Joint Sample Position Based Noise Filtering and Mean Shift Clustering for Imbalanced Classification Learning

The problem of imbalanced data classification learning has received much attention.Conventional classification algorithms are susceptible to data skew to favor majority samples and ignore minority samples.Majority weighted minority oversampling technique(MWMOTE)is an effective approach to solve this problem,however,it may suffer from the shortcomings of inadequate noise filtering and synthesizing the same samples as the original minority data.To this end,we propose an improved MWMOTE method named joint sample position based noise filtering and mean shift clustering(SPMSC)to solve these problems.Firstly,in order to effectively eliminate the effect of noisy samples,SPMsC uses a new noise filtering mechanism to determine whether a minority sample is noisy or not based on its position and distribution relative to the majority sample.Note that MWMOTE may generate duplicate samples,we then employ the mean shift algorithm to cluster minority samples to reduce synthetic replicate samples.Finally,data cleaning is performed on the processed data to further eliminate class overlap.Experiments on extensive benchmark datasets demonstrate the effectiveness of SPMsC compared with other sampling methods.

Improving Software Quality Prediction by Noise Filtering Techniques

Accuracy of machine learners is affected by quality of the data the learners are induced on. In this paper, quality of the training dataset is improved by removing instances detected as noisy by the Partitioning Filter. The fit dataset is first split into subsets, and different base learners are induced on each of these splits. The predictions are combined in such a way that an instance is identified as noisy if it is misclassified by a certain number of base learners. Two versions of the Partitioning Filter are used： Multiple-Partitioning Filter and Iterative-Partitioning Filter. The number of instances removed by the filters is tuned by the voting scheme of the filter and the number of iterations. The primary aim of this study is to compare the predictive performances of the final models built on the filtered and the un-filtered training datasets. A case study of software measurement data of a high assurance software project is performed. It is shown that predictive performances of models built on the filtered fit datasets and evaluated on a noisy test dataset are generally better than those built on the noisy （un-filtered） fit dataset. However, predictive performance based on certain aggressive filters is affected by presence of noise in the evaluation dataset.

A Δ ∑ fractional-N frequency synthesizer for FM tuner using low noise filter and quantization noise suppression technique

A △∑ fractional-N frequency synthesizer fabricated in a 130 nm CMOS technology is presented for the application of an FM tuner. A low noise filter, occupying a small die area and decreasing the output noise, is integrated on a chip. A quantization noise suppression technique, using a reduced step size of the frequency divider, is also adopted. The proposed synthesizer needs no off-chip components and occupies an area of 0.7 mm2. The in-band phase noise （from 10 to 100 kHz） below -108 dBc/Hz and out-of-band phase noise of -122.9 dBc/Hz （at 1 MHz offset） are measured with a loop bandwidth of 200 kHz. The quantization noise suppression technique reduces the in-band and out-of band phase noise by 15 dB and 7 dB respectively. The integrated RMS phase error is no more than 0.48°. The proposed synthesizer consumes a total power of 7.4 mW and the frequency resolution is less than 1 Hz.

Trigger and noise filtering algorithms for the LHAASO water Cherenkov detector array

Introduction The Large High Altitude Air Shower Observatory(LHAASO)will be constructed at Mt.Haizishan,Sichuan Province,China.Among several detector components of the LHAASO,the Water Cherenkov Detector Array(WCDA)is of great importance for gamma-ray astronomy from 100 GeV to 30 TeV.The single-channel counting rate of a photo-multiplier tube can reach as high as 30-35 kHz,most of them are background noise hits from the low energy cosmic ray showers,bringing a big challenge on data transferring,data storage and event reconstruction.Methods In this paper,a dedicated trigger scheme and a fast noise filtering method aiming to deal with these high rate background noise hits are introduced.These methods are tested with some Monte Carlo simulation data,showing a fair efficiency in filtering background noise hits,while most of the real shower signals are kept.Conclusion This method is proposed to be applied in a stage of the online processing just after the data are acquired in LHAASO-WCDA.

A 55 nm CMOS ΔΣ fractional-N frequency synthesizer for WLAN transceivers with low noise filters

A fully integrated △∑ fractional-N frequency synthesizer fabricated in a 55 nm CMOS technology is presented for the application of IEEE 802.11b/g wireless local area network （WLAN） transceivers. A low noise filter, occupying a small die area, whose power supply is given by a high PSRR and low noise LDO regulator, is integrated on chip. The proposed synthesizer needs no off-chip components and occupies an area of 0.72 mm^2excluding PAD. Measurement results show that in all channels, the phase noise of the synthesizer achieves -99 dBc/Hz and -119 dBc/Hz in band and out of band respectively with a reference frequency of 40 MHz and a loop bandwidth of 200 kHz. The integrated RMS phase error is no more than 0.6°. The proposed synthesizer consumes a total power of 15.6 mW.

Stochastic Response Analysis of Piled Offshore Platform Excited by Stationary Filtered White Noise

In this paper, the analysis method of stochastic response of piled offshore platform excited by stationary filtered white noise is presented. With this method, the strong ground motion is considered as three direction stationary filtered white noise process, the theoretic solutions of three special integration equations are derived with the residue theorem, and the expression of response nodal displacements and member forces of offshore platform excited by the stationary filtered white noise is put forward. The stochastic response of a piled offshore platform excited by the stationary filtered white noise, which is located 114.3 m in water depth, is computed. The results are compared with those obtained with the response spectrum analysis method and the stationary white noise model analysis method, and the corresponding conclusion is drawn.

Noise level estimation method with application to EMD-based signal denoising

This paper proposes a new signal noise level estimation approach by local regions. The estimated noise variance is applied as the threshold for an improved empirical mode decomposition（EMD） based signal denoising method. The proposed estimation method can effectively extract the candidate regions for the noise level estimation by measuring the correlation coefficient between noisy signal and a Gaussian filtered signal. For the improved EMD based method, the situation of decomposed intrinsic mode function（IMFs） which contains noise and signal simultaneously are taken into account. Experimental results from two simulated signals and an X-ray pulsar signal demonstrate that the proposed method can achieve better performance than the conventional EMD and wavelet transform（WT） based denoising methods.

Refinement of Enhanced Speech Using Hybrid-Median Filter and Harmonic Regeneration Approach

This study proposes a post-processor to improve the harmonic structure of a vowel in an enhanced speech, enabling the speech quality to be improved. Initially, a speech enhancement algorithm is employed to reduce the background noise for a noisy speech. Hence the enhanced speech is post-processed by a hybrid-median filter to reduce the musical effect of residual noise. Since the harmonic spectra are impacted by background noise and a speech enhancement process, the quality of a vowel is deteriorated. A harmonic regenerated method is developed to improve the quality of post-processed speech. Experimental results show that the proposed method can improve the quality of post-processed speech by adequately regenerating harmonic spectra.

Self-tuning measurement fusion white noise deconvolution estimator with correlated noises

For the multisensor linear discrete time-invariant stochastic systems with correlated noises and unknown noise statistics,an on-line noise statistics estimator is presented by using the correlation method.Substituting it into the steady-state Riccati equation,the self-tuning Riccati equation is obtained.Using the Kalman filtering method,based on the self-tuning Riccati equation,a self-tuning weighted measurement fusion white noise deconvolution estimator is presented.By the dynamic error system analysis（DESA） method,it is proved that the self-tuning fusion white noise deconvolution estimator converges to the optimal fusion steadystate white noise deconvolution estimator in a realization,so that it has the asymptotic global optimality.A simulation example for Bernoulli-Gaussian input white noise shows its effectiveness.

Methods of Data Processing for Trace Elements Analysis Using Laser Induced Breakdown Spectroscopy

With the development of Laser Induced Breakdown Spectroscopy （LIBS）, increasing numbers of researchers have begun to focus on problems of the application. We are not just satisfied with analyzing what kinds of elements are in the samples but are also eager to accomplish quantitative detection with LIBS. There are several means to improve the limit of detection and stability, which are important to quantitative detection, especially of trace elements, increasing the laser energy and the resolution of spectrometer, using dual pulse setup, vacuuming the ablation environment etc. All of these methods are about to update the hardware system, which is effective but expensive. So we establish the following spectrum data processing methods to improve the trace elements analysis in this paper： spectrum sifting, noise filtering, and peak fitting. There are small algorithms in these three method groups, which we will introduce in detail. Finally, we discuss how these methods affect the results of trace elements detection in an experiment to analyze the lead content in Chinese cabbage.

An Approach Using Fuzzy Sets and Boosting Techniques to Predict Liver Disease

The aim of this research is to develop a mechanism to help medical practitioners predict and diagnose liver disease.Several systems have been proposed to help medical experts by diminishing error and increasing accuracy in diagnosing and predicting diseases.Among many existing methods,a few have considered the class imbalance issues of liver disorder datasets.As all the samples of liver disorder datasets are not useful,they do not contribute to learning about classifiers.A few samples might be redundant,which can increase the computational cost and affect the performance of the classifier.In this paper,a model has been proposed that combines noise filter,fuzzy sets,and boosting techniques(NFFBTs)for liver disease prediction.Firstly,the noise filter(NF)eliminates the outliers from the minority class and removes the outlier and redundant pair from the majority class.Secondly,the fuzzy set concept is applied to handle uncertainty in datasets.Thirdly,the AdaBoost boosting algorithm is trained with several learners viz,random forest(RF),support vector machine(SVM),logistic regression(LR),and naive Bayes(NB).The proposed NFFBT prediction system was applied to two datasets(i.e.,ILPD and MPRLPD)and found that AdaBoost with RF yielded 90.65%and 98.95%accuracy and F1 scores of 92.09%and 99.24%over ILPD and MPRLPD datasets,respectively.

Design of low noise class D amplifiers using an integrated filter

This paper investigates the noise sources in a single-ended class D amplifier（SECDA） and suggests corresponding ways to lower the noise.The total output noise could be expressed as a function of the gain and noises from different sources.According to the function,the bias voltage（V_B） is a primary noise source,especially for a SECDA with a large gain.A low noise SECDA is obtained by integrating a filter into the SECDA to lower the noise of the V_B.The filter utilizes an active resister and an 80 pF capacitance to get a 3 Hz pole.A noise test and fast Fourier transform analysis show that the noise performance of this SECDA is the same as that of a SECDA with an external filter.

Towed line array sonar platform noise suppression based on spatial matrix filtering technology

The spatial matrix filter was designed and used for solving the problem to detect a weak target who was influenced by the strong nearby platform noise interference of the towed line array sonar. The MFP technology and the DOA estimation technology were combined together by using the sound propagation characteristics of both target and interference. The spatial matrix filter with platform noise zero response constraint was designed by the near-field platform noise normal modes copy vectors and the far-field plane wave bearing vectors together. The optimal solution of the optimization problem for designing the spatial matrix filter was deduced directly, and it was simplified by the generalized singular value decomposition. The total response error to the plane wave bearing vectors and the total response to the platform noise copy vectors were given. The phenomena that strong interferences existed in the bearing course and blind areas existed after filtering were analyzed by the correlation between the plat- form noise copy vectors and the plane wave bearing vectors. It could be found from simulations that it has less blind area and higher detection ability by using the spatial matrix filtering technology.

Multi-Level Fusion in Ultrasound for Cancer Detection Based on Uniform LBP Features

Collective improvement in the acceptable or desirable accuracy level of breast cancer image-related pattern recognition using various schemes remains challenging.Despite the combination of multiple schemes to achieve superior ultrasound image pattern recognition by reducing the speckle noise,an enhanced technique is not achieved.The purpose of this study is to introduce a features-based fusion scheme based on enhancement uniform-Local Binary Pattern(LBP)and filtered noise reduction.To surmount the above limitations and achieve the aim of the study,a new descriptor that enhances the LBP features based on the new threshold has been proposed.This paper proposes a multi-level fusion scheme for the auto-classification of the static ultrasound images of breast cancer,which was attained in two stages.First,several images were generated from a single image using the pre-processing method.Themedian andWiener filterswere utilized to lessen the speckle noise and enhance the ultrasound image texture.This strategy allowed the extraction of a powerful feature by reducing the overlap between the benign and malignant image classes.Second,the fusion mechanism allowed the production of diverse features from different filtered images.The feasibility of using the LBP-based texture feature to categorize the ultrasound images was demonstrated.The effectiveness of the proposed scheme is tested on 250 ultrasound images comprising 100 and 150 benign and malignant images,respectively.The proposed method achieved very high accuracy(98%),sensitivity(98%),and specificity(99%).As a result,the fusion process that can help achieve a powerful decision based on different features produced from different filtered images improved the results of the new descriptor of LBP features in terms of accuracy,sensitivity,and specificity.

Flexible polarization demultiplexing method based on an adaptive process noise covariance Kalman filter

A flexible polarization demultiplexing method based on an adaptive Kalman filter（AKF） is proposed in which the process noise covariance has been estimated adaptively. The proposed method may significantly improve the adaptive capability of an extended Kalman filter（EKF） by adaptively estimating the unknown process noise covariance. Compared to the conventional EKF, the proposed method can avoid the tedious and time consuming parameter-by-parameter tuning operations. The effectiveness of this method is confirmed experimentally in 128 Gb/s 16 QAM polarization-division-multiplexing（PDM） coherent optical transmission systems. The results illustrate that our proposed AKF has a better tracking accuracy and a faster convergence（about 4 times quicker）compared to a conventional algorithm with optimal process noise covariance.

A low-noise widely tunable Gm-C filter with frequency calibration

A fourth-order Gm-C Chebyshev low-pass filter is presented as channel selection filter for reconfigurable multi-mode wireless receivers. Low-noise technologies are proposed in optimizing the noise characteristics of both the Gm cells and the filter topology. A frequency tuning strategy is used by tuning both the transconductance of the Gm cells and the capacitance of the capacitor banks. To achieve accurate cut-off frequencies, an on-chip calibration circuit is presented to compensate for the frequency inaccuracy introduced by process variation. The filter is fabricated in a 0.13 m CMOS process. It exhibits a wide programmable bandwidth from 322.5 k Hz to20 MHz. Measured results show that the filter has low input referred noise of 5.9 n V/（Hz）^（1/2） and high out-of-band IIP3 of 16.2 d Bm. It consumes 4.2 and 9.5 m W from a 1 V power supply at its lowest and highest cut-off frequencies respectively.

Applying nonlinear filtering in reverberation time measurement under strong background noise

Nonlinear filtering of impulse response obtained by M-sequence correlation method under strong background noise is presented. The research shows that the new method works very efficiently without the need to cut off impulse response data. Even if the ratio of signal to noise is below -15 dB, the same decay curve ranges can still be obtained as when S/N > 40 dB

Mode shape description of an aero-engine casing structure using Zernike moment descriptors

Vibration mode shape description of an aero-engine casing structure using Zernike moment descriptor(ZMD) was introduced in this paper.The mode shapes of the aero-engine casing structure can be decomposed as a linear combination of a series of Zernike polynomials,with the feature of each Zernike polynomial reflecting a part of characteristic of mode shapes,based on Zernike moment transformation.Meanwhile,the reconstruction of mode shapes with ZMD was explored and its ability to filtering the noise contaminated in the mode shapes was studied.Simulation of the aero-engine casing structure indicated the advantage of this method to depict the mode shapes of a symmetric structure.Results demonstrate that the Zernike moment description of the mode shapes can effectively describe the double modes in the symmetric structure and also has the ability to remove or significantly reduce the influence of noise in the mode shapes.Such feature shows great practical value for further research on the correlation,model updating and model validation of the symmetric structure's finite element model.

Accurate Identification of Mass Peaks for Tandem Mass Spectra Using MCMC Model

In proteomics, many methods for the identification of proteins have been developed. However, because of limited known genome sequences, noisy data, incomplete ion sequences, and the accuracy of protein identification,it is challenging to identify peptides using tandem mass spectral data. Noise filtering and removing thus play a key role in accurate peptide identification from tandem mass spectra. In this paper, we employ a Bayesian model to identify proteins based on the prior information of bond cleavages. A Markov Chain Monte Carlo（MCMC）algorithm is used to simulate candidate peptides from the posterior distribution and to estimate the parameters for the Bayesian model. Our simulation and computational experimental results show that the model can identify peptide with a higher accuracy.

A low power 2.4 GHz transceiver for ZigBee applications

This paper presents a low power 2.4 GHz transceiver for ZigBee applications.This transceiver adopts low power system architecture with a low-IF receiver and a direct-conversion transmitter.The receiver consists of a new low noise amplifier（LNA） with a noise cancellation function,a new inverter-based variable gain complex filter （VGCF） for image rejection,a passive quadrature mixer,and a decibel linear programmable gain amplifier（PGA）. The transmitter adopts a quadrature mixer and a class-B mode variable gain power amplifier（PA） to reduce power consumption.This transceiver is implemented in 0.18μm CMOS technology.The receiver achieves—95 dBm of sensitivity,28 dBc of image rejection,and -8 dBm of third-order input intercept point（IIP3）.The transmitter can deliver a maximum of＋3 dBm output power with PA efficiency of 30%.The whole chip area is less than 4.32 mm^2. It only consumes 12.63 mW in receiving mode and 14.22 mW in transmitting mode,respectively.