A web version of POCASA is freely available at http://altair.sci.hokudai.ac.jp/g6/Research/POCASA_e.html.

This work investigates the possibility of automated malaria parasite detection in thick blood smears with smartphones. Methods: We have developed the first deep learning method that can detect malaria parasites in thick blood smear images and can run on smartphones. Our method consists of two processing steps. First, we apply an intensity-based Iterative Global Minimum Screening (IGMS), which performs a fast screening of a thick smear image to find parasite candidates. Then, a customized Convolutional Neural Network (CNN) classifies each candidate as either parasite or background. Together with this paper, we make a dataset of 1819 thick smear images from 150 patients publicly available to the research community. We used this dataset to train and test our deep learning method, as described in this paper. Results: A patient-level five-fold cross-evaluation demonstrates the effectiveness of the customized CNN model in discriminating between positive (parasitic) and negative image patches in terms of the following performance indicators: accuracy (93.46% ± 0.32%), AUC (98.39% ± 0.18%), sensitivity (92.59% ± 1.27%), specificity (94.33% ± 1.25%), precision (94.25% ± 1.13%), and negative predictive value (92.74% ± 1.09%). High correlation coefficients (>0.98) between automatically detected parasites and ground truth, on both image level and patient level, demonstrate the practicality of our method. Conclusion: Promising results

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Abstract-In the power demand side, responsive loads can provide fast regulation and ancillary services as reserve capacities in interconnected power systems. This paper presents a distributed pinning demand side control (DSC) strategy for coordinating multiple load aggregators, i.e., aggregated responsive loads, to provide frequency regulation services. Specifically, a leaderfollowing communication protocol is considered for the load aggregators, in which there is a centralized pinner (leader) and multiple load aggregators (followers). The regulation objective is generated from the pinner and only shared with a small fraction of load aggregators. Moreover, a multi-step algorithm is proposed to determine the control gains in the DSC, which not only guarantees the stability of the close-loop system, but also restrains the plant disturbance. Furthermore, the distributed pinning DSC algorithm is integrated into the traditional centralized PI-based AGC framework, which has formed the coupled secondary frequency control structure. It has been shown that the total power mismatch in each control area is shared with both AGC units and load aggregators and the system frequency can be improved by considering the distributed pinning DSC for load aggregators. Finally, simulation results are provided to demonstrate the effectiveness of the proposed coupled frequency control strategy.