second with $640 \times 360$ resolution on the Cityscapes dataset.

ACS Style

Taha Emara; Hossam E. Abd El Munim; Hazem M. Abbas. LiteSeg: A Novel Lightweight ConvNet for Semantic Segmentation. 2019, 1 .

AMA Style

Taha Emara, Hossam E. Abd El Munim, Hazem M. Abbas. LiteSeg: A Novel Lightweight ConvNet for Semantic Segmentation. . 2019; ():1.

Chicago/Turabian Style

Taha Emara; Hossam E. Abd El Munim; Hazem M. Abbas. 2019. "LiteSeg: A Novel Lightweight ConvNet for Semantic Segmentation." , no. : 1.

Conference paper
Published: 15 May 2019 in Transactions on Petri Nets and Other Models of Concurrency XV
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The performance of speech recognition systems can be significantly improved when visual information is used in conjunction with the audio ones, especially in noisy environments. Prompted by the great achievements of deep learning in solving Audio-Visual Speech Recognition (AVSR) problems, we propose a deep AVSR model based on Long Short-Term Memory Bidirectional Recurrent Neural Network (LSTM-BRNN). The proposed deep AVSR model utilizes the Gabor filters in both the audio and visual front-ends with Early Integration (EI) scheme. This model is termed as BRNN\(_{av}\) model. The Gabor features simulate the underlying spatiotemporal processing chain that occurs in the Primary Audio Cortex (PAC) in conjunction with Primary Visual Cortex (PVC). We named it Gabor Audio Features (GAF) and Gabor Visual Features (GVF). The experimental results show that the deep Gabor (LSTM-BRNNs)-based model achieves superior performance when compared to the (GMM-HMM)-based models which utilize the same front-ends. Furthermore, the use of GAF and GVF in both audio and visual front-ends attain significant improvement in the performance compared to the traditional audio and visual features.

ACS Style

Ali S. Saudi; Mahmoud I. Khalil; Hazem M. Abbas. Improving Audio-Visual Speech Recognition Using Gabor Recurrent Neural Networks. Transactions on Petri Nets and Other Models of Concurrency XV 2019, 71 -83.

AMA Style

Ali S. Saudi, Mahmoud I. Khalil, Hazem M. Abbas. Improving Audio-Visual Speech Recognition Using Gabor Recurrent Neural Networks. Transactions on Petri Nets and Other Models of Concurrency XV. 2019; ():71-83.

Chicago/Turabian Style

Ali S. Saudi; Mahmoud I. Khalil; Hazem M. Abbas. 2019. "Improving Audio-Visual Speech Recognition Using Gabor Recurrent Neural Networks." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 71-83.

Journal article
Published: 08 March 2019 in Digital Signal Processing
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This paper investigates the enhancement of a speech recognition system that uses both audio and visual speech information in noisy environments by presenting contributions in two main system stages: front-end and back-end. The double use of Gabor filters is proposed as a feature extractor in the front-end stage of both modules to capture robust spectro-temporal features. The performance obtained from the resulted Gabor Audio Features (GAFs) and Gabor Visual Features (GVFs) is compared to the performance of other conventional features such as MFCC, PLP, RASTA-PLP audio features and DCT2 visual features. The experimental results show that a system utilizing GAFs and GVFs has a better performance, especially in a low-SNR scenario. To improve the back-end stage, a complete framework of synchronous Multi-Stream Hidden Markov Model (MSHMM) is used to solve the dynamic stream weight estimation problem for Audio-Visual Speech Recognition (AVSR). To demonstrate the usefulness of the dynamic weighting in the overall performance of AVSR system, we empirically show the preference of Late Integration (LI) compared to Early Integration (EI) especially when one of the modalities is corrupted. Results confirm the superior recognition accuracy for all SNR levels the superiority of the AVSR system with the Late Integration.

ACS Style

Ali S. Saudi; Mahmoud I. Khalil; Hazem M. Abbas. Improved features and dynamic stream weight adaption for robust Audio-Visual Speech Recognition framework. Digital Signal Processing 2019, 89, 17 -29.

AMA Style

Ali S. Saudi, Mahmoud I. Khalil, Hazem M. Abbas. Improved features and dynamic stream weight adaption for robust Audio-Visual Speech Recognition framework. Digital Signal Processing. 2019; 89 ():17-29.

Chicago/Turabian Style

Ali S. Saudi; Mahmoud I. Khalil; Hazem M. Abbas. 2019. "Improved features and dynamic stream weight adaption for robust Audio-Visual Speech Recognition framework." Digital Signal Processing 89, no. : 17-29.

Conference paper
Published: 30 August 2018 in Transactions on Petri Nets and Other Models of Concurrency XV
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Millions of tweets are published every day which contain massive amount of opinions and sentiments. Thus, twitter is used heavily in research and business areas. Twitter is a global platform that is accessed from all the globe. Users express their opinions freely, using informal language, without any rules and with different languages. We propose a unified system that could be applied on any raw tweets and could be applied without any man-made intervention. We use emoticons as heuristic labels for our system and extract features statistically or with unsupervised techniques. We combine classical and deep learning algorithms with an ensemble algorithm to make use of different features of each model and achieve better accuracy. The results show that our approach is reliable and achieves accuracy near the state-of-the-art with a smaller set of labeled tweets.

ACS Style

Mohammad Hanafy; Mahmoud I. Khalil; Hazem M. Abbas. Combining Classical and Deep Learning Methods for Twitter Sentiment Analysis. Transactions on Petri Nets and Other Models of Concurrency XV 2018, 281 -292.

AMA Style

Mohammad Hanafy, Mahmoud I. Khalil, Hazem M. Abbas. Combining Classical and Deep Learning Methods for Twitter Sentiment Analysis. Transactions on Petri Nets and Other Models of Concurrency XV. 2018; ():281-292.

Chicago/Turabian Style

Mohammad Hanafy; Mahmoud I. Khalil; Hazem M. Abbas. 2018. "Combining Classical and Deep Learning Methods for Twitter Sentiment Analysis." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 281-292.

Conference paper
Published: 30 August 2018 in Transactions on Petri Nets and Other Models of Concurrency XV
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Handwriting recognition is a research topic with a lot of challenges and complications. One of the main complications is big databases that affect classifier complexities and their ability to perform correctly. This paper introduces a new ranking approach that is proposed as a solution to this point of research. Per input test image, the approach sorts database classes from the nearest to the furthest based on the calculated ranks. Accordingly, the classification process is applied on only subset of best nearest neighbor classes rather than the whole database classes. The approach starts with computing simple regional-type features to group similar competitive database classes together using decision trees. This grouping process aims to split big database to multiple smaller ones. Decision trees match between test image and one of the constructed smaller databases. Finally, Kullback-Leibler divergence is measured between the pyramid histogram of gradients (PHoGs) features extracted from the test image and the members of the matched smaller database. This measurement sorts the matching classes to select smaller subset from them. This subset represents best nearest neighbors of test image that can be used for final classification. Reducing database size and focusing classification on subset of best nearest neighbor classes reduce the classifier complexity and increase the overall system classification accuracy. The proposed approach was applied on IFN-ENIT database, and its effect was tested on the SVM classifier.

ACS Style

Taraggy M. Ghanim; Mahmoud I. Khalil; Hazem M. Abbas. PHoG Features and Kullback-Leibler Divergence Based Ranking Method for Handwriting Recognition. Transactions on Petri Nets and Other Models of Concurrency XV 2018, 293 -305.

AMA Style

Taraggy M. Ghanim, Mahmoud I. Khalil, Hazem M. Abbas. PHoG Features and Kullback-Leibler Divergence Based Ranking Method for Handwriting Recognition. Transactions on Petri Nets and Other Models of Concurrency XV. 2018; ():293-305.

Chicago/Turabian Style

Taraggy M. Ghanim; Mahmoud I. Khalil; Hazem M. Abbas. 2018. "PHoG Features and Kullback-Leibler Divergence Based Ranking Method for Handwriting Recognition." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 293-305.

Article
Published: 05 June 2018 in Neural Processing Letters
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ACS Style

Edmondo Trentin; Friedhelm Schwenker; Neamat El Gayar; Hazem M. Abbas. Off the Mainstream: Advances in Neural Networks and Machine Learning for Pattern Recognition. Neural Processing Letters 2018, 48, 643 -648.

AMA Style

Edmondo Trentin, Friedhelm Schwenker, Neamat El Gayar, Hazem M. Abbas. Off the Mainstream: Advances in Neural Networks and Machine Learning for Pattern Recognition. Neural Processing Letters. 2018; 48 (2):643-648.

Chicago/Turabian Style

Edmondo Trentin; Friedhelm Schwenker; Neamat El Gayar; Hazem M. Abbas. 2018. "Off the Mainstream: Advances in Neural Networks and Machine Learning for Pattern Recognition." Neural Processing Letters 48, no. 2: 643-648.

Article
Published: 27 October 2017 in Neural Processing Letters
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In the context of Arabic optical characters recognition, Arabic poses more challenges because of its cursive nature. We purpose a system for recognizing a document containing Arabic text, using a pipeline of three neural networks. The first network model predicts the font size of an Arabic word, then the word is normalized to an 18pt font size that will be used to train the next two models. The second model is used to segment a word into characters. The problem of words segmentation in the Arabic language, as in many similar cursive languages, presents a challenge to the OCR systems. This paper presents a multichannel neural network to solve the offline segmentation of machine-printed Arabic documents. The segmented characters are then fed as an input to a convolutional neural network for Arabic characters recognition. The font size prediction model produced a test accuracy of 99.1%. The accuracy of the segmentation model using one font is 98.9%, while four-font model showed 95.5% accuracy. The whole pipeline showed an accuracy of 94.38% on Arabic Transparent font of size 18pt from APTI data set.

ACS Style

Mohamed A. Radwan; Mahmoud I. Khalil; Hazem Abbas. Neural Networks Pipeline for Offline Machine Printed Arabic OCR. Neural Processing Letters 2017, 48, 769 -787.

AMA Style

Mohamed A. Radwan, Mahmoud I. Khalil, Hazem Abbas. Neural Networks Pipeline for Offline Machine Printed Arabic OCR. Neural Processing Letters. 2017; 48 (2):769-787.

Chicago/Turabian Style

Mohamed A. Radwan; Mahmoud I. Khalil; Hazem Abbas. 2017. "Neural Networks Pipeline for Offline Machine Printed Arabic OCR." Neural Processing Letters 48, no. 2: 769-787.

Journal article
Published: 30 May 2017 in IET Intelligent Transport Systems
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The formulation of data-driven short-term traffic state prediction models is highly dependent on the characteristics of collected data. Mobile sensors, specifically, on-board cellular phones (CPs) have proven success in wide scale real-time traffic data collection, in areas with limited traffic surveillance infrastructure. In this research, four short-term travel speed prediction models have been examined to cater the CP-based traffic data environment. Time-series concepts were adopted for speed prediction by autoregressive integrated moving average model and non-linear autoregressive exogenous model that is trained by neural networks. Alternatively, Bayesian networks (BNTs) and dynamic BNTs (DBNs) speed prediction models, from the graphical-based arena, have been investigated. The developed prediction models were tested in MATLAB environment on data from a simulation platform for 26-of-July corridor in Greater Cairo, Egypt. Testing results revealed the advantage of graphical-based models in restricting the propagation of prediction errors from one time step to the next. BNT reported a mean absolute percentage error (MAPE) of 6.31 ± 1.03, whereas the DBN model reported a MAPE of 5.34 ± 1.90.

ACS Style

Yarah Basyoni; Hazem M. Abbas; Hoda Talaat; Ibrahim El Dimeery. Speed prediction from mobile sensors using cellular phone‐based traffic data. IET Intelligent Transport Systems 2017, 11, 387 -396.

AMA Style

Yarah Basyoni, Hazem M. Abbas, Hoda Talaat, Ibrahim El Dimeery. Speed prediction from mobile sensors using cellular phone‐based traffic data. IET Intelligent Transport Systems. 2017; 11 (7):387-396.

Chicago/Turabian Style

Yarah Basyoni; Hazem M. Abbas; Hoda Talaat; Ibrahim El Dimeery. 2017. "Speed prediction from mobile sensors using cellular phone‐based traffic data." IET Intelligent Transport Systems 11, no. 7: 387-396.

Conference paper
Published: 01 December 2016 in 2016 11th International Conference on Computer Engineering & Systems (ICCES)
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Ladies and Gentlemen, welcome to the 2016 11th International Conference on Computer Engineering & Systems (ICCES). Organizing this annual event has been a continuous success for more than a decade. This conference provides a forum to exchange experiences and promote new trends in the field of computer and systems engineering. This year, the conference adopts four tracks of scientific research: • Hardware, • Networks, • Software, and • Systems & DSP

ACS Style

Hazem M. Abbas. Welcome message from the conference chair. 2016 11th International Conference on Computer Engineering & Systems (ICCES) 2016, 1 .

AMA Style

Hazem M. Abbas. Welcome message from the conference chair. 2016 11th International Conference on Computer Engineering & Systems (ICCES). 2016; ():1.

Chicago/Turabian Style

Hazem M. Abbas. 2016. "Welcome message from the conference chair." 2016 11th International Conference on Computer Engineering & Systems (ICCES) , no. : 1.

Conference paper
Published: 09 September 2016 in Transactions on Petri Nets and Other Models of Concurrency XV
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This article offers an open vocabulary Arabic text recognition system using two neural networks, one for segmentation and another one for characters recognition. The problem of words segmentation in Arabic language, like many cursive languages, presents a challenge to the OCR systems. This paper presents a multichannel neural network to solve offline segmentation of machine-printed Arabic documents. The segmented characters are then used as input to a convolutional neural network for Arabic characters recognition. The accuracy of the segmentation model using one font is 98.9 %, while four-font model showed 95.5 % accuracy. The accuracy of characters recognition on Arabic Transparent font of size 18 pt from APTI data set is 94.8 %.

ACS Style

Mohamed A. Radwan; Mahmoud I. Khalil; Hazem Abbas. Predictive Segmentation Using Multichannel Neural Networks in Arabic OCR System. Transactions on Petri Nets and Other Models of Concurrency XV 2016, 233 -245.

AMA Style

Mohamed A. Radwan, Mahmoud I. Khalil, Hazem Abbas. Predictive Segmentation Using Multichannel Neural Networks in Arabic OCR System. Transactions on Petri Nets and Other Models of Concurrency XV. 2016; ():233-245.

Chicago/Turabian Style

Mohamed A. Radwan; Mahmoud I. Khalil; Hazem Abbas. 2016. "Predictive Segmentation Using Multichannel Neural Networks in Arabic OCR System." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 233-245.

Journal article
Published: 25 February 2016 in Journal of Circuits, Systems and Computers
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Satisfiability modulo theories (SMT) is an area concerned with checking the satisfiability of logical formulas over one or more theories. SMT can be well tuned to solve several of the most intriguing problems in electronic design automation (EDA). Analog placers use physical constraints to automatically generate small sections of layout. The work presented in this paper shows that SMT solvers can be used for the automation of analog placement, given some physical constraints. We propose a tool that uses Microsoft Z3 SMT solver to find valid placement solutions for the given analog blocks. Accordingly, it generates multiple layouts that fulfill some given constraints and provides a variety of alternative layouts. The user has the option to choose one of the feasible solutions. The proposed system uses the quantifier-free linear real arithmetic (QFLRA), which makes the problem decidable. The proposed system is able to generate valid placement solutions for benchmarks. For benchmarks that have many constraints and few geometries, the proposed system achieves a speedup that is 10 times faster than other recently used approaches.

ACS Style

Sherif M. Saif; Mohamed Dessouky; M. Watheq El-Kharashi; Hazem Abbas; Salwa Nassar. A Platform for Placement of Analog Integrated Circuits Using Satisfiability Modulo Theories. Journal of Circuits, Systems and Computers 2016, 25, 1650047 .

AMA Style

Sherif M. Saif, Mohamed Dessouky, M. Watheq El-Kharashi, Hazem Abbas, Salwa Nassar. A Platform for Placement of Analog Integrated Circuits Using Satisfiability Modulo Theories. Journal of Circuits, Systems and Computers. 2016; 25 (5):1650047.

Chicago/Turabian Style

Sherif M. Saif; Mohamed Dessouky; M. Watheq El-Kharashi; Hazem Abbas; Salwa Nassar. 2016. "A Platform for Placement of Analog Integrated Circuits Using Satisfiability Modulo Theories." Journal of Circuits, Systems and Computers 25, no. 5: 1650047.

Conference paper
Published: 01 January 2016 in Proceedings of the 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE)
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This paper presents an analog layout placement tool with emphasis on Pareto front generation. In order to handle the exploding number of analog physical constraints, a new approach based on the use of a Satisfiability Modulo Theories (SMT) solver is suggested. SMT is an area concerned with checking the satisfiability of logical formulas over one or more theories. SMT is usually well-tuned to solve specific problems. To our knowledge, this is the first effort to use SMT to tackle analog placement. The proposed tool implicitly generates multiple layouts that fulfill the given constraints. Therefore, it gives the user the option to choose from the feasible solutions through specifying an aspect ratio or by selecting the optimum solution from the Pareto front of the generated shape function. In contrast to most of the existing techniques, as the number of physical constraints increases the SMT solver processing time decreases. The proposed system yielded layouts with a competitive area and run time compared to other techniques.

ACS Style

Sherif M. Saif; Mohamed Dessouky; M. Watheq El-Kharashi; Hazem Abbas; Salwa Nassar. Pareto Front Analog Layout Placement using Satisfiability Modulo Theories. Proceedings of the 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE) 2016, 1411 -1416.

AMA Style

Sherif M. Saif, Mohamed Dessouky, M. Watheq El-Kharashi, Hazem Abbas, Salwa Nassar. Pareto Front Analog Layout Placement using Satisfiability Modulo Theories. Proceedings of the 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE). 2016; ():1411-1416.

Chicago/Turabian Style

Sherif M. Saif; Mohamed Dessouky; M. Watheq El-Kharashi; Hazem Abbas; Salwa Nassar. 2016. "Pareto Front Analog Layout Placement using Satisfiability Modulo Theories." Proceedings of the 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE) , no. : 1411-1416.

Conference paper
Published: 01 December 2015 in 2015 Tenth International Conference on Computer Engineering & Systems (ICCES)
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Accurate optical flow techniques are widely used in spatio-temporal object detection in videos. However, the computational complexity of the currently used techniques limits the effectiveness of spatio-temporal detection in applications such as action detection and event recognition. Therefore, in this paper we aim at employing rapid yet accurate optical flow techniques to promote the effectiveness of the detection system. The proposed design uses novel optical flow estimation techniques that are based on learned flow basis, known as PCA-Flow and PCA-Layers. PCA-Flow estimates dense flow from a linear flow model based on principle components of natural flow. PCA-Layers is an extension of PCA-Flow. PCA-Layers technique uses Markov random field (MRF) to combine several motion layers into dense optical flow. The motion in each layer is estimated by PCA-Flow. Our experimental results show that our approach maintains the overall performance of the baseline framework while 64% reduction in the computation time is achieved.

ACS Style

Rana O. Elnaggar; Mahmoud I. Khalil; Hossam Abdelmunim; Hazem M. Abbas. Optical flow-based enhancement of spatio-temporal detection in videos. 2015 Tenth International Conference on Computer Engineering & Systems (ICCES) 2015, 378 -383.

AMA Style

Rana O. Elnaggar, Mahmoud I. Khalil, Hossam Abdelmunim, Hazem M. Abbas. Optical flow-based enhancement of spatio-temporal detection in videos. 2015 Tenth International Conference on Computer Engineering & Systems (ICCES). 2015; ():378-383.

Chicago/Turabian Style

Rana O. Elnaggar; Mahmoud I. Khalil; Hossam Abdelmunim; Hazem M. Abbas. 2015. "Optical flow-based enhancement of spatio-temporal detection in videos." 2015 Tenth International Conference on Computer Engineering & Systems (ICCES) , no. : 378-383.

Conference paper
Published: 01 September 2015 in 2015 IEEE International Conference on Image Processing (ICIP)
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This paper introduces an approach for fast tracking, quantization and centreline extraction of small vascular structures in medical imaging, with the problem of coronary segmentation used as an example. The tracking is achieved by propagating an explicit surface. This surface is represented as a triangular adaptive mesh where the element size is adjusted based on the current size of the vessel. Adaptive re-meshing is performed on-the-fly during propagation in an efficient manner. An effective self-intersection prevention method is introduced to address one of the major issues in triangular mesh offsetting.

ACS Style

Yusuf I. Afifi; Mahmoud I. Khalil; Hazem M. Abbas. Fast 3D tracking and quantization of small vascular structures in 3D medical images. 2015 IEEE International Conference on Image Processing (ICIP) 2015, 877 -881.

AMA Style

Yusuf I. Afifi, Mahmoud I. Khalil, Hazem M. Abbas. Fast 3D tracking and quantization of small vascular structures in 3D medical images. 2015 IEEE International Conference on Image Processing (ICIP). 2015; ():877-881.

Chicago/Turabian Style

Yusuf I. Afifi; Mahmoud I. Khalil; Hazem M. Abbas. 2015. "Fast 3D tracking and quantization of small vascular structures in 3D medical images." 2015 IEEE International Conference on Image Processing (ICIP) , no. : 877-881.

Conference paper
Published: 01 April 2015 in 2015 10th International Conference on Design & Technology of Integrated Systems in Nanoscale Era (DTIS)
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This paper exploits one of the formal methods to generate layouts for an analog circuit, where these layouts satisfy some given analog constraints. The analog constraints are provided by the user through a text file and the used method is the Satisfiability Modulo Theories solving. After generating the layouts as valid solutions for the given constraints, the paper shows how different aspect ratios can be used in order to draw the shape function. The shape function allows the user to find optimal layouts and accordingly select one of them, given some acceptable ranges for aspect ratios, width, and height.

ACS Style

Sherif M. Saif; Mohamed Dessouky; Hazem Abbas; M. Watheq El-Kharashi; Salwa Nassar. Analog layout constraints resolution and shape function generation using Satisfiability Modulo Theories. 2015 10th International Conference on Design & Technology of Integrated Systems in Nanoscale Era (DTIS) 2015, 1 -6.

AMA Style

Sherif M. Saif, Mohamed Dessouky, Hazem Abbas, M. Watheq El-Kharashi, Salwa Nassar. Analog layout constraints resolution and shape function generation using Satisfiability Modulo Theories. 2015 10th International Conference on Design & Technology of Integrated Systems in Nanoscale Era (DTIS). 2015; ():1-6.

Chicago/Turabian Style

Sherif M. Saif; Mohamed Dessouky; Hazem Abbas; M. Watheq El-Kharashi; Salwa Nassar. 2015. "Analog layout constraints resolution and shape function generation using Satisfiability Modulo Theories." 2015 10th International Conference on Design & Technology of Integrated Systems in Nanoscale Era (DTIS) , no. : 1-6.

Conference paper
Published: 01 December 2014 in 2014 9th International Design and Test Symposium (IDT)
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This paper explores the use of Satisfiability Modulo Theories to handle mirror symmetry and common-centroid analog layout placement constraints. The proposed system reads the constraints and generates the corresponding equations or inequalities needed for Microsoft Z3 solver. These inequalities are resolved using quantifier free nonlinear real arithmetic theory. This theory has doubly exponential complexity in the worst case and it guarantees generating a solution if one exists. The proposed system produces multiple layouts that satisfy the constraints and allows the designer to choose the appropriate one according to designer's experience.

ACS Style

Sherif M. Saif; Mohamed Dessouky; Salwa Nassar; Hazem Abbas; M. Watheq El-Kharashi; Mohammad Abdulaziz. Exploiting satisfiability modulo theories for analog layout automation. 2014 9th International Design and Test Symposium (IDT) 2014, 1 -6.

AMA Style

Sherif M. Saif, Mohamed Dessouky, Salwa Nassar, Hazem Abbas, M. Watheq El-Kharashi, Mohammad Abdulaziz. Exploiting satisfiability modulo theories for analog layout automation. 2014 9th International Design and Test Symposium (IDT). 2014; ():1-6.

Chicago/Turabian Style

Sherif M. Saif; Mohamed Dessouky; Salwa Nassar; Hazem Abbas; M. Watheq El-Kharashi; Mohammad Abdulaziz. 2014. "Exploiting satisfiability modulo theories for analog layout automation." 2014 9th International Design and Test Symposium (IDT) , no. : 1-6.