Using a computer model of a realistically shaped human torso with lungs and intraventricular blood masses, we have assessed how torso geometry and composition affect the extracorporal magnetic field produced by a current dipole in the centre of the ventricular mass. The magnetic induction vector B arising from the dipole has been calculated at points of a precordial measuring grid and the influence of boundaries has been assessed qualitatively, by comparing contour maps of the B component normal to the torso's frontal plane. We found that the maps reflected relatively faithfully the underlying dipolar source for the homogeneous torso and even for the torso with lungs. However, the intraventricular blood masses caused a noticeable rotation of the maps' extrema. Both lungs and blood masses tended to swing the distribution towards the distribution that would have been caused by a dipole oriented along the anatomical axis of the heart.
Named Data Networking (NDN) is a model that has been proposed by many researchers to alter the long-established IP based networking model. It derives the content centric approach rather than host-based approach. This is gaining even more traction in the wireless network and is able to replace the conventional IP-based networking. Up to now, NDN has proven to be fruitful when used with certain limitations in vehicular networks. Vehicular networks deal with exchanging information across fast moving complex vehicle network topology. The sending and receiving of information in such a scenario acts as a challenge and thus requires an effective forwarding strategy to address this problem. Different research work has provided with multiple forwarding strategy that solves the current problem up to some limit but further research work is still longed for to get an optimum solution. This paper provides a brief survey on current existing forwarding strategies related to vehicular networks using NDN as well as providing information on various resources and technologies used in it.
Spectrum handoff (SH) in the cognitive radio network (CRN) is considered as a key challenging area to enhance the performance of secondary users (SUs) in CRN. If the primary user is detected, the SU may pause and stay on the same channel or may perform SH to another idle channel. An accurate and precise handoff decision improves the overall throughput and quality of experience of end-users. In this paper, we introduce a new SH algorithm and continuous short-sensing strategy to improve the overall throughput of SUs. In addition, we have derived the minimum length of the target channel sequence based on network-specific parameters like desired call dropping probability. Further, an optimum channel search time is obtained to minimize the handoff delay. The simulation result shows that the proposed scheme improves the overall throughput of CRN, and the mean opinion score of different video applications increases by 10%, 4.6%, and 1% for rapid motion, gentle walk, and slight motion types of video applications. In the case of VoIP applications, the maximum simultaneous call is improved by 2 times in the case of G.711, 1.
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