Omar M. El-Habbak¹, Abdelrahman M. Abdelalim¹, Nour H. Mohamed¹, Habiba M. Abd-Elaty¹, Mostafa A. Hammouda¹, Yasmeen Y. Mohamed¹, Mohanad A. Taifor¹, Ali W. Mohamed^2,3,*

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2953-2969, 2022, DOI:10.32604/cmc.2022.020109

Abstract Parkinson’s disease (PD), one of whose symptoms is dysphonia, is a prevalent neurodegenerative disease. The use of outdated diagnosis techniques, which yield inaccurate and unreliable results, continues to represent an obstacle in early-stage detection and diagnosis for clinical professionals in the medical field. To solve this issue, the study proposes using machine learning and deep learning models to analyze processed speech signals of patients’ voice recordings. Datasets of these processed speech signals were obtained and experimented on by random forest and logistic regression classifiers. Results were highly successful, with 90% accuracy produced by the random forest classifier and 81.5% by… More >

Rakesh Kumar¹, Meenu Gupta¹, Shakeel Ahmed^2,*, Abdulaziz Alhumam², Tushar Aggarwal¹

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 693-706, 2022, DOI:10.32604/iasc.2022.019811

Abstract Hearing a species in a tropical rainforest is much easier than seeing them. If someone is in the forest, he might not be able to look around and see every type of bird and frog that are there but they can be heard. A forest ranger might know what to do in these situations and he/she might be an expert in recognizing the different type of insects and dangerous species that are out there in the forest but if a common person travels to a rain forest for an adventure, he might not even know how to recognize these species,… More >

Radek Martinek¹, Rene Jaros¹, Jan Baros¹, Lukas Danys¹, Aleksandra Kawala-Sterniuk², Jan Nedoma^3,*, Zdenek Machacek¹, Jiri Koziorek¹

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1073-1096, 2021, DOI:10.32604/cmc.2021.017568

Abstract This paper discusses the reduction of background noise in an industrial environment to extend human-machine-interaction. In the Industry 4.0 era, the mass development of voice control (speech recognition) in various industrial applications is possible, especially as related to augmented reality (such as hands-free control via voice commands). As Industry 4.0 relies heavily on radiofrequency technologies, some brief insight into this problem is provided, including the Internet of things (IoT) and 5G deployment. This study was carried out in cooperation with the industrial partner Brose CZ spol. s.r.o., where sound recordings were made to produce a dataset. The experimental environment comprised… More >

Yulin Jiang^*

FDMP-Fluid Dynamics & Materials Processing, Vol.17, No.4, pp. 759-772, 2021, DOI:10.32604/fdmp.2021.014470

Abstract When a pipe is partially filled with a given working liquid, the relationship between the electromotive force (EMF) measured by the sensor (flowmeter) and the average velocity is nonlinear and non-monotonic. This relationship varies with the inclination of the pipe, the fluid density, the pipe wall friction coefficient, and other factors. Therefore, existing measurement methods cannot meet the accuracy requirements of many industrial applications. In this study, a new processing method is proposed by which the flow rate can be measured with an ordinary electromagnetic flowmeter even if the pipe is only partially filled. First, a B-spline curve fitting method… More >

T. Jayasree^1,*, S.Emerald Shia²

Sound & Vibration, Vol.55, No.2, pp. 141-161, 2021, DOI:10.32604/sv.2021.011734

Abstract This paper presents the pathological voice detection and classification techniques using signal processing based methodologies and Feed Forward Neural Networks (FFNN). The important pathological voices such as Autism Spectrum Disorder (ASD) and Down Syndrome (DS) are considered for analysis. These pathological voices are known to manifest in different ways in the speech of children and adults. Therefore, it is possible to discriminate ASD and DS children from normal ones using the acoustic features extracted from the speech of these subjects. The important attributes hidden in the pathological voices are extracted by applying different signal processing techniques. In this work, three… More >

Haiming Du^1,*, Han Gao¹, Wenjing Jia²

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3907-3919, 2021, DOI:10.32604/cmc.2021.015969

Abstract This paper presents a novel scheme for joint frequency and direction of arrival (DOA) estimation, that pairs frequencies and DOAs automatically without additional computations. First, when the property of the Kronecker product is used in the received array signal of the multiple-delay output model, the frequency-angle steering vector can be reconstructed as the product of the frequency steering vector and the angle steering vector. The frequency of the incoming signal is then obtained by searching for the minimal eigenvalue among the smallest eigenvalues that depend on the frequency parameters but are irrelevant to the DOAs. Subsequently, the DOA related to… More >

B. Nagaraj^1,*, Danilo Pelusi², Joy I.-Z. Chen³

Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 737-739, 2020, DOI:10.32604/iasc.2020.010107

Abstract This article has no abstract. More >

R. Uma Maheswari^1,*, R. Umamaheswari²

Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 479-488, 2020, DOI:10.32604/iasc.2020.013924

Abstract To enhance the predictive condition-based maintenance (CBMS), a reliable automatic Drivetrain fault detection technique based on vibration monitoring is proposed. Accelerometer sensors are mounted on a wind turbine drivetrain at different spatial locations to measure the vibration from multiple vibration sources. In this work, multi-channel signals are fused and monocomponent modes of oscillation are reconstructed by the Multivariate Empirical Mode Decomposition (MEMD) Technique. Noise assisted methodology is adapted to palliate the mixing of modes with common frequency scales. The instantaneous amplitude envelope and instantaneous frequency are estimated with the Hilbert transform. Low order and high order statistical moments, signal feature… More >

Fecir Duran¹, Mustafa Teke²

Intelligent Automation & Soft Computing, Vol.25, No.3, pp. 441-449, 2019, DOI:10.31209/2018.11006161

Abstract Ultrasonic cleaners are devices that perform ultrasonic cleaning by using ultrasonic converters. Ultrasonic cleaners have been employed to clean dirty and rusty materials such as optic, jewelers, automotive and dental prosthesis sectors. Due to non-identified correctly cleaning time, cavitation erosion has been occurred at some materials, which desire for cleaning. In this study, an intelligent cleaning device that runs autonomously identified cleaning time, saves energy, and makes the cleaning process safely has been designed and implemented. An ultrasonic cleaning time has been adjusted automatically by monitoring of turbidity and conductivity values of liquid that is put in to the cleaning… More >

Jiaqi Zhen^{1, *}

CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 445-455, 2020, DOI:10.32604/cmc.2020.06385

Abstract In array signal processing, number of signals is often a premise of estimating other parameters. For the sake of determining signal number in the condition of strong additive noise or a little sample data, an algorithm for detecting number of wideband signals is provided. First, technique of focusing is used for transforming signals into a same focusing subspace. Then the support vector machine (SVM) can be deduced by the information of eigenvalues and corresponding eigenvectors. At last, the signal number can be determined with the obtained decision function. Several simulations have been carried on verifying the proposed algorithm. More >