Information Transmission Time Research Articles

Delay-adaptive compensation for 3-D formation control of leader-actuated multi-agent systems

This paper focuses on the control of collective dynamics in large-scale multi-agent systems (MAS) operating in a 3-D space, with a specific emphasis on compensating for the influence of an unknown delay affecting the actuated leaders. The communication graph of the agents is defined on a mesh-grid 2-D cylindrical surface. We model the agents’ collective dynamics by a complex- and a real-valued reaction–advection–diffusion 2-D partial differential equations (PDEs) whose states represent the 3-D position coordinates of the agents. The leader agents on the boundary suffer unknown actuator delay due to the cumulative computation and information transmission time. We design a delay-adaptive controller for the 2-D PDE by using PDE backstepping combined with a Lyapunov functional method, where the latter is employed to design an update law that generates real-time estimates of the unknown delay. Capitalizing on our recent result on the control of 1-D parabolic PDEs with unknown input delay, we use Fourier series expansion to bridge the control of 1-D PDEs to that of 2-D PDEs. To design the update law for the 2-D system, a new target system is defined to establish the closed-loop local boundedness of the system trajectories in H2 norm and the regulation of the states to zero assuming a measurement of the spatially distributed plant’s state. We illustrate the performance of the delay-adaptive controller by numerical simulations.

Delay estimation for cortical-muscular interaction with wavelet coherence time lag

BackgroundCortico-muscular coherence (CMC) between the cerebral cortex and muscle activity is an effective tool for studying neural communication in the motor control system. To accurately evaluate the coherence between electroencephalogram (EEG) and electromyogram (EMG) signals, it is necessary to accurately calculate the time delay between physiological signals to ensure signal synchronization. New methodWe proposed a new delay estimation method, named wavelet coherence time lag (WCTL) and the significant increase areas (SIA) index as a measure of the specific region enhancement effect of the magnitude squared coherence (MSC) image. ResultsThe grip strength level had a small effect on the information transmission time from the cortex to the muscles, while the transmission time from the cortex to different muscle channels was different for the same task. A positive correlation was found between the grip strength level and the SIA index on the β band of C3-B and the α and β bands of C3-FDS.Comparison with Existing Method: The WCTL method was found to accurately calculate the delay time even when the number of repeated segments was low in a simple motor control model, and the results were more accurate than the rate of voxels change (RVC) and CMC with time lag (CMCTL) methods. ConclusionsThe WCTL is an effective method for detecting the transmission time of information between the cortex and muscles, laying the foundation for future rehabilitation treatment for stroke patients.

Computation Offloading and Resource Allocation in C-RAN Supporting Wireless Charging

In this paper, we consider a Cloud-Radio Access Network (C-RAN) with multiple enhanced remote radio heads (RRHs), which support for wireless charging, computation offloading and information transmission. The employment of enhanced RRHs requires a redesign for efficient resource allocation. Our goal is to minimize the weighted sum of energy transfer time and computation time by jointly optimizing energy transmission time, energy beamforming vectors, offloading factors, information transmission time, and combining vectors, while satisfying the communication performance requirements of terminal nodes. The problem is formulated as a mixed-integer nonlinear programming, which can be solved by alternating optimization (AO) utilizing semidefinite relaxation (SDR) and nonlinear transformation. To reduce the online computation delay of AO algorithm, we propose an optimization method based on deep learning framework, which can determine optimal solution with trained neural network in real-time application. To improve the performance of learning algorithm, we use two methods to set labels for training deep neural network to obtain the optimal offloading decision: pre-optimization and self-evolution. Simulation results show that with trained neural network, the algorithms based on deep learning can achieve similar performance to AO algorithm with less computation time.

Performance Analysis of the LoRaWAN Protocol for Data Transmission In Urban Area Scenarios

Long Reach Wide Region Organization (LoRaWAN) is a kind of organization for remote media transmission region intended to empower far off correspondence with significant distance correspondence and low piece rate. LoRaWAN can be utilized on correspondence networks that have a wide reach utilizing end gadgets. LoRaWAN offers a great many benefits to the test of IoT execution, however the ongoing execution of LoRaWAN innovation is still exceptionally restricted, requiring practicality testing and examination how much LoRaWAN innovation is equipped for being carried out in metropolitan regions in Indonesia. In this examination, an investigation of the exhibition of the LoRaWAN convention for information transmission with metropolitan region situations in Malang City is done. The strategy utilized in this study is to reproduces LoRaWAN convention in Organization Test system 3 (NS-3) application by changing the boundaries to decide the impact on the quantity of end gadgets, sweep of the passage reach, and information sending period to the boundaries throughput and parcel misfortune. From the aftereffects of exploration demonstrated that for the utilization of convention LoRaWAN in metropolitan regions, the more end gadgets utilized, the more prominent the throughput esteem and the more prominent the worth of bundle misfortune acquired. What's more, the more extensive the region required, the throughput and bundle misfortune acquired are steady. And afterward, the bigger the time of information transmission, the more modest the throughput esteem is acquired and the more modest the worth of bundle misfortune got

In Situ Loading of Polypyrrole onto Aramid Nanofiber and Carbon Nanotube Aerogel Fibers as Physiology and Motion Sensors.

Nanocomposite conductive fiber has been newly developed as a lightweight material with high flexibility and strong weavability, which can meet the requirements of flexible wearable devices. Herein, lightweight porous aramid nanofibers (ANF) and carbon nanotube (CNT) aerogel fibers coated with polypyrrole (PPy) layers are prepared by a wet spinning method for motion detection and information transmission. The ANF/CNT/PPy aerogel fiber with low density (56.3 mg/cm3), conductivity (6.43 S/m), and tensile strength (2.88 MPa) were used as motion sensors with high sensitivity (0.12) and long life (1000 cycles). At the same time, the differential conductivity of aerogel fibers is utilized to reduce the information transmission time (up to 46%). High- and low-temperature-resistant (-196 to 100 °C) aerogel fibers are also available as a quick heater and ionic solution detector. In summary, the prepared ANF/CNT/PPy aerogel fiber can be used as a multifunctional sensor for human-health detection and motion monitoring.

Real-Time Analysis and Research of Vehicle CAN

Abstract The real-time evaluation method of CAN bus is studied. The event-triggered CAN protocol has no limitation on the worst transmission time of information. Based on the study of ratio monotone analysis (RMA), a single step recursive relation equation is deduced. Using LabWindows /CVI design and analysis software, the research method is applied to offline realtime analysis of CAN bus information of a certain vehicle. The results show that this method is of great significance to the real-time evaluation of CAN bus.

Exploration on Signal Processing Performance of Wireless Sensor in Electromagnetic Interference Environment

In order to deeply analyze the performance of Electromagnetic Field (EMF) on wireless sensor Signal Processing (SP), this paper first examines the influencing factors of wireless sensor SP performance based on the relevant theories of EMF and wireless sensor. Then, it discusses the influence of EMF on wireless sensor information Transmission and Reception (T & R) time, Package Reception Rate (PRR), and communication throughput. Furthermore, the Packet Loss Rate (PLR) and Bit Error Rate (BER) of the sensor are described, and finally, a conclusion is drawn. The research findings are as follows. This paper makes a statistical study on whether the wireless sensor is disturbed by EMI from three aspects (wireless sensor’s information T & R time, PLR, and communication throughput). In any case, EMF will prolong the wireless sensor’s information T & R time and reduce the PRR and communication throughput. When the distance between the transmitter and the receiver is set to 5 m, the change of field intensity at the transmitter significantly impacts the BER but has little impact on the PLR. However, when the field intensity of the transmitter and the receiver is the same, PLR and BER show no evident difference. Therefore, this paper studies the SP performance of the wireless sensor in the EMI environment, which provides an excellent reference significance for the rapid development of wireless sensors in the future.

Topology characteristic analysis of vehicular ad hoc network based on time-varying complex network

The dynamic network topology of the Vehicular Ad Hoc Network (VANET) directly affects network performances. The VanetMobiSim software was used to study the dynamic topology of VANET in detail. A time-varying model of VANET was built. First, the information transmission model of VANET was established on the basis of the time-varying network theory. The evolution of VANET characteristic parameters in a time-varying process was stimulated by using the information transmission time τ and the maximum tolerated delay time δ accessibility graph. Then, we perform mathematical analysis of the relations of (τ, δ) to the out-degrees, average packet accessibility, and asymmetry ratio. Finally, the relations of the node communication radius to the global efficiency and the average harmonic mean of the distances were analyzed. The results indicated that the time-varying accessibility graph made VANET information transmission more efficient; reducing the information transmission time τ or increasing the maximum tolerated delay time δ tends to bring out the improving temporal connectivity of the whole network, thus improving the efficiency of packet transmission.

Multi-Stage Enhanced Rain Image Restoration Network

The restoration task of rain images is beneficial to the further development of computer vision tasks. However, recent rain removal methods have difficulty coping with dense rain streaks. In this paper, we propose a Multi-stage Enhanced Rain Image Restoration Network (MERNet) for the task of rain removal. Our main idea is to design a three-stage network architecture, which makes full use of the enhanced information of the previous stage and complements the information of the next stage through transmission step by step. We give consideration to information enhancement at the same time of information transmission, fully improve the information utilization rate and the overall network effect.

A Review on Secure Data Transmission for Banking Application using Machine Learning

Security on the Internet of Things (IoT) accentuates safeguarding the Internet-empowered devices that connect to remote networks. IoT Safety endeavors to shield IoT gadgets and frameworks against cybercrime, and it is considered a vital security element linked to the IoT. Conversely, banking applications are dynamically being regulated for their inability to give an adequate level of client assistance and insure themselves against and react to digital assaults. One of the primary components for this is the weakness of Fintech systems and organizations to breaking down. Therefore, wireless organizations covering these IoT items are incredibly unprotected. IoT is a lightweight framework, and it is ideal when utilizing lightweight and energy-effective cryptography for assurance. Deep learning is a proficient technique to examine dangers and react to assaults and security occurrences. So this business locales both security and energy productivity in IoT utilizing two novel strategies helped out through the deep learning. This work adds to the most inventive method of saving energy in IoT gadgets through diminishing the utilization of energy-costly '1' values in the interface of Dynamic RAM. This should be possible by utilizing Base + XOR encoding of information during information transmission. Utilizing Conditional Generative Adversarial Network (CGAN) based deep learning strategy, the Base + XOR encoding technique and C.X.E. are prepared or trained quite well in the banking/financial application. The information age in CGAN is done dependent on rules delivered utilizing the generator model. This work is ended up being burning-through less energy, less information transmission time, and gives greater security when thought about the existing frameworks.

Deterministic generation of high-dimensional entanglement between distant atomic memories via multiphoton exchange

Promising access to high-speed quantum networks relies on the creation of high-dimensional entangled memories that provide quantum communication with higher capacity of noisy quantum channels, thereby reducing the transmission time of information. Yet, the distribution of multidimensional entanglement between remote memory nodes is still faintly investigated. We propose an experimentally feasible protocol of deterministic generation of high-dimensional entanglement between distant multilevel atoms confined in high-finesse optical cavities and driven by laser pulses. Three-dimensional entanglement is generated deterministically between remote atoms by triggering a two-photon wave packet, which mediates a superposition of states chosen by the laser pulse parameters. The efficient transfer of atomic states between remote nodes allows the construction of a three-dimensional quantum repeater, where the successful creation of entanglement can be verified by a simple method for reliable measurement of atomic ground Zeeman states.

Optimization of Energy Efficiency for Uplink Wireless Information and Downlink Power Transfer System with Imperfect CSI

As an emerging paradigm, supplying power by radio frequency signal has been a key technology for the wireless powered communication network (WPCN) to prolong the lifetime. This paper considers a multiple input multiple output (MIMO) system where users are charged only by one source. The source is equipped with multiple antennas while each user with one antenna. Besides receiving information as the traditional way, the source has the capacity to transfer energy with beamforming, which can be harvested by users to store for information transmission in the later. However, the unknown channel state information (CSI), low energy efficiency, and various demands of transmitting volume jointly raise inaccurate, wasteful, and flexible conditions in transmitting design. On the other hand, energy and spectrum efficient solutions are indispensable to the success of Internet of Things (IoT). In this case, we put forward a novel design of downlink energy transfer, uplink information transmission, and channel estimation to achieve a practical efficient transmission. By jointly optimizing the source antenna number, power allocation, energy beamforming vectors, and each phase time of channel estimation, energy harvest, and information transmission, we aim to achieve the optimized system energy efficiency with constraints of signal-to-noise ratio (SNR), data transmission volume, and transmitting power. Based on fractional programming and Lagrangian dual functions, we also put forward a distributed iterative algorithm to solve the formulated problem optimally. Simulation results verify the convergence of our proposed algorithm and illustrate the relationship between variables of antenna number, data volume requirement, pathloss factor and system performance of sum-throughput, energy efficiency, and user fairness. Our proposed transmitting design can achieve the optimized energy efficiency, whose upper bound is improved by appropriate massive antenna employment.

Information carrier and resource optimization of counterfactual quantum communication

Counterfactual quantum communication is unique in its own way that allows remote parties to transfer information without sending any message carrier in the channel. Although no message carrier travels in the channel at the time of successful information transmission, it is impossible to transmit information faster than the speed of light, thus without an information carrier. In this paper, we address an important question What carries the information in counterfactual quantum communication? and optimize the resource efficiency of the counterfactual quantum communication in terms of the number of channels used, time consumed to transmit 1-bit classical information between two remote parties, and the number of qubits required to accomplish the counterfactual quantum communication.

A novel model to eliminate the doubly near‐far problem in wireless powered communication network

In this paper, the newly emerging wireless powered communication network is studied. In doing so, the performance of the global controller (GC) is evaluated, which coordinates the wireless energy transmissions between two sensor nodes. Both the sensors have the same harvested energy for uplink (UL) transmission of information through time-division-multiple-access. Afterwards, the information transmission time is optimised to maximise the common throughput of both the sensors with a total time constraint based on the user's UL channels along with the same harvested energy value. Further, due to the “doubly near-far” phenomenon, a remote sensor from the GC, which has poor channel conditions than a nearer user, has to transmit more time in the UL for maximum common throughput. To overcome this problem, the energy exchange (EEx) model is proposed where both sensors first harvest the same amount of wireless energy and then exchange energy to nullify the different channel conditions between sensors and GC to send their independent information in the UL. Simulation results demonstrate the EEx Model's effectiveness over without energy exchange (WEEx) model in eliminating the doubly near-far problem in wireless powered communication network but at the cost of maximum sum-throughput. The maximum sum-throughput of the proposed EEx model is 35 % lower than the WEEx model. However, the average BER in the proposed EEx model is 74.6 % lower than the WEEx model, which increases the reliability of the model.

OPTIMUM FLOW DISTRIBUTION IN THE NETWORK WITH ADAPTIVE DATA TRANSFER

This paper considers a fundamentally new model of data network construction for servicing the flows given by the gravitational matrix with restrictions on the time of information transmission. The proposed network model differs from the known ones because it has an expanded capacity of node and channel resources, combining the lowest possible cost of channels and switching nodes. As part of the development of the proposed model and algorithm for allocation of flows in a full mesh network, the developed mathematical apparatus provides a high degree of reliability and survivability of the synthesized network as a whole. One of the used approaches to the solution of the linear programming problem is based on the choice of the target function, the type of which is determined by the consumer conditions of the synthesis of a particular data network. Within the framework of the article, it is established that the linear programming problem, for each specific case, has an admissible, practically realizable solution with the optimal choice of the target function without additional simplex transformations. An important obtained result of the research is the simplicity of flow control, which is in direct dependence on the strict ordering of the proposed structure, because it was possible to connect by analytical dependences the flows in the branches and the path data transfer flows. The obtained analytical results can be used as a basis for statistical algorithms of information flow control.

Proposal for a standardized discharge letter after hospital stay for acute myocardial infarction.

In patients admitted for acute myocardial infarction, the communication and transition from specialists to primary care physicians is often delayed, and the information imparted to subsequent healthcare providers (HCPs) may be sub-optimal. A French group of cardiologists, lipidologists and diabetologists decided to establish a consensus to optimize the discharge letter after hospitalization for acute myocardial infarction. The aim is to improve both the timeframe and the quality of the content transmitted to subsequent HCPs, including information regarding baseline assessment, procedures during hospitalization, residual risk, discharge treatments, therapeutic targets and follow-up recommendations in compliance with European Society of Cardiology guidelines. A consensus was obtained regarding a template discharge letter, to be released within two days after patient's discharge, and containing the description of the patient's history, risk factors, acute management, risk assessment, discharge treatments and follow-up pathway. Specifically for post acute MI patients, tailored details are necessary regarding the antithrombotic regimen, lipid-lowering and anti-diabetic treatments, including therapeutic targets. Lastly, the follow-up pathway needs to be precisely mentioned in the discharge letter. Additional information such as technical descriptions, imaging, and quality indicators may be provided separately. A template for a standardized discharge letter based on 8 major headings could be useful for implementation in routine practice and help to improve the quality and timing of information transmission between HCPs after acute MI.

Model of control access process in wireless computer network

The problem of ensuring the minimum delivery time of information with prevention of collisions is a pressing problem in ACS TP, since collisions often occur during transmission or the delay time of information is not optimal. This problem is solved by the use of wireless computer networks and methods of information transmission with collision prevention, which affect the delay time of information transmission. The developed model of the access control process in wireless computer networks solves the problem of effective information transfer with collision prevention. The purpose of using this model is to reduce the delay in information transmission. ACS TP often needs modernization as technical innovations are becoming available. At the same time, the quality of production should not fluctuate. The most important element of modernization is the use of wireless technologies that save money and time, compared to the deployment of wired networks. The aim of the work is to develop the model of control access process in a wireless computer network with collision prevention. The process of forming delay slots for information transfer has been researched. The object of the study is a wireless computer network in the ACS TP. The subject of the study is a model of control access process in a wireless computer network with collision prevention. The research objectives are to create requirements for the model, to propose a structure, to create a model, to conduct tests and evaluate the obtained results. The model of access control process in a wireless computer network designed to transmit information with prevention of collisions in a wireless computer network has been developed.

Quantum correlations and artificial intelligence

The possibility of using quantum correlations in the process of exchanging information between artificial intelligence systems has been considered. The benefits of using quantum correlations in such process include: high degree of information protection both on the physical level (electric and magnetic noise does not affect quantum correlations) and on the level of confidentiality preservation of information transmitted (any external interference results in data corruption without information disclosure); the possibility of full automation of information transmission process; the process is dissipation free (which follows from that the quantum correlations are performed between the quantum obj ects described by the same wave function); high speed of information transmission (higher than the speed of light). In this case the special relativity postulate concerning the speed of light is not violated, because it relates to inertial systems only; however, quantum correlation due to being dissipation free is an inertialess process (which follows from the well-known, experimentally-verified relationship between mass m and energy E: E = mc 2 , where с is the speed of light). Feasible approaches to using quantum correlations for information exchange between artificial intelligence systems have been reviewed in the paper, in particular two versions of information transmission between artificial intelligence systems have been considered: with external synchronization of information transmission time and autonomous synchronization performed by interacting artificial intelligence systems. The main difficulty in implementing information transmission by means of quantum correlations is the necessity of using the so-called entangled quantum objects, that is the quantum objects having some mutually-dependent characteristics of their wave functions.

DewMusic: crowdsourcing-based internet of music things in dew computing paradigm

Internet of Things is a promising paradigm that integrates a plethora of heterogeneous computational devices, incorporating the crowd, frameworks, additional system elements, and infrastructure. Information sensing, modeling, retrieval, and distribution perform an emerging role in the Internet of Things network. Dew computing is a challenging research issue, which needs to demonstrate its impact on the sensor data in the domain of parallel and distributed computing. We have presented a dew-cloud computing-based music crowdsourcing framework in this paper, to address the dew computing effectiveness in the context of the Internet of Things. The crowdsourcing paradigms are efficient to collect and analyze billions of information efficiently with a diminutive cost. In this promising paradigm, participated sound sensing devices sense acoustic information from the environment; transmit the sensor data to fog computing devices through dew repository, and eventually, cloud data center stores the processed data for providing aggregated musical information and relevant services to the end-users. This paper presents a Dew-Cloud based music crowdsourcing framework in the ambiance of the Internet of Things. We have illustrated a semantic mathematical background for the proposed crowdsourcing-based Internet of Music Things architecture in the dew-cloud computing framework. We have also discussed the system performance metrics, in terms of information transmission time, service latency, and energy dissipation in this endeavor. We have additionally illustrated a comparative analysis between the proposed paradigm and the conventional cloud computing schema in terms of data transmission time and overall system energy dissipation. The goal of this paper is to conceptualize, how the end-users can be benefitted from data analytics through data sensing, computing, and distributed scenario using a dew-cloud computational framework in the Internet of Things environment.

Completion Time Minimization for Turning Angle-Constrained UAV-to-UAV Communications

This paper investigates UAV-to-UAV (unmanned aerial vehicle) communications with a turning angle constraint, in which the information transmission time is minimized by joint UAV flight path planning and transmit power control. Considering the communication throughput requirement, peak transmit power, energy consumption, UAV flight speed, collision avoidance, and turning angle constraints, the joint UAV flight path planning and transmit power control problem is formulated as a non-convex optimization. Different from majority of prior studies that separately optimize the UAV flight path and transmit power, we develop a successive convex approximation based time minimization algorithm that simultaneously updates the UAV path and transmit power. We further prove that the proposed algorithm is guaranteed to converge. In addition, an initialization scheme is designed to start the proposed time minimization algorithm by solving an energy minimization problem. Simulation results verify that the proposed time minimization algorithm can shorten the completion time length and reduce the computational complexity compared with state of the art methods.