Congenital nevocellular or nevomelanocytic nevi (CNN) are considered to be hemartomatous lesions of melanocytes apparent at birth(l-3). Though CNN has an incidence of 1% of all newborns, its spectacular variantthe giant or bathing trunk nevus is extremely rare, occuring once in 5,00,000 live births(l). Giant CNN is of devastating consequence due to the cosmetic disfigurement and the great risk ...

To phased microphone array for sound source localization, algorithm with both high computational efficiency and high precision is a persistent pursuit. In this paper convolutional neural network (CNN) a kind of deep learning is preliminarily applied as a new algorithm. At high frequency CNN can reconstruct the sound localizations with excellent spatial resolution as good as DAMAS, within a very...

In this study, a 1-D Convolutional Neural Network (CNN) architecture was developed, trained and utilized to classify single (summer) and three seasons (spring, summer, fall) of hyperspectral imagery over the San Francisco Bay Area, California for the year 2015. For comparison, the Random Forests (RF) and Support Vector Machine (SVM) classifiers were trained and tested with the same data. In ord...

Convolutional Neural Network (CNN) is the state-ofthe-art algorithm of many mobile vision fields. It is also applied in many vision tasks such as face detection and augmented reality on mobile devices. Though benefited from the high accuracy achieved via deep CNN models, nowadays commercial mobile devices are often short in processing capacity and battery to continuously carry out such CNN-driv...

The computational paradigm represented by Cellular Neural/nonlinear Networks (CNN) and the CNN Universal Machine (CNN-UM) as a Cellular Wave Computer, gives new perspectives for computational physics. Many numerical problems and simulations can be elegantly addressed on this fully parallelized and analogic architecture. Here we study the possibility of performing stochastic simulations on this ...

On the other hand, since its definition by Chua&Yang [2], Cellular Neural Networks (CNNs) received a great deal of interest. Although many of these applications were proposed for numerous disciplines like artificial life and image processing [3], some have been adapted for chaos [4-7]. In one of these applications, Arena et al. realized unfolded Chua’s circuit using the connection of three simp...

The purpose of this study is to successfully train our vehicle detector using R-CNN, Faster R-CNN deep learning methods on a sample vehicle data sets and to optimize the success rate of the trained detector by providing efficient results for vehicle detection by testing the trained vehicle detector on the test data. The working method consists of six main stages. These are respectively; loading...

3D Object recognition is an important task in computer vision applications. After the success of convolutional neural networks for object recognition in 2D images, many researchers have designed convolution neural network (CNN) for 3D object recognition. The state of art methods provide favourable results. However, the availability of large/dynamic 3D dataset and computational complexity of CNN...

Convolutional Neural Networks (CNNs) require large image corpora to be trained on classification tasks. The variation in image resolutions, sizes of objects and patterns depicted, and image scales, hampers CNN training and performance, because the task-relevant information varies over spatial scales. Previous work attempting to deal with such scale variations focused on encouraging scale-invari...

Retrieving similar questions in online Q&A community sites is a difficult task because different users may formulate the same question in a variety of ways, using different vocabulary and structure. In this work, we propose a new neural network architecture to perform the task of semantically equivalent question retrieval. The proposed architecture, which we call BOW-CNN, combines a bag-ofwords...