Accurate Frequency Estimator of Sinusoid Based on Interpolation of FFT and DTFT

Fan, Lei; Qi, Guoqing; Jin, Jiyu; Liu, Jinyu; Wang, Zhisen

doi:10.1109/access.2020.2977978

Cited by 32 publications

(16 citation statements)

References 28 publications

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“…The boom height variation data were divided into 100 millisecond intervals consisting of 3000 points. Zero padding was used to increase the number of points to 3072 before the FFT was applied to each interval [27]. The primary frequency and amplitude were obtained using MATLAB® R2016b (MathWorks, USA) software.…”

mentioning

confidence: 99%

Field Variation Characteristics of Sprayer Boom Height Using a Newly Designed Boom Height Detection System

et al. 2021

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To ensure boom sprayer safety and spraying efficiency, the sprayer boom height must be adjusted during pesticide application. The field variation characteristics of the sprayer boom height are the basis of the boom balance adjustment. A boom height detection system based on ultrasonic sensors was designed. Field tests were performed in 2.0 ha of vacant fields and 13.44 ha of wheat stubble fields. A signal processing method based on the K-means clustering algorithm was used to preprocess the ultrasonic sensor data. The results showed that the K-means clustering algorithm could effectively improve the detection accuracy of an ultrasonic sensor. The boom height variation was greatest at the sides of the boom, and the primary frequencies of the boom height variation were concentrated within a low-frequency band from 0 Hz to 1 Hz. The U-turn operation was more likely to cause the boom to contact the crop canopy or the ground than row operation. As the spraying speed increased, the maximum boom height variation and maximum roll angle increased; these primary components decreased in frequency, and the amplitude clearly increased. The maximum boom height variation exceeded 50 cm, and the maximum roll angle exceeded 3°, which not only aggravated the droplet drift but also caused damage to the boom and nozzles due to contact with the ground or the crop canopy. These findings can provide a theoretical basis for use in the development of an automatic boom height adjustment system.

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confidence: 99%

Field Variation Characteristics of Sprayer Boom Height Using a Newly Designed Boom Height Detection System

et al. 2021

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“…In [ 15 , 16 , 17 ] and the references therein, good DFT interpolators were presented; however, despite some accuracy gains, they were computationally more expensive than earlier methods in the literature were.…”

Section: A Brief Overview Of Classical Fe For Single Tonesmentioning

confidence: 99%

Deep Learning versus Spectral Techniques for Frequency Estimation of Single Tones: Reduced Complexity for Software-Defined Radio and IoT Sensor Communications

Almayyali

Hussain

2021

Sensors

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Despite the increasing role of machine learning in various fields, very few works considered artificial intelligence for frequency estimation (FE). This work presents comprehensive analysis of a deep-learning (DL) approach for frequency estimation of single tones. A DL network with two layers having a few nodes can estimate frequency more accurately than well-known classical techniques can. While filling the gap in the existing literature, the study is comprehensive, analyzing errors under different signal-to-noise ratios (SNRs), numbers of nodes, and numbers of input samples under missing SNR information. DL-based FE is not significantly affected by SNR bias or number of nodes. A DL-based approach can properly work using a minimal number of input nodes N at which classical methods fail. DL could use as few as two layers while having two or three nodes for each, with the complexity of O{N} compared with discrete Fourier transform (DFT)-based FE with O{Nlog2 (N)} complexity. Furthermore, less N is required for DL. Therefore, DL can significantly reduce FE complexity, memory cost, and power consumption, which is attractive for resource-limited systems such as some Internet of Things (IoT) sensor applications. Reduced complexity also opens the door for hardware-efficient implementation using short-word-length (SWL) or time-efficient software-defined radio (SDR) communications.

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“…In [1], an iterative approach for single-tone frequency estimation is presented; however, despite their benefits in accuracy, such approaches can be computationally expensive or complex. In [15,16,17] and the References therein, good DFT-interpolators have been presented, however, despite some accuracy gains, they are computationally more expensive that earlier methods in the literature. Reduced complexity is a property that can be very useful for some resource-limited systems like wireless sensor networks [18].…”

Section: A Brief Overview Of Classical Fe For Single-tonesmentioning

confidence: 99%

Deep Learning versus Spectral Techniques for Frequency Estimation of Single-Tones: Reduced Complexity for SDR and IoT Sensor Communications

Almayyali¹,

Zm²

2021

Preprint

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Despite the increasing role of machine learning in various fields, very few works considered artificial intelligence for frequency estimation (FE). This work presents a comprehensive analysis of deep-learning (DL) approach for frequency estimation of single-tones. It is shown that DL network with two layers having a few nodes can estimate frequency more accurately than well-known classical techniques. The study is comprehensive, filling gaps of existing works, where it analyzes error under different signal-to-noise ratios, numbers of nodes, and numbers of input samples; also, under missing SNR information. It is found that DL-based FE is not significantly affected by SNR bias or number of nodes. DL-based approach can work properly using minimal number of input nodes N at which classical methods fail. It is possible for DL to use as little as two layers with two or three nodes each, with complexity of O{N} versus O{Nlog2 (N)} for DFT-based FE, noting that less N is required for DL. Hence, DL can significantly reduce FE complexity, memory, cost, and power consumption, making DL-based FE attractive for resource-limited systems like some IoT sensor applications. Also, reduced complexity opens the door for hardware-efficient implementation using short word-length (SWL) or time-efficient software-defined radio (SDR) communications.

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Accurate Frequency Estimator of Sinusoid Based on Interpolation of FFT and DTFT

Cited by 32 publications

References 28 publications

Field Variation Characteristics of Sprayer Boom Height Using a Newly Designed Boom Height Detection System

Field Variation Characteristics of Sprayer Boom Height Using a Newly Designed Boom Height Detection System

Deep Learning versus Spectral Techniques for Frequency Estimation of Single Tones: Reduced Complexity for Software-Defined Radio and IoT Sensor Communications

Deep Learning versus Spectral Techniques for Frequency Estimation of Single-Tones: Reduced Complexity for SDR and IoT Sensor Communications

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