Smoothed Airtime Linear Tuning and Optimized REACT with Multi-hop Extensions

abstract: Medium access control (MAC) is a fundamental problem in wireless networks. In ad-hoc wireless networks especially, many of the performance and scaling issues these networks face can be attributed to their use of the core IEEE 802.11 MAC protocol: distributed coordination function (DCF). Smoothed Airtime Linear Tuning (SALT) is a new contention window tuning algorithm proposed to address some of the deficiencies of DCF in 802.11 ad-hoc networks. SALT works alongside a new user level and optimized implementation of REACT, a distributed resource allocation protocol, to ensure that each node secures the amount of airtime allocated to it by REACT. The algorithm accomplishes that by tuning the contention window size parameter that is part of the 802.11 backoff process. SALT converges more tightly on airtime allocations than a contention window tuning algorithm from previous work and this increases fairness in transmission opportunities and reduces jitter more than either 802.11 DCF or the other tuning algorithm. REACT and SALT were also extended to the multi-hop flow scenario with the introduction of a new airtime reservation algorithm. With a reservation in place multi-hop TCP throughput actually increased when running SALT and REACT as compared to 802.11 DCF, and the combination of protocols still managed to maintain its fairness and jitter advantages. All experiments were performed on a wireless testbed, not in simulation.Dissertation/ThesisMasters Thesis Computer Science 201

Data path analysis for dynamic circuit specialisation

Dynamic Circuit Specialisation (DCS) is a method that exploits the reconfigurability of modern FPGAs to allow the specialisation of FPGA circuits at run-time. Currently, it is only explored as part of Register-transfer level design. However, at the Register-transfer level (RTL), a large part of the design is already locked in. Therefore, maximally exploiting the opportunities of DCS could require a costly redesign. It would be interesting to already have insight in the opportunities for DCS from the higher abstraction level. Moreover, the general design trend in FPGA design is to work on higher abstraction levels and let tool(s) translate this higher level description to RTL. This paper presents the first profiler that, based on the high-level description of an application, estimates the benefits of an implementation using DCS. This allows a designer to determine much earlier in the design cycle whether or not DCS would be interesting. The high-level profiling methodology was implemented and tested on a set of PID designs

Quantifying the Impact of Parameter Tuning on Nature-Inspired Algorithms

The problem of parameterization is often central to the effective deployment of nature-inspired algorithms. However, finding the optimal set of parameter values for a combination of problem instance and solution method is highly challenging, and few concrete guidelines exist on how and when such tuning may be performed. Previous work tends to either focus on a specific algorithm or use benchmark problems, and both of these restrictions limit the applicability of any findings. Here, we examine a number of different algorithms, and study them in a "problem agnostic" fashion (i.e., one that is not tied to specific instances) by considering their performance on fitness landscapes with varying characteristics. Using this approach, we make a number of observations on which algorithms may (or may not) benefit from tuning, and in which specific circumstances.Comment: 8 pages, 7 figures. Accepted at the European Conference on Artificial Life (ECAL) 2013, Taormina, Ital

The Smooth-Lasso and other ℓ1+ℓ2\ell_1+\ell_2-penalized methods

We consider a linear regression problem in a high dimensional setting where the number of covariates $p$ can be much larger than the sample size $n$. In such a situation, one often assumes sparsity of the regression vector, \textit i.e., the regression vector contains many zero components. We propose a Lasso-type estimator $\hat{\beta}^{Quad}$ (where '$Quad$' stands for quadratic) which is based on two penalty terms. The first one is the $\ell_1$ norm of the regression coefficients used to exploit the sparsity of the regression as done by the Lasso estimator, whereas the second is a quadratic penalty term introduced to capture some additional information on the setting of the problem. We detail two special cases: the Elastic-Net $\hat{\beta}^{EN}$, which deals with sparse problems where correlations between variables may exist; and the Smooth-Lasso $\hat{\beta}^{SL}$, which responds to sparse problems where successive regression coefficients are known to vary slowly (in some situations, this can also be interpreted in terms of correlations between successive variables). From a theoretical point of view, we establish variable selection consistency results and show that $\hat{\beta}^{Quad}$ achieves a Sparsity Inequality, \textit i.e., a bound in terms of the number of non-zero components of the 'true' regression vector. These results are provided under a weaker assumption on the Gram matrix than the one used by the Lasso. In some situations this guarantees a significant improvement over the Lasso. Furthermore, a simulation study is conducted and shows that the S-Lasso $\hat{\beta}^{SL}$ performs better than known methods as the Lasso, the Elastic-Net $\hat{\beta}^{EN}$, and the Fused-Lasso with respect to the estimation accuracy. This is especially the case when the regression vector is 'smooth', \textit i.e., when the variations between successive coefficients of the unknown parameter of the regression are small. The study also reveals that the theoretical calibration of the tuning parameters and the one based on 10 fold cross validation imply two S-Lasso solutions with close performance

Implications of Cosmic Repulsion for Gravitational Theory

In this paper we present a general, model independent analysis of a recently detected apparent cosmic repulsion, and discuss its potential implications for gravitational theory. In particular, we show that a negatively spatially curved universe acts like a diverging refractive medium, to thus naturally cause galaxies to accelerate away from each other. Additionally, we show that it is possible for a cosmic acceleration to only be temporary, with some accelerating universes actually being able to subsequently recontract.Comment: RevTeX, 13 page

Passivity-based harmonic control through series/parallel damping of an H-bridge rectifier

Nowadays the H-bridge is one of the preferred solutions to connect DC loads or distributed sources to the single-phase grid. The control aims are: sinusoidal grid current with unity power factor and optimal DC voltage regulation capability. These objectives should be satisfied, regardless the conditions of the grid, the DC load/source and the converter nonlinearities. In this paper a passivity-based approach is thoroughly investigated proposing a damping-based solution for the error dynamics. Practical experiments with a real converter validate the analysis.