Analysing neurobiological models using communicating automata

Two important issues in computational modelling in cognitive neuroscience are: first, how to formally describe neuronal networks (i.e. biologically plausible models of the central nervous system), and second, how to analyse complex models, in particular, their dynamics and capacity to learn. We make progress towards these goals by presenting a communicating automata perspective on neuronal networks. Specifically, we describe neuronal networks and their biological mechanisms using Data-rich Communicating Automata, which extend classic automata theory with rich data types and communication. We use two case studies to illustrate our approach. In the first case study, we model a number of learning frameworks, which vary in respect of their biological detail, for instance the Backpropagation (BP) and the Generalized Recirculation (GeneRec) learning algorithms. We then used the SPIN model checker to investigate a number of behavioral properties of the neural learning algorithms. SPIN is a well-known model checker for reactive distributed systems, which has been successfully applied to many non-trivial problems. The verification results show that the biologically plausible GeneRec learning is less stable than BP learning. In the second case study, we presented a large scale (cognitive-level) neuronal network, which models an attentional spotlight mechanism in the visual system. A set of properties of this model was verified using Uppaal, a popular real-time model checker. The results show that the asynchronous processing supported by concurrency theory is not only a more biologically plausible way to model neural systems, but also provides a better performance in cognitive modelling of the brain than conventional artificial neural networks that use synchronous updates. Finally, we compared our approach with several other related theories that apply formal methods to cognitive modelling. In addition, the practical implications of the approach are discussed in the context of neuronal network based controllers.     

Dissecting the protocol and network traffic of the OnLive cloud gaming platform

Cloud gaming is a new paradigm that is envisaged to play a pivotal role in the video game industry in forthcoming years. Cloud gaming, or gaming on demand, is a type of online gaming that allows on-demand streaming of game content onto non-specialised devices (e.g. PC, smart TV, etc.). This approach requires no downloads or game installation because the actual game is executed on the game company’s server and is streamed directly to the client. Nonetheless, this revolutionary approach significantly affects the network load generated by online games. As cloud gaming presents new challenges for both network engineers and the research community, both groups need to be fully conversant with these new cloud gaming platforms. The purpose of this paper is to investigate OnLive, one of the most popular cloud gaming platforms. Our key contributions are: (a) a review of the state-of-the-art of cloud gaming; (b) reverse engineering of the OnLive protocol; and (c) a synthetic traffic model for OnLive.     

空间资源索引与top-k查询研究

以空间资源索引结构R-tree为基础, 考虑人们对空间资源能力指标的要求, 利用道路网络模型进行空间距离的计算, 提出了一种包含能力维度信息的空间资源索引结构和top-k查询算法, 并与传统遍历算法进行对比实验, 验证了查询算法的有效性。     

Industrial TiO2 based nacreous pigments as functional building materials: Photocatalytic removal of NO

In this paper, the photocatalytic activity of industrial titanium dioxide (TiO₂) based nacreous pigments was researched as functional building materials for photocatalytic NO remove. Three industrial TiO₂ based nacreous pigments were selected to estimate the photocatalytic activity for NO remove. This study is a good proof that pearlescent pigments can eliminate NO, and its performance is positively correlated with its titanium dioxide content. And this research will widen the application of nacreous pigments in functional building materials, and provide a new way to eliminate in door nitric oxide pollution.     

Morphologies and microstructures of tree-like carbon produced at different reaction conditions in a CVD process

Centimeter-size multi-branched tree-like carbon structures have been generated by the catalytic chemical vapor deposition of toluene using ferrocene as the catalyst precursor and investigated by means of SEM, TEM, and EDX. It is found that a temperature of 1000-1200 °C and a carrier gas flow rate of 1000-2500 ml/min are necessary for the generation of the carbon trees. Their morphologies and microstructures change greatly with the changing reaction conditions. The fractal dimensions of the trees are calculated to quantitatively investigate the influence of different reaction temperatures on the morphologies.     

Detecting changes of the Yellow River Estuary via SAR images based on a local fit-search model and kernel-induced graph cuts

The Yellow River Estuary area of China is under great pressure from both human intervention and natural processes. For analysis of the changes in this area, this article presents a novel change-detection method based on a local fit-search model and kernel-induced graph cuts in multitemporal synthetic aperture radar images. Change detection involves assigning a label to every pixel. This task is naturally formulated in terms of energy minimization, which can be effectively solved by graph cuts. The difference image is transformed implicitly by a kernel function so that an alternative to complex modelling of the original data makes the piecewise constant model become applicable for graph cuts formulation. An issue is that graph cuts are sensitive to the initial estimate. The local fit-search model is proposed to approximate to the local histogram while selecting an optimal threshold for the initial labelling, which leads to an effective constraint for graph cuts and computational benefits as well. Visual and quantitative analyses obtained on the Yellow River Estuary data set confirm the effectiveness of the proposed method and that it outperforms the other state-of-the-art methods of change detection.     

Synthesis and characterization of copolythiophene

Copolythiophenes (Co‐PTs), poly(3‐hexylthiophene‐co‐3‐thiophene carboxylic acid) (P3HT‐TCa), poly(3‐hexyloxylthiophene‐co‐3‐thiophene carboxylic acid) (P3HOT‐TCa), and poly(3‐phenylthiophene‐co‐3‐thiophene carboxylic acid) (P3PhT‐TCa), were synthesized by chemical oxidized polymerization to investigate the effect of copolymerization on the properties of polythiophenes (PTs). Gel permeation chromatography showed that the molecular weight (MW) of Co‐PT was lower than that of homopolythiophene. Fourier transform infrared (FTIR) spectra indicated that the copolymerization was successful between the monomers. The λ_max of Co‐PTs gave a “blue shift” in ultraviolet‐visible (UV‐VIS) spectra. Photoluminescence (PL) spectra showed that the PL intensity of Co‐PT became weaker than that of homopolythiophene and the disappearance of PL had been observed in P3HOT‐TCa. The thermal stability of Co‐PT was influenced by the carboxyl for its low decomposition temperature. Furthermore, the copolymerization between multi‐wall carbon nanotube containing thiophene ring (MWNT‐Th) and 3‐hexyloxylthiphene could also take place successfully. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 2007     

Dynamic deviation reduction‐based concurrent dual‐band digital predistortion

This article introduces the concurrent dual‐band digital predistortion (DPD) architecture with only one upconvertion unit, which is suitable for the linearization of wideband power amplifiers (PAs) excited by concurrent dual‐band signals. By extending the conventional dynamic deviation reduction (DDR) model to the concurrent dual‐band mode, we propose two DDR‐based concurrent dual‐band models, the dual‐band DDR (DB‐DDR) model and the simplified dual‐band DDR (SDB‐DDR) model. The performance of these two models is experimentally assessed with two types of wideband PAs (a GaN Class F PA and a GaN Doherty PA) driven by the concurrent dual‐band signal, and compared with the prior two‐dimensional digital predistortion (2D‐DPD) model and the two‐dimensional modified memory polynomial (2D‐MMP) model. The results prove the good DPD performance and low computational complexity of the proposed models. © 2013 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:401–411, 2014.     

Logo detection with extendibility and discrimination

Logos are specially designed marks that identify goods, services, and organizations using distinguished characters, graphs, signals, and colors. Identifying logos can facilitate scene understanding, intelligent navigation, and object recognition. Although numerous logo recognition methods have been proposed for printed logos, a few methods have been specifically designed for logos in photos. Furthermore, most recognition methods use codebook-based approaches for the logos in photos. A codebook-based method is concerned with the generation of visual words for all the logo models. When new logos are added, the codebook reconstruction is required if effectiveness is a crucial factor. Moreover, logo detection in natural scenes is difficult because of perspective tilt and non-rigid deformation. Therefore, this study develops an extendable, but discriminating, model-based logo detection method. The proposed logo detection method is based on a support vector machine (SVM) using edge-based histograms of oriented gradient (HOGE) as features through multi-scale sliding window scanning. Thereafter, anti-distortion affine scale invariant feature transform (ASIFT) is used for logo verification with constraints on the ASIFT matching pairs and neighbors. The experimental results using the public Flickr-Logo database confirm that the proposed method has a higher retrieval and precision accuracy compared to existing model-based methods.