D. Venugopal¹, V. Mohan², S. Ramesh³, S. Janupriya⁴, Sangsoon Lim^5,*, Seifedine Kadry⁶

Computer Systems Science and Engineering, Vol.43, No.2, pp. 545-558, 2022, DOI:10.32604/csse.2022.024239

Abstract In recent times, pattern recognition of communication modulation signals has gained significant attention in several application areas such as military, civilian field, etc. It becomes essential to design a safe and robust feature extraction (FE) approach to efficiently identify the various signal modulation types in a complex platform. Several works have derived new techniques to extract the feature parameters namely instant features, fractal features, and so on. In addition, machine learning (ML) and deep learning (DL) approaches can be commonly employed for modulation signal classification. In this view, this paper designs pattern recognition of communication signal modulation using fractal features… More >

B. Chinthamani^1,*, N. S. Bhuvaneswari², R. Senthil Kumar³, N. R. Shanker⁴

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 515-530, 2022, DOI:10.32604/iasc.2022.025410

Abstract Motor Torque (MT) measurement plays a vital role for evaluating the performance of squirrel cage induction motor during operating conditions. Accurate and continuous measurements of MT provide information regarding driving load capacity, performance degradation of motor, reduces downtime and increases the efficiency. Traditional inline torque sensors-based measurement becomes inaccurate during abrupt change in load during starting condition of motor due to torque spikes. Mounting of torque sensor on motor is a major problem during torque measurement. Improper mounting of sensor acquires signals from other inefficient driveline components such as gearbox, couplings, and bearing. In this paper, we propose a non-contact… More >

Mahdi Souzanchi-K¹, Mohammad-R Akbarzadeh-T^1,*, Nadia Naghavi¹, Ali Sharifnezhad², Vahab Khoshdel³

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 279-294, 2022, DOI:10.32604/iasc.2022.023717

Abstract Sliding mode control is often used for systems with parametric uncertainties due to its desirable robustness and stability, but this approach carries undesirable chattering. Similarly, joint elasticity is a common phenomenon induced by transmission systems in robots, but it presents additional complexity in robot dynamics that could lead to robot vibrations or even instability. Coupling these two phenomena presents further compounded challenges, particularly when faced with the human interface's added uncertainties. Here, a stable voltage-based adaptive fuzzy strategy to sliding mode control is proposed for an elastic joint robot arm that uses a human's upper limb electromyogram (EMG) signals to… More >

Manar Ahmed Hamza^1,*, Siwar Ben Haj Hassine², Souad Larabi-Marie-Sainte³, Mohamed K. Nour⁴, Fahd N. Al-Wesabi^5,6, Abdelwahed Motwakel¹, Anwer Mustafa Hilal¹, Mesfer Al Duhayyim⁷

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3055-3071, 2022, DOI:10.32604/cmc.2022.024490

Abstract Modulation signal classification in communication systems can be considered a pattern recognition problem. Earlier works have focused on several feature extraction approaches such as fractal feature, signal constellation reconstruction, etc. The recent advent of deep learning (DL) models makes it possible to proficiently classify the modulation signals. In this view, this study designs a chaotic oppositional satin bowerbird optimization (COSBO) with bidirectional long term memory (BiLSTM) model for modulation signal classification in communication systems. The proposed COSBO-BiLSTM technique aims to classify the different kinds of digitally modulated signals. In addition, the fractal feature extraction process takes place by the use… More >

Jae Myung Kim^1,2, Gyu Ho Choi², Min-Gu Kim², Sung Bum Pan^1,2,*

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1213-1227, 2022, DOI:10.32604/cmc.2022.025213

Abstract Recently, user recognition methods to authenticate personal identity has attracted significant attention especially with increased availability of various internet of things (IoT) services through fifth-generation technology (5G) based mobile devices. The EMG signals generated inside the body with unique individual characteristics are being studied as a part of next-generation user recognition methods. However, there is a limitation when applying EMG signals to user recognition systems as the same operation needs to be repeated while maintaining a constant strength of muscle over time. Hence, it is necessary to conduct research on multidimensional feature transformation that includes changes in frequency features over… More >

Ahmed S. Almasoud¹, Taiseer Abdalla Elfadil Eisa², Fahd N. Al-Wesabi^3,4, Abubakar Elsafi⁵, Mesfer Al Duhayyim⁶, Ishfaq Yaseen⁷, Manar Ahmed Hamza^7,*, Abdelwahed Motwakel⁷

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 871-886, 2022, DOI:10.32604/cmc.2022.024596

Abstract Early detection of Parkinson's Disease (PD) using the PD patients’ voice changes would avoid the intervention before the identification of physical symptoms. Various machine learning algorithms were developed to detect PD detection. Nevertheless, these ML methods are lack in generalization and reduced classification performance due to subject overlap. To overcome these issues, this proposed work apply graph long short term memory (GLSTM) model to classify the dynamic features of the PD patient speech signal. The proposed classification model has been further improved by implementing the recurrent neural network (RNN) in batch normalization layer of GLSTM and optimized with adaptive moment… More >

Ayman Altameem¹, Jaideep Singh Sachdev², Vijander Singh², Ramesh Chandra Poonia³, Sandeep Kumar⁴, Abdul Khader Jilani Saudagar^5,*

Computer Systems Science and Engineering, Vol.42, No.3, pp. 1095-1107, 2022, DOI:10.32604/csse.2022.023256

Abstract Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals; these signals can be recorded, processed and classified into different hand movements, which can be used to control other IoT devices. Classification of hand movements will be one step closer to applying these algorithms in real-life situations using EEG headsets. This paper uses different feature extraction techniques and sophisticated machine learning algorithms to classify hand movements from EEG brain signals to control prosthetic hands for amputated persons. To achieve good classification accuracy, denoising and feature extraction of EEG signals is a significant step. We… More >

S. Karthiga^*, A. M. Abirami

Computer Systems Science and Engineering, Vol.42, No.3, pp. 851-866, 2022, DOI:10.32604/csse.2022.021935

Abstract Much attention has been given to the Internet of Things (IoT) by citizens, industries, governments, and universities for applications like smart buildings, environmental monitoring, health care and so on. With IoT, network connectivity is facilitated between smart devices from anyplace and anytime. IoT-based health monitoring systems are gaining popularity and acceptance for continuous monitoring and detect health abnormalities from the data collected. Electrocardiographic (ECG) signals are widely used for heart diseases detection. A novel method has been proposed in this work for ECG monitoring using IoT techniques. In this work, a two-stage approach is employed. In the first stage, a… More >

Marwa M. Eid^1,*, Fawaz Alassery², Abdelhameed Ibrahim³, Mohamed Saber⁴

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4627-4641, 2022, DOI:10.32604/cmc.2022.024043

Abstract Digital signal processing of electroencephalography (EEG) data is now widely utilized in various applications, including motor imagery classification, seizure detection and prediction, emotion classification, mental task classification, drug impact identification and sleep state classification. With the increasing number of recorded EEG channels, it has become clear that effective channel selection algorithms are required for various applications. Guided Whale Optimization Method (Guided WOA), a suggested feature selection algorithm based on Stochastic Fractal Search (SFS) technique, evaluates the chosen subset of channels. This may be used to select the optimum EEG channels for use in Brain-Computer Interfaces (BCIs), the method for identifying… More >

Bei Liu¹, Xian Zhang^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1547-1563, 2022, DOI:10.32604/cmes.2022.018130

Abstract During high-intensity focused ultrasound (HIFU) treatment, the accurate identification of denatured biological tissue is an important practical problem. In this paper, a novel method based on the improved variational mode decomposition (IVMD) and autoregressive (AR) model was proposed, which identified denatured biological tissue according to the characteristics of ultrasonic scattered echo signals during HIFU treatment. Firstly, the IVMD method was proposed to solve the problem that the VMD reconstruction signal still has noise due to the limited number of intrinsic mode functions (IMF). The ultrasonic scattered echo signals were reconstructed by the IVMD to achieve denoising. Then, the AR model… More >