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This paper describes the use of convolutional neural network(CNN) method to classify various image and photo of Indonesia ancient temple. The method itself implements Deep Learning technique designed for Computer Vision task. The idea behind CNN is image pre-processing through a stack of convolution layers to create many patterns that can be easily recognized. The result shows that the learning model...
In a computer vision system, handwritten digits recognition is a complex task that is central to a variety of emerging applications. It has been widely used by machine learning and computer vision researchers for implementing practical applications like computerized bank check numbers reading. In this study, we implemented a multi-layer fully connected neural network with one hidden layer for handwritten...
Road traffic accident is a serious threat to human life and safety of living environment. In this paper, a new road traffic accident prediction model (TAP-CNN) is established by using traffic accident influencing factors, such as traffic flow, weather, light to build a state matrix to describe the traffic state and CNN model. This paper uses samples to test the accuracy of the new model. The experimental...
In this work, we investigate the robustness of 1-transistor-1-resistor (1T1R) synaptic array to implement a low-precision neural network. The experimental results on 1 kb HfOx-based RRAM array show a large on/off ratio (i.e. > 105×) and 5 stable resistance states can be reliably achieved with 10× window between adjacent two states. As the RRAM has the resistance drift over time under read voltage...
The paper presents a deep analysis of the literature on the problems of optimization of parameters and the structure of the neural networks and the basic disadvantages that are present in the observed algorithms and methods. As a result, there are suggested a new algorithm for neural network structure optimization, which is devoided of the major shortcomings of other algorithms. The paper includes...
The aim of this work is the detection of solar photovoltaic panels in low-quality satellite photos. It is important to receive the geospatial data (such as country, zip code, street and home number) of installed solar panels, because they are connected directly to the local power. It will be helpful to estimate a power capacity and an energy production using the satellite photos. For this purpose,...
Communication is changing to a wireless world. Any wireless communication system needs radio frontends (transceivers) to link higher layer signals to the air interface. The increasing standard and technology diversity require transceivers with high flexibility supporting many of frequencies, standards and signal requirements. Design to cost, the demand for highest energy efficiency and MIMO systems...
Different from training common neural networks (NNs) for inference on general-purpose processors, the development of NNs for neuromorphic chips is usually faced with a number of hardware-specific restrictions. This paper proposes a systematic methodology to address the challenge. It can transform an existing trained, unrestricted NN (usually for software execution substrate) into an equivalent network...
A human action recognition method is introduced that detects a set of actions in videos by a temporal expansion with hidden Markov models of a pose detection with an artificial neural network. The method was set-up and tested using eleven actions from the MOCAP motion capture database comprising 3,947 frames. A poses alphabet of fourteen relevant poses was defined to be learned by an artificial neural...
In this paper, we propose a Wavelet Neural Network (WNN) classifier for breast cancer. WNN is a new kind of artificial neural network which is coming more popular these days. This method is based on the Wavelet Transform (WT) and classical neural networks. This paper explains how WNN classifies and uses formulas. The results of the experiments made to obtain the best results and the parameters affecting...
This paper provides a voice transformation model that uses pitch data and Feed-forward Neural Networks on Line Spectral Frequency. The aim of this work is to achieve the transformation of a speech signal produced by a source speaker by modifying voice individuality parameters such that it appears to be spoken by a chosen target speaker, without modifying the message contents. Most of the previous...
To overcome the unsatisfying trend prediction results of network public opinion in the present research, this paper put forward a method of Levenberg-Marquardt-based Back-Propagation (LM-BP) neural network algorithm to predict the network public opinion trend. Taking the microblog as the research object, the effectiveness and reliability of the method are proved with some real data in this article...
Deep learning has gained considerable attention in the scientific community, breaking benchmark records in many fields such as speech and visual recognition [1]. Motivated by extending advancement of deep learning approaches to brain imaging classification, we propose a framework, called “deep neural network (DNN)+ layer-wise relevance propagation (LRP)”, to distinguish schizophrenia patients (SZ)...
Software Defined Networking (SDN) is a new promising networking concept which has a centralized control over the network and separates the data and control planes. This new approach provides abstraction of lower-level functionality and allows the network administrators to initialize, control, change, and manage network behavior programmatically. The centralized control, being the major advantage of...
Ransomware is one type of malware that covertly installs and executes a cryptovirology attack on a victims computer to demand a ransom payment for restoration of the infected resources. This kind of malware has been growing largely in recent days and causes tens of millions of dollars losses to consumers. In this paper, we evaluate shallow and deep networks for the detection and classification of...
Network traffic prediction aims at predicting the subsequent network traffic by using the previous network traffic data. This can serve as a proactive approach for network management and planning tasks. The family of recurrent neural network (RNN) approaches is known for time series data modeling which aims to predict the future time series based on the past information with long time lags of unrevealed...
This paper proposes a low-cost video-based Real-Time Pupil-Tracking embedded system which will allow people with reduced mobility to control a wheelchair through their eyes. The main aspect of the method is its capacity to be implemented in a portable computing system, reduced both in computing power and in RAM memory. The Pupil-Tracking system is based on Feedforward Neural Networks-using offline...
This paper proposes an optimized pedestrian and vehicle detection method based on deep learning technique. We optimize the convolutional neural network architecture by three mainly methods. The first one is the choice of the learning policy. The second one is to simplify the convolutional neural network architecture. The last one is careful choice of training samples. With limited loss of accuracy,...
Spiking Neural Networks offer low precision communication, robustness, and low power consumption and are attractive for autonomous applications. One of the well accepted learning rules for these networks is spike time dependent plasticity which is governed by the pre- and postsynaptic spike timings. To stabilize the plasticity and avoid saturation in these learning rules, synaptic normalization is...
In this paper, an extraction and classification of steady state-visual evoked potentials using the IIR Chebyshev I of 4 order and the adaptive feed-forward Neural Networks algorithm, respectively are applied. The classification results of the extracted signals is directly used to make a user able of controlling the directions (stop, forward, right, and left with stimuli frequencies of 7.5, 10, 15,...
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