Showing papers on "Sampling (signal processing) published in 2021"

A Digital Twin Based Estimation Method for Health Indicators of DC–DC Converters

Abstract: This article proposes a health indicator estimation method based on the digital-twin concept aiming for condition monitoring of power electronic converters. The method is noninvasive, without additional hardware circuits, and calibration requirements. An application for a buck dc–dc converter is demonstrated with theoretical analyses, practical considerations, and experimental verifications. The digital replica of an experimental prototype is established, which includes the power stage, sampling circuit, and close-loop controller. Particle swarm optimization algorithm is applied to estimate the unknown circuit parameters of interest based on the incoming data from both the digital twin and the physical prototype. Cluster-data of the estimated health indicators under different testing conditions of the buck converter is analyzed and used for observing the degradation trends of key components, such as capacitor and MOSFET. The outcomes of this article serve as a key step for achieving noninvasive, cost-effective, and robust condition monitoring for power electronic converters.

Quantum computational advantage via 60-qubit 24-cycle random circuit sampling

Abstract: To ensure a long-term quantum computational advantage, the quantum hardware should be upgraded to withstand the competition of continuously improved classical algorithms and hardwares. Here, we demonstrate a superconducting quantum computing systems Zuchongzhi 2.1, which has 66 qubits in a two-dimensional array in a tunable coupler architecture. The readout fidelity of Zuchongzhi 2.1 is considerably improved to an average of 97.74%. The more powerful quantum processor enables us to achieve larger-scale random quantum circuit sampling, with a system scale of up to 60 qubits and 24 cycles, and fidelity of F XEB = ( 3.66 ± 0.345 ) × 10 - 4 . The achieved sampling task is about 6 orders of magnitude more difficult than that of Sycamore [Nature 574, 505 (2019)] in the classic simulation, and 3 orders of magnitude more difficult than the sampling task on Zuchongzhi 2.0 [arXiv:2106.14734 (2021)]. The time consumption of classically simulating random circuit sampling experiment using state-of-the-art classical algorithm and supercomputer is extended to tens of thousands of years (about 4.8 × 10 4 years), while Zuchongzhi 2.1 only takes about 4.2 h, thereby significantly enhancing the quantum computational advantage.

On Unlimited Sampling and Reconstruction

Abstract: Shannon's sampling theorem, at the heart of digital signal processing, is well understood and explored. However, its practical realization still suffers from a fundamental bottleneck due to dynamic range limitations of the underlying analog–to–digital converters (ADCs). This results in clipping or saturation for signal amplitudes exceeding their maximum recordable voltage thus leading to a significant information loss. In this paper, we develop an alternative paradigm for sensing and recovery, called the Unlimited Sampling Framework . The key observation is that applying a modulo operation to the signal before the ADC prevents saturation; instead, one encounters a different type of information loss. Such a setup can be implemented, for example, via so-called folding or self-reset ADCs, as proposed in various contexts in the circuit design literature. The key challenge for this new type of information loss is to recover a bandlimited signal from its modulo samples. We derive conditions when perfect recovery is possible and complement them with a stable recovery algorithm. The required sampling density is independent of the maximum recordable ADC voltage and depends on the signal bandwidth only. Our guarantees extend to measurements affected by bounded noise, which includes round-off quantization. Numerical experiments validate our approach. For example, it is possible to recover functions with amplitudes orders of magnitude higher than the ADC's threshold from quantized modulo samples up to the unavoidable quantization error. Applications of the unlimited sampling paradigm can be found in a number of fields such as signal processing, communication and imaging.

Electronic Bottleneck Suppression in Next‐Generation Networks with Integrated Photonic Digital‐to‐Analog Converters

Abstract: Digital-to-analog converters (DAC) are indispensable functional units in signal processing instrumentation and wide-band telecommunication links for both civil and military applications. Since photonic systems are capable of high data throughput and low latency, an increasingly found system limitation stems from the required domain-crossing such as digital-to-analog, and electronic-to-optical. A photonic DAC implementation, in contrast, enables a seamless signal conversion with respect to both energy efficiency and short signal delay, often require bulky discrete optical components and electric-optic transformation hence introducing inefficiencies. Here, we introduce a novel coherent parallel photonic DAC concept along with an experimental demonstration capable of performing this digital-to-analog conversion without optic-electric-optic domain crossing. This design hence guarantees a linear intensity weighting among bits operating at high sampling rates, yet at a reduced footprint and power consumption compared to other photonic alternatives. Importantly, this photonic DAC could create seamless interfaces of next-generation data processing hardware for data-centers, task-specific compute accelerators such as neuromorphic engines, and network edge processing applications.

Compressed Sensing Framework for Heart Sound Acquisition in Internet of Medical Things

Abstract: For continuous monitoring of cardiovascular diseases, this paper presents a novel framework for heart sound acquisition. The proposed approach uses compressed sensing for signal sampling, and a two-stage reconstruction is developed for reconstruction. The first stage aims to give a tentative recovered signal, on which a peak detection technique is developed to identify whether there is a peak in current segment and, if so, its location. With such information, an adaptive dictionary is selected for the second round reconstruction. Because the selected dictionary is adaptive to the morphology of current frame, the signal reconstruction performance is consequently promoted. Experiment results indicate that a satisfactory performance can be obtained when the frame length is 256 and the signal morphology is divided into 16 categories. Furthermore, the proposed algorithm is compared with a series of counterparts and the results well demonstrate the advantages of our proposal, especially at high compression ratios.

Unlimited Sampling from Theory to Practice: Fourier-Prony Recovery and Prototype ADC

Abstract: Following the Unlimited Sampling strategy to alleviate the omnipresent dynamic range barrier, we study the problem of recovering a bandlimited signal from point-wise modulo samples, aiming to connect theoretical guarantees with hardware implementation considerations. Our starting point is a class of non-idealities that we observe in prototyping an unlimited sampling based analog-to-digital converter. To address these non-idealities, we provide a new Fourier domain recovery algorithm. Our approach is validated both in theory and via extensive experiments on our prototype analog-to-digital converter, providing the first demonstration of unlimited sampling for data arising from real hardware, both for the current and previous approaches. Advantages of our algorithm include that it is agnostic to the modulo threshold and it can handle arbitrary folding times. We expect that the end-to-end realization studied in this paper will pave the path for exploring the unlimited sampling methodology in a number of real world applications.

Periodic Event-Triggered Synchronization for Discrete-Time Complex Dynamical Networks

Abstract: In this article, we investigate the periodic event-triggered synchronization of discrete-time complex dynamical networks (CDNs). First, a discrete-time version of periodic event-triggered mechanism (ETM) is proposed, under which the sensors sample the signals in a periodic manner. But whether the sampling signals are transmitted to controllers or not is determined by a predefined periodic ETM. Compared with the common ETMs in the field of discrete-time systems, the proposed method avoids monitoring the measurements point-to-point and enlarges the lower bound of the inter-event intervals. As a result, it is beneficial to save both the energy and communication resources. Second, the ``discontinuous'' Lyapunov functionals are constructed to deal with the sawtooth constraint of sampling signals. The functionals can be viewed as the discrete-time extension for those discontinuous ones in continuous-time fields. Third, sufficient conditions for the ultimately bounded synchronization are derived for the discrete-time CDNs with or without considering communication delays, respectively. A calculation method for simultaneously designing the triggering parameter and control gains is developed such that the estimation of error level is accurate as much as possible. Finally, the simulation examples are presented to show the effectiveness and improvements of the proposed method.

Sampling and Remote Estimation for the Ornstein-Uhlenbeck Process Through Queues: Age of Information and Beyond

Abstract: Recently, a connection between the age of information and remote estimation error was found in a sampling problem of Wiener processes: If the sampler has no knowledge of the signal being sampled, the optimal sampling strategy is to minimize the age of information; however, by exploiting causal knowledge of the signal values, it is possible to achieve a smaller estimation error. In this paper, we generalize the previous study by investigating a problem of sampling a stationary Gauss-Markov process named the Ornstein-Uhlenbeck (OU) process, where we aim to find useful insights for solving the problems of sampling more general signals. The optimal sampling problem is formulated as a constrained continuous-time Markov decision process (MDP) with an uncountable state space. We provide an exact solution to this MDP: The optimal sampling policy is a threshold policy on instantaneous estimation error and the threshold is found. Further, if the sampler has no knowledge of the OU process, the optimal sampling problem reduces to an MDP for minimizing a nonlinear age of information metric. The age-optimal sampling policy is a threshold policy on expected estimation error and the threshold is found. In both problems, the optimal sampling policies can be computed by low-complexity algorithms (e.g., bisection search and Newton’s method), and the curse of dimensionality is circumvented. These results hold for (i) general service time distributions of the queueing server and (ii) sampling problems both with and without a sampling rate constraint. Numerical results are provided to compare different sampling policies.

Fuzzy intermittent extended dissipative control for delayed distributed parameter systems with stochastic disturbance: A spatial point sampling approach

Abstract: In this paper, the extended dissipative performance of distributed parameter systems (DPSs) with stochastic disturbances and multiple time-varying delays is studied by using a new fuzzy aperiodic intermittent sampled-data control strategy. Different from the previous fuzzy sampled-data control results, the state sampling of the proposed sampled-data controller occurs only in space and is intermittent rather than continuous in the time domain. By introducing a novel multi-time-delay-dependent switched Lyapunov functional to explore the dynamic characteristics of the controlled system, and by means of the famous Jensen's inequality with reciprocally convex approach, Wirtinger's inequality, the criterion of the system's mean square stabilization is established based on the LMI technique, which quantitatively reveals the relationship between the control period, the control length, and the upper bound of the control sampling interval. Especially, the optimal control gain is given by designing an optimized algorithm in the paper, which greatly reduces the cost. Finally, a numerical example is presented to demonstrate the effectiveness and superiority of the proposed approach.

Electronically steered metasurface antenna.

Abstract: Mobile devices, climate science, and autonomous vehicles all require advanced microwave antennas for imaging, radar, and wireless communications. We propose a waveguide-fed metasurface antenna architecture that enables electronic beamsteering from a lightweight circuit board with varactor-tuned elements. Our approach uses a unique feed structure and layout that enables spatial sampling at the Nyquist limit of half a wavelength. We detail the design of this Nyquist metasurface antenna and experimentally demonstrate electronic beamsteering in two directions. Nyquist metasurface antennas can realize high performance without costly and power hungry phase shifters, making them a compelling technology for future antenna hardware.

Microneedle Arrays for Sampling and Sensing Skin Interstitial Fluid

Abstract: Dermal interstitial fluid (ISF) is a novel source of biomarkers that can be considered as an alternative to blood sampling for disease diagnosis and treatment. Nevertheless, in vivo extraction and analysis of ISF are challenging. On the other hand, microneedle (MN) technology can address most of the challenges associated with dermal ISF extraction and is well suited for long-term, continuous ISF monitoring as well as in situ detection. In this review, we first briefly summarise the different dermal ISF collection methods and compare them with MN methods. Next, we elaborate on the design considerations and biocompatibility of MNs. Subsequently, the fabrication technologies of various MNs used for dermal ISF extraction, including solid MNs, hollow MNs, porous MNs, and hydrogel MNs, are thoroughly explained. In addition, different sensing mechanisms of ISF detection are discussed in detail. Subsequently, we identify the challenges and propose the possible solutions associated with ISF extraction. A detailed investigation is provided for the transport and sampling mechanism of ISF in vivo. Also, the current in vitro skin model integrated with the MN arrays is discussed. Finally, future directions to develop a point-of-care (POC) device to sample ISF are proposed.

A 16-Element Fully Integrated 28-GHz Digital RX Beamforming Receiver

Abstract: A 16-element fully integrated 28-GHz digital beamformer combines with a custom eight-layer LTCC substrate with a 4 $\times \,\,{4}$ patch antenna array for a complete 16-element single-chip 28-GHz millimeter-wave (mm-wave)-to-digital beamforming system. Sixteen-element digital beamforming in a single integrated circuit (IC) represents an excellent tradeoff between die size, signal loss, and I/O routing complexity. Per-element RX slices with an inductor-less mm-wave front end and 4 $\times $ parallel continuous-time bandpass delta–sigma analog-to-digital conversion (ADC) arrays enable compact mm-wave-to-digital conversion. The 4 $\times $ parallel ADC array provides in-built finite-impulse response (FIR) filtering for additional harmonic suppression and anti-alias filtering. ADC sampling of a high (1 GHz) IF facilitates single-phase mm-wave local oscillator (LO) routing and moves the I/Q mixing into the digital domain. Optimum bump and RX slice placement shortens LO and mm-wave signal routing and reduces signal loss. Bit-stream processing (BSP) takes advantage of the narrow bit-width raw outputs of the RX slices to implement digital beamforming with area- and energy-efficient MUXes. The prototype 16-element beamformer generates four independent, simultaneous beams. Over-the-air measurements confirm accurate 3-D beam patterns and indicate a measured overall noise figure of 7 dB and QAM-4 error vector magnitude (EVM) of −18 dB.

Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning

Abstract: Federated learning (FL) is a novel machine learning setting which enables on-device intelligence via decentralized training and federated optimization. The rapid development of deep neural networks facilitates the learning techniques for modeling complex problems and emerges into federated deep learning under the federated setting. However, the tremendous amount of model parameters burdens the communication network with a high load of transportation. This paper introduces two approaches for improving communication efficiency by dynamic sampling and top-$k$ selective masking. The former controls the fraction of selected client models dynamically, while the latter selects parameters with top-$k$ largest values of difference for federated updating. Experiments on convolutional image classification and recurrent language modeling are conducted on three public datasets to show the effectiveness of our proposed methods.

SAR Waveform and Mismatched Filter Design for Countering Interrupted-Sampling Repeater Jamming

Abstract: The interrupted-sampling repeater jamming (ISRJ) is coherent and has the characteristic of suppression and deception to degrade the synthetic aperture radar (SAR) image quality. The anti-ISRJ methods are studied in this work in order to suppress the ISRJ based on the waveform and filter design for SAR. First, the relationship between the ISRJ and waveform is obtained by analyzing the principle of the ISRJ using the ambiguity function. The ISRJ produces multiple false targets based on the high Doppler tolerance of waveform and the characteristic of the matched filter. Next, a method is proposed to counter the ISRJ by transmitting a phase-coded (PC) waveform with low Doppler tolerance and designing the corresponding mismatched filter. The joint design method is then developed to improve the anti-ISRJ and imaging performance. In the proposed methods, the majorization minimization framework is introduced to solve the nonconvex waveform and filter design problem. Finally, several simulations are conducted to demonstrate the effectiveness of the proposed methods. Simulation results show that the joint design method shows better anti-ISRJ and imaging performance in comparison with the separate design method, but it is more sensitive to the ISRJ sampling duty ratio and period.

False-Target Recognition Against Interrupted-Sampling Repeater Jamming Based on Integration Decomposition

Abstract: The interrupted-sampling repeater jamming (ISRJ) is a kind of intrapulse coherent deception jamming, it produces false-target peaks, which obfuscate the real target detection and tracking. In this article, a novel recognition method is proposed to identify the false-target peaks caused by ISRJ. The proposed method is realized via the integration decomposition of pulse compression, and the intermediate data in pulse compression are extracted to identify the false-target peaks. Due to the time-sharing transmit–receive antenna of the jammer, the jamming signal is short or discontinuous pieces compared with the real echo. This intrinsic property provides important feature for jamming identification. In the proposed method, a variance value is generated from the intermediate data, to evaluate the temporal energy distribution evenness of the signal component corresponding to each target peak. Real target echo corresponds to small variance whereas the jamming signal corresponds to large variance. In this way, false-target peaks caused by jamming signal can be identified efficiently. Besides, the computational cost of the proposed method is low, thus, it is suitable for real-time applications in practical radar systems. Numerical experiments under different jamming parameters demonstrate the promising performance of the proposed false-target recognition method. Moreover, Monte Carlo simulations under different SNR levels verify the reliable classification capability of the proposed method. In summary, this article provides a new perspective for ISRJ identification, and it is also the proof-of-concept example for other potential applications concerning integration decomposition.

Design and Control of a Highly Redundant Rigid-Flexible Coupling Robot to Assist the COVID-19 Oropharyngeal-Swab Sampling

Abstract: The outbreak of novel coronavirus pneumonia (COVID-19) has caused mortality and morbidity worldwide. Oropharyngeal-swab (OP-swab) sampling is widely used for the diagnosis of COVID-19 in the world. To avoid the clinical staff from being affected by the virus, we developed a 9-degree-of-freedom (DOF) rigid-flexible coupling (RFC) robot to assist the COVID-19 OP-swab sampling. This robot is composed of a visual system, UR5 robot arm, micro-pneumatic actuator and force-sensing system. The robot is expected to reduce risk and free up the clinical staff from the long-term repetitive sampling work. Compared with a rigid sampling robot, the developed force-sensing RFC robot can facilitate OP-swab sampling procedures in a safer and softer way. In addition, a varying-parameter zeroing neural network-based optimization method is also proposed for motion planning of the 9-DOF redundant manipulator. The developed robot system is validated by OP-swab sampling on both oral cavity phantoms and volunteers.

Synchronization of Complex Dynamical Networks Subject to Noisy Sampling Interval and Packet Loss.

Abstract: This article focuses on the sampled-data synchronization issue for a class of complex dynamical networks (CDNs) subject to noisy sampling intervals and successive packet losses. The sampling intervals are subject to noisy perturbations, and categorical distribution is used to characterize the sampling errors of noisy sampling intervals. By means of the input delay approach, the CDN under consideration is first converted into a delay system with delayed input subject to dual randomness and probability distribution characteristic. To verify the probability distribution characteristic of the delayed input, a novel characterization method is proposed, which is not the same as that of some existing literature. Based on this, a unified framework is then established. By recurring to the techniques of stochastic analysis, a probability-distribution-dependent controller is designed to guarantee the mean-square exponential synchronization of the error dynamical network. Subsequently, a special model is considered where only the lower and upper bounds of delayed input are utilized. Finally, to verify the analysis results and testify the effectiveness and superiority of the designed synchronization algorithm, a numerical example and an example using Chua's circuit are given.

Task-Based Analog-to-Digital Converters

Abstract: Obtaining digital representations of multivariate continuous-time (CT) signals is a challenge encountered in many signal processing systems. In practice, these signals are often acquired to extract some underlying information, i.e., for a specific task. Employing conventional task-agnostic analog-to-digital converters (ADCs), typically designed to minimize the mean squared error (MSE) in reconstructing the CT input signal, can be costly and energy-inefficient in such cases. In this work, we study task-based ADCs, which are designed to obtain a digital representation of a multivariate CT input process with the goal of recovering an underlying statistically related parameter vector, referred to as the task . The proposed system employs analog filtering, uniform sampling, and scalar uniform quantization of the input process before recovering the task vector using a linear digital recovery filter. We optimize the analog and digital filters and derive closed-form expressions for the achievable MSE in recovering the task vector from a set of analog signals when utilizing ADCs with a fixed sampling rate and amplitude resolution. Based on our derivation, we provide guidelines for designing practical acquisition systems subject to a constraint on the bit rate. Our analysis proves that the intuitive approaches of either recovering the task vector solely in digital or designing the analog filter to estimate the task vector are inferior to the proposed joint design. We then consider the recovery of a set of matched filter outputs under a rate budget. We numerically verify our theoretical observations and demonstrate that task-based ADCs substantially outperform analog matched filtering as well as applying the matched filter solely in the digital domain. When acquiring signals for a task under tight bit budgets, we also show that it is often preferable to sample below the Nyquist rate instead of reducing the quantization resolution.

Robust Predictive Control for Modular Solid-State Transformer With Reduced DC Link and Parameter Mismatch

Abstract: This article presents the analysis and implementation of a predictive control method for dc-link regulation and voltage balance in a cascaded modular reduced dc-link solid-state transformer (SST). Passive components like bulky dc links limit the power density of power converters, especially medium-voltage (MV) SST. Reduced dc-link or low-inertia converters can dramatically reduce the size, cost, and weight by tolerating larger dc-link ripples and improve the reliability with electrolytic capacitor-less dc link. However, a small dc link leads to tight coupling between the input and the output stages, which is a challenge for control design. In stacked low-inertia converters (SLIC), the low-inertia converter modules are stacked for MV applications, resulting in coupling between the modules and making the control more challenging. A new model predictive control method that can achieve deadbeat regulation on the dc link without weighting factors has been proposed to address this novel problem. This article focuses on analyzing the condition of the low-inertia dc link up to 80% ripple, the robustness of the control under parameter mismatches, high-order terms, and important implementation issues, such as model-based sampling and computation delay compensation. Significantly, the high-order terms are introduced because of the large dc-link ripple. These high-order terms are unique to the SLIC and negligible in conventional high-inertia converters. A discrete-time large-signal model is built to capture the dc-link's nonlinear dynamics, and the eigenvalues of a small-signal Jacobian matrix are analyzed with Floquet theory to evaluate stability, using the modular soft-switching SST (M-S4T) as an example of the SLIC. Simulation and experimental results of an MVDC M-S4T verify the analysis and the predictive control method. Finally, the general application of the predictive control to low-inertia converters is compared against a conventional PI controller using a reduced dc-link active-front-end rectifier as an example.

Nonlinear calibration of frequency modulated continuous wave LIDAR based on a microresonator soliton comb.

Abstract: Traditional frequency modulated continuous wave (FMCW) LIDAR ranging is based on heterodyne detection, calculating unknown distance by extracting the frequency of the interference signal, while the main error source is frequency modulation (FM) nonlinearity. In this paper, a ranging system based on a microresonator soliton comb is demonstrated to correct the nonlinearity by sampling the ranging signals at equal frequency intervals, producing a ranging error lower than 20 µm, while at the range of 2 m. Advantages of fast data acquisition, light computation requirements, and a simple optical path, without long optical fiber, give this method a high practical value in precision manufacturing.

Adaptive Dual-Input Analog RF Predistorter for Wideband 5G Communication Systems

Abstract: Advanced communication technologies like 5G focuses on the wideband signals and high data rate. The quality of wideband signal transmission deteriorates due to the nonlinear characteristics of the power amplifiers (PA). The digital predistortion (DPD) method reduces the distortion and spectrum regrowth, but the sampling speed of the converters restricts its operation to support 5 to 7 times the signal bandwidth. The analog predistortion (APD) method is not limited by the bandwidth of the distorted signal, but its linearization performance is inferior to the DPD. This paper presents an adaptive dual-input APD that establishes the benefit of digital control over the variable parameters to linearize the wideband signals. For the LTE signal with a bandwidth of 20 MHz and RF frequency of 3.5 GHz, the proposed method achieves an ACPR improvement of 24.01 dB. For another LTE-A signal with 100 MHz bandwidth, the proposed linearizer achieves an ACPR improvement of 9.2 dB and 15.7 dB over PA with and without conventional APD. Though the predistorter (PD) is designed for 3.5 GHz, it is reported that due to the wideband operation of APD, digital adaptation allows satisfactory performance for carrier frequency from 2 to 4 GHz.

A 4-GS/s 10-ENOB 75-mW Ringamp ADC in 16-nm CMOS With Background Monitoring of Distortion

Abstract: A $4\times $ interleaved pipelined ADC for direct-RF sampling applications is presented. It leverages the performance advantages of ring amplifiers to unlock greater architectural freedom. The first pipeline stage MDAC with a “passive-hold” mode eliminates the sub-ADC sampling path and associated problems. A high-speed ringamp topology employs digital bias control, robust common-mode feedback (CMFB), and an elegant self-resetting behavior. An asynchronous, event-driven timing control system improves several aspects of performance and enables fully dynamic power consumption and modular design re-use. A general technique is presented whereby the signal-to-distortion ratio (SDR) of any amplifier in the system can be measured in the background with an analog hardware overhead of only one comparator. In this amplifier-intensive architecture utilizing 36 ringamps, the 4-GS/s ADC fabricated in 16-nm CMOS achieves 62-dB SNDR and 75-dB SFDR at Nyquist, consumes 75 mW (including input buffer), and has a Walden figure of merit (FoM) of 18 fJ/conversion-step and a Schreier FoM of 166 dB, advancing the state of the art in direct-RF sampling ADCs by roughly an order of magnitude.