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This paper presents a new type of coarse-grained reconfigurable architecture (CGRA) for the object inference domain in machine learning. The proposed CGRA is optimized for stream processing and a correspondent programming model called dual-track model is proposed. The CGRA is realized in Verilog HDL and implemented in SMIC 55 nm process, with the footprint of 3.79 mm2 and consuming 1.79 W at 500 MHz...
Software logging is an essential programming practice that saves important runtime information that can be used later by software developers for troubleshooting, debugging and monitoring the software. Even though software logging has numerous benefits this practice is underutilized because of lack of any formal guiding principles to developers for making strategic and efficient logging decisions....
In this paper, the brief survey of data mining classification by using the machine learning techniques is presented. The machine learning techniques like decision tree and support vector machine play the important role in all the applications of artificial intelligence. Decision tree works efficiently with discrete data and SVM is capable of building the nonlinear boundaries among the classes. Both...
Through multiple levels of abstraction, deep learning takes advantage of multiple layers models to find the complicated structure and learn the high level representations of data. In recent years, deep learning has made great progress in object detection, speech recognition, and many other domains. The robustness of learning systems with deep architectures is however rarely studied and needs further...
Feature extraction and classification are two important components in object recognition. While the traditional methods design these components individually, the deep neural networks jointly learn these two parts. In this paper, we propose a method of the convolutional neural network combined with Gabor filters for strengthening the learning of texture information. We called this model as Gabor-CNN...
Detection of stealthy attacks on alternating current (AC) static state estimation through false data injection is considered in this paper. To detect the presence of such cyber attacks, we follow a statistical outlier detection approach using a recently proposed machine learning technique called density ratio estimation. The proposed method offers an improved detection especially since our technique...
Deep learning techniques have claimed state-of-the-art results in a wide range of tasks, including classification. Despite the promising results, there are limitations for these large networks. In fact, deep neural networks have a poor generalisation performance on small data sets, such as biologic data. This paper describes a new machine learning algorithm for classification tasks. We introduce a...
This paper presents a fast algorithmic method to train convolutional neural network (CNN) classifiers through extreme learning which has been verified on popular datasets on classification and pedestrian detection. CNN has been one of the best classifiers for images and object recognition. However, the Backpropagation (BP) algorithm, mostly used for training CNN, suffers from slow learning, local...
We summarize the history and state of the art in Convolutional Neural Networks (CNNs), which constitute a significant advancement in pattern recognition. As a demonstration of capability, we address the problem of automatic aircraft identification during refueling approach. In this paper we describe the history of CNN development and provide a high level overview of the state of the art and a summary...
In this paper, an asymmetric kernel is proposed for extracting sparse features from two-dimensional visual face images for identity recognition. Essentially, the kernel consists of an inner product of two vectors where one of them has been raised to power terms element-wise. The impact of such a power term is suppression of less influential features where only relevant ones are used for estimation...
Efficient spectrum sensing can be realized by predicting the future idle times of primary users' activity in a cognitive radio network. In dynamic spectrum access, based on a reliable prediction scheme, a secondary user chooses a channel with the longest idle time for data transmission. In this paper, four supervised machine learning techniques, two from ANN, i.e. Multilayer Perceptron & Recurrent...
As a kind of important structured data, tree is widely used in domains like biology, molecular chemical, and text processing etc. However, many traditional machine learning methods cannot directly deal with tree-structured data. Currently, the commonly adopted approach is based on the subtree. It is supposed that the more common structure between two trees the more similarity between them. To effectively...
A novel classifier architecture is introduced and its performances are evaluated against state of the art shallow classifiers. Its main advantage consists in a very fast learning ensured by a novelty detection algorithm, selecting a list of prototypes among the training samples, used as centers in a radial basis functions neurons layer. Only the radius of the basis functions is optimized to improve...
Quantitative trading strategies are designed to look for relationships between data about an underlying security and its future price and then to generate alpha on a trading desk. Recent years have witnessed the increasing attention from both academic and corporate sectors on enhancing quantitative trading by machine learning techniques due to their excellent predictive powers, with a few successful...
Machine learning from brain images is a central tool for image-based diagnosis and diseases characterization. Predicting behavior from functional imaging, brain decoding, analyzes brain activity in terms of the behavior that it implies. While these multivariate techniques are becoming standard brain mapping tools, like mass-univariate analysis, they entail much larger computational costs. In an time...
Quality monitoring and prediction plays a key role in improving product quality and achieving automated quality control in manufacturing processes such as the abrasion-resistant material manufacturing process. Traditional methods that rely on the use of first-principle models are difficult to formulate due to the increasing complexity and high dimensionality of manufacturing processes. Data-driven...
Recent advances in deep convolutional neural networks enable researchers and developers to apply machine learning to a much broader number of applications. With the proliferation of deep learning applications, widely used deep learning frameworks, such as Caffe, Theano and Torch, have been significantly improved with the support of powerful GPUs and GPU-accelerated libraries. However, lack of frameworks...
Classification is one of the core tasks in machine learning data mining. One of several models of classification are classification rules, which use a set of if-then rules to describe a classification model. In this paper we present a set of FPGA-based compute kernels for accelerating classification rule induction. The kernels can be combined to perform specific procedures in rule induction process,...
Billions of photographs are uploaded to the Internet every day through image sharing services, e.g., Flickr, Instagram, etc. The growing size of these social media poses new challenges in social popularity prediction. In this paper, we try to use a multimodal learning approach which uses both tag feature and visual feature for popularity prediction. We compare several multimodal approaches with unimodal...
In this paper a survey on fault diagnosing techniques of electronic circuits are presented which are related mainly to industrial applications. Diagnozing the faults in circuit boards is very essential for achieving better reliability and easy maintainance of electronic systems. The circuit fault finding diagnosis is treated as the pattern recognition case and uses machine learning methodology. Increasing...
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