Communication Noise Research Articles

Communication noise of product innovation promotion process

The communicative noise phenomenon as a destructive element of marketing communication aimed at promoting product innovation has been analyzed. In the process of solving the set tasks the attention has been focused on the study of communicative noise of recipient and addressee of marketing information about an innovative product, since the noises of this type are the most complex and problematic ones due to their latency and weak controllability. Their essence lies in errors/failures of coding-decoding of marketing information in the process of a company’s interaction with potential consumers of its product innovation. As the conducted research result, the following communicative noises of recipient and addressee of marketing information about an innovative product have been identified and analyzed: incorrect positioning of an innovation; semantic dissonance of the brand image and its product innovation; incorrect advertising concept of an innovation; weak or incorrect creative idea of the innovation advertising; incorrect choice of tools for promoting an innovation; differences in the perception of an innovation related to social and demographic characteristics of consumers (gender, sex, age, education, and income), consumers’ belonging to one or another culture (cultural and ethnic or religious affiliation), and individual-psychological characteristics (personality type, individual value system, degree of dependence on the social environment, and the level of resistance to psychological influence).

Practical considerations for assessing crew noise exposure in armored vehicles.

Measurement and analysis of the continuous and intermittent noise produced by armored vehicle (AV) platforms, including the output from communications systems as experienced by crew, are necessary for the purposes of exposure prediction, to support the selection of hearing protection and communication devices, and to facilitate assessments of compliance with occupational health and safety legislation. Practical estimation of the personal noise exposure of AV crews requires the assessment of the vehicle, communications and special-to-role activity noise sources, and an understanding of how these sources combine. Procedures are described that consider instrumentation requirements, AV configuration and build standard, operating conditions representative of actual use, the application of speed thresholding to measurements, and derivation of communications noise levels. Real-world examples are given where these procedures have been applied to an in-service tracked AV to estimate crew noise exposure. The procedures and methods presented are a compromise between precision, repeatability, reproducibility, and pragmatism. Measurements of AV noise are expected to be obtained during the commissioning stage of vehicle design, immediately prior to the vehicle being put into operational service and following any major modifications to the vehicle to inform the necessary engineering, administrative, and personal protective equipment control measures.

Containment control of fractional complex networks with stochastic communication noises

Containment control of fractional complex networks with stochastic communication noises

Quantum multicast based on joint remote state preparation

Effective propagation of information among multiple users is the purpose of realizing large-scale quantum communication networks. In this paper, multicast protocols for any single, two and three qubits with real amplitude and complex phase information are presented. They were realized using a composite of Greenberger–Horne–Zeilinger states as shared channels. Joint remote state preparation was the main method for completing quantum multicast. At the same time, quantum state tomography of the schemes was carried out on the IBM Quantum platform. The obtained states were compared with the target states by fidelity. The analysis of communication efficiency and noise effects shows that our protocol has advantages in the case of complex coefficients.

The Denoising Method With Variational Mode Decomposition for Signal-Dependent Counting Noise in Molecular Communications

The Denoising Method With Variational Mode Decomposition for Signal-Dependent Counting Noise in Molecular Communications

Distributed Global Consensus of LTI Mass with Heterogeneous Actuator Saturation and Communication Noises

Distributed Global Consensus of LTI Mass with Heterogeneous Actuator Saturation and Communication Noises

Geometric Modelling of a Family of 4-Point Ternary Approximating Subdivision Scheme U_φ with Visual Performance

Making signals better than noise in communication has always been challenging for scientists. Researchers have been working on it in different ways. The computer-aided geometric design is a new research field emerging from the collaboration of computer algorithms and mathematical logic towards curve designing, in which the subdivision schemes used have a key position due to their flexible and smooth behaviour. Using parameters in these schemes allows for increased control over designing. A parameterized framework for generating a wide range of subdivision surfaces with tunable degrees of shape control is presented in the family of schemes. The properties of the proposed family make it suitable for use in isogeometric analysis, computer animation, and geometric modelling. The purpose of this paper is to construct and analyze a family of 4-point ternary subdivision schemes to smooth the curves based on the Laurant polynomial. This family is generated by tuning the weight parameter. The scheme is analysed for its different properties. The scheme has continuity. Visual performance of the subdivision scheme is also provided as an application of this proposed study.

A deep reinforcement learning based distributed multi-UAV dynamic area coverage algorithm for complex environment

Dynamic area coverage algorithm is often used in multiple unmanned aerial vehicle (multi-UAV) systems to realize searching or monitoring an area of interest (AOI). Improving coverage efficiency is one of the important research issues of dynamic area coverage. In this paper, we propose a distributed dynamic area coverage algorithm based on a reinforcement learning (RL) algorithm to enhance the coverage efficiency in complex application environments. A coverage information fusion based discrete soft actor–critic algorithm (herein FDSAC) is proposed to solve the non-stationary problem of RL algorithm in area coverage task under the communication constraint environment. Considering the equivalence of UAVs in the area coverage tasks, we introduce a multi-UAV cooperative learning method for FDSAC by sharing the strategy and experience of the UAVs to accelerate the learning speed of FDSAC in multi-UAV system. With the FDSAC algorithm, the UAVs can intelligently select the best coverage points and achieve almost optimal coverage point planning for the entire coverage process. In addition, we also provide a coverage point adjustment method based on the distribution of obstacles to enable the proposed algorithm adapt to obstacle environments. The experimental results demonstrate that the proposed algorithm can efficiently complete the area coverage task in complex communication and stochastic control noise environment. The good scalability, environment adaptability, and practicality of our algorithm have been also confirmed in different complex environments of different experimental platforms.

A Lyapunov-based control design for centralised networked control systems under false-data-injection attacks

ABSTRACT A central processing unit receives data from all agents and transmits control commands in a Networked Control System (NCS) which is centralised. Centralised NCSs have numerous applications in industrial settings due to their efficiency, simplicity and cost-effective design. However, centralised NCSs are vulnerable to false data injection (FDI) attacks. Despite the fact that researchers have developed detection and mitigation defense mechanisms during past several years, most of these methods have focused on systems with linear dynamics. Furthermore, the existing literature only assumes the injection of FDI attacks on measurement signals. In this paper, we assume that an adversary has injected the FDI attack into both state measurements and control signals with nonlinear dynamics while considering communication noises and disturbances. We propose a secure nonlinear control design that mitigates FDI attacks in real time by combining learning and model-based approaches. We used Lyapunov stability analysis to design the controller, estimator and updating laws of the neural network (NN). In addition, we selected a network of two robots with Euler–Lagrange dynamics to illustrate the robustness of the proposed controller and estimator.

Adjusting Optical Polarization with Machine Learning for Enhancing Practical Security of Continuous-Variable Quantum Key Distribution

An available trick to mitigate the interference of environmental noise in quantum communications is to modulate signals with time-polarization multiplexing. Conversely, due to effects of the atmospheric turbulence in free space, the polarization of signals fluctuates randomly, resulting in feasible information leakage when direct polarization demultiplexing is carried out at the receiver, drowning out the noise-contained signals. For enhancing the practical security of the continuous-variable quantum key distribution (CVQKD), we propose a machine learning (ML) approach for optimization of the dynamic polarization control (DPC) of signals transmitted through atmospheric turbulence. An optimal DPC scheme can be adaptively adjusted with ML algorithms, which is based on the received signals at the receiver for solving the loophole problem of information leakage since it provides an accurate response to the polarization changes regarding the anamorphic signals. The performance of the CVQKD system can be increased in terms of secret key rates and maximal transmission distance as well. Numerical simulation shows the positive effect of the ML-based DPC while taking into account the secret key rate of the CVQKD system. The ML-based DPC effectively reduces the feasibility of information leakage and hence results in an increased secret key rate of the practical CVQKD system.

Consensus of networked nonlinear systems based on the distributed stochastic approximation algorithm

Consensus of networked nonlinear nonparametric time-varying systems is studied, and the nonlinearity at input may possess arbitrary growth rate. In addition, the communication noise is also under consideration. The purely distributed control algorithm is designed on the basis of distributed stochastic approximation algorithm with expanding truncations (DSAAWET). The truncation mechanism neutralizes the divergent tendency caused by unknown nonlinearities and noises, which makes the algorithm well tackle more general nonlinearities with high growth rate and more complicated structure noises. We first prove the average consensus over fixed digraph, and then extend the results to averagely connected time-varying digraphs. Finally, the validity of the algorithm is justified by numerical simulations.

Indoor visible light communication denoising system combining iterative variational mode decomposition and multiple frequency shift keying modulation

To reduce noise in indoor visible light communication (IVLC), the Pearson correlation coefficient difference (PCCD), a denoising system combining iterative variational mode decomposition (IVMD) and multiple frequency shift keying modulation (MFSK), is proposed. Compared with VMD, the method can directly determine the optimal number of VMD modes and solve the issue of VMD penalty factor selection to some extent. The simulation results show that, when the input SNRs vary from −15 to −8dB, the proposed method can improve the output SNRs of the 2FSK signal by an average of 15.5 dB and reduce the BER by 55.8%, improve the output SNR of the 4FSK signal by an average of 13 dB, and reduce the BER by 54.4%. The proposed method can also effectively suppress noise interference in real IVLC experiments at a distance of 1 m. In addition, the IVMD-MFSK denoising system can be applied to denoise all frequency-modulated signals with high applicability.

Privacy preserving Mean-square consensus for discrete-time heterogeneous multi-agent systems with Communication Noises

Privacy preserving Mean-square consensus for discrete-time heterogeneous multi-agent systems with Communication Noises

Stochastic Consensus for Heterogeneous Multi-agent Networks With Constraints and Communication Noises

Stochastic Consensus for Heterogeneous Multi-agent Networks With Constraints and Communication Noises

Autoencoder based image quality metric for modelling semantic noise in semantic communications

AbstractSemantic communication has attracted significant attention as a key technology for emerging 6G communications. This paper proposes an autoencoder based image quality metric to quantify the semantic noise. An autoencoder is initially trained with the reference image to generate the encoder‐decoder model and calculate its Latent Vector Space (LVS) and then a semantically generated/received image is inserted into the same autoencoder to create the corresponding LVS. Finally, both LVS are used to define the Euclidean space to calculate the mean square error between two LVS. Results indicate that the proposed model has a high correlation coefficient of 88% with subjective quality assessment and commonly used conventional metrics completely failed in semantic noise modelling.

Distributed Localization for Multi-Agent Systems With Random Noise Based on Iterative Learning.

This article is concerned with the real-time localization problem for the dynamic multi-agent systems with measurement and communication noises under directed graphs. The barycentric coordinates are introduced to describe the relative position between agents. A novel robust distributed localization estimation algorithm based on iterative learning is proposed. The relative-distance unbiased estimator constructed from the historical iterative information is used to suppress the measurement noise. The designed stochastic approximation method with two iterative-varying gains is used to inhibit the communication noise. Under the zero-mean and independent distributed conditions on the measurement and communication noises, the asymptotic convergence of the proposed methods is derived. The numerical simulation and the QBot-2e robot experiment are conducted to test and verify the effectiveness and the practicability of the proposed methods.

Adaptive Neural Consensus of Unknown Non-Linear Multi-Agent Systems with Communication Noises under Markov Switching Topologies

In this paper, the adaptive consensus problem of unknown non-linear multi-agent systems (MAs) with communication noises under Markov switching topologies is studied. Based on the adaptive control theory, a novel distributed control protocol for non-linear multi-agent systems is designed. It consists of the local interfered relative information and the estimation of the unknown dynamic. The Radial Basis Function networks (RBFNNs) approximate the nonlinear dynamic, and the estimated weight matrix is updated by utilizing the measurable state information. Then, using the stochastic Lyapunov analysis method, conditions for attaining consensus are derived on the consensus gain and the weight of RBFNNs. The main findings of this paper are as follows: the consensus control of multi-agent systems under more complicated and practical circumstances, including unknown nonlinear dynamic, Markov switching topologies and communication noises, is discussed; the nonlinear dynamic is approximated based on the RBFNNs and the local interfered relative information; the consensus gain k must to be small to guarantee the consensus performance; and the proposed algorithm is validated by the numerical simulations finally.

A Study on MXene/Graphene Composite Coatings for Electromagnetic Shielding Paints

In recent years, rapid advancements in digital and semiconductor technology have made it possible to miniaturize electronic devices, increase their speed, and provide broadband capabilities. However, the problem of malfunctioning due to even minor electromagnetic interference has become increasingly common. The increase in electromagnetic wave density creates an unfavorable electromagnetic environment, which applies not only to civilians but also military weapons and battlefields. In this study, we conducted research on a new composite material-based electromagnetic shielding paint to reduce communication noise. We introduced MXene/Graphene and nano organic and inorganic particles and analyzed the dispersion characteristics of the composite material and the optimal coating structure for using it as a coating pigment.

From black screens to mediating means

The paper reports on an empirical case study that explores innovative teaching practices in higher education, including digital, physical, and blended learning activities. The study followed the Master’s course ‘Experts in Teamwork’, where the students worked with practical assignments based on real-life challenges formulated by an external collaboration partner, which included actors from the health care department, the school management, and the ICT management in the local municipality. The students also gained experience with creative media production and worked with various digital tools and software.
 The findings show that the students experienced the project as motivating, highly educational and relevant to working life. It seems that digital tools contributed to flexibility, particularly for interactivity with the external partners and for group work. Both for students and the teachers the use of screens and software like Zoom and Teams opened for new ways of thinking about collaboration and provided an extended learning space and increased learning potential. However, screens also imply limitations, for example in terms of communication noise, possible passive forms of participation or even withdrawal from some of the students. Moreover, practices that include digital, physical and hybrid learning activities are highly complex, which require that the activities are particularly well planned and thoughtfully organized. The learning situation is therefore characterized by increased demands and expectations - for both students, teachers, and the institutions.

Distributed online optimization for heterogeneous linear multi-agent systems with coupled constraints

This paper studies a class of distributed online convex optimization problems for heterogeneous linear multi-agent systems. Agents in a network, knowing only their own outputs, need to minimize the time-varying costs through neighboring interaction subject to time-varying coupled inequality constraints. Based on the saddle-point technique, we design a continuous-time distributed controller which is shown to achieve constant regret bound and sublinear fit bound, matching those of the standard centralized online method. We further extend the control law to the event-triggered communication mechanism and show that the constant regret bound and sublinear fit bound are still achieved while reducing the communication frequency. Additionally, we study the situation of communication noise, i.e., the agent’s measurement of the relative states of its neighbors is disturbed by a noise. It is shown that, if the noise is not excessive, the regret and fit bounds are unaffected, which indicates the controller’s noise-tolerance capability to some extent. Finally, a numerical simulation is provided to support the theoretical conclusions.