The structure-hallucinogenic activity relationships of a series of phenylethylamine and phenylisopropylamine derivatives have been investigated in the frameworks of electron-conformational method. The calculated geometry and electronic structure parameters accompanying to each atom and bond of each molecule in view were arranged as a matrix called electron-conformational matrix of contiguity (E...

A localization algorithm of weighted maximum likelihood estimation is presented for wireless sensor network based on ZigBee. Location accuracy is sensitive to ranging accuracy which has an inverse relationship with distance between reference node and blind node. Thus weight matrix is introduced to the traditional maximum likelihood estimation method. Theoretical analysis and simulation results ...

Acquisition-weighting improves the localization of MRI experiments. An approach to acquisition-weighting in a purely phase-encoded experiment is presented that is based on a variation of the sampling density in k-space. In contrast to conventional imaging or to accumulation-weighting, where k-space is sampled with uniform increments, density-weighting varies the distance between neighboring sam...

Let $D$ be a diameter and $d_G(v_i, v_j)$ be the distance between the vertices $v_i$ and $v_j$ of a connected graph $G$. The complementary distance signless Laplacian matrix of a graph $G$ is $CDL^+(G)=[c_{ij}]$ in which $c_{ij}=1+D-d_G(v_i, v_j)$ if $ineq j$ and $c_{ii}=sum_{j=1}^{n}(1+D-d_G(v_i, v_j))$. The complementary transmission $CT_G(v)$ of a vertex $v$ is defined as $CT_G(v)=sum_{u in ...

In this article, we integrate the spatial-spectral information of hyperspectral image (HSI) samples into nonnegative matrix factorization (NMF) for affinity learning to address issue HSI clustering. This technique consists three main components: 1) oversegmentation computing spectral-spatial matrix; 2) NMF with guidance obtained and 3) density-based spectral clustering on final matrix. First, i...

The energy of a graph G is equal to the sum of absolute values of the eigenvalues of the adjacency matrix of G, whereas the Laplacian energy of a graph G is equal to the sum of the absolute value of the difference between the eigenvalues of the Laplacian matrix of G and the average degree of the vertices of G. Motivated by the work from Sharafdini an...

Let G be a finite connected graph. If x and y are vertices of G, one may define a distance function d, on G by letting d&x, y) be the minimal length of any path between x and y in G (with d&, x) = 0). Thus, for example, d&x, y) = 1 if and only if {x, y} is an edge of G. Furthermore, we define the distance matrix D(G) for G to be the square matrix with rows and columns indexed by the vertex set ...

A distance based classification is one of the popular methods for classifying instances using a point-to-point distance based on the nearest neighbour or k-NEAREST NEIGHBOUR (k-NN). The representation of distance measure can be one of the various measures available (e.g. Euclidean distance, Manhattan distance, Mahalanobis distance or other specific distance measures). In this paper, we propose ...