Models developed using Nanopore direct RNA sequencing data from in vitro synthetic RNA with all adenosine replaced by N6-methyladenosine (m6A), are likely distorted due to superimposed signals from saturated m6A residues. Here, we develop a neural network, DENA, for m6A quantification using the sequencing data of in vivo transcripts from Arabidopsis. DENA identifies 90% of miCLIP-detected m6A sites in Arabidopsis, and obtains modification rates in human consistent to those found by SCARLET, demonstrating its robustness across species. We sequence the transcriptome of two additional m6A-deficient Arabidopsis, mtb and fip37-4, using Nanopore and evaluate their single-nucleotide m6A profiles using DENA.
Abstract. Radar signal processing is a promising tool for vital sign monitoring.
For contactless observation of breathing and heart rate a precise measurement of the distance between radar antenna and the patient's skin is required.
This results in the need to detect small movements in the range of 0.5 mm and below.
Such small changes in distance are hard to be measured with a limited radar bandwidth when relying on the frequency based range detection alone.
In order to enhance the relative distance resolution a precise measurement of the observed signal's phase is required.
Due to radar reflections from surfaces in close proximity to the main area of interest the desired signal of the radar reflection can get superposed.
For superposing signals with little separation in frequency domain the main lobes of their discrete Fourier transform (DFT) merge into a single lobe, so that their peaks cannot be differentiated.
This paper evaluates a method for reconstructing the phase and amplitude of such superimposed signals.
<div>This paper proposes an energy-efficient resource allocation framework for the AmBC-enabled NOMA IoV network under imperfect Successive Interference Cancellation (SIC) decoding. In particular, multiple Road-Side Units (RSUs) transmit superimposed signals to their associated IoVs utilizing downlink NOMA transmission. Meanwhile, the Backscatter Tags (BackTags) also transmit data symbols towards nearby IoVs by reflecting the superimposed signals of RSUs. Thus, the objective is to maximize the total energy efficiency of the NOMA IoV network subject to the minimum data rate of all IoVs. A joint problem that simultaneously optimizes the total power budget of each RSU, power allocation coefficient of IoVs and reflection power of BackTags under imperfect SIC decoding is formulated.</div>
<div>This paper proposes an energy-efficient resource allocation framework for the AmBC-enabled NOMA IoV network under imperfect Successive Interference Cancellation (SIC) decoding. In particular, multiple Road-Side Units (RSUs) transmit superimposed signals to their associated IoVs utilizing downlink NOMA transmission. Meanwhile, the Backscatter Tags (BackTags) also transmit data symbols towards nearby IoVs by reflecting the superimposed signals of RSUs. Thus, the objective is to maximize the total energy efficiency of the NOMA IoV network subject to the minimum data rate of all IoVs. A joint problem that simultaneously optimizes the total power budget of each RSU, power allocation coefficient of IoVs and reflection power of BackTags under imperfect SIC decoding is formulated.</div>
Abstract It is found using triboluminescence (TL) that nanocrystals of silicon (ncSi) with linear dimensions of about 4 nm are formed from a single crystal of silicon (Si) under friction. The time dependence of the intensity of TL signals is investigated. It consists of many superimposed signals. An analysis of their dynamics shows that nanocrystals are located on the microcrack faces formed during fracture. At the same time, a powder flies out of the friction region. Using the photoluminescence (PL) and Raman spectroscopy methods, the size of silicon nanocrystals in the powder was determined. It is about 2.2 nm. Probably, microcracks grow further in the time interval between friction and recording the Raman and PL spectra (several hours), which leads to an approximately twofold decrease in the size of nanocrystals.
This paper presents a developed dislocation superimposed method (DSM) for automatically extracting the component of impulsive signals from abnormal noise signals of an engine at a single speed range on the basis of the initial DSM. This method consists of three steps: using a correlation analysis to select an appropriate starting superposition point, superimposing abnormal sound signals to improve the signal-to-noise ratio, and intercepting superimposed signals to separate the fault component. Experimental results show that the developed DSM can effectively extract the fault characteristics of cylinder knocking and connecting rod bearing knocking. The developed approach can be applied to separate the fault characteristics of other types of rotating machines.
DENA: training an authentic neural network model using Nanopore sequencing data of Arabidopsis transcripts for detection and quantification of N6-methyladenosine on RNA