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We report the recent observation of a density duct structure in the equatorial plasmasphere, identified using data from the NASA Van Allen Probes (RBSP). The structure consists of a low‐density duct located above a high‐density duct, forming a “double duct” system capable of simultaneously trapping and guiding multiple whistler‐mode waves at distinct frequencies. Simulations based on the electron‐MHD model confirm that such density double ducts enable efficient confinement and propagation of whistler‐mode waves along the background magnetic field.

Salient object detection in wavelet domain has recently begun to attract researchers’ effort due to its desired ability to provide multi-scale analysis of an image simultaneously in both frequency and spatial domains. The proposed algorithm exploits the inherent multi-scale structure of the double-density dual-tree complex-oriented wavelet transform (DDDTCWT) to decompose each input image into four approximate sub-band images and 32 high-pass detailed sub-band images at each scale. These 32 detailed high-pass sub-bands at each scale are adequate to represent singularities of any geometric object with high precision and to mimic zooming-in and zooming-out process of human vision system. In the proposed model, we first compute a rough segmented saliency map (RSSM) by fusing multi-scale edge-to-texture features generated from DDDTCWT with segmentation results obtained from bipartite graph partitioning-based segmentation approach. Then, each pixel in RSSM is categorized into either background region or salient region based on a threshold. Finally, the pixels of the two regions are considered as samples to be drawn from a multivariate kernel function whose parameters are estimated using expectation maximization algorithm, to generate a saliency map. The performance of the proposed model is evaluated in terms of precision, recall, F-measure, area under the ROC curve and computation time using six publicly available image datasets. Extensive experimental results on six benchmark datasets demonstrate that the proposed model outperformed the existing 29 state-of-the-art methods in terms of F-measure on all five datasets, recall on four datasets and area under ROC curve on two datasets. In terms of mean recall value, mean F-measure value and mean AUC value on all six datasets, the proposed method outperforms all state-of-the-art methods. The proposed method also takes comparatively less computation time in comparison with many existing spatial domain methods.

Background: In males with extremely severe oligospermia (MESO), single density gradient centrifugation (SDGC) has low sperm enrichment efficiency, making intracytoplasmic sperm injection (ICSI) challenging. This study aimed to determine whether double density gradient centrifugation (DDGC) can efficiently enrich sperm from MESO samples and whether these sperm are safe for clinical use. Methods: MESO was defined as having ≤2000 motile sperm/mL of semen, whereas males with severe oligospermia (MSO) were defined as having 2000–10,000 motile sperm/mL. We compared sperm recovery between SDGC and DDGC in MESO samples and retrospectively analyzed in vitro fertilization (IVF) data from 39 MESO cases (sperm prepared using DDGC) and 78 MSO cases (sperm prepared using SDGC) collected from 2017 to 2023. The SDGC group served as the control group. Results: The results showed that the sperm recovery rate of DDGC was approximately three-fold higher than that of SDGC in MESO samples. We hypothesized that in normal semen samples, sperm aggregate into a pellet during centrifugation, enabling efficient enrichment by SDGC. In MESO, where sperm count is extremely low, sperm fail to form a pellet, leading to slower sedimentation and lower recovery rates with SDGC, thereby necessitating additional centrifugation. Importantly, sperm prepared by DDGC from MESO semen samples showed comparable in vitro and in vivo embryo developmental parameters to sperm prepared by SDGC. Interestingly, the DDGC group showed a significantly higher usable blastocyst formation rate compared to SDGC group (73.48% vs. 62.63%, p = 0.009). Conclusions: In conclusion, DDGC can effectively enrich sperm from MESO samples, and no obvious adverse clinical outcomes were observed. The method is simple, requires no additional equipment, and may be considered as a routine sperm preparation technique for MESO in clinical use. However, the long-term safety of using DDGC for sperm preparation from MESO for ICSI still requires further confirmation, and more effective methods for sperm enrichment from MESO are needed.

We aim to show the effectiveness of the double density dual-tree complex wavelet transform to denoise the Raman signal. A comparative study is carried out of the double density dual-tree complex wavelet transform with the discrete wavelet transform, dual-tree complex wavelet transforms, and Savitzky–Golay smoothing method to show its capability and effectiveness. Results show that denoising based on the double density dual-tree complex wavelet transform can improve the quantitative and qualitative analysis of the Raman signal.

Digital photo acquisition, manipulation, and redistribution are ubiquitous because of digital globalization. Digital watermarking provides a comparative solution for leak information tracking and retrieval problems and improves authentication, security, and copyright protection in today’s digital globalization. This paper presents a watermarking scheme joined with the Arnold encryption technique for Screen-Camera (SC) rebroadcast attacks. This research aims to extract watermark information from the stego (or watermarked) content even after a rebroadcast attack from the LCD screens. The technique is resilient to lens distortion, light source imbalance, and aliasing artifacts. To resist the geometric distortions caused by the recapturing process, we propose a cascaded Double-Density Dual-Tree Discrete Wavelet Transform (DDD-DWT) domain and Singular Value Decomposition (SVD) based algorithm. However, most SVD-based watermarking techniques are prone to false and missed alarm rates and cause security issues. On that account, this paper presents the Arnold Transform-based watermark encryption technique using a unique security key for authentication on the decoder side. The evaluated results showed that the scheme achieved a high Peak Signal-to-Noise Ratio (PSNR) value of 64 dB, an Structural Similarity Index (SSIM) value of about 0.99, and robustness to several geometrical, signal processing, compression, and recapturing attacks.

The mirror neuron system (MNS), mainly including the premotor cortex (PMC), inferior frontal gyrus (IFG), superior parietal lobule (SPL), and rostral inferior parietal lobule (IPL), has attracted extensive attention as a possible neural mechanism of social interaction. Owing to high ecological validity, functional near-infrared spectroscopy (fNIRS) has become an ideal approach for exploring the MNS. Unfortunately, for the feasibility of fNIRS to detect the MNS, none of the four dominant regions were found in previous studies, implying a very limited capacity of fNIRS to investigate the MNS. Here, we adopted an experimental paradigm in a real-life situation to evaluate whether the MNS activity, including four dominant regions, can be detected by using fNIRS. Specifically, 30 right-handed subjects were asked to complete a table-setting task that included action execution and action observation. A double density probe configuration covered the four regions of the MNS in the left hemisphere. We used a traditional channel-based group analysis and also a ROI-based group analysis to find which regions are activated during both action execution and action observation. The results showed that the IFG, adjacent PMC, SPL, and IPL were involved in both conditions, indicating the feasibility of fNIRS to detect the MNS. Our findings provide a foundation for future research to explore the functional role of the MNS in social interaction and various disorders using fNIRS.

Image segmentation is often faced by low contrast, bad boundaries, and inhomogeneity that made it difficult to separate normal and abnormal tissue. Therefore, it takes long periodto read and diagnose brain tumor patients. The aim of this study was to applied hybrid methods to optimize segmentation process of magnetic resonance image of brain. In this study, we divide the brain tumor images with double density dual-tree complex wavelet transform (DDDTCWT), continued by convolutional neural network (CNN), and optimized by genetic algorithm (GA) with 48 combinations yielding excellent results. The F-1 score was 99.42%, with 913 images test data. The training images consist of 1397 normal MRI images and 302 tumor magnetic resonance imaging (MRI) images resized by 32 x32 pixels. The DDDTCWT transforms the input images into more detail than ordinary wavelet transforms, and the CNNs will recognize the pattern of the output images. Additionally, we applied the GA to optimize the weights and biases from the first layer of the CNNs layers. The parameters used for evaluating were dice similarity coefficient (DSC), positive present value (PPV), sensitivity, and accuracy. The result showed that the combination of DDDTCWT, CNN, and GA could be used to brain MRI images and it generated parameters value more that 95%.

[This corrects the article DOI: 10.3389/fnhum.2018.00086.].

Steganography, the art of concealing information within another message or physical object to evade detection, has potential applications across multiple digital content types, including text, photos, videos, and audio. The hidden data size significantly influences the difficulty of detection. Conversely, the data amount that can be concealed within an image is largely dependent on the cover image dimensions, a concept often overlooked by steganographers. Despite numerous attempts to improve embedding capacity, the quality of generated stego-images remains subpar, and embedding capacity continues to be restricted by the cover image size. This study introduces an image steganography approach, leveraging double density dual tree wavelet transform (DDDT-DWT), designed to enhance capacity while preserving optimal quality. The performances of discrete wavelet transform (DWT), double density DWT (DD-DWT), and double density dual tree DWT (DDDT-DWT) are implemented, evaluated, and comparatively assessed. Key performance parameters, such as peak signal-to-noise ratio (PSNR) and mean squared error (MSE), are calculated, guiding the selection of the most efficient methodology. The stego-image quality is also measured using the Structural Similarity Index Metric (SSIM). Experimental results indicate that the proposed DDDT-DWT-based method yields superior imperceptibility for the stego image, with a PSNR of 47.8582 and an SSIM of 0.9945. This advancement in steganography presents opportunities for increasingly undetectable and efficient data concealment.

The existing density clustering algorithms have high error rates on processing data sets with mixed density clusters. For overcoming shortcomings of these algorithms, a double-density clustering method based on Nearest-to-First-in strategy, DDNFC, is proposed, which calculates two densities for each point by using its reverse k nearest neighborhood and local spatial position deviation, respectively. Points whose densities are both greater than respective average densities of all points are core. By searching the strongly connected subgraph in the graph constructed by the core objects, the data set is clustered initially. Then each non-core object is classified to its nearest cluster by using a strategy dubbed as ‘Nearest-to-First-in’: the distance of each unclassified point to its nearest cluster calculated firstly; only the points with the minimum distance are placed to their nearest cluster; this procedure is repeated until all unclassified points are clustered or the minimum distance is infinite. To test the proposed method, experiments on several artificial and real-world data sets are carried out. The results show that DDNFC is superior to the state-of-art methods like DBSCAN, DPC, RNN-DBSCAN, and so on.