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Classifying HEp-2 fluorescence patterns in Indirect Immunofluorescence (IIF) HEp-2 cell imaging is important for the differential diagnosis of autoimmune diseases. The current technique, based on human visual inspection, is time-consuming, subjective and dependent on the operator's experience. Automating this process may be a solution to these limitations, making IIF faster and more reliable. This work proposes a classification approach based on Subclass Discriminant Analysis (SDA), a dimensionality reduction technique that provides an effective representation of the cells in the feature space, suitably coping with the high within-class variance typical of HEp-2 cell patterns. In order to generate an adequate characterization of the fluorescence patterns, we investigate the individual and combined contributions of several image attributes, showing that the integration of morphological, global and local textural features is the most suited for this purpose. The proposed approach provides an accuracy of the staining pattern classification of about 90%.

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Nowadays, the continuously increasing demand for high data traffic and providing different quality of services (QoS) to the customer are very challenging tasks for all network operators. In the last few years, mobile data traffic is increased to a significant extent and half of the traffic is provided through WiFi technology which is known as WiFi offloading. To overcome the increasing traffic demand, WiFi offloading is the best option to reduce the burden of cellular networks. So, by aggregating existing indoor WiFi technology to the cellular network increases the network capacity and provides better QoS to customers. In this article, we propose and implement the LTE-WiFi aggregation system where eNodeB is responsible for the aggregation of the WiFi access point without modifying the core network. Furthermore, the proposed system is integrated with the mobile-CORD (M-CORD) platform which leverages software defined networking (SDN), network function virtualization (NFV), and cloud technologies for providing a 5G environment. M-CORD platform has three main modules: service orchestrator (XOS), SDN controller ONOS, and OpenStack. One of the important features of M-CORD is to provide virtualized core network functions that enable the users to automatically customize, monitor, and control the resources of the network. Due to ONOS controller support, we can easily scale up the network instances by giving the configurations to service orchestrator XOS of the M-CORD. The implementation of the proposed system is based on the OpenAirInterface (OAI) platform which provides open sources implementation of core and access networks. The aggregation of both LTE and WiFi technologies is done at the PDCP layer in a very tight coupling way. Moreover, we test our proposed system with three kinds of policies for UDP and TCP traffic: LTE only, WiFi only, and LTE-WiFi aggregated. The experimental results show that our proposed LTE-WiFi aggregated system gives better performance and provides high bandwidth as compared to the LTE network.