Biologically inspired task oriented gist model for scene classification

Capturing the scene gist is account for rapid and accurate scene classification in human visual system. This paper presents a biologically inspired task oriented gist model (BT-Gist) that attempts to emulate two important attributes of biological gist: holistic scene centered spatial layout representation and task oriented resolution determination. For the first attribute, we enrich the model of Oliva and Torralba by refining the low-level features in several biological plausible ways, extending the spatial layout to multiple resolution and followed by perceptually meaningful manifold analysis for a set of multi-resolution biologically inspired intrinsic manifold spatial layouts (BMSLs). Since the optimal resolution that best represents the spatial layout varies from task to task, we embody the second attribute as learning the combination of BMSLs of multiple resolution with respect to their optimal discriminative invariance trade-off for the task at hand, and then cast it in the SVM based localized multiple kernel learning (LMKL) framework, by which the kernel of each scene gist is approximated as a local combination of kernels associated to multi-resolution BMSLs. By exploring the task specific category distribution pattern over BMSL, we define the local model as a category distribution sensitive (CDS) kernel, which can accommodate both the diverse individuality of specific BMSL and the universality shared within the whole category space. Via CDS-LMKL, both the optimal resolution for spatial layouts and the final classifier can be efficiently obtained in a joint manner. We evaluate BT-Gist on four natural scene databases and one cluttered indoor scene database with a range of comparison: From different MKL methods, to various biologically inspired models and BoF based computer vision models. CDS-LMKL leads to better results compared to several existing MKL algorithms. Given the two biological attributes that the framework has to follow, BT-Gist, despite its holistic nature, outperforms existing biologically inspired models and BoF based computer vision models in natural scene classification, and competes with the object segmentation based ROI-Gist in cluttered indoor scene classification.     

Application of the Soil and Water Assessment Tool for six watersheds of Lake Erie: Model parameterization and calibration

The Soil and Water Assessment Tool (SWAT), a physically-based watershed-scale model, holds promise as a means to predict tributary sediment and nutrient loads to the Laurentian Great Lakes. In the present study, model performance is compared across six watersheds draining into Lake Erie to determine the applicability of SWAT to watersheds of differing characteristics. After initial model parameterization, the Huron, Raisin, Maumee, Sandusky, Cuyahoga, and Grand SWAT models were calibrated (1998-2001) and confirmed, or validated (2002-2005), individually for stream water discharge, sediment loads, and nutrient loads (total P, soluble reactive P, total N, and nitrate) based on available datasets. SWAT effectively predicted hydrology and sediments across a range of watershed characteristics. SWAT estimation of nutrient loads was weaker although still satisfactory at least two-thirds of the time across all nutrient parameters and watersheds. SWAT model performance was most satisfactory in agricultural and forested watersheds, and was less so in urbanized settings. Model performance was influenced by the availability of observational data with high sampling frequency and long duration for calibration and confirmation evaluation. In some instances, it appeared that parameter adjustments that improved calibration of hydrology negatively affected subsequent sediment and nutrient calibration, suggesting trade-offs in calibrating for hydrologic vs. water quality model performance. Despite these considerations, SWAT accurately predicted average stream discharge, sediment loads, and nutrient loads for the Raisin, Maumee, Sandusky, and Grand watersheds such that future use of these SWAT models for various scenario testing is reasonable and warranted.     

污水处理厂有毒有害及易燃易爆气体在线监测系统建设与应用

小红门污水处理厂是较早建设有毒有害及易燃易爆气体在线监测系统的污水处理厂之一.介绍了小红门污水处理厂监测系统的建设背景、实施方案及应用效果.系统包括在线检测、视频检测、广播等3个子系统.系统的应用促进了安全生产,有利于保障职工职业健康,取得了较好的经济效益和社会效益.建议城镇污水处理厂的设计中增设此种监测系统.     

柬埔寨甘再水电站计算机监控系统设计

着重介绍了作为柬埔寨甘再水电站枢纽工程中装机最大的PH1电站计算机监控系统的结构、配置及功能,并重点总结了该电站计算机监控系统的特点.     

基于Qt的虚拟示波器的软面板设计

为了解决用户无法自定义示波器界面的缺陷,文中以实际示渡器为模型,使用可视化图形编程环境-Qt平台,开发设计了虚拟示波器的软面板.以实际示波器需要的功能为基础,通过组件化的方式,实现对测试数据的存储、波形显示、回放及仪器界面控制等功能.结果达到了预期效果,设计出的虚拟示波器操作简单、功能强大,同时可以根据用户需要,重组组件设计出更为形象直观的界面,具有很强的可扩展性与可移植性.虚拟示波器势必成为当代示波器发展的方向.     

Poisson image fusion based on Markov random field fusion model

In this paper, we present a gradient domain image fusion framework based on the Markov Random Field (MRF) fusion model. In this framework, the salient structures of the input images are fused in the gradient domain, then the final fused image is reconstructed by solving a Poisson equation which forces the gradients of the fused image to be close to the fused gradients. To fuse the structures in the gradient domain, an effective MRF-based fusion model is designed based on both the per-pixel fusion rule defined by the local saliency and also the smoothness constraints over the fusion weights, which is optimized by graph cut algorithm. This MRF-based fusion model enables the accurate estimation of region-based fusion weights for the salient objects or structures. We apply this method to the applications of multi-sensor image fusion, including infrared and visible image fusion, multi-focus image fusion and medical image fusion. Extensive experiments and comparisons show that the proposed fusion model is able to better fuse the multi-sensor images and produces high-quality fusion results compared with the other state-of-the-art methods.     

A new image fusion performance metric based on visual information fidelity

Because subjective evaluation is not adequate for assessing work in an automatic system, using an objective image fusion performance metric is a common approach to evaluate the quality of different fusion schemes. In this paper, a multi-resolution image fusion metric using visual information fidelity (VIF) is presented to assess fusion performance objectively. This method has four stages: (1) Source and fused images are filtered and divided into blocks. (2) Visual information is evaluated with and without distortion information in each block. (3) The visual information fidelity for fusion (VIFF) of each sub-band is calculated. (4) The overall quality measure is determined by weighting the VIFF of each sub-band. In our experiment, the proposed fusion assessment method is compared with several existing fusion metrics using the subjective test dataset provided by Petrovic. We found that VIFF performs better in terms of both human perception matching and computational complexity.     

Multiconsensus of second order multiagent systems with directed topologies

A novel multiconsensus problem is introduced in multiagent systems. The states of multiple agents in each subnetwork asymptotically converge to a consistent value with information exchanges among subnetworks. The multiconsensus problem of second order multiagent systems with directed topologies is studied. Three linear protocols are proposed to solve the multiconsensus problem. Necessary and sufficient conditions are derived based on matrix theory. Simulations are provided to demonstrate the effectiveness of the theoretical results.     

Parameter-dependent robust H ∞ filter design for uncertain discrete-time systems with quantized measurements

This paper deals with the problem of parameter-dependent robust H _∞ filter design for uncertain discrete-time systems with output quantization. The uncertain parameters are supposed to reside in a polytope. The system outputs are quantized by a memoryless logarithmic quantizer before being transmitted to a filter. Attention is focused on the design of a robust H _∞ filter to mitigate quantization effects and ensure a prescribed H _∞ noise attenuation level. Via introducing some slack variables and using the parameter-dependent Lyapunov function, sufficient conditions for the existence of a robust H _∞ filter are expressed in terms of linear matrix inequalities (LMIs). Finally, a numerical example is provided to demonstrate the effectiveness of the proposed approach.