We propose a method for the recovery of projective structure and motion by the factorization of the rank 1 matrix containing the images of all points in all views. In our method, the unknowns are the 3D motion and relative depths of the set of points, not their 3D positions. The coordinates of the points along the camera plane are given by their image positions in the first frame. The knowledge of the coordinates along the camera plane enables us to solve the SFM problem by iteratively factorizing the rank 1 matrix. This simplifies the decomposition compared with the SVD (Singular Value Decomposition). Experiments with both simulated and real data show that the method is efficient for the recovery of projective structure and motion.

With the increase of commercial multimedia applications using digital video, the security of video data becomes more and more important. Although several techniques have been proposed in order to protect these video data, they provide limited security or introduce significant overhead. This paper proposes a video security scheme for MPEG video compression standard, which includes two methods: DCEA (DC Coefficient Encryption Algorithm) and "Event Shuffle." DCEA is aim to encrypt group of codewords of DC coefficients. The feature of this method is the usage of data permutation to scatter the ciphertexts of additional codes in DC codewords. These additional codes are encrypted by block cipher previously. With the combination of these algorithms, the method provides enough security for important DC component of MPEG video data. "Event Shuffle" is aim to encrypt the AC coefficients. The prominent feature of this method is a shuffling of AC events generated after DCT transformation and quantization stages. Experimental results show that these methods introduce no bit overhead to MPEG bit stream while achieving low processing overhead to MPEG codec.

The proportionate normalized least mean square algorithm (PNLMS) greatly improves the convergence of the sparse impulse response. It exploits the shape of the impulse response to decide the proportionate step gain for each coefficient. This is not always suitable. Actually, the proportionate step gain should be determined according to the difference between the current estimate of the coefficient and its optimal value. Based on this idea, an approach is proposed to determine the proportionate step gain. The proposed approach can improve the convergence of proportionate adaptive algorithms after a fast initial period. It even behaves well for the non-sparse impulse response. Simulations verify the effectiveness of the proposed approach.

Recently, proportionate adaptive algorithms have been proposed to speed up convergence in the identification of sparse impulse response. Although they can improve convergence for sparse impulse responses, the steady-state misalignment is limited by the constant step-size parameter. In this article, based on the principle of least perturbation, we first present a derivation of normalized version of proportionate algorithms. Then by taking the disturbance signal into account, we propose a variable step-size proportionate NLMS algorithm to combine the benefits of both variable step-size algorithms and proportionate algorithms. The proposed approach can achieve fast convergence with a large step size when the identification error is large, and then considerably decrease the steady-state misalignment with a small step size after the adaptive filter reaches a certain degree of convergence. Simulation results verify the effectiveness of the proposed approach.

The control and diagnosis networks in Maglev Train are the most important parts. In the paper, the control and diagnosis network structures are discussed, and the disadvantages of them are described and analyzed. In virtue of role automation decentralized system (RoADS), some basic ideas of RoADS are applied in new network. The structure, component parts and application of new network are proposed, designed and discussed in detail. The comparison results show that new network not only embodies some RoADS' ideas but also better meets the demands of control and diagnosis networks in Maglev Train.

In speech recognition, confidence annotation adopts a single confidence feature or a combination of different features for classification. These confidence features are always extracted from decoding information. However, it is proved that about 30% of knowledge of human speech understanding is mainly derived from high-level information. Thus, how to extract a high-level confidence feature statistically independent of decoding information is worth researching in speech recognition. In this paper, a novel confidence feature extraction algorithm based on latent topic similarity is proposed. Each word topic distribution and context topic distribution in one recognition result is firstly obtained using the latent Dirichlet allocation (LDA) topic model, and then, the proposed word confidence feature is extracted by determining the similarities between these two topic distributions. The experiments show that the proposed feature increases the number of information sources of confidence features with a good information complementary effect and can effectively improve the performance of confidence annotation combined with confidence features from decoding information.

In this letter, we propose two antenna grouping schemes for uplink Nx SC-FDMA MIMO systems, where the multiple component carriers can be divided into several groups which are handled by different antennas, thus the number of component carriers on each antenna will be reduced by the group method. As a result, the peak-to-average power ratio (PAPR) of each antenna has been reduced. To further enhance the performance, an interleaving method is proposed to achieve better diversity gain due to the channel varying in the spatial domain and the frequency domain during one turbo coded stream. Our simulation figures clearly demonstrate that in all examples, the proposed schemes are shown to be effective in improving the Block Error Rate (BLER) performance while reducing the PAPR.

The diagnosis system of Maglev Train is one of most important parts, which can obtain kinds of status messages of electric and electronic devices in vehicle to ensure the whole train safety. In this paper, diagnosis system structure and diagnosis method are analyzed and discussed in detail. The disadvantages of diagnosis system are described. In virtue of the theory of ADS, some basic ideas of ADS are applied in new diagnosis system. The structure, component parts and diagnosis method of new diagnosis system are proposed, designed and discussed in detail. The analysis results show that new diagnosis not only embodies some ADS' ideas but also better meets the demands of Maglev Train Diagnosis System.

Driver fatigue detection has become crucial in vehicle safety technology. Achieving high accuracy and real-time performance in detecting driver fatigue is paramount. In this paper, we propose a novel driver fatigue detection algorithm based on dynamic tracking of Facial Eyes and Yawning using YOLOv7, named FEY-YOLOv7. The Coordinate Attention module is inserted into YOLOv7 to enhance its dynamic tracking accuracy by focusing on coordinate information. Additionally, a small target detection head is incorporated into the network architecture to promote the feature extraction ability of small facial targets such as eyes and mouth. In terms of compution, the YOLOv7 network architecture is significantly simplified to achieve high detection speed. Using the proposed PERYAWN algorithm, driver status is labeled and detected by four classes: open_eye, closed_eye, open_mouth, and closed_mouth. Furthermore, the Guided Image Filtering algorithm is employed to enhance image details. The proposed FEY-YOLOv7 is trained and validated on RGB-infrared datasets. The results show that FEY-YOLOv7 has achieved mAP of 0.983 and FPS of 101. This indicates that FEY-YOLOv7 is superior to state-of-the-art methods in accuracy and speed, providing an effective and practical solution for image-based driver fatigue detection.

Photo animation is to transform photos of real-world scenes into anime style images, which is a challenging task in AIGC (AI Generated Content). Although previous methods have achieved promising results, they often introduce noticeable artifacts or distortions. In this paper, we propose a novel double-tail generative adversarial network (DTGAN) for fast photo animation. DTGAN is the third version of the AnimeGAN series. Therefore, DTGAN is also called AnimeGANv3. The generator of DTGAN has two output tails, a support tail for outputting coarse-grained anime style images and a main tail for refining coarse-grained anime style images. In DTGAN, we propose a novel learnable normalization technique, termed as linearly adaptive denormalization (LADE), to prevent artifacts in the generated images. In order to improve the visual quality of the generated anime style images, two novel loss functions suitable for photo animation are proposed: 1) the region smoothing loss function, which is used to weaken the texture details of the generated images to achieve anime effects with abstract details; 2) the fine-grained revision loss function, which is used to eliminate artifacts and noise in the generated anime style image while preserving clear edges. Furthermore, the generator of DTGAN is a lightweight generator framework with only 1.02 million parameters in the inference phase. The proposed DTGAN can be easily end-to-end trained with unpaired training data. Extensive experiments have been conducted to qualitatively and quantitatively demonstrate that our method can produce high-quality anime style images from real-world photos and perform better than the state-of-the-art models.