Multi-object filtering with second-order moment statistics

The focus of this work lies on multi-object estimation techniques, in particular the Probability Hypothesis Density (PHD) filter and its variations. The PHD filter is a recursive, closed-form estimation technique which tracks a population of objects as a group, hence avoiding the combinatorics of data association and therefore yielding a powerful alternative to methods like Multi-Hypothesis Tracking (MHT). Its relatively low computational complexity stems from strong modelling assumptions which have been relaxed in the Cardinalized PHD (CPHD) filter to gain more flexibility, but at a much higher computational cost. We are concerned with the development of two suitable alternatives which give a compromise between the simplicity and elegance of the PHD filter and the versatility of the CPHD filter. The first alternative generalises the clutter model of the PHD filter, leading to more accurate estimation results in the presence of highly variable numbers of false alarms; the second alternative provides a closed-form recursion of a second-order PHD filter that propagates variance information along with the target intensity, thus providing more information than the PHD filter while keeping a much lower computational complexity than the CPHD filter. The discussed filters are applied on simulated data, furthermore their practicality is demonstrated on live-cell super-resolution microscopy images to provide powerful techniques for molecule and cell tracking, stage drift estimation and estimation of background noise

Optimization of adsorptive removal of α-toluic acid by CaO2 nanoparticles using response surface methodology

The present work addresses the optimization of process parameters for adsorptive removal of α-toluic acid by calcium peroxide (CaO2) nanoparticles using response surface methodology (RSM). CaO2 nanoparticles were synthesized by chemical precipitation method and confirmed by Transmission electron microscopy (TEM) and high-resolution TEM (HRTEM) analysis which shows the CaO2 nanoparticles size range of 5–15 nm. A series of batch adsorption experiments were performed using CaO2 nanoparticles to remove α-toluic acid from the aqueous solution. Further, an experimental based central composite design (CCD) was developed to study the interactive effect of CaO2 adsorbent dosage, initial concentration of α-toluic acid, and contact time on α-toluic acid removal efficiency (response) and optimization of the process. Analysis of variance (ANOVA) was performed to determine the significance of the individual and the interactive effects of variables on the response. The model predicted response showed a good agreement with the experimental response, and the coefficient of determination, (R2) was 0.92. Among the variables, the interactive effect of adsorbent dosage and the initial α-toluic acid concentration was found to have more influence on the response than the contact time. Numerical optimization of process by RSM showed the optimal adsorbent dosage, initial concentration of α-toluic acid, and contact time as 0.03 g, 7.06 g/L, and 34 min respectively. The predicted removal efficiency was 99.50%. The experiments performed under these conditions showed α-toluic acid removal efficiency up to 98.05%, which confirmed the adequacy of the model prediction