Plasmonic metasurfaces have been employed for tuning and controlling light enabling various novel applications. Their appeal is enhanced with the incorporation of an active element with the metasurfaces paving the way...
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In this paper, we design and simulate the 19 mm × 16 mm × 3.15 mm meander implantable antenna operating in industrial, scientific, and medical (ISM) 2.4-2.5 GHz band. The resonant frequency and impedance mat...
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In this paper, we design and simulate the 19 mm × 16 mm × 3.15 mm meander implantable antenna operating in industrial, scientific, and medical (ISM) 2.4-2.5 GHz band. The resonant frequency and impedance matching are fine tuned to around 2.45 GHz by changing the height and position of antenna components. The comparisons of three proposed antennas are performed and then discussed. The antenna design with graphene patch shows the most outstanding results. The bandwidth and 3 dB-beamwidths increase significantly and the minimum average specific absorption rate (SAR) can be achieved.
The development of the pattern recognition techniques in voice recognition has rapidly increased. Many methods are used to create a system that is reliable and easily accessible so that the recognition accuracy is hig...
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ISBN:
(纸本)9781509041404
The development of the pattern recognition techniques in voice recognition has rapidly increased. Many methods are used to create a system that is reliable and easily accessible so that the recognition accuracy is high. To find the pattern recognition system that is robust and reliable, proper feature extraction methods are needed. In this case, the proper method of the feature extraction is an efficient and effective method when it is used in recognizing specific features of data. This paper presents an effective and efficient method of the extracting features for speech processing. The focus of this work is to explore the feature extraction methods which are the most efficient and effective in recognizing Indonesian phonemes. The Feature extraction methods used and compared in the study are the Discrete Wavelet Transform (DWT) and Wavelet Packet Transform (WPT). The wavelet transform was done on the 2 nd level until 4 th level of decomposition. The comparison of the performance of both feature extraction methods are presented at the end of this section, with a statistical comparison, to draw a conclusion that the DWT methods have a better performance in terms of effectiveness and efficiency compared with the WPT method. The results of the study show that the effectiveness ratio is 60% versus 40% and the efficiency ratio is 57% versus 43%.
Fast radio bursts (FRBs) are brief, bright, extragalactic radio flashes1, 2. Their physical origin remains unknown, but dozens of possible models have been postulated3. Some FRB sources exhibit repeat bursts4–7. Thou...
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Focusing on the quality of education, skills, and employability of our graduates in computing-related fields, this work proposes a cost-effective approach to achieve these goals. The Evaluation and Proficiency Infrast...
Despite substantial declines since 2000, lower respiratory infections (LRIs), diarrhoeal diseases, and malaria remain among the leading causes of nonfatal and fatal disease burden for children under 5 years of age (un...
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Concerns for the environment, health and safety are of major importance and have been attracting considerable attention around the globe due to the new environmental challenges that are threatening our planet. In this...
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ISBN:
(纸本)9781509034086
Concerns for the environment, health and safety are of major importance and have been attracting considerable attention around the globe due to the new environmental challenges that are threatening our planet. In this paper, we propose to enhance the fault detection of an air quality monitoring network (AQMN) by using wavelet principal component analysis (WPCA)-based on generalized likelihood ratio test (GLRT). The presence of measurement noise in the data and model uncertainties degrade the quality of fault detection (FD) techniques by increasing the rate of false alarms. Therefore, the objective of this paper is to enhance the FD of an AQMN by using wavelet representation of data, which is a powerful feature extraction tool to remove the noises from the data. Wavelet data representation has been used to enhance the FD abilities of principal component analysis. Therefore, in the current work, we propose to use WPCA-based on GLRT technique for FD. The fault detection performances of the WPCA-based GLRT technique are shown using air quality monitoring network (AQMN). The results showed the detection efficiency of developed WPCA-based GLRT technique, when compared to classical PCA and WPCA techniques.
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