Noise Reduction Algorithm Research Articles

Fabrication and Application of Customized Breast Phantom for Noise Reduction Algorithms in Digital Mammography: An Experimental Study

Fabrication and Application of Customized Breast Phantom for Noise Reduction Algorithms in Digital Mammography: An Experimental Study

Magnetic Resonance Imaging Images under Deep Learning in the Identification of Tuberculosis and Pneumonia.

This work aimed to explore the application value of deep learning-based magnetic resonance imaging (MRI) images in the identification of tuberculosis and pneumonia, in order to provide a certain reference basis for clinical identification. In this study, 30 pulmonary tuberculosis patients and 27 pneumonia patients who were hospitalized were selected as the research objects, and they were divided into a pulmonary tuberculosis group and a pneumonia group. MRI examination based on noise reduction algorithms was used to observe and compare the signal-to-noise ratio (SNR) and carrier-to-noise ratio (CNR) of the images. In addition, the apparent diffusion coefficient (ADC) value for the diagnosis efficiency of lung parenchymal lesions was analyzed, and the best b value was selected. The results showed that the MRI image after denoising by the deep convolutional neural network (DCNN) algorithm was clearer, the edges of the lung tissue were regular, the inflammation signal was higher, and the SNR and CNR were better than before, which were 119.79 versus 83.43 and 12.59 versus 7.21, respectively. The accuracy of MRI based on a deep learning algorithm in the diagnosis of pulmonary tuberculosis and pneumonia was significantly improved (96.67% vs. 70%, 100% vs. 62.96%) (P < 0.05). With the increase in b value, the CNR and SNR of MRI images all showed a downward trend (P < 0.05). Therefore, it was found that the shadow of tuberculosis lesions under a specific sequence was higher than that of pneumonia in the process of identifying tuberculosis and pneumonia, which reflected the importance of deep learning MRI images in the differential diagnosis of tuberculosis and pneumonia, thereby providing reference basis for clinical follow-up diagnosis and treatment.

Listening Effort in Cochlear Implant Users: The Effect of Speech Intelligibility, Noise Reduction Processing, and Working Memory Capacity on the Pupil Dilation Response.

This study aimed to evaluate the effect of speech recognition performance, working memory capacity (WMC), and a noise reduction algorithm (NRA) on listening effort as measured with pupillometry in cochlear implant (CI) users while listening to speech in noise. Speech recognition and pupil responses (peak dilation, peak latency, and release of dilation) were measured during a speech recognition task at three speech-to-noise ratios (SNRs) with an NRA in both on and off conditions. WMC was measured with a reading span task. Twenty experienced CI users participated in this study. With increasing SNR and speech recognition performance, (a) the peak pupil dilation decreased by only a small amount, (b) the peak latency decreased, and (c) the release of dilation after the sentences increased. The NRA had no effect on speech recognition in noise or on the peak or latency values of the pupil response but caused less release of dilation after the end of the sentences. A lower reading span score was associated with higher peak pupil dilation but was not associated with peak latency, release of dilation, or speech recognition in noise. In CI users, speech perception is effortful, even at higher speech recognition scores and high SNRs, indicating that CI users are in a chronic state of increased effort in communication situations. The application of a clinically used NRA did not improve speech perception, nor did it reduce listening effort. Participants with a relatively low WMC exerted relatively more listening effort but did not have better speech reception thresholds in noise.

An Improved Adaptive IVMD-WPT-Based Noise Reduction Algorithm on GPS Height Time Series.

To improve the reliability of Global Positioning System (GPS) signal extraction, the traditional variational mode decomposition (VMD) method cannot determine the number of intrinsic modal functions or the value of the penalty factor in the process of noise reduction, which leads to inadequate or over-decomposition in time series analysis and will cause problems. Therefore, in this paper, a new approach using improved variational mode decomposition and wavelet packet transform (IVMD-WPT) was proposed, which takes the energy entropy mutual information as the objective function and uses the grasshopper optimisation algorithm to optimise the objective function to adaptively determine the number of modal decompositions and the value of the penalty factor to verify the validity of the IVMD-WPT algorithm. We performed a test experiment with two groups of simulation time series and three indicators: root mean square error (RMSE), correlation coefficient (CC) and signal-to-noise ratio (SNR). These indicators were used to evaluate the noise reduction effect. The simulation results showed that IVMD-WPT was better than the traditional empirical mode decomposition and improved variational mode decomposition (IVMD) methods and that the RMSE decreased by 0.084 and 0.0715 mm; CC and SNR increased by 0.0005 and 0.0004 dB, and 862.28 and 6.17 dB, respectively. The simulation experiments verify the effectiveness of the proposed algorithm. Finally, we performed an analysis with 100 real GPS height time series from the Crustal Movement Observation Network of China (CMONOC). The results showed that the RMSE decreased by 11.4648 and 6.7322 mm, and CC and SNR increased by 0.1458 and 0.0588 dB, and 32.6773 and 26.3918 dB, respectively. In summary, the IVMD-WPT algorithm can adaptively determine the number of decomposition modal functions of VMD and the optimal combination of penalty factors; it helps to further extract effective information for noise and can perfectly retain useful information in the original time series.

Detecting Arsenic Contamination Using Satellite Imagery and Machine Learning.

Arsenic, a potent carcinogen and neurotoxin, affects over 200 million people globally. Current detection methods are laborious, expensive, and unscalable, being difficult to implement in developing regions and during crises such as COVID-19. This study attempts to determine if a relationship exists between soil’s hyperspectral data and arsenic concentration using NASA’s Hyperion satellite. It is the first arsenic study to use satellite-based hyperspectral data and apply a classification approach. Four regression machine learning models are tested to determine this correlation in soil with bare land cover. Raw data are converted to reflectance, problematic atmospheric influences are removed, characteristic wavelengths are selected, and four noise reduction algorithms are tested. The combination of data augmentation, Genetic Algorithm, Second Derivative Transformation, and Random Forest regression ( and normalized root mean squared error (re-scaled to [0,1]) = ) shows strong correlation, performing better than past models despite using noisier satellite data (versus lab-processed samples). Three binary classification machine learning models are then applied to identify high-risk shrub-covered regions in ten U.S. states, achieving strong accuracy (=0.693) and F1-score (=0.728). Overall, these results suggest that such a methodology is practical and can provide a sustainable alternative to arsenic contamination detection.

EFFECT OF DIGITAL NOISE REDUCTION AND DIRECTIONALITY ALGORITHMS IN HEARING AIDS ON TEMPORAL ENVELOPE DISTORTION AND SPEECH RECOGNITION

Background Digital signal processing algorithms tend to alter temporal cues in the speech signal and yet individuals with hearing impairment rely strongly on these cues for speech perception. Hence, there is a need to assess the effect of these algorithms on temporal cues and speech perception. The present study aimed to quantify, in a wide dynamic range compression hearing aid, the individual and combined effects of digital noise reduction and directionality algorithms on temporal cues, syllable recognition, and sentence recognition. Temporal cues were quantified by the envelope difference index (EDI). Material and methods The study included 20 individuals (in the age range of 21 to 44 years) with mild to moderate sensorineural hearing loss. Sentence recognition, syllable recognition, and EDI were obtained at different levels in four different aided conditions – digital noise reduction algorithm only, directionality only, both digital noise reduction and directionality on, and both algorithms off. Sentences were presented in noise at +5 dB signal-to-noise ratio from 180° azimuth. Results Compared to independent activation of the algorithms, the combined algorithms significantly improved speech recognition scores at all presentation levels. The temporal changes induced by the algorithms were only mild, even though EDI was the highest when all the algorithms and the directional microphone were activated. Conclusions The noise reduction algorithms and compression induce temporal changes; however speech recognition improved when the algorithms were activated, presumably due to countervailing psychophysical factors.

Intelligent Noise Reduction Algorithm to Evaluate the Correlation between Human Fat Deposits and Uterine Fibroids under Ultrasound Imaging.

This study aimed to realize the automatic diagnosis of fatty degeneration of uterine fibroids. In this study, the traditional nonlocal means (NLM) algorithm was improved by changing the Euclidean distance and introducing a cosine function and applied to the ultrasonic imaging intelligent diagnosis of patients with fatty degeneration of uterine fibroids. Then, the noise reduction effect of the improved NLM algorithm was evaluated based on several indicators, such as peak signal-to-noise ratio (PSNR), mean square error (MSE), contrast-to-noise ratio (CNR), figure of merit (FOM), and structural similarity (SSIM). The accuracy, sensitivity, specificity, and F1 score were adopted to evaluate the improved NLM algorithm for diagnosing fatty degeneration of uterine fibroids, and the Perona–Malik (PM) algorithm and NLM algorithm were used for comparative analysis. The results showed that after the ultrasound images of patients with uterine fibroids were denoised using the improved NLM algorithm, the PSNR, MSE, CNR, FOM, and SSIM were obviously better than the same indicators of the image processed with the PM algorithm and the NLM algorithm, and the differences were statistically significant (P < 0.05). The diagnosis results of patients with fatty degeneration of uterine fibroids found that there was only one patient with missed diagnosis after the ultrasound image was processed with NLM algorithm, and there was no statistical difference between the improved NLM algorithm and the assisted diagnosis accuracy of the pathological examination results (P > 0.05). The average noise reduction time of the PM algorithm, NLM algorithm, and the improved NLM algorithm was 16.38 ± 4.33 s, 18.01 ± 5.14 s, and 23.81 ± 4.62 s, respectively. The diagnosis rate before improvement was 75.0%, the diagnosis accuracy rate for PM was 79.69%, and that after improvement was 85.94%. In summary, the improved NLM algorithm showed a good noise reduction effect on ultrasound images of patients with uterine fibroids, could improve the diagnosis accuracy of fatty degeneration of uterine fibroids, and could assist clinicians in the ultrasound imaging diagnosis of patients with uterine fibroids.

Advanced Noise-Optimized Dual-Energy Virtual Monochromatic Imaging vs. Conventional 120-kVp CT Imaging: Image Quality Assessment

Purpose: This study aimed at evaluating the image quality characteristics of advanced noise-optimized and traditional virtual monochromatic images compared with conventional 120-kVp images from second-generation Dual-Source CT. Materials and Methods: For spiral scans six syringes filled with diluted iodine contrast material (1, 2, 5, 10, 15, 20 mg I/ml) were inserted into the test phantom and scanned with a second-generation dual-source CT in both single-energy (120-kVp) and dual-energy modes. Images set contain conventional single-energy 120-kVp, and virtual monochromatic were reconstructed with energies ranging from 40 to 190-keV in 1-keV steps. An energy-domain noise reduction algorithm was applied and the mean CT number, image noise, and iodine CNR were calculated. Results: The iodine CT number of conventional 120-kVp images compared with monochromatic of 40-, 50-, 60- and 70-keV images showed increase. The improvement ratio of image noise on Advanced Virtual Monochromatic Images (AVMIs) compared with the Traditional Virtual Monochromatic Images (TVMIs) at energies of 40-, 50-, 60, 70-keV was 52.9%, 35.7%, 8.1%, 2.1%, respectively. At AVMIs from 75- to 190-keV, the image noise value was less than conventional 120-kVp images. CNR improvement ratio at 20 mg/ml of iodinated contrast material for TVMIs and AVMIs compared to 120-kVp CT images and AVMIs compared to TVMI was 18.3% and 56.3%, 32.1% respectively. Conclusion: Both TVMIs (in energies ranging from 54 to 71-keV) and AVMIs (in energies ranging from 40 to 74-keV) represent improvement in the iodine contrast-to-noise ratio than conventional 120-kVp CT images for the same radiation dose. Also, AVMIs compared to TVMIs have been obtained considerable noise reduction and CNR improvement for low-energy virtual monochromatic images. In the present study, we show that virtual monochromatic image and its Advanced version (AVMI) may boost the dual-energy CT advantages by providing higher CNR images in the same exposure value compared to routinely acquired single-energy CT images.

Conditional Generative Adversarial Networks for low-dose CT image denoising aiming at preservation of critical image content.

X-ray Computed Tomography (CT) is an imaging modality where patients are exposed to potentially harmful ionizing radiation. To limit patient risk, reduced-dose protocols are desirable, which inherently lead to an increased noise level in the reconstructed CT scans. Consequently, noise reduction algorithms are indispensable in the reconstruction processing chain. In this paper, we propose to leverage a conditional Generative Adversarial Networks (cGAN) model, to translate CT images from low-to-routine dose. However, when aiming to produce realistic images, such generative models may alter critical image content. Therefore, we propose to employ a frequency-based separation of the input prior to applying the cGAN model, in order to limit the cGAN to high-frequency bands, while leaving low-frequency bands untouched. The results of the proposed method are compared to a state-of-the-art model within the cGAN model as well as in a single-network setting. The proposed method generates visually superior results compared to the single-network model and the cGAN model in terms of quality of texture and preservation of fine structural details. It also appeared that the PSNR, SSIM and TV metrics are less important than a careful visual evaluation of the results. The obtained results demonstrate the relevance of defining and separating the input image into desired and undesired content, rather than blindly denoising entire images. This study shows promising results for further investigation of generative models towards finding a reliable deep learning-based noise reduction algorithm for low-dose CT acquisition.

Noise Reduction Method of Φ-OTDR System Based on EMD-TFPF Algorithm

The phase-sensitive optical time domain reflectometry ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Phi $ </tex-math></inline-formula> -OTDR) system has been gradually applied to vertical seismic profile exploration due to its excellent anti-electromagnetic interference, extremely high resolution and sensitivity, and wide detection range. However, the actual data is often disturbed by random noise, which seriously affects the system’s ability to recognize low-frequency disturbances. In order to suppress the background noise and improve the signal-to-noise ratio (SNR), a fusion noise reduction method based on empirical mode decomposition and time-frequency peak filtering (EMD-TFPF) algorithm is studied and introduced. In terms of seismic exploration data processing, the algorithm has good denoising performance. Using the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Phi $ </tex-math></inline-formula> -OTDR system based on optical synchronous reference heterodyne detection as the experimental platform, the EMD-TFPF noise reduction algorithm is applied to the position information and compared with the VSS-NLMS, VMD-NWT and EMD-soft methods. Experimental results show that under the conditions of 0.1 Hz and 5 V vibration interference, the EMD-TFPF method improves the SNR to 37.6 dB, which is much higher than the other three noise reduction algorithms. In addition, this method broadens the low-frequency response range of the system to 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−5</sup> Hz. The improvement of the system’s ability to recognize low-frequency disturbance events will undoubtedly accelerate the practical process of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Phi $ </tex-math></inline-formula> -OTDR system in seismic exploration.

Неразрушающий контроль стальных тосов с использованием оптимизированного метода опорных векторов

In this paper, to address the problems of poor signal noise reduction and low recognition rate in wire rope leakage magnetic detection, we propose the algorithm MSVDW, which uses a combination of median filtering, singular value decomposition (SVD) and wavelet transform, to denoise the collected three-dimensional MFL signals. Then, false color is used to enhance the image. The image is then localized and segmented using the modulus maximum method. The color moments are extracted from the images and used as the input of the particle swarm algorithm optimized support vector machine (PSO-SVM) for training and recognition. The experimental results show that the noise reduction algorithm proposed in this paper effectively reduces the noise of the leakage signal, the false color image enhances the defect image information, and the algorithm of PSO-SVM greatly improves the recognition rate of defects.

A Bayesian Modeling Approach to Situated Design of Personalized Soundscaping Algorithms

Effective noise reduction and speech enhancement algorithms have great potential to enhance lives of hearing aid users by restoring speech intelligibility. An open problem in today’s commercial hearing aids is how to take into account users’ preferences, indicating which acoustic sources should be suppressed or enhanced, since they are not only user-specific but also depend on many situational factors. In this paper, we develop a fully probabilistic approach to “situated soundscaping”, which aims at enabling users to make on-the-spot (“situated”) decisions about the enhancement or suppression of individual acoustic sources. The approach rests on a compact generative probabilistic model for acoustic signals. In this framework, all signal processing tasks (source modeling, source separation and soundscaping) are framed as automatable probabilistic inference tasks. These tasks can be efficiently executed using message passing-based inference on factor graphs. Since all signal processing tasks are automatable, the approach supports fast future model design cycles in an effort to reach commercializable performance levels. The presented results show promising performance in terms of SNR, PESQ and STOI improvements in a situated setting.

Development of learning-based noise reduction and image reconstruction algorithm in two dimensional Rayleigh thermometry

Consistent research efforts have been invested to deal with particles’ Mie scattering interference in Rayleigh measurements. This work reports an alternative learning-based image denoising and reconstruction algorithm to remove high value Mie scattering noise in Rayleigh thermometry. The algorithm, named denoising generative adversarial network (DNGAN), consists of a generator and a discriminator to learn the texture features and infer denoised image with a given noisy counterpart. Training data of the neural network come from artificially generated noise-free Rayleigh image superimposed with experimentally acquired Mie scattering image. Extensive evaluations are then conducted to prove the superiority of the proposed DNGAN network, compared with several other neural networks reported in literature. Quantitative results show that a convincing denoising with an overall reconstruction error of ~ 0.6% a peak signal-to-noise ratio of ~ 43 dB can be reached. Moreover, the pre-trained network is able to denoise image of the jet flame with a different Reynolds number without retraining the network parameters.

Subcortical measures of individual tolerance to background noise and sensitivity to speech distortions induced by noise-reduction processing

Listeners with sensorineural hearing loss have significant difficulty understanding speech in noisy environments. Thus, current digital hearing aids employ noise-reduction (NR) algorithms to suppress background noise. Unfortunately, the NR algorithms distort some speech cues. Though considerable variability exists in hearing-aid users’ reactions to these conflicting effects, little is known about the neural underpinnings contributing to the variance in perceptual outcomes with the addition of noise and the use of NR processing. Recently, we documented that neuroimaging-based measures of cortical responses can predict individual speech-in-noise perception benefits (or lack thereof) from NR processing. Here, we describe novel objective subcortical measures for quantifying individual noise tolerance and individual sensitivity to speech-cue distortions. Preliminary results show that the effect of adding noise and introducing NR processing on brainstem responses varies considerably across individuals. Because the brainstem responses are relatively unaffected by cognitive processes like selective attention, these preliminary results suggest an independent “bottom-up” contribution to individual variations in NR benefits. We will also discuss how the neurophysiological measures of subcortical encoding of speech sounds relate to individual profiles of peripheral (cochlear) pathophysiology and to individual perceptual outcomes.

SureTypeSCR: R package for rapid quality control and genotyping of SNP arrays from single cells.

Genotyping of single cells using single nucleotide polymorphism arrays is a cost-effective technology that provides good coverage and precision, but requires whole genome amplification (WGA) due to the low amount of genetic material. Since WGA introduces noise, we recently developed SureTypeSC, an algorithm to minimize genotyping errors. Here, we present SureTypeSCR, an R package that integrates a state-of-the-art algorithm (SureTypeSC) for noise reduction in single cell genotyping and unites all common parts of genotyping workflow in a single tool. SureTypeSCR is built on top of the tidyverse ecosystem, which facilitates common operations over the data and allows users to create and experiment with the genotyping pipeline. Furthermore, the workflow of SureTypeSCR can also be used for standard genotyping of bulk DNA for batch processing in a single pipeline.SureTypeSCR is avaliable from: https://github.com/Meiomap/SureTypeSCR.

Feasibility of late gadolinium enhancement (LGE) in ischemic cardiomyopathy using 2D-multisegment LGE combined with artificial intelligence reconstruction deep learning noise reduction algorithm

BackgroundDespite the low spatial resolution of 2D-multisegment late gadolinium enhancement (2D-MSLGE) sequences, it may be useful in uncooperative patients instead of standard 2D single segmented inversion recovery gradient echo late gadolinium enhancement sequences (2D-SSLGE). The aim of the study is to assess the feasibility and comparison of 2D-MSLGE reconstructed with artificial intelligence reconstruction deep learning noise reduction (NR) algorithm compared to standard 2D-SSLGE in consecutive patients with ischemic cardiomyopathy (ICM). MethodsFifty-seven patients with known ICM referred for a clinically indicated CMR were enrolled in this study. 2D-MSLGE were reconstructed using a growing level of NR (0%,25%,50%,75%and 100%). Subjective image quality, signal to noise ratio (SNR) and contrast to noise ratio (CNR) were evaluated in each dataset and compared to standard 2D-SSLGE. Moreover, diagnostic accuracy, LGE mass and scan time were compared between 2D-MSLGE with NR and 2D-SSLGE. ResultsThe application of NR reconstruction ≥50% to 2D-MSLGE provided better subjective image quality, CNR and SNR compared to 2D-SSLGE (p < 0.01). The best compromise in terms of subjective and objective image quality was observed for values of 2D-MSLGE 75%, while no differences were found in terms of LGE quantification between 2D-MSLGE versus 2D-SSLGE, regardless the NR applied. The sensitivity, specificity, negative predictive value, positive predictive value and accuracy of 2D-MSLGE NR 75% were 87.77%,96.27%,96.13%,88.16% and 94.22%, respectively.Time of acquisition of 2D-MSLGE was significantly shorter compared to 2D-SSLGE (p < 0.01). ConclusionWhen compared to standard 2D-SSLGE, the application of NR reconstruction to 2D-MSLGE provides superior image quality with similar diagnostic accuracy.

Feasibility of MMWF noise reduction algorithm in brain SPECT images according to various reconstruction methods: A phantom study

In order to improve image quality in single-photon emission computed tomography (SPECT)/computed tomography (CT), various studies focusing on reconstruction methods and noise reduction algorithms have been conducted. Therefore, the purpose of this study was to evaluate the efficiency of the filtered back projection (FBP) and ordered subset expectation maximization (OSEM) reconstruction methods, and to evaluate image quality after applying the median modified Wiener filter (MMWF) as a complementary noise reduction filter using 3 × 3 and 5 × 5 mask sizes. The 3D Hoffman brain phantom was used, and the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were evaluated in regions of interest for quantitative analysis. Based on the results, the image quality obtained by the OSEM reconstruction method was 37.7% and 25.9% higher than that of the FBP reconstruction method regarding SNR and CNR, respectively. In addition, the average SNR and CNR values obtained by applying the MMWF algorithm were 35.9% and 17.1% higher than those without the MMWF algorithm. Therefore, our results indicate that the OSEM reconstruction method is more efficient than the FBP reconstruction method, and that the MMWF algorithm improves image quality in SPECT images, regardless of which reconstruction method is used.

An online algorithm for echo cancellation, dereverberation and noise reduction based on a Kalman-EM Method

Many modern smart devices are equipped with a microphone array and a loudspeaker (or are able to connect to one). Acoustic echo cancellation algorithms, specifically their multi-microphone variants, are essential components in such devices. On top of acoustic echos, other commonly encountered interference sources in telecommunication systems are reverberation, which may deteriorate the desired speech quality in acoustic enclosures, specifically if the speaker distance from the array is large, and noise. Although sub-optimal, the common practice in such scenarios is to treat each problem separately. In the current contribution, we address a unified statistical model to simultaneously tackle the three problems. Specifically, we propose a recursive EM (REM) algorithm for solving echo cancellation, dereverberation and noise reduction. The proposed approach is derived in the short-time Fourier transform (STFT) domain, with time-domain filtering approximated by the convolutive transfer function (CTF) model. In the E-step, a Kalman filter is applied to estimate the near-end speaker, based on the noisy and reveberant microphone signals and the echo reference signal. In the M-step, the model parameters, including the acoustic systems, are inferred. Experiments with human speakers were carried out to examine the performance in dynamic scenarios, including a walking speaker and a moving microphone array. The results demonstrate the efficiency of the echo canceller in adverse conditions together with a significant reduction in reverberation and noise. Moreover, the tracking capabilities of the proposed algorithm were shown to outperform baseline methods.

Establish intelligent detection system to evaluate the sugar smoking of chicken thighs

The objective of this study was to establish a standardized color detection method to achieve low-cost, rapid, nonintrusive and accurate characterization of the color change of smoked chicken thighs during the smoking process. This study was based on machine vision technology using the Mean algorithm, K-means algorithm and K-means algorithm + image noise reduction algorithm to establish 3 colorimetric cards for the color of sugar-smoked chicken thighs. The accuracy of the 3 colorimetric cards was verified by the K-medoids algorithm and sensory analysis, respectively. Results showed that all 3 colorimetric cards had significant color gradient changes. From the K-medoids algorithm, the accuracy of the colorimetric card produced by the Mean algorithm, K-means algorithm and K-means algorithm + image noise reduction algorithm was 87.2, 95.1, and 96.7%, respectively. Meanwhile, the verification results of the sensory analysis showed that the accuracy of the Mean algorithm, K-means algorithm and K-means algorithm + image noise reduction algorithm colorimetric card was 69.4, 80.9, and 79.2%, respectively. A comparative analysis found that the colorimetric cards produced by the K-means algorithm and K-means algorithm + image noise reduction have excellent accuracy. These 2 colorimetric cards could become a suitable method for rapid, low-cost, and accurate online color monitoring of smoked chicken.

RETRACTED ARTICLE: Distinguish between the data of the Internet of Things based on abnormal audio detection and the image of athletes’ human actions

This article discusses methods of abnormal audio detection. Extract audio information from audio acquisition hardware, convert analog signals into digital signals, perform pre-focused audio processing on digital signals, and compare and analyze audio noise reduction algorithms, such as wave noise reduction, homomorphic filtering, and Wiener filtering. After noise reduction processing is performed on the audio data, endpoint detection is performed on the continuous noise reduction frequency data, and the actual audio segment is separated from the continuous audio signal. Consistent with this, the blockchain-based IoT data service reputation evaluation model designed and implemented in this paper can support the data service reputation evaluation requirements. Users can upload IoT data sources to the platform or retrieve data from the platform, and research the data based on reputation or data provider analysis. The interaction with the data authority is realized through access control based on permissions. Finally, according to the latest requirements of the national sports team, based on the research foundation of the “athlete military division,” a new visual word model is used to explain the research on the image discrimination of athletes’ movements, and introduces the athletes’ movements, including traditional word models and automatic algorithms, and then introduces the research content and research methods to improve the traditional new visual word model to adapt to the characteristics of athletes. This algorithm minimizes the impact of short recognition rate and slow recognition rate, the latter causing problems such as the background similarity of the athlete’s image and the difference between the athlete’s actions. By understanding the available modules in the athlete’s motion recognition system, the prototype design of the basic functions and the athlete’s video system was completed.