Frequency Spectrum Growth Index

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The Frequency Spectrum Growth Index is a quantitative measure used to assess the expansion of frequency bands in a given spectrum over time, often applied in telecommunications and signal processing to evaluate the efficiency and capacity of frequency utilization in various applications.

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Key research themes

1. How can frequency domain methods characterize long-term cycles in economic growth data?

This research area focuses on applying and comparing frequency domain techniques, including spectral analysis, wavelet transforms, and empirical mode decomposition, to analyze cyclical patterns in macroeconomic growth data, particularly US real GNP. The aim is to evaluate the existence, duration, and strength of economic growth cycles beyond standard short business cycles, addressing issues of non-stationarity and the validity of the classical 'typical' spectral shape hypothesis first proposed by Granger.

Long Cycles in Growth: Explorations Using New Frequency Domain Techniques with US Data

by Patrick Crowley

2016, SSRN Electronic Journal

Key finding: Using recent frequency domain methods such as discrete and continuous wavelet analysis and empirical mode decomposition on US real GNP data, the study finds no support for the classical 'typical' spectral shape implying... Read more

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N On-Spectral Frequency Indicators

by María Navascués

2022

Key finding: By employing polynomial orthogonal expansions, specifically Legendre series, as an alternative to Fourier-based spectral methods, the authors developed trend curves acting as low-pass filters for non-periodic or... Read more

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2. How does spectral analysis assist in predicting epidemic outbreak periodicities within time series data?

This theme investigates the applicability of spectral methods, especially Fourier analysis and its fast computational variant FFT, to epidemiological time series data for infectious diseases such as COVID-19. The research examines how smoothing techniques coupled with spectral analysis can reveal underlying periodicities in case numbers, to forecast future waves and assess temporal dynamics, especially useful given the nonlinear growth and complex transmission characteristics of pandemics.

Outbreaks Predictions of Covid19 in Tabasco, Mexico, using Fast Fourier Transform

by IJEACS UK

2022, Empirical Research Press Ltd., United Kingdom

Key finding: By applying a 7-day moving average smoothing to COVID-19 infection data from Tabasco, Mexico, followed by Fast Fourier Transform analysis, the authors identified significant underlying frequencies corresponding to periodic... Read more

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3. What are the advanced spectral analysis techniques and their applications for atmospheric and environmental variability monitoring?

This research area explores innovative spectral and wave amplitude indices developed for characterizing low-frequency atmospheric variability modes, planetary wave amplitudes, and environmental spectral indices from remote sensing data. These methods assist in modeling complex nonlinear, non-stationary climatic processes and spatially heterogeneous environmental changes, aiding in the interpretation and monitoring of phenomena such as blocking patterns, urban built-up land detection, land-surface temperature, and vegetative dynamics using satellite imagery.

An intercomparison between low-frequency variability indices

by Paolina Bongioannini Cerlini

2016

Key finding: Through probabilistic analysis of the wave amplitude index (WAI) derived from Northern Hemisphere winter geopotential heights, this study found that hemispheric planetary-wave activity modes modulate both the mean and... Read more

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The development of spectral indices for detecting built-up land areas and their relationship with land-surface temperature

by Majid Kiavarz

2022, International Journal of Remote Sensing

Key finding: By integrating multispectral and panchromatic Landsat satellite images, the authors introduced a novel spectral index combining blue band and shortwave infrared data (VbSWIR1-BI) and compared it with six existing indices for... Read more

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Mapping and analyzing temporal variability of spectral indices in the lowland region of Far Western Nepal

by Mahesh P R A S A D Awasthi

2023, Water Practice & Technology

Key finding: Using multi-decadal Landsat 5 TM and Landsat 8 OLI/TIRS data, the authors applied key spectral indices (NDVI, NDBI, NDWI) to quantify spatiotemporal changes in vegetation, urbanization, and water bodies in Far Western Nepal.... Read more

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All papers in Frequency Spectrum Growth Index

SPECTRUM ANALYSIS OF PARTIAL DISCHARGE SIGNALS

by BABAK SADEGHI

Partial Discharge (PD) is the major cause for insulation degradation. These discharges occur due to presence of voids or cracks or impurities inside the insulation. There are different types of partial discharges occurring in the... more

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CR-MAC: A MULTICHANNEL MAC PROTOCOL FOR COGNITIVE RADIO AD HOC NETWORKS

by International Journal of Computer Networks & Communications (IJCNC)

This paper proposes a cross-layer based cognitive radio multichannel medium access control (MAC) protocol with TDMA, which integrate the spectrum sensing at physical (PHY) layer and the packet scheduling at MAC layer, for the ad hoc... more

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Performance Analysis and Simulation of Rain Attenuation Models at 12–40 GHz Band for an Earth Space Path over Indian Cities

by Parth Panchal and

2016

As the shortage of the radio spectrum infers challenges in the Earth–Space Communication Link (ESCL) particularly in satellite communication, it is important to gauge the measure of attenuation to guarantee proficient usage of the... more

Fig. 1. Schematic presentation of an Earth—-Space path [5]. Parameters k and a can be obtained from ITU-R P.838-3 [4]. The predicted slant path attenuation exceeded for 0.01% of an average year is obtained from, where, Lz is the effective path length. The predicted attenuation to be exceeded for other percentages of an average year can be obtained from the value of Ap .9;(dB) as,

Fig. 2. At 40 GHz (a) ITU-R model; (b) Garcia—Lopez model. near 35 GHz as after 35 GHz, the attenuation values approach towards minor constant increment and shows near to zero increment at extremely high frequencies and at higher rain rate of above 80 mm/h as shown in Fig. 3(b).

Fig. 3. (a) SAM model (40 GHz); (b) Frequency response of SAM model. For frequency band like K, band (12 to 18 GHz), SAM model gives better results for rain rates of up to 60 mm/h, but if the rain rate exceed 60 mm/h, the attenuation values tend towards minor increment. For frequency near 40 GHz, the attenuation values starts reducing from the rain rate near 20 mm/h. Hence, the SAM model is not desirable and cannot be applied for higher frequencies above 35 GHz and even for higher rain rates. But for the operations in K,, band, SAM model gives reliable results and have varied attenuation values compared to the rain attenuation estimation models that are simulated.

4. Conclusion After analysing and simulating different rain attenuation models over different locations by integrating different parameters, it is observed that attenuation models gives results based on the parameters considered and each of the model have their own advantages and limitations. The ITU-R model and Garcfa—Lépez model performs well for higher frequencies and even for higher rain rate. Garcfa—Lo6pez’s method for rain attenuation considers more number of parameters and coefficients bringing result of good accuracy where ITU-R model underestimates higher rain rates. While SAM model gives varied attenuation values for lower frequencies as well as for lower rain rates. SAM model can be applicable in the Ku band and it is more suitable in cold regions where the rainfall rate is quite lower.

Fig. 4. At 40 GHz (a) Ahmedabad; (b) Bangalore.

Table 2. Ground station and satellite data from major cities of India The performance analysis of different rain attenuation models over major Indian cities is graphically represented. The rain attenuation models are implemented over MATLAB® software and the graph of frequency versus varying rain rate has been plotted. Fig. 2 shows the performance analysis of ITU-R model and Garcia—Lépez model at 40 GHz of operating frequency. It can be observed from Fig. 2 that the response of ITU-R model with respect to the attenuation is somewhat linear with different rain rates. The Garcia—Lépez model’s response of attenuation with varied rain rates is linear to the extent and gives kind of less or average attenuation values as compared to ITU-R model. Fig. 3 shows the attenuation and frequency response of SAM model. It can be observed from Fig. 3; SAM model gives varied attenuation response for different rain rates. SAM model performs well for the frequency up to

3. Simulation Table 1. Comparison of rain attenuation models

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Robust health evaluation of gearbox subject to tooth failure with wavelet decomposition

by Qiang Miao

Machinery condition monitoring is a key step to perform condition-based maintenance (CBM) policy. In this paper, a novel health evaluation method based on wavelet decomposition is proposed. In the process of wavelet decomposition, a new... more

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Frequency Spectrum Growth Index

Key research themes

1. How can frequency domain methods characterize long-term cycles in economic growth data?

2. How does spectral analysis assist in predicting epidemic outbreak periodicities within time series data?

3. What are the advanced spectral analysis techniques and their applications for atmospheric and environmental variability monitoring?

Related Topics

All papers in Frequency Spectrum Growth Index