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arXiv 2023年

作者： Álvarez, Josefa Díaz Risco-Martín, José L. Colmenar, J. Manuel Dpt. of Computer Architecture and Communications University of Extremadura Spain Dpt. of Computer Architecture and Automation Complutense University of Madrid Spain Dpt. of Computer Science King Juan Carlos University Spain

The memory hierarchy has a high impact on the performance and power consumption in the system. Moreover, current embedded systems, included in mobile devices, are specifically designed to run multimedia applications, which are memory intensive. This increases the pressure on the memory subsystem and affects the performance and energy consumption. In this regard, the thermal problems, performance degradation and high energy consumption, can cause irreversible damage to the devices. We address the optimization of the whole memory subsystem with three approaches integrated as a single methodology. Firstly, the thermal impact of register file is analyzed and optimized. Secondly, the cache memory is addressed by optimizing cache configuration according to running applications and improving both performance and power consumption. Finally, we simplify the design and evaluation process of general-purpose and customized dynamic memory manager, in the main memory. To this aim, we apply different evolutionary algorithms in combination with memory simulators and profiling tools. This way, we are able to evaluate the quality of each candidate solution and take advantage of the exploration of solutions given by the optimization *** also provide an experimental experience where our proposal is assessed using well-known benchmark applications. © 2023, CC BY-SA.

关键词： Cache memory

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A HYPOTHESIS-DRIVEN METHOD BASED ON MACHINE LEARNING FOR NEUROIMAGING DATA ANALYSIS

arXiv

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arXiv 2022年

作者： Saez, Juan Manuel Gorriz Clemente, R. Martin Puntonet, C.G. Ortiz, A. Ramirez, J. Suckling, John Dasci Institute University of Granada Granada Spain Dpt. Signal Theory and Communications University of Seville Seville Spain Dpt. Computer Architecture and Tech. University of Granada Granada Spain Dpt. Communication Engineering University of Malaga Malaga Spain Department of Psychiatry University of Cambridge Cambridge United Kingdom

There remains an open question about the usefulness and the interpretation of Machine learning (MLE) approaches for discrimination of spatial patterns of brain images between samples or activation states. In the last few decades, these approaches have limited their operation to feature extraction and linear classification tasks for between-group inference. In this context, statistical inference is assessed by randomly permuting image labels or by the use of random effect models that consider between-subject variability. These multivariate MLE-based statistical pipelines, whilst potentially more effective for detecting activations than hypotheses-driven methods, have lost their mathematical elegance, ease of interpretation, and spatial localization of the ubiquitous General linear Model (GLM). Recently, the estimation of the conventional GLM has been demonstrated to be connected to an univariate classification task when the design matrix is expressed as a binary indicator matrix. In this paper we explore the complete connection between the univariate GLM and MLE regressions. To this purpose we derive a refined statistical test with the GLM based on the parameters obtained by a linear Support Vector Regression (SVR) in the inverse problem (SVR-iGLM). Subsequently, random field theory (RFT) is employed for assessing statistical significance following a conventional GLM benchmark. Experimental results demonstrate how parameter estimations derived from each model (mainly GLM and SVR) result in different experimental design estimates that are significantly related to the predefined functional task. Moreover, using real data from a multisite initiative the proposed MLE-based inference demonstrates statistical power and the control of false positives, outperforming the regular GLM. © 2022, CC BY.

关键词： Magnetic resonance imaging

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Clustering approach for the classificarion of SPECT images

Clustering approach for the classificarion of SPECT images

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2008 IEEE Nuclear Science Symposium Conference Record, NSS/MIC 2008

作者： Lassl, A. Górriz, J.M. Ramírez, J. Salas-Gonzalez, D. Puntonet, C.G. Lang, E.W. Dpt.of Signal Theory and Communications University of Granada Spain Dpt. of Computer Architecture and Tech. University of Granada Spain Computational Intelligence and Machine Learning Group University of Regensburg Germany

ISBN: (纸本)9781424427154

We present a method to cluster the information contained in 3-dimensional brain images where each cluster incorporates a contiguous brain region with similar activation. The grey-level distribution of a brain image is approximated by a sum of Gaussian functions and the parameters of the Gaussian mixture are determined by a maximum likelihood criterion via the expectation maximization (EM) algorithm. Each cluster, therefore, is represented by a multivariate Gaussian function with a definite centre coordinate and a certain shape. This approach leads to a drastic compression of the information contained in the brain image and serves as a starting point for a variety of possible feature extraction methods for the diagnosis of brain diseases. © 2008 IEEE.

关键词： Maximum principle

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Clustering approach for the classificarion of SPECT images

Clustering approach for the classificarion of SPECT images

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： A. Lassl J. M. Gorriz J. Ramirez D. Salas-Gonzalez C. G. Puntonet E. W. Lang Dpt. of Signal Theory and Communications University of Granada Spain Dpt. of Computer Architecture and Tech. University of Granada Spain Computational Intelligence and Machine Learning Group University of Regensburg Germany

关键词： Brain Alzheimer's disease Clustering algorithms Magnetic resonance imaging Positron emission tomography Image reconstruction Electronic mail Probability distribution Gaussian distribution Histograms

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Noise subspace fuzzy C-means clustering for robust speech recognition

Noise subspace fuzzy C-means clustering for robust speech re...

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ICCSA 2006: International Conference on Computational Science and Its Applications

作者： Gárriz, J.M. Ramárez, J. Segura, J.C. Puntonet, G.G. Gonzalez, J.J. Dpt. Signal Theory Networking and Communications University of Granada Spain Dpt. Computer Architecture and Technology University of Granada Spain

ISBN: (纸本)3540340793

In this paper a fuzzy C-means (FCM) based approach for speech/non-speech discrimination is developed to build an effective voice activity detection (VAD) algorithm. The proposed VAD method is based on a soft-decision clustering approach built over a ratio of subband energies that improves recognition performance in noisy environments. The accuracy of the FCM-VAD algorithm lies in the use of a decision function defined over a multiple-observation (MO) window of averaged subband energy ratio and the modeling of noise subspace into fuzzy prototypes. In addition, time efficiency is also reached due to the clustering approach which is fundamental in VAD real time applications, i.e. speech recognition. An exhaustive analysis on the Spanish SpeechDat-Car databases is conducted in order to assess the performance of the proposed method and to compare it to existing standard VAD methods. The results show improvements in detection accuracy over standard VADs and a representative set of recently reported VAD algorithms. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Fuzzy sets

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