Frequency-aware optical coherence tomography image super-resolution via conditional generative adversarial neural network

Optical coherence tomography (OCT) has stimulated a wide range of medical image-based diagnosis and treatment in fields such as cardiology and ophthalmology. Such applications can be further facilitated by deep learning-based super-resolution technology, which improves the capability of resolving morphological structures. However, existing deep learning-based method only focuses on spatial distribution and disregard frequency fidelity in image reconstruction, leading to a frequency bias. To overcome this limitation, we propose a frequency-aware super-resolution framework that integrates three critical frequency-based modules (i.e., frequency transformation, frequency skip connection, and frequency alignment) and frequency-based loss function into a conditional generative adversarial network (cGAN). We conducted a large-scale quantitative study from an existing coronary OCT dataset to demonstrate the superiority of our proposed framework over existing deep learning frameworks. In addition, we confirmed the generalizability of our framework by applying it to fish corneal images and rat retinal images, demonstrating its capability to super-resolve morphological details in eye imaging.Comment: 13 pages, 7 figures, submitted to Biomedical Optics Express special issu

A Novel Non-Volatile Inverter-based CiM: Continuous Sign Weight Transition and Low Power on-Chip Training

In this work, we report a novel design, one-transistor-one-inverter (1T1I), to satisfy high speed and low power on-chip training requirements. By leveraging doped HfO2 with ferroelectricity, a non-volatile inverter is successfully demonstrated, enabling desired continuous weight transition between negative and positive via the programmable threshold voltage (VTH) of ferroelectric field-effect transistors (FeFETs). Compared with commonly used designs with the similar function, 1T1I uniquely achieves pure on-chip-based weight transition at an optimized working current without relying on assistance from off-chip calculation units for signed-weight comparison, facilitating high-speed training at low power consumption. Further improvements in linearity and training speed can be obtained via a two-transistor-one-inverter (2T1I) design. Overall, focusing on energy and time efficiencies, this work provides a valuable design strategy for future FeFET-based computing-in-memory (CiM)

GE-BASED HIGH MOBILITY CHANNEL TRANSISTORS FOR LOW POWER LOGIC AND BACK-END-OF-LINE COMPATIBLE 3D INTEGRATED CIRCUITS

Ph.DDOCTOR OF PHILOSOPHY (FOE

A Time-Delay-Bounded Data Scheduling Algorithm for Delay Reduction in Distributed Networked Control Systems

As a key feature of networked control systems (NCSs), the time delays induced by communication medium sharing and data exchange over the system components could largely degrade the NCS performances or may even cause system instability, and thus, it is of critical importance to reduce time delays within NCSs. This paper studies the time-delay reduction problem in distributed NCSs and presents a dual-way data scheduling mechanism for time-delay reductions in delay-bounded NCSs with time-varying delays. We assess the time delays and their influences on the NCSs first with various delay factors being considered and then describe a one-way scheduling mechanism for network-delay reductions in NCSs. Based upon such a method, a dual-way scheduling algorithm is finally proposed for distributed NCSs with different types of transmitted data packets. Experiments are conducted on a remote teaching platform to verify the effectiveness of the proposed dual-way scheduling mechanism. Results demonstrate that, with the stability time-delay bound considered within the scheduling process, the proposed mechanism is effective for NCS time-delay reductions while addressing the stability, control accuracy, and settling time issues efficiently. Such a proposed mechanism could also be implemented together with some other existing control algorithms for time-delay reductions in NCSs. Our work could provide both useful theoretical guidance and application references for stable tracking control of delay-bounded NCSs

Ge₀.₉₅Sn₀.₀₅ gate-all-around p-channel metal-oxide-semiconductor field-effect transistors with sub-3 nm nanowire width

We demonstrate Ge0.95Sn0.05 p-channel gate-all-around field-effect transistors (p-GAAFETs) with sub-3 nm nanowire width (WNW) on a GeSn-on-insulator (GeSnOI) substrate using a top-down fabrication process. Thanks to the excellent gate control by employing an aggressively scaled nanowire structure, Ge0.95Sn0.05 p-GAAFETs exhibit a small subthreshold swing (SS) of 66 mV/decade, a decent on-current/off-current (ION/IOFF) ratio of ∼1.2 × 106, and a high-field effective hole mobility (μeff) of ∼115 cm2/(V s). In addition, we also investigate quantum confinement effects in extremely scaled GeSn nanowires, including threshold voltage (VTH) shift and IOFF reduction with continuous scaling of WNW under 10 nm. The phenomena observed from experimental results are substantiated by the calculation of GeSn bandgap and TCAD simulation of electrical characteristics of devices with sub-10 nm WNW. This study suggests Ge-based nanowire p-FETs with extremely scaled dimension hold promise to deliver good performance to enable further scaling for future technology nodes.National Research Foundation (NRF)This work at NUS was supported by Singapore Ministry of Education (MOE) Tier 2 (MOE2018-T2-2-154) and MOE Tier 1 (R-263-000-D65-114). Prof. Fan Weijun acknowledges the support from the National Research Foundation Singapore (NRF-CRP19-2017-01)

The pathological risk score: A new deep learning‐based signature for predicting survival in cervical cancer

Abstract Purpose To develop and validate a deep learning‐based pathological risk score (RS) with an aim of predicting patients' prognosis to investigate the potential association between the information within the whole slide image (WSI) and cervical cancer prognosis. Methods A total of 251 patients with the International Federation of Gynecology and Obstetrics (FIGO) Stage IA1–IIA2 cervical cancer who underwent surgery without any preoperative treatment were enrolled in this study. Both the clinical characteristics and WSI of each patient were collected. To construct a prognosis‐associate RS, high‐dimensional pathological features were extracted using a convolutional neural network with an autoencoder. With the score threshold selected by X‐tile, Kaplan–Meier survival analysis was applied to verify the prediction performance of RS in overall survival (OS) and disease‐free survival (DFS) in both the training and testing datasets, as well as different clinical subgroups. Results For the OS and DFS prediction in the testing cohort, RS showed a Harrell's concordance index of higher than 0.700, while the areas under the curve (AUC) achieved up to 0.800 in the same cohort. Furthermore, Kaplan–Meier survival analysis demonstrated that RS was a potential prognostic factor, even in different datasets or subgroups. It could further distinguish the survival differences after clinicopathological risk stratification. Conclusion In the present study, we developed an effective signature in cervical cancer for prognosis prediction and patients' stratification in OS and DFS

Strain relaxation of germanium-tin (GeSn) fins

Strain relaxation of biaxially strained Ge1-xSnx layer when it is patterned into Ge1-xSnx fin structures is studied. Ge1-xSnx-on-insulator (GeSnOI) substrate was realized using a direct wafer bonding (DWB) technique and Ge1-xSnx fin structures were formed by electron beam lithography (EBL) patterning and dry etching. The strain in the Ge1-xSnx fins having fin widths (WFin) ranging from 1 μm down to 80 nm was characterized using micro-Raman spectroscopy. Raman measurements show that the strain relaxation increases with decreasing WFin. Finite element (FE) simulation shows that the strain component in the transverse direction relaxes with decreasing WFin, while the strain component along the fin direction remains unchanged. For various Ge1-xSnx fin widths, transverse strain relaxation was further extracted using micro-Raman spectroscopy, which is consistent with the simulation results