Discrete-time Markov-based dynamic control approach for compressed sampling

To solve the problem that the signal sparsity level is time-varying and not known as a priori in most cases,a signal sparsity level prediction and optimal sampling rate determination scheme is proposed.The discrete-time Markov chain is used to model the signal sparsity level and analyze the transition between different states.According to the current state,the signal sparsity level state in the next sampling period and its probability are predicted.Furthermore,based on the prediction results,a dynamic control approach is proposed to find out the optimal sampling rate with the aim of maximizing the expected reward which considers both the energy consumption and the recovery accuracy.The proposed approach can balance the tradeoff between the energy consumption and the recovery accuracy.Simulation results show that the proposed dynamic control approach can significantly improve the sampling performance compared with the existing approach.

Individual Identification of Electronic Equipment Based on Electromagnetic Fingerprint Characteristics

With the rapid development of communication and computer,the individual identification technology of communication equipment has been brought to many application scenarios.The identification of the same type of electronic equipment is of considerable significance,whether it is the identification of friend or foe in military applications,identity determination,radio spectrum management in civil applications,equipment fault diagnosis,and so on.Because of the limited-expression ability of the traditional electromagnetic signal representation methods in the face of complex signals,a new method of individual identification of the same equipment of communication equipment based on deep learning is proposed.The contents of this paper include the following aspects:(1)Considering the shortcomings of deep learning in processing small sample data,this paper provides a universal and robust feature template for signal data.This paper constructs a relatively complete signal template library from multiple perspectives,such as time domain and transform domain features,combined with high-order statistical analysis.Based on the inspiration of the image texture feature,characteristics of amplitude histogram of signal and the signal amplitude co-occurrence matrix(SACM)are proposed in this paper.These signal features can be used as a signal fingerprint template for individual identification.(2)Considering the limitation of the recognition rate of a single classifier,using the integrated classifier has achieved better generalization ability.The final average accuracy of 5 NRF24LE1 modules is up to 98%and solved the problem of individual identification of the same equipment of communication equipment under the condition of the small sample,low signal-to-noise ratio.

Machine Learning for Data Fusion:A Fuzzy AHP Approach for Open Issues

Data fusion generates fused data by combining multiple sources,resulting in information that is more consistent,accurate,and useful than any individual source and more reliable and consistent than the raw original data,which are often imperfect,inconsistent,complex,and uncertain.Traditional data fusion methods like probabilistic fusion,set-based fusion,and evidential belief reasoning fusion methods are computationally complex and require accurate classification and proper handling of raw data.Data fusion is the process of integrating multiple data sources.Data filtering means examining a dataset to exclude,rearrange,or apportion data according to the criteria.Different sensors generate a large amount of data,requiring the development of machine learning(ML)algorithms to overcome the challenges of traditional methods.The advancement in hardware acceleration and the abundance of data from various sensors have led to the development of machine learning(ML)algorithms,expected to address the limitations of traditional methods.However,many open issues still exist as machine learning algorithms are used for data fusion.From the literature,nine issues have been identified irrespective of any application.The decision-makers should pay attention to these issues as data fusion becomes more applicable and successful.A fuzzy analytical hierarchical process(FAHP)enables us to handle these issues.It helps to get the weights for each corresponding issue and rank issues based on these calculated weights.The most significant issue identified is the lack of deep learning models used for data fusion that improve accuracy and learning quality weighted 0.141.The least significant one is the cross-domain multimodal data fusion weighted 0.076 because the whole semantic knowledge for multimodal data cannot be captured.

Design and research of signal-level airborne electronic reconnaissance simulation system

Airborne passive electronic reconnaissance equipment has developed rapidly during recent years.However,because of its expensive and unavailable military features,the simulation realization of these equipment needs to be solved.And the smaller the simulation particle is,the better the simulation system will be.In this study,a signal-level airborne electronic reconnaissance simulation system was built.Mathematical model and simulation realization of each part are introduced in this study.Focusing on the shortness of traditional signal sorting methods,we creatively proposed a presorting method based on the Euclidean distance inside signal flows.Simulation results show that the presorting method based on Euclidean distance successfully reduced the pressure on main sorting and appeared good for signal sorting.Simulation test results of each model built in this study are also shown in the study.This study provides a new thought on the realization of airborne electronic reconnaissance equipment and benefits the development of electronic countermeasure.

Long-Distance Signaling in bypass1 Mutants： Bioassay Development Reveals the bps Signal to Be a Metabolite

Root-to-shoot signaling is used by plants to coordinate shoot development with the conditions experienced by the roots. A mobile and biologically active compound, the bps signal, is over-produced in roots of an Arabidopsis thaliana mutant called bypass1 （bpsl）, and might also be a normally produced signaling molecule in wild-type plants. Our goal is to identify the bps signal chemically, which will then allow us to assess its production in normal plants. To identify any signaling molecule, a bioassay is required, and here we describe the development of a robust, simple, and quantitative bioassay for the bps signal. The developed bioassay follows the growth-reducing activity of the bps signal using the pCYCB1;I：：GUS cell cycle marker. Wild-type plants carrying this marker, and provided the bps signal through either grafts or metabolite extracts, showed reduced cell division. By contrast, control grafts and treatment with control extracts showed no change in pCYCB1;I：：GUS expression. To determine the chemical nature of the bps signal, extracts were treated with RNase A, Proteinase K, or heat. None of these treatments diminished the activity of bpsl extracts, sug- gesting that the active molecule might be a metabolite. This bioassay will be useful for future biochemical fractionation and analysis directed toward bps signal identification.

Dispersion-Induced Waveform Distortion Detection in 42.7 Gbps CS-RZ Signals by Optical Time Domain Level Monitoring

We propose a technique for chromatic dispersion monitoring based on optical time domain level monitoring. Experimental and simulation results show that the technique is effective for the monitoring of dispersion in 42.7-Gbps CS-RZ signals for dynamic dispersion compensation.