17,510 research outputs found
A Vision-based Scheme for Kinematic Model Construction of Re-configurable Modular Robots
Re-configurable modular robotic (RMR) systems are advantageous for their
reconfigurability and versatility. A new modular robot can be built for a
specific task by using modules as building blocks. However, constructing a
kinematic model for a newly conceived robot requires significant work. Due to
the finite size of module-types, models of all module-types can be built
individually and stored in a database beforehand. With this priori knowledge,
the model construction process can be automated by detecting the modules and
their corresponding interconnections. Previous literature proposed theoretical
frameworks for constructing kinematic models of modular robots, assuming that
such information was known a priori. While well-devised mechanisms and built-in
sensors can be employed to detect these parameters automatically, they
significantly complicate the module design and thus are expensive. In this
paper, we propose a vision-based method to identify kinematic chains and
automatically construct robot models for modular robots. Each module is affixed
with augmented reality (AR) tags that are encoded with unique IDs. An image of
a modular robot is taken and the detected modules are recognized by querying a
database that maintains all module information. The poses of detected modules
are used to compute: (i) the connection between modules and (ii) joint angles
of joint-modules. Finally, the robot serial-link chain is identified and the
kinematic model constructed and visualized. Our experimental results validate
the effectiveness of our approach. While implementation with only our RMR is
shown, our method can be applied to other RMRs where self-identification is not
possible
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A discrete-time performance model for congestion control mechanism using queue thresholds with QOS constraints
This paper presents a new analytical framework for the congestion control of Internet traffic using a
queue threshold scheme. This framework includes two discrete-time analytical models for the performance
evaluation of a threshold based congestion control mechanism and compares performance measurements through
typical numerical results. To satisfy the low delay along with high throughput, model-I incorporates one
threshold to make the arrival process step reduce from arrival rate Âż1 directly to Âż2 once the number of packets in
the system has reached the threshold value L1. The source operates normally, otherwise. Model-II incorporates
two thresholds to make the arrival rate linearly reduce from Âż1 to Âż2 with system contents when the number of
packets in the system is between two thresholds L1 and L2. The source operates normally with arrival rate Âż1
before threshold L1, and with arrival rate Âż2 after the threshold L2. In both performance models, the mean packet
delay W, probability of packet loss PL and throughput S have been found as functions of the thresholds and
maximum drop probability. The performance comparison results for the two models have also been made
through typical numerical results. The results clearly demonstrate how different load settings can provide
different tradeoffs between throughput, loss probability and delay to suit different service requirements
Compressive sensing based Bayesian sparse channel estimation for OFDM communication systems: high performance and low complexity
In orthogonal frequency division modulation (OFDM) communication systems,
channel state information (CSI) is required at receiver due to the fact that
frequency-selective fading channel leads to disgusting inter-symbol
interference (ISI) over data transmission. Broadband channel model is often
described by very few dominant channel taps and they can be probed by
compressive sensing based sparse channel estimation (SCE) methods, e.g.,
orthogonal matching pursuit algorithm, which can take the advantage of sparse
structure effectively in the channel as for prior information. However, these
developed methods are vulnerable to both noise interference and column
coherence of training signal matrix. In other words, the primary objective of
these conventional methods is to catch the dominant channel taps without a
report of posterior channel uncertainty. To improve the estimation performance,
we proposed a compressive sensing based Bayesian sparse channel estimation
(BSCE) method which can not only exploit the channel sparsity but also mitigate
the unexpected channel uncertainty without scarifying any computational
complexity. The propose method can reveal potential ambiguity among multiple
channel estimators that are ambiguous due to observation noise or correlation
interference among columns in the training matrix. Computer simulations show
that propose method can improve the estimation performance when comparing with
conventional SCE methods.Comment: 24 pages,16 figures, submitted for a journa
Exotic pairing in 1D spin-3/2 atomic gases with symmetry
Tuning interactions in the spin singlet and quintet channels of two colliding
atoms could change the symmetry of the one-dimensional spin-3/2 fermionic
systems of ultracold atoms while preserving the integrability. Here we find a
novel symmetry integrable point in thespin-3/2 Fermi gas and derive the
exact solution of the model using the Bethe ansatz. In contrast to the model
with and symmetries, the present model with symmetry
preserves spin singlet and quintet Cooper pairs in two sets of spin subspaces. We obtain full phase diagrams, including the
Fulde-Ferrel-Larkin-Ovchinnikov like pair correlations, spin excitations and
quantum criticality through the generalized Yang-Yang thermodynamic equations.
In particular, various correlation functions are calculated by using
finite-size corrections in the frame work of conformal field theory. Moreover,
within the local density approximation, we further find that spin singlet and
quintet pairs form subtle multiple shell structures in density profiles of the
trapped gas.Comment: 8 figures, 2 tables, 37 page
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