ia-PNCC: Noise Processing Method for Underwater Target Recognition Convolutional Neural Network

Underwater target recognition is a key technology for underwater acoustic countermeasure.How to classify and recognize underwater targets according to the noise information of underwater targets has been a hot topic in the field of underwater acoustic signals.In this paper,the deep learning model is applied to underwater target recognition.Improved anti-noise Power-Normalized Cepstral Coefficients(ia-PNCC)is proposed,based on PNCC applied to underwater noises.Multitaper and normalized Gammatone filter banks are applied to improve the anti-noise capacity.The method is combined with a convolutional neural network in order to recognize the underwater target.Experiment results show that the acoustic feature presented by ia-PNCC has lower noise and are wellsuited to underwater target recognition using a convolutional neural network.Compared with the combination of convolutional neural network with single acoustic feature,such as MFCC(Mel-scale Frequency Cepstral Coefficients)or LPCC(Linear Prediction Cepstral Coefficients),the combination of the ia-PNCC with a convolutional neural network offers better accuracy for underwater target recognition.

Noise Removal in Speech Processing Using Spectral Subtraction

Spectral subtraction is used in this research as a method to remove noise from noisy speech signals in the frequency domain. This method consists of computing the spectrum of the noisy speech using the Fast Fourier Transform (FFT) and subtracting the average magnitude of the noise spectrum from the noisy speech spectrum. We applied spectral subtraction to the speech signal “Real graph”. A digital audio recorder system embedded in a personal computer was used to sample the speech signal “Real graph” to which we digitally added vacuum cleaner noise. The noise removal algorithm was implemented using Matlab software by storing the noisy speech data into Hanning time-widowed half-overlapped data buffers, computing the corresponding spectrums using the FFT, removing the noise from the noisy speech, and reconstructing the speech back into the time domain using the inverse Fast Fourier Transform (IFFT). The performance of the algorithm was evaluated by calculating the Speech to Noise Ratio (SNR). Frame averaging was introduced as an optional technique that could improve the SNR. Seventeen different configurations with various lengths of the Hanning time windows, various degrees of data buffers overlapping, and various numbers of frames to be averaged were investigated in view of improving the SNR. Results showed that using one-fourth overlapped data buffers with 128 points Hanning windows and no frames averaging leads to the best performance in removing noise from the noisy speech.

Nonlinear Model of Image Noise: An Application on Computed Tomography including Beam Hardening and Image Processing Algorithms

This paper proposes a more inclusive statistical model for predicting image noise in Computed Tomography (CT), associated with scanning factors, considering the effect of beam hardening and image processing filters. It is based on power functions where the levels of the parameters will determine the rate of noise variation with respect to a given scanning factor. It includes the influence of tube potential, tube current, slice thickness, Field of View (FOV), reconstruction methods and post-processing filters. To validate the model, tomographic measurements were made by using a PMMA phantom that simulates paediatric head and adult abdomen, a PET bottle was used to simulate the head of the new-born. The influence of ROI (Region Of Interest) size over nonlinear model parameters was analysed, and high variations of powers of attenuation and FOV were found depending on ROI size. A nonlinear robust regression method was used. The validation was performed graphically by weighted residual analysis. A nonlinear noise model was obtained with an adjusted coefficient of determination for ROI sizes between 10% and 70% of the phantom diameter or FOV. The model confirms the significance of the tube current, slice thickness and beam hardening effect on image. The process of estimation of the parameters of the model by Nonlinear Robust Regression turned out to be optimal.

Automatic de-noising and recognition algorithm for drilling fluid pulse signal

Wavelet forced de-noising algorithm is suitable for denoising of unsteady drilling fluid pulse signal, including baseline drift rectification and two-stage de-noising processing of frame synchronization signal and instruction signal. Two-stage de-noising processing can reduce the impact of baseline drift and determine automatic peak detection threshold range for signal recognition by distinguishing different features of frame synchronization pulse and instruction pulse. Rising and falling edge relative protruding threshold is defined for peak detection in signal recognition, which can make full use of the degree of the signal peak change and detect peaks flexibly with rising and falling edge relative protruding threshold combination. A synchronous decoding method was designed to reduce position uncertainty of the frame synchronization pulse and eliminate the accumulative error of time base drift, which determines the first instruction pulse position according to position of the frame synchronization pulse and decodes subsequent instruction pulse by taking current instruction pulse as new bit synchronization pulse. Special tool software was developed to tune algorithm parameters, which has a decoding success rate of about 95% for the universal coded signals. For the special coded signals with check byte, decoding success rate using the automatic threshold adjustment algorithm is as high as 99%.

An Algorithm for Ship Wake Detection from the SAR Images Using the Radon Transform and Morphological Image Processing

Using the Radon transform and morphological image processing, an algorithm for ship's wake detection in the SAR (synthetic aperture radar) image is developed. Being manipulated in the Radon space to invert the gray-level and binary images, the linear texture of ship wake in oceanic clutter can be well detected. It has been applied to the automatic detection of a moving ship from the SEASAT SAR image. The results show that this algorithm is well robust in a strong noisy background and is not very sensitive to the threshold parameter and the working window size.

Extraction of Signals Buried in Noise: Non-Ergodic Processes

In this paper, we propose extraction of signals buried in non-ergodic processes. It is shown that the proposed method extracts signals defined in a non-ergodic framework without averaging or smoothing in the direct time or frequency domain. Extraction is achieved independently of the nature of noise, correlated or not with the signal, colored or white, Gaussian or not, and locations of its spectral extent. Performances of the pro-posed extraction method and comparative results with other methods are demonstrated via experimental Doppler velocimetry measurements.

Extraction of Buried Signals in Noise: Correlated Processes

In this paper, we propose extraction of signals correlated with noise in which they are buried. Proposed extraction method uses no a-priori information on the buried signal and works independently of the nature of noise, correlated or not with the signal, colored or white, Gaussian or not, and locations of its spectral extent. Extraction of buried correlated signals is achieved without averaging in the time or frequency domain.

Fine Geological Radar Processing and Interpretation

Geological radar probing technology finds wide application in engineering projects.The high-precision characteristics of geologic radar should be studied in connection with fine processing and interpretation. This article discusses such issues as ( 1 ) geologic radar noise source and (2) fine processing and interpretation of radar data. It is foct^sed on bow to achieve fine processing and interpretation.

The Response to Arbitrarily Bandlimited Gaussian Noise of the Complex Stretch Processor Using a Conventional Range-Sidelobe-Reduction Window

This paper derives a mathematical description of the complex stretch processor’s response to bandlimited Gaussian noise having arbitrary center frequency and bandwidth. The description of the complex stretch processor’s random output comprises highly accurate closed-form approximations for the probability density function and the autocorrelation function. The solution supports the complex stretch processor’s usage of any conventional range-sidelobe-reduction window. The paper then identifies two practical applications of the derived description. Digital-simulation results for the two identified applications, assuming the complex stretch processor uses the rectangular, Hamming, Blackman, or Kaiser window, verify the derivation’s correctness through favorable comparison to the theoretically predicted behavior.

Evolutionary Game Dynamics in a Fitness-Dependent Wright-Fisher Process with Noise

Evolutionary game dynamics in finite size populations can be described by a fitness-dependent WrightFisherprocess.We consider symmetric 2x2 games in a well-mixed population.In our model,two parameters todescribe the level of player’s rationality and noise intensity in environment are introduced.In contrast with the fixationprobability method that used in a noiseless case,the introducing of the noise intensity parameter makes the processan ergodic Markov process and based on the limit distribution of the process,we can analysis the evolutionary stablestrategy (ESS) of the games.We illustrate the effects of the two parameters on the ESS of games using the Prisoner’sdilemma games (PDG) and the snowdrift games (SG).We also compare the ESS of our model with that of the replicatordynamics in infinite size populations.The results are determined by simulation experiments.

Interference Mitigation Techniques Using Receiver Processing and Resource Allocation

Heterogeneous network consists of the pico cells overlaid over the macro cell coverage area in a wireless cellular network. The pico cells are deployed to increase the capacity of the homogeneous network by reusing the spectrum further. However, more users will tend to be associated to the macro cell due to the fact that the transmit power of the pico cell is low. In order to increase the number of users associated to the pico cell, range extension techniques like biased association are used. This will cause severe interference to cell edge users of the pico cell from the macro cell causing degradation in throughput performance in the cell range extension area. In this paper, interference mitigation using receiver processing along with different scheduling techniques is proposed to improve the throughput, average delay, and the packet delivery ratio performance of the system. The performance comparison of the round robin, proportional fair and modified largest weighted delay first (MLWDF) algorithm for resource allocation using interference suppressing receiver is done, and analyzed. It is shown that the MLWDF algorithm achieves the highest throughput with minimum average delay of packets with the best delivery ratio.

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.

Some Applications of Higher Moments of the Linear Gaussian White Noise Process

The Linear Gaussian white noise process is an independent and identically distributed (iid) sequence with zero mean and finite variance with distribution N (0, σ2 ) . Hence, if X1, x2, …, Xn is a realization of such an iid sequence, this paper studies in detail the covariance structure of X1d, X2d, …, Xnd, d=1, 2, …. By this study, it is shown that: 1) all powers of a Linear Gaussian White Noise Process are iid but, not normally distributed and 2) the higher moments (variance and kurtosis) of Xtd, d=2, 3, … can be used to distinguish between the Linear Gaussian white noise process and other processes with similar covariance structure.

On the Small SNR Processing Ability of IR Point Target Detection

Three-dimensional (3-D) matched filtering has been suggested as a powerful processing technique for detecting weak, moving IR point target immersed in a noisy field. Based on the theory of the 3-D matched filtering and the optimal linear processing, the optimal point target detector is being analyzed in this paper. The performance of the detector is introduced in detail. The results provide a standard reference to evaluate the performance of any other point target detection algorithms.

Process Noise Parameters of Beamforming Green Nodes

Signals arrive out of phase at the intended receiver from collaborative beamforming （CB） nodes due to the instability in the output frequency signals of the universal software radio peripheral＇s （USRP） local oscillator （LO）. These nodes including the target must synchronize their oscillator frequencies for coherent signal reception. In order to do this, frequencies and phases of the signals should be estimated in software defined radio （SDR） and smoothen with nonlinear filters such as the extended Kalman filter （EKF）. The process noise parameters of the NI USRP-2920 nodes will have to be calculated and used with the EKF process noise covariance matrix. These nodes are green communication hardware devices where most of the hardware units are now software defined. This article uses the direct spectrum method to obtain the phase noise values at various frequency offsets of the NI USRP-2920 in order to calculate the power spectral density of fractional frequency fluctuation. By applying the power-law noise model to this obtained value, the generated white frequency noise and random walk frequency noise values are q_1=1.93x10^-21 and q_2=5.86x10^-18, respectively.

The Distribution of Multiple Shot Noise Process and Its Integral

In this paper, we study multiple shot noise process and its integral. We analyse these two processes systematically for their theoretical distributions, based on the piecewise deterministic Markov process theory developed by Davis [1] and the martingale methodology used by Dassios and Jang [2]. The analytic expressions of the Laplace transforms of these two processes are presented. We also obtain the multivariate probability generating function for the number of jumps, for which we use a multivariate Cox process. To derive these, we assume that the Cox processes jumps, intensity jumps and primary event jumps are independent of each other. Using the Laplace transform of the integral of multiple shot noise process, we obtain the tail of multivariate distributions of the first jump times of the Cox processes, i.e. the multivariate survival functions. Their numerical calculations and other relevant joint distributions’ numerical values are also presented.

Dose Reduction by the Use of a Wavelet-Based Denoising Method for Digital Radiography

The primary purpose of this paper is to provide a novel wavelet-domain method for digital radiography with low dose examination. Approach of this study is an improved wavelet-transform-based method for potentially reducing radiation dose while maintaining clinically acceptable image quality. The wavelet algorithm integrates the advantages of wavelet-coefficient-weighted method and the existing Bayes Shrink thresholding method. In order to confirm the usefulness of the proposed method, the resolving and noise characteristics of the processed computed radiography images were measured. In addition, variations of contrast and noise with respect to radiation dose were also examined. Finally, to verify the effect of clinical examination, visual evaluations were also performed in lower abdominal area using phantom. Our quantitative results demonstrated that our wavelet algorithm could improve resolution characteristics while keeping the noise level within acceptable limits. Visual evaluation result demonstrated that the proposed method was superior to other published methods. Our proposed method recognized effect on decreasing in exposure dose in lower abdominal radiographs. As a conclusion, our proposed method’s performance is better when compared with that of the 3 conventional methods. The proposed method has the potential to improve visibility in radiographs when a lower radiation dose is applied.

Measuring Tail Dependence for Aggregate Collateral Losses Using Bivariate Compound Shot-Noise Cox Process

In this paper, we introduce tail dependene measures for collateral losses from catastrophic events. To calculate these measures, we use bivariate compound process where a Cox process with shot noise intensity is used to count collateral losses. A homogeneous Poisson process is also examined as its counterpart for the case where the catastrophic loss frequency rate is deterministic. Joint Laplace transform of the distribution of the aggregate collateral losses is derived and joint Fast Fourier transform is used to obtain the joint distributions of aggregate collateral losses. For numerical illustrations, a member of Farlie-Gumbel-Morgenstern copula with exponential margins is used. The figures of the joint distributions of collateral losses, their contours and numerical calculations of risk measures are also provided.

Micro-process model of hydraulic shock absorber with abnormal structural noise

In order to discover the causes of the abnormal noise of shock absorbers, it is necessary to identify the operating characteristics of the shock absorbers. A micro-process model for operation of the hydraulic shock absorber was presented. A novel concept, which describes the process of hydraulic shock absorber by dividing it into smaller steps, was proposed. The dynamic model and the differential equations were established. The results of numerical simulation agree well with data obtained from the vibrostand test, indicating that the collision between the piston and the oil, the alternation of static friction and sliding friction acted between the piston and the cylinder, and the adherence between valve plate and piston result in impact on the piston head near the top dead center and the bottom dead center. Ultimately, the impact excites the high-frequency vibration of the piston structure, which can generate the abnormal noise in the hydraulic shock absorber after its transfer. And the maximum vibration acceleration on the piston head and the abnormal noise increase with the increase of the gap between the oil and piston rod head, the maximum static friction force and the adhering function, respectively.

Air Quality Impacts of Smallholder Oil Palm Processing in Nigeria

Air emissions during palm oil processing by smallholders are issues of public health concern demanding urgent intervention by environmentalist. In this study, six smallholder oil palm processing mills were studied inElele,Nigeria. Air emission parameters (NO2, NH3, CO, H2S, SO2, VOC), noise and meteorology (wind speed, temperature, relative humidity and pressure) were determined at three distances (10 ft,25 ftand50 ft) in both wind ward and lee ward directions from the mills covering boiling and digestion activities. The emissions from biomass were found to be significantly higher than that from fossil diesel, while noise was higher during digestion. The health implications of air emissions were discussed. The study concluded by directing attentions of regulatory agencies to monitor the activities of smallholder oil palm processing to ensure the environmental sustainability of their operations. In summary, evidence during boiling activity revealed that: · H2S ranged from - 2.400 ppm at10 ft, - 2.067 ppm at25 ftand - 0.833 ppm at50 ftfrom the mills in the wind ward direction, and - 1.167 ppm at10 ft, - 0.567 ppm at25 ftand - 0.367 ppm at50ftdistance from the mills in lee ward direction and was significantly lower during digestion. · SPM ranged from 1634 - 7853 μg/m3 at10 ft, 657 - 1110 μg/m3 at25 ftand 81 - 854 μg/m3 at50 ftfrom the mills in the wind ward direction, and 46 - 236 μg/m3 at10 ft, 44 - 120 μg/m3 at25 ftand 30 - 58 μg/m3 at50 ftfrom the mills in lee ward direction. SPM was significantly lower during digestion. · VOC ranged from 67 - 13.933 ppm at10 ft, 1.033 - 13.133 ppm at25ftand 0.500 -