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The widespread and massive application of multi-function radars (MFRs) pushes MFR modeling to become a recognized need in electronic intelligence (ELINT). Since pioneer research has made trials in the model-centric ELINT from various viewpoints, a universal hierarchical modeling framework is needed at the emergence. To fill the absence and capture future trends, we illustrate the mathematical logic in modeling MFR and propose an MFR modeling framework (named MFRModeling) by studying and summarizing current literature. To be specific, the MFRModeling contains pulse interception, pulse representation, hierarchical model design, pulse dictation, and evaluation. For the pulse dictation step, we design a novel two-stage processor, comprising of the pulse-to-symbol and symbol-to-symbol transformer. Based on the proposed MFRModeling, current literature’s phases are extracted, grouped, and compared. Finally, recent research trends and the overarching future outlook for MFR modeling in next-generation ELINT are discussed.

In this letter, a data-driven single-input/multi-output (SIMO) demodulator is proposed, to demodulate concurrently cochannel signals. The SIMO demodulation is formed as a signal-wise sequence labelling problem, which we propose to solve by multi-layer recurrent neural networks (RNN). Numerical results validated the low bit-error rates of the proposed demodulator against cochannel signals of varied power ratios. Comparing with existing model-based and data-driven SIMO demodulators, the proposed demodulator provided superior BER performance, especially when cochannel signals were of distinct powers. More importantly, the proposed demodulator avoids one major deficiency of existing model-based schemes, which require the symbol rates of cochannel signals to be strictly identical. Meanwhile, the offline-trained demodulator generalized well in varied open set tests.

This letter addresses the issue of underfitting or failure of deep learning models caused by insufficient training samples. Unlike previous supervised methods, a new few-shot learning semi-supervised automatic modulation recognition method based on multimodal information and domain adversarial network is proposed herein. The fusion input of multimodal information realizes the joint utilization of modulated signal modal features in the time and frequency domains. Domain adversarial training mines the potential knowledge information of a large number of unlabeled target domain data and introduces the convolutional block attention module (CBAM) to enhance the ability of the network to represent the key features of data. Numerical results validate the high-average classification accuracy of the proposed scheme compared to that of state-of-the-art schemes using fewer samples, particularly in high-order modulation classification. Alternative network structures are compared to confirm the applicability of multimodal information, domain adversarial training, and CBAM.

Multiobjectivization has emerged as a new promising paradigm to solve single-objective optimization problems (SOPs) in evolutionary computation, where an SOP is transformed into a multiobjective optimization problem (MOP) and solved by an evolutionary algorithm to find the optimal solutions of the original SOP. The transformation of an SOP into an MOP can be done by adding helper-objective(s) into the original objective, decomposing the original objective into multiple subobjectives, or aggregating subobjectives of the original objective into multiple scalar objectives. Multiobjectivization bridges the gap between SOPs and MOPs by transforming an SOP into the counterpart MOP, through which multiobjective optimization methods manage to attain superior solutions of the original SOP. Particularly, using multiobjectivization to solve SOPs can reduce the number of local optima, create new search paths from local optima to global optima, attain more incomparability solutions, and/or improve solution diversity. Since the term “multiobjectivization” was coined by Knowles et al. in 2001, this subject has accumulated plenty of works in the last two decades, yet there is a lack of systematic and comprehensive survey of these efforts. This article presents a comprehensive multifacet survey of the state-of-the-art multiobjectivization methods. Particularly, a new taxonomy of the methods is provided in this article and the advantages, limitations, challenges, theoretical analyses, benchmarks, applications, as well as future directions of the multiobjectivization methods are discussed.

Specific emitter identification (SEI) is to authenticate communication devices via analyzing unintentional modulation (UIM) from hardware imperfection. A great challenge existing in current methods is the interference of intentional modulation (IM) and channel noise. To address this issue, this letter firstly analyzes how IM, UIM, and additive noise (AN) affect reconstructed states, which describes the dynamic characteristics of communication devices. The findings indicate that IM and UIM bring a scaling transform, while AN corresponds to a translation transform. Based on theoretical analysis, a novel SEI method concentrating on UIM is proposed. An advanced PointNet architecture is designed with stacked spatial transformer networks (STN), namely PointNet-Alignment Point by Point (PointNet-APBP). PointNet-APBP aligns reconstructed states into a canonical space and extracts spatial interaction as fingerprint features. Simulated and real-world experiments validate that the proposed method can mitigate the effects of IM as well as AN, concentrate feature extractors on UIM, and in turn achieve higher accuracy and stronger robustness.

This paper takes the micro tilting-pad gas bearing as the research object. Based on the continuous model, first-order slip model, and Wu's new slip model, the static and dynamic Reynolds equations with the slip effect are established and solved by MATLAB software. First, a hybrid method integrating the Newton–Raphson iteration and finite difference methods was used to solve the static Reynolds equation. Then, the dynamic Reynolds equation was calculated by combining the partial derivative method with the equivalent coefficient method. So, the static and dynamic performance of the micro tilting-pad gas bearing can be obtained. The calculation results indicate that the bearing capacity and film pressure of the tilting-pad gas bearing are reduced, and the dynamic stiffness is reduced after considering the slip effect. In the calculation results, the value of the continuous model is the largest, followed by the first-order slip model, Wu's new slip model is minimum. It provides a reference for the design of the micro tilting-pad gas bearing.

State-of-the-art model-driven Direction-Of-Arrival (DOA) estimation methods for multipath signals face great challenges in practical application because of the dependence on the precise multipath model. In this paper, we introduce a framework, based on deep learning, for synchronizing perturbation auto-elimination with effective DOA estimation in multipath environment. Firstly, a signal selection mechanism is introduced to roughly locate specific signals to spatial subregion via frequency domain filters and compressive sensing-based method. Then, we set the mean of the correlation matrix’s row vectors as the input feature to construct the spatial spectrum by the corresponding single network within the parallel deep capsule networks. The proposed method enhances the generalization capability to untrained scenarios and the adaptability to non-ideal conditions, e.g., lower SNRs, smaller snapshots, unknown reflection coefficients and perturbational steering vectors, which make up for the defects of the previous model-driven methods. Simulations are carried out to demonstrate the superiority of the proposed method.

Potassium-ion batteries (PIBs) as a new electrochemical energy storage system have been considered as a desirable candidate in the post-lithium-ion battery era. Nevertheless, the study on this realm is in its infancy; it is urgent to develop electrode materials with high electrochemical performance and low cost. Iron sulfides as anode materials have aroused wide attention by virtue of their merits of high theoretical capacities, environmental benignity, and cost competitiveness. Herein, we constructed carbon-free crystal-like Fe1–xS and demonstrated its feasibility as a PIB anode. The unique structural feature endows the prepared Fe1–xS with plentiful active sites for electrochemical reactions and short transmission pathways for ions/electrons. The Fe1–xS electrode retained capacities of 420.8 mAh g–1after 100 cycles at 0.1 A g–1and 212.9 mAh g–1after 250 cycles at 1.0 A g–1. Even at a high rate of 5.0 A g–1, an average capacity of 167.6 mAh g–1was achieved. In addition, a potassium-ion full cell is assembled by employing Fe1–xS as an anode and potassium Prussian blue as a cathode; it delivered a discharge capacity of 127.6 mAh g–1at 100 mA g–1after 50 cycles.

In this letter, we propose a signal recovery scheme for interference mitigation in K-user interference channels. The recurrent structure of communication signals is investigated, based on which a single-user region (SUR) detection scheme involving sparse coding (SC) is proposed. A dictionary is constructed directly over SURs detected. Recovery of signal in the principal link is then implemented via a template matching step over the dictionary. Comparing with existing signal recovery schemes involving SC, main contributions include: 1) the proposed scheme is significantly more practical as it no more relies on clean signals as a prior for dictionary construction and 2) traditional time-consuming dictionary training is avoided, thus the computation complexity is remarkably lowered. Simulation results show that SURs in the received signal can be accurately recognized via the proposed SUR detection scheme. Moreover, the signal recovered by the proposed scheme is of higher quality, under lower time consumption and without clean signal as a prior, comparing with existing scheme.

Nutrients such as nitrogen (N) and phosphorus (P) discharged from mariculture wastewater pose a serious threat to the environment. In this study, an electrochemically assisted submerged fixed bed bioreactor (E-SFBBR) with intermittent aeration was used to explore the effect of aeration strategies (aeration time, aeration rate and aeration position) on the removal of N and P. Phosphate (PO43--P) removal was maintained above 95% under different aeration strategies. Although PO43--P removal predominantly occurred in the cathode areas, the aeration position affected the main functional area for PO43--P removal by influencing the pattern of DO distribution within E-SFBBR; and chemical precipitation with iron ions was the main removal pathway as revealed by XPS analysis. E-SFBBR with upper aeration (i.e., UA-E-SFBBR), aerated for 6 h d−1at 0.8 L min−1showed the best N removal performance, i.e., 83.78 ± 4.67%. The cathode area mainly contributed to N removal through mixotrophic denitrification. Aeration position plays a greater role in affecting N removal compared to aeration time and rate. Upper aeration favored the growth of heterotrophic denitrification bacteria (f_Rhodobacteraceaeand Marinicella) in the cathode through providing more anaerobic environment, resulting in a better N removal performance. Besides, results of N transformation pathway predicted by PICRUSt 2.0 revealed that upper aeration promoted the ammonia assimilation but inhibited ammonia oxidation and NO3--N reduction processes in the cathode area. This study provided a theoretical basis for the application of aerated E-SFBBR in mariculture wastewater treatment.

One-dimensional (1D) organic–inorganic hybrid perovskite nanowires (NWs) with well-defined structures possess superior optical and electrical properties for optoelectronic applications. However, most of the perovskite NWs are synthesized in air, which makes the NWs susceptible to water vapor, resulting in large amounts of grain boundaries or surface defects. Here, a template-assisted antisolvent crystallization (TAAC) method is designed to fabricate CH3NH3PbBr3NWs and arrays. It is found that the as-synthesized NW array has designable shapes, low crystal defects, and ordered alignment, which is attributed to the sequestration of water and oxygen in air by the introduction of acetonitrile vapor. The photodetector based on the NWs exhibits an excellent response to light illumination. Under the illumination of a 532 nm laser with 0.1 μW and a bias of −1 V, the responsivity and detectivity of the device reach 1.55 A/W and 1.21 × 1012Jones, respectively. The transient absorption spectrum (TAS) shows a distinct ground state bleaching signal only at 527 nm, which corresponds to the absorption peak induced by the interband transition of CH3NH3PbBr3. Narrow absorption peaks (a few nanometers) indicate that the energy-level structures of CH3NH3PbBr3NWs only have a few impurity-level-induced transitions leading to additional optical loss. This work provides an effective and simple strategy to achieve high-quality CH3NH3PbBr3NWs, which exhibit potential application in photodetection.

To develop a system supporting rapid growth of juvenile freshwater mussels, a recirculating aquaculture system was designed and built at the Laboratory for Conservation Aquaculture and Aquatic Ecology, Virginia Polytechnic Institute and State University. The system included a rectangular trough tank, a sump (biofilter), a pump, a microalgae drip feeder, and an air delivery system. An algal turf scrubber (ATS) was evaluated for its potential to maintain and improve water quality within the system. Growth and survival rates of juvenile rainbow mussels Villosa irisafter 90 d were compared between system units with and without ATSs. Flow rate through the culture units was approximately 23.3 L/min. Results showed no statistically significant differences between the growth and survival rates of juvenile mussels reared in systems with ATSs and those reared in systems without ATSs. Ammonia and nitrite levels were low and did not differ among treatments. However, systems with ATSs exhibited significantly lower levels of nitrate and phosphate than systems without ATSs. Our results show that freshwater mussel culture systems can be scaled up to increase production and that the use of ATSs may help to maintain water quality in recirculating aquaculture systems during long-term culture of freshwater mussels.

AbstractTo develop a system supporting rapid growth of juvenile freshwater mussels, a recirculating aquaculture system was designed and built at the Laboratory for Conservation Aquaculture and Aquatic Ecology, Virginia Polytechnic Institute and State University. The system included a rectangular trough tank, a sump (biofilter), a pump, a microalgae drip feeder, and an air delivery system. An algal turf scrubber (ATS) was evaluated for its potential to maintain and improve water quality within the system. Growth and survival rates of juvenile rainbow mussels Villosa irisafter 90 d were compared between system units with and without ATSs. Flow rate through the culture units was approximately 23.3 L/min. Results showed no statistically significant differences between the growth and survival rates of juvenile mussels reared in systems with ATSs and those reared in systems without ATSs. Ammonia and nitrite levels were low and did not differ among treatments. However, systems with ATSs exhibited significantly lower levels of nitrate and phosphate than systems without ATSs. Our results show that freshwater mussel culture systems can be scaled up to increase production and that the use of ATSs may help to maintain water quality in recirculating aquaculture systems during long-term culture of freshwater mussels.Received February 4, 2013; accepted July 12, 2013

Lead halide perovskites have emerged as attractive photoelectric materials owing to their excellent performance, which demonstrates great potential for application in the optoelectronic field. A basic understanding of the photogenerated carrier transport in perovskite photodetectors is critical to achieving high-performance devices. Here, we reported the ultraviolet photodetectors with a fast response based on the Au-coated CH3NH3PbBr3perovskite nanowires, which were grown by an in situ solution method. Interestingly, the devices exhibited negative differential resistance (NDR) behaviors at high power intensity, relevant to the ion migration in the nanowires. The carrier transport process and the state of energy band bending were discussed in detail. This work provides a valuable idea for designing fast-response photodetectors with the function of memristors.

With the development of wireless communication technology, the electromagnetic environment has become more and more complex. Conventional signal identification methods are difficult to accurately identify illegal devices. However, electromagnetic signals have an unavoidable device-specific characteristic unintentionally generated by a transmitter, appearing in the form of an UnIntentional Modulation (UIM), namely Radio Frequency Fingerprint (RFF). RFFs can be used to uniquely identify an emitter to match a received signal with its source. In this paper, the authors propose a novel RFF scheme to separate UIM part from the original signals from the time and frequency domain, and then utilize non-Gaussian measuring tools to extract a set of dimension-reduced secondary features. Additionally, Singular Value Reconstruction (SVR) is developed to extract UIM in the frequency spectrum. In time domain, a curve-fitting residual method is proposed to extract the UIM on the estimated instantaneous phase based on Maximum Likelihood Estimator (MLE). Various aspects of the proposed method are evaluated, including identification accuracy under various Signal-to-Noise Ratio (SNR) conditions, energy relationships between the UIM and the whole signal, and sensitivity to training set size. Compared with other methods, experimental results based on real-world signals prove that the proposed method has remarkable performance and high practicability.