We introduce Focal Split, a handheld, snapshot depth camera with fully onboard power and computing based on depth-from-differential-defocus (DfDD). Focal Split is passive, avoiding power consumption of light sources. Its achromatic optical system simultaneously forms two differentially defocused images of the scene, which can be independently captured using two photosensors in a snapshot. The data processing is based on the DfDD theory, which efficiently computes a depth and a confidence value for each pixel with only 500 floating point operations (FLOPs) per pixel from the camera measurements. We demonstrate a Focal Split prototype, which comprises a handheld custom camera system connected to a Raspberry Pi 5 for real-time data processing. The system consumes 4.9 W and is powered on a 5 V, 10,000 mAh battery. The prototype can measure objects with distances from 0.4 m to 1.2 m, outputting 480×360 sparse depth maps at 2.1 frames per second (FPS) using unoptimized Python scripts. Focal Split is DIY friendly. A comprehensive guide to building your own Focal Split depth camera, code, and additional data can be found at https://focal-split.qiguo.org.

Human studies often rely on wearable lifelogging cameras that capture videos of individuals and their surroundings to aid in visual confirmation or recollection of daily activities like eating, drinking, and smoking. However, this may include private or sensitive information that may cause some users to refrain from using such monitoring devices. Also, short battery lifetime and large form factors reduce applicability for long-term capture of human activity. Solving this triad of interconnected problems is challenging due to wearable embedded systems’ energy, memory, and computing constraints. Inspired by this critical use case and the unique design problem, we developed NIR-sighted, an architecture for wearable video cameras that navigates this design space via three key ideas: (i) reduce storage and enhance privacy by discarding masked pixels and frames, (ii) enable programmers to generate effective masks with low computational overhead, and (iii) enable the use of small MCUs by moving masking and compression off-chip. Combined together in an end-to-end system, NIR-sighted’s masking capabilities and off-chip compression hardware shrinks systems, stores less data, and enables programmer-defined obfuscation to yield privacy enhancement. The user’s privacy is enhanced significantly as nowhere in the pipeline is any part of the image stored before it is obfuscated. We design a wearable camera called NIR-sightedCam based on this architecture; it is compact and can record IR and grayscale video at 16 and 20+ fps, respectively, for 26 hours nonstop (59 hours with IR disabled) at a fraction of comparable platforms power draw. NIR-sightedCam includes a low-power Field Programmable Gate Array that implements our mJPEG compress/obfuscate hardware, Blindspot. We additionally show the potential for privacy-enhancing function and clinical utility via an in-lab eating study, validated by a nutritionist.

Perceiving the environment for better and more efficient situational awareness is essential in applications such as wildlife surveillance, wildfire detection, crop irrigation, and building management. Energy-harvesting, intermittently-powered sensors have emerged as a zero maintenance solution for long-term environmental perception. However, these devices suffer from intermittent and varying energy supply, which presents three major challenges for executing perceptual tasks: (1) intelligently scaling computation in light of constrained resources and dynamic energy availability, (2) planning communication and sensing tasks, (3) and coordinating sensor nodes to increase the total perceptual range of the network. We propose an adaptive framework, AdaSens, which adapts the operations of intermittently-powered sensor nodes in a coordinated manner to cover as much as possible of the targeted scene, both spatially and temporally, under interruptions and constrained resources. We evaluate AdaSens on a real-world surveillance video dataset, VideoWeb, and show at least 16% improvement on the coverage of the important frames compared with other methods.

This paper presents a decentralized control frame-work for lattice-based Modular Self-Reconfigurable Robots (MSRR) which utilizes a novel magnetic fiducial system to facilitate neighbor identification and to enable algorithms which promise scalable functionality for systems with many modules. In this system individual modules autonomously follow simple behaviors while periodically accepting input from a centralized controller. This system is demonstrated with three initial behaviors: (1) Path following: modules follow a three dimensional path based on magnetic fiducial tags embedded in their neighbors, (2) Line formation: modules transform from a 3D structure into a line following a partially decentralized control algorithm, and (3) Light gradient aggregation: the formation of a group of modules guided by a global stimulus (i.e. visible light). This paper provides details of the neighbor identification system, introduces the three behaviors and presents the results of physical experiments performed with a system of twelve 3D M-Block robotic modules.