Towards robust autonomous driving systems through adversarial test set generation

Correct environmental perception of objects on the road is vital for the safety of autonomous driving. Making appropriate decisions by the autonomous driving algorithm could be hindered by data perturbations and more recently, by adversarial attacks. We propose an adversarial test input generation approach based on uncertainty to make the machine learning (ML) model more robust against data perturbations and adversarial attacks. Adversarial attacks and uncertain inputs can affect the ML model’s performance, which can have severe consequences such as the misclassification of objects on the road by autonomous vehicles, leading to incorrect decision-making. We show that we can obtain more robust ML models for autonomous driving by making a dataset that includes highly-uncertain adversarial test inputs during the re-training phase. We demonstrate an improvement in the accuracy of the robust model by more than 12%, with a notable drop in the uncertainty of the decisions returned by the model. We believe our approach will assist in further developing risk-aware autonomous systems.acceptedVersio

Towards robust autonomous driving systems through adversarial test set generation

Correct environmental perception of objects on the road is vital for the safety of autonomous driving. Making appropriate decisions by the autonomous driving algorithm could be hindered by data perturbations and more recently, by adversarial attacks. We propose an adversarial test input generation approach based on uncertainty to make the machine learning (ML) model more robust against data perturbations and adversarial attacks. Adversarial attacks and uncertain inputs can affect the ML model's performance, which can have severe consequences such as the misclassification of objects on the road by autonomous vehicles, leading to incorrect decision-making. We show that we can obtain more robust ML models for autonomous driving by making a dataset that includes highly-uncertain adversarial test inputs during the re-training phase. We demonstrate an improvement in the accuracy of the robust model by more than 12%, with a notable drop in the uncertainty of the decisions returned by the model. We believe our approach will assist in further developing risk-aware autonomous systems

Improvement of heat sink performance using paraffin/graphite/hydrogel phase change composite coating

Phase-change materials offer high latent heat and are widely used for energy storage applications. Paraffin wax is usually used as a phase-change material. However, its application in energy storage is restricted due to its low thermal conductivity. In the present work, graphite and graphite-hydrogel are used to enhance the thermal conductivity and heat release properties of paraffin wax. Wax-graphite (W-G) and wax-graphite-hydrogel (W-G-H) composites were synthesized by the dispersion of graphite and graphite-hydrogel in paraffin wax above its melting temperature. Scanning electron microscope (SEM) analysis was used to investigate the graphite and graphite-hydrogel distribution in the paraffin wax matrix. Thermogravimetric analysis (TGA) and differential scanning calorimeter (DSC) characterization were performed to measure the thermal stability and phase transition properties, respectively. DSC revealed that all composites have a similar melting temperature. The W-G-H composite displayed nearly 12 folds more thermal conductivity compared to the pure paraffin wax. High temperature brings adverse impacts on energy efficiency, and even destroys a semiconductor device. The synthesized W-G-H composite is proposed to decrease the working temperature of semiconductor devices. As an applicative demonstration, the W-G-H composite film was coated at the back of the solar panel. The W-G-H composite coated solar panel displayed a surface temperature that was near ∼4 °C lower than the bare solar panel while operating. The real-time experiment indicates that the W-G-H composite has high thermal conductivity and heat release properties. The study reports fundamentally new low-cost, simple, scalable, and self-adaptive, passive cooling technology to the semiconductor industry. The proposed material can further be developed in the form of paint and its heat sink properties can be improved by introducing hydrogels doped with Li+ and Br− ions.This work was supported by Qatar National Research Fund under the grant no. NPRP12S-0131-190030 . Open Access funding was provided by the Qatar National Librar

Self-sanitizing reusable glove via 3D-printing and common mold making method

In health care and public health practice, it is critical to settings control practices that are critical to reducing the transmission of infections through cross-contamination. To provide protection from cross-contamination, use and throw gloves are routinely used. However, single-time use and inconsistent sanitization of used gloves remain a large problem and elevate the risk of catching viruses, germs, pathogens, and contaminants. The study reports reusable self-sanitizing gloves via 3D-printing and common hand molding methods. The major contribution is frequent self-sanitization of gloves without any manual intervention. The elastomeric material is used for fabricating gloves and continuous channels are embedded within the elastomeric material that runs through the entire glove surface, covering the front, back, and fingers. Elastomeric material allows the engagement of fingers for gripping objects. While the embedded channel is provided with uniformly spaced openings to eject the sanitizing solution. The glove surface is textured with a porous morphology that acts as mini and micro reservoirs for sterilizing solution ejected through embedded channel opening. The embedded channel is connected to a sanitizing solution storage tank. The incorporation of sanitizing solution storage tank enables its usage over a longer period. This uniquely constructed design of the gloves even assists in the effective sterilization of infected surface that comes in contact with the gloves. The gloves can be customized to improve comfortability by fabricating them from the 3D-printed mound developed based on the palm size of the user. The developed technology can be used by individuals working in hospitals, the transport sector, delivery units, schools, offices, industries, etc. We strongly believe that this technology will be highly useful in minimizing the risk of getting infected through cross-contamination and will help in maintaining hygienic as well as safe surroundings.This work was supported by the RRC-2-063-133 grant from the Qatar National Research Fund (a member of Qatar Foundation). Open Access funding was provided by the Qatar National Library

Design of Embedded 3D Printed Sensors on a Robot for Monitoring and Capturing Atmospheric Carbon Dioxide

Gas detection is a critical task in dangerous environments that involve hazardous or contaminant gases. Quick detection of gas leaks and their rectification ensure the protection of lives and safeguards equipment installed in workplaces and industrial sites. Extensive work has been done in remote monitoring of the gas leaks via sensors installed at the locations. Even though these devices can detect harmful gases in the environment, they are not designed to take instant action for gas/smoke removal. We have surveyed the literature and gathered information that smoke from the fire accidents causes a lack of visibility to the exit, and many untimely lives are lost. Thus, further research has to be carried out to develop devices that can not only detect the harmful gases but also have the technology for gas/smoke removal supported by standard protocols to mitigate the effects of the harmful gas on the surrounding environment, such as an explosion, asphyxiation, fire. This thesis addresses the tasks required for developing a mobile robot that not only detects the presence of leakage of harmful gas like carbon dioxide (CO2), carbon monoxide(CO), ammonia NH3, liquid petroleum gas (LPG) and broadcast the information via wireless communication modeling system but also has gas filtration mechanism that helps in purifying the surrounding air that has been contaminated by the leakage of the harmful gas. The contributions presented in this thesis are three-fold. Firstly, the fabrication of gas sensors via a 3D printing technique. The 3D printing approach enables the bulk production of the sensors in a short period. It gives control over the repeatability, reproducibility, and sensitivity of the fabricated sensors, especially for CO2

Anti-spoofing device for biometric fingerprint scanners

Biometrics is a promising technology for safeguarding the personal identity and digital information of every individual. This paper describes an easy-to-integrate and inexpensive method to improve the security of fingerprint scanners. An added layer of protection is proposed by integrating an anti-spoofing device that can be integrated to any commercial fingerprint scanners to enhance their security and prevent spoofing from prosthetic or dismembered fingers. The proposed device senses the capacitance and pulses from human fingers. We conducted over 300 tests on human and fake fingers. Our experimental results demonstrate that this novel device can identify the fake fingers with 100% accuracy. The device has a potential for being a cost-efficient and robust solution against spoofing. 2017 IEEE.ACKNOWLEDGMENT The work is supported by an NPRP grant from the Qatar National Research Fund under the grant No. NPRP 7-673-2-251. The statements made herein are solely the responsibility of the authors.Scopu

Designing a quick fix shutter for auto-disinfecting scan glass surface in biometric scanners

Fingerprint scanners are significant devices in professional life, and its contamination can be potential sources of COVID 19 transmission. Manual disinfection of the fingerprint scanner after every single use is time consuming and even can adversely affect its electronics/functioning. Thus, with an aim to prevent the spread of infectious disease by cross contamination and implement the safe use of fingerprint scanner, we have developed a smart quick fix technology for automatic disinfection of finger print scanner glass after every single use. The smart portable top mount assembly uses two different disinfecting methods that ensures higher degree of disinfection. The disinfection is based on the simultaneous ultraviolet (UV) and heat treatment for a specific short time, and required to kill all the viruses on the scan glass surface. Moreover, developing this disinfecting technology with a universal design that can be fitted to any finger print scanner irrespective to its size, makes it a novel idea

A New Deep Learning Method for Accurate Cardiac Heart Failure Prediction from RR Interval Measurements

cardiovascular diseases are the major cause of death worldwide. Early detection of heart failure will assist patients and medical professionals in taking better precautions to reduce risks. The objective of this study is to find a technique that can reliably forecast the risk of cardiovascular illnesses. With the help of the training data we offer, deep learning algorithms like Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) make these predictions. Prediction accuracy will be reduced by a lack of medical data. As a part of our study, we examined DNN architectures to forecast cardiac failure. Over the training data, existing deep learning methods were employed. A new deep learning method that can predict heart failure using RR interval measurements is developed by comparing the accuracy performance of the proposed and existing models. The Physiobank NSR-RR and CHF-RR databases were used to compile the findings. The new model, which was based on experimental findings using these two free RR interval databases, attained a 94% accuracy rate compared to the existing model's 93.1% accuracy rate.Qatar University IRCC progra

Green energy powered - vapor, thermal and UV light assisted disinfection technology

Infectious diseases are responsible for an immense global burden of disease that impacts public health systems and economies worldwide. The rapid increase in the use of protective items has introduced an urge for their reuse and safe dumping to minimize the threat of disease transmission through cross-contamination. The existing disinfection processes are mostly powered with non-renewable energy sources and focus on single disinfecting technology that limits their installation to in-house applications and reduces the degree of disinfection, respectively. Thus there is a need to develop new strategies and innovations powered by renewable energy sources that enhance the degree of disinfection of daily-use objects and curtail the spread of the infection through cross-contamination. The present study reports a smart automatic technology called “REACTIV-FIT”. The technology is developed as an exercise bike with a disinfection chamber that efficiently kills viruses, bacteria, and other germs/pathogens. The current developed technology is equipped with three disinfecting protocols in one system. The system is powered by renewable solar energy and utilizes the mechanical energy during exercise into electrical energy using a generator-based mechanism. The portable smart automatic disinfection box embedded in the bike utilizes the synergetic effect of ultraviolet (UV), thermal, and vapor treatment for sterilizing objects. During vapor treatment, the continuous supply of the sterilizing solution is maintained through a storage tank attached to the outer body of the box and can be easily refilled. In the final step of the disinfecting protocol, the objects are exposed to UV-A light having a peak wavelength of 365–370 nm. The automatic disinfection process is powered by renewable solar and mechanical energy during cycling. The developed technology can be installed in schools, offices, and industries, and its renewable energy-powered feature offers installation in parks, tourist places, streets, remote areas, etc.This work was supported by the RRC-2-063-133 grant from the Qatar National Research Fund (a member of Qatar Foundation)