Online Teaching - Coronavirus

It was much easier for me to switch the lectures-based classes I'm teaching to online classes due to Coronavirus pandemic than conducting Labs online. Specifically, it was challenging to deliver a Digital Design Lab I'm teaching at AUC online without breaking some of the Intended Learning Outcomes (ILOs). I feel really proud of how we've managed to cope with such challenge so far!

We enabled each student to connect remotely from his/her home to a PC in the lab to use and access the lab licensed software and tools. The hardware material needed for each lab experiment are physically connected to all the lab machines. We also connect a camera to each PC that monitors the connected hardware to allow students to see it remotely while programming it. Interestingly, we enabled students to mimic pressing hardware push button or switches remotely through their home PC keyboard!

In these images as example, each student, from home, remotely uses Xilinx Vivado to design and develop his/her digital circuit and to program the physical FPGA in the lab, and to remotely see the outcome of their circuits through a camera, while having me and the TA connected with him/her via Zoom. Students can use some keyboard key presses if they want to press a switch or a button on the physical hardware board. In short, students can remotely program, see, and control/interact the hardware and to use the lab software and tools. One picture is old actually to show the lab before switching to online teaching :)

This is a team effort, and I have to thank Dr. Mohamed Shalan and Eng. Amr ElShorbagy for their great contribution to it. This would not have been possible without the full support received from the university and my department (CSE) in terms of providing the cameras and the remote access tools. And for sure I would like to thank my amazing students who showed great levels of agility and cooperation. Also, it's important to mention that any instructor who implements similar approaches should account for the overhead students consume compared to before having all of this online.

Camera Sees the Hardware, and students work remotely from their homes on our empty lab

Students program, see, and control/interact the hardware remotely

 Old picture for the lab before switching to online teaching

Students program, see, and control/interact the hardware remotely

Comments like this motivate :)

Distracted Driver Dataset

Distracted Driver Dataset

Hesham M. Eraqi ^1,3,*, Yehya Abouelnaga ^2,*, Mohamed H. Saad ³, Mohamed N. Moustafa ¹

¹ The American University in Cairo
² Technical University of Munich
³ Valeo Egypt
^* Both authors equally contributed to this work.

Institutions:

Our work is being used by researches across academia and research labs:

 


 
 
 

Dataset:

	Distracted Driver V1	Distracted Driver V2
Key Contributions	First publicly available dataset for distracted driving Training and testing datasets are split randomly	Collected more data with more drivers More precise labeling and better sampling per class Training and testing datasets are split based on drivers
Dataset Information	31 drivers	44 drivers
License Agreement	License Agreement V1	License Agreement V2
Download Link	If you agree with terms and conditions, please fill out the license agreement and send it to: Yehya Abouelnaga: yehya.abouelnaga@tum.de or Hesham M. Eraqi: heraqi@aucegypt.edu.Upon receiving a filled and signed license agreement, we will send you the dataset and our training/testing splits.
Publication	H. Eraqi, Y. Abouelnaga, M. Saad, M. Moustafa, "Driver Distraction Identification with an Ensemble of Convolutional Neural Networks", Journal of Advanced Transportation, Machine Learning in Transportation (MLT) Issue, 2019. Y. Abouelnaga, H. Eraqi, and M. Moustafa. "Real-time Distracted Driver Posture Classification". Neural Information Processing Systems (NIPS 2018), Workshop on Machine Learning for Intelligent Transportation Systems, Dec. 2018.

Terms & Conditions:

• The dataset is the sole property of the Machine Intelligence group at the American University in Cairo (MI-AUC) and is protected by copyright. The dataset shall remain the exclusive property of the MI-AUC.
• The End User acquires no ownership, rights or title of any kind in all or any parts with regard to the dataset.
• Any commercial use of the dataset is strictly prohibited. Commercial use includes, but is not limited to: Testing commercial systems; Using screenshots of subjects from the dataset in advertisements, Selling data or making any commercial use of the dataset, broadcasting data from the dataset.
• The End User shall not, without prior authorization of the MI-AUC group, transfer in any way, permanently or temporarily, distribute or broadcast all or part of the dataset to third parties.
• The End User shall send all requests for the distribution of the dataset to the MI-AUC group.
• All publications that report on research that uses the dataset should cite our publications.

Description:

This is the first publicly available dataset for distracted driver detection. We had 44 participants from 7 different countries: Egypt (37), Germany (2), USA (1), Canada (1), Uganda (1), Palestine (1), and Morocco (1). Out of all participants, 29 were males and 15 were females. Some drivers participated in more than one recording session with different time of day, driving conditions, and wearing different clothes. Videos were shot in 5 different cars: Proton Gen2, Mitsubishi Lancer, Nissan Sunny, KIA Carens, and a prototyping car. We extracted 14,478 frames distributed over the following classes: Safe Driving (2,986), Phone Right (1,256), Phone Left (1,320), Text Right (1,718), Text Left (1,124), Adjusting Radio (1,123), Drinking (1,076), Hair or Makeup (1,044), Reaching Behind (1,034), and Talking to Passenger (1,797). The sampling is done manually by inspecting the video files with eye and giving a distraction label for each frame. The transitional actions between each consecutive distraction types are manually removed. The figure below shows samples for the ten classes in our dataset.

Citation:

All publications that report on research that use the dataset should cite our work(s):

Hesham M. Eraqi, Yehya Abouelnaga, Mohamed H. Saad, Mohamed N. Moustafa, “Driver Distraction Identification with an Ensemble of Convolutional Neural Networks”, Journal of Advanced Transportation, Machine Learning in Transportation (MLT) Issue, 2019.

Yehya Abouelnaga, Hesham M. Eraqi, and Mohamed N. Moustafa, “Real-time Distracted Driver Posture Classification”, Machine Learning for Intelligent Transportation Systems Workshop in the 32nd Conference on Neural Information Processing Systems (NeuroIPS), Montréal, Canada, 2018.

Clustering Lectures [English]

Lecture 1: Clustering 1 (K-means)

Lecture 2: Clustering 2 (DBSCAN - Hierarchical - GMM - Validation)

Arabot Robot

Arabot robot is my graduation project (BSc thesis) from Cairo University Electrical Communications Engineering department in 2010. The team was composed of 5 colleagues and friends; Hany Ahmed, Mahmoud Sami, Mostafa Khattab, and Mahmoud Serag. I wanted to share some pics and videos with you here :)

We have developed a robot waiter that serves the customers of restaurants and hotels; it understands Arabic speech (continuous speech) and interacts with the customers and the kitchen-man by listening to their orders. It moves around the place, chatting with customers, taking orders, delivering it, and sending text orders to the chef’s PC wirelessly in a fully-automated way. It also has a user interface which enables the customization of its behavior, look and technical parameters (even the color of the LED's on Arabot chest). The robot is fully designed and built by us.

Arabot was awarded:
- The best project of the Egyptian Engineering Day 2010
- SAMSUNG Real Dreams Award 2011
- Young Innovator Award 2010

Download manual and technical details book

Download manual and technical details book