93,892 research outputs found

    Linear Support Vector Machines for Error Correction in Optical Data Transmission

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    Reduction of bit error rates in optical transmission systems is an important task that is difficult to achieve. As speeds increase, the difficulty in reducing bit error rates also increases. Channels have differing characteristics, which may change over time, and any error correction employed must be capable of operating at extremely high speeds. In this paper, a linear support vector machine is used to classify large-scale data sets of simulated optical transmission data in order to demonstrate their effectiveness at reducing bit error rates and their adaptability to the specifics of each channel. For the classification, LIBLINEAR is used, which is related to the popular LIBSVM classifier. It is found that is possible to reduce the error rate on a very noisy channel to about 3 bits in a thousand. This is done by a linear separator that can be built in hardware and can operate at the high speed required of an operationally useful decode

    Mitigation of Distortions in Radio-Over-Fiber Systems Using Machine Learning

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    Introducción: El constante crecimiento de usuarios conectados a internet por medio de dispositivos móviles ha conllevado a incrementar la investigación en el paradigma de las redes híbridas conocido como Radio-sobre-Fibra. Estas redes aprovechan las ventajas del ancho de banda de la fibra óptica y la movilidad de las transmisiones inalámbricas, evitando el cuello de botella que se da por la conversión óptico a eléctrico. No obstante, la dispersión cromática propia de la fibra óptica genera distorsiones en la señal de radiofrecuencia modulada ópticamente, lo cual limita su alcance.  Objetivo: Mejorar el desempeño de un sistema de radio sobre fibra en términos de la tasa de error de bit, usando demodulación no simétrica por medio del algoritmo de aprendizaje automático Máquina de Soporte Vectorial.  Metodología: Se simula un sistema de Radio-sobre-Fibra en el software especializado VPIDesignSuite. Se transmiten señales de radiofrecuencia moduladas en formatos 16 y 64-QAM con diferentes anchos de línea de láser sobre fibra óptica. Se aplica el algoritmo Máquina de Soporte Vectorial para la demodulación de la señal.  Resultados: La implementación del algoritmo de aprendizaje automático para la demodulación de la señal mejora significativamente el desempeño de la red permitiendo alcanzar los 30 km de transmisión por fibra óptica. Esto implica una reducción de la tasa de error de bit hasta en dos órdenes de magnitud en comparación con la demodulación tradicional.  Conclusiones: Se demuestra que con el uso de umbrales asimétricos usando algoritmo de Máquina de Soporte Vectorial se logran mitigar distorsiones en términos de la tasa de error de bit. Así, esta técnica se hace atractiva para futuras redes de acceso de alta capacidad.  Introduction: The ever-growing number of users connected to internet via mobile devices has driven to increase the research in the paradigm of hybrid optical networks called Radio-over-Fiber. These networks take advantages of the bandwidth given by the optical fiber and the mobility given by wireless transmissions, avoiding the bottleneck of optical-to-electrical conversion interfaces. However, the chromatic dispersion of the optical fiber generates distortions in the radiofrequency signals optically modulated, limiting the reach of transmission.  Objective: To improve the performance of a Radio-over-Fiber system in terms of bit-error-rate, using nonsymmetrical demodulation by means of the machine learning algorithm Support Vector Machine.  Methodology: A Radio-over-Fiber System is simulated in the specialized software VPIDesignSuite. The radiofrequency signals are modulated at 16 and 64-QAM formats with different laser linewidths and transmitted over optical fiber. The Support Vector Machine algorithm is applied to carry out nonsymmetrical demodulation.  Results: The implementation of the machine learning algorithm for signal demodulation significantly improves the network performance, reaching transmissions up to 30 km. It implies a reduction of the bit-error-rate up to two Introduction: The ever-growing number of users connected to internet via mobile devices has driven to increase the research in the paradigm of hybrid optical networks called Radio-over-Fiber. These networks take advantages of the bandwidth given by the optical fiber and the mobility given by wireless transmissions, avoiding the bottleneck of optical-to-electrical conversion interfaces. However, the chromatic dispersion of the optical fiber generates distortions in the radiofrequency signals optically modulated, limiting the reach of transmission.  Objective: To improve the performance of a Radio-over-Fiber system in terms of bit-error-rate, using nonsymmetrical demodulation by means of the machine learning algorithm Support Vector Machine.   Methodology: A Radio-over-Fiber System is simulated in the specialized software VPIDesignSuite. The radiofrequency signals are modulated at 16 and 64-QAM formats with different laser linewidths and transmitted over optical fiber. The Support Vector Machine algorithm is applied to carry out nonsymmetrical demodulation.  Results: The implementation of the machine learning algorithm for signal demodulation significantly improves the network performance, reaching transmissions up to 30 km. It implies a reduction of the bit-error-rate up to two orders of magnitude in comparison with conventional demodulation.  Conclusions: Mitigation of distortions in terms of bit-error-rate is demonstrated in a Radio-over-Fiber system using nonsymmetrical demodulation by using the Support Vector Machine algorithm. Thus, the proposed technique can be suitable for future high-capacity access networks. &nbsp

    A compiler extension for parallelizing arrays automatically on the cell heterogeneous processor

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    This paper describes the approaches taken to extend an array programming language compiler using a Virtual SIMD Machine (VSM) model for parallelizing array operations on Cell Broadband Engine heterogeneous machine. This development is part of ongoing work at the University of Glasgow for developing array compilers that are beneficial for applications in many areas such as graphics, multimedia, image processing and scientific computation. Our extended compiler, which is built upon the VSM interface, eases the parallelization processes by allowing automatic parallelisation without the need for any annotations or process directives. The preliminary results demonstrate significant improvement especially on data-intensive applications

    Pharmacoprint -- a combination of pharmacophore fingerprint and artificial intelligence as a tool for computer-aided drug design

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    Structural fingerprints and pharmacophore modeling are methodologies that have been used for at least two decades in various fields of cheminformatics: from similarity searching to machine learning (ML). Advances in silico techniques consequently led to combining both these methodologies into a new approach known as pharmacophore fingerprint. Herein, we propose a high-resolution, pharmacophore fingerprint called Pharmacoprint that encodes the presence, types, and relationships between pharmacophore features of a molecule. Pharmacoprint was evaluated in classification experiments by using ML algorithms (logistic regression, support vector machines, linear support vector machines, and neural networks) and outperformed other popular molecular fingerprints (i.e., Estate, MACCS, PubChem, Substructure, Klekotha-Roth, CDK, Extended, and GraphOnly) and ChemAxon Pharmacophoric Features fingerprint. Pharmacoprint consisted of 39973 bits; several methods were applied for dimensionality reduction, and the best algorithm not only reduced the length of bit string but also improved the efficiency of ML tests. Further optimization allowed us to define the best parameter settings for using Pharmacoprint in discrimination tests and for maximizing statistical parameters. Finally, Pharmacoprint generated for 3D structures with defined hydrogens as input data was applied to neural networks with a supervised autoencoder for selecting the most important bits and allowed to maximize Matthews Correlation Coefficient up to 0.962. The results show the potential of Pharmacoprint as a new, perspective tool for computer-aided drug design.Comment: Journal of Chemical Information and Modeling (2021
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