Using short-term fourier transform for particle detection and recognition in a CMOS oscillator-based chain

Author(s):  
H. Aziza ◽  
K. Coulie ◽  
W. Rahajandraibe ◽  
R. Vauche
2020 ◽  
Vol 151 ◽  
pp. 106379
Author(s):  
Yeping Peng ◽  
Junhao Cai ◽  
Tonghai Wu ◽  
Guangzhong Cao ◽  
Ngaiming Kwok ◽  
...  

1992 ◽  
Vol 35 (4) ◽  
pp. 942-949 ◽  
Author(s):  
Christopher W. Turner ◽  
David A. Fabry ◽  
Stephanie Barrett ◽  
Amy R. Horwitz

This study examined the possibility that hearing-impaired listeners, in addition to displaying poorer-than-normal recognition of speech presented in background noise, require a larger signal-to-noise ratio for the detection of the speech sounds. Psychometric functions for the detection and recognition of stop consonants were obtained from both normal-hearing and hearing-impaired listeners. Expressing the speech levels in terms of their short-term spectra, the detection of consonants for both subject groups occurred at the same signal-to-noise ratio. In contrast, the hearing-impaired listeners displayed poorer recognition performance than the normal-hearing listeners. These results imply that the higher signal-to-noise ratios required for a given level of recognition by some subjects with hearing loss are not due in part to a deficit in detection of the signals in the masking noise, but rather are due exclusively to a deficit in recognition.


2021 ◽  
Author(s):  
Mohammed Jahirul Islam

The CN Tower is a transmission tower and it is not unexpected that recorded lightning current signals be corrupted by noise. The existence of noise may affect the calculation of current waveform parameters (current peak, 10-90% risetime to current peak, maximum steepness, and pulse width at half value of current peak). But accurate statistics of current waveform parameters are required to design systems for the protection of structures and devices, especially those with electrical and electronic components, exposed to hazards of lightning. Since more electrical devices are used nowadays, lightning protection becomes more important. So to determine accurate statistics of current waveform parameters, the interfering noise must be removed. In this thesis we describe a technique for de-noising the CN Tower lightning current by modifying its Fourier Transform (FT) where a simulated current waveform (Heidler function) is used to represent the lightning current signal.The limitations of Discrete Fourier Transform (DFT) for removal of non-stationary noise signals, including the noise connected with CN Tower lightning current signals and its properties are discussed. The Short Term Fourier Transform (STFT) is explored to analyze non-stationary signals and to deal with the limitations of DFT. Last of all, an STFT-based Spectral Subtraction method is developed to denoise the CN Tower lightning current signal. In order to evaluate the Spectral Subtraction method, a simulated current derivative waveform ( obtained by differentiating Heidler function) is artificially distorted by a noise signal measured at the CN Tower in the absence of lightning. The Spectral Subtraction method is then used to de-noise the distorted waveform. The de-noised waveform proved to be very close to the original simulated waveform. A signal-peak to noise-peak ratio (SPNPR) of the CN Tower lightning current signal is defined and calculated before and after the de-noising process. For example, for a typical measured current derivative signal, the SPNPR before de-noising is 7.27, and after de-noising it becomes 151.30. Similarly for its current waveform (obtained by numerical integration) the SPNPR before de-noising is 20,16 and it becomes 361.39 after de-noising. Statistics of current waveform parameters are obtained from the de-noised waveforms. The Spectral Subtraction method is also applied for de-noising the electric and magnetic field waveforms generated by lightning to the CN Tower which enables the calculation of their waveform parameters.


AITI ◽  
2020 ◽  
Vol 17 (1) ◽  
pp. 22-32
Author(s):  
Dyah Ayu Pratiwi ◽  
Achmad Rizal ◽  
Rita Magdalena

Sinyal elektrokardiogram adalah sinyal bio-electrik yang dihasilkan dari aktifitas kelistrikan jantung. Informasi dari kondisi kesehatan jantung bisa diketahui dengan menganalisis bentuk, irama, durasi, maupun orientasi nya. Berbagai metode dikembangkan untuk melakukan analisis atau mengklasifikasi sinyal EKG secara otomatis. Beberapa diantaranya menggunakan metode transformasi untuk mengubah sinyal dari domain waktu ke domain sinyal yang lain. Pada penelitian ini digunakan Stockwell transform (S-transform) untuk mengubah sinyal dari domain waktu ke domain waktu-frekuensi. Nilai minimum dan maksimum dari pada deretan waktu dari S-transform digunakan sebagai masukan K-NN sebagai classifer. Akurasi dari penggunaan S-transform dibandingkan dengan akurasi penggunaan short-term Fourier transform (STFT) yang merupakan transormasi yang setara. Hasil pengujian menunjukkan akurasi S-transform lebih tinggi dibandingkan dengan FFT pada enam kelas data sinyal EKG yang diuji.


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