scholarly journals Automatic Wireless Signal Classification: A Neural-Induced Support Vector Machine-Based Approach

Information ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 338
Author(s):  
Arfan Haider Wahla ◽  
Lan Chen ◽  
Yali Wang ◽  
Rong Chen
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Signal To Noise Ratio ◽  
Radio Spectrum ◽  
Spectrum Management ◽  
Support Vector ◽  
Intermediate Step ◽  
Signal To Noise ◽  
Feature Based ◽  
Noise Ratio

Automatic Classification of Wireless Signals (ACWS), which is an intermediate step between signal detection and demodulation, is investigated in this paper. ACWS plays a crucial role in several military and non-military applications, by identifying interference sources and adversary attacks, to achieve efficient radio spectrum management. The performance of traditional feature-based (FB) classification approaches is limited due to their specific input feature set, which in turn results in poor generalization under unknown conditions. Therefore, in this paper, a novel feature-based classifier Neural-Induced Support Vector Machine (NSVM) is proposed, in which the features are learned automatically from raw input signals using Convolutional Neural Networks (CNN). The output of NSVM is given by a Gaussian Support Vector Machine (SVM), which takes the features learned by CNN as its input. The proposed scheme NSVM is trained as a single architecture, and in this way, it learns to minimize a margin-based loss instead of cross-entropy loss. The proposed scheme NSVM outperforms the traditional softmax-based CNN modulation classifier by managing faster convergence of accuracy and loss curves during training. Furthermore, the robustness of the NSVM classifier is verified by extensive simulation experiments under the presence of several non-ideal real-world channel impairments over a range of signal-to-noise ratio (SNR) values. The performance of NSVM is remarkable in classifying wireless signals, such as at low signal-to-noise ratio (SNR), the overall averaged classification accuracy is > 97% at SNR = −2 dB and at higher SNR it achieves overall classification accuracy at > 99%, when SNR = 10 dB. In addition to that, the analytical comparison with other studies shows the performance of NSVM is superior over a range of settings.

scholarly journals Machine learning applied to wind turbine blades impact detection

2019 ◽  
Vol 44 (3) ◽  
pp. 325-338 ◽  
Author(s):  
Congcong Hu ◽  
Roberto Albertani
Keyword(s):  
Support Vector Machine ◽  
Predictive Model ◽  
Signal To Noise Ratio ◽  
Turbine Blades ◽  
Environmental Benefits ◽  
Support Vector ◽  
Signal To Noise ◽  
Impact Detection ◽  
Species Vulnerability ◽  
Noise Ratio

The significant development of wind power generation worldwide brings, together with environmental benefits, wildlife concerns, especially for volant species vulnerability to interactions with wind energy facilities. For surveying such events, an automatic system for continuous monitoring of blade collisions is critical. An onboard multi-senor system capable of providing real-time collision detection using integrated vibration sensors is developed and successfully tested. However, to detect low signal-to-noise ratio impact can be challenging; hence, an advanced impact detection method has been developed and presented in this article. A robust automated detection algorithm based on support vector machine is proposed. After a preliminary signal pre-processing, geometric features specifically selected for their sensitivity to impact signals are extracted from raw vibration signal and energy distribution graph. The predictive model is formulated by training conventional support vector machine using extracted features for impact identification. Finally, the performance of the predictive model is evaluated by accuracy, precision, and recall. Results indicate a linear regression relationship between signal-to-noise ratio and model overall performance. The proposed method is much reliable on higher signal-to-noise ratio [Formula: see text], but it shows to be ineffective at lower signal-to-noise ratio [Formula: see text].

scholarly journals Near-field sound source localization using principal component analysis–multi-output support vector regression

2020 ◽  
Vol 16 (4) ◽  
pp. 155014772091640
Author(s):  
Lanmei Wang ◽  
Yao Wang ◽  
Guibao Wang ◽  
Jianke Jia
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Regression ◽  
Covariance Matrix ◽  
Signal To Noise Ratio ◽  
Near Field ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Signal To Noise ◽  
Noise Ratio

In this article, principal component analysis method, which is applied to image compression and feature extraction, is introduced into the dimension reduction of input characteristic variable of support vector regression, and a method of joint estimation of near-field angle and range based on principal component analysis dimension reduction is proposed. Signal-to-noise ratio and calculation amount are the decisive factors affecting the performance of the algorithm. Principal component analysis is used to fuse the main characteristics of training data and discard redundant information, the signal-to-noise ratio is improved, and the calculation amount is reduced accordingly. Similarly, support vector regression is used to model the signal, and the upper triangular elements of the signal covariance matrix are usually used as input features. Since the covariance matrix has more upper triangular elements, training it as a feature input will affect the training speed to some extent. Principal component analysis is used to reduce the dimensionality of the upper triangular element of the covariance matrix of the known signal, and it is used as the input feature of the multi-output support vector regression machine to construct the near-field parameter estimation model, and the parameter estimation of unknown signal is herein obtained. Simulation results show that this method has high estimation accuracy and training speed, and has strong adaptability at low signal-to-noise ratio, and the performance is better than that of the back-propagation neural network algorithm and the two-step multiple signal classification algorithm.

scholarly journals Deep Feature Autoextraction Method for Intrapulse Data of Radar Emitter Signal

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Shiqiang Wang ◽  
Caiyun Gao ◽  
Chang Luo ◽  
Huiyong Zeng ◽  
Guimei Zheng ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machine Classifier ◽  
Signal To Noise Ratio ◽  
Support Vector ◽  
Large Signal ◽  
Signal To Noise ◽  
Training Scheme ◽  
Deep Feature ◽  
Novel Method ◽  
Sparse Autoencoder

Concerned with the problems that the extracted features are the absence of objectivity for radar emitter signal intrapulse data because of relying on priori knowledge, a novel method is proposed. First, this method gets the sparse autoencoder by adding certain restrain to the autoencoder. Second, by optimizing the sparse autoencoder and confirming the training scheme, intrapulse deep features are autoextracted with encoder layer parameters. The method extracts the eigenvectors of six typical radar emitter signals and uses them as inputs to a support vector machine classifier. The experimental results show that the method has higher accuracy in the case of large signal-to-noise ratio. The simulation verifies that the extracted features are feasible.

scholarly journals A Two-Level Speaker Identification System via Fusion of Heterogeneous Classifiers and Complementary Feature Cooperation

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5097
Author(s):  
Mohammad Al-Qaderi ◽  
Elfituri Lahamer ◽  
Ahmad Rad
Keyword(s):  
Speaker Identification ◽  
Signal To Noise Ratio ◽  
Support Vector ◽  
Spectral Features ◽  
Prosodic Features ◽  
Signal To Noise ◽  
Short Term ◽  
Classifier System ◽  
Cepstral Coefficients ◽  
Noise Ratio

We present a new architecture to address the challenges of speaker identification that arise in interaction of humans with social robots. Though deep learning systems have led to impressive performance in many speech applications, limited speech data at training stage and short utterances with background noise at test stage present challenges and are still open problems as no optimum solution has been reported to date. The proposed design employs a generative model namely the Gaussian mixture model (GMM) and a discriminative model—support vector machine (SVM) classifiers as well as prosodic features and short-term spectral features to concurrently classify a speaker’s gender and his/her identity. The proposed architecture works in a semi-sequential manner consisting of two stages: the first classifier exploits the prosodic features to determine the speaker’s gender which in turn is used with the short-term spectral features as inputs to the second classifier system in order to identify the speaker. The second classifier system employs two types of short-term spectral features; namely mel-frequency cepstral coefficients (MFCC) and gammatone frequency cepstral coefficients (GFCC) as well as gender information as inputs to two different classifiers (GMM and GMM supervector-based SVM) which in total leads to construction of four classifiers. The outputs from the second stage classifiers; namely GMM-MFCC maximum likelihood classifier (MLC), GMM-GFCC MLC, GMM-MFCC supervector SVM, and GMM-GFCC supervector SVM are fused at score level by the weighted Borda count approach. The weight factors are computed on the fly via Mamdani fuzzy inference system that its inputs are the signal to noise ratio and the length of utterance. Experimental evaluations suggest that the proposed architecture and the fusion framework are promising and can improve the recognition performance of the system in challenging environments where the signal-to-noise ratio is low, and the length of utterance is short; such scenarios often arise in social robot interactions with humans.

scholarly journals Deep Learning-Based Signal-To-Noise Ratio Estimation Using Constellation Diagrams

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Xiaojuan Xie ◽  
Shengliang Peng ◽  
Xi Yang
Keyword(s):  
Deep Learning ◽  
Data Transmission ◽  
Signal To Noise Ratio ◽  
Estimation Algorithm ◽  
Spectrum Management ◽  
Signal To Noise ◽  
Estimation Errors ◽  
Snr Estimation ◽  
Low Snr ◽  
Noise Ratio

Signal-to-noise ratio (SNR) estimation is a fundamental task of spectrum management and data transmission. Existing methods for SNR estimation usually suffer from significant estimation errors when SNR is low. This paper proposes a deep learning (DL) based SNR estimation algorithm using constellation diagrams. Since the constellation diagrams exhibit different patterns at different SNRs, the proposed algorithm achieves SNR estimation via constellation diagram recognition, which can be easily handled based on DL. Three DL networks, AlexNet, InceptionV1, and VGG16, are utilized for DL based SNR estimation. Experimental results show that the proposed algorithm always performs well, especially in low SNR scenarios.

Novel Acoustic Source Localization Method in WSN Based on LSSVR Regression Modeling

2012 ◽  
Vol 468-471 ◽  
pp. 2296-2303
Author(s):  
Xiao Ping Zhang ◽  
Yang Wang
Keyword(s):  
Source Localization ◽  
Feature Vector ◽  
Signal To Noise Ratio ◽  
Parameters Optimization ◽  
Support Vector ◽  
Acoustic Source ◽  
Signal To Noise ◽  
Localization Method ◽  
Acoustic Source Localization ◽  
Noise Ratio

To solve the problem of acoustic source localization in wireless sensor networks (WSN) under interference of environmental noise, a novel acoustic source localization method in WSN based on Least Square Support Vector Regression (LSSVR) modeling (ASL-LRM) was proposed. The ideal measured values of acoustic sensors were used to compose feature vector at first. Then LSSVR models were built by LSSVR modeling on the mapping relation between feature vector and acoustic source coordinate. The acoustic source was then located by inputting feature vector composed of real measured values of the sensors into LSSVR models. The modeling parameters optimization method based on localization effect in sample locations was also discussed. Experiments were performed in 100 test locations. RMSE values by ASL-LRM method in 72-76 test locations were less than MLE method and reduced by 60%-74% at most. In lower signal-to-noise ratio case, there were 87 test locations where RMSE values by ASL-LRM method were less than 2 meters, while there were only 12 test locations by MLE method. It shows ASL-LRM method achieves better localization effects in a large part of the region surrounded by sensor nodes. It especially has advantage on the occasions like lower signal-to-noise ratio or high precision localization.

scholarly journals Influence of Acquisition Parameters on Pigment Classification using Hyperspectral Imaging

2021 ◽  
Author(s):  
Dipendra J. Mandal ◽  
Sony George ◽  
Marius Pedersen ◽  
Clotilde Boust
Keyword(s):  
Hyperspectral Imaging ◽  
Classification Accuracy ◽  
Near Infrared ◽  
Hyperspectral Image ◽  
Signal To Noise Ratio ◽  
Hyperspectral Data ◽  
Integration Time ◽  
Signal To Noise ◽  
Scientific Analysis ◽  
Noise Ratio

Pigment classification of paintings is considered an important task in the field of cultural heritage. It helps to analyze the object and to know its historical value. This information is also essential for curators and conservators. Hyperspectral imaging technology has been used for pigment characterization for many years and has potential in its scientific analysis. Despite its advantages, there are several challenges linked with hyperspectral image acquisition. The quality of such acquired hyperspectral data can be influenced by different parameters such as focus, signal-to-noise ratio, illumination geometry, etc. Among several, we investigated the effect of four key parameters, namely focus distance, signal-to-noise ratio, integration time, and illumination geometry on pigment classification accuracy for a mockup using hyperspectral imaging in visible and near-infrared regions. The results obtained exemplify that the classification accuracy is influenced by the variation in these parameters. Focus distance and illumination angle have a significant effect on the classification accuracy compared to signal-to-noise ratio and integration time.

scholarly journals Improving P300 Speller performance by means of optimization and machine learning

2021 ◽  
Author(s):  
Luigi Bianchi ◽  
Chiara Liti ◽  
Giampaolo Liuzzi ◽  
Veronica Piccialli ◽  
Cecilia Salvatore
Keyword(s):  
Machine Learning ◽  
Signal To Noise Ratio ◽  
Event Related Potentials ◽  
Support Vector ◽  
Early Stopping ◽  
Signal To Noise ◽  
Linear Discriminant ◽  
Related Potentials ◽  
Noise Ratio ◽  
Number Of Iterations

AbstractBrain-Computer Interfaces (BCIs) are systems allowing people to interact with the environment bypassing the natural neuromuscular and hormonal outputs of the peripheral nervous system (PNS). These interfaces record a user’s brain activity and translate it into control commands for external devices, thus providing the PNS with additional artificial outputs. In this framework, the BCIs based on the P300 Event-Related Potentials (ERP), which represent the electrical responses recorded from the brain after specific events or stimuli, have proven to be particularly successful and robust. The presence or the absence of a P300 evoked potential within the EEG features is determined through a classification algorithm. Linear classifiers such as stepwise linear discriminant analysis and support vector machine (SVM) are the most used discriminant algorithms for ERPs’ classification. Due to the low signal-to-noise ratio of the EEG signals, multiple stimulation sequences (a.k.a. iterations) are carried out and then averaged before the signals being classified. However, while augmenting the number of iterations improves the Signal-to-Noise Ratio, it also slows down the process. In the early studies, the number of iterations was fixed (no stopping environment), but recently several early stopping strategies have been proposed in the literature to dynamically interrupt the stimulation sequence when a certain criterion is met in order to enhance the communication rate. In this work, we explore how to improve the classification performances in P300 based BCIs by combining optimization and machine learning. First, we propose a new decision function that aims at improving classification performances in terms of accuracy and Information Transfer Rate both in a no stopping and early stopping environment. Then, we propose a new SVM training problem that aims to facilitate the target-detection process. Our approach proves to be effective on several publicly available datasets.

Determination of the Best Emulsion for the Microscopy of Crystals

1981 ◽  
Vol 39 ◽  
pp. 32-33
Author(s):  
David A. Grano ◽  
Kenneth H. Downing
Keyword(s):  
Spatial Frequency ◽  
Information Transfer ◽  
Signal To Noise Ratio ◽  
Experimental Situation ◽  
Photographic Emulsion ◽  
Modulation Transfer ◽  
Signal To Noise ◽  
Frequency Space ◽  
Noise Ratio

The retrieval of high-resolution information from images of biological crystals depends, in part, on the use of the correct photographic emulsion. We have been investigating the information transfer properties of twelve emulsions with a view toward 1) characterizing the emulsions by a few, measurable quantities, and 2) identifying the “best” emulsion of those we have studied for use in any given experimental situation. Because our interests lie in the examination of crystalline specimens, we've chosen to evaluate an emulsion's signal-to-noise ratio (SNR) as a function of spatial frequency and use this as our critereon for determining the best emulsion.The signal-to-noise ratio in frequency space depends on several factors. First, the signal depends on the speed of the emulsion and its modulation transfer function (MTF). By procedures outlined in, MTF's have been found for all the emulsions tested and can be fit by an analytic expression 1/(1+(S/S0)2). Figure 1 shows the experimental data and fitted curve for an emulsion with a better than average MTF. A single parameter, the spatial frequency at which the transfer falls to 50% (S0), characterizes this curve.

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