Ahmed Badr
Ph.D. in Computer Engineering
Software Developer
Systems Engineer
System Architect
Android Developer

Ahmed Badr

Ahmed is a proficient software engineer with over seven years of professional experience in various industries. He enjoys working with diverse teams to build scalable and sustainable technologies for business that uniquely combine innovation and entrepreneurship.

Core expertise lies in building the foundation of product road maps, influencing growth through solution-driven leadership, and utilizing data insights to help improve software performance. Ability to maintain current knowledge of new technologies and trends, focusing on adding business value while ensuring continuity, success, and scalability.

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7+ Years of Experience

Experience

2022- Present
Next Structural Integrity

Lead Software Engineer / DevOps Engineer

Roles:

  1. Led the software development team to build multi-disciplinary systems using Backstage.io and Laravel frameworks.
  2. Worked on designing and developing a companywide Intranet system integrating project management tools like P6, ADP, and Oracle NetSuite.
  3. Ensured that systems were safe and secure against cybersecurity threats using identity controls and permissions.
  4. Developed an autonomous AI web application for holding live 1-on-1 Interviews with candidates using ChatGPT and Azure Speech AI services.
  5. Developed, and maintained enterprise-level infrastructure on bare-metal servers using infrastructure as a code concept and Kubernetes technologies.
Technology used:
Agile, SDLC, Backstage.io (TypeSciprt), Laravel (PHP), ChatGPT, React, NodeJS, MS Azure, REST APIs, Proxmox, ArgoCD, Kubernetes, Docker, Automation, Helm Charts
2019 - 2021
Udacity

Android Developer

Roles:
  1. Reviewed and audited Android graduation projects / Provided 1-on-1 mentoring calls.
Tools & Software: 
Android Studio
Programming Languages:
Java
2018 - 2022
OntarioTech University

Research Teaching Assistant (Software Engineer)

Roles:

  1. Member of the IoT Research Lab at OntarioTech University.
  2. X-Beats: Developed a state-of-the-art real-time monitoring platform, diagnoses, and anomaly detection for healthcare applications.
  3. Built a comprehensive IoT framework that can be widely deployed and used by medical wearable devices and patients for real-time data acquisition.
  4. Prototyped a wearable sensor for continuous ECG data acquisition and transmission over Bluetooth Low Energy (XBeats – Patent Pending).
Sessional Lecturer:
Full course lecturer: Distributed Systems / Human-Computer Interaction (HCI)

Technology used:

RTOS for Embedded System (Embedded C), Apache Kafka (Confluent), Python, KSQL, Java, NodeJS, Docker, Android (Java). ThingsBoard, HiveMQ (MQTT), MongoDB, Bluetooth BLE, Android
2021 - 2022
Bosten-Group.net

Senior Software Engineer / Architect (Remote)

Roles:

  1. Developed a CRM and ticketing system for tracking services and internal procedures using Laravel framework.
  2. Designed and implemented automation scripts for billing customers and generating invoices.
  3. Designed and deployed the company's infrastructure hosting software and services.
  4. Deploy a Lightweight CalDAV + CardDAV server hosting more than 7k contacts.

Technologies Used:
MS Azure, Laravel (PHP), JavaScript, MySQL, Linux, Nginx, CardDAV, CalDAV.

2016 - 2019
SSTM

Software Engineer / IoT System Architect

Roles:
  1. Developed and Implemented a comprehensive web application for customers to configure and track their assets using Laravel (PHP).
  2. Designed and deployed a scalable intranet system for IoT sensors data logging and monitoring using ThingsBoard and HiveMQ.
  3. Designed and deployed the company's infrastructure hosting software and services.
Technology used: 
AWS, Laravel, PHP, JavaScript, MQTT, REST APIs, MySQL, Python, Nginx, Linux, ThingsBoard.
2015 - 2015
GUC - ITS-Research Group

Java Developer

Roles:

  1. Designed and implemented a Graphical User Interface (GUI) for the software of the project “Planning Platform for Roadside Networks, PRONET”.
  2. Led a team of three members using project and time management techniques.
  3. Supervised the implementation process of the project on GitHub.

Technology used:
Java - XML

Eduaction

2018 - 2022
OntarioTech University

PhD in Electrical & Computer Engineering

Thesis Theme:
IoT in Healthcare (ECG Monitoring Platform)
Objective:
  • Design a novel platform for real-time electrocardiogram monitoring and analysis that uses edge computing and machine learning for early anomaly detection.
  • Build a complete IoT platform that can be widely deployed and used by
    the healthcare providers and patients.
  • Hardware design by building innovative ECG sensors providing maximum
    efficiency and patients' experience.
Skills: 
Hardware Prototyping - System Design - Protocol Design - Real-time Stream Analysis
Tools & Software:
Kafka Confluent - KsqlDB - RTOS - TI Code Composer - TI Sensor Controller - Bluetooth Low Energy Service Discovery - Android Studio - MongoDB - Docker
Programming Languages:
Java - Embedded C - Python
2016 - 2018
The German University in Cairo

Master of Science in Networking Engineering

Topic:
Misbehavior Detection Framework Using Subjective Logic for IoT Security.
Objective:
Design an algorithm to detect anomalous power consumption in smart grids.
Skills: 
Algorithm Design - System Design - Protocol Design
Programming Languages:
Java - MATLAB
2017 - 2017
Udacity

Android Development Nanodegree

Course Link

About: Built-in collaboration with Google, this program will prepare you to become a professional Android developer and allow you to create a diverse portfolio of projects to show employers. By the end of this program, you will be able to use Android development platform best practices, Android Studio, Android Jetpack, and Kotlin to build your own apps for the world’s most-used mobile platform.

2011 - 2016
The German University in Cairo

Bachelor of Science - Networking Engineering

Thesis Theme:
Conception and Implementation of SoIs Prioritization Policy and Algorithms for RSUs Distribution in Urban and Rural Areas (Grade: Excellent).
Main Projects:
Video Streaming Web Service + Android Application, VoIP System (IP-PBX Asterisk, SIP), Android Game “Connect 4” over Java Server, MIPS simulator using Java, Matlab Video encoder/decoder.
Tools & Software: 
MATLAB - Android Studio - Eclipse IDE - Ubuntu Server - MySQL Studio IDE - Asterisk Server
Programming Languages:
Java - Android - MATLAB - PHP - MySQL

Design Skills

Distributed Systems

Real-time Event Processing

IoT Applications

Software Design

Cloud Applications

RESTfull APIs

Hardware Design

eCommerce

WordPress

Platforms

Kafka Confluent

Amazon Web Services

RTOS

Google Cloud Platform

ThingsBoard

Microsoft Azure

Coding Skills

Java

Android

PHP

Embedded C

MySQL

HTML / CSS

Python

Publications

Publications

Research

2023

A Framework for Real-time Remote ECG Monitoring and Diagnoses

2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA)
Abstract >>
  • Enabled by the fast development of Internet of Things (IoT) technologies in recent years, the healthcare domain has witnessed significant advancements in wearable devices that seamlessly collect vital medical information. With the availability of IoT devices serving the healthcare domain, extraordinary amounts of sensory data are generated in real-time, requiring immediate diagnoses and attention in critical medical conditions. The provision of remote patient monitoring (RPM) and analytics infrastructure proved to be fundamental components of the healthcare domain during the Coronavirus pandemic. Traditional healthcare services are digitized and offered virtually, where patients are monitored and managed remotely without the need to go to hospitals. This paper presents a comprehensive RPM framework for real-time telehealth operations with scalable data monitoring, real-time analytics and decision-making, fine-grained data access and robust notification mechanisms in emergencies and critical health conditions. We focus on the overall framework architecture, enabling technologies integration, various system-level integrations and deployment options. Furthermore, we provide a use case application for patients with chronic heart conditions for real-time electrocardiogram (ECG) monitoring. We are releasing the framework as open-source software to the active research community..
Authors:
Ahmed Badr and Khalid Elgazzar
Conference:
5th ICCSPA 2023
2022

An Application-Specific Power Consumption Optimization for Wearable Electrocardiogram Devices

2022 IEEE 47th Conference on Local Computer Networks (LCN)
Abstract >>
  • This paper explores ways for energy consumption reduction in wearable and Remote Patient Monitoring (RPM) devices. We use the XBeats ECG patch as a case study application for remote Electrocardiogram (ECG) wearable device power consumption benchmarking. Systematic energy consumption profiling criteria are proposed for evaluating participating components in an RPM device. We isolate each hardware component to find power-intensive processes in the XBeats system, discover energy consumption patterns, and measure voltage, current, power, and energy consumption for a given time period. The proposed optimization techniques demonstrate significant improvements to the hardware components on the ECG patch. The results show that optimizing the data acquisition process saves 8.2% compared to the original power consumption and 1.62% in data transmission over BLE, thus extending the device lifetime. Lastly, we optimize the data logging operation to save 54% of data initially written to an external drive.
Authors:
Ahmed Badr, Abdulmonem Rashwan and Khalid Elgazzar
Conference:
IEEE 47th LCN 2022
2022

XBeats: A Real-Time Electrocardiogram Monitoring and Analysis System

Signals an MDPI Journal
Abstract >>
  • This work presents XBeats, a novel platform for real-time electrocardiogram monitoring and analysis that uses edge computing and machine learning for early anomaly detection. The platform encompasses a data acquisition ECG patch with 12 leads to collect heart signals, perform on-chip processing, and transmit the data to healthcare providers in real-time for further analysis. The ECG patch provides a dynamically configurable selection of the active ECG leads that could be transmitted to the backend monitoring system. The selection ranges from a single ECG lead to a complete 12-lead ECG testing configuration. XBeats implements a lightweight binary classifier for early anomaly detection to reduce the time to action should abnormal heart conditions occur. This initial detection phase is performed on the edge (i.e., the device paired with the patch) and alerts can be configured to notify designated healthcare providers. Further deep analysis can be performed on the full fidelity 12-lead data sent to the backend. A fully functional prototype of the XBeats has been implemented to demonstrate the feasibly and usability of the proposed system. Performance evaluation shows that XBeats can achieve up to 95.30% detection accuracy for abnormal conditions, while maintaining a high data acquisition rate of up to 441 samples per second. Moreover, the analytical results of the energy consumption profile show that the ECG patch provides up to 37 h of continuous 12-lead ECG streaming.
Authors:
Ahmed Badr, Abeer Badawi, Abdulmonem Rashwan and Khalid Elgazzar
Journal:
Signals 2022, Volume 3, Issue 2, 189-208
2022

12-Lead ECG Platform for Real-time Monitoring and Early Anomaly Detection

IWCMC
Abstract >>
  • In response to the rapid digital revolution and the COVID-19 pandemic, the healthcare landscape has significantly shifted from physical to virtual care and telemedicine. As a result, healthcare providers and patients have shown increased interest and adoption for up-to-date technologies to monitor ongoing health conditions, including cardiovascular diseases. Driven by the importance of an efficient remote cardiovascular monitor for virtual care, we present a platform that enables remote ECG testing and provides ubiquitous data access to patients and their healthcare providers. A patent-pending 12-lead data acquisition ECG patch is attached to the patient's body to simultaneously collect heart signals, perform binary classification, and transmit the data to healthcare providers for further analysis at a high rate of up to 480 samples per second. As a preliminary classification phase, the presented platform introduces a machine learning technique to classify ECG signals near the ECG patch. The classification function is optimized for power-constrained devices using machine learning techniques. Moreover, the preliminary results of the energy consumption profile show that the ECG patch provides up to 37 hours of continuous 12-lead ECG streaming.
Authors:
Ahmed Badr, Abeer Badawi, Abdulmonem Rashwan and Khalid Elgazzar
Venue:
IWCMC 2022: e-Health Symposium
2022

Optimizing Real-Time ECG Data Transmission in Constrained Environments

IEEE ICC
Abstract >>
  • The electrocardiogram (ECG) test is developed to monitor the functionality of the cardiovascular system. Nowadays, numerous attentions have been given to the accurate and early detection of heartbeat anomalies in real-time to prevent complications and take necessary measures. This paper proposes a robust real-time binary classification for ECG signals to detect possible anomalies. We implement an initial detection phase right where ECG data is collected through lightweight deep learning analysis. We evaluate the system on two widely used datasets, PTB and MIT-BIH datasets from PhysioNet. Our experiments suggest using artificial neural network (ANN) algorithms for their superior performance over other machine learning algorithms with an accuracy up to 99.3%. Furthermore, we implemented our system on a Raspberry Pi B+ representing an ECG patch to collect and process ECG signals and detect any abnormalities using the proposed ANN model. To create a scalable system, we stream the data in real-time using Apache Kafka and MQTT to keep records of patients’ ECG data and use it for further analysis to identify causes and support medical diagnosis. The system notifies healthcare providers when abnormalities are detected.
Authors:
Hebatalla Ouda, Ahmed Badr, Abdulmonem Rashwan, Hossam S. Hassanein and Khalid Elgazzar
Venue:
ICC 2022 - IEEE International Conference on Communications
2021

ECG Real-time Monitoring and Heart Anomaly Detection Reimagined

WF-IoT
Abstract >>
  • The electrocardiogram (ECG) test is developed to monitor the functionality of the cardiovascular system. Nowadays, numerous attentions have been given to the accurate and early detection of heartbeat anomalies in real-time to prevent complications and take necessary measures. This paper proposes a robust real-time binary classification for ECG signals to detect possible anomalies. We implement an initial detection phase right where ECG data is collected through lightweight deep learning analysis. We evaluate the system on two widely used datasets, PTB and MIT-BIH datasets from PhysioNet. Our experiments suggest using artificial neural network (ANN) algorithms for their superior performance over other machine learning algorithms with an accuracy up to 99.3%. Furthermore, we implemented our system on a Raspberry Pi B+ representing an ECG patch to collect and process ECG signals and detect any abnormalities using the proposed ANN model. To create a scalable system, we stream the data in real-time using Apache Kafka and MQTT to keep records of patients’ ECG data and use it for further analysis to identify causes and support medical diagnosis. The system notifies healthcare providers when abnormalities are detected.
Authors:
Abeer Badawi, Ahmed Badr, Khalid Elgazzar
Venue:
2021 IEEE 7th World Forum on Internet of Things (WF-IoT)
2019

A Permissioned Blockchain-Based System for Verification of Academic Records

NTMS
Abstract >>
  • While academic institutions maintain records such as transcripts and certificates, they are often requested to share these records with other institutions at the request of students for credit transfer, or prerequisites for acceptance into new academic programs. While the transfer of academic records is a regular daily activity for the institutions, there is often significant overhead involved as the process of transfer and verification is extremely manual. The need for an automated end-to-end solution for the transfer and verification of academic records between institutions is on the edge to reduce wait times for students to transfer their records, as well as to provide a reliable verification method to avoid academic fraud. This paper presents a permissioned blockchain-based system to allow institutions to securely and dependably transfer and verify academic records at the student request. Permissioned blockchains, such as Hyperledger, provide a more scalable and cost-effective and private solution for enterprise applications. Our solution is comprised of a web interface for enrolling and requesting the transfer, with a backend using Hyperledger Fabric and Hyperledger Composer to retain the hash of the records on the blockchain for verification.
Authors:
Ahmed Badr, Laura Raferty, Qusay H. Mahmoud, Khalid Elgazzar, Patrick C. K. Hung
Venue:
10th IFIP International Conference on New Technologies, Mobility and Security (NTMS), Canary Islands - Spain, 24 -26 June 2019

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ahmed@a-badr.com

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