Review of natural fibre-reinforced hybrid composites

Natural fibre-reinforced hybrid composites which contain one or more types of natural reinforcement are gaining increasing research interest. This paper presents a review of natural fibre-reinforced hybrid composites. Both thermoplastic and thermoset composites reinforced by hybrid/synthetic fibres or hybrid/hybrid fibres are reviewed. The properties of natural fibres, the properties and processing of composites are summarised

Automatic Detection and Classification of Audio Events for Road Surveillance Applications

This work investigates the problem of detecting hazardous events on roads by designing an audio surveillance system that automatically detects perilous situations such as car crashes and tire skidding. In recent years, research has shown several visual surveillance systems that have been proposed for road monitoring to detect accidents with an aim to improve safety procedures in emergency cases. However, the visual information alone cannot detect certain events such as car crashes and tire skidding, especially under adverse and visually cluttered weather conditions such as snowfall, rain, and fog. Consequently, the incorporation of microphones and audio event detectors based on audio processing can significantly enhance the detection accuracy of such surveillance systems. This paper proposes to combine time-domain, frequency-domain, and joint time-frequency features extracted from a class of quadratic time-frequency distributions (QTFDs) to detect events on roads through audio analysis and processing. Experiments were carried out using a publicly available dataset. The experimental results conform the effectiveness of the proposed approach for detecting hazardous events on roads as demonstrated by 7% improvement of accuracy rate when compared against methods that use individual temporal and spectral features

LFR face dataset:Left-Front-Right dataset for pose-invariant face recognition in the wild

In this work, a new multitask convolutional neural network (CNN) is proposed aiming for the recognition of face under pose variations. Furthermore, the combination of pose estimation for each corresponding pose in a separate fashion allows robust face recognition in presence of various facial expressions as well as low illuminations. First, a CNN model for pose estimation is proposed. The pose estimation model is trained using a self-collected dataset built from three popular datasets including FLW, CEP, and CASIA-WebFace using three categories of face image capture such as Left side, Frontal and right side. Experimental evaluation has been conducted using two datasets: Pointing'04 and Schneiderman. Results reveal the robustness of the proposed pose estimation model. Moreover, the proposed face pose estimation is applied on three datasets to widen the dataset and make it bigger for training and testing deep learning models.identity. Compared with the recent and related techniques, the proposed system has been shown to outperform related works and deliver state-of-the-art performance for pose estimation. The built large-scale dataset for training may improve the accuracy of the proposed system as increasing the number of images for each identity will cover a wider range of variations of the face. Also, the generated dataset can be improved in terms of the number of identities using the same methodology on other datasets like VGGFace2. Acknowledgment This publication was made by NPRP grant # NPRP8-140-2-065 from the Qatar National Research Fund (a member of the Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Towards the design of smart video-surveillance system

Security and monitoring systems are increasingly demanding in terms of quality, reliability and flexibility especially those dedicated to video surveillance. The aim of this study is to identify some limiting factors in the existing video-surveillance systems and to propose a set of best practices for developing a smart platform for a security monitoring system incorporating advanced techniques for video processing and analysis. In this work, we focus on the effect of the video quality on the biometric part of the video-surveillance systems for public security. In such systems, face detection and recognition from video sequences acquired from surveillance cameras, are challenging tasks, due to presence of strong illumination variations, noise, and changes in facial expressions. In this paper, we mainly focus on the illumination issue occurred in video surveillance. The low light video data is processed using a perceptual based approach, namely multi-scale Retinex method, to improve the video quality, followed by face detection. The experimental results demonstrate significant performance improvement in face detection and recognition, by improving the illumination of video sequences over the unprocessed video data. � 2018 IEEE.This publication was made possible by NPRP grant # NPRP8-140-2-065 from the Qatar National Research Fund (a member of the Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

An image steganography approach based on k-least significant bits (k-LSB)

Image steganography is the operation of hiding a message into a cover image. the message can be text, codes, or image. Hiding an image into another is the proposed approach in this paper. Based on LSB coding, a k-LSB-based method is proposed using k least bits to hide the image. For decoding the hidden image, a region detection operation is used to know the blocks contains the hidden image. The resolution of stego image can be affected, for that, an image quality enhancement method is used to enhance the image resolution. To demonstrate the effectiveness of the proposed approach, we compare it with some of the state-of-the-art methods.This publication was made by NPRP grant # NPRP 11S - 0113 - 180276 from the Qatar National Research Fund (a member of the Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Chemical modification of graphene with grape seed extract: Its structural, optical and antimicrobial properties

Herein, we modified for the first time thermally reduced graphene oxide (TRG) using grape seed extract (GSE), by simple probe sonication method. The effect of GSE on the structural changes of TRG has been carefully analyzed through Fourier Transform Infrared (FT-IR), X-ray photoelectron spectroscopy (XPS) and Raman spectroscopy and these spectral data proved that the TRG has been modified successfully. Furthermore, X-ray investigations proved the change in crystallinity and coherence length of TRG, which could be further, authenticated by Transmission electron microscopy (TEM). The optical properties of as prepared modified TRG (m-TRG) were investigated with the help of UV–Visible and photoluminescence spectroscopy. The band gap of m-TRG was found to be 4.1 eV and it exerted the luminescence in the visible region. Moreover, the antibacterial results showed that m-TRG has enhanced antibacterial activity and 80% of mortality was observed in both the gram positive and gram negative bacteria at a minimum concentration of 40 μg ml−1 and 60 μg ml−1. Thus, this m-TRG could find many applications in the future semiconductor and optoelectronic devices and it could be considered as a novel antibacterial agent that can find potential application in the areas of healthcare and engineering.The authors of this manuscript would like to thank the University Grants Commission ( 2061010223 ) (U.G.C), New Delhi and Department of Science and Technology (D.S.T) (Inspire Fellowship) New Delhi for their financial support. The authors would like to extend their thanks to Centre for Advanced Materials (CAM), Qatar University for supporting this work.Scopu