Stress is an emotional state which although experienced in a subjective way, it shares specific common characteristics. Objective stress recognition has proven to be a complicated issue, due to the number of parameter...
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ISBN:
(纸本)9781728138916
Stress is an emotional state which although experienced in a subjective way, it shares specific common characteristics. Objective stress recognition has proven to be a complicated issue, due to the number of parameters involved. Thus, the investigation of reliable indices associated with the stress response is of utmost importance. Heart activity may provide useful information towards this goal. Traditional machine learning techniques have been used in the area of emotion recognition but they sometimes present specific limitations. The emergence of deeplearning (DL) techniques permits the reveal underlying patterns in electrocardiography (ECG) which, otherwise, would not be easily observed. The proposed DL architecture utilizes a variety of kernels per module to compute complex feature maps and enables a multi-level modelling of the unique heart rate variability signature for stress state identification. The proposed methodology using 6-fold cross-validation outperforms single kernel networks achieving classification accuracy up to 99.1%, better overall performance (avg. F1-score 88.1%, avg. accuracy 89.8%) and more consistent behaviour across study's experimental phases.
In recent years, there has been significant progress in recognition of text from scene images. However, very less work is done for Kannada language. The focus of this paper is to identify Kannada character set. Resear...
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ISBN:
(纸本)9781728108995
In recent years, there has been significant progress in recognition of text from scene images. However, very less work is done for Kannada language. The focus of this paper is to identify Kannada character set. Researchers have purported a number of deeplearning procedures for natural scene text detection and recognition. In this work, basic transfer learning neural network classification models are trained with 1700 scene Kannada character images extracted from Chars74K standard dataset, and labelled individual folders as per character names. Stochastic gradient descent solver with different epoch, learning rate and momentum is applied. Also, the performance of Alex net model is improved by inducing a batch normalization layer in the hidden layers. The results show that for characteristic features of Kannada text, the combination of Vgg19 network model used with higher split ratio and stated training options and values gave 100% accuracy. Also, the modified Alex net model performed better with accuracy up to 96 percent and lower error rate of 0.0134.
The growing popularity of the mini-camera is posing a serious threat to privacy and personal security. Disguised as common tools in rooms, these devices can become undetectable. Moreover, conventional active laser det...
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The growing popularity of the mini-camera is posing a serious threat to privacy and personal security. Disguised as common tools in rooms, these devices can become undetectable. Moreover, conventional active laser detection systems often fail to recognize them owing to their small lens size, weak reflectivity, and the influence of interference targets. In this paper, a method for building a laser active detection system for minicameras is proposed. Using a monostatic optical system and a deep learning classification algorithm, this anti-camera system can detect mini-cameras accurately in real time. This article describes the system components including its optical design, core components and image processing algorithm. The capability of the system for detecting mini-cameras and identifying interference is also experimentally demonstrated. This work successfully overcomes the limit of mini-camera detection using deeplearning methods in active laser detection systems.
Remote sensing hyperspectral imaging can obtain rich spectral information of terrestrial objects, which allows the indistinguishable matter in the traditional wideband remote sensing to be distinguished in hyperspectr...
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ISBN:
(纸本)9789881563958
Remote sensing hyperspectral imaging can obtain rich spectral information of terrestrial objects, which allows the indistinguishable matter in the traditional wideband remote sensing to be distinguished in hyperspectral remote sensing. Hyperspectral image has the characteristics of "combining image with spectnim". Making hill use of spectral information and spatial information in hyperspectral image is the premise of obtaining accurate classification results. At present, most of hyperspectral data feature extraction algorithms mainly utilize local spatial information in the same channel and spectral information in the same spatial location of different channels. However, these methods require a large amount of prior knowledge, it is difficult to fully grasp the hyperspectral data of all spatial and spectral information, and the model generalization ability is poor. With the development of deeplearning, convolutional neural network shows superior performance in all kinds of visual tasks, especially in the two-dimensional image classification, and could get a high classification accuracy. In this paper, an image classification method based on three-dimensional convolution neural network is proposed based on the stnictural properties of hyperspectral data. In the proposed method, first the stereo image blocks of hyperspectral data are intercepted, then multi-layer convolution and pooling operation of extracted blocks by convolutional neural network are implemented to obtain the essential information of hyperspectral data, finally the classification of hyperspectral data is completed. The experimental results show the proposed method could provide better feature expression and classification accuracy for hyperspectral image.
Remote sensing hyperspectral imaging can obtain rich spectral information of terrestrial objects, which allows the indistinguishable matter in the traditional wideband remote sensing to be distinguished in hyperspectr...
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Remote sensing hyperspectral imaging can obtain rich spectral information of terrestrial objects, which allows the indistinguishable matter in the traditional wideband remote sensing to be distinguished in hyperspectral remote *** image has the characteristics of "combining image with spectrum". Making full use of spectral information and spatial information in hyperspectral image is the premise of obtaining accurate classification results. At present, most of hyperspectral data feature extraction algorithms mainly utilize local spatial information in the same channel and spectral information in the same spatial location of different channels. However, these methods require a large amount of prior knowledge, it is difficult to fully grasp the hyperspectral data of all spatial and spectral information, and the model generalization ability is poor. With the development of deeplearning, convolutional neural network shows superior performance in all kinds of visual tasks, especially in the two-dimensional image classification, and could get a high classification accuracy. In this paper, an image classification method based on three-dimensional convolution neural network is proposed based on the structural properties of hyperspectral data. In the proposed method, first the stereo image blocks of hyperspectral data are intercepted, then multi-layer convolution and pooling operation of extracted blocks by convolutional neural network are implemented to obtain the essential information of hyperspectral data, finally the classification of hyperspectral data is completed. The experimental results show the proposed method could provide better feature expression and classification accuracy for hyperspectral image.
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