The outbreak of jellyfish blooms poses a serious threat to human life and marine ecology. Therefore, jellyfish detection techniques have earned great interest. This paper investigates the jellyfish ...detection and classification algorithm based on optical images and deep learning theory. Firstly, we create a dataset comprising 11,926 images. A MSRCR underwater image enhancement algorithm with fusion is proposed. Finally, an improved YOLOv4-tiny algorithm is proposed by incorporating a CBMA module and optimizing the training method. The results demonstrate that the detection accuracy of the improved algorithm can reach 95.01%, the detection speed is 223FPS, both of which are better than the compared algorithms such as YOLOV4. In summary, our method can accurately and quickly detect jellyfish. The research in this paper lays the foundation for the development of an underwater jellyfish real-time monitoring system.
In order to solve the problem that the gridless DOA estimation algorithms based on generalized finite rate of innovation (FRI) signal reconstruction model are not suitable for two-dimensional DOA ...estimation using planar array, a separable gridless DOA estimation algorithm exploiting bi-orthogonal sparse linear array (BSLA) structure is proposed in this article, which is called 2D-SGFRI. The 2D-SGFRI algorithm firstly recovers the covariance data of the virtual array formed by BSLA through the matrix completion method, so as to obtain the complete covariance data vectors about two independent parameters respectively. Next, since the covariance data vector satisfies the constraints of annihilation filter equations, the generalized FRI signal reconstruction model can be utilized to retrieve DOA from the covariance data vector. Compared with the existing DOA estimation algorithms based on generalized FRI signal reconstruction model, the 2D-SGFRI algorithm can be can be effectively applied to two-dimensional DOA estimation, and can obtain stable estimation results. At the same time, due to the reduction of the dimension of positive semidefinite matrix, the 2D-SGFRI algorithm can significantly reduce the computational complexity compared with the two-dimensional DOA estimation algorithms based on atomic norm minimization (ANM). A series of simulation experiments are shown to verify the effectiveness and superiority of 2D-SGFRI algorithm.
Jellyfish detection is a challenge in the field of underwater biological identification. Although early works have solved some of the problems of jellyfish detection, there are still shortcomings. ...Earlier jellyfish datasets were not as extensive, necessitating heavy reliance on data augmentation. Moreover, the detection capability is limited, especially in scenarios involving jellyfish gatherings and occlusions. To overcome these limitations, we establish a high-quality dataset with more jellyfish species and propose a more robust real-time detection algorithm. Our algorithm primarily consists of a multi-gradient flow backbone and a feature fusion module GFPN. Additionally, we have designed a receptive field expansion module SPPFCSPC_G. The entire network employs the FReLU activation function, while the bounding box regression utilizes the WIOU loss function. Our methods demonstrate accuracy and run-time performance in comparison with the state-of-the-art yolo series algorithms. Results show that our algorithm achieves the highest Precision, Recall and mAP50, exceeding the baseline yolov5 by 1.1%, 4.1%, and 4.5%, and outperforming the latest yolov8 by 0.9%, 1.3%, and 2.5%. Importantly, our method effectively addresses aggregation, occlusion, and deformation issues commonly encountered in jellyfish detection.
Dealing with the bit-error problem of high-speed optical links in electronic reconnaissance system, this paper investigates a typical application architecture which composed of FPGA, optical module ...and optical links. The comprehensive testing method of this optical link is discussed, which is composed of bit error ratio test, and eye diagram test. According to the testing results, the impact of input electrical signal of the transmitting optical module, the jitter and signal-to-noise of optical signal on the bit error ratio is confirmed. Finally, through extended tests to adjust the parameters of Gigabit Transceiver at the transmitting end and decrease the output jitter of transmitting optical module, the bit error ratio of the optical transmission link can be reduced effectively.
Specific emitter identification is one of the important part of radar countermeasure signal processing. Current specific radar emitter recognition algorithm highly relies on prior knowledge and ...labeled training samples. In this paper, a radar emitter identification method based on manifold learning is proposed to solve such problem. Based on the characteristics of radar emitters, this method extracts representative slices of ambiguity function as signal features, and proposes a limited looseness geodesic Gaussian Locality Preserving Projection (LLGGLPP) algorithm to reduce the dimension of signal feature samples, which has the advantage of unsupervised learning and easy sample expansion. Simulation results show that the proposed method achieves fine sample clustering characteristics, and provides a new method for specific identification of radar emitters.
In order to reduce the influences of spectrum leakage and scalloping loss of fast Fourier transform channelizer on the performance of digital receivers, an efficient real-time digital channelization ...method based on frequency-domain windowing was proposed. This method utilized a special window on the basis of windowed fast Fourier transform model. Theoretical derivation revealed that the proposed method could be realized only by adding an adder to each channel calculation, which facilitated the implementation in FPGAs. Moreover, the sidelobe is reduced by 10dB and the gain of channel intersection is increased by 2dB compared with fast Fourier transform channelizer. The simulation results showed that the proposed method had improved the signal-to-noise ratio, the accuracy of amplitude calculation, and the instantaneous dynamic range of the receivers.
Resveratrol (RES), a natural polyphenol in fruits, has shown promising anti-cancer properties. Due to its relative low toxicity which limits the adverse effects observed for conventional ...chemotherapeutics, RES has been proposed as an alternative. However, the therapeutic applications of RES have been limited due to low water solubility, as well as chemical and physical instability. This study investigated enhancing the anti-cancer activity of RES against non-small-cell-lung-cancer (NSCLC) by complexing with sulfobutylether-β-cyclodextrin (CD-RES) and loading onto polymeric nanoparticles (NPs). The physicochemical properties of the CD-RES NPs were then characterized. The CD-RES inclusion complex increased the water solubility of RES by ~66-fold. CD-RES NPs demonstrated very good aerosolization potential with a mass median aerodynamic diameter of 2.20 μm. Cell-based studies demonstrated improved therapeutic efficacy of CD-RES NPs compared to RES. This included enhanced cellular uptake, cytotoxicity, and apoptosis, while retaining antioxidant activity. The 3D spheroid study indicated an intensified anti-cancer effect of CD-RES NPs. Altogether, these findings marked CD-RES NPs as a potential inhalable delivery system of RES for the treatment NSCLC.
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•CD-RES inclusion complex was prepared to improve the aqueous solubility of RES by 66-folds.•CD-RES NPs proved to have excellent aerosolization potential using the Next-Generation Impactor™.•CD-RES loaded onto PLGA NPs significantly enhanced the anti-cancer effects when tested on NSCLC cell lines.•3D spheroid study also indicated a considerably intensified anti-cancer effect of the CD-RES NPs compared to the plain drug.
Discovery of advanced soft-magnetic high entropy alloy (HEA) thin films are highly pursued to obtain unidentified functional materials. The figure of merit in current nanocrystalline HEA thin films ...relies in integration of a simple single-step electrochemical approach with a complex HEA system containing multiple elements with dissimilar crystal structures and large variation of melting points. A new family of Cobalt-Copper-Iron-Nickel-Zinc (Co-Cu-Fe-Ni-Zn) HEA thin films are prepared through pulse electrodeposition in aqueous medium, hosts nanocrystalline features in the range of ~ 5-20 nm having FCC and BCC dual phases. The fabricated Co-Cu-Fe-Ni-Zn HEA thin films exhibited high saturation magnetization value of ~ 82 emu/g, relatively low coercivity value of 19.5 Oe and remanent magnetization of 1.17%. Irrespective of the alloying of diamagnetic Zn and Cu with ferromagnetic Fe, Co, Ni elements, the HEA thin film has resulted in relatively high saturation magnetization which can provide useful insights for its potential unexplored applications.
In the development of a fire, all kinds of equipment in ship cabins cause flames to flow around them, which greatly affects the combustion characteristics of the flames. Experimental studies were ...conducted on a pool of 80 cm × 6 cm × 5 cm containing a bluff body with a diameter of 2 cm and a height of 15 cm. Using diesel fuel as an ignition source, the flame flow around the bluff body propagating as a crosswind at speeds of 0.8–2.4 m/s was studied. The results showed that the fluctuation of the mass loss in a rising period decreased. A new flame spreading phenomenon of “continuous-flow around-broken-retract” appeared. The average spreading rate affected by the bluff body had a quadratic nonlinear relationship with the intensity of airflow. A prediction model of the flame drag length for the rectangular pool containing a bluff body was established. In addition, there were two vortices near a bluff body that were symmetrical and flowed in opposite directions. The temperature at the standing vortices reached a maximum of approximately 260 °C. At the same time, the vortices presented the characteristics of periodic formation and shedding with an interval of approximately 3.6 s.
To investigate whether the number of years of schooling are causally associated traumatic brain injury (TBI). We aimed to investigate whether the number of years of schooling are causally associated ...TBI.
We investigate the prospective causal effect of years of schooling on TBI using summary statistical data. The statistical dataset comprising years of schooling (n = 293,723) from genome-wide association studies (GWASs) deposited in the UK Biobank was used for exposure. We used the following GWAS available in the FinnGen dataset: individuals with TBI (total = 13,165; control = 136,576; number of single nucleotide polymorphisms SNPs = 16,380,088).
Seventy significant genome-wide SNPs from GWAS datasets with annotated years of schooling were selected as instrumental variables. The inverse variance weighted method results supported a causal relationship between years of schooling and TBI (odds ratio (OR), 0.78; 95 % confidence interval (CI), 0.62–0.98; P = 0.029). MR-Egger regression showed that polydirectionality was unlikely to bias the results (intercept = 0.007, SE = 0.01, P = 0.484) and demonstrated no causal relationship between years of schooling and TBI (OR, 0.52; 95%CI, 0.17–1.64; P = 0.270). The weighted median method revealed a causal relationship with TBI (OR, 0.73; 95%CI, 0.55–0.98; P = 0.047). A Cochran's Q test and funnel plot did not show heterogeneity nor asymmetry, indicating no directional pleiotropy.
The current investigation yields substantiation of a causal association between years of schooling and TBI development. More years of schooling may be causally associated with a reduced risk of TBI, which has implications for clinical and public health practices and policies.
•To explore the causal association between years of schooling and traumatic brain injury with Mendelian randomization.•More years of schooling results in a lower risk of traumatic brain injury.•Our data provides the evidence required to develop clinical and public health policies.
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