The detection of weak signals is a well-established application in chaos theory. This theory leverages the inherent robustness of chaotic systems, enabling them to resist noise and thus serve as effective tools for identifying weak signals. However, challenges remain in selecting appropriate chaotic systems and in their practical implementation—areas that are still under-explored. In this paper, we analyze a simplified fractional-order Genesio–Tesi chaotic system, which exhibits a unique chaos-divergence characteristic. Based on this characteristic, we propose a new detection method that uses the chaos-divergence state as a criterion for determining the presence or absence of a signal when detecting weak signal amplitudes. This approach makes the simplified fractional-order Genesio–Tesi chaotic system more suitable for chaotic weak signal detection. Notably, the significant variance observed in the divergent state’s independent variables emerges as a key feature, enhancing the system’s ability to detect the frequencies of weak signals. Our numerical simulations focus on detecting weak cosine signals masked by three different types of noise. The results demonstrate successful detection of a weak signal at a frequency of 100 rad/s under the specified conditions, with the lowest detectable signal-to-noise ratio of −40.83 dB. Overall, these results highlight the effectiveness and feasibility of our proposed method for weak signal detection.

Objective Single-pixel imaging is an indirect imaging technique that uses only one detector element instead of an array of imaging sensors to acquire images.Compared to traditional methods,it offers better detection efficiency in scenarios with limited resources or specific environmental conditions.However,the sampling speed and image quality of current single-pixel imaging methods are insufficient for practical applications.To address this,improvements in sampling methods are needed to reduce time costs while obtaining high-quality images—specifically,optimizing the calibration and sampling strategy to enhance the speed of single-pixel imaging.Many research institutes and universities,both domestically and internationally,have investigated single-pixel imaging sampling and achieved significant results.After continuous innovation and optimization of the sampling method,the sampling rate has decreased under the same signal-to-noise ratio,and the sampling time has been markedly reduced.However,previous research has neglected the processing of non-essential coefficients.When focusing on sampling important regional information,concentrating solely on important coefficients can lead to sampling lag for local information within those regions.Prioritizing the sampling of important coefficients first,followed by non-essential coefficients,can help restore the important regions more completely.Based on this,we propose a new method to tackle these shortcomings and reduce the number of samples required for imaging while ensuring image quality. Methods The two-dimensional reflectance spatial distribution function of the target object is first converted into wavelet coefficients using the Haar wavelet transform,which reveals its energy distribution at different frequencies and scales.Initially,based on the information available about the measured target,each scale is assigned an orthorhombic diagonal diameter and subsequent sampling is performed within this orthorhombic area.In the pre-subsampling step,the number of subsamples is set for wavelet coefficients at each scale:the number of sampling points for low-scale coefficients(1st to 4th levels)is either minimal or sampled fully,while the number of high-scale coefficients(5th level and above)is reduced.Finally,random subsampling of the target object is performed.The wavelet coefficients collected through subsampling are first arranged according to the absolute values of their magnitudes,and the corresponding subsampling points are determined to guide subsequent sampling.Next,the remaining wavelet coefficients are sorted in terms of their sampling order.For each scale from the first to the eighth level,the subsampling coefficients are arranged by the absolute value,and sampling points are expanded accordingly.Repeated sampling points are skipped,and the remaining points are sampled to complete the process.All points are then sorted and organized to create a new sampling order for further sampling.In this paper,the peak signal-to-noise ratio(PSNR)of the reconstructed image using the proposed algorithmic sampling method is compared to that of the reconstructed image with standard sampling.The difference in PSNR values is used as the evaluation index. Results and Discussions The comparison of reconstructed PSNR differences shows that the proposed method significantly outperforms the orthogonal sampling one with the same number of samples(Figs.5 and 8).The detail comparison figures for landscape and people images(Figs.7 and 11)further illustrate that,with the same number of samples,the proposed method excels in image reconstruction,particularly in preserving detail.This method requires fewer samples to achieve reconstruction,which maintains the main features and structure of the original image while providing a clearer and more natural effect at the detail level.Consequently,it reduces the computational and storage resources needed and allows for more valuable data acquisition within the same timeframe.Our method notably boosts data acquisition efficiency,enabling effective and accurate data collection even with limited resources. Conclusions Single-pixel imaging can accurately reconstruct an image with a small amount of sampling data.We put forward a subsampling fast single-pixel imaging method based on sample reordering.It guides the subsequent sampling order by wavelet subsampling and devises an ordering strategy from the results of random subsampling at each image scale in the previous stage.Theoretical analysis and simulations show that,with the same number of samples,the proposed method considerably improves the signal-to-noise ratio and strengthens imaging efficiency.However,the method is highly dependent on pre-subsampling,which requires continual optimization.Future research should focus on mitigating the effects of pre-subsampling and exploring additional optimization strategies to strengthen the robustness and applicability of the method in real imaging scenarios.

The Duffing Chaos System can detect weak signals that are obscured by Gaussian noise because it is sensitive to specific signal functions and can withstand noise. In this paper, we investigate the use of intermittent chaotic phenomena in fractional-order incommensurate Duffing chaotic systems for weak signal detection. This new intermittent chaotic state has not appeared in integer-order Duffing systems before, so this phenomenon reflects the superiority of fractional-order Duffing systems. We start by giving the incommensurate fractional-order Duffing system’s weak signal detection model. Then design a time series-based judgment method that successfully separates chaotic, intermittent chaotic, and limit cycle states. Finally, the intermittent chaotic of fractional-order detection system is used to determine the amplitude and frequency of the weak signals to calculate the detection performance. The results show that the weak signal can be detected at a maximum signal-to-noise ratio of - 13.26 dB for single-detection oscillator amplitude detection. When detecting the frequency, a single-detection oscillator can detect the frequency range of 1050 rad/s, proving that the fractional-order chaos detection system is better than the integer-order chaos detection system.

A high-quality single-photon blockade system can effectively enhance the quality of single-photon sources. Conventional photon blockade(CPB) suffers from low single-photon purity and high requirements for system nonlinearity, while unconventional photon blockade(UPB) has the disadvantage of low brightness. Recent research by [Laser Photon.Rev 14,1900279,2020] demonstrates that UPB can be used to enhance the strength of CPB, thereby improving the purity of single-photon sources. Research by [Opt. Express 30(12),21787,2022] shows that there is an intersection point between CPB and UPB in certain nonlinear systems, where the performance of single photons is better. In this study, we investigated the phenomenon of photon blockade in a non-degenerate four-wave mixing system, where CPB and UPB can occur simultaneously within the same parameter range. We refer to this phenomenon as composite photon blockade. Particularly, when the system achieves composite photon blockade, the value of g(2)(0) is smaller, and there are more single photons. We conducted analytical analysis and numerical calculations to study the conditions for the realization of CPB, UPB, and 2PB in the system, and discussed in detail the influence of system parameters on various blockade effects.

This work investigates a fractional-order multi-wing chaotic system for detecting weak signals. The influence of the order of fractional calculus on chaotic systems’ dynamical behavior is examined using phase diagrams, bifurcation diagrams, and SE complexity diagrams. Then, the principles and methods for determining the frequencies and amplitudes of weak signals are examined utilizing fractional-order multi-wing chaotic systems. The findings indicate that the lowest order at which this kind of fractional-order multi-wing chaotic system appears chaotic is 2.625 at a=4, b=8, and c=1, and that this value decreases as the driving force increases. The four-wing and double-wing change dynamics phenomenon will manifest in a fractional-order chaotic system when the order exceeds the lowest order. This phenomenon can be utilized to detect weak signal amplitudes and frequencies because the system parameters control it. A detection array is built to determine the amplitude using the noise-resistant properties of both four-wing and double-wing chaotic states. Deep learning images are then used to identify the change in the array’s wing count, which can be used to determine the test signal’s amplitude. When frequencies detection is required, the MUSIC method estimates the frequencies using chaotic synchronization to transform the weak signal’s frequencies to the synchronization error’s frequencies. This solution adds to the contact between fractional-order calculus and chaos theory. It offers suggestions for practically implementing the chaotic weak signal detection theory in conjunction with deep learning.

We developed a novel method based on self-supervised learning to improve the ghost imaging of occluded objects. In particular, we introduced a W-shaped neural network to preprocess the input image and enhance the overall quality and efficiency of the reconstruction method. We verified the superiority of our W-shaped self-supervised computational ghost imaging (WSCGI) method through numerical simulations and experimental validations. Our results underscore the potential of self-supervised learning in advancing ghost imaging.

E Se tea, processed by the tender leaves of Malus toringoides (Rehd) Hughes, has been traditionally used as healthy tea to prevent hypoglycemic, hypolipidemia and hypertensive disease. This paper firstly performed to explore its bioactive compounds and the possible protective mechanism on lipid accumulation. As a result, 13 flavonoids were isolated and identified, and phlorizin (2) was the most abundant compound in E Se tea. All the flavonoids could reduce TG level in 3T3-L1 preadipocytes. Oil red O staining results also verified that these flavonoids could inhibit the production of lipid droplet. Among them, compounds 2 and 6 have the strongest inhibitory effects on lipid accumulation. In addition, the synergistic effects between compound 2 and other compounds were further evaluated. Compared with individual flavonoid, most of combinations significantly reduced lipid accumulation, especially compounds 2 and 6. Western blotting analysis suggested compounds 2 and 6 could promote the phosphorylation of AMPK and ACC proteins. Furthermore, molecular docking results suggested that compounds 2 and 6 tightly bonded to AMPK by hydrogen bonding and electrostatic interaction. All the results suggested there was a significant synergistic effect between compounds 2 and 6 for inhibiting the lipid accumulation in 3T3-L1 preadipocytes. Therefore, E Se tea could be served as healthy tea to regulate lipid metabolism.

A multi-channel computational ghost imaging method based on multi-scale speckle optimization is proposed. We not only reduce imaging time and enhance imaging quality but also reduce interference among different channels. Using one bucket detector to receive total light intensity, the color speckle is formed by combining components obtained through the singular value decomposition of three self-designed multi-scale measurement matrices. Simulation and experimental results demonstrate that our designed method contributes to reducing imaging time and enhancing imaging quality, achieving improved visual quality even at low sampling rates. This approach enhances ghost imaging flexibility and holds potential for diverse applications, including target recognition and biomedical imaging.

To improve the imaging speed of ghost imaging and ensure the accuracy of the images, an adaptive ghost imaging scheme based on 2D-Haar wavelets has been proposed. This scheme is capable of significantly retaining image information even under under-sampling conditions. By comparing the differences in light intensity distribution and sampling characteristics between Hadamard and 2D-Haar wavelet illumination patterns, we discovered that the lateral and longitudinal information detected by the high-frequency 2D-Haar wavelet measurement basis could be used to predictively adjust the diagonal measurement basis, thereby reducing the number of measurements required. Simulation and experimental results indicate that this scheme can still achieve high-quality imaging results with about a 25% reduction in the number of measurements. This approach provides a new perspective for enhancing the efficiency of computational ghost imaging.

We have achieved a conventional photon blockade and two -photon blockade in a second -order nonlinear system with a two -level atom embedded in a high -frequency cavity. The physical mechanisms behind the implementation of both types of photon blockade are explained, and analytical conditions for achieving a conventional photon blockade are derived, which are consistent with the numerical solutions of the master equation in the steady-state limit. By appropriately setting the system parameters, we can achieve simultaneous conventional photon blockade in the high -frequency cavity and two -photon blockade in the low -frequency cavity. The effects of driving factors and environmental temperature on photon blockade are analyzed. The adjustability of the coupling coefficient between the high -frequency cavity and the atom, as well as the nonlinear coupling coefficient between different nanocavities, is discussed in the context of implementing conventional photon blockades. The tunability of these coupling coefficients may significantly reduce the experimental complexity of implementing the system.

Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by multiple autoantibody types, some of which are produced by long-lived plasma cells (LLPC). Active SLE generates increased circulating antibody-secreting cells (ASC). Here, we examine the phenotypic, molecular, structural, and functional features of ASC in SLE. Relative to post-vaccination ASC in healthy controls, circulating blood ASC from patients with active SLE are enriched with newly generated mature CD19 − CD138 + ASC, similar to bone marrow LLPC. ASC from patients with SLE displayed morphological features of premature maturation and a transcriptome epigenetically initiated in SLE B cells. ASC from patients with SLE exhibited elevated protein levels of CXCR4, CXCR3 and CD138, along with molecular programs that promote survival. Furthermore, they demonstrate autocrine production of APRIL and IL-10, which contributed to their prolonged in vitro survival. Our work provides insight into the mechanisms of generation, expansion, maturation and survival of SLE ASC.

Objective To investigate the effectiveness of Baduanjin exercise on executive function in community-dwelling older adults with cognitive frailty. Design Randomized controlled trial. Setting Community residential centers. Subjects 120 eligible older adults. Interventions Baduanjin training group received supervised Baduanjin training, 60 min sessions three times per week for 24 weeks. The control group did not receive any exercise intervention. Main measures Primary outcome was executive function, assessed using Clock Drawing Test. Secondary outcomes included the subcomponents of executive function (working memory, inhibitory control and cognitive flexibility), attention and cognitive frailty (global cognitive function, physical frailty) assessed using Verbal Fluency Test, Trail Making Test-A/B, Stroop Test, Montreal Cognitive Assessment and Edmonton Frailty Scale, respectively, at baseline and 24 weeks after intervention. Results After the 24-week intervention, the scores of Clock Drawing Test and Verbal Fluency Test, the Trail Making Test-B time and the Card correct numbers of Stroop Test in Baduanjin training group showed significant improvement compared with control group (all P < 0.05) with small to moderate effect sizes and the significant interaction effect of group by time in the Clock Drawing Test and Trail Making Test-B test ( P = 0.003 and P = 0.043); cognitive frailty variables, including Montreal Cognitive Assessment and Edmonton Frail Scale scores, also showed significant improvement ( P = 0.002 and P = 0.004) with a moderate effect sizes and a significant interaction effect ( P < 0.001, P = 0.013). No adverse events were reported. Conclusion Regular Baduanjin training may be an effective and safe intervention to improve cognitive frailty and executive function in community-dwelling older adults with cognitive frailty. Trial registration Chinese Clinical Trial Registry, ChiCTR2100050857. Data of registration: 8/5/2020, https://www.chictr.org.cn/showproj.html?proj = 133037 .

The resolution is an important factor in evaluating image quality. In general, the resolution of correlation imaging is taken to the full width at half maximum (FWHM) of the point spread function (PSF) produced by the second-order correlation function. In this paper, we show that the resolution of correlation imaging can be improved by the fluctuation characteristic of the second-order correlation function. It is demonstrated both experimentally and theoretically that the resolution of the system can be drastically improved. We also prove that the FWHM of the PSF can be narrowed by 2n by extracting the n-order fluctuation information of the second-order correlation function.

The paper introduces a deep decryption method based on chaotic ghost imaging, termed Chaotic Ghost Imaging with Deep Decryption (CGI-DD). Utilizing chaotic sequences generated by the Hénon system as encryption keys, combined with ghost imaging technology, the intensity information of an object is acquired using a single-pixel detector to complete image encryption. To improve the quality of decrypted images, this paper introduces a deep learning model based on deep image prior technology. This model can effectively remove noise and restore high-quality decrypted images even at low sampling rates. Experimental results show that the CGI-DD method outperforms traditional ghost imaging methods in decryption effectiveness across various sampling rates, particularly demonstrating accurate reconstruction of target images even under conditions of low sampling rates.

High-quality computational ghost imaging under low sampling rates has always attracted much attention and plays an important role in practical applications. In this paper, a novel optical field optimization method based on multi-scale light fields singular value decomposition which can greatly reduce the number of computational ghost imaging measurements is proposed. The computational ghost imaging measurement matrix is derived from the components obtained by singular value decomposition of self-designed special measurement matrices. When the measurement matrix is fully sampled, high-quality reconstructed image can be obtained. Similarly, when the measurement matrix is under-sampled, it is still possible to obtain high-quality reconstructed image and show the performance of multi-resolution imaging. Simulation and experimental results show that our method can obtain high-quality computational ghost imaging, even at low sampling rates, and as the number of splicing matrices increases, the number of measurements is further reduced.

Correlated imaging and interference is currently a topic of intense research interest.However,research based on interference is limited.This study introduces the basic theory of second-order correlation imaging based on the properties of correlation functions and simulates the ghost imaging of double-slit interference.In addition,when the incident wavelengths of the two optical paths are different,the ghost imaging of double-slit interference exhibits more regular behavior.This observation indicates that the wavelength in the reference optical path significantly impacts correlation information.In contrast,the selected wavelength in the signal optical path has a relatively small impact on the correlation results.This provides a reference for selecting lighting sources for ghost imaging experiments.Finally,the second-order correlation function's correlation characteristics were studied,and interference behavior occurred in double-slit ghost imaging after second-order correlation,laying a good foundation for applications in imaging resolution and other aspects.

Multi-wavelength ghost imaging usually involves extensive data processing and faces challenges such as poor reconstructed image quality. In this paper, we propose a multi-wavelength computational ghost imaging method based on feature dimensionality reduction. This method not only reconstructs high-quality color images with fewer measurements but also achieves low-complexity computation and storage. First, we utilize singular value decomposition to optimize the multi-scale measurement matrices of red, green, and blue components as illumination speckles. Subsequently, each component image of the target object is reconstructed using the second-order correlation function. Next, we apply principal component analysis to perform feature dimensionality reduction on these reconstructed images. Finally, we successfully recover a high-quality color reconstructed image. Simulation and experimental results show that our method not only improves the quality of the reconstructed images but also effectively reduces the computational and storage burden. When extended to multiple wavelengths, our method demonstrates greater advantages, making it more feasible to handle large-scale data.

The conventional edge detection encounters limitations in practical applications due to its low imaging quality.By contrast,the edge detection encounters based on ghost imaging can achieve a high signal-to-noise ratio for the edge imaging of object.Accordingly,this paper proposes a computational ghost imaging based on the edge detection using the Scharr operator.The Scharr operator has low computational complexity,enhancing its effectiveness for image processing.Hence,a new set of speckle functions is generated by applying the Scharr operator to speckle.When the Scharr operator template is applied to speckle movement,information will miss along a certain direction in edge extraction results.To address this problem,a new operator template is generated by converting the positive and negative values of the operator template.Thus,a new illumination speckle is created by applying the newly generated operator template to the moving speckle,thereby obtaining complete information along all directions in the edge detection results.Additionally,based on the basic method of computational ghost imaging,edges of unknown images are extracted theoretically and experimentally.The simulation and experimental results show that the proposed method can obtain complete and clear edges of the tested object.

XLS file, 193K, Sorted Affymetrix gene array data.

PDF file, 176K, Assessment of baseline gene expression in RG7212 responders and non-responders in qPCR array.