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Dynamical Study of S-Wave $$\bar{Q}Q\bar{q}q$$ Q ¯ Q q ¯ q System
2018
Few-body systems
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In this note we prove that a set of class [1, q + 1, 2q + 1] 2 in PG(3, q) is either a line, or an ovoid, or a (q 2 + q + 1)-set of type (1, q + 1, 2q + 1) 2 or a (q + 1) 2 -set of type (q + 1, 2q + 1) ...
If a = q + 1, then k = q 2 + q + 1, t 1 = q(q − 1)/2, t q+1 = q 3 + q + 1, and t 2q+1 = q(q + 1)/2; so K is a (q 2 + q + 1)-set of type (1, q + 1, 2q + 1) 2 . ...
Finally, we have that q ≤ a ≤ q + 2 or a = 2q. If a = q, then k = q 2 + 1, t 1 = q 2 + 1, t q+1 = q 3 + q, and t 2q+1 = 0; so K is a (q 2 + 1)-set of type (1, q + 1) 2 , i.e. ...
doi:10.1007/s00601-018-1477-5
fatcat:ahkxfl6m2jdupjzjru2cia6enu
Reversible Data Hiding
[chapter]
2005
Lecture Notes in Computer Science
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Shi is an IEEE Fellow for his contribution to multidimensional signal processing, the chairman of Signal Processing Chapter of IEEE North Jersey Section, the founding Editor ...
Q. Shi and N. Ansari are with the Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102-1982 USA (e-mail: Shi@ADM.njit.edu; nirwan.ansari@njit.edu). ...
doi:10.1007/978-3-540-31805-7_1
fatcat:w2chv3bnfjgz7fvrpaukho2eom
Steganalysis Versus Splicing Detection
[chapter]
2008
Lecture Notes in Computer Science
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The block diagram of this image model (thereafter Shi et al.'s) is given in Fig. Feature generation block diagram of Shi et al.'s method
Fig. 5 . 5 Feature generation block diagram of Zou et al.' ...
9 . 9 Mapping images to feature space
Table 1 . 1 Detecting spliced images using four natural image models established in universal steganalysis (standard deviation in parentheses) Hyu and Farid's
Shi ...
doi:10.1007/978-3-540-92238-4_13
fatcat:xeopzwnwfzg6bm5hfx23wypltq
Adaptive image watermarking scheme based on visual masking
1998
Electronics Letters
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Shi (Depurtment of ECE, New Jersey Institute of Technology, University Heights, Newark, JN 07102, USA) Corresponding author: Yun Q. ...
Sel.Jiwu Huang and Yun Q. ShiAn image watermarking scheme in a DCT domain is proposed. ...
doi:10.1049/el:19980545
fatcat:wycpsofzg5gvbeb4ngbvo4rkty
Mini-max initialization for function approximation
2004
Neurocomputing
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Neural networks have been successfully applied to various pattern recognition and function approximation problems. However, the training process remains a time-consuming procedure that often gets stuck in a local minimum. The optimum network size and topology are usually unknown. In this paper, we formulate the concept of extrema equivalence for estimating the complexity of a function. Based on this formulation, the optimal network size and topology can be selected according to the number of
doi:10.1016/j.neucom.2003.10.014
fatcat:7z75bznxd5df3fxzppzzsw66aa
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... rema. Mini-max initialization method is then proposed to select the initial values of the weights for the network that is proven to greatly speed up training. The superior performance of our method in terms of convergence and generalization has been substantiated by experimental results.
MPEG Video Traffic Models: Sequentially Modulated Self-Similar Processes
[chapter]
2000
Broadband Communications
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. + X tm ), t E Q,m E Q, (4) m and Q is a positive integer set. X is said to be exactly second-order selfsimilar [11] if (5) and (6) for all m E {I, 2, 3,··· } and k E {O, 1,2,··· }. ...
doi:10.1007/978-0-387-35579-5_6
fatcat:s56uvqaun5g27b6jwbirt7olau
Steganalyzing Texture Images
2007
2007 IEEE International Conference on Image Processing
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In [2] , another universal steganalysis system was proposed by Shi et al. ...
The prediction-error is given by
, N is the total number of different value level in a 2
Table 1 . 1 stego images with Farid's method [1] (hereinafter Farid), Shi et al's method [2] (hereinafter Shi ...
doi:10.1109/icip.2007.4379115
dblp:conf/icip/ChenSX07
fatcat:dewltkkpv5b6dhjsdm2a3kalhq
Spread Spectrum Video Data Hiding, Interleaving and Synchronization
[chapter]
2004
Intelligent Watermarking Techniques
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, f p: predicted video frame, f r : reconstructed video frame, q: quantized transform coefficients, v: motion vector. ...
Figure 4 . 4 Forward motion estimation and compensation, T: transformer, Q: quantizer, FB: frame buffer, MCP: motion compensated predictor, ME: motion estimator, e: prediction error, f: input video frame ...
doi:10.1142/9789812562524_0018
fatcat:rv766byvmfeergdoyeswwxsjwi
An Enhanced Statistical Approach to Identifying Photorealistic Images
[chapter]
2009
Lecture Notes in Computer Science
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Yun Shi's research group. There are totally 3,000 CG and 3,000 PG. The Q-factors of PG range from 75 to 100 and those of CG from 65 to 98. ...
According to Popescu [54] , double compression introduces periodic peak artifacts to a JPEG mode histogram when the ratio of second quantization step (q 2 ) over the first one (q 1 ) is not an integer ...
doi:10.1007/978-3-642-03688-0_28
fatcat:oxca6jfktfaxxky32tshvd5zoi
Dynamic Bandwidth Allocation for VBR Video Transmission
2003
Journal of Computing and Information Technology
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His current research interests cover xDSL, multimedia communications, digital signal processing, and IP networks.YUN Q. ...
SHI joined the Department of Electrical and Computer Engineering at the New Jersey Institute of Technology, Newark, NJ in 87, and is currently a professor there. ...
doi:10.2498/cit.2003.04.05
fatcat:46ahz6cstbacdkhiw6ybdtpxjy
Do biometric images follow Benford's law?
2014
2nd International Workshop on Biometrics and Forensics
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A natural image model approach to splicing detection
2007
Proceedings of the 9th workshop on Multimedia & security - MM&Sec '07
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Image splicing detection is of fundamental importance in digital forensics and therefore has attracted increasing attention recently. In this paper, we propose a blind, passive, yet effective splicing detection approach based on a natural image model. This natural image model consists of statistical features extracted from the given test image as well as 2-D arrays generated by applying to the test images multi-size block discrete cosine transform (MBDCT). The statistical features include
doi:10.1145/1288869.1288878
dblp:conf/mmsec/ShiCC07
fatcat:bh3phwkrgjemrpq7mupp5vnlp4
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... s of characteristic functions of wavelet subbands and Markov transition probabilities of difference 2-D arrays. To evaluate the performance of our proposed model, we further present a concrete implementation of this model that has been designed for and applied to the Columbia Image Splicing Detection Evaluation Dataset. Our experimental works have demonstrated that this new splicing detection scheme outperforms the state of the art by a significant margin when applied to the above-mentioned dataset, indicating that the proposed approach possesses promising capability in splicing detection.
Feature Selection based on the Bhattacharyya Distance
2006
18th International Conference on Pattern Recognition (ICPR'06)
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This paper presents a Bhattacharyya distance based feature selection method, which utilizes a recursive algorithm to obtain the optimal dimension reduction matrix in terms of the minimum upper bound of classification error under normal distribution for multi-class classification problem. In our scheme, PCA is incorporated as a pre-processing to reduce the intractably heavy computation burden of the recursive algorithm. The superior experimental results on the handwritten-digit recognition with
doi:10.1109/icpr.2006.557
dblp:conf/icpr/XuanZCZSF06a
fatcat:jrjxr7pdsfhz7ijn455ilxoq5y
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... he MNIST database and the steganalysis applications have demonstrated the effectiveness of our proposed method.
Run-Length and Edge Statistics Based Approach for Image Splicing Detection
[chapter]
2009
Lecture Notes in Computer Science
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In [14] , Chen and Shi focused on Fourier phase and made analysis on 2-D phase congruency to extract features for splicing detection. ...
Run-length based statistic moments The motivation of using run-length based statistic moments for splicing detection is due to a recent study by Shi et al. [15] . ...
doi:10.1007/978-3-642-04438-0_7
fatcat:qg7viq45gbhdpazln3kkjx4ss4
Computer graphics identification using genetic algorithm
2008
Pattern Recognition (ICPR), Proceedings of the International Conference on
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This paper proposes the use of genetic algorithm to select an optimal feature set for distinguishing computer graphics from digital photographic images. Our previously developed approach has derived a 234-D feature vector from each test image in HSV color space. The statistical moments of characteristic functions of the image and its wavelet subbands were selected as the distinguishing image features. Since it is possible that only certain image features contain significant information with
doi:10.1109/icpr.2008.4761552
dblp:conf/icpr/ChenSXS08
fatcat:y2ukfpjd3jen7e3qwdojo35uza
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... ect to the classification, the image features with insignificant contributions to classification may be eliminated to reduce the dimensionality of the feature vectors while maximizing the classification performance. Famous for its efficiency in searching the optimal solution in a very large space, the genetic algorithm is applied to find a reduced feature set which consists of only 100-D features per image in our investigation. The experimental results have demonstrated that the 100-D reduced feature set outperforms the 234-D full feature set.
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