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In this paper, we employ probabilistic neural network (PNN) with image and data processing techniques to implement a general purpose automated leaf recognition for plant classification. 12 leaf features are extracted and orthogonalized into 5 principal variables which consist the input vector of the PNN. The PNN is trained by 1800 leaves to classify 32(More)
RNA silencing functions as an important antiviral defense mechanism in a broad range of eukaryotes. In plants, biogenesis of several classes of endogenous small interfering RNAs (siRNAs) requires RNA-dependent RNA Polymerase (RDR) activities. Members of the RDR family proteins, including RDR1and RDR6, have also been implicated in antiviral defense, although(More)
As a newly-proposed weighing-based clustering algorithm, WCA has improved performance compared with other previous clustering algorithms. But the high mobility of nodes will lead to high frequency of re-affiliation which will increase the network overhead. To solve this problem, we propose an entropy- based WCA (EWCA) which can enhance the stability of the(More)
Predicting the helpfulness of product reviews is a key component of many e-commerce tasks such as review ranking and recommendation. However, previous work mixed review helpfulness prediction with those outer layer tasks. Using non-text features, it leads to less transferable models. This paper solves the problem from a new angle by hypothesizing that(More)
Epilepsy is one of the most common neurological disorders that greatly impair patients' daily lives. Traditional epileptic diagnosis relies on tedious visual screening by neurologists from lengthy EEG recording that requires the presence of seizure (ictal) activities. Nowadays, there are many systems helping the neurologists to quickly find interesting(More)
Computer-aided diagnosis of neural diseases from EEG signals (or other physiological signals that can be treated as time series, e.g., MEG) is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of EEG signals. In our previous works, we have implemented many EEG feature extraction functions(More)
Over 50 million people worldwide suffer from epilepsy. Traditional diagnosis of epilepsy relies on tedious visual screening by highly trained clinicians from lengthy EEG recording that contains the presence of seizure (ictal) activities. Nowadays, there are many automatic systems that can recognize seizure-related EEG signals to help the diagnosis. However,(More)
Autonomous operation is a key challenge to the deployment of mobile robots in real-world domains such as homes and offices. The partial observ-ability, non-determinism and unforeseen dynamic changes of these domains frequently make it difficult for robots to operate without any domain knowledge or human inputs. It is however infeasible to provide robots(More)
Mobile robots equipped with multiple sensors and deployed in real-world domains frequently find it difficult to process all sensor inputs, or to operate without any human input and domain knowledge. At the same time, robots cannot be equipped with all relevant domain knowledge in advance, and humans are unlikely to have the time and expertise to provide(More)