Xem 1-20 trên 240 kết quả Detection data
  • • Network errors are in the form of corrupted data or lost data. • Network errors occur naturally on all networks due to electrical noise and distortion and must be detected and corrected by either hardware or software. • Bit Error Rates (BERs) are calculated as the number of bits in error divided by the number of bits transmitted. A BER of 1 in 100,000 might be shown as 1:105 or simply as a BER of 10-5 . • Errors often occur in bursts where many bits in a sequence will be in...

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  • Data fusion is a research area that is growing rapidly due to the fact that it provides means for combining pieces of information coming from different sources/sensors, resulting in ameliorated overall system performance (improved decision making, increased detection capabilities, diminished number of false alarms, improved reliability in various situations at hand) with respect to separate sensors/sources.

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  • Today, the data acquisition technology has found its way into virtually every segment of electronics. A digital signal processing (DSP) system accepts analog signals as input, converts those analog signals to numbers, performs computations using the numbers and eventually converts the results of the computations back into analog signals. Once converted to numbers, signals are unconditionally stable. Error detection and correction methods can be applied to store,transmit and reproduce numbers with no corruption. Signals stored digitally are really just large arrays of numbers.

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  • Networks must be able to transfer data from one device to another with complete accuracy. Data can be corrupted during transmission. For reliable communication, errors must be detected and corrected.Single bit errors are the least likely type of errors in serial data transmission because the noise must have a very short duration which is very rare. However this kind of errors can happen in parallel transmission.

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  • (BQ) Data Communications and Computer Networks: A Business User's Approach will give you the thorough understanding you need of basic features, operations, and limitations of ifferent types of computer networks. This book offers full coverage of wireless technologies, industry convergence, compression techniques, network security, LAN technologies, VoIP, and error detection and correction.

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  • What are anomalies/outliers? The set of data points that are considerably different than the remainder of the data Variants of Anomaly/Outlier Detection Problems Given a database D, find all the data points x  D with anomaly scores greater than some threshold t Given a database D, find all the data points x  D having the top-n largest anomaly scores f(x) Given a database D, containing mostly normal (but unlabeled) data points, and a test point x, compute the anomaly score of x with respect to D Applications: Credit card fraud detection, telecommunication fraud detection, netwo...

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  • Today, the data acquisition technology has found its way into virtually every segment of electronics. A digital signal processing (DSP) system accepts analog signals as input, converts those analog signals to numbers, performs computations using the numbers and eventually converts the results of the computations back into analog signals. Once converted to numbers, signals are unconditionally stable. Error detection and correction methods can be applied to store, transmit and reproduce numbers with no corruption....

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  • Data can be corrupted during transmission. For reliable communication, error must be detected and corrected are implemented either at the data link layer or the transport layer of the OSI model.Error detection uses the concept of redundancy, which means adding extra bits for detecting errors at the destination

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  • After completing this lesson, you should be able to do the following: Define levels of locking Identify causes of contention Prevent locking problems Use Oracle utilities to detect lock contention Resolve contention in an emergency Resolve deadlock conditions

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  • We address the issue of on-line detection of communication problems in spoken dialogue systems. The usefulness is investigated of the sequence of system question types and the word graphs corresponding to the respective user utterances. By applying both ruleinduction and memory-based learning techniques to data obtained with a Dutch train time-table information system, the current paper demonstrates that the aforementioned features indeed lead to a method for problem detection that performs significantly above baseline.

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  • Research on the discovery of terms from corpora has focused on word sequences whose recurrent occurrence in a corpus is indicative of their terminological status, and has not addressed the issue of discovering terms when data is sparse. This becomes apparent in the case of noun compounding, which is extremely productive: more than half of the candidate compounds extracted from a corpus are attested only once. We show how evidence about established (i.e.

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  • In this paper we deal with Named Entity Recognition (NER) on transcriptions of French broadcast data. Two aspects make the task more difficult with respect to previous NER tasks: i) named entities annotated used in this work have a tree structure, thus the task cannot be tackled as a sequence labelling task; ii) the data used are more noisy than data used for previous NER tasks. We approach the task in two steps, involving Conditional Random Fields and Probabilistic Context-Free Grammars, integrated in a single parsing algorithm.

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  • Wavelet analysis is one of the most effective solutions to overcome the drawbacks of Fourier analysis, and since the seminal work of Ingrid Daubechies was published in the late 1980s, it has been applied very successfully in various branches of science and engineering. The fractional wavelet techniques started to play an important role in data treatment corresponding to complex systems during the last decade.

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  • Data transmission can contain errors. – Single-bit. – Burst errors of length n. (n: distance between the first and last errors in data block) • How to detect errors. – If only data is transmitted, errors cannot be detected. Send more information with data that satisfies a special relationship. -Add redundancy

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  • Unrehearsed spoken language often contains disfluencies. In order to correctly interpret a spoken utterance, any such disfluencies must be identified and removed or otherwise dealt with. Operating on transcripts of speech which contain disfluencies, we study the effect of language model and loss function on the performance of a linear reranker that rescores the 25-best output of a noisychannel model.

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  • We have analyzed 607 sentences of spontaneous human-computer speech data containing repairs, drawn from a total corpus of 10,718 sentences. We present here criteria and techniques for automatically detecting the presence of a repair, its location, and making the appropriate correction. The criteria involve integration of knowledge from several sources: pattern matching, syntactic and semantic analysis, and acoustics. INTRODUCTION Spontaneous spoken language often includes speech that is not intended by the speaker to be part of the content of the utterance. ...

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  • This paper describes a system designed to support event detection over Twitter. The system operates by querying the data stream with a user-specified set of keywords, filtering out non-English messages, and probabilistically geolocating each message. The user can dynamically set a probability threshold over the geolocation predictions, and also the time interval to present data for.

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  • (The cost-effectiveness analysis was based on the baseline screening results of the Morelos HPV study. 20 ) Results showed that using clinician-HPV testing alone or in combination with Pap is more cost-effective for women aged 30 to 80 years than using the Pap test alone in Mexico. The analysis took into account the cost of false negatives and false positives, colposcopy and biopsy, and treatment for CIN2/3 or cancer, as well as the cost of the screening tests.

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  • We outline different methods to detect errors in automatically-parsed dependency corpora, by comparing so-called dependency rules to their representation in the training data and flagging anomalous ones. By comparing each new rule to every relevant rule from training, we can identify parts of parse trees which are likely erroneous. Even the relatively simple methods of comparison we propose show promise for speeding up the annotation process.

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  • This paper presents an approach to detection of the semantic types of relation arguments employing the WordNet hierarchy. Using the SemEval-2007 data, we show that the method allows to generalize relation arguments with high precision for such generic relations as Origin-Entity, Content-Container, Instrument-Agency and some other.

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