Applications of Independent Component Analysis in Wireless Communication Systems

Uddin, Zahoor; Ahmad, Ayaz; Iqbal, Muhammad; Naeem, Muhammad

doi:10.1007/s11277-015-2565-1

Cited by 32 publications

(12 citation statements)

References 71 publications

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“…Independent component analysis (ICA) is one of the most important BSS techniques. ICA is based on the statistical independence and non‐Gaussianity of the source signals in order to separate the recorded mixed signals [19].…”

Section: Impact Of Emi On Derivative Methodsmentioning

confidence: 99%

“…Furthermore, we examine our method in the presence of substation EMI. Since our method delivers false peaks due to the substation EMI, we employ the fast independent component analysis (FastICA) algorithm of the blind source separation (BSS) technique [19] to blindly estimate our grounding grid signal. Moreover, we also investigate the performance of the FastICA algorithm by taking the error evaluation criteria into account.…”

Section: Introductionmentioning

confidence: 99%

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Inverse features extraction for substation grounding grid: derivative and ICA combinatorial approach

Qamar¹,

Uddin²,

Yang

2019

IET Generation, Transmission & Distribution

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Section: Impact Of Emi On Derivative Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Inverse features extraction for substation grounding grid: derivative and ICA combinatorial approach

Qamar¹,

Uddin²,

Yang

2019

IET Generation, Transmission & Distribution

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“…ICA requires statistically independent and non‐Gaussian source signals. Working principles of ICA can be observed from 50 where a multi‐user environment with “ n ” number of sending and receiving antennas are considered. Let the transmitted signals’ vectors are [ s 1 , s 2 , s 3 , …, s n ] and received mixed data vectors are [ x 1 , x 2 , x 3 , …, x n ].…”

Section: Independent Component Analysismentioning

confidence: 99%

“…At the receiving end, in Figure 2, a linear mixture of the transmitted source signals is received. The mixed received signals are first un‐mixed through the FastICA algorithm of ICA 50 and then processed to observe various faults in the system. The FastICA algorithm is summarized in Algorithm , where E is the expectation operator, g is the contrast function used for performance optimization of the algorithm, P is the maximum number of iterations, W is the un‐mixing matrix and is inverse of the mixing matrix A , and T is the transpose.…”

Section: The Proposed Workmentioning

confidence: 99%

ICA ‐based solar photovoltaic fault diagnosis

Qureshi

Uddin

Satti

et al. 2020

Int Trans Electr Energ Syst

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Summary Faults are natural for any practical system including photovoltaic (PV) power generation systems that need to be diagnosed accordingly to maximize its efficiency. However, faults can be rectified only if they are diagnosed correctly. Therefore, a credible and reliable diagnosis is required for maintenance of the system. In this paper, independent component analysis (ICA)‐based wireless fault diagnosis technique is proposed for solar PV systems. ICA enables us to communicate without having any prior information about the signals and the wireless channel. The proposed technique can diagnose faults related to electrical failures, climatic impacts, variable irradiance, and irregular cell temperature. Effectiveness of the proposed technique is evaluated through extensive simulations utilizing practical data.

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“…Independent component analysis (ICA) is the most used algorithms to realize BSS [18]. More improved ICA algorithms, such as FastICA and ASS-GAICA [19], have been proposed by researchers to enhance the applicability of the ICA in various fields.…”

Section: Introductionmentioning

confidence: 99%

An EEMD-Based Electromagnetic Induction Method for Nondestructive Testing of Buried Metal Conductors

et al. 2019

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Nondestructive testing of substation grounding grids is an issue that has increasing importance. The traditional EMI method transforms the condition of the undergrounding conductors to the surficial induced electric signal in the sensing coil. However, The EMI signals excited by multiple coexisted faults combining with other unknown noises surrounding the substation often cause the failure of detection. Therefore, the observed EMI signals rather complex and cannot be used directly. To address this problem, the separation of individual signatures from the mixture is posed as an SCBSS problem. To extract the induced signal, an EEMD-based EMI method is proposed. The desired signal is then reconstructed to visualize the structure of the grounding grids by a virtual instrument that consists of DAQ and digital signal processing modules. The numerical simulation and practical experiments are employed. The results show the proposed method can be used to effectively detect the topological structure of grounding grid in real substations' electromagnetic environment.INDEX TERMS Grounding grid, electromagnetic induction (EMI), ensemble empirical mode decomposition (EEMD), single channel blind source separation (SCBSS), nondestructive testing (NDT).

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Applications of Independent Component Analysis in Wireless Communication Systems

Cited by 32 publications

References 71 publications

Inverse features extraction for substation grounding grid: derivative and ICA combinatorial approach

Inverse features extraction for substation grounding grid: derivative and ICA combinatorial approach

ICA ‐based solar photovoltaic fault diagnosis

An EEMD-Based Electromagnetic Induction Method for Nondestructive Testing of Buried Metal Conductors

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