AirCode: Unobtrusive Physical Tags for Digital Fabrication

We present AirCode, a technique that allows the user to tag physically fabricated objects with given information. An AirCode tag consists of a group of carefully designed air pockets placed beneath the object surface. These air pockets are easily produced during the fabrication process of the object, without any additional material or postprocessing. Meanwhile, the air pockets affect only the scattering light transport under the surface, and thus are hard to notice to our naked eyes. But, by using a computational imaging method, the tags become detectable. We present a tool that automates the design of air pockets for the user to encode information. AirCode system also allows the user to retrieve the information from captured images via a robust decoding algorithm. We demonstrate our tagging technique with applications for metadata embedding, robotic grasping, as well as conveying object affordances.Comment: ACM UIST 2017 Technical Paper

Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening

This work introduces a number of algebraic topology approaches, such as multicomponent persistent homology, multi-level persistent homology and electrostatic persistence for the representation, characterization, and description of small molecules and biomolecular complexes. Multicomponent persistent homology retains critical chemical and biological information during the topological simplification of biomolecular geometric complexity. Multi-level persistent homology enables a tailored topological description of inter- and/or intra-molecular interactions of interest. Electrostatic persistence incorporates partial charge information into topological invariants. These topological methods are paired with Wasserstein distance to characterize similarities between molecules and are further integrated with a variety of machine learning algorithms, including k-nearest neighbors, ensemble of trees, and deep convolutional neural networks, to manifest their descriptive and predictive powers for chemical and biological problems. Extensive numerical experiments involving more than 4,000 protein-ligand complexes from the PDBBind database and near 100,000 ligands and decoys in the DUD database are performed to test respectively the scoring power and the virtual screening power of the proposed topological approaches. It is demonstrated that the present approaches outperform the modern machine learning based methods in protein-ligand binding affinity predictions and ligand-decoy discrimination

Survey and Systematization of Secure Device Pairing

Secure Device Pairing (SDP) schemes have been developed to facilitate secure communications among smart devices, both personal mobile devices and Internet of Things (IoT) devices. Comparison and assessment of SDP schemes is troublesome, because each scheme makes different assumptions about out-of-band channels and adversary models, and are driven by their particular use-cases. A conceptual model that facilitates meaningful comparison among SDP schemes is missing. We provide such a model. In this article, we survey and analyze a wide range of SDP schemes that are described in the literature, including a number that have been adopted as standards. A system model and consistent terminology for SDP schemes are built on the foundation of this survey, which are then used to classify existing SDP schemes into a taxonomy that, for the first time, enables their meaningful comparison and analysis.The existing SDP schemes are analyzed using this model, revealing common systemic security weaknesses among the surveyed SDP schemes that should become priority areas for future SDP research, such as improving the integration of privacy requirements into the design of SDP schemes. Our results allow SDP scheme designers to create schemes that are more easily comparable with one another, and to assist the prevention of persisting the weaknesses common to the current generation of SDP schemes.Comment: 34 pages, 5 figures, 3 tables, accepted at IEEE Communications Surveys & Tutorials 2017 (Volume: PP, Issue: 99

A high dynamic-range instrument for SPICA for coronagraphic observation of exoplanets and monitoring of transiting exoplanets

This paper, first, presents introductory reviews of the Space Infrared Telescope for Cosmology and Astrophysics (SPICA) mission and the SPICA Coronagraph Instrument (SCI). SPICA will realize a 3m class telescope cooled to 6K in orbit. The launch of SPICA is planned to take place in FY2018. The SPICA mission provides us with a unique opportunity to make high dynamic-range observations because of its large telescope aperture, high stability, and the capability for making infrared observations from deep space. The SCI is a high dynamic-range instrument proposed for SPICA. The primary objectives for the SCI are the direct coronagraphic detection and spectroscopy of Jovian exoplanets in the infrared region, while the monitoring of transiting planets is another important target owing to the non-coronagraphic mode of the SCI. Then, recent technical progress and ideas in conceptual studies are presented, which can potentially enhance the performance of the instrument: the designs of an integral 1-dimensional binary-shaped pupil mask coronagraph with general darkness constraints, a concentric ring mask considering the obscured pupil for surveying a wide field, and a spectral disperser for simultaneous wide wavelength coverage, and the first results of tests of the toughness of MEMS deformable mirrors for the rocket launch are introduced, together with a description of a passive wavefront correction mirror using no actuator.Comment: 15 pages, 10 figures, 2 table

Microfluidics for protein biophysics

Microfluidics has the potential to transform experimental approaches across the life sciences. In this review, we discuss recent advances enabled by the development and application of microfluidic approaches to protein biophysics. We focus on areas where key fundamental features of microfluidics open up new possibilities and present advantages beyond low volumes and short time-scale analysis, conventionally provided by microfluidics. We discuss the two most commonly used forms of microfluidic technology, single-phase laminar flow and multiphase microfluidics. We explore how the understanding and control of the characteristic physical features of the microfluidic regime, the integration of microfluidics with orthogonal systems and the generation of well-defined microenvironments can be used to develop novel devices and methods in protein biophysics for sample manipulation, functional and structural studies, detection and material processing

Proceedings, MSVSCC 2018

Proceedings of the 12th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 19, 2018 at VMASC in Suffolk, Virginia. 155 pp

Identification of Tuna and Mackerel based on DNA Barcodes using Support Vector Machine

Tuna and mackerel are important fish in Indonesia that have great demand in the community and contain good nutrients for health. Many of the processed products have been faked including processed fish, by replacing the content of products that have high sales value to other lower price one. For ensuring food safety, fraudulent should be prevented by identifying the content of refined product. In this research, we implemented support vector machine (SVM), one of the popular methods in machine learning, to yield a model for identifying the content of refined product based on DNA barcode sequences. The feature extraction of DNA barcode Sequences was conducted by calculating k-mers frequency of each sequences. In this study, we used trinucleotide (3-mers) and tetranucleotide (4-mers). These features were inputted to SVM to classify and identify whether the DNA barcode sequences belong to the class of tuna, mackerel, or other fish. The evaluation results showed model SVM was able to perform identification with the accuracy 88%