Feature representation is a key component of most visual tracking algorithms. It is difficult to deal with complex appearance changes with low-level hand-crafted features due to weak representation capacities of such features. In this paper, we propose a novel tracking algorithm through combining a joint dictionary pair learning with convolutional neural networks (CNN). We utilize CNN model that is trained on ImageNet-Vid to extract target features. The CNN includes three convolutional layers and two fully connected layers. A dictionary pair learning follows the second fully connected layer. The joint dictionary pair is learned upon extracted deep features by the trained CNN model. The temporal variations of target appearances are learned in the dictionary learning. We use the learned dictionaries to encode target candidates. A linear combination of atoms in the learned dictionary is used to represent target candidates. Extensive experimental evaluations on OTB2015 demonstrate the superior performances against SOTA trackers.

In this paper, estimation accuracy of channel frequency response (CFR) according to least squared (LS) criterion with two transmit antennas for the time domain synchronous-orthogonal frequency division multiplexing (TDS-OFDM) system is investigated. To minimize the estimation variance, the conditions to guide the pseudo-noise (PN) sequence design are discussed and three training sequence design schemes are proposed accordingly. Simulations show that the proposed PN sequence design scheme is effective, while the implementation complexity for the channel estimation is low.

An improved spectrum sensing method based on PN autocorrelation (PNAC) for Digital Terrestrial Television Multimedia Broadcasting (DTMB) system is proposed in this paper. The low bound of miss-detection probability and the decision threshold for a given false alarm probability are studied. The performances of proposed method and existing methods are compared through computer simulations under both non-time dispersive channel and time dispersive channel. Simulation results show that the proposed method has better performance than the original PNAC-based method, and is more robust to both carrier frequency offset (CFO) and time dispersion of the channel than the existing method based on PN cross-correlation (PNCC).

Both gender and identity recognition task with hand vein information is solved based on the proposed cross-selected-domain transfer learning model. State-of-the-art recognition results demonstrate the effectiveness of the proposed model for pattern recognition task, and the capability to avoid over-fitting of fine-tuning DCNN with small-scaled database.

Image boundary detection or image segmentation is an important step in image analysis. However, choosing appropriate parameters for boundary detection algorithms is necessary to achieve good boundary detection results. Image boundary detection fusion with unsupervised parameters can output a final consensus boundary, which is generally better than using unsupervised or supervised image boundary detection algorithms. In this study, we theoretically examine why image boundary detection fusion can work well and we propose a mixture model for image boundary detection fusion (MMIBDF) to achieve good consensus segmentation in an unsupervised manner. All of the segmentation algorithms are treated as new features and the segmentation results obtained by the algorithms are the values of the new features. The MMIBDF is designed to sample the boundary according to a discrete distribution. We present an inference method for MMIBDF and describe the corresponding algorithm in detail. Extensive empirical results demonstrate that MMIBDF significantly outperforms other image boundary detection fusion algorithms and the base image boundary detection algorithms according to most performance indices.

This letter provides an efficient massive multiple-input multiple-output (MIMO) detector based on quasi-newton methods to speed up the convergence performance under realistic scenarios, such as high user load and spatially correlated channels. The proposed method leverages the information of the Hessian matrix by merging Barzilai-Borwein method and Limited Memory-BFGS method. In addition, an efficient initial solution based on constellation mapping is proposed. The simulation results demonstrate that the proposed method diminishes performance loss to 0.7dB at the bit-error-rate of 10-2 at 128×32 antenna configuration with low complexity, which surpasses the state-of-the-art (SOTA) algorithms.

A differential inter-symbol interference (ISI) cancellation method for time domain synchronous orthogonal frequency division multiplexing (TDS-OFDM) systems is proposed. The differential output of an OFDM system can greatly reduce the impact of ISI in the frequency domain and it constructs a convolutional structure, thus the Viterbi decoding algorithm can be used to recover the transmitted information from the output signal. Simulation results show the effectiveness of the proposed method.

The accuracy of non-rigid 3D face recognition is highly influenced by the capability to model the expression deformations. Given a training set of non-neutral and neutral 3D face scan pairs from the same subject, a set of Fourier series coefficients for each face scan is reconstructed. The residues on each frequency of the Fourier series between the finely aligned pairs contain the expression deformation patterns and PCA is applied to learn these patterns. The proposed expression deformation model is then built by the eigenvectors with top eigenvalues from PCA. Recognition experiments are conducted on a 3D face database that features a rich set of facial expression deformations, and experimental results demonstrate the feasibility and merits of the proposed model.

This paper presents an efficient multi-service allocation scheme for the digital television terrestrial broadcasting systems in which the fixed service is modulated by orthogonal frequency division multiplexing and quadrature amplitude modulation (OFDM/QAM) with larger FFT size and the added mobile service is modulated by OFDM and offset quadrature amplitude modulation (OQAM) with smaller FFT size. The two different types of services share one 8MHz broadcasting channel. The isotropic orthogonal transform algorithm (IOTA) is chosen as the shaping filter for OQAM because of its isotropic convergence in time and frequency domain and the proper FFT size is selected to maximum the transmission capacity under mobile environment. The corresponding transceiver architecture is also proposed and analyzed. Simulations show that the newly added mobile service generates much less out-of-band interference to the fixed service and has a better performance under fast fading wireless channels.

Usually an FIR filter is used to model the physical paths in an active noise control system. However, the order of the filter to be modeled is a key factor for determining the computational load for the adaptive algorithms associated with active noise control (ANC), particularly for multi-channel algorithms. In this letter, the relationships among the filter's order, the plant modeling error and the location of poles for the transfer functions of the physical paths in an ANC system are theoretically examined and numerical examples are given to verify the theoretical results.

To realize transmit diversity for the time domain synchronous OFDM (TDS-OFDM) system, this letter proposes the space-time-frequency orthogonal training sequence and the corresponding flexible channel estimation methods. Simulation results indicate that an significant performance improvement could be achieved for low-density parity-check code (LDPC) coded TDS-OFDM system over multi-path fading channels.

The continuous advances in sensing and positioning technologies have resulted in a dramatic increase in popularity of Location-Based Services (LBS). Nevertheless, the LBS can lead to user privacy breach due to sharing location information with potentially malicious services. A high degree of location privacy preservation for LBS is extremely required. In this paper, a clustering K-anonymity scheme for location privacy preservation (namely CK) is proposed. The CK scheme does not rely on a trusted third party to anonymize the location information of users. In CK scheme, the whole area that all the users reside is divided into clusters recursively in order to get cloaked area. The exact location information of the user is replaced by the cloaked spatial temporal boundary (STB) including K users. The user can adjust the resolution of location information with spatial or temporal constraints to meet his personalized privacy requirement. The experimental results show that CK can provide stringent privacy guarantees, strong robustness and high QoS (Quality of Service).

Player detection is an important part in sports video analysis. Over the past few years, several learning based detection methods using various supervised two-class techniques have been presented. Although satisfactory results can be obtained, a lot of manual labor is needed to construct the training set. To overcome this drawback, this letter proposes a player detection method based on one-class SVM (OCSVM) using automatically generated training data. The proposed method is evaluated using several video clips captured from World Cup 2010, and experimental results show that our approach achieves a high detection rate while keeping the training set construction's cost low.

In this study, a novel forest method based on specific random trees (SRT) was proposed for a multiclass classification problem. The proposed SRT was built on one specific class, which decides whether a sample belongs to a certain class. The forest can make a final decision on classification by ensembling all the specific trees. Compared with the original random forest, our method has higher strength, but lower correlation and upper error bound. The experimental results based on 10 different public datasets demonstrated the efficiency of the proposed method.

Malware phylogeny refers to inferring evolutionary relationships between instances of families. It has gained a lot of attention over the past several years, due to its efficiency in accelerating reverse engineering of new variants within families. Previous researches mainly focused on tree-based models. However, those approaches merely demonstrate lineage of families using dendrograms or directed trees with rough evolution information. In this paper, we propose a novel malware phylogeny construction method taking advantage of persistent phylogeny tree model, whose nodes correspond to input instances and edges represent the gain or lost of functional characters. It can not only depict directed ancestor-descendant relationships between malware instances, but also show concrete function inheritance and variation between ancestor and descendant, which is significant in variants defense. We evaluate our algorithm on three malware families and one benign family whose ground truth are known, and compare with competing algorithms. Experiments demonstrate that our method achieves a higher mean accuracy of 61.4%.

Due to the distributed nature, mobile ad-hoc networks (MANETs) are vulnerable to various attacks, resulting in distrusted communications. To achieve trusted communications, it is important to build trusted routes in routing algorithms in a self-organizing and decentralized fashion. This paper proposes a trusted routing to locate and to preserve trusted routes in MANETs. Instead of using a hard security mechanism, we employ a new dynamic trust mechanism based on multiple constraints and collaborative filtering. The dynamic trust mechanism can effectively evaluate the trust and obtain the precise trust value among nodes, and can also be integrated into existing routing protocols for MANETs, such as ad hoc on-demand distance vector routing (AODV) and dynamic source routing (DSR). As an example, we present a trusted routing protocol, based on dynamic trust mechanism, by extending DSR, in which a node makes a routing decision based on the trust values on its neighboring nodes, and finally, establish a trusted route through the trust values of the nodes along the route in MANETs. The effectiveness of our approach is validated through extensive simulations.