The paper considers a problem statement for the synthesis of a robust stochastic regulator based on the principles of control on manifolds and a new algorithm for designing a regulator for stochastic discrete objects....
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The paper considers a problem statement for the synthesis of a robust stochastic regulator based on the principles of control on manifolds and a new algorithm for designing a regulator for stochastic discrete objects. The object of study is presented in the form of a system of stochastic difference nonlinear equations. The new algorithm for the analytical design of a stochastic nonlinear control system is based on the classical method for the analytical design of aggregated regulators, previously developed for a deterministic nonlinear object with a complete description. The robust stochastic nonlinear regulator provides the following characteristics of the control system: a) the minimal variance of the output variable;b) the minimal dispersion of the target invariant;c) the minimum of the average value of the quality functional.
Aiming at data from different data sources were data preprocessing and the establishment of a common mathematical model to analyze how to improve in the face of massive high-dimensional data mining efficiency and qual...
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Aiming at data from different data sources were data preprocessing and the establishment of a common mathematical model to analyze how to improve in the face of massive high-dimensional data mining efficiency and quality of the methods of association rules, and the rules for existing association metrics prone to a lot of redundant and loop rule this situation put forward the corresponding solutions, while the paper applied the high-dimensional data clustering algorithm based on hypergraph. Research article on high-dimensional data mining methods of data analysis for large data era has a certain significance to explore the methods and tools of mathematical analysis of big data era.
Government and commercial customers are increasingly interested in robust, reusable flight software. For many spacecraft, Attitude Determination and Control Subsystem (ADCS) contributes a significant portion of the FS...
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ISBN:
(纸本)9781479916191
Government and commercial customers are increasingly interested in robust, reusable flight software. For many spacecraft, Attitude Determination and Control Subsystem (ADCS) contributes a significant portion of the FSW. Thus refinements to ADCS code pay dividends for code development and reuse. Sierra Nevada Corporation (SNC) has recently developed an ADCS model and code set that follows model based design techniques. It uses Simulink for algorithm design and verification, a modular parameter database to customize mission profiles, and automatic code generation to create production quality embedded software. The code will be used on a LEO Earth imaging nanosat. This paper presents an overview of the algorithms, model architecture, parameter database, and code generation process.
To empower an autonomous robot to perform long-term navigation in a given area, a concurrent localization and map update algorithm is required. In this paper, we tackle this problem by providing both theoretical analy...
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ISBN:
(纸本)9781538679012;9781538679265
To empower an autonomous robot to perform long-term navigation in a given area, a concurrent localization and map update algorithm is required. In this paper, we tackle this problem by providing both theoretical analysis and algorithm design for robotic systems equipped with 2D laser range finders. The first key contribution of this paper is that we propose a hybrid signed distance field (SDF) framework for laser based localization. The proposed hybrid SDF integrates two methods with complementary characteristics, namely Euclidean SDF (ESDF) and Truncated SDF (TSDF). With our framework, accurate pose estimation and fast map update can be performed simultaneously. Moreover, we introduce a novel sliding window estimator which attains better accuracy by consistently utilizing sensor and map information with both scan-to-scan and scan-to-map data association. Real-world experimental results demonstrate that the proposed algorithm can be used for commercial robots in various environments with long-term usage. Experiments also show that our approach outperforms competing approaches by a wide margin.
Theory of Computing (ToC) is an important aspect of nearly every undergraduate CS curriculum, as it concerns what computation fundamentally means. However, there has been little research into ToC pedagogy, both within...
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ISBN:
(纸本)9798400706042
Theory of Computing (ToC) is an important aspect of nearly every undergraduate CS curriculum, as it concerns what computation fundamentally means. However, there has been little research into ToC pedagogy, both within the classroom and how it fits within its institutional context. We propose in this working group to create a survey of current ToC pedagogy. Our goals are to create a standard for teaching ToC, find trends, determine under-researched areas, and to build a community among ToC educators.
This paper proposes the use of subspace tracking algorithms for performing MIMO channel estimation at millimeter wave (mmWave) frequencies. Using a subspace approach, we develop a protocol enabling the estimation of t...
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This paper proposes the use of subspace tracking algorithms for performing MIMO channel estimation at millimeter wave (mmWave) frequencies. Using a subspace approach, we develop a protocol enabling the estimation of the right (resp. left) singular vectors at the transmitter (resp. receiver) side; then, we adapt the projection approximation subspace tracking with deflation (PASTd) and the orthogonal Oja (OOJA) algorithms to our framework and obtain two channel estimation algorithms. The hybrid analog/digital nature of the beamformer is also explicitly taken into account at the algorithm design stage. Numerical results show that the proposed estimation algorithms are effective, and that they perform better than two relevant competing alternatives available in the open literature.
We present Geospatial Intelligence (GEOINT) as a context for computing education. GEOINT is a rich source of ideas for programming projects, algorithm design and use of databases. Students are interested in GEOINT due...
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ISBN:
(纸本)9781450318686
We present Geospatial Intelligence (GEOINT) as a context for computing education. GEOINT is a rich source of ideas for programming projects, algorithm design and use of databases. Students are interested in GEOINT due to its inherently visual subject matter and its strong ties with crime, espionage and social changes and upheavals. In short we are motivating computation as a subject that can provide solutions and insights into crime mysteries and complex events that unfold over time and space. In addition to keeping Computer Science majors interested, we also seek to attract students of other disciplines into computing. GEOINT and computing knowledge can provide initial preparation for certain jobs that are in demand and also for graduate school. Our assignments and programming projects that are inspired by GEOINT can be used in an introductory programming course or a more advanced course. These materials derive from well known case studies and also fundamentals concepts in computing. Some programming projects are based on exploration of chronologies and timelines as tools that enable the geographical display of information as an order sequence of events. In general these geospatial displays can correlate information to help correct for possible gaps and inconsistencies in knowledge. These materials use multiple layered techniques of presenting information that use time, geographical location, weather conditions and static features of the earth's surface.
We present a novel algorithm for digital halftoning. The algorithm combines a technique based on error diffusion with the use of a cost function to determine termination. Its chief advantages include the use of random...
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ISBN:
(纸本)0909925828
We present a novel algorithm for digital halftoning. The algorithm combines a technique based on error diffusion with the use of a cost function to determine termination. Its chief advantages include the use of randomness to avoid visual artifacts in the binary image and its amenability to parallel execution. The algorithm is a member of the class of "dynamic communication algorithms" which make novel use of dynamically-routed messages to structure the execution of a program.
In this work we propose a novel unsupervised algorithm for designing multispectral filters that are tuned for local anomaly detection algorithms. This problem is formulated as a problem of channel reduction in hypersp...
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ISBN:
(纸本)9781617388767
In this work we propose a novel unsupervised algorithm for designing multispectral filters that are tuned for local anomaly detection algorithms. This problem is formulated as a problem of channel reduction in hyperspectral images, which is performed by replacing subsets of adjacent spectral bands by their means. An optimal partition of hyperspectral bands is obtained by minimizing the Maximum of Maha-lanobis Norms (MXMN) of errors, obtained due to misrepresentation of hyperspectral bands by constants. By minimizing the MXMN of errors, one reduces the anomaly contribution to the errors, which allows to retain more anomaly-related information in the reduced channels. We demonstrate that the proposed algorithm produces better results, in terms of the Receiver Operation Characteristic (ROC) curve of a benchmark anomaly detection algorithm (RX) - applied after the dimensionality reduction, as compared to two other dimensionality reduction techniques, including Principal Component Analysis (PCA).
This paper considers a network of agents whose objective is for the aggregate of their states to converge to a solution of a linear program. We assume that each agent has limited information about the problem data and...
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ISBN:
(纸本)9781467360890
This paper considers a network of agents whose objective is for the aggregate of their states to converge to a solution of a linear program. We assume that each agent has limited information about the problem data and communicates with other agents at discrete times of its choice. Our main contribution is the development of a distributed continuous-time dynamics and a set of state-based rules, termed triggers, that an individual agent can use to determine when to broadcast its state to neighboring agents to ensure convergence. Our technical approach to the algorithm design and analysis overcomes a number of challenges, including establishing convergence in the absence of a common smooth Lyapunov function, ensuring that the triggers are detectable by agents using only local information, and accounting for the asynchronism in the state broadcasts of the agents. Simulations illustrate our results.
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