SAR Tomography with Improved Non-Local Means Filtering Based on Adaptive Window

In order to mitigate speckle noise in synthetic aperture radar(SAR)images and enhance the accuracy of SAR tomography,non-local means(NL-means)filtering has been proven to be an effective method for improving the quality of SAR interferograms.Apart from considerations like noise type and the definition of similarity,the size and shape of filtering windows are critical factors influencing the efficacy of NL-means filtering,yet there has been limited research on this aspect.This paper introduces an enhanced NL-means filtering method based on adaptive windows,allowing for the automatic adjustment of filtering window size according to the amplitude information of the SAR interferogram.Simultaneously,a directional window is incorporated to align SAR interferograms,achieving the dual objective of preserving filtering standards and retaining detailed information.Experimental results on interferogram filtering and tomography,based on TerraSAR-X data,demonstrate that the proposed method effectively reduces phase noise while maintaining texture accuracy,thereby improving tomography quality.

Drift DetectionMethod Using DistanceMeasures and Windowing Schemes for Sentiment Classification

Textual data streams have been extensively used in practical applications where consumers of online products have expressed their views regarding online products.Due to changes in data distribution,commonly referred to as concept drift,mining this data stream is a challenging problem for researchers.The majority of the existing drift detection techniques are based on classification errors,which have higher probabilities of false-positive or missed detections.To improve classification accuracy,there is a need to develop more intuitive detection techniques that can identify a great number of drifts in the data streams.This paper presents an adaptive unsupervised learning technique,an ensemble classifier based on drift detection for opinion mining and sentiment classification.To improve classification performance,this approach uses four different dissimilarity measures to determine the degree of concept drifts in the data stream.Whenever a drift is detected,the proposed method builds and adds a new classifier to the ensemble.To add a new classifier,the total number of classifiers in the ensemble is first checked if the limit is exceeded before the classifier with the least weight is removed from the ensemble.To this end,a weighting mechanism is used to calculate the weight of each classifier,which decides the contribution of each classifier in the final classification results.Several experiments were conducted on real-world datasets and the resultswere evaluated on the false positive rate,miss detection rate,and accuracy measures.The proposed method is also compared with the state-of-the-art methods,which include DDM,EDDM,and PageHinkley with support vector machine(SVM)and Naive Bayes classifiers that are frequently used in concept drift detection studies.In all cases,the results show the efficiency of our proposed method.

A 12-bit 30-MS/s VCO-based SAR ADC with NOC-assisted multiple adaptive bypass windows

This paper proposes a technique that uses the number of oscillation cycles(NOC)of a VCO-based comparator to set multiple adaptive bypass windows in a 12-bit successive approximation register(SAR)analog-to-digital converter(ADC).The analysis of the number of bit cycles,power and static performance shows that three adaptive bypass windows reduce power consumption,and decrease DNL and have similar INL,compared with the SAR ADC without bypass windows.In addition,a 1-bit split-and-recombination redundancy technique and a general bypass logic digital error correction method are proposed to address the settling issues and optimize the size of the bypass window.This design is implemented in 40 nm CMOS technology.The conversion frequency of the ADC reaches up to 30 MS/s.The ADC achieves an SFDR of 85.35 dB and 11.12-bit ENOB with Nyquist input,consuming 380μW,down from 427μW without multiple adaptive bypass windows,at a 1.1 V supply,resulting in a figure of merit(FoM)of 5.69 fJ/conversion-step.

RFID unreliable data filtering by integrating adaptive sliding Window and Euclidean distance

Through improving the redundant data filtering of unreliable data filter for radio frequency identification（RFID） with sliding-window,a data filter which integrates self-adaptive sliding-window and Euclidean distance is proposed.The input data required being filtered have been shunt by considering a large number of redundant data existing in the unreliable data for RFID and the redundant data in RFID are the main filtering object with utilizing the filter based on Euclidean distance.The comparison between the results from the method proposed in this paper and previous research shows that it can improve the accuracy of the RFID for unreliable data filtering and largely reduce the redundant reading rate.

Random Forests Algorithm Based Duplicate Detection in On-Site Programming Big Data Environment

On-site programming big data refers to the massive data generated in the process of software development with the characteristics of real-time,complexity and high-difficulty for processing.Therefore,data cleaning is essential for on-site programming big data.Duplicate data detection is an important step in data cleaning,which can save storage resources and enhance data consistency.Due to the insufficiency in traditional Sorted Neighborhood Method(SNM)and the difficulty of high-dimensional data detection,an optimized algorithm based on random forests with the dynamic and adaptive window size is proposed.The efficiency of the algorithm can be elevated by improving the method of the key-selection,reducing dimension of data set and using an adaptive variable size sliding window.Experimental results show that the improved SNM algorithm exhibits better performance and achieve higher accuracy.

Speckle filtering of Synthetic Aperture Radar images using filters with object-size-adapted windows

Speckle degrades the radiometric quality of a Synthetic Aperture Radar(SAR)image.Previous methods for speckle reduction have used a fixedsize window for filtering the entire image.This,however,may not be effective for the entire image,as land covers of different sizes require different filtering windows.In this paper,a novel method is proposed by which each pixel in the image is filtered with a window appropriate for the size of object within it.The real in-phase and the imaginary quadrature components of the SAR images determine the best window size and the pixels in the intensity image are filtered using their own optimal windows.The proposed method is presented for both singleand multi-polarized SAR images,and the results of several common filters that were modified are presented.This approach is applied to two RADARSAT-2 images:one over San Francisco,California,USA and the other over St.John’s,Newfoundland and Labrador,Canada,producing results that were similar to,or outperformed,comparable filters while retaining details and suppressing speckle effectively.While the method was successful for single-look intensity data,it offers great potential for multi-look and amplitude data as well.