一种新的基于压缩传感理论解决Pan-Sharpening问题的算法

Pan-Sharpening问题是将高分辨率全色图像和低分辨率多光谱图像融合,重构出高分辨率多光谱图像的过程。压缩传感理论解决了相应目标函数的稀疏解问题(求解含有l0范数的极小化问题)。利用压缩传感理论,提出一种新的解决Pan-Sharpening问题的算法:以离散余弦变换矩阵作为字典进行重构。然后针对重构过程中产生的块效应、色彩偏差和洗涤效应等问题进行相应的处理。QuickBird卫星图像的实验结果表明:与经典算法进行对比,该算法在效果上有一定的优势。     

一种梯度交替迭代设计测量矩阵的方法

在压缩采样中,测量矩阵应该和表达字典有尽可能小的相干性,随机测量矩阵一直被使用是因为其和任何表达字典都有较小的相干性。提出一种基于梯度迭代最小化方法,作为格拉斯曼框架设计的一种变体,通过优化一个初始的随机测量矩阵来得到相干性更小的测量矩阵。仿真结果表明所设计的测量矩阵具有更好的性能。     

一种压缩待测气体增强气体传感的方法

针对普通气体传感器在低体积分数的气体检测中灵敏度低的问题,提出将待测气体进行压缩以增强气体传感器响应的方法,根据检测压缩后的气体响应的特征,间接得到普通状态下该气体的体积分数。设计了一种小型的样机系统来进行气体压缩和气体传感器检测,描述了系统各个模块的设计,给出了该系统的软件设计和控制流程。以H2S气体为检测对象进行了实验,分析结果表明:该方法提高了现有传感器检测低体积分数气体的能力,尤其是提高了对毒害气体的及时发现和预警防范能力。样机的试制也为该压缩传感系统的进一步小型化和提升检测性能提供了条件,具有较高的应用价值。     

制袋机薄膜速度传感方法与定长控制

提出了实现薄膜张力传感的薄膜速度传感方法,设计了基于ARM处理器和嵌入式实时操作系统的薄膜定长伺服控制系统。采用巧妙的机械变换技术实现薄膜长度变化的传感,从而建立具有线性特性的薄膜速度传感数学模型。采用ARM7 TDMI核心处理器和μC/OS-II嵌入式实时操作系统平台,研发了薄膜定长伺服控制系统。核心技术成功应用到双列中封高速制袋机的控制系统中,运行速度达到24000 P/h,成品率超过99.6%。     

基于P-Ifourier观测矩阵的宽带压缩感知方法

针对压缩感知理论在宽带频谱感知领域应用时重构精度差的问题,根据平稳信号在频域所表现出的稀疏特性,提出了一种基于P-Ifourier(Partial-Inverse fourier)观测矩阵的宽带压缩频谱感知方法。新方法首先将频谱感知问题建模为一个典型的压缩感知问题,利用相关性能优良的标准正交傅里叶基构造观测矩阵,使观测矩阵具有良好的重构性能和重构精度。仿真结果表明,相比于高斯随机观测矩阵和嵌入式混沌序列-循环Toeplitz结构观测矩阵,该方法在较低信噪比环境下能够明显降低信号重构的均方误差,并且在相同条件下的重构概率得到了明显改善。     

溶液雾化微波等离子体CVD法制备SrTiO3薄膜

研究了一种新型高效制备多组分陶瓷薄膜的方法，它采用可溶性无机盐溶液雾化为反应膜，利用微波等离子体化学气相沉积工艺在Al2O3基片上制备了SrTiO3基陶瓷薄膜，实验结果表明，薄膜沉积时衬底温度对成膜的相组成与结构产生重要影响，本实验中当沉积薄膜衬底温度在700℃时，可以制备出单一相组成，符合化学计量比，结晶性较好，晶粒度呈球形且均匀分布的SrTiO3薄膜，通过对不同氧分压下薄膜电阻测试，发现此SrTiO3薄膜在O2 N2气氛中氧浓度由1%变化到20%时，其电阻值由5.0MΩ变化到2.5MΩ，从而显示出一定氧敏性能。     

无人机遥感在水土保持领域的应用

我国水土流失面积大并分布广泛,传统水土保持研究调查方法已不能满足目前水土流失数据的获取需求。无人机遥感技术作为一种新型遥感数据获取手段,非常适合小范围内高分辨率遥感数据的即时获取,在水土保持领域的应用越加广泛。主要介绍了无人机遥感高分辨、高精度、高效率、低成本的应用特点,分析了无人机遥感数据获取和解译过程,总结了其在水土保持方案编制、动态监测、监督执法及设施验收评估等方面的具体应用,论述了该项技术在水土保持领域的重要性和发展前景。     

水土流失遥感调查中植被覆盖度因子提取研究

植被覆盖度是水土流失的主要影响因子,而利用遥感影像判读可以快速有效地获得大范围的植被覆盖度。简要介绍了水土流失遥感调查中植被覆盖度因子提取的方法。基于遥感与实地野外调查数据,采用MODIS时序数据融合高分遥感数据的方法,以湖南省蓝山县为试验区,对植被覆盖度因子进行了提取。结果表明,该方法提取植被覆盖度因子的结果较理想。提出的植被覆盖度因子提取方法对今后的水土流失动态监测研究具有重要的理论意义和实践意义。     

Ubicon and its applications for ubiquitous social computing

The combination of ubiquitous and social computing is an emerging research area which integrates different but complementary methods, techniques, and tools. In this paper, we focus on the Ubicon platform, its applications, and a large spectrum of analysis results. Ubicon provides an extensible framework for building and hosting applications targeting both ubiquitous and social environments. We summarize the architecture and exemplify its implementation using four real-world applications built on top of Ubicon. In addition, we discuss several scientific experiments in the context of these applications in order to give a better picture of the potential of the framework, and discuss analysis results using several real-world data sets collected utilizing Ubicon.     

A Class of Cross‐Layer Optimization Design for Congestion and Energy Efficiency with Compressed Sensing in Wireless Sensing Networks

In wireless sensor networks (WSNs), the congestion problem not only causes packet loss, but also leads to an increase in delays and energy consumption. The actual performance of wireless sensor networks (WSNs) can be severely influenced by the quality of the communication channel and the bit in transmission. In this paper, the distributed protocols, which attain global optimum control for signals by the compressed sensing technique and achieve fair channel allocation by the scheduling algorithm, are proposed for WSNs. We take into account the congestion problem by robust optimization with congestion ratio for two classic aspects in energy limited WSNs: minimum transmission rate and maximum transmitted information. To achieve the goal, three protocols are developed. In the first protocol, the desired control input is designed based on the compressed sensing technique. A minimal bit of signal is provided to reduce the transmission flow for the congestion model. The second protocol is resource allocation. The resources can be allocated increasingly to the channel in order to avoid more severe congestion. This can also avoid conservative reduction of resource allocation for eliminating congestion. Channel selection abides by the fair resource allocation principle. The above protocols separately are implemented through a congestion ratio at network layer, transport layer, and MAC layer. Simulation results demonstrate that the proposed algorithm effectively relieves congestion, and achieves higher throughput and lower energy consumption.