Sampling strong tracking nonlinear unscented Kalman filter and its application in eye tracking

The unscented Kalman filter is a developed well-known method for nonlinear motion estimation and tracking. However, the standard unscented Kalman filter has the inherent drawbacks, such as numerical instability and much more time spent on calculation in practical applications. In this paper, we present a novel sampling strong tracking nonlinear unscented Kalman filter, aiming to overcome the difficulty in nonlinear eye tracking. In the above proposed filter, the simplified unscented transform sampling strategy with n＋ 2 sigma points leads to the computational efficiency, and suboptimal fading factor of strong tracking filtering is introduced to improve robustness and accuracy of eye tracking. Compared with the related unscented Kalman filter for eye tracking, the proposed filter has potential advantages in robustness, convergence speed, and tracking accuracy. The final experimental results show the validity of our method for eye tracking under realistic conditions.

SMC-PHD based multi-target track-before-detect with nonstandard point observations model

Detection and tracking of multi-target with unknown and varying number is a challenging issue, especially under the condition of low signal-to-noise ratio(SNR). A modified multi-target track-before-detect(TBD) method was proposed to tackle this issue using a nonstandard point observation model. The method was developed from sequential Monte Carlo(SMC)-based probability hypothesis density(PHD) filter, and it was implemented by modifying the original calculation in update weights of the particles and by adopting an adaptive particle sampling strategy. To efficiently execute the SMC-PHD based TBD method, a fast implementation approach was also presented by partitioning the particles into multiple subsets according to their position coordinates in 2D resolution cells of the sensor. Simulation results show the effectiveness of the proposed method for time-varying multi-target tracking using raw observation data.

Optimized determination of airborne tetracycline resistance genes in laboratory atmosphere

Antibiotic resistance genes(ARGs)have been detected in various atmospheric environments.Airborne ARGs transmission presents the public health threat.However,it is very difficult to quantify airborne ARGs because of the limited availability of collectable airborne particulate matter and the low biological content of samples.In this study,an optimized protocol for collecting and detecting airborne ARGs was presented.Experimental results showed that recovery efficiency tended to increase initially and then declined over time,and a range of 550-780 copies/mmz of capture loading was recommended to ensure that the recovery efficiency is greater than 75%.As the cell walls were mechanically disrupted and nucleic acids were released,the buffer wash protects ARGs dissolution.Three ratios of buffer volume to membrane area in buffer wash were compared.The highest concentrations of airborne ARGs were detected with 1.4μL/mm^2 buffer wash.Furthermore,the majority of the cells were disrupted by an ultrasonication pretreatment(5 min),allowing the efficiency ARGs detection of airborne samples.While,extending the ultrasonication can disrupt cell structures and gene sequence was broken down into fragments.Therefore,this study could provide a theoretical basis for the efficient filter collection of airborne ARGs in different environments.An optimized sampling method was proposed that the buffer wash was 1.4 nL/mm and the ultrasonication duration was 5 min.The indoor airborne ARGs were examined in accordance with the improved protocol in two laboratories.The result demonstrated that airborne ARGs in an indoor laboratory atmosphere could pose the considerable health risk to inhabitants and we should pay attention to some complicated indoor air environment.

Modified filtered importance sampling for virtual spherical Gaussian lights

This paper proposes a modification of the filtered importance sampling method, and improves the quality of virtual spherical Gaussian light(VSGL)-based real-time glossy indirect illumination using this modification. The original filtered importance sampling method produces large overlaps of and gaps between filtering kernels for high-frequency probability density functions(PDFs). This is because the size of the filtering kernel is determined using the PDF at the sampled center of the kernel. To reduce those overlaps and gaps, this paper determines the kernel size using the integral of the PDF within the filtering kernel. Our key insight is that these integrals are approximately constant, if kernel centers are sampled using stratified sampling. Therefore, an appropriate kernel size can be obtained by solving this integral equation. Using the proposed kernel size for filtered importance samplingbased VSGL generation, undesirable artifacts are significantly reduced with a negligibly small overhead.