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Search: L773:9781665483605 > (2022)

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1.
  • Jiang, Meng, et al. (author)
  • Performance Comparison of Omni and Cardioid Directional Microphones for Indoor Angle of Arrival Sound Source Localization
  • 2022
  • In: Conference Record - IEEE Instrumentation and Measurement Technology Conference. - : IEEE. - 9781665483605
  • Conference paper (peer-reviewed)abstract
    • The sound source localization technology brings the possibility of mapping the sound source positions. In this paper, angle-of-arrival (AOA) has been chosen as the method for achieving sound source localization in an indoor enclosed environment. The dynamic environment and reverberations bring a challenge for AOA-based systems for such applications. By the acknowledgement of microphone directionality, the cardioid-directional microphone systems have been chosen for the localization performance comparison with omni-directional microphone systems, in order to investigate which microphone is superior in AOA indoor sound source localization. To reduce the hardware complexity, the number of microphones used during the experiment has been limited to 4. A localization improvement has been proposed with a weighting factor. The comparison has been done for both types of microphones with 3 different array manifolds under the same system setup. The comparison shows that the cardioid-directional microphone system has an overall higher accuracy. 
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2.
  • Nie, Yali, et al. (author)
  • Skin Cancer Classification based on Cosine Cyclical Learning Rate with Deep Learning
  • 2022
  • In: Conference Record - IEEE Instrumentation and Measurement Technology Conference. - : IEEE. - 9781665483605
  • Conference paper (peer-reviewed)abstract
    • Since early-stage skin cancer identification can improve melanoma prognosis and significantly reduce treatment costs, AI-based diagnosis systems might greatly benefit patients suffering from suspicious skin lesions. The study proposes a cosine cyclical learning rate with a skin cancer classification model to improve melanoma prediction. The contributions of models involve three critical CNNs, which are standard deep feature extraction modules for the skin cancer classification in this study (Vgg19, ResNet101 and InceptionV3). Each CNN model applies three different learning rates: fixed learning rate(LR), Cosine Annealing LR, and Cosine Annealing with WarmRestarts. HAM10000 is a large collection of publicly available dermoscopic images dataset used for our experiments. The performance of the proposed approach was appraised through comparative experiments. The outcome has indicated that the proposed method has high efficiency in diagnosing skin lesions with a cosine cyclical learning rate. 
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