2,231 research outputs found
A Vertical Channel Model of Molecular Communication based on Alcohol Molecules
The study of Molecular Communication(MC) is more and more prevalence, and
channel model of MC plays an important role in the MC System. Since different
propagation environment and modulation techniques produce different channel
model, most of the research about MC are in horizontal direction,but in nature
the communications between nano machines are in short range and some of the
information transportation are in the vertical direction, such as transpiration
of plants, biological pump in ocean, and blood transportation from heart to
brain. Therefore, this paper we propose a vertical channel model which
nano-machines communicate with each other in the vertical direction based on
pure diffusion. We first propose a vertical molecular communication model, we
mainly considered the gravity as the factor, though the channel model is also
affected by other main factors, such as the flow of the medium, the distance
between the transmitter and the receiver, the delay or sensitivity of the
transmitter and the receiver. Secondly, we set up a test-bed for this vertical
channel model, in order to verify the difference between the theory result and
the experiment data. At last, we use the data we get from the experiment and
the non-linear least squares method to get the parameters to make our channel
model more accurate.Comment: 5 pages,7 figures, Accepted for presentation at BICT 2015 Special
Track on Molecular Communication and Networking (MCN). arXiv admin note: text
overlap with arXiv:1311.6208 by other author
NRPA: Neural Recommendation with Personalized Attention
Existing review-based recommendation methods usually use the same model to
learn the representations of all users/items from reviews posted by users
towards items. However, different users have different preference and different
items have different characteristics. Thus, the same word or similar reviews
may have different informativeness for different users and items. In this paper
we propose a neural recommendation approach with personalized attention to
learn personalized representations of users and items from reviews. We use a
review encoder to learn representations of reviews from words, and a user/item
encoder to learn representations of users or items from reviews. We propose a
personalized attention model, and apply it to both review and user/item
encoders to select different important words and reviews for different
users/items. Experiments on five datasets validate our approach can effectively
improve the performance of neural recommendation.Comment: 4 pages, 4 figure
PROACTIVE REAL-TIME WEATHER INFORMATION
A computing device (e.g., a cellular phone, a smartphone, a desktop computer, a laptop computer, a tablet computer, a portable gaming device, a watch, etc.) may provide navigation and weather information to assist a user of the computing device in selecting a route to a destination. The computing device may execute an application that provides both navigation functionality and periodic weather updates. A user of the computing device may input a destination into the application, and the application may output a route to the destination as well as weather information for locations along that route. As the user moves along the route, the application may receive updated weather information for the updated locations and alerts warning about extreme weather. The application may receive, via the computing device, the updated weather information in response to a periodic request, automatically (e.g., in response to a weather alert being issued), or a combination thereof. As a result, the application may output (e.g., visually, auditorily, tactilely, etc.) current weather information about the user’s current and upcoming locations, thus providing the user a stream of accurate weather information
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