Harbin Engineering University

About: Harbin Engineering University is a education organization based out in Harbin, Heilongjiang, China. It is known for research contribution in the topics: Control theory & Computer science. The organization has 31149 authors who have published 27940 publications receiving 276787 citations. The organization is also known as: HEU.

DOA Estimation Using Compressed Sparse Array

Abstract: Sparse arrays, such as nested arrays and coprime arrays, can achieve a high number of degrees of freedom (DOFs) for direction of arrival (DOA) estimation with a reduced number of antennas. On the other hand, the compressive measurement method provides an effective way to reduce the number of front-end circuit chains. In this paper, we generalized current works on the two categories of methods to a compressed sparse array (CSA) scheme, which combines the compressive measurement method, and the sparse array together to significantly reduce the system complexity. After introducing the proposed scheme, the Cramer-Rao bound (CRB) of the proposed CSA scheme is derived. We then determine the corresponding existing conditions of the CRB, based on which the number of DOFs is derived and examined for the first time. It is proved that, for a CSA which compressed the output of an $L$ -element sparse array to $M chains, a higher number of DOFs is obtained as compared to that of the $M$ -element array with the same sparse structure. Furthermore, the DOA estimation accuracy using the $M$ -chain CSA is higher than that using the $M$ -element sparse array due to the extended array aperture. Numerical simulations verify the superiority of the proposed CSA scheme.

Blocked Maximum Correntropy Criterion Algorithm for Cluster-Sparse System Identifications

Abstract: A blocked proportionate normalized maximum correntropy criterion (PNMCC) is presented to improve the estimation behavior of the traditional maximum correntropy criterion (MCC) algorithm for identifying the blocked sparse systems. The proposed blocked MCC is implemented by constructing a new cost function based on a hybrid-norm constraint (HNC) of the filter coefficient vector to adaptively utilize the cluster-sparse characteristic of unknown systems, denoting as hybrid-norm constrained PNMCC (HNC-PNMCC). The proposed HNC-PNMCC algorithm is achieved by using the basis pursuit. Various simulations are brought out to confirm the validity of the HNC-PNMCC. Simulation results indicate that the HNC-PNMCC is better than the PNMCC, MCC, and sparse MCC with respect to the estimation performance for the cluster-sparse system identification under the impulsive noises.

From biomass with irregular structures to 1D carbon nanobelts: a stripping and cutting strategy to fabricate high performance supercapacitor materials

Abstract: One-dimensional (1D) nanostructures have been identified as the most viable structures for high-performance supercapacitors from the view of high ion-accessible surface area and rapid electron transport path as well as excellent mechanical properties. Herein, we report a “stripping and cutting” strategy to produce 1D carbon nanobelts (CNB) from tofu with irregular structures through a molten salts assisted technique. It is a completely novel and green avenue for constructing 1D carbon materials from biomass, showing large commercial potential. The resultant CNB electrode delivers a high specific capacitance (262 F g−1 at 0.5 A g−1) and outstanding cycling stability with capacitance retention up to 102% after 10 000 continuous charging/discharging cycles. Additionally, a CNB//CNB symmetric supercapacitor and CNB//MnO2–CNB asymmetric supercapacitor are assembled and reach energy densities of 18.19 and 29.24 W h kg−1, respectively. Therefore, such a simple, one-pot and low-cost process may have great potential for preparing eco-friendly biomass-derived carbon materials for high-performance supercapacitor electrodes.