A Group of Inverse Filters Based on Stabilized Solutions of Fredholm Integral Equations of the First Kind

Zhang, Zhijie; Wang, Dai-Hua; Wang, Wenlian; Du, Hongliang; Jing, Zu

doi:10.1109/imtc.2008.4547120

Cited by 8 publications

(5 citation statements)

References 10 publications

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“…Inverse filters are commonly used in communication [1], speech processing, audio and acoustic systems [2,3], and instrumentation [4] to reverse the distortion of the signal incurred due to signal processing and transmission. The transfer characteristics of the system that caused the distortion should be known a priori and the inverse filter to be used should have a reciprocal transfer characteristic so as to result in an undistorted desired signal.…”

Section: Introductionmentioning

confidence: 99%

CDBA Based Universal Inverse Filter

Pandey

Negi

et al. 2013

ISRN Electronics

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Current difference buffered amplifier (CDBA) based universal inverse filter configuration is proposed. The topology can be used to synthesize inverse low-pass (ILP), inverse high-pass (IHP), inverse band-pass (IBP), inverse band-reject (IBR), and inverse all-pass filter functions with appropriate admittance choices. Workability of the proposed universal inverse filter configuration is demonstrated through PSPICE simulations for which CDBA is realized using current feedback operational amplifier (CFOA). The simulation results are found in close agreement with the theoretical results.

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Section: Introductionmentioning

confidence: 99%

CDBA Based Universal Inverse Filter

Pandey

Negi

et al. 2013

ISRN Electronics

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“…Additive colored Gaussian noise has nonflat power spectral density with correlated samples. Hence, y ( t ) is a random process and can be represented via Karhunen‐Loeve (KL) expansion as follows:

y false(t false) = \sum_{i = 0}^{\infty} Y_{i} ϕ_{i} false(t false),

where ϕ i ( t ) is the set of orthonormal basis functions, and Y i is

Y_{i} = \int_{0}^{T_{K}} y false(t false) ϕ_{i} false(t false) d t .

Here, KL coefficients Y i are statistically independent random variables since the orthonormal basis functions satisfy the necessary and sufficient condition, as follows:

\int_{0}^{T_{K}} R_{Y Y} false(t_{1}, t_{2} false) ϕ_{i} false(t_{2} false) d t_{2} = λ_{i} ϕ_{i} false(t_{1} false),

where t 1 , t 2 ∈[0, T K ] such that t 1 < t 2 as y ( t ) is WSS. Since the coefficients Y i are uncorrelated, it is therefore also true that

R_{Y Y} false(t_{1}, t_{2} false) = E ([], y false(t_{1} false) y false(t_{2} false)) = E false[Y_{h} Y_{i} false] = λ_{i} δ_{h i} .

Substituting y ( t ) from Equations and in gives

\begin{matrix} alignleft \\ align-1 Y_{i} & align-2 = \int_{0}^{T_{K}} n (t) {normalϕ}_{i} (t \end{matrix}

…”

Section: Mathematical Modeling Of Plidsmentioning

confidence: 99%

Physical‐layer intrusion detection system for smart jamming attacks

Yousaf

Loan

Babiceanu

et al. 2017

Trans Emerging Tel Tech

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In modern electronic warfare, physical-layer security threats have evolved from traditional jammers to smart jammers. Smart jammers, due to their stealthy nature, make wireless communication systems vulnerable. They can easily deceive a detection system. In this paper, a physical-layer intrusion detection system (PLIDS) for direct-sequence spread-spectrum systems is developed against smart jammers, which is efficient due to its high detection rates, usability, and accuracy. Smart jamming noise is modeled as wide-sense stationary, additive, and colored. This random process is decomposed into a series expansion of independent and uncorrelated random variables. The decomposition helps in designing a signal processing algorithm for PLIDS. The algorithm is easily implemented via correlators. PLIDS is evaluated using a simulated electronic warfare environment, and its performance is measured in terms of detection capability, precision, and accuracy. It performs error-free detection by adjusting its tuning parameter. Moreover, different performance tradeoffs are observed for different values of the tuning parameter. Finally, comparisons of PLIDS are done with competitive intrusion detection systems.

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“…Since inverse filters have a frequency response which is reciprocal of the frequency response of the system which caused the distortions, it is expected that this can be corrected by using an inverse filter. A survey of the work done on the realization of inverse filters indicates that before the publication of the 1997 paper of Adrian Leuciuc [1], although there had been mention of digital inverse filters from time-to-time [2][3][4][5][6], only the 1964 paper of Burch, Green and Grote [6] had discussed earlier about the restoration and correction of time functions by the synthesis of inverse filters on analog computers. Thus, with the sole exception of [6] and before the 1997 work of Leuciuc [1], no procedure or circuit appears to have been presented in the open technical literature to realize any kind of inverse analog filters using the now prevalent analog circuit building blocks.…”

Section: Introductionmentioning

confidence: 99%

Inverse Analog Filters: History, Progress and Unresolved Issues

2022

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This paper traces the history of the evolution of inverse analog filters (IAF) and presents a review of the progress made in this area to date. The paper, thus, presents the current state-of-the art of IAFs by providing an appraisal of a variety of realizations of IAFs using commercially available active building blocks (ABB), such as operational amplifiers (Op-amp), operational transconductance amplifiers (OTA), current conveyors (CC) and current feedback operational amplifiers (CFOA) as well as those realized with newer active building blocks of more recent origin, such as operational transresistance amplifiers (OTRA), current differencing buffered amplifiers (CDBA) and variants of current conveyors which, although not available as off-the-shelf ICs yet, can be implemented as complementary metal–oxide–semiconductors (CMOS) or be realized in discrete form using other commercially available integrated circuits (IC). In the end, some issues related to IAFs have been highlighted which need further investigation.

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A Group of Inverse Filters Based on Stabilized Solutions of Fredholm Integral Equations of the First Kind

Cited by 8 publications

References 10 publications

CDBA Based Universal Inverse Filter

CDBA Based Universal Inverse Filter

Physical‐layer intrusion detection system for smart jamming attacks

Inverse Analog Filters: History, Progress and Unresolved Issues

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