Design of a Web Based Underwater Acoustic Communication Testbed and Simulation Platform

Wireless Personal Communications - Underwater Wireless Sensor Networks (UWSNs) are playing a vital role in exploring the unseen underwater (UW) natural resources. However, performance evaluation of...     

Ontology-Centered Knowledge Management Using Information Retrieval Techniques

The paper argues that an effective solution to information and knowledge management (KM) needs of practitioners in the construction industry can be found in the provision of an adapted knowledge environment that makes use of user profiling and document summarization techniques based on information retrieval sciences. The conceptualization of the domain through ontology takes a pivotal role in the proposed knowledge environment and provides a semantic referential to ensure relevance, accuracy, and completeness of information. A set of KM services articulated around the selected ontology have been developed, using the Web services model, tested, and validated in real organizational settings. This provided the basis for formulating recommendations and key success factors for any KM project development.     

Joint routing and location-based service in VANETs

Geographic routing protocols use location information when they need to route packets. In the meantime, location information are maintained by location-based services provided by network nodes in a distributed manner. Routing and location services are very related but are used separately. Therefore, the overhead of the location-based service is not considered when we evaluate the geographic routing overhead. Our aim is to combine routing protocols with location-based services in order to reduce communication establishment latency and routing overhead.     

Multimedia framework to support eHealth applications

Limited bandwidth resources lead to a number of challenges especially for eHealth applications, which are communicated over IP and wireless networks. These multimedia services include high-resolution videos and have very large file sizes that require a high level of compression to overcome this limitation. Therefore, there is an acute demand for the research community to provide an efficient multimedia framework to encode medical videos with high quality specifically under the conditions of an error-prone environment. Both an affordable delivery framework and effective coding techniques are extremely desirable for the delivery of high-quality eHealth video applications for transmission over heterogeneous networks and devices. In this paper, we propose and demonstrate a multimedia framework to support eHealth applications, which has an improved coding scheme that uses an SVC-scalable extension of MPEC-4 AVC/H.264. Simulation results show that the proposed scheme achieves a significant improvement in terms of the PSNR-Y gain and reduces the picture quality degradation caused by artifacts and distortions, compared to the existing scheme.     

Performance tuning of glass fiber/epoxy composites through interfacial modification upon integrating with dendrimer functionalized graphene oxide

In this study, glass fiber/epoxy composites were interfacially tailored by introducing polyamidoamine (PAM) dendrimer functionalized graphene oxide (GO) into epoxy matrix. Two different composites each containing varying loading fraction (0.5, 1.0, and 1.5 wt%) of GO and GO-PAM were fabricated via hot press processing. Composites were evaluated for interlaminar shear strength (ILSS), dynamic mechanical properties and thermal conductivity. The inclusion of 1.5 wt% GO-PAM resulted ~57.3%, ~42.7%, and ~54% enhancement in ILSS, storage modulus and thermal conductivity, respectively. Almost, ~71% reduction in coefficient of thermal expansion was also observed at same GO-PAM loading. Moreover, higher glass transition temperature was observed with GO-PAM addition. GO-PAM substantially improved fiber/matrix interfacial adhesion, which was witnessed through scanning electron microscopy. The enhanced thermo-mechanical performance was attributed to interfacial covalent interactions engendered by ring opening reaction between epoxy and amine moieties of PAM dendrimers. These multiscale composites with extraordinary functional properties can outperform conventional counterparts with improved reliability and performance.     

Target point‐based path‐following controller for a car‐type vehicle using bounded controls

In this paper, we have studied the control problem of target point‐based path following for car‐type vehicles. This special path‐following task arises from the needs of vision‐based guidance systems, where a given target point located ahead of the vehicle, in the visual range of the camera, must follow a specified path. A solution to this problem is developed through a nonlinear transformation of the path‐following problem into a reference trajectory tracking problem, by modeling the target point as a virtual vehicle. The use of target point complicates the control problem, as the development produces a first‐order nonlinear nonglobally Lipschitz differential equation with finite escape time. This problem is solved by using small control signals. Bounded feedback laws are designed to control the real vehicle's angular acceleration and the virtual vehicle's velocity, to achieve stability. The resulting controller is globally asymptotically stable with respect to the origin, the proof of which is derived from Lyapunov‐based arguments and a bootstrap argument. It is also shown that the use of exponentially convergent observers/differentiators does not affect the stability of the closed‐loop system. The effectiveness of this controller has been illustrated through simulations. Copyright © 2014 John Wiley & Sons, Ltd.     

Ant-based service selection framework for a smart home monitoring environment

Selecting ambient media services in a smart home monitoring environment is challenging. Services in such an environment should be ubiquitous, adaptive, and robust with respect to access and delivery. Many different techniques exist for selecting services in smart environments, for example, dynamic programming, genetic algorithms, and fuzzy logic. However, existing approaches to service selection fail to address the dynamic nature of the services and the requirement of considering the user context and user satisfaction. We address this issue by proposing an ant-inspired service selection framework based on dynamic user preferences and satisfaction. This ant-inspired approach is robust to failures and adaptive to dynamic context. The proposed framework enables different categories of residents (e.g., elderly people, fathers with children, mothers, and so on) to access various media services in such a way that their experiences are optimized with regard to their surrounding environment. Experimental results demonstrate the viability of the proposed framework.     

Extended Kalman filter based speeds estimation of series-connected five-phase two-motor drive system

The multi-phase machines enables independent control of a few number of machines that are connected in series in a particular manner, and the supply is fed from a single voltage source inverter (VSI). The idea was first implemented for a five-phase series-connected two-motor drive system, but is now applicable to any number of phases. The number of series-connected machines is a function of the phase number of VSI. Variable speed induction motor drives without mechanical speed sensors at the motor shaft have the attractions of low cost and high reliability. To replace the sensor, information of the rotor speed is extracted from measured stator currents and voltages at motor terminals. Open-loop estimators or closed-loop observers are used for this purpose. They differ with respect to accuracy, robustness, and sensitivity against model parameter variations. This paper analyses operation of an EKF-based sensorless control of vector controlled series-connected two-motor five-phase drive system with current control in the stationary reference frame. Results, obtained with fixed voltage and fixed frequency supply fed and hysteresis current control, is presented for various operating conditions on the basis of simulation. The purpose of this paper is to report first time, the simulation results on a sensorless control of a five-phase two-motor series-connected drive system using EKF estimator.     

End-to-End 2D Convolutional Neural Network Architecture for Lung Nodule Identification and Abnormal Detection in Cloud

The extent of the peril associated with cancer can be perceived from the lack of treatment, ineffective early diagnosis techniques, and most importantly its fatality rate. Globally, cancer is the second leading cause of death and among over a hundred types of cancer; lung cancer is the second most common type of cancer as well as the leading cause of cancer-related deaths. Anyhow, an accurate lung cancer diagnosis in a timely manner can elevate the likelihood of survival by a noticeable margin and medical imaging is a prevalent manner of cancer diagnosis since it is easily accessible to people around the globe. Nonetheless, this is not eminently efficacious considering human inspection of medical images can yield a high false positive rate. Ineffective and inefficient diagnosis is a crucial reason for such a high mortality rate for this malady. However, the conspicuous advancements in deep learning and artificial intelligence have stimulated the development of exceedingly precise diagnosis systems. The development and performance of these systems rely prominently on the data that is used to train these systems. A standard problem witnessed in publicly available medical image datasets is the severe imbalance of data between different classes. This grave imbalance of data can make a deep learning model biased towards the dominant class and unable to generalize. This study aims to present an end-to-end convolutional neural network that can accurately differentiate lung nodules from non-nodules and reduce the false positive rate to a bare minimum. To tackle the problem of data imbalance, we oversampled the data by transforming available images in the minority class. The average false positive rate in the proposed method is a mere 1.5 percent. However, the average false negative rate is 31.76 percent. The proposed neural network has 68.66 percent sensitivity and 98.42 percent specificity.