Signal processing algorithms for noise reduction in high-dimensional systems

In the last years Independent Component Analysis (ICA) has been applied with success in signal processing and many algorithms have been developed in order to perform ICA. In this paper we review some algorithms, like INFOMAX (Bell and Sejnowski 1995), extended-INFOMAX (Lee, Girolami and Sejniowski 1997), FastICA (OjA, and Hyvarinen 1999), that solve the ICA problem under the assumption of the linear mixture model. We also show an overview of the nonlinear ICA algorithms and we discuss the MISEP (Almeida 2003). In order to test the performances of the reviewed algorithms, we present some applications of ICA in biomedical signal processing. In particular the application of ICA to the electroencephalographic (EEG) and surface electromyographic (sEMG) recordings are shown.

Independent component analysis (ICA) is a technique that extracts independent source signals by searching for a linear or nonlinear transformation which minimizes the statistical dependence between components. ICA has been used in a variety of signal processing applications including dimensionality reduction in hyperspectral image (HSI) analysis. Due to the computation complexities and convergence rates, ICA is very time-consuming for high volume or dimension data set like hyperspectral images. Hardware implementation provides not only an optimal parallelism environment, but also a potential faster and real-time solution. This work synthesizes a parallel ICA (pICA) algorithm on field programmable gate array (FPGA). In the proposed implementation method, the pICA is partitioned into three temporally independent functional modules, and each of which is synthesized individually with several ICA-related reconfigurable components (RCs) that are developed for reuse and retargeting purpose. All modules are then integrated into a design and development environment for performing many subtasks such as FPGA synthesis, optimization, placement and routing. In a case study, we synthesize the pICA algorithm for hyperspectral image dimensionality reduction on the pilchard reconfigurahle computing platform embedded with Xilinx: VIRTEX V1000E. The FPGA executes at the maximum frequency of 20.161 MHz, and the pilchard board transfers data directly with CPU on the 64-bit memory bus at the maximum frequency of 133MHz. The performance comparisons between the proposed and another two ICA-related FPGA implementations show that the proposed FPGA implementation of pICA has potential in performing complicated algorithms on large volume data sets.

Enhancing microplastic classification through filter-interfered FTIR spectra using dimensionality reduction and deep learning in low-dimensional spaces.

This study tested a combination of algorithms designed to improve cochlear implant performance in noise. A noise reduction (NR) algorithm, based on signal to noise ratio estimation was evaluated in combination with several directional microphone algorithms available in the Cochlear CP810 sound processor. Fourteen adult unilateral cochlear implant users participated in the study. Evaluation was conducted using word recognition in quiet, sentence recognition in noise, and subjective feedback via questionnaire after a period of take-home use. Music appreciation was also evaluated in a controlled listening task. The sentence recognition task measured speech reception threshold for 50% morphemes correct. The interfering maskers were speech-weighted noise and competing talkers, which were spatially separated from the target speech. In addition, the locations of the noise maskers changed during the test in an effort to replicate relevant real-world listening conditions. SmartSound directionality settings Standard, Zoom, and Beam (used in the SmartSound programs Everyday, Noise, and Focus, respectively) were all evaluated with and without NR. Microphone directionality demonstrated a consistent benefit in sentence recognition in all noise conditions tested. The group average speech reception threshold benefit over the Standard setting was 3.7 dB for Zoom and 5.3 dB for Beam. Addition of the NR algorithm further improved sentence recognition by 1.3 dB when the noise maskers were speech-weighted noise. There was an overall group preference for the NR algorithm in noisy environments. Group mean word recognition in quiet, preference in quiet conditions, and music appreciation were all unaffected by the NR algorithm. Multimicrophone directionality was effective in improving speech understanding in spatially separated noisy conditions. The single-channel NR algorithm further enhanced speech intelligibility in speech-weighted noise for cochlear implant users while maintaining equivalent performance in quiet situations and when listening to music.

Motion-induced noise is the main noise that limits extremely low-frequency communication for both shore-based and deep-sea submarines. This manuscript starts from the generation mechanism of motion-induced noise in magnetic antennas, and focuses on the causes of motion-induced noise in magnetic antennas on fixed platforms. The theoretical conclusion reveals that the vibration of magnetic antennas will induce their internal motion-induced noise in either differential or integral form, indicating a clear linear relationship between the vibration and motion-induced noise of magnetic antennas. Based on the generated principle of motion-induced noise presented in this manuscript, corresponding noise reduction algorithms are proposed, and an effective way to apply adaptive filtering algorithm in motion-induced noise cancellation is also presented to accurately reduce this type of noise. In different types of experiments on motion-induced noise testing, the noise reduction results obtained by different noise reduction algorithms demonstrate that the method proposed in this manuscript can achieve effective cancellation of motion-induced noise in magnetic antennas. This experimental result further confirms the research conclusions in this manuscript and provides a feasible approach for practical motion-induced noise cancellation.

An adaptive filter is the utmost essential filter castoff in statistical signal dealing. The fine-tuning of the filter factor in relation to the response signal is the adaptive filter's key feature due to fewer calculations, Least Mean Square (LMS) adaptive filters are widely used to remove noise from Electrocardiograms (ECG). The adaptive filters are realized as signal processing algorithms in Digital Signal Processors (DSPs) or in VLSI Signal Processors (VSPs). The technique provides a way to create a folded adaptive lattice LMS filter, which requires less hardware than an adaptive lattice filter. Folding is an algorithm that uses a time scheduling technique that combines arithmetic operations into one operation which reduces Register and silicon chip areas. The design and implementation of a folded lattice adaptive filter remove Power Line Interference (PLI) noise from ECG signals. The MATLAB Xilinx System Generator tool is used to design the Adaptive Lattice LMS Filter and Folded Adaptive Lattice LMS Filter with Folding Order K = 2 and K = 4 and realized in the Virtex 5 FPGA KIT. The results of the folded architecture show that the area is reduced for K = 2 and K = 4 by 82.60% and 91.05%, respectively compared with a normal adaptive lattice filter.

Classification of human hand movements based on EMG signals using nonlinear dimensionality reduction and data fusion techniques

Financial distress identification remains an essential topic in the scientific literature due to its importance for society and the economy. The advancements in information technology and the escalating volume of stored data have led to the emergence of financial distress that transcends the realm of financial statements and its’ indicators (ratios). The feature space could be expanded by incorporating new perspectives on feature data categories such as macroeconomics, sectors, social, board, management, judicial incident, etc. However, the increased dimensionality results in sparse data and overfitted models. This study proposes a new approach for efficient financial distress classification assessment by combining dimensionality reduction and machine learning techniques. The proposed framework aims to identify a subset of features leading to the minimization of the loss function describing the financial distress in an enterprise. During the study, 15 dimensionality reduction techniques with different numbers of features and 17 machine-learning models were compared. Overall, 1,432 experiments were performed using Lithuanian enterprise data covering the period from 2015 to 2022. Results revealed that the artificial neural network (ANN) model with 30 ranked features identified using the Random Forest mean decreasing Gini (RF_MDG) feature selection technique provided the highest AUC score. Moreover, this study has introduced a novel approach for feature extraction, which could improve financial distress classification models.

Distance similarity matrix using ensemble of dimensional data reduction techniques: Vibration and aerocoustic case studies

PurposeThe paper aims to enhance Arabic machine translation (MT) by proposing novel approaches: (1) a dimensionality reduction technique for word embeddings tailored for Arabic text, optimizing efficiency while retaining semantic information; (2) a comprehensive comparison of meta-embedding techniques to improve translation quality; and (3) a method leveraging self-attention and Gated CNNs to capture token dependencies, including temporal and hierarchical features within sentences, and interactions between different embedding types. These approaches collectively aim to enhance translation quality by combining different embedding schemes and leveraging advanced modeling techniques.Design/methodology/approachRecent works on MT in general and Arabic MT in particular often pick one type of word embedding model. In this paper, we present a novel approach to enhance Arabic MT by addressing three key aspects. Firstly, we propose a new dimensionality reduction technique for word embeddings, specifically tailored for Arabic text. This technique optimizes the efficiency of embeddings while retaining their semantic information. Secondly, we conduct an extensive comparison of different meta-embedding techniques, exploring the combination of static and contextual embeddings. Through this analysis, we identify the most effective approach to improve translation quality. Lastly, we introduce a novel method that leverages self-attention and Gated convolutional neural networks (CNNs) to capture token dependencies, including temporal and hierarchical features within sentences, as well as interactions between different types of embeddings. Our experimental results demonstrate the effectiveness of our proposed approach in significantly enhancing Arabic MT performance. It outperforms baseline models with a BLEU score increase of 2 points and achieves superior results compared to state-of-the-art approaches, with an average improvement of 4.6 points across all evaluation metrics.FindingsThe proposed approaches significantly enhance Arabic MT performance. The dimensionality reduction technique improves the efficiency of word embeddings while preserving semantic information. Comprehensive comparison identifies effective meta-embedding techniques, with the contextualized dynamic meta-embeddings (CDME) model showcasing competitive results. Integration of Gated CNNs with the transformer model surpasses baseline performance, leveraging both architectures' strengths. Overall, these findings demonstrate substantial improvements in translation quality, with a BLEU score increase of 2 points and an average improvement of 4.6 points across all evaluation metrics, outperforming state-of-the-art approaches.Originality/valueThe paper’s originality lies in its departure from simply fine-tuning the transformer model for a specific task. Instead, it introduces modifications to the internal architecture of the transformer, integrating Gated CNNs to enhance translation performance. This departure from traditional fine-tuning approaches demonstrates a novel perspective on model enhancement, offering unique insights into improving translation quality without solely relying on pre-existing architectures. The originality in dimensionality reduction lies in the tailored approach for Arabic text. While dimensionality reduction techniques are not new, the paper introduces a specific method optimized for Arabic word embeddings. By employing independent component analysis (ICA) and a post-processing method, the paper effectively reduces the dimensionality of word embeddings while preserving semantic information which has not been investigated before especially for MT task.

Chapter 19 - Dimensionality Reduction and Latent Variable Modeling

Electrocardiogram (ECG) signal processing aims basically 1) at artifact reduction to make the ECG signals cleaner and better interpretable by human or machine observers, 2) at revealing aspects not immediately observable in plain measured ECG signals even after artifact reduction, or 3) at diagnostics decision support and automated ECG signal interpretation, including classification of ECG signals into different classes associated with normal or pathological heart function. Thus, sports related applications aside, body surface ECG signal processing aims at enhancing ECG based diagnostics. In this Chapter, we review and demonstrate a statistical signal processing approach, independent component analysis (ICA), which is inherently very suitable for ECG signal processing regarding the aims 1) and 2) above, and also equally applicable as a component in systems aimed at accomplishing the aim 3). For more on general ECG signal processing, the reader is directed to the textbook written by Sornmo & Laguna (2005), and for a thorough treatment of ICA to the Hyvarinen’s book (Hyvarinen et al., 2001). A concise review on ICA in ECG signal processing has been presented by Castells et al. (2007a). In this Chapter, we describe and illustrate several widely adopted applications of ICA in ECG signal processing, and discuss associated practical aspects, some of which are not generally found in the literature. The treatment of the matter is aimed at conceptual and practical understanding, leaving the mathematical derivations and proofs far mostly for the interested reader to find in the references. ICA (Castells et al., 2007a; Comon, 1994; Hyvarinen et al., 2001; Hyvarinen & Oja, 2000; Naik & Kumar, 2011) is a statistical signal processing method for decomposing a set of signals into a set of mutually independent component signals. In general, in the applications of ICA, including in ECG signal processing, the objective is that the resulting independent component signals are the original source signals. Since ICA operates purely based on the input signals and a few assumptions, ICA belongs to the class of methods called blind source separation methods. For ICA, a source signal is called an independent component (IC). The terms ‘IC’ and ‘source signal’ are here used interchangeably. For ECG, the source signals are the bioelectrical signals generated by the heart, and all the possible artifact signals. Generally, ICA input signals are the observed signals, which may be measurement time series, such as sampled voltage values in time as in the case of ECGs, image pixel values, or basically any sets of values fulfilling the assumptions of ICA. In the sequel, the term

Background: Many studies on the efficacy of directional microphones (DIRMs) and noise reduction (NR)algorithms were not conducted under realistic signal-to-noise ratio (SNR) conditions. A Repeat–RecallTest (RRT) was developed previously to partially address this issue.<br />Purpose: This study evaluated whether the RRT could provide a more comprehensive understanding ofthe efficacy of a DIRM and NR algorithm under realistic SNRs. Possible interaction with listener workingmemory capacity (WMC) was assessed.<br />Research Design: This study uses a double-blind, within-subject repeated measures design.<br />Study Sample: Nineteen listeners with a moderate degree of hearing loss participated.<br />Data Collection and Analysis: The RRT was administered with participants wearing the study hearingaids (HAs) under two microphones (omnidirectional versus directional) by two NR (on versus off) conditions.Speech was presented from 0° at 75 dB SPL and a continuous noise from 180° at SNRs of 0, 5,10, and 15 dB. The order of SNR and HA conditions was counterbalanced across listeners. Each testcondition was completed twice in two 2-hour sessions separated by one month.<br />Results: The recall scores of listeners were used to group listeners into good and poor WMC groups.Analysis using linear mixed-effects models revealed significant effects of context, SNR, and microphonefor all four measures (repeat, recall, listening effort, and tolerable time). NR was only significant on thelistening effort scale in the DIRM mode at an SNR of 5 dB. Listeners with good WMC performed better onall measures of the RRT and benefitted more from context. Although DIRM benefitted listeners with goodand poor WMC, the benefits differed by context and SNR.<br />Conclusions: The RRT confirmed the efficacy of DIRM and NR on several outcome measures underrealistic SNRs. It also highlighted interactions between WMC and sentence context on feature efficacy.<br />

Objective: The objectives of this study were: (1) to examine the effect of wide dynamic range compression (WDRC) and modulation-based noise reduction (NR) algorithms on wind noise levels at the hearing aid output; and (2) to derive effective strategies for clinicians and engineers to reduce wind noise in hearing aids. Design: Three digital hearing aids were fitted to KEMAR. The noise output was recorded at flow velocities of 0, 4.5, 9.0, and 13.5 m/s in a wind tunnel as the KEMAR head was turned from 0° to 360°. Study sample: Flow noise levels were compared between the 1:1 linear and 3:1 WDRC conditions, and between NR-activated and NR-deactivated conditions when the hearing aid was programmed to the directional and omnidirectional modes. Results: The results showed that: (1) WDRC increased low-level noise and reduced high-level noise; and (2) different noise reduction algorithms provided different amounts of wind noise reduction in different microphone modes, frequency regions, flow velocities, and head angles. Conclusions: Wind noise can be reduced by decreasing the gain for low-level inputs, increasing the compression ratio for high-level inputs, and activating modulation-based noise reduction algorithms.

Financial engineering is a synthesis of modern finance, information technology, engineering, and other fields. Under the complex and volatile market conditions, how to allocate assets reasonably to minimize the benefits and risks assumed is a very important topic. Using the principle of random matrix, the correlation statistical characteristics of stocks are analyzed, the noise reduction and reconstruction algorithm of the correlation matrix is discussed, and a new noise reduction algorithm, two-point determination method, is given. The investment portfolio is numerically simulated, and its parameters are set by the orthogonal experimental method. Through analysis, it is found that the eigenvector contained in the maximum eigenvalue of the correlation matrix can be regarded as a market indicator. The “two-point determination method” has a good effect in “denoising” and can provide operational decision-making and technical support for portfolios.