Spectrum sensing is a key technology for cognitive *** present spectrum sensing as a classification problem and propose a sensing method based on deep learning *** normalize the received signal power to overcome the e...
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Spectrum sensing is a key technology for cognitive *** present spectrum sensing as a classification problem and propose a sensing method based on deep learning *** normalize the received signal power to overcome the effects of noise power *** train the model with as many types of signals as possible as well as noise data to enable the trained network model to adapt to untrained new *** also use transfer learning strategies to improve the performance for real-world *** experiments are conducted to evaluate the performance of this *** simulation results show that the proposed method performs better than two traditional spectrum sensing methods,i.e.,maximum-minimum eigenvalue ratio-based method and frequency domain entropy-based *** addition,the experimental results of the new untrained signal types show that our method can adapt to the detection of these new ***,the real-world signal detection experiment results show that the detection performance can be further improved by transfer ***,experiments under colored noise show that our proposed method has superior detection performance under colored noise,while the traditional methods have a significant performance degradation,which further validate the superiority of our method.
Target detecting algorithm in infrared image is drawing extensive attention both at home and abroad, expecially when the infrared images own complex backgrounds and low *** to make sure of the accuracy of infrared dim...
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Target detecting algorithm in infrared image is drawing extensive attention both at home and abroad, expecially when the infrared images own complex backgrounds and low *** to make sure of the accuracy of infrared dim-small target detecting is the key point during the processing *** this paper, a novel region of interest detecting algorithm is proposed for aerial small dim infrared target with clutter *** from traditional region of interest extraction method, we extract region of interest based on the grayscale merging algorithm and connected components analysising ***, the infrared image is reconstructed by separating the grayscale into several interval sections, then binarization is used to segment the processed image according to estimate the threshold value using minimum error probability;finally, analysis and select the connected components, to sift the accurate connected domain which contain the *** validity of the proposed approach is demonstrated by experiment of infrared target region extraction.
Target detection and location in infrared clutter background is very important to infrared search and track system. Especially for small target detection in infrared image in background of sea and sky, there are no ge...
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Memristor has received significant attentions since Strukov et al released their invention on April 30, 2008 at Nature Letters. Based on the model of Strukov et al, analytic expression of the internal state is obtaine...
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This paper proposes a fast and robust algorithm for classification and recognition of ships based on the Principal Component Analysis (PCA) method. The three-dimensional ship models are achieved by modeling software o...
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This paper proposes a fast and robust algorithm for classification and recognition of ships based on the Principal Component Analysis (PCA) method. The three-dimensional ship models are achieved by modeling software of MultiGen, and then they are projected by Vega simulating software for two-dimensional ship silhouettes. The PCA method as against the Back-Propagation (BP) neural network method for simulated ship recognition using training and testing experiments, we can see that there is a sharp contrast between them. Some recognition results from simulated data are presented, the correct recognition rate of PCA method improved rapidly for each of the five ship types than that of neural network method, the number of times a ship type is recognized as one of the other ships is reduced greatly.
A Linear Quadratic robust control algorithm is proposed for the system with non-stochastic *** this paper,the uncertainties are the noises which lie in a bounded ellipsoid *** assumption weakens the requirements of th...
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ISBN:
(纸本)9781509046584
A Linear Quadratic robust control algorithm is proposed for the system with non-stochastic *** this paper,the uncertainties are the noises which lie in a bounded ellipsoid *** assumption weakens the requirements of the known Gaussian distribution in the traditional Linear Quadratic Gaussian(LQG) *** on the linear characteristic of the system and robust optimization theory,the linear quadratic robust controller is *** simulation results show the effectiveness of the algorithm.
In this paper, we take the advantages of color contrast and color distribution to get high quality saliency maps. The overall procedure flow of our unified framework contains superpixel pre-segmentation, color contras...
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A new change detection approach based on non-parametric density estimation and Markov random fields is proposed in this paper. As the concrete form of gray statistical distribution of remote sensing images is often di...
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C-mode imaging is one of the ultrasound imaging modalities. Compared with other modalities, e.g. A-mode, B-mode, M-mode, and Doppler, C-mode is mainly developed and used in industry testing. The potential of C-mode im...
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Facial expression recognition (FER) is challenging, when transiting from the laboratory to in-the-wild situations. In this paper, we present a general framework for the Learning from Synthetic Data Challenge in the 4t...
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