The smoke detection algorithm has wide application value in fire warning, environmental monitoring, and other fields. To improve the accuracy and real‐time performance of smoke detection, a smoke detection algorithm based on improved feature fusion is proposed. A new feature matrix is constructed by combining the Haar‐like algorithm and the LBP algorithm. To improve the two feature extraction methods, the study compares the pixel values of the neighborhood and the central pixel and weights the pixels of each region according to the difference. The improved LBP matrix is obtained. The longitudinal, transverse, diagonal, and cross‐shaped feature differences of the Haar template are extracted, and these feature differences are used as the weighting coefficients of the Haar feature matrix. Then, the extracted features are input into the improved YOLOv7, and the efficient pyramid split attention network (EPSANet) is introduced into YOLOv7. Meanwhile, the dual compressed attention mechanism (shuffle attention network, SANet) integrates spatial channels. EPSANet extracts multilevel smoke feature information by squeezing and allocating attention to features of different scales through a pyramid‐shaped structure. SANet further enhances the model’s focus on key features and reduces background interference by integrating a dual compression attention mechanism of space and channels. The results show that the detection accuracy of the research design method in different practical application scenarios is above 85%, and the first detection time is also within 5s. The detection error value of the research method mainly fluctuates in the range of 0.02∼0.04, which has high detection accuracy and detection efficiency. In summary, it can be seen that the smoke detection method designed in this study can provide more reliable technical support for fire warning, environmental monitoring, and other fields.

Categorizing web-based videos is an important yet challenging task. The difficulties arise from large data diversity within a category, lack of labeled data, and degradation of video quality. This paper presents a large scale video taxonomic classification scheme (with more than 1000 categories) tackling these issues. Taxonomic structure of categories is deployed in classifier training. To compensate for the lack of labeled video data, a novel method is proposed to adapt the web-text documents trained classifiers to video domain so that the availability of a large corpus of labeled text documents can be leveraged. Video content based features are integrated with text-based features to gain power in the case of degradation of one type of features. Evaluation on videos from hundreds of categories shows that the proposed algorithms generate significant performance improvement over text classifiers or classifiers trained using only video content based features.

To deal with the multi-path delay spread in non- line-of-sight (NLOS) environments, single-carrier frequency- domain equalization (SC-FDE), a.k.a., single-carrier block transmission (SCBT), is thought to be an effective alternative of orthogonal frequency division multiplexing (OFDM). On the other hand, the low-density parity-check (LDPC) coding is considered as the high-performance channel coding scheme due to its good tradeoff between performance and complexity. To facilitate using LDPC for SC-FDE, we proposed a virtual channel based log-likelihood ratio (LLR) calculation method aided by the Unique Word (UW). The proposed method can significantly improve the LDPC decoding performance in SC-FDE system, and does not need additional overhead or complexity. This is very promising to support ultra high-data-rate wireless transmission.

Recently non-negative matrix factorization (NMF) has received a lot of attentions in information retrieval, computer vision and pattern recognition. NMF aims to find two non-negative matrices whose product can well approximate the original matrix. The sizes of these two matrices are usually smaller than the original matrix. This results in a compressed version of the original data matrix. The solution of NMF yields a natural parts-based representation for the data. When NMF is applied for data representation, a major disadvantage is that it fails to consider the geometric structure in the data. In this paper, we develop a graph based approach for parts-based data representation in order to overcome this limitation. We construct an affinity graph to encode the geometrical information and seek a matrix factorization which respects the graph structure. We demonstrate the success of this novel algorithm by applying it on real world problems.

In this paper, we consider a multiuser relay network under amplify and forward (AF) scheme, where each mobile station (MS) is supported by a unique relay station (RS). While the RSs forwards the scaled signal to the MSs, the interference caused by the multiuser nature of the system propagates via the RSs, which significantly degrades the performance gain of the relay technique. We propose two beamforming methods, zero forcing (ZF) method and maximizing signal to leakage ratio (MSLR) method, to suppress the multiuser interference in the relay networks. ZF method can effectively cancel multiuser interference for all MSs and RSs, while it requires a relatively large number of transmit antennas equipped at the base station (BS). The other method, MSLR, aims to maximize the received signal at each MS and RS, while minimize the energy leaking to other MSs and RSs. The advantage of MSLR method is that it does not impose a condition on the relation between the number of transmit antennas equipped at the BS and the receive antennas at the RSs and MSs. Simulations show that MSLR method has significant performance gains over ZF method.

In 802.16e OFDMA systems, noise and co-channel interference on pilots degrade the accuracy of channel state information (CSI) obtained from channel estimation (CE) and system performance is deteriorated. To evaluate the system performance loss due to imperfect CSI, CE model should be used to incorporate noise and interference's impact on CSI into system level simulator (SLS). In this paper, a CE model, which considers 802.16e OFDMA system's pilot pattern and subcarrier randomization, are proposed. The signal model of 802.16e OFDMA system's CE under noise and interference is presented first. Then the error of CSI estimation by LMMSE channel estimator is derived. The proposed CE model covers the main parts of CSI error. Simulation results show it is suitable for SLS by providing a good approximation to estimated CSI instead of performing true LMMSE CE.

Relational data appear frequently in many machine learning applications. Relational data consist of the pairwise relations (similarities or dissimilarities) between each pair of implicit objects, and are usually stored in relation matrices and typically no other knowledge is available. Although relational clustering can be formulated as graph partitioning in some applications, this formulation is not adequate for general relational data. In this paper, we propose a general model for relational clustering based on symmetric convex coding. The model is applicable to all types of relational data and unifies the existing graph partitioning formulation. Under this model, we derive two alternative bound optimization algorithms to solve the symmetric convex coding under two popular distance functions, Euclidean distance and generalized I-divergence. Experimental evaluation and theoretical analysis show the effectiveness and great potential of the proposed model and algorithms.

In this document, a novel MIMO HARQ scheme is proposed, which jointly utilizes both chase combining and MLD. By utilizing the bit-wise soft-information (BWSI) derived from channel decoder iteratively for next retransmission's MLD effectively, the performances, such as PER and throughout seen from simulation results, outperforms conventional HARQ scheme.

Clustering on multi-type relational data has attracted more and more attention in recent years due to its high impact on various important applications, such as Web mining, e-commerce and bioinformatics. However, the research on general multi-type relational data clustering is still limited and preliminary. The contribution of the paper is three-fold. First, we propose a general model, the collective factorization on related matrices, for multi-type relational data clustering. The model is applicable to relational data with various structures. Second, under this model, we derive a novel algorithm, the spectral relational clustering, to cluster multi-type interrelated data objects simultaneously. The algorithm iteratively embeds each type of data objects into low dimensional spaces and benefits from the interactions among the hidden structures of different types of data objects. Extensive experiments demonstrate the promise and effectiveness of the proposed algorithm. Third, we show that the existing spectral clustering algorithms can be considered as the special cases of the proposed model and algorithm. This demonstrates the good theoretic generality of the proposed model and algorithm.

Various data mining applications involve data objects of multiple types that are related to each other, which can be naturally formulated as a k-partite graph. However, the research on mining the hidden structures from a k-partite graph is still limited and preliminary. In this paper, we propose a general model, the relation summary network, to find the hidden structures (the local cluster structures and the global community structures) from a k-partite graph. The model provides a principal framework for unsupervised learning on k-partite graphs of various structures. Under this model, we derive a novel algorithm to identify the hidden structures of a k-partite graph by constructing a relation summary network to approximate the original k-partite graph under a broad range of distortion measures. Experiments on both synthetic and real data sets demonstrate the promise and effectiveness of the proposed model and algorithm. We also establish the connections between existing clustering approaches and the proposed model to provide a unified view to the clustering approaches.

Like many purely data-driven machine learning methods, Support Vector Machine (SVM) classifiers are learned exclusively from the evidence presented in the training dataset; thus a larger training dataset is required for better performance. In some applications, there might be human knowledge available that, in principle, could compensate for the lack of data. In this paper, we propose a simple generalization of SVM: Weighted Margin SVM (WMSVMs) that permits the incorporation of prior knowledge. We show that Sequential Minimal Optimization can be used in training WMSVM. We discuss the issues of incorporating prior knowledge using this rather general formulation. The experimental results show that the proposed methods of incorporating prior knowledge is effective.

This paper proposes a new approach to digital watermarking of image and video objects with arbitrary regions of support (AROS) based on the 2D and 3D shape adaptive wavelet transforms. The hierarchical nature of the wavelet representation of objects allows detection of the digital watermark at various resolutions. We show that, when subjected to image/video compression, the corresponding watermark of objects can still be correctly identified at each resolution (excluding the lowest one) in the wavelet domain. Such a multiresolution watermarking scheme for objects has computational advantage, especially for the video case. Potential applications of our proposed scheme include watermark-based object searching and indexing.