To effectively detect stroke patients after falling under e-health, the fall situation of patients is graded, and warnings are given to improve the survival probability of stroke patients after falling. First, the fal...
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To effectively detect stroke patients after falling under e-health, the fall situation of patients is graded, and warnings are given to improve the survival probability of stroke patients after falling. First, the fall model of stroke patients is analysed. According to the model, multi-modal information fusion fall detection technology is proposed, including data fusion algorithm and feature recognition technology. Also, various sensor data are adjusted. The fall detection cascade algorithm is proposed to classify data with different features in order, thereby completing target detection. Finally, combined with heart rate sensor, height sensor and microcontroller unit software and patients' e-health information, the research and development of an information collection system for fall detection are realized. Six young volunteers are selected to test the system performance. The results show that for the six testers, the heart rate detected by the ordinary device and the device of this investigation is the same when it is resting in different states of resting, walking, as well as walking and falling. The heart rate difference between walking and falling detection is not large (within the allowable error range of the device). But the best detection effect is to measure after the patient falls, which not only reduces power consumption but also keeps the detection error to a minimum. The height sensor is in the static state, increased by 75 m in the vertical direction and decreased by 75 cm from the static position in the vertical direction. The height difference of the data information exported from these three cases has some errors compared with the actual 75 cm. The tester's three situations of resting, walking and falling, standing up after sitting still and falling are observed. The waveform when resting is stable, and the acceleration information also fluctuates significantly when walking and after standing up. The accuracy of the developed system is above 80%, wh
As the light of the belt conveyor changes drastically and unevenly, it will cause the longitudinal tear detection rate of the traditional methods drop sharply. To improve the situation, a novel method, which replaces ...
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As the light of the belt conveyor changes drastically and unevenly, it will cause the longitudinal tear detection rate of the traditional methods drop sharply. To improve the situation, a novel method, which replaces the traditional geometric features with the Haar features, is proposed. First, the Haar feature was employed to train weak classifiers. Then, the AdaBoost algorithm is utilized to upgrade the weak classifiers to strong classifiers. Finally, the cascade algorithm is introduced to combine strong classifiers in series into a cascade classifier that can reduce the training and processing time. The experiment following the above proposed method has shown that the recall, accuracy, and precision of tear detection under uneven light almost approaches the level under the uniform light (less than 3% difference), which indicates that our method is more accurate and robust than the existing methods in real-time belt tear detection.
In the desire to quantify the success of neural networks in deep learning and other applications, there is a great interest in understanding which functions are efficiently approximated by the outputs of neural networ...
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In the desire to quantify the success of neural networks in deep learning and other applications, there is a great interest in understanding which functions are efficiently approximated by the outputs of neural networks. By now, there exists a variety of results which show that a wide range of functions can be approximated with sometimes surprising accuracy by these outputs. For example, it is known that the set of functions that can be approximated with exponential accuracy (in terms of the number of parameters used) includes, on one hand, very smooth functions such as polynomials and analytic functions and, on the other hand, very rough functions such as the Weierstrass function, which is nowhere differentiable. In this paper, we add to the latter class of rough functions by showing that it also includes refinable functions. Namely, we show that refinable functions are approximated by the outputs of deep ReLU neural networks with a fixed width and increasing depth with accuracy exponential in terms of their number of parameters. Our results apply to functions used in the standard construction of wavelets as well as to functions constructed via subdivision algorithms in Computer Aided Geometric Design.
In view of the problem that the convolution neural network research of facial expression recognition ignores the internal relevance of the key links, which leads to the low accuracy and speed of facial expression reco...
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ISBN:
(纸本)9781728186160
In view of the problem that the convolution neural network research of facial expression recognition ignores the internal relevance of the key links, which leads to the low accuracy and speed of facial expression recognition, and can't meet the recognition requirements, a series cascade algorithm model for expression recognition of educational robot is constructed and enables the educational robot to recognize multiple students' facial expressions simultaneously, quickly and accurately in the process of movement, in the balance of the accuracy, rapidity and stability of the algorithm, based on the cascade convolution neural network model. Through the CK+ and Oulu-CASIA expression recognition database, the expression recognition experiments of this algorithm are compared with the commonly used STM-ExpLet and FN2EN cascade network algorithms. The results show that the accuracy of the expression recognition method is more than 90%. Compared with the other two commonly used cascade convolution neural network methods, the accuracy of expression recognition is significantly improved.
Cancer is a disease that is found in many forms. Early diagnosis process significantly affects follow-up of the disease. As in other diseases, it is important to classify the data in cancer cases to determine whether ...
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Cancer is a disease that is found in many forms. Early diagnosis process significantly affects follow-up of the disease. As in other diseases, it is important to classify the data in cancer cases to determine whether the person belongs to healthy-patient or high-low risk groups. For this purpose, machine learning based on artificial intelligence can be used as a very effective method to follow both the progress and the treatment response process of such diseases and to reveal important features of data sets. In this publication, breast cancer diagnosis was carried out using Principal Component Analysis-Support Vector Machine (PCA-SVM) and proposed parallel Principal Component Analysis-Linear Discriminant Analysis-Support Vector Machine (PCA-LDA-SVM) model classifier algorithms, by LabVIEW. LabVIEW, known as Virtual Instrument (VI), is a graphical programming language. The durableness of the used algorithms is analyzed using accuracy, sensitivity, specificity, rand index, False Positive Rate (FPR), False Discovery Rate (FDR), False Negative Rate (FNR), Negative Predictive Value (NPV), Matthews Correlation Coefficient (MCC) parameters and status detection. The obtained results are compared with each other. After training, of the 140 data used in the test set, 130 were used for the test performance analysis and 10 data were used for the status determination of the newly entered data. Performance analysis has been examined for Polynomial and Gaussian kernel functions. The proposed parallel model provides improvement especially for the Polynomial kernel function. With the proposed model, an increase in classification accuracy was observed in the test phase compared to PCA-SVM, and it was observed that 10 data used for status determination were correctly classified. (C) 2020 Elsevier B.V. All rights reserved.
In this study, we present a new family of discrete wavelets which are constructed with the help of Laguerre polynomials and the Daubechies biorthogonal wavelets construction method. Our aim is to propose the discrete ...
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In this study, we present a new family of discrete wavelets which are constructed with the help of Laguerre polynomials and the Daubechies biorthogonal wavelets construction method. Our aim is to propose the discrete version of some previously constructed continuous Laguerre wavelets and also to present a method of discrete wavelets construction by several iterations. With this scheme, we use two different sets of finite impulse response filters for the signal decomposition and their duals for reconstruction. The quadruplet finite impulse response filters respect the a nti-aliasing and the perfect reconstruction conditions, and at the same time, they resemble as much as possible the continuous Laguerre wavelets when using the cascade algorithm. We use the mean squared error, the maximum deviation, and the standard deviation to quantify the similarity between the continuous Laguerre wavelets and the constructed discrete Laguerre *** results show that, they are both the same wavelets due to the small nature of these parameters. Our method is important because, it can permit the determination of the finite impulse response filter coefficients corresponding to many other continuous wavelets.
Face detection algorithm based on a cascade of ensembles of decision trees (CEDT) is presented. The new approach allows detecting faces other than the front position through the use of multiple classifiers. Each class...
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Face detection algorithm based on a cascade of ensembles of decision trees (CEDT) is presented. The new approach allows detecting faces other than the front position through the use of multiple classifiers. Each classifier is trained for a specific range of angles of the rotation head. The results showed a high rate of productivity for CEDT on images with standard size. The algorithm increases the area under the ROC-curve of 13% compared to a standard Viola-Jones face detection algorithm. Final realization of given algorithm consist of 5 different cascades for frontal/non-frontal faces. One more thing which we take from the simulation results is a low computational complexity of CEDT algorithm in comparison with standard Viola-Jones approach. This could prove important in the embedded system and mobile device industries because it can reduce the cost of hardware and make battery life longer.
We introduce Evenly cascaded convolutional Network (ECN), a neural network taking inspiration from the cascade algorithm of wavelet analysis. ECN employs two feature streams - a low-level and high-level steam. At each...
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ISBN:
(纸本)9781538650356
We introduce Evenly cascaded convolutional Network (ECN), a neural network taking inspiration from the cascade algorithm of wavelet analysis. ECN employs two feature streams - a low-level and high-level steam. At each layer these streams interact, such that low-level features are modulated using advanced perspectives from the high-level stream. ECN is evenly structured through resizing feature map dimensions by a consistent ratio, which removes the burden of ad-hoc specification of feature map dimensions. ECN produces easily interpretable features maps, a result whose intuition can be understood in the context of scale-space theory. We demonstrate that ECN's design facilitates the training process through providing easily trainable shortcuts. We report new state-of-the-art results for small networks, without the need for additional treatment such as pruning or compression - a consequence of ECN's simple structure and direct training. A 6-layered ECN design with under 500k parameters achieves 95.24% and 78.99% accuracy on CIFAR-10 and CIFAR-100 datasets, respectively, outperforming the current state-of-the-art on small parameter networks, and a 3 million parameter ECN produces results competitive to the state-of-the-art.
In this paper, we propose a novel cascade detection algorithm which focuses on point and line defects on TFT-LCD. At the first step of the algorithm, we use the gray level difference of su-bimage to segment the abnorm...
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ISBN:
(纸本)9781510613058;9781510613041
In this paper, we propose a novel cascade detection algorithm which focuses on point and line defects on TFT-LCD. At the first step of the algorithm, we use the gray level difference of su-bimage to segment the abnormal area. The second step is based on phase only transform (POT) which corresponds to the Discrete Fourier Transform (DFT), normalized by the magnitude. It can remove regularities like texture and noise. After that, we improve the method of setting regions of interest (ROI) with the method of edge segmentation and polar transformation. The algorithm has outstanding performance in both computation speed and accuracy. It can solve most of the defect detections including dark point, light point, dark line, etc.
Let A be a dilation matrix, an n x n expansive matrix that maps Z(n) into itself. Let. be a finite subset of Z(n), and for kappa is an element of Lambda let c kappa be r x r complex matrices. The refinement equation c...
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Let A be a dilation matrix, an n x n expansive matrix that maps Z(n) into itself. Let. be a finite subset of Z(n), and for kappa is an element of Lambda let c kappa be r x r complex matrices. The refinement equation corresponding to A, Z(n),Lambda and c = {c(kappa)}(kappa is an element of Lambda) is f(x) = Sigma(kappa is an element of Lambda)c(kappa) f(Ax - kappa). A solution f : R-n -> C-r, if one exists, is called a refinable vector function or a vector scaling function of multiplicity r. This paper characterizes the higher-order smoothness of compactly supported solutions of the refinement equation, in terms of the p-norm joint spectral radius of a finite set of finite matrices determined by the coefficients c(kappa).
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