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In this paper, a dual-frequency control method for regulating the output power in class-E resonant DC-DC converters has been introduced. As in the standard ON-OFF control or other recently proposed dual-frequency controls, the approach is based on the ability of the converter to alternately operate in a high- and a low-power state. The proposed solution has a twofold advantage: on the one hand, soft-switching capabilities (i.e., Zero-Voltage and Zero-Voltage-Derivative switching) are preserved in both operating states; on the other hand, it is possible to reduce to zero the transient time required to switch from one state to the other one. The most straightforward consequence is the possibility to increase to very large values the frequency at which the two operating states are switched, up to the same order of magnitude as the main switching frequency of the converter. In this way, the additional ripple introduced by the proposed dual-frequency control can be decreased to a negligible value. The approach has been validated by measurements on a prototype operating between 4MHz and 8MHz and in which it has been possible to increase the control frequency up to 500kHz.

Compressed Sensing (CS) has been addressed as a paradigm capable of lowering energy requirements in acquisition systems. Furthermore, the capability of simultaneously acquiring and compressing an input signal makes this paradigm perfectly suitable for low-power devices. However, the need for analog hardware blocks makes the adoption of most of standard solutions proposed so far in the literature problematic when an aggressive voltage and energy scaling is considered, as in the case of ultra-low-power IoT devices that need to be battery-powered or energy harvesting-powered. Here, we investigate a recently proposed architecture that, due to the lack of any analog block (except for the comparator required in the following A/D stage) is compatible with the aggressive voltage scaling required by IoT devices. Feasibility and expected performance of this architecture are investigated according to the most recent state-of-the-art literature.

Leakage discharge currents represent one of the most detrimental factors for the maximum hold time in analog sample-and-hold circuits. Apart from the obvious passive solution of enlarging the sampling capacitor, alternatives based on active circuits have been proposed. We focus here on an existing solution which has proven to be effective in reducing the leakage discharge, hence extending the hold time, by a factor of 20. Being based on a feedback circuit built around the hold capacitor, it is paramount to understand its stability properties. This work tries to close the gap by analyzing the closed-loop stability of the nominal circuit. Classical control systems techniques are employed to thoroughly analyze the dynamic behaviour of the feedback circuit, highlighting the detrimental effect of device mismatches.

In this paper, the design of a wireless power transfer system (WPT) targeting biomedical implants is considered. The novelty of the approach is to propose a co-design of the transmitter and receiver side based on the design of class-E isolated DC-DC converters. The solution, along with the simple introduction of a shunt regulator at the receiver, allows us to solve the problem of ensuring optimal efficiency in the WPT link. In conventional solutions, in order to cope with coupling factor and load variations, information from the receiver is needed, which is usually relayed back onto the transmitter by means of telemetry. With the proposed approach, a very simple minimum power point tracking (mPPT) algorithm can be used to maximize the WPT efficiency based on the information already available at the transmitter side. This reduces the complexity of the circuitry of the implant and thereby its power overhead and possibly its size, both being crucial constraints of a biomedical implant.

The amount of data generated by distributed monitoring systems that can be exploited for anomaly detection, along with real time, bandwidth, and scalability requirements leads to the abandonment of centralized approaches in favor of processing closer to where data are generated. This increases the interest in algorithms coping with the limited computational resources of gateways or sensor nodes. We here propose two dual and lightweight methods for anomaly detection based on generalized spectral analysis. We monitor the signal energy laying along with the principal and anti -principal signal subspaces, and call for an anomaly when such energy changes significantly with respect to normal conditions. A streaming approach for the online estimation of the needed subspaces is also proposed. The methods are tested by applying them to synthetic data and real-world sensor readings. The synthetic setting is used for design space exploration and highlights the tradeoff between accuracy and computational cost. The real-world example deals with structural health monitoring and shows how, despite the extremely low computations costs, our methods are able to detect permanent and transient anomalies that would classically be detected by full spectral analysis.