The Northbound (NB) APIs that SDN controllers provide differ in terms of architecture, syntax, naming convention, data resources, and usage. Using NB APIs to write SDN applications makes each application dependent on the API of a specific controller. To bring NB APIs from different vendors under one umbrella and make programming of SDN applications independent of specific controllers, we propose a unified software defined development framework that we call Umbrella. This paper presents the key components of the software and reports some preliminary results.

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In this work we present a calibration-free system for locating wireless local area network devices, based on the radio frequency characteristics of such networks. Calibration procedures are applied in a great number of proposed location techniques and are considered to be not practical or a considerable barrier to wider adoption of such methods. Thus, we addressed issues related to some aspects of location systems through, an architecture based on wireless sniffers and by constructing a location model based on signal propagation models, in which its parameters are calculated in real time. This guarantee good self-sufficiency and adaptation capacity to the proposed system, once it does not need human intervention to work, neither from the network administrator or the wireless user being located. Moreover, a probabilistic method was used for estimating wireless devices positions, based on the previous constructed model. We later demonstrate the feasibility of our approach by reporting results of field tests in which the proposed technique was implemented and validated in a real-world indoor environment.

This deep learning pipeline may lend towards diagnostic worklist prioritization, standardization, and generalizability in assessing anterior cruciate ligament lesions, in addition to point-of-care communication with patients by non-experts.