Matthew S. Reynolds

ElectriSense: single-point sensing using EMI for electrical event detection and classification in the home

This paper presents ElectriSense, a new solution for automatically detecting and classifying the use of electronic devices in a home from a single point of sensing. ElectriSense relies on the fact that most modern consumer electronics and fluorescent lighting employ switch mode power supplies (SMPS) to achieve high efficiency. These power supplies continuously generate high frequency electromagnetic interference (EMI) during operation that propagates throughout a homes power wiring. We show both analytically and by in-home experimentation that EMI signals are stable and predictable based on the devices switching frequency characteristics. Unlike past transient noise-based solutions, this new approach provides the ability for EMI signatures to be applicable across homes while still being able to differentiate between similar devices in a home. We have evaluated our solution in seven homes, including one six-month deployment. Our results show that ElectriSense can identify and classify the usage of individual devices with a mean accuracy of 93.82%.

Disaggregated End-Use Energy Sensing for the Smart Grid

This article surveys existing and emerging disaggregation techniques for energy-consumption data and highlights signal features that might be used to sense disaggregated data in an easily installed and cost-effective manner.

Metamaterial Apertures for Computational Imaging

Compressed Sampling It is often said that a picture is worth a thousand words. But images often contain a lot of redundant information—effectively creating huge data files of meaningless information. While algorithms can compress the size of a file without loss of information, such processing is done after the picture has been taken. Hunt et al. (p. 310) used a metamaterial sensor to compress the sampled scene directly, obviating the need for postprocessing. Tuning the response of the metamaterial allowed imaging of a scene with a 40:1 compression ratio, which may mean that finding that needle in a haystack may be much easier using a metamaterial camera. Metamaterial-based sensors can be used for compressive image reconstruction. By leveraging metamaterials and compressive imaging, a low-profile aperture capable of microwave imaging without lenses, moving parts, or phase shifters is demonstrated. This designer aperture allows image compression to be performed on the physical hardware layer rather than in the postprocessing stage, thus averting the detector, storage, and transmission costs associated with full diffraction-limited sampling of a scene. A guided-wave metamaterial aperture is used to perform compressive image reconstruction at 10 frames per second of two-dimensional (range and angle) sparse still and video scenes at K-band (18 to 26 gigahertz) frequencies, using frequency diversity to avoid mechanical scanning. Image acquisition is accomplished with a 40:1 compression ratio.

The magic carpet: physical sensing for immersive environments

An interactive environment has been developed that uses a pair of Doppler radars to measure upper-body kinematics (velocity, direction of motion, amount of motion) and a grid of piezoelectric wires hidden under a 6 x 10 foot carpet to monitor dynamic foot position and pressure. This system has been used in an audio installation, where users launch and modify complex musical sounds and sequences as they wander about the carpet. This paper describes the floor and radar systems, quantifies their performance, and outlines the musical application.

Quadrature Amplitude Modulated Backscatter in Passive and Semipassive UHF RFID Systems

Passive and semipassive UHF RF identification (RFID) systems have traditionally been designed using scalar-valued differential radar cross section (DRCS) methods to model the backscattered signal from the tag. This paper argues that scalar-valued DRCS analysis is unnecessarily limiting because of the inherent coherence of the backscatter link and the complex-valued nature of load-dependent antenna-mode scattering from an RFID tag. Considering modulated backscatter in terms of complex-valued scattered fields opens the possibility of quadrature modulation of the backscatter channel. When compared with binary amplitude shift keying (ASK) or phase shift keying (PSK) based RFID systems, which transmit 1 bit of data per symbol period, and thus 1 bit per on-chip clock oscillator period, tags employing vector backscatter modulation can transmit more than 1 bit per symbol period. This increases the data rate for a given on-chip symbol clock rate leading to reduced on-chip power consumption and extended read range. Alternatively, tags employing an M-ary modulator can achieve log2 M higher data throughput at essentially the same dc power consumption as a tag employing binary ASK or PSK. In contrast to the binary ASK or PSK backscatter modulation employed by passive and semipassive UHF RFID tags, such as tags compliant with the widely used ISO18000-6c standard, this paper explores a novel CMOS-compatible method for generating M-ary quadrature amplitude modulated (QAM) backscatter modulation. A new method is presented for designing an inductorless M-ary QAM backscatter modulator using only an array of switched resistances and capacitances. Device-level simulation and measurements of a four-state phase shift keying (4-PSK)/four-state quadrature amplitude modulated (4-QAM) modulator are provided for a semipassive (battery-assisted) tag operating in the 850-950-MHz band. This first prototype modulator transmits 4-PSK/4-QAM at a symbol rate of 200 kHz and a bit rate of 400 kb/s at a static power dissipation of only 115 nW.

At the Flick of a Switch: Detecting and Classifying Unique Electrical Events on the Residential Power Line (Nominated for the Best Paper Award)

Activity sensing in the home has a variety of important applications, including healthcare, entertainment, home automation, energy monitoring and post-occupancy research studies. Many existing systems for detecting occupant activity require large numbers of sensors, invasive vision systems, or extensive installation procedures. We present an approach that uses a single plug-in sensor to detect a variety of electrical events throughout the home. This sensor detects the electrical noise on residential power lines created by the abrupt switching of electrical devices and the noise created by certain devices while in operation. We use machine learning techniques to recognize electrically noisy events such as turning on or off a particular light switch, a television set, or an electric stove. We tested our system in one home for several weeks and in five homes for one week each to evaluate the system performance over time and in different types of houses. Results indicate that we can learn and classify various electrical events with accuracies ranging from 85-90%.

Magnetic metamaterial superlens for increased range wireless power transfer.

The ability to wirelessly power electrical devices is becoming of greater urgency as a component of energy conservation and sustainability efforts. Due to health and safety concerns, most wireless power transfer (WPT) schemes utilize very low frequency, quasi-static, magnetic fields; power transfer occurs via magneto-inductive (MI) coupling between conducting loops serving as transmitter and receiver. At the “long range” regime – referring to distances larger than the diameter of the largest loop – WPT efficiency in free space falls off as (1/d)6; power loss quickly approaches 100% and limits practical implementations of WPT to relatively tight distances between power source and device. A “superlens”, however, can concentrate the magnetic near fields of a source. Here, we demonstrate the impact of a magnetic metamaterial (MM) superlens on long-range near-field WPT, quantitatively confirming in simulation and measurement at 13–16 MHz the conditions under which the superlens can enhance power transfer efficiency compared to the lens-less free-space system.

The Emergence of RF-Powered Computing

Over the past decade, personal computers have been transformed into small, often mobile devices that are rapidly multiplying. Aside from the ever-present smartphone, a growing set of computing devices has become part of our everyday world, from thermostats and wristwatches, to picture frames, personal activity monitors, and even implantable devices such as pacemakers. All of these devices bring us closer to an “Internet of Things,” but supplying power to sustain this future is a growing burden. Technological advances have so far largely failed to improve power delivery to these machines. Power cords tie devices down, prohibiting their free movement, while batteries add weight, bulk, cost, the need for maintenance, and an undesirable environmental footprint. Fortunately, running small computing devices using only incident RF signals as the power source is increasingly possible. We call such devices RF-powered computers. As might be expected, the amount of power that can be harvested from typical RF signals is small. However, the energy efficiency of the computers themselves has improved exponentially for decades-a lesser-known consequence of Moores law. This relentless improvement has recently brought the power requirements of small computational workloads into the microwatt realm, roughly equal to the power available from RF sources in practical settings.

Intrabody buses for data and power

While wearable computers are empowering fashion accessories, they bring with them a tangle of wires which connect their parts. As these subsystems begin to decouple and operate on less power, it becomes possible to wirelessly distribute their required data and power using to the wearers body. We have demonstrated systems that transmit and receive both data and power, and are working to combine the two.

An ultra-low-power human body motion sensor using static electric field sensing

Wearable sensor systems have been used in the ubiquitous computing community and elsewhere for applications such as activity and gesture recognition, health and wellness monitoring, and elder care. Although the power consumption of accelerometers has already been highly optimized, this work introduces a novel sensing approach which lowers the power requirement for motion sensing by orders of magnitude. We present an ultra-low-power method for passively sensing body motion using static electric fields by measuring the voltage at any single location on the body. We present the feasibility of using this sensing approach to infer the amount and type of body motion anywhere on the body and demonstrate an ultra-low-power motion detector used to wake up more power-hungry sensors. The sensing hardware consumes only 3.3 μW, and wake-up detection is done using an additional 3.3 μW (6.6 μW total).