Many sensing tasks, such as plant stress sensing and blood pressure estimation, require co-located multi-modal measurements from two to five sensors at one site. RF backscatter enables low-power sensing, but existing tags usually support only one sensor; using multiple tags increases footprint and antenna coupling. We present Matrix, a fully-analog single-chain backscatter tag that supports multiple onboard sensors by multiplexing them into a composite voltage for transmission through one analog modulation chain. Unlike time-division polling, which introduces inter-sensor sampling offsets, or frequency-division, which requires separate chains, Matrix uses voltage-division multiplexing. Each sensor is encoded as a PWM waveform whose duty cycle represents the measurement, while amplitude enables multiplexing. Binary-weighted voltage-division weights make each active-sensor set uniquely invertible for reliable demultiplexing. The composite voltage is then converted into backscatter frequency shifts through the same chain. At the receiver, Matrix uses a Hidden Markov Model to recover per-sensor readings. Its ASIC consumes 25.56μW. A five-sensor prototype achieves 20 dB average reconstruction SNR at 30 kHz sampling, and we validate Matrix in plant sensing, health monitoring, and microphone-based direction finding.

Sign-to-911 offers a compact mobile system solution to fast and runtime American Sign Language (ASL) and English translations. It is designated as 911 call services for ASL users with hearing disabilities upon emergencies. It enables bidirectional translations of ASL-to-English and English-to-ASL. The signer wears the AR glasses, runs Sign-to-911 on his/her smartphone and glasses, and interacts with a 911 operator. The design of Sign-to-911 departs from the popular deep learning based solution paradigm, and adopts simpler traditional AI/machine learning (ML) models. The key is to exploit ASL linguistic features to simplify the model structures and improve accuracy and speed. It further leverages recent component solutions from graphics, vision, natural language processing, and AI/ML. Our evaluation with six ASL signers and 911 call records has confirmed its viability.