Poly[[hexa­aqua­(μ2-fumarato-κ4 O 1,O 1′:O 4,O 4′)bis­(μ3-maleato-κ4 O 1,O 1′:O 4:O 4′)disamarium(III)] hexa­hydrate]

In the title coordination polymer, {[Sm2(C4H2O4)3(H2O)6]·6H2O}n, the SmIII ion is nine-coordinated by four O atoms from three different maleate ligands, two O atoms from one fumarate ligand and three O atoms from three water mol­ecules. The fumarate ligand lies on an inversion center. Adjacent SmIII ions are bridged by the maleate and fumarate ligands, forming a layer parallel to (011). The layers are further linked by inter­molecular O—H⋯O hydrogen bonds into a three-dimensional supra­molecular network

The Reverse Hölder Inequality for the Solution to p-Harmonic Type System

Some inequalities to A-harmonic equation A(x,du)=d∗v have been proved. The A-harmonic equation is a particular form of p-harmonic type system A(x,a+du)=b+d∗v only when a=0 and b=0. In this paper, we will prove the Poincaré inequality and the reverse Hölder inequality for the solution to the p-harmonic type system

Bayesian Non-parametric Hidden Markov Model for Agile Radar Pulse Sequences Streaming Analysis

Multi-function radars (MFRs) are sophisticated types of sensors with the capabilities of complex agile inter-pulse modulation implementation and dynamic work mode scheduling. The developments in MFRs pose great challenges to modern electronic reconnaissance systems or radar warning receivers for recognition and inference of MFR work modes. To address this issue, this paper proposes an online processing framework for parameter estimation and change point detection of MFR work modes. At first, this paper designed a fully-conjugate Bayesian non-parametric hidden Markov model with a designed prior distribution (agile BNP-HMM) to represent the MFR pulse agility characteristics. The proposed model allows fully-variational Bayesian inference. Then, the proposed framework is constructed by two main parts. The first part is the agile BNP-HMM model for automatically inferring the number of HMM hidden states and emission distribution of the corresponding hidden states. An estimation error lower bound on performance is derived and the proposed algorithm is shown to be close to the bound. The second part utilizes the streaming Bayesian updating to facilitate computation, and designed an online work mode change detection framework based upon a weighted sequential probability ratio test. We demonstrate that the proposed framework is consistently highly effective and robust to baseline methods on diverse simulated data-sets.Comment: 15 pages, 10 figures, submitted to IEEE transactions on signal processin