Measurement and Data Correction of Channel Sampling Timing Walk-Off of Photonic Analog-to-Digital Converter in Signal Recovery

A two-channel, time-wavelength interleaved photonic analog-to-digital converter (PADC) system with a sampling rate of 10.4 GSa/s was established, and a concise method for measuring and data correcting the channel sampling timing walk-off of PADCs for signal recovery was proposed. The measurements show that for the two RF signals of f1 = 100 MHz and f2 = 200 MHz, the channel sampling timing walk-off was 12 sampling periods, which results in an ENOB = -0.1051 bits for the 100 MHz directly synthesized signal, while the ENOB improved up to 4.0136 bits using shift synthesis. In addition, the peak limit method (PLM) and normalization processing were introduced to reduce the impacts of signal peak jitter and power inconsistency between two channels, which further improve the ENOB of the 100 MHz signal up to 4.5668 bits. All signals were analyzed and discussed in both time and frequency domains. The 21.1 GHz signal was also collected and converted using the established two-channel PADC system with the data correction method, combining the PLM, normalization, and shift synthesis, showing that the ENOB increased from the initial -0.9181 to 4.1913 bits, which demonstrates that our method can be effectively used for signal recovery in channel-interleaved PADCs.

Effects of Optical Sampling Pulse Power, RF Power, and Electronic Back-End Bandwidth on the Performance of Photonic Analog-to-Digital Converter

The effects of optical sampling pulse power, RF power, and electronic back-end bandwidth on the performance of time- and wavelength-interleaved photonic analog-to-digital converter (PADC) with eight-channel 41.6 GHz pulses have been experimentally investigated in detail. The effective number of bits (ENOB) and peak-to-peak voltage (Vpp) of converted 10.6 GHz electrical signals were used to characterize the effects. For the 1550.116 nm channel with 5.2 G samples per second, an average pulse power of 0 to -10 dBm input to the photoelectric detector (PD) has been tested. The Vpp increased with increasing pulse power. And the ENOB for pulse power -9~-3 dBm was almost the same and all were greater than four. Meanwhile, the ENOB decreased either when the pulse power was more than -2 dBm due to the saturation of PD or when the pulse power was less than -10 dBm due to the non-ignorable noise relative to the converted weak signal. In addition, RF powers of -10~15 dBm were loaded into the Mach-Zehnder modulator (MZM). The Vpp increased with the increase in RF power, and the ENOB also showed an increasing trend. However, higher RF power can saturate the PD and induce greater nonlinearity in MZM, leading to a decrease in ENOB, while lower RF power will convert weak electrical signals with more noise, also resulting in lower ENOB. In addition, the back-end bandwidths of 0.2~8 GHz were studied in the experiments. The Vpp decreased as the back-end bandwidth decreased from 8 to 3 GHz, and remained nearly constant for the bandwidth between the Nyquist bandwidth and the subsampled RF signal frequency. The ENOB was almost the same and all greater than four for a bandwidth from 3 to 8 GHz, and gradually increased up to 6.5 as the back-end bandwidth decreased from the Nyquist bandwidth to 0.25 GHz. A bandwidth slightly larger than the Nyquist bandwidth was recommended for low costs and without compromising performance. In our experiment, the -3 to -5 dBm average pulse power, about 10 dBm RF power, and 3 GHz back-end bandwidth were recommended to accomplish both a high ENOB more than four and large Vpp. Our research provides a solution for selecting optical sampling pulse power, RF power, and electronic back-end bandwidth to achieve low-cost and high-performance PADC.

Compressive Sensing via Variational Bayesian Inference under Two Widely Used Priors: Modeling, Comparison and Discussion

Compressive sensing is a sub-Nyquist sampling technique for efficient signal acquisition and reconstruction of sparse or compressible signals. In order to account for the sparsity of the underlying signal of interest, it is common to use sparsifying priors such as Bernoulli-Gaussian-inverse Gamma (BGiG) and Gaussian-inverse Gamma (GiG) priors on the components of the signal. With the introduction of variational Bayesian inference, the sparse Bayesian learning (SBL) methods for solving the inverse problem of compressive sensing have received significant interest as the SBL methods become more efficient in terms of execution time. In this paper, we consider the sparse signal recovery problem using compressive sensing and the variational Bayesian (VB) inference framework. More specifically, we consider two widely used Bayesian models of BGiG and GiG for modeling the underlying sparse signal for this problem. Although these two models have been widely used for sparse recovery problems under various signal structures, the question of which model can outperform the other for sparse signal recovery under no specific structure has yet to be fully addressed under the VB inference setting. Here, we study these two models specifically under VB inference in detail, provide some motivating examples regarding the issues in signal reconstruction that may occur under each model, perform comparisons and provide suggestions on how to improve the performance of each model.

ECG Monitoring Based on Dynamic Compressed Sensing of Multi-Lead Signals

This paper presents an innovative method for multiple lead electrocardiogram (ECG) monitoring based on Compressed Sensing (CS). The proposed method extends to multiple leads signals, a dynamic Compressed Sensing method, that were previously developed on a single lead. The dynamic sensing method makes use of a sensing matrix in which its elements are dynamically obtained from the signal to be compressed. In this method, for the application to multiple leads, it is proposed to use a single sensing matrix for which its elements are obtained from a combination of multiple leads. The proposed method is evaluated on a wide set of signals and acquired on healthy subjects and on subjects affected by different pathologies, such as myocardial infarction, cardiomyopathy, and bundle branch block. The experimental results demonstrated that the proposed method can be adopted for a Compression Ratio (CR) up to 10, without compromising signal quality. In particular, for CR= 10, it exhibits a percentage of root-mean-squared difference average among a wide set of ECG signals lower than 3%.

Analytical Blind Beamforming for a Multi-Antenna UAV Base-Station Receiver in Millimeter-Wave Bands

Over the last decade, unmanned aerial vehicles (UAVs) with antenna arrays have usually been employed for the enhancement of wireless communication in millimeter-wave bands. They are commonly used as aerial base stations and relay platforms in order to serve multiple users. Many beamforming methods for improving communication quality based on channel estimation have been proposed. However, these methods can be resource-intensive due to the complexity of channel estimation in practice. Thus, in this paper, we formulate an MIMO blind beamforming problem at the receivers for UAV-assisted communications in which channel estimation is omitted in order to save communication resources. We introduce one analytical method, which is called the analytical constant modulus algorithm (ACMA), in order to perform blind beamforming at the UAV base station; this relies only on data received by the antenna. The feature of the constant modulus (CM) is employed to restrict the target user signals. Algebraic operations, such as singular value decomposition (SVD), are applied to separate the user signal space from other interferences. The number of users in the region served by the UAV can be detected by exploring information in the measured data. We seek solutions that are expressible as one Kronecker product structure in the signal space; then, the beamformers that correspond to each user can be successfully estimated. The simulation results show that, by using this analytically derived blind method, the system can achieve good signal recovery accuracy, a reasonable system sum rate, and acceptable complexity.

Application of an integrated radio-frequency/shim coil technology for signal recovery in fMRI

PURPOSE

Gradient-echo echo-planar imaging (EPI), which is typically used for blood oxygenation level-dependent (BOLD) functional MRI (fMRI), suffers from distortions and signal loss caused by localized B₀ inhomogeneities. Such artifacts cannot be effectively corrected for with the low-order spherical harmonic (SH) shim coils available on most scanners. The integrated parallel reception, excitation, and shimming (iPRES) coil technology allows radiofrequency (RF) and direct currents to flow on each coil element, enabling imaging and localized B₀ shimming with one coil array. iPRES was previously used to correct for distortions in spin-echo EPI and is further developed here to also recover signal loss in gradient-echo EPI.

METHODS

The cost function in the shim optimization, which typically uses a single term representing the B₀ inhomogeneity, was modified to include a second term representing the signal loss, with an adjustable weight to optimize the trade-off between distortion correction and signal recovery. Simulations and experiments were performed to investigate the shimming performance.

RESULTS

Slice-optimized shimming with iPRES and the proposed cost function substantially reduced the signal loss in the inferior frontal and temporal brain regions compared to shimming with iPRES and the original cost function or 2^nd -order SH shimming with either cost function. In breath-holding fMRI experiments, the ΔB₀ and signal loss root-mean-square errors decreased by -34.3% and -56.2%, whereas the EPI signal intensity and number of activated voxels increased by 60.3% and 174.0% in the inferior frontal brain region.

CONCLUSION

iPRES can recover signal loss in gradient-echo EPI, which is expected to improve BOLD fMRI studies in brain regions suffering from signal loss.

ZTE imaging with long-T2 suppression

Three-dimensional radial zero echo time (ZTE) imaging enables efficient direct MRI of tissues with rapid transverse relaxation. Yet, the feature of capturing signals with a wide range of T2 and T2 * values is accompanied by a lack of contrast between the corresponding tissues. In particular, the targeted short-T2 tissues may not be easily identified, and various approaches have been proposed to generate T2 contrast by reducing the long-T2 signal of water and/or fat. The aim of this work was to provide efficient long-T2 suppression for selective direct MRI of short-T2 tissues using the ZTE technique. For magnetization preparation, suppression pulses for water and fat were designed to provide both good T2 selectivity and off-resonance performance. To obtain high efficiency at short TRs, the pulses were applied in a segmented sequence scheme with minimized timing overhead, thus leading to a quasi-steady state of magnetization. The sequence timing was adjusted for optimal tissue contrast in musculoskeletal applications by means of simulations and experiments, incorporating both T2 and T1 of the involved tissues. The developed technique was employed for imaging of a lamb joint sample at 4.7 T. ZTE images were obtained with effective suppression of signals from tissues with long-T2 water, such as muscle or articular spaces, and fat. Hence, primarily short-T2 tissues were visible, such as bone and tendon. The MR image intensity of bone showed strong similarity with bone density imaged with micro-computed tomography.

Signal Discrimination Between Fluorescent Proteins in Live Cells by Long-wavelength Optical Modulation

Fluorescent proteins (FPs) have revolutionized molecular and cellular biology; yet, discrimination over cellular autofluorescence, spectral deconvolution, or detection at low concentrations remain challenging problems in many biological applications. By optically depopulating a photoinduced dark state with orange secondary laser co-excitation, the higher-energy green AcGFP fluorescence is dynamically increased. Modulating this secondary laser then modulates the higher-energy, collected fluorescence; enabling its selective detection by removing heterogeneous background from other FPs. Order-of-magnitude reduction in obscuring fluorophore background emission has been achieved in both fixed and live cells. This longwavelength modulation expands the dimensionality to discriminate FP emitters based on dark state lifetimes and enables signal of interest to be recovered by removing heterogeneous background emitter signals. Thus, AcGFP is not only useful for extracting weak signals from systems plagued by high background, but it is a springboard for further FP optimization and utilization for improving sensitivity and selectivity in biological fluorescence imaging.