In this paper, we fuse EEG and forehead EOG to detect drivers' fatigue level by using discriminative graph regularized extreme learning machine (GELM). Twenty-one healthy subjects including twelve men and nine wom...
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ISBN:
(纸本)9781509006212
In this paper, we fuse EEG and forehead EOG to detect drivers' fatigue level by using discriminative graph regularized extreme learning machine (GELM). Twenty-one healthy subjects including twelve men and nine women participate in our driving simulation experiments. Two fusion strategies are adopted: feature level fusion (FLF) and decision level fusion (DLF). PERCLOS (the percentage of eye closure) is calculated by using the eye movement data recorded by eye tracking glasses as the indicator of drivers' fatigue level. The prediction correlation coefficient and root mean square error (RMSE) between the estimated fatigue level and the real fatigue level are both used to evaluate the performance of single modality and fusion modality. A comparative study on modality performance is conducted between GELM and support vector machine (SVM). The experimental results show that fusion modality can improve the performance of driving fatigue detection with a higher prediction correlation coefficient and a lower RMSE value in comparison with solely using EEG or forehead EOG. And FLF achieves better performance than DLF. GELM is more suitable for driving fatigue detection than SVM. Moreover, feature level fusion with GELM achieves the best performance with the prediction correlation coefficient of 0.8080 and the RMSE value of 0.0712 on average.
We demonstrate light-emitting hyperbolic metasurfaces in the 1200-1600nm spectral range. The multilayer configuration, ideal for planar integration, enables characterization of hyperbolic dispersion by polarization an...
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This study aims at measuring last-night sleep quality from electroencephalography (EEG). We design a sleep experiment to collect waking EEG signals from eight subjects under three different sleep conditions: 8 hours s...
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ISBN:
(纸本)9781509006212
This study aims at measuring last-night sleep quality from electroencephalography (EEG). We design a sleep experiment to collect waking EEG signals from eight subjects under three different sleep conditions: 8 hours sleep, 6 hours sleep, and 4 hours sleep. We utilize three machine learning approaches, k-Nearest Neighbor (kNN), support vector machine (SVM), and discriminative graph regularized extreme learning machine (GELM), to classify extracted EEG features of power spectral density (PSD). The accuracies of these three classifiers without feature selection are 36.68%, 48.28%, 62.16%, respectively. By using minimal-redundancy-maximal-relevance (MRMR) algorithm and the brain topography, the classification accuracy of GELM with 9 features is improved largely and increased to 83.57% in average. To investigate critical frequency bands for measuring sleep quality, we examine the features of each band and observe their energy changing. The experimental results indicate that Gamma band is more relevant to measuring sleep quality.
Speech separation or enhancement algorithms seldom exploit information about phoneme identities. In this study, we propose a novel phoneme-specific speech separation method. Rather than training a single global model ...
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Speech separation or enhancement algorithms seldom exploit information about phoneme identities. In this study, we propose a novel phoneme-specific speech separation method. Rather than training a single global model to enhance all the frames, we train a separate model for each phoneme to process its corresponding frames. A robust ASR system is employed to identify the phoneme identity of each frame. This way, the information from ASR systems and language models can directly influence speech separation by selecting a phoneme-specific model to use at the test stage. In addition, phoneme-specific models have fewer variations to model and do not exhibit the data imbalance problem. The improved enhancement results can in turn help recognition. Experiments on the corpus of the second CHiME speech separation and recognition challenge (task-2) demonstrate the effectiveness of this method in terms of objective measures of speech intelligibility and quality, as well as recognition performance.
This paper investigates a new voice conversion technique using phone-aware Long Short-Term Memory Recurrent Neural Networks（LSTM-RNNs）. Most existing voice conversion methods, including Joint Density Gaussian Mixtur...
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This paper investigates a new voice conversion technique using phone-aware Long Short-Term Memory Recurrent Neural Networks（LSTM-RNNs）. Most existing voice conversion methods, including Joint Density Gaussian Mixture Models（JDGMMs）, Deep Neural Networks（DNNs）and Bidirectional Long Short-Term Memory Recurrent Neural Networks（BLSTM-RNNs）, only take acoustic information of speech as features to train models. We propose to incorporate linguistic information to build voice conversion system by using monophones generated by a speech recognizer as linguistic features. The monophones and spectral features are combined together to train LSTM-RNN based voice conversion models,reinforcing the context-dependency modelling of *** results of the 1st voice conversion challenge shows our system achieves significantly higher performance than baseline（GMM method） and was found among the most competitive scores in similarity test. Meanwhile, the experimental results show phone-aware LSTM-RNN method obtains lower Melcepstral distortion and higher MOS scores than the baseline LSTM-RNNs.
Artificial neural networks(ANN) have been used in many applications such like handwriting recognition and speech recognition. It is well-known that learning rate is a crucial value in the training procedure for artifi...
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Artificial neural networks(ANN) have been used in many applications such like handwriting recognition and speech recognition. It is well-known that learning rate is a crucial value in the training procedure for artificial neural networks. It is shown that the initial value of learning rate can confoundedly affect the final result and this value is always set manually in practice. A new parameter called beta stabilizer has been introduced to reduce the sensitivity of the initial learning rate. But this method has only been proposed for deep neural network(DNN) with sigmoid activation function. In this paper we extended beta stabilizer to long short-term memory(LSTM) and investigated the effects of beta stabilizer parameters on different models, including LSTM and DNN with relu activation *** is concluded that beta stabilizer parameters can reduce the sensitivity of learning rate with almost the same performance on DNN with relu activation function and LSTM. However, it is shown that the effects of beta stabilizer on DNN with relu activation function and LSTM are fewer than the effects on DNN with sigmoid activation function.
Recent developments in miniaturized microscopes have furthered the quest to visualize brain activities and structural dynamics in animals engaged in self-determined behaviors. However, it remains an unmet challenge to...
Recent developments in miniaturized microscopes have furthered the quest to visualize brain activities and structural dynamics in animals engaged in self-determined behaviors. However, it remains an unmet challenge to resolve activity at single dendritic spines, the elemental units underlying neuronal computation, in freely-behaving animals. Here, we report the design, testing, and application of a fast, high-resolution, miniaturized two-photon microscope（FIRM-TPM） that accomplishes this goal. With a headpiece weighing 2.15 g and a new type of hollow-core photonic crystal fiber to deliver 920-nm femtosecond laser pulses, the FIRM-TPM is capable of imaging commonly used biosensors at high spatiotemporal resolution（0.64 μm laterally and 3.35 μm axially, 40 Hz at 256 × 256 pixels）. Its micro-electromechanical systems scanner also enables random-access capability and free-line scanning at up to 10,000 Hz. It compares favorably with benchtop two-photon microscopy and miniature wide-field fluorescence microscopy in the structural and functional imaging of Thy1-GFP-or GCa MP6 f-labeled neurons. Further, we demonstrate its unique application and robustness with hour-long recording of neuronal activities down to the level of spines in mice experiencing vigorous body and head movements or engaging in social interaction. Thus, our new generation miniature microscope provides neuroscientists the long-sought tool-of-choice for imaging the brain at the synaptic level in freely-behaving animals.
We examine the effect of carrier localization due to random alloy fluctuations on the radiative and Auger recombination rates in InGaN quantum wells as a function of alloy composition, crystal orientation, carrier den...
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