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Multitarget tracking is an important topic in visual surveillance system. Considering imperfections of the cardinalized probability hypothesis density (CPHD) filter and the target maneuvers, we propose an adaptive genetic multiple-model CPHD filter in this paper. First, we discuss the filtering process and combined the standard CPHD filter with the multiple-model-based framework. Afterward, the sequential Monte Carlo implementation of the proposed filter for the nonlinear and non-Gaussian state estimates is presented in detail. To enhance the tracking performance as target start to maneuver, the adaptive genetic algorithm is used to improve the target state estimation accuracy at the time of state switching with the excellent particles. On the other hand, the undetected component of the measurement-updated weight of survival particle is compensated by the excess weight of newborn particle to correct the number estimates of targets. The simulation results are provided to illustrate the reliability and efficiency of the proposed filter.

To prepare iodine-131(Collagen-chitosan microspheres (CCMSs) were prepared with type-Ⅰ collagen and chitosan using emulsification-chemical cross-linking method. The size of the CCMSs were determined by electron microscope.CCMSs were synthesized with a smooth and spherical shape and an average diameter of (5.1±1.2) μm. A radiolabeling rate of 86.10% was achieved.Biodegradable CCMSs were successfully prepared and radiolabeled. The

To establish a mouse model bearing human prostate cancer xenograft with bone metastasis by the monitoring with X-Ray, Micro CT, and ¹⁸F-NaF PET/CT.Sixteen male Balb/c nude mice were allocated into control (6 mice) and experimental group (10 mice). In experimental group, the mice were subjected to percutaneous injection of 2×10At 21 d post modeling, the significant radioactive ¹⁸F--NaF signals were found in the tibial intramedullary cavity of all 10 mice in experimental group. The ROI value evaluation showed that SUVBone metastasis model of prostate PC-3 cancer cells were successfully established by intratibial injection. ¹⁸F-NaF PET/CT could detect tumor invasion and bone osteogenesis in the early stage of modeling.

To predict dynamic targets by using tracking gains, an improved adaptive α-β filtering algorithm is presented. First, we introduce the pseudo acceleration to build generalized model for uniform linear and acceleration motions. Then, the tracking gains based on tracking index are adaptively determined. Simulation results demonstrate that the proposed filtering algorithm can track both maneuvering and non-maneuvering targets with good performance.

Computing time and decision correctness are the measurable indicators in vessel collision risk (CR) assessment. However, the existing CR assessment approaches, based on fuzzy theory or neural network, have lower accuracy and longer computing times. To overcome these drawbacks and obtain a compromised evaluation, an approach of vessel CR assessment based on the Dempster–Shafer (D–S) evidence theory is proposed in this paper. Considering that CR is associated with the membership functions corresponding to navigation parameters such as the distance to the closest point of approach (DCPA), the time to closest point of approach (TCPA) and the relative distance, we use the multiradar network to achieve them. Afterwards, applying the D–S evidence theory, we successfully assess CR with joint basic probability assignment (JBPA). Finally, the simulation results confirm the validity of the proposed approach.

Multitarget tracking (MTT) is important in radar and/or sonar surveillance systems. However, as a popular implementation of MTT, the standard probability hypothesis density (PHD) filter has computerized intractability and imprecise estimation. To overcome these drawbacks, an improved PHD filter is proposed in this paper. First, we apply particle approximation to achieve the closed-form solution to the PHD propagations. Afterward, the weight formula is simplified by the method of kernel estimate. Meanwhile, the excess weights of newborn particles are increased to survival particles on average. As a result, the overestimated target number and the missed detection are balanced, and the accuracy of MMT is improved by our lemmas. Finally, simulations are presented to compare the performance of the proposed filter with that of the standard one. The results show that our filter can achieve MTT with better performance.

Orthogonal Frequency Division Multiplexing Access (OFDMA) relay system is a promising technique for increasing the capacity and the range of wireless communication systems. Dynamic resource allocation for OFDMA relay systems plays an important role in improving the system performance. However, most of the resource allocation algorithms are designed for the unicast traffics, and resource allocation algorithm for multicast traffics in OFDMA relay systems has received little attention. In this paper, the resource allocation problem for the decode-and-forward relay assisted OFDMA multicast system is considered. Our goal is to maximize the sum rate of the multicast system under the constraints of total power limit and exclusive subcarrier usage. The multicast resource allocation problem is formulated as a mixed integer programming problem. The problem is firstly solved by using Lagrangian dual decomposition method, resulting in an asymptotically optimal joint subcarrier and power allocation algorithm with computational complexity being linear with the number of multicast groups and subcarriers. Furthermore, a low complexity suboptimal resource allocation algorithm is presented to further reduce the computational complexity. Simulation results show that both the proposed optimal and suboptimal algorithms can significantly improve the system performance and the proposed suboptimal algorithm can achieve a near optimal solution.

To synthesize and study the specific binding affinity of tumor-penetrating peptide YCCS to non-small cell lung carcinoma (NSCLC) cells in vitro.YCCS peptide was designed by fusing the neuropilin-1 (NRP-1) binding sequence and NSCLC binding peptide CS. YCCS peptide was synthesized and fluorescent labeled with N-terminal FITC. NRP-1 positive human NSCLC cell A549, NRP-1 positive human breast cancer cell MDA-MB-231, normal human bronchial epithelium HBE135-E6E7 and human liver cell HL-7702 were incubated respectively, then we observed the specific binding affinity of tumor-penetrating peptide YCCS to NSCLC cells.After treated with 5 μmol/L peptide, significant fluorescent signals of FITC-YCCS peptide were demonstrated only in NSCLC A549 cells but marginal captured signal in MDA-MB-231, normal human HBE135-E6E7 or HL-7702 cells, which revealed specific NSCLC cells binding affinity. In 20 μmol/L treated group, non-specific binding were found in MDA-MB-231 and HL-7702 cells.The results of this novel designed YCCS peptide indicated a promising strategy for improving tumor penetrating with delivery capability of drugs to NSCLC A549 cells when treated with 5 μmol/L peptide.