scholarly journals An Automatic Modulation Classification Network for IoT Terminal Spectrum Monitoring under Zero-sample Situations

2021 ◽  
Author(s):  
Quan Zhou ◽  
Ronghui Zhang ◽  
Fangpei Zhang ◽  
Xiaojun Jing
Keyword(s):  
Cooperative Communication ◽  
Feature Vector ◽  
Recognition Accuracy ◽  
Original Data ◽  
Training Data ◽  
Modulation Classification ◽  
Spectrum Monitoring ◽  
Automatic Modulation Classification ◽  
Feature Vectors ◽  
Wireless Signal

Abstract Rely on powerful computing resources, a large number of internet of things (IoT) sensors are placed in various locations to sense the environment around where we live and improve the service. The proliferation of IoT end devices has led to the misuse of spectrum resources, making spectrum regulation an important task. Automatic modulation classification (AMC) is a task in spectrum monitoring, which senses the electromagnetic space and is carried out under non-cooperative communication. However, DL-based methods are data-driven and require large amounts of training data. In fact, under some non-cooperative communication scenarios, it is challenging to collect the wireless signal data directly. How can the DL-based algorithm complete the inference task under zero-sample conditions? In this paper, a signal zero-shot learning network (SigZSLNet) is proposed for AMC under the zero-sample situations firstly. Specifically, for the complexity of the original signal data, SigZSLNet generates the convolutional layer output feature vector instead of directly generating the original data of the signal. The semantic descriptions and the corresponding semantic vectors are designed to generate the feature vectors of the modulated signals. The generated feature vectors act as the training data of zero-sample classes, and the recognition accuracy of AMC is greatly improved in zero-sample cases as a consequence. The experimental results demonstrate the effectiveness of the proposed SigZSLNet. Simultaneously, we show the generated feature vectors and the intermediate layer output of the model.

scholarly journals Walsh Transform based Feature vector generation for Image Database Classification

2016 ◽  
Vol 6 (3) ◽  
pp. 1176
Author(s):  
Jagruti Ketan Save
Keyword(s):  
Nearest Neighbor ◽  
Feature Vector ◽  
Similarity Measures ◽  
Image Database ◽  
Training Data ◽  
Walsh Transform ◽  
Test Image ◽  
Feature Vectors ◽  
Vector Generation ◽  
Automated Classifier

Thousands of images are generated everyday, which implies the need to build an easy, faster, automated classifier to classify and organize these images. Classification means selecting an appropriate class for a given image from a set of pre-defined classes. The main objective of this work is to explore feature vector generation using Walsh transform for classification. In the first method, we applied Walsh transform on the columns of an image to generate feature vectors. In second method, Walsh wavelet matrix is used for feature vector generation. In third method we proposed to apply vector quantization (VQ) on feature vectors generated by earlier methods. It gives better accuracy, fast computation and less storage space as compared with the earlier methods. Nearest neighbor and nearest mean classification algorithms are used to classify input test image. Image database used for the experimentation contains 2000 images. All these methods generate large number of outputs for single test image by considering four similarity measures, six sizes of feature vector, two ways of classification, four VQ techniques, three sizes of codebook, and five combinations of wavelet transform matrix generation. We observed improvement in accuracy from 63.22% to 74% (55% training data) through the series of techniques.

Circular Construction of Iris Feature Vectors for Human Iris Recognition Systems

2013 ◽  
Vol 330 ◽  
pp. 991-995
Author(s):  
Li Na ◽  
Danu Widatama ◽  
Tony Mulia ◽  
Benyamin Kusumoputro
Keyword(s):  
Conventional Method ◽  
Euclidean Distance ◽  
Iris Recognition ◽  
Feature Vector ◽  
Recognition Accuracy ◽  
Distance Method ◽  
Pixel Intensity ◽  
Feature Vectors ◽  
Human Iris ◽  
Recognition Systems

In this paper, the authors propose a new method to construct feature vectors of human iris based on circular technique. Since an iris has a circular shape, the circular construction of the iris feature vector is expected to have higher ability to capture iris characteristics. Different from the conventional method which constructs iris feature vectors through left to right scanning process, the circular technique scans the pixel-intensity value of iris circularly. As the result, the length of the constructed feature vectors is dependent on the length of iris radius. In the experiments, various recognition methods of feature vectors were compared. The iris recognition results show that the proposed system with Statistical Euclidean Distance method gives the highest recognition accuracy with 84.5%.

scholarly journals On Supervised Classification of Feature Vectors with Independent and Non-Identically Distributed Elements

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1045
Author(s):  
Farzad Shahrivari ◽  
Nikola Zlatanov
Keyword(s):  
Error Probability ◽  
Supervised Classification ◽  
Feature Vector ◽  
Training Data ◽  
Finite Alphabet ◽  
Feature Vectors ◽  
Optimal Classifier ◽  
Mutually Independent ◽  
Distributed Elements

In this paper, we investigate the problem of classifying feature vectors with mutually independent but non-identically distributed elements that take values from a finite alphabet set. First, we show the importance of this problem. Next, we propose a classifier and derive an analytical upper bound on its error probability. We show that the error probability moves to zero as the length of the feature vectors grows, even when there is only one training feature vector per label available. Thereby, we show that for this important problem at least one asymptotically optimal classifier exists. Finally, we provide numerical examples where we show that the performance of the proposed classifier outperforms conventional classification algorithms when the number of training data is small and the length of the feature vectors is sufficiently high.

Walsh Transform based Feature vector generation for Image Database Classification

2016 ◽  
Vol 6 (3) ◽  
pp. 1176
Author(s):  
Jagruti Ketan Save
Keyword(s):  
Nearest Neighbor ◽  
Feature Vector ◽  
Similarity Measures ◽  
Image Database ◽  
Training Data ◽  
Walsh Transform ◽  
Test Image ◽  
Feature Vectors ◽  
Vector Generation ◽  
Automated Classifier

Thousands of images are generated everyday, which implies the need to build an easy, faster, automated classifier to classify and organize these images. Classification means selecting an appropriate class for a given image from a set of pre-defined classes. The main objective of this work is to explore feature vector generation using Walsh transform for classification. In the first method, we applied Walsh transform on the columns of an image to generate feature vectors. In second method, Walsh wavelet matrix is used for feature vector generation. In third method we proposed to apply vector quantization (VQ) on feature vectors generated by earlier methods. It gives better accuracy, fast computation and less storage space as compared with the earlier methods. Nearest neighbor and nearest mean classification algorithms are used to classify input test image. Image database used for the experimentation contains 2000 images. All these methods generate large number of outputs for single test image by considering four similarity measures, six sizes of feature vector, two ways of classification, four VQ techniques, three sizes of codebook, and five combinations of wavelet transform matrix generation. We observed improvement in accuracy from 63.22% to 74% (55% training data) through the series of techniques.

scholarly journals Automatic Modulation Classification Based on Deep Feature Fusion for High Noise Level and Large Dynamic Input

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2117
Author(s):  
Hui Han ◽  
Zhiyuan Ren ◽  
Lin Li ◽  
Zhigang Zhu
Keyword(s):  
Neural Network ◽  
Frequency Domain ◽  
Noise Level ◽  
Background Noise ◽  
Dynamic Range ◽  
Feature Fusion ◽  
Superior Performance ◽  
Modulation Classification ◽  
High Background ◽  
Automatic Modulation Classification

Automatic modulation classification (AMC) is playing an increasingly important role in spectrum monitoring and cognitive radio. As communication and electronic technologies develop, the electromagnetic environment becomes increasingly complex. The high background noise level and large dynamic input have become the key problems for AMC. This paper proposes a feature fusion scheme based on deep learning, which attempts to fuse features from different domains of the input signal to obtain a more stable and efficient representation of the signal modulation types. We consider the complementarity among features that can be used to suppress the influence of the background noise interference and large dynamic range of the received (intercepted) signals. Specifically, the time-series signals are transformed into the frequency domain by Fast Fourier transform (FFT) and Welch power spectrum analysis, followed by the convolutional neural network (CNN) and stacked auto-encoder (SAE), respectively, for detailed and stable frequency-domain feature representations. Considering the complementary information in the time domain, the instantaneous amplitude (phase) statistics and higher-order cumulants (HOC) are extracted as the statistical features for fusion. Based on the fused features, a probabilistic neural network (PNN) is designed for automatic modulation classification. The simulation results demonstrate the superior performance of the proposed method. It is worth noting that the classification accuracy can reach 99.8% in the case when signal-to-noise ratio (SNR) is 0 dB.

scholarly journals The Performance of Post-Fall Detection Using the Cross-Dataset: Feature Vectors, Classifiers and Processing Conditions

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4638
Author(s):  
Bummo Koo ◽  
Jongman Kim ◽  
Yejin Nam ◽  
Youngho Kim
Keyword(s):  
Time Series ◽  
Fall Detection ◽  
Discrete Data ◽  
Classification Performance ◽  
Training Data ◽  
Processing Conditions ◽  
Feature Vectors ◽  
External Data ◽  
Public Dataset ◽  
The Cross

In this study, algorithms to detect post-falls were evaluated using the cross-dataset according to feature vectors (time-series and discrete data), classifiers (ANN and SVM), and four different processing conditions (normalization, equalization, increase in the number of training data, and additional training with external data). Three-axis acceleration and angular velocity data were obtained from 30 healthy male subjects by attaching an IMU to the middle of the left and right anterior superior iliac spines (ASIS). Internal and external tests were performed using our lab dataset and SisFall public dataset, respectively. The results showed that ANN and SVM were suitable for the time-series and discrete data, respectively. The classification performance generally decreased, and thus, specific feature vectors from the raw data were necessary when untrained motions were tested using a public dataset. Normalization made SVM and ANN more and less effective, respectively. Equalization increased the sensitivity, even though it did not improve the overall performance. The increase in the number of training data also improved the classification performance. Machine learning was vulnerable to untrained motions, and data of various movements were needed for the training.

A Novel Automatic Modulation Classification Method Using Attention Mechanism and Hybrid Parallel Neural Network

2021 ◽  
Vol 11 (3) ◽  
pp. 1327
Author(s):  
Rui Zhang ◽  
Zhendong Yin ◽  
Zhilu Wu ◽  
Siyang Zhou
Keyword(s):  
Neural Network ◽  
Communication Systems ◽  
Parallel Structure ◽  
Modulation Classification ◽  
Automatic Modulation Classification ◽  
Spatial Features ◽  
Temporal Features ◽  
Different Types ◽  
In Series ◽  
Parallel Network

Automatic Modulation Classification (AMC) is of paramount importance in wireless communication systems. Existing methods usually adopt a single category of neural network or stack different categories of networks in series, and rarely extract different types of features simultaneously in a proper way. When it comes to the output layer, softmax function is applied for classification to expand the inter-class distance. In this paper, we propose a hybrid parallel network for the AMC problem. Our proposed method designs a hybrid parallel structure which utilizes Convolution Neural Network (CNN) and Gate Rate Unit (GRU) to extract spatial features and temporal features respectively. Instead of superposing these two categories of features directly, three different attention mechanisms are applied to assign weights for different types of features. Finally, a cosine similarity metric named Additive Margin softmax function, which can expand the inter-class distance and compress the intra-class distance simultaneously, is adopted for output. Simulation results demonstrate that the proposed method can achieve remarkable performance on an open access dataset.

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