Ensemble filtering for state space models

The state space model has been widely used in various fields including economics, finance, bioinformatics, oceanography, and tomography. The goal of the filtering problem is to find the posterior distribution of the hidden state given the current and past observations. The first part of my thesis focuses on designing efficient proposal distributions for particle filters. I propose a new approach named the augmented particle filter (APF), which combines two sets of particles from the observation and state equations. The APF can be applied to general state space models, and it does not require special structures of the model or any approximation to the target or proposal distribution. I find through simulation studies that the APF performs similarly to or better than other filtering algorithms in the literature. The convergence of the augmented particle filter has been established. The second part of my thesis develops the localization methods for particle filters in high dimensional state space models. Under high dimensional state space models, the computational constraints prevent us from having a large number of particles to avoid the degeneracy problem of the importance weights. When the dimension of the state vector is high, it is common that only a few components of the state vector are dependent on any single component or a set of a few components of the observation vector. In filtering problems, the concept of localization is to use the information in the components of the observation vector to update only the corresponding a few components of the hidden state vector. I propose the localized augmented particle filter. This new approach divides state vectors into small blocks, and it updates each block of the state vectors through state dynamics and observations. By considering blocks, the influence of observations in updating state vectors is restricted to a few blocks of the state vectors, so the localized augmented particle filter allows constructing the proposal distribution in a lower dimension than the original model. The localized augmented particle filter can outperform many other methods in the literature. The convergence of the localized augmented particle filter has been proved for some class of models. The method to improve particle filters by dividing the particles into independent batches is presented. The development of the method is motivated by the particle Markov chain Monte Carlo method proposed by Andrieu et al. (2010). Often, the combination of particle filters in batches outperforms the standard particle filter. Parallel computing techniques can be easily adapted to make the implementation fast. The convergence property of the batched particle filter has been established. As the number of batches goes to infinity, the estimate based on the combination of batches converges to the target

Percutaneous radiofrequency ablation: A novel treatment of facial venous malformation

We performed radiofrequency ablation to treat a symptomatic facial venous malformation of a 24-year-old woman under ultrasound scan-guidance. The 20.25-cm sized original facial venous malformation in her right cheek markedly reduced without any scar formation and was grossly not visible after 1 month of the procedure. In the 3-month follow-up magnetic resonance imaging, original venous malformation reduced in volume to 5.40 cm. Radiofrequency ablation may provide an alternative treatment for facial venous malformations

Spin relaxation in mesoscopic superconducting Al wires

We studied the diffusion and the relaxation of the polarized quasiparticle spins in superconductors. To that end, quasiparticles of polarized spins were injected through an interface of a mesoscopic superconducting Al wire in proximity contact with an overlaid ferromagnetic Co wire in the single-domain state. The superconductivity was observed to be suppressed near the spin-injecting interface, as evidenced by the occurrence of a finite voltage for a bias current below the onset of the superconducting transition. The spin diffusion length, estimated from finite voltages over a certain length of Al wire near the interface, was almost temperature independent in the temperature range sufficiently below the superconducting transition but grew as the transition temperature was approached. This temperature dependence suggests that the relaxation of the spin polarization in the superconducting state is governed by the condensation of quasiparticles to the paired state. The spin relaxation in the superconducting state turned out to be more effective than in the normal state.Comment: 9 pages, 8 figure

The Effect of Competitive Advantage and Human Advantage on Industrial Competitive Strategy (Case Study: Smis in Gorontalo Province)

Small and Medium Industries (SMIs) have a strategic role in the Indonesian economy, as they earn 61.9 percent of the foreign exchange which goes to make up the nation\u27s Gross Domestic Product, and nationally they are able to absorb 97 percent of the workforce. The Global Competitiveness Report also notes that SMIs serve as the business units that affect every nation\u27s competitiveness. Considering this strategic role, the selection of a competitive strategy for these SMIs is absolutely necessary. Through an in-depth literature review, this study aims to explore what variables influence the competitive strategy of industries, particularly the SMIs. By using a Systematic Literature Review (SLR) with a total of 31 main literature (articles, papers and books), this study has found two dominant factors that influence industrial competitive strategy: Competitive advantage and human advantage, which are subsequently developed into six independent variables (construct variables), i.e. cost, delivery, product quality, product variety, know-how and innovativeness, with a total of 44 indicators. The results of measurements of the sample of SMIs in Gorontalo Province, using Structural Equation Modeling, found that both competitive advantage and human advantage jointly influence 40.2 percent of the industrial competitive strategies. These results indicate that competitive strategies, such as creating products with unique features, on-time delivery, flexibility in production, and employee involvement in the innovations, are indispensable to SMIs in order for them to produce quality products and be able to maintain their advantage

Fully automatic integration of dental CBCT images and full-arch intraoral impressions with stitching error correction via individual tooth segmentation and identification

We present a fully automated method of integrating intraoral scan (IOS) and dental cone-beam computerized tomography (CBCT) images into one image by complementing each image's weaknesses. Dental CBCT alone may not be able to delineate precise details of the tooth surface due to limited image resolution and various CBCT artifacts, including metal-induced artifacts. IOS is very accurate for the scanning of narrow areas, but it produces cumulative stitching errors during full-arch scanning. The proposed method is intended not only to compensate the low-quality of CBCT-derived tooth surfaces with IOS, but also to correct the cumulative stitching errors of IOS across the entire dental arch. Moreover, the integration provide both gingival structure of IOS and tooth roots of CBCT in one image. The proposed fully automated method consists of four parts; (i) individual tooth segmentation and identification module for IOS data (TSIM-IOS); (ii) individual tooth segmentation and identification module for CBCT data (TSIM-CBCT); (iii) global-to-local tooth registration between IOS and CBCT; and (iv) stitching error correction of full-arch IOS. The experimental results show that the proposed method achieved landmark and surface distance errors of 112.4 $\mu$m and 301.7 $\mu$m, respectively

Synergistic nanoarchitecture of mesoporous carbon and carbon nanotubes for lithium-oxygen batteries

A rechargeable lithium–oxygen battery (LOB) operates via the electrochemical formation and decomposition of solid-state Li2O2 on the cathode. The rational design of the cathode nanoarchitectures is thus required to realize high-energy-density and long-cycling LOBs. Here, we propose a cathode nanoarchitecture for LOBs, which is composed of mesoporous carbon (MPC) integrated with carbon nanotubes (CNTs). The proposed design has the advantages of the two components. MPC provides sufficient active sites for the electrochemical reactions and free space for Li2O2 storage, while CNT forests serve as conductive pathways for electron and offer additional reaction sites. Results show that the synergistic architecture of MPC and CNTs leads to improvements in the capacity (~ 18,400 mAh g− 1), rate capability, and cyclability (~ 200 cycles) of the CNT-integrated MPC cathode in comparison with MPC. © 2021, The Author(s).1