scholarly journals Spatial Modulation for Beyond 5G Communications: Capacity Calculation and Link Adaptation

Proceedings ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 26 ◽  
Author(s):  
Anxo Tato ◽  
Carlos Mosquera
Keyword(s):  
Neural Network ◽  
Spectral Efficiency ◽  
Spatial Modulation ◽  
Modulation Scheme ◽  
Hardware Complexity ◽  
Simple Method ◽  
Communications Systems ◽  
Capacity Calculation ◽  
Good Trade ◽  
The One

Spatial Modulation (SM) is a candidate modulation scheme for beyond 5G communications systems due to its reduced hardware complexity and good trade-off between energy and spectral efficiency. This paper proposes two Machine Learning based solutions for easing the implementation of adaptive SM systems. On the one hand, a shallow neural network is shown to be an accurate and simple method for obtaining the capacity of SM. On the other hand, a deep neural network is proposed to select the coding rate in practical adaptive SM systems.

scholarly journals Application of Deep Learning in Fault Diagnosis of Rotating Machinery

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 919
Author(s):  
Wanlu Jiang ◽  
Chenyang Wang ◽  
Jiayun Zou ◽  
Shuqing Zhang
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Generative Adversarial Networks ◽  
Extraction Ability ◽  
One Dimensional ◽  
Adversarial Networks ◽  
Diagnosis Model ◽  
The One

The field of mechanical fault diagnosis has entered the era of “big data”. However, existing diagnostic algorithms, relying on artificial feature extraction and expert knowledge are of poor extraction ability and lack self-adaptability in the mass data. In the fault diagnosis of rotating machinery, due to the accidental occurrence of equipment faults, the proportion of fault samples is small, the samples are imbalanced, and available data are scarce, which leads to the low accuracy rate of the intelligent diagnosis model trained to identify the equipment state. To solve the above problems, an end-to-end diagnosis model is first proposed, which is an intelligent fault diagnosis method based on one-dimensional convolutional neural network (1D-CNN). That is to say, the original vibration signal is directly input into the model for identification. After that, through combining the convolutional neural network with the generative adversarial networks, a data expansion method based on the one-dimensional deep convolutional generative adversarial networks (1D-DCGAN) is constructed to generate small sample size fault samples and construct the balanced data set. Meanwhile, in order to solve the problem that the network is difficult to optimize, gradient penalty and Wasserstein distance are introduced. Through the test of bearing database and hydraulic pump, it shows that the one-dimensional convolution operation has strong feature extraction ability for vibration signals. The proposed method is very accurate for fault diagnosis of the two kinds of equipment, and high-quality expansion of the original data can be achieved.

N. Belknap's Four-Valued Logic, Belknap Computer and New Proximity Functions for Comparing Discrete Objects

2021 ◽  
Vol 1 (2 (6)) ◽  
Author(s):  
Valerii Dmitrienko ◽  
Sergey Leonov ◽  
Mykola Mezentsev
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
The Other ◽  
Proximity Function ◽  
Input Information ◽  
Proximity Measures ◽  
The One ◽  
True Values

The idea of ​​Belknap's four-valued logic is that modern computers should function normally not only with the true values ​​of the input information, but also under the conditions of inconsistency and incompleteness of true failures. Belknap's logic introduces four true values: T (true - true), F (false - false), N (none - nobody, nothing, none), B (both - the two, not only the one but also the other).  For ease of work with these true values, the following designations are introduced: (1, 0, n, b). Belknap's logic can be used to obtain estimates of proximity measures for discrete objects, for which the functions Jaccard and Needhem, Russel and Rao, Sokal and Michener, Hamming, etc. are used. In this case, it becomes possible to assess the proximity, recognition and classification of objects in conditions of uncertainty when the true values ​​are taken from the set (1, 0, n, b). Based on the architecture of the Hamming neural network, neural networks have been developed that allow calculating the distances between objects described using true values ​​(1, 0, n, b). Keywords: four-valued Belknap logic, Belknap computer, proximity assessment, recognition and classification, proximity function, neural network.

A Low-Complexity High-Rate Spatial Multiplexing Aided Generalized Spatial Modulation Scheme

2021 ◽  
Author(s):  
Yen-Ming Chen ◽  
Kuo-Chun Lin ◽  
Yao-Hsien Peng ◽  
Aswin Balaji ◽  
Chih-Peng Li
Keyword(s):  
Low Complexity ◽  
Spatial Multiplexing ◽  
Spatial Modulation ◽  
High Rate ◽  
Modulation Scheme

scholarly journals Dual-Branch Pre-Distorted Enhanced ADO-OFDM for Full-Duplex Underwater Optical Wireless Communication System

Photonics ◽  
2021 ◽  
Vol 8 (9) ◽  
pp. 368
Author(s):  
Zixian Wei ◽  
Yibin Li ◽  
Zhaoming Wang ◽  
Junbin Fang ◽  
Hongyan Fu
Keyword(s):  
Wireless Communication ◽  
Spectral Efficiency ◽  
Orthogonal Frequency Division Multiplexing ◽  
Channel Model ◽  
Full Duplex ◽  
Optical Channel ◽  
Optical Wireless Communication ◽  
Optical Wireless ◽  
Hardware Complexity ◽  
Double Gamma Function

In this paper, dual-branch pre-distorted enhanced asymmetrically clipped direct current (DC) biased optical orthogonal frequency division multiplexing (PEADO-OFDM) for underwater optical wireless communication (UOWC) is firstly proposed and simulated. The performances of PEADO-OFDM on the underwater optical channel model (UOCM) are analyzed and further compared with the typical ADO-OFDM. Using the Monte Carlo method for the modeling of UOCM, we adopt a double-gamma function to represent three different water qualities including clear, coastal and harbor waters. The full-duplex architecture enables the removal of Hermitian symmetry (HS) from conventional optical OFDM and can increase the spectral efficiency at the cost of hardware complexity. A new PEADO-OFDM transmitter is also proposed to reduce the complexity of the transmitter. The simulation results exhibit that our proposed dual-branch PEADO-OFDM scheme outperforms the typical ADO-OFDM scheme in spectral efficiency, bit error rate (BER) and stability over the underwater channels of three different water qualities.

scholarly journals Statistical and Artificial Neural Networks Models for Electricity Consumption Forecasting in the Brazilian Industrial Sector

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 588
Author(s):  
Felipe Leite Coelho da Silva ◽  
Kleyton da Costa ◽  
Paulo Canas Rodrigues ◽  
Rodrigo Salas ◽  
Javier Linkolk López-Gonzales
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Statistical Approach ◽  
Electricity Consumption ◽  
Industrial Sector ◽  
Neural Network Approach ◽  
Artificial Neural ◽  
The One ◽  
Mlp Model

Forecasting the industry’s electricity consumption is essential for energy planning in a given country or region. Thus, this study aims to apply time-series forecasting models (statistical approach and artificial neural network approach) to the industrial electricity consumption in the Brazilian system. For the statistical approach, the Holt–Winters, SARIMA, Dynamic Linear Model, and TBATS (Trigonometric Box–Cox transform, ARMA errors, Trend, and Seasonal components) models were considered. For the approach of artificial neural networks, the NNAR (neural network autoregression) and MLP (multilayer perceptron) models were considered. The results indicate that the MLP model was the one that obtained the best forecasting performance for the electricity consumption of the Brazilian industry under analysis.

scholarly journals Convolution Neural Network Based Ecg Classifier

2019 ◽  
Vol 10 (3) ◽  
pp. 1626-1630
Author(s):  
Sharanya S ◽  
Sridhar PA ◽  
Poornakala J ◽  
Muppala Vasishta ◽  
Tharani U
Keyword(s):  
Neural Network ◽  
Noise Removal ◽  
Convolution Neural Network ◽  
P Wave ◽  
Time Interval ◽  
Ecg Signal ◽  
Related Condition ◽  
Simple Method ◽  
Medical Practitioners ◽  
Ecg Signals

Classification of Electrocardiogram (ECG) signals plays a significant role in the identification of the functioning of the heart. This work pertains with the ECG signals, where the classifier is developed for identification of normal or abnormal conditions of the heart. The raw ECG signals are collected from an online database (www.physioNet.org) for classification. The raw ECG signal is pre-processed for noise removal, and the frequency spectrum is analysed to compare raw and denoised ECG signal. Attributes (P, Q, R, S, T time intervals) from denoised ECG signal is analysed and classified using Convolution Neural Network (CNN). The paper reports a classification technique to differentiate ECG signals from the MIT-BIH database (arrhythmia database, arrhythmia p-wave annotations, atrial fibrillation). The CNN analyses the deviation between nominal ranges of attributes (amplitude and time interval) and classifies between the abnormality and normal ECG wave. This work provides a simple method for interpreting ECG related condition for the clinician and helps medical practitioners to make diagnostic decisions.

scholarly journals Proton drip line evaluation by complementing the Finite Range Droplet Model with neural networks

2020 ◽  
Vol 15 ◽  
pp. 258
Author(s):  
S. Athanasopoulos ◽  
E. Mavrommatis ◽  
K. A. Gernoth ◽  
J. W. Clark
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Atomic Mass ◽  
Drip Line ◽  
Finite Range ◽  
Mass Excess ◽  
Droplet Model ◽  
Mass Model ◽  
Proton Drip Line ◽  
The One

We evaluate the location of the proton drip line in the regions 31≤Z≤49 and 73≤Z≤91 based on the one- and two-proton separation energies predicted by our latest Hybrid Mass Model. The latter is constructed by complementing the mass-excess values ΔM predicted by the Finite Range Droplet Model (FRDM) of Moeller et al. with a neural network model trained to predict the differences ΔMexp − ΔMFRDM between these values and the experimental mass-excess values published in the 2003 Atomic Mass Evaluation AME03.

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