Noise Tradeoff Research Articles

Fuel consumption and trailing-edge noise tradeoff studies via mission-based airfoil shape optimization

Aerodynamic drag is often used as a proxy for fuel consumption in aircraft design. However, the aeroacoustic byproduct of airfoil design, such as trailing-edge noise (TEN), is yet to be considered in aircraft applications, despite its inclusion in wind turbine designs. To explore the impact of TEN in airfoil design for aircraft, we establish a mission-based aerodynamic shape optimization framework. A surrogate-based detailed flight mission analysis (that can simulate a flight mission from takeoff to landing) is incorporated into the optimization framework to obtain realistic flight conditions during takeoff and landing—which are important to assess TEN metrics—and calculate accurate fuel consumption, where both are used to evaluate the objective function. To assess its effectiveness, we compare the results against those of the conventional single-point optimization. Not surprisingly, the mission-based optimization simultaneously reduces fuel consumption and TEN, while the single-point optima (performed at the low Mach region) reduce TEN at the expense of higher fuel consumption. This is due to the tradeoff between aerodynamic performance in the transonic region and aeroacoustic performance in the low Mach region, which cannot be properly analyzed without including flight mission analysis in the optimization loop.

A Low-Noise Low-Power 0.001Hz-1kHz Neural Recording System-on-Chip With Sample-Level Duty-Cycling.

Advances in brain-machine interfaces and wearable biomedical sensors for healthcare and human-computer interactions call for precision electrophysiology to resolve a variety of biopotential signals across the body that cover a wide range of frequencies, from the mHz-range electrogastrogram (EGG) to the kHz-range electroneurogram (ENG). Existing integrated wearable solutions for minimally invasive biopotential recordings are limited in detection range and accuracy due to trade-offs in bandwidth, noise, input impedance, and power consumption. This article presents a 16-channel wide-band ultra-low-noise neural recording system-on-chip (SoC) fabricated in 65nm CMOS for chronic use in mobile healthcare settings that spans a bandwidth of 0.001 Hz to 1 kHz through a featured sample-level duty-cycling (SLDC) mode. Each recording channel is implemented by a delta-sigma analog-to-digital converter (ADC) achieving 1.0 μ V rms input-referred noise over 1Hz-1kHz bandwidth with a Noise Efficiency Factor (NEF) of 2.93 in continuous operation mode. In SLDC mode, the power supply is duty-cycled while maintaining consistently low input-referred noise levels at ultra-low frequencies (1.1 μV rms over 0.001Hz-1Hz) and 435 M Ω input impedance. The functionalities of the proposed SoC are validated with two human electrophysiology applications: recording low-amplitude electroencephalogram (EEG) through electrodes fixated on the forehead to monitor brain waves, and ultra-slow-wave electrogastrogram (EGG) through electrodes fixated on the abdomen to monitor digestion.

Power efficient ReLU design for neuromorphic computing using spin Hall effect

We demonstrate that a magnetic tunnel junction injected with a spin Hall current can exhibit linear rotation of the magnetization of the free-ferromagnet using only the spin current. Using the linear resistance change of the magnetic tunnel junction (MTJ), we devise a circuit for the rectified linear activation (ReLU) function of the artificial neuron. We explore the role of different spin Hall effect (SHE) heavy metal (HM) layers on the power consumption of the ReLU circuit. We benchmark the power consumption of the ReLU circuit with different SHE layers by defining a new parameter called the spin Hall power factor. It combines the spin Hall angle, resistivity, and thickness of the HM layer, which translates to the power consumption of the different SHE layers during spin-orbit switching/rotation of the free FM. We employ a hybrid spintronics-CMOS simulation framework that couples Keldysh non-equilibrium Green’s function formalism with Landau–Lifshitz–Gilbert–Slonzewski equations and the HSPICE circuit simulator to account for the diverse physics of spin-transport and the CMOS elements in our proposed ReLU design. We also demonstrate the robustness of the proposed ReLU circuit against thermal noise and a non-trivial power-error trade-off that enables the use of an unstable free-ferromagnet for energy-efficient design. Using the proposed circuit, we evaluate the performance of the convolutional neural network for MNIST datasets and demonstrate comparable classification accuracies to the ideal ReLU with an energy consumption of 75 pJ per sample.

Noise-Aware FET Circuit Design Based on C/ID-Invariant

This article presents an analytical approach, supported by experimental data, to design analog circuits for given noise and speed specifications. As an extension to traditional design methodologies, i.e., Inversion Coefficient (IC) and gm/ID (GmID), the proposed approach aims to gain more in-depth insight, while providing appropriate analytical means for circuit design, at lower levels of computing complexity. Based on a recently proposed design approach, the C/ID method relies on device characteristic capacitance, i.e., CS = CS/ID as an invariant, where CS refers to the Field-Effect Transistor (FET) device self-loading capacitance. C/ID provides the opportunity to derive closed-form solutions to some key design optimization problems such as power versus Gain Bandwidth (GBW) and noise trade-offs. Examples for common-source amplifiers and Ring Oscillators (RO) have been provided. A set of current-steering ROs has been designed and fabricated in 0.18 lm technology based on the proposed flow. The circuit Phase Noise (PN) was characterized and compared to the predictions of the proposed design methodology, demonstrating favorable matching.

Impact of TOF on Brain PET With Short-Lived 11C-Labeled Tracers Among Suspected Patients With AD/PD: Using Hybrid PET/MRI.

ObjectiveTo explore the impact of the time-of-flight (TOF) reconstruction on brain PET with short-lived 11C-labeled tracers in PET magnetic resonance (PET/MR) brain images among suspected patients with Alzheimer's and Parkinson's disease (AD/PD).MethodsPatients who underwent 11C-2-ß-carbomethoxy-3-b-(4-fluorophenyl) tropane (11C-CFT) and 2-(4-N-[11C] methylaminophenyl)-6-hydroxybenzothiazole (11C-PiB) PET/MRI were retrospectively included in the study. Each PET LIST mode data were reconstructed with and without the TOF reconstruction algorithm. Standard uptake values (SUVs) of Caudate Nucleus (CN), Putamen (PU), and Whole-brain (WB) were measured. TOF and non-TOF SUVs were assessed by using paired t-test. Standard formulas were applied to measure contrast, signal-to-noise ratio (SNR), and percentage relative average difference of SUVs (%RAD-SUVs).ResultsTotal 75 patients were included with the median age (years) and body mass index (BMI-kg/m2) of 60.2 ± 10.9 years and 23.9 ± 3.7 kg/m2 in 11C-CFT (n = 41) and 62.2 ± 6.8 years and 24.7 ± 2.9 kg/m2 in 11C-PiB (n = 34), respectively. Higher average SUVs and positive %RAD-SUVs were observed in CN and PU in TOF compared with non-TOF reconstructions for the two 11C-labeled radiotracers. Differences of SUVmean were significant (p < 0.05) in CN and PU for both 11C-labeled radiotracers. SUVmax was enhanced significantly in CN and PU for 11C-CFT and CN for 11C-PiB, but not in PU. Significant contrast enhancement was observed in PU for both 11C-labeled radiotracers, whereas SNR gain was significant in PU, only for 11C-PiB in TOF reconstruction.ConclusionTime-of-flight leads to a better signal vs. noise trade-off than non-TOF in 11C-labeled tracers between CN and PU, improving the SUVs, contrast, and SNR, which were valuable for reducing injected radiation dose. Improved timing resolution aided the rapid decay rate of short-lived 11C-labeled tracers, and it shortened the scan time, increasing the patient comfort, and reducing the motion artifact among patients with AD/PD. However, one should adopt the combined TOF algorithm with caution for the quantitative analysis because it has different effects on the SUVmax, contrast, and SNR of different brain regions.

Image Quality and Diagnostic Performance of Accelerated Shoulder MRI With Deep Learning-Based Reconstruction.

BACKGROUND. Shoulder MRI using standard multiplanar sequences requires long scan times. Accelerated sequences have tradeoffs in noise and resolution. Deep learning-based reconstruction (DLR) may allow reduced scan time with preserved image quality. OBJECTIVE. The purpose of this study was to compare standard shoulder MRI sequences and accelerated sequences without and with DLR in terms of image quality and diagnostic performance. METHODS. This retrospective study included 105 patients (45 men, 60 women; mean age, 57.6 ± 10.9 [SD] years) who underwent a total of 110 3-T shoulder MRI examinations. Examinations included standard sequences (scan time, 9 minutes 23 seconds) and accelerated sequences (3 minutes 5 seconds; 67% reduction), both including fast spin-echo sequences in three planes. Standard sequences were reconstructed using the conventional pipeline; accelerated sequences were reconstructed using both the conventional pipeline and a commercially available DLR pipeline. Two radiologists independently assessed three image sets (standard sequence, accelerated sequence without DLR, and accelerated sequence with DLR) for subjective image quality and artifacts using 4-point scales (4 = highest quality) and identified pathologies of the subscapularis tendon, supraspinatus-infraspinatus tendon, long head of the biceps brachii tendon, and glenoid labrum. Interobserver agreement and agreement between image sets for the evaluated pathologies were assessed using weighted kappa statistics. In 27 patients who underwent arthroscopy, diagnostic performance was calculated using arthroscopic findings as a reference standard. RESULTS. Mean subjective image quality scores for readers 1 and 2 were 10.6 ± 1.2 and 10.5 ± 1.4 for the standard sequence, 8.1 ± 1.3 and 7.2 ± 1.1 for the accelerated sequence without DLR, and 10.7 ± 1.2 and 10.5 ± 1.6 for the accelerated sequence with DLR. Mean artifact scores for readers 1 and 2 were 9.3 ± 1.2 and 10.0 ± 1.0 for the standard sequence, 7.3 ± 1.3 and 9.1 ± 0.8 for the accelerated sequence without DLR, and 9.4 ± 1.2 and 9.8 ± 0.8 for the accelerated sequence with DLR. Interobserver agreement ranged from kappa of 0.813-0.951 except for accelerated sequence without DLR for the supraspinatus-infraspinatus tendon (κ = 0.673). Agreement between image sets ranged from kappa of 0.809-0.957 except for reader 1 for supraspinatus-infraspinatus tendon (κ = 0.663-0.700). Accuracy, sensitivity, and specificity for tears of the four structures were not different (p > .05) among image sets. CONCLUSION. Accelerated sequences with DLR provide 67% scan time reduction with similar subjective image quality, artifacts, and diagnostic performance to standard sequences. CLINICAL IMPACT. Accelerated sequences with DLR may provide an alternative to standard sequences for clinical shoulder MRI.

Diversity-enabled sweet spots in layered architectures and speed–accuracy trade-offs in sensorimotor control

Nervous systems sense, communicate, compute, and actuate movement using distributed components with severe trade-offs in speed, accuracy, sparsity, noise, and saturation. Nevertheless, brains achieve remarkably fast, accurate, and robust control performance due to a highly effective layered control architecture. Here, we introduce a driving task to study how a mountain biker mitigates the immediate disturbance of trail bumps and responds to changes in trail direction. We manipulated the time delays and accuracy of the control input from the wheel as a surrogate for manipulating the characteristics of neurons in the control loop. The observed speed-accuracy trade-offs motivated a theoretical framework consisting of two layers of control loops-a fast, but inaccurate, reflexive layer that corrects for bumps and a slow, but accurate, planning layer that computes the trajectory to follow-each with components having diverse speeds and accuracies within each physical level, such as nerve bundles containing axons with a wide range of sizes. Our model explains why the errors from two control loops are additive and shows how the errors in each control loop can be decomposed into the errors caused by the limited speeds and accuracies of the components. These results demonstrate that an appropriate diversity in the properties of neurons across layers helps to create "diversity-enabled sweet spots," so that both fast and accurate control is achieved using slow or inaccurate components.

A Time-Domain 147fsrms 2.5-MHz Bandwidth Two-Step Flash-MASH 1-1-1 Time-to-Digital Converter With Third-Order Noise-Shaping and Mismatch Correction

A 50 MS/s two-step flash-MASH 1-1-1 time-to-digital converter (TDC) employing a two-channel time-interleaved time-domain register with an implicit adder/subtractor realizes an error-feedback topology. Such an error-feedback unit of 1st-order noise-shaping TDC can be cascaded as a multi-stage noise shaping (MASH) configuration to achieve higher-order noise-shaping and, thereby high resolution. This paper also discusses different noise sources, linearity and noise tradeoffs in noise-shaping TDC and then demonstrates a histogram testing technique to correct the mismatch of 1st stage flash TDC. An on/off-chip delay modulation (DM) measurement technique is presented to characterize the TDC linearity and noise performance. Fabricated in 40-nm CMOS technology, the proposed TDC consumes 1.32 mW from a 1.1 V supply. At frequency below 2.5 MHz, the TDC error integrates to 147fsrms, which is equal to equivalent flash resolution of 1.6 ps.

Improved myocardial perfusion PET imaging using artificial neural networks

Myocardial perfusion (MP) PET imaging plays a key role in risk assessment and stratification of patients with coronary artery disease. In this work, we proposed a patch-based artificial neural network (ANN) fusion approach that integrates information from the ML and the post-smoothed ML reconstruction to improve MP PET imaging. The proposed method was applied to images reconstructed from different noise levels to enhance quantification and task-based MP defect detection. Using the XCAT phantom, we simulated three MP PET imaging cases, one with normal perfusion and the other two with non-transmural and transmural regionally reduced perfusion of the left ventricular (LV) myocardium. The proposed ANN fusion technique was quantitatively evaluated in terms of the noise versus bias and noise versus contrast tradeoff, and compared with the post-smoothed ML reconstruction. Using the channelized Hotelling observer, we evaluated the detectability of the non-transmural and transmural defects through the receiver operating characteristic analysis. The quantitative results demonstrated that the ANN enhancement method reduced bias and improved contrast while reaching comparable noise to what the post-smoothed ML reconstruction achieved. Moreover, the ANN fusion technique significantly improved the defect detectability of both the non-transmural and transmural defects. In addition to the simulation study, we further evaluated the proposed method using patient data. Compared with the post-smoothed ML reconstruction, the ANN fusion improved the tradeoff between noise and the mean value on the LV myocardium, indicating its potential clinical application in MP PET imaging.

Generative adversarial network based regularized image reconstruction for PET

Positron emission tomography (PET) is an ill-posed inverse problem and suffers high noise due to limited number of detected events. Prior information can be used to improve the quality of reconstructed PET images. Deep neural networks have also been applied to regularized image reconstruction. One method is to use a pretrained denoising neural network to represent the PET image and to perform a constrained maximum likelihood estimation. In this work, we propose to use a generative adversarial network (GAN) to further improve the network performance. We also modify the objective function to include a data-matching term on the network input. Experimental studies using computer-based Monte Carlo simulations and real patient datasets demonstrate that the proposed method leads to noticeable improvements over the kernel-based and U-net-based regularization methods in terms of lesion contrast recovery versus background noise trade-offs.

A Reconfigurable Analog Baseband Circuitry for LFMCW RADAR Receivers in 130-nm SiGe BiCMOS Process

A highly reconfigurable open-loop analog baseband circuitry with programmable gain, bandwidth and filter order are proposed for integrated linear frequency modulated continuous wave (LFMCW) radar receivers in this paper. This analog baseband chain allocates noise, gain and channel selection specifications to different stages, for the sake of noise and linearity tradeoffs, by introducing a multi-stage open-loop cascaded amplifier/filter topology. The topology includes a course gain tuning pre-amplifier, a folded Gilbert variable gain amplifier (VGA) with a symmetrical dB-linear voltage generator and a 10-bit R-2R DAC for fine gain tuning, a level shifter, a programmable Gm-C low pass filter, a DC offset cancellation circuit, two fixed gain amplifiers with bandwidth extension and a novel buffer amplifier with active peaking for testing purposes. The noise figure is reduced with the help of a low noise pre-amplifier stage, while the linearity is enhanced with a power-efficient buffer and a novel high linearity Gm-C filter. Specifically, the Gm-C filter improves its linearity specification with no increase in power consumption, thanks to an alteration of the trans-conductor/capacitor connection style, instead of pursuing high linearity but power-hungry class-AB trans-conductors. In addition, the logarithmic bandwidth tuning technique is adopted for capacitor array size minimization. The linear-in-dB and DAC gain control topology facilitates the analog baseband gain tuning accuracy and stability, which also provides an efficient access to digital baseband automatic gain control. The analog baseband chip is fabricated using 130-nm SiGe BiCMOS technology. With a power consumption of 5.9~8.8 mW, the implemented circuit achieves a tunable gain range of −30~27 dB (DAC linear gain step guaranteed), a programmable −3 dB bandwidth of 18/19/20/21/22/23/24/25 MHz, a filter order of 3/6 and a gain resolution of better than 0.07 dB.

Benefits of Using a Spatially-Variant Penalty Strength With Anatomical Priors in PET Reconstruction.

In this study, we explore the use of a spatially-variant penalty strength in penalized image reconstruction using anatomical priors to reduce the dependence of lesion contrast on surrounding activity and lesion location. This work builds on a previous method to make the local perturbation response (LPR) approximately spatially invariant. While the dependence of lesion contrast on the local properties introduced by the anatomical penalty is intentional, the method aims to reduce the influence from surroundings lying along the lines of response (LORs) but not in the penalty neighborhood structure. The method is evaluated using simulated data, assuming that the anatomical information is absent or well-aligned with the corresponding activity images. Since the parallel level sets (PLS) penalty is convex and has shown promising results in the literature, it is chosen as the representative anatomical penalty and incorporated into the previously proposed preconditioned algorithm (L-BFGS-B-PC) for achieving good image quality and fast convergence rate. A 2D disc phantom with a feature at the center and a 3D XCAT thorax phantom with lesions inserted in different slices are used to study how surrounding activity and lesion location affect the visual appearance and quantitative consistency. A bias and noise analysis is also performed with the 2D disc phantom. The consistency of the algorithm convergence rate with respect to different data noise and background levels is also investigated using the XCAT phantom. Finally, an example of reconstruction for a patient dataset with inserted pseudo lesions is used as a demonstration in a clinical context. We show that applying the spatially-variant penalization with PLS can reduce the dependence of the lesion contrast on the surrounding activity and lesion location. It does not affect the bias and noise trade-off curves for matched local resolution. Moreover, when using the proposed penalization, significant improvement in algorithm convergence rate and convergence consistency is observed.

A 22-to-36.8 GHz low phase noise Colpitts VCO array in 0.13-μm SiGe BiCMOS technology

This paper describes a 22-to-36.8 GHz low phase noise Colpitts voltage-controlled oscillator (VCO) array using 0.13-μm SiGe BiCMOS technology. The VCO array consists of four Colpitts VCO cores and three millimeter-wave (MMW) selectors. In the VCO core, in order to achieve the best phase noise and power consumption trade-off, an optimization method based on symbolic expressions is presented. In a further research, a switchable bias current technique is proposed to achieve lower phase noise. The MMW selectors are designed to select the desired VCO core and cover the whole frequency band with sufficient output power. The fabricated VCO array features a wide tuning range of 50.3%, low phase noise between −100.7 dBc/Hz and −95.3 dBc/Hz at 1 MHz offset and high output power of 4.5 dBm, which results in the FOMT between −183.2 dBc/Hz and −177.8 dBc/Hz.

Full-Differential Folded-Cascode Front-End Receiver Amplifier Integrated Circuit for Capacitive Micromachined Ultrasonic Transducers.

This paper describes the design of a front-end receiver amplifier for capacitive micromachined ultrasonic transducer (CMUT). The proposed operational amplifier (op amp) consists of a full differential folded-cascode amplifier stage followed by a class AB output stage. A feedback resistor is applied between the input and the output of the op amp to make a transimpedance amplifier. We analyzed the equivalent circuit model of the CMUT element operating in the receiving mode and obtained the static output impedance and center frequency characteristics of the CMUT. The op amp gain, bandwidth, noise, and power consumption trade-offs are discussed in detail. The amplifier was fabricated using GlobalFoundries 0.18-μm complementary metal-oxide-semiconductor (CMOS) technology. The open loop gain of the amplifier is approximately 65 dB, and its gain bandwidth product is approximately 29.5 MHz. The measured input reference noise current was 56 nA/√Hz@3 MHz. The amplifier chip area is 325 μm × 150 μm and the op amp is powered by ±3.3 V, the static power consumption is 11 mW. We verified the correct operation of our amplifier with CMUT and echo-pulse shown that the CMUT center frequency is 3 MHz with 92% fractional bandwidth.

Quantum efficiency and spatial noise tradeoffs for III–V focal plane arrays

Recent advances in quantum well detector materials such as SLS, QWIP, and QDIP photodetectors have made the performance of those technologies competitive with traditional MCT and InSb detectors while offering the potential of lower unit costs. Some III–V focal plane arrays operating in the MWIR spectral region have already achieved similar quantum efficiency performance as MCT and InSb devices, and are therefore, highly competitive for those applications. In the LWIR spectral region, quantum efficiency performance for III–V FPAs still lags MCT devices by a significant margin, but recent improvements have begun to close this gap. In addition, the improved spatial noise and operability typically associated with III–V devices can mitigate some of the shortfall in quantum efficiency when assessing overall system level performance. This paper will seek to quantify this tradeoff based on the most recent reported results for both III–V and MCT devices in order to help focus future research.This paper will first seek to quantify the current state of MCT and III–V (primarily SLS) FPAs in both MWIR and LWIR using a detailed literature survey to assess recent results, particularly in terms of reported quantum efficiency and spatial noise (quantified as residual fixed pattern noise). This data is then used to bound system-level performance modeling using the US Army’s Night Vision Integrated Performance Model (NV-IPM), which calculates image contrast and spatial frequency content using data from the target/background, intervening atmosphere, and system components. The modeling analyzes various tradeoffs in quantum efficiency and spatial noise under relevant operating conditions, including scenarios with both high and low target photon fluxes, and using realistic constraints for integration time (based on both frame rate and ROIC parameters). The results of this analysis are then compared to the current state of the art performance for both III–V and MCT FPAs to determine relative performance between the material systems and to recommend areas of improvement for each. This will provide insights to the FPA development community regarding the most pressing needs for improvements to each material system.

A 21-dm-OP1 dB 20.3%-Efficiency -131.8-dBm/Hz-Noise X-Band Cartesian Error Feedback Transmitter With Fully Integrated Power Amplifier in 65-nm CMOS

This article presents an $X$ -band Cartesian error feedback loop transmitter (CEFLT) with a fully integrated power amplifier (PA) in a 65-nm CMOS. The extra reference path cancels the feedback signal to mitigate the inherent linearity–noise tradeoff in the conventional Cartesian feedback loop transmitter (CFLT). In this way, the nonlinearities from the feedback path are suppressed without using large attenuation, and thus the noise contribution from the feedback path is reduced as well. Compared with the conventional CFLT, the proposed CEFLT can improve the linearity without degrading the noise performance. The PA-included transmitter delivers a maximum power ( $P_{\mathrm {SAT}}$ ) of 21.4 dBm and output-referred 1-dB compression point ( OP 1 dB) of 21 dBm, with 33.3% and 30.3% PA output stage efficiency, respectively, and 22.3% and 20.3% system efficiency, respectively. Compared with the open-loop condition, the closed-loop transmitter demonstrates up to 13.6-dB suppression of adjacent channel leakage ratio (ACLR) error vector magnitude (EVM) using 4 Msps 64 quadratic-amplitude modulation (QAM) signals. The out-of-band (OOB) noise at 100 MHz is −133/−131.8 dBm/Hz for the lower and upper sidebands, respectively.

A 0.13-&lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;$\mu\text{m}$ &lt;/tex-math&gt; &lt;/inline-formula&gt; CMOS SoC for Simultaneous Multichannel Optogenetics and Neural Recording

This paper presents a 0.13- $\mu \text{m}$ CMOS system-on-chip (SoC) for simultaneous multichannel optogenetics and multichannel neural recording in freely moving laboratory animals. This fully integrated system provides 10 multimodal recording channels with analog-to-digital conversion and a four- channel LED driver circuit for optogenetic stimulation. The bio-amplifier design includes a programmable bandwidth (BW) (0.5 Hz–7 kHz) to collect either the action potentials (APs) and/or the local field potentials (LFPs) and has a noise efficiency factor (NEF) of 2.30 for an input-referred noise of 3.2 $\mu V_{\text {rms}}$ within a BW of 10–7 kHz. The low-power delta–sigma ( $\Delta \Sigma $ ) MASH 1-1-1 analog-to-digital converter (ADC) is designed to work at low oversampling ratios (OSRs) (≤50) and has an effective number of bits (ENOB) of 9.75 bits at an OSR of 25 (BW of 10 kHz). The utilization of a $\Delta \Sigma $ ADC is the key to address the flexibility needed to address different noise versus power consumption tradeoff of various experimental settings. It leverages a new technique that reduces its size by subtracting the output of each $\Delta \Sigma $ branch in the digital domain, instead of in the analog domain as done conventionally. The ADC is followed by an on-chip fourth-order cascaded integrator-comb (CIC4) decimation filter (DF). A whole recording channel, including the bio-amplifier, the $\Delta \Sigma $ MASH 1-1-1, and the DF consumes 11.2 $\mu \text{W}$ . Optical stimulation is performed with an LED driver using a regulated cascode current source with feedback that can accommodate a wide range of LED parameters and battery voltages. The SoC is validated in vivo within a wireless experimental platform in both the ventral posteromedial nucleus (VPM) and cerebral motor cortex brain regions of a virally mediated Channelrhodopsin-2 (ChR2) rat.

An image reconstruction method with a locally adaptive gating scheme for PET data

In conventional gating approaches for positron emission tomography (PET), a single number of gates is predetermined for the whole field of view (FOV) regardless of spatially variant motion blurring effects, which compromises image quality by under-gating regions of large motion and over-gating static regions. To achieve the best resolution–noise trade-off for the whole FOV, we proposed a new approach that incorporates a spatially variant number of gates into gated image reconstruction. The first step was to estimate the motion amplitude of each spatial location. A preliminary set of gated image reconstructions was generated from the PET data. The spatially variant motion amplitudes were approximated based on the registration of 2D maximum intensity projections of the gated reconstructions as well as prior knowledge. Second, the spatially varying motion amplitudes were used to determine the optimal number of gates for each region. Finally, the adaptive gating image reconstruction algorithm that incorporates a gating transform function to model the spatially variant number of gates was applied to generate adaptively gated 4D images. Scans from large FOV systems were simulated using actual multi-bed patient data from a clinical scanner for evaluation purposes. Images reconstructed with the conventional gating scheme as well as static reconstruction were obtained for comparison with the results obtained using our new method. In areas with lower estimated motion amplitudes (such as the spine), the reconstructed images using the new approach showed reduced noise compared to images with conventional gated reconstructions and comparable quality with non-gated images. In areas with large estimated motion amplitudes, such as in the lung and liver, contrast and resolution of images using the new method and conventional gated-reconstructions were comparable, and both were higher than those of non-gated images. The results indicate that using a locally adaptive number of gates based on respiratory motion amplitude instead of a fixed number of gates can improve the statistics of gated PET images by optimizing the local noise–resolution trade-off.

Influences of operating parameters on the aerodynamics and aeroacoustics of a horizontal-axis wind turbine

A computational framework used to evaluate the aerodynamics and aeroacoustics is developed and validated against the experimental data in the previous work. In the present work, the different operating parameters of inlet flow velocity, tip speed ratio and turbulence intensity have been considered separately. The aerodynamic performance, the vortex dynamics and the aerodynamic acoustics of the full scale horizontal-axis wind turbine under different operating conditions have been investigated. And the analysis of the impact of different operating parameters is discussed. It is observed that the model has an extension to different conditions and it is sensitive and accurate for simulating the results of different condition parameters. According to the results, the wind with lower turbulence intensity will be better for the operating, and the wind turbine operation can be optimized by adjusting the rotating speed (TSR) according to the inflow wind velocity. In the end, a noise and power trade-off graphics has been proposed based on the wind turbine acoustics and performance results. With enough operating conditions available for reference, selecting the optimal operating parameters under specific operating conditions according to the noise and power trade-offs graphics becomes feasible.

Theoretical study of the benefit of long axial field-of-view PET on region of interest quantification

The aim of this study is to evaluate the benefit of long axial field-of-view (AFOV) PET scanners on region of interest (ROI) quantification. We simulated a series of PET scanners with an AFOV ranging from 22 cm to 220 cm. A theoretical framework was used to predict the contrast recovery coefficient (CRC) and the variance of ROI quantification in penalized maximum likelihood (ML) image reconstruction, in which the resolution and noise tradeoff was controlled by a regularization parameter with a quadratic penalty function. The characterization was based on the converged penalized ML reconstruction with an accurate system model. We examined quantification of a 2 mm ROI and 10 mm ROI in a clinically relevant scan range of 110 cm. Multiple bed positions with 50% overlap were used for scanners with shorter AFOV to provide a relatively uniform sensitivity across the 110 cm axial range. A uniform water cylinder of 20 cm in diameter and 230 cm in length was chosen to model the attenuation and background activity. We computed the variance reduction factor at fixed resolution. Effects of different detector capabilities, including TOF (time-of-flight) resolution (320 ps, 500 ps, and non-TOF) and DOI (depth-of-interaction) resolution (4 mm, 10 mm, and no DOI), were evaluated. The results show that at a normal activity level (370 MBq), the 220 cm AFOV scanner offers a ∼17-fold variance reduction for the 2 mm ROI and ∼26-fold variance reduction for the 10 mm ROI (both measured at CRC = 0.5) over the 22 cm AFOV scanner when both using detectors with 500 ps TOF resolution no DOI capability. The variance reduction factors of trues-only are higher than those of including scatters and randoms. Combining 320 ps TOF and 4 mm DOI, the 220 cm long scanner offers a ∼45-fold variance reduction over the 22 cm long reference scanner (500 ps TOF, no DOI) for imaging 2 mm and 10 mm ROIs. The variance reduction factors are higher at a lower activity level due to lower random fraction. In conclusion, our study demonstrates that a long AFOV scanner can greatly improve the quantitative accuracy of PET imaging compared to current state-of-the-art clinical PET scanners.