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Supervised and Semi-Supervised Learning for Failure Identification in Microwave Networks.

, , , , , , , , and . IEEE Trans. Netw. Serv. Manag., 18 (2): 1934-1945 (2021)

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Supervised and Semi-Supervised Learning for Failure Identification in Microwave Networks., , , , , , , , and . IEEE Trans. Netw. Serv. Manag., 18 (2): 1934-1945 (2021)Relaxation Digital-to-Analog Converter with Radix-based Digital Correction., , and . ISCAS, page 1402-1406. IEEE, (2022)A 880 nW, 100 kS/s, 13 bit Differential Relaxation-DAC in 180 nm., , , , and . PRIME, page 269-272. IEEE, (2023)Emerging Relaxation and DDPM D/A Converters: Overview and Perspectives., , , and . MWSCAS, page 1-6. IEEE, (2022)Relaxation Digital-to-Analog Converter with Foreground Digital Self-Calibration., , and . ISCAS, page 1-5. IEEE, (2020)Design of an Analog and of a Digital-Based OTA in Flexible Integrated Circuit Technology., , , and . ICECS 2022, page 1-4. IEEE, (2022)Explainable Artificial Intelligence in communication networks: A use case for failure identification in microwave networks., , , , , , , , , and . Comput. Networks, (2022)FPGA-Based Relaxation D/A Converters With Parasitics-Induced Error Suppression and Digital Self-Calibration., , and . IEEE Trans. Circuits Syst. I Regul. Pap., 68 (6): 2494-2507 (2021)Direct Digital Sensing Potentiostat targeting Body-Dust., , and . BioCAS, page 280-283. IEEE, (2022)Design of Relaxation Digital-to-Analog Converters for Internet of Things Applications in 40nm CMOS., , and . APCCAS, page 13-16. IEEE, (2019)