Article
Computer Science, Information Systems
Zhongqiang Luo, Ruiming Guo, Chengjie Li
Summary: This paper proposes an independent vector analysis (WA) detection receiver for blindly deconvolving the convolutive mixtures of digitally modulated signals for wireless communications. The method jointly carries out separation work for different frequency bin data fusion, and solves the random permutation problem of separation signals by exploiting the dependencies of frequency bins. Simulation results and analysis demonstrate the effectiveness of the proposed detection method.
Article
Engineering, Electrical & Electronic
Andreas Brendel, Thomas Haubner, Walter Kellermann
Summary: In many scenarios, acoustic sources recorded in an enclosure are often observed together with interfering sources, making convolutive Blind Source Separation (BSS) a crucial problem in audio signal processing. Methods based on Independent Component Analysis (ICA) are particularly significant as they have minimal assumptions and allow for blindness in the original source signals and acoustic propagation path. This paper aims to establish a comprehensive framework by exploring the common building blocks and differences between various algorithms, including Frequency Domain ICA (FD-ICA), Independent Vector Analysis (IVA), and TRIple-N Independent component analysis for CONvolutive mixtures (TRINICON), thus bridging the gap in understanding the relation to TRINICON.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2023)
Article
Acoustics
Jianjun Gu, Dingding Yao, Junfeng Li, Yonghong Yan
Summary: This study introduces a new algorithm called SCGC-AuxIVA, which solves the problem of output scale ambiguity in blind source separation. It achieves comparable separation performance to existing methods while requiring less computational cost.
Article
Anthropology
Enrique Cerrillo-Cuenca, Marcela Sepulveda, Zaray Guerrero-Bueno
Summary: Independent Component Analysis (ICA) is an effective method for separating different colors found in rock paintings into discrete images or components, showing more accuracy in separating panels with multiple color types compared to Principal Component Analysis (PCA).
JOURNAL OF ARCHAEOLOGICAL SCIENCE
(2021)
Article
Engineering, Electrical & Electronic
YunPeng Li
Summary: Independent Component Analysis (ICA) is a method to recover mutually independent sources from their linear mixtures. This study proposes a novel method based on the second-order approximation of minimum discrimination information (MDI) to improve the performance of FastICA algorithm when introducing more nonlinear functions.
IEEE SIGNAL PROCESSING LETTERS
(2022)
Article
Computer Science, Artificial Intelligence
Zahoor Uddin, Muhammad Altaf, Ayaz Ahmad, Aamir Qamar, Farooq Alam Orakzai
Summary: This article presents a technique using independent vector analysis (IVA) for eliminating various artifacts in electrocardiogram (ECG) signals. The technique combines canonical correlation analysis (CCA) and independent component analysis (ICA) to effectively separate mixed data. The proposed technique is practical and outperforms CCA and ICA by minimizing changes to the ECG signals while removing artifacts.
PEERJ COMPUTER SCIENCE
(2023)
Article
Engineering, Electrical & Electronic
Jianjun Gu, Longbiao Cheng, Dingding Yao, Junfeng Li, Yonghong Yan
Summary: In this study, the impact of source sparsity on the performance of the independent vector analysis (IVA) algorithm for blind source separation is investigated. The IVA algorithm, originally developed under the assumption of statistical independence between the sources, has made significant progress in recent years. However, there is limited research on its performance under different sparsity conditions. The study mathematically analyzes the performance of IVA in permutation alignment and establishes a direct correlation with the frame-level W-disjoint orthogonality (F-WDO) of the sources. The experimental results demonstrate a strong positive correlation between a quantitative measure of F-WDO and the performance of the IVA algorithm under various conditions.
Article
Geosciences, Multidisciplinary
Guangde Zhang, Huaibang Zhang, Li You, Yuyong Yang, Huailai Zhou, Bohan Zhang, Wujin Chen, Liyuan Liu
Summary: A joint denoising method using seismic velocity and acceleration signals is proposed in this study. It utilizes Independent Component Analysis (ICA) to obtain initial effective signals and noise, and then applies a Kalman filter to improve the denoising results. The method combines the advantages of low-frequency seismic velocity signals and high-frequency and high-resolution acceleration signals, and overcomes the problem of inconsistent stratigraphic reflection caused by large spacing between adjacent traces, thus improving the signal-to-noise ratio (SNR) of seismic data.
FRONTIERS IN EARTH SCIENCE
(2023)
Article
Optics
Wei Wang
Summary: A multivariate Gaussian statistics model has been applied to study the higher-order statistics of polarization speckle at two spatial or temporal points. By assuming a Gaussian distribution for the random electric field, the joint probability density functions of the Stokes parameters and parameters characterizing the polarization ellipse are obtained. The corresponding statistics of isotropic polarization speckle at two points are investigated to obtain the joint and conditional probability densities of these random variables.
JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION
(2022)
Article
Engineering, Electrical & Electronic
Rintaro Ikeshita, Tomohiro Nakatani
Summary: The proposed method integrates weighted prediction error dereverberation and independent vector extraction algorithms for blind source separation in a noisy reverberant environment, achieving faster convergence compared to conventional methods. The optimization problem is simplified by exploiting the stationary condition, making it computationally efficient.
IEEE SIGNAL PROCESSING LETTERS
(2021)
Article
Telecommunications
Zhongqiang Luo, Mingchun Li, Chengjie Li
Summary: This paper proposes a new detection mechanism called independent vector analysis (IVA) for blind adaptive signal recovery in MIMO IoT green communication. The IVA method reduces inter-carrier interference and multiple access interference, enhancing the separation performance.
CHINA COMMUNICATIONS
(2022)
Article
Mathematics, Applied
Wenpo Yao, Wenli Yao, Rongshuang Xu, Jun Wang
Summary: This paper conducts a systematic comparative analysis of the relationship between time irreversibility (TIR) and amplitude irreversibility (AIR) based on statistical descriptions and numerical simulations. The results show that TIR and AIR are fundamentally different nonequilibrium descriptors and have similar outcomes when used to analyze both model series and real-world signals. Overall, comparative analysis of TIR and AIR contributes to our understanding of nonequilibrium features and broadens the scope of quantitative nonequilibrium measures.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2023)
Article
Engineering, Electrical & Electronic
Zbynek Koldovsky, Vaclav Kautsky, Petr Tichavsky
Summary: This article introduces new algorithms for ICE/IVE by combining nonstationary mixing and source models, allowing for a moving source-of-interest distribution. The proposed Gaussian source model shows benefits in frequency-domain speaker extraction. The algorithms are verified in simulations and demonstrate superior performance in convergence speed and extraction accuracy compared to existing algorithms.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2022)
Article
Chemistry, Analytical
Mengjie Xu, Jianhan Wang, Jiahui Mo, Xingfei Li, Lei Yang, Feng Ji
Summary: This article introduces a linear vibration sensor based on MHD technology, which meets the requirements for low-noisy measurement of acceleration, velocity, and micro-vibration in spacecraft during their development, launch, and orbit operations. To address the narrowband interference issue in practical testing, a single-channel blind signal separation method based on SSA and FastICA is proposed, which effectively separates the useful linear vibration signals.
Article
Engineering, Electrical & Electronic
Paris A. Karakasis, Nicholas D. Sidiropoulos
Summary: This study revisits the recent deterministic extensions of deep canonical correlation analysis (CCA) and explores their strengths and limitations. To overcome these limitations, a novel and efficient formulation is proposed, which models private components as conditionally independent given the common ones. Experiments with synthetic and real datasets demonstrate the effectiveness of the proposed approach.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2023)
