To solve the single-channel blind separation problem of time-frequency overlapped communications signals, this paper proposes a new method joint multi-scale continuous wavelet transform (CWT) and independent component analysis (ICA). By using multi-scale CWT decomposition on single-channel signal, the original mixed signal can be expanded to a multi-dimensional signal without any prior knowledge, then use the ICA algorithm to realize its blind separation. Next, determine the number of source signals and eliminate the redundancy and irrelevancy to complete the separation. Finally, the separation experiments and simulations confirmed this proposed method valid and feasible.
The identification of seal authenticity is an important part of document inspection. Confocal laser Raman spectroscopy combined with convolutional neural (CNN) and recurrent neural (RNN) networks was used to distinguish red stamp‐pad ink of different brands and aging. A total of 536 spectral samples from 16 brands were collected in this study and 53,600 amplification data samples were obtained by adding noise. The joint neural network has significant classification performance compared to partial least squares (PLS) discriminant and common CNN. In the three kinds of stamp‐pad inks, photosensitive, atomic, and common, the recognition rate for different brands and aging both reached 100%.
Memory is an intricate process involving various faculties of the brain and is a central component in human cognition. However, the exact mechanism that brings about memory in our brain remains elusive and the performance of the existing memory models is not satisfactory. To overcome these problems, this paper puts forward a brain-inspired spatio-temporal sequential memory model based on spiking neural networks (SNNs). Inspired by the structure of the neocortex, the proposed model is structured by many mini-columns composed of biological spiking neurons. Each mini-column represents one memory item, and the firing of different spiking neurons in the mini-column depends on the context of the previous inputs. The Spike-Timing-Dependant Plasticity (STDP) is used to update the connections between excitatory neurons and formulates association between two memory items. In addition, the inhibitory neurons are employed to prevent incorrect prediction, which contributes to improving the retrieval accuracy. Experimental results demonstrate that the proposed model can effectively store a huge number of data and accurately retrieve them when sufficient context is provided. This work not only provides a new memory model but also suggests how memory could be formulated with excitatory/inhibitory neurons, spike-based encoding, and mini-column structure.
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