Image fusion for a nighttime driving display

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (leaves 39-40).An investigation into image fusion for a nighttime driving display application was performed. Most of the image fusion techniques being investigated in this application were developed for other purposes. When comparing the images of a typical night driving scene to the images used in medical or satellite image fusion it becomes apparent that most of the techniques developed for other fields would be overkill in a nighttime driving display application. This observation motivated the development of a set of image fusion techniques specifically for the nighttime driving display application using the computationally simple Discrete Haar Wavelet Transform. This thesis presents the techniques that were developed and the details of their implementation.by William Frederick Herrington, Jr.S.M

Batch Policy Learning under Constraints

When learning policies for real-world domains, two important questions arise: (i) how to efficiently use pre-collected off-policy, non-optimal behavior data; and (ii) how to mediate among different competing objectives and constraints. We thus study the problem of batch policy learning under multiple constraints, and offer a systematic solution. We first propose a flexible meta-algorithm that admits any batch reinforcement learning and online learning procedure as subroutines. We then present a specific algorithmic instantiation and provide performance guarantees for the main objective and all constraints. To certify constraint satisfaction, we propose a new and simple method for off-policy policy evaluation (OPE) and derive PAC-style bounds. Our algorithm achieves strong empirical results in different domains, including in a challenging problem of simulated car driving subject to multiple constraints such as lane keeping and smooth driving. We also show experimentally that our OPE method outperforms other popular OPE techniques on a standalone basis, especially in a high-dimensional setting

Microscopic model of a phononic refrigerator

We analyze a simple microscopic model to pump heat from a cold to a hot reservoir in a nanomechanical system. The model consists of a one-dimensional chain of masses and springs coupled to a back gate through which a time-dependent perturbation is applied. The action of the gate is to modulate the coupling of the masses to a substrate via additional springs that introduce a moving phononic barrier. We solve the problem numerically using non-equilibrium Green function techniques. For low driving frequencies and for sharp traveling barriers, we show that this microscopic model realizes a phonon refrigerator.Comment: 9 pages, 4 figure

How dissipation constrains fluctuations in nonequilibrium liquids: Diffusion, structure and biased interactions

The dynamics and structure of nonequilibrium liquids, driven by non-conservative forces which can be either external or internal, generically hold the signature of the net dissipation of energy in the thermostat. Yet, disentangling precisely how dissipation changes collective effects remains challenging in many-body systems due to the complex interplay between driving and particle interactions. First, we combine explicit coarse-graining and stochastic calculus to obtain simple relations between diffusion, density correlations and dissipation in nonequilibrium liquids. Based on these results, we consider large-deviation biased ensembles where trajectories mimic the effect of an external drive. The choice of the biasing function is informed by the connection between dissipation and structure derived in the first part. Using analytical and computational techniques, we show that biasing trajectories effectively renormalizes interactions in a controlled manner, thus providing intuition on how driving forces can lead to spatial organization and collective dynamics. Altogether, our results show how tuning dissipation provides a route to alter the structure and dynamics of liquids and soft materials.Comment: 21 pages, 7 figure

Return-Map Cryptanalysis Revisited

As a powerful cryptanalysis tool, the method of return-map attacks can be used to extract secret messages masked by chaos in secure communication schemes. Recently, a simple defensive mechanism was presented to enhance the security of chaotic parameter modulation schemes against return-map attacks. Two techniques are combined in the proposed defensive mechanism: multistep parameter modulation and alternative driving of two different transmitter variables. This paper re-studies the security of this proposed defensive mechanism against return-map attacks, and points out that the security was much over-estimated in the original publication for both ciphertext-only attack and known/chosen-plaintext attacks. It is found that a deterministic relationship exists between the shape of the return map and the modulated parameter, and that such a relationship can be used to dramatically enhance return-map attacks thereby making them quite easy to break the defensive mechanism.Comment: 11 pages, 7 figure

Evaluation of ecodriving performances and teaching method: comparing training and simple advice

Eco-driving style is widely known to induce up to 20% fuel consumption reduction, but little is known about differences due to different learning methods. In order to evaluate the potential impacts of future ecological driving assistance system (EDAS) in comparison with usual techniques, a statistical approach is proposed. Two kinds of experiments are analysed in this paper: In the first one, simple advice were given to the participants, while in the second one, full courses with eco-driving experts were used. Performance indicators were derived from five commonly referred golden rules of eco-driving and used as model inputs. Different kind of statistical models are discussed, among which we choose to apply the ordinary logistic regression to assess the effects of each driving advice separately Results show that ecodriving advices are better applied after a course than just providing tips. The approach is then extended to build a generic model that can be used both to characterize and evaluate eco-driving style