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This article offers a general approach to developing methods of determining operation tolerances for the parameters' values of memristor-based artificial neural networks (ANNM), as a system that constitutes an united physical and informational object implemented by the hardware and software learning facilities. While looking for a solution to the issues of analysis and synthesis of this system's tolerances,...
In software projects, there is a data repository which contains the bug reports. These bugs are required to carefully analyse and resolve the problem. Handling these bugs humanly is extremely time consuming process, and it can result the deleying in addressing some important bugs resolutions. To overcome this problem, researchers have introduced many techniques. One of the commonly used algorithm...
This paper introduces the design and implementation of a Bluetooth based on indoor location tracking system. This system utilizes the integrated Bluetooth modules in any today's mobile phones to specify and display the location of the individuals in a certain building. The proposed system aim for location tracking/monitoring and marketing applications for whom want to locate individuals carrying mobile...
Post-Classification Comparison(PCC) method is widely used in change detection for remote sensing images, but it is affected by a significant cumulative error caused by single remote sensing image classification during change detection, which leads to the excessive evaluation of changed types and quantity. To solve this problem, this paper proposes a change detection method for remote sensing images...
Deep neural networks (DNNs) have recently proved their effectiveness in complex data analyses such as object/speech recognition. As their applications are being expanded to mobile devices, their energy efficiencies are becoming critical. In this paper, we propose a novel concept called big/LITTLE DNN (BL-DNN) which significantly reduces energy consumption required for DNN execution at a negligible...
In order to improve the accuracy of INS/GPS integrated navigation system during GPS signals blockage, an effective and low-cost method is to design the corresponding linear or non-linear predictor to predict the position and velocity errors between INS and GPS during GPS blockage and then to correct the results of INS. Based on the distributed data fusion system, a novel hybrid prediction method that...
The comparison of two classifiers, the Extreme Learning Machine (ELM) and the Support Vector Machine (SVM) is considered for performance, resources used (neurons or support vector kernels) and computational complexity (speed). Both implementations are of similar type (C++ compiled as Octave .mex files) to have a better evaluation of speed and computational complexity. Our results indicate that ELM...
The paper presents one of the possible modifications of the Projective Adaptive Resonance Theory (PART) clustering algorithm and its application in the processing of text documents. Clustering on the basis of PART can be applied, for example, to generate a dictionary of keywords from a text. The principle of this method is based on clustering of words with the same root word. In order to demonstrate...
The growing interests in multi-way data analysis have made the tensor factorization and classification a crucial issue in machine learning for signal processing. Conventional neural network (NN) classifier is estimated from a set of input vectors. The multi-way data are unfolded as high-dimensional vectors for model training. The classification performance is constrained because the neighboring temporal...
Experienced pipeline operators utilize Magnetic Flux Leakage (MFL) sensors to probe oil and gas pipelines for the purpose of localizing and sizing different defect types. A large number of sensors is usually used to cover the targeted pipelines. The sensors are equally distributed around the circumference of the pipeline, and every three millimeters the sensors measure MFL signals. Thus, the collected...
Artificial neural networks have been investigated for many years as a technique for automated diagnosis of defects causing partial discharge (PD). While good levels of accuracy have been reported, disadvantages include the difficulty of explaining results, and the need to hand-craft appropriate features for standard two-layer networks. Recent advances in the design and training of deep neural networks,...
In this paper an easy-to-use, wearable sensor system capable of deciphering upper-limb based functional tasks associated with stroke rehabilitation is introduced. Such a system can assist the therapist with monitoring a patient's progress during rehabilitation, and hence can increase the efficiency of the rehabilitation process. The developed system provides quantitative, real-time feedback of a user's...
This paper presents an activity detection system using dendrite threshold logic neuron models. This method generates a dendrite weight matrix from the background image and detect the changes in the subsequent images through the trained neuron outputs. Using only one layer of dendrite neuron cells with simplistic threshold logic cells, an accuracy of 98% is reported in realistic imaging conditions...
Enterprise in financial trouble is a comprehensive event and the enterprise financial situation can be reflected through the liquidity ratio, earnings per share and net assets per share and cash content per share. Artificial neural network method is used to establish the financial early warning model to find the potential financial crisis at an early age. The experiment results show that BP neural...
Two advanced modelling approaches, Multi-Level Models and Artificial Neural Networks are employed to model house prices. These approaches and the standard Hedonic Price Model are compared in terms of predictive accuracy, capability to capture location information, and their explanatory power. These models are applied to 2001–2013 house prices in the Greater Bristol area, using secondary data from...
Deep neural networks such as Convolutional Networks (ConvNets) and Deep Belief Networks (DBNs) represent the state-of-the-art for many machine learning and computer vision classification problems. To overcome the large computational cost of deep networks, spiking deep networks have recently been proposed, given the specialized hardware now available for spiking neural networks (SNNs). However, this...
This article presents a methodology for intelligent, biologically inspired fault detection system for generic complex systems of systems. The proposed methodology utilizes the concepts of associative memory and vector symbolic architectures, commonly used for modeling cognitive abilities of human brain. Compared to classical methods of artificial intelligence used in the context of fault detection...
This paper presents algorithm and digital hardware design, inspired by biological spiking neural networks, to perform unsupervised, online spike-clustering with high accuracy and low-power consumption in the context of deep-brain sensing and stimulation systems. The proposed hardware contains 1220 digital neurons and 4.86k latch-based synapses, and achieves the average sorting accuracy of 91% whereas...
ELM works for the “generalized” singlehidden layer feedforward networks (SLFNs) but the hidden layer (or called feature mapping) in ELM needs not be tuned. Extreme Support Vector Machine (ESVM), combining Support Vector Machine (SVM) and Extreme Learning Machine (ELM) kernels, can lead to a better prediction capability. ESVM can usually have a relatively good predictive capability, and its training...
Science learned models based on limited data are usually fragile, researchers suggest the adoption of virtual samples to improve the prediction model. In this study, nonparametric statistical tool, Kolmogorov-Smirnov test, is introduced to examine the distribution of virtual samples without any assumption about the underlying population. The examination procedure would help control the quality of...
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