64 research outputs found
Body powered thermoelectric systems
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 107-111).Great interest exists for and progress has be made in the effective utilization of the human body as a possible power supply in hopes of powering such applications as sensors and continuously monitoring medical devices [1]. This report furthers into the area of thermal energy harvesting, which focuses on using the temperature differential generated between the human body and the ambient environment to generate power. More specifically, a body-powered, thermoelectric-based power supply and system will be introduced and examined, with hopes that this technology will be utilized alongside low-power, medical monitoring applications in order to achieve self-sufficiency. This report also analyzes the performance of existing thermoelectric-based body-powered energy harvesting applications and compares that with the new design introduced in this work. The new designs were able to output upwards of 25[mu]W/cm2 or, equivalently, 280µW for the entire heat sink system. Additionally, this report details the physics associated with thermoelectric modules, addresses the issues with modern thermoelectric heat-sinks, introduces two new types of wearable, conformal heat sinks, quantifies the performance of the body-powered thermoelectric supply, tests a flexible EKG processing board, and analyzes future paths for this project.by Krishna Tej Settaluri.M.Eng
Identification of Wheat Varieties with a Parallel-Plate Capacitance Sensor Using Fisher’s Linear Discriminant Analysis
Fisher’s linear discriminant (FLD) models for wheat variety classification were developed and validated. The inputs to the FLD models were the capacitance (C), impedance (Z), and phase angle (θ), measured at two frequencies. Classification of wheat varieties was obtained as output of the FLD models. Z and θ of a parallel-plate capacitance system, holding the wheat samples, were measured using an impedance meter, and the C value was computed. The best model developed classified the wheat varieties, with accuracy of 95.4%, over the six wheat varieties tested. This method is simple, rapid, and nondestructive and would be useful for the breeders and the peanut industry
Recommended from our members
Practical Solutions to Accelerating ASIC Design Development Using Machine Learning
This thesis presents a method for analyzing the efficacy of automated ASIC design tools, specifically by assessing the accuracy, practicability, automation, interpretability, generalizability and run-time efficiency of the algorithm. This is established by presenting one in-depth case study and two projects where machine learning can be used to address in-efficiencies in ASIC design, and include: 1) an analog circuit design framework that uses reinforcement learning (RL) to size parameters for a given circuit topology to meet a target specification, 2) an analog sub-clustering tool that uses graphical convolutional neural networks (GCNNs), and 3) using convolutional neural networks (CNNs) to detect defects in circuit yield. The goal is demonstrate that machine learning techniques can not only be successfully used for these three applications, but can be comprehensively analyzed to obtain practical and feasible solutions in circuit design.Specifically, the results of the RL analog circuit framework show that this solution achieves state-of-the-art run-time efficiency across six unique circuit topologies of varying complexity while considering layout parasitics. Additional analyses is also conducted to explain the decision-making of the algorithm, establishing that the obtained performances are explainable and analyzable in the context of circuit design. Furthermore, for several non-linear circuits, the algorithm obtains initial designs that are better than that of an expert, providing potential for better intuition into the boundaries of performance for these circuits.The GCNN framework for analog sub-clustering project demonstrates that high run-time efficiency with over 91% accuracy can be achieved while being fully automated, requiring no input from the designer for classification. In addition, the algorithm successfully scales to a variety of analog circuits, which is crucial in establishing practicality. The yield defect detection framework using CNNs shows that ML can be applied to a post-silicon application, successfully resulting in identification of yield defects in real and noisy scan diagnosis tests while reducing the layout search space significantly
- …