Multicasting with selective delivery: a SafetyNet for vertical handoffs

In the future, mobility support will require handling roaming in heterogeneous access networks. In order to enable seamless roaming it is necessary to minimize the impact of the vertical handoffs. Localized mobility management schemes such as Fast Handovers for Mobile IPv6 (FMIPv6) and Hierarchical Mobile IPv6 do not provide sufficient handoff performance, since they have been designed for horizontal handoffs. In this paper, we propose the SafetyNet protocol, which allows a Mobile Node to perform seamless vertical handoffs. Further, we propose the SafetyNet handoff timing algorithm, to enable a Mobile Node to delay or even completely avoid upward vertical handoffs. We implement the SafetyNet protocol and compare its performance with the FMIPv6 protocol in our wireless test bed and analyze the results. The experimental results indicate that the proposed SafetyNet protocol can provide an improvement of up to 95% for TCP performance in vertical handoffs, when compared with FMIPv6 and an improvement of 64% over FMIPv6 with bicasting. We use numerical analysis of the protocol to show that its over the air signaling and data transmission overhead is comparable to FMIPv6 and significantly smaller than that of FMIPv6 with bicasting.     

Machine vision system for automated spectroscopy

This paper describes a novel system based on the machine vision and machine learning techniques for fully automated, real-time identification of constituent elements in a sample specimen using laser-induced breakdown spectroscopy (LIBS) images. The proposed system is developed as a compact spectrum analyzer for rapid element detection using a commercially available video camera. We proposed a correlation-based pattern matching algorithm for analyzing single element spectra. However, the use of a high-speed laser and presence of numerous imperfections in the experimental setup require advanced techniques for analyzing multi-element spectra. We cast the element detection problem as a multi-label classification problem that uses support vector machines and artificial neural networks for multi-element classification. The proposed algorithms were evaluated using actual LIBS images. The machine learning approaches yielded correct identification of elements to an accuracy of 99%. Our system is useful in instances where a qualitative analysis is sufficient over a quantitative element analysis.     

Octopus: privacy preserving peer-to-peer transactions system with InterPlanetary file system (IPFS)

International Journal of Information Security - Blockchain-based decentralized infrastructure is intended to achieve peer-to-peer transactions without relying on centralized/trusted third parties....