Disparity estimation using TI multi-wavelet transform

A multi-resolution image matching technique based on translation invariant discrete multi-wavelet transform followed by a coarse to fine matching strategy is presented. The technique addresses the estimation of optimal corresponding points and the corresponding disparity maps in the presence of occlusion, ambiguity and illuminative variations in the two perspective views taken by two different cameras or at different lighting conditions. The problem of occlusion and ambiguity is addressed explicitly by a geometric optimization approach along with the uniqueness constraint whereas the illuminative variation is dealt with by using windowed normalized correlation on the discrete multi-wavelet coefficients.

Passive angle measurement based localization consistency via geometric constraints

In this paper, we examine the geometric relations between various measured parameters and their corresponding errors in angle-measurement based emitter localization scenarios. We derive a geometric constraint formulating the relationship among the measurement errors in such a scenario. Using this constraint, we formulate the localization task as a constrained optimization problem that can be performed on the measurements in order to provide the optimal values such that the solution is consistent with the underlying geometry.

Bearing-only localization using geometrically constrained optimization

We examine the problem of optimal bearing-only localization of a single target using synchronous measurements from multiple sensors. We approach the problem by forming geometric relationships between the measured parameters and their corresponding errors in the relevant emitter localization scenarios. Specifically, we derive a geometric constraint equation on the measurement errors in such a scenario. Using this constraint, we formulate the localization task as a constrained optimization problem that can be performed on the measurements in order to provide the optimal values such that the solution is consistent with the underlying geometry. We illustrate and confirm the advantages of our approach through simulation, offering detailed comparison with traditional maximum likelihood (TML) estimation.

Optimization-based formation of autonomous mobile robots

The formation of autonomous mobile robots to an arbitrary geometric pattern in a distributed fashion is a fundamental problem in formation control. This paper presents a new asynchronous, memoryless (oblivious) algorithm to the formation problem via distributed optimization techniques. The optimization minimizes an appropriately defined difference function between the current robot distribution and the target geometric pattern. The optimization processes are performed independently by individual robots in their local coordinate systems. A movement strategy derived from the results of the distributed optimizations guarantees that every movement makes the current robot configuration approaches the target geometric pattern until the final pattern is reached.

A decentralized cloud firewall framework with resources provisioning cost optimization

Cloud computing is becoming popular as the next infrastructure of computing platform. Despite the promising model and hype surrounding, security has become the major concern that people hesitate to transfer their applications to clouds. Concretely, cloud platform is under numerous attacks. As a result, it is definitely expected to establish a firewall to protect cloud from these attacks. However, setting up a centralized firewall for a whole cloud data center is infeasible from both performance and financial aspects. In this paper, we propose a decentralized cloud firewall framework for individual cloud customers. We investigate how to dynamically allocate resources to optimize resources provisioning cost, while satisfying QoS requirement specified by individual customers simultaneously. Moreover, we establish novel queuing theory based model M/Geo/1 and M/Geo/m for quantitative system analysis, where the service times follow a geometric distribution. By employing Z-transform and embedded Markov chain techniques, we obtain a closed-form expression of mean packet response time. Through extensive simulations and experiments, we conclude that an M/Geo/1 model reflects the cloud firewall real system much better than a traditional M/M/1 model. Our numerical results also indicate that we are able to set up cloud firewall with affordable cost to cloud customers.

Linear optimization for ecological indices based on aggregation functions

We consider an optimization problem in ecology where our objective is to maximize biodiversity with respect to different land-use allocations. As it turns out, the main problem can be framed as learning the weights of a weighted arithmetic mean where the objective is the geometric mean of its outputs. We propose methods for approximating solutions to this and similar problems, which are non-linear by nature, using linear and bilevel techniques.