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Image-based automatic classification of vector mosquitoes has been investigated for decades for its practical applications such as early detection of potential mosquitoes-borne diseases. However, the classification accuracy of previous approaches has never been close to human experts' and often images of mosquitoes with certain postures and body parts, such as flatbed wings, are required to achieve good classification performance. Deep convolutional neural networks (DCNNs) are state-of-the-art approach to extracting visual features and classifying objects, and, hence, there exists great interest in applying DCNNs for the classification of vector mosquitoes from easy-to-acquire images. In this study, we investigated the capability of state-of-the-art deep learning models in classifying mosquito species having high inter-species similarity and intra-species variations. Since no off-the-shelf dataset was available capturing the variability of typical field-captured mosquitoes, we constructed a dataset with about 3,600 images of 8 mosquito species with various postures and deformation conditions. To further address data scarcity problems, we investigated the feasibility of transferring general features learned from generic dataset to the mosquito classification. Our result demonstrated that more than 97% classification accuracy can be achieved by fine-tuning general features if proper data augmentation techniques are applied together. Further, we analyzed how this high classification accuracy can be achieved by visualizing discriminative regions used by deep learning models. Our results showed that deep learning models exploit morphological features similar to those used by human experts. Mosquitoes cause global infectious disease burden, as vectors of numerous fatal diseases like malaria and dengue thrive by climate change and insecticide resistance. For example, in 2017, an estimated 219 million cases of malaria occurred and an estimated 435,000 deaths from malaria globally 1. For this reason, there have been significant efforts to develop a surveillance system for early detection and diagnosis of potential outbreaks of mosquito-borne diseases. Although, mosquito monitoring programs have been intensively developed worldwide, current mosquito monitoring procedures have many limitations. In particular, it takes at least a few days to detect potential pathogens of mosquito-borne diseases. Furthermore, one of the major bottlenecks in mosquito monitoring is that even classification by human experts of collected mosquitoes is a laborious and time-consuming procedure. Traditionally, trained researchers or technicians classify the species of mosquitoes by visual examination of morphological keys that provide step-by-step instructions on taxonomic characteristics of a given mosquito species 2. However, the number of taxonomists and classification experts has drastically decreased so far 3. Therefore, alternative automatic identification methods with expert-level classification accuracy are highly required in this field. Aut...

3G Long Term Evolution, which aims for various mobile multimedia services provision by enhanced wireless performance, proposes the VoIP-based voice service through the PS domain. When delay and loss-sensitive VoIP traffic flows through the PS domain, more challenging technical difficulties are expected than in the existing 3G systems which provide the CS domain based voice service. Moreover, since 3G LTE, which adopts the OFDM as its physical layer, introduces Physical Resource Block (PRB) as the unit for the transmission resources, it becomes necessary to develop new types of resource management schemes. This paper proposes a MAC layer PRB scheduling algorithm for the efficient VoIP service in 3G LTE and shows the simulation results regarding its performance. The key idea of the algorithm consists of two parts; dynamic activation of a VoIP priority mode for the voice QoS satisfaction and adaptive adjustment of the VoIP priority mode duration in order to minimize the performance degradation induced by its priority mode application.

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