There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

CA : A Cancer Journal for Clinicians

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

The aim of this review paper is to summarize recent developments in the field of wearable sensors and systems that are relevant to the field of rehabilitation. The growing body of work focused on the application of wearable technology to monitor older adults and subjects with chronic conditions in the home and community settings justifies the emphasis of this review paper on summarizing clinical applications of wearable technology currently undergoing assessment rather than describing the development of new wearable sensors and systems. A short description of key enabling technologies (i.e. sensor technology, communication technology, and data analysis techniques) that have allowed researchers to implement wearable systems is followed by a detailed description of major areas of application of wearable technology. Applications described in this review paper include those that focus on health and wellness, safety, home rehabilitation, assessment of treatment efficacy, and early detection of disorders. The integration of wearable and ambient sensors is discussed in the context of achieving home monitoring of older adults and subjects with chronic conditions. Future work required to advance the field toward clinical deployment of wearable sensors and systems is discussed.

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A review of wearable sensors and systems with application in rehabilitation.

Environmental sound classification task (ESC) is still open and challenging. In contrast to speech, sounds of a specific acoustic event may be produced by a wide variety of sources. Thus for one class, feature spectrums of acoustic events are much more transformative than human speech. In order to learn better high-level feature representations from these transformative feature spectrums, convolution neural network (CNN) has been applied to ESC tasks and achieved state-of-the-art results. However, it is noted that existing CNN-based ESC systems only use small convolution filters (typically 2×2 or 3×3) in CNN model which results in deep CNN model for learning long contextual information of sound events. Besides, to our knowledge, there is no work of evaluating the effect of activation functions on the performance of CNN-based ESC systems, which is actually very critical for improving the performance. Motivated by these findings, in this study, we propose a dilated CNN-based ESC (D-CNN-ESC) system where dilated filters and LeakyReLU activation function are adopted. The main ideas behind our research are that the dilated filters will increase receptive field of convolution layers to incorporate more contextual information. Moreover, the LeakyReLU function brings the tradeoff between the network sparsity and the performance. To evaluate the effectiveness of our proposed D-CNN-ESC system, we conduct several experiments on three sound event datasets. It is encouraged to see that our proposed D-CNN-ESC system outperforms the state-of-the-art ESC results obtained by very deep CNN-ESC system on UrbanSound8K dataset, the absolute error of our method is about 10% less than that of compared method.

Dilated convolution neural network with LeakyReLU for environmental sound classification

This paper studies the problem of robust adaptive filtering in impulsive noise environment using a recursive least M-estimate algorithm (RLM). The RLM algorithm minimizes a robust M-estimator-based cost function instead of the conventional mean square error function (MSE). Previous work has showed that the RLM algorithm offers improved robustness to impulses over conventional recursive least squares (RLS) algorithm. In this paper, the mean and mean square convergence behaviors of the RLM algorithm under the contaminated Gaussian impulsive noise model is analyzed. A lattice structure-based fast RLM algorithm, called the Huber Prior Error Feedback-Least Squares Lattice (H-PEF-LSL) algorithm is derived. Part of the H-PEF-LSL algorithm was presented in ICASSP 2001. It has an order O(N) arithmetic complexity, where N is the length of the adaptive filter, and can be viewed as a fast implementation of the RLM algorithm based on the modified Huber M-estimate function and the conventional PEF-LSL adaptive filtering algorithm. Simulation results show that the transversal RLM and the H-PEF-LSL algorithms have better performance than the conventional RLS and other RLS-like robust adaptive algorithms tested when the desired and input signals are corrupted by impulsive noise. Furthermore, the theoretical and simulation results on the convergence behaviors agree very well with each other.

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A recursive least M-estimate algorithm for robust adaptive filtering in impulsive noise: fast algorithm and convergence performance analysis

This paper proposes two gradient-based adaptive algorithms, called the least mean M estimate and the transform domain least mean M-estimate (TLMM) algorithms, for robust adaptive filtering in impulse noise. A robust M-estimator is used as the objective function to suppress the adverse effects of impulse noise on the filter weights. They have a computational complexity of order O(N) and can be viewed, respectively, as the generalization of the least mean square and the transform-domain least mean square algorithms. A robust method fur estimating the required thresholds in the M-estimator is also given. Simulation results show that the TLMM algorithm, in particular, is more robust and effective than other commonly used algorithms in suppressing the adverse effects of the impulses.

/pdf/least-mean-m-estimate-algorithms-for-robust-adaptive-3eowp06q2t.pdf

Least mean M-estimate algorithms for robust adaptive filtering in impulse noise

Speech emotion recognition (SER) is a challenging task since it is unclear what kind of features are able to reflect the characteristics of human emotion from speech. However, traditional feature extractions perform inconsistently for different emotion recognition tasks. Obviously, different spectrogram provides information reflecting difference emotion. This paper proposes a systematical approach to implement an effectively emotion recognition system based on deep convolution neural networks (DCNNs) using labeled training audio data. Specifically, the log-spectrogram is computed and the principle component analysis (PCA) technique is used to reduce the dimensionality and suppress the interferences. Then the PCA whitened spectrogram is split into non-overlapping segments. The DCNN is constructed to learn the representation of the emotion from the segments with labeled training speech data. Our preliminary experiments show the proposed emotion recognition system based on DCNNs (containing 2 convolution and 2 pooling layers) achieves about 40% classification accuracy. Moreover, it also outperforms the SVM based classification using the hand-crafted acoustic features.

An experimental study of speech emotion recognition based on deep convolutional neural networks

This paper introduces a mobile human airbag system designed for fall protection for the elderly. A Micro Inertial Measurement Unit ( muIMU) of 56 mm times 23 mm times 15 mm in size is built. This unit consists of three dimensional MEMS accelerometers, gyroscopes, a Bluetooth module and a Micro Controller Unit (MCU). It records human motion information, and, through the analysis of falls using a high-speed camera, a lateral fall can be determined by gyro threshold. A human motion database that includes falls and other normal motions (walking, running, etc.) is set up. Using a support vector machine (SVM) training process, we can classify falls and other normal motions successfully with a SVM filter. Based on the SVM filter, an embedded digital signal processing (DSP) system is developed for real-time fall detection. In addition, a smart mechanical airbag deployment system is finalized. The response time for the mechanical trigger is 0.133 s, which allows enough time for compressed air to be released before a person falls to the ground. The integrated system is tested and the feasibility of the airbag system for real-time fall protection is demonstrated.

Yuexian Zou

Papers

Dilated convolution neural network with LeakyReLU for environmental sound classification

A recursive least M-estimate algorithm for robust adaptive filtering in impulsive noise: fast algorithm and convergence performance analysis

Least mean M-estimate algorithms for robust adaptive filtering in impulse noise

An experimental study of speech emotion recognition based on deep convolutional neural networks

Mobile Human Airbag System for Fall Protection Using MEMS Sensors and Embedded SVM Classifier