934 resultados para Approximate filtering
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We experimentally confirm the optimum combination of modulator delay and filter bandwidth to maximize the dispersion tolerance of partial DPSK.
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Adaptive information filtering is a challenging research problem. It requires the adaptation of a representation of a user’s multiple interests to various changes in them. We investigate the application of an immune-inspired approach to this problem. Nootropia, is a user profiling model that has many properties in common with computational models of the immune system that have been based on Franscisco Varela’s work. In this paper we concentrate on Nootropia’s evaluation. We define an evaluation methodology that uses virtual user’s to simulate various interest changes. The results show that Nootropia exhibits the desirable adaptive behaviour.
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A high frequency sensing interrogation system by using fiber Bragg grating based microwave photonic filtering is proposed, in which the wavelength measurement sensitivity is proportional to the RF modulation frequency applied to the optical signal.
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We present the impact of frequency offsetting of strong (e.g. 35 GHz) optical filters on the performance of 42.7 Gb/s 50% RZ-DPSK systems. The performance is evaluated when offsetting the filter by substantial amounts and it is found that with an offset of almost half the bit rate there is a significant improvement in the calculated 'Q' (> 1 dB). We deployed balanced, constructive single ended and destructive single ended detection, so that we could investigate the physical origins of the penalty reduction of asymmetric filtering of 42.7 Gb/s 50% RZ-DPSK system.
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We present a novel differential phase shift keying receiver design under strong optical filtering. The receiver design is based on asymmetrical filtering at the destructive port of the Mach Zehnder Interferometer. The asymmetrical filtered receiver design can significantly increase performance by 2 to 4.7dB in calculated "Q".
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We investigate the impact of a duty cycle on a wavelength allocated transmission at 40 Gbit/s with narrow, off-centre, optical filtering. We also study how the shape of the off-centred VSB filter affects the performance of the optical system. © 2004 Elsevier Inc. All rights reserved.
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Effect of the carrier shape in the ultra high dense wavelength division multiplexing (WDM) return to zero differential phase shift keying (RZ-DPSK) transmission has been examined through numerical optimization of the pulse form, duty cycle and narrow multiplex/de-multiplex (MUX/DEMUX) filtering parameters. © 2007 Springer Science+Business Media, LLC.
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We propose a scheme for 211 optical regeneration based on self-phase modulation in fiber and quasi-continuous filtering. Numerical simulations demonstrate the possibility of increasing the transmission reach from 3500 to more than 6000 km at 10 Gb/s using 100-km spans. Spectral broadening is shown to be small using this technique, indicating its suitability for wavelength-division-multiplexing regeneration.
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Impact of duty cycle on the optimisation of ultra-narrow VSB filtering in wavelength allocated CS-RZ Nx40Gbit/s DWDM transmission is investigated. A feasibility has been confirmed of over 600 km with 0.64 bit/s/Hz spectral efficiency.
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Microwave photonic filtering is realised using a superstructured fibre Bragg grating. The time delay of the optical taps is precisely controlled by the grating characteristics and fibre dispersion. A bandpass response with a rejection level of >45 dB is achieved.
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Traditional content-based filtering methods usually utilize text extraction and classification techniques for building user profiles as well as for representations of contents, i.e. item profiles. These methods have some disadvantages e.g. mismatch between user profile terms and item profile terms, leading to low performance. Some of the disadvantages can be overcome by incorporating a common ontology which enables representing both the users' and the items' profiles with concepts taken from the same vocabulary. We propose a new content-based method for filtering and ranking the relevancy of items for users, which utilizes a hierarchical ontology. The method measures the similarity of the user's profile to the items' profiles, considering the existing of mutual concepts in the two profiles, as well as the existence of "related" concepts, according to their position in the ontology. The proposed filtering algorithm computes the similarity between the users' profiles and the items' profiles, and rank-orders the relevant items according to their relevancy to each user. The method is being implemented in ePaper, a personalized electronic newspaper project, utilizing a hierarchical ontology designed specifically for classification of News items. It can, however, be utilized in other domains and extended to other ontologies.
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This thesis objective is to discover “How are informal decisions reached by screeners when filtering out undesirable job applications?” Grounded theory techniques were employed in the field to observe and analyse informal decisions at the source by screeners in three distinct empirical studies. Whilst grounded theory provided the method for case and cross-case analysis, literature from academic and non-academic sources was evaluated and integrated to strengthen this research and create a foundation for understanding informal decisions. As informal decisions in early hiring processes have been under researched, this thesis contributes to current knowledge in several ways. First, it locates the Cycle of Employment which enhances Robertson and Smith’s (1993) Selection Paradigm through the integration of stages that individuals occupy whilst seeking employment. Secondly, a general depiction of the Workflow of General Hiring Processes provides a template for practitioners to map and further develop their organisational processes. Finally, it highlights the emergence of the Locality Effect, which is a geographically driven heuristic and bias that can significantly impact recruitment and informal decisions. Although screeners make informal decisions using multiple variables, informal decisions are made in stages as evidence in the Cycle of Employment. Moreover, informal decisions can be erroneous as a result of a majority and minority influence, the weighting of information, the injection of inappropriate information and criteria, and the influence of an assessor. This thesis considers these faults and develops a basic framework of understanding informal decisions to which future research can be launched.
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Recommender systems are now widely used in e-commerce applications to assist customers to find relevant products from the many that are frequently available. Collaborative filtering (CF) is a key component of many of these systems, in which recommendations are made to users based on the opinions of similar users in a system. This paper presents a model-based approach to CF by using supervised ARTMAP neural networks (NN). This approach deploys formation of reference vectors, which makes a CF recommendation system able to classify user profile patterns into classes of similar profiles. Empirical results reported show that the proposed approach performs better than similar CF systems based on unsupervised ART2 NN or neighbourhood-based algorithm.
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WDM signal degradation from pump phase-modulation in a one-pump 20dB net-gain fibre optical parametric amplifier is experimentally and numerically characterised for the first time using 10x59Gb/s QPSK signals.
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Real-time systems are usually modelled with timed automata and real-time requirements relating to the state durations of the system are often specifiable using Linear Duration Invariants, which is a decidable subclass of Duration Calculus formulas. Various algorithms have been developed to check timed automata or real-time automata for linear duration invariants, but each needs complicated preprocessing and exponential calculation. To the best of our knowledge, these algorithms have not been implemented. In this paper, we present an approximate model checking technique based on a genetic algorithm to check real-time automata for linear durration invariants in reasonable times. Genetic algorithm is a good optimization method when a problem needs massive computation and it works particularly well in our case because the fitness function which is derived from the linear duration invariant is linear. ACM Computing Classification System (1998): D.2.4, C.3.