Information Capacity Research Articles

SDRNet: Camouflaged object detection with independent reconstruction of structure and detail

The simultaneous reconstruction of structure and detail is a prevalent strategy in camouflaged object detection. However, the reconstruction features required for structure and detail exhibit disparities, a facet overlooked in existing methods. Therefore, we present a novel methodology, termed SDRNet, which employs a dual-branch approach for the independent reconstruction of structure and detail, aiming to discern camouflaged targets and their edges. Specifically, we propose a decomposition block to segregate encoded features into distinct structure and detail components. Furthermore, structure enhancement block and detail enhancement block are proposed as feature enhancement methods to boost the capacity of structure and detail information. Subsequently, the introduced structure fusion block and detail fusion block progressively amalgamate the enhanced features. Additionally, the shared feature block is designed to serve as a bridge for the interaction between structure and detail information. Experimental results demonstrate that SDRNet outperforms existing state-of-the-art methods significantly on benchmark datasets. Our code is available at https://github.com/whyandbecause/SDRNet/.

An intelligent threshold selection method to improve orbital angular momentum-encoded quantum key distribution under turbulence

High-dimensional quantum key distribution (HD-QKD) encoded by orbital angular momentum (OAM) presents significant advantages in terms of information capacity. However, perturbations caused by free-space atmospheric turbulence decrease the performance of the system by introducing random fluctuations in the transmittance of OAM photons. Currently, the theoretical performance analysis of OAM-encoded QKD systems exists a gap when concerning the statistical distribution under the free-space link. In this article, we analyzed the security of QKD systems by combining probability distribution of transmission coefficient (PDTC) of OAM with decoy-state BB84 method. To address the problem that the invalid key rate is calculated in the part transmittance interval of the post-processing process, an intelligent threshold method based on neural network is proposed to improve OAM-encoded QKD, which aims to conserve computing resources and enhance system efficiency. Our findings reveal that the ratio of root mean square (RMS) OAM-beam radius to Fried constant plays a crucial role in ensuring secure key generation. Meanwhile, the training error of neural network is at the magnitude around 10−3, indicating the ability to predict optimization parameters quickly and accurately. Our work contributes to the advancement of parameter optimization and prediction for free-space OAM-encoded HD-QKD systems. Furthermore, it provides valuable theoretical insights to support the development of free-space experimental setups.

Robust PUF Label Authentication System Synergistically Constructed by Hierarchical Pattern of Self‐assembled Phase‐Separation Encrypted Wrinkle and Deep Learning Model

AbstractWith the increasing demand for information security, current anti‐counterfeiting methods not only suffer from issues such as low information density and vulnerability to forgery, but also inherently involve a trade‐off between information capacity and readout methods. This paper reports a high‐security solution using hierarchical pattern with perfect combination of micro self‐wrinkle and nano phase‐separation as physical unclonable function (PUF) labels, which is generated through self‐organization of anthracene‐functionalized poly(styrene‐block‐butadiene‐block‐styrene) (SBS‐CAN) under UV exposure. The double‐layer morphologies formed by the wrinkle and phase separation are adjustable, independent, and stable. The obtained hierarchical PUF labels exhibit random and unique features similar to the minutiae of fingerprints at both micro and nano scales, ensuring a well‐balanced bit uniformity (>0.492), high uniqueness (>0.496), and outstanding reliability (>96%). As a consequence of the multi‐layered combination of morphologies, the designed PUF label possesses an information density about 1010 times higher than that of human fingerprints. The PUF labels can be quickly obtained through simple visual scanning and exhibit sufficient security. To cope with various application environments, the advanced authentication pipeline designed LPLA guarantees robust label recognition capability in real‐world scenarios. A practical integrated anti‐counterfeiting authentication system is developed by combining hierarchical PUF labels and authentication pipeline.

Underwater Image Enhancement Algorithm Based on Adversarial Training

Ocean observation is the first step in the development of the ocean, whose abundant resources and strategic significance are attracting increasing attention. Observation methods based on visual sensor networks have received great attention from researchers due to their visualization capability and high information capacity. However, below the sea surface, objective factors such as blurriness, turbulence, and underwater color casting can cause image distortion and affect the acquisition of images. In this paper, the enhancement of underwater images is tackled using an adversarial learning-based approach. First, pre-processing is applied to address the significant color casting in the dataset, thus enhancing feature learning for subsequent style transfer. Then, corresponding improvements are made to a generative adversarial network’s structure and loss functions to better restore the features of the network output. Finally, evaluations and comparisons are performed using underwater image quality assessment metrics and several public datasets. Through multidimensional experiments, the proposed algorithm is shown to exhibit excellent performance in both subjective and objective evaluation metrics compared to state-of-the-art algorithms, as well as in practical visual applications.

Student Attendance Management System

The unique traces a real-time face recognition and attendance system designed to streamline attendance tracking in instructive settings. Leveraging OpenCV, face_recognition, and MongoDB, the framework mechanizes the recognizable proof of understudies from video streams or IP camera nourishes, recording their participation precisely and productively. Conventional attendance methods are regularly labor-intensive and inclined to mistakes, inciting the advancement of robotized arrangements. This investigate addresses these challenges by joining strong facial recognition algorithms with real-time video preparing capabilities and adaptable information capacity. By utilizing OpenCV for image processing, face_recognition for facial include location and acknowledgment, and MongoDB for information administration, the framework offers a comprehensive arrangement for participation following. The abstract emphasizes the system's common sense, effectiveness, and potential to revolutionize participation administration in instructive educate. Besides, it highlights the significance of exactness, preparing speed, and versatility in assessing the system's execution. Generally, the theoretical sets the organize for a detailed investigation of the system's design, execution, results, and suggestions for participation administration in educational settings.

Model for predicting metabolic activity in athletes based on biochemical blood test analysis

Improving the efficiency of athletic performance and reducing the likelihood of overtraining are primarily determined goals that can be achieved by the correct organization of the training process. The nature of adaptation to physical stress is associated with the specificity, focus, and degree of biochemical and functional changes that occur during muscular work. In this study, we aimed to develop a diagnostic model for predicting metabolic processes in athletes based on standard biochemical blood analysis indicators. The study involved athletes from the track and field athletics team (men, n ​= ​42, average age was [22.55 ​± ​3.68] years). Blood samples were collected in the morning at the beginning and end of the training week during the annual cycle. During the entire period, 3 625 laboratory parameter tests were conducted. Capillary blood sampling in athletes was conducted from the distal phalanx of the finger after overnight fasting, according to standard diagnostic procedures. To determine the predominance of anabolic or catabolic processes, equations were derived from a linear discriminant function. The discriminant function of predicting metabolic processes in athletes has a high information capacity (92.1%), as confirmed by the biochemical results of neuroendocrine system activity, which characterized the body's stage of adaptive regulatory mechanisms in response to stress factors. The classification matrix used to predict the metabolic processes based on the results of the discriminant function calculation demonstrates the statistical significance of the model (p ​< ​0.01). Consequently, an informative mathematical model was developed, which enabled the reliable and timely prediction of the prevalence of one of the metabolic activity phases in the athlete's body. The use of the developed model will also allow us to assess the nature of adaptation to specific muscular work, identify an athlete's weaknesses, forecast the success of their performance, and timely adjust both the training process and the recovery program.

Climate Communication, Public Health, and Social Media: Examining the Role of Health Agencies in Addressing the Impacts of Climate Change on Human Health

The impacts of climate change on human health are a critical public health concern, with previous studies highlighting its clear effects on well-being. Understanding how state and territorial health agencies (STHAs) are addressing these emerging risks is important. This study conducted 26 in-depth interviews across the U.S. to explore the perceptions and communication strategies of STHA officials regarding climate change’s impact on human health. Additionally, the study sought to identify the primary challenges faced by these officials to effectively communicate the impacts. Thematic analysis of the data revealed three major themes: community building and coalitions, climate denialism on social media, and misinformation about climate change and its effects on human health. These findings offer valuable insights for climate change and public health communication. The importance of the STHAs’ development of technical and informational capacity to effectively communicate climate-related risks and threats is emphasized.

Broadband spin and angle co-multiplexed waveguide-based metasurface for six-channel crosstalk-free holographic projection

Metasurface-based holograms, or metaholograms, offer unique advantages including enhanced imaging quality, expanded field of view, compact system size, and broad operational bandwidth. Multi-channel metaholograms, capable of switching between multiple projected images based on the properties of illuminating light such as state of polarization and angle of incidence, have emerged as a promising solution for realizing switchable and dynamic holographic displays. Yet, existing designs typically grapple with challenges such as limited multiplexing channels and unwanted crosstalk, which severely constrain their practical use. Here, we present a new type of waveguide-based multi-channel metaholograms, which support six independent and fully crosstalk-free holographic display channels, simultaneously multiplexed by the spin and angle of guided incident light within the glass waveguide. We employ a k-space translation strategy that allows each of the six distinct target images to be selectively translated from evanescent-wave region to the center of propagation-wave region and projected into free space without crosstalk, when the metahologram is under illumination of a guided light with specific spin and azimuthal angle. In addition, by tailoring the encoded target images, we implement a three-channel polarization-independent metahologram and a two-channel full-color (RGB) metahologram. Moreover, the number of multiplexing channels can be further increased by expanding the k-space’s central-period region or combing the k-space translation strategy with other multiplexing techniques such as orbital angular momentum multiplexing. Our work provides a novel approach towards realization of high-performance and compact holographic optical elements with substantial information capacity, opening avenues for applications in AR/VR displays, image encryption, and information storage.

Frequency-polarization multiplexing reflective metasurface for orbital angular momentum generation

Frequency-polarization multiplexing metasurfaces (FPMMSs) capable of generating vortex beams carrying orbital angular momentum (OAM) have facilitated the actualization of high information capacity in optical and microwave communication. However, poor frequency controllability and the deficiency of theoretical methodology for frequency modulation of meta-atom remain challenges for existing FPMMSs with orthogonal linear polarization. In this work, a strategy to customize the operating band of the “I”-shaped meta-atom (ISMA) based on phase analysis of the equivalent circuit is proposed. A frequency modulation factor is introduced to adjust the operating band of the ISMA over a wide range based on the relationships between element geometry parameters, capacitance and inductance values in the equivalent circuit model, and the reflected phase of ISMA-type meta-atom. Then, a dual-band dual-polarized meta-atom is proposed by placing subunits operating at different bands cross-orthogonally, allowing the subunits to modulate the phase independently. A dual-band dual-polarized dual-mode OAM beam generator is designed and constructed as a proof-of-concept to verify the methodology. The measured results are in excellent agreement with the simulations. The proposed methodology for customizing frequencies establishes the groundwork for the implementation of OAM-based FPMMSs for secure and high-capacity communication in 6G massive-MIMO systems.

Характеристика Rf-линий подсолнечника коллекции ВНИИМК с помощью микросателлитных маркеров ДНК

Sunflower is one of the most profitable oil crops. The task of sunflower breeding is development of highly productive hybrids. The genetic diversity of initial germplasm is very important in this case. Molecular-genetic markers, particularly microsatellites, are used for the estimation of such material. The purpose of this research concluded in the genotyping of an Rf-lines collection, analyzing its genetic diversity using microsatellite markers. The analysis of 54 Rf-lines from a collection of V.S. Pustovoit All-Russian Research Institute of Oil Crops was carried out using 12 SSR-markers. Genetic passports of all studied lines were composed by these markers, the main indicators of SSR-loci information capacity were determined. The collection is characterized with a high genetic diversity, in total (Nа) 55 alleles were obtained, in average 4.58 alleles per locus. An effective allele amount (Ne) averaged 2.48. An index of polymorphic information content (PIC) was 0.51. A level of line heterogeneity was quite low (5.5%) that certifies their good genetic uniformity. Due to the results, the collection was divided in two cluster at a joint level of 15.8. A minimal genetic distance between lines was equal 0. Eleven lines formed one cluster. The most lines (43 pcs) formed another cluster. The uniqueness of the collection was 91%.

Multispectral Polarization States Generation with a Single Metasurface

AbstractPolarization state generation plays an important role in optics and are applied in many fields. Multispectral polarization manipulation can increase information capacity. To meet the requirement of miniaturization and integration, it is desirable to generate multispectral polarization states with a compact optical device. a metasurface device is proposed and experimentally demonstrated, which can simultaneously realize wavelength separation and polarization generation. A geometric metasurface is used to realize the phase and wavelength multiplexing. Six polarization states with different wavelengths are generated. Customized polarization states with predesigned wavelength information can add extra degrees of freedom for carrying information, opening a new window for portable polarization optics where low‐cost and miniaturized systems are in high demand.

Information propagation of focus wave mode localized waves in anisotropic turbulent seawater

The evolution of the information transfer capability of an optical system for underwater focused wave mode localized wave (FWMLW) in anisotropic weakly turbulent absorbing seawater is studied. By developing the probability distribution function as well as the detection probability of the vortex modes carried by the FWMLW and the average bit error rate of the FWMLW underwater system, the information capacity of the FWMLW system with a pointing error is modeled. Through a numerical analysis of the effects of turbulent seawater and optical system parameters on the built light intensity, the detection probability, and the information capacity models, we find that the FWMLW system has an optimal delay time determined by the spectrum bandwidth when the spectrum bandwidth is greater than 1. The information capacity of the FWMLW system is higher than that of the X localized wave system under the same turbulent seawater channel condition, and FWMLW is a better optical signal source for vortex mode division multiplexing underwater systems than a Bessel–Gaussian beam.

Coupling Relationships between the Brain and the Central Pattern Generator Based on a Fractional-Order Extended Hindmarsh-Rose Model.

The states of the central nervous system (CNS) can be classified into subcritical, critical, and supercritical states that endow the system with information capacity, transmission capabilities, and dynamic range. A further investigation of the relationship between the CNS and the central pattern generators (CPG) is warranted to provide insight into the mechanisms that govern the locomotion system. In this study, we established a fractional-order CPG model based on an extended Hindmarsh-Rose model with time delay. A CNS model was further established using a recurrent excitation-inhibition neuronal network. Coupling between these CNS and CPG models was then explored, demonstrating a potential means by which oscillations generated by a neural network respond to periodic stimuli. These simulations yielded two key sets of findings. First, frequency sliding was observed when the CPG was sent to the CNS in the subcritical, critical, and supercritical states with different external stimulus and fractional-order index values, indicating that frequency sliding regulates brain function on multiple spatiotemporal scales when the CPG and CNS are coupled together. The main frequency range for these simulations was observed in the gamma band. Second, with increasing external inputs the coherence index for the CNS decreases, demonstrating that strong external inputs introduce neuronal stochasticity. Neural network synchronization is then reduced, triggering irregular neuronal firing. Together these results provide novel insight into the potential mechanisms that may underlie the locomotion system.

Double-Ring perfect vectorial vortex beams distribution regulation

Perfect vector beams with radius independent of topological charges have garnered significant interest owing to their unique capabilities in optical manipulation, microscopy imaging, and laser micro-machining. However, their polarization state is limited to four modes, restricting their information capacity and re-usability. To address this, we introduce double-ring perfect vectorial vortex beams that leverage the axicon phase to theoretically generate double-ring structures through a Fourier transform. By applying a transformation matrix and introducing a cross phase, we can manipulate the beam distribution. This approach is expected to enhance for development of particle capture and optical communications.

Learning context-aware local feature descriptors for 3D reconstruction

The generation of generalizable and discriminative descriptors plays a crucial role in image matching and 3D reconstruction. While numerous existing solutions are concentrated on encoding specific invariances, such as illumination or viewpoint invariance, they often face challenges in achieving robustness and generalization. These challenges arise from the frequent inadequacy of these solutions to effectively adapt to diverse and demanding environments due to their limited information capacity. In this paper, we introduce a novel approach aimed at maximizing the utilization of hidden feature informativeness to address these challenges. Specifically, we propose the Hierarchical Context-aware Aggregation Network (HCNet), which employs a hierarchical dense features constraint in a coarse-to-refinement description manner. In this approach, a coarse-level descriptor is used to present the overall information, while the refinement descriptor captures the detailed information of the image. Leveraging the strengths of both CNN and Transformer architectures, our hierarchical dense feature constraint encodes both local features and long-range information to efficiently generate dense feature descriptions. To boost descriptor informativeness and enhance matching accuracy, we introduce the Context-aware Attention Aggregation (CAA) model, which adaptively aggregates features from various scales through an efficient coarse-to-refinement manner. Additionally, we design a hierarchical triplet training strategy that considers both variant and invariant properties of hierarchical features, aiming to enhance descriptor informativeness while preserving their strong discriminative qualities. Our experiments, conducted on two popular feature-matching benchmarks, as well as a challenging long-term visual localization benchmark, demonstrate that our method significantly improves matching accuracy and outperforms state-of-the-art descriptors. Moreover, our approach exhibits superior generalization capabilities in various 3D reconstruction scenarios.

Learning by doing using the Life Cycle Assessment tool: LCA projects in collaboration with industries

Active learning, also called "learning by doing" (LbD), has resulted in positive learning outcomes in several higher education degrees. This paper describes an LbD experience within Chemical Engineering education aiming to enhance learning and transferable competencies using a Life Cycle Assessment course as a vehicle. This compulsory course belongs to the European Project Semester (EPS) program taught in the fourth year of the Chemical Engineering Degree at the University of Cantabria. From the beginning, the activity has targeted LCA practice with a strong emphasis on performance and its application as a decision-making tool in real case studies through close collaboration with regional companies. Working in partnership with industrial companies has favoured a win-win-win situation as students could apply knowledge as future LCA specialists. In contrast, companies gained valuable insights to improve their environmental performance, and lecturers enhanced their industrial networks. A public session carried out at the end of the activity created an enriching debate on subjects from a diversity of points of view (e.g., the selection of impact categories, the proposed improvements for environmental impact reduction, etc.). According to the lecturers, the competencies acquired by students through this LbD experience in life cycle assessment have notably evolved, demonstrating not only an enhanced understanding of environmental impacts across a product life cycle but also a significant improvement in critical thinking, team collaboration, and practical problem-solving skills, thereby bridging the gap between theoretical knowledge and its application in real-world scenarios. This is in line with the student’s perception that considered, such as "problem resolution", "capacity for analysing" and synthesis and "capacity for information" management. These are essential not only for future LCA practitioners but for chemical engineers.

Multi‐Dimensional Meta‐Holography Encrypted by Orbital Angular Momentum, Frequency, and Polarization

AbstractMetasurface holographic encryption, as an emerging and highly promising technique, presents the advantages of multi‐dimension and high security and consequently provides a flexible and convenient platform for information encryption. Recent years have witnessed rapid progress in meta‐holographic encryption through the utilization of different dimensions of electromagnetic (EM) waves, yet enhancing the capacity and security of encrypted information remains an endless goal. In this context, the concept of three physical dimensions encrypted metasurface holography is proposed in which orbital angular momentum (OAM) is fully synergized with frequency and polarization state. The mechanism relies on the simultaneous control of these dimensions and arbitrarily overlaying them in each operation channel, thereby acting as a multi‐dimensional key to realize information encryption and decryption. As a proof‐of‐concept experiment, a three‐physical dimension encrypted meta‐hologram is designed that conceals four real images as encrypted information in 32 information channels. Remarkably, a specific OAM order, frequency, and polarization state is required to decrypt the designed meta‐hologram. This work opens a new avenue for multi‐dimensional encrypted holography, which has broad application prospects for information security and data storage in the era of big data.

Low-dimensional criticality embedded in high-dimensional awake brain dynamics.

Whether cortical neurons operate in a strongly or weakly correlated dynamical regime determines fundamental information processing capabilities and has fueled decades of debate. We offer a resolution of this debate; we show that two important dynamical regimes, typically considered incompatible, can coexist in the same local cortical circuit by separating them into two different subspaces. In awake mouse motor cortex, we find a low-dimensional subspace with large fluctuations consistent with criticality-a dynamical regime with moderate correlations and multi-scale information capacity and transmission. Orthogonal to this critical subspace, we find a high-dimensional subspace containing a desynchronized dynamical regime, which may optimize input discrimination. The critical subspace is apparent only at long timescales, which explains discrepancies among some previous studies. Using a computational model, we show that the emergence of a low-dimensional critical subspace at large timescales agrees with established theory of critical dynamics. Our results suggest that the cortex leverages its high dimensionality to multiplex dynamical regimes across different subspaces.

Limited information-processing capacity in vision explains number psychophysics.

Humans and other animals are able to perceive and represent a number of objects present in a scene, a core cognitive ability thought to underlie the development of mathematics. However, the perceptual mechanisms that underpin this capacity remain poorly understood. Here, we show that our visual sense of number derives from a visual system designed to efficiently encode the location of objects in scenes. Using a mathematical model, we demonstrate that an efficient but information-limited encoding of objects' locations can explain many key aspects of number psychophysics, including subitizing, Weber's law, underestimation, and effects of exposure time. In two experiments (N = 100 each), we find that this model of visual encoding captures human performance in both a change-localization task and a number estimation task. In a third experiment (N = 100), we find that individual differences in change-localization performance are highly predictive of differences in number estimation, both in terms of overall performance and inferred model parameters, with participants having numerically indistinguishable inferred information capacities across tasks. Our results therefore indicate that key psychophysical features of numerical cognition do not arise from separate modules or capacities specific to number, but rather as by-products of lower level constraints on perception. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

What role does information capacity play in the tourism livelihood of farm households in ethnic villages?

Abstract Tourism livelihood has become an essential livelihood decision for people to get rid of poverty. Under the background of the internet era, it is of great significance to study how ethnic village farm households can participate in tourism livelihood with the help of information capacity. Applying the Resource Orchestration Theory and taking Guolan Yao Village in Jiangyong County of Hunan Province as an example, this paper uses binary logistic regression model and fuzzy set qualitative comparative analysis method to explore the mechanism of farm households’ information capacity affecting their tourism livelihood. The findings are as follows: Firstly, in addition to financial capital, other livelihood capital and information capacity have a significant impact on farm households’ participation in tourism livelihood. Secondly, when financial capital is lacking, farm households in ethnic villages with information capacity can unite with human capital, social capital, natural capital or physical capital to make up for the non-existent defect of financial capital and help realize tourism livelihood. Thirdly, when human capital is lacking, farm households in ethnic villages with information capacity can make up for the absence of human capital by combining natural capital, financial capital and social capital to help realize tourism livelihood.