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Traditional approaches to simultaneous localization and mapping (SLAM) rely on low-level geometric features such as points, lines, and planes. They are unable to assign semantic labels to landmarks observed in the environment. Furthermore, loop closure recognition based on low-level features is often viewpoint-dependent and subject to failure in ambiguous or repetitive environments. On the other hand,...
The problem of distance evaluation from a point to an algebraic manifold in Rn is treated in the framework of elimination of variables procedure applied for the algebraic equation system generated by Lagrange's method. The resulting univariate distance equation possesses a zero set coinciding with that of critical values of the squared distance function. We discuss also the problem of nearest point...
Cloud computing enables end users to execute high-performance computing applications by renting the required computing power. This pay-for-use approach enables small enterprises and startups to run HPC-related businesses with a significant saving in capital investment and a short time to market. When deploying an application in the cloud, the users may a) fail to understand the interactions of the...
The in-memory graph layout affects performance of distributed-memory graph computations. Graph layout could refer to partitioning or replication of vertex and edge arrays, selective replication of data structures that hold meta-data, and reordering vertex and edge identifiers. In this work, we consider one-dimensional graph layouts, where disjoint sets of vertices and their adjacencies are partitioned...
In this paper, we develop and analyze a novel performance metric, called interference efficiency (IE), that shows the number of transmitted bits per unit of interference energy imposed on the primary users (PUs) in an underlay cognitive radio network (CRN). Specifically, we develop a framework to maximize the IE of a CRN with multiple secondary users (SUs) while satisfying target constraints on the...
Techniques known as Nonlinear Set Membership prediction, Kinky Inference or Lipschitz Interpolation are fast and numerically robust approaches to nonparametric machine learning that have been proposed to be utilised in the context of system identification and learning-based control. They utilise presupposed Lipschitz properties in order to compute inferences over unobserved function values. Unfortunately,...
Project selection is a very important decision that every firm has to take; in fact, this decision plays a major role in the prosperity of the firm. Pinch analysis, which was initially developed to conserve energy and improve energy efficiency in industrial process, is now being extended to non-conventional areas. In this paper, pinch analysis is applied to select multiple independent projects from...
Rescheduling is a necessary procedure when new jobs come and are inserted into existing schedule in remanufacturing. The stability is an important metrics to evaluate the quality of rescheduling solution. This paper proposed a novel discrete Jaya algorithm to solve flexible job shop rescheduling problem with five objectives, including Makespan, total flow time, maximum machine workload, total machine...
Exhausters and coexhausters were proposed by V.F. Demyanov and is used for studying nonsmooth functions. These objects are families of convex compact sets in terms of which optimality conditions are described. This makes possible to construct effective optimization algorithms for nonsmooth problems. Exhausters and coexhausters are not uniquely defined. The smaller families the easier computations...
In this paper, we design a game theoretical framework for improving the Quality of Service (QoS) in cooperative RAN caching. Considering the cooperation under both single cell transmission and joint transmission, the QoS metric is uniformly quantified as the total content delivery time. Although the formulated cooperative content placement problem is proved NP-hard, noticing the local cooperative...
We present a method for the design optimization of a soft, inflatable robot. The method described utilizes a multi-objective fitness function together with custom, platform-specific metrics related to the dexterity and load-bearing capacity of inflatable manipulators. Candidate designs are scored by computing these metrics at many randomly generated configurations and then by appropriately combining...
Distance metric learning focuses on learning one global or multiple local distance functions to draw similar instances close to each other and push away dissimilar ones. Most existing work has to do matrix projection to learn distance functions. In this paper, we present a novel distance function learning model which is based on eigenvalue fine tuning. Our model not only is able to learn the global...
Real-time nonlinear stabilization techniques are often limited by inefficient or intractable online and/or offline computations, or a lack guarantee for global stability. In this paper, we explore the use of Control Contraction Metrics (CCM) for nonlinear stabilization because it offers tractable offline computations that give formal guarantees for global stability. We provide a method to solve the...
In cyber-physical systems (CPS), the problem of controlling resources can be depicted as an actuator selection problem. Given a large library of actuators and a control objective, what is the least number of actuators to be selected, and what is the corresponding optimal control law? These dynamic design questions are inherently coupled. In this paper, we show that a breadth of actuator selection...
This paper studies the benefits of time-varying actuator scheduling to the controllability of complex networks. The network dynamics are described by a single-input discrete-time linear system over an undirected graph. Taking the trace of the controllability Gramian as the measure of network controllability, we identify a new notion of nodal communicability and unveil its role in the time-varying...
In this paper, we study the performance of initial access beamforming schemes in the cases with large but finite number of transmit antennas and users. Particularly, we develop an efficient beamforming scheme using genetic algorithms. Moreover, taking the millimeter wave communication characteristics and different metrics into account, we investigate the effect of various parameters such as number...
Increasing volumes of data and the desire for realtime query capability make the development of efficient streaming algorithms for data analytics valuable. Streaming graph algorithms that avoid unnecessary recomputation through clever application of data dependency analysis are often more complex to derive than their static counterparts. This paper discusses a method to derive algorithms for streaming...
In this work, a framework based on maximum likelihood estimation and mutual information is proposed to design a metaheuristic. A multilevel decomposition of metaheuristics is proposed that allow to have a unified vision on this optimization approach. Then, a new layer based on machine learning is added to take profit from the evolution of the algorithm to adapt it to the considered problem to alleviate...
A completely controllable linear dynamical system can be steered from any given initial state to any specified final state with an application of input control energy. The input control energy is provided through a combination of actuators. It is desirable to have a limited number of actuators, which also presents the possibility of multiple actuator combinations that render the system completely...
Accurate spectrum sensing plays a decisive role in determining the performance of any cognitive radio network (CRN). Spectrum sensing enables the unlicensed secondary users (SUs) to operate in the licensed band until the primary user (PU) is detected. By allowing SUs in the same band to cooperate, we can reduce the detection time and increase overall agility and this forms the basis of cooperative...
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