Portable system for monitoring and controlling driver behavior and the use of a mobile phone while driving

There is an utmost requirement for technology to control a driver's phone while driving, which will prevent the driver from being distracted and thus saving the driver's and passenger;s lives. Information from recent studies has shown that 70% of the young and aware drivers are used to texting while driving. There are many different technologies used to control mobile phones while driving, including electronic device control, global positioning system (GPS), onboard diagnostics (OBD)-II-based devices, mobile phone applications or apps, etc. These devices acquire the vehicle information such as the car speed and use the information to control the driver's phone such as preventing them from making or receiving calls at specific speed limits. The information from the devices is interfaced via Bluetooth and can later be used to control mobile phone applications. The main aim of this paper is to propose the design of a portable system for monitoring the use of a mobile phone while driving and for controlling a driver's mobile phone, if necessary, when the vehicle reaches a specific speed limit (>10 km/h). A paperbased self-reported questionnaire survey was carried out among 600 teenage drivers from different nationalities to see the driving behavior of young drivers in Qatar. Finally, a mobile application was developed to monitor the mobile usage of a driver and an OBD-II module-based portable system was designed to acquire data from the vehicle to identify drivers' behavior with respect to phone usage, sudden lane changes, and abrupt breaking/sharp speeding. This information was used in a mobile application to control the driver's mobile usage as well as to report the driving behavior while driving. The application of such a system can significantly improve drivers' behavior all over the world.Author Contributions: Experiments were designed by A.K., M.C.; Experiments were performed by A.D.,M.M.; Results were analyzed by A.K.,M.C.,R.D.,N.E. and D.M.; All authors were involved in interpretation of data and paper writing. Funding: The publication of this article was funded by the Qatar National Library. Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the resultsScopu

Kalman tracking of linear predictor and harmonic noise models for noisy speech enhancement

This paper presents a speech enhancement method based on the tracking and denoising of the formants of a linear prediction (LP) model of the spectral envelope of speech and the parameters of a harmonic noise model (HNM) of its excitation. The main advantages of tracking and denoising the prominent energy contours of speech are the efficient use of the spectral and temporal structures of successive speech frames and a mitigation of processing artefact known as the ‘musical noise’ or ‘musical tones’.The formant-tracking linear prediction (FTLP) model estimation consists of three stages: (a) speech pre-cleaning based on a spectral amplitude estimation, (b) formant-tracking across successive speech frames using the Viterbi method, and (c) Kalman filtering of the formant trajectories across successive speech frames.The HNM parameters for the excitation signal comprise; voiced/unvoiced decision, the fundamental frequency, the harmonics’ amplitudes and the variance of the noise component of excitation. A frequency-domain pitch extraction method is proposed that searches for the peak signal to noise ratios (SNRs) at the harmonics. For each speech frame several pitch candidates are calculated. An estimate of the pitch trajectory across successive frames is obtained using a Viterbi decoder. The trajectories of the noisy excitation harmonics across successive speech frames are modeled and denoised using Kalman filters.The proposed method is used to deconstruct noisy speech, de-noise its model parameters and then reconstitute speech from its cleaned parts. Experimental evaluations show the performance gains of the formant tracking, pitch extraction and noise reduction stages

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

Scene understanding through semantic image segmentation in augmented reality

Abstract. Semantic image segmentation, the task of assigning a label to each pixel in an image, is a major challenge in the field of computer vision. Semantic image segmentation using fully convolutional neural networks (FCNNs) offers an online solution to the scene understanding while having a simple training procedure and fast inference speed if designed efficiently. The semantic information provided by the semantic segmentation is a detailed understanding of the current context and this scene understanding is vital for scene modification in augmented reality (AR), especially if one aims to perform destructive scene augmentation. Augmented reality systems, by nature, aim to have a real-time modification of the context through head-mounted see-through or video-see-through displays, thus require efficiency in each step. Although there are many solutions to the semantic image segmentation in the literature such as DeeplabV3+, Deeplab DPC, they fail to offer a low latency inference due to their complex architectures in aim to acquire the best accuracy. As a part of this thesis work, we provide an efficient architecture for semantic image segmentation using an FCNN model and achieve real-time performance on smartphones at 19.65 frames per second (fps) while maintaining a high mean intersection over union (mIOU) of 67.7% on Cityscapes validation set with our "Basic" variant and 15.41 fps and 70.3% mIOU on Cityscapes test set using our "DPC" variant. The implementation is open-sourced and compatible with Tensorflow Lite, thus able to run on embedded and mobile devices. Furthermore, the thesis work demonstrates an augmented reality implementation where semantic segmentation masks are tracked online in a 3D environment using Google ARCore. We show that the frequent calculation of semantic information is not necessary and by tracking the calculated semantic information in 3D space using inertial-visual odometry that is provided by the ARCore framework, we can achieve savings on battery and CPU usage while maintaining a high mIOU. We further demonstrate a possible use case of the system by inpainting the objects in 3D space that are found by the semantic image segmentation network. The implemented Android application performs real-time augmented reality at 30 fps while running the computationally efficient network that was proposed as a part of this thesis work in parallel

SLS: Smart localization service: human mobility models and machine learning enhancements for mobile phone’s localization

In recent years we are witnessing a noticeable increment in the usage of new generation smartphones, as well as the growth of mobile application development. Today, there is an app for almost everything we need. We are surrounded by a huge number of proactive applications, which automatically provide relevant information and services when and where we need them. This switch from the previous generation of passive applications to the new one of proactive applications has been enabled by the exploitation of context information. One of the most important and most widely used pieces of context information is location data. For this reason, new generation devices include a localization engine that exploits various embedded technologies (e.g., GPS, WiFi, GSM) to retrieve location information. Consequently, the key issue in localization is now the efficient use of the mobile localization engine, where efficient means lightweight on device resource consumption, responsive, accurate and safe in terms of privacy. In fact, since the device resources are limited, all the services running on it have to manage their trade-off between consumption and reliability to prevent a premature depletion of the phone’s battery. In turn, localization is one of the most demanding services in terms of resource consumption. In this dissertation I present an efficient localization solution that includes, in addition to the standard location tracking techniques, the support of other technologies already available on smartphones (e.g., embedded sensors), as well as the integration of both Human Mobility Modelling (HMM) and Machine Learning (ML) techniques. The main goal of the proposed solution is the provision of a continuous tracking service while achieving a sizeable reduction of the energy impact of the localization with respect to standard solutions, as well as the preservation of user privacy by avoiding the use of a back-end server. This results in a Smart Localization Service (SLS), which outperforms current solutions implemented on smartphones in terms of energy consumption (and, therefore, mobile device lifetime), availability of location information, and network traffic volume