An Integrated Approach to Energy Harvester Modeling and Performance Optimization

This paper proposes an integrated approach to energy harvester (EH) modeling and performance optimization where the complete mixed physical-domain EH (micro generator, voltage booster, storage element and load) can be modeled and optimized. We show that electrical equivalent models of the micro generator are inadequate for accurate prediction of the voltage booster’s performance. Through the use of hardware description language (HDL) we demonstrate that modeling the micro generator with analytical equations in the mechanical and magnetic domains provide an accurate model which has been validated in practice. Another key feature of the integrated approach is that it facilitates the incorporation of performance enhanced optimization, which as will be demonstrated is necessary due to the mechanicalelectrical interactions of an EH. A case study of a state-of-the-art vibration-based electromagnetic EH has been presented. We show that performance optimization can increase the energy harvesting rate by about 40%

Neuromorphic In-Memory Computing Framework using Memtransistor Cross-bar based Support Vector Machines

This paper presents a novel framework for designing support vector machines (SVMs), which does not impose restriction on the SVM kernel to be positive-definite and allows the user to define memory constraint in terms of fixed template vectors. This makes the framework scalable and enables its implementation for low-power, high-density and memory constrained embedded application. An efficient hardware implementation of the same is also discussed, which utilizes novel low power memtransistor based cross-bar architecture, and is robust to device mismatch and randomness. We used memtransistor measurement data, and showed that the designed SVMs can achieve classification accuracy comparable to traditional SVMs on both synthetic and real-world benchmark datasets. This framework would be beneficial for design of SVM based wake-up systems for internet of things (IoTs) and edge devices where memtransistors can be used to optimize system's energy-efficiency and perform in-memory matrix-vector multiplication (MVM).Comment: 4 pages, 5 figures, MWSCAS 201

Design and fabrication of a basic mass analyzer and vacuum system

A two-inch hyperbolic rod quadrupole mass analyzer with a mass range of 400 to 200 amu and a sensitivity exceeding 100 packs per billion has been developed and tested. This analyzer is the basic hardware portion of a microprocessor-controlled quadrupole mass spectrometer for a Gas Analysis and Detection System (GADS). The development and testing of the hyperbolic-rod quadrupole mass spectrometer and associated hardware are described in detail

Integrated approach to energy harvester mixed technology modelling and performance optimisation

An energy harvester is a system consisting of several components from different physical domains including mechanical, magnetic and electrical as well as the external circuits which regulate and store the generated energy. To design highly efficient energy harvesters, we believe that the various components of the energy harvesters need to be modelled together and in systematic manner using one simulation platform. We propose an accurate HDL model for the energy harvester and demonstrate its accuracy by validating it experimentally and comparing it with recently reported models. It is crucial to consider the various parts of the energy harvester in the context of a complete system, or else the gain at one part may come at the price of efficiency loss else where, rending the energy harvester much less efficient than before. The close mechanical-electrical interaction that takes place in energy harvesters, often lead to significant performance loss when the various parts of the energy harvesters are combined. Therefore, to address the performance loss, we propose an integrated approach to the energy harvester modelling and performance optimisation and demonstrate the effectiveness of employing such an approach by showing that it is possible to improve the performance of vibration-based energy harvester, in terms of the effective energy stored in the super-capacitor, by 33% through optimising the micro-generator mechanical parameters and the voltage booster circuit components