Farook Sattar

Vehicle localization in VANETs using data fusion and V2V communication

In Vehicular Ad-hoc Networks (VANETs), one of the challenging issues is to find an accurate localization information. In this paper, we have addressed this problem by introducing a novel approach based on the idea of cooperative localization. Our proposed scheme incorporates different techniques of localization along with data fusion as well as vehicle-to-vehicle communication, to integrate the available data and cooperatively improve the accuracy of the localization information of the vehicles. The simulation results show that sharing the localization information and deploying that of the neighboring vehicles, not only can assure the vehicles in a vicinity to obtain more accurate localization information, but also find the results robust to sensor inaccuracies or even to failures.

New approaches for spectro-temporal feature extraction with applications to respiratory sound classification

Auscultation based diagnosis of pulmonary disorders relies on the presence of adventitious sounds. In this paper, we propose a new set of features based on temporal characteristics of filtered narrowband signal to classify respiratory sounds (RSs) into normal and continuous adventitious types. RS signals are first decomposed in the time-frequency domain and features are extracted over selected frequency bins containing distinct signal characteristics based on auto-regressive averaging, the recursively measured instantaneous kurtosis, and the sample entropy histograms distortion. The presented features are compared with existing features using a modified clustering index with different distance metrics. Mean classification accuracies of 97.7% and 98.8% for inspiratory and expiratory segments respectively have been achieved using Support Vector Machine on real recordings.

Vehicular ad-hoc networks(VANETs): capabilities, challenges in information gathering and data fusion

Vehicular Ad-hoc Network (VANET) has become an active area of research due to its major role to improve vehicle and road safety, traffic efficiency, and convenience as well as comfort to both drivers and passengers. This paper thus addresses some of the attributes and challenging issues related to Vehicular Ad-hoc Networks (VANETs). A lot of VANET research work have focused on specific areas including routing, broadcasting, Quality of Service (QoS), and security. In this paper, a detailed overview of the current information gathering and data fusion capabilities and challenges in the context of VANET is presented. In addition, an overall VANET framework, an illustrative VANET scenario are provided in order to enhance safety, flow, and efficiency of the transportation system.

Vehicular ad-hoc Networks(VANETs): capabilities, challenges in context-aware processing and communication gateway

Vehicular Ad-hoc Networks (VANETs) have attracted attention in the support of safe driving, intelligent navigation, and emergency and entertainment applications. VANET can be viewed as an intelligent component of the Transportation Systems as vehicles communicate with each other as well as with roadside base stations located at critical points of the road, such as intersections or construction sites. In this paper, we provide an overview of the context-aware processing and communication gateway associated with Vehicular Ad-hoc Network (VANET). The concept of context-awareness, the recent advances and various challenges involved in context-aware processing are discussed. Some arising ideas such as based on context ontology, relevancy, hybrid dissemination, service oriented routing are also presented. This paper further briefly describes the communication gateway in VANET which includes its functional view together with the standards and their detailed preliminary specifications applicable to VANET.

Localization in vehicular ad hoc networks using data fusion and V2V communication

The paper deals with challenging localization problem in vehicular ad-hoc networks.A novel approach is proposed based on the idea of cooperative localization.Our scheme integrates available data and cooperatively improves location accuracy.Localization is more accurate and robust to sensor inaccuracies or even to failures.The estimation of vehicle prior and sequential decentralized EKF improve further. In Vehicular ad-hoc networks (VANETs), one of the challenging tasks is to find an accurate localization information. In this paper, we have addressed this problem by introducing a novel approach based on the idea of cooperative localization. Our proposed scheme incorporates different techniques of localization along with data fusion as well as vehicle-to-vehicle communication, to integrate the available data and cooperatively improve the accuracy of the localization information of the vehicles. The simulation results show that sharing the localization information and deploying that of the neighboring vehicles, not only assures the vehicles in a vicinity to obtain more accurate localization information, but also find the results robust to sensor inaccuracies or even to failures. Moreover, further improvement has been achieved by estimating the vehicle prior (prior mean and covariance) using unscented transform (UT) together with sequential decentralized extended Kalman filtering.

Recent Advances on Context-Awareness and Data/Information Fusion in ITS

Intelligent transportation systems (ITS) involve various emerging technologies and applications. This paper presents a comprehensive review of recent advances on data/information fusion and context-awareness referring to ITS. Data/Information fusion is necessary to fuse the data from different sensors and thereby extract relevant information on the target sources. On the other hand, context-aware information processing provides awareness of the driving environments by deploying intelligent query processing and smart information dissemination. The fusion and context-awareness should help in improving ITS operations with better road-awareness service, traffic monitoring, vehicle detection as well as development of new methods. This paper is centered on data fusion and context aware methodologies developed recently in the areas of ITS rather than on their ITS applications. We found that the recent progresses in ITS fusion are devoted to the potential cooperative approaches providing real-time/dynamic vehicle sensing technologies, whereas the recent context awareness techniques are deploying service concepts (e.g. location aware service) and frameworks. It is believed that the newly developed advanced fusion/context-aware techniques are becoming more effective to tackle complex traffic scenarios (e.g. traffic intersection) as well as complex urban environments.

Log-frequency spectrogram for respiratory sound monitoring

Computerized patient monitoring provides valuable information on clinical disorders in medical practice, and it triggers the need to simplify the extent of resources required to describe large set of complex biomedical signals. In this paper, we present a new signal quantification method based on block-wise similarity measurement between the neighboring regions in the optimized log-frequency spectrogram of audio signals. Low dimensional cepstral feature set for signal quantification is then formed from the reconstructed similarity matrix using 2D principal component analysis. The effectiveness of the method is verified with real respiratory sound (RS) signals for the purpose of abnormal RS detection towards RS monitoring. Unlike conventional pathological RS detection methods which extract features from well-segmented inspiratory/expiratory phase segments, the proposed scheme is able to perform fast detection of various types of abnormality for unsegmented signals.

VANET IR-CAS: utilizing IR techniques in developing context aware system for VANET

The proposed VANET IR-CAS is a context aware system that utilizes information retrieval (IR) techniques, such as indexing, document scoring and document similarity, to enhance context aware information dissemination in VANET. It uses a hybrid context model; spatial model for service filtering, ontology model for context reasoning and knowledge sharing, markup model for file exchange, and situational model for safety and convenience services. Its VANET OWL ontology managed by Jena semantic web framework succeeded in formalizing the semantics of VANET context domain and heightened the system abstraction level. Relevance of dispatched information to prospective recipients is enhanced by employing IR techniques and partial relevance. For commercial services, we used the hybrid vehicular communication (HVC) to increase the decentralized processing, exploit the vehicle processing power and increase user satisfaction and privacy. V2V is used for safety and convenience services where the level of abstraction has increased by using high level situation context attributes. In addition, more precise application notifications are now feasible after improving reasoning about situation certainty and severity. Hence, the main novelty of VANET IR-CAS is that it provides a highly abstract hybrid context model with IR based processing that raises the notification relevance, certainty and precision beside increasing decentralization and user satisfaction.

A multiscale mean shift localization approach for robust extraction of heart sounds in respiratory signals

This paper addresses the problem of heart sound (HS) extraction in different types of single-channel respiratory sound (RS) signals by proposing a multiscale mean shift localization approach. First, the incoming respiratory signal (RS) are identified into linear/nonlinear portions by using third-order cumulant. Second, the identified linear and nonlinear portions are processed separately to tackle the large variations in the signal characteristics of adventitious sounds. The time-varying mean-shifts of the weighted log likelihood ratios of wavelet features are then calculated to capture the signal dynamics of various noisy RS signals. The proposed approach provides promising results giving an overall false localization rate as low as (1.8 ± 1.8)% for normal lung sound (LS) and (0.1 ± 1.7)% for adventitious sound signals. Therefore, the presented approach successfully attempts to solve the key clinical challenges faced by the existing localization methods in terms of respiratory ailments.

Acoustic analysis of big ocean data to monitor fish sounds

Abstract This paper presents a novel framework for monitoring fish sounds based on acoustic analysis of noisy big ocean data. The proposed method involves multiresolution acoustic features (MRAF) extraction and RPCA (robust principal component analysis) based feature selection for monitoring of natural fish sounds produced in situ by the plainfin midshipman (Porichthys notatus); here, we investigate this fishs grunts, growls and groans. Both local and contextual information are exploited by MRAF, while sparse components of the MRAF matrix obtained through RPCA is found to be more robust to overlapping low-frequency spectral contents among different classes. The simulation results obtained from real-recorded ocean data reveal the advantages of the proposed scheme for monitoring underwater soundscapes and determining a variety of fish sounds in natural marine habitats.