Sequential extraction and recognition of pulse group structure for multi‐function radar

Detection of the changes in Multi-Functional Radar (MFR) work modes is a critical situation assessment task for Electronic Support Measure (ESM) systems. There are two major challenges that must be addressed: (i) The received radar pulse stream may contain multiple work mode segments of unknown number and duration, which makes the Change Point Detection (CPD) difficult. (ii) Modern MFRs can produce a variety of parameter-level (fine-grained) work modes with complex and flexible patterns, which are challenging to detect through traditional statistical methods and basic learning models. To address the challenges, a deep learning framework is proposed for fine-grained work mode CPD in this paper. First, the fine-grained MFR work mode model is established. Then, a multi-head attention-based bi-directional long short-term memory network is introduced to abstract high-order relationships between successive pulses. Finally, temporal features are adopted to predict the probability of each pulse being a change point. The framework further improves the label configuration and the loss function of training to mitigate the label sparsity problem effectively. The simulation results showed that compared with existing methods, the proposed framework effectively improves the CPD performance at parameter-level. Moreover, the F1-score was increased by 4.15% under hybrid non-ideal conditions.

The multi-function radar (MFR) is capable of transmitting complex signals and achieving beam agility according to different tasks, thus leading to many challenges in the work mode recognition field. This paper, therefore, develops an Encoder-Decoder model based on the gated recurrent units (GRU) network to achieve work mode recognition. It involves the use of the Encoder structure to extract the temporal features and the work mode transition regulations of the intercepted pulse group sequence while leveraging the Decoder structure to decode the features and transition regulations as a part of the input for the next decoding process. Additionally, the label substitution (LS) method is utilized for improving the ability of the Decoder structure to recognize work mode under non-ideal situations. Simulation results show that the proposed method is less sensitive in the presence of non-ideal situations and shares better performance than existing work mode recognition methods.

Multi-function radar signals account for a large proportion of radar reconnaissance datasets with a rapidly increasing amount of data. Automatic and accurate analysis of the pulse group of these radars is an important task to research and explain the working mode and behavior intention of radar system. In order to address this problem, this paper proposes an automatic analysis method of radar pulse group based on the joint extraction and extension of multi-parameter frequent items, including pulse repetition interval (PRI) and radio frequency (RF). The multi-parameter frequent items extracted from the pulse train are used to implement pulses extension and pulse segments extension. Finally, the pulse group of multi-function radar are obtained. The last part of this paper sets up several typical simulations to verify the feasibility and performance of the new method.

Broadband metamaterial absorber with double resonant modes using single-layer substrate

Nonlinear spectral filtering provides a simple method for reducing noise in optical pulses to below the shot noise level. We show its effectiveness for excess noise reduction in an ultrafast optical pulse train by demonstrating 23 dB reduction of low frequency 1/f noise in pulses from a passively mode-locked erbium-doped fiber laser. The noise reduction is achieved by spectrally filtering pulses propagated as solitons through a 1.5 km length of optical fiber.

Sequential model-based segmentation and recognition of image structures driven by visual features and spatial relations

The intensity-dependent rotation of elliptically polarized light by carbon disulfide is used to discriminate against low-intensity laser light and pass high-intensity pulses through crossed polarizers. The transmitted intensity is shown to be proportional to the third power of the incident intensity. This technique has been used to reduce base-line noise and low-intensity pulses in the output pulse train of a mode-locked Nd : YAG oscillator.

Recent work demonstrates that learning to understand noise-vocoded (NV) speech alters sublexical perceptual processes but is enhanced by the simultaneous provision of higher-level, phonological, but not lexical content (Hervais-Adelman, Davis, Johnsrude, & Carlyon, 2008), consistent with top-down learning (Davis, Johnsrude, Hervais-Adelman, Taylor, & McGettigan, 2005; Hervais-Adelman et al., 2008). Here, we investigate whether training listeners with specific types of NV speech improves intelligibility of vocoded speech with different acoustic characteristics. Transfer of perceptual learning would provide evidence for abstraction from variable properties of the speech input. In Experiment 1, we demonstrate that learning of NV speech in one frequency region generalizes to an untrained frequency region. In Experiment 2, we assessed generalization among three carrier signals used to create NV speech: noise bands, pulse trains, and sine waves. Stimuli created using these three carriers possess the same slow, time-varying amplitude information and are equated for naïve intelligibility but differ in their temporal fine structure. Perceptual learning generalized partially, but not completely, among different carrier signals. These results delimit the functional and neural locus of perceptual learning of vocoded speech. Generalization across frequency regions suggests that learning occurs at a stage of processing at which some abstraction from the physical signal has occurred, while incomplete transfer across carriers indicates that learning occurs at a stage of processing that is sensitive to acoustic features critical for speech perception (e.g., noise, periodicity).

The paper presents a new task scheduling model for phased array radar based on priority assessment of task request. The method is used to analyses the radar tasks choosing process for different kinds of targets. Considered with the work mode and task of the multi-function radar, a task scheduling model using Adaptive Neuro Fuzzy Inference System (ANFIS) was adopted which preprocess the interfering factors of task priority such as target working mode, parameter and amount et al. which help the scheduler finds the task to be executed. The scheduling policies that give better priority to the most important and critical targets. An adaptive time length of scheduling model is used to adjust dynamically time and energy resources of multi-function radar. The proposed method can effectively improve the performance of the radar scheduler and make the radar tasks scheduling more feasibly.KeywordsMulti-function radarTask schedulingPriorityFuzzy-reasoning

A one-year study of biochar in immobilizing PFOA in soil-plant rainfall leaching systems: Residue fraction and metabolomics perspectives.

Detecting randomly modulated pulses in noise

The X-band Substrate Integrated Waveguide (SIW) antennas with different radiating slots etched on top layer is presented in this paper. The main objective is to evaluate the performance of five different antennas on the basis of standing wave ratio, return loss and bandwidth. The SIW structures are designed with Square Ring, U-shape, Inverted U-shape, C-shape and Inverted C-shape slots. This results the design in compact and planar structure. It is observed that the square ring slot antenna provides excellent return loss of -39.78 dB and VSWR of 1.03 at frequency 10GHz and the C-shape slot antenna achieves very broad VSWR<; 2 bandwidth of 2.4 GHz. HFSS simulation software is used to carry out the simulation for the proposed antennas.

This paper proposes an air-core transformer integration method, which mounts the transformer straight into the multilayer printed circuit board (PCB), and maintains a proper distance between the inner transformer and other components on the top layer. The integration method reduces the PCB area significantly, ensuring higher power density. The design of the complete structure is accomplished with finite element analysis (FEA) to ensure that the unconstrained transformer magnetic field does not affect other components. It is applied to three resonant flyback converters operating at 20 MHz with Si mosfet s, 30 MHz, and 50 MHz with enhancement-mode gallium nitride (eGaN) high-electron mobility transistors, respectively. The 30-MHz eGaN prototype achieves the full load efficiency of 80.1% (an increase of 1.1% compared with the 20-MHz Si prototype) and power density of 32 W/in3 at 5-V input and 5 V/ 2 W output. The 50-MHz eGaN prototype achieves the power density of 39.4 W/in3, which is 41% higher than the 20-MHz Si prototype. With the similar efficiency, the overall height of the converters in this paper is less than half of the commercial products, very suitable for low thickness and profile applications.

Design and optimization of ceramic membrane structure: From the perspective of flux matching between support and membrane

Phosphorus (P) fractionation and distribution in sediments are of great concern in the Florida Everglades ecosystem because potential eutrophication of surface waters usually results from P external loading and stability. Intact core sediment samples were collected to a depth of 35 cm from wetlands and canals across Water Conservation Area 3 (WCA‐3) of the Florida Everglades. These sediment cores were sliced into 5 cm increments and analyzed for P contents in different fractions by sequential extraction. These fractions mainly included total P (TP), readily available P (Pi‐KCl), Fe/Al‐bound P (Pi‐NaOH), Ca/Mg‐bound P (Pi‐HCl), organic P (Po‐NaOH), and residual P (PoResidue). Results showed that the canal sediments had the highest concentrations of TP, with about 87% in the form of Ca/Mg‐bound fraction, and the concentrations of TP in these sediments increased with depth. In contrast, the wetland sediments contained the lowest concentrations of TP (predominantly in the organic fraction), with 43% residual P and 27% Po‐NaOH, and the concentrations of TP in these sediments decreased with depth. In addition, a large amount of the readily available P (up to 1500 mg kg−1) in the canal sediments was accumulated at the top layer of 0–5 cm. This study suggests that any disturbance and/or environmental alterations, such as high canal flow and dredging in canal sediments, could pose a potential risk of a P increase in the water column and, consequently, in the wetlands because of the release of readily available P despite the relatively stable nature of such P fractions in these sediments.