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We present detection of various fdters using neural networks usable for our Long wave infrared (LWIR) hyperspectral detection system (HDES). Some reduction techniques are shown, for our aim of the small neural network with small computing requirements. In addition, the filter measurement is usable for calibration and verification of the HDES properties.
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...
In this paper, we present a model for rainfall rate prediction 30 seconds ahead of time using an artificial neural network. The resultant predicted rainfall rate can then be used in determining an appropriate fade counter-measure, for instance, digital modulation scheme ahead of time, to keep the bit error rate (BER) on the link within acceptable levels to allow constant flow of data on the link during...
Ultrasonic NDE uses high frequency acoustic waves to evaluate materials, and often signal processing is required to detect echoes from defects in the presence of microstructure scattering noise. Scattering noise, also known as clutter, interferes with the flaw signal and cannot be completely eliminated by using classical signal processing methods such as band-pass filtering. In this paper, neural...
At the present time, nanomaterials are used in the medicine and biology applications such as drug and gene delivery, bio-detection of pathogens, MRI contrast enhancement, tumor destruction via heating, and protein detection. Tissue engineering which is another of these applications is being increasingly popular. Because extracellular matrix (ECM) consists nano-sized structure; the usage of nanomaterials...
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...
Ultrasonic Non-Destructive Evaluation (NDE) uses high frequency acoustic waves to evaluate materials, and often signal processing is required to detect echoes from defects in the presence of micro-structure scattering noise. Scattering noise is known as the clutter. The clutter interferes with the flaw signal and cannot be completely separated from it by using conventional signal processing methods...
Randomized feed-forward artificial neural networks (ANNs) have been employed in various domains. This paper was written in order to assess the efficiency of the basic forms of randomized feed-forward ANNs, which are randomized weight artificial neural network, random vector functional link network, extreme learning machine, and radial bases function neural network. In order to compare these methods,...
This paper presents a decision support system for classification of hotel guests in the terms of additional spending. The research is conducted on three stars medium-sized hotel. Guests are classified on arrival, during check-in, in one of the two groups: low spending group or high spending group. A low spending group consists of visitors that are anticipated to spend less than 25 Euros per day for...
The prosthetic knees have been improved and developed to support the amputee to be able to walk as normal people and help them on a daily basis. This research is concerned with a swing phase of a semi-active prosthetic knees utilizing magnetorheological (MR) damper. Although the referred work which use a neural network predictive control (NNPC) has a satisfying results with low error, it has a possibility...
Forecasting the returns of stock markets is gaining importance nowadays in finance. For this aim, in the last decade, Artificial Neural Networks (ANN) have been widely used to forecast stock market movements. In Baltic countries, artificial neural networks are not commonly used in predicting financial failures. This study aims using artificial neural networks to predict OMX Baltic Benchmark GI (OMXBBGI)...
The article deals with the features of creation of tools for monitoring and neuronet identification of complex gasair mixtures using devices such as “electronic nose” equipped with semiconductor gas sensitive sensors in the form of a matrix are considered. The results of experimental studies on the analysis and recognition of various gas mixtures based on the use of artificial neural networks in the...
In order to train neural networks (NN) for text-to-speech synthesis (TTS), phonetic segmentation must be performed. The most accurate segmentation is performed manually, but the process of creating manual alignments is costly and time-consuming, so automatic procedures are preferable. In this paper, a simple alignment method based on models trained during hidden Markov Model (HMM) based TTS system...
In this work, an artificial neural network is designed for predicting the heating and cooling loads for buildings. The paper develops two neural models that make use of a dataset with 8 input attributes with the output as a numeric value of the heating and cooling loads of the buildings. The predictive abilities of the neural nets are compared with linear regression under conventional validation,...
Feature selection is addressed an important problem in data mining. To be high dimension of the data obtained from the sources is encountered as an issue in many issues such as computation cost. For this reason, eliminating the unnecessary ones among these data and choosing the appropriate ones makes it possible to evaluate the information correctly. In this study, it is tried to suggest a method...
Automatic segmentation of the left ventricle (LV) can become a useful tool in echocardiography, for instance to provide automatic ejection fraction measurements or to initialize deformation imaging algorithms. Deep neural networks have recently shown very promising results for improving image classification and segmentation. These methods learn using only a set of input and output data, but require...
In this paper, a new hybrid model is proposed to control the DC/DC converter of Grid Connected Photovoltaic System. The approach used to build the developed controller is divided into three steps, the first step is to generate a data based on the fuzzy logic controller (FLC), the second step is to choose an artificial neural network (ANN) structure to model the FLC and the last step is the training...
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...
A spatial analysis of magnitude distribution is presented in this paper to identify the optimal number of clusters based on seismic data of all region in Indonesia. The data were obtained from Indonesian Agency for Meteorological, Climatological and Geophysics (BMKG) and United States Geological Survey's (USGS). Clustering process consist of two steps: finding the global optimum number of clusters...
The paper presents an approach to localize human body joints in 3D coordinates based on a single low resolution depth image. First a framework to generate a database of 80k realistic depth images from a 3D body model is described. Then data preprocessing and normalization procedure, and DNN and MLP artificial neural networks architectures and training are presented. The robustness against camera distance...
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