Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (13)
  • Open Access

    ARTICLE

    Emotion Recognition with Short-Period Physiological Signals Using Bimodal Sparse Autoencoders

    Yun-Kyu Lee1, Dong-Sung Pae2, Dae-Ki Hong3, Myo-Taeg Lim1, Tae-Koo Kang4,*

    Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 657-673, 2022, DOI:10.32604/iasc.2022.020849 - 17 November 2021

    Abstract With the advancement of human-computer interaction and artificial intelligence, emotion recognition has received significant research attention. The most commonly used technique for emotion recognition is EEG, which is directly associated with the central nervous system and contains strong emotional features. However, there are some disadvantages to using EEG signals. They require high dimensionality, diverse and complex processing procedures which make real-time computation difficult. In addition, there are problems in data acquisition and interpretation due to body movement or reduced concentration of the experimenter. In this paper, we used photoplethysmography (PPG) and electromyography (EMG) to record… More >

  • Open Access

    ARTICLE

    Deep Learning Based Stacked Sparse Autoencoder for PAPR Reduction in OFDM Systems

    A. Jayamathi1, T. Jayasankar2,*

    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 311-324, 2022, DOI:10.32604/iasc.2022.019473 - 03 September 2021

    Abstract Orthogonal frequency division multiplexing is one of the efficient and flexible modulation techniques, and which is considered as the central part of many wired and wireless standards. Orthogonal frequency division multiplexing (OFDM) and multiple-input multiple-output (MIMO) achieves maximum spectral efficiency and data rates for wireless mobile communication systems. Though it offers better quality of services, high peak-to-average power ratio (PAPR) is the major issue that needs to be resolved in the MIMO-OFDM system. Earlier studies have addressed the high PAPR of OFDM system using clipping, coding, selected mapping, tone injection, peak windowing, etc. Recently, deep… More >

  • Open Access

    ARTICLE

    Pseudo Zernike Moment and Deep Stacked Sparse Autoencoder for COVID-19 Diagnosis

    Yu-Dong Zhang1, Muhammad Attique Khan2, Ziquan Zhu3, Shui-Hua Wang4,*

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3145-3162, 2021, DOI:10.32604/cmc.2021.018040 - 24 August 2021

    Abstract (Aim) COVID-19 is an ongoing infectious disease. It has caused more than 107.45 m confirmed cases and 2.35 m deaths till 11/Feb/2021. Traditional computer vision methods have achieved promising results on the automatic smart diagnosis. (Method) This study aims to propose a novel deep learning method that can obtain better performance. We use the pseudo-Zernike moment (PZM), derived from Zernike moment, as the extracted features. Two settings are introducing: (i) image plane over unit circle; and (ii) image plane inside the unit circle. Afterward, we use a deep-stacked sparse autoencoder (DSSAE) as the classifier. Besides, multiple-way… More >

Displaying 11-20 on page 2 of 13. Per Page