Wireless communication plays an important role in mission plan activities of various military and battlefield situations. In each of these situations, a frequency assignment problem arises with application specific ch...
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ISBN:
(纸本)9783319049601;9783319049595
Wireless communication plays an important role in mission plan activities of various military and battlefield situations. In each of these situations, a frequency assignment problem arises with application specific characteristics. This paper presents a novel extension to binary constraint [1] method for automation of frequency assignment in battlefield scenarios. The proposed method calculates the cost function considering each node of the radio net in battlefield equally important. Two cases for calculation of cost function are considered;in the first case the average of the signal (from intra radio nodes) and interference (from inter radio nodes) is considered whereas in the second case the intra radio nodes causing minimum signal and inter radio nodes producing maximum interference are taken into consideration for cost calculation. A simulation exercise in lab environment has been done for validation of proposed methods by creating a battlefield scenario with three radio nets each consisting three-radio nodes.
Multi-task learning (MTL) is a joint learning paradigm to improve the generalization performance of the tasks. At present, most of MTL methods are all based on one hypothesis that all learning tasks are related and ap...
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Multi-task learning (MTL) is a joint learning paradigm to improve the generalization performance of the tasks. At present, most of MTL methods are all based on one hypothesis that all learning tasks are related and approximate for joint learning. However, this hypothesis may not be held in some scenarios, which may further lead to the problem of negative transfer. Therefore, in this paper, we aim to deal with the negative transfer problem and simultaneously improve the generalization performance in the joint learning. Combining with the subspace learning, we proposed a calibrated multi-task subspace learning method (CMTSL) under the binary group constraint. With the low-rank constraint on subspaces and the binary group indicator, our model can identify "with whom" one task should share and perform the multi-task inference on the high-dimensional parameter space in the meantime. To better approximate the low-rank constraint, we introduce a capped rank function as the tight relaxation term. Last, an iteration based re-weighted algorithm is proposed to solve our model and the convergence analysis is also proved in theory. Experimental results on benchmark datasets demonstrate the superiority of our model.
In this paper we present a method for navigating a multi-robot system through an environment while additionally maintaining a predefined set of constraints. Possible constraints are the requirement to keep up the dire...
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ISBN:
(纸本)9781424466757
In this paper we present a method for navigating a multi-robot system through an environment while additionally maintaining a predefined set of constraints. Possible constraints are the requirement to keep up the direct line-of-sight between robots or to ensure that robots stay within a certain distance. Our approach is based on graph structures that model movements and constraints separately, in order to cover different robots and a large class of possible constraints. Additionally, the partition of movement and constraint graph allows us to use known graph algorithms like Steiner trees to solve the problem of finding a target configuration for the robots. We construct so called separated distance graphs from the Steiner tree and the underlying roadmap graph, which allow assembling valid navigation plans fast.
We present a new copositive Farkas lemma for a general conic quadratic system with binary constraints under a convexifiability requirement. By employing this Farkas lemma, we establish that a minimally exact conic pro...
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We present a new copositive Farkas lemma for a general conic quadratic system with binary constraints under a convexifiability requirement. By employing this Farkas lemma, we establish that a minimally exact conic programming relaxation holds for a convexifiable robust quadratic optimization problem with binary and quadratic constraints under a commonly used ellipsoidal uncertainty set of robust optimization. We then derive a minimally exact copositive relaxation for a robust binary quadratic program with conic linear constraints where the convexifiability easily holds. (C) 2019 Elsevier B.V. All rights reserved.
This paper proposes a method for low resolution QR-code recognition. A QR-code is a two-dimensional binary symbol that can embed various information such as characters and numbers. To recognize a QR-code correctly and...
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ISBN:
(纸本)9780769545202
This paper proposes a method for low resolution QR-code recognition. A QR-code is a two-dimensional binary symbol that can embed various information such as characters and numbers. To recognize a QR-code correctly and stably, the resolution of an input image should be high. In practice, however, recognition of a QR-code is usually difficult due to low resolution when it is captured from a distance. In this paper, we propose a method to improve the performance of low resolution QR-code recognition by using the super-resolution technique that generates a high resolution image from multiple low-resolution images. Although a QR-code is a binary pattern, it is observed as a grayscale image due to the degradation through the capturing process. Especially the pixels around the borders between white and black regions become ambiguous. To overcome this problem, the proposed method introduces a binary pattern constraint to generate super-resolved images appropriate for recognition. Experimental results showed that a recognition rate of 98% can be achieved by the proposed method, which is a 15.7% improvement in comparison with a method using a conventional super-resolution method.
binary constraints are a general representation for constraints and is used in constraint Satisfaction Problems (CSPs). However, many problems are more easily modelled with non-binary constraints (constraints with ari...
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ISBN:
(纸本)9783030192129;9783030192112
binary constraints are a general representation for constraints and is used in constraint Satisfaction Problems (CSPs). However, many problems are more easily modelled with non-binary constraints (constraints with arity >2). Several well-known binary encoding methods can be used to transform non-binary CSPs to binary CSPs. Historically, work on constraint satisfaction began with binary CSPs with many algorithms proposed to maintain Arc Consistency (AC) on binary constraints. In more recent times, research has focused on non-binary constraints and efficient Generalized Arc Consistency (GAC) algorithms for non-binary constraints. Existing results and "folklore" suggest that AC algorithms on the binary encoding of a non-binary CSP do not compete with GAC algorithms on the original problem. We propose new algorithms to enforce AC on binary encoded instances. Preliminary experiments show that our AC algorithm on the binary encoded instances is competitive to state-of-the-art GAC algorithms on the original non-binary instances and faster in some instances. This result is surprising and is contrary to the "folklore" on AC versus GAC algorithms. We believe our results can lead to a revival of AC algorithms as binary constraints and resulting algorithms are simpler than the non-binary ones.
We compare and contrast case-only designs for detecting gene x gene (G x G) interaction in rheumatoid arthritis (RA) using the genome-wide data provided by Genetic Analysis Workshop 16 Problem 1. Logistic as well as n...
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We compare and contrast case-only designs for detecting gene x gene (G x G) interaction in rheumatoid arthritis (RA) using the genome-wide data provided by Genetic Analysis Workshop 16 Problem 1. Logistic as well as novel multinomial and proportional odds models that do not depend on the specification of additive or dominant models for susceptibility loci were applied to the case-only sample. We identified 519 significant interactions (p < 1 x 10-4 in at least one test). All methods detected unique significant interactions; 169 were common to more than one model and only 21 were common to all models. Results emphasize that categorization of the genetic variables and choice of regression model are critical and hugely influential in the identification of G x G. Porportional odds and multinomial methods provide new tools for identification of G x G interactions.
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