The Fast Fourier Transform

Nussbaumer, Henri J.

doi:10.1007/978-3-662-00551-4_4

Cited by 221 publications

(97 citation statements)

References 6 publications

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“…A sliding window extracts noise free features such as mean, standard deviation, energy, and correlation in time or frequency domain. Additionally, the FFT [110] is used to compute the energy in the frequency domain. MLP, along with Bayesian Regularization Neural Network (BRNN), is used to model the relationship between the input, gait, and output (BAC value).…”

Section: Gharani Et Al [109]mentioning

confidence: 99%

Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

et al. 2020

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Gait is the locomotion attained through the movement of limbs and gait analysis examines the patterns (normal/abnormal) depending on the gait cycle. It contributes to the development of various applications in the medical, security, sports, and fitness domains to improve the overall outcome. Among many available technologies, two emerging technologies that play a central role in modern day gait analysis are: A) wearable sensors which provide a convenient, efficient, and inexpensive way to collect data and B) Machine Learning Methods (MLMs) which enable high accuracy gait feature extraction for analysis. Given their prominent roles, this paper presents a review of the latest trends in gait analysis using wearable sensors and Machine Learning (ML). It explores the recent papers along with the publication details and key parameters such as sampling rates, MLMs, wearable sensors, number of sensors, and their locations. Furthermore, the paper provides recommendations for selecting a MLM, wearable sensor and its location for a specific application. Finally, it suggests some future directions for gait analysis and its applications.

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Section: Gharani Et Al [109]mentioning

confidence: 99%

Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

et al. 2020

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“…The Fourier operator allows us to switch from the time domain to the frequency domain through a projection of the signal acquired over time on an orthonormal basis of complex exponentials. When we apply the Fourier transform, we do nothing but switch between two different representations of the same signal: the original signal is in the time domain, while the transformed signal is in the frequency domain [19,20]. The fast Fourier transform (FFT) is a mathematical transformation to a function, f, represented by Equation (1):…”

Section: Signal Descriptor Selectionmentioning

confidence: 99%

Sound Event Detection in Underground Parking Garage Using Convolutional Neural Network

Ciaburro

2020

BDCC

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Parking is a crucial element in urban mobility management. The availability of parking areas makes it easier to use a service, determining its success. Proper parking management allows economic operators located nearby to increase their business revenue. Underground parking areas during off-peak hours are uncrowded places, where user safety is guaranteed by company overseers. Due to the large size, ensuring adequate surveillance would require many operators to increase the costs of parking fees. To reduce costs, video surveillance systems are used, in which an operator monitors many areas. However, some activities are beyond the control of this technology. In this work, a procedure to identify sound events in an underground garage is developed. The aim of the work is to detect sounds identifying dangerous situations and to activate an automatic alert that draws the attention of surveillance in that area. To do this, the sounds of a parking sector were detected with the use of sound sensors. These sounds were analyzed by a sound detector based on convolutional neural networks. The procedure returned high accuracy in identifying a car crash in an underground parking area.

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“…The fast Fourier transform (FFT) [17] is an algorithm that computes the discrete Fourier transform (DFT) of a signal and converts it from the time or space domain to a representation in the frequency domain. FFTs are widely used in various fields and for different purposes, such as digital signal processing, data compression, polynomial multiplication, or even multiplication of large integers [36], [37].…”

Section: Appendix Fast Fourier Transformmentioning

confidence: 99%

“…One of the major challenges with searching over the 3D models themselves, as opposed to solely the metadata, is how to derive important characteristics from the models to allow for a robust search mechanism that encompasses both exact and partial matching. To solve this problem, we propose using the Fast Fourier Transform (FFT) [17] to convert models to the frequency domain 1 and deduce peak frequencies. We can use this peak information, as well as distances between peaks, to create a unique identifier, or "fingerprint", for each object.…”

Section: Introductionmentioning

confidence: 99%

Peak Your Frequency: Advanced Search of 3D CAD Files in the Fourier Domain

et al. 2020

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An ever-increasing number of industries are adopting additive manufacturing (AM), also known as 3D printing, to their production lifecycles for manufacturing parts. A computer aided design (CAD) model is used to manufacture the part. The capability for efficient search and retrieval of the CAD models from the database has become an essential need for designers and users. However, traditional search techniques perform poorly in the context of searching CAD designs. In this paper, we propose Fourier Fingerprint Search (FFS), a retrieval framework for 3D models that deduces and leverages critical shape characteristics for search. FFS introduces a novel search methodology that incorporates these characteristics and uses two advanced matching techniques that operate at different granularities and take into account unique patterns associated with each design. In addition, FFS supports both exact and partial matching in order to provide helpful and robust search results for any scenario. We investigate a diverse set of features and enhancements for search that allows for high adaptability in all situations, such as dividing shapes into smaller parts, surface interpolation, and two different types of rotation. We evaluate FFS using the FabWave CAD dataset with approximately 3000 manufacturing models with different configurations. Our experimental results demonstrate the efficiency and high accuracy of our approach for both exact and partial matching, rendering FFS a powerful framework for CAD model search.

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The Fast Fourier Transform

Cited by 221 publications

References 6 publications

Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

Sound Event Detection in Underground Parking Garage Using Convolutional Neural Network

Peak Your Frequency: Advanced Search of 3D CAD Files in the Fourier Domain

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