A Novel Image Segmentation Algorithm Based on Neutrosophic Filtering and Level Set

Image segmentation is an important step in image processing and analysis, pattern recognition, and machine vision. A few of algorithms based on level set have been proposed for image segmentation in the last twenty years. However, these methods are time consuming, and sometime fail to extract the correct regions especially for noisy images. Recently, neutrosophic set (NS) theory has been applied to image processing for noisy images with indeterminant information. In this paper, a novel image segmentation approach is proposed based on the filter in NS and level set theory

Sayısal modülasyonlu haberleşme işaretlerinden Wigner-Ville zaman-frekans dağılımlarına dayalı öznitelik çıkarımı

Bir haberleşme işaretinin modülasyon türünü otomatik olarak algılayabilen sistemlerin önemi gittikçe artmaktadır. Bu sistemler genellikle askeri ve/veya sivil amaçlı olarak geliştirilmektedirler. Elektronik savaş en önemli askeri uygulama alanı olurken, izge yönetimi, işaret onaylama, girişim tespit etme, engelleme ve son zamanlarda oldukça önemli bir uygulama alanı olan internet radyoları ise sivil amaçlı gerçekleştirilen uygulamalardandır. Literatürde, haberleşme işaretlerinin otomatik algılanması için genellikle analitik zaman ve/veya frekans bölgesi işaret işleme teknikleri kullanılır. Fakat haberleşme işaretleri gürültünün etkisiyle durağan olmayan bir yapıdadırlar ve dolayısıyla geleneksel analitik zaman ve/veya frekans bölgesi işaret işleme teknikleriyle elde edilen özniteliklerle yeterince karakterize edilemezler ve teorik karar yöntemleriyle de yeterince doğru sınıflandırılamazlar. Ye ve Wenbo, sayısal modülasyonlu işaretlerin zaman-frekans dağılımlarına bağlı öznitelikleriyle yapay sinir ağlarını sınıflandırıcısı kullanarak düşük SNR’lerde bile dayanıklı bir sistem önermiştir (Ye ve Wenbo, 2007). Çalışma, Wignet-Ville ve çapraz Margenau-Hill zaman-frekans dağılımını kullanarak ASK-2, ASK-4, FSK-2, FSK-4, PSK-2 ve PSK-4 işaretlerini karakterize edecek öznitelikleri elde etmiştir. Oysaki sadece Wigner-Ville zaman frekans dağılımı kullanılarak da aynı sayısal modülasyon işaretlerini karakterize edecek öznitelikler elde edilebilir. Dolayısıyla, bu çalışmada, sayısal modülasyonlu haberleşme işaretlerinin otomatik sınıflandırılmasına yönelik zaman-frekans dağılımlarına dayalı öznitelik çıkarımı amaçlanmıştır. Sayısal modülasyonlardan ASK-2, ASK-4, FSK-2, FSK-4, PSK-2 ve PSK-4 işaretlerinin Wigner-Ville zaman frekans dağılımlarının zamana göre marjinalleri, yine Wigner-Ville dağılımlarının frekansa göre birinci momentleri öznitelik çıkarımında kullanılmıştır. Bilgisayar benzetimleriyle Wigner-Ville zaman-frekans dağılımına dayalı özniteliklerin sayısal modülasyonlu haberleşme işaretlerini yeterince karakterize ettiği görülmüştür. Anahtar Kelimeler: Sayısal modülasyonlar, zaman-frekans dağılımları, öznitelik çıkarımı, Wigner-Ville dağılımı.Signal identification/classification is a process that is based on feature extraction from the signal of interest. Interest in communication signal classification algorithms has increased with the emergence of new communication technologies. Automatic classification of the digital modulation type of an intercepted signal is a rapidly evolving field. Its applications include both military and civilian purposes such as electronic warfare, spectrum management, and signal confirmation and interference identification. Several methods have been proposed helpfully explored the classification of digitally modulated signals in literacy. Most of them use the analytic signal representation to calculate the time domain or frequency domain features that are classified by the decision theoretic classifier. When traveling in a wireless channel, communication signals are corrupted by noise, and are generally no stationary and time-varying. The time domain features or frequency domain features extracted intuitively can not describe the non stationarity of communication signals, and the threshold values of a decision theoretic classifier cannot be chosen adaptively. Considering that real communication signals corrupted by noise are generally non stationary, and time frequency distributions are especially suitable for the analysis of non stationary signals, time-frequency distributions are introduced for the modulation classification of communication signals. Thus, Ye and Wenbo proposed Wigner-Ville and cross Margenau-Hill distribution based features for efficient characterization of the MASK, MPSK and MFSK signals where M is limited to 2 and 4. (Ye and Wenbo, 2007). Moreover, they employed a multilayer perceptron (MLP) classifier as the robust classifier. They concluded that the MLP classifier with time-frequency features improves the probability of correct classification in a noisy environment. In this work, we demonstrated that only Wigner-Ville time-frequency distribution based features can be used to characterize the MASK, MFSK and MPSK digital modulations. As we aforementioned that Ye and Wembo (2007) used both Wigner-Ville and cross Margenau-Hill distribution based features for characterization of the MASK, MPSK and MFSK signals. Here, we limited our works to feature extraction. As we know that features are key in pattern classification and features carry distinctive information about the digital modulation types and allow the classifier to work with smaller datasets.  Thus, a feature extraction mechanism is carried out based on Wigner-Ville time-frequency distribution and no classification schema is indented in this study. We proposed three key features for identification of the MASK, MPSK and MFSK signals. The first key feature is the marginals of Wigner-Ville time frequency distribution with respect to time. The marjinals of Wigner-Ville time frequency distribution for MFSK and MPSK signals are constants, whereas, those of MASK signals are multistep functions, after removing the peaks by median filtering. This feature can be used to discriminate between MASK signals and MPSK and MFSK. The second key feature is the first moment of the Wigner-Ville time frequency distribution, with respect to frequency. The first moments of Wigner-Ville distribution for MASK and MPSK signals are constants, whereas, those of MFSK signals are multistep functions, after removing the peaks by median filtering. Thus, this feature can be used to discriminate between MFSK signals and MPSK and MASK. The third key feature is used to discriminate between MPSK signals and MASK and MFSK. The instantaneous frequency of the Wigner-Ville distribution of MPSK signals has peaks at phase changes. If median filtering is not applied to the instantaneous frequency of the Wigner-Ville distribution of the MPSK signals, these peaks, which occur in the phase chances, provide useful information for MPSK signals. The computer simulations of the proposed Wigner-Ville time frequency distribution based feature extraction mechanism are carried out by using MATLAB. The digital modulations were simulated according to the following parameters; Sample numbers of message signal 1000 samples Sampling frequency, 25 KHz Carrier frequency, 10 KHz and Baud rate 300 baud. The message signal is generated by using random integer up to M = 8 level. Then, this message signal is re-sampled at baud rate (300 baud) for pulse shaping before passing through respective modulators. The robustness of the proposed feature extraction schema is tested with 15 dB SNR rate.  Based on the computer simulations, it is seen that the features based on the Wigner-Ville time frequency distributions are well enough to characterize the digital modulations. In conclusions, we intended to extract features for characterization of digital modulation signals. We did not utilize any classification process for performance evaluation. Performance evaluation of different classifiers will be considered in our future works.   Keywords: Digital modulations, time-frequency distributions, feature extraction, Wigner-Ville distribution

A Novel Image Segmentation Algorithm Based on Neutrosophic Filtering and Level Set

Image segmentation is an important step in image processing and analysis, pattern recognition, and machine vision. A few of algorithms based on level set have been proposed for image segmentation in the last twenty years. However, these methods are time consuming, and sometime fail to extract the correct regions especially for noisy images. Recently, neutrosophic set (NS) theory has been applied to image processing for noisy images with indeterminant information. In this paper, a novel image segmentation approach is proposed based on the filter in NS and level set theory. At first, the image is transformed into NS domain, which is described by three membership sets (T, I and F). Then, a filter is newly defined and employed to reduce the indeterminacy of the image. Finally, a level set algorithm is used in the image after filtering operation for image segmentation. Experiments have been conducted using different images. The results demonstrate that the proposed method can segment the images effectively and accurately. It is especially able to remove the noise effect and extract the correct regions on both the noise-free images and the images with different levels of noise

Investigation of complex modulus of base and EVA modified bitumen with Adaptive-Network-Based Fuzzy Inference System

This study aims to model the complex modulus of base and ethylene-vinyl-acetate (EVA) modified bitumen by using Adaptive-Network-Based Fuzzy Inference System (ANFIS). The complex modulus of base and EVA polymer modified bitumen (PMB) samples were determined using dynamic shear rheometer (DSR). PMB samples have been produced by mixing a 50/70 penetration grade base bitumen with EVA copolymer at five different polymer contents. In ANFIS modeling, the bitumen temperature, frequency and EVA content are the parameters for the input layer and the complex modulus is the parameter for the output layer. The hybrid learning algorithm related to the ANFIS has been used in this study. The variants of the algorithm used in the study are two input membership functions and three input membership functions for each of the all inputs. The input membership functions are triangular, gbell, gauss2, and gauss. The results showed that EVA polymer modified bitumens display reduced temperature susceptibility than base bitumens. In the light of analysis the Adaptive-Network-Based Fuzzy Inference System and statistical methods can be used for modeling the complex modulus of bitumen under varying temperature and frequency. The analysis indicated that the training accuracy is improved by decreasing the number of input membership functions and the utilization of the two gauss input membership functions appeared to be most optimal topology. Besides, it is realized that the predicted complex modulus is closely related with the measured (actual) complex modulus

Investigation of complex modulus of base and SBS modified bitumen with artificial neural networks

This study aims to model the complex modulus of base and styrene–butadiene–styrene (SBS) modified bitumens by using artificial neural networks (ANNs). The complex modulus of base and SBS polymer modified bitumen samples (PMB) were determined by using dynamic shear rheometer (DSRs). PMB samples have been produced by mixing a 50/70 penetration grade base bitumen with SBS Kraton D1101 copolymer at five different polymer contents. In ANN model, the bitumen temperature, frequency and SBS contents are the parameters for the input layer where as the complex modulus is the parameter for the output layer. The variants of the algorithm used in the study are the Levenberg–Marquardt (LM), scaled conjugate gradient (SCG) and Pola-Ribiere conjugate gradient (CGP) algorithms. A tangent sigmoid transfer function was used for both hidden layer and the output layer. The statistical indicators, such as the root-mean squared (RMS), the coefficient of multiple determination (R2) and the coefficient of variation (cov) was utilized to compare the predicted and measured values for model validation. The analysis indicated that the LM algorithm appeared to be the most optimal topology which gained 0.0039 mean RMS value, 20.24 mean cov value and 0.9970 mean R2 value

Neural network modeling of SBS modified bitumen produced with different methods

Various types of polymers are added to bitumen in order to improve its properties under low and high temperatures. It is important to determine accurately the complex modulus of polymer-modified bitumen samples (PMBs) in order to make a suitable mix design. Moreover the determination of the complex modulus is important in order to evaluate the efficiency of the additives. However the manufacture processes of PMBs involve many factors. This study aims to model the complex modulus of styrene–butadiene–styrene (SBS) modified bitumen samples that were produced by different methods using artificial neural networks (ANNs). PMB samples were produced by mixing a 160/220 penetration grade base bitumen with 4% SBS Kraton D1101 copolymer at 18 different combinations of three mixing temperatures, three mixing times and two mixing rates. The complex modulus of PMBs was determined at five different test temperatures and at ten different frequencies. Therefore a total of 900 combinations were evaluated. Various different results were obtained for the same PMB produced at different conditions. In the ANN model, the mixing temperature, rate and time as well as the test temperature and frequency were the parameters for the input layer whereas the complex modulus was the parameter for the output layer. The most suitable algorithm and the number of neurons in the hidden layer were determined as Levenberg–Marguardt with 3 neurons. It was concluded that, ANNs could be used as an accurate method for the prediction of the complex modulus of PMBs, which were produced using different methods

COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network

The reliable and rapid identification of the COVID-19 has become crucial to prevent the rapid spread of the disease, ease lockdown restrictions and reduce pressure on public health infrastructures. Recently, several methods and techniques have been proposed to detect the SARS-CoV-2 virus using different images and data. However, this is the first study that will explore the possibility of using deep convolutional neural network (CNN) models to detect COVID-19 from electrocardiogram (ECG) trace images. In this work, COVID-19 and other cardiovascular diseases (CVDs) were detected using deep-learning techniques. A public dataset of ECG images consisting of 1937 images from five distinct categories, such as normal, COVID-19, myocardial infarction (MI), abnormal heartbeat (AHB), and recovered myocardial infarction (RMI) were used in this study. Six different deep CNN models (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, and MobileNetv2) were used to investigate three different classification schemes: (i) two-class classification (normal vs COVID-19); (ii) three-class classification (normal, COVID-19, and other CVDs), and finally, (iii) five-class classification (normal, COVID-19, MI, AHB, and RMI). For two-class and three-class classification, Densenet201 outperforms other networks with an accuracy of 99.1%, and 97.36%, respectively; while for the five-class classification, InceptionV3 outperforms others with an accuracy of 97.83%. ScoreCAM visualization confirms that the networks are learning from the relevant area of the trace images. Since the proposed method uses ECG trace images which can be captured by smartphones and are readily available facilities in low-resources countries, this study will help in faster computer-aided diagnosis of COVID-19 and other cardiac abnormalities