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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.

The recovery of sparse signals from their linear mapping on a lower-dimensional space is traditionally performed by finding the sparsest solution compatible with such solutions. This task can be partitioned in two phases: support estimation and coefficient estimation. We propose to perform the former with a deep neural network jointly trained with the encoder that divines a support that is used in the latter phase to estimate the coefficients by pseudo-inversion. Numerical evidence demonstrates that the proposed encoder-decoder architecture outperforms state-of-the-art Compressed Sensing (CS) approaches in the recovery of synthetic ECG signals for a compression ratio higher than 2.5. Further tests on real ECG prove the applicability in real-world scenarios.

In recently published papers, an innovative analytical approach for the design of a class-E resonant dc-dc converter has been first proposed and further extended to many other class-E converter topologies. Its peculiarity is to be dimensionless and based on the exact solution of the system of differential equations regulating the behavior of the circuit, ensuring very high precision and reliability with respect to all methodologies previously proposed by the state-of-the-art and based on the so-called sinusoidal approximation. Here, we review this methodology and improve it in a twofold way. On the one hand, we propose alternative modeling for some devices (in particular the transformer), increasing both flexibility and generality, with the possibility to extend the application to more topologies and more working points. On the other hand, a new normalization is proposed, showing that the actual dimension of the design workspace is 2, and not 3 as assumed in the previous works. This has important consequences. As an example, the solution existence condition can be represented on a simple 2D plot, with the possibility to immediately check whether the optimal class-E condition can be ensured or not. Furthermore, we can completely and conveniently explore the entire design space to investigate properties such as the stress on the switching devices or the root-mean-square currents, allowing further optimization of the converter design.

An innovative analog-to-digital converter (ADC) architecture is proposed, with the aim of acquiring an input signal according to the Compressed Sensing (CS) paradigm and without the need for dedicated active analog blocks. Its core is the capacitive array employed in traditional successive-approximation-register (SAR) ADCs. Introducing only a few additional switches, the array can compute the linear combination of consecutive signal samples, as required by the CS encoding. To manage the presence of leakage currents, which may impair signal reconstruction, a compensation circuit is considered, allowing close-to-ideal performance of the system when properly designed. A neural network-based decoding strategy is also analyzed, with up to 20 dB of additional reconstruction quality with respect to standard algorithms. Synthetic electrocardiogram signals are used to validate optimizations both at the hardware level in the encoding block and at the software level in the decoder.

In this article, we propose a methodology to create a through-the-barrier bidirectional communication channel between the two sides of an isolated class-E resonant dc-dc converter. Our methodology is innovative since communication occurs through the same transformer used for energy transfer, and relies on connecting and disconnecting an auxiliary capacitance. By doing so, the converter switches between a faster and a slower response without any change in the clock controlling its activity. With this, it is possible to achieve a very high-rate bidirectional communication (up to 1 b per switching clock period) with virtually no limitations in the converter power with respect to a standard class-E implementation. Measurements performed on a prototype operating at 1 MHz and capable of delivering up to 1.2 W is presented as a proof-of-concept.

We investigate the advantage of a two-step approach in the recovery of Compressed Sensing (CS) encoded signals in a realistic environment. First, the support of the signal is computed from the compressed measurements exploiting a Deep Neural Network (DNN). Once the support is known, the input signal can be easily recovered by a pseudoinverse operation. We consider a case study involving realistic biomedical signals and a processing architecture based on a limited precision fixed-point arithmetic unit for the implementation of the DNN and the pseudoinverse operation. In this setting, we show that the proposed approach results in a performance improvement of more than 5 dB in terms of average reconstructed signal to noise ratio (ARSNR) compared to CS state-of-the-art approach. This has been possible thanks to two main contributions reported in this paper. The first one is a theoretical investigation of the relationship between the definition of support and both the properties of the input signal and the adopted compression technique. The second one relies on replacing the pseudoinverse operation with a least mean square filter, whose small sensitivity to numerical errors grants advantages in architectures relying on limited precision fixed-point arithmetic units.

Despite the universal nature of the compressed sensing mechanism, additional information on the class of sparse signals to acquire allows adjustments that yield substantial improvements. In facts, proper exploitation of these priors allows to significantly increase compression for a given reconstruction quality. Since one of the most promising scopes of application of compressed sensing is that of IoT devices subject to extremely low resource constraint, adaptation is especially interesting when it can cope with hardware-related constraint allowing low complexity implementations. We here review and compare many algorithmic adaptation policies that focus either on the encoding part or on the recovery part of compressed sensing. We also review other more hardware-oriented adaptation techniques that are actually able to make the difference when coming to real-world implementations. In all cases, adaptation proves to be a tool that should be mastered in practical applications to unleash the full potential of compressed sensing.

The recovery of sparse signals given their linear mapping on lower-dimensional spaces can be partitioned into a support estimation phase and a coefficient estimation phase. We propose to estimate the support with an oracle based on a deep neural network trained jointly with the linear mapping at the encoder. The divination of the oracle is then used to estimate the coefficients by pseudo-inversion. This architecture allows the definition of an encoding-decoding scheme with state-of-the-art recovery capabilities when applied to biological signals such as ECG and EEG, thus allowing extremely low-complex encoders. As an additional feature, oracle-based recovery is able to self-assess, by indicating with remarkable accuracy chunks of signals that may have been reconstructed with a non-satisfactory quality. This self-assessment capability is unique in the CS literature and paves the way for further improvements depending on the requirements of the specific application. As an example, our scheme is able to satisfyingly compress by a factor of 2.67 an ECG or EEG signal with a complexity equivalent to only 24 signed sums per processed sample.

Compressed Sensing (CS) has been proposed as a method able to reduce the amount of data needed to represent sparse signals. Nowadays, different approaches have been proposed in order to increase the performance of this technique in each stage that composes it. Particularly, this paper provides a critical review of the state-of-the art of some CS adaptations in the sensing stage to identify the strengths and limitations of each of them. In addition, a new method is proposed (Nearly Orthogonal Rakeness-based CS) that aims to overcome limits of the CS adaptations covered in this work. After intensive numerical simulations on synthetic signals and electroencephalographic (EEG) signals, the proposed approach outperforms discussed state-of-the-art approaches in terms of compression capability required to achieve a target quality of service.

A recent paper improved the state of the art for resonant class-E dc/dc converters by relaying their design on the solution of an associated non-linear dimensionless mathematical system. We show in this paper that when the associated nonlinear system can be solved, the solution is not always unique. By considering a simple case study we are able to compute two different solutions leading to two different designs. In the first one the main spectral component of voltage and current waveforms is located around the first clock harmonic, and around the second harmonic in the other solution. Interestingly, the latter design leads to a reduction either in the size of inductors/transformers or in the oscillation frequency (a 2.46x factor), and also a non-negligible improvement in the converter efficiency (from 74.1% to 75.8%).

In this paper we propose an innovative A/D architecture with the ability to acquire an input signal according to the recently introduced Compressed Sensing (CS) paradigm. The architecture relies on the hardware blocks already found in traditional successive-approximation-register (SAR) A/D converter, requiring only the addition of a limited number of switches. The capacitive array at the core of the circuit is used both by the SAR conversion algorithm and to realize the linear combination of consecutive signal samples, as required by the CS framework. The lack of additional active blocks allows for a remarkable saving in sampling energy with respect to published solutions. The role of some design parameters is investigated and solutions to ease the circuital implementation are analyzed.

Compressed sensing can be used to yield both compression and a limited form of security to the readings of sensors. This can be most useful when designing the low-resources sensor nodes that are the backbone of IoT applications. Here, we propose to use chaining of subsequent plaintexts to improve the robustness of CS-based encryption against ciphertext-only attacks, known-plaintext attacks and man-in-the-middle attacks.

This paper presents the design of an ultra-low energy, rakeness-based compressed sensing (CS) system that utilizes time-mode (TM) signal processing (TMSP). To realize TM CS operation, the presented implementation makes use of monostable multivibrator based analog-to-time converters, fixed-width pulse generators, basic digital gates and an asynchronous time-to-digital converter. The TM CS system was designed in a standard 0.18 µm IC process and operates from a supply voltage of 0.6V. The system is designed to accommodate data from 128 individual sensors and outputs 9-bit digital words with an average reconstruction SNR of 35.31 dB, a compression ratio of 3.2, with an energy dissipation per channel per measurement vector of 0.621 pJ at a rate of 2.23 k measurement vectors per second.

Atrial electrogram (AEG) acquired with a high spatio-temporal resolution is a promising approach for early detection of atrial fibrillation. Due to the high data rate, transmission of AEG signals requires considerable energy, making its adoption a challenge for low-power wireless devices. In this paper, we investigate the feasibility of using compressed sensing (CS) for the acquisition of AEGs while reducing redundant data without losing information. We apply two CS approaches, standard CS and rakeness-based CS (rak-CS) on real medical recordings. We find that the AEGs are compressible in time, and, more interestingly, in the spatial domain. The performance of rak-CS is better than standard CS, especially at higher compression ratios (CR), both during sinus rhythm (SR) and atrial fibrillation (AF). More specifically, the difference in the achieved average reconstruction signal-to-noise (ARSNR) in rak-CS and standard CS, for CR = 4.26, in the time domain is 7.7 dB and 2.6 dB for AF and SR, respectively. Multi-channel data is modeled as a multiple-measurement-vector problem and a suitable mixed norm is used to exploit the group structure of the signals in the spatial domain to obtain improved reconstruction performance over l 1 norm minimization. Using the mixed-norm recovery approach, for CR = 4.26, the difference in achieved ARSNR performance between rak-CS and standard CS is 5 dB and 2 dB for AF and SR, respectively.

The incoming Internet of Things revolution requires the adoption of innovative paradigms for the design of low-power ubiquitous sensor nodes. This can be achieved by exploiting Compressed Sensing (CS), that is a recently introduced approach capable of simultaneously sampling and compressing an input signal with a limited amount of resources. While the underlying basic theory is well developed, in recent years we have seen a flourishing of CS techniques capable of exploiting some additional priors on the input signal to improve performance. In this paper, we propose a survey and a comparison of the most promising ones. We use a classification mechanism based on which prior is used and which processing block is modified with respect to the standard CS.

Compressed sensing can be seen as a lossy data compression stage processing vectors of digital words that correspond to time windows of the signal to acquire. We here show that if the second-order statistical features of such a signal are known, they may be exploited to obtain extremely high compression ratios by means of an almost zero-complexity hardware that is limited to signed adders and very few other elementary algebraic blocks. Optimization is obtained and demonstrated against non-optimized compressed sensing both by specializing classical rakeness-based design and by employing and even simpler and novel principal-component-based method that in some cases may outperform the former. Simulations are performed taking into account bit-wise operations and yield the true compression ratios that would be produced by the real system entailing only very low-depth fixed-point arithmetic. In the case of real-workd ECGs, good reconstruction with bitwise compression ratios up to 9 is demonstrated.