SUMMARYA superharmonic voltage-controlled injection-locked frequency divider, implemented using a modified Colpitts oscillator operating at 2.5, 5 and 10 GHz and a cross-coupled LC oscillator operating at 1.25, 2.5 and 5 GHz, is demonstrated. The proposed triple-band operation is achieved by employing a novel technique that uses pin-diodes and negative power supply. The discrete dividers, built with low noise hetero-junction FETs and high-frequency SiGe BJTs, are described theoretically while their functionality is proven experimentally. Additionally, a short phase noise analysis, which is missing in the literature, is given. Phase noise, frequency range of operation, and locking range measurement results are presented. Finally, post-layout simulation results of a 5 GHz fully differential injection-locked frequency divider, implemented in a 0.25 m SiGe process are provided.

, eliska chalupova 2 , fotis c. plessas 3 & panagiotis Alexiou 1 ✉ Genomic regions that encode small RNA genes exhibit characteristic patterns in their sequence, secondary structure, and evolutionary conservation. Convolutional Neural Networks are a family of algorithms that can classify data based on learned patterns. Here we present MuStARD an application of Convolutional Neural Networks that can learn patterns associated with user-defined sets of genomic regions, and scan large genomic areas for novel regions exhibiting similar characteristics. We demonstrate that MuStARD is a generic method that can be trained on different classes of human small RNA genomic loci, without need for domain specific knowledge, due to the automated feature and background selection processes built into the model. We also demonstrate the ability of MuStARD for inter-species identification of functional elements by predicting mouse small RNAs (pre-miRNAs and snoRNAs) using models trained on the human genome. MuStARD can be used to filter small RNA-Seq datasets for identification of novel small RNA loci, intra-and inter-species, as demonstrated in three use cases of human, mouse, and fly pre-miRNA prediction. MuStARD is easy to deploy and extend to a variety of genomic classification questions. Code and trained models are freely available at gitlab.com/ RBP_Bioinformatics/mustard.

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