Tropical cyclone intensity estimation is a challenging task as it required domain knowledge while extracting features, significant pre-processing, various sets of parameters obtained from satellites, and human intervention for analysis. The inconsistency of results, significant pre-processing of data, complexity of the problem domain, and problems on generalizability are some of the issues related to intensity estimation. In this study, we design a deep convolutional neural network architecture for categorizing hurricanes based on intensity using graphics processing unit. Our model has achieved better accuracy and lower root-mean-square error by just using satellite images than 'state-of-the-art' techniques. Visualizations of learned features at various layers and their deconvolutions are also presented for understanding the learning process.

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With the increasing need for different energy saving mechanisms in Wireless Sensor Networks (WSNs), data aggregation techniques for reducing the number of data transmissions by eliminating redundant information have been studied as a significant research problem. These studies have shown that data aggregation in WSNs may produce various trade‐offs among some network related performance metrics such as energy, latency, accuracy, fault‐tolerance and security. In this paper, we investigate the impact of data aggregation on these networking metrics by surveying the existing data aggregation protocols in WSNs. Our aim is twofold: First, providing a comprehensive summary and comparison of the existing data aggregation techniques with respect to different networking metrics. Second, pointing out both the possible future research issues and the need for collaboration between data management and networking research communities working on data aggregation in WSNs. Copyright © 2006 John Wiley & Sons, Ltd.

In this paper, we describe the design and implementation of a stand-alone real-time system for protein crystallization image acquisition and classification with a goal to assist crystallographers in scoring crystallization trials. In-house assembled fluorescence microscopy system is built for image acquisition. The images are classified into three categories as non-crystals, likely leads, and crystals. Image classification consists of two main steps – image feature extraction and application of classification based on multilayer perceptron (MLP) neural networks. Our feature extraction involves applying multiple thresholding techniques, identifying high intensity regions (blobs), and generating intensity and blob features to obtain a 45-dimensional feature vector per image. To reduce the risk of missing crystals, we introduce a max-class ensemble classifier which applies multiple classifiers and chooses the highest score (or class). We performed our experiments on 2250 images consisting 67% non-crystal, 18% likely leads, and 15% clear crystal images and tested our results using 10-fold cross validation. Our results demonstrate that the method is very efficient (< 3 seconds to process and classify an image) and has comparatively high accuracy. Our system only misses 1.2% of the crystals (classified as non-crystals) most likely due to low illumination or out of focus image capture and has an overall accuracy of 88%.

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