Cancellation of nonlinear distortion based on integration of FCM clustering algorithm and adaptive-two-stage linear approximation

A hybrid system of the fuzzy c-means （FCM） clustering algorithm and adaptive-two-stage linear approximation was presented for nonlinear distortion cancellation of radio frequency （RF） power amplifier （PA）. This mechanism can effectively eliminate noise, adaptively model PA＇s instantaneous change, and efficiently correct nonlinear distortion. This article puts forward the FCM clustering algorithm for clustering received signals to eliminate white noise, and then uses the adaptive-two-stage linear approximation to fit the inverse function of the amplitude＇s and phase＇s nonlinear mapping during the training phase. Parameters of the linear function and similarity function are trained using the gradient-descent and minimum mean-square error criteria. The proposed approach＇s training results is directly employed to eliminate sampling signal＇s nonlinear distortion. This hybrid method is realized easier than the multi-segment linear approximation and could reduce the received signal＇s bit error rate （BER） more efficiently.

Construction of Air Quality Evaluation System Based on FCM Algorithm and BP Neural Network

In order to solve the limitations of existing air quality evaluation system, a new air quality evaluation system was established based on FCM, the BP neural network, with the aim to provide scientific bases for the targeted and efficient control of air pollution, formulation of prevention and control strategy, and improvement of living environment. Based on the existing data of 6 air quality indices, the air quality data were reclassified by using FCM algorithm, obtaining the clustering center, which minimized the cost function of non-similar index. Then, the reclassified 6 classes of data were proceeded with BP neural network training and simulation, so as to achieve the purpose of identification, thereby forming a new air quality evaluation system.

Adaptive Image Digital Watermarking with DCT and FCM

A novel adaptive digital image watermark algorithm is proposed. Fuzzy c-means clustering （FCM） is used to classify the original image blocks into two classes based on several characteristic parameters of human visual system （HVS）. One is suited for embedding a digital watermark, the other is not. So the appropriate blocks in an image are selected to embed the watermark. The wetermark is embedded in the middle-frequency part of the host image in conjunction with HVS and discrete cosine transform （DCT）. The maximal watermark strength is fixed according to the frequency masking. In the same time, for the good performance, the watermark is modulated into a fractal modulation array. The simulation results show that we can remarkably extract the hiding watermark and the algorithm can achieve good robustness with common signal distortion or geometric distortion and the quality of the watermarked image is guaranteed.

An Airborne Radar Clutter Tracking Algorithm Based on Multifractal and Fuzzy C-Mean Cluster

For an airborne Iookdown radar, clutter power often changes dynamically about 80 dB with wide distributions as the platform moves. Therefore, clutter tracking techniques are required to guide the selection of const false alarm rate （CFAR） schemes. In this work, clutter tracking is done in image domain and an algorithm combining multifractal and fuzzy C-mean （FCM） cluster is proposed. The clutter with large dynamic distributions in power density is converted to steady distributions of multifractal exponents by the multifractal transformation with the optimum moment. Then, later, the main lobe and side lobe are tracked from the multifractal exponents by FCM clustering method.

Characterization of particulate products for aging of ethylbenzene secondary organic aerosol in the presence of ammonium sulfate seed aerosol

Aging of secondary organic aerosol（SOA） particles formed from OH– initiated oxidation of ethylbenzene in the presence of high mass（100–300 μg/m^3） concentrations of（NH_4）_2SO_4seed aerosol was investigated in a home-made smog chamber in this study.The chemical composition of aged ethylbenzene SOA particles was measured using an aerosol laser time-of-flight mass spectrometer（ALTOFMS） coupled with a Fuzzy C-Means（FCM） clustering algorithm.Experimental results showed that nitrophenol,ethyl-nitrophenol,2,4-dinitrophenol,methyl glyoxylic acid,5-ethyl-6-oxo-2,4-hexadienoic acid,2-ethyl-2,4-hexadiendioic acid,2,3-dihydroxy-5-ethyl-6-oxo-4-hexenoic acid,1H-imidazole,hydrated N-glyoxal substituted1H-imidazole,hydrated glyoxal dimer substituted imidazole,1H-imidazole-2-carbaldehyde,N-glyoxal substituted hydrated 1H-imidazole-2-carbaldehyde and high-molecular-weight（HMW） components were the predominant products in the aged particles.Compared to the previous aromatic SOA aging studies,imidazole compounds,which can absorb solar radiation effectively,were newly detected in aged ethylbenzene SOA in the presence of high concentrations of（NH_4）_2SO_4seed aerosol.These findings provide new information for discussing aromatic SOA aging mechanisms.

Personalized Emotion Space for Video Affective Content Representation

A personalized emotion space is proposed to bridge the ＂affective gap＂ in video affective content understanding. In order to unify the discrete and dimensional emotion model, fuzzy C-mean （FCM） clustering algorithm is adopted to divide the emotion space. Gaussian mixture model （GMM） is used to determine the membership functions of typical affective subspaces. At every step of modeling the space, the inputs rely completely on the affective experiences recorded by the audiences. The advantages of the improved V-A （Velance-Arousal） emotion model are the per- sonalization, the ability to define typical affective state areas in the V-A emotion space, and the convenience to explicitly express the intensity of each affective state. The experimental results validate the model and show it can be used as a personalized emotion space for video affective content representation.