University of Macau

About: University of Macau is a education organization based out in Macao, Macau, China. It is known for research contribution in the topics: Computer science & Population. The organization has 6636 authors who have published 18324 publications receiving 327384 citations. The organization is also known as: UM & UMAC.

Tunable Bandpass Filter Design Based on External Quality Factor Tuning and Multiple Mode Resonators for Wideband Applications

Abstract: In this paper, a tunable bandpass filter using cross-shaped multiple mode resonators (MMRs) and N:1 transformer based external quality factor tuning structures is proposed. The use of a cross-shaped MMR simplifies inter-resonators control while two Qe tuning structures are investigated and incorporated with the MMR to implement simultaneous center frequency agility and narrow and wide bandwidth tuning. Compared with traditional tunable filters, the proposed architecture requires less tuning elements and is easier to realize wideband and high-order tunable filters. Two examples (Filter I and II) are presented to validate the design. Both filters use a single MMR and six tuning elements to achieve a third-order wideband tunable filter. Filter I reports 58% center frequency tuning with constant bandwidth and 14%-64.4% fractional bandwidth (FBW) tuning when center frequency locates at 1 GHz. Filter II achieves larger frequency agility and wider FBW tuning of 82.9% and 95%, respectively, for the same bandwidth and center frequency of Filter I.

Web query expansion by wordnet

Abstract: In this paper, we address a novel method of Web query expansion by using WordNet and TSN. WordNet is an online lexical dictionary which describes word relationships in three dimensions of Hypernym, Hyponym and Synonym. And their impacts to expansions are different. We provide quantitative descriptions of the query expansion impact along each dimension. However, WordNet may bring many noises for the expansion due to its collection independent characteristic. Furthermore, it may not catch current state of words and their relationships because of the explosive increase of the Web. To overcome those problems, collection-based TSN (Term Semantic Network) is created with respect to word co-occurrence in the collection. We use TSN both as a filter and a supplement for WordNet. We also provide a quantitatively study as what is the best way for the expansion with TSN. In our system, we combine the query expansions along each semantic dimension as our overall solution. Our experiments reveal that the combined expansion can provide a satisfied result for the Web query performance. The methodologies in this paper have been already employed in our Web image search engine system.

Fabrication of Graphene-isolated-Au-nanocrystal Nanostructures for Multimodal Cell Imaging and Photothermal-enhanced Chemotherapy

Abstract: Using nanomaterials to develop multimodal systems has generated cutting-edge biomedical functions. Herein, we develop a simple chemical-vapor-deposition method to fabricate graphene-isolated-Au-nanocrystal (GIAN) nanostructures. A thin layer of graphene is precisely deposited on the surfaces of gold nanocrystals to enable unique capabilities. First, as surface-enhanced-Raman-scattering substrates, GIANs quench background fluorescence and reduce photocarbonization or photobleaching of analytes. Second, GIANs can be used for multimodal cell imaging by both Raman scattering and near-infrared (NIR) two-photon luminescence. Third, GIANs provide a platform for loading anticancer drugs such as doxorubicin (DOX) for therapy. Finally, their NIR absorption properties give GIANs photothermal therapeutic capability in combination with chemotherapy. Controlled release of DOX molecules from GIANs is achieved through NIR heating, significantly reducing the possibility of side effects in chemotherapy. The GIANs have high surface areas and stable thin shells, as well as unique optical and photothermal properties, making them promising nanostructures for biomedical applications.

Force Sensorless Admittance Control for Teleoperation of Uncertain Robot Manipulator Using Neural Networks

Abstract: In this paper, a force sensorless control scheme based on neural networks (NNs) is developed for interaction between robot manipulators and human arms in physical collision. In this scheme, the trajectory is generated by using geometry vector method with Kinect sensor. To comply with the external torque from the environment, this paper presents a sensorless admittance control approach in joint space based on an observer approach, which is used to estimate external torques applied by the operator. To deal with the tracking problem of the uncertain manipulator, an adaptive controller combined with the radial basis function NN (RBFNN) is designed. The RBFNN is used to compensate for uncertainties in the system. In order to achieve the prescribed tracking precision, an error transformation algorithm is integrated into the controller. The Lyapunov functions are used to analyze the stability of the control system. The experiments on the Baxter robot are carried out to demonstrate the effectiveness and correctness of the proposed control scheme.