Summary form only given. Over the recent years the role of mathematics in innovations for Circuits, Systems and signalprocessing has increased considerably. The talk will overview the dynamical forces and their impac...
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Summary form only given. Over the recent years the role of mathematics in innovations for Circuits, Systems and signalprocessing has increased considerably. The talk will overview the dynamical forces and their impact on the research and education. Examples will be given of mathematical methodologies for signal and image classification, data fusion, biomedical diagnostics with support vector machines, matrix and tensor decompositions. Also cryptographic algorithms are crucial in our modern society. Important lessons can be learned for research planning, dissemination and reproducibility as well as for teaching in engineering.
The concept of USG based noninvasive articulograph has been presented for speech production registration. The proposed articulograph is a portable device with OMAP board as a signal processor unit. Combining the infor...
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The concept of USG based noninvasive articulograph has been presented for speech production registration. The proposed articulograph is a portable device with OMAP board as a signal processor unit. Combining the information about tongue position and labiograms the progress in linguistic theory of speech can be expected.
This paper presents a review of selected methods with use of sound in diagnosis and therapy. Methods proposed by the authors are based mainly on advanced methods of spectral analysis and use of disharmonic multitones,...
This paper presents a review of selected methods with use of sound in diagnosis and therapy. Methods proposed by the authors are based mainly on advanced methods of spectral analysis and use of disharmonic multitones, particularly those produced by special sound bowls. Usefulness of various DSP methods has been proven for creating and assessing the acoustic materials for the therapy.
Functional magnetic resonance imaging (fMRI) investigations are increasingly important for the in vivo study and modeling of integrative brain functions in health and disease, where sophisticated mathematical and stat...
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Functional magnetic resonance imaging (fMRI) investigations are increasingly important for the in vivo study and modeling of integrative brain functions in health and disease, where sophisticated mathematical and statistical algorithms for fMRI signalprocessing and interpretation have come into play. Apart from neuroanatomical, neurophysiological, and neuropsychological competence, the progress in cognitive neuroscience and brain mapping is critically dependent on expertise from other disciplines - such as statistics, computer science, and electrical and electronic engineering dealing with signalprocessing, circuits and systems. During the last years, there has also been a trend towards funding of "open science" consortia, providing huge and well-curated image data repositories together with advanced software tools and processing pipelines to be used and further developed by the research community.
In this paper we present methods of parallel programming on graphics processor unit (GPU). We describe and consider usefulness of NVIDIA's SDK CUDA (Compute Unified Device Architecture), which make GPGPU easier. F...
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In this paper we present methods of parallel programming on graphics processor unit (GPU). We describe and consider usefulness of NVIDIA's SDK CUDA (Compute Unified Device Architecture), which make GPGPU easier. Finally we try to estimate potential capacity of new graphics processors and also look closer for edge detection example program.
In this paper we show the process of a class of algorithms parallelization which are used in digital signalprocessing. We present this approach on the instance of the popular LMS algorithm which is used in noise redu...
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In this paper we show the process of a class of algorithms parallelization which are used in digital signalprocessing. We present this approach on the instance of the popular LMS algorithm which is used in noise reduction, echo cancelation problems and digital signalprocessing in general. We propose an approach which uses a GPGPU technology. Parallel approach allows us decomposing the problem into a number of smaller ones, which can be computed faster. Obtained results, especially increase of speed and efficiency, show that the parallel method implemented on GPU is much more effective than other existing procedures and it can be used in the real-time systems.
The paper presents a non-linear signalprocessing system that is suitable for detection and tracking individual, possibly non-stationary, components of the complex signal. The core signalprocessing algorithm is inspi...
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The paper presents a non-linear signalprocessing system that is suitable for detection and tracking individual, possibly non-stationary, components of the complex signal. The core signalprocessing algorithm is inspired by the ways human auditory nerve responds to tonal and noise stimuli. The performed experiments confirm ability of the system to lock its attention onto the sinusoidal signals embedded in the noise with low SNR and produce accurate estimates of sinusoid frequencies. Precision of the estimates is tested with Monte-Carlo trials over the entire space of possible amplitudes, frequencies and initial phases. The amplitude has spanned a reasonably wide dynamic range of 100 dB.
An idea of the use of two accumulators for improvement of the precision of floating-point computations with graphic processing units (GPUs) is presented in this paper for applications in digital signalprocessing. The...
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An idea of the use of two accumulators for improvement of the precision of floating-point computations with graphic processing units (GPUs) is presented in this paper for applications in digital signalprocessing. The increase of the precision of computations does not need any increase of the length of the data words. This is particularly important if hardware limits for the precision of computations exist, which is just the case for graphic processors. A history of development of the cores of graphic cards is analyzed together with the idea of general purpose computing using GPU's (GPGPU). Special attention has been paid to efficiency and precision of computations. The so-called maximum accuracy property has been analyzed and technically realized with no additional costs in hardware and computation time. The proposed approach has been tested with illustrative frequency modulated sine waveform generators.
This paper presents a short survey on current technology available in hearing aids with a focus on digital signalprocessing techniques used. First, factors influencing the hearing aid effectiveness are introduced. Th...
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ISBN:
(纸本)9781509026616
This paper presents a short survey on current technology available in hearing aids with a focus on digital signalprocessing techniques used. First, factors influencing the hearing aid effectiveness are introduced. Then, examples of the present DSP methods and strategies are provided. Also, a description of current limitations of hearing aids and future trends of development are shown. Finally, the notion of computational auditory scene analysis is presented as a possible solution for improving quality of speech and music perception while using a hearing prosthesis.
Many classes of data are composed as purely additive combinations of latent parts that do not result in subtraction or diminishment of the parts. Compositional models such as non-negative matrix factorization can effe...
Many classes of data are composed as purely additive combinations of latent parts that do not result in subtraction or diminishment of the parts. Compositional models such as non-negative matrix factorization can effectively learn these latent structures of the data. Even though such models most naturally applies to non-signal data such as counts of populations, they can be employed to explain other forms of data as well. On signalprocessing, these models can be used to give more interpretable representations than what is obtained with many established signalprocessing methods. Therefore, during the last few years such models have provided new paradigms to solve old standing signalprocessing problems, e.g. source separation and robust pattern recognition. For example in the field of audio processing where we often deal with mixtures of sounds, the models have been used as parts of processing systems to advance the state of the art on many problems, for example on the analysis of polyphonic music and recognition of noisy speech. In this presentation we show how compositional models can be powerful tools for signalprocessing, providing highly interpretable representations, and enabling diverse applications such as signal analysis, recognition, manipulation, and enhancement. We will use several examples from the field of audio processing to demonstrate the effectiveness of the models.
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