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Barium esophagography is an invaluable radiologic technique for detecting a host of morphologic abnormalities in the esophagus. Double-contrast barium studies are particularly well suited for diagnosing reflux esophagitis and its complications, including peptic strictures and Barrett’s esophagus. Double-contrast esophagography is also useful for detecting infectious esophagitis and for differentiating the underlying causes, including Candida albicans, the herpes simplex virus, cytomegalovirus, and human immunodeficiency virus. Barium studies can also facilitate the diagnosis of drug-induced esophagitis, eosinophilic esophagitis, and other less common forms of esophagitis. In patients with dysphagia, barium esophagography is a sensitive test for detecting the two common malignant tumors of the esophagus—squamous cell carcinoma and adenocarcinoma. Finally, esophagography can be used to diagnose other morphologic abnormalities in the esophagus, including webs, rings, diverticula, varices, foreign body impactions, fistulas, and perforation. All of these conditions are discussed in the following chapter.

A wireless sensor network is an information technology infrastructure capable of processing theoretically unlimited sequences of data commonly called as data streams. dA heterogeneous sensor network consists of sensing nodes creating the streams of atomic data items and clustering nodes joining several data streams into one stream of complex data items and performing the preliminary data processing. This work investigates the microprogramming implementation of join operation at the clustering nodes. We define a data stream join operation and we prove that it can be computed in an incremental way. Then, we propose a number of optimisation techniques for its processing. The micro implementation of join operation on 8-bit microprocessor controller with memory and interfacing ports is derived from the incremental processing algorithm. We describe architecture of firmware implementation of join operation and we propose a network implementation of a number of join modules. The experimental results include testing of join operation on two and many data streams.

Implementation of the data stream processing applications requires a method for formal specification of the computations at a dataflow level. The logical models of stream processing hide the lower level implementation details. To solve this problem, we propose a new model of data stream processing based on the concepts of relational data stream, extensible system of elementary operations on relational streams, and data stream processing network integrating the dataflows and elementary operations. Next, we present the transformations of grouped data stream processing applications into data stream processing networks. The transformations proposed in the paper integrate the networks and optimize the implementations through elimination of the redundant elementary operations and dataflows. Finally, the paper introduces a timestamp based synchronization of data flows in our model and discusses its correctness.

This paper investigates the use of an off the shelf database management system for the processing of continuous queries. It describes the utilization of existing features in a DBMS and their adoption to synchronize processing of unlimited data streams and discusses the performance of the proposed solution. It provides a solution for the inherent limitations in the architecture of a DBMS to be able to react to continual inputs from several sources. The outcome of this paper shows that it is possible to process continuous data streams from several sources and in some ways a better performance is also achievable.