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opencc-by-4.0Oct 2013View details →

BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 3. Applying Wiener Filter on noisy image

The Wiener filter is the mean square error-optimal stationary linear filter for images degraded by additive noise and blurring. It removes the additive noise and inverts the blurring simultaneously. The Wiener filtering is a linear estimation of the original image. The approach is based on a stochastic framework.

opencc-by-4.0Jun 2012View details →

Figure 6. (a1), (a2), (a3), (a4), (a5), (a6), (a7) and (a8) watermarked image is degraded respectively through JPEG2000 compression, JPEG compression, median filtering, adding Salt&Pepper noise, rotating, center cropping, surrounding cropping and scaling. (b1), (b2), (b3), (b4), (b5), (b6), (b7) and (b8) The corresponding extracted watermarks.-Discrete Wavelet Transform Method: A New Optimized Robust Digital Image Watermarking Scheme

This paper has described a scheme for digital watermarking of still images based on discrete wavelet transform. In the proposed method, the embedded logo watermark can be extracted without access to the original image. It has been confirmed that the proposed watermarking method is able to extract the embedded logo watermark from the watermarked images that have degraded through compression, filtering, cropping and scaling. Although this algorithm is not robust against rotation, it can completely extract the watermark from watermarked images that lose about 35% of their areas by cropping attack.

Figure 17. Algorithm A9pseudocode-Efficient Filtering of Noisy Fingerprint Images

The proposed algorithms refer to two directions: suppressing the noise resulted from acquisition of the fingerprints, the Gaussian noise in filters: A1 to A8 and to “salt and pepper” reduction noise in A9. The set of 9 filters make use of some methods like: &thresholds for segmentation of the digital images and adapted for filters A4, A5 and A6; &quartiles that dive ranked data set in four equal groups and they are applied on the filters: A1, A2, A4, A6, A7, A8, A9; &selections of parameters, like in: A7, A8 and the dimension of the local neighborhood (in our case 10x10 pixels);

Figure 2. Algorithm A1pseudocode-Efficient Filtering of Noisy Fingerprint Images

A2&uses the principle of quartiles over the entire image in conjunction with applying the quartiles calculated on small areas "locally"(Figure 2.);

Figure 3. Algorithm A3pseudocode-Efficient Filtering of Noisy Fingerprint Images

A3&is achieved by the extrapolation of the values that are "in the immediate" neighborhood of extremes (0 and 255), made in a "local" manner (Figure 3.);

Figure 1.Algorithm A1pseudocode-Efficient Filtering of Noisy Fingerprint Images

In the image processing field, the quartiles are used also for eliminating the outliers (aberrant values) and replacing them with a median value, but in this paper we would like to avoid the median value because often introduces blurring in the resulting image. The set of algorithms uses the quartiles to transform the values under or upper the quartiles Q1, Q3 (in the overall image) and the q1, q3 (in the 10X10 vicinity) in black or white, leaving the rest of pixels in greyscale or introducing other type of processing like increasing or decreasing the values of pixels in the vicinity of the quartiles or simply by locally adjust values according to the vicinity values. A1 & uses quartile (figure 1.) which applies "locally" in small areas, usually 10x10 pixels or estimated using a sample representing approximately 40% of all image data (columns) and transform;

Figure 4. Algorithm A4pseudocode-Efficient Filtering of Noisy Fingerprint Images

A4& uses quartiles principle applied globally, in conjunction with the thresholds referred to A3 (Figure 4.);

Figure 19. Dynamics of the filtered images with the 9 algorithms and the 2 methods of classification-Efficient Filtering of Noisy Fingerprint Images

The classification (Malik, Gautam, Sahai, Jha & Singh, 2013) and ranking stage can be visualized in the Figure 19, the summary of the filtered images is shown in Table 3 and the pseudocode of the current step can be visualized in Figure 18. The overall results show that the two selection criterion: fuzzy and aggregation indicate that the most efficient algorithm is A6 and according to each criterion there can be made certain decisions to choose the best filters for each situation. Also, the results are influenced by the parameters set to calibrate the filtering, the fuzzy profiles, the weighted sum or the vicinity approach.

Figure 6. Algorithm A6pseudocode-Efficient Filtering of Noisy Fingerprint Images

A6&the principle of quartiles applied to the entire image, using the quartiles q1 and q3 as thresholds, but the update refers only for the pixel values only if Q1 < q1 or Q3 > q3, where q1 and q3 are the quartiles calculated in the 10x10 pixels area (Figure 6.);

Figure 18. Classification/ranking algorithmpseudocode-Efficient Filtering of Noisy Fingerprint Images

Figure 7. Algorithm A7pseudocode-Efficient Filtering of Noisy Fingerprint Images

A7&applied to the entire surface using quartiles(Q1 and Q3) in conjunction with an algorithm for the processing of gray tones between Q1 and Q3 is as follows: if in addition is full field the condition that (q2 >Q2), when the initial value of the pixel is adjusted at (initial_value*(1&(q2/q1))), and on the other hand if (q2<Q2) then the value is adjusted to (initial_value*(1+q3/q2 )), please see Figure 7.;

Figure 8. Algorithm A8pseudocode-Efficient Filtering of Noisy Fingerprint Images

A8 & uses the principles of quartiles per global and additionally adjusts the gray tones between Q1 and Q3 as follows: &if (q2<Q2), the pixel shall be updated with the following value (initial_value *(1.1)); &if (q2 >Q2) when the pixel value is updated with (initial_value *(0.9)), and the purpose is to bring q2(local quartile or median) as much closer as possible to Q2(2 quartile or median overall global), please see Figure 8.;

opencc-by-4.0Apr 2018View details →

Data associated with "A collaborative filtering based approach to biomedical knowledge discovery"

This is the data set associated with the publication: "A collaborative filtering based approach to biomedical knowledge discovery" published in Bioinformatics. The data are sets of cooccurrences of biomedical terms extracted from published abstracts and full text articles. The cooccurrences are then represented in sparse matrix form. There are three different splits of this data denoted by the prefix number on the files. 1. All - All cooccurrences combined in a single file 2. Training/Validation - All cooccurrences in publications before 2010 in training, all novel cooccurrences in publication in 2010 go in validation 3. Training+Validation/Test - All cooccurrences in publication upto and including 2010 in training+validation. All novel cooccurrences after 2010 in year by year increments and also all combined together   Furthermore there are subset files which are used in some experiments to deal with the computational cost of evaluating the full set. The associated cuids.txt file containing a link between the row/column in the matrix with the UMLS Metathesaurus CUIDs. Hence the first row of cuids.txt matches up to the 0th row/column in the matrix. Note that the matrix is square and symmetric. This work was done with UMLS Metathesaurus 2016AB.

opencc-by-4.0Oct 2018View details →

An Ultra Low-Loss Silicon-Micromachined Waveguide Filter for D-Band Telecommunication Applications

The dataset contains S-parameter measurements between 110-170 GHz, for a silicon micromachined filter. It also contains quality factor data for the filter and the complex propagation constant data.

Figure 5 in Ecology and life cycle of the filter-feeding Amphipsyche meridiana Ulmer 1902 (Trichoptera: Hydropsychidae) in an irrigation canal, central Thailand

Figure 5. The percentage of larval instars of Amphipsyche meridiana Ulmer 1902 at the sampling site was calculated using the distribution of head capsule width for each month.

Figure 8 in Ecology and life cycle of the filter-feeding Amphipsyche meridiana Ulmer 1902 (Trichoptera: Hydropsychidae) in an irrigation canal, central Thailand

Figure 8. Food item in Amphipsyche meridiana's digestive system under a bright field microscope (magnification x40).

Figure 7 in Ecology and life cycle of the filter-feeding Amphipsyche meridiana Ulmer 1902 (Trichoptera: Hydropsychidae) in an irrigation canal, central Thailand

Figure 7. Food item proportions in the gut contents of the larval instar of Amphipsyche meridiana in each month from the irrigation canal. The gut content of A. meridiana larvae (n = 120) from the study area. The presence (%) represents the percentage of larvae with guts containing this type of material.

Figure 4 in Ecology and life cycle of the filter-feeding Amphipsyche meridiana Ulmer 1902 (Trichoptera: Hydropsychidae) in an irrigation canal, central Thailand

Figure 4. The frequency distribution of larval instars of Amphipsyche meridiana Ulmer 1902 based on head capsule width (n = 12,513) from December 2021 to November 2022.

opencc-by-4.0Aug 2024View details →

Biomechanical filtering supports efficient tactile encoding in the human hand

This repository contains the data and code used to produce the results in the publication "Biomechanical filtering supports efficient tactile encoding in the human hand." If you use these data or code, please cite our publication. Full citation: N. Tummala, G. Reardon, B. Dandu, Y. Shao, H. P. Saal, and Y. Visell, "Biomechanical filtering supports efficient tactile encoding in the human hand". bioRxiv, 2024. doi: 10.1101/2023.11.10.565040   Abstract From Manuscript Touching an object elicits skin oscillations that are biomechanically transmitted throughout the hand, driving responses in thousands of tactile receptors, including numerous exquisitely sensitive Pacinian corpuscles (PCs). Accepted descriptions of PC functionality characterize their response properties as highly stereotyped, based on experimental data gathered when stimuli are applied near the receptor. However, during natural touch, spiking activity in the majority of PCs is evoked by transmitted skin oscillations that are modified by biomechanical filtering. This filtering mechanism, stemming from dispersive wave dynamics in the skin, bears some similarity to the pre-neuronal filtering of auditory signals by the basilar membrane, a mechanical process that is instrumental to perception. Thus, we sought to clarify how skin biomechanics might influence tactile information encoding in the periphery. We used vibrometry imaging and computational neural experiments to examine the influence of biomechanical filtering on neural activity in whole-hand PC populations. We observed complex, location- and frequency-dependent patterns of filtering that were shaped by tissue mechanics and hand morphology. This source of biomechanical modulation diversified PC population spiking activity and enhanced tactile information encoding efficiency. These findings indicate that biomechanics furnishes a pre-neuronal mechanism that facilitates efficient tactile encoding and processing.