DDI: Drones Detection and Identification using Deep Learning Techniques

Drones are becoming increasingly popular not only for recreational purposes but in day-to-day applications in engineering, medicine, logistics, security and others. Besides their useful applications, an alarming concern in regards to the physical infrastructure security, safety and privacy arose due to the potential of their use in malicious activities. To address this problem, wework towards the proposed solution by the following twofold contribution, first we propose a novel solution that automates the drone detection and identification processes using drone's acoustic features with different deep learning algorithms. However, the lack of acoustic drone datasets hinders the ability to implement an effective solution. Therefore, we aim to fulfil this gap by introducing a hybrid drone acoustic dataset composed of recorded drone audio clips and artificially generated drone audio clips using a state of the art deep learning model known as the Generative Adversarial Network. Furthermore, we examine the effectiveness of using drone audio with different deep learning algorithms, namely, the Convolutional Neural Network, the Recurrent Neural Network and the Convolutional Recurrent Neural Network in drone detection and identification. Moreover, we investigate the impact our proposed hybrid dataset has on drone detection. The second contribution is laying the foundation for the next step of the anti-drone proposed system which is focused around swarm drones localisation and tracking using data fusion of audio and radio frequency signals using deep learning techniques. This is made possible through the design of a novel swarm of drones simulator. Our findings prove the advantage of using deep learning techniques with acoustic data for drone detection and identification while confirming our hypothesis on the benefits of using the Generative Adversarial Networks to generate real-like drone audio clips with an aim of enhancing the detection of new and unfamiliar drones

Personalized Quantification of Facial Normality using Artificial Intelligence

While congenital facial deformities are not rare, and surgeons typically perform operations to improve these deformities, currently the success of the surgical reconstruction operations can only be “measured” subjectively by surgeons and specialists. No efficient objective mechanisms of comparing the outcomes of plastic reconstruction surgeries or the progress of different surgery techniques exist presently. The aim of this research project is to develop an efficient software application that can be used by plastic surgeons as an objective measurement tool for the success of an operation. The long-term vision is to develop a software application that is user-friendly and can be downloaded on a regular laptop and used by doctors and patients to assess the progress of their surgical reconstruction procedures. The application would work by first scanning a face before and after an operation and providing the surgeon with a normality score of the face from 0 to 3 where 3 represents normal and 0 represents extreme abnormality. A score will be given when the face is scanned before and after surgery. The difference between those scores is what we will call the delta. A high delta value would point to a high improvement in the normality of a face post-surgery, and a low delta value would indicate a small improvement. The first chapter of the thesis represents the introduction which describes the general aspects of the project. The second chapter presents the methodology employed for building the application and the existing solutions and proposed functional model structure. The results chapter presents the process behind collecting and labeling the image database and analyzes the scores produced by the program when fed with new images from the database. Finally, the last chapter of this thesis presents the conclusions. The list of references completes this work

Audio-Based Drone Detection and Identification Using Deep Learning Techniques with Dataset Enhancement through Generative Adversarial Networks

Drones are becoming increasingly popular not only for recreational purposes but in day-to-day applications in engineering, medicine, logistics, security and others. In addition to their useful applications, an alarming concern in regard to the physical infrastructure security, safety and privacy has arisen due to the potential of their use in malicious activities. To address this problem, we propose a novel solution that automates the drone detection and identification processes using a drone’s acoustic features with different deep learning algorithms. However, the lack of acoustic drone datasets hinders the ability to implement an effective solution. In this paper, we aim to fill this gap by introducing a hybrid drone acoustic dataset composed of recorded drone audio clips and artificially generated drone audio samples using a state-of-the-art deep learning technique known as the Generative Adversarial Network. Furthermore, we examine the effectiveness of using drone audio with different deep learning algorithms, namely, the Convolutional Neural Network, the Recurrent Neural Network and the Convolutional Recurrent Neural Network in drone detection and identification. Moreover, we investigate the impact of our proposed hybrid dataset in drone detection. Our findings prove the advantage of using deep learning techniques for drone detection and identification while confirming our hypothesis on the benefits of using the Generative Adversarial Networks to generate real-like drone audio clips with an aim of enhancing the detection of new and unfamiliar drones

دراسة التعليم في قطر 2012 : تقرير دافعية الطلاب و مشاركة أولياء الأمور

This report examines the views of children, parents, teachers, and administrators toward K-12 education in Qatar. It is based on results from the Qatar Education Study (QES), which is a series of surveys conducted by the Social and Economic Survey Research Institute (SESRI) in December 2012. Together, the surveys included more than 4,200 participants from 39 preparatory and secondary schools.يتناول هذا التقرير آراء الطلاب وأولياء الأمور والمعلمين والإداريين حول نظام التعليم في قطر من الصف الثامن و حتى الصف 12( المرحلتان الإعدادية والثانوية). يعمتد هذا التقرير عىل نتائج دراسة التعليم في قطر (QES )وهي سلسلةّ من المسوح أجراها معهد البحوث الاجمتاعية والاقتصادية المسحية (SESRI )في شهر ديمبسر 2012 و قد شملت المسوح مجتمعة أكثر من 4200 مشارك من 39 مدرسة إعدادية وثانوية

A systematic approach to the design and characterization of a smart insole for detecting vertical ground reaction force (vGRF) in gait analysis

Gait analysis is a systematic study of human locomotion, which can be utilized in various applications, such as rehabilitation, clinical diagnostics and sports activities. The various limitations such as cost, non-portability, long setup time, post-processing time etc., of the current gait analysis techniques have made them unfeasible for individual use. This led to an increase in research interest in developing smart insoles where wearable sensors can be employed to detect vertical ground reaction forces (vGRF) and other gait variables. Smart insoles are flexible, portable and comfortable for gait analysis, and can monitor plantar pressure frequently through embedded sensors that convert the applied pressure to an electrical signal that can be displayed and analyzed further. Several research teams are still working to improve the insoles' features such as size, sensitivity of insoles sensors, durability, and the intelligence of insoles to monitor and control subjects' gait by detecting various complications providing recommendation to enhance walking performance. Even though systematic sensor calibration approaches have been followed by different teams to calibrate insoles' sensor, expensive calibration devices were used for calibration such as universal testing machines or infrared motion capture cameras equipped in motion analysis labs. This paper provides a systematic design and characterization procedure for three different pressure sensors: force-sensitive resistors (FSRs), ceramic piezoelectric sensors, and flexible piezoelectric sensors that can be used for detecting vGRF using a smart insole. A simple calibration method based on a load cell is presented as an alternative to the expensive calibration techniques. In addition, to evaluate the performance of the different sensors as a component for the smart insole, the acquired vGRF from different insoles were used to compare them. The results showed that the FSR is the most effective sensor among the three sensors for smart insole applications, whereas the piezoelectric sensors can be utilized in detecting the start and end of the gait cycle. This study will be useful for any research group in replicating the design of a customized smart insole for gait analysis. 2020 by the authors. Licensee MDPI, Basel, Switzerland.This research was partially funded by Qatar National Research Foundation (QNRF), grant number NPRP12S-0227-190164 and Research University Grant DIP-2018-017. The publication of this article was funded by the Qatar National Library. The authors would like to thank Engr. Ayman Ammar, Electrical Engineering, Qatar University for helping in printing the printed circuit boards (PCBs). This research was partially funded by Qatar National Research Foundation (QNRF), grant number NPRP12S-0227-190164 and Research University Grant DIP-2018-017. The publication of this article was funded by the Qatar National Library.Scopu

Web Accessibility Regulations: How are they effective for people with disabilities?

 Equitable access to the web is a critical element of the Right to Life affirmed by the UN Convention on the Rights of People with Disabilities (CRPD). In response to signing the CRPD, nations from all around the world have enacted web accessibility legislation to eliminate discrimination and to ensure equity of access to the web for people with disabilities. This research aims to understand the scope and maturity of accessibility regulations worldwide and their enforcement. Since the CRPD came into existence in 2008, governments throughout the world have passed or revised their laws guaranteeing disabled people's civil liberties and full participation in society, including full and equal use of information and communications technologies. But a fundamental question that remains is whether such regulations are effective in holding businesses or even governments liable for their violations. Examining a sample of countries across six regions of the world the paper underlines the need for more related action from governments to ensure full participation for people with disabilities in the digital economy.</p

Audio based drone detection and identification using deep learning

In recent years, unmanned aerial vehicles (UAVs) have become increasingly accessible to the public due to their high availability with affordable prices while being equipped with better technology. However, this raises a great concern from both the cyber and physical security perspectives since UAVs can be utilized for malicious activities in order to exploit vulnerabilities by spying on private properties, critical areas or to carry dangerous objects such as explosives which makes them a great threat to the society. Drone identification is considered the first step in a multi-procedural process in securing physical infrastructure against this threat. In this paper, we present drone detection and identification methods using deep learning techniques such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Convolutional Recurrent Neural Network (CRNN). These algorithms will be utilized to exploit the unique acoustic fingerprints of the flying drones in order to detect and identify them. We propose a comparison between the performance of different neural networks based on our dataset which features audio recorded samples of drone activities. The major contribution of our work is to validate the usage of these methodologies of drone detection and identification in real life scenarios and to provide a robust comparison of the performance between different deep neural network algorithms for this application. In addition, we are releasing the dataset of drone audio clips for the research community for further analysis. - 2019 IEEE.ACKNOWLEDGMENT This publication was supported by Qatar University Internal Grant No. QUCP-CENG-2018/2019-1. The findings achieved herein are solely the responsibility of the authors.Scopu

دراسة التعليم في قطر : دليل المرافق 2012

This report examines the views of students, teachers and administrators toward K-12 education in Qatar. It is based on results from the Qatar Education Study (QES), which is a series of surveys conducted by the Social and Economic Survey Research Institute (SESRI) in December 2012. In the spring of 2014 the school operators in each of the original 39 schools that participated in the QES were asked to complete a Supplemental Facilities Questionnaire (SFQ) to enhance information collected in the original QES.يتناول هذا التقرير أراء الطالب والمعلمين والإداريين حول التعليم من الروضة إلى الصف 12 في دولة قطر. يعتمد هذا التقرير على نتائج دراسة التعليم في قطر وهي سلسلة من الدراسات المسحية قام بها معهد البحوث االجتماعية والاقتصادية المسحية في شهر ديسمبر من عام 2012 .وفي ربيع 2014 طلب من أصحاب تراخيص المدارس الـ 39 األصلية التي شاركت في دراسة التعليم في قطر تعبئة استبيان تكميلي خاص بالمرافق لتعزيز المعلومات التي تم جمعها في الدراسة الأصلية للتعليم في قطر. وقد شمل االستبيان إجمالا أكثر من 4200 مشارك من 39 مدرسة إعدادية وثانوية

A Systematic Approach to the Design and Characterization of a Smart Insole for Detecting Vertical Ground Reaction Force (vGRF) in Gait Analysis

Gait analysis is a systematic study of human locomotion, which can be utilized in various applications, such as rehabilitation, clinical diagnostics and sports activities. The various limitations such as cost, non-portability, long setup time, post-processing time etc., of the current gait analysis techniques have made them unfeasible for individual use. This led to an increase in research interest in developing smart insoles where wearable sensors can be employed to detect vertical ground reaction forces (vGRF) and other gait variables. Smart insoles are flexible, portable and comfortable for gait analysis, and can monitor plantar pressure frequently through embedded sensors that convert the applied pressure to an electrical signal that can be displayed and analyzed further. Several research teams are still working to improve the insoles&rsquo; features such as size, sensitivity of insoles sensors, durability, and the intelligence of insoles to monitor and control subjects&rsquo; gait by detecting various complications providing recommendation to enhance walking performance. Even though systematic sensor calibration approaches have been followed by different teams to calibrate insoles&rsquo; sensor, expensive calibration devices were used for calibration such as universal testing machines or infrared motion capture cameras equipped in motion analysis labs. This paper provides a systematic design and characterization procedure for three different pressure sensors: force-sensitive resistors (FSRs), ceramic piezoelectric sensors, and flexible piezoelectric sensors that can be used for detecting vGRF using a smart insole. A simple calibration method based on a load cell is presented as an alternative to the expensive calibration techniques. In addition, to evaluate the performance of the different sensors as a component for the smart insole, the acquired vGRF from different insoles were used to compare them. The results showed that the FSR is the most effective sensor among the three sensors for smart insole applications, whereas the piezoelectric sensors can be utilized in detecting the start and end of the gait cycle. This study will be useful for any research group in replicating the design of a customized smart insole for gait analysis