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This paper presents an adaptive noise reduction method based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) to address the non-stationary characteristics and noise interference present in friction vibration signals from mechanical equipment. and friction testing machine simulation experiments. The performance of CEEMD and Ensemble Empirical Mode Decomposition (EEMD) was compared through MATLAB R2023b simulations and experiments conducted on a friction testing machine. CEEMD achieved a computational efficiency 85.6% higher than that of EEMD and effectively reduced mode aliasing. Among them, the adaptive correlation coefficient screening method performed well in signal reconstruction, and the high correlation (correlation coefficient > 0.8) between the denoised signal and the laboratory noise signal was verified using the multi-scale permutation entropy (MPE) theory, which is of great significance for early diagnosis of mechanical faults, prediction of equipment life and timely maintenance decisions.

Based on big data analysis, this paper clarifies the basic elements and other elements in visual communication through the expression of visual communication elements in art design and constructs the formation mechanism of visual art design and the characteristics of information cognition. Based on the image processing algorithm for image brightness equalization processing, the image fusion and wavelet noise reduction processing are combined with the pixel point quantization tracking method, and specific cases demonstrate the optimization effect and practicality. The results show that in the example solution, the visual communication index of each module is = 4289, while the optimized visual communication index &prime = 4834 is obtained after processing by the image-based processing algorithm, which improves the communication effect and quality of the art design. This paper proposes a new idea and method that provides inspiration and reference for art design practice and research.

This paper first investigates cognitive psychology and art design, divides cognitive memory into sensory memory, short-term memory and long-term memory according to cognitive psychology, and investigates art design's needs, psychology and creative thinking ability. Then, an art design is proposed that uses image processing technology. The first step in designing an art design using image processing technology is to use an image processing algorithm. Based on the image luminance equalization processing in art design, image fusion and wavelet noise reduction processing are carried out, and ANOVA cognitive psychology and art design experimental data are used to realize art design human-computer interaction. The results show that the analysis of the model with artistic design creative thinking variability as the dependent variable found that the R2 of the model was 0.036, and the adjusted R2 was 0.023. F=2.633, p=0.015, and the grade level had a significant effect on artistic design creative thinking variability. This study is a comprehensive and thorough cognitive psychology and art and design relationship that can contribute to development.

Han, P. and Han, Y., 2018. Noise reduction and recovery algorithm for underwater distorted image. In: Liu, Z.L. and Mi, C. (eds.), Advances in Sustainable Port and Ocean Engineering. Journal of Coastal Research, Special Issue No. 83, pp. 720–723. Coconut Creek (Florida), ISSN 0749-0208. Due to some unavoidable internal factors of digital cameras and other imaging equipment, as well as human and environmental external factors, the underwater photographing images will be distorted, thus affecting the recognition effect of optical character recognition. The traditional noise reduction algorithm has low efficiency and short time-consuming. Therefore, in this paper, a document image distortion recognition algorithm based on full automatic image splicing is proposed. Firstly, two distorted images captured from different angles for the same document page are processed by feature extraction, image registration, transformation and so on, and then spliced into a nearly non-deformation document image. The experimental results show that the algorithm proposed in this paper can effectively deal with the problem of document image distortion recognition, and has a very high development prospect.

The problems of data abnormalities and missing data are puzzling the traditional multi-modal heterogeneous big data clustering. In order to solve this issue, a multi-view heterogeneous big data clustering algorithm based on improved Kmeans clustering is established in this paper. At first, for the big data which involve heterogeneous data, based on multi view data analyzing, we propose an advanced Kmeans algorithm on the base of multi view heterogeneous system to determine the similarity detection metrics. Then, a BP neural network method is used to predict the missing attribute values, complete the missing data and restore the big data structure in heterogeneous state. Last, we ulteriorly propose a data denoising algorithm to denoise the abnormal data. Based on the above methods, we construct a framework namely BPK-means to resolve the problems of data abnormalities and missing data. Our solution approach is evaluated through rigorous performance evaluation study. Compared with the original algorithm, both theoretical verification and experimental results show that the accuracy of the proposed method is greatly improved.

This chapter contains sections titled: Introduction Signal Analysis and Synthesis Voice Activity Detection Noise Power Spectrum Estimation Adaptive Filters for Signal Enhancement ASR Performance Conclusions References

Progress in many scientific disciplines is hindered by the presence of independent noise. Technologies for measuring neural activity (calcium imaging, extracellular electrophysiology and functional magnetic resonance imaging (fMRI)) operate in domains in which independent noise (shot noise and/or thermal noise) can overwhelm physiological signals. Here, we introduce DeepInterpolation, a general-purpose denoising algorithm that trains a spatiotemporal nonlinear interpolation model using only raw noisy samples. Applying DeepInterpolation to two-photon calcium imaging data yielded up to six times more neuronal segments than those computed from raw data with a 15-fold increase in the single-pixel signal-to-noise ratio (SNR), uncovering single-trial network dynamics that were previously obscured by noise. Extracellular electrophysiology recordings processed with DeepInterpolation yielded 25% more high-quality spiking units than those computed from raw data, while DeepInterpolation produced a 1.6-fold increase in the SNR of individual voxels in fMRI datasets. Denoising was attained without sacrificing spatial or temporal resolution and without access to ground truth training data. We anticipate that DeepInterpolation will provide similar benefits in other domains in which independent noise contaminates spatiotemporally structured datasets.DeepInterpolation is a self-supervised deep learning-based denoising approach for calcium imaging, electrophysiology and fMRI data. The approach increases the signal-to-noise ratio and allows extraction of more information from the processed data than from the raw data.

A fundamental challenge in fluorescence microscopy is the photon shot noise arising from the inevitable stochasticity of photon detection. Noise increases measurement uncertainty and limits imaging resolution, speed and sensitivity. To achieve high-sensitivity fluorescence imaging beyond the shot-noise limit, we present DeepCAD-RT, a self-supervised deep learning method for real-time noise suppression. Based on our previous framework DeepCAD, we reduced the number of network parameters by 94%, memory consumption by 27-fold and processing time by a factor of 20, allowing real-time processing on a two-photon microscope. A high imaging signal-to-noise ratio can be acquired with tenfold fewer photons than in standard imaging approaches. We demonstrate the utility of DeepCAD-RT in a series of photon-limited experiments, including in vivo calcium imaging of mice, zebrafish larva and fruit flies, recording of three-dimensional (3D) migration of neutrophils after acute brain injury and imaging of 3D dynamics of cortical ATP release. DeepCAD-RT will facilitate the morphological and functional interrogation of biological dynamics with a minimal photon budget. DeepCAD-RT denoises fluorescence time-lapse images in real time.

Practical and useful quantum information processing requires substantial improvements with respect to current systems, both in the error rates of basic operations and in scale. The fundamental qualities of individual trapped-ion 1 qubits are promising for long-term systems 2 , but the optics involved in their precise control are a barrier to scaling 3 . Planar-fabricated optics integrated within ion-trap devices can make such systems simultaneously more robust and parallelizable, as suggested by previous work with single ions 4 . Here we use scalable optics co-fabricated with a surface-electrode ion trap to achieve high-fidelity multi-ion quantum logic gates, which are often the limiting elements in building up the precise, large-scale entanglement that is essential to quantum computation. Light is efficiently delivered to a trap chip in a cryogenic environment via direct fibre coupling on multiple channels, eliminating the need for beam alignment into vacuum systems and cryostats and lending robustness to vibrations and beam-pointing drifts. This allows us to perform ground-state laser cooling of ion motion and to implement gates generating two-ion entangled states with fidelities greater than 99.3(2) per cent. This work demonstrates hardware that reduces noise and drifts in sensitive quantum logic, and simultaneously offers a route to practical parallelization for high-fidelity quantum processors 5 . Similar devices may also find applications in atom- and ion-based quantum sensing and timekeeping 6 . Scalable optics co-fabricated with a cryogenic surface-electrode ion trap are used to drive high-fidelity multi-ion quantum logic gates, demonstrating a route to simultaneously scale and reduce errors in quantum processors.

Imaging Mass Cytometry (IMC) is an emerging multiplexed imaging technology for analyzing complex microenvironments using more than 40 molecularly-specific channels. However, this modality has unique data processing requirements, particularly for patient tissue specimens where signal-to-noise ratios for markers can be low, despite optimization, and pixel intensity artifacts can deteriorate image quality and downstream analysis. Here we demonstrate an automated content-aware pipeline, IMC-Denoise, to restore IMC images deploying a differential intensity map-based restoration (DIMR) algorithm for removing hot pixels and a self-supervised deep learning algorithm for shot noise image filtering (DeepSNiF). IMC-Denoise outperforms existing methods for adaptive hot pixel and background noise removal, with significant image quality improvement in modeled data and datasets from multiple pathologies. This includes in technically challenging human bone marrow; we achieve noise level reduction of 87% for a 5.6-fold higher contrast-to-noise ratio, and more accurate background noise removal with approximately 2 × improved F1 score. Our approach enhances manual gating and automated phenotyping with cell-scale downstream analyses. Verified by manual annotations, spatial and density analysis for targeted cell groups reveal subtle but significant differences of cell populations in diseased bone marrow. We anticipate that IMC-Denoise will provide similar benefits across mass cytometric applications to more deeply characterize complex tissue microenvironments. Multiplexed imaging technologies can reveal the complex cellular and molecular profiles of tissue. Here, the authors develop and implement a denoising pipeline to significantly enhance imaging mass cytometry quality and improve single-cell analyses.