A Space-Time with Weight Algorithm for RFID Data Interpolation

In recent years, RFID technology is gradually applied to the field of real-time monitoring, positioning, tracking, etc. As the unreliability of RFID data, the development and the application of RFID technology are limited. In certain practical applications, false negative reading can be solved by multi-reading periodically. However, false positive reading (ie, noise) provided the wrong information to high-level RFID applications. For example, readers are installed on the shopping shelf, they send false anti-theft alarms due to false positive reading. False positive reading has seriously affected the promotion and application of RFID technology. Therefore, it is necessary to design an efficient and feasible method to eliminate false positive reading. The paper proposes an efficient cleaning mechanism to solve the false positive problem in RFID data streams. The result of experiment shows that the method is effective and efficient.

To compensate for the inherent unreliability of RFID data streams, most RFID middleware systems employ a filter, a sliding-window aggregate that interpolates for lost readings. In this paper, we propose SMURF, the first declarative, adaptive smoothing filter for RFID data cleaning. SMURF models the unreliability of RFID readings by viewing RFID streams as a statistical sample of tags in the physical world, and exploits techniques grounded in sampling theory to drive its cleaning processes. Through the use of tools such as binomial sampling and π-estimators, SMURF continuously adapts the smoothing window size in a principled manner to provide accurate RFID data to applications.

RFID complex event processing technology, which is proved to be a powerful tool in describing relationship between different events, such as timing, causality, and membership in RFID data streams or events, is a key point of RFID researching in these years. For the trend of great network application of RFID, to manage the tremendous and potentially infinite volumes of raw data generated by RFID systems poses new challenges. This paper presents an RFID complex event processing model in the context of distributed RFID network according to the characteristics of RFID data. Besides, some key methods to realize the modules and the application in real scenario are also discussed.

Due to the high sensitivity of RFID tag-reader performance to the operating environment, RFID data streams generated are unreliable and contain a significant amount of missed readings. RFID data cleaning is therefore an essential task for successful deployment of RFID systems. One of the common techniques used by RFID middleware systems to compensate for the missed readings is the use of sliding-window filters. However, setting an optimum window size is non-trivial task especially in mobile tag environments. In this paper we present a new adaptive data cleaning scheme called WSTD based on some of the concepts proposed in SMURF but with an improved transition detection mechanism. WSTD uses the comparison of the two window subrange observations or estimated tag counts to detect when transitions occur within a window. In the mobile environment, our experimental results show that the WSTD scheme performs better than SMURF producing an improvement of about 30% less overall errors than that produced by SMURF.

The use of mixed-oxide (MOX) fuel in commercial nuclear power reactors operated in Europe has expanded rapidly over the past decade. The predicted characteristics of MOX fuel such as the nuclide inventories, thermal power from decay heat, and radiation sources are required for design and safety evaluations, and can provide valuable information for non-destructive safeguards verification activities. This report describes the development of computational methods and cross-section libraries suitable for the analysis of irradiated MOX fuel with the widely-used and recognized ORIGEN-ARP isotope generation and depletion code of the SCALE (Standardized Computer Analyses for Licensing Evaluation) code system. The MOX libraries are designed to be used with the Automatic Rapid Processing (ARP) module of SCALE that interpolates appropriate values of the cross sections from a database of parameterized cross-section libraries to create a problem-dependent library for the burnup analysis. The methods in ORIGEN-ARP, originally designed for uranium-based fuels only, have been significantly upgraded to handle the larger number of interpolation parameters associated with MOX fuels. The new methods have been incorporated in a new version of the ARP code that can generate libraries for low-enriched uranium (LEU) and MOX fuel types. The MOX data libraries and interpolation algorithms in ORIGEN-ARP have been verified using a database of declared isotopic concentrations for 1042 European MOX fuel assemblies. The methods and data are validated using a numerical MOX fuel benchmark established by the Organization for Economic Cooperation and Development (OECD) Working Group on burnup credit and nuclide assay measurements for irradiated MOX fuel performed as part of the Belgonucleaire ARIANE International Program.

Background: A prospective study to estimate the serum lactate levels and as a prognostic marker in patients with sepsis.Methods: 170 patients admitted with sepsis in B.L.D.E. (Deemed to be University) Shri. B. M. Patil Medical College, Hospital and Research Centre, Vijayapur from October 2014 to June 2016.Results: In this study the mean serum lactate value of first sample in survivors (146 patients) is 3.8±1.2 and non-survivors (24 patients) is 6.2±1.9 with p value<0.001 which is significant. The serum lactate value of the second sample in survivors (146) is 2.7±1.0 and in non survivors (24) is 6.3±1.8 with p value<0.001 which is significant. The mean value of serum lactate 1st sample collected at the time of admission is 4.1±1.6 and the mean value of serum lactate second sample collected at 24 - 48 hours after admission is 3.1±1.6.Conclusions: Lactate level more than 4 mmol/l, patients are at highest risk of mortality and an aggressive resuscitation strategy shall be warranted. Hence serum lactate is considered as an independent and significant prognostic marker in patients with sepsis and evaluates the treatment outcome.

The quality of products depends on a stability of production process. In practice to identify reasons of the process degradation, the Shewhart control chart is typically used. At the construction of control X-charts, it is supposed that the dispersion of sample means in subgroups of data measured in process is caused by the influence of random factors and the limited sample size. In such cases it is an improbable event to obtain the output sample values, which are outside the interval ±3σ. Its appearance indicates the presence of systematic influence and it is the need to adjust the controlled parameters of technological process. Based on practical experience in the ISO 7870-2: 2013 standard it is recommended to pay attention to “… any unusual structure of data points, which may indicate about a manifestation of special (non-random) reasons”. In the numerical example presented in this work the analysis of a structure of points on the control chart showed the presence of non-random values, although if the sample mean values were within interval ±3σ. The indicator of existence of non-randomness was the probability that the minimum number of consecutive selective averages, which got to a certain area did not exceed 0.003. As a result of executed analysis the criteria are established and the algorithm is developed. It helped to identify the dysfunction of technological process at an early stage.

Interpolation and prediction of spatiotemporal data are integral components of many real-world applications. Thus, approaches to interpolating and predicting spatiotemporal data have been extensively investigated. Currently, the grey dynamic model has been used to enhance the performance of interpolating and predicting spatiotemporal data. Meanwhile, the extensible markup language (XML) has unique characteristics of information representation and exchange. In this chapter, the authors first couple the grey dynamic model with the spatiotemporal XML model. Based on a definition of the position part of the spatiotemporal XML model, they extract the corresponding position information of each time interval and propose an algorithm for constructing an AVL tree to store them. Then, the authors present the architecture of an interpolating and predicting process and investigate change operations in positions. On this basis, the chapter presents an algorithm for interpolation and prediction of spatiotemporal data based on XML integrated with the grey dynamic model.

As point-of-care testing becomes a popular form of clinical testing, the demand for compact devices and test tools has been increasing. A prototype "Palm LC," a palmtop-sized white blood cell (WBC) counter that has been developed for point-of-care testing of clinical hematology was evaluated. The Palm LC was downsized to palmtop size using Micro Electro Mechanical Systems. Its microelectrode cartridge and reagent vial are disposable to prevent infection. As a result of reproducibility tests, the coefficient of variation was 4.6% in healthy normal sample (mean value, 4.01 × 1000/μL). The coefficient of variations for samples with abnormally low and high WBC (mean values, 0.52 and 22.24 × 1000/μL) were 40.4% and 4.4%, respectively. Linearity test results showed a 13.7% linearity error in the range of 0.5 to 118.15 × 1000/μL and 8.5% in the range of 2.2 to 27.3/μL. The regression equation for the assayed value against expected value was log(y) = 0.937 × log(x) + 0.053, and the SE regression was 0.036. In the low WBC range, results were slightly higher than expected, and in the high WBC range, they were slightly lower. Correlation with the XE-2100 (Sysmex, Kobe, Japan) was studied in 2 hospitals, and a regression analysis was carried out. The result from Tenri Hospital gave a regression equation of log(y) = 0.913 × log(x) − 0.004 (R2 = 0.887) and an SE of 0.071. Result from Osaka City University Hospital gave a regression equation of log(y) = 1.096 × log(x) − 0.175 (R2 = 0.846), and the SE was 0.058. These results will be used in marketing model development.

The use of the bootstrap technique to estimate the reference level of137 Cs in an uneroded site is tested. The analysis is developed using 137Cs measurements made in a small experimental Sicilian basin. In the reference area the 137Cs activity is normally distributed with a known sample mean value, m equal to 94.4 mBq cm−2. The influence of137 Cs reference site sampling was determined generating samples having a fixed size, N and six different values of the sample coefficient of variation, CV, by a Monte Carlo technique. Then, for each size N, the probability distribution of the mean μ of the sequences generated by Monte Carlo technique is defined. The soil redistribution is determined, both at morphological unit and basin scale, using the proportional method of Martz & de Jong for calculating the net soil loss. The analysis showed that the spatial distribution of the net soil loss E i, and the basin value E b are independent of the sample size, N, and the coefficient of variation, CV, of the samples drawn from the reference area, if the bootstrap technique is applied for estimating the mean μ(μ) to use as reference value. The soil redistribution is also examined using as reference value the quantiles μ2.5, μ25, μ75, μ97.5 corresponding to a frequency F(μ) equal to 2.5, 25, 75 and 97.5%, respectively. In conclusion, the analysis established that a robust estimate of the reference value can be obtained even in fields where a small number of samples was drawn (high CV of the 137Cs activity of the field samples), using the bootstrap technique for generating sequences of reference values having known mean value m (the mean value of the 137Cs activity of the drawn field samples) and large sample size (N = 50).

Many modern seismic data interpolation and redatuming algorithms rely on the promotion of transform-domain sparsity for high-quality results. Amongst the large diversity of methods and different ways of realizing sparse reconstruction lies a central question that often goes unaddressed: is it better for the transform-domain sparsity to be achieved through explicit construction of sparse representations (e.g., by thresholding of small transform-domain coefficients), or by demanding that the algorithm return physical signals which produces sparse coefficients when hit with the forward transform? Recent results show that the two approaches give rise to different solutions when the transform is redundant, and that the latter approach imposes a whole new class of constraints related to where the forward transform produces zero coefficients. From this framework, a new reconstruction algorithm is proposed which may allow better reconstruction from subsampled signaled than what the sparsity assumption alone would predict. In this work we apply the new framework and algorithm to the case of seismic data interpolation under the curvelet domain, and show that it admits better reconstruction than some existing L1 sparsity-based methods derived from compressive sensing for a range of subsampling factors.

Compressive measurement and feature reconstruction method for autonomous star trackers

Traditional decision support systems (DSS) and executive information systems (EIS) gather and present information from several sources for business purposes. It is an information technology to help the knowledge worker (executive, manager, analyst) make faster and better decisions. So far, these data were stored statically and persistently in a database, typically in a data warehouse. Data warehouses collect masses of operational data, allowing analysts to extract information by issuing decision support queries on the otherwise discarded data. In a typical scenario, an organization stores a detailed record of its operations in a database, which is then analyzed to improve efficiency, detect sales opportunities, and so on. Performing complex analysis on these data is an essential component of these organizations’ businesses. Chaudhuri and Dayal (1997) present an excellent survey on decision-making and online analytical processing (OLAP) technologies for traditional database systems. ?n many applications however, it may not be possible to process queries within a database management system (DBMS). These applications involve data items that arrive online from multiple sources in a continuous, rapid and time-varying fashion (Babcock et. al., 2002). These data may or may not be stored in a database. As a result, a new class of data-intensive applications has recently attracted a lot of attention: applications in which the data is modeled not as persistent relations but rather as transient data streams. Examples include financial applications (streams of transactions or ticks), network monitoring (stream of packets), security, telecommunication data management (stream of calls or call packets), web applications (clickstreams), manufacturing, wireless sensor networks (measurements), RFID data, and others. In data streams we usually have “continuous” queries (Terry et. al., 1992; Babu & Widom, 2002) rather than “one-time.” The answer to a continuous query is produced over time, reflecting the stream data seen so far. Answers may be stored and updated as new data arrives or may be produced as data streams themselves. Continuous queries can be used for monitoring, alerting, security, personalization, etc. Data streams can be either transactional (i.e., log interactions between entities, such as credit card purchases, web clickstreams, phone calls), or measurement (i.e., monitor evolution of entity states, such as physical phenomena, road traffic, temperature, network). How to best model, express and evaluate complex queries over data streams is an open and difficult problem. This involves data modeling, rich querying capabilities to support real-time decision support and mining, and novel evaluation and optimization processing techniques. In addition, the kind of decision support over data streams is quite different from “traditional” decision-making: decisions are “tactical” rather than “strategic.” Research on data streams is currently among the most active areas in database research community. Flexible and efficient stream querying will be a crucial component of any future data management and decision support system (Abiteboul et al., 2005).

Multispectral vision and time series techniques were cooperated firstly to study early detection of rice blast. The gray value of NIR(near Infrared) image was selected as characteristic parameter for early detection of rice blast by analyzing significant differences on mean value of samples’ characteristics between inoculation and no-inoculation samples and comparing change trend of seedling multispectral images information. The time series of NIR image gray value was set up, and then the similarity of different time series was calculated by pattern distance. The early classification detection of rice blast was accomplished by K-nearest neighbor arithmetic. The classification accuracy of infected seedlings samples was 94.1%. The results show that pattern distance is suitable for describing the change trend of time series. A key technique of rice blast early detection researched in this study provides theoretical foundation and technical support for variable sprayer and disease forecast, and advances a new idea in plant disease detection.

Statistical estimation of mean values of fish stock indicators from trawl surveys