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In this paper, a dual-polarized four-port 2 × 2 series fed antenna array operating at 28 GHz with beam-switching capability is proposed. The antenna array uses a simple passive beamforming network to switch the main beam. The presented antenna design is suitable for 5G user equipment and high data rates applications by which it has a compact size with low cost and complexity. The size of the antenna is 37.2 × 37.2 mm2 including the ground plane, and it produces 10 different switched beams by using only two simple 3 dB/90∘ couplers which create the required amplitudes and phase excitations for the antenna elements. A one-port simple feeding mechanism including Peregrine PE42525 SPDT switch modules and a power divider is used to generate and measure the 10 switched beams. The antenna design is implemented on a two-layer 0.203 mm thick low-loss (tanδ = 0.0027) Rogers 4003C substrate, and it has a measured 10 dB impedance bandwidth of 4 GHz (14.3%, from 26 GHz to 30 GHz) for all ports. Measured peak isolation between any dual-polarized ports of the antenna is better than 30 dB. The antenna has an average measured realized gain of 8.9 dBi and around 10 dB side lobe level (SLL) for all beams. The antenna has 3-dB coverage of 80∘ to 90∘ in 2D space and it has a maximum of ±26∘ beam-steering angle. The antenna is designed and simulated using Ansys HFSS and fabricated using regular PCB processing.
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In this paper, an indoor positioning system using Global Positioning System (GPS) signals in the 433 MHz Industrial Scientific Medical (ISM) band is proposed, and an experimental demonstration of how the proposed system operates under both line-of-sight and non-line-of-sight conditions on a building floor is presented. The proposed method is based on down-converting (DC) repeaters and an up-converting (UC) receiver. The down-conversion is deployed to avoid the restrictions on the use of Global Navigation Satellite Systems (GNSS) repeaters, to achieve higher output power, and to expose the GPS signals to lower path loss. The repeaters receive outdoor GPS signals at 1575.42 MHz (L1 band), down-convert them to the 433 MHz ISM band, then amplify and retransmit them to the indoor environment. The front end up-converter is combined with an off-the-shelf GPS receiver. When GPS signals at 433 MHz are received by the up-converting receiver, it then amplifies and up-converts these signals back to the L1 frequency. Subsequently, the off-the-shelf GPS receiver calculates the pseudo-ranges. The raw data are then sent from the receiver over a 2.4 GHz Wi-Fi link to a remote computer for data processing and indoor position estimation. Each repeater also has an attenuator to adjust its amplification level so that each repeater transmits almost equal signal levels in order to prevent jamming of the off-the-shelf GPS receiver. Experimental results demonstrate that the indoor position of a receiver can be found with sub-meter accuracy under both line-of-sight and non-line-of-sight conditions. The estimated position was found to be 54 and 98 cm away from the real position, while the 50% circular error probable (CEP) of the collected samples showed a radius of 3.3 and 4 m, respectively, for line-of-sight and non-line-of-sight cases.
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In this paper, a wideband four port 2-6 GHz antenna is proposed. One-, two-, and four-port antennas are implemented and characterized between 2 and 6 GHz. The isolation between the ports is improved by connecting and optimizing the ground plane sections. The results show that the antennas' reflection coefficients are better than 10 dB in the frequency band. The measured isolation between the ports is greater than 15 dB (between 2.3 and 6 GHz) and 10 dB in the whole band for two- and four-port antennas, respectively, however, it is more than 20 dB around 2.4 and 5-6 GHz for both antennas. The calculated correlation coefficient between ports is below -30 dB (>2.14 GHz) and -15 dB for the two- and four-port antennas, respectively. The measured gain and efficiency scale are 3.1-6.75 dBi and 62-98%, respectively. To the best of our knowledge, an antenna both being wideband from 2 to 6 GHz and having independent four ports is only addressed in this work. The four-port antenna can be used for MIMO systems or smartphones operating on many wireless systems simultaneously such as 3G/4G/5G Sub-6 GHz and WLAN including the next generation WiFi7 with full-duplex operation.
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Electrostatic microelectromechanical (MEMS) switches are the basic building blocks for various radio-frequency (RF) transceivers. However, conventional cantilever-based designs of MEMS switches require a large actuation voltage, exhibit limited RF performance, and suffer from many performance tradeoffs due to their flat geometries restricted in two dimensions (2D). Here, by leveraging the residual stress in thin films, we report a novel development of three-dimensional (3D) wavy microstructures, which offer the potential to serve as high-performance RF switches. Relying on standard IC-compatible metallic materials, we devise a simple fabrication process to repeatedly manufacture out-of-plane wavy beams with controllable bending profiles and yields reaching 100%. We then demonstrate the utility of such metallic wavy beams as RF switches achieving both extremely low actuation voltage and improved RF performance owing to their unique geometry, which is tunable in three dimensions and exceeds the capabilities of current state-of-the-art flat-cantilever switches with 2D-restricted topology. As such, the wavy cantilever switch presented in this work actuates at voltages as low as 24 V while simultaneously exhibiting RF isolation and insertion loss of 20 dB and 0.75 dB, respectively, for frequencies up to 40 GHz. Wavy switch designs with 3D geometries break through the design limits set by traditional flat cantilevers and provide an additional degree of freedom or control knob in the switch design process, which could enable further optimization of switching networks used in current 5G and upcoming 6G communication scenarios.
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The formation of a Through Silicon Via (TSV) includes a deep Si trench etching and the formation of an insulating layer along the high-aspect-ratio trench and the filling of a conductive material into the via hole. The isolation of the filling conductor from the silicon substrate becomes more important for higher frequencies due to the high coupling of the signal to the silicon. The importance of the oxide thickness on the via wall isolation can be verified using electromagnetic field simulators. To satisfy the needs on the Silicon dioxide deposition, a sub-atmospheric chemical vapor deposition (SA-CVD) process has been developed to deposit an isolation oxide to the walls of deep silicon trenches. The technique provides excellent step coverage of the 100 microm depth silicon trenches with the high aspect ratio of 20 and more. The developed technique allows covering the deep silicon trenches by oxide and makes the high isolation of TSVs from silicon substrate feasible which is the key factor for the performance of TSVs for mm-wave 3D packaging.
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Background and Aim: The ability to predict survival in cirrhosis is essential to management. Artificial intelligence models are promising alternatives to current scores and staging systems. The objective of this study was to test the feasibility of such a model to predict the short- and long-term survival of patients with different stages of cirrhosis. Materials and Methods: Clinical, laboratory, and survival data of patients with cirrhosis were collected retrospectively. A machine learning model was designed using feature selection. The model's prediction performance was compared with the Model for End-stage Liver Disease-serum sodium (MELD-Na) and the Child-Turcotte-Pugh (CTP) scores using area under the curve (AUC) analysis. Results: The study population consisted of 124 cirrhotic patients. The AUC of the CTP score for 1-, 3-, and 12-month overall survival was 0.75 (CI:0.61-0.88), 0.77 (0.65-0.88), and 0.69 (CI:0.60-0.79), respectively. The AUC of the MELD-Na scores for the same time points was 0.7 (CI:0.62-0.86), 0.73 (CI:0.63-0.83), and 0.68 (CI:0.59-0.78). The machine learning model mean AUC for the entire study population was 0.87 (±0.082) for 1 month, 0.85 (±0.077) for 3 months, and 0.76 (±0.076) for 12 months. The model predicted 1-, 3-, and 12-month survival with an AUC of 0.91 (±0.03), 0.88 (±0.10), and 0.91 (±0.06), respectively, in patients with variceal bleeding. Conclusion: To the best of our knowledge, this is the first study to test a machine learning model in this context. The model outperformed the MELD-Na and CTP scores in the prediction of short- and long-term survival and also successfully predicted high risk variceal bleeding.
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Background and Aim: Hepatocellular carcinoma (HCC) is a complex disease with heterogenous outcomes influenced by disease- and patient-related factors. The prediction of outcomes requires a comprehensive approach, and artificial intelligence could provide a feasible means of estimating HCC outcomes. This study was designed to assess the viability of a machine learning model to predict survival in HCC patients. Materials and Methods: HCC patient data with at least 5 years of follow-up were retrospectively reviewed. Patients with accessible data on the primary liver disease, tumor and laboratory values at the time of diagnosis, and length of survival were included. A gradient boosting machine learning algorithm was constructed to predict patient survival at 6 time points. Results: A total of 100 HCC patients (80% male) with a median overall survival of 43 months (range: 0.7-256 months) were included. The survival rate for 6, 12, 24, 36, 60, and 120 months was 88%, 81%, 67%, 60%, 40%, and 11%, respectively. The mean area under the curve of the model prediction was 0.92 (0.061) for >6 months, 0.81 (0.107) for >1 year, 0.78 (0.11) for >2 years, 0.81 (0.083) for >3 years, 0.82 (0.079) for >5 years, 0.81 (0.96) for >8 years, and 0.66 (0.14) for >10 years. Conclusion: The machine learning model successfully predicted short- and long-term survival of patients with HCC.