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Neural networks (NNs) have been widely used in microwave device modeling. One of the greatest challenges is how to speed up the model training process and reduce the development cost. To address the issue, this paper exploits FPGAs to accelerate NN training. Experimental results demonstrate that the model training time can be reduced by up to 99.1%, compared to the traditional software implementation.
We consider long-haul sensor networks where sensors are remotely deployed over a large geographical area to perform certain tasks, such as tracking and/or monitoring of one or more dynamic targets. A remote fusion center fuses the information provided by these sensors to improve the accuracy of the final estimates of certain target characteristics. In this work, we pursue artificial neural network...
As one of the most popular recommender system technologies, neighborhood-based collaborative filtering algorithm has obtained great favor due to its simplicity, justifiability, and stability. However, when faced with large-scale, sparse, or noise affected data, nearest-neighbor collaborative filtering performs not so well, as the calculation of similarity between user or item pairs is costly and the...
In this paper, we used max-min distance method to determine Kohonen Network' initial weights, as well as introduced energy function as the convergence condition of network, and as such improved Kohonen network' unsupervised-learning algorithm. We then used the improved Kohonen' learning algorithm combined with penalty formula to carry out supervised classification of remote sensing data. Experiments...
With the rapid pace of the computer and information development, software projects are becoming increasingly complicated. Teamwork and soft skills are especially important in software engineering and computer programming, where projects are general too strenuous for individuals to accomplish and effective teamwork is vital for efficient completion of the projects. However, these soft skills, such...
This paper proposes a semantic-block-based hidden Markov model. Semantic block is segmented from the elicited information of various websites based on their characteristic of semi-structure. The model adopts semantic block as the basic element in an observation sequence, replacing the original element — word, in order to improve the accuracy and efficiency of the transition matrix. Also, it optimizes...
Objective: Discussion based on neural networks in the 31P MR spectroscopy to distinguish hepatocellular carcinoma, normal liver and cirrhosis in value. Methods: Using self-organizing map neural network (SOM) analyse 66 data of 31P MRS, including hepatocellular carcinoma (13 samples), normal liver (16 samples) and liver cirrhosis (37 samples). Results: 31P MRS can be used for the diagnosis and differential...
Through the evaluation of the 31Phosphorus Magnetic Resonance Spectroscopy (31P-MRS), we can distinguish three types of diagnosis: hepatocellular carcinoma, normal and cirrhosis. 71 samples of 31P-MRS data are selected including hepatocellular carcinoma, normal and cirrhosis tissue. Back-propagation neural network (BP) and Radial Basis Function Neural Network (RBF) are applied to analyze 31P-MRS data,...
Aiming at the deficiency of the current meridian diagnosis algorithms, SVM is applied to meridian diagnosis system. The system structure is described firstly, then the model selection of SVM is discussed in detail by taking chronic pharyngitis as an example: one-against-one method is used to realize multi-class; the problem of non-symmetrical samples of C-SVM is solved by giving positive and negative...
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