The signal lamp is affected by the haze weather conditions, and the color of the signal lamp image taken will be disturbed to a certain extent. Before color threshold segmentation, it is necessary to restore and enhan...
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In this paper, Fabric defect detection is a challenging task because of the complex texture. Deep learning technology provide a promising solution. As a kind of deep learning object detection model. Single Shot Multib...
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ISBN:
(纸本)9781450363860
In this paper, Fabric defect detection is a challenging task because of the complex texture. Deep learning technology provide a promising solution. As a kind of deep learning object detection model. Single Shot Multibox Detector(SSD) achieves good detection performance. However, the original SSD model may fail to detect the small objects. In this paper, we proposed a novel SSD model for fabric defect detection. Experimental results showed that the improved SSD model can accurately detect the defect region.
A very active expansion of data and convenience in modern period accept to motivate extremely significant automation tasks with the advanced algorithmic models with the technologies such as Artificial Intelligence, De...
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An adaptive algorithm for selection of Intrinsic Mode Functions ( IMF) of Empirical Mode Decomposition (EMD) is a time demand in the field of signalprocessing. This paper presents a new model of an effective algorith...
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ISBN:
(纸本)9781509057856
An adaptive algorithm for selection of Intrinsic Mode Functions ( IMF) of Empirical Mode Decomposition (EMD) is a time demand in the field of signalprocessing. This paper presents a new model of an effective algorithm for the adaptive selection of IMFs for the EMD. Our proposed model suggests the decomposition of an input signal using EMD, and the resultant IMFs are classified into two categories the relevant noise free IMFs and the irrelevant noise dominant IMFs using a trained Support Vector machine (SVM). The Pearson Correlation Coefficient (PCC) is used for the supervised training of SVM. Noise dominant IMFs are then de-noised using the Savitzky-Golay filter. The signal is reconstructed using both noise free and de-noised IMFs. Our proposed model makes the selection process of IMFs adaptive and it achieves high signal to Noise Ratio (SNR) while the Percentage of RMS Difference (PRD) and Max Error values are low. Experimental result attained up to 41.79% SNR value, PRD and Max Error value reduced to 0.814% and 0.081%, respectively compared to other models.
In this paper, a cognitive jammer is developed which can adaptively and optimally jam the radar and protect the target from being detected. The interaction of the cognitive jammer and the environment is modeled as a f...
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ISBN:
(纸本)9781728151021
In this paper, a cognitive jammer is developed which can adaptively and optimally jam the radar and protect the target from being detected. The interaction of the cognitive jammer and the environment is modeled as a finite Markov Decision Process based on the framework of reinforcement learning. Q-learning algorithm is used to solve the optimal jamming frequency selection problem. After several interactions, the jammer can learn the radar's strategy and optimize its jamming frequency to achieve a larger jamming-plus-noise to signal ratio (JNSR) during the whole process. Numerical results are given to illustrate the effectiveness of the proposed method. Compared with a random jamming frequency selection method, the JNSR of the proposed method is significantly larger.
In recent years, many researches focus on sound source localization based on neural networks, which is an appealing but difficult problem. In this paper, a novel time-domain end-to-end method for sound source localiza...
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ISBN:
(纸本)9781728151021
In recent years, many researches focus on sound source localization based on neural networks, which is an appealing but difficult problem. In this paper, a novel time-domain end-to-end method for sound source localization is proposed, where the model is trained by two strategies with both cross entropy loss and mean square error loss. Based on the idea of multi-task learning, CNN is used as the shared hidden layers to extract features and DNN is used as the output layers for each task. Compared with SRP-PHAT, MUSIC and a DNN-based method, the proposed method has better performance.
We propose a graph-based deep network for predicting the associations pertaining to field labels and field values in heterogeneous handwritten form images. We consider forms in which the field label comprises printed ...
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ISBN:
(纸本)9781728150543
We propose a graph-based deep network for predicting the associations pertaining to field labels and field values in heterogeneous handwritten form images. We consider forms in which the field label comprises printed text and field value can be the handwritten text. Inspired by the relationship predicting capability of the graphical models, we use a Graph Autoencoder to perform the intended field label to field value association in a given form image. To the best of our knowledge, it is the first attempt to perform label-value association in a handwritten form image using a machinelearning approach. We have prepared our handwritten form image dataset comprising 300 images from 30 different templates having 10 images per template. Our framework is experimented on different network parameter and has shown promising results.
An emerging trend in digital design is approximate computing, which trades off the need for precise computation for increased speed and efficiency. These circuits have been the promising solution for energy-efficient ...
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Asthma is a chronic respiratory disorder characterised by airway inflammation and constriction, leading to difficulty in breathing and recurrent attacks of wheezing, coughing, and shortness of breath. In asthma, vario...
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We study the problem of learning a mixture model of non-parametric product distributions. The problem of learning a mixture model is that of finding the component distributions along with the mixing weights using obse...
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We study the problem of learning a mixture model of non-parametric product distributions. The problem of learning a mixture model is that of finding the component distributions along with the mixing weights using observed samples generated from the mixture. The problem is well-studied in the parametric setting, i.e., when the component distributions are members of a parametric family such as Gaussian distributions. In this work, we focus on multivariate mixtures of non-parametric product distributions and propose a two-stage approach which recovers the component distributions of the mixture under a smoothness condition. Our approach builds upon the identifiability properties of the canonical polyadic (low-rank) decomposition of tensors, in tandem with Fourier and Shannon-Nyquist sampling staples from signalprocessing. We demonstrate the effectiveness of the approach on synthetic and real datasets.
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