Efficiency Of Information Transmission Research Articles

Utilizing topological invariants for encoding and manipulating chiral phonon devices

As a fundamental degree of freedom, phonon chirality is expected to promote the development of quantum information technology just like electron spin. Currently, central to this area is the realization of efficient transmission and control of chiral information. In this paper, we propose an approach by integrating topological theory, leveraging topologically invariant Chern numbers, to encode hexagonal lattice systems. Our investigation reveals the presence of topologically protected chiral interface states within the shared band gaps of both trivial and non-trivial system units. By precisely modulating the magnetic field distribution within the encoding system, we can effectively manipulate the topological pathways. Building upon this framework, we design and implement a chiral phonon three-port device. Through dynamic calculations, we demonstrate the transmission process of chiral information, showcasing the chiral phonon switching effect and logical OR operation. Our findings not only establish a fundamental mechanism for the manipulation and control of phonon chiral information but also provide a promising direction for research in harnessing chirality degrees of freedom in practical applications.

Emergence of input selective recurrent dynamics via information transfer maximization

Network structures of the brain have wiring patterns specialized for specific functions. These patterns are partially determined genetically or evolutionarily based on the type of task or stimulus. These wiring patterns are important in information processing; however, their organizational principles are not fully understood. This study frames the maximization of information transmission alongside the reduction of maintenance costs as a multi-objective optimization challenge, utilizing information theory and evolutionary computing algorithms with an emphasis on the visual system. The goal is to understand the underlying principles of circuit formation by exploring the patterns of wiring and information processing. The study demonstrates that efficient information transmission necessitates sparse circuits with internal modular structures featuring distinct wiring patterns. Significant trade-offs underscore the necessity of balance in wiring pattern development. The dynamics of effective circuits exhibit moderate flexibility in response to stimuli, in line with observations from prior visual system studies. Maximizing information transfer may allow for the self-organization of information processing functions similar to actual biological circuits, without being limited by modality. This study offers insights into neuroscience and the potential to improve reservoir computing performance.

Enhancing information transmission in FSO-OAM systems through multiscale interpretable neural networks under turbulent conditions

The paper proposes a solution to improve the information transmission efficiency of FSO-OAM systems under turbulent conditions by combining a multiscale interpretable neural network model, 4RK-MSNN. We use a multiscale structure to design the overall architecture of the neural network, which enables the comprehensive analysis of information in different dimensions. Based on the fourth-order Runge-Kutta correlation theory, a core network module, 4RK, is constructed, which can be explained in terms of dynamical systems. The 4RK-MSNN model, which couples the multiscale structure and the 4RK module, has a lower number of parameters, allowing for layered feature extraction in an interpretable framework. This facilitates low-cost, rapid sharing and transmission of feature information at different scales. The proposed solution is validated by transmitting image data under different turbulence intensities and transmission distances. The results indicate the feasibility of the proposed information transfer system. After adding redundant training data, the 4RK-MSNN model significantly improves the quality of the transmitted data and maintains satisfactory results even under strong turbulence and long distances.

Enhancing Communication in CPS Using Graph-Based Reply Relationship Identification in Multi-Party Conversations

To enhance communication and collaborative work efficiency in cyber–physical systems (CPSs) within the Industry 4.0 environment, this study investigates a graph-based machine learning approach aimed at optimizing information interaction during multi-party conversations. Devices within CPSs must efficiently exchange information in real time to synchronize operations and responses. This research treats these interactions as intricate graph structures and uses graph learning techniques to accurately identify communication links and dependencies among devices. This improvement leads to more accurate decision-making and smoother operations. Our methodology involves a real-time analysis of structural patterns and node attributes within conversations, improving information flow and comprehension. The empirical findings demonstrate that this approach significantly enhances production efficiency, system adaptability, and minimizes delays attributed to communication misunderstandings. Our method can effectively identify the communication relationships between devices, significantly improving the efficiency and accuracy of information transmission. This improved communication capability leads to an enhanced production efficiency of the entire system.

Noiseless and efficient quantum information transmission for fiber-based continuous-variable quantum networks

Noiseless and efficient quantum information transmission for fiber-based continuous-variable quantum networks

Toward efficient and lightweight sea–land segmentation for remote sensing images

Sea–land segmentation is of great significance for autonomous coastline monitoring, which is fundamental research in the remote sensing community. Due to the diverse contents and easily confused sea–land boundaries contained in remote sensing images, it is always challenging to achieve precise sea–land segmentation for complex scenarios. Although existing deep learning-based methods have exhibited promising performance, excessive computational load and insufficient use of hierarchical features remain unresolved. In this paper, we contribute to addressing the problems by developing an efficient and lightweight convolutional neural network (CNN) termed E-Net. On the one hand, the proposed network adopts a novel E-shaped architecture that reforms the conventional U-codec structure to make full use of hierarchical features at different depths, so that the sea–land segmentation effect can be significantly improved without excessive computational load. On the other hand, a contextual aggregation attention mechanism (CA2M) is designed to further facilitate efficient aggregation and transmission of contextual information, so that the fuzzy and irregular sea–land boundaries can be accurately distinguished. Extensive experiments reveal that our approach not only produces superior sea–land segmentation effect but also demonstrates promising computational efficiency. Specifically, the proposed E-Net achieves state-of-the-art sea–land segmentation performance with 92.78% and 93.62% mean Intersection over Union (mIoU) on the SLSD and HRSC2016 datasets, respectively, while the frames per second (FPS) reaches 108.032 with as low as 52.287G floating point operations per second (FLOPs).

GGT-SNN: Graph learning and Gaussian prior integrated spiking graph neural network for event-driven tactile object recognition

Spiking neural networks possess asynchronous, discrete, and sparse characteristics that enable direct processing of event-based tactile sensor data and more efficient transmission of information. These networks have been widely applied in the tactile perception field. However, due to the sparsity of tactile data, these models are prone to generalization issues. Additionally, existing methods for tactile graph construction are constrained when spatially connecting high-dimensional tactile data, making it challenging for the model to fuse and process such information effectively. To address these challenges, we propose the GGT-SNN, which introduces a parameter estimation method with Gaussian priors to alleviate generalization problems caused by sparse tactile data. Furthermore, we design M-tree and Z-tree tactile graph construction methods to compensate for deficiencies of other approaches when handling high-dimensional tactile spatial connections. This approach enhances the model’s ability to efficiently process high-dimensional event-driven tactile information. We introduce an approximate LIF neuron activation function to enable backpropagation within the model and comprehensively compare and evaluate its performance against different approximate functions. The experimental results demonstrate that our proposed method performs significantly better than SOTA methods, exhibiting a 10.83 % improvement over TactileSGNet on the EvTouch-Containers dataset and a 2.92 % improvement over TactileSGNet on the EvTouch-Objects dataset.

Active ballistic orbital transport in Ni/Pt heterostructure

Orbital current, defined as the orbital character of Bloch states in solids, can travel with larger coherence length through a broader range of materials than its spin counterpart, facilitating a robust, higher density and energy efficient information transmission. Hence, active control of orbital transport plays a pivotal role in the progress of the evolving field of quantum information technology. Unlike spin angular momentum, orbital angular momentum couples to phonon angular momentum efficiently via orbital-crystal momentum (L-k) coupling, allowing us to control orbital transport through crystal field potential mediated angular momentum transfer. Here, leveraging the orbital dependant efficient L-k coupling, we have experimentally demonstrated the active control of orbital current velocity in Ni/Pt heterostructure. We observe terahertz emission from Ni/Pt heterostructure via long-range ballistic orbital transport, as evidenced by the delay, and chirping in the emitted THz pulse correlating with increased Pt thickness. Additionally, we also have identified a critical energy density required to overcome collisions in orbital transport, enabling a swifter flow of orbital current. Femtosecond light driven active control of the ballistic orbital transport lays the foundation for the development of dynamic optorbitronics for transmitting information over extended distance.

Dependency task offloading in semantic communications

In the realm of advanced wireless communications, the significance of semantic communication has become increasingly pronounced, particularly in its focus on the meaning and efficiency of information transmission. Given the voluminous nature of task data and the intricacy of task interconnections, executing semantic segmentation tasks directly on user-end devices often falls short due to limitations in processing power. Leveraging mobile edge computing, however, presents a strategic enhancement in performing semantic segmentation tasks by delegating computational workloads to proximal devices, thus diminishing latency and reducing energy expenditure. This document delineates a task offloading framework aimed at refining the management of interdependent tasks, thereby boosting the efficiency of processing complex tasks within environments of semantic communication. The framework abstracts tasks in the form of directed acyclic graphs (DAG) and approaches the optimization challenge through a Markov Decision Process (MDP), targeting an optimal balance between task completion, delay reduction, and energy savings. To adapt to fluctuating network conditions, a novel approach employing the Deep Deterministic Policy Gradient (DTO-DDPG) algorithm is introduced for optimization decision-making. Our empirical tests underscore the framework’s capacity to significantly elevate task completion rates while simultaneously decreasing both energy consumption and latency in scenarios of semantic communication.

Research on the Impact of the Digital Economy on International Trade Barriers and Its Countermeasures

The rapid development of the digital economy has not only changed traditional business models and trade patterns, promoting trade facilitation, but has also to some extent reduced international trade barriers, providing unprecedented opportunities for enterprises worldwide, especially small and medium-sized enterprises, to expand into international markets. In the era of the digital economy, the modes and patterns of international trade are undergoing profound changes. The high efficiency and universality of information transmission, as one of the core features of the digital economy, not only significantly reduce trade costs but also break the limitations of traditional trade models, laying a solid foundation for the extensive expansion of international trade. However, with the rapid rise of digital trade, new digital barriers are also emerging, which are impacting the international trade landscape. A comprehensive analysis of the role of the digital economy in reducing international trade barriers and the opportunities and challenges it brings is of great significance for understanding and grasping the current trends in global trade development.

Origin of the light-induced spin currents in heavy metal/magnetic insulator bilayers.

Light-induced spin currents with the faster response is essential for the more efficient information transmission and processing. Herein, we systematically explore the effect of light illumination energy and direction on the light-induced spin currents in the W/Y3Fe5O12 heterojunction. Light-induced spin currents can be clearly categorized into two types. One is excited by the low light intensity, which mainly involves the photo-generated spin current from spin photovoltaic effect. The other is caused by the high light intensity, which is the light-thermally induced spin current and mainly excited by spin Seebeck effect. Under low light-intensity illumination, light-thermally induced temperature gradient is very small so that spin Seebeck effect can be neglected. Furthermore, the mechanism on spin photovoltaic effect is fully elucidated, where the photo-generated spin current in Y3Fe5O12 mainly originates from the process of spin precession induced by photons. These findings provide some deep insights into the origin of light-induced spin current.

Distributed angle‐only orbit determination algorithm for non‐cooperative spacecraft based on factor graph

AbstractBayesian filtering provides an effective approach for the orbit determination of a non‐cooperative target using angle measurements from multiple CubeSats. However, existing methods face challenges such as low reliability and limited estimation accuracy. Two distributed filtering algorithms based on factor graphs employed in the sub‐parent and distributed cluster spacecraft architectures are proposed. Two appropriate factor graphs representing different cluster spacecraft structures are designed and implement distributed Bayesian filtering within these models. The Gaussian messages transmitted between nodes and the probability distributions of variable nodes are calculated using the derived non‐linear Gaussian belief propagation algorithm. Gaussian messages propagate from the deputy spacecraft to the chief spacecraft in the sub‐parent spacecraft architecture, demonstrating that the estimation accuracy converges to the centralised extended Kalman filter (EKF). Simulation results indicate that the algorithm enhances system robustness in observation node failures without compromising accuracy. In the distributed spacecraft architecture, neighbouring spacecraft iteratively exchanges Gaussian messages. The accuracy of the algorithm can rapidly approach the centralised EKF, benefiting from the efficient and unbiased transmission of observational information. Compared to existing distributed consensus filtering algorithms, the proposed algorithm improves estimation accuracy and reduces the number of iterations needed to achieve consensus.

Tunable Negative and Positive Photoconductance in Van Der Waals Heterostructure for Image Preprocessing.

The processing of visual information occurs mainly in the retina, and the retinal preprocessing function greatly improves the transmission quality and efficiency of visual information. The artificial retina system provides a promising path to efficient image processing. Here, graphene/InSe/h-BN heterogeneous structure is proposed, which exhibits negative and positive photoconductance (NPC and PPC) effects by altering the strength of a single wavelength laser. Moreover, a modified theoretical model is presented based on the power-dependent photoconductivity effect of laser: , which can reveal the internal physical mechanism of negative/positive photoconductance effects. The present 2D structure design allows the field effect transistor (FET) to exhibit excellent photoelectric performance (RNPC=1.1× 104AW-1, RPPC=13AW-1) and performance stability. Especially, the retinal pretreatment process is successfully simulated based on the negative and positive photoconductive effects. Moreover, the pulse signal input improves the device responsivity by 167%, and the transmission quality and efficiency of the visual signal can also be enhanced. This work provides a new design idea and direction for the construction of artificial vision, and lay a foundation for the integration of the next generation of optoelectronicdevices.

Sustainable Food Value Chains: Approaches to Transaction Costs in Agro-Alimentary Systems of Developing Countries—A Chile Case Study

This study delves into the dynamics of price linkages and transaction costs in agricultural markets, emphasizing the sustainability of food supply chains. By exploring vertical and horizontal price linkages in agro-farming value chains of a developing country, it addresses the efficiency of market information transmission and the capacity for arbitrage among chain participants. The methodological core of the research involves analyzing price linkages in Chilean horticultural wholesale markets, focusing on key vegetables such as, onions, lettuce, maize, and tomatoes. This analysis is underpinned by a novel approach that models and estimates time-dependent, conditional threshold bands, extending the traditional cointegration models. This method allows a more nuanced understanding of how agricultural market linkages evolve over time, enhancing our comprehension of price transmission behavior and market integration. The results reveal significant non-linear relationships between fuel prices and vegetable prices, particularly in central Chilean regions. This finding challenges the traditional linear perspective, suggesting that factors such as storage capacity and arbitrage behavior can influence price signal transmission. Such insights are crucial for stakeholders in the agribusiness value chain, offering a deeper understanding of market dynamics and aiding in the development of more sustainable and efficient market strategies. This research contributes significantly to the field of agricultural economics by providing a more robust framework for analyzing market behaviors and transaction costs in the context of sustainability and value chains. Its findings have profound implications for both theory and practice, informing policy-making and strategic decision-making in the agribusiness sector.

Risk Prediction of the Development of the Digital Economy Industry Based on a Machine Learning Model in the Context of Rural Revitalization

In today's society, rural areas face challenges such as complex terrain and uneven population distribution, and infrastructure construction is exceptionally difficult. At the same time, poor information transmission and low communication efficiency have also become a major obstacle to the promotion of the digital economy in rural areas. This study aims to use gradient advancement models to identify potential risks in the growth of the digital economy sector related to rural revitalization. In this study, we used an enhanced hierarchical gradient boosting algorithm. The research results indicate that the introduction of this technology can provide us with a more comprehensive and reliable risk prediction model, thereby more scientifically assisting the development and decision-making of the digital economy in rural areas. This article provides a new perspective and solutions for development issues in rural areas, promoting sustainable development and economic growth in rural areas.

A Study of Huawei’s 5G Technology Innovation Ecosystem Based on Actor-Network Theory

The 5G technology enables a more comprehensive satisfaction of individuals' varied requirements by capitalizing on its improved information transmission efficiency and stronger signals. Investigating the innovative development of 5G technology contributes to enhancing network security and stability, thereby furthering enterprise digital transformation. This paper analyzes all 55 texts from the “News” section of Huawei’s official website’s 5G topic, which contain descriptions of Huawei's 5G innovation ecosystem. Building upon the actor-network theory, the study employs text coding to discern and classify actors and other elements that participate in the enterprise innovation ecosystem, and describes the collaborative network construction process with Huawei as the central actor. The study finds that heterogeneity in actor network theory plays an important role in the study of corporate innovation ecosystems. The four links of "Problematisation", "Interesssement", "Enrollment" and "Mobilization" clearly describe the process of constructing a network of actors in STI activities. The actor network of Huawei's 5G technology innovation ecosystem is characterised by continuous openness and stable operation. Facilitating the digital transformation of the industry is the main reason for other actors to join Huawei's 5G technology innovation ecosystem actor network.

Deep multi-threshold spiking-UNet for image processing

U-Net, known for its simple yet efficient architecture, is widely utilized for image processing tasks and is particularly suitable for deployment on neuromorphic chips. This paper introduces the novel concept of Spiking-UNet for image processing, which combines the power of Spiking Neural Networks (SNNs) with the U-Net architecture. To achieve an efficient Spiking-UNet, we face two primary challenges: ensuring high-fidelity information propagation through the network via spikes and formulating an effective training strategy. To address the issue of information loss, we introduce multi-threshold spiking neurons, which improve the efficiency of information transmission within the Spiking-UNet. For the training strategy, we adopt a conversion and fine-tuning pipeline that leverage pre-trained U-Net models. During the conversion process, significant variability in data distribution across different parts is observed when utilizing skip connections. Therefore, we propose a connection-wise normalization method to prevent inaccurate firing rates. Furthermore, we adopt a flow-based training method to fine-tune the converted models, reducing time steps while preserving performance. Experimental results show that, on image segmentation and denoising, our Spiking-UNet achieves comparable performance to its non-spiking counterpart, surpassing existing SNN methods. Compared with the converted Spiking-UNet without fine-tuning, our Spiking-UNet reduces inference time by approximately 90%. This research broadens the application scope of SNNs in image processing and is expected to inspire further exploration in the field of neuromorphic engineering. The code for our Spiking-UNet implementation is available at https://github.com/SNNresearch/Spiking-UNet.

Research on Dynamic Graphic Features in Digital Media Interfaces

In digital media interfaces, dynamic graphics integrate elements of animation design, graphic design, andcinematic language, presenting in various forms and demonstrating significant inclusivity. From the perspective of visual cognition, by capturing dynamic graphics within the interface, three-dimensional information such as time and space is acquired, and the cognition process exhibits characteristics of spatial and temporal correlation. In terms of information cognition, dynamic representation fragments information, resulting in a sense of discontinuity and intermittent memory. The efficiency of information cognition and transmission is enhanced through the coordination of audio, video, and other elements. In aesthetic perception, with the rise of Internet culture, people’s aesthetic perceptions towards dynamic graphic design have evolved, showcasing a generalization of form and the instantaneous nature of content. Technically, the complexity of design and the diversity of applications have established dynamic graphics as a typical design style label in digital media interfaces.

Multi-object detection and behavior tracking of sea cucumbers with skin ulceration syndrome based on deep learning

Skin ulceration syndrome of sea cucumbers is one of the most serious diseases in intensive aquaculture, and it is the most effective way of preventing the spread of this disease to detect the abnormal behavior of sea cucumbers in time and take corresponding measures. However, the detection and tracking of multi-object is a hard problem in sea cucumber behavior analysis. To solve this problem, this paper first proposes a novel one-stage algorithm SUS-YOLOv5 for multi-object detection and tracking of sea cucumbers. The proposed SUS-YOLOv5 optimizes the maximum suppression algorithm in the overlapping region of the object detection box. Next, the SE-BiFPN feature fusion structure is proposed to enhance the transmission efficiency of feature information between deep and shallow layers of the network. Then, a MO-Tracking algorithm is proposed integrated with DeepSORT to achieve real-time multi-object tracking. Experimental results show that the mAP@0.5 and mAP@0.5:0.95 of the proposed object detector reach 95.40% and 83.80%, respectively, which are 3.30% and 4.10% higher than the original YOLOv5s. Compared with the traditional SSD, YOLOv3, and YOLOv4, the mAP of SUS-YOLOv5 is improved by 5.49%, 1.57%, and 3.76%, respectively. This research can realize the multi-object detection and tracking, which lays the foundation for the prediction of skin ulceration syndrome in sea cucumbers and has a certain practical application value for improving the intelligence level of aquaculture.

User scheduling for non-orthogonal multiple access-enabled 5G aeronautical mobile airport communications system

The recent concept of aeronautical mobile airport communications system with the 5th generation mobile communication technology (5G AeroMACS) for enhancing real-time and efficient flight information transmission has attracted wide attention from the civil aviation industry. In addition, non-orthogonal multiple access (NOMA) is also a promising solution to further improve the sum rate. However, the fundamental limit of the conventional 5G AeroMACS is that, the base station (BS) equipped with dozens of antennas cannot support more users than its number of antennas using the same time-frequency resources. To break this limit, for the first time, we investigate user scheduling policies for the integration of NOMA in 5G AeroMACS, i.e., 5G AeroMACS-NOMA, employing a half-duplex (HD) BS for serving multiple HD downlink users simultaneously. The two proposed user scheduling policies are obtained from the sub-optimal solution of mixed combinatorial non-convex optimization problems for the maximization of the achievable sum rate of the entire system. To mitigate the inter-beam interference (IBI), a user clustering algorithm based on the principle of maximizing the channel spatial correlation is designed, and an iterative power allocation algorithm is utilized for maximizing the achievable sum rate of the entire system. Furthermore, to mitigate the serious error propagation caused by the nature in NOMA, a scenario with user division is considered, and an user scheduling policy based on successive convex approximation is designed to strike a balance between computational complexity and optimality. Simulation results show that the proposed 5G AeroMACS-NOMA schemes can achieve a higher achievable sum rate compared with conventional 5G AeroMACS.