4,808 research outputs found
Energy-Efficient Resource Allocation for Device-to-Device Underlay Communication
Device-to-device (D2D) communication underlaying cellular networks is
expected to bring significant benefits for utilizing resources, improving user
throughput and extending battery life of user equipments. However, the
allocation of radio and power resources to D2D communication needs elaborate
coordination, as D2D communication can cause interference to cellular
communication. In this paper, we study joint channel and power allocation to
improve the energy efficiency of user equipments. To solve the problem
efficiently, we introduce an iterative combinatorial auction algorithm, where
the D2D users are considered as bidders that compete for channel resources, and
the cellular network is treated as the auctioneer. We also analyze important
properties of D2D underlay communication, and present numerical simulations to
verify the proposed algorithm.Comment: IEEE Transactions on Wireless Communication
Activity recognition from videos with parallel hypergraph matching on GPUs
In this paper, we propose a method for activity recognition from videos based
on sparse local features and hypergraph matching. We benefit from special
properties of the temporal domain in the data to derive a sequential and fast
graph matching algorithm for GPUs.
Traditionally, graphs and hypergraphs are frequently used to recognize
complex and often non-rigid patterns in computer vision, either through graph
matching or point-set matching with graphs. Most formulations resort to the
minimization of a difficult discrete energy function mixing geometric or
structural terms with data attached terms involving appearance features.
Traditional methods solve this minimization problem approximately, for instance
with spectral techniques.
In this work, instead of solving the problem approximatively, the exact
solution for the optimal assignment is calculated in parallel on GPUs. The
graphical structure is simplified and regularized, which allows to derive an
efficient recursive minimization algorithm. The algorithm distributes
subproblems over the calculation units of a GPU, which solves them in parallel,
allowing the system to run faster than real-time on medium-end GPUs
Occlusion reasoning for multiple object visual tracking
Thesis (Ph.D.)--Boston UniversityOcclusion reasoning for visual object tracking in uncontrolled environments is a challenging problem. It becomes significantly more difficult when dense groups of indistinguishable objects are present in the scene that cause frequent inter-object interactions and occlusions. We present several practical solutions that tackle the inter-object occlusions for video surveillance applications.
In particular, this thesis proposes three methods. First, we propose "reconstruction-tracking," an online multi-camera spatial-temporal data association method for tracking large groups of objects imaged with low resolution. As a variant of the well-known Multiple-Hypothesis-Tracker, our approach localizes the positions of objects in 3D space with possibly occluded observations from multiple camera views and performs temporal data association in 3D. Second, we develop "track linking," a class of offline batch processing algorithms for long-term occlusions, where the decision has to be made based on the observations from the entire tracking sequence. We construct a graph representation to characterize occlusion events and propose an efficient graph-based/combinatorial algorithm to resolve occlusions.
Third, we propose a novel Bayesian framework where detection and data association are combined into a single module and solved jointly. Almost all traditional tracking systems address the detection and data association tasks separately in sequential order. Such a design implies that the output of the detector has to be reliable in order to make the data association work. Our framework takes advantage of the often complementary nature of the two subproblems, which not only avoids the error propagation issue from which traditional "detection-tracking approaches" suffer but also eschews common heuristics such as "nonmaximum suppression" of hypotheses by modeling the likelihood of the entire image.
The thesis describes a substantial number of experiments, involving challenging, notably distinct simulated and real data, including infrared and visible-light data sets recorded ourselves or taken from data sets publicly available. In these videos, the number of objects ranges from a dozen to a hundred per frame in both monocular and multiple views. The experiments demonstrate that our approaches achieve results comparable to those of state-of-the-art approaches
Media-Based MIMO: A New Frontier in Wireless Communications
The idea of Media-based Modulation (MBM), is based on embedding information
in the variations of the transmission media (channel state). This is in
contrast to legacy wireless systems where data is embedded in a Radio Frequency
(RF) source prior to the transmit antenna. MBM offers several advantages vs.
legacy systems, including "additivity of information over multiple receive
antennas", and "inherent diversity over a static fading channel". MBM is
particularly suitable for transmitting high data rates using a single transmit
and multiple receive antennas (Single Input-Multiple Output Media-Based
Modulation, or SIMO-MBM). However, complexity issues limit the amount of data
that can be embedded in the channel state using a single transmit unit. To
address this shortcoming, the current article introduces the idea of Layered
Multiple Input-Multiple Output Media-Based Modulation (LMIMO-MBM). Relying on a
layered structure, LMIMO-MBM can significantly reduce both hardware and
algorithmic complexities, as well as the training overhead, vs. SIMO-MBM.
Simulation results show excellent performance in terms of Symbol Error Rate
(SER) vs. Signal-to-Noise Ratio (SNR). For example, a LMIMO-MBM is
capable of transmitting bits of information per (complex) channel-use,
with SER at dB (or SER
at dB). This performance is achieved using a single transmission
and without adding any redundancy for Forward-Error-Correction (FEC). This
means, in addition to its excellent SER vs. energy/rate performance, MBM
relaxes the need for complex FEC structures, and thereby minimizes the
transmission delay. Overall, LMIMO-MBM provides a promising alternative to MIMO
and Massive MIMO for the realization of 5G wireless networks.Comment: 26 pages, 11 figures, additional examples are given to further
explain the idea of Media-Based Modulation. Capacity figure adde
Geometric Multi-Model Fitting with a Convex Relaxation Algorithm
We propose a novel method to fit and segment multi-structural data via convex
relaxation. Unlike greedy methods --which maximise the number of inliers-- this
approach efficiently searches for a soft assignment of points to models by
minimising the energy of the overall classification. Our approach is similar to
state-of-the-art energy minimisation techniques which use a global energy.
However, we deal with the scaling factor (as the number of models increases) of
the original combinatorial problem by relaxing the solution. This relaxation
brings two advantages: first, by operating in the continuous domain we can
parallelize the calculations. Second, it allows for the use of different
metrics which results in a more general formulation.
We demonstrate the versatility of our technique on two different problems of
estimating structure from images: plane extraction from RGB-D data and
homography estimation from pairs of images. In both cases, we report accurate
results on publicly available datasets, in most of the cases outperforming the
state-of-the-art
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