464 research outputs found
Food Phone Application
This project is about implementing a food menu application for users to search and upload food information by using a mobile phone. People sometimes may just know what food they wish to eat instead of the restaurants\u27 name. Without knowing any restaurants\u27 names, our food application\u27s search only requires the name of the dish (e.g., hamburger, spaghetti, etc) in order to get the list of restaurants that serve these items and their corresponding information (e.g., location, hours, phone number, item\u27s price, etc.). An advantage of using my food application is the system not only includes Google Map, but any information other users have inputted. When a user wants to input a food item, one can either upload the item\u27s picture or a template picture to the server and input the rating and comments about the specific food item. With the rating option, my project calculates a cumulative rating result based around the original and other user\u27s input. There is also the option of having the users input a zip code to better identify where to find the food. Based on the phone\u27s capability, the system also needs to figure out the physical phone location. This requires the phone to receive the GPS signal. As a result, users can search/upload the local restaurants\u27 food without inputting the current location
Rigidity of bordered polyhedral surfaces
This paper investigates the rigidity of bordered polyhedral surfaces. Using
the variational principle, we show that bordered polyhedral surfaces are
determined by boundary value and discrete curvatures on the interior. As a
corollary, we reprove the classical result that two Euclidean cyclic polygons
(or hyperbolic cyclic polygons) are congruent if the lengths of their sides are
equal
Ground-VIO: Monocular Visual-Inertial Odometry with Online Calibration of Camera-Ground Geometric Parameters
Monocular visual-inertial odometry (VIO) is a low-cost solution to provide
high-accuracy, low-drifting pose estimation. However, it has been meeting
challenges in vehicular scenarios due to limited dynamics and lack of stable
features. In this paper, we propose Ground-VIO, which utilizes ground features
and the specific camera-ground geometry to enhance monocular VIO performance in
realistic road environments. In the method, the camera-ground geometry is
modeled with vehicle-centered parameters and integrated into an
optimization-based VIO framework. These parameters could be calibrated online
and simultaneously improve the odometry accuracy by providing stable
scale-awareness. Besides, a specially designed visual front-end is developed to
stably extract and track ground features via the inverse perspective mapping
(IPM) technique. Both simulation tests and real-world experiments are conducted
to verify the effectiveness of the proposed method. The results show that our
implementation could dramatically improve monocular VIO accuracy in vehicular
scenarios, achieving comparable or even better performance than state-of-art
stereo VIO solutions. The system could also be used for the auto-calibration of
IPM which is widely used in vehicle perception. A toolkit for ground feature
processing, together with the experimental datasets, would be made open-source
(https://github.com/GREAT-WHU/gv_tools)
Multi-Constraint Molecular Generation using Sparsely Labelled Training Data for Localized High-Concentration Electrolyte Diluent Screening
Recently, machine learning methods have been used to propose molecules with
desired properties, which is especially useful for exploring large chemical
spaces efficiently. However, these methods rely on fully labelled training
data, and are not practical in situations where molecules with multiple
property constraints are required. There is often insufficient training data
for all those properties from publicly available databases, especially when
ab-initio simulation or experimental property data is also desired for training
the conditional molecular generative model. In this work, we show how to modify
a semi-supervised variational auto-encoder (SSVAE) model which only works with
fully labelled and fully unlabelled molecular property training data into the
ConGen model, which also works on training data that have sparsely populated
labels. We evaluate ConGen's performance in generating molecules with multiple
constraints when trained on a dataset combined from multiple publicly available
molecule property databases, and demonstrate an example application of building
the virtual chemical space for potential Lithium-ion battery localized
high-concentration electrolyte (LHCE) diluents
Equilibria in Second Price Auctions with Information Acquisition
This paper studies equilibria in second price auctions with information acquisition in an
independent private value setting. We focus on the existence and uniqueness of equilibrium in
the information acquisition stage for both homogenous and heterogenous bidders. It is shown
that, when the relative probability gain of information acquisition is increasing, there always
exists an equilibrium and further it is symmetric and unique when bidders are homogenous.
Moreover, we show that different type of bidders must choose different information levels, and
further the advantaged groups with lower marginal information cost have stronger incentive
to acquire information. An illustrative example with two bidders and Gaussian specification
is presented to provide intuition and implications on equilibrium behavior of bidders
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