Camera self-calibration and analysis of singular cases

Master'sMASTER OF ENGINEERIN

Managing Inventory and Financing Decisions Under Ambiguity

Micro, small and medium-sized enterprises (MSMEs) face persistent challenges in raising capitals, and one of the practical reasons could be the high level of ambiguity in this sector. As many not-for-profit organizations or governmental agencies strengthen financial supports to MSMEs, the important issue of stimulating growth while protecting fund providers under ambiguity arises. We propose a robust optimization framework to jointly determine the firm's production planning and financing decisions in a principal-agent model with the presence of distributional ambiguity. We apply the notion of absolute robustness to derive a financing agreement that is both feasibility-robust and performance-robust. We assume that both the firm and the investor base their decisions on two fundamental descriptive statistics: the mean and the variance of the demand. The firm jointly determines the production quantity and financial agreement to maximize the worst-case expected profit, while the investor approves the financial agreement if the worst case expected return can cover the cost of capital. We show that equity financing is one of the robust optimal financing agreements. We also consider loan financing as an alternative. We derive the firm's robust optimal interest rate and production quantity in closed forms. Notably, the robust optimal interest rate depends on the demand variability and the asset recovery ratio, which comprehensively considers the value of collateral, initial capital, and production quantity

Divide and Conquer Partition for Fourier Reconstruction Sparse Inversion with its Applications

A partition method, with an efficient divide and conquer partition strategy, for the non-uniform sampling signal reconstruction based on Fourier reconstruction sparse inversion (FRSI) is developed. The novel partition FRSI(P-FRSI) is motivated by the observation that the partition processing of multi-dimensional signals can reduce the reconstruction difficulty and save the reconstruction time. Moreover, it is helpful to choose suitable reconstruction parameters. The P-FRSI employs divide and conquer strategy, and the signal is firstly partitioned into some blocks. Following that, traditional FRSI is applied to reconstruct signals in each block. We adopt linear or nonlinear superposition to determine the weight coefficients during integrating these blocks. Finally, P-FRSI is applied to two-dimensional seismic signal reconstruction. The superiority of the new method over conventional FRSI is demonstrated by numerical reconstruction experiments

SEABED INFRASTRUCTURE DEFENSE ANALYSIS

Traditional fleet operations and technologies are not adequately suited to counter the growing threat to undersea infrastructure from autonomous undersea systems. A cost-effective unmanned and manned system of systems is required to provide defense of this seabed infrastructure. This paper proposes possible system architectures to defend against this emerging threat to include passive barriers and active defense systems. The effectiveness of those candidate systems is evaluated through multiple agent-based modeling simulations of UUV versus UUV engagements. Analysis resulted in two major findings. First, point defense of critical assets is more effective than barrier defense. Second, system design must focus on minimizing the time required to effectively engage and neutralize threats, either through improvement to defensive UUV speed or investment in more UUV docking stations and sensor arrays. Cost analysis suggests that acquisition and operations cost of the recommended defensive system is less than the projected financial impact of a successful attack.http://archive.org/details/seabedinfrastruc1094562767Lieutenant, United States NavyLieutenant, United States NavyLieutenant, United States NavyMajor, Israel Defence ForcesMajor, Republic of Singapore Air ForceMajor, Republic of Singapore Air ForceCaptain, Singapore ArmyLieutenant, United States NavyLieutenant, United States NavyLieutenant, United States NavyMajor, Republic of Singapore Air ForceCaptain, Singapore ArmyCivilian, Ministry of Defense, SingaporeLieutenant, United States NavyLieutenant Commander, United States NavyLieutenant Junior Grade, United States NavyCivilian, Ministry of Defense, SingaporeCivilian, Ministry of Defense, SingaporeMajor, Republic of Singapore Air ForceMajor, United States Marine CorpsMajor, Singapore ArmyApproved for public release; distribution is unlimited

A Transportation Network Paradox: Consideration of Travel Time and Health Damage due to Pollution

In cities with serious air pollution, travel time and health damage significantly affect route choice by travelers (e.g., motorcycle and scooter drivers). Consequently, the classical Braess paradox is no longer realistic because it only considers the traveler’s value of time (VOT). In the current study, we describe a new transportation network paradox that considers both the VOT and the traveler’s perception of pollution damage. To examine the conditions that create the new paradox, we developed a novel method to compute a total comprehensive cost that combines the VOT with health damage. We analyzed the conditions for the new paradox and the system’s performance using a user equilibrium model and system optimization. Furthermore, an improved model is used to analyze how different transport modes influence the Braess paradox. We found that whether the new paradox occurs and the potential improvement of the system’s performance depend on whether the total travel demand falls within critical ranges. The bounds of these ranges depend on the values of the parameters in the function that describes the health damage and the link travel time function. In addition, high health damage significantly affects route choices and traffic flow distribution. This paper presents a new perspective for decision-making by transportation planners and for route choices in cities with serious air pollution

Research Article Determining Vision Graphs for Distributed Camera Networks Using Feature Digests

We propose a decentralized method for obtaining the vision graph for a distributed, ad-hoc camera network, in which each edge of the graph represents two cameras that image a sufficiently large part of the same environment. Each camera encodes a spatially well-distributed set of distinctive, approximately viewpoint-invariant feature points into a fixed-length “feature digest ” that is broadcast throughout the network. Each receiver camera robustly matches its own features with the decompressed digest and decides whether sufficient evidence exists to form a vision graph edge. We also show how a camera calibration algorithm that passes messages only along vision graph edges can recover accurate 3D structure and camera positions in a distributed manner. We analyze the performance of different message formation schemes, and show that high detection rates (> 0.8) can be achieved while maintaining low false alarm rates (< 0.05) using a simulated 60-node outdoor camera network. Copyright © 2007 Zhaolin Cheng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1

Determining Vision Graphs for Distributed Camera Networks Using Feature Digests

<p/> <p>We propose a decentralized method for obtaining the vision graph for a distributed, ad-hoc camera network, in which each edge of the graph represents two cameras that image a sufficiently large part of the same environment. Each camera encodes a spatially well-distributed set of distinctive, approximately viewpoint-invariant feature points into a fixed-length "feature digest" that is broadcast throughout the network. Each receiver camera robustly matches its own features with the decompressed digest and decides whether sufficient evidence exists to form a vision graph edge. We also show how a camera calibration algorithm that passes messages only along vision graph edges can recover accurate 3D structure and camera positions in a distributed manner. We analyze the performance of different message formation schemes, and show that high detection rates ( <inline-formula><graphic file="1687-6180-2007-057034-i1.gif"/></inline-formula>) can be achieved while maintaining low false alarm rates ( <inline-formula><graphic file="1687-6180-2007-057034-i2.gif"/></inline-formula>) using a simulated 60-node outdoor camera network.</p