Search for: All records

The emergence of Audio-based Augmented Reality has been calling for increasing data-rates for audio signals, with significant reduction in power to enable extremely energy-constrained sensor nodes. Typically, the communication power dominates sensing and computing power in a node [1]. For highly energy constrained scenarios, compressive sensing (CS) have been demonstrated (Fig. 1), where samples are first compressed at the sensor to contain the same information in a smaller number of samples, before transmitting to a receiver, where the signal is reconstructed. Previous CS works [2]-[5] have focused entirely on “sparse” physiological signals, operating in low speed regime. This work illustrates the first CS design, enabled with a discrete wavelet transform (DWT) sparsifier for catering to non-sparse signals such as high definition audio. Audio recording and playback are quite sensitive to quality, thereby requiring audio codecs, such as. aac, for efficient compression and decompression of audio streams, which usually consume power in the order of mW [6]. Audio inferencing operated in intelligent assistants are more tolerant to input quality, functioning effectively when the Perceptual Evaluation of Audio Quality Mean Opinion Score (PAEQ MOS) [7], an ITU-R standard objective metric for characterizing perceived audio quality, exceeds 1.5. CS presents an opportunity to achieve >10X reduction in transmitted audio data with orders of magnitude lower power, as compared to codecs. The design is implemented in 65 nm CMOS and consumes 238 uW power at 0.65 V and 15 Mbps. 

Applications like Connected Healthcare through physiological signal monitoring and Secure Authentication using wearable keys can benefit greatly from battery-less operation. Low power communication along with energy harvesting is critical to sustain such perpetual battery-less operation. Previous studies have used techniques such as Tribo-Electric, Piezo-Electric, RF energy harvesting for Body Area Network devices, but they are restricted to on-body node placements. Human body channel is known to be a promising alternative to wireless radio wave communication for low power operation [1-4], through Human Body Communication, as well as very recently as a medium for power transfer through body coupled power transfer [5]. However, channel length (L) dependency of the received power makes it inefficient for L>40cm. There have also been a few studies for low power communication through the human body, but none of them could provide sustainable battery-less operation. In this paper, we utilize Resonant Electro Quasi-Static Human Body Communication (Res-EQS HBC) with Maximum Resonance Power Tracking (MRPT) to enable channel length independent whole-body communication and powering (Fig.1). We design the first system to simultaneously transfer Power and Data between a HUB device and a wearable through the human body to enable battery-less operation. Measurement results show 240uW, 28uW and 5uW power transfer through the body in a MachineMachine (large devices with strong ground connection) Tabletop (small devices kept on a table, as in [5]) and Wearable-Wearable (small form factor battery operated devices) scenario respectively, independent of body channel length, while enabling communication with a power consumption of only 2.19uW. This enables >25x more power transfer with >100x more efficiency compared to [5] for Tabletop and 100cm Body distance by utilizing the benefits of EQS. The MRPT loop automatically tracks device and posture dependent resonance point changes to maximize power transfer in all cases.