Instruct: Given the title of a scientific paper, retrieve paper titles that are cited by the given paper
Query: A Near-Zero-Power Wake-Up Receiver Achieving −69-dBm Sensitivity
A 4.5 μW 2.4 GHz wake-up receiver based on complementary current-reuse RF detector
A 2.4 GHz passive wake-up circuit for power minimization in wireless sensor nodes
Low-Cost Wake-Up Receiver for RF Energy Harvesting Wireless Sensor Networks
A Low-Power Self-Forward-Body-Bias CMOS LNA for 3–6.5-GHz UWB Receivers
A 14.4nW 122KHz dual-phase current-mode relaxation oscillator for near-zero-power sensors
Low-Power, High-Data Rate 915 MHz Transceiver with Fully Passive Wake-Up Receiver for Biomedical Implants
Low-Power 2.4 GHz Wake-Up Radio for Wireless Sensor Networks
A 0.45-V Low-Power OOK/FSK RF Receiver in 0.18 $\mu\text{m}$ CMOS Technology for Implantable Medical Applications
WaCo: A Wake-Up Radio COOJA Extension for Simulating Ultra Low Power Radios
21.6 A 1.2cm2 2.4GHz self-oscillating rectifier-antenna achieving −34.5dBm sensitivity for wirelessly powered sensors
A 0.4V ultra low-power UWB CMOS LNA employing noise cancellation
A 3.6cm2 wirelessly-powered UWB SoC with −30.7dBm rectifier sensitivity and sub-10cm range resolution
Zero-IF receivers for phased array radars
Low-Noise Active Cancellation of Transmitter Leakage and Transmitter Noise in Broadband Wireless Receivers for FDD/Co-Existence
A Broadband Noise-Canceling CMOS LNA for 3.1–10.6-GHz UWB Receivers
Wake-up receiver operating at 433 MHz
Design of miniaturized wireless power receivers for mm-sized implants
An ultrawideband CMOS low-noise amplifier for 3.1-10.6-GHz wireless receivers
Development of high sensitivity amplifier for VLF receiver application
A low-voltage 5.1-5.8-GHz image-reject receiver with wide dynamic range
Active varactor tunable bandpass filter
Applications of Graph Theory in Computer Science
Comparison between Caprini and Padua risk assessment models for hospitalized medical patients at risk for venous thromboembolism: a retrospective study
Endodontic Repair Filling Materials: A Review Article
Learning Inverse Mapping by Autoencoder based Generative Adversarial Nets