9,482 research outputs found
Facial expressions emotional recognition with NAO robot
Human-robot interaction research is diverse and covers a wide range of topics. All aspects of human factors and robotics are within the purview of HRI research so far as they provide insight into how to improve our understanding in developing effective tools, protocols, and systems to enhance HRI. For example, a significant research effort is being devoted to designing human-robot interface that makes it easier for the people to interact with robots. HRI is an extremely active research field where new and important work is being published at a fast pace.
It is crucial for humanoid robots to understand the emotions of people for efficient human robot interaction. Initially, the robot detects human face by Viola- Jones technique. Later, facial distance measurements are accumulated by geometric based facial distance measurement method. Then facial action coding system is used to detect movements of measured facial points. Finally, measured facial movements are evaluated to get instant emotional properties of human face in this research; it has been specifically applied to NAO humanoid robot
Efficient and Privacy-Preserving Ride Sharing Organization for Transferable and Non-Transferable Services
Ride-sharing allows multiple persons to share their trips together in one
vehicle instead of using multiple vehicles. This can reduce the number of
vehicles in the street, which consequently can reduce air pollution, traffic
congestion and transportation cost. However, a ride-sharing organization
requires passengers to report sensitive location information about their trips
to a trip organizing server (TOS) which creates a serious privacy issue. In
addition, existing ride-sharing schemes are non-flexible, i.e., they require a
driver and a rider to have exactly the same trip to share a ride. Moreover,
they are non-scalable, i.e., inefficient if applied to large geographic areas.
In this paper, we propose two efficient privacy-preserving ride-sharing
organization schemes for Non-transferable Ride-sharing Services (NRS) and
Transferable Ride-sharing Services (TRS). In the NRS scheme, a rider can share
a ride from its source to destination with only one driver whereas, in TRS
scheme, a rider can transfer between multiple drivers while en route until he
reaches his destination. In both schemes, the ride-sharing area is divided into
a number of small geographic areas, called cells, and each cell has a unique
identifier. Each driver/rider should encrypt his trip's data and send an
encrypted ride-sharing offer/request to the TOS. In NRS scheme, Bloom filters
are used to compactly represent the trip information before encryption. Then,
the TOS can measure the similarity between the encrypted trips data to organize
shared rides without revealing either the users' identities or the location
information. In TRS scheme, drivers report their encrypted routes, an then the
TOS builds an encrypted directed graph that is passed to a modified version of
Dijkstra's shortest path algorithm to search for an optimal path of rides that
can achieve a set of preferences defined by the riders
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