Clinical researches show that the morbidity of coronary artery disease (CAD) is gradually increasing in many countries every year, and it causes hundreds of thousands of people all over the world dying for each year. As the optical coherence tomography with high resolution and better contrast applied to the lesion tissue investigation of human vessel, many more micro-structures of the vessel could be easily and clearly visible to doctors, which help to improve the CAD treatment effect. Manual qualitative analysis and classification of vessel lesion tissue are time-consuming to doctors because a single-time intravascular optical coherence (IVOCT) data set of a patient usually contains hundreds of in-vivo vessel images. To overcome this problem, we focus on the investigation of the superficial layer of the lesion region and propose a model based on local multi-layer region for vessel lesion components (lipid, fibrous and calcified plaque) features characterization and extraction. At the pre-processing stage, we applied two novel automatic methods to remove the catheter and guide-wire respectively. Based on the detected lumen boundary, the multi-layer model in the proximity lumen boundary region (PLBR) was built. In the multi-layer model, features extracted from the A-line sub-region (ALSR) of each layer was employed to characterize the type of the tissue existing in the ALSR. We used 7 human datasets containing total 490 OCT images to assess our tissue classification method. Validation was obtained by comparing the manual assessment with the automatic results derived by our method. The proposed automatic tissue classification method achieved an average accuracy of 89.53%, 93.81% and 91.78% for fibrous, calcified and lipid plaque respectively.

Joint transmit and receive antenna selection (JTRAS) for transceive spatial modulation (TRSM) is investigated in this paper. A couple of low-complexity and efficient JTRAS algorithms are proposed to improve the reliability of TRSM systems by maximizing the minimum Euclidean distance (ED) among all received signals. Specifically, the QR decomposition based ED-JTRAS achieves near-optimal error performance with a moderate complexity reduction as compared to the optimal ED-JTRAS method. The singular value decomposition based ED-JTRAS achieves sub-optimal error performance with a significant complexity reduction. Simulation results show that the proposed methods remarkably improve the system reliability in both uncorrelated and spatially correlated Rayleigh fading channels, as compared to the conventional norm based JTRAS method.

Wireless communication security has become a hot topic in recent years. The directional modulation (DM) is a promising secure communication technique that has attracted attentions of many researchers. Several different frequency diverse arrays (FDAs) are used to obtain the direction-range-dependent DM signals in previous literatures. However, most of them are not ideal enough to obtain a nonperiodic dot-shaped secure area. In this paper, the symmetrical multi-carrier frequency diverse array with logarithmical frequency increment, named the symmetrical-multilog-FDA, is used to obtain the direction-range-dependent DM signals that are normal at the desired locations while disordered at other locations. Based on the symmetrical-multilog-FDA, we derive the closed-form expression of baseband-weighted vector using the artificial-noise-aided zero-forcing approach. Compared with previous schemes, the proposed scheme can obtain a more fine-focusing nonperiodic dot-shaped secure area at the desired location. In addition, it can achieve a point-to-multipoint secure communication for multiple cooperative receivers at different locations.

A novel secure spatial modulation (SM) scheme based on dynamic multi-parameter weighted-type fractional Fourier transform (WFRFT), abbreviated as SMW, is proposed. Each legitimate transmitter runs WFRFT on the spatially modulated super symbols before transmit antennas, the parameters of which are dynamically updated using the transmitting bits. Each legitimate receiver runs inverse WFRFT to demodulate the received signals, the parameters of which are also dynamically generated using the recovered bits with the same updating strategies as the transmitter. The dynamic update strategies of WFRFT parameters are designed. As a passive eavesdropper is ignorant of the initial WFRFT parameters and the dynamic update strategies, which are indicated by the transmitted bits, it cannot recover the original information, thereby guaranteeing the communication security between legitimate transmitter and receiver. Besides, we formulate the maximum likelihood (ML) detector and analyze the secrecy capacity and the upper bound of BER. Simulations demonstrate that the proposed SMW scheme can achieve a high level of secrecy capacity and maintain legitimate receiver's low BER performance while deteriorating the eavesdropper's BER.

Compressive sensing (CS) shows that a sparse or compressible signal can be exactly recovered from its linear measurements at a rate significantly lower than the Nyquist rate. As an extreme case, 1-bit compressive sensing (1-bit CS) states that an original sparse signal can be recovered from the 1-bit measurements. In this paper, we intrduce a Fast and Accurate Two-Stage (FATS) algorithm for 1-bit compressive sensing. Simulations show that FATS not only significantly increases the signal reconstruction speed but also improves the reconstruction accuracy.

This paper presents an RK-means clustering algorithm which is developed for reliable data grouping by introducing a new reliability evaluation to the K-means clustering algorithm. The conventional K-means clustering algorithm has two shortfalls: 1) the clustering result will become unreliable if the assumed number of the clusters is incorrect; 2) during the update of a cluster center, all the data points belong to that cluster are used equally without considering how distant they are to the cluster center. In this paper, we introduce a new reliability evaluation to K-means clustering algorithm by considering the triangular relationship among each data point and its two nearest cluster centers. We applied the proposed algorithm to track objects in video sequence and confirmed its effectiveness and advantages.

In this paper, we present a general object tracking method based on a newly proposed pixel-wise clustering algorithm. To track an object in a cluttered environment is a challenging issue because a target object may be in concave shape or have apertures (e.g. a hand or a comb). In those cases, it is difficult to separate the target from the background completely by simply modifying the shape of the search area. Our algorithm solves the problem by 1) describing the target object by a set of pixels; 2) using a K-means based algorithm to detect all target pixels. To realize stable and reliable detection of target pixels, we firstly use a 5D feature vector to describe both the color ("Y, U, V") and the position ("x, y") of each pixel uniformly. This enables the simultaneous adaptation to both the color and geometric features during tracking. Secondly, we use a variable ellipse model to describe the shape of the search area and to model the surrounding background. This guarantees the stable object tracking under various geometric transformations. The robust tracking is realized by classifying the pixels within the search area into "target" and "background" groups with a K-means clustering based algorithm that uses the "positive" and "negative" samples. We also propose a method that can detect the tracking failure and recover from it during tracking by making use of both the "positive" and "negative" samples. This feature makes our method become a more reliable tracking algorithm because it can discover the target once again when the target has become lost. Through the extensive experiments under various environments and conditions, the effectiveness and efficiency of the proposed algorithm is confirmed.

Sensor node localization is an important issue in wireless sensor networks (WSNs) due to the dynamic nature of sensor deployment. Generally, in wireless sensor network localization, the absolute positions of certain anchor nodes are required based on the use of global positioning systems, then all the other nodes are approximately localized using various algorithms based on a coordinate system of the anchors. This paper proposes a neighbor position-based localization algorithm (NPLA) that can greatly enhance the positioning accuracy when compared with current overlapping connectivity localization algorithms that attempt to use the observation of connectivity to a set of anchors to determine a node's position. The proposed method localizes the sensor nodes using both the anchors' positions and neighbor node information. However, unlike existing overlapping-based methods, the proposed NPLA does not assume the same radio transmission range. A simulation study is used to demonstrate the positioning accuracy of the proposed method with different anchor and sensor node densities.

Quasi cyclic LDPC (QC-LDPC) codes consisting of circulant permutation matrices (CPM-QC-LDPC) are one of the most attractive types of LDPC codes due to their many advantages. In this paper, we mainly do some research on CPM-QC-LDPC codes. We first propose a two-stage decoding scheme mainly based on parity check matrix transform (MT), which can efficiently improve the bit error rate performance. To optimize the tradeoff between hardware implementation complexity and decoding performance, an improved method that combines our proposed MT scheme with the existing CPM-RID decoding scheme is presented. An experiment shows that both schemes can improve the bit error rate (BER) performance. Finally, we show that the MT decoding mechanism can be applied to other types of LDPC codes. We apply the MT scheme to random LDPC codes and show that it can efficiently lower the error floor.

A low-complexity time-invariant angle-range dependent directional modulation (DM) based on time-modulated frequency diverse array (TM-FDA-DM) is proposed to achieve point-to-point physical layer security communications. The principle of TM-FDA is elaborated and the vector synthesis method is utilized to realize the proposal, TM-FDA-DM, where normalization and orthogonal matrices are designed to modulate the useful baseband symbols and inserted artificial noise, respectively. Since the two designed matrices are time-invariant fixed values, which avoid real-time calculation, the proposed TM-FDA-DM is much easier to implement than time-invariant DMs based on conventional linear FDA or logarithmical FDA, and it also outperforms the time-invariant angle-range dependent DM that utilizes genetic algorithm (GA) to optimize phase shifters on radio frequency (RF) frontend. Additionally, a robust synthesis method for TM-FDA-DM with imperfect angle and range estimations is proposed by optimizing normalization matrix. Simulations demonstrate that the proposed TM-FDA-DM exhibits time-invariant and angle-range dependent characteristics, and the proposed robust TM-FDA-DM can achieve better BER performance than the non-robust method when the maximum range error is larger than 7km and the maximum angle error is larger than 4°.

Wireless communication security has been increasingly important nowadays. Directional modulation (DM) is seen as a promising wireless physical layer security technology. Traditional DM is a transmit-side technology that projects digitally modulated information signals in the desired directions (or at the desired locations) while simultaneously distorting the constellation formats of the same signals in other directions (or at all other locations). However, these directly exposed digitally modulated information signals are easily intercepted by eavesdroppers along the desired directions (or around the desired locations). A new DM scheme for secure point-to-multipoint communication based on the spread spectrum assisted orthogonal frequency diverse array (short for SS-OFDA-M-DM) is proposed in this paper. It can achieve point-to-multipoint secure communication for multiple cooperative receivers at different locations. In the proposed SS-OFDA-M-DM scheme, only cooperative users that use specific DM receivers with right spread spectrum parameters can retrieve right symbols. Eavesdroppers without knowledge of spread spectrum parameters cannot intercept useful signals directly at the desired locations. Moreover, they cannot receive normal symbols at other locations either even if the right spread spectrum parameters are known. Numerical simulation results verify the validity of our proposed scheme.

This paper describes a sophisticated method to estimate visual direction using iris contours. This method requires only one monocular image taken by a camera with unknown focal length. In order to estimate the visual direction, we assume the visual directions of both eyes are parallel and iris boundaries are circles in 3D space. In this case, the two planes where the iris boundaries reside are also parallel. We estimate the normal vector of the two planes from the iris contours extracted from an input image by using an extended "two-circle" algorithm. Unlike most existing gaze estimation algorithms that require information about eye corners and heuristic knowledge about 3D structure of the eye in addition to the iris contours, our method uses two iris contours only. Another contribution of our method is the ability of estimating the focal length of the camera. It allows one to use a zoom lens to take images and the focal length can be adjusted at any time. The extensive experiments over simulated images and real images demonstrate the robustness and the effectiveness of our method.