A novel graph-based clustering method using noise cutting

Recently, many methods have appeared in the field of cluster analysis. Most existing clustering algorithms have considerable limitations in dealing with local and nonlinear data patterns. Algorithms based on graphs provide good results for this problem. However, some widely used graph-based clustering methods, such as spectral clustering algorithms, are sensitive to noise and outliers. In this paper, a cut-point clustering algorithm (CutPC) based on a natural neighbor graph is proposed. The CutPC method performs noise cutting when a cut-point value is above the critical value. Normally, the method can automatically identify clusters with arbitrary shapes and detect outliers without any prior knowledge or preparatory parameter settings. The user can also adjust a coefficient to adapt clustering solutions for particular problems better. Experimental results on various synthetic and real-world datasets demonstrate the obvious superiority of CutPC compared with k-means, DBSCAN, DPC, SC, and DCore.     

结合MNF变换和Canny算子的遥感影像变化检测

针对大面积区域的多时相遥感影像变化检测的需求，提出了一种基于最小噪声分离（MNF）的Canny边缘检测提取影像变化信息的检测方法。对多时相影像采用多种变换组合成具有多维波段信息的影像，采用最小噪声分离法分离噪声并得到单波段差异图，通过Canny边缘检测法计算梯度幅值，采用高低双阈值法细化边缘，从而提取差异图变化边缘，有效突出了变化信息。以1995年和2003年加扎勒河的两期遥感影像为例，利用两时相影像进行土地覆被变化检测。实验结果表明，该方法适用于监测大面积区域内地物的突变情况。在数据基础上进行最小噪声分离可以有效解决传统Canny边缘检测提取边缘时造成的伪边缘现象，同时采用高低双阈值法有效去除伪边缘点，从而获得更加精确、直观的变化检测效果，在自然地理变化监测、地理国情灾害监测等有很好的应用价值。     

2.3–21 GHz broadband and high linearity distributed low noise amplifier

This paper focuses on the design of a 2.3–21 GHz Distributed Low Noise Amplifier (LNA) with low noise figure (NF), high gain (S₂₁), and high linearity (IIP3) for broadband applications. This distributed amplifier (DA) includes S/C/X/Ku/K-band, which makes it very suitable for heterodyne receivers. The proposed DA uses a 0.18 μm GaAs pHEMT process (OMMIC ED02AH) in cascade architecture with lines adaptation and equalization of phase velocity techniques, to absorb their parasitic capacitances into the gate and drain transmission lines in order to achieve wide bandwidth and to enhance gain and linearity. The proposed broadband DA achieved an excellent gain in the flatness of 13.5 ± 0.2 dB, a low noise figure of 3.44 ± 1.12 dB, and a small group delay variation of ±19.721 ps over the range of 2.3–21 GHz. The input and output reflection coefficients S₁₁ and S₂₂ are less than −10 dB. The input compression point (P_1dB) and input third-order intercept point (IIP3) are −1.5 dBm and 11.5 dBm, respectively at 13 GHz. The dissipated power is 282 mW and the core layout size is 2.2 × 0.8 mm².     

Random-valued impulse noise removal using adaptive dual threshold median filter

Noise detection and its removal is very important in the image processing. Detection of noise is very crucial and significant in random valued impulse noise because it does not hamper the image pixels uniformly. This paper presents a novel and unique concept of adaptive dual threshold for the detection of random valued impulse noise along with simple median filter at noise removal stage. Simulation results shows that an efficient noise detection leads to a superior quality of de-noised image as compared to existing adaptive threshold based image de-noising techniques. Proposed threshold computation is based on averaging of pixel values of window which enhances the PSNR of our system as compared to existing median filter based image de-noising methods.     

Effect of noise emitted by forestry equipment on workers' hearing capacity

Forest logging involves the use of various noise-producing equipment, which may be harmful to the hearing capabilities of operators in the immediate area. Yet, little research has been performed to determine the long term effect of noise on forest loggers. Therefore, the purpose of this study was to determine how noise from forestry work affects operators' hearing capabilities at different frequencies, in relation to their age, years of experience as forest loggers, and whether or not they regularly wore hearing protection equipment (HPE). Twenty-six male forest loggers who were directly involved with logging equipment operations participated in this study. They were divided into different groups depending on age (20–29, 30–39, 40–49, and 50–59), years of experience (1–10, 11–20, 21–30, and 31–40), and whether they used HPEs. A Beltone audiometer was used to measure hearing thresholds at different frequencies (125, 250, 500, 750, 1000, 2000, 4000, and 8000 Hz). To determine whether the loggers experienced threshold shifts, their hearing thresholds were compared with average persons’ hearing thresholds. The hearing threshold and threshold shift were found at 4000 Hz, and the lowest hearing thresholds were found at 500, 750, and 1000 Hz. According to results, as age increased, hearing threshold either increased or remained the same. This study also concluded that the years of experience or exposure to forestry equipment can adversely affect hearing. Participants that used HPE had lower hearing thresholds than participants that did not use HPE. By wearing HPE, forest workers may prevent hearing loss at 4000 Hz.Relevance to industryForest logging has adverse effect on the hearing of workers. Specifically the loggers who do not use hearing protection equipment are more susceptible to hearing loss. Therefore, the hearing of workers needs to be monitored on a regular basis and the use of HPE should be made mandatory.     

Underwater image quality enhancement through integrated color model with Rayleigh distribution

The physical properties of water cause light-induced degradation of underwater images. Light rapidly loses intensity as it travels in water, depending on the color spectrum wavelength. Visible light is absorbed at the longest wavelength first. Red and blue are the most and least absorbed, respectively. Underwater images with low contrast are captured due to the degradation effects of light spectrum. Therefore, the valuable information from these images cannot be fully extracted for further processing. The current study proposes a new method to improve the contrast and reduce the noise of underwater images. The proposed method integrates the modification of image histogram into two main color models, Red–Green–Blue (RGB) and Hue-Saturation-Value (HSV). In the RGB color model, the histogram of the dominant color channel (i.e., blue channel) is stretched toward the lower level, with a maximum limit of 95%, whereas the inferior color channel (i.e., red channel) is stretched toward the upper level, with a minimum limit of 5%. The color channel between the dominant and inferior color channels (i.e., green channel) is stretched to both directions within the whole dynamic range. All stretching processes in the RGB color model are shaped to follow the Rayleigh distribution. The image is converted into the HSV color model, wherein the S and V components are modified within the limit of 1% from the minimum and maximum values. Qualitative analysis reveals that the proposed method significantly enhances the image contrast, reduces the blue-green effect, and minimizes under- and over-enhanced areas in the output image. For quantitative analysis, the test with 300 underwater images shows that the proposed method produces average mean square error (MSE) and peak signal to noise ratio (PSNR) of 76.76 and 31.13, respectively, which outperform six state-of-the-art methods.     

Optimization of exhaust emissions,vibration, and noise of a hydrogen enriched fuelled diesel engine

In this study, the effects of various input parameters are examined on exhaust emissions, vibration, and noise of an unmodified diesel engine. The primary aim of this study is to optimize the vibration, noise, and exhaust emissions of the engine to get optimal configuration parameters. Experiments were carried out on a four-stroke, four-cylinder, diesel engine fuelled with diesel-biodiesel-hydrogen blends. To minimize the number of experiments Box-Behnken design (BBD) has been adopted. Optimum desirability is found as 0.862 with hydrogen addition of 4.63 L/min, fuel blend of 26.8% and 1500 rpm engine speed for the diesel engine. When the diesel engine is operated at 1500 rpm engine speed and fuelled with 4.63 L/min hydrogen addition and 26.8% biodiesel blend ratio; the optimum responses of CO, CO₂, NO_x, vibration, and noise are established as 214 ppm, 1.35%, 90.4 ppm, 38.6 m/s², and 91.3 dB[A], respectively. The predicted values were confirmed experimentally and the errors in predicted values are found in a limit range.     

Reducing over- and under-estimation of the a priori SNR in speech enhancement techniques

Most speech enhancement methods based on short-time spectral modification are generally expressed as a spectral gain depending on the estimate of the local signal-to-noise ratio (SNR) on each frequency bin. Several studies have analyzed the performance of a priori SNR estimation algorithms to improve speech quality and to reduce speech distortions. In this paper, we concentrate on the analysis of over- and under estimation of the a priori SNR in speech enhancement and noise reduction systems. We first show that conventional approaches such as the decision-directed approach proposed by Ephraïm and Malah lead to a biased estimator for the a priori SNR. To reduce this bias, our strategy relies on the introduction of a correction term in the a priori SNR estimate depending on the current state of both the available a posteriori SNR and the estimated a priori one. The proposed solution leads to a bias-compensated a priori SNR estimate, and allows to finely estimating the output speech signal to be very close to the original one on each frequency bin. Such refinement procedure in the a priori SNR estimate can be inserted in any type of spectral gain function to improve the output speech quality. Objective tests under various environments in terms of the Normalized Covariance Metric (NCM) criterion, the Coherence Speech Intelligibility Index (CSII) criterion, the segmental SNR criterion and the Perceptual Evaluation of Speech Quality (PESQ) measure are presented showing the superiority of the proposed method compared to competitive algorithms.     

离心风机噪声控制研究

在通风设备中，尽管经常使用离心风机，但是它在运转过程中产生的强噪声，令人心烦，危险极大。本文主要从如何改进风机本身结构参数，而不是采用一种消声器来降低风机噪声的角度进行探讨。     

铝电解槽针振信息元分析和诊断系统的开发与应用

针对铝电解槽针振信息元诊断手段的不足,提出基于小波-神经网络技术的铝电解槽针振信息元分析与诊断的新方法。获取5种针振信息元的特征波谱作为神经网络的训练样本,建立神经网络诊断系统。分析了系统软硬件结构及其特点,并在350kA预焙铝电解槽上进行实验和仿真。实验证明该系统有很高的精度,且具有很好的应用价值。