Multi-resolution Image Fusion in Remote Sensing () || Use of Self-similarity and Gabor Prior

摘要： In this chapter, we introduce a concept called self-similarity and use the same for obtaining the initial fused image. We also use a new prior called Gabor prior for regularizing the solution. In Chapter 4, degradation matrix entries were estimated by modelling the relationship between the Pan-derived initial estimate of the fused MS image and the LR MS image. This may lead to inaccurate estimate of the final fused image since we make use of the Pan data suffering from low spectral resolution in getting the initial estimate. However, if we derive the initial fused image using the available LR MS image, which has high spectral resolution, mapping between LR and HR would be better and the derived degradation matrix entries are more accurate. This makes the estimated degradation matrix better represent the aliasing since we now have an initial estimate that has both high spatial and spectral resolutions. To do this, we need to obtain the initial estimate using only the available LR MS image since the true fused image is not available. We perform this by using the property of natural images that the probability of the availability of redundant information in the image and its downsampled versions is high [89]. We exploit this self-similarity in the LR observation and the sparse representation theory in order to obtain the initial estimate of the fused image. Finally, we solve the Pan-sharpening or multi-resolution image fusion problem by using a model based approach in which we regularize the solution by proposing a new prior called the Gabor prior.

Surface velocity model of the flow caused by a DBD plasma actuator; DBDプラズマアクチュエータによる流れの表面速度モデル;

摘要： The surface velocity model is proposed for modelling the flow caused by a DBD plasma actuator. While numerical model presented up to now for the DBD plasma actuator, i.e. Suzen’s model, needs to calibrate the five parameters that must be determined by comparison with the experimental results, the surface velocity model presented here requires calibrating three parameters only. The flow by the surface velocity model is compared with the experimental results and that of Suzen’s model. When DBD plasma actuators are activated on the surface of the flat plate or on that of the circular cylinder, the induced flow appears near the electrode. And then the temporal and spatial development of the flow by the surface velocity model is consistent with the results from the experiment and the Suzen’s model. The surface velocity model is very easy to use compared with the Suzen’s model, and it can well simulate the induced flow caused by the plasma actuator.

Multi-resolution Image Fusion in Remote Sensing () || Image Fusion: Application to Super-resolution of Natural Images

摘要： Increasing the spatial resolution of a given test image is of interest to the image processing community since the enhanced resolution of the image has better details when compared to the corresponding low resolution image. Super-resolution (SR) is an algorithmic approach in which a high spatial resolution image is obtained by using single/multiple low resolution observations or by using a database of LR–HR pairs. The linear image formation model discussed for image fusion in Chapter 4 is extended here to obtain an SR image for a given LR test observation. In the image fusion problem, the available Pan image was used in obtaining a high resolution fused image. Similar to the fusion problem, SR is also concerned with the enhancement of spatial resolution. However, we do not have a high resolution image such as a Pan image as an additional observation. Hence, we make use of a database of LR–HR pairs in order to obtain the SR for the given LR observation. Here, we use contourlet based learning to obtain the initial SR estimate which is then used in obtaining the degradation as well as the MRF parameter. Similar to the fusion problem discussed in Chapter 4, an MAP–MRF framework is used to obtain the final SR image.

Multi-resolution Image Fusion in Remote Sensing () || Conclusion and Directions for Future Research

Effect of Zn Content on the physicochemical Characteristics and Photoactivity of ZnO supported Activated Carbon

摘要： In the present work, synthesis of ZnO dispersed onto activated carbon (ZnO/AC) is reported. Synthesis was conducted by using impregnation method by zinc acetate as precursor at varied Zn content. A systematic characterization of ZnO/C materials was performed using XRD, SEM, gas sorption analyzer and diffuse reflectance UV-Visible spectrophotometer. Photoactivity of the materials was studied in phenol removal via photooxidation mechanism. From the study it is confirmed that ZnO dispersion give acceleration of phenol removal refer to surface area contribution. It is no linear correlation of each physical character to the photocatalytic activity.

http://www.orientjchem.org/vol34no1/optical-pattern-of-liquid-crystalline-structure-in-singular-and-mixed-anionic-cationic-surfactant-system/

摘要： A preliminary study to examine the liquid crystalline region formed by surfactant, DTAB and AOT, individually and mixed, in the presence of various chain length alcohols was carried out. Several ternary phase equilibrium were constructed. Results showed that all the investigated systems apart from forming lyotropic liquid crystalline structure were also able to form self-assembled aggregates such as micelle and reversed micelles. From the optical pattern observation, it was observed that two types of liquid crystalline were formed namely hexagonal and lamellar.

[American Society of Agricultural and Biological Engineers 2017 Spokane, Washington July 16 - July 19, 2017 - ()] 2017 Spokane, Washington July 16 - July 19, 2017 - <i>Variety classification of maize kernels using near infrared (NIR) hyperspectral imaging</i>

摘要： Variety classification of maize kernels was evaluated using near infrared (NIR) hyperspectral imaging in this work. Firstly, NIR hyperspectral images of kernels of four widely used maize varieties were acquired within effective spectral range of 1000-2500 nm. Spectral math was used to compensate for minor lighting differences, and band math combined with threshold method was used to remove the background from images. Minimum noise fraction (MNF) was adopted to reduce noise. Texture features (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation) as appearance character of each maize kernel were calculated and extracted to establish classification model combined with spectra data. Moving average smoothing and standard normal variate were applied on the raw spectra extracted from hyperspectral images. Four optimal wavelengths (1352.20 nm, 1615.50 nm, 1733.10 nm, and 2478.20 nm) were selected by competitive adaptive reweighted sampling (CARS) method. Partial least squares discriminant analysis (PLSDA) was employed to build varieties classification models, based on full wavelength data, the four wavelengths data, and combination of spectral and textural features at four wavelengths, respectively. Results demonstrated that PLSDA model based on combination of spectral and textural features had the best performance with accuracies of 0.89, 0.83 for calibration and prediction set, which indicated the hyperspectral imaging technique with combination of spectral and textural features had a potential of application for variety classification.

Sensing Multi Business Model Innovationvia Advanced Sensor Technology

摘要： The ability to innovate Business Models (BM) and do Multi Business Model Innovation (MBMI) has become more complicated today, but also a very hot topic in many businesses and even for governments. As BM’s lifetime are becoming shorter and shorter, BM’s are also changing typologies and a larger part of these becomes disruptive. Danish Government already in 2017 formed a disruption council – as a reflection on this evolvement – its primary task was to discuss, analyze and come up with proposals for how businesses and society should prepare for future MBMI, disruptive BM’s and Business Model Ecosystems (BMES) [1–4, 6, 7, 16]. Sensing BM’s seems to hold some solutions to meet this development. The academic classification of Sensing BM’s and Sensing MBMI have until now been very fragmented defined. The Sensing BM and Sensing MBMI have not been clearly defined, also compared to other types of BM’s and Business Model Innovation (BMI). Several businesses believe they innovate Sensing BM’s (SBM), Operate Industrial 4.0 with advanced sensors – but are challenged when asked to classify SBM and SBMI. The paper addresses – when can a BM be classified as a SBM. In relation to this topic – When can a business be classified as doing SBMI. The aim of the paper is: (1) to add to the development of conceptual framework models and typologies [10–12] for classifying SBM’s and disruptive, radical and incremental SBM’s and SBMIs. (2) to add to the development of MBMI framework and language using advanced sensor and wireless technologies. The preliminary research was supported by a BM and BMI framework [29], combined with advanced sensing technology, enabling researchers to observe and do experiments with SBM’s and SBMI.

Feasibility Study of Patient Specific Quality Assurance Using Transit Dosimetry Based on Measurement with an Electronic Portal Imaging Device

摘要： This study was designed to measure transit dose with an electronic portal imaging device (EPID) in eight patients treated with intensity modulated radiotherapy (IMRT), and to verify the accuracy of dose delivery to patients. The calculated dose map of the treatment planning system (TPS) was compared with the EPID based dose measured on the same plane with a gamma index method. The plan for each patient was verified prior to treatment with a diode array (MapCHECK) and portal dose image prediction (PDIP). To simulate possible patient positioning errors during treatment, outcomes were evaluated after an anthropomorphic phantom was displaced 5 and 10 mm in various directions. Based on 3%/3 mm criteria, the mean±SD passing rates of MapCHECK, PDIP (pre-treatment QA) for 47 IMRT were 99.8±0.1%, 99.0±0.7%, and, respectively. Besides, passing rates using transit dosimetry was 90.0±1.5% for the same condition. Setup errors of 5 and 10 mm reduced the mean passing rates by 1.3% and 3.0% (inferior to superior), 2.2% and 4.3% (superior to inferior), 5.9% and 10.9% (left to right), and 8.9% and 16.3% (right to left), respectively. These findings suggest that the transit dose-based IMRT verification method using EPID, in which the transit dose from patients is compared with the dose map calculated from the TPS, may be useful in verifying various errors including setup and/or patient positioning error, inhomogeneity and target motions.

An effective image denoising using PPCA and classification of CT images using artificial neural networks

摘要： The main aim of denoising is to remove the noise while recollecting as much possible important signal features. This appears to be very simple when considered under practical situations, where the type of images and noises are all variable parameters. This paper deals with removal of combination of noises from image and classification of normal and abnormal images. At first phase, median filter is used to remove the noises present in the images. To improve the denoised output, we are using PSM and PPCA with morphological operations, filter and region props. In the second phase, to analyse the denoised output, neural network-based classification is proposed. The use of artificial intelligent techniques for classification shows a great potential in this field. Hence the performance of neural network classifier is estimated in terms of training performance and classification accuracy and is compared with the existing method to show the system is effective.