2,092 research outputs found

    A radio channel emulator for WCDMA, based on the hidden Markov model (HMM)

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    One of the main development and research subjects within the telecommunications area activity is the 3G mobile systems standardisation. The radio access is, of course, the main trouble in mobile systems, so it is important to investigate its implication. This paper describes a radio channel emulator for the UTRA-FDD made, based on the hidden Markov model (HMM). Since a statistical system behaviour is needed to train the HMM, off-line simulations have been made. The results between simulated and emulated statistics are presented. The use of emulation models implies a loss of accuracy with respect to simulation models, but is adequate to operate in real time. Certainly, the main advantage of using HMM in the emulator is the huge reduction in time, resources and effort with regard to a real simulation of the system. The emulator will allow in future works, for fast testing and comparison of several higher layer protocols and error control schemes.Peer ReviewedPostprint (published version

    A generic radio channel emulator to evaluate higher layer protocols in a CDMA system

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    Currently, we are involved in the standardisation process to specify the next mobile system generation. A wideband code division multiple access (WCDMA) system is considered in most of the region versions. It would be very useful to count on a radio channel emulator which allows one to evaluate higher layers protocols within this context. This paper presents a radio channel emulator developed for a code division multiple access (CDMA) based system. Its versatility and low complexity have been exposed, and the validation process to check the model accuracy has also been shown for this system as an example.Peer ReviewedPostprint (published version

    On-line adaptive learning of the correlated continuous density hidden Markov models for speech recognition

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    We extend our previously proposed quasi-Bayes adaptive learning framework to cope with the correlated continuous density hidden Markov models (HMMs) with Gaussian mixture state observation densities in which all mean vectors are assumed to be correlated and have a joint prior distribution. A successive approximation algorithm is proposed to implement the correlated mean vectors' updating. As an example, by applying the method to an on-line speaker adaptation application, the algorithm is experimentally shown to be asymptotically convergent as well as being able to enhance the efficiency and the effectiveness of the Bayes learning by taking into account the correlation information between different model parameters. The technique can be used to cope with the time-varying nature of some acoustic and environmental variabilities, including mismatches caused by changing speakers, channels, transducers, environments, and so on.published_or_final_versio

    Building Combined Classifiers

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    This chapter covers different approaches that may be taken when building an ensemble method, through studying specific examples of each approach from research conducted by the authors. A method called Negative Correlation Learning illustrates a decision level combination approach with individual classifiers trained co-operatively. The Model level combination paradigm is illustrated via a tree combination method. Finally, another variant of the decision level paradigm, with individuals trained independently instead of co-operatively, is discussed as applied to churn prediction in the telecommunications industry

    MobiBits: Multimodal Mobile Biometric Database

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    This paper presents a novel database comprising representations of five different biometric characteristics, collected in a mobile, unconstrained or semi-constrained setting with three different mobile devices, including characteristics previously unavailable in existing datasets, namely hand images, thermal hand images, and thermal face images, all acquired with a mobile, off-the-shelf device. In addition to this collection of data we perform an extensive set of experiments providing insight on benchmark recognition performance that can be achieved with these data, carried out with existing commercial and academic biometric solutions. This is the first known to us mobile biometric database introducing samples of biometric traits such as thermal hand images and thermal face images. We hope that this contribution will make a valuable addition to the already existing databases and enable new experiments and studies in the field of mobile authentication. The MobiBits database is made publicly available to the research community at no cost for non-commercial purposes.Comment: Submitted for the BIOSIG2018 conference on June 18, 2018. Accepted for publication on July 20, 201
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