Automatic extracellular spike denoising using wavelet neighbor coefficients and level dependency

Liu, Xinyu; Wan, Hong; Shang, Zhigang; Shi, Li

doi:10.1016/j.neucom.2014.08.055

Cited by 17 publications

(6 citation statements)

References 23 publications

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“…The LIBS spectra acquired from the sensor were very noisy. Wavelet multivariate denoising, 16,–19 an efficient method, was applied to smooth the data. The db4 (Daubechies wavelet) with a decomposition level of four was used for denoising the data.…”

Section: Methodsmentioning

confidence: 99%

Detection of Anomalies in Citrus Leaves Using Laser-Induced Breakdown Spectroscopy (LIBS)

2015

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Nutrient assessment and management are important to maintain productivity in citrus orchards. In this study, laser-induced breakdown spectroscopy (LIBS) was applied for rapid and real-time detection of citrus anomalies. Laser-induced breakdown spectroscopy spectra were collected from citrus leaves with anomalies such as diseases (Huanglongbing, citrus canker) and nutrient deficiencies (iron, manganese, magnesium, zinc), and compared with those of healthy leaves. Baseline correction, wavelet multivariate denoising, and normalization techniques were applied to the LIBS spectra before analysis. After spectral pre-processing, features were extracted using principal component analysis and classified using two models, quadratic discriminant analysis and support vector machine (SVM). The SVM resulted in a high average classification accuracy of 97.5%, with high average canker classification accuracy (96.5%). LIBS peak analysis indicated that high intensities at 229.7, 247.9, 280.3, 393.5, 397.0, and 769.8 nm were observed of 11 peaks found in all the samples. Future studies using controlled experiments with variable nutrient applications are required for quantification of foliar nutrients by using LIBS-based sensing.

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Section: Methodsmentioning

confidence: 99%

Detection of Anomalies in Citrus Leaves Using Laser-Induced Breakdown Spectroscopy (LIBS)

2015

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“…The dominant frequency band was extracted by a wavelet filter due to its advantage of wavelet transform in nonstationary signal analysis. A quadratic B-spline mother wavelet was used as the wavelet filter because it has compact support, while it is smooth, symmetric, and, most importantly, it can provide optimal time-frequency resolution [ 30 , 31 ].…”

Section: Methodsmentioning

confidence: 99%

Decoding Pigeon Behavior Outcomes Using Functional Connections among Local Field Potentials

Chen

Liu

et al. 2018

Computational Intelligence and Neuroscience

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Recent studies indicate that the local field potential (LFP) carries information about an animal's behavior, but issues regarding whether there are any relationships between the LFP functional networks and behavior tasks as well as whether it is possible to employ LFP network features to decode the behavioral outcome in a single trial remain unresolved. In this study, we developed a network-based method to decode the behavioral outcomes in pigeons by using the functional connectivity strength values among LFPs recorded from the nidopallium caudolaterale (NCL). In our method, the functional connectivity strengths were first computed based on the synchronization likelihood. Second, the strength values were unwrapped into row vectors and their dimensions were then reduced by principal component analysis. Finally, the behavioral outcomes in single trials were decoded using leave-one-out combined with the k-nearest neighbor method. The results showed that the LFP functional network based on the gamma-band was related to the goal-directed behavior of pigeons. Moreover, the accuracy of the network features (74 ± 8%) was significantly higher than that of the power features (61 ± 12%). The proposed method provides a powerful tool for decoding animal behavior outcomes using a neural functional network.

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“…Nasri and Nezamabadi-pour [16] proposed a new thresholding function to be further used in a new subband-adaptive thresholding neural network to improve the efficiency of the denoising procedure. Liu et al [17] found that a wavelet denoising using neighbor coefficients and level dependency was proposed to separate spikes from background noise.…”

Section: Wavelet Threshold Shrinkagementioning

confidence: 99%

“…Therefore, it is necessary to further enhance the image texture detail. Using (17), the denoised image is subtracted from the original noiseless image to yield the details of the loss in . Positive number is to control and balance the degree of its stacking:…”

Section: Image Enhancementmentioning

confidence: 99%

Color Image Denoising Based on Guided Filter and Adaptive Wavelet Threshold

Sun

Zhang

et al. 2017

Applied Computational Intelligence and Soft Computing

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In the process of denoising color images, it is very important to enhance the edge and texture information of the images. Image quality can usually be improved by eliminating noise and enhancing contrast. Based on the adaptive wavelet threshold shrinkage algorithm and considering structural characteristics on the basis of color image denoising, this paper describes a method that further enhances the edge and texture details of the image using guided filtering. The use of guided filtering allows edge details that cannot be discriminated in grayscale images to be preserved. The noisy image is decomposed into low-frequency and highfrequency subbands using discrete wavelets, and the contraction function of threshold shrinkage is selected according to the energy in the vicinity of the wavelet coefficients. Finally, the edge and texture information of the denoised color image are enhanced by guided filtering. When the guiding image is the original noiseless image itself, the guided filter can be used as a smoothing operator for preserving edges, resulting in a better effect than bilateral filtering. The proposed method is compared with the adaptive wavelet threshold shrinkage denoising algorithm and the bilateral filtering algorithm. Experimental results show that the proposed method achieves superior color image denoising compared to these conventional techniques.

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Automatic extracellular spike denoising using wavelet neighbor coefficients and level dependency

Cited by 17 publications

References 23 publications

Detection of Anomalies in Citrus Leaves Using Laser-Induced Breakdown Spectroscopy (LIBS)

Detection of Anomalies in Citrus Leaves Using Laser-Induced Breakdown Spectroscopy (LIBS)

Decoding Pigeon Behavior Outcomes Using Functional Connections among Local Field Potentials

Color Image Denoising Based on Guided Filter and Adaptive Wavelet Threshold

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