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To address the problem that the performance of the detector in airborne magnetic anomaly detection (MAD) is terrible, a stochastic resonance (SR) detection algorithm based on orthonormalized basis function (OBF-SR) is proposed for MAD under low signal-to-noise ratio conditions. The signal contaminated by noise is first preprocessed by the OBF method, where the sum of the three components in the OBF space is selected as the SR system input. Then, a parallel SR system with different initial states is designed to detect the signal. Finally, the simulation analysis of MAD methods is performed to draw a comparison between the OBF-SR method, the typical SR method, and the OBF method. The results show that the OBF-SR method outperforms the SR and OBF methods in the detection probability and detection range under the same conditions., (© 2023 Author(s). Published under an exclusive license by AIP Publishing.)

The segmentation of pulmonary lobes is important in clinical assessment, lesion location, and surgical planning. Automatic lobe segmentation is challenging, mainly due to the incomplete fissures or the morphological variation resulting from lung disease. In this work, we propose a learning-based approach that incorporates information from the local fissures, the whole lung, and priori pulmonary anatomy knowledge to separate the lobes robustly and accurately. The prior pulmonary atlas is registered to the test CT images with the aid of the detected fissures. The result of the lobe segmentation is obtained by mapping the deformation function on the lobes-annotated atlas. The proposed method is evaluated in a custom dataset with COPD. Twenty-four CT scans randomly selected from the custom dataset were segmented manually and are available to the public. The experiments showed that the average dice coefficients were 0.95, 0.90, 0.97, 0.97, and 0.97, respectively, for the right upper, right middle, right lower, left upper, and left lower lobes. Moreover, the comparison of the performance with a former learning-based segmentation approach suggests that the presented method could achieve comparable segmentation accuracy and behave more robustly in cases with morphological specificity.

Organic room-temperature long-persistent luminescent materials are promising light-emitting materials for encryption, architectural decoration, organic solar cells, and biomedical applications. However, their unstable structures and thermal- and humidity-induced emission quenching have greatly limited their utility and reliability. Here, we report a metal-free nonconjugated copolymer that possesses stable photoluminescence at both high temperature and humidity. The room-temperature long-persistent luminescence (LPL) of this copolymer lasts for more than 15 s and can be recovered in high humidity conditions by heating to remove moisture. Copolymer LPL can be achieved with various excitation wavelengths, ranging from ultraviolet to near-infrared, and the LPL color can be adjusted accordingly. The high initial LPL intensity and ultrafast filling time of the copolymer makes it suitable for low flicker alternating current-driven light-emitting diodes (AC-LEDs)., (© 2022 Wiley-VCH GmbH.)

This article deals with the problem of electromagnetic source localization (ESL). An evolutionary particle filter, which is first used to make a decision on the positions of electromagnetic sources, has two characteristics. One characteristic is that the number of particles can be significantly reduced while the other characteristic is that the particle diversity can be well improved. On the basis of the estimated positions of electromagnetic sources, the position and velocity of the virtual leader can be determined. Then, an event-based fixed-time consensus control approach is proposed such that the positions and velocities of robots reach consensus with the virtual leader over a fixed-time interval while saving resource consumption by reducing the communication frequencies and updating times of control inputs. Finally, simulation and experimental results show the effectiveness of the proposed decision and event-based fixed-time consensus control approach for ESL.

Effective removal of mercury (Hg) pollutants from contaminated water/wastewater to prevent severe environmental pollution is of great significance due to the extremely high toxicity of Hg. In this study, granular biochar and gravel (control) were packed into intermittently aerated constructed wetland (CW) microcosms to treat Hg(Ⅱ)-containing wastewater over 100 d. The results showed that the biochar-filled CWs exhibited notably better Hg(Ⅱ) removal than the gravel systems by facilitating chemical and microbial Hg(Ⅱ) reduction and volatilization and promoting plant growth and Hg assimilation. More than ten times more Hg was absorbed by the plants (L. salicaria) in biochar CWs than in the gravel systems, with the roots acting as the major sink. In contrast, substrate binding in a predominantly oxidizable fraction was the dominant pathway for Hg removal in the gravel CWs. Biochar substrates also exhibited higher levels of COD, N and P removal, and Hg(Ⅱ) import impacted the removal of these pollutants only slightly. Filling material played a more crucial role than Hg input in shaping the microbial communities in the CWs. The proportions of some dominant genera, including Arenimonas, Lysobacter, Micropruina and Hydrogenophaga, increased in the presence of Hg, implying their tolerance to Hg toxicity and potential roles in Hg detoxification in the CWs. Granular biochar-based CW has high potential for treating Hg(Ⅱ)-contaminated wastewater., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Attention mechanisms have demonstrated great potential in improving the performance of deep convolutional neural networks (CNNs). However, many existing methods dedicate to developing channel or spatial attention modules for CNNs with lots of parameters, and complex attention modules inevitably affect the performance of CNNs. During our experiments of embedding Convolutional Block Attention Module (CBAM) in light-weight model YOLOv5s, CBAM does influence the speed and increase model complexity while reduce the average precision, but Squeeze-and-Excitation (SE) has a positive impact in the model as part of CBAM. To replace the spatial attention module in CBAM and offer a suitable scheme of channel and spatial attention modules, this paper proposes one Spatio-temporal Sharpening Attention Mechanism (SSAM), which sequentially infers intermediate maps along channel attention module and Sharpening Spatial Attention (SSA) module. By introducing sharpening filter in spatial attention module, we propose SSA module with low complexity. We try to find a scheme to combine our SSA module with SE module or Efficient Channel Attention (ECA) module and show best improvement in models such as YOLOv5s and YOLOv3-tiny. Therefore, we perform various replacement experiments and offer one best scheme that is to embed channel attention modules in backbone and neck of the model and integrate SSAM into YOLO head. We verify the positive effect of our SSAM on two general object detection datasets VOC2012 and MS COCO2017. One for obtaining a suitable scheme and the other for proving the versatility of our method in complex scenes. Experimental results on the two datasets show obvious promotion in terms of average precision and detection performance, which demonstrates the usefulness of our SSAM in light-weight YOLO models. Furthermore, visualization results also show the advantage of enhancing positioning ability with our SSAM.

Owing to their high energy density, lithium-oxygen batteries (LOBs) have been drawn great attention as one of the promising electrochemical energy sources. However, the sluggish kinetics of oxygen reduction/evolution reaction (ORR/OER) hamper the widespread application of LOBs. Herein, an elaborate designed catalysts which are constructed by FeN x moieties dispersed on the network-like hollow dodecahedral carbon and then decorated with Ru nanoparticles (FeN x -HDC@Ru). Since the homogeneously dispersed FeN x moieties could promote ORR performance, and the Ru nanoparticles could facilitate OER capability, the FeN x -HDC@Ru nanocomposites used as cathode catalysts can significantly improve LOBs performance. A lower discharge and charge overpotentials of 0.15 V and 0.78 V can be detected in the first cycle, respectively, and an excellent cycle performance of 90 cycles at 200 mA g -1 and 89 cycles at 500 mA g -1 can be demonstrated. Herein, the charge transfer kinetics has been enhanced with the internal network-like hollow structure and a low impedance Li 2 O 2 /catalysts contact interface could be earned by the constructed Ru nanoparticles, these factors would lead to an efficient acceleration to the formation and decomposition of Li 2 O 2 during discharge and charge process., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier Inc. All rights reserved.)

In a traditional antenna array direction finding system, all the antenna sensors need to work or shut down at the same time, which often leads to signal crosstalk, signal distortion, and other electromagnetic compatibility problems. In addition, the direction-finding algorithm in a traditional system needs a tremendous spectral search, which consumes considerable time. To compensate for these deficiencies, a reconfigurable antenna array direction finding system is established in this paper. This system can dynamically load part or all of the antennas through microwave switches (such as a PIN diode) and conduct a fast direction of arrival (DOA) search. First, the hardware structure of the reconfigurable antenna is constructed. Then, based on the conventional spatial domain search algorithm, an improved transform domain (TD) search algorithm is proposed. The effectiveness of the system has been proven by real experiments, and the advantage of the system has been verified by detailed simulations.

Solid-state lithium batteries (SSLBs) are promising owing to enhanced safety and high energy density but plagued by the relatively low ionic conductivity of solid-state electrolytes and large electrolyte-electrode interfacial resistance. Herein, we design a poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP)-based polymer-in-salt solid electrolyte (PISSE) with high room-temperature ionic conductivity (1.24×10 -4  S cm -1 ) and construct a model integrated TiO 2 /Li SSLB with 3D fully infiltration of solid electrolyte. With forming aggregated ion clusters, unique ionic channels are generated in the PISSE, providing much faster Li + transport than common polymer electrolytes. The integrated device achieves maximized interfacial contact and electrochemical and mechanical stability, with performance close to liquid electrolyte. A pouch cell made of 2 SSLB units in series shows high voltage plateau (3.7 V) and volumetric energy density comparable to many commercial thin-film batteries., (© 2021 Wiley-VCH GmbH.)

Incremental Learning is a particular form of machine learning that enables a model to be modified incrementally, when new data becomes available. In this way, the model can adapt to the new data without the lengthy and time-consuming process required for complete model re-training. However, existing incremental learning methods face two significant problems: 1) noise in the classification sample data, 2) poor accuracy of modern classification algorithms when applied to modern classification problems. In order to deal with these issues, this paper proposes an integrated classification model, known as a Pre-trained Truncated Gradient Confidence-weighted (Pt-TGCW) model. Since the pre-trained model can extract and transform image information into a feature vector, the integrated model also shows its advantages in the field of image classification. Experimental results on ten datasets demonstrate that the proposed method outperform the original counterparts., (© Springer Science+Business Media, LLC, part of Springer Nature 2020.)

The accuracy of X-ray pulsar-based navigation is greatly affected by the Doppler effect caused by the spacecraft orbital motion and the systematic biases introduced by the pulsar directional error, spacecraft-borne clock error, etc. In this paper, an innovative navigation method simultaneously employing the pulse phase (PP), the difference of two neighbor PPs (DPP) and the Doppler frequency (DF) of X-ray pulsars as measurements is proposed to solve this problem. With the aid of the spacecraft orbital dynamics, a single pair of PP and DF relative to the spacecraft's state estimation error can be estimated by using the joint probability density function of the arrival photon timestamps as the likelihood function. The systematic biases involved to the PP is proved to be nearly invariant over two adjacent navigation periods and the major part of it is eliminated in the DPP; therefore, the DPP is also exploited as additional navigation measurement to weaken the impact of systematic biases on navigation accuracy. Results of photon-level simulations show that the navigation accuracy of the proposed method is remarkably better than that of the method only using PP, the method using both PP and DF and the method using both PP and DPP for Earth orbit., Competing Interests: The authors declare that they have no conflict of interest.

In this paper, we study the multi-sensor multi-target tracking problem in the formulation of random finite sets. The Gaussian Mixture probability hypothesis density (GM-PHD) method is employed to formulate the sequential fusing multi-sensor GM-PHD (SFMGM-PHD) algorithm. First, the GM-PHD is applied to multiple sensors to get the posterior GM estimations in a parallel way. Second, we propose the SFMGM-PHD algorithm to fuse the multi-sensor GM estimations in a sequential way. Third, the unbalanced weighted fusing and adaptive sequence ordering methods are further proposed for two improved SFMGM-PHD algorithms. At last, we analyze the proposed algorithms in four different multi-sensor multi-target tracking scenes, and the results demonstrate the efficiency.

A localization and tracking algorithm for an early-warning tracking system based on the information fusion of Infrared (IR) sensor and Laser Detection and Ranging (LADAR) is proposed. The proposed Kalman filter scheme incorporates Out-of-Sequence Measurements (OOSMs) to address long-range, high-speed incoming targets to be tracked by networked Remote Observation Sites (ROS) in cluttered environments. The Rauch⁻Tung⁻Striebel (RTS) fixed lag smoothing algorithm is employed in the proposed technique to further improve tracking accuracy, which, in turn, is used for target profiling and efficient filter initialization at the targeted platform. This efficient initialization increases the probability of target engagement by increasing the distance at which it can be effectively engaged. The increased target engagement range also reduces risk of any damage from debris of the engaged target. Performance of the proposed target localization algorithm with OOSM and RTS smoothing is evaluated in terms of root mean square error (RMSE) for both position and velocity, which accurately depicts the improved performance of the proposed algorithm in comparison with existing retrodiction-based OOSM filtering algorithms. The effects of assisted target state initialization at the targeted platform are also evaluated in terms of Time to Impact (TTI) and true track retention, which also depict the advantage of the proposed strategy.

Multitarget tracking in clutter using bearings-only measurements is a challenging problem. In this paper, a performance improved nonlinear filter is proposed on the basis of the Random Finite Set (RFS) theory and is named as Gaussian mixture measurements-based cardinality probability hypothesis density (GMMbCPHD) filter. The GMMbCPHD filter enables to address two main issues: measurement-origin-uncertainty and measurement nonlinearity, which constitutes the key problems in bearings-only multitarget tracking in clutter. For the measurement-origin-uncertainty issue, the proposed filter estimates the intensity of RFS of multiple targets as well as propagates the posterior cardinality distribution. For the measurement-origin-nonlinearity issue, the GMMbCPHD approximates the measurement likelihood function using a Gaussian mixture rather than a single Gaussian distribution as used in extended Kalman filter (EKF). The superiority of the proposed GMMbCPHD are validated by comparing with several state-of-the-art algorithms via intensive simulation studies.

An enhanced anomalous Hall effect is observed in heterogeneous uniform Fe cluster assembled films with different film thicknesses (ta = 160-1200 nm) fabricated by a plasma-gas-condensation method. The anomalous Hall coefficient (Rs) at ta = 1200 nm reaches its maximum of 2.4 × 10(-8) Ω cm G(-1) at 300 K, which is almost four orders of magnitude larger than bulk Fe. The saturated Hall resistivity (ρ(A)xy) first increases and then decreases with the increase of temperature accompanied by a sign change from positive to negative. Analysis of the results revealed that ρ(A)xy decreases with increasing longitudinal resistivity (ρxx) on a double-logarithmic scale and obeys a new scaling relation of log(ρ(A)xy/ρxx) = a0 + b0 log ρxx.