This paper proposes a quad-core, quad-mode millimeter-wave (mmWave) voltage-controlled oscillator (VCO) utilizing electromagnetic hybrid coupling. This design achieves a wide frequency tuning range and low phase noise, fulfilling the needs of integrated sensing and communication (ISAC) systems. The oscillator addresses the challenge of concurrent oscillations in multi-mode switching oscillators by implementing a switched transconductance network. The VCO also employs a controllable tail transistor array to suppress flicker noise up-conversion across a wide bandwidth, effectively reducing the 1/f3corner frequency and enhancing the phase noise performance. The VCO is fabricated in a 55-nm CMOS process occupying a core area of 0.08 mm2. Measurement results show that the VCO achieves a tuning range of 51.9% spanning from 18.7 to 31.8 GHz, and a phase noise of -110.0 dBc/Hz at 1 MHz offset while consuming 8.4 mW of power, leading to a figure of merit (FoM) of 186.2 dBc/Hz and FoMTof 200.5 dBc/Hz at 1 MHz offset.

This paper proposes a frequency-domain model for fast and accurate prediction of frequency modulation (FM) error in frequency-modulated continuous wave (FMCW) radar chirp generators. Obtaining FM error from the output frequency of a phase-locked loop (PLL) requires lengthy simulation times due to the simulator’s limited frequency quantization accuracy, which can result in discrepancies between simulated and actual performance. To address this issue, the feedback signal (DIV) sent to the phase-frequency detector (PFD) is employed in this study to quickly and accurately determine the FM error through spectrum analysis. During frequency sweeps, the feedback signal with a relatively constant frequency receives the modulating signal directly. To validate the effectiveness of the proposed model, a nested PLL-based frequency synthesizer operating from 24-27.52 GHz is designed in a 55-nm CMOS process. Measurement results show good agreement with those obtained from the model, with the difference between the simulated and measured root mean square (RMS) FM error being less than 22 kHz.

Because social networks have become a vital part of people's lives, cyberbullying becomes the most common risk encountered by young people on social networking platforms and raised serious concerns in society. Over the past few decades, most existing work on cyberbullying has focused on text analysis. Yet, the cyberbullying develops into multi-objective, multi-channel, and multi-form. Traditional text analysis methods cannot satisfy the diversity of bullying data in social networks. To deal with the new type of cyberbullying, we propose a multi-modal detection framework that takes into multi-modal information(e.g., image, video, comments, time) on social networks. Specifically, we not only extract textual features but also use the hierarchical attention networks to capture the session feature in social networks and encode several media information(e.g., video, image). Based on these features, we model the multi-modal cyberbullying detection framework to solve the new form of cyberbullying. Experimental analysis on two real-world datasets shows that our framework outperforms several existing state-of-the-art models.