scholarly journals Efficient Informative Sensing using Multiple Robots

2009 ◽  
Vol 34 ◽  
pp. 707-755 ◽  
Author(s):  
A. Singh ◽  
A. Krause ◽  
C. Guestrin ◽  
W. J. Kaiser
Keyword(s):  
Mutual Information ◽  
Resource Constraints ◽  
Temporal Dynamics ◽  
Optimal Solution ◽  
Water Quality Monitoring ◽  
General Technique ◽  
Amount Of Information ◽  
Sensing Applications ◽  
Spatial Coverage ◽  
Multi Robot

The need for efficient monitoring of spatio-temporal dynamics in large environmental applications, such as the water quality monitoring in rivers and lakes, motivates the use of robotic sensors in order to achieve sufficient spatial coverage. Typically, these robots have bounded resources, such as limited battery or limited amounts of time to obtain measurements. Thus, careful coordination of their paths is required in order to maximize the amount of information collected, while respecting the resource constraints. In this paper, we present an efficient approach for near-optimally solving the NP-hard optimization problem of planning such informative paths. In particular, we first develop eSIP (efficient Single-robot Informative Path planning), an approximation algorithm for optimizing the path of a single robot. Hereby, we use a Gaussian Process to model the underlying phenomenon, and use the mutual information between the visited locations and remainder of the space to quantify the amount of information collected. We prove that the mutual information collected using paths obtained by using eSIP is close to the information obtained by an optimal solution. We then provide a general technique, sequential allocation, which can be used to extend any single robot planning algorithm, such as eSIP, for the multi-robot problem. This procedure approximately generalizes any guarantees for the single-robot problem to the multi-robot case. We extensively evaluate the effectiveness of our approach on several experiments performed in-field for two important environmental sensing applications, lake and river monitoring, and simulation experiments performed using several real world sensor network data sets.

Capacity Analysis for Correlated Multi-Hop MIMO Channels under Colored Noise

2015 ◽  
Vol 1 ◽  
pp. 41
Author(s):  
Nguyen N. Tran ◽  
Ha X. Nguyen
Keyword(s):  
Computational Complexity ◽  
Mutual Information ◽  
Closed Form Solution ◽  
Optimal Solution ◽  
Form Solution ◽  
Maximization Problem ◽  
Capacity Analysis ◽  
Mimo Channels ◽  
Average Mutual Information ◽  
Channel Output

A capacity analysis for generally correlated wireless multi-hop multi-input multi-output (MIMO) channels is presented in this paper. The channel at each hop is spatially correlated, the source symbols are mutually correlated, and the additive Gaussian noises are colored. First, by invoking Karush-Kuhn-Tucker condition for the optimality of convex programming, we derive the optimal source symbol covariance for the maximum mutual information between the channel input and the channel output when having the full knowledge of channel at the transmitter. Secondly, we formulate the average mutual information maximization problem when having only the channel statistics at the transmitter. Since this problem is almost impossible to be solved analytically, the numerical interior-point-method is employed to obtain the optimal solution. Furthermore, to reduce the computational complexity, an asymptotic closed-form solution is derived by maximizing an upper bound of the objective function. Simulation results show that the average mutual information obtained by the asymptotic design is very closed to that obtained by the optimal design, while saving a huge computational complexity.

Mutual Information as an Index of Diagnostic Test Performance

2003 ◽  
Vol 42 (03) ◽  
pp. 260-264 ◽  
Author(s):  
W. A. Benish
Keyword(s):  
Mutual Information ◽  
Diagnostic Test ◽  
Test Performance ◽  
Random Variable ◽  
Disease State ◽  
Necessary Condition ◽  
Conditional Probabilities ◽  
Amount Of Information ◽  
Diagnostic Test Performance ◽  
Test Result

Summary Objectives: This paper demonstrates that diagnostic test performance can be quantified as the average amount of information the test result (R) provides about the disease state (D). Methods: A fundamental concept of information theory, mutual information, is directly applicable to this problem. This statistic quantifies the amount of information that one random variable contains about another random variable. Prior to performing a diagnostic test, R and D are random variables. Hence, their mutual information, I(D;R), is the amount of information that R provides about D. Results: I(D;R) is a function of both 1) the pretest probabilities of the disease state and 2) the set of conditional probabilities relating each possible test result to each possible disease state. The area under the receiver operating characteristic curve (AUC) is a popular measure of diagnostic test performance which, in contrast to I(D;R), is independent of the pretest probabilities; it is a function of only the set of conditional probabilities. The AUC is not a measure of diagnostic information. Conclusions: Because I(D;R) is dependent upon pretest probabilities, knowledge of the setting in which a diagnostic test is employed is a necessary condition for quantifying the amount of information it provides. Advantages of I(D;R) over the AUC are that it can be calculated without invoking an arbitrary curve fitting routine, it is applicable to situations in which multiple diagnoses are under consideration, and it quantifies test performance in meaningful units (bits of information).

S-DolLion-MSVNN: A Hybrid Model for Developing the Super-Resolution Image From the Multispectral Satellite Image

2020 ◽  
Author(s):  
Anil B Gavade ◽  
Vijay S Rajpurohit
Keyword(s):  
Hybrid Model ◽  
Satellite Image ◽  
Optimal Solution ◽  
Super Resolution ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Resolution Image ◽  
Sensing Applications ◽  
Multispectral Satellite Image ◽  
Lion Optimization Algorithm

Abstract Super-resolution offers a new image with high resolution from the low-resolution (LR) image that is highly employed for the numerous remote sensing applications. Most of the existing techniques for formation of the super-resolution image exhibit the loss of quality and deviation from the original multi-spectral LR image. Thus, this paper aims at proposing an efficient super-resolution method using the hybrid model. The hybrid model is developed using the support vector regression model and multi-support vector neural network (MSVNN), and the weights of the MSVNN is tuned optimally using the proposed algorithm. The proposed DolLion algorithm is the integration of the dolphin echolocation algorithm and lion optimization algorithm that exhibits better convergence and offers a global optimal solution. The experimentation is performed using the datasets taken from the multi-spectral scene images. The optimal and effective formation of the super-resolution image using the proposed hybrid model outperforms the existing methods, and the analysis using the second-derivative-like measure of enhancement (SDME) ensures that the proposed method is better and yields a maximum SDME of 67.6755 dB.

A New Testability Optimization Allocation Approach

2013 ◽  
Vol 328 ◽  
pp. 444-449 ◽  
Author(s):  
Gang Liu ◽  
Fang Li
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Iterative Process ◽  
Resource Constraints ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Numerical Example ◽  
Set Up ◽  
Optimization Allocation ◽  
Allocation Approach

This paper describes a methodology based on improved genetic algorithms (GA) and experiments plan to optimize the testability allocation. Test resources were reasonably configured for testability optimization allocation, in order to meet the testability allocation requirements and resource constraints. The optimal solution was not easy to solve of general genetic algorithm, and the initial parameter value was not easy to set up and other defects. So in order to more efficiently test and optimize the allocation, migration technology was introduced in the traditional genetic algorithm to optimize the iterative process, and initial parameters of algorithm could be adjusted by using AHP approach, consequently testability optimization allocation approach based on improved genetic algorithm was proposed. A numerical example is used to assess the method. and the examples show that this approach can quickly and efficiently to seek the optimal solution of testability optimization allocation problem.

APPLICATION OF DISTRIBUTED PARAMETER CONTROL MODEL IN WILDLIFE DAMAGE MANAGEMENT

1992 ◽  
Vol 02 (04) ◽  
pp. 423-439 ◽  
Author(s):  
SUZANNE M. LENHART ◽  
MAHADEV G. BHAT
Keyword(s):  
Temporal Dynamics ◽  
Real Life ◽  
Optimal Solution ◽  
Distributed Parameter ◽  
Parameter Control ◽  
Wildlife Damage Management ◽  
Distributed Parameter Control ◽  
Time Problem ◽  
Wildlife Damage ◽  
Spatio Temporal

A bioeconomic model for optimal control of wildlife damage by migratory small mammal populations is developed under the framework of a nonlinear distributed parameter control problem. The model first simulates the spatio-temporal dynamics of dispersal population by parabolic diffusive Volterra-Lotka partial differential equation and then optimizes a criterion function of present value combined costs of wildlife damage and harvesting. The existence of a unique optimal solution for a finite time problem is proved. An iterative procedure for numerical solution of the Optimality System with parabolic equations of opposite orientations is developed. The theoretical model is applied to a real life problem using biological and economic data for beaver populations under certain simplistic assumptions.

scholarly journals New perspectives on multilocus ancestry informativeness

2018 ◽  
Author(s):  
Omri Tal ◽  
Tat Dat Tran
Keyword(s):  
Mutual Information ◽  
Genetic Markers ◽  
Genetic Variants ◽  
Population Genetic ◽  
Finite Population ◽  
Axiomatic Approach ◽  
Amount Of Information ◽  
Individual Ancestry

AbstractWe present an axiomatic approach for multilocus informativeness measures for determining the amount of information that a set of polymorphic genetic markers provides about individual ancestry. We then reveal several surprising properties of a decision-theoretic based measure that is consistent with the set of proposed criteria for multilocus informativeness. In particular, these properties highlight the interplay between information originating from population priors and the information extractable from the population genetic variants. This analysis then reveals a certain deficiency of mutual information based multilocus informativeness measures when such population priors are incorporated. Finally, we analyse and quantify the inevitable inherent decrease in informativeness due to learning from finite population samples.

scholarly journals Quantifying The Significance of Distance to Temporal Dynamics of Covid-19 Cases in Nigeria Using a Geographic Information System

2021 ◽  
Vol 6 (1) ◽  
pp. 40
Author(s):  
Ifeyinwa Sarah Obuekwe ◽  
Umar Saleh Anka ◽  
Sodiq Opeyemi Ibrahim ◽  
Usman Ahmad Adam
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Spatial Autocorrelation ◽  
Temporal Dynamics ◽  
Close Contact ◽  
Geographic Information ◽  
Movement Restriction ◽  
Creative Commons ◽  
Spatial Coverage ◽  
Significant Difference

The coronavirus disease 2019 (COVID-19) is caused by a new strain of coronavirus that spreads primarily by close contact. Although Nigeria adopted lockdown measures, no defined strategies were used in setting the distance threshold for these lockdowns. Hence, understanding the drivers of COVID-19 is pivotal to an informed decision for containment measures in the absence of vaccines. Spatial and temporal analyses are crucial drivers to apprehending the pattern of diseases over space and time. Thus, this study aimed to quantify the significance of distance to the temporal dynamics of COVID-19 cases in Nigeria using the Geographic Information System. Incremental spatial autocorrelation was used to analyze datasets of each month in ArcGIS. March, April, May, and June exhibited patterns with no significant peaks, while July and August exhibited patterns with two statistically significant peaks. The first and second peaks of July were 301,338.39 and 365,947.83 meters, respectively, while August was 301,338.39 and 336,128.09 meters, respectively. Therefore, a significant difference in the clustering of COVID-19 over distances between July and August was established. This indicated that progression in the spread of the virus increased the virus's spatial coverage while the distance of risk of exposure decreased. This study's findings could be utilized to establish maximum movement restriction areas to contain the spread of COVID-19. Keywords: Distance; Incremental spatial autocorrelation; Covid-19; Disease; Nigeria Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

scholarly journals A Constraint-based Mission Planning Approach for Reconfigurable Multi-Robot Systems

2018 ◽  
Vol 21 (62) ◽  
pp. 25
Author(s):  
Thomas M Roehr
Keyword(s):  
Degrees Of Freedom ◽  
Resource Constraints ◽  
Mission Planning ◽  
General Nature ◽  
Planning Problem ◽  
Modular Systems ◽  
Planning Approach ◽  
Robot Systems ◽  
Revised Definitions ◽  
Multi Robot

The application of reconfigurable multi-robot systems introduces additional degrees of freedom to design robotic missions compared to classical multi-robot systems. To allow for autonomous operation of such systems, planning approaches have to be investigated that cannot only cope with the combinatorial challenge arising from the increased flexibility of modular systems, but also exploit this flexibility to improve for example the safety of operation. While the problem originates from the domain of robotics it is of general nature and significantly intersects with operations research. This paper suggests a constraint-based mission planning approach, and presents a set of revised definitions for reconfigurable multi-robot systems including the representation of the planning problem using spatially and temporally qualified resource constraints. Planning is performed using a multi-stage approach and a combined use of knowledge-based reasoning, constraint-based programming and integer linear programming. The paper concludes with the illustration of the solution of a planned example mission.

Dynamic Programming: Special Cases

1997 ◽  
Author(s):  
R. Giancarlo
Keyword(s):  
Dynamic Programming ◽  
Optimal Policy ◽  
Decision Process ◽  
Optimization Problem ◽  
Optimal Solution ◽  
General Technique ◽  
Optimal Cost ◽  
Initial Node ◽  
Principle Of Optimality ◽  
Algorithmic Techniques

In this Chapter we present some general algorithmic techniques that have proved to be useful in speeding up the computation of some families of dynamic programming recurrences which have applications in sequence alignment, paragraph formation and prediction of RNA secondary structure. The material presented in this chapter is related to the computation of Levenshtein distances and approximate string matching that have been discussed in the previous three chapters. Dynamic programming is a general technique for solving discrete optimization (minimization or maximization) problems that can be represented by decision processes and for which the principle of optimality holds. We can view a decision process as a directed graph in which nodes represent the states of the process and edges represent decisions. The optimization problem at hand is represented as a decision process by decomposing it into a set of subproblems of smaller size. Such recursive decomposition is continued until we get only trivial subproblems, which can be solved directly. Each node in the graph corresponds to a subproblem and each edge (a, b) indicates that one way to solve subproblem a optimally is to solve first subproblem b optimally. Then, an optimal solution, or policy, is typically given by a path on the graph that minimizes or maximizes some objective function. The correctness of this approach is guaranteed by the principle of optimality which must be satisfied by the optimization problem: An optimal policy has the property that whatever the initial node (state) and initial edge (decision) are, the remaining edges (decisions) must be an optimal policy with regard to the node (state) resulting from the first transition. Another consequence of the principle of optimality is that we can express the optimal cost (and solution) of a subproblem in terms of optimal costs (and solutions) of problems of smaller size. That is, we can express optimal costs through a recurrence relation. This is a key component of dynamic programming, since we can compute the optimal cost of a subproblem only once, store the result in a table, and look it up when needed.

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