2,163 research outputs found
A Tutorial on Clique Problems in Communications and Signal Processing
Since its first use by Euler on the problem of the seven bridges of
K\"onigsberg, graph theory has shown excellent abilities in solving and
unveiling the properties of multiple discrete optimization problems. The study
of the structure of some integer programs reveals equivalence with graph theory
problems making a large body of the literature readily available for solving
and characterizing the complexity of these problems. This tutorial presents a
framework for utilizing a particular graph theory problem, known as the clique
problem, for solving communications and signal processing problems. In
particular, the paper aims to illustrate the structural properties of integer
programs that can be formulated as clique problems through multiple examples in
communications and signal processing. To that end, the first part of the
tutorial provides various optimal and heuristic solutions for the maximum
clique, maximum weight clique, and -clique problems. The tutorial, further,
illustrates the use of the clique formulation through numerous contemporary
examples in communications and signal processing, mainly in maximum access for
non-orthogonal multiple access networks, throughput maximization using index
and instantly decodable network coding, collision-free radio frequency
identification networks, and resource allocation in cloud-radio access
networks. Finally, the tutorial sheds light on the recent advances of such
applications, and provides technical insights on ways of dealing with mixed
discrete-continuous optimization problems
Efficient Mapping of Neural Network Models on a Class of Parallel Architectures.
This dissertation develops a formal and systematic methodology for efficient mapping of several contemporary artificial neural network (ANN) models on k-ary n-cube parallel architectures (KNC\u27s). We apply the general mapping to several important ANN models including feedforward ANN\u27s trained with backpropagation algorithm, radial basis function networks, cascade correlation learning, and adaptive resonance theory networks. Our approach utilizes a parallel task graph representing concurrent operations of the ANN model during training. The mapping of the ANN is performed in two steps. First, the parallel task graph of the ANN is mapped to a virtual KNC of compatible dimensionality. This involves decomposing each operation into its atomic tasks. Second, the dimensionality of the virtual KNC architecture is recursively reduced through a sequence of transformations until a desired metric is optimized. We refer to this process as folding the virtual architecture. The optimization criteria we consider in this dissertation are defined in terms of the iteration time of the algorithm on the folded architecture. If necessary, the mapping scheme may utilize a subset of the processors of a given KNC architecture if it results in the most efficient simulation. A unique feature of our mapping is that it systematically selects an appropriate degree of parallelism leading to a highly efficient realization of the ANN model on KNC architectures. A novel feature of our work is its ability to efficiently map unit-allocating ANN\u27s. These networks possess a dynamic structure which grows during training. We present a highly efficient scheme for simulating such networks on existing KNC parallel architectures. We assume an upper bound on size of the neural network We perform the folding such that the iteration time of the largest network is minimized. We show that our mapping leads to near-optimal simulation of smaller instances of the neural network. In addition, based on our mapping no data migration or task rescheduling is needed as the size of network grows
When Being Soft Makes You Tough: A Collision Resilient Quadcopter Inspired by Arthropod Exoskeletons
Flying robots are usually rather delicate, and require protective enclosures
when facing the risk of collision. High complexity and reduced payload are
recurrent problems with collision-tolerant flying robots. Inspired by
arthropods' exoskeletons, we design a simple, easily manufactured, semi-rigid
structure with flexible joints that can withstand high-velocity impacts. With
an exoskeleton, the protective shell becomes part of the main robot structure,
thereby minimizing its loss in payload capacity. Our design is simple to build
and customize using cheap components and consumer-grade 3D printers. Our
results show we can build a sub-250g, autonomous quadcopter with visual
navigation that can survive multiple collisions at speeds up to 7m/s that is
also suitable for automated battery swapping, and with enough computing power
to run deep neural network models. This structure makes for an ideal platform
for high-risk activities (such as flying in a cluttered environment or
reinforcement learning training) without damage to the hardware or the
environment
Automation of road feature extraction from high resolution images
Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesThe detection of road features from remotely sensed images has become a critical factor in maintaining a reliable and updated road network in a country to provide a base reference for transportation, emergency planning, and navigation. With the recent advances of convolutional neural networks in image processing, several publications are devoted to the development of a method for automatically extract roads from satellite images. However, a reliable feature extraction method has not yet been developed with the desired accuracy and precision, and always seems to be a proportionality between the accuracy and the complexity of these developed methods. The aim of this study was therefore to develop an accurate road extraction method without compromising computational efficiency. In this paper, a semantic segmentation neural network that combines the strengths of transfer learning and U-net architecture is proposed with a minimal network complexity. Further, post-processing based on morphological operations and regional properties of the extracted segments were used to remove the noises from the final output. The results have been compared with different automatic classification and segmentation methods and the results of the proposed method produced an F1 score of 0.83 and high accuracy of 95.57%, more accurate and precise than all the other models for the freely available Massachusetts dataset. Finally, the developed method stood superior to the preexisting methods in terms of performance measure and network complexity
Integration of aerial and terrestrial locomotion modes in a bioinspired robotic system
In robotics, locomotion is a fundamental task for the development of high-level activities such as navigation. For a robotic system, the challenge of evading environmental obstacles depends both on its physical capabilities and on the strategies followed to achieve it. Thus, a robot with the ability to develop several modes of locomotion (walking, flying or swimming) has a greater probability of success in achieving its goal than a robot that develops only one.
In nature, Hymenoptera insects use terrestrial and aerial modes of locomotion to carry out their activities. Mimicry the physical capabilities of these insects opens the possibility of improvements in the area of robotic locomotion. Therefore, this work seeks to generate a bio-inspired robotic system that integrates the terrestrial and aerial modes of locomotion.
The methodology used in this research project has considered the anatomical study and characterization of Hymenoptera insects locomotion, the proposal of conceptual models that integrate terrestrial and aerial modes locomotion, the construction of a physical platform and experimental testing of the system. In addition, a gait generation approach based on an artificial nervous system of coupled nonlinear oscillators has been proposed. This approach has resulted in the generation of a coherent and functional gait pattern that, in combination with the flight capabilities of the system, has constituted an aero-terrestrial robot.
The results obtained in this work include the construction of a bioinspired physical platform, the generation of the gait process using an artificial nervous system and the experimental tests on the integration of aero-terrestrial locomotion.Conacyt - Becario Naciona
A Survey of Developable Surfaces: From Shape Modeling to Manufacturing
Developable surfaces are commonly observed in various applications such as
architecture, product design, manufacturing, and mechanical materials, as well
as in the development of tangible interaction and deformable robots, with the
characteristics of easy-to-product, low-cost, transport-friendly, and
deformable. Transforming shapes into developable surfaces is a complex and
comprehensive task, which forms a variety of methods of segmentation,
unfolding, and manufacturing for shapes with different geometry and topology,
resulting in the complexity of developable surfaces. In this paper, we reviewed
relevant methods and techniques for the study of developable surfaces,
characterize them with our proposed pipeline, and categorize them based on
digital modeling, physical modeling, interaction, and application. Through the
analysis to the relevant literature, we also discussed some of the research
challenges and future research opportunities.Comment: 20 pages, 24 figures, Author submitted manuscrip
NASA Space Engineering Research Center Symposium on VLSI Design
The NASA Space Engineering Research Center (SERC) is proud to offer, at its second symposium on VLSI design, presentations by an outstanding set of individuals from national laboratories and the electronics industry. These featured speakers share insights into next generation advances that will serve as a basis for future VLSI design. Questions of reliability in the space environment along with new directions in CAD and design are addressed by the featured speakers
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