Design of Exponentially Weighted Median Filter Cascaded With Adaptive Median Filter

The objective of this paper is to design an II phase algorithm employing median filters for enlightening the performance in removing impulse noise during the processing of the image. The cascaded filter section employs an Adaptive median filter in the first phase followed by a Recursive weighted median filter (RWM) in the second phase. The RWM filter weight is selected with the Median Controlled Algorithm. As a design parameter, the exponential weights of RWM filters are used in the feedback path. The projected algorithm can achieve suggestively improved quality of image when compared to fixed weight or the Center Weighted Median filters.

A double layer FSS filter for sub-THz applications

This work presents the simulated and measured performance of single- and double-layer frequency selective surface filters for operation at sub-THz frequencies (250 GHz center frequency). They were composed of concentric square loops with a split as a unit cell resonator on top of a low dielectric permittivity, low thickness material (RT5880). Both a single layer filter and a cascaded two layer filter with varied distances were investigated. The simulated bandwidth for the cascaded filter was 27 GHz and 16 GHz and 9 GHz bandwidth measured with a THz-TDS and microwave system.

An Adaptive Weighted Image Denoising Method Based on Morphology

In its generation, transmission and record, image signal is often interfered by various noises, which have severally affected the visual effects of images; therefore, it is a very important pre-processing step to take proper approaches to reduce noises. Conventional denoising methods have also blurred image edge information while removing noises, which can be overcome by the method based on mathematical morphology. While eliminating different noises from images, it can not only keep clear object edges, but also preserve as many image details as possible and it also has excellent capacities in noise resistance and edge preservation. With image denoising and mathematical morphology as the research subject, this paper analyzes the generation and characteristics of common image noises, studies the basic theories of mathematical morphology and its applications in image processing, discusses the method to select structural elements in mathematical morphology and proposes a filtering algorithm which combines image denoising and mathematical morphology. This method conducts morphological filtering and denoising on noised image with filter cascade and its performance is verified with stimulation testing. The experiment results prove that the approach to build the morphological filter into cascaded filter through series and parallel connection can to a certain extent, affect the effect of common filter while being applied to different image processing.

Revealing and evaluating the influence of filters position in cascaded filter: application on the ECG de-noising performance disparity

In this paper, a new optimization on windowing technique based on finite impulse response (FIR) filters is proposed for revealing and evaluating the Influence of filters position in cascaded filter tested on the ECG signal de-noising. baseline wander (BLW), power line interference (PLI) and electromyography (EMG) noises are getting removed. The performance of the adopted method is evaluated on the PTB diagnostic database. Subsequently, the comparisons are based on signal to noise ratio (SNR) improvement and mean square error (MSE) minimization. Where the Rectangular, and Kaiser windows have been used for the more potent performances. The disparity average (DA) of SNR values is detected; in both Kaiser and Rectangular windows are assessed by ±0.38046dB and ±0.70278dB respectively, while the MSE values were constant. The excellent configuration or filters position (H-B-L) of the filtration system is selected according to high measurements of SNR and low MSE too, to de-noise the ECG signals. First of all, this applied approach has led to 31.30 dB SNR improvement with MSE minimization of 26. 43%. This means that there is a significant contribution to improving the field of filtration.

Cascaded filter deterministic lateral displacement microchips for isolation and molecular analysis of circulating tumor cells and fusion cells

Precise isolation and analysis of circulating tumor cells (CTCs) from blood samples offer considerable potential for cancer research and personalized treatment. Currently, available CTCs isolation approaches remain challenging in the...

PointSite: a point cloud segmentation tool for identification of protein ligand binding atoms

Accurate identifications of ligand binding sites (LBS) on protein structure is critical for understanding protein function and designing structure-based drug. As the previous pocket-centric methods are usually based on the investigation of pseudo surface points (PSPs) outside the protein structure, thus inherently cannot incorporate the local connectivity and global 3D geometrical information of the protein structure. In this paper, we propose a novel point clouds segmentation method, PointSite, for accurate identification of protein ligand binding atoms, which performs protein LBS identification at the atom-level in a protein-centric manner. Specifically, we first transfer the original 3D protein structure to point clouds and then conduct segmentation through Submanifold Sparse Convolution (SSC) based U-Net. With the fine-grained atom-level binding atoms representation and enhanced feature learning, PointSite can outperform previous methods in atom-IoU by a large margin. Furthermore, our segmented binding atoms can work as a filter on predictions achieved by previous pocket-centric approaches, which significantly decreases the false-positive of LBS candidates. Through cascaded filter and re-ranking aided by the segmented atoms, state-of-the-art performance can be achieved over various canonical benchmarks and CAMEO hard targets in terms of the commonly used DCA criteria. Our code is publicly available through https://github.com/PointSite.