Building Trustworthy And Resourceful Interrogation Services In The Cloud Using Knn-R Algorithm

Today’s , peoples are prevalently used cloud computing platform. In this platform user can save their outlay and time by using interrogation services in cloud info. In these info sometimes data owner does not transfer in  to cloud, because information may be factotum from the malevolent users when they use in cloud if not the secure data and also secrecy of a interrogation is guaranteed. In cloud, to intensification the efficiency of interrogation processing and to save the workload of interrogation processing, it is necessary to provide secure interrogation service to user. To fully realize the benefits of cloud computing the workload must be reduced and resourceful interrogation processing must be provided. Therefore, to provide trustworthy and resourceful interrogation service RASP method is proposed, where RASP denotes Random Space Perturbation. Data Perturbation technique allows users to ascertain key summary information about the data that is not distorted and does not lead to a safe keeping breach. Exclusive safe keeping features are provided by the RASP. The RASP approach satisfies the data Trustworthyity, interrogation Secrecy, Resourceful interrogation processing and Low working outlay (CPEL) criteria for hosting queries in the cloud. KNN R algorithm is used here to process the Range interrogation to the kNN interrogation. The random space perturbation (RASP) data perturbation method to provide secure and resourceful range interrogation and kNN interrogation services for protected data in the cloud. The RASP data perturbation method combines order preserving encryption, dimensionality expansion, random noise injection, and random projection, to provide strong resilience to attacks on the perturbed data and queries. It also preserves multidimensional ranges, which allows prevailing indexing techniques to be applied to speedup range interrogation processing. The kNN R algorithm is designed to work with the RASP range interrogation algorithm to process the kNN queries.Key Words: interrogation services in the cloud, low in house processing, RASP perturbation, Range interrogation, KNN interrogation

The utility of elaborative interrogation for university students studying expository text in preparation for matching and multiple-choice tests.

The purpose of the present research was to investigate the efficacy of elaborative interrogation for the types of tasks university students confront. The utility of the technique was evaluated for university students studying a university-level text on Canadian physiography in preparation for matching and multiple-choice (MC) tests. Three types of processing were contrasted (elaborative interrogation versus imagery versus self-study). Text information was presented in one of two formats (individually-presented facts or text) (i.e., a 3 x 2 factorial design). Hypotheses concerning the pattern of findings were based on consideration of both the processing used by the students and the processing prompted by the passage. It was expected that for both format conditions, elaborative-interrogation and imagery students would outperform the corresponding self-study controls on the matching and factual MC questions. Overall poor performance on the higher-level MC questions was expected for all groups regardless of format. Contrary to expectation, matching performance did not differ for the processing groups for either format condition. A secondary analysis was conducted including only those elaborative-interrogation and imagery students who ranked in the top half of their respective groups in generation of adequate responses. All self-study students were retained. For this supplementary analysis, the pattern of results changed. Students using elaborative interrogation achieved significantly higher matching scores than their corresponding self-study controls, for both format conditions. For this secondary analysis, it was only for the text-format condition that the imagery students outperformed their self-study controls. Think-aloud data indicated that students in the self-study groups were primarily relying on rote-learning techniques. There was no facilitation of factual MC performance for the elaborative-interrogation or imagery groups relative to the self study controls. For the higher-level MC questions only the elaborative-interrogation text-format students significantly outperformed their self-study controls (for primary and secondary analysis). Findings were discussed in terms of the types of generative processing prompted by elaborative-interrogation and imagery in contrast to the processing used by the self-study students.Dept. of Psychology. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis1994 .M3775. Source: Dissertation Abstracts International, Volume: 56-11, Section: B, page: 6419. Adviser: A. Kobasigawa. Thesis (Ph.D.)--University of Windsor (Canada), 1994

Instantaneous Wavenumber Estimation for Damage Quantification in Layered Plate Structures

This paper illustrates the application of instantaneous and local wavenumber damage quantification techniques for high frequency guided wave interrogation. The proposed methodologies can be considered as first steps towards a hybrid structural health monitoring/ nondestructive evaluation (SHM/NDE) approach for damage assessment in composites. The challenges and opportunities related to the considered type of interrogation and signal processing are explored through the analysis of numerical data obtained via EFIT simulations of damage in CRFP plates. Realistic damage configurations are modeled from x-ray CT scan data of plates subjected to actual impacts, in order to accurately predict wave-damage interactions in terms of scattering and mode conversions. Simulation data is utilized to enhance the information provided by instantaneous and local wavenumbers and mitigate the complexity related to the multi-modal content of the plate response. Signal processing strategies considered for this purpose include modal decoupling through filtering in the frequency/wavenumber domain, the combination of displacement components, and the exploitation of polarization information for the various modes as evaluated through the dispersion analysis of the considered laminate lay-up sequence. The results presented assess the effectiveness of the proposed wavefield processing techniques as a hybrid SHM/NDE technique for damage detection and quantification in composite, plate-like structures

Chemometric Strategies for Sensitive Annotation and Validation of Anatomical Regions of Interest in Complex Imaging Mass Spectrometry Data

Imaging mass spectrometry (IMS) is a promising new chemical imaging modality that generates a large body of complex imaging data, which in turn can be approached using multivariate analysis approaches for image analysis and segmentation. Processing IMS raw data is critically important for proper data interpretation and has significant effects on the outcome of data analysis, in particular statistical modeling. Commonly, data processing methods are chosen based on rational motivations rather than comparative metrics, though no quantitative measures to assess and compare processing options have been suggested. We here present a data processing and analysis pipeline for IMS data interrogation, processing and ROI annotation, segmentation, and validation. This workflow includes (1) objective evaluation of processing methods for IMS datasets based on multivariate analysis using PCA. This was then followed by (2) ROI annotation and classification through region-based active contours (AC) segmentation based on the PCA component scores matrix. This provided class information for subsequent (3) OPLS-DA modeling to evaluate IMS data processing based on the quality metrics of their respective multivariate models and for robust quantification of ROI-specific signal localization. This workflow provides an unbiased strategy for sensitive annotation of anatomical regions of interest combined with quantitative comparison of processing procedures for multivariate analysis allowing robust ROI annotation and quantification of the associated molecular histology

Measurement potential of laser speckle velocimetry

Laser speckle velocimetry, the measurement of fluid velocity by measuring the translation of speckle pattern or individual particles that are moving with the fluid, is described. The measurement is accomplished by illuminating the fluid with consecutive pulses of Laser Light and recording the images of the particles or the speckles on a double exposed photographic plate. The plate contains flow information throughout the image plane so that a single double exposure may provide data at hundreds or thousands of points in the illuminated region of the fluid. Conventional interrogation of the specklegram involves illuminating the plate to form Young's fringes, whose spacing is inversely proportional to the speckle separation. Subsequently the fringes are digitized and analyzed in a computer to determine their frequency and orientation, yielding the velocity magnitude and orientation. The Young's fringe technique is equivalent to performing a 2-D spatial correlation of the double exposed specklegram intensity pattern, and this observation suggests that correlation should be considered as an alternative processing method. The principle of the correlation technique is examined

Syntax-directed documentation for PL360

PL360 is a phrase-structured programming language which provides the facilities of a symbolic machine language for the IBM 360 computers. An automatic process, syntax-directed documentation, is described which acquires programming documentation through the syntactical analysis of a program, followed by the interrogation of the originating programmer. This documentation can be dispensed through reports of file query replies when other programmers later need to know the program structure and its details. A key principle of the programming documentation process is that it is managed solely on the basis of the syntax of programs

Accelerated High-Resolution Photoacoustic Tomography via Compressed Sensing

Current 3D photoacoustic tomography (PAT) systems offer either high image quality or high frame rates but are not able to deliver high spatial and temporal resolution simultaneously, which limits their ability to image dynamic processes in living tissue. A particular example is the planar Fabry-Perot (FP) scanner, which yields high-resolution images but takes several minutes to sequentially map the photoacoustic field on the sensor plane, point-by-point. However, as the spatio-temporal complexity of many absorbing tissue structures is rather low, the data recorded in such a conventional, regularly sampled fashion is often highly redundant. We demonstrate that combining variational image reconstruction methods using spatial sparsity constraints with the development of novel PAT acquisition systems capable of sub-sampling the acoustic wave field can dramatically increase the acquisition speed while maintaining a good spatial resolution: First, we describe and model two general spatial sub-sampling schemes. Then, we discuss how to implement them using the FP scanner and demonstrate the potential of these novel compressed sensing PAT devices through simulated data from a realistic numerical phantom and through measured data from a dynamic experimental phantom as well as from in-vivo experiments. Our results show that images with good spatial resolution and contrast can be obtained from highly sub-sampled PAT data if variational image reconstruction methods that describe the tissues structures with suitable sparsity-constraints are used. In particular, we examine the use of total variation regularization enhanced by Bregman iterations. These novel reconstruction strategies offer new opportunities to dramatically increase the acquisition speed of PAT scanners that employ point-by-point sequential scanning as well as reducing the channel count of parallelized schemes that use detector arrays.Comment: submitted to "Physics in Medicine and Biology

Wearable flexible lightweight modular RFID tag with integrated energy harvester

A novel wearable radio frequency identification (RFID) tag with sensing, processing, and decision-taking capability is presented for operation in the 2.45-GHz RFID superhigh frequency (SHF) band. The tag is powered by an integrated light harvester, with a flexible battery serving as an energy buffer. The proposed active tag features excellent wearability, very high read range, enhanced functionality, flexible interfacing with diverse low-power sensors, and extended system autonomy through an innovative holistic microwave system design paradigm that takes antenna design into consideration from the very early stages. Specifically, a dedicated textile shorted circular patch antenna with monopolar radiation pattern is designed and optimized for highly efficient and stable operation within the frequency band of operation. In this process, the textile antenna's functionality is augmented by reusing its surface as an integration platform for light-energy-harvesting, sensing, processing, and transceiver hardware, without sacrificing antenna performance or the wearer's comfort. The RFID tag is validated by measuring its stand-alone and on-body characteristics in free-space conditions. Moreover, measurements in a real-world scenario demonstrate an indoor read range up to 23 m in nonline-of-sight indoor propagation conditions, enabling interrogation by a reader situated in another room. In addition, the RFID platform only consumes 168.3 mu W, when sensing and processing are performed every 60 s