Ahmad El Sallab is the Senior Chief Engineer of Deep Learning at Valeo Egypt, and Senior Expert at Valeo Group. Ahmad has 14 years of experience in Machine Learning and Deep Learning, where he acquired his M.Sc. and Ph.D. on 2009 and 2013 in the field. He has worked for reputable multi-national organization in the industry since 2005 like Intel and Valeo. He has many publications and book chapters in Deep Learning in top IEEE and ACM journals and conferences, in addition to many patents, with applications in Speech, NLP, Computer Vision and Robotics.
Personal Information
Name: Ahmad Abdel Monem El Sallab
Date of birth: 29 January, 1983
Address: Building F1, New Garden City Compound, District 7, Sheikh Zayed City , Giza
17th European Signal Processing Conference (EUSIPCO) – Aug. 24-28, 2009, Strathclyde University Glasgow, Scotland
4th International Design and Test Workshop (IDT)- 2009 , Riyadh, Saudi-Arabia
2nd International Conference on Soft Computing and Pattern Recognition (SoCPaR) -2011, Dalian, China
1st International Conference on Pattern Recognition Applications and Methods (ICPRAM) – 2012, Vilamouro, Algarve, Portugal
Arabic NLP workshop, EMNLP 2014: Conference on Empirical Methods in Natural Language Processing, October 25-29, 2014, Doha, Qatar.
Machine learning and Data analytics symposium, 8,9 March 2015, Doha, Qatar.
Arabic NLP workshop, ACL-IJCNLP, The 53rd Annual Meeting of the Association for Computational Linguistics and The 7th International Joint Conference of the Asian Federation of Natural Language Processing, Beijing, China, July 26-31, 2015.
The Thirtieth Annual Conference on Neural Information Processing Systems (NIPS), Machine Learning in Inetelligent Transportation MLITS workshop, 05 Dec – 10 Dec, 2016, Barcelona Spain.
Electronic Imaging 2017, Autonomous Vehicles and Machines, 29 Jan – 2 Feb 2017, Burlingame, California USA.
Journals and Conferences reviewer
Designated reviewer on the technical program committee (TPC) of ISIEA 2012 (2012 IEEE Symposium on Industrial Electronics and Applications) – Bandung, Indonesia and other conferences.
Designated reviewer of the 22nd European Signal Processing Conference (EUSIPCO) – Lisbon, Portugal, March 2014.
Chair of Machine Learning Workshop of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC 2016) – Rio de Janiro, Brazil, Nov 1-4 2016.
Designated reviewer of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC 2016).
Reviewer at the Natural Language Engineering Journal, Cambridge University
Associate Editor, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC 2017) – Yokohama, Japan, Nov 1-4 2016.
IEEE Intelligent Transportation Systems Magazine
ACM Transactions on Asian and Low-Resource Language Information Processing
Conferences session sharing and co-organization
Program Committee member for The Third Arabic Natural Language Processing Workshop in 2017, Valencia, Spain, on 3-4 April 2017.
Program Committee member for The Fifth Arabic Natural Language Processing Workshop co-located with ACL 2019, Florence, Italy, July 28-Aug 2, 2019.
Chair and Program committee member of 3D-DLAD 2019, Workshop on 3D Deep Learning for Automated Driving, IEEE Intelligent Vehicles Symposium (IV’19) – Paris, France, June 9-12, 2019
Chair and Program committee member of Deep Learning in Automated Driving (DLAD) Workshop of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC 2017) – Yokohama, Japan, Oct 10-20 2017.
Teaching Lectures and Courses
Practical Deep Learning, ITI Mechatronics Diploma, for 14 engineers, 14 lectures
Online eLearning course for Practical Deep Learning (ITI e-Learning) at ITI MOOCs (16hrs of videos)
CDA Academy Full Course on Deep Learning (8 lectures), Valeo Comfort and Driving Assistance (CDA), regularly conducted in Egypt, France and Germany
Deep Learning crash course (4 lectures): Faculty of Computer Informatics (FCI), Cairo University
Deep Learning crash course (4 lectures): Faculty of Engineering Cairo University (FOECU)
Artificial Intelligence in Autonomous driving Diploma (10 lectures, 5 labs): Information Technology Institute (ITI).
Artificial Intelligence and Machine learning as part of the Machine Learning Diploma in the Faculty of Computer Informatics (FCI), Cairo University.
Title: Algorithms to enhance the adaptability and accuracy of deep learning models
Electronics and communications department, Faculty of Engineering, Cairo University
Supervisors: Supervised by Prof. Mohsen Rashwan
Proposal of Deep Belief Networks (DBN) and Restricted Boltzmann Machine (RBM) to various applications
Proposal of Self learning machine to various applications
Proposal of classifiers fusion technique using classifier mapping into deep neural network
Testing proposed techniques on: Enron dataset for Email classification, Word Sense Disambiguation in Arabic, Arabic Diacritization task
M.Sc.
Title: Hardware implementation of speech recognition front end system based on DSR standard
Electronics and communications department, Faculty of Engineering, Cairo University
Supervisors: Supervised by Prof. Mohsen Rashwan and Dr. Hossam Ali Fahmy, Cairo University
Implementation of a Front End Processor in Distributed Speech Recognition Systems, based on Mel-Frequency Cepstrum (MFCC) Features.
Hardware implementation was done in VHDL and synthesized on Altera Cyclone III FPGA outperforming reference designs in area and time performance.
The Front End Processor is intended to be used in the new version of “Hafss” program, which is a speech recognition program for teaching how to read Holly Quran
Recommendations and Testimonials
Recommendation letter for joining Ph.D program at Cairo University by Dr. M. Khairy – Professor at Dept. of ELC, Cairo University, Co-founder of SySDSoft, 2009: “I have known Eng. Ahmed El Sallab for over five years. I first knew him as a student in two signal processing courses that I teach at Cairo University. Eng . El Sallab was one of the top 3% of the students out of over 500 students attending the classes. Following graduation, I immediately asked Eng. El Sallab to join SySDSoft, where he showed great potential and was always on top of the tasks assigned to him. He usually came up with innovative ideas that helped him finish his tasks in a timely and efficient manner. He also has a pleasant personality and is a team player”
Recommendation letter for joining Ph.D program at Cairo University by Dr. M. Rashwan– Professor at Dept. of ELC, Cairo University CEO of RDI corp., 2009
“I supervised Eng. Ahmed El Sallab in his M.Sc. thesis, on which he acquired his M.Sc. degree on 2009. Mr. Ahmed is a very highly motivated person. He is a self-dependent, hard worker, self-organized and very pleasant character. I can easily rank him among the best 7 students that I have supervised along my academic life (I have supervised more than 60 M.Sc. and Ph.D. students)”
Projects History
Valeo AI: Team of 4
2019
LiDAR sensor modeling and data augmentation with GANs
Lead PI
Ahmad El Sallab, Ibrahim Sobh, Mohamed Zahran, Nader Essam, “LiDAR Sensor modeling and Data augmentationwith GANs for Autonomous driving”, Proceedings of the 36th International Conference on MachineLearning (ICML), 2019, Long Beach, California.
Valeo AI: Team of 6
2019
LiDAR and Camera Fusion for Semantic Segmentation of LiDAR Point Cloud
Lead PI
Joint Research Project - Valeo-AUC
Valeo AI: Team of 6
2018-2019
Multi-stream and Multi-task learning for Semantic Segmentation and Moving Object Detection
PI
Hazem Rashed, Senthil Yogamani, Ahmad El-Sallab, Arindam Das, and Mohamed El-Helw, “Depth augmented Semantic Segmentation networks for Automated Driving”, 11th Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP 2018), December 18 - 22, Hyderabad, India
Rashed, Hazem, Senthil Yogamani, Ahmad El-Sallab, Pavel Krizek, and Mohamed El-Helw. “Optical Flow augmented Semantic Segmentation networks for Automated Driving.” arXiv preprint arXiv:1901.07355 (2019)
Hazem Rashed, Senthil Yogamani, Ahmad A. Al Sallab, “Deep Semantic Segmentation in Autonomous Driving” Book chapter in Deep Learning in Computer Vision (DLCV) 2019, IEEE book
Siam, M., Mahgoub, H., Zahran, M., Yogamani, S., Jagersand, M. and Ahmad A. Al Sallab, “Motion and Appearance Based Multi-Task Learning Network for Autonomous Driving.”, Neural Information Processing Systems (NIPS), Machine Learning in Inetelligent Transportation MLITS workshop, 05 Dec – 10 Dec, 2017, Long Beach, California, USA.
Siam, M., Mahgoub, H., Zahran, M., Yogamani, S., Jagersand, M. and Ahmad A. Al Sallab, “MODNet: Moving Object Detection Network with Motion and Appearance for Autonomous Driving”, The 21st IEEE International Conference on Intelligent Transportation Systems, November 4-7, 2018 , Maui, Hawaii, USA
Valeo AI: Team of 6
2017-2018
LiDAR Environment Perception: Semantic segmentation and 3D Object detection
Lead PI
Khaled ElMadawy, Ahmad A. Al Sallab, Mostafa Gamal, Moemen Abdelrazek, Hesham Eraqi, Jens Honer. “Deep Convolutional Long-Short Term Memory Network for LIDAR Semantic Segmentation”, IEEE 20th International Conference on Intelligent Transportation Systems, Yokohama, Japan, October 16 - 19, 2017.
Ali, W., Abdelkarim, S., Zahran, M., Zidan, M., & Sallab, A. E. (2018). “YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud”, European Conference on Computer Vision (ECCV), 3D Reconstruction meets Semantics workshop, 8-14 September 2018, Munich, Germany.
Ahmad A. Al Sallab, Ibrahim Sobh, Mahmoud Zidan, Mohamed Zahran and Sherif Abdelkarim. “YOLO4D: A Spatio-temporal Approach for Real-time Multi-object Detection and Classification from LiDAR Point Clouds.”, Neural Information Processing Systems (NIPS), Machine Learning in Inetelligent Transportation MLITS workshop, 05 Dec – 10 Dec, 2018, Canada
“End-To-End Multi-Modal Sensors Fusion System For Urban Automated Driving.”, Neural Information Processing Systems (NIPS), Machine Learning in Inetelligent Transportation MLITS workshop, 05 Dec – 10 Dec, 2018, Canada"
“End-to-End 3D-PointCloud Semantic Segmentation for Autonomous Driving”,2019 IEEE Intelligent Vehicles Symposium (IV) (IV2019), June 9-12, 2019, in Paris, France."
Valeo AI: Team of 6
2018
End-to-end automated driving Conditional imitation Learning, from fusion of LiDAR + Camera
Lead PI
Ahmad A. Al Sallab, Ibrahim Sobh, Khaled El Medawy, Loay Amin, Mahmoud Gamal, Mostafa Gamal, Omar Abdeltawab and Sherif Abdelkarim. “End-To-End Multi-Modal Sensors Fusion System For Urban Automated Driving.”, Neural Information Processing Systems (NIPS), Machine Learning in Inetelligent Transportation MLITS workshop, 05 Dec – 10 Dec, 2018, Canada
Valeo AI: Team of 6
2017
End-to-end automated driving with Meta learning
Lead PI
Ahmad A. Al Sallab, Mahmoud Saeed, Omar AbdelTawab, Mohammed Abdo. “Meta learning Framework for Automated Driving”, International Conference on Machine Learning (ICML), Machine Learning in Automated Driving workshop, 4-10 August 2017, Syndey, Australia.
Valeo AI: Team of 5
2016
End-end high way driving using deep reinforcement learning
Leading Deep Learning developments
CES 2016 Valeo Demo: Human vs. AI Racing
Ahmad A. Al Sallab, Mohammed Abdo, Senthil Yogamani, Etienne Perot. “End to end Deep Reinforcemet learning framework for Lane Keeping Assist”, The Thirtieth Annual Conference on Neural Information Processing Systems (NIPS), Machine Learning in Intelligent Transportation MLITS workshop, 05 Dec – 10 Dec, 2016, Barcelona Spain.
Ahmad A. Al Sallab, Mohammed Abdo, Senthil Yogamani, Etienne Perot. “Deep reinforcement learning framework for autonomous driving”, Electronic Imaging 2017, Autonomous Vehicles and Machines, 29 Jan – 2 Feb 2017, Burlingame, California USA.
Team of 3
6 months in 2016
End-end occupancy grid map from ultrasonic and laser scanner sensors using recurrent neural networks
Lead PI
Patent: Locating an object in an environment of a motor vehicle by means of an ultrasonic sensor system, DE102017101476B3, https://patents.google.com/patent/DE102017101476B3/en
PhD: Team of 6
6 months in 2018
Optical Character Recognition (OCR) on Medical Records
Leading Deep Learning developments
PhD: Team of 8 (OMA)
2014-2017
Opinion Mining in Arabic
Senior Researcher
Joint project between American University in Beirut (AUB) and Qatar University
The aim is to advance Opinion Mining in Arabic state-of-the art
The main contribution is in applying recursive models to Arabic sentiment classification, resulting in the new system AROMA
In addition, a survey on Arabic sentiment classification is published
The team participated and won in SemEval 2017
Ahmad A. Al Sallab, Ramy Baly, Gilbert Badaro, Hazem Hajj, Wassim El Hajj, Khaled Bashir Shaaban “Towards Deep Learning Models for Sentiment Analysis in Arabic”, Machine learning and Data analytics symposium, 8,9 March 2015, Doha, Qatar
Ahmad A. Al Sallab, Ramy Baly, Gilbert Badaro, Hazem Hajj, Wassim El Hajj, Khaled Bashir Shaaban “Deep Learning models for sentiment analysis in Arabic”, Arabic NLP workshop, ACL-IJCNLP, The 53rd Annual Meeting of the Association for Computational Linguistics and The 7th International Joint Conference of the Asian Federation of Natural Language Processing, Beijing, China, July 26-31, 2015
Baly, Ramy, Roula Hobeica, Hazem Hajj, Wassim El-Hajj, Khaled Bashir Shaban, and Ahmad Al-Sallab. “A Meta-Framework for Modeling the Human Reading Process in Sentiment Analysis.“ ACM Transactions on Information Systems (TOIS) 35, no. 1 (2016): 7.
Ahmad A. Al Sallab, Ramy Baly, Gilbert Badaro, Hazem Hajj, Wassim El Hajj, Khaled Bashir Shaaban “AROMA: A Recursive Deep Learning Model for Opinion Mining in Arabic as a Low Resource Language”, ACM Transactions on Asian and Low Resources Language Information Processing (TALLIP), 2017
Baly R, Badaro G, Hamdi A, Moukalled R, Aoun R, El-Khoury G, Al Sallab A, Hajj H, Habash N, Shaban K, El-Hajj W. OMAM at SemEval-2017 Task 4: Evaluation of English State-of-the-Art Sentiment Analysis Models for Arabic and a New Topic-based Model. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017). Association for Computational Linguistics, Vancouver, Canada 2017 (pp. 601-608).
Magooda, Ahmed, Amr M. Sayed, Ashraf Y. Mahgoub, Hany Ahmed, Mohsen Rashwan, Hazem Raafat, Eslam Kamal, and Ahmad A. Al Sallab. “RDI_Team at SemEval-2016 Task 3: RDI Unsupervised Framework for Text Ranking” Proceedings of SemEval (2016): 822-827.
Gilber Badaro, Ramy Baly, Hazem Hajj, Wassim El-Hajj, Khaled Bashir Shaaban, Nizar Habash, Ahmad A. Al Sallab, Ali Hamdi, “A Survey of Opinion Mining in Arabic: A Comprehensive System Perspective Covering Challenges and Advances in Tools, Resources, Models, Applications and Visualizations”, ACM Transactions on Asian and Low Resources Language Information Processing (TALLIP), 2018
PhD: Team of 6
2014-2016
Sentiment analysis on twitter stock market tweets to obtain stock indicators based on sentiment of Arabic and English tweets
Leading Deep Learning developments
PhD
6 months in 2012
Reuters and 20Newsgroups document classifier
Part of PhD projects with Deep Learning
A. Sallab, M. Rashwan “E-mail Classification using Deep Networks”, Journal of Theoretical and Applied Information Technology (JATIT), ISSN 1992-8645, pp. 241 – 251, Vol. 37. No. 2 - March, 2012
A. Sallab, H. Rashwan “Self Learning Machines using Deep Networks”, in Proceedings of 2011 International Conference of Soft Computing and Pattern Recognition (SoCPaR), pp. 21 – 26, 14-16 Oct. 2011
A. Sallab, M. Rashwan “Adaptation of a deep learning machine to real world data”, International Journal of Computer Information Systems and Industrial Management Applications (IJCISIM), ISSN 2150-7988, pp. 216-226, Vol. 5 – 2013
PhD
1 year in 2013
Arabic automatic diacritization
Part of PhD projects with Deep Learning
A. Sallab, M. Rashwan “E-mail Classification using Deep Networks”, Journal of Theoretical and Applied Information Technology (JATIT), ISSN 1992-8645, pp. 241 – 251, Vol. 37. No. 2 - March, 2012
A. Sallab, M. Rashwan, A. Rafea, H.M. Rafaat “Automatic Arabic diacritics restoration based on deep nets”, Arabic NLP workshop, EMNLP 2014: Conference on Empirical Methods in Natural Language Processing, October 25-29, 2014, Doha, Qatar.
A. Sallab, M. Rashwan, A. Rafea, H.M. Rafaat “Deep learning framework with Confused Sub-set Resolution architecture for Automatic Arabic Diacritization”, IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 23, NO. 3, MARCH 2015
PhD
6 months in 2012
E-mail categorization on Enron dataset
Part of PhD projects with Deep Learning
A. Sallab, M. Rashwan “E-mail Classification using Deep Networks”, Journal of Theoretical and Applied Information Technology (JATIT), ISSN 1992-8645, pp. 241 – 251, Vol. 37. No. 2 - March, 2012
A. Sallab, H. Rashwan “Self Learning Machines using Deep Networks”, in Proceedings of 2011 International Conference of Soft Computing and Pattern Recognition (SoCPaR), pp. 21 – 26, 14-16 Oct. 2011
A. Sallab, M. Rashwan “Adaptation of a deep learning machine to real world data”, International Journal of Computer Information Systems and Industrial Management Applications (IJCISIM), ISSN 2150-7988, pp. 216-226, Vol. 5 – 2013
PhD
6 months in 2012
Arabic word sense disambiguation
Part of PhD projects with Deep Learning
6 months in 2012
Speech phoneme recognition on TIMIT using deep networks
Part of PhD projects with Deep Learning
MSc
6 months in 2007
Speech front-end based on MFCC features
MSc. Thesis project
A. Sallab, H. A. Fahmy, M. Rashwan, “Hardware Implementation of Distributed Speech Recognition System Front-end”, In proceedings of 17th European Signal Processing Conference (EUSIPCO) – Aug. 2009
A. Sallab, H. A. Fahmy, M. Rashwan “Optimized Hardware Implementation of FFT processor”, In proceedings of 4th International Design and Test Workshop (IDT)- 2009 , Riyadh, Saudi-Arabia
SysDSoft (Intel Mobile Communications - IMC): Team of 23
2013-2014
Wireless USB design service project for the Indian Company Wipro
Senior Team Leader
Development of MCAL layer for RL78 Renesas target for Wipers ECU
Writting the software requirements for the next Gen ECU
Valeo: Team of 6
2008-2012
Basic Software (BSW) components development of Engine Control Unit (ECU)
Team Leader
Leading seven projects in Basic Software (BSW) components development of Engine Control Unit (ECU) for the following customers:
Renault
Peugeot
Toyota
VolksWagen
Daimler
BMW
Scope includes development of standard BSW components for different microcontroller platforms, together with their validation
SysDSoft (Intel Mobile Communications - IMC): Team of 23
2006-2007
Wireless USB design service project for the Indian Company Wipro
Software Design Engineer
The project was Software Implementation of the Wireless USB Protocol Stack to be portable to different target platforms and operating systems, customized to eCos Real Time Operating Systems, and ARM Processor
The Wireless USB device was third party certified against INTEL Host
Responsible for the following features:
Data Transfer
Media Reliability Considerations
Wireless Channel Change Considerations
For the above mentioned features, was responsible for the following activities:
Features Extraction and Requirements Specification
Defining the System APIs to external entities
System Design and modeling using Rational Rose
Implementation (coding) using C Language
Documenting the System Level design, Component Interaction Level design, and Component Level design details
Test design of the End User Test Scenarios
Performing Component Level Testing (White box and Black box) on PC
Performing Hardware Testing with ARM Processor on FireFly board
Performing Third Party Testing with our Device against INTEL reference Host (INTEL PDK)
SysDSoft (Intel Mobile Communications - IMC): Team of 12
2005-2006
CDMA-DO 3G Partner-Ship Project with VIA Telecom
Software Design Engineer
Implemented the following MAC protocols in C Language
Broadcast Multi-Cast Protocol Suite (BCMCS)
Test Application Protocol (TAP)
Route Update Protocol (RUP)
B.Sc. Graduation Project: Team of 5
2004-2005
Robocon 2005: Team of Autonomous and Manual Robots
A team of 2 Autonomous robots and 1 Manual robot. The autonomous robots mission to collect balls from dedicated area, with the color of the team (blue/red), and place those balls in elevated baskets (beacons) that are distributed across a court. The central beacon is internally divided into 3 colors, and the robot shall put the ball in its corresponding color section, with an extra color (Green) for bonus points. The Manual robot mission is to collect the balls and slide them to a whole in the ground.
First time for Cairo University in Robocon
Soft skills
Trainings attended
Time Management | 2009 | Logic Trainig Center
Communications Skills | 2009 | Logic Trainig Center
Technical and e-mail writing | 2010 | Brilliance Trainig Center
High impact presentation | 2011 | Dale Carnegie
PMP | 2009 | AMIDEAST
Problem Solving | 2011 | Dale Carnegie
Stress Management | 2010 | Dale Carnegie
Team building | 2010 | Dale Carnegie
Interviewing skills | 2014 | Dale Carnegie
Effective Leadership | 2015 | Dale Carnegie
How to motivate your team | 2015 | Dale Carnegie
First time manager | 2015 | Dale Carnegie
Train of the trainer | 2016 | Brilliance
Languages
Arabic : Mother tongue
English : Fluent (spoken and written)
French : Fair (spoken and written)
Technical Skills and Tools
Deep Learning and Machine Learning
Frameworks
Excellent command of TensorFlow
Excellent command of Keras
Excellent command of Pytorch
Excellent command of Theano
Excellent command of scikit learn
Good knowledge of CUDA
Familiar with Torch
Familiar with Gluon and Neon
Very good knowledge of Open AI Gym and Universe Reinforcement Learning benchmarks
Cloud and Tools
Amazon EC2 configuration and administration
Amazon S3 buckets, with Python API
Excellent command of AWS SageMaker deployment
Familiar with AWS Textract, AWS GroundTruth
Excellent command of Docker, Linux and NVIDIA tools
Familiar with NIVIDIA DIGITS DevBox
Computer Vision Tools
Excellent command of OpenCV
Excellent command of OCR tesseract
Natural Language Processing
Excellent command of NLP toolkits: spaCy, nltk, Stanford NLP tools
Software Skills
Programming Languages and IDEs
Solid knowledge of object oriented programming concepts
Embedded C, C++, VisualC++ (Data Structures, and Windows Applications)
C# .Net (Console, Windows and Web Applications)
Familiar with .NET framework and Visual Studio
Java 2, J2SE and JavaScript Programming
MATLAB and SIMULINK
Familiar with PyCharm
Familiar with NetBeans IDE
Familiar with Eclipse IDE
Familiar with Visual Studio IDE
Familiar with AVR for ATMEL Micro-controllers
Android Programming
Practical experience with unit testing and RTRT
Amazon EC2 configuration and administration
Web
Excellent command of Django
Excellent command of Jekyll
Very good knowledge of CSS, HTML, JavaScript
Very good knowledge of SQL, MySQL
Familiar with PHP Laravel
Scripting Languages
Excellent command of Pyhton
Excellent command of Perl
Very good knowledge and practice of PHP
Very good knowledge and practice of VBA Macros scripting on MS Excel and MS Word
Very good knowledge and practice of CAPL scripts
Code Analysis tools
Valgrind tool for testing memory leakage
LCOV, GCOV tool for code coverage inspection
QAC tool for code quality and MISRA rules checking
Debugging tools
Familiar with GDB debugger
Familiar with ARM simulator (AXD)
Configuration Management tools
Excellent command of Git
Familiar with Github, Gitlab
Good knowledge of CVS tools and concepts
Good knowledge of SVN
Excellent knowledge of PVCS
Bug tracking systems
Familiar with JIRA
Familiar with Gitlab and Github issue tracking
Familiar with Bugzilla bug tracking system
Familiar of STICKER issue management system
Compilers
Familiar with GCC compiler
Familiar with ARM GCC compiler
Familiar with the building process using the “make” tool under Linux, and make files
Familiar with Hitachi compilers
Real-Time Operating Systems (RTOS)
Inter-Task communication
Memory management techniqes
Multi-threading and thread priority
Byte Ordering (little- big endianess)
Application of all the above on eCos RTOS
Project Management
Solid knowledge and practice of Agile and Scrum methodologies
MS Project
PSNext by Serena
Embedded Systems
Electronics
PSpice (Capture and Layout)
Hardware Design Languages (HDLs)
Excellent command of VHDL
Familiar with Altera MAX PLUS
Familiar with Altera Quartus II
Micro controllers and Micro processors
Familiar with Atmel
Familiar with PIC
Familiar with ARM and related debugging and development tools
Familiar with Renesas / Hitachi
Digital Signal Processors (DSP)
Working on digital filters design using MATLAB
Good experience with Texas Instruments DSP Kit (C500, C600 Series) using Simulink
Familiar with Code Composer Studio
Hardware testing tools
Practical experience with Hardware test benches
Practical experience using CAN, CANoe and CANalyzer
Practical experience using different type of oscilloscopes and their automation
System Design and Modeling
Excellent knowledge MATLAB
Fair knowledge of Simulink
System modeling using IBM Rational Rose
Knowledge of UML concepts
Linux
Familiar with development under Ubuntu, Madriva, Red Hat, Fedora and SUSE Linux
