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  • Open Access

    ARTICLE

    Classification of Elephant Sounds Using Parallel Convolutional Neural Network

    T. Thomas Leonid1,*, R. Jayaparvathy2

    Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1415-1426, 2022, DOI:10.32604/iasc.2022.021939

    Abstract Human-elephant conflict is the most common problem across elephant habitat Zones across the world. Human elephant conflict (HEC) is due to the migration of elephants from their living habitat to the residential areas of humans in search of water and food. One of the important techniques used to track the movements of elephants is based on the detection of Elephant Voice. Our previous work [] on Elephant Voice Detection to avoid HEC was based on Feature set Extraction using Support Vector Machine (SVM). This research article is an improved continuum of the previous method using Deep learning techniques. The current… More >

  • Open Access

    ARTICLE

    Adaptive Virtual Source Imaging Using the Sequence Intensity Factor: Simulation and Experimental Study

    Chichao Zheng, Yazhong Wang, Yadan Wang*, Qing He, Hu Peng

    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.1, pp. 397-413, 2022, DOI:10.32604/cmes.2022.016308

    Abstract Virtual source (VS) imaging has been proposed to improve image resolution in medical ultrasound imaging. However, VS obtains a limited contrast due to the non-adaptive delay-and-sum (DAS) beamforming. To improve the image contrast and provide an enhanced resolution, adaptive weighting algorithms were applied in VS imaging. In this paper, we proposed an adjustable generalized coherence factor (aGCF) for the synthetic aperture sequential beamforming (SASB) of VS imaging to improve image quality. The value of aGCF is adjusted by a sequence intensity factor (SIF) that is defined as the ratio between the effective low resolution scan lines (LRLs) intensity and total… More >

  • Open Access

    ARTICLE

    Heart Sound Analysis for Abnormality Detection

    Zainab Arshad1, Sohail Masood Bhatti2,*, Huma Tauseef3, Arfan Jaffar2

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1195-1205, 2022, DOI:10.32604/iasc.2022.022160

    Abstract According to the World Health Organization, 31% death rate in the World is because of cardiovascular diseases like heart arrhythmia and heart failure. Early diagnosis of heart problems may help in timely treatment of the patients and hence control death rate. Heart sounds are good signals of heart health if examined by an expert. Moreover, heart sounds can be analyzed with inexpensive and portable medical devices. Automatic heart sound classification can be very useful in diagnosing heart problems. Major focus of this research is to study the existing techniques for heart sound classification and develop a more sophisticated method. A… More >

  • Open Access

    ARTICLE

    Prioritizing and Providing Sound Pollution Control Strategies at the CPF of North Azadegan Oilfield Project

    Ali Askari1, Ali Salehi Sahl Abadi2,*, Alimardan Alinia3, Milad Pourjaafar4, Aref Honairi Haghighi5, Elham Akhlaghi Pirposhteh6

    Sound & Vibration, Vol.55, No.4, pp. 329-341, 2021, DOI:10.32604/sv.2021.016662

    Abstract Among the harmful occupational factors, noise is the most common exposure in the oil industrial workplaces. The present study aimed to prioritize sound pollution areas in central processing facilities (CPF) of an oil field in order to provide corrective action in the studied industry and similar industries. After reviewing the issued permit to work, job description and noise dosimetry test, the evaluated areas were selected then sound pressure levels in the referenced areas investigated according to ISO 9612–2009 (E) next the noise map prepared for all selected areas. For identifying the prioritized areas to implement the control measures NCPI was… More >

  • Open Access

    ARTICLE

    Lactoferrin-Conjugated Polylactic Acid Nanobubbles Encapsulated Perfluoropentane as a Contrast Agent for Ultrasound/Magnetic Resonance Dual-Modality Imaging

    Liqiong Ding1, Pingsheng Li2, Liu He1, Fengnan Xu1, Jieqiong Ding3,*, Binhua Luo1,*

    Journal of Renewable Materials, Vol.10, No.3, pp. 767-780, 2022, DOI:10.32604/jrm.2022.016903

    Abstract The development of contrast agents that can be activated by multiple modes is of great significance for tumor diagnosis. In this study, the lactoferrin (Lf)-conjugated polylactic acid (PLLA) nanobubbles (Lf-PLLA NBs) were used to encapsulate liquid perfluoropentane (PFP) with the double emulsion method, creating PFP loaded (PFP/Lf-PLLA) NBs for the ultrasound/magnetic resonance dual-modality imaging of subcutaneous tumor. The particle diameter and stability of nanobubbles were investigated by photon correlation spectroscopy. The biocompatibility of nanobubbles was preliminarily evaluated by cell proliferation and migration assay, hemolysis rate, and blood biochemistry analysis. A B-mode clinical ultrasound real-time imaging system was used to perform… More >

  • Open Access

    ARTICLE

    Adversarial Neural Network Classifiers for COVID-19 Diagnosis in Ultrasound Images

    Mohamed Esmail Karar1,2, Marwa Ahmed Shouman3, Claire Chalopin4,*

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1683-1697, 2022, DOI:10.32604/cmc.2022.018564

    Abstract The novel Coronavirus disease 2019 (COVID-19) pandemic has begun in China and is still affecting thousands of patient lives worldwide daily. Although Chest X-ray and Computed Tomography are the gold standard medical imaging modalities for diagnosing potentially infected COVID-19 cases, applying Ultrasound (US) imaging technique to accomplish this crucial diagnosing task has attracted many physicians recently. In this article, we propose two modified deep learning classifiers to identify COVID-19 and pneumonia diseases in US images, based on generative adversarial neural networks (GANs). The proposed image classifiers are a semi-supervised GAN and a modified GAN with auxiliary classifier. Each one includes… More >

  • Open Access

    REVIEW

    Deep Learning Applications for COVID-19 Analysis: A State-of-the-Art Survey

    Wenqian Li1, Xing Deng1,2,*, Haijian Shao1, Xia Wang3

    CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.1, pp. 65-98, 2021, DOI:10.32604/cmes.2021.016981

    Abstract The COVID-19 has resulted in catastrophic situation and the deaths of millions of people all over the world. In this paper, the predictions of epidemiological propagation models, such as SIR and SEIR, are introduced to analyze the earlier COVID-19 propagation. The deep learning methods combined with transfer learning are familiar with classification-detection approaches based on chest X-ray and CT images are presented in detail. Besides, deep learning approaches have also been applied to lung ultrasound (LUS), which has been shown to be more sensitive than chest X-ray and CT images in detecting COVID-19. In the absence of a vaccine, the… More > Graphic Abstract

    Deep Learning Applications for COVID-19 Analysis: A <i>State-of-the-Art</i> Survey

  • Open Access

    ARTICLE

    Intelligent IoT-Aided Early Sound Detection of Red Palm Weevils

    Mohamed Esmail Karar1,2, Omar Reyad1,3,*, Abdel-Haleem Abdel-Aty4, Saud Owyed5, Mohd F. Hassan6

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4095-4111, 2021, DOI:10.32604/cmc.2021.019059

    Abstract Smart precision agriculture utilizes modern information and wireless communication technologies to achieve challenging agricultural processes. Therefore, Internet of Things (IoT) technology can be applied to monitor and detect harmful insect pests such as red palm weevils (RPWs) in the farms of date palm trees. In this paper, we propose a new IoT-based framework for early sound detection of RPWs using fine-tuned transfer learning classifier, namely InceptionResNet-V2. The sound sensors, namely TreeVibes devices are carefully mounted on each palm trunk to setup wireless sensor networks in the farm. Palm trees are labeled based on the sensor node number to identify the… More >

  • Open Access

    ARTICLE

    Implementing Delay Multiply and Sum Beamformer on a Hybrid CPU-GPU Platform for Medical Ultrasound Imaging Using OpenMP and CUDA

    Ke Song1,*, Paul Liu2, Dongquan Liu3

    CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.3, pp. 1133-1150, 2021, DOI:10.32604/cmes.2021.016008

    Abstract A novel beamforming algorithm named Delay Multiply and Sum (DMAS), which excels at enhancing the resolution and contrast of ultrasonic image, has recently been proposed. However, there are nested loops in this algorithm, so the calculation complexity is higher compared to the Delay and Sum (DAS) beamformer which is widely used in industry. Thus, we proposed a simple vector-based method to lower its complexity. The key point is to transform the nested loops into several vector operations, which can be efficiently implemented on many parallel platforms, such as Graphics Processing Units (GPUs), and multi-core Central Processing Units (CPUs). Consequently, we… More >

  • Open Access

    ARTICLE

    Robust Sound Source Localization Using Convolutional Neural Network Based on Microphone Array

    Xiaoyan Zhao1,*, Lin Zhou2, Ying Tong1, Yuxiao Qi1, Jingang Shi3

    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 361-371, 2021, DOI:10.32604/iasc.2021.018823

    Abstract In order to improve the performance of microphone array-based sound source localization (SSL), a robust SSL algorithm using convolutional neural network (CNN) is proposed in this paper. The Gammatone sub-band steered response power-phase transform (SRP-PHAT) spatial spectrum is adopted as the localization cue due to its feature correlation of consecutive sub-bands. Since CNN has the “weight sharing” characteristics and the advantage of processing tensor data, it is adopted to extract spatial location information from the localization cues. The Gammatone sub-band SRP-PHAT spatial spectrum are calculated through the microphone signals decomposed in frequency domain by Gammatone filters bank. The proposed algorithm… More >

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