Maximum-likelihood registration of range images with missing data

Missing data are common in range images, due to geometric occlusions, limitations in the sensor field of view, poor reflectivity, depth discontinuities, and cast shadows. Using registration to align these data often fails, because points without valid correspondences can be incorrectly matched. This paper presents a maximum likelihood method for registration of scenes with unmatched or missing data. Using ray casting, correspondences are formed between valid and missing points in each view. These correspondences are used to classify points by their visibility properties, including occlusions, field of view, and shadow regions. The likelihood of each point match is then determined using statistical properties of the sensor, such as noise and outlier distributions. Experiments demonstrate a high rates of convergence on complex scenes with varying degrees of overlap.     

Collapse-free thermal bonding technique for large area microchambers in plastic lab-on-a-chip applications

Bonding is an essential step to form microchannels or microchambers in lab-on-a-chip applications. In this paper, we present a novel plastic thermal bonding technique to seal and form large area microchambers (planar characteristic width and length on the order of 1 mm and characteristic thickness on the order of 10–100 μm) without collapse by introducing a holed pressure equalizing plate (HPEP) that includes holes of the same size and shape as the microchambers. To demonstrate the proposed technique, two types of large area microchambers [(1) 20 × 10 mm and 40 μm thick and (2) 12 × 2.5 mm and 120 μm thick] with microchannels were designed and replicated on plastic substrates by means of hot embossing and injection molding processes with prepared two nickel mold inserts. The replicated large area microchambers as well as the microchannels in the plastic lab-on-a-chip were successfully sealed (i.e., no leakage) and formed without any collapse by the proposed thermal bonding technique with the help of the HPEP.     

Regulation of Polymer Configurations Enables Green Solvent-Processed Large-Area Binary All-Polymer Solar Cells With Breakthrough Performance and High Efficiency Stretchability Factor

With the great potential of the all-polymer solar cells for large-area wearable devices, both large-area device efficiency and mechanical flexibility are very critical but attract limited attention. In this work, from the perspective of the polymer configurations, two types of terpolymer acceptors PYTX-A and PYTX-B (X = Cl or H) are developed. The configuration difference caused by the replacement of non-conjugated units results in distinct photovoltaic performance and mechanical flexibility. Benefiting from a good match between the intrinsically slow film-forming of the active materials and the technically slow film-forming of the blade-coating process, the toluene-processed large-area (1.21 cm²) binary device achieves a record efficiency of 14.70%. More importantly, a new parameter of efficiency stretchability factor (ESF) is proposed for the first time to comprehensively evaluate the overall device performance. PM6:PYTCl-A and PM6:PYTCl-B yield significantly higher ESF than PM6:PY-IT. Further blending with non-conjugated polymer donor PM6-A, the best ESF of 3.12% is achieved for PM6-A:PYTCl-A, which is among the highest comprehensive performances.     

Nanotextured superhydrophobic micromesh

A superhydrophobic micromesh covered with nanoprotrusions has been introduced and its applicability to a waterproof mobile phone speaker has been evaluated. The nanotextured superhydrophobic micromesh showed excellent water repellency, self-cleaning and waterproofing performances. In a waterproof speaker test using the fabricated nanotextured micromesh, the micromesh did not lose its waterproof function at 2 m water depth and did not form a remnant water film after being removed from the water. The packaged speaker showed almost the same sound quality before and after dipping at a 2 m water depth. These results demonstrate that the superhydrophobic nanotextured micromesh could be directly applicable for various products that need to resist water penetration, yet allow the transmission of gases and sound/light waves.     

Autonomous topological modeling of a home environment and topological localization using a sonar grid map

This paper presents a method of autonomous topological modeling and localization in a home environment using only low-cost sonar sensors. The topological model is extracted from a grid map using cell decomposition and normalized graph cut. The autonomous topological modeling involves the incremental extraction of a subregion without predefining the number of subregions. A method of topological localization based on this topological model is proposed wherein a current local grid map is compared with the original grid map. The localization is accomplished by obtaining a node probability from a relative motion model and rotational invariant grid-map matching. The proposed method extracts a well-structured topological model of the environment, and the localization provides reliable node probability even when presented with sparse and uncertain sonar data. Experimental results demonstrate the performance of the proposed topological modeling and localization in a real home environment.     

BSPNN: boosted subspace probabilistic neural network for email security

In the modern age of Internet connectivity, advanced information systems have accumulated huge volumes of data. Such fast growing, tremendous amount of data, collected and stored in large databases has far exceeded our human ability to comprehend without proper tools. There has been a great deal of research conducted to explore the potential applications of Machine Learning technologies in Security Informatics. This article studies the Network Security Detection problems in which predictive models are constructed to detect network security breaches such as spamming. Due to overwhelming volume of data, complexity and dynamics of computer networks and evolving cyber threats, current security systems suffer limited performance with low detection accuracy and high number of false alarms. To address such performance issues, a novel Machine Learning algorithm, namely Boosted Subspace Probabilistic Neural Network (BSPNN), has been proposed which combines a Radial Basis Function Neural Network with an innovative diversity-based ensemble learning framework. Extensive empirical analyses suggested that BSPNN achieved high detection accuracy with relatively small computational complexity compared with other conventional detection methods.     

Self-absorption correction for solid-state photoluminescence quantum yields obtained from integrating sphere measurements

A new method is presented for analyzing the effects of self-absorption on photoluminescence integrating sphere quantum yield measurements. Both the observed quantum yield and luminescence spectrum are used to determine the self-absorption probability, taking into account both the initial emission and subsequent absorption and reemission processes. The analysis is experimentally validated using the model system of the laser dye perylene red dispersed in a polymer film. This approach represents an improvement over previous methods that tend to overestimate the true quantum yield, especially in cases with high sample absorbance or quantum yield values.