Powerline communication has become interesting as a new choice of communication media by using OFDM in the Europe Internet application and using TDMA in power meters' reading in Japan mimicking the function of phone-line DSL. However, this raw copper media without hefty infrastructure investment such as the telephone twisted pair DSL has many challenges, because it was designed to transmit an electrical current that had isolated power grid with transformers, it has nothing that is suitable to convey data but it is everywhere in the last mile of connecting households. To make everything worse, the powerline is also easily interfered by unpredictable impulsive noise, background colored noise, and fatal attenuation of signal. Our goal is to take the US city grid power-line as a supplement to the concept of a single-user & multiple- sensor-broadcasting applications for security. To successfully communicate through the power-line for the household/stadium/subway/traffic-lights security application, e.g. separated video feds find themselves the single owner PC through the common household/stadium/subway/traffic-lights power-line, we develop and test an appropriate sparse coding, compression, and error correction to succeed the task with a limited bandwidth technique. In this paper we did not apply out human sensory preserving compression code, but to concentrate on sparse coding BSS with the less-bandwidth- demanding audio signals. In this paper, we describe a detail model of the power-line topologies based on realistic powerline data, and quantify the error rates of transmitted data on the powerline in the cases when noise is presented in the channel.
This paper has analyzed the day and night images using the novel concept called hard and soft singularity maps (SM) that are biologically extracted by the lateral redundant data. Consequently, the correspondence exists uniquely among neighborhood frames in terms of the different slope values at image corners solving the optical flow problem for the video compression. In this paper, some efficient computational methods: min-max picking, and next order Cellular Neural Network implementing the anti-diffusion Laplacian, can obtain the SM without the convolution broadening based on Sobel and Canny edge operators. However, the differentiation operation may produce a false singularity under noise, and thus we apply the Hermitian wavelets to obtain the noisy singularity.
We wish to generalize the covariance matrix approach (PCA) by the statistical Independent Component Analyses (ICA), which have been implemented by Bell-Sejnowski efficiently using ANN methodology. The gain of the statistics is the los of the geometry. In this research, we preserve the texture geometry with a so-called local ICA, in order to extract separately independent features from each class of natural textures. To avoid the curse of the dimensionality due to the local ICA, we furthermore use the divide-and-conquer strategy. A single ICA basis vector is chosen from each texture class, based on the maximum associative recalls from the class training set. Subsequently, another ICA basis is chosen, if necessary, to minimize the false alarm rate, namely the spread of confusion matrix. For the visible remote sensing application, we have designed such an optimum classifier of all natural scene textures with a minimum spread of the confusion matrix.