Design and Fabrication of ZnO-Based FBAR Microwave Devices for Mobile WiMAX Applications

In this letter, we present the design and fabrication of a novel ZnO-based film bulk acoustic wave resonator (FBAR) microwave devices. The novel FBAR devices employ a new-type of Bragg reflector with very thin chromium (Cr) layer formed between SiO₂ and W films. The Cr layer seems to enhance the adhesion between SiO₂ and W layers. The novel FBAR devices show good return losses (S₁₁) and high Q-factors at the frequency range of 2.7-3.0 GHz. This approach will be very helpful for mobile worldwide interoperability for microwave access applications.     

REINFORCEMENT LEARNING FOR POMDP USING STATE CLASSIFICATION

Reinforcement learning (RL) has been widely used to solve problems with a little feedback from environment. Q learning can solve Markov decision processes (MDPs) quite well. For partially observable Markov decision processes (POMDPs), a recurrent neural network (RNN) can be used to approximate Q values. However, learning time for these problems is typically very long. We present a new combination of RL and RNN to find a good policy for POMDPs in a shorter learning time. This method contains two phases: firstly, state space is divided into two groups (fully observable state group and hidden state group); secondly, a Q value table is used to store values of fully observable states and an RNN is used to approximate values for hidden states. Results of experiments in two grid world problems show that the proposed method enables an agent to acquire a policy with better learning performance compared to the method using only a RNN.     

EXPERIMENTS WITH ONLINE REINFORCEMENT LEARNING IN REAL-TIME STRATEGY GAMES

Real-time strategy (RTS) games provide a challenging platform to implement online reinforcement learning (RL) techniques in a real application. Computer, as one game player, monitors opponents’ (human or other computers) strategies and then updates its own policy using RL methods. In this article, we first examine the suitability of applying the online RL in various computer games. Reinforcement learning application depends on both RL complexity and the game features. We then propose a multi-layer framework for implementing online RL in an RTS game. The framework significantly reduces RL computational complexity by decomposing the state space in a hierarchical manner. We implement an RTS game—Tank General—and perform a thorough test on the proposed framework. We consider three typical profiles of RTS game players and compare two basic RL techniques applied in the game. The results show the effectiveness of our proposed framework and shed light on relevant issues in using online RL in RTS games.     

Deflation-based power iteration clustering

Spectral clustering (SC) is currently one of the most popular clustering techniques because of its advantages over conventional approaches such as K-means and hierarchical clustering. However, SC requires the use of computing eigenvectors, making it time consuming. To overcome this limitation, Lin and Cohen proposed the power iteration clustering (PIC) technique (Lin and Cohen in Proceedings of the 27th International Conference on Machine Learning, pp. 655–662, 2010), which is a simple and fast version of SC. Instead of finding the eigenvectors, PIC finds only one pseudo-eigenvector, which is a linear combination of the eigenvectors in linear time. However, in certain critical situations, using only one pseudo-eigenvector is not enough for clustering because of the inter-class collision problem. In this paper, we propose a novel method based on the deflation technique to compute multiple orthogonal pseudo-eigenvectors (orthogonality is used to avoid redundancy). Our method is more accurate than PIC but has the same computational complexity. Experiments on synthetic and real datasets demonstrate the improvement of our approach.     

Moment-based alignment for shape prior with variational B-spline level set

This paper presents a new shape prior-based implicit active contour model for image segmentation. The paper proposes an energy functional including a data term and a shape prior term. The data term, inspired from the region-based active contour approach, evolves the contour based on the region information of the image to segment. The shape prior term, defined as the distance between the evolving shape and a reference shape, constraints the evolution of the contour with respect to the reference shape. Especially, in this paper, we present shapes via geometric moments, and utilize the shape normalization procedure, which takes into account the affine transformation, to align the evolving shape with the reference one. By this way, we could directly calculate the shape transformation, instead of solving a set of coupled partial differential equations as in the gradient descent approach. In addition, we represent the level-set function in the proposed energy functional as a linear combination of continuous basic functions expressed on a B-spline basic. This allows a fast convergence to the segmentation solution. Experiment results on synthetic, real, and medical images show that the proposed model is able to extract object boundaries even in the presence of clutter and occlusion.     

Urban growth and change analysis using remote sensing and spatial metrics from 1975 to 2003 for Hanoi,Vietnam

Since the beginning of the ‘Doi Moi’ policy in 1986, Hanoi has witnessed significant changes in its urban areas. Landsat and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) scenes were used to identify built-up areas in Hanoi, and spatial metrics were used to characterize urban change patterns from 1975 to 2003. Firstly, a spatial metric called the ‘percentage of like adjacency’ was used to discern urban growth patterns, which were classified into three sub-patterns: expansion growth, infill growth and outlying growth. Secondly, the driving force underlying the urbanization of the city for the 1975–1984, 1984–1992, 1992–2001, 2001–2003 periods was investigated using a spatial metric analysis programme (FRAGSTATS). The expansion of urban areas along major transportation routes in the latter 1980s was identified as the main form of urbanization in Hanoi. This paper shows the potential application of spatial metrics as secondary sources of information for supporting remotely sensed data and their use to characterize urban growth patterns.     

New criteria for exponential stability of nonlinear difference systems with time-varying delay

General nonlinear time-varying difference systems with time-varying delay are considered. Some new explicit criteria for global exponential stability are given. Two examples are given to illustrate the obtained results.     

Estimating aboveground biomass of a mangrove plantation on the Northern coast of Vietnam using machine learning techniques with an integration of ALOS-2 PALSAR-2 and Sentinel-2A data

ABSTRACT

Aboveground biomass (AGB) of mangrove forest plays a crucial role in global carbon cycle by reducing greenhouse gas emissions and mitigating climate change impacts. Monitoring mangrove forests biomass accurately still remains challenging compared to other forest ecosystems. We investigated the usability of machine learning techniques for the estimation of AGB of mangrove plantation at a coastal area of Hai Phong city (Vietnam). The study employed a GIS database and support vector regression (SVR) to build and verify a model of AGB, drawing upon data from a survey in 25 sampling plots and an integration of Advanced Land Observing Satellite-2 Phased Array Type L-band Synthetic Aperture Radar-2 (ALOS-2 PALSAR-2) dual-polarization horizontal transmitting and horizontal receiving (HH) and horizontal transmitting and vertical receiving (HV) and Sentinel-2A multispectral data. The performance of the model was assessed using root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R²), and leave-one-out cross-validation. Usability of the SVR model was assessed by comparing with four state-of-the-art machine learning techniques, i.e. radial basis function neural networks, multi-layer perceptron neural networks, Gaussian process, and random forest. The SVR model shows a satisfactory result (R² = 0.596, RMSE = 0.187, MAE = 0.123) and outperforms the four machine learning models. The SVR model-estimated AGB ranged between 36.22 and 230.14 Mg ha^?1 (average = 87.67 Mg ha^?1). We conclude that an integration of ALOS-2 PALSAR-2 and Sentinel-2A data used with SVR model can improve the AGB accuracy estimation of mangrove plantations in tropical areas.     

Formation and crosslinking of latex films through the reaction of acetoacetoxy groups with diamines under ambient conditions

We investigated the processes of film formation, polymer diffusion, and crosslinking of latex films at ambient temperature, using low T_g methacrylate latex bearing acetoacetoxy groups, and curing the systems with 1,6-hexanediamine as the crosslinker. The addition of diamine induces floc formation, which modifies the rheological properties of the dispersion and increases its drying rate when coated onto a substrate. The crosslinking reaction between diamine and acetoacetoxy groups occurs at a rapid rate, even in the dispersed state. Although the crosslinking reaction precedes polymer diffusion in the two systems we examined, latex films with relatively good solvent resistance are obtained. Department of Chemistry, Toronto, Ontario, Canada M5S 3H6. Department of Polymer Chemistry, P. O. Box 513, 5600 MB Eindhoven, The Netherlands.     

Blind separation of instantaneous mixture of sources based on orderstatistics

In this paper, we introduce a novel procedure for separating an instantaneous mixture of sources based on order statistics. The method is derived in a general context of independence component analysis, using a contrast function defined in term of the Kullback-Leibler divergence or of the mutual information. We introduce a discretized form of this contrast permitting its easy estimation through order statistics. We show that the local contrast property is preserved and derive a global contrast, exploiting only the information of the support of the distribution (in case this support is finite). Some simulations are given, illustrating the good performance of the method