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A Hybrid BCI for Robotic Device Navigation 58

A Hybrid BCI for Robotic Device Navigation

引用

58th Annual conference on Information Sciences and Systems (CISS)

作者： Yu, Yih-Choung Fisher, Hayden Busheska, Angela Thompson, Lily Gabel, Lisa Lafayette Coll Dept Elect & Comp Engn Easton PA 18042 USA Childrens Hosp Philadelphia Philadelphia PA 19104 USA Lafayette Coll Neurosci Program Easton PA 18042 USA Lafayette Coll Dept Psychol Easton PA 18042 USA

ISBN: (纸本)9798350369298;9798350369304

Applications of brain-computer interface (BCI) systems have grown in importance for assisting individuals with severe motor disabilities in navigating our increasingly technologically dependent society. With applications such as electric wheelchairs and advanced prosthetics in mind, the goal of this research is to develop a system that enables the use of electroencephalographic (EEG) and electromyographic (EMG) signals to control the movement of a robot. An EEG cap was used to obtain occipital alpha power density, frontal muscular artifacts, and sensorimotor mu rhythms, which were then sent back to a PC via Bluetooth for further processing. signal-processing algorithms and models were developed and implemented to determine the user's mental activity and send signals to the external physical device. The preliminary results from the pilot experiments were very promising. The algorithms will be implemented for real-time signal processing and tested with a BCI-controlled robotic device.

关键词： brain-computer interface biomedical signal processing feature extraction and classification

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A spectral phase algorithm for detecting and estimating pitch

A spectral phase algorithm for detecting and estimating pitc...

引用

conference on advanced signal processing algorithms, Architectures, and Implementations XI

作者： Tempkin, JA Nelson, DJ USA Dept Def Ft George G Meade MD 20755 USA

ISBN: (纸本)0819441880

We describe a fast and efficient algorithm for automatic detection and estimation of the fundamental frequency F0 of a harmonic time-domain signal. The method is based on differentiation of the short time Fourier transform (STFT) phase, which is implemented as a cross-spectral product. In estimating and isolating the fundamental frequency, several enhancement processes are developed and applied to the TF surface to improve the signal quality. We describe the algorithm in detail and demonstrate the processing gain achieved at each step. In addition, we apply the algorithm to human speech to recover the pitch fundamental F0 and report the evaluation of the algorithm's performance on the Western Michigan vowel corpus [3].

关键词： signal processing

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Digital signal processing technology and applications in hearing aids 6

Digital Signal Processing technology and applications in hea...

引用

6th International conference on signal processing

作者： Luo, H Arndt, H Unitron Hearing Kitchener ON N2G 4X1 Canada

ISBN: (纸本)0780374886

Digital signal processing has found applications in almost every industry and the hearing aid industry is no exception. Building practical DSP platforms for hearing aids has been difficult due to the physical limitations of small size, low power consumption and low operating voltage, and although progress was initially very slow, real advances have been made during the last decade. Now DSP based hearing aids make use of many well-known digital signal processing technologies developed for communication, speech processing, radar and sonar systems. More sophisticated digital platforms are going to be created and more advanced digital signal processing technologies will be developed and implemented on this new digital hardware. This paper addresses DSP hardware, algorithms, and their application in hearing aids.

关键词： Digital signal processing

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Review of MRI brain tumor segmentation and MGMT promoter classification methods on BraTs dataset based on Deep learning 7

Review of MRI brain tumor segmentation and MGMT promoter cla...

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IEEE 7th International conference on advanced Technologies, signal and Image processing (ATSIP)

作者： Amor, Feriel Mzoughi, Hiba Njeh, Ines Ben Slima, Mohamed Adv Technol Image & Signal Proc ATISP Lab Sfax Tunisia Sfax Univ Natl Engn Sch Sfax ATMS LAB Sfax Tunisia Gabes Univ Higher Inst Comp Sci & Multimedia Gabes Gabes Tunisia Sfax Univ Natl Sch Elect & Telecommun Sfax Sfax Tunisia

ISBN: (纸本)9798350351491;9798350351484

During clinical decision-making, the segmentation process Is considered an important task because it provides valuable information about the location, size, and characterization of the brain tumor. However, manual human segmentation iserror-prone, time-consuming, and requires skilled neuroradiologists. Magnetic resonance imaging (MRI) could give extremely detailed images for the investigation and diagnosis of glioblastoma brain cancers. We compared evaluated approaches on the BRATS 2021 and BRATS 2022 datasets and found that they outperformed and could compete with state-of-the-art algorithms in comparable settings. In our research, we focused on two crucial tasks: segmentation and MGMT classification. This study also addresses asn objective evaluation through performance evaluation of cutting-edge DL-based techniques for MR image analysis (Brats 2021-Brats 2022). Based on the findings of the contrasted methods, we can confirm that using a combination of DL techniques will produce more accurate segmentation results than depending on a single, unique methodology. For the second task, five distinct deep learning-based methods were evaluated to predict the methylation state of the MGMT promoter.

关键词： Deep Learning (DL) Resonance Imaging (MRI) Segmentation Gliomas MGMT BraTS2021 BraTS 2022

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Study of Statistical signal Models in Low-Frequency Underwater Acoustic Applications

Study of Statistical Signal Models in Low-Frequency Underwat...

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MTS/IEEE OCEANS conference

作者： Xu, Wen Li, Jianlong Zhejiang Univ Dept Informat Sci & Elect Engn Hangzhou 310027 Zhejiang Peoples R China

ISBN: (纸本)9780933957398

Modern acoustic signal processing is often a combination of sound propagation physics and advanced signal processing algorithms, which involves intensive acoustic, environmental, and statistical modeling. This paper discusses three different statistical signal models in low-frequency matched-field processing accounting for different source and channel conditions, and presents the Cramer-Rao bound (CRB) associated with each model. CRB evaluation examples show that under the same signal-to-noise ratios, the source localization performance degrades with increasingly-complicated as well more realistic data models.

关键词： matched field processing statistical signal model correlated noise mode decomposition Cramer-Rao bound

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Approximation by structured lower rank matrices

Approximation by structured lower rank matrices

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SPIE conference on advanced signal processing algorithms, Architectures, and Implementations VIII

作者： Chu, MT Funderlic, RE Plemmons, RJ N Carolina State Univ Dept Math Raleigh NC 27695 USA

ISBN: (纸本)0819429163

We consider two general procedures for constructing the nearest approximation of a given matrix by one with any lower rank and any linear structure. Nearness can be measured in any matrix norm. Structured low rank matrices arise in various applications, including image enhancement and model reduction. In practice, the empirical data collected in the matrix often either do not maintain the specified structure or do not induce the desirable rank. It is an important task to search for the nearest structured lower rank approximation of a given matrix. The techniques developed in this paper can easily be implemented for numerical computation. In particular, it is shown that the computations can be approached using efficient optimization packages. The special case of Toeplitz structure using the Frobenius matrix norm is discussed in detail to illustrate the ideas, and numerical tests are reported. The procedures developed herein can be generalized to consider a much broader range of approximation problems.

关键词： Matrices signal processing MATLAB Optimization (mathematics) Image enhancement Computer programming Computer science Fourier transforms Image processing Mathematics

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An adaptive algorithm for interference cancelling in array processing 6

An adaptive algorithm for interference cancelling in array p...

引用

conference on advanced signal processing algorithms, Architectures, and Implementations VI

作者： Luk, FT Qiao, SZ RENSSELAER POLYTECH INST DEPT COMP SCITROYNY 12180

ISBN: (纸本)0819422347

In array processing, one technique for cancelling interference in the presence of colored noise is the ULLV decomposition of a pair of matrices. The factorization is stable and accurate, and is easy to update when a row is added to either one of the two matrices. In earlier work, we made the assumptions that both matrices must have at least as many rows as columns and that the matrix representing interference must have full column rank. In this paper, we relax the latter restriction and allow the interference matrix to be rank deficient. We present an algorithm for computing the decomposition and two techniques for updating it. The details on updating are quite different from those in the full rank case. © 1996 SPIE. All rights reserved.

关键词： ULLV decomposition matrix updating noise subspace tracking

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Wavelet-domain hidden Markov models for signal detection and classification

Wavelet-domain hidden Markov models for signal detection and...

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conference on advanced signal processing - algorithms, Architectures, and Implementations VII

作者： Crouse, MS Nowak, RD Mhirsi, K Baraniuk, RG Rice Univ. (United States) Michigan State Univ. (United States)

ISBN: (纸本)0819425842

This paper addresses the problem of detection and classification of complicated signals in noise. Classical detection methods such as energy detectors and linear discriminant analysis do not perform well in many situations of practical interest. We introduce a new approach based on hidden Markov modeling in the wavelet domain. Using training data, we fit a hidden Markov model (HMM) to the wavelet transform to concisely represent its probabilistic time-frequency structure. The HMM provides a natural framework for performing likelihood ratio tests used in signal detection and classification. We compare our approach with classical methods for classification of nonlinear processes, change-point detection, and detection with unknown delay.

关键词： wavelets hidden Markov models detection classification

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Microsystem for signal processing applications

Microsystem for signal processing applications

引用

conference on advanced Sensor Technologies for Nondestructive Evaluation and Structural Health Monitoring

作者： Frankenstein, B Fröhlich, KJ Hentschel, D Reppe, G Fraunhofer Inst Nondestruct Testing Dresden Branch D-01326 Dresden Germany

ISBN: (纸本)0819457515

Acoustic monitoring of technological processes requires methods that eliminate noise as much as possible. Sensor-near signal evaluation can contribute substantially. Frequently, a further necessity exists to integrate the measuring technique into the monitored structure. The solution described contains components for analog preprocessing of acoustic signals, their digitization, algorithms for data reduction, and digital communication. The core component is a digital signal processor (DSP), which performs the basic algorithms necessary for filtering, down sampling, FFT computation and correlation of spectral components particularly effective. A compact, sensor-near signal processing structure was realized. It meets the Match-X standard, which was specified by the German Association for Mechanical and Plant Engineering (VDMA) for development of micro-technical modules, which can be combined to application specific systems. The solution is based on AL(2)O(3) ceramic components including different signal processing modules as ADC, as well as memory and power supply. An arbitrary waveform generator has been developed and combined with a power amplifier for piezoelectric transducers in a special module. A further module interfaces to these transducers. It contains a multi-channel preamplifier, some high-pass and low-pass filters for analog signal processing and an ADC-driver. A Bluetooth communication chip for wireless data transmission and a DiscOnChip module are under construction.

关键词： process monitoring acoustic signal processing microsystem

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Big Data Stream Learning with SAMOA 14

Big Data Stream Learning with SAMOA

引用

14th IEEE International conference on Data Mining (IEEE ICDM)

作者： Bifet, Albert De Francisci Morales, Gianmarco HUAWEI Noahs Ark Lab Hong Kong Hong Kong Peoples R China Yahoo Labs Barcelona Spain

ISBN: (纸本)9781479942749

Big data is flowing into every area of our life, professional and personal. Big data is defined as datasets whose size is beyond the ability of typical software tools to capture, store, manage and analyze, due to the time and memory complexity. Velocity is one of the main properties of big data. In this demo, we present SAMOA (Scalable advanced Massive Online Analysis), an open-source platform for mining big data streams. It provides a collection of distributed streaming algorithms for the most common data mining and machine learning tasks such as classification, clustering, and regression, as well as programming abstractions to develop new algorithms. It features a pluggable architecture that allows it to run on several distributed stream processing engines such as Storm, S4, and Samza. SAMOA is written in Java and is available at http://*** under the Apache Software License version 2.0.

关键词： Big Data data mining learning (artificial intelligence) pattern classification pattern clustering regression analysis Apache Software License version 2.0 Java S4 SAMOA Samza Storm big data stream learning big data stream mining classification task clustering task distributed stream processing engines distributed streaming algorithms machine learning tasks open-source platform pluggable architecture programming abstractions regression task scalable advanced massive online analysis Algorithm design and analysis Big data Data mining Digital signal processing Engines Machine learning algorithms Storms Classification Clustering Data Streams Distributed Systems Machine Learning Regression Toolbox big data storms data mining Samoan Islands Algorithm design and analysis Cluster Analysis Machine learning algorithms Engines tool cases Java Java programming language Distributed Systems Digital signal processing Pattern recognition Regression (Psychology)

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