Satellite SAR Remote Sensing of Great Lakes Ice Cover,Part 2. Ice Classification and Mapping

During the 1997 winter season, shipborne polarimetric backscatter measurements of Great Lakes (freshwater) ice types using the Jet Propulsion Laboratory C-band scatterometer, together with surface-based ice physical characterization measurements and environmental parameters, were acquired concurrently with Earth Resource Satellite 2 (ERS-2) and RADARSAT Synthetic Aperture Radar (SAR) data. This polarimetric data set, composed of over 20 variations of different ice types measured at incident angles from 0° to 60° for all polarizations, was processed to radar cross-section to establish a library of signatures (look-up table) for different ice types. The library is used in the computer classification of calibrated satellite SAR data. Computer analysis of ERS-2 and RADARSAT ScanSAR images of Great Lakes ice cover using a supervised classification technique indicates that different ice types in the ice cover can be identified and mapped, and that wind speed and direction can have an influence on the classification of water as ice based on single frequency, single polarization data. Once satellite SAR data are classified into ice types, the ice map provides important and necessary input for environmental protection and management, ice control and ice breaking operations, and ice forecasting and modeling efforts.     

BANDC——一种适合于从遥感图象识别地理环境的分类器

本文所提出的新算法 BANDC 是 BAyes Normal Dynamic Clustering 的缩写。这个算法的目的是从遥感图象识别地理环境。它的优点是:能由机器自动按照图象估算出分类器的参数;作为一种特殊的 Bayes 分类器,它能够依靠计算机估算出{wi}的先验概率,于是能剔除野点,提高分类器的鲁棒性。这个算法的关键是应用拟合法从灰度等级直方图估算出参数。     

Enhancing cognitive radio systems with robust reasoning

Cognitive radio systems dynamically reconfigure the algorithms and parameters they use, in order to adapt to the changing environment conditions. However, reaching proper reconfiguration decisions presupposes a way of knowing, with high enough assurance, the capabilities of the alternate configurations, especially in terms of achievable transmission capacity and coverage. The present paper addresses this problem, firstly, by specifying a complete process for extracting estimations of the capabilities of candidate configurations, in terms of transmission capacity and coverage, and, secondly, by enhancing these estimations with the employment of a machine learning technique. The technique is based on the use of Bayesian Networks, in conjunction with an effective learning and adaptation strategy, and aims at extracting and exploiting knowledge and experience, in order to reach robust (i.e. stable and reliable) estimations of the configurations' capabilities. Comprehensive results of the proposed method are presented, in order to validate its functionality. Copyright © 2007 John Wiley & Sons, Ltd.     

Anion Sensing Porphyrin Functionalized Nanoparticles

Disulfide and dithiocarbamate functionalized porphyrins have been synthesized and used as protecting ligands for gold nanoparticle formation either via ligand substitution reactions or by direct synthesis. These nanoparticles have been shown to recognize anions via changes in the absorbance spectrum of the surface adsorbed porphyrin moieties. Association constants, derived from quantitative titrations, indicate a remarkable surface enhancement effect where the surface bound porphyrins bind anions much more strongly than the free receptor in solution.     

A survey on approaches to gridification

The Grid shows itself as a globally distributed computing environment, in which hardware and software resources are virtualized to transparently provide applications with vast capabilities. Just like the electrical power grid, the Grid aims at offering a powerful yet easy‐to‐use computing infrastructure to which applications can be easily ‘plugged’ and efficiently executed. Unfortunately, it is still very difficult to Grid‐enable applications, since current tools force users to take into account many details when adapting applications to run on the Grid. In this paper, we survey some of the recent efforts in providing tools for easy gridification of applications and propose several taxonomies to identify approaches followed in the materialization of such tools. We conclude this paper by describing common features among the proposed approaches, and by pointing out open issues and future directions in the research and development of gridification methods. Copyright © 2007 John Wiley & Sons, Ltd.     

Monitoring water quality in reservoirs with IRS-1A-LISS-I

An attempt has been made to quantify the relationship between the variation in IRS-IA-LISS-I (Indian Remote Sensing Satellite-1A Linear Imaging Self-Scanning System) radiance data and field measured change in secchi disc depth. Secchi disc depth was measured for 47 predetermined sampling locations on reservoir surface water. At extinction depth (secchi depth), water samples were collected from all the sampling locations. Suspended sediments of eight locations representing various reaches of the reservoir were selected for mineralogical, particle size and optical properties analysis. The LISS-I radiance value in band 1 (0.45–0.52µm) band 2 (0-52–0.59 µm) and band 3 (0.62–0.68 µm) were used in a regression analysis. The absorption infrared band 4 (0.77–0.86 µm) was not included in the analysis. In these, the dependable variable was secchi depth (SD) and the LISS-I-radiance data was the estimator variable. Forty-seven data sets of 20 October 1988 from Tawa reservoir surface water were used to obtain an estimator equation for SD. The verification of the estimator equation was tested by applying it to a data set of 21 measurements of 28 September 1988 for this reservoir. The coefficient of correlation between observed and estimated values for the 28 September 1988 data set wasr=0.92 for SD, indicating that the equation could accurately predict the water clarity (SD) for this reservoir on new occasions from IRS-IA-LISS-I spectral data. It is shown that mineral composition and optical properties of suspended sediments influence the reflected radiance of water quality. It is concluded that IRS-IA-LISS-I data provide a useful means of mapping water quality in reservoir.     

Target differentiation with simple infrared sensors using statistical pattern recognition techniques

This study compares the performances of various statistical pattern recognition techniques for the differentiation of commonly encountered features in indoor environments, possibly with different surface properties, using simple infrared (IR) sensors. The intensity measurements obtained from such sensors are highly dependent on the location, geometry, and surface properties of the reflecting feature in a way that cannot be represented by a simple analytical relationship, therefore complicating the differentiation process. We construct feature vectors based on the parameters of angular IR intensity scans from different targets to determine their geometry and/or surface type. Mixture of normals classifier with three components correctly differentiates three types of geometries with different surface properties, resulting in the best performance (100%) in geometry differentiation. Parametric differentiation correctly identifies six different surface types of the same planar geometry, resulting in the best surface differentiation rate (100%). However, this rate is not maintained with the inclusion of more surfaces. The results indicate that the geometrical properties of the targets are more distinctive than their surface properties, and surface recognition is the limiting factor in differentiation. The results demonstrate that simple IR sensors, when coupled with appropriate processing and recognition techniques, can be used to extract substantially more information than such devices are commonly employed for.     

The Synthesis of Covalent Triazine-Based Frameworks via Friedel–Crafts Reactions of Cyanuric Chloride with Thienyl and Carbazolyl Derivatives for Fluorescence Sensing to Picric Acid,Iodine and Capturing Iodine

A multifunctional thiophene-based covalent triazine framework (TTPATTh) with triphenylamine as core is synthesized by Friedel–Crafts reaction of cyanuric chloride with thienyl derivative for the first time. The yield of TTPATTh (99.59%) is far higher than that of the carbazole-based CTF (TTPATCz, 47.03%). TTPATTh and TTPATCz possess high BET surface areas with 1235 and 2501 m² g^–1 as well as high pore volumes with 1.60 and 2.23 cm³ g^–1, respectively. TTPATTh and TTPATCz have high thermal stability with high thermal decomposition temperatures of 514 and 598 °C in nitrogen atmosphere. With the introduction of triazine rings, the fluorescence sensing sensitivities of TTPATTh and TTPATCz to picric acid and iodine are improved significantly. Especially for TTPATTh, the K_sv values reach 5.95 × 10⁵ and 1.61 × 10⁴ L mol^–1, and LODs reach 1.02 × 10^–12 and 1.86 × 10^–12 mol L^–1. To the best of the knowledge, this is the most sensitive value among thiophene-based porous organic polymers to PA and I₂. Furthermore, TTPATTh and TTPATCz can also adsorb iodine in vapor phase, cyclohexane solution and aqueous solution, and release iodine by heating or in ethanol solution. The efficiency of controlling the release of iodine is higher than that in ethanol.     

Smart pricing scheme: A multi-layered scoring rule application

Defining appropriate pricing strategy for smart environment is important and complex task at the same time. It holds the primal fraction in Demand Response (DR) program. In our work, we devise an incentive based smart dynamic pricing scheme for consumers facilitating a multi-layered scoring rule. The proposed strategy characterizes both incentive based DR and price based DR programs facilities. This mechanism is applied between consumer agents (CA) to electricity provider agent (EP) and EP to Generation Company (GENCO). Based on the Continuous Ranked Probability Score (CRPS), a hierarchical scoring system is formed among these entities, CA–EP–GENCO. As CA receives the dynamic day-ahead pricing signal from EP, it will schedule the household appliances to lower price period and report the prediction in a form of a probability distribution function to EP. EP, in similar way reports the aggregated demand prediction to GENCO. Finally, GENCO computes the base discount after running a cost-optimization problem. GENCO will reward EP with a fraction of discount based on their prediction accuracy. EP will do the same to CA based on how truthful they were reporting their intentions on device scheduling. The method is tested on real data provided by Ontario Power Company and we show that this scheme is capable to reduce energy consumption and consumers’ payment.     

离网型太阳能光伏发电系统设计研究

离网型太阳能光伏发电系统是太阳能光伏发电系统的重要组成。它具有灵活度高、分布广、项目成本低等优点。本文主要对现有离网光伏发电系统设计过程进行分析。