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Journal articles on the topic 'Rfsoi'

Author: Grafiati
Published: 29 March 2025

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1

Abbadie, Alexandra, C. Pribat, V. Gredy, V. Brouzet, and E. Sereix. "Impact of Oxygen on the Generation of Slip Lines and the Electronic Properties of Si-based Substrates." ECS Transactions 112, no. 1 (September 29, 2023): 147–56. http://dx.doi.org/10.1149/11201.0147ecst.

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We focus on slip line formation and propagation on different types of Si and SOI substrates that underwent high temperature anneals during 65nm RFSOI device fabrication. Different parameters are found to contribute to slip line formation and dislocations propagation, mainly the substrate properties (thickness, layer and oxygen in silicon handle), the temperature gradients and the support between furnace carriers and backsides of substrates. Several parameters contribute to substrate deformation, e.g. the warpage increase. The warp degradation is found to be directly linked to low oxygen concentrations in silicon handle. This understanding is very helpful to introduce and process new substrates in advanced front-end process flows for MOS device fabrication.
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2

Dai, Ruofan. "A 0.6-V sub-mW X-band RFSOI CMOS LNA with novel complementary current-reused technique." International Journal of Circuit Theory and Applications 45, no. 12 (March 7, 2017): 2046–56. http://dx.doi.org/10.1002/cta.2336.

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3

Costa, Julio. "Passing the Plateau of Productivity: Development of RFSOI Technologies on HR Silicon Substrates for Reconfigurable Wireless Solutions." IEEE Microwave Magazine 15, no. 7 (November 2014): S61—S73. http://dx.doi.org/10.1109/mmm.2014.2356152.

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4

Wu, Zhuo-Jie, Haojun Zhang, and John Malinowski. "Understanding and Improving Reliability for Wafer Level Chip Scale Package: A Study Based on 45nm RFSOI Technology for 5G Applications." IEEE Journal of the Electron Devices Society 8 (2020): 1305–14. http://dx.doi.org/10.1109/jeds.2020.3023007.

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5

Ergesh, Toktonur, Jian Li, Xue-Feng Duan, Xin Pei, and Zhi-Gang Wen. "Development of Pulsar Digital Backend Based on RFSoC." Research in Astronomy and Astrophysics 22, no. 2 (February 1, 2022): 025002. http://dx.doi.org/10.1088/1674-4527/ac3ad9.

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Abstract Radio Frequency System on Chip (RFSoC) offers great potential for implementing a complete next generation signal processing system on a single board for radio astronomy. We designed a pulsar digital backend system based on the ZCU111 board. The system uses RFSoC technology to implement digitization, channelization, correlation and high-speed data transmission in the Xilinx ZU28DR device. We have evaluated the performance of the eight 12-bit RF-ADCs, which are integrated in RFSoC, with the maximum sampling rate of 4.096 GSPS. The RF-ADC sampling frequency, channel bandwidth and time resolution can be configured dynamically in our designed system. To verify the system performance, we deployed the RFSoC board on the Nanshan 26 m radio telescope and observed the pulsar signal with a frequency resolution of 1 MHz and time resolution of 64 μs. In the observation test, we obtained pulsar profiles with high signal-to-noise ratio and accurately searched the DM values. The experimental results show that the performance of RF-ADCs, FPGA and CPU cores in RFSoC is sufficient for radio astronomy signal processing applications.
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Park, Kun Hee, Yung Szen Yap, Yuanzheng Paul Tan, Christoph Hufnagel, Long Hoang Nguyen, Karn Hwa Lau, Patrick Bore, et al. "ICARUS-Q: Integrated control and readout unit for scalable quantum processors." Review of Scientific Instruments 93, no. 10 (October 1, 2022): 104704. http://dx.doi.org/10.1063/5.0081232.

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We present a control and measurement setup for superconducting qubits based on the Xilinx 16-channel radio-frequency system-on-chip (RFSoC) device. The proposed setup consists of four parts: multiple RFSoC boards, a setup to synchronize every digital to analog converter (DAC) and analog to digital converter (ADC) channel across multiple boards, a low-noise direct current supply for tuning the qubit frequency, and cloud access for remotely performing experiments. We also designed the setup to be free of physical mixers. The RFSoC boards directly generate microwave pulses using sixteen DAC channels up to the third Nyquist zone, which are directly sampled by its eight ADC channels between the fifth and the ninth zones.
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Axani, S. N., S. Futagi, M. Garcia, C. Grant, K. Hosokawa, S. Ieki, K. Inoue, et al. "RFSoC-based front-end electronics for pulse detection." Journal of Instrumentation 19, no. 03 (March 1, 2024): P03013. http://dx.doi.org/10.1088/1748-0221/19/03/p03013.

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Abstract Radiation measurement relies on pulse detection, which can be performed using various configurations of high-speed analog-to-digital converters (ADCs) and field-programmable gate arrays (FPGAs). For optimal power consumption, design simplicity, system flexibility, and the availability of DSP slices, we consider the Radio Frequency System-on-Chip (RFSoC) to be a more suitable option than traditional setups. To this end, we have developed custom RFSoC-based electronics and verified its feasibility. The ADCs on RFSoC exhibit a flat frequency response of 1–125 MHz. The root-mean-square (RMS) noise level is 2.1 ADC without any digital signal processing. The digital signal processing improves the RMS noise level to 0.8 ADC (input equivalent 40 μVrms). Baseline correction via digital signal processing can effectively prevent photomultiplier overshoot after a large pulse. Crosstalk between all channels is less than -55 dB. The measured data transfer speed can support up to 32 kHz trigger rates (corresponding to 750 Mbps). Overall, our RFSoC-based electronics are highly suitable for pulse detection, and after some modifications, they will be employed in the Kamioka Liquid Scintillator Anti-Neutrino Detector (KamLAND).
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8

Ahmed, Faroque. "Dynamic spillovers of various uncertainties to Russian financial stress: Evidence from quantile dependency and frequency connectedness approaches." Russian Journal of Economics 10, no. 3 (October 2, 2024): 246–73. http://dx.doi.org/10.32609/j.ruje.10.126926.

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Being geopolitically exposed, the Russian financial sector is vulnerable to various ­uncertainties. The aim of the article is to examine the quantile movements and dynamic connectedness of uncertainty indices with the financial stress index of Russia employing the cross-quantilogram (CQ), recursive cross-quantilogram (R-CQ) and TVP‑VAR dynamic connectedness using monthly data from July 2011 to August 2023. It is found that for Tweeter-based Economic Uncertainty (TEU) and Global Economic Policy Uncertainty (GEPU), there is strong positive dependence on the Russian Financial Stress Index (RFSI) in the bearish states of market in the initial memory, and the strength of this positive spillover effect gradually wilts towards longer memory structures. Unlike the GEPU, Russian Economic Policy Uncertainty (REPU) has long-lasting heterogeneous spillover effects on RFSI. Though there are significant positive, as well as negative, spillover ­effects of Global Geopolitical Risk (GGPR) on RFSI in the initial memory, across the longer­ memory structures these entire heterogeneous effects wash out. However, Russian Geopolitical Risk (RGPR) have long-lasting heterogeneous spillover effects on RFSI, unlike GGPR. GEPU, GGPR and RGPR were the net transmitters while RFSI, TEU and REPU were net receivers of volatility shocks. Since RFSI shows resilience over the long-term horizon to global geopolitics and economic uncertainty, investors are advised to keep patience and hold their capital/investment up to a minimum of 1 year in Russian financial system in order to be rewarded with positive returns.
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9

Liu, Chao, Ryan Herbst, Larry Ruckman, and Emilio Nanni. "Next Generation LLRF Control Platform for Compact C-band Linear Accelerator." EPJ Web of Conferences 315 (2024): 02008. https://doi.org/10.1051/epjconf/202431502008.

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The Low-Level RF (LLRF) control circuits of linear accelerators (LINACs) are conventionally realized with heterodyne based architectures, which have analog RF mixers for up and down conversion with discrete data converters. We have developed a new LLRF platform for C-band linear accelerator based on the Frequency System-on-Chip (RFSoC) device from AMD Xilinx. The integrated data converters in the RFSoC can directly sample the RF signals in C-band and perform the up and down mixing digitally. The programmable logic and processors required for signal processing for the LLRF control system are also included in a single RFSoC chip. With all the essential components integrated in a device, the RFSoC-based LLRF control platform can be implemented more cost-effectively and compactly, which can be applied to a broad range of accelerator applications. In this paper, the structure and configuration of the newly developed LLRF platform will be described. The LLRF prototype has been tested with high power test setup with a Cool Cooper Collider (C3) accelerating structure. The LLRF and the solid state amplifier (SSA) loopback setup demonstrated phase jitter in 1 s as low as 115 fs, which is lower than the requirement of C3. The rf signals from the klystron forward and accelerating structure captured with peak power up to 16.45 MW will be presented and discussed.
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10

Bergamaschi, L., and A. Martínez. "Parallel RFSAI-BFGS Preconditioners for Large Symmetric Eigenproblems." Journal of Applied Mathematics 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/767042.

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We propose a parallel preconditioner for the Newton method in the computation of the leftmost eigenpairs of large and sparse symmetric positive definite matrices. A sequence of preconditioners starting from an enhanced approximate inverse RFSAI (Bergamaschi and Martínez, 2012) and enriched by a BFGS-like update formula is proposed to accelerate the preconditioned conjugate gradient solution of the linearized Newton system to solveAu=q(u)u,q(u)being the Rayleigh quotient. In a previous work (Bergamaschi and Martínez, 2013) the sequence of preconditioned Jacobians is proven to remain close to the identity matrix if the initial preconditioned Jacobian is so. Numerical results onto matrices arising from various realistic problems with size up to 1.5 million unknowns account for the efficiency and the scalability of the proposed low rank update of the RFSAI preconditioner. The overall RFSAI-BFGS preconditioned Newton algorithm has shown comparable efficiencies with a well-established eigenvalue solver on all the test problems.
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11

Grylls, Bethan. "Spectrum Support." New Electronics 52, no. 4 (February 26, 2019): 8. http://dx.doi.org/10.12968/s0047-9624(22)60920-9.

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12

Asafo-Adjei, Emmanuel, Ebenezer Boateng, Zangina Isshaq, Anthony Adu-Asare Idun, Peterson Owusu Junior, and Anokye M. Adam. "Financial sector and economic growth amid external uncertainty shocks: Insights into emerging economies." PLOS ONE 16, no. 11 (November 11, 2021): e0259303. http://dx.doi.org/10.1371/journal.pone.0259303.

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The study aims to shed new lights on the lead-lag relationships between the financial sector (RFSI) and economic growth (GDP) in the midst of global economic policy uncertainty (GEPU) shocks for BRICS economies. Hence, the bivariate, partial, and wavelet multiple correlations techniques are employed. From the bivariate analysis, we document positive bi-directional causality between the RFSI and economic growth over the sample period. The partial wavelet reveals that GEPU shocks distort the significance and directional comovements between the RFSI and GDP. Moreover, the outcome from the wavelet multiple cross correlations (WMCC) indicates that the RFSI is a first mover at most time scales for the BRICS economies. This is followed by GEPU which either leads or lags for most scales, especially for South Africa. The impact of GEPU on RFSI and GDP is worst for South Africa in about four cases in the medium-, and long-terms. This signifies that South Africa’s financial markets and economic growth are vulnerable to GEPU. However, the impetus for GEPU to drive the comovements between the financial sector and economic activity was less pronounced in the pre-COVID analysis conducted with the WMCC. The study supports both the supply-leading and demand-following hypotheses. Our findings also underscore the need for policymakers, investors and academics alike to incessantly observe the dynamics between finance and growth across time and periodicity while considering adverse shocks from global economic policy uncertainty in tandem.
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13

Sekulić, Aleksandar, Milan Kilibarda, Gerard B. M. Heuvelink, Mladen Nikolić, and Branislav Bajat. "Random Forest Spatial Interpolation." Remote Sensing 12, no. 10 (May 25, 2020): 1687. http://dx.doi.org/10.3390/rs12101687.

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For many decades, kriging and deterministic interpolation techniques, such as inverse distance weighting and nearest neighbour interpolation, have been the most popular spatial interpolation techniques. Kriging with external drift and regression kriging have become basic techniques that benefit both from spatial autocorrelation and covariate information. More recently, machine learning techniques, such as random forest and gradient boosting, have become increasingly popular and are now often used for spatial interpolation. Some attempts have been made to explicitly take the spatial component into account in machine learning, but so far, none of these approaches have taken the natural route of incorporating the nearest observations and their distances to the prediction location as covariates. In this research, we explored the value of including observations at the nearest locations and their distances from the prediction location by introducing Random Forest Spatial Interpolation (RFSI). We compared RFSI with deterministic interpolation methods, ordinary kriging, regression kriging, Random Forest and Random Forest for spatial prediction (RFsp) in three case studies. The first case study made use of synthetic data, i.e., simulations from normally distributed stationary random fields with a known semivariogram, for which ordinary kriging is known to be optimal. The second and third case studies evaluated the performance of the various interpolation methods using daily precipitation data for the 2016–2018 period in Catalonia, Spain, and mean daily temperature for the year 2008 in Croatia. Results of the synthetic case study showed that RFSI outperformed most simple deterministic interpolation techniques and had similar performance as inverse distance weighting and RFsp. As expected, kriging was the most accurate technique in the synthetic case study. In the precipitation and temperature case studies, RFSI mostly outperformed regression kriging, inverse distance weighting, random forest, and RFsp. Moreover, RFSI was substantially faster than RFsp, particularly when the training dataset was large and high-resolution prediction maps were made.
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14

Hobden, Peter, Edmond Nurellari, and Saket Srivastava. "RFSoC Softwarisation of a 2.45 GHz Doppler Microwave Radar Motion Sensor." Journal of Sensor and Actuator Networks 13, no. 5 (September 23, 2024): 58. http://dx.doi.org/10.3390/jsan13050058.

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Microwave Doppler sensors are used extensively in motion detection as they are energy-efficient, small-size and relatively low-cost sensors. Common applications of microwave Doppler sensors are for detecting intrusion behind a car roof liner inside an automotive vehicle and to detect moving objects. These applications require a millisecond response from the target for effective detection. A Doppler microwave sensor is ideally suited to the task, as we are only interested in movement of a large water-based mass (i.e., a person) (FMCW Radar also detect static objects). Although microwave components at 2.45 GHz are now relatively cheap due to mass production of other Industrial Scientific and Medical application (ISM) devices, they do require tuning for temperature compensation, dielectric, and manufacturing variability. A digital solution would be ideal, as chip solutions are known to be more repeatable, but Application-Specific Integrated Circuits (ASICs) are expensive to initially prototype. This paper presents the first completely digital Doppler motion sensor solution at 2.45 GHz, implemented on the new RFSoC from Xilinx without the need to up/downconvert the frequency externally. Our proposed system uses a completely digital approach bringing the benefits of product repeatability, better overtemperature performance and softwarisation, without compromising any performance metric associated with a comparable analogue motion sensor. The RFSoC shows to give superior distance versus false detection, as the Signal-to-Noise Ratio (SNR) is better than a typical analogue system. This is mainly due to the high gain amplification requirement of an analogue system, making it susceptible to electrical noise appearing in the intermediate-frequency (IF) baseband. The proposed RFSoC-based Doppler sensor shows how digital technology can replace traditional analogue radio frequency (RF). A case study is presented showing how we can use a novel method of using multiple Doppler channels to provide range discrimination, which can be performed in both analogue and in a digital implementation (RFSoC).
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Stefanazzi, Leandro, Kenneth Treptow, Neal Wilcer, Chris Stoughton, Collin Bradford, Sho Uemura, Silvia Zorzetti, et al. "The QICK (Quantum Instrumentation Control Kit): Readout and control for qubits and detectors." Review of Scientific Instruments 93, no. 4 (April 1, 2022): 044709. http://dx.doi.org/10.1063/5.0076249.

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We introduce a Xilinx RF System-on-Chip (RFSoC)-based qubit controller (called the Quantum Instrumentation Control Kit, or QICK for short), which supports the direct synthesis of control pulses with carrier frequencies of up to 6 GHz. The QICK can control multiple qubits or other quantum devices. The QICK consists of a digital board hosting an RFSoC field-programmable gate array, custom firmware, and software and an optional companion custom-designed analog front-end board. We characterize the analog performance of the system as well as its digital latency, important for quantum error correction and feedback protocols. We benchmark the controller by performing standard characterizations of a transmon qubit. We achieve an average gate fidelity of [Formula: see text]. All of the schematics, firmware, and software are open-source.
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16

Stefanazzi, Leandro, Kenneth Treptow, Neal Wilcer, Chris Stoughton, Collin Bradford, Sho Uemura, Silvia Zorzetti, et al. "The QICK (Quantum Instrumentation Control Kit): Readout and control for qubits and detectors." Review of Scientific Instruments 93, no. 4 (April 1, 2022): 044709. http://dx.doi.org/10.1063/5.0076249.

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We introduce a Xilinx RF System-on-Chip (RFSoC)-based qubit controller (called the Quantum Instrumentation Control Kit, or QICK for short), which supports the direct synthesis of control pulses with carrier frequencies of up to 6 GHz. The QICK can control multiple qubits or other quantum devices. The QICK consists of a digital board hosting an RFSoC field-programmable gate array, custom firmware, and software and an optional companion custom-designed analog front-end board. We characterize the analog performance of the system as well as its digital latency, important for quantum error correction and feedback protocols. We benchmark the controller by performing standard characterizations of a transmon qubit. We achieve an average gate fidelity of [Formula: see text]. All of the schematics, firmware, and software are open-source.
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17

Stefanazzi, Leandro, Kenneth Treptow, Neal Wilcer, Chris Stoughton, Collin Bradford, Sho Uemura, Silvia Zorzetti, et al. "The QICK (Quantum Instrumentation Control Kit): Readout and control for qubits and detectors." Review of Scientific Instruments 93, no. 4 (April 1, 2022): 044709. http://dx.doi.org/10.1063/5.0076249.

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We introduce a Xilinx RF System-on-Chip (RFSoC)-based qubit controller (called the Quantum Instrumentation Control Kit, or QICK for short), which supports the direct synthesis of control pulses with carrier frequencies of up to 6 GHz. The QICK can control multiple qubits or other quantum devices. The QICK consists of a digital board hosting an RFSoC field-programmable gate array, custom firmware, and software and an optional companion custom-designed analog front-end board. We characterize the analog performance of the system as well as its digital latency, important for quantum error correction and feedback protocols. We benchmark the controller by performing standard characterizations of a transmon qubit. We achieve an average gate fidelity of [Formula: see text]. All of the schematics, firmware, and software are open-source.
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18

Jabłońska, Ewa, Dierk Michaelis, Marlena Tokarska, Klara Goldstein, Mateusz Grygoruk, Mateusz Wilk, Tomasz Wyszomirski, and Wiktor Kotowski. "Alleviation of Plant Stress Precedes Termination of Rich Fen Stages in Peat Profiles of Lowland Mires." Ecosystems 23, no. 4 (September 16, 2019): 730–40. http://dx.doi.org/10.1007/s10021-019-00437-y.

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Abstract Mesotrophic rich fens, that is, groundwater-fed mires, may be long-lasting, as well as transient ecosystems, displaced in time by poor fens, bogs, forests or eutrophic reeds. We hypothesized that fen stability is controlled by plant stress caused by waterlogging with calcium-rich and nutrient-poor groundwater, which limits expansion of hummock mosses, tussock sedges and trees. We analysed 32 European Holocene macrofossil profiles of rich fens using plant functional traits (PFTs) which indicate the level of plant stress in the environment: canopy height, clonal spread, diaspore mass, specific leaf area, leaf dry matter content, Ellenberg moisture value, hummock-forming ability, mycorrhizal status and plant functional groups. Six PFTs, which formed long-term significant trends during mire development, were compiled as rich fen stress indicator (RFSI). We found that RFSI values at the start of fen development were correlated with the thickness of subsequently accumulated rich fen peat. RFSI declined in fens approaching change into another mire type, regardless whether it was shifting into bog, forest or eutrophic reeds. RFSI remained comparatively high and stable in three rich fens, which have not terminated naturally until present times. By applying PFT analysis to macrofossil data, we demonstrated that fens may undergo a gradual autogenic process, which lowers the ecosystem’s resistance and enhances shifts to other mire types. Long-lasting rich fens, documented by deep peat deposits, are rare. Because autogenic processes tend to alleviate stress in fens, high levels of stress are needed at initial stages of rich fen development to enable its long persistence and continuous peat accumulation.
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19

Lv, Fulu. "Development of Athlete Health Monitoring and Early Warning System Based on Sensor Data." Journal of Electrical Systems 20, no. 6s (April 29, 2024): 2037–47. http://dx.doi.org/10.52783/jes.3118.

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An athlete health monitoring and early warning system based on sensor data utilizes wearable sensors to track various physiological parameters, such as heart rate, body temperature, oxygen saturation, and movement patterns. These sensors continuously collect real-time data during training sessions and competitions. Machine learning algorithms analyze the data to detect deviations from normal patterns and identify potential indicators of injury, fatigue, or overtraining. By monitoring athletes' health parameters over time and comparing them to established baseline values, the system can provide early warnings of potential health issues or performance declines. Coaches and medical staff can then intervene promptly with appropriate interventions, adjustments to training loads, or rest periods to prevent injuries and optimize athletes' performance and well-being. . The paper introduces the Reliable Fuzzy Sensor Optimized Automated Monitoring (RFSO-DL) system, a novel approach to athlete health monitoring leveraging fuzzy logic and sensor data optimization techniques. RFSO-DL offers a comprehensive solution for real-time assessment of athlete well-being by analyzing physiological measurements and activity levels. Through a series of experiments, the system demonstrates high accuracy, precision, recall, and F1 score in classifying athlete health status. Results indicate RFSO-DL's effectiveness in capturing subtle variations in health status and providing timely interventions. Through experiments, the system achieves impressive results, with accuracy ranging from 90.5% to 94.1%, precision from 85.8% to 91.2%, recall from 91.7% to 95.6%, and F1 score from 88.6% to 93.3%. These findings underscore RFSO-DL's potential to revolutionize athlete health monitoring by accurately classifying health status based on physiological measurements and activity levels.
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20

Pei, Xin, Na Wang, Dan Werthimer, Xue-Feng Duan, Jian Li, Toktonur Ergesh, Qi Liu, and Ming-Hui Cai. "Design of RFSoC-based Digital Phased Array Feed (PAF) and Hybrid Architecture Beamforming System." Research in Astronomy and Astrophysics 22, no. 4 (March 31, 2022): 045016. http://dx.doi.org/10.1088/1674-4527/ac56cb.

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Abstract As the number of array elements and bandwidth increase, the design challenges of the Phased Array Feed (PAF) front-end and its signal processing system increase. Aiming at the ng-PAF of the 110 m radio telescope, this article introduces the concept of fully digital receivers and attempts to use Radio Frequency System-on-Chip (RFSoC) technology to digitize close to the feed array, reduce the complexity and analog components of the front-end, and improve the fidelity of the signals. The article discusses the digital beamforming topology and designs a PAF signal processing experimental system based on RFSoC+GPU hybrid architecture. The system adopts a ZCU111 board to design RF-direct digitization and preprocessing front-end, which can sample eight signals up to 2.048 GSPS, 12 bit, channelize the signals into 1024 chunks, then reorder into four data streams and select one of the 256 MHz frequency bands to output through four 10 Gb links. A GPU server is equipped with four RTX 3090 GPUs running four HRBF_HASHPIPE instances, each receiving a 64 MHz bandwidth signal for high-throughput real-time beamforming. The experimental system uses a signal generator to emulate Sa-like signals and propagates through rod antennas, which verifies the effectiveness of the beamforming algorithm. Performance tests show that after algorithm optimization, the average processing time for a given 4 ms data is less than 3 ms in the four-GPU parallel processing mode. The RFSoC integrated design shows significant advantages in power consumption and electromagnetic radiation compared with discrete circuits according to the measurement results.
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Wang, Yihao, Linghua Meng, Huanjun Liu, Chong Luo, Yilin Bao, Beisong Qi, and Xinle Zhang. "Construction and Assessment of a Drought-Monitoring Index Based on Multi-Source Data Using a Bias-Corrected Random Forest (BCRF) Model." Remote Sensing 15, no. 9 (May 8, 2023): 2477. http://dx.doi.org/10.3390/rs15092477.

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Agricultural drought significantly impacts agricultural production, highlighting the need for accurate monitoring. Accurate agricultural-drought-monitoring models are critical for timely warning and prevention. The random forest (RF) is a popular artificial intelligence method but has not been extensively studied for agricultural drought monitoring. Here, multi-source remote sensing data, including surface temperature, vegetation index, and soil moisture data, were used as independent variables; the 3-month standardized precipitation evapotranspiration index (SPEI_3) was used as the dependent variable. Soil texture and terrain data were used as auxiliary variables. The bias-corrected RF model was used to construct a random forest synthesized drought index (RFSDI). The drought-degree determination coefficients (R2) of the training and test sets reached 0.86 and 0.89, respectively. The RFSDI and SPEI_3 fit closely, with a correlation coefficient (R) above 0.92. The RFSDI accurately reflected typical drought years and effectively monitored agricultural drought in Northeast China (NEC). In the past 18 years, agricultural drought in NEC has generally decelerated. The degree and scope of drought impacts from 2003 to 2010 were greater than those from 2010 to 2020. Agricultural drought occurrence in NEC was associated with dominant climatic variables such as precipitation (PRE), surface temperature (Ts), relative humidity (RHU), and sunshine duration (SSD), alongside elevation and soil texture differences. The agricultural drought occurrence percentage at 50–500 m elevations reached 94.91%, and the percentage of occurrence in loam and sandy soils reached 90.31%. Water and temperature changes were significantly correlated with the occurrence of agricultural drought. Additionally, NEC showed an alternating cycle of drought and waterlogging of about 10 years. These results have significant application potential for agricultural drought monitoring and drought prevention in NEC and demonstrate a new approach to comprehensively evaluating agricultural drought.
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G. P, Naveen Kumar, Prachi Misra Sahoo, Pankaj Das, Tauqueer Ahmad, and Ankur Biswas. "Random Forest Spatial Interpolation Techniques for Crop Yield Estimation at District Level." Journal of the Indian Society of Agricultural Statistics 78, no. 1 (May 10, 2024): 9–19. http://dx.doi.org/10.56093/jisas.v78i1.2.

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General Crop Estimation Surveys (GCES) based on Crop Cutting Experiments (CCEs) are conducted for estimation of crop yield following random sampling approach for almost all major crops. About 13 lakh CCEs are conducted every year which has now increased rapidly due to the Pradhan Mantri Fasal Bima Yojana (PMFBY) which is yield based insurance scheme. As suggested by Ministry of Agriculture and Farmers’ Welfare (MoA&FW), this number needs to be reduced drastically by developing sampling procedures based on the use of advanced technologies and advanced survey techniques for crop yield estimation. In this study, an attempt has been made to develop crop yield estimation procedures using Random Forest Spatial Interpolation (RFSI) technique including the spatial variables like spatial distance and nearest neighbours as covariates. RFSI is one of the most adaptable and user-friendly interpolation techniques, as well as one of the fastest in large training datasets. Estimates of yield of wheat were obtained for all the six tehsils of Barabanki district using the estimator under stratified two stage sampling technique. The district level estimates were also obtained by pooling area under wheat crop in each tehsil along with the district level estimate of crop yield, estimate of variance, estimate of standard error (SE) and percentage SE (%SE) of these estimates were also computed in order to make comparison. The results of this study suggest that the estimates derived using RFSI are comparable to kriging and superior to inverse distance weighting (IDW) for the prediction of yield at unknown locations using distance and nearest neighbours.
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Nomaru, R., G. Mitsuka, L. Ruckman, and R. Herbst. "Development of a novel bunch oscillation recorder with RFSoC technology." Journal of Instrumentation 19, no. 12 (December 1, 2024): P12026. https://doi.org/10.1088/1748-0221/19/12/p12026.

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Abstract The SuperKEKB accelerator is designed to achieve unprecedented luminosity levels, but this goal is currently hindered by Sudden Beam Loss (SBL) events. These events not only obstruct luminosity improvement but also pose a significant risk to accelerator components, the Belle II detectors, and the superconducting focusing system, potentially leading to severe damage and quenching of the superconducting system. To address this critical challenge, we have developed a novel Bunch Oscillation Recorder (BOR) based on RFSoC technology. The BOR has demonstrated high precision with a position resolution of 0.03 mm, making it a powerful tool for real-time beam monitoring. In its initial deployment, the BOR successfully recorded multiple SBL events, providing valuable data for further analysis. By strategically positioning BORs at the suspected points of SBL origin, we aim to directly identify sources of beam instability. We anticipate that this portable, high-speed BOR monitor will play a crucial role in resolving the SBL issue, ultimately helping achieve SuperKEKB's luminosity targets.
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Farley, Brendan, John McGrath, and Christophe Erdmann. "An All-Programmable 16-nm RFSoC for Digital-RF Communications." IEEE Micro 38, no. 2 (March 2018): 61–71. http://dx.doi.org/10.1109/mm.2018.022071136.

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Ruckman, L., A. Dragone, and R. Herbst. "Performance evaluation of an RFSoC operating in a 1.25 Tesla magnetic field." Journal of Instrumentation 19, no. 12 (December 1, 2024): P12011. https://doi.org/10.1088/1748-0221/19/12/p12011.

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Abstract The Belle II experiment at the SuperKEKB collider is preparing for an upgrade after 2027–2028 to handle higher luminosity and increased background rates. A Radio Frequency System-on-Chip (RFSoC) has been identified as a potential candidate for a common front-end upgrade for subsystems requiring high-speed waveform digitization. The RFSoC's ADC and DAC channels were tested across various magnetic field strengths and a few different field orientations. Power consumption and boot memory functionality were also assessed. Results indicate stable operation with negligible performance degradation, suggesting the RFSoC's viability for high-speed digitization tasks in high magnetic field environments.
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Gartmann, R., T. Muscheid, M. E. Garcia Redondo, M. Fuchs, L. E. Ardila-Perez, and O. Sander. "Evaluating the RFSoC as a Software-Defined Radio readout system for Magnetic Microcalorimeters." Journal of Instrumentation 19, no. 02 (February 1, 2024): C02078. http://dx.doi.org/10.1088/1748-0221/19/02/c02078.

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Abstract Arrays of superconducting sensors enable particle spectrum analysis with superior energy resolution. To efficiently acquire data from frequency multiplexed sensors, the readout electronics operating at room temperature must perform multiple tasks, such as low-noise probe tone generation, frequency demodulation, and data decimation. We designed a Software-Defined Radio (SDR) system composed of an MPSoC board, an analogue-digital conversion stage, and a radio frequency front-end mixing stage to meet the system requirements of 4 GHz instantaneous bandwidth and real-time data analysis. Nevertheless, utilising a Radio Frequency System-on-Chip (RFSoC) could simplify the overall system by integrating the conversion stage. This work investigates the applicability of RFSoCs for the aforementioned use case.
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Lacruz, Jesus O., and Rafael Ruiz. "MIMORPH." GetMobile: Mobile Computing and Communications 26, no. 1 (May 27, 2022): 5–8. http://dx.doi.org/10.1145/3539668.3539670.

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With the rapid increase in performance and complexity of wireless networks, it has become challenging to build experimentation platforms that can meet such performance requirements but at the same time are comparatively easy to use and flexible. The lack of suitable platforms inspired us to build MIMORPH, a single experimentation platform that supports massive MIMO sub-6 GHz systems, ultra-high bandwidth Millimeter Wave (mmWave) MIMO, as well as mixed sub-6 GHz and mmWave configurations. It can be operated in a closed-loop manner and is intended for WLAN, 5G-NR and future 6G research. MIMORPH is built on top of standard components, such as a state-of-the-art RFSoC FPGA system and its implementation is made freely available to the research community.
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Jeon, Joongnam, Bo-Seok Seo, Youngkwan Ju, Kyung-Cheol Lim, and Seong-Jun Lee. "Development of a Real-Time Spectrum Analyzer for Radar Pulse Signals Using Xilinx RFSoC." Journal of Korean Institute of Electromagnetic Engineering and Science 34, no. 1 (January 2023): 69–78. http://dx.doi.org/10.5515/kjkiees.2023.34.1.69.

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Smith, Jennifer Pearl, J. I. Bailey, John Tuthill, Leandro Stefanazzi, Gustavo Cancelo, Ken Treptow, and Benjamin A. Mazin. "A High-Throughput Oversampled Polyphase Filter Bank Using Vivado HLS and PYNQ on a RFSoC." IEEE Open Journal of Circuits and Systems 2 (2021): 241–52. http://dx.doi.org/10.1109/ojcas.2020.3041208.

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Olesiński, Adam, and Zbigniew Piotrowski. "A Radio Frequency Region-of-Interest Convolutional Neural Network for Wideband Spectrum Sensing." Sensors 23, no. 14 (July 18, 2023): 6480. http://dx.doi.org/10.3390/s23146480.

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Wideband spectrum sensing plays a crucial role in various wireless communication applications. Traditional methods, such as energy detection with thresholding, have limitations like detecting signals with low signal-to-noise ratio (SNR). This article proposes a novel deep learning-based approach for RF signal detection in the wideband spectrum. The objective is to accurately estimate the noise distribution in a wideband radio spectrogram and improve the detection performance by substracting it. The proposed method utilizes convolutional neural networks to analyze radio spectrograms. Model evaluation demonstrates that the RFROI-CNN approach outperforms the traditional energy detection with thresholding method by achieving significantly better detection results, even up to 6 dB, and expanding the capabilities of wideband spectrum sensing systems. The proposed approach, with its precise estimation of noise distribution and consideration of neighboring signal power values, proves to be a promising solution for RF signal detection.
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Pei, Xin, Jian Li, Xuefeng Duan, and Hailong Zhang. "QTT Ultra-wideband Signal Acquisition and Baseband Data Recording System Design Based on the RFSoC Platform." Publications of the Astronomical Society of the Pacific 135, no. 1049 (July 1, 2023): 075003. http://dx.doi.org/10.1088/1538-3873/ace12d.

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Abstract The 110 m QiTai radio Telescope (QTT) will be equipped with multiple Ultra-WideBand (UWB) receivers in the primary and Gregory focus to achieve continuous frequency coverage from 270 MHz to 115 GHz, which poses great challenges to signal acquisition, transmission, and real-time processing. Aiming at 10 GHz and above full-bandwidth acquisition and multi-scientific processing for the QTT UWB signals, an experimental system with high-speed signal acquisition, 100 Gb network multi-path distribution, and fast recording is designed by using advanced direct RF-sampling technology and heterogeneous architecture. The system employs a ZCU111 board to digitize dual-polarization signals with a sampling rate of 4.096 GigaSamples-Per-Second and 12-bit quantization. The collected wideband signals are channelized into 2048 chunks, which are then assembled into 16 sets of digital narrow basebands with 128 MHz bandwidth and transmitted to the processing servers through two 100 Gb ports. A HASPIPE pipeline, UWB_HASHPIPE is designed to receive and store multiple subbands in parallel. Data distribution links can be flexibly configured based on IP addresses and port numbers. The system is verified by pulsar observation experiments on the Nanshan 26 m telescope. 512 MHz bandwidth is selected from the collected L-band receiver signals and recorded in VDIF file format with 8 parallel instances. The test results show that the data integrity is excellent, and the signal-to-noise ratio of the band-merged pulsar profile is stronger than single subband data. This paper provides a high-performance and flexible solution for the design of versatile UWB backends. Meanwhile, the developed platform can be integrated into QTT backends for baseband data collection and Very Long Baseline Interferometry observation.
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Shamseera KK, Selvavinayaki K. "An investigation of Glaucoma Detection in RFI Using Bio-Inspired Multivariable FE with ML Models for Early Diagnosis." Tuijin Jishu/Journal of Propulsion Technology 44, no. 4 (October 16, 2023): 7551–66. http://dx.doi.org/10.52783/tjjpt.v44.i4.2607.

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To propose a bio-inspired multi-variant feature extraction technique in retinal fundus images to improve glaucoma detection for easy diagnosis. To improve the classification accuracy of glaucoma disease, a machine learning approach is employed based on the features extracted in RFI. Methods: The Fish School Search MOFS (FSS-MOFS) bio-inspired technique is employed to extract the multivariable features from digital RFI and spot the glaucoma variations to maximize the discrimination power and minimize the dimensionality. The Binary Robust Independent Elementary Feature (BRIEF-LD) technique is employed to identify the latent spots in RFI. The PAPILA dataset is utilized for this research study, which includes the clinical records of 244 patients and 488 images of the right and left eye of all the patients under 2 categories (male and female) with results in healthy, glaucoma, and suspect categories. In order to perform deep pixel identification and RFI segmentation, the Densenet-121 architecture is used along with a deep-lab model to incarcerate the built-in fine points of RFI with the help of dilated convolutions. The MATLAB tool is used to assess the suggested bio-inspired technique, and comparative analysis is done against the prevailing glaucoma detection and feature extraction methods such as I-SVM, RFSO, SE-GSO, VGG-Net, and DBN classifiers. Findings: The suggested FSS-MOFS multi-variant feature extraction technique outperforms the prevailing ML methods such as I-SVM, RFSO, SE-GSO, VGG-Net, and DBN classifiers. The result of the comparative study shows 94% accuracy, 96% sensitivity, 97% specificity, 96% F-measure, 96.8% Precision, 97% recall, 95% detection speed, 92.06% TPR, 92.15% TNR, and 8% FPR & FNR. Novelty: The promising result of the FSS-MOFS method boosts the accuracy level of glaucoma detection and classification compared to other classification techniques. The suggested model helps the ophthalmologists identify and diagnose glaucoma in a robust way. FSS-MOFS overcomes the shortcomings of prevailing feature extraction methods.
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Ieki, S., S. Asami, S. Axani, S. Futagi, K. Hosokawa, K. Ishidoshiro, T. Nakahata, and K. Nakamura. "Concept of KamLAND2 DAQ system." Journal of Physics: Conference Series 2374, no. 1 (November 1, 2022): 012104. http://dx.doi.org/10.1088/1742-6596/2374/1/012104.

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The KamLAND-Zen experiment is searching for neutrinoless double-beta decay of 136Xe. We are preparing for the upgrade of the KamLAND detector, KamLAND2. It is designed to improve the discrimination power of two-neutrino double-beta decay and cosmic-ray muon spallation backgrounds. Data acquisition of all neutrino events from nearby supernova is also an important issue of the KamLAND2 experiment and the data rate is a key factor to design a new DAQ system. High speed sampling will be realized with RFSoC on the front-end electronics. Data is read out from FEE to DAQ computers via 10 GbE, and FairMQ is one of the candidates for KamLAND2 DAQ software. In the KamLAND DAQ, the whole trigger system is implemented in hardware, while the KamLAND2 DAQ adopts hardware trigger for photon detection and software trigger to extract physics events. A simulation study of the software trigger is proceeding in order to detect low energy events using timing and charge information.
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34

Goldsmith, Josh, Craig Ramsay, David Northcote, Kenneth W. Barlee, Louise H. Crockett, and Robert W. Stewart. "Control and Visualisation of a Software Defined Radio System on the Xilinx RFSoC Platform Using the PYNQ Framework." IEEE Access 8 (2020): 129012–31. http://dx.doi.org/10.1109/access.2020.3008954.

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35

Kirij, Natali V., Sergej V. Pasenok, Yurij L. Yagupolskii, Dieter Naumann, and Wieland Tyrra. "Synthesis of bismuthio methanides containing the RfSO2 group, (C6H5)3Bi=C(SO2Rf)2 [Rf=CF3, n-C4F9, OCH2CF2CF2H]." Journal of Fluorine Chemistry 66, no. 1 (January 1994): 75–78. http://dx.doi.org/10.1016/0022-1139(93)02898-o.

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ZHANG, BAI-LING, LEI XU, and MINYUE FU. "LEARNING MULTIPLE CAUSES BY COMPETITION ENHANCED LEAST MEAN SQUARE ERROR RECONSTRUCTION." International Journal of Neural Systems 07, no. 03 (July 1996): 223–36. http://dx.doi.org/10.1142/s0129065796000208.

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In this paper we studied a self-organization principle that input should be best reconstructed from a factorial distributed hidden representation, which has been addressed in the literature recently. An auto-encoder network is trained by the Least Mean Square Error Reconstruction (LMSER) while the redundance in the representation is reduced by a proposed anti-Hebbian scheme, in which a penalty term called Receptive Field Overlapping Index (RFOI) is combined into the objective function for enhancing competition among nodes in the network. Our learning scheme provides a way for balancing the cooperation and competition necessary for the self-organization process thus realizes the multiple causes model, which accounts for an observed data by combining assertions from the discovered causes or features in the data. Our experiment results demonstrate again the powerful information processing capability inherent to the popular weighted sum followed by sigmoid squashing. Comparing with previous probability theory based multiple causes models, our scheme is much easier to implement and quite reliable.
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37

Brennsteiner, Stefan, Tughrul Arslan, John Thompson, and Andrew McCormick. "A Real-Time Deep Learning OFDM Receiver." ACM Transactions on Reconfigurable Technology and Systems 15, no. 3 (September 30, 2022): 1–25. http://dx.doi.org/10.1145/3494049.

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Machine learning in the physical layer of communication systems holds the potential to improve performance and simplify design methodology. Many algorithms have been proposed; however, the model complexity is often unfeasible for real-time deployment. The real-time processing capability of these systems has not been proven yet. In this work, we propose a novel, less complex, fully connected neural network to perform channel estimation and signal detection in an orthogonal frequency division multiplexing system. The memory requirement, which is often the bottleneck for fully connected neural networks, is reduced by ≈ 27 times by applying known compression techniques in a three-step training process. Extensive experiments were performed for pruning and quantizing the weights of the neural network detector. Additionally, Huffman encoding was used on the weights to further reduce memory requirements. Based on this approach, we propose the first field-programmable gate array based, real-time capable neural network accelerator, specifically designed to accelerate the orthogonal frequency division multiplexing detector workload. The accelerator is synthesized for a Xilinx RFSoC field-programmable gate array, uses small-batch processing to increase throughput, efficiently supports branching neural networks, and implements superscalar Huffman decoders.
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Zhua, Shi Zheng, Qi Chen Huang, Qing Son Liu, and Bin Xu. "Bis(perfluoroalkanesulfonyl)methylene dimethyloxosulfonium ylids(RFSO2)2C−−S+(O)Me2: formation and X-ray structure ofMe2(O)S−−O2." Journal of Fluorine Chemistry 67, no. 2 (May 1994): 109–12. http://dx.doi.org/10.1016/0022-1139(93)02957-g.

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39

Wahyuningrum, Tenia, Gita Fadila Fitriana, and Dyah Wahyuningsih. "The combination of user experience evaluation method in asessing the application of suicide risk idea identification." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 2 (August 1, 2022): 1025. http://dx.doi.org/10.11591/ijeecs.v27.i2.pp1025-1033.

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The global pandemic COVID-19 has led to extreme loss of human life worldwide. <span lang="EN-US">The pandemic has made a profound impact on the psyche of some people, and it has led to suicide. The need for confident handling of specific risk factors for suicide is needed. Therefore, to support Indonesian governments to control the increment of suicidal ideas for adolescents using a combination of unmoderated remote usability testing (URUT) and usability metric for user experience (UMUX)-Lite. Then we adopted the school-based mental health program to make a prototype mobile application using the risk factor for suicidal ideation (RFSI) instrument to identify suicidal ideas in adolescents. Participants’ characteristics included late adolescents aged 19-22 years, male and female. The results show that the time-based efficiency on the registered task obtained from the calculation is 0.025 goals/sec. In one second, participants could complete a 0.025 of the job; although all participants could complete all tasks well and quickly, they provided good satisfaction scores. Some design improvements are needed on the prototype by considering user input</span>.
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40

Lamoral Coines, Adrián, and Víctor P. Gil Jiménez. "CCSDS 131.2-B-1 Transmitter Design on FPGA with Adaptive Coding and Modulation Schemes for Satellite Communications." Electronics 10, no. 20 (October 12, 2021): 2476. http://dx.doi.org/10.3390/electronics10202476.

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Satellite communications are a well-established research area in which the main innovation of last decade has been the use of multi-carrier modulations and more robust channel coding techniques. However, in recent years, novel advanced signal processing has started being developed for these communications due to the increase in the signal processing capacity of transmitters and receivers. Although signal processing capabilities are increasing, they are still constrained by large limitations because these techniques need to be implemented in real hardware, thus making complexity a matter of critical importance. Therefore, this paper presents the design and implementation of a transmitter with adaptable coding and modulation on a field-programmable-gate-array (FPGA). The main motivation came from the standard CCSDS 131.2-B-1 which recommends that such a novel transmitter which has to date not been implemented in a real system The system was modeled by MATLAB with the purpose of being programmed in VHDL following the AXI-stream protocol between components. Behavioral simulation results were obtained in VIVADO and compared with MATLAB for verification purposes. The transmitter logical circuit was synthesized in a FPGA Zynq Ultrascale RFSoC ZU28DR, showing low resource consumption and correct functioning, leading us to conclude that the deployment of new communication systems in state-of-the-art hardware in satellite communications is justified.
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Jiang, Yiyue, Andrius Vaicaitis, John Dooley, and Miriam Leeser. "Efficient Neural Networks on the Edge with FPGAs by Optimizing an Adaptive Activation Function." Sensors 24, no. 6 (March 13, 2024): 1829. http://dx.doi.org/10.3390/s24061829.

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The implementation of neural networks (NNs) on edge devices enables local processing of wireless data, but faces challenges such as high computational complexity and memory requirements when deep neural networks (DNNs) are used. Shallow neural networks customized for specific problems are more efficient, requiring fewer resources and resulting in a lower latency solution. An additional benefit of the smaller network size is that it is suitable for real-time processing on edge devices. The main concern with shallow neural networks is their accuracy performance compared to DNNs. In this paper, we demonstrate that a customized adaptive activation function (AAF) can meet the accuracy of a DNN. We designed an efficient FPGA implementation for a customized segmented spline curve neural network (SSCNN) structure to replace the traditional fixed activation function with an AAF. We compared our SSCNN with different neural network structures such as a real-valued time-delay neural network (RVTDNN), an augmented real-valued time-delay neural network (ARVTDNN), and deep neural networks with different parameters. Our proposed SSCNN implementation uses 40% fewer hardware resources and no block RAMs compared to the DNN with similar accuracy. We experimentally validated this computationally efficient and memory-saving FPGA implementation of the SSCNN for digital predistortion of radio-frequency (RF) power amplifiers using the AMD/Xilinx RFSoC ZCU111. The implemented solution uses less than 3% of the available resources. The solution also enables an increase of the clock frequency to 221.12 MHz, allowing the transmission of wide bandwidth signals.
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Bertolucci, Matteo, Riccardo Cassettari, and Luca Fanucci. "On the Frequency Carrier Offset and Symbol Timing Estimation for CCSDS 131.2-B-1 High Data-Rate Telemetry Receivers." Sensors 21, no. 9 (April 21, 2021): 2915. http://dx.doi.org/10.3390/s21092915.

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In recent years there have been significant developments in satellite transmitter technology to follow the rapid innovation of sensors on-board new satellites. The CCSDS 131.2-B-1 standard for telemetry downlink, released in 2012, is part of the next generation of standards that aims to support the increased data-rate caused by these improvements in resolution. As a result of its relative novelty, this standard currently lacks in-depth analysis by researchers, but it is also strongly supported by the European Space Agency (ESA) for future missions. For these reasons, it seems important to evaluate how major receiver sub-components, such as timing recovery and carrier frequency correction, can be designed and implemented in new receivers that support this standard. The timing error detectors (TED) and frequency error detectors (FED) were therefore studied on the specific peculiarities of CCSDS 131.2-B-1 in its usual environment of Low Earth Orbit (LEO). Estimators have been evaluated highlighting performances, trade-offs and peculiarities of each one with respect to corresponding architectural choices. Finally, a receiver architecture derived from the paper considerations is proposed in the aim of supporting very different mission scenarios. Specifically, the realized architecture employs a parallel feedforward estimator for the timing recovery section and a novel multi-algorithm feedback frequency correction loop to efficiently cover both low symbol rates (5 Mbaud) and high data-rates (up to 500 Mbaud). This solution represents a good trade-off to support these scenarios in a very compact footprint by pushing the clock frequency to the FPGA limit. The FPGA resources occupation on a Zynq Ultrascale+ RFSoC XCZU28DR FPGA is 5202 LUT, 4851 FF, 5 BRAM, and 21 DSP for the timing recovery part, while the frequency recovery section occupies 1723 LUT, 1511 FF, 2.5 BRAM and 32 DSP.
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43

Tejas Prashantrao Adhau. "Incentivized BiLSTM with Triplet Attention for Predecting Congestion in Network Traffic." Panamerican Mathematical Journal 35, no. 1s (November 13, 2024): 72–89. http://dx.doi.org/10.52783/pmj.v35.i1s.2099.

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In the world of online live video streaming, network traffic congestion poses a serious risk to user experience and service quality. Several factors contributing to the congestion include insufficient bandwidth, an increase in user demand, as well as ineffective data routing. The incapacity of the prediction algorithms to Adjust to modifications in network circumstances and challenges in detecting long-range relationships may restrict their ability to effectively estimate congestion in dynamic contexts when it comes to online streaming video. In order to overcome these constraints, this study created an Incentivized-RF-Triplet ASTM (“Incentivized learning-based triplet attention enabled rat fierce hunting optimized Bidirectional Long Short-Term Memory) for network traffic congestion prediction in online streaming video. A reward” scheme built into the Incentivized learning mechanism pushes the algorithm to prioritize online live video streaming congestion prediction. The sequential structure of network traffic data is handled by the BiLSTM architecture, that is well-known for capturing temporal dependencies. By utilizing the triplet Attention method, the model is better able to identify relevant regions within the input data as well as identify congestion patterns more successfully. The RFSO method incorporates social behavior with selection and searching qualities to further improve the classifier's parameters. Therefore, the characteristics of the model can be tuned more effectively and robustly, improving its effectiveness in congestion detection. The outcomes of the experiment show how well the Incentivized-RF-Triplet ASTM technique works to precisely estimate traffic congestion for the Darpa99week1 dataset. 95.97% accuracy, 96.08% specificity, and 0.22 mean square error are reported.
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44

Karaman, Ç. H., and Z. Akyurek. "IMPROVING THE ACCURACY OF SATELLITE-BASED NEAR SURFACE AIR TEMPERATURE AND PRECIPITATION PRODUCTS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-M-1-2023 (August 15, 2023): 537–45. http://dx.doi.org/10.5194/isprs-archives-xlviii-m-1-2023-537-2023.

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Abstract. In this study, we evaluate the performance of several reanalyses and satellite-based products of near-surface air temperature and precipitation to determine the best product in estimating daily and monthly variables across the complex terrain of Turkey. Each product’s performance was evaluated using 1120 ground-based gauge stations from 2015 to 2019, covering a range of complex topography with different climate classes according to the Köppen-Geiger classification scheme and land surface types according to the Moderate Resolution Imaging Spectroradiometer (MODIS). Furthermore, various traditional and more advanced machine learning downscaling algorithms were applied to improve the spatial resolution of the products. We used distance-based interpolation, classical Random Forest, and more innovative Random Forest Spatial Interpolation (RFSI) algorithms. We also investigated several satellite-based covariates as a proxy to downscale the precipitation and near-surface air temperature, including MODIS Land Surface Temperature, Vegetation Index (NDVI and EVI), Cloud Properties (Cloud Optical Properties, Cloud Effective Radius, Cloud Water Path), and topography-related features. The agreement between the ground observations and the different products, as well as the downscaled temperature products, was examined using a range of commonly employed measures. The results showed that AgERA5 was the best-performing product for air temperature estimation, while MSWEP V2.2 was superior for precipitation estimation. Spatial downscaling using bicubic interpolation improved air temperature product performance, and the Random Forest (RF) machine learning algorithm outperformed all other methods in certain seasons. The study suggests that combining ground-based measurements, precipitation products, and features related to topography can substantially improve the representation of spatiotemporal precipitation distribution in data-scarce regions.
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45

Baietto, Anthony, Jayson Boubin, Patrick Farr, Trevor J. Bihl, Aaron M. Jones, and Christopher Stewart. "Lean Neural Networks for Autonomous Radar Waveform Design." Sensors 22, no. 4 (February 9, 2022): 1317. http://dx.doi.org/10.3390/s22041317.

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In recent years, neural networks have exploded in popularity, revolutionizing the domains of computer vision, natural language processing, and autonomous systems. This is due to neural networks ability to approximate complex non-linear functions. Despite their effectiveness, they generally require large labeled data sets and considerable processing power for both training and prediction. Some of these bottlenecks have been mitigated by recent increased availability of high-quality data sets, improvements in neural network development software, and greater hardware support. Due to algorithmic bloat, neural network inference times and imprecision make them undesirable for some problems where fast classical algorithm solutions already exist, other classes of algorithms, such as convex optimization, with non-trivial execution times could be reduced using neural solutions. These algorithms could be replaced with light-weight neural networks, benefiting from their high degree of parallelization and high accuracy when properly trained. Previous work has explored how low size, weight, and power (low SWaP) neural networks and neuromorphic computing can be used to improve autonomous radar waveform design techniques that currently rely on convex optimization. Autonomous radar waveform design helps meet the need for interference mitigation caused by an ever-growing number of consumer and commercial technologies which pollute the radio frequency (RF) spectrum. Spectral notching, a radar waveform design technique, augments transmitted radar waveforms to avoid frequencies with excessive interference while maintaining the integrity of the waveform. In this paper, we extend that work, demonstrating that lean neural networks and specialized hardware can improve inference time for waveform design without sacrificing accuracy. Our lean neural solution incorporates problem-specific information into the layer structures and loss functions to decrease network size and improve accuracy. We provide model outcomes implemented on radio frequency system on a chip (RFSoC) hardware that support our simulation results. Our neural network solution decreases inference time on traditional CPU hardware by 1057× and on GPU hardware accelerators by 883× while maintaining 99% cosine similarity.
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46

Moulin, M., M. Rack, T. Fache, Z. Chalupa, C. Plantier, Y. Morand, J. Lacord, et al. "High-Resistivity Silicon-based Substrate using Buried PN Junctions towards RFSOI applications." Solid-State Electronics, April 2022, 108301. http://dx.doi.org/10.1016/j.sse.2022.108301.

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47

Patil, Shubham, Abhishek Kadam, Jay Sonawane, Shreyas Deshmukh, R. Gaurav, Ajay Kumar Singh, Sandip Lashkare, Veeresh Deshpande, Laxmeesha Somappa, and Udayan Ganguly. "Area and Energy-Efficient Quantum Tunneling-Based Thermal Sensor on 45nm RFSOI Technology." IEEE Transactions on Electron Devices, 2024, 1–5. http://dx.doi.org/10.1109/ted.2024.3469177.

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48

Uddin, Syed Mohammad Ashab, and Wooram Lee. "Design and Implementation of a D-Band Bidirectional Common-Gate Amplifier in 45-nm RFSOI." IEEE Journal of Solid-State Circuits, 2024, 1–11. https://doi.org/10.1109/jssc.2024.3504358.

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49

Baek, Seungjae, Jooseok Lee, Kihyun Kim, Seungwon Park, Hansik Oh, Taewan Kim, Joonho Jung, et al. "A Large-Scale, Low-Power, Compact 5G mm-Wave Phased-Array Transceiver in 45 nm RFSOI CMOS." IEEE Transactions on Microwave Theory and Techniques, 2025, 1–14. https://doi.org/10.1109/tmtt.2025.3544620.

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50

Li, Siwei, Zhe Zhang, Bhaskara Rupakula, and Gabriel M. Rebeiz. "An Eight-Element 140-GHz Wafer-Scale IF Beamforming Phased-Array Receiver With 64-QAM Operation in CMOS RFSOI." IEEE Journal of Solid-State Circuits, 2021, 1. http://dx.doi.org/10.1109/jssc.2021.3102876.

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