Dynamic stabilization versus fusion for treatment of degenerative spine conditions.

Study design Comparative effectiveness review.Study rationale Spinal fusion is believed to accelerate the degeneration of the vertebral segment above or below the fusion site, a condition called adjacent segment disease (ASD). The premise of dynamic stabilization is that motion preservation allows for less loading on the discs and facet joints at the adjacent, non-fused segments. In theory, this should decrease the rate of ASD. However, clinical evidence of this theoretical decrease in ASD is still lacking. We performed a systematic review to evaluate the evidence in the literature comparing dynamic stabilization with fusion.Clinical question In patients 18 years or older with degenerative disease of the cervical or lumbar spine, does dynamic stabilization lead to better outcomes and fewer complications, including ASD, than fusion in the short-term and the long-term?Methods A systematic search and review of the literature was undertaken to identify studies published through March 7, 2011. PubMed, Cochrane, and National Guideline Clearinghouse Databases as well as bibliographies of key articles were searched. Two individuals independently reviewed articles based on inclusion and exclusion criteria which were set a priori. Each article was evaluated using a predefined quality-rating scheme.Results No significant differences were identified between fusion and dynamic stabilization with regard to VAS, ODI, complications, and reoperations. There are no long-term data available to show whether dynamic stabilization decreases the rate of ASD.Conclusions There are no clinical data from comparative studies supporting the use of dynamic stabilization devices over standard fusion techniques

Dynamic Fusion of Web Data

Mashups exemplify a workflow-like approach to dynamically integrate data and services from multiple web sources. Such integration workflows can build on existing services for web search, entity search, database querying, and information extraction and thus complement other data integration approaches. A key challenge is the efficient execution of integration workflows and their query and matching steps at runtime. We relate mashup data integration with other approaches, list major challenges, and outline features of a first prototype design

Dynamic displacement estimation using data fusion

The paper describes a Kalman filtering technique for dynamic displacement estimation using accelerometer and laser sensor measurements. Data fusion of measurements from multiple sensors can give the more accurate results because of different advantages of sensors. Since the acceleration and displacement have different sampling rates, the multi-rate Kalman filter is applied. The filter is expanded with the fixed interval smoother to improve reconstruction accuracy of displacements. A modelled signal consisting of two sinus functions and Gaussian distributed noise is used to validate developed state-space model

APPLICATION OF DATA FUSION TO FLUID DYNAMIC DATA

In recent years, there have been improvements in the methods of obtaining fluid dynamic data, which has led to the generation of vast amounts of data. Extracting the useful information from large data sets can be a challenging task when investigating data from a single source. However, most experiments use data from multiple sources, such as particle image velocimetry (PIV), pressure sensors, acoustic measurements, and computational fluid dynamics (CFD), to name a few. Knowing the strengths and weaknesses of each measurement technique, one can fuse the data together to improve the understanding of the problem being studied. Concepts from the data fusion community are used to combine fluid dynamic data from the different data sources. The data is fused using techniques commonly used by the fluid dynamics community, such as proper orthogonal decomposition (POD), linear stochastic estimation (LSE), and wavelet analysis. This process can generate large quantities of data and a method of handling all of the data and the techniques in an efficient manner is required. To accomplish this, a framework was developed that is capable of tracking, storing, and, manipulating data. With the framework and techniques, data fusion can be applied. Data fusion is first applied to a synthetic data set to determine the best methods of fusing data. Data fusion was then applied to airfoil data that was obtained from PIV, CFD, and pressure to test the ideas from the synthetic data. With the knowledge gained from applying fusion to the synthetic data and airfoil data, these techniques are ultimately applied to data for a Mach 0.6 jet obtained from large-window PIV (LWPIV), time-resolved PIV (TRPIV), and pressure. Through the fusion of the different data sets, occlusion in the jet data were estimated within 6% error using a new POD based technique called Fused POD. In addition, a technique called Dynamic Gappy POD was created to fuse TRPIV and LWPIV to generate a large-window time-resolved data set. This technique had less error than other standard techniques for accomplishing this such as pressure-based stochastic estimation. The work presented in this document lays the groundwork for future applications of data fusion to fluid dynamic data. With the success of the work in this document, one can begin to apply the ideas from data fusion to other types of fluid dynamic problems, such as bluff bodies, unsteady aerodynamics, and other. These ideas could be used to help improve understanding in the field of fluid dynamics due to the current limitations of obtaining data and the need to better understand flow phenomena

The moving crowd: collecting and processing of crowd behaviour data

The MOVE project focuses on the collection and analyses of crowd behavior data. The two main goals of the project are first, the collection of data through mobile phones. The second goal is to develop new technologies to process and mine the collected data for crowd behaviour analysis. The technology will allow to make advanced interpretations of historic and dynamic mobile crowd data coming from GSM/GPS and from different classes of users (vehicle, pedestrian, indoor/outdoor). Fusion will be made between data coming from different sources (smartphone, navigation device) and external map data. The interpretation will allow the mining of advanced features/geometry from the crowd data as well as interprete the dynamic behaviour of the population

Compressed Sensing based Dynamic PSD Map Construction in Cognitive Radio Networks

In the context of spectrum sensing in cognitive radio networks, collaborative spectrum sensing has been proposed as a way to overcome multipath and shadowing, and hence increasing the reliability of the sensing. Due to the high amount of information to be transmitted, a dynamic compressive sensing approach is proposed to map the PSD estimate to a sparse domain which is then transmitted to the fusion center. In this regard, CRs send a compressed version of their estimated PSD to the fusion center, whose job is to reconstruct the PSD estimates of the CRs, fuse them, and make a global decision on the availability of the spectrum in space and frequency domains at a given time. The proposed compressive sensing based method considers the dynamic nature of the PSD map, and uses this dynamicity in order to decrease the amount of data needed to be transmitted between CR sensors’ and the fusion center. By using the proposed method, an acceptable PSD map for cognitive radio purposes can be achieved by only 20 % of full data transmission between sensors and master node. Also, simulation results show the robustness of the proposed method against the channel variations, diverse compression ratios and processing times in comparison with static methods