A Hybrid Neural Network Framework and Application to Radar Automatic Target Recognition

Deep neural networks (DNNs) have found applications in diverse signal processing (SP) problems. Most efforts either directly adopt the DNN as a black-box approach to perform certain SP tasks without taking into account of any known properties of the signal models, or insert a pre-defined SP operator into a DNN as an add-on data processing stage. This paper presents a novel hybrid-NN framework in which one or more SP layers are inserted into the DNN architecture in a coherent manner to enhance the network capability and efficiency in feature extraction. These SP layers are properly designed to make good use of the available models and properties of the data. The network training algorithm of hybrid-NN is designed to actively involve the SP layers in the learning goal, by simultaneously optimizing both the weights of the DNN and the unknown tuning parameters of the SP operators. The proposed hybrid-NN is tested on a radar automatic target recognition (ATR) problem. It achieves high validation accuracy of 96\% with 5,000 training images in radar ATR. Compared with ordinary DNN, hybrid-NN can markedly reduce the required amount of training data and improve the learning performance

The Insecticidal and Repellent Activity of Soil Containing Cinnamon Leaf debris against Red Imported Fire Ant Workers

In the study, the amount of cinnamaldehyde and eugenol in soil containing cinnamon leaf debris were determined at different depths by high performance liquid chromatography (HPLC). The insecticidal activity and repellence of the soil was tested separately. Results showed that higher contents of cinnamic aldehyde and eugenol were found in soil at depths of 5 - 10 cm. In the insecticidal toxicity bioassay, the corrected mortality of major workers treated with cinnamon soil at depths of 5 - 10 cm, which was higher than the other soil depths, increased from 13.3% to 80.0% with contact time from 1 - 5 d. Likewise, the corrected mortality of minor workers also increased from 6.7% to 100.0%. In the repellent activity bioassay, the repellency (96.3%) of major and minor workers treated with cinnamon soil at depths of 5 - 10 cm for 24 h were significantly higher than the other treatments. This result revealed ecological value of cinnamon. Soil underneath cinnamon contained cinnamaldehyde and eugenol from fallen leaves, and these components showed insecticidal activity and repellence against red imported fire ants. Perhaps we could control the red imported fire ants by planting cinnamon in some possible regions or by incorporating cinnamon leaves into soil where cinnamon will not grow

Higher bottomonium zoo

In this work, we study higher bottomonia up to the $nL=8S$, $6P$, $5D$, $4F$, $3G$ multiplets using the modified Godfrey-Isgur (GI) model, which takes account of color screening effects. The calculated mass spectra of bottomonium states are in reasonable agreement with the present experimental data. Based on spectroscopy, partial widths of all allowed radiative transitions, annihilation decays, hadronic transitions, and open-bottom strong decays of each state are also evaluated by applying our numerical wave functions. Comparing our results with the former results, we point out difference among various models and derive new conclusions obtained in this paper. Notably, we find a significant difference between our model and the GI model when we study $D, F$, and $G$ and $n\ge 4$ states. Our theoretical results are valuable to search for more bottomonia in experiments, such as LHCb, and forthcoming Belle II.Comment: 40 pages, 4 figures and 40 tables. Accepted by Eur. Phys. J.