A Concurrent Dual-Band Inverter-Based Low Noise Amplifier (LNA) for WLAN Applications

low noise amplifier (LNA); concurrent; dual-band; inverter-basedIn this paper, a two-stage concurrent dual-band low noise amplifier (DB-LNA) operating at 2.4/5.2-GHz is presented for Wireless Local Area Network (WLAN) applications. The current-reused structure using resistive shunt-shunt feedback is employed to reduce power dissipation and achieve a wide frequency band from DC to-5.5-GHz in the inverter-based LNA. The second inverter-based stage is employed to increase the gain and obtain a flat gain over the frequency band. An LC network is also inserted at the proposed circuit output to shape the dual-band frequency response. The proposed concurrent DB-LNA is designed by RF-TSMC 0.18-µm CMOS technology, which consumes 10.8 mW from a power supply of 1.5 V. The simulation results show that the proposed DB-LNA achieves a direct power gain (S 21 ) of 13.7/14.1 dB, a noise figure (NF) of 4.2/4.6 dB, and an input return loss (S 11 ) of −12.9/−14.6 dBm at the 2.4/5.2-GHz bands

A New CMOS Fully Differential Low Noise Amplifier for Wideband Applications

In this paper, a multi-stage fully differential low noise amplifier (LNA) has been presented for wideband applications. A common-gate input stage is used to improve the input impedance matching and linearity. A common-source stage is also used as the second stage to enhance gain and reduce noise. A shunt-shunt feedback is employed to extend bandwidth and enhance linearity. The proposed low noise amplifier has been designed and simulated using RF-TSMC 0.18 μm CMOS process technology. In frequency band of 3.5-7.5 GHz, this amplifier has a flat power gain (S21) of 16.5 ± 1.5 dB, low noise figure (NF) of 3dB, input (S11) and output (S22) return losses less than -10 dB and high linearity with input thirdorder intercept point (IIP3) of -3dBm. It’s power consumption is also less than 10 mw with low power supply voltage of 0.8v

Nonlinear Modeling for Distortion Analysis in Silicon Bulk-Mode Ring Resonators

A distributed modeling approach has been developed to describe the dynamic behavior of ring resonators. The model includes the effect of large amplitudes around primary resonance frequencies, material and electrostatic nonlinearities. Through a combination of geometric and material nonlinearities, closed-form expression for third-order nonlinearity in mechanical stiffness of bulk-mode ring resonators is obtained. Moreover, to avoid dynamic pull-in instability, the choices of the quality factor, <em>ac</em>-drive<em> </em>and DC-bias voltages of the ring resonators, with a given geometry are limited by a resonant pull-in condition. Using the perturbation technique and the method of harmonic balance, the expressions for describing the effect of nonlinearities on the resonance frequency and displacement are derived. The results are discussed in detail, showing the effect of varying operating conditions and the quality factor on the harmonic distortions and third-order intermodulation distortion. The detailed nonlinear modeling and distortion analysis are applied as appropriate tools to design bulk-mode ring resonators with low motional resistance and high linearity

COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients

Background: We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients. Methods: Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported. Results: In the test dataset (4,301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95%: 0.81-0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + Random Forest (RF) classifier. Similar results were achieved in RT-PCR-only positive test sets (3,644). In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in the highest performance of AUC, reaching 0.83 ± 0.01 (CI95%: 0.81-0.85), with a sensitivity and specificity of 0.77 and 0.74, respectively. ComBat harmonization did not depict statistically significant improvement compared to a non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and RF classifier resulted in the highest performance. Conclusion: Lung CT radiomics features can be used for robust prognostic modeling of COVID-19. The predictive power of the proposed CT radiomics model is more reliable when using a large multicentric heterogeneous dataset, and may be used prospectively in clinical setting to manage COVID-19 patients.</p