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Filters
Published in Afshin Samani, An Introduction to Signal Processing for Non-Engineers, 2019
What in practice one can achieve with frequency-based filters does not have a sharp and clear-cut frequency response as shown in Figure 8.1. There will always be a transition phase around the cut-off frequency. The cut-off frequency is often defined as a threshold where the power of the output signal drops to half of the power of components with no attenuation (Widmann, Schröger, and Maess, 2015) (often where the magnitude of the frequency response is 1, the filter does not attenuate the frequency components in that range). I already stated that the power spectrum of the output is proportional to the square of the magnitude of the system frequency response, and therefore, the cut-off frequency is the frequency limit where the magnitude of the frequency response drops to 12 of its magnitude when no attenuation is applied (often where the magnitude is 1).
Digital Filters and z-Transform
Published in Francis F. Li, Trevor J. Cox, Digital Signal Processing in Audio and Acoustical Engineering, 2019
Filters are synthesised systems to achieve desirable design objectives. Filter design represents the major part of classical digital signal processing. Although there are dozens of digital filter implementations, they can be categorised into two general types, namely FIR and IIR filters. The FIR filters have impulse responses of limited lengths, while the impulse responses of IIR filters are infinitely long. From a perspective of filter structure, FIR filters take a feed-forward structure; IIR filters employ recursive configurations or feed-back loops. This chapter has presented basic concepts of these two types of digital filters through some intuitive examples. Filter design is a vast area; the content included in this chapter is by no means comprehensive. The purpose of this chapter is to outline essential concepts and provide a starting point of filter design. With these concepts, readers can make use of filter design tools, such as those available on the MATLAB platform, to design filters for their application needs. For readers interested in more in-depth understanding and the state-of-the-art of filter design, they are referred to specialist texts.
Digital Filters
Published in Jerry C. Whitaker, Microelectronics, 2018
Jonathon A. Chambers, Sawasd Tantaratana, Bruce W. Bomar
Digital filtering is concerned with the manipulation of discrete data sequences to remove noise, extract information, change the sample rate, and perform other functions. Although an infinite number of numerical manipulations can be applied to discrete data (e.g., finding the mean value, forming a histogram), the objective of digital filtering is to form a discrete output sequence y(n) from a discrete input sequence x(n). In some manner or another, each output sample is computed from the input sequence—not just from any one sample, but from many, in fact, possibly from all of the input samples. Those filters that compute their output from the present input and a finite number of past inputs are termed finite impulse response (FIR), whereas those that use all past inputs are infinite impulse response (IIR). This chapter will consider the design and realization of both FIR and IIR digital filters and will examine the effect of finite wordlength arithmetic on implementing these filters.
A low complexity and high modularity design for continuously variable bandwidth digital filters
Published in International Journal of Electronics, 2023
Sushmitha Sajeevu, Sakthivel Vellaisamy
Digital filtering is one of the most dynamic tools of digital signal processing. Compared to analog filters, digital filters are less sensitive towards environmental changes, more flexible, programmable and can be easily standardised since they are simply software modules. With the advent of VLSI technology, digital filters are employed in a wide variety of signal processing platforms. Variable bandwidth digital FIR filters have several critical applications in the field of speech signal processing, digital communications, biomedical signal processing etc. (Laakso et al., 1996; Stoyanov & Kawamata, 1997). Digital channelizer is a prominent part of the digital front end (DFE) in a software-defined radio (SDR)-based Internet of things (IOT) platform (Zeineddine et al., 2019). The ability to support multiple bands of signal frequencies according to the user demand is a special feature of SDR (Venosa & Palmieri, 2011). Sharp filtering is necessary to reduce interference from adjacent channels in a digital channelizer. Variable bandwidth digital FIR filter can be used as an efficient digital channelizer.
Design of woven meta-materials for electronic textiles for functional applications
Published in The Journal of The Textile Institute, 2023
Hanen Zribi, Amine Hadj Taieb, Ignacio Gil, Raúl Fernández-García, Mònica Ardanuy
Metamaterials have recently received a great deal of attention because they are capable of achieving unusual electromagnetic responses, such as a negative index, not found in natural materials (Driscoll et al., 2006; Zhang et al., 2005). A metamaterial can be defined as an artificial crystal in which mesoscopic inclusion structures replace the microscopic atoms or molecular structures of natural materials (Driscoll et al., 2007). A frequency selective surface (FSS) or "spatial filter" can be integrated into the materials to obtain a metamaterial. In fact, it is a structure typically consisting of two-dimensional periodic metallic elements on a dielectric substrate (Dalkiliç, 2014). These structures provide either a band-stop filter frequency response to reject unwanted frequency bands, or a bandpass filter response by selecting a specific frequency range and allowing different signals to pass through that are not attenuated (Anwar et al., 2018).
Evaluation of the airport runway flexible pavement macro-texture using digital image processing technique (DIPT)
Published in International Journal of Pavement Engineering, 2022
Omid Ghaderi, Mohammad Abedini
Butterworth band-pass filter is a type of signal processing filter. There are generally three different forms of frequency filters: low-pass, high-pass and band-pass filters. Low-pass filters (LPF) only pass the low spatial frequency details of the input image, effectively smoothing the sharp intensity changes in the image. In contrast, high-pass filters (HPF) yield edge enhancement or edge detection in the spatial domain, because of high frequencies content of edges. Areas of rather constant grey-level consist of mainly low frequencies and are therefore suppressed. Band-pass filters leave the desired band of frequencies unchanged by multiplying an LPF and an HPF in the frequency domain. A band-pass filter is defined by setting two cut-off frequencies (f0 and f1; f0 > f1) as borders of band-pass for low-pass and high-pass filtering process that attenuating all frequencies smaller than f0 and higher than f1 and passes the frequencies between these two cut-offs. Band-pass filters could be used for edge enhancement by suppressing low frequencies while noises are reduced at the same time (Gonzalez and Woods 2008, Qidwai and Chen 2009).