A heterogeneous short-range communication platform for internet of vehicles

The automotive industry is rapidly accelerating toward the development of innovative industry applications that feature management capabilities for data and applications alike in cars. In this regard, more internet of vehicles solutions are emerging through advancements of various wireless medium access-control technologies and the internet of things. In the present work, we develop a short-range communication–based vehicular system to support vehicle communication and remote car control. We present a combined hardware and software testbed that is capable of controlling a vehicle’s start-up, operation and several related functionalities covering various vehicle metric data. The testbed is built from two microcontrollers, Arduino and Raspberry Pi 3, each of which individually controls certain functions to improve the overall vehicle control. The implementation of the heterogeneous communication module is based on the Institute of Electrical and Electronics Engineers (IEEE) 802.11 and IEEE 802.15 medium access control technologies. Further, a control module on a smartphone was designed and implemented for efficient management. Moreover, we study the system connectivity performance by measuring various important parameters including the coverage distance, signal strength, download speed and latency. This study covers the use of this technology setup in different geographical areas over various time spans

AI Modeling to Combat COVID-19 Using CT Scan Imaging Algorithms and Simulations: A Study

The coronavirus disease 2019 (COVID-19) outbreak has been designated as a worldwide pandemic by World Health Organization (WHO) and raised an international call for global health emergency. In this regard, recent advancements of technologies in the field of artificial intelligence and machine learning provide opportunities for researchers and scientists to step in this battlefield and convert the related data into a meaningful knowledge through computational-based models, for the task of containment the virus, diagnosis and providing treatment. In this study, we will provide recent developments and practical implementations of artificial intelligence modeling and machine learning algorithms proposed by researchers and practitioners during the pandemic period which suggest serious potential in compliant solutions for investigating diagnosis and decision making using computerized tomography (CT) scan imaging. We will review the modern algorithms in CT scan imaging modeling that may be used for detection, quantification, and tracking of Coronavirus and study how they can differentiate Coronavirus patients from those who do not have the disease

Computation and memory efficient face recognition using binarized eigenphases and component-based linear discriminant analysis for wide range applications.

Face recognition finds many important applications in many life sectors and in particular in commercial and law enforcement. This thesis presents two novel methods which make face recognition more practical. In the first method, we propose an attractive solution for efficient face recognition systems that utilize low memory devices. The new technique applies the principal component analysis to the binarized phase spectrum of the Fourier transform of the covariance matrix constructed from the MPEG-7 Fourier Feature Descriptor vectors of the face images. Most of the algorithms proposed for face recognition are computationally exhaustive and hence they can not be used on devices constrained with limited memory; hence our method may play an important role in this area. The second method presented in this thesis proposes a new approach for efficient face representation and recognition by finding the best location component-based linear discriminant analysis. In this regard, the face image is decomposed into a number of components of certain size. Then the proposed scheme finds the best representation of the face image in most efficient way, taking into consideration both the recognition rate and the processing time. Note that the effect of the variation in a face image, when it is taken as a whole, is reduced when it is divided into components. As a result the performance of the system is enhanced. This method should find applications in systems requiring very high recognition and verification rates. Further, we demonstrate a solution to the problem of face occlusion using this method. The experimental results show that both proposed methods enhance the performance of the face recognition system and achieve a substantial saving in the computation time when compared to other known methods. It will be shown that the two proposed methods are very attractive for a wide range of applications for face recognition

Computation and memory efficient face recognition using binarized eigenphases and component-based linear discriminant analysis for wide range applications.

Face recognition finds many important applications in many life sectors and in particular in commercial and law enforcement. This thesis presents two novel methods which make face recognition more practical. In the first method, we propose an attractive solution for efficient face recognition systems that utilize low memory devices. The new technique applies the principal component analysis to the binarized phase spectrum of the Fourier transform of the covariance matrix constructed from the MPEG-7 Fourier Feature Descriptor vectors of the face images. Most of the algorithms proposed for face recognition are computationally exhaustive and hence they can not be used on devices constrained with limited memory; hence our method may play an important role in this area. The second method presented in this thesis proposes a new approach for efficient face representation and recognition by finding the best location component-based linear discriminant analysis. In this regard, the face image is decomposed into a number of components of certain size. Then the proposed scheme finds the best representation of the face image in most efficient way, taking into consideration both the recognition rate and the processing time. Note that the effect of the variation in a face image, when it is taken as a whole, is reduced when it is divided into components. As a result the performance of the system is enhanced. This method should find applications in systems requiring very high recognition and verification rates. Further, we demonstrate a solution to the problem of face occlusion using this method. The experimental results show that both proposed methods enhance the performance of the face recognition system and achieve a substantial saving in the computation time when compared to other known methods. It will be shown that the two proposed methods are very attractive for a wide range of applications for face recognition

Binarized Eigenphases for Limited Memory Face Recognition Applications

Most of the algorithms proposed for face recognition involve considerable amount of calculations, and hence they can not be used on devices of limited memory constraints. In this paper, we propose a novel solution for efficient face recognition problem for the systems that utilize low memory devices. The new technique applies the principal component analysis to the binarized phase spectrum of the Fourier transform of the covariance matrix constructed from the MPEG-7 Fourier Feature Descriptor vectors of the images. The binarization step that is applied to the phases adds many interesting advantages to the system. It will be shown that the proposed technique maximizes the recognition rate while achieving substantial savings in computational time, when compared to other known systems