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Annual International Conference of the IEEE engineering in Medicine and Biology Society

作者： Jeffrey D. Hoffman James McNames Biomedical Signal Processing Laboratory and graduate student in the Department of Electrical and Computer Engineering at Portland State University Department of Electrical and Computer Engineering at Portland State University

ISBN: (纸本)9781424441211

Functional motor impairment caused by Parkinson's disease and other movement disorders is currently measured with rating scales such as the Unified Parkinson's Disease Rating Scale (UPDRS). These are typically comprised of a series of simple tasks that are visually scored by a trained rater. We developed a method to objectively quantify three upper extremity motor tasks directly with a wearable inertial sensor. Specifically, we used triaxial gyroscopes and adaptive filters to quantify how predictable and regular the signals were. We found that simply using the normalized mean squared error (NMSE) as a test statistic permitted us to distinguish between subjects with and without Parkinson's disease who were matched for age, height, and weight. A forward linear predictor based on the Kalman filter was able to attain areas under the curve (AUC) in receiver operator characteristic (ROC) curves in the range of 0.76 to 0.83. Further studies and development are warranted. This technology has the potential to more accurately measure the motor signs of Parkinson's disease. This may reduce statistical bias and variability of rating scales, which could lead to trials with fewer subjects, less cost, and shorter duration.

关键词： Parkinson Disease inertia sensor Rating Scales Motor Disorders Statistical Biases Kalman filters Upper Extremity adaptive filters

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Semi-supervised learning in nonstationary environments

Semi-supervised learning in nonstationary environments

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International Joint Conference on Neural Networks (IJCNN)

作者： Gregory Ditzler Robi Polikar Signal Processing and Pattern Recognition Laboratory in the Electrical & Computer Engineering Department Rowan University Glassboro NJ USA

Learning in nonstationary environments, also called learning concept drift, has been receiving increasing attention due to increasingly large number of applications that generate data with drifting distributions. These applications are usually associated with streaming data, either online or in batches, and concept drift algorithms are trained to detect and track the drifting concepts. While concept drift itself is a significantly more complex problem than the traditional machine learning paradigm of data coming from a fixed distribution, the problem is further complicated when obtaining labeled data is expensive, and training must rely, in part, on unlabelled data. Independently from concept drift research, semi-supervised approaches have been developed for learning from (limited) labeled and (abundant) unlabeled data; however, such approaches have been largely absent in concept drift literature. In this contribution, we describe an ensemble of classifiers based approach that takes advantage of both labeled and unlabeled data in addressing concept drift: available labeled data are used to generate classifiers, whose voting weights are determined based on the distances between Gaussian mixture model components trained on both labeled and unlabeled data in a drifting environment.

关键词： Classification algorithms Data models Clustering algorithms Training data Training Testing Algorithm design and analysis

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A New NSCT Based Contrast Enhancement Algorithm for Amplification of Early Signs of Ischemic Stroke in Brain CT Images

A New NSCT Based Contrast Enhancement Algorithm for Amplific...

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Iranian Machine Vision and Image Processing (MVIP)

作者： Mehdi Sajjadi Rassoul Amirfattahi Mohammad Reza Ahmadzadeh Digital Signal Processing Research Laboratory Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran

Brain CT images are extensively used in detection of Cerebrovascular Accidents (CVA). But CT scan is not really a sensitive test for diagnosis of ischemic stroke. Most of the times, early CT images barely show or even don 't show any noticeable sign of ischemia. But it is possible to put emphasis on subtle signs of ischemia by means of image contrast enhancement. Contrast enhancement using multiscale transforms is one of the most popular approaches in recent years. Nonsubsampled Contourlet Transform (NSCT) is a directional multiscale transform that is able to take geometrical structures into account. In this paper we are about to customize this transform and improve its performance in contrast enhancement of brain CT images. To this end, we propose a set of simpler and more efficient filters for Nonsubsampled Pyramid (NSP) and introduce an improved method for modification of directional subbands' coefficients.

关键词： Filter banks Finite impulse response filter Computed tomography Equations Wavelet transforms Image enhancement

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A block-based 2D Discrete Wavelet Transform structure with new scan method for overlapped sections

A block-based 2D Discrete Wavelet Transform structure with n...

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Middle East Conference on Biomedical engineering (MECBME)

作者： Sayed Ahmad Salehi Rasoul Amirfattahi Department of Electrical and Computer Engineering Digital Signal Processing Research Laboratory Isfahan University of Technology Isfahan Iran

Regarding internal memory size, we proposed a new scan method for block-based structure to provide an efficient parametric structure for two Dimensional Discrete Wavelet Transform (2-D DWT). The main challenge in 2-D DWT structure is the amount of internal memory required to produce wavelet coefficients. The previous structures need fixed memory size depend on input frame size, but in proposed architecture, memory size is parametric and in trade off with the number of output memory access. Lifting scheme is incorporated to implement DWT.

关键词： Discrete wavelet transforms Memory management Hardware Pixel

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282

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Cytokine 2013年第3期63卷 310-310页

作者： Elia D. Tait Wojno Mario Noti Brian S. Kim Mark C. Siracusa Paul R. Giacomin Meera G. Nair Alain J. Benitez Kathryn R. Ruymann Amanda B. Muir David A. Hill Kudakwashe R. Chikwava Amin E. Moghaddam Quentin J. Sattentau Aneesh Alex Chao Zhou Paul Menard-Katcher Masato Kubo Kazushige Obata-Ninomiya Hajime Karasuyama Michael R. Comeau David Artis Department of Microbiology University of Pennsylvania Philadelphia PA USA Institute for Immunology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Queensland Tropical Health Alliance and School of Public Health and Tropical Medicine James Cook University Cairns Australia Division of Gastroenterology Hepatology and Nutrition Children’s Hospital of Philadelphia Philadelphia PA USA Department of Pediatrics Division of Allergy and Immunology Children’s Hospital of Philadelphia Philadelphia PA USA Department of Pathology and Laboratory Medicine Children’s Hospital of Philadelphia Philadelphia PA USA The Sir William Dunn School of Pathology The University of Oxford Oxford UK Department of Electrical and Computer Engineering Lehigh University Bethlehem PA USA Center for Photonics and Nanoelectronics Lehigh University BethlehemPA USA Bioengineering Program Lehigh University Bethlehem PA USA Division of Gastroenterology Hospital of the University of Pennsylvania Philadelphia PA USA Laboratory for Signal Network Research Center for Allergy and Immunology RIKEN Yokohama Institute Yokohama Japan Division of Molecular Pathology Research Institute for Biological Science Tokyo University of Science Chiba Japan Department of Immune Regulation Tokyo Medical and Dental University Graduate School Tokyo Japan JST CREST Tokyo Medical and Dental University Graduate School Tokyo Japan Inflammation Research Amgen Inc. Seattle WA USA Department of Pathobiology School of Veterinary Medicine University of Pennsylvania Philadelphia PA USA

Eosinophilic esophagitis (EoE) is a food allergy-associated disease characterized by esophageal eosinophilia and associated inflammation. EoE has become increasingly common in industrialized countries. However, current management strategies, such as treatment with swallowed steroids and dietary restrictions, are nonspecific or negatively impact the quality of life of EoE patients. In addition, use of more specific therapies that target immunoglobulin E (IgE) and interleukin (IL)-5 have been largely unsuccessful in ameliorating EoE. Thus, there is an urgent need to identify novel, specific immunological pathways that underlie the pathogenesis of EoE that could be targeted to treat this disease. Recently, a genome-wide association study identified that EoE is associated with a gain-of-function polymorphism in the gene that encodes thymic stromal lymphopoietin (TSLP). TSLP is an IL-7 family member cytokine that promotes allergic inflammation by eliciting T helper type 2 cytokine responses, IgE production, and the expansion of a distinct population of basophils . However, whether TSLP directly promotes allergic inflammation in the esophagus and the mechanisms by which TSLP might contribute to the pathogenesis of EoE remain unknown. Here, we describe a new murine model of experimental EoE-like disease that was used to investigate the role of TSLP in allergic inflammation of the esophagus. Murine experimental EoE-like disease developed independently of IgE but was dependent on TSLP-elicited basophils. Critically, therapeutic antibody-mediated neutralization of TSLP or depletion of basophil populations ameliorated established EoE-like disease in mice. Finally, elevated TSLP levels and exaggerated basophil responses were observed in esophageal biopsies from EoE patients, and a gain-of-function polymorphism in TSLP correlated with increased basophil responses in patients with EoE. Together, these data indicate that TSLP-elicited basophil responses may play a key role in medi

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Evaluation of some physiological statements about seizure, using processing of epileptic EEG signals

Evaluation of some physiological statements about seizure, u...

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Iranian Conference on electrical engineering (ICEE)

作者： F. Shayegh R. Amirfattahi S. Sadri K. Ansari-Asl Digital signal Processing Laboratory Department of Electrical and Computer Engineering Isfahan University of Technology Iran Electrical Department Engineering Faculty Shahid Chamran University of Ahvaz Iran

Seizure genesis is usually attributed to imbalance of the excitation and inhibition activities of neurons. It is claimed that slow inhibition mechanism is responsible for controlling the variations of the excitation mechanism, and any deficiency in this function will lead to disturbance of their natural balance. Fast inhibition mechanism is also known to collaborate with slow inhibitory processes. Any proof about the excitation and inhibition mechanisms refers to the ex-vivo or in-vitro experiments, i.e. from the microscopic point of view. Up to now, there is not a straight road into evaluating these hypotheses from the macroscopic view. In this paper we try to interpret the excitation-inhibition sequences extracted from some depth-EEG signals through an identification-based procedure. The aim is to check out whether the statements gained from microscopic observations are re-established in population of neurons in the case of MTLE or not. Using Neyman-Pearson hypothesis test technique we try to answer these questions. We observe that even in population of neurons slow inhibition is responsible for compensating excitatory processes to balance inhibitory /excitatory ratio, such that when it is impaired causes seizures to arise; also the average fast inhibitory process of population of neurons follows its slow inhibition dynamics. Also, we check the differentiation of the excitation-inhibition ratio between normal and ictal states, as well as between normal and pre-ictal states. Ictal activity is properly a state of abnormal high excitation-inhibition ratio but before beginning of seizures there are not unique patterns.

关键词： Brain modeling Electroencephalography Noise Mathematical model Neurons Cost function Equations

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New image compression algorithm using proposed quantization approach

New image compression algorithm using proposed quantization ...

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International Conference on Pattern Analysis and Intelligent Robotics (ICPAIR)

作者： Seyed Mehdi Moghadas Hossein Pourghasem Rasoul Amirfattahi Department of Electrical Engineering Islamic Azad University Najafabad Iran Digital Signal Processing Research Laboratory Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran

A new quantization method is proposed in this paper. This method is useful for enhancement of compression quality when each kind of neural network is used to compress the image. By quantizing the image with the proposed method, the numbers of samples which must be reconstructed by neural network is reduced. This causes a remarkable increase in quality of the reconstructed image. For testing the proposed method we use autoassociative transform coding and by merging it with the proposed quantization method a new compression algorithm is obtained. Then results of compression by the merged method are compared with some previous works. Obtained results show that the proposed compression algorithm increases the compression quality of the images remarkably. compression time and complexity in the merged method is also better than JPEG and make it suitable for the systems with low processor and hardware implementation.

关键词： Image coding Biological neural networks Image reconstruction Transform coding Neurons PSNR Training

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Bayesian Spatiotemporal Multitask Learning for Radar HRRP Target Recognition

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IEEE Transactions on signal Processing 2011年第7期59卷 3182-3196页

作者： Lan Du Penghui Wang Hongwei Liu Mian Pan Feng Chen Zheng Bao Department of Electrical and Computer Engineering Duke University Durham NC USA National Laboratory of Radar Signal Processing Xidian University Xi'an China School of Electronic Information and Electrical Engineering Shanghai Jiaotong University Shanghai China

A Bayesian dynamic model based on multitask learning (MTL) is developed for radar automatic target recognition (RATR) using high-resolution range profile (HRRP). The aspect-dependent HRRP sequence is modeled using a truncated stick-breaking hidden Markov model (TSB-HMM) with time-evolving transition probabilities, in which the spatial structure across range cells is described by the hidden Markov structure and the temporal dependence between HRRP samples is described by the time evolution of the transition probabilities. This framework imposes the belief that temporally proximate HRRPs are more likely to be drawn from similar HMMs, while also allowing for possible distant repetition or “innovation”. In addition, as formulated the stick-breaking prior and MTL mechanism are employed to infer the number of hidden states in an HMM and learn the target-dependent states collectively for all targets. The form of the proposed hierarchical model allows efficient variational Bayesian (VB) inference, of interest for large-scale problems. To validate the formulation, example results are presented for an illustrative synthesized dataset and our main application-RATR, for which we consider the measured HRRP data. For the latter, we also make comparisons to the model with the independent state-transition statistics and some other existing statistical models for radar HRRP data.

关键词： Hidden Markov models Data models Radar Bayesian methods Target recognition Analytical models

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A higher radix FFT FPGA implementation suitable for OFDM systems

A higher radix FFT FPGA implementation suitable for OFDM sys...

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IEEE International Conference on Electronics, Circuits and Systems (ICECS)

作者： Marwan A. Jaber Daniel Massicotte Youssef Achouri Electrical and Computer Engineering Department Laboratory of Signal and System Integrations Université du Québec á Trois-Rivières Canada

This article describes a new approach for higher radix butterflies suitable for pipeline implementation. Based on the butterfly computation introduced by Cooley-Tukey [1], we will introduce a novel approach for the Discrete Fourier Transform (DFT) factorization, by redefining the butterfly computation, which is more suitable for efficient VLSI implementation. The proposed factorization motivated us to present a new concept of a radix-r Fast Fourier Transform (FFT), in which the radix-r butterfly computation concept was formulated as composite engines to implement each of the butterfly computations. This concept enables the radix r butterfly-processing element (BPE) to be designed by maintaining only one complex value multiplier in the butterfly critical path for any given r [2]. Algorithmic description and performance of low complexity FFT methods are considered in this paper where the speed and accuracy evaluation of the proposed method in fixed point is also elaborated.

关键词： Discrete Fourier transforms signal processing algorithms Algorithm design and analysis Complexity theory Accuracy Delay Field programmable gate arrays

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Unsupervised classification and spectral unmixing for sub-pixel labelling

Unsupervised classification and spectral unmixing for sub-pi...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： A. Villa J. Chanussot J. A. Benediktsson C. Jutten GIPSA-Laboratory Signal & Image Department INPG Grenoble Institute of Technology France Faculty of Electrical and Computer Engineering University of Iceland Iceland

The unsupervised classification of hyperspectral images containing mixed pixels is addressed in this paper. Hyperspectral images are characterized by a trade-off between the spectral and the spatial resolution, this leading to data sets containing mixed pixels, e.g. pixels jointly occupied by more than a single land cover class. In [1], a preliminary research based on spectral unmixing concepts was conducted, in order to handle mixed pixels and to obtain thematic maps at a finer spatial resolution. In this work, we extend the investigation by proposing a new methodology based on image clustering. Experiments conducted on real data show the comparative effectiveness of the proposed method, which provides good results in terms of accuracy and is less sensitive to pixels with extreme values of reflectance.

关键词： Spatial resolution Hyperspectral imaging Accuracy Simulated annealing

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