Analog backscatter enables sensing and communication at lower power consumption than digital backscatter. In analog tags, a sensor typically changes the resistance or capacitance value that then translates into a frequency change that is backscattered on top of a carrier. However, to read the sensor data with high resolution, a powerful receiver is required. The resolution of sensor data is limited by the FFT size and computation at the receiver. This limits the use of low-cost radio receivers, like RTL-SDR, that are gaining popularity in the RF community. We propose to use higher order harmonic frequencies, that are inherently generated by square wave backscattering at no extra cost, to derive sensor data using such low-cost radio receivers. We present experimental results that demonstrate the viability of our approach.

This paper presents Eclipse, a platform for low-cost urban environmental sensing using solar-powered and cellular-connected devices. Dense sensor networks promise to monitor pollution at fine spatial and temporal resolutions, yet few cities have actually implemented such networks due to high costs and limited accuracy. We address these barriers by developing an end-to-end framework for urban air quality sensing with minimal infrastructure requirements. We designed an unobtrusive device that collects data on fine particulate matter (PM2.5), temperature, relative humidity, and barometric pres-sure. A modular design further includes four low-cost gas sensors - Ozone (03), Nitrogen Dioxide (NO2), Sulfur Dioxide (SO2), and Carbon Monoxide (CO) - selected based on local priorities. We deployed 115 devices across Chicago, reliably collecting data for over 90% of expected sensor-hours from July 2 - September 30, 2021. We further developed a calibration strategy that reduced errors by 41.2 – 98.8%, improving accuracy to levels recommended for hotspot detection (PM2.5and 03) or education (NO2and SO2). Through this work, we offer insights on the real-world deployment of a replicable, large-scale, end-to-end platform for hyperlocal urban environmental sensing.

This work highlights the need for a low-cost and low-overhead solution to monitor pallet-level environment in the food supply chain to create traceability, accountability and reduce wastage. We identify post-harvest sensing through the supply chain as a key need to reduce food waste. Toward this end, we develop initial prototypes of two different wireless environmental sensing architectures. The first leverages an ultra-low power timer with a current consumption of 35 nA to power gate and periodically wake up the system. The second mode explores a sparse event driven sensing model leveraging the threshold detection features of low power sensors to log events of interest. We demonstrate a millimeter scale prototypes that can read and backscatter temperature and humidity data with as little as 3.2 μW of power.

The landscape of everyday fashion has been transformed due to the COVID-19 pandemic. Part of this can be attributed to different types of communities (e.g. fashion, makers, sewing), who have designed and fabricated masks to counter global shortages and negative culture backlashes. In this paper, we present a mix-methods study of individuals and groups within these communities on their motivations and practices in designing and creating face masks during a global crisis. We conducted a survey with 66 mask makers in the Summer of 2020, and we interviewed 23 of them about their attitudes and reflections on their mask making processes, their unique innovations, and the meaning of contributing in an impactful manner in local and global communities. We unpack themes around technology, self-expression and statement making, making and remixing, sustainable practices, as well as the role of design inspirations on methods and practices for mask makers during a crisis.

Continuously monitoring a wide variety of performance and fault metrics has become a crucial part of operating large-scale datacenter networks. In this work, we ask whether we can reduce the costs to monitor - in terms of collection, storage and analysis - by judiciously controlling how much and which measurements we collect. By positing that we can treat almost all measured signals as sampled time-series, we show that we can use signal processing techniques such as the Nyquist-Shannon theorem to avoid wasteful data collection. We show that large savings appear possible by analyzing tens of popular measurement systems from a production datacenter network. We also discuss some challenges that must be solved when applying these techniques in practice.

Analytics with three-dimensional imagery from drones are driving the next generation of remote monitoring applications. Today, there is an unmet need in providing such analytics in an interactive manner, especially over weak Internet connections, to quickly diagnose and solve problems in the commercial industry space of monitoring assets using drones in remote parts of the world. Existing mechanisms either compromise on the quality of insights by not building 3D images and analyze individual 2D images in isolation, or spend tens of minutes building a 3D image before obtaining and uploading insights. We present Visage, a system that accelerates 3D image analytics by identifying smaller parts of the data that can actually benefit from 3D analytics and prioritizing building, and uploading the localized 3D images for those parts. To achieve this, Visage uses a graph to represent raw 2D images and their relative content overlap, and then identifies the various subgraphs using application knowledge that are good candidates for localized 3D image based insights. We evaluate Visage using data from multiple real deployments and show that it can reduce analytics-latency by up to four orders of magnitude.

Implantable neural recording and stimulation devices hold great promise in monitoring and treatment of neurological disorders, limb reanimation and, development of brain-computer interfaces among other applications. However, transcutaneous wires limit the lifetime of such devices and there is a need for self-contained fully implantable solutions. In this work, we propose a novel dual-frequency approach for simultaneous wireless power transfer and low-power communication for small form factor fully implantable neural devices. We deliver wireless power using efficient magnetically coupled resonators operating at 13.56MHz and communicate using ultra-low power backscatter communication at 915 MHz. We leverage the frequency separation to combine wireless power and communication resonators with minimal interference using a novel concentric design, which meets the stringent size restrictions. We implement the wireless power receiver and communication front end of the implanted device in 65 nm CMOS and demonstrate 25 mW power delivery and 6 Mbps communication link.

Growing number of important application areas, including automotive and industrial applications as well as space, avionics, combustion engine, intelligent propulsion systems, and geo-thermal exploration require electronics that can work reliable at extreme conditions - in particular at a temperature > 250°C and at high radiation (1-30 Mrad), where conventional electronics fail to work reliably. Traditionally, existing wideband-gap semiconductors, e.g., silicon carbide (SiC) transistor-based electronics have been considered most viable for high temperature and high radiation applications. However, the large-size, high threshold voltage, low switching speed and high leakage current make logic design with these devices unattractive. Additionally, the leakage current markedly increases at high temperature (in the range of 10 μA for a 2-input NAND gate), which induces self-heating effect and makes power delivery at high temperature very challenging. To address these issues, in this paper we present a computing platform for low-power reliable operation at extreme environment using SiC electromechanical switches. We show that a device-circuit-architecture co-design approach can provide reliable long-term operation with virtually zero leakage power.