Harmonic currents in a receiver in wireless power transfer (WPT) systems can degrade performance of the receiver device by corrupting the DC power plane. The DC plane at the receiver serves as a constant reference that allows for dependent, sensitive electronics to operate with acceptable precision. However, if that reference is corrupted by noise, then the functions of the system, such as sensing and wireless communication, can degrade significantly. To reduce the harmonic current content in the receiver, and thereby improve the DC supply constancy, the optimal arbitrary voltage transmitter excitation is derived. To physically generate the derived arbitrary voltage, the signal is converted to a multilevel voltage using a phase-shifted carrier modulation. This multilevel voltage is then realized via the design of a full-bridge flying capacitor multilevel inverter and circuit simulations demonstrated the reduction of the harmonic currents in the receiver. By comparison to using a square-wave input voltage, circuit simulations demonstrate that a 5-level multilevel inverter reduces the total harmonic distortion (THD) of the received current from 6.79 % to 0.69 %, effectively reducing the harmonic currents in the receiver by 90 %.

To increase the transmission distance of Wireless Power Transfer systems, engineering an intermediate coil (IC) is a common approach. In this paper, we provide methodologies and formulas for choosing the optimal size and location of an IC that provides a required transferred power to the load in a standard four-coil systems. Increasing the size of the IC will result in diminishing returns, so the optimal size depends on the target power ratio. The optimal location depends on the size of the IC and the separation of transmitting and receiving coils.

We present a minimally invasive, needle-injectable, wirelessly-powered diffuse optical spectroscopy sensor implant that fits within a 12G breast biopsy needle. The sensor uses red and near-infrared lasers to measure tumor hemoglobin concentration changes. It contains two photodiodes controlled by an analog front end integrated circuit with an integrated transimpedance amplifier. This ultralow power sensor is wirelessly powered using near-field resonant inductive coupling and communicates via backscattered load shift keying. Characterization in liquid tissue mimicking phantoms showed a response to absorption concentrations consistent with estimated tumor hemoglobin concentrations. After implantation in murine breast cancer models, tissue stainings revealed no adverse effects.

Brain–computer interfaces (BCIs) are neural prosthetics that enable closed-loop electrophysiology procedures. These devices are currently used in fundamental neurophysiology research, and they are moving toward clinical viability for neural rehabilitation. State-of-the-art BCI experiments have often been performed using tethered (wired) setups in controlled laboratory settings. Wired tethers simplify power and data interfaces but restrict the duration and types of experiments that are possible, particularly for the study of sensorimotor pathways in freely behaving animals. To eliminate tethers, there is significant ongoing research to develop fully wireless BCIs having wireless uplink of broadband neural recordings and wireless recharging for long-duration deployment, but significant challenges persist. BCIs must deliver complex functionality while complying with tightly coupled constraints in size, weight, power, noise, and biocompatibility. In this article, we provide an overview of recent progress in wireless BCIs and a detailed presentation of two emerging technologies that are advancing the state of the art: ultralow-power wireless backscatter communication and adaptive inductive resonant (AIR) wireless power transfer (WPT).

The Mostly Printed Field Characterization System (MPFCS) is an accurate, inexpensive, and open-source platform for high-fidelity, rapid, large volume WPT coil field characterizations that provides a significant reduction in scan duration in comparison to similar simulations.

This paper describes Packet Assay, a power efficient sparse neural network (NN) that can discriminate between wireless transmissions, such as WLAN packets, based solely on the RF signal envelope, a feature that can be measured with much less power than fully demodulating and decoding the packets. The NN was trained on a Wireless Local Area Networks (WLAN) dataset developed in-house with over 600K labeled samples and achieved above 88% accuracy while maintaining a memory footprint of only 4.9KB. This approach can reduce the power consumption of wireless modules (WM), can minimize the signal processing in IoT devices, and provides a foundation for future protocol development.