example, the use of noninvasive mechanical home ventilation (MHV) [14,28] in the care of patients with chronic respiratory failure. This kind of treatment raises ventilatory effectiveness, improves physical
detail records are generated by telephone exchanges at the end of each user session and describe the session in detail. By analyzing streams of CIMs and CDRs, telecommunication companies can develop procedures to improve the number of calls completed successfully, identify potential revenue leakage, identify the most and the least profitable services (through minutes of usage), find out how existing products are consumed by end users, and discover services to be either expanded or limited. They can also detect logical network problems caused by routing errors,
Abstract.Overload management has become very important in public safety systems that analyse high performance multimedia data streams, especially in the case of detection of terrorist and criminal dangers. Efficient overload management improves the accuracy of automatic identification of persons suspected of terrorist or criminal activity without requiring interaction with them. We argue that in order to improve the quality of multimedia data stream processing in the public safety arena, the innovative concept of a Multimedia Data Stream Management System (MMDSMS) using load-shedding techniques should be introduced into the infrastructure to monitor and optimize the execution of multimedia data stream queries. In this paper, we present a novel content-centered load shedding framework, based on searching and matching algorithms, for analysing video tuples arriving within multimedia data streams. The framework tracks and registers all symptoms of overload, and either prevents overload before it occurs, or minimizes its effects. We have extended our Continuous Query Language (CQL) syntax to enable this load shedding technique. The effectiveness of the framework has been verified using both artificial and real data video streams collected from monitoring devices.
Over the last several years, processing of high performance data streams has become very important in various domains. A new type of data processing is needed for applications where input data streams are modeled as multimedia data streams, such as audio and video feeds. For example, in the public safety sector, monitoring and automatic identification of particular individuals suspected of terrorist or criminal activity requires the processing of complex audio and video streams, which is beyond the capabilities of a typical data stream management system (DSMS). The concept of a multimedia data stream management system (MMDSMS) has recently been introduced in order to effectively process continuous queries over dynamic multimedia data streams. In this paper, we address MMDSMS functionalities related to speaker recognition problems in the area of detecting individuals who may pose security threats. We focus on audio feed processing using our novel similarity‐based join and on parameterization of the multimedia signal for the process of recognition. We propose a set of signal parameters which a clearly discriminate among individual voices by describing the signal using a homomorphic processing method. Our research was primarily focused on assessing the applicability of cepstral analysis in speech recognition systems, based on a set of acquired digitized voice samples. We developed a research prototype to assess the proposed concepts, and verified the effectiveness of our framework in a lab environment. © 2013 Alcatel‐Lucent.
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