Speckle pattern interferometry : vibration measurement based on a novel CMOS camera

A digital speckle pattern interferometer based on a novel custom complementary metaloxide- semiconductor (CMOS) array detector is described. The temporal evolution of the dynamic deformation of a test object is measured using inter-frame phase stepping. The flexibility of the CMOS detector is used to identify regions of interest with full-field time averaged measurements and then to interrogate those regions with time-resolved measurements sampled at up to 7 kHz. The maximum surface velocity that can be measured and the number of measurement points are limited by the frame rate and the data transfer rate of the detector. The custom sensor used in this work is a modulated light camera (MLC), whose pixel design is still based on the standard four transistor active pixel sensor (APS), but each pixel has four large independently shuttered capacitors that drastically boost the well capacity from that of the diode alone. Each capacitor represents a channel which has its own shutter switch and can either be operated independently or in tandem with others. The particular APS of this camera enables a novel approach in how the data are acquired and then processed. In this Thesis we demonstrate how, at a given frame rate and at a given number of measurement points, the data transfer rate of our system is increased if compared to the data transfer rate of a system using a standard approach. Moreover, under some assumptions, the gain in system bandwidth doesn’t entail any reduction in the maximum surface velocity that can be reliably measured with inter-frame phase stepping

Millimeter-Scale and Energy-Efficient RF Wireless System

This dissertation focuses on energy-efficient RF wireless system with millimeter-scale dimension, expanding the potential use cases of millimeter-scale computing devices. It is challenging to develop RF wireless system in such constrained space. First, millimeter-sized antennae are electrically-small, resulting in low antenna efficiency. Second, their energy source is very limited due to the small battery and/or energy harvester. Third, it is required to eliminate most or all off-chip devices to further reduce system dimension. In this dissertation, these challenges are explored and analyzed, and new methods are proposed to solve them. Three prototype RF systems were implemented for demonstration and verification. The first prototype is a 10 cubic-mm inductive-coupled radio system that can be implanted through a syringe, aimed at healthcare applications with constrained space. The second prototype is a 3x3x3 mm far-field 915MHz radio system with 20-meter NLOS range in indoor environment. The third prototype is a low-power BLE transmitter using 3.5x3.5 mm planar loop antenna, enabling millimeter-scale sensors to connect with ubiquitous IoT BLE-compliant devices. The work presented in this dissertation improves use cases of millimeter-scale computers by presenting new methods for improving energy efficiency of wireless radio system with extremely small dimensions. The impact is significant in the age of IoT when everything will be connected in daily life.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147686/1/yaoshi_1.pd

IoT for measurements and measurements for IoT

The thesis is framed in the broad strand of the Internet of Things, providing two parallel paths. On one hand, it deals with the identification of operational scenarios in which the IoT paradigm could be innovative and preferable to pre-existing solutions, discussing in detail a couple of applications. On the other hand, the thesis presents methodologies to assess the performance of technologies and related enabling protocols for IoT systems, focusing mainly on metrics and parameters related to the functioning of the physical layer of the systems

Image compression and energy harvesting for energy constrained sensors

Title from PDF of title page, viewed on June 21, 2013Dissertation advisor: Walter D. Leon-SalasVitaIncludes bibliographic references (pages 176-[187])Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2013The advances in complementary metal-oxide-semiconductor (CMOS) technology have led to the integration of all components of electronic system into a single integrated circuit. Ultra-low power circuit techniques have reduced the power consumption of circuits. Moreover, solar cells with improved efficiency can be integrated on chip to harvest energy from sunlight. As a result of all the above, a new class of miniaturized electronic systems known as self-powered system on a chip has emerged. There is an increasing research interest in the area of self-powered devices which provide cost-effective solutions especially when these devices are used in the areas that changing or replacing batteries is too costly. Therefore, image compression and energy harvesting are studied in this dissertation. The integration of energy harvesting, image compression, and an image sensor on the same chip provides the energy source to charge a battery, reduces the data rate, and improves the performance of wireless image sensors. Integrated circuits of image compression, solar energy harvesting, and image sensors are studied, designed, and analyzed in this work. In this dissertation, a hybrid image sensor that can perform the tasks of sensing and energy harvesting is presented. Photodiodes of hybrid image sensor can be programmed as image sensors or energy harvesting cells. The hybrid image sensor can harvest energy in between frames, in sleep mode, and even when it is taking images. When sensing images and harvesting energy are both needed at the same time, some pixels have to work as sensing pixels, and the others have to work as solar cells. Since some pixels are devoted to harvest energy, the resolution of the image will be reduced. To preserve the resolution or to keep the fair resolution when a lot of energy collection is needed, image reconstruction algorithms and compressive sensing theory provide solutions to achieve a good image quality. On the other hand, when the battery has enough charge, image compression comes into the picture. Multiresolution decomposition image compression provides a way to compress image data in order to reduce the energy need from data transmission. The solution provided in this dissertation not only harvests energy but also saves energy resulting long lasting wireless sensors. The problem was first studied at the system level to identify the best system-level configuration which was then implemented on silicon. As a proof of concept, a 32 x 32 array of hybrid image sensor, a 32 x 32 array of image sensor with multiresolution decomposition compression, and a compressive sensing converter have been designed and fabricated in a standard 0.5 [micrometer] CMOS process. Printed circuit broads also have been designed to test and verify the proposed and fabricated chips. VHDL and Matlab codes were written to generate the proper signals to control, and read out data from chips. Image processing and recovery were carried out in Matlab. DC-DC converters were designed to boost the inherently low voltage output of the photodiodes. The DC-DC converter has also been improved to increase the efficiency of power transformation.Introduction -- Hybrid imager system and circuit design -- Hybrid imager energy harvesting and image acquisition results and discussion -- Detailed description and mathematical analysis for a circuit of energy harvesting using on-chip solar cells -- Multiresolution decomposition for lossless and near-lossless compression -- An incremental [sigma-delta] converter for compressive sensing -- Detailed description of a sigma-delta random demodulator converter architecture for compressive sensing applications -- Conclusion -- Appendix A. Chip pin-out -- Appendix B. Schematics -- Appendix C. Pictures of custom PC

Spectrum sensing algorithms and software-defined radio implementation for cognitive radio system

The scarcity of spectral resources in wireless communications, due to a fixed frequency allocation policy, is a strong limitation to the increasing demand for higher data rates. However, measurements showed that a large part of frequency channels are underutilized or almost unoccupied. The cognitive radio paradigm arises as a tempting solution to the spectral congestion problem. A cognitive radio must be able to identify transmission opportunities in unused channels and to avoid generating harmful interference with the licensed primary users. Its key enabling technology is the spectrum sensing unit, whose ultimate goal consists in providing an indication whether a primary transmission is taking place in the observed channel. Such indication is determined as the result of a binary hypothesis testing experiment wherein null hypothesis (alternate hypothesis) corresponds to the absence (presence) of the primary signal. The first parts of this thesis describes the spectrum sensing problem and presents some of the best performing detection techniques. Energy Detection and multi-antenna Eigenvalue-Based Detection algorithms are considered. Important aspects are taken into account, like the impact of noise estimation or the effect of primary user traffic. The performance of each detector is assessed in terms of false alarm probability and detection probability. In most experimental research, cognitive radio techniques are deployed in software-defined radio systems, radio transceivers that allow operating parameters (like modulation type, bandwidth, output power, etc.) to be set or altered by software.In the second part of the thesis, we introduce the software-defined radio concept. Then, we focus on the implementation of Energy Detection and Eigenvalue-Based Detection algorithms: first, the used software platform, GNU Radio, is described, secondly, the implementation of a parallel energy detector and a multi-antenna eigenbased detector is illustrated and details on the used methodologies are given. Finally, we present the deployed experimental cognitive testbeds and the used radio peripherals. The obtained algorithmic results along with the software-defined radio implementation may offer a set of tools able to create a realistic cognitive radio system with real-time spectrum sensing capabilities