Strongly Enhanced Sensitivity in Planar Microwave Sensors Based on Metamaterial coupling

Limited sensitivity and sensing range are arguably the greatest challenges in microwave sensor design. Recent attempts to improve these properties have relied on metamaterial- (MTM-) inspired open-loop resonators (OLRs) coupled to transmission lines (TLs). Although the strongly resonant properties of the OLR sensitively reflect small changes in the environment through a shift in its resonance frequency, the resulting sensitivities remain ultimately limited by the level of coupling between the OLR and the TL. This work introduces a novel solution to this problem that employs negative-refractiveindex TL (NRI-TL) MTMs to substantially improve this coupling so as to fully exploit its resonant properties. A MTM-infused planar microwave sensor is designed for operation at 2.5 GHz, and is shown to exhibit a significant improvement in sensitivity and linearity. A rigorous signal-flow analysis (SFA) of the sensor is proposed and shown to provide a fully analytical description of all salient features of both the conventional and MTM-infused sensors. Full-wave simulations confirm the analytical predictions, and all data demonstrate excellent agreement with measurements of a fabricated prototype. The proposed device is shown to be especially useful in the characterization of commonly-available high-permittivity liquids as well as in sensitively distinguishing concentrations of ethanol/methanol in water.Comment: 11 pages, 18 Figures, 4 table

Hybrid Random/Deterministic Parallel Algorithms for Nonconvex Big Data Optimization

We propose a decomposition framework for the parallel optimization of the sum of a differentiable {(possibly nonconvex)} function and a nonsmooth (possibly nonseparable), convex one. The latter term is usually employed to enforce structure in the solution, typically sparsity. The main contribution of this work is a novel \emph{parallel, hybrid random/deterministic} decomposition scheme wherein, at each iteration, a subset of (block) variables is updated at the same time by minimizing local convex approximations of the original nonconvex function. To tackle with huge-scale problems, the (block) variables to be updated are chosen according to a \emph{mixed random and deterministic} procedure, which captures the advantages of both pure deterministic and random update-based schemes. Almost sure convergence of the proposed scheme is established. Numerical results show that on huge-scale problems the proposed hybrid random/deterministic algorithm outperforms both random and deterministic schemes.Comment: The order of the authors is alphabetica

Lean manufacturing in a mass customization plant : improved efficiencies in raw material presentation

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 70-71).This thesis focuses on the application of the principles of lean manufacturing at Varian Semiconductor Equipment Associates (VSEA). The company faces the challenges of highly customized assembly as well as fluctuating demand, both of which cause lead times that are longer than expected. Value Stream Mapping was used to identify the main sources of waste in the VSEA manufacturing plant. After evaluating all factors contributing to longer cycle times, it was found that one of the main problems encountered by VSEA was unorganized presentation of raw material to the shop floor. Using the 5S methodology, a framework was created to appropriately categorize the raw material into smaller groups, and deliver them to the flow line according to Just-in-Time (JIT) principles. After the new presentation method for raw material is implemented, the cycle time will be reduced by 6% due to the elimination of the non value added activity from the process. In addition, the first steps toward kaizen process improvement will be in place.by Moojan Daneshmand.M.Eng

Distributed Dictionary Learning

The paper studies distributed Dictionary Learning (DL) problems where the learning task is distributed over a multi-agent network with time-varying (nonsymmetric) connectivity. This formulation is relevant, for instance, in big-data scenarios where massive amounts of data are collected/stored in different spatial locations and it is unfeasible to aggregate and/or process all the data in a fusion center, due to resource limitations, communication overhead or privacy considerations. We develop a general distributed algorithmic framework for the (nonconvex) DL problem and establish its asymptotic convergence. The new method hinges on Successive Convex Approximation (SCA) techniques coupled with i) a gradient tracking mechanism instrumental to locally estimate the missing global information; and ii) a consensus step, as a mechanism to distribute the computations among the agents. To the best of our knowledge, this is the first distributed algorithm with provable convergence for the DL problem and, more in general, bi-convex optimization problems over (time-varying) directed graphs