Recent Experimental Progress of Fractional Quantum Hall Effect: 5/2 Filling State and Graphene

The phenomenon of fractional quantum Hall effect (FQHE) was first experimentally observed 33 years ago. FQHE involves strong Coulomb interactions and correlations among the electrons, which leads to quasiparticles with fractional elementary charge. Three decades later, the field of FQHE is still active with new discoveries and new technical developments. A significant portion of attention in FQHE has been dedicated to filling factor 5/2 state, for its unusual even denominator and possible application in topological quantum computation. Traditionally FQHE has been observed in high mobility GaAs heterostructure, but new materials such as graphene also open up a new area for FQHE. This review focuses on recent progress of FQHE at 5/2 state and FQHE in graphene.Comment: 17 pages, 13 figure

SURFACE PROPERTIES AND CATALYTIC PERFORMANCE OF Pt/LaSrCoO4 CATALYSTS IN THE OXIDATION OF HEXANE

Perovskite-type La2 –xSrxCoO4 mixed oxides have been prepared by calcination at various temperatures of precipitates obtained from aqueous solutions in the presence of citric or ethylenediamintetraacetic (EDTA) acids, and have been studied by X-ray diffraction (XRD), surface area (BET) measurements, temperature programmed desorption (TPD), temperature programmed reduction (TPR) and X-ray photoelectron spectroscopy (XPS). These oxides are catalysts for hexane oxidation, with the greatest activity for LaSrCoO4 calcined at 750 C. This has extensive oxygen vacancies and large internal surface area. Pt-modified LaSrCoO4 catalysts are significantly more active than the Pt-free system. Both surface and bulk phases of the preovskitetype oxides contribute to hexane oxidation. KEY WORDS: Perovskite-type A2BO4, Surface properties, Catalytic performance, Temperature programmed desorption (TPD), Temperature programmed reduction (TPR), and X-ray photoelectron spectroscopy (XPS), Hexane oxidation Bull. Chem. Soc. Ethiop. 2007, 21(2), 271-280

Channel Covariance Matrix Estimation via Dimension Reduction for Hybrid MIMO MmWave Communication Systems

Hybrid massive MIMO structures with lower hardware complexity and power consumption have been considered as a potential candidate for millimeter wave (mmWave) communications. Channel covariance information can be used for designing transmitter precoders, receiver combiners, channel estimators, etc. However, hybrid structures allow only a lower-dimensional signal to be observed, which adds difficulties for channel covariance matrix estimation. In this paper, we formulate the channel covariance estimation as a structured low-rank matrix sensing problem via Kronecker product expansion and use a low-complexity algorithm to solve this problem. Numerical results with uniform linear arrays (ULA) and uniform squared planar arrays (USPA) are provided to demonstrate the effectiveness of our proposed method

Fast Low-rank Representation based Spatial Pyramid Matching for Image Classification

Spatial Pyramid Matching (SPM) and its variants have achieved a lot of success in image classification. The main difference among them is their encoding schemes. For example, ScSPM incorporates Sparse Code (SC) instead of Vector Quantization (VQ) into the framework of SPM. Although the methods achieve a higher recognition rate than the traditional SPM, they consume more time to encode the local descriptors extracted from the image. In this paper, we propose using Low Rank Representation (LRR) to encode the descriptors under the framework of SPM. Different from SC, LRR considers the group effect among data points instead of sparsity. Benefiting from this property, the proposed method (i.e., LrrSPM) can offer a better performance. To further improve the generalizability and robustness, we reformulate the rank-minimization problem as a truncated projection problem. Extensive experimental studies show that LrrSPM is more efficient than its counterparts (e.g., ScSPM) while achieving competitive recognition rates on nine image data sets.Comment: accepted into knowledge based systems, 201