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Abstract Hafnium oxide (HfO2)‐based devices have been extensively evaluated for high‐speed and low‐power memory applications. Here, the influence of aluminum (Al) and lanthanum (La) co‐doping HfO2 thin films on the ferroelectric characteristics of hafnium‐based devices is investigated. Among devices with different La/Al ratios, the Al and La co‐doped hafnium oxide (HfAlAO) device with 4.2% Al and 2.17% La exhibited the excellent remanent polarization and thermostability. Meanwhile, first principal analyses verified that hafnium‐based thin films with 4.2% Al and 2.17% La promoted the formation of the o‐phase against the paraelectric phase, providing theoretical support for supporting experimental results. Furthermore, a vertical ferroelectric HfO2 memory based on 3D macaroni architecture is reported. The devices show excellent ferroelectric characteristics of 22 µC cm−2 under 4.5 MV cm−1 and minimal coercive field of ≈1.6 V. In addition, the devices exhibit great memory performance, including the response speed of device can achieve 20 ns and endurance characteristic can achieve 1010 cycles.

Abstract With the development of wearable devices and hafnium-based ferroelectrics (FE), there is an increasing demand for high-performance flexible ferroelectric memories. However, developing ferroelectric memories that simultaneously exhibit good flexibility and significant performance has proven challenging. Here, we developed a high-performance flexible field-effect transistor (FeFET) device with a thermal budget of less than 400 °C by integrating Zr-doped HfO2 (HZO) and ultra-thin indium tin oxide (ITO). The proposed FeFET has a large memory window (MW) of 2.78 V, a high current on/off ratio (ION/IOFF) of over 108, and high endurance up to 2×107 cycles. In addition, the FeFETs under different bending conditions exhibit excellent neuromorphic properties. The device exhibits excellent bending reliability over 5×105 pulse cycles at a bending radius of 5 mm. The efficient integration of hafnium-based ferroelectric materials with promising ultrathin channel materials (ITO) offers unique opportunities to enable high-performance back-end-of-line (BEOL) compatible wearable FeFETs for edge intelligence applications.

Abstract We determined if a convolutional neural network (CNN) deep learning model can accurately segment acute ischemic changes on non-contrast CT compared to neuroradiologists. Non-contrast CT (NCCT) examinations from 232 acute ischemic stroke patients who were enrolled in the DEFUSE 3 trial were included in this study. Three experienced neuroradiologists independently segmented hypodensity that reflected the ischemic core on each scan. The neuroradiologist with the most experience (expert A) served as the ground truth for deep learning model training. Two additional neuroradiologists’ (experts B and C) segmentations were used for data testing. The 232 studies were randomly split into training and test sets. The training set was further randomly divided into 5 folds with training and validation sets. A 3-dimensional CNN architecture was trained and optimized to predict the segmentations of expert A from NCCT. The performance of the model was assessed using a set of volume, overlap, and distance metrics using non-inferiority thresholds of 20%, 3 ml, and 3 mm, respectively. The optimized model trained on expert A was compared to test experts B and C. We used a one-sided Wilcoxon signed-rank test to test for the non-inferiority of the model-expert compared to the inter-expert agreement. The final model performance for the ischemic core segmentation task reached a performance of 0.46 ± 0.09 Surface Dice at Tolerance 5mm and 0.47 ± 0.13 Dice when trained on expert A. Compared to the two test neuroradiologists the model-expert agreement was non-inferior to the inter-expert agreement, $$p < 0.05$$ p < 0.05 . The before, CNN accurately delineates the hypodense ischemic core on NCCT in acute ischemic stroke patients with an accuracy comparable to neuroradiologists.

Neuromorphic computing memristors are attractive to construct low-power- consumption electronic textiles. Here, authors report an ultralow-power textile memristor network of Ag/MoS2/HfAlOx/carbon nanotube with reconfigurable characteristics and firing energy consumption of 1.9 fJ/spike.

Abstract HfAlO film‐based ferroelectric memory is a strong contender for the next‐generation nonvolatile memories. However, the remanent polarization intensity of HfAlO films is small compared to other Hf‐based ferroelectric films at low annealing temperatures. In order to further improve the remnant polarization of the device, the ferroelectric memory with metal‐ferroelectric‐metal structure using ZrO2 as the regulating layer (RL) is designed and fabricated. Experimental results show that the device with the ZrO2 regulating layer exhibits triple enhancement, which may be due to the fact that ZrO2 RL has an effect on the enhancement of the ferroelectric phase. In addition, the device with ZrO2 regulating layer exhibits a superior ON/OFF conductance ratio, endurance, and retention characteristics, demonstrating potential for application to memory. This work provides an effective way to improve the ferroelectricity in HfAlO films at low annealing temperatures.

Piezoelectric fast steering mirror (PZT FSM) is the core component of the fine tracking system for space laser communication, and its actuator is a piezoelectric ceramic. Consequently, there is a hysteretic nonlinear disturbance throughout the entire range of the FSM’s steering. To enhance the fine tracking system’s performance, this paper innovatively analyzes and verifies the effect of the PZT FSM hysteresis characteristics on the error suppression bandwidth of the fine tracking system. Firstly, the rate-dependent hysteresis model is established by serially connecting the Prandtl–Ishlinskii (P-I) model with the dynamic linear mode. The inverse model is designed as a feedforward controller, followed by the conduction of open-loop feedforward compensation experiments. Subsequently, we propose a compound control method based on the rate-dependent hysteresis mode and conduct a simulation analysis. Finally, the experimental platform for the fine tracking system is set up, and the optimization effect of compensating for hysteresis nonlinearity on the fine tracking system is verified. The experimental results show that the nonlinearity of the PZT FSM is improved by 30% in the middle- and high-frequency ranges, and the error suppression bandwidth of the fine tracking system is improved by 41.7%. This effectively enhances the fine tracking system’s error suppression capability.

Whole-prostate gland (WPG) segmentation plays a significant role in prostate volume measurement, treatment, and biopsy planning. This study evaluated a previously developed automatic WPG segmentation, deep attentive neural network (DANN), on a large, continuous patient cohort to test its feasibility in a clinical setting. With IRB approval and HIPAA compliance, the study cohort included 3,698 3T MRI scans acquired between 2016 and 2020. In total, 335 MRI scans were used to train the model, and 3,210 and 100 were used to conduct the qualitative and quantitative evaluation of the model. In addition, the DANN-enabled prostate volume estimation was evaluated by using 50 MRI scans in comparison with manual prostate volume estimation. For qualitative evaluation, visual grading was used to evaluate the performance of WPG segmentation by two abdominal radiologists, and DANN demonstrated either acceptable or excellent performance in over 96% of the testing cohort on the WPG or each prostate sub-portion (apex, midgland, or base). Two radiologists reached a substantial agreement on WPG and midgland segmentation (κ = 0.75 and 0.63) and moderate agreement on apex and base segmentation (κ = 0.56 and 0.60). For quantitative evaluation, DANN demonstrated a dice similarity coefficient of 0.93 ± 0.02, significantly higher than other baseline methods, such as DeepLab v3+ and UNet (both p values < 0.05). For the volume measurement, 96% of the evaluation cohort achieved differences between the DANN-enabled and manual volume measurement within 95% limits of agreement. In conclusion, the study showed that the DANN achieved sufficient and consistent WPG segmentation on a large, continuous study cohort, demonstrating its great potential to serve as a tool to measure prostate volume.

Automatic segmentation of prostatic zones on multi-parametric MRI (mpMRI) can improve the diagnostic workflow of prostate cancer. We designed a spatial attentive Bayesian deep learning network for the automatic segmentation of the peripheral zone (PZ) and transition zone (TZ) of the prostate with uncertainty estimation. The proposed method was evaluated by using internal and external independent testing datasets, and overall uncertainties of the proposed model were calculated at different prostate locations (apex, middle, and base). The study cohort included 351 MRI scans, of which 304 scans were retrieved from a de-identified publicly available datasets (PROSTATEX) and 47 scans were extracted from a large U.S. tertiary referral center (external testing dataset; ETD)). All the PZ and TZ contours were drawn by research fellows under the supervision of expert genitourinary radiologists. Within the PROSTATEX dataset, 259 and 45 patients (internal testing dataset; ITD) were used to develop and validate the model. Then, the model was tested independently using the ETD only. The segmentation performance was evaluated using the Dice Similarity Coefficient (DSC). For PZ and TZ segmentation, the proposed method achieved mean DSCs of 0.80±0.05 and 0.89± 0.04 on ITD, as well as 0.79±0.06 and 0.87±0.07 on ETD. For both PZ and TZ, there was no significant difference between ITD and ETD for the proposed method. This DL-based method enabled the accuracy of the PZ and TZ segmentation, which outperformed the state-of-art methods (DeeplabV3+, Attention U-Net, R2U-Net, USE-Net and U-Net). We observed that segmentation uncertainty peaked at the junction between PZ, TZ and AFS. Also, the overall uncertainties were highly consistent with the actual model performance between PZ and TZ at three clinically relevant locations of the prostate.

Based on the whole life cycle process of the economic exploitation of natural gas hydrate, this paper proposes the basic problem of stabilizing the wellbore for the basic conditions that must be met to ensure the integrity of the wellbore for exploitation: revealing the complex mechanism of fluid–solid–heat coupling in the process of the physical exchange of equilibrium among gas, water, and multiphase sand flows in the wellbore, hydrate reservoir, and wellbore, defining the interface conditions to ensure wellbore stability during the entire life cycle of hydrate production and proposing a scientific evaluation system of interface parameters for wellbore integrity.

Non-Kolmogorov turbulence has been widely observed in free-space optical communication links and should be used to evaluate the system performance. We calculated the wavefront residual variance in the condition of the non-Kolmogorov turbulence model and deduced the mathematical expression of the probability density function (PDF) for the coupling efficiency (CE) of a single-mode fiber (SMF). The PDF was simulated, and the results showed its robustness and rationality. Moreover, an experiment was set up to verify the PDF with experimental distribution. The correlation coefficients are above 0.95 in all cases, which means the statistical model of the CE fitted the experimental distribution well.

Our main objective in the paper is to develop a novel deep learning-based algorithm for automatic segmentation of prostate zones and to evaluate the performance of the algorithm on an additional independent testing dataset in comparison with inter-reader agreement between two experts. With IRB approval and HIPAA compliance, we designed a novel convolutional neural network (CNN) for automatic segmentation of the prostatic transition zone (TZ) and peripheral zone (PZ) on T2-weighted (T2w) 3 Tesla (3T) MRI. The total study cohort included 359 MRI scans of patients in subcohorts; 313 scans from a deidentified publicly available dataset (SPIE-AAPM-NCI PROSTATEX challenge) and 46 scans from a large U.S. tertiary referral center (external testing dataset (ETD)). The TZ and PZ contours were manually annotated by research fellows, supervised by expert genitourinary (GU) radiologists. The model was developed using 250 patients and tested internally using the remaining 63 patients from the PROSTATEX (internal testing dataset (ITD)) and tested again (n=46) externally using the ETD. The Dice Similarity Coefficient (DSC) was used to evaluate the segmentation performance. DSCs for PZ and TZ were 0.74±0.08 and 0.86±0.07 in the ITD respectively. In the ETD, DSCs for PZ and TZ were 0.74±0.07 and 0.79±0.12, respectively. The inter-reader consistency (Expert 2 vs. Expert 1) were 0.71±0.13 (PZ) and 0.75±0.14 (TZ). This novel DL algorithm enabled automatic segmentation of PZ and TZ with high accuracy on both ITD and ETD without a performance difference for PZ and less than 10% TZ difference. In the ETD, the proposed method can be comparable to experts in the segmentation of prostate zones. Part of our source code and datasets with annotations is available at https://github.com/ykl-ucla/prostate_zonal_seg.

The alignment accuracy and stability of shipborne electro-optical system are affected by external attitude disturbance and internal non-linear disturbance. Therefore, this study examined a new digital sliding mode control based on a novel reaching law with an n-order disturbance compensator to solve this problem. Specially, the proposed novel reaching law was a chatter-free method, in which, as the switching gain was replaced by an adaptive function, chattering on the sliding surface tended to zero. For perturbed systems, the new sliding mode controller was able to guarantee a narrow quasi-sliding-mode domain and the ultimate width was the order of O(T3), such that the narrower the quasi-sliding-mode domain width was, the more robust toward nonlinear disturbances. Both mathematical calculations and simulations verified the convergence and stability of the new controller. Finally, based on a compound control strategy that combined feedforward control with a sliding mode control position loop and proportional-integral velocity loop control structure, shipborne equivalent dynamic target tracking experiments were performed. The results, compared with existing controllers, showed that the robustness of the new controller to nonlinear disturbances was stronger and the tracking system accuracy clearly improved, while the influence of sliding mode chattering on the system was avoided.

In order to improve the tracking accuracy of the fine tracking system of free-space optical communications, a proposed active disturbance rejection control (ADRC) with iterative learning control (ILC) strategy is designed to regulate the fast steering mirror (FSM) system. First, based on the open-loop frequency response results of the system, we can approximately confirm the system transfer function and prepare for the design of the controller. Then, an improved ADRC that using sliding mode control instead of the traditional nonlinear state error feedback method is proposed to attenuate the influence of the unknown and unmolded parts. In addition, the ILC method is used to compensate the hysteresis of piezoelectric actuators and improve the overall system performance. The experimental results indicate that the tracking accuracy of the system is 1 μrad at 1k sampling frequency. In addition, the recommended method shows the better disturbance suppression performance than traditional ADRC in simulation and experimental investigations. This method has a very high application value in the field of visual axis stability, adaptive optics, and other fields.

The current standardized scheme for interpreting MRI requires a high level of expertise and exhibits a significant degree of inter-reader and intra-reader variability. An automated prostate cancer (PCa) classification can improve the ability of MRI to assess the spectrum of PCa. The purpose of the study was to evaluate the performance of a texture-based deep learning model (Textured-DL) for differentiating between clinically significant PCa (csPCa) and non-csPCa and to compare the Textured-DL with Prostate Imaging Reporting and Data System (PI-RADS)-based classification (PI-RADS-CLA), where a threshold of PI-RADS ≥ 4, representing highly suspicious lesions for csPCa, was applied. The study cohort included 402 patients (60% (n = 239) of patients for training, 10% (n = 42) for validation, and 30% (n = 121) for testing) with 3T multiparametric MRI matched with whole-mount histopathology after radical prostatectomy. For a given suspicious prostate lesion, the volumetric patches of T2-Weighted MRI and apparent diffusion coefficient images were cropped and used as the input to Textured-DL, consisting of a 3D gray-level co-occurrence matrix extractor and a CNN. PI-RADS-CLA by an expert reader served as a baseline to compare classification performance with Textured-DL in differentiating csPCa from non-csPCa. Sensitivity and specificity comparisons were performed using Mcnemar’s test. Bootstrapping with 1000 samples was performed to estimate the 95% confidence interval (CI) for AUC. CIs of sensitivity and specificity were calculated by the Wald method. The Textured-DL model achieved an AUC of 0.85 (CI [0.79, 0.91]), which was significantly higher than the PI-RADS-CLA (AUC of 0.73 (CI [0.65, 0.80]); p < 0.05) for PCa classification, and the specificity was significantly different between Textured-DL and PI-RADS-CLA (0.70 (CI [0.59, 0.82]) vs. 0.47 (CI [0.35, 0.59]); p < 0.05). In sub-analyses, Textured-DL demonstrated significantly higher specificities in the peripheral zone (PZ) and solitary tumor lesions compared to the PI-RADS-CLA (0.78 (CI [0.66, 0.90]) vs. 0.42 (CI [0.28, 0.57]); 0.75 (CI [0.54, 0.96]) vs. 0.38 [0.14, 0.61]; all p values < 0.05). Moreover, Textured-DL demonstrated a high negative predictive value of 92% while maintaining a high positive predictive value of 58% among the lesions with a PI-RADS score of 3. In conclusion, the Textured-DL model was superior to the PI-RADS-CLA in the classification of PCa. In addition, Textured-DL demonstrated superior performance in the specificities for the peripheral zone and solitary tumors compared with PI-RADS-based risk assessment.

In this study, a sliding mode control (SMC) algorithm was proposed based on a novel reaching law to solve the nonlinear disturbance problem of a ground-based laser communication turntable. This algorithm is a chatter-free method, in which the coefficient of sliding mode variable structure function is designed as an adaptive function, so the chattering of the sliding mode approaches zero. For any perturbed system, this algorithm can ensure a finite time for the system state to reach the sliding mode surface from any initial state. Additionally, the system will stabilize in the quasi-sliding mode domain (QSMD) with O(T3) width, where a narrower QSMD width corresponds to stronger robustness toward nonlinear disturbances. Both mathematical calculations and simulations verified the sliding mode and stability of this control algorithm. Experimental results of the velocity closed-loop of pitch axis show that the proposed algorithm effectively improved the anti-nonlinear disturbance ability of the control system compared with the effects of the traditional digital PID and the existing chatter-reduced SMC algorithms, for smooth system operation.

In this study, Zernike polynomials and optical fiber field theory are applied to build a mathematical model of coupling efficiency (CE) and spatial mode of aberrations. The theory built in this paper can be used to quickly calculate the CE affected by a single aberration as well as the aberrations caused by atmospheric turbulence. The aberrations are classified based on Zernike polynomials and the effects of aberrations on CE of different types and different spatial frequencies are analyzed. The influence of the effects of AO system residual errors is also analyzed. Adaptive optics (AO) equipment is applied to build a system on which the proposed theory was tested; the experimental results validate the theoretical analysis.

Background Outlining acutely infarcted tissue on non-contrast CT is a challenging task for which human inter-reader agreement is limited. We explored two different methods for training a supervised deep learning algorithm: one that used a segmentation defined by majority vote among experts and another that trained randomly on separate individual expert segmentations. Methods The data set consisted of 260 non-contrast CT studies in 233 patients with acute ischemic stroke recruited from the multicenter DEFUSE 3 (Endovascular Therapy Following Imaging Evaluation for Ischemic Stroke 3) trial. Additional external validation was performed using 33 patients with matched stroke onset times from the University Hospital Lausanne. A benchmark U-Net was trained on the reference annotations of three experienced neuroradiologists to segment ischemic brain tissue using majority vote and random expert sampling training schemes. The median of volume, overlap, and distance segmentation metrics were determined for agreement in lesion segmentations between (1) three experts, (2) the majority model and each expert, and (3) the random model and each expert. The two sided Wilcoxon signed rank test was used to compare performances (1) to 2) and (1) to (3). We further compared volumes with the 24 hour follow-up diffusion weighted imaging (DWI, final infarct core) and correlations with clinical outcome (modified Rankin Scale (mRS) at 90 days) with the Spearman method. Results The random model outperformed the interexpert agreement ((1) to (2)) and the majority model ((1) to (3)) (dice 0.51±0.04 vs 0.36±0.05 (P<0.0001) vs 0.45±0.05 (P<0.0001)). The random model predicted volume correlated with clinical outcome (0.19, P<0.05), whereas the median expert volume and majority model volume did not. There was no significant difference when comparing the volume correlations between random model, median expert volume, and majority model to 24 hour follow-up DWI volume (P>0.05, n=51). Conclusion The random model for ischemic injury delineation on non-contrast CT surpassed the interexpert agreement ((1) to (2)) and the performance of the majority model ((1) to (3)). We showed that the random model volumetric measures of the model were consistent with 24 hour follow-up DWI. [ABSTRACT FROM AUTHOR]