Deep Learning-Based Frequency Offset Estimation

In wireless communication systems, the asynchronization of the oscillators in the transmitter and the receiver along with the Doppler shift due to relative movement may lead to the presence of carrier frequency offset (CFO) in the received signals. Estimation of CFO is crucial for subsequent processing such as coherent demodulation. In this brief, we demonstrate the utilization of deep learning for CFO estimation by employing a residual network (ResNet) to learn and extract signal features from the raw in-phase (I) and quadrature (Q) components of the signals. We use multiple modulation schemes in the training set to make the trained model adaptable to multiple modulations or even new signals. In comparison to the commonly used traditional CFO estimation methods, our proposed IQ-ResNet method exhibits superior performance across various scenarios including different oversampling ratios, various signal lengths, and different channel

Radio–Image Transformer: Bridging Radio Modulation Classification and ImageNet Classification

Radio modulation classification is widely used in the field of wireless communication. In this paper, in order to realize radio modulation classification with the help of the existing ImageNet classification models, we propose a radio–image transformer which extracts the instantaneous amplitude, instantaneous phase and instantaneous frequency from the received radio complex baseband signals, then converts the signals into images by the proposed signal rearrangement method or convolution mapping method. We finally use the existing ImageNet classification network models to classify the modulation type of the signal. The experimental results show that the proposed signal rearrangement method and convolution mapping method are superior to the methods using constellation diagrams and time–frequency images, which shows their performance advantages. In addition, by comparing the results of the seven ImageNet classification network models, it can be seen that, except for the relatively poor performance of the architecture MNASNet1_0, the modulation classification performance obtained by the other six network architectures is similar, indicating that the proposed methods do not have high requirements for the architecture of the selected ImageNet classification network models. Moreover, the experimental results show that our method has good classification performance for signal datasets with different sampling rates, Orthogonal Frequency Division Multiplexing (OFDM) signals and real measured signals

The RING Finger E3 Ligase SpRing is a Positive Regulator of Salt Stress Signaling in Salt-Tolerant Wild Tomato Species

Protein ubiquitination in plants plays critical roles in many biological processes, including adaptation to abiotic stresses. Previously, RING finger E3 ligase has been characterized during salt stress response in several plant species, but little is known about its function in tomato. Here, we report that SpRing, a stress-inducible gene, is involved in salt stress signaling in wild tomato species Solanum pimpinellifolium 'PI365967'. In vitro ubiquitination assay revealed that SpRing is an E3 ubiquitin ligase and the RING finger conserved region is required for its activity. SpRing is expressed in all tissues of wild tomato and up-regulated by salt, drought and osmotic stresses, but repressed by low temperature. Green fluorescent protein (GFP) fusion analysis showed that SpRing is localized at the endoplasmic reticulum. Silencing of SpRing through a virus-induced gene silencing approach led to increased sensitivity to salt stress in wild tomato. Overexpression of SpRing in Arabidopsis thaliana resulted in enhanced salt tolerance during seed germination and early seedling development. The expression levels of certain key stress-related genes are altered both in SpRing-overexpressing Arabidopsis plants and virus-induced gene silenced tomato seedlings. Taken together, our results indicate that SpRing is involved in salt stress and functions as a positive regulator of salt tolerance

Source Reconstruction of Atmospheric Releases by Bayesian Inference and the Backward Atmospheric Dispersion Model: An Application to ETEX-I Data

Source term reconstruction methods attempt to calculate the most likely source parameters of an atmospheric release given measurements, including both location and release amount. However, source term reconstruction is vulnerable to uncertainties. In this paper, a method combining Bayesian inference with the backward atmospheric dispersion model is developed for robust source term reconstruction. The backward model is used to quantify the relationship between the source and measurements and to reduce the search range of the Bayesian inference. A Markov chain Monte Carlo method is used to sample from the multidimensional parameter space of the source term. The source location and release rate are estimated simultaneously, and the posterior probability distribution is produced by applying Bayes’ theorem. The proposed method is applied to a set of real concentration data from the ETEX-I experiment. The results demonstrate that the source location is estimated to be −2.86° ± 1.01°E, 48.25° ± 0.33°N, and the release rate is estimated to be 20.16 ± 3.56 kg/h. The true source location is correctly estimated to be within a one standard deviation interval, and the release rate is correctly determined to be within a three standard deviation interval