A 2.6-to-4.1GHz Fractional-N Digital PLL Based on a Time-Mode Arithmetic Unit Achieving -249.4dB FoM and -59dBc Fractional Spurs

In a fractional-N PLL, it is beneficial to minimize the input range of its phase detector (PD) as it promotes better linearity and higher PD gain for suppressing noise contributions of the following loop components. This can be done by canceling the predicted instantaneous time offset between the frequency reference (FREF) and the variable...

A Low-Spur Fractional-N PLL Based on a Time-Mode Arithmetic Unit

This article introduces a low-jitter low-spur fractional-N phase-locked loop (PLL) adopting a new concept of a time-mode arithmetic unit (TAU) for phase error extraction. The TAU is a time-signal processor that calculates the weighted sum of input time offsets. It processes two inputs - the period of a digitally controlled oscillator (DCO)...

From Deterministic to Generative: Multimodal Stochastic RNNs for Video Captioning

Video captioning, in essential, is a complex natural process, which is affected by various uncertainties stemming from video content, subjective judgment, and so on. In this paper, we build on the recent progress in using encoder-decoder framework for video captioning and address what we find to be a critical deficiency of the existing...

Adaptive DDK Filter for GRACE Time‐Variable Gravity Field with a Novel Anisotropic Filtering Strength Metric

Filtering for GRACE temporal gravity fields is a necessary step before calculating surface mass anomalies. In this study, we propose a new denoising and decorrelation kernel (DDK) filtering scheme called adaptive DDK filter. The involved error covariance matrix (ECM) adopts nothing but the monthly time‐variable released by several data...

Unified Binary Generative Adversarial Network for Image Retrieval and Compression

Binary codes have often been deployed to facilitate large-scale retrieval tasks, but not that often for image compression. In this paper, we propose a unified framework, BGAN+, that restricts the input noise variable of generative adversarial networks to be binary and conditioned on the features of each input image, and simultaneously learns...