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In addition, a comparison is made to investigate the effectiveness of the proposed method compared to that of the EEMD method [24]. The rest of the paper is organized as follows. Underdetermined blind source separation is discussed in Section 2. In Section 3, the VMD algorithm is reviewed. Section 4 presents the ICA algorithm. The proposed method can be seen in Section 5. Section 6 contains a description of the experimental test, the algorithm application and a comparison of the proposed method and EEMD. The paper is concluded in Section 7. 2. Underdetermined Blind Source Separation Blind source separation (BSS), also called blind signal separation, is the process of recovering each source signal only from the observed signal based on the statistical characteristics of the input Afatinib chemical structure signal without the parameters of the source signal and the transmission channel. The purpose of BSS is to confirm the parameters of the separation process and to obtain the estimation of the source signal based on the observed signal. It can be represented as the following formula: xi(t)=��j=in��ijsj(t)+nj(t)?i=1,2,?,n?t=1,2,?,T (1) where xi(t) is the observed signal, si(t) is the source signal, ��ij is the mixing matrix, ni(t) is the noise signal and T is the sampling data. Formula (1) can be described as a matrix form: X(t)=AS(t)+N(t) (2) where S(t)=[s1(t),s2(t),?,sm(t)]T is m original signals, A is the n��m hybrid matrix and X(t)=[x1(t),?x2(t),?,?xn(t)]T is the n-d observed signal. In general, X is considered as the combination of original signal S and hybrid matrix A (X=AS). Under the condition that PTPRJ both S and A are unknown, demixing matrix B is acquired to make sure that Y obtained by X and B is the best estimation of S. Because the prior knowledge of the original signal and the hybrid matrix are unknown, some basic assumptions must be stipulated as follows: (1) source signals are statistically independent of each other; (2) hybrid matrix A is a matrix with full column rank; (3) noise signals are statistically independent Dasatinib order of each other and are irrelevant to the original signals. (4) The number of source signals (N) is less than or equal to the number of observed signals (M). In practice, the position and quantity of sensors are limitative, which usually causes the number of sensors to be less than the number of source signals. This condition that M