• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권12호
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• pp.6188-6204
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• 2017

Over the years, more password-based authentication key agreement schemes using chaotic maps were susceptible to attack by off-line password guess attack. This work approaches this problem by a new method--new theorem of chaotic maps: $T_{a+b}(X)+T_{a-b}(X)=2T_a(X)T_b(X)$,(a>b). In fact, this method can be used to design two-party, three-party, even in N-party intelligently. For the sake of brevity and readability, only a two-party instance: a novel Two-party Password-Authenticated Key Agreement Protocol is proposed for resisting password guess attack in this work. Compared with the related literatures recently, our proposed scheme can be not only own high efficiency and unique functionality, but is also robust to various attacks and achieves perfect forward secrecy. For capturing improved ratio of security and efficiency intuitively, the paper firstly proposes a new parameter called security/efficiency ratio(S/E Ratio). The higher the value of the S/E Ratio, the better it is. Finally, we give the security proof and the efficiency analysis of our proposed scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권5호
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• pp.2171-2185
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• 2020

In order to judge that whether the vehicles in different images which are captured by surveillance cameras represent the same vehicle or not, we proposed a novel vehicle face recognition algorithm based on improved Nonnegative Matrix Factorization (NMF), different from traditional vehicle recognition algorithms, there are fewer effective features in vehicle face image than in whole vehicle image in general, which brings certain difficulty to recognition. The innovations mainly include the following two aspects: 1) we proposed a novel idea that the vehicle type can be determined by a few key regions of the vehicle face such as logo, grille and so on; 2) Through adding weight, sparseness and classification property constraints to the NMF model, we can acquire the effective feature bases that represent the key regions of vehicle face image. Experimental results show that the proposed algorithm not only achieve a high correct recognition rate, but also has a strong robustness to some non-cooperative factors such as illumination variation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권1호
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• pp.451-465
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• 2017

This paper presents a novel discriminative visual tracking algorithm with an adaptive incremental extreme learning machine. The parameters for an adaptive incremental extreme learning machine are initialized at the first frame with a target that is manually assigned. At each frame, the training samples are collected and random Haar-like features are extracted. The proposed tracker updates the overall output weights for each frame, and the updated tracker is used to estimate the new location of the target in the next frame. The adaptive learning rate for the update of the overall output weights is estimated by using the confidence of the predicted target location at the current frame. Our experimental results indicate that the proposed tracker can manage various difficulties and can achieve better performance than other state-of-the-art trackers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권11호
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• pp.4573-4584
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• 2015

The traditional feature extraction algorithms for recognition of communication signals can hardly realize the balance between computational complexity and signals' interclass gathered degrees. They can hardly achieve high recognition rate at low SNR conditions. To solve this problem, a novel feature extraction algorithm based on Holder coefficient was proposed, which has the advantages of low computational complexity and good interclass gathered degree even at low SNR conditions. In this research, the selection methods of parameters and distribution properties of the extracted features regarding Holder coefficient theory were firstly explored, and then interval gray relation algorithm with improved adaptive weight was adopted to verify the effectiveness of the extracted features. Compared with traditional algorithms, the proposed algorithm can more accurately recognize signals at low SNR conditions. Simulation results show that Holder coefficient based features are stable and have good interclass gathered degree, and interval gray relation classifier with adaptive weight can achieve the recognition rate up to 87% even at the SNR of -5dB.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권3호
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• pp.941-953
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• 2012

A novel and universal method of video image text detection is proposed. A coarse-to-fine text detection method is implemented. Firstly, the spectral clustering (SC) method is adopted to coarsely detect text regions based on the stationary wavelet transform (SWT). In order to make full use of the information, multi-parameters kernel function which combining the features similarity information and spatial adjacency information is employed in the SC method. Secondly, 28 dimension classifying features are proposed and support vector machine (SVM) is implemented to classify text regions with non-text regions. Experimental results on video images show the encouraging performance of the proposed algorithm and classifying features.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권4호
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• pp.1140-1152
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• 2012

A novel and universal method of video image text detection is proposed. A coarse-to-fine text detection method is implemented. Firstly, the spectral clustering (SC) method is adopted to coarsely detect text regions based on the stationary wavelet transform (SWT). In order to make full use of the information, multi-parameters kernel function which combining the features similarity information and spatial adjacency information is employed in the SC method. Secondly, 28 dimension classifying features are proposed and support vector machine (SVM) is implemented to classify text regions with non-text regions. Experimental results on video images show the encouraging performance of the proposed algorithm and classifying features.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제4권6호
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• pp.1209-1221
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• 2010

Three dimensional (3D) video is expected to be an important application for broadcast and IP streaming services. One of the main limitations for the transmission of 3D video over IP networks is network bandwidth mismatch due to the large size of 3D data, which causes fatal decoding errors and mosaic-like damage. This paper presents a novel selective frame discard method to address the problem. The main idea of the proposed method is the symmetrical discard of the two dimensional (2D) video frame and the depth map frame. Also, the frames to be discarded are selected after additional consideration of the playback deadline, the network bandwidth, and the inter-frame dependency relationship within a group of pictures (GOP). It enables the efficient utilization of the network bandwidth and high quality 3D IPTV service. The simulation results demonstrate that the proposed method enhances the media quality of 3D video streaming even in the case of bad network conditions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권4호
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• pp.1463-1480
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• 2014

It is well known that sparse code is effective for feature extraction of face recognition, especially sparse mode can be learned in the kernel space, and obtain better performance. Some recent algorithms made use of single kernel in the sparse mode, but this didn't make full use of the kernel information. The key issue is how to select the suitable kernel weights, and combine the selected kernels. In this paper, we propose a novel multiple kernel sparse representation based classification for face recognition (MKSRC), which performs sparse code and dictionary learning in the multiple kernel space. Initially, several possible kernels are combined and the sparse coefficient is computed, then the kernel weights can be obtained by the sparse coefficient. Finally convergence makes the kernel weights optimal. The experiments results show that our algorithm outperforms other state-of-the-art algorithms and demonstrate the promising performance of the proposed algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권2호
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• pp.935-949
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• 2016

In this paper, we present a novel authenticated group key distribution scheme for large and dynamic multicast groups without employing traditional symmetric and asymmetric cryptographic operations. The security of our scheme is mainly based on the basic theories for solving linear equations. In our scheme, a large group is divided into many subgroups, where each subgroup is managed by a subgroup key manager (SGKM) and a group key generation center (GKGC) further manages all SGKMs. The group key is generated by the GKGC and then propagated to all group members through the SGKMs, such that only authorized group members can recover the group key but unauthorized users cannot. In addition, all authorized group members can verify the authenticity of group keys by a public one-way function. The analysis results show that our scheme is secure and efficient, and especially it is very appropriate for secure multicast communications in large and dynamic client-server networks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권12호
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• pp.5401-5421
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• 2016

Both low-density parity-check (LDPC) codes and the multiple access technique of spatially coupling data transmission (SCDT) can be expressed in bipartite graphs. To improve the performance of iterative demodulation and decoding for SCDT, a novel joint sparse graph (JSG) with SCDT and LDPC codes is constructed. Based on the JSG, an approach for iterative joint demodulation and decoding by belief propagation (BP) is presented as an exploration of the flooding schedule, and based on BP, density evolution equations are derived to analyze the performance of the iterative receiver. To accelerate the convergence speed and reduce the complexity of joint demodulation and decoding, a novel serial schedule is proposed. Numerical results show that the joint demodulation and decoding for SCDT based on JSG can significantly improve the system's performance, while roughly half of the iterations can be saved by using the proposed serial schedule.