Spectrum Anomaly Detection Based on Spatio-Temporal Network Prediction

With the miniaturization of communication devices, the number of distributed electromagnetic devices is increasing. In order to achieve effective management of the electromagnetic spectrum, prediction and anomaly detection of the spectrum has become increasingly critical. This paper proposes an algorithmic framework for detecting spectrum anomalies using deep learning techniques. More specifically, the framework includes spectrum prediction and anomaly detection. We use the sliding window method to divide the time series, construct multi-timescale historical data, and train the model with normal data to have high accuracy spectrum prediction capability. We analyze and determine the discriminant function to distinguish the spectral anomalies by calculating the differences between the predicted and real data. The experimental results show that the proposed method outperforms existing baseline algorithms based on real-world spectrum measurement data and simulated anomaly data

Spectrum Anomaly Detection Based on Spatio-Temporal Network Prediction

With the miniaturization of communication devices, the number of distributed electromagnetic devices is increasing. In order to achieve effective management of the electromagnetic spectrum, prediction and anomaly detection of the spectrum has become increasingly critical. This paper proposes an algorithmic framework for detecting spectrum anomalies using deep learning techniques. More specifically, the framework includes spectrum prediction and anomaly detection. We use the sliding window method to divide the time series, construct multi-timescale historical data, and train the model with normal data to have high accuracy spectrum prediction capability. We analyze and determine the discriminant function to distinguish the spectral anomalies by calculating the differences between the predicted and real data. The experimental results show that the proposed method outperforms existing baseline algorithms based on real-world spectrum measurement data and simulated anomaly data

A Method for Detecting Anomalies in an Electromagnetic Environment Situation Using a Dual-Branch Prediction Network

Electromagnetic environment situation anomaly detection is a prerequisite for electromagnetic threat level assessment, and its research is of great practical value. However, because of the complexity of the electromagnetic environment, electromagnetic environment situation anomaly detection is not efficient. Therefore, we propose a dual-branch prediction network-based electromagnetic environment situation anomaly detection method to predict the future and achieve anomaly detection by fusing different development characteristics of electromagnetic environment situations learned by other branches. We extract the electromagnetic environment situation state and trend features using the manual feature extraction module and mine the electromagnetic environment situation in-depth data distribution features using ConvLSTM, improve the dynamic time regularization model according to the physical characteristics of electromagnetic space, and then provide the anomaly detection method. We experimentally demonstrate the effectiveness of the proposed method in electromagnetic environment situation prediction and anomaly detection accuracy

An Effective Sharding Consensus Algorithm for Blockchain Systems

Sharding is the widely used approach to the trilemma of simultaneously achieving decentralization, security, and scalability in traditional blockchain systems. However, existing schemes generally involve problems such as uneven shard arithmetic power and insecure cross-shard transaction processing. In this study, we used the Practical Byzantine Fault Tolerance (PBFT) as the intra-shard consensus and, here, we propose a new sharding consensus mechanism. Firstly, we combined a jump consistent hash algorithm with signature Anchorhash to minimize the mapping of the node assignment. Then, we improved the process of the cross-shard transaction and used the activity of nodes participating in intra-shard transactions as the criterion for the shard reconfiguration, which ensured the security of the blockchain system. Meanwhile, we analyzed the motivation mechanism from two perspectives. Finally, through theoretical analysis and related experiments, we not only verified that the algorithm can ensure the security of the entire system, but also further clarified the necessary conditions to ensure the effectiveness of the shards and the system on the original basis

Theory and Application of Weak Signal Detection Based on Stochastic Resonance Mechanism

Stochastic resonance is a new type of weak signal detection method. Compared with traditional noise suppression technology, stochastic resonance uses noise to enhance weak signal information, and there is a mechanism for the transfer of noise energy to signal energy. The purpose of this paper is to study the theory and application of weak signal detection based on stochastic resonance mechanism. This paper studies the stochastic resonance characteristics of the bistable circuit and conducts an experimental simulation of its circuit in the Multisim simulation environment. It is verified that the bistable circuit can achieve the stochastic resonance function very well, and it provides strong support for the actual production of the bistable circuit. This paper studies the stochastic resonance phenomenon of FHN neuron model and bistable model, analyzes the response of periodic signals and nonperiodic signals, verifies the effect of noise on stochastic resonance, and lays the foundation for subsequent experiments. It proposes to feedback the link and introduces a two-layer FHN neural network model to improve the weak signal detection performance under a variable noise background. The paper also proposes a multifault detection method based on the total empirical mode decomposition of sensitive intrinsic mode components with variable scale adaptive stochastic resonance. Using the weighted kurtosis index as the measurement index of the system output can not only maintain the similarity between the system output signal and the original signal but also be sensitive to impact characteristics, overcoming the missed or false detection of the traditional kurtosis index. Experimental research shows that this method has better noise suppression ability and a clear reproduction effect on details. Especially for images contaminated by strong noise (D = 500), compared with traditional restoration methods, it has better performance in subjective visual effects and signal-to-noise ratio evaluation

Health Assessment of Typical Reservoirs in Eastern Jilin: A Case Study of Niligou Reservoir

Niligou Reservoir, a typical reservoir in the eastern region of Jilin Province, is subordinate to Niligou River, which is located in the Changbai Mountains in eastern Jilin, with abundant plant and animal resources and well protected biodiversity in the basin, but there are also some related problems such as fragile ecosystem of forest wetland. This paper carried out a health assessment of Niligou River, including water quality monitoring, aquatic organism monitoring, and riparian zone investigation. According to the requirements of the Technical Guidelines for River and Lake Health Assessment, 13 assessment indexes were selected to build a river and lake health assessment index system. By sorting and analyzing the monitoring and investigation data, we got a clear picture of the ecological environment status and existing problems of Niligou Reservoir. Based on the actual situation of Niligou River, we evaluated the hydrologic integrity, chemical integrity, morphological and structural integrity, biological integrity and sustainability of social service function, and put forward corresponding countermeasures according to the results, in order to provide a technical support for the health treatment of rivers and lakes in Jilin Province

Comparative Transcriptome Analysis in Oilseed Rape (<i>Brassica napus</i>) Reveals Distinct Gene Expression Details between Nitrate and Ammonium Nutrition

Nitrate (NO3&#8722;) and ammonium (NH4+) are the main inorganic nitrogen (N) sources absorbed by oilseed rape, a plant that exhibits genotypic differences in N efficiency. In our previous study, the biomass, N accumulation, and root architecture of two oilseed rape cultivars, Xiangyou 15 (high N efficiency, denoted &#8220;15&#8221;) and 814 (low N efficiency, denoted &#8220;814&#8221;), were inhibited under NH4+ nutrition, though both cultivars grew normally under NO3&#8722; nutrition. To gain insight into the underlying molecular mechanisms, transcriptomic changes were investigated in the roots of 15 and 814 plants subjected to nitrogen-free (control, CK), NO3&#8722; (NT), and NH4+ (AT) treatments at the seedling stage. A total of 14,355 differentially expressed genes (DEGs) were identified. Among the enriched Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway categories of these DEGs, carbohydrate metabolism, lipid metabolism, protein metabolism, and cell wall biogenesis were inhibited by AT treatment. Interestingly, DEGs such as N transporters, genes involved in N assimilation and CESA genes related to cellulose synthase were also mostly downregulated in the AT treatment group. This downregulation of genes related to crucial metabolic pathways resulted in inhibition of oilseed rape growth after AT treatment