We investigate theoretical and practical aspects of the design of far-field RF power extraction systems consisting of antennas, impedance matching networks and rectifiers. Fundamental physical relationships that link the operating bandwidth and range are related to technology dependent quantities like threshold voltage and parasitic capacitances. This allows us to design efficient planar antennas, coupled resonator impedance matching networks and low-power rectifiers in standard CMOS technologies (0.5-mum and 0.18-mum) and accurately predict their performance. Experimental results from a prototype power extraction system that operates around 950 MHz and integrates these components together are presented. Our measured RF power-up threshold (in 0.18-mum, at 1 muW load) was 6 muWplusmn10%, closely matching the predicted value of 5.2 muW.

We analyze the performance of wireless data telemetry links for implanted biomedical systems. An experimental realization of a bidirectional half-duplex link that uses near-field inductive coupling between the implanted system and an external transceiver is described. Our system minimizes power consumption in the implanted system by using impedance modulation to transmit high-bandwidth information in the uplink direction, i.e., from the implanted to the external system. We measured a data rate of 2.8 Mbps at a bit error rate (BER) of <10^-6 (we could not measure error rates below 10^-6 ) and a data rate of 4.0 Mbps at a BER of 10^-3. Experimental results also demonstrate data transfer rates up to 300 kbps in the opposite, i.e., downlink direction. We also perform a theoretical analysis of the bit error rate performance. An important effect regarding the asymmetry of rising and falling edges that is inherent to impedance modulation is predicted by theory and confirmed by experiment. The link dissipates 2.5 mW in the external system and only 100 muW in the implanted system, making it among the most power-efficient inductive data links reported. Our link is compatible with FCC regulations on radiated emissions.

Radar is a key component of the suite of perception sensors used for safe and reliable navigation of autonomous vehicles. Its unique capabilities include high-resolution velocity imaging, detection of agents in occlusion and over long ranges, and robust performance in adverse weather conditions. However, the usage of radar data presents some challenges: it is characterized by low resolution, sparsity, clutter, high uncertainty, and lack of good datasets. These challenges have limited radar deep learning research. As a result, current radar models are often influenced by lidar and vision models, which are focused on optical features that are relatively weak in radar data, thus resulting in under-utilization of radar’s capabilities and diminishing its contribution to autonomous perception. This review seeks to encourage further deep learning research on autonomous radar data by 1) identifying key research themes, and 2) offering a comprehensive overview of current opportunities and challenges in the field. Topics covered include early and late fusion, occupancy flow estimation, uncertainty modeling, and multipath detection. The paper also discusses radar fundamentals and data representation, presents a curated list of recent radar datasets, and reviews state-of-the-art lidar and vision models relevant for radar research.

The paper presents the design and validation of body-conformal active ultrasound patches with integrated imaging and modulation modalities for image-guided neural therapy. A mechanically-flexible linear 64-element array of piezoelectric transducers with a resonance frequency of 5 MHz was designed for nerve localization. A second 8-element array using larger elements was integrated on the wearable probe for low intensity focused ultrasound neuromodulation at a resonance frequency of 1.3 MHz. Full-wave simulations were used to model the flexible arrays and estimate their generated pressure profiles. A focal depth of 10-20 mm was assumed for beamforming and focusing to support modulation of the vagus, tibial, and other nerves. A strain sensor integrated on the probe provides patient-specific feedback information on array curvature for real-time optimization of focusing and image processing. Each patch also includes high voltage (HV) multiplexers, transmit/receive switches, and pre-amplifiers that simplify connectivity and also improve the signal-to-noise ratio (SNR) of the received echo signals by ∼5 dB. Experimental results from a flexible prototype show a sensitivity of 80 kPa/V with ∼3 MHz bandwidth for the modulation and 20 kPa/V with ∼6 MHz bandwidth for the imaging array. An algorithm for accurate and automatic localization of targeted nerves based on using nearby blood vessels (e.g., the carotid artery) as image markers is also presented.

Ground-penetrating radar (GPR) is a widely popular sensing method with broad applications in non-destructive subsurface imaging. This paper presents a multistatic GPR for vehicle-mounted roadway and utility monitoring applications that employs several methods to improve performance compared to the state-of-the-art. The proposed system illuminates the subsurface with pseudo-random codes (m-sequences) that have nearideal autocorrelation properties. As a result, the received signal can be matched-filtered to provide pulse-compression, which improves both range-resolution and depth of scan compared to impulse-based GPRs. It also uses a highly-digital transmit and receive architecture based on direct FPGA-based transmit pulse generation and direct radio frequency (RF) sampling of the received echoes. Further, the analog front-end uses an 8×8 multistatic antenna array design with broadband antipodal Vivaldi elements to provide spatial diversity, which leads to improved object localization and reduced drift between scans. Experimental results from indoor and outdoor test-beds confirm the functionality of the proposed GPR system.

This paper presents work on ultra-low-power circuits for brain–machine interfaces with applications for paralysis prosthetics, stroke, Parkinson's disease, epilepsy, prosthetics for the blind, and experimental neuroscience systems. The circuits include a micropower neural amplifier with adaptive power biasing for use in multi-electrode arrays; an analog linear decoding and learning architecture for data compression; low-power radio-frequency (RF) impedance-modulation circuits for data telemetry that minimize power consumption of implanted systems in the body; a wireless link for efficient power transfer; mixed-signal system integration for efficiency, robustness, and programmability; and circuits for wireless stimulation of neurons with power-conserving sleep modes and awake modes. Experimental results from chips that have stimulated and recorded from neurons in the zebra finch brain and results from RF power-link, RF data-link, electrode-recording and electrode-stimulating systems are presented. Simulations of analog learning circuits that have successfully decoded prerecorded neural signals from a monkey brain are also presented.