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Journal articles on the topic 'Algoritmo de Goertzel'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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1

De Lima, Alfredo Rodrigues. "ANÁLISE DA APLICABILIDADE DO FILTRO DIGITAL DE GOERTZEL NA MEDIÇÃO DE DESEQUILÍBRIOS DE TENSÃO." HOLOS 4 (September 19, 2017): 327. http://dx.doi.org/10.15628/holos.2017.6028.

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Este artigo apresenta a utilização de um algoritmo simples de detecção de frequências no cálculo do Fator de Desequilíbrio de Tensão. O algoritmo de Goertzel permite que sejam encontradas, em um sinal distorcido, a amplitude e a fase de uma harmônica específica, sem requerer grande capacidade de armazenamento ou de processamento. Dessa forma, é possível implementar o método das componentes simétricas para o cálculo do fator de desequilíbrio de tensão, utilizando o algoritmo de Goertzel para obter o ângulo de fase e a amplitude da componente fundamental das tensões. A simulação é feita através da ferramenta Scilab. São gerados sinais que correspondem às tensões desequilibradas, tanto por módulo como por ângulo. O programa principal é implementado de forma a fornecer o valor do fator de desequilíbrio para diversas situações. Em seguida são plotados gráficos com as formas de onda das tensões e com os respectivos fasores. A análise dos resultados mostra a simplicidade, utilidade e viabilidade do uso do filtro de Goertzel em aplicações relacionadas com a qualidade da energia elétrica, permitindo que, com menor investimento em hardware, seja possível projetar equipamentos capazes de detectar e mensurar desequilíbrios de forma mais confiável e generalista.

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2

Kliwati, Sri. "ALGORITMA DETEKSI FREKUENSI DTMF MENGGUNAKAN KORELASI SILANG UNTUK TELEKOMANDO WAHANA TERBANG." Jurnal Teknologi Dirgantara 14, no. 1 (July 22, 2016): 1. http://dx.doi.org/10.30536/j.jtd.2016.v14.a2562.

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In general, the frequency DTMF detection algorithm using FFT (Fast Fourier Transform) and Goertzel Algorithm. However, in certain circumstances a more suitable signal detected by the other algorithms. For example in a non-periodic signal as the measurement sensors for flight vehicle. The purpose of this study is to develop a system telecommand for flight vehicle. And this paper discusses the frequency DTMF (Dual Tone Multi Frequency) detection method using cross correlation algorithm and data-base DTMF. The simulation results show the frequency detection successfully carried out in accordance with the accuracy of the data-base created (1 Hz) to change the frequency of zero to 1700 Hz (DTMF frequency limit). Abstrak Deteksi frekuensi DTMF secara umum menggunakan algoritma Fast Fourier Transform (FFT) dan Goertzel Algorithm. Akan tetapi pada kondisi tertentu sebuah sinyal lebih cocok dideteksi dengan algoritma yang lain. Misalnya pada sinyal yang non-periodik seperti pada pengukuran sensor-sensor wahana terbang. Tujuan penelitian ini adalah membangun sistem telekomando untuk wahana terbang. Dan tulisan ini membahas metode deteksi frekuensi Dual Tone Multi Frequency (DTMF) dengan menggunakan algoritma korelasi silang dan data-base DTMF. Hasil simulasi menunjukkan deteksi frekuensi berhasil dilakukan sesuai dengan akurasi database yang dibuat (1 Hz) untuk perubahan frekuensi antara nol hingga 1700 Hz (batas frekuensi DTMF).

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3

Kliwati, Sri. "ALGORITMA DETEKSI FREKUENSI DTMF MENGGUNAKAN KORELASI SILANG UNTUK TELEKOMANDO WAHANA TERBANG (DTMF FREQUENCY DETECTION ALGORITHM USING CROSSCORRELATION FOR VEHICLE TELECOMMAND)." Jurnal Teknologi Dirgantara 14, no. 1 (March 19, 2018): 1. http://dx.doi.org/10.30536/j.jtd.2016.v14.a2942.

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In general, the frequency DTMF detection algorithm using FFT (Fast Fourier Transform) and Goertzel Algorithm. However, in certain circumstances a more suitable signal detected by the other algorithms. For example in a non-periodic signal as the measurement sensors for flight vehicle. The purpose of this study is to develop a system telecommand for flight vehicle. And this paper discusses the frequency DTMF (Dual Tone Multi Frequency) detection method using cross correlation algorithm and data-base DTMF. The simulation results show the frequency detection successfully carried out in accordance with the accuracy of the data-base created (1 Hz) to change the frequency of zero to 1700 Hz (DTMF frequency limit). ABSTRAKDeteksi frekuensi DTMF secara umum menggunakan algoritma Fast Fourier Transform (FFT) dan Goertzel Algorithm. Akan tetapi pada kondisi tertentu sebuah sinyal lebih cocok dideteksi dengan algoritma yang lain. Misalnya pada sinyal yang non-periodik seperti pada pengukuran sensor-sensor wahana terbang. Tujuan penelitian ini adalah membangun sistem telekomando untuk wahana terbang. Dan tulisan ini membahas metode deteksi frekuensi Dual Tone Multi Frequency (DTMF) dengan menggunakan algoritma korelasi silang dan data-base DTMF. Hasil simulasi menunjukkan deteksi frekuensi berhasil dilakukan sesuai dengan akurasi database yang dibuat (1 Hz) untuk perubahan frekuensi antara nol hingga 1700 Hz (batas frekuensi DTMF).

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4

Chicharo, Joe F., and Mehdi T. Kilani. "A sliding Goertzel algorithm." Signal Processing 52, no. 3 (August 1996): 283–97. http://dx.doi.org/10.1016/0165-1684(96)00066-7.

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5

Bharadwaj B, Sai, V. Sharmila Gowri, and V. Bindu Madhuri. "Dtmf Detection Using Goertzel Algorithm." IOSR journal of VLSI and Signal Processing 4, no. 3 (2014): 34–43. http://dx.doi.org/10.9790/4200-04323443.

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6

Fedorenko, Sergei Valentinovich. "Improving the Goertzel–Blahut Algorithm." IEEE Signal Processing Letters 23, no. 6 (June 2016): 824–27. http://dx.doi.org/10.1109/lsp.2016.2557486.

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7

Farhan, Mhnd. "Glance on Parallelization of FFT Algorithms." Land Forces Academy Review 24, no. 1 (March 1, 2019): 77–82. http://dx.doi.org/10.2478/raft-2019-0009.

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Abstract This paper implores the parallelization of Fast Fourier Transform (FFT) algorithms and evaluates the resultant parallelized source codes. The FFT algorithm is considered to be among the most important algorithms in the digital era. There are various FFT algorithms but just a few are considered in this paper. The Cooley-Tukey FFT is the most widely known and used. With no exception, in this paper, the radix-2 Decimation in Time (DIT) and Decimation in Frequency (DIF) are studied and implemented. Another important FFT algorithm that is the Goertzel is also considered in this paper.

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8

V.Siva, V. Siva, and shaik saidulu. "Design and Implementation of Goertzel Algorithm for DTMF application on FPGA." International Journal of Scientific Research 1, no. 6 (June 1, 2012): 62–64. http://dx.doi.org/10.15373/22778179/nov2012/22.

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9

KULAÇ, Selman, and Murat Hüsnü SAZLI. "Effective cooperative DTV detection using the Goertzel algorithm." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 23 (2015): 1384–92. http://dx.doi.org/10.3906/elk-1304-177.

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10

Verma, Sanjay, and Devendra Kumar. "Detection of Protein Coding Regions using Goertzel Algorithm." International Journal of Computer Applications 124, no. 2 (August 18, 2015): 1–4. http://dx.doi.org/10.5120/ijca2015905329.

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11

Gao, Z., P. Reviriego, X. Li, J. A. Maestro, M. Zhao, and J. Wang. "A fault tolerant implementation of the Goertzel algorithm." Microelectronics Reliability 54, no. 1 (January 2014): 335–37. http://dx.doi.org/10.1016/j.microrel.2013.08.012.

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12

Howard, Carl Q. "A sliding Goertzel algorithm for adaptive passive neutralizers." Journal of Sound and Vibration 331, no. 9 (April 2012): 1985–93. http://dx.doi.org/10.1016/j.jsv.2012.01.007.

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13

Li Tang′an, 李唐安, 李世阳 Li Shiyang, 张家明 Zhang Jiaming, 孙. 轩. Sun Xuan, and 郭荣静 Guo Rongjing. "Infrared detection method of gas based on Goertzel algorithm." Infrared and Laser Engineering 48, no. 3 (2019): 304003. http://dx.doi.org/10.3788/irla201948.0304003.

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14

Chen, Hao, GongLiang Chen, and JianHua Li. "The generalized Goertzel algorithm and its parallel hardware implementation." Science in China Series A: Mathematics 51, no. 1 (January 2008): 37–41. http://dx.doi.org/10.1007/s11425-007-0183-4.

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15

Najafi, Ehsan, A. H. M. Yatim, and Amin Mirzaei. "An improved sag detection approach based on modified Goertzel algorithm." International Journal of Electronics 106, no. 1 (August 2, 2018): 36–47. http://dx.doi.org/10.1080/00207217.2018.1501612.

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16

Onchis, Darian M., and Pavel Rajmic. "Generalized Goertzel algorithm for computing the natural frequencies of cantilever beams." Signal Processing 96 (March 2014): 45–50. http://dx.doi.org/10.1016/j.sigpro.2013.07.026.

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17

Beraldin, J. A., and W. Steenaart. "Overflow analysis of a fixed-point implementation of the Goertzel algorithm." IEEE Transactions on Circuits and Systems 36, no. 2 (1989): 322–24. http://dx.doi.org/10.1109/31.20217.

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18

Smoktunowicz, Alicja, and Iwona Wr�bel. "On improving the accuracy of Horner?s and Goertzel?s algorithms." Numerical Algorithms 38, no. 4 (May 2005): 243–58. http://dx.doi.org/10.1007/s11075-004-4570-4.

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19

CHICHARO, JOE F., and MEHDI T. KILANI. "Arbitrary multifrequency estimation and the relationship between the LMS and Goertzel algorithms." International Journal of Electronics 83, no. 3 (September 1997): 287–98. http://dx.doi.org/10.1080/002072197135364.

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20

Chen, Nan, Shangchun Fan, and Dezhi Zheng. "A Phase Difference Measurement Method Based on the Extended Kalman Filter for Coriolis Mass Flowmeters." Measurement Science Review 19, no. 2 (April 1, 2019): 48–52. http://dx.doi.org/10.2478/msr-2019-0008.

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Abstract According to the characteristics of stable single-phase flow, a phase difference measurement method based on the extended Kalman filter is proposed in this paper for use with Coriolis mass flowmeters. Firstly, the Mallat algorithm is applied to filter out interference signals. Then, the frequency and phase difference of the two reconstructed signals are detected through the extended Kalman filter. Compared with the sliding Goertzel algorithm or discrete time Fourier transform, the proposed method does not need to predict the signal frequency and avoids quadratic error. Simulations and experiments show that the proposed method has stronger anti-interference, higher measurement accuracy and lower relative error than the existing method based on the Hilbert transformation.

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21

Yoon, Jae-Seung, Kyoung-Gu Lee, June-Seok Lee, and Kyo-Beum Lee. "Off-Line Parameter Identification of Permanent Magnet Synchronous Motor Using a Goertzel Algorithm." Journal of Electrical Engineering and Technology 10, no. 6 (November 1, 2015): 2262–70. http://dx.doi.org/10.5370/jeet.2015.10.6.2262.

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22

Singh, Ksh Milan. "Simultaneous Estimation of Moving-Vibration Parameters by Sliding Goertzel Algorithm in PLL Technique." IEEE Transactions on Instrumentation and Measurement 68, no. 2 (February 2019): 334–43. http://dx.doi.org/10.1109/tim.2018.2844898.

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23

Yeh, Cheng-Yu, and Shaw-Hwa Hwang. "Efficient Detection Approach for DTMF Signal Detection." Applied Sciences 9, no. 3 (January 27, 2019): 422. http://dx.doi.org/10.3390/app9030422.

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A novel tone detection approach, designated as the multi-frequency detecting (MFD) algorithm, is presented in this work as an alternative to conventional single point detection approaches but it is an efficient way to achieve the aim of further computational load reduction for a dual-tone multi-frequency (DTMF) signal detection. The idea is that an optimal phase search is performed over the frequency band of interest in each tone detection, and then the optimal frequency response of a detector is built accordingly. In this manner, a DTMF detection task is done following one-time detection computation. This proposal demonstrates an overall computational load reduction of 80.49% and 74.06% in comparison with a discrete Fourier transform (DFT) approach and the Goertzel algorithm, respectively. This detection complexity reduction is an advantage and an important issue for applying DTMF detection technique to embedded devices.

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24

Kim, Jae-Hyung, Jun-Gu Kim, Young-Hyok Ji, Yong-Chae Jung, and Chung-Yuen Won. "An Islanding Detection Method for a Grid-Connected System Based on the Goertzel Algorithm." IEEE Transactions on Power Electronics 26, no. 4 (April 2011): 1049–55. http://dx.doi.org/10.1109/tpel.2011.2107751.

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25

Howard, Carl Q., and Richard A. Craig. "An adaptive quarter-wave tube that uses the sliding-Goertzel algorithm for estimation of phase." Applied Acoustics 78 (April 2014): 92–97. http://dx.doi.org/10.1016/j.apacoust.2013.12.002.

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26

Tu, Yaqing, Huiyue Yang, Haitao Zhang, and Xiangyu Liu. "CMF Signal Processing Method Based on Feedback Corrected ANF and Hilbert Transformation." Measurement Science Review 14, no. 1 (February 1, 2014): 41–47. http://dx.doi.org/10.2478/msr-2014-0007.

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Abstract In this paper, we focus on CMF signal processing and aim to resolve the problems of precision sharp-decline occurrence when using adaptive notch filters (ANFs) for tracking the signal frequency for a long time and phase difference calculation depending on frequency by the sliding Goertzel algorithm (SGA) or the recursive DTFT algorithm with negative frequency contribution. A novel method is proposed based on feedback corrected ANF and Hilbert transformation. We design an index to evaluate whether the ANF loses the signal frequency or not, according to the correlation between the output and input signals. If the signal frequency is lost, the ANF parameters will be adjusted duly. At the same time, singular value decomposition (SVD) algorithm is introduced to reduce noise. And then, phase difference between the two signals is detected through trigonometry and Hilbert transformation. With the frequency and phase difference obtained, time interval of the two signals is calculated. Accordingly, the mass flow rate is derived. Simulation and experimental results show that the proposed method always preserves a constant high precision of frequency tracking and a better performance of phase difference measurement compared with the SGA or the recursive DTFT algorithm with negative frequency contribution

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27

Abdelghany, A. M., M. S. Meikhail, and R. Hamdy. "Enrichment of Poly Vinyl Chloride (PVC) Biological uses Through Sodium Chloride Filler, Density Functional Theory (DFT) Supported Experimental Study." JOURNAL OF ADVANCES IN PHYSICS 14, no. 3 (September 25, 2018): 5682–92. http://dx.doi.org/10.24297/jap.v14i3.7744.

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Samples of pristine poly vinyl chloride (PVC) and other samples of the same matrix containing a variable amount of inorganic sodium salt (sodium chloride NaCl) were prepared via simple casting route in a dimethyl sulfoxide (DMSO) solvent were prepared. Prepared samples were characterized via x-ray diffraction (XRD) measurements and Fourier transform infrared spectrophotometric measurements (FTIR) in combination with computational density functional theory approaches (DFT) into account for a detailed picture of the possible reaction mechanisms within the polymeric matrices. All measurements were performed on the possible basis set. Local density approximate (LDA) and generalized Goertzel algorithm (GGA) was also tested. A compatibility between computed and experimental results suggested that physical interaction was preferred in such matrix and nominate use of this new material to be used in the medical field as a result of dioxin illumination.

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Varadarajan, K. K., P. R. Suhasini, K. Manikantan, and S. Ramachandran. "Face Recognition Using Block Based Feature Extraction with CZT and Goertzel-algorithm as a Preprocessing Technique." Procedia Computer Science 46 (2015): 1458–67. http://dx.doi.org/10.1016/j.procs.2015.02.065.

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29

Cordoneanu, Daniel. "Bearing fault diagnosis using the Kolmogorov-Smirnov test on frequency features extracted using the Goertzel algorithm." IOP Conference Series: Materials Science and Engineering 997 (December 25, 2020): 012041. http://dx.doi.org/10.1088/1757-899x/997/1/012041.

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30

Najafi, E., and A. H. M. Yatim. "A novel current mode controller for a static compensator utilizing Goertzel algorithm to mitigate voltage sags." Energy Conversion and Management 52, no. 4 (April 2011): 1999–2008. http://dx.doi.org/10.1016/j.enconman.2010.11.020.

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31

Sundararajan, Prasanth, Mohamed Halick Mohamed Sathik, Firman Sasongko, Chuan Seng Tan, Josep Pou, Frede Blaabjerg, and Amit Kumar Gupta. "Condition Monitoring of DC-Link Capacitors Using Goertzel Algorithm for Failure Precursor Parameter and Temperature Estimation." IEEE Transactions on Power Electronics 35, no. 6 (June 2020): 6386–96. http://dx.doi.org/10.1109/tpel.2019.2951859.

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Slekas, Gediminas, Paulius Ragulis, Dalius Seliuta, and Zilvinas Kancleris. "Using of Generalized Goertzel Algorithm for FDTD Calculation of the Transmission and Reflection Spectra of Periodic Structures." IEEE Transactions on Electromagnetic Compatibility 59, no. 6 (December 2017): 2038–41. http://dx.doi.org/10.1109/temc.2017.2678547.

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33

Dobiński, Grzegorz, Sławomir Pawłowski, and Marek Smolny. "Amplitude Estimation Technique for Intermittent Contact Atomic Force Microscopy." International Journal of Measurement Technologies and Instrumentation Engineering 6, no. 2 (July 2017): 29–42. http://dx.doi.org/10.4018/ijmtie.2017070103.

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This article describes how one of the biggest challenges in designing of high-speed intermittent contact atomic force microscope (AFM) is the construction of a fast amplitude detector. The measurement techniques commonly used in commercial microscopes, such as RMS to DC converters or lock-in amplifiers often do not provide sufficient bandwidth to perform high speed imaging. On the other hand, many techniques developed especially for high-speed AFM are characterized by poor signal-to-noise ratio. In this paper, a novel amplitude estimation method based on the generalized Goertzel algorithm is presented. The detection system, composed of 16-bit 100 mega-samples per second analog-to-digital converter and field-programmable gate array device, allows to measure the signal amplitude within the time comparable to one oscillation cycle of the AFM cantilever. The effectiveness and validity of the designed detector were investigated by computer simulation. High spatial resolution of the presented method implemented in the actual atomic force microscopy system is also demonstrated.

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Saberkari, Hamidreza, Mousa Shamsi, Hamed Heravi, and Mohammad Hossein Sedaaghi. "A Novel Fast Algorithm for Exon Prediction in Eukaryotic Genes using Linear Predictive Coding Model and Goertzel Algorithm based on the Z-Curve." International Journal of Computer Applications 67, no. 17 (April 18, 2013): 25–38. http://dx.doi.org/10.5120/11489-7194.

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35

Reljic, Dejan, Josif Tomic, and Zeljko Kanovic. "Application of the Goertzel’s algorithm in the airgap mixed eccentricity fault detection." Serbian Journal of Electrical Engineering 12, no. 1 (2015): 17–32. http://dx.doi.org/10.2298/sjee1501017r.

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In this paper, a suitable method for the on-line detection of the airgap mixed eccentricity fault in a three-phase cage induction motor has been proposed. The method is based on a Motor Current Signature Analysis (MCSA) approach, a technique that is often used for an induction motor condition monitoring and fault diagnosis. It is based on the spectral analysis of the stator line current signal and the frequency identification of specific components, which are created as a result of motor faults. The most commonly used method for the current signal spectral analysis is based on the Fast Fourier transform (FFT). However, due to the complexity and memory demands, the FFT algorithm is not always suitable for real-time systems. Instead of the whole spectrum analysis, this paper suggests only the spectral analysis on the expected airgap fault frequencies employing the Goertzel?s algorithm to predict the magnitude of these frequency components. The method is simple and can be implemented in real-time airgap mixed eccentricity monitoring systems without much computational effort. A low-cost data acquisition system, supported by the LabView software, has been used for the hardware and software implementation of the proposed method. The method has been validated by the laboratory experiments on both the line-connected and the inverter-fed three-phase fourpole cage induction motor operated at the rated frequency and under constant load at a few different values. In addition, the results of the proposed method have been verified through the motor?s vibration signal analysis.

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36

Papathanasopoulos, Dimitrios A., Konstantinos N. Giannousakis, Evangelos S. Dermatas, and Epaminondas D. Mitronikas. "Vibration Monitoring for Position Sensor Fault Diagnosis in Brushless DC Motor Drives." Energies 14, no. 8 (April 16, 2021): 2248. http://dx.doi.org/10.3390/en14082248.

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A non-invasive technique for condition monitoring of brushless DC motor drives is proposed in this study for Hall-effect position sensor fault diagnosis. Position sensor faults affect rotor position feedback, resulting in faulty transitions, which in turn cause current fluctuations and mechanical oscillations, derating system performance and threatening life expectancy. The main concept of the proposed technique is to detect the faults using vibration signals, acquired by low-cost piezoelectric sensors. With this aim, the frequency spectrum of the piezoelectric sensor output signal is analyzed both under the healthy and faulty operating conditions to highlight the fault signature. Therefore, the second harmonic component of the vibration signal spectrum is evaluated as a reliable signature for the detection of misalignment faults, while the fourth harmonic component is investigated for the position sensor breakdown fault, considering both single and double sensor faults. As the fault signature is localized at these harmonic components, the Goertzel algorithm is promoted as an efficient tool for the harmonic analysis in a narrow frequency band. Simulation results of the system operation, under healthy and faulty conditions, are presented along with the experimental results, verifying the proposed technique performance in detecting the position sensor faults in a non-invasive manner.

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Farsani, MahsaSaffari, Masoud RezaAghabozorgi Sahhaf, and Vahid Abootalebi. "Performance improvement of the Goertzel algorithm in estimating of protein coding regions using modified anti-notch filter and linear predictive coding model." Journal of Medical Signals & Sensors 6, no. 3 (2016): 130. http://dx.doi.org/10.4103/2228-7477.186882.

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Rubiano-Muriel, Borja, José Luis Lázaro-Galilea, Alfredo Gardel-Vicente, Álvaro De-La-Llana-Calvo, and Ignacio Bravo-Muñoz. "Development of an Optical Signal-Based IPS from an MCU-SoC." Electronics 9, no. 5 (May 9, 2020): 782. http://dx.doi.org/10.3390/electronics9050782.

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In this work, we have studied the integration of an optical signal-based Indoor Positioning System (IPS) capable of supporting multi-access discrimination techniques. The research analyzes the different techniques and conditions that can be used to develop an IPS using a microcontroller unit (MCU)-based system-on-chip (SoC) systems. The main goal is to be able to integrate into the MCU both the hardware and software requirements for an IPS detector. In this way, different strategies that can implement multi-access discrimination using Frequency-division multiple access (FDMA) have been tested, such as I/Q demodulation, digital filtering, and discrete Fourier transform (DFT). This analysis has found a good technique to be executed in an MCU-based SoC, the DFT implemented through the Goertzel’s algorithm. The empirical tests carried out concluded that, using only one an MCU with the required HW and tuned SW, 15 position measurements per second were computed, with high accuracy in the 3-D positioning, with errors of less than 1 cm in a test area of 3.5 × 3.5 m 2 . The main contribution of the paper is the implementation of the optical signal based IPS in an MCU-SoC that includes signal acquisition and processing. The digital filtering or spectral processing for up to 16 received signals makes this IPS system very attractive from a design and cost point of view.

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Trzeciak, Justin, and James A. Mynderse. "Application of Goertzel Algorithm to Piezoresistive Pressure Sensing Fabric for Custom Fitting of Wheelchair Cushions." ASME Letters in Dynamic Systems and Control 1, no. 2 (November 19, 2020). http://dx.doi.org/10.1115/1.4048934.

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Abstract Pressure sores are a costly, painful problem for wheelchair users, caused by prolonged periods of mechanical loading. A common location of pressure sores in wheelchair users is the tissue between the ischial tuberosities and the seating surface. The pressure exerted on the tissue, or interface pressure, can be measured using piezoresistive fabric. This work demonstrates the use of the Goertzel algorithm for efficiently acquiring interface pressure data from a fabric assembly consisting of a layer of piezoresistive fabric between two layers of fabric with conducting and non-conducting stripes. The Goertzel algorithm was used to sample sums of sine waves from the conductive columns of the fabric assembly and calculate the amplitudes of each component sine wave corresponding to the local interface pressure. The Goertzel algorithm is more efficient for this application than a fast Fourier transform due to the limited number of calculated frequency bins needed for this application and more freedom in choice of sample size. The algorithm was successfully used to generate two-dimensional, 32 × 32 sensel interface pressure maps once per second. The Goertzel algorithm can be used in tandem with generated sine waves to measure interface pressure from piezoresistive fabric. Low-cost, accurate interface pressure measurements will help lessen the risk of pressure sores in wheelchair users.

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"Determination of Spectral Characteristics in a Vibration Sensor Microcircuit." International Journal of Innovative Technology and Exploring Engineering 9, no. 3 (January 10, 2020): 2044–50. http://dx.doi.org/10.35940/ijitee.c8834.019320.

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This article discusses aspects of the implementation of digital signal processing algorithms in devices with multifunctional diagnostics of bearing assemblies in integrated-circuit form. The possibility of applying the effective implementation of spectral analysis algorithms on a defined basis is considered. The structural flow chart of the filter which performs Goertzel transformation and schemes for implementation of the fast Fourier transform unit is shown. To solve these problems in Matlab environment were developed mathematical models of Goertzel filter of fast Fourier transform unit was simulated operation of these units for specifying of parameters and evaluation of achieved characteristics. After the evaluation of simulation results, 2 types of spectral analysis units were developed: Goertzel transformation units for accurate calculations and fast Fourier transform units for analysis during the whole operating band. The created units of spectral analysis make it possible to efficiently solve the problems of bearing diagnostics, obtain information on the video spectrum of the vibration signal in the full frequency band and at the same time to determine the exact value or vibration levels at the characteristic frequencies.

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Sysel, Petr, and Pavel Rajmic. "Goertzel algorithm generalized to non-integer multiples of fundamental frequency." EURASIP Journal on Advances in Signal Processing 2012, no. 1 (March 6, 2012). http://dx.doi.org/10.1186/1687-6180-2012-56.

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"FREQUENCY-BASED FEATURES FOR BEARINGS CONDITION MONITORING EXTRACTED WITH GOERTZEL ALGORITHM." International Journal of Mechatronics and Applied Mechanics 1, no. 5 (August 1, 2019). http://dx.doi.org/10.17683/ijomam.issue5.28.

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Maity, Arunit, Sarthak Bhargava, and Prakasam P. "Efficient Dual-tone Multi-frequency Signal Detection using a KNN Classifier." International Journal of Scientific Research in Science and Technology, October 1, 2020, 208–24. http://dx.doi.org/10.32628/ijsrst207543.

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The requirement for an efficient method for noise-robust detection of Dual-tone Multi-frequency (DTMF) signals keeping in mind the continuous evolution of telecommunication equipment is conspicuous. A machine learning based approach has been proposed in this research article to detect DTMF tones under the influence of various noises and frequency variations by employing the K-Nearest Neighbor (KNN) Algorithm. In order to meet accurate classification/detection requirements for various real-world requirements, a total of four KNN models have been created and compared, and the best one proposed for real-time deployment. Two datasets have been amassed, a clean dataset without noise and a noisy augmented dataset with perturbations that are observed in telecommunication channels such as additive white gaussian noise (AWGN), amplitude attenuation, time shift/stretch etc. Mel-Frequency Cepstral Coefficients (MFCC) and Goertzel’s Algorithm (used to estimate the absolute Discrete Fourier Transform (DFT) values for the fundamental DTMF frequencies) are employed to calculate features to be fed to the KNN models. The four models differ in being trained with and without the augmented data using the two aforementioned feature extraction algorithms, namely MFCCs calculation and the Goertzel’s algorithm. The proposed models have been verified and validated with unseen noisy testing data and it was found that the proposed KNN model D outperformed all the other models with a macro recall, precision and F1 classification score of 97.7, 97.70625 and 97.70046 respectively. The proposed model is also computationally inexpensive and showcases relatively low computing time and complexity.

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Kim, Jeongkuk. "Comparison of the Number of Iterations for Noise Cancelling of ECG with DFT Algorithm vs. Goertzel Algorithm." Journal of Korean Institute of Information Technology 12, no. 8 (August 31, 2014). http://dx.doi.org/10.14801/kitr.2014.12.8.33.

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Goswami, Soumyajit, Arghya Sarkar, and Samarjit Sengupta. "Digital Metering of Electrical Power Components Using Adaptive Non-Uniform Discrete Short Time Fourier Transform." International Journal of Emerging Electric Power Systems 20, no. 4 (August 13, 2019). http://dx.doi.org/10.1515/ijeeps-2018-0283.

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Abstract This paper presents an adaptive Goertzel filter bank based discrete short time Fourier transform (DSTFT) implementation algorithm, called adaptive non-uniform discrete short time Fourier transform (ANDSTFT) for online measurement of electrical power components using IEEE Standard 1459–2010. The proposed ANDSTFT algorithm utilizes effective combination of non-uniform discrete Fourier transform (NDFT) and window method to detect the spectrum of each individual finite short time segment of power signals at distinct, arbitrarily located frequencies. Compared with the well-established technique such as windowed FFT interpreted DSTFT based approaches, the proposed method offers (i) better accuracy (ii) higher degree of immunity and insensitivity to noise, and (iii) reduced computational complexity per sample interval. The simulation results have been given and its response time and accuracy have been compared with the conventional windowed FFT interpreted DSTFT based techniques. Real-time implementation of the proposed approach has also been presented.

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Kececioglu, O. Fatih, Ahmet Gani, and Mustafa Sekkeli. "A performance comparison of static VAr compensator based on Goertzel and FFT algorithm and experimental validation." SpringerPlus 5, no. 1 (March 31, 2016). http://dx.doi.org/10.1186/s40064-016-2034-7.

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Martinez-Roman, Javier, Ruben Puche-Panadero, Carla Terron-Santiago, Angel Sapena-Bano, Jordi Burriel-Valencia, and Manuel Pineda-Sanchez. "Low-Cost Diagnosis of Rotor Asymmetries of Induction Machines at Very Low Slip with the Goertzel Algorithm Applied to the Rectified Current." IEEE Transactions on Instrumentation and Measurement, 2021, 1. http://dx.doi.org/10.1109/tim.2021.3115216.

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Maity, Arunit, P. Prakasam, and Sarthak Bhargava. "Robust dual-tone multi-frequency tone detection using k-nearest neighbour classifier for a noisy environment." Applied Computing and Informatics ahead-of-print, ahead-of-print (April 1, 2021). http://dx.doi.org/10.1108/aci-10-2020-0105.

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Purpose Due to the continuous and rapid evolution of telecommunication equipment, the demand for more efficient and noise-robust detection of dual-tone multi-frequency (DTMF) signals is most significant. Design/methodology/approach A novel machine learning-based approach to detect DTMF tones affected by noise, frequency and time variations by employing the k-nearest neighbour (KNN) algorithm is proposed. The features required for training the proposed KNN classifier are extracted using Goertzel's algorithm that estimates the absolute discrete Fourier transform (DFT) coefficient values for the fundamental DTMF frequencies with or without considering their second harmonic frequencies. The proposed KNN classifier model is configured in four different manners which differ in being trained with or without augmented data, as well as, with or without the inclusion of second harmonic frequency DFT coefficient values as features. Findings It is found that the model which is trained using the augmented data set and additionally includes the absolute DFT values of the second harmonic frequency values for the eight fundamental DTMF frequencies as the features, achieved the best performance with a macro classification F1 score of 0.980835, a five-fold stratified cross-validation accuracy of 98.47% and test data set detection accuracy of 98.1053%. Originality/value The generated DTMF signal has been classified and detected using the proposed KNN classifier which utilizes the DFT coefficient along with second harmonic frequencies for better classification. Additionally, the proposed KNN classifier has been compared with existing models to ascertain its superiority and proclaim its state-of-the-art performance.

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