物联网关键技术及其发展探讨

本文分析了物联网的技术架构、物联网的关键技术、物联网的安全问题研究、物联网发展面临的问题及其解决措施.为物联网相关问题的探讨奠定了理论基础.     

Classifying mental tasks based on features of higher-order statistics from EEG signals in brain-computer interface

In order to characterize the non-Gaussian information contained within the EEG signals, a new feature extraction method based on bispectrum is proposed and applied to the classification of right and left motor imagery for developing EEG-based brain-computer interface systems. The experimental results on the Graz BCI data set have shown that based on the proposed features, a LDA classifier, SVM classifier and NN classifier outperform the winner of the BCI 2003 competition on the same data set in terms of either the mutual information, the competition criterion, or misclassification rate.     

Model driven middleware: A new paradigm for developing distributed real-time and embedded systems

Distributed real-time and embedded (DRE) systems have become critical in domains such as avionics (e.g., flight mission computers), telecommunications (e.g., wireless phone services), tele-medicine (e.g., robotic surgery), and defense applications (e.g., total ship computing environments). These types of system are increasingly interconnected via wireless and wireline networks to form systems of systems. A challenging requirement for these DRE systems involves supporting a diverse set of quality of service (QoS) properties, such as predictable latency/jitter, throughput guarantees, scalability, 24x7 availability, dependability, and security that must be satisfied simultaneously in real-time. Although increasing portions of DRE systems are based on QoS-enabled commercial-off-the-shelf (COTS) hardware and software components, the complexity of managing long lifecycles (often ∼15-30 years) remains a key challenge for DRE developers and system integrators. For example, substantial time and effort is spent retrofitting DRE applications when the underlying COTS technology infrastructure changes.This paper provides two contributions that help improve the development, validation, and integration of DRE systems throughout their lifecycles. First, we illustrate the challenges in creating and deploying QoS-enabled component middleware-based DRE applications and describe our approach to resolving these challenges based on a new software paradigm called Model Driven Middleware (MDM), which combines model-based software development techniques with QoS-enabled component middleware to address key challenges faced by developers of DRE systems — particularly composition, integration, and assured QoS for end-to-end operations. Second, we describe the structure and functionality of CoSMIC (Component Synthesis using Model Integrated Computing), which is an MDM toolsuite that addresses key DRE application and middleware lifecycle challenges, including partitioning the components to use distributed resources effectively, validating software configurations, assuring multiple simultaneous QoS properties in real-time, and safeguarding against rapidly changing technology.     

Efficient embedded wavelet codec based on list structure

A new efficient image codec called embedded wavelet coding based on list structure (EWCBL) is proposed in this paper. By combining the techniques of morphological representation and quadtree partitioning, EWCBL effectively exploits both within-subband clustering and cross-subband similarity of wavelet coefficients. Based on the list structure, a fine fractional bit-plane coding is employed to achieve excellent rate-distortion performance. The patterned morphological dilation is introduced to greatly reduce the redundant test operations. Experimental results show that the proposed EWCBL outperforms state-of-the-art embedded codecs for both lossy and lossless compression. Moreover, the good performance and features of embeddedness and scalability are achieved with relatively low complexity.     

Extracting Takagi-Sugeno Fuzzy Rules with Interpretable Submodels via Regularization of Linguistic Modifiers

In this paper, a method for constructing Takagi-Sugeno (TS) fuzzy system from data is proposed with the objective of preserving TS submodel comprehensibility, in which linguistic modifiers are suggested to characterize the fuzzy sets. A good property held by the proposed linguistic modifiers is that they can broaden the cores of fuzzy sets while contracting the overlaps of adjoining membership functions (MFs) during identification of fuzzy systems from data. As a result, the TS submodels identified tend to dominate the system behaviors by automatically matching the global model (GM) in corresponding subareas, which leads to good TS model interpretability while producing distinguishable input space partitioning. However, the GM accuracy and model interpretability are two conflicting modeling objectives, improving interpretability of fuzzy models generally degrades the GM performance of fuzzy models, and vice versa. Hence, one challenging problem is how to construct a TS fuzzy model with not only good global performance but also good submodel interpretability. In order to achieve a good tradeoff between GM performance and submodel interpretability, a regularization learning algorithm is presented in which the GM objective function is combined with a local model objective function defined in terms of an extended index of fuzziness of identified MFs. Moreover, a parsimonious rule base is obtained by adopting a QR decomposition method to select the important fuzzy rules and reduce the redundant ones. Experimental studies have shown that the TS models identified by the suggested method possess good submodel interpretability and satisfactory GM performance with parsimonious rule bases.     

基于规则引擎的智能决策支持系统开发

提出了解决基于规则专家系统的规则冲突的方法。构建了使用Eclipse和Drools的集成开发环境。     

Detection of urinary biomarkers for early diagnosis of acute renal allograft rejection by proteomic analysis

Acute allograft rejection has been recognized as a major impediment to improved success in renal transplantation. Timely detection and control of rejection are very important for the improvement in long-term renal allograft survival. Thus, biomarkers for early diagnosis of acute rejection are required urgently to clinical medication. This study seeks to search for such biomarker candidates by comparing patients' pre-treatment urinary protein profiling with their post-treatment urinary protein profiling. A total of 15 significantly and consistently down-regulated protein candidates were identified. Among them, alpha-1-antichymotrypsin precursor (AACT), tumor rejection antigen gp96 (GP96) and Zn-Alpha-2-Glycoprotein (ZAG) were selected for further analysis. The results indicated that Western Blot assay of AACT, GP96 and ZAG had advanced the diagnosis time of acute renal rejection by 3 days, compared with current standard clinical observation and laboratory examination. Furthermore, the double-blind detection revealed that the accuracy, sensitivity and specificity of the diagnosis of acute renal rejection of AACT, GP96 and ZAG were 66.67%/100%/60%, 83.33%/100%/80% and 66.67%/100%/60%, respectively, and 100%/100%/100% in combination. In conclusion, urinary protein AACT, GP96 and ZAG could be a set of potential biomarkers for early non-invasive diagnosis of the acute rejection after renal transplantation.     

基于网格的一种新的动态演化算法

近年来,越来越多的演化计算研究者对动态优化问题产生了很大的兴趣,并产生了很多解决动态优化问题的方法。提出一种新的动态演化算法,与传统的演化算法有所不同,它是建立在划分网格基础上的,故而称它为网格优化算法。通过测试典型的动态优化问题,并与经典的SOS算法进行比较,证明了算法的有效性。     

基于概率A*的智能车路径规划算法

为保证智能车辆能够按照规划的路径安全行驶,且满足车辆动力学特性,针对混合A*算法中没有引导性策略所产生较多不必要的搜索问题,提出概率A*算法先得到搜索粗路径,提高在随后搜索过程中搜索效率。然后利用概率A*算法得到的路径点引导节点走向,避免节点向障碍物等搜寻。最后对节点的代价函数进行优化。仿真实验结果表明,与混合A*算法相比,本文所提出的算法平均减少搜索时间10.8%,且得到的路径相对规整平滑。该算法可以在较短时间内为智能车辆规划一条安全可行平滑的路径。     

Simultaneous CO2 and H2O Activation via Integrated Cu Single Atom and N Vacancy Dual-Site for Enhanced CO Photo-Production

Photocatalytic conversion of CO₂ into fuels using pure water as the proton source is of immense potential in simultaneously addressing the climate-change crisis and realizing a carbon-neutral economy. Single-atom photocatalysts with tunable local atomic configurations and unique electronic properties have exhibited outstanding catalytic performance in the past decade. However, given their single-site features they are usually only amenable to activations involving single molecules. For CO₂ photoreduction entailing complex activation and dissociation process, designing multiple active sites on a photocatalyst for both CO₂ reduction and H₂O dissociation simultaneously is still a daunting challenge. Herein, it is precisely construct Cu single-atom centers and two-coordinated N vacancies as dual active sites on CN (Cu₁/N_2CV-CN). Experimental and theoretical results show that Cu single-atom centers promote CO₂ chemisorption and activation via accumulating photogenerated electrons, and the N_2CV sites enhance the dissociation of H₂O, thereby facilitating the conversion from COO* to COOH*. Benefiting from the dual-functional sites, the Cu₁/N_2CV-CN exhibits a high selectivity (98.50%) and decent CO production rate of 11.12 µmol g⁻¹ h⁻¹. An ingenious atomic-level design provides a platform for precisely integrating the modified catalyst with the deterministic identification of the electronic property during CO₂ photoreduction process.