Communication Transmission Research Articles

The impact of face masks on face-to-face neural tracking of speech: Auditory and visual obstacles

Face masks provide fundamental protection against the transmission of respiratory viruses but hamper communication. We estimated auditory and visual obstacles generated by face masks on communication by measuring the neural tracking of speech. To this end, we recorded the EEG while participants were exposed to naturalistic audio-visual speech, embedded in 5-talker noise, in three contexts: (i) no-mask (audio-visual information was fully available), (ii) virtual mask (occluded lips, but intact audio), and (iii) real mask (occluded lips and degraded audio). Neural tracking of lip movements and of the sound envelope of speech was measured through backward modeling, that is, by reconstructing stimulus properties from neural activity. Behaviorally, face masks increased perceived listening difficulty and phonological errors in speech content retrieval. At the neural level, we observed that the occlusion of the mouth abolished lip tracking and dampened neural tracking of the speech envelope at the earliest processing stages. By contrast, degraded acoustic information related to face mask filtering altered neural tracking of speech envelope at later processing stages. Finally, a consistent link emerged between the increment of perceived listening difficulty and the drop in reconstruction performance of speech envelope when attending to a speaker wearing a face mask. Results clearly dissociated the visual and auditory impact of face masks on the neural tracking of speech. While the visual obstacle related to face masks hampered the ability to predict and integrate audio-visual speech, the auditory filter generated by face masks impacted neural processing stages typically associated with auditory selective attention. The link between perceived difficulty and neural tracking drop also provides evidence of the impact of face masks on the metacognitive levels subtending face-to-face communication.

Performance analysis of 1.28 terabit/s transmission system for dense wavelength division multiplexing free-space optical communications

Performance analysis of 1.28 terabit/s transmission system for dense wavelength division multiplexing free-space optical communications

Improving the efficiency of channel evaluation during high-speed data transmission in underwater acoustic communication with OFDM

Problem statement. The organization of noise-resistant wireless underwater acoustic communication is not a simple and solved task. One of the main problems is the multipath propagation of a sound acoustic wave as a result of re–reflections from the seabed surface and a change in the frequency of signal vibrations when the receiver and transmitter move relative to each other. Thus, finding a channel evaluation solution that can improve the quality of communication is an urgent task for developers of underwater acoustic communication systems. Purpose. To consider the possibility of developing effective methods for processing underwater acoustic communication signals with high noise immunity by evaluating the communication channel. Results. The possibility of using an adaptive filter with a sliding window to evaluate the channel during high-speed data transmission in underwater acoustic communication is shown. The simulation results showed that the proposed adaptive filter with a sliding window surpasses the normalized adaptive filters in terms of the BER bit error and wins over additive filters with an exponential window in reducing the number of mathematical operations. When modeling wireless acoustic communication systems with OFDM, error-free data transmission with a spectral efficiency of 0.33 bits/s/Hz was achieved. Practical significance. The obtained results can be used to improve the wireless transmission of data in underwater acoustic communication due to the proposed algorithm of the adaptive filter.

Intellicise model transmission for semantic communication in intelligence-native 6G networks

Intellicise model transmission for semantic communication in intelligence-native 6G networks

Reducing Door-to-Balloon Time Using EMS-Initiated App-Based Communication

BackgroundReducing door-to-balloon (D2B) time for ST-segment elevation myocardial infarction (STEMI) has been shown to improve outcomes. Delays still occur due to various factors such as time to laboratory activation and diagnostic clarification in equivocal cases. We propose that early communication through a mobile application (app) between emergency medical services (EMS) and in-hospital providers can reduce EMS-to-balloon time and provide coordinated care to impact D2B time. MethodsThe General Devices e-Bridge mobile app, which allows real-time communication and data transmission between EMS providers and the cardiac catheterization laboratory and emergency department staff, was implemented on May 14, 2019. A single-center, retrospective observational study was conducted on 795 STEMI activations undergoing emergent coronary angiography between January 2017 and July 2020. After exclusions, EMS transmissions of 428 cases were analyzed, of which 132 used the app. ResultsImplementation of e-Bridge significantly reduced D2B time by 7.2 minutes (P = .005) and emergency department board time by 4.41 minutes (P = .01). First medical contact to balloon (FMC2B) time trended toward reduction but was not statistically significant. The app did not significantly reduce D2B or FMC2B time during the day shift but significantly improved these metrics during the night shift. ConclusionsImplementation of app-based communication between EMS and in-hospital providers resulted in statistically significant reductions in D2B time at our institution. This approach holds promise in improving the timely management of STEMI patients, particularly during the night shift.

Event-Triggered Interval Observer Fault Detection and Isolation for Multiagent Systems.

This article investigates an event-triggered interval observer (ETIO) fault detection and isolation method for multiagent systems. First, an event-triggered mechanism is developed to reduce unnecessary communication transmission. Then, a distributed ETIO is designed by combining an interval observer and the proposed event-triggered mechanism. Furthermore, for achieving the desired tradeoff between the robustness to disturbances and the sensitivity to faults, the ETIO is formulated as a multiobjective optimization with l1 / H∞ performance. Second, a bank of ETIOs are interpreted to isolate the faulty agent on a local agent using only the output information from itself and its neighbors. Comparison result with the existing method is given to highlight the superiority of our methodology. Finally, the multiunmanned aerial vehicles system is utilized as the case research, and specific simulation results are presented.

Event-Based Nonsingular Fixed-Time Tracking Control of an Uncertain Manipulator System Subject to Full-State Static Constraints.

This article centers around investigating the event-triggered nonsingular fixed-time tracking issue for an n -link rigid robot manipulator with full-state constraints, external disturbances, and model uncertainties. We propose the definition of the constrainedly practically fixed-time stability (CPFTS) and provide a sufficient condition for CPFTS. A novel auxiliary function is developed to address the singularity issue caused by repeated differentiation in achieving the fixed-time tracking control. The uncertain parameters are approximated using the radial basis function neural network (RBFNN). This study proposes the model-based and the neutral network-based tracking control approaches, designed using the scaling function technique and the barrier Lyapunov function, respectively, to ensure that the tracking error systems are CPFTS and the full-state constraints comply. Moreover, the communication transmission load is reduced using the relative threshold event-triggered control strategy. Simulation results demonstrate the effectiveness of the proposed tracking control algorithms.

Security and Privacy for Smart Transportation Management using Big Data Analytics

Security and privacy are vital aspects of smart transportation management with big data analytics because they assure the security of sensitive information, prevent unwanted access to essential systems, and retain public trust in the safety and dependability of the transportation infrastructure. Protecting data from cyber threats, ensuring secure communication and data transmission, protecting passengers' personal information and addressing privacy concerns related to data collection and usage to maintain transparency and accountability in data handling practices are all obstacles to smart transportation management using big data analytics. This paper proposes a Secure Data Encryption Control based Big Data Framework (SDEC-BDF) to strike a middle ground between data analytics and privacy protection, establishing the way for more private and secure transportation systems that benefit everyone involved. The intention of this approach is to offer strong security while simultaneously safeguarding people's privacy. The method has many potential uses in the Intelligent transportation sector (ITS), including traffic control, passenger security, fleet management, preventative maintenance, and road network design. It ensures privacy and security while facilitating effective data analysis. Furthermore, it protects the public confidence in the security and dependability of the transportation system, protects sensitive passenger data, and stops hackers from breaking into vital systems. The simulation analysis is conducted on the assumption that the system can maximize its security, privacy, and efficiency to create a more trustworthy transportation network.

Hybrid-driven based reliable control for uncertain semi-Markov jump cyber–physical systems via two-sided looped-functional

In this paper, the problem of input–output finite-time mean square stabilisation for semi-Markov jump cyber–physical systems is addressed under the frame of generally uncertain transition rates through the hybrid-triggering-based reliable control technique. And, the undertaken system is susceptible to several vulnerable elements such as linear fractional uncertainty, actuator faults, deception attacks and external disturbances. Moreover, a hybrid-triggered approach that fuses the both time-triggered and event-triggered strategies is implemented for the addressed system which significantly reduces the number of unimportant communication transmissions inside the network. Subsequently, a reliable control law is configured by utilising the established hybrid-trigger mechanism and taking into account the actuator fault. In addition, the deception attacks and hybrid-trigger mechanism are characterised by the stochastic variables that adhere to the Bernoulli distribution. Furthermore, by framing a mode-dependent two-side looped Lyapunov–Krasovskii functional and deploying the advanced integral inequalities, adequate criteria are developed within the context of linear matrix inequalities which ensures that the desired result. After that, the controller gain values is determined based on the inferred appropriate criteria. Ultimately, the simulation results, including the RLC circuit series, are shown in order to verify the suitability and potential of the demonstrated findings.

Research on Wearable Devices Based on the Internet of Things

Wearable devices are closely related to people's lives. This project is based on WIFI wireless communication transmission technology, supplemented by Cortex-M3 microprocessor and MQ-7 gas sensor to design a wearable device. This wearable device can not only achieve basic clock timing, but also achieve functions such as CO concentration detection, voice intelligent recognition, and automatic alarm. In addition, the device adopts an embedded design and adds blockchain technology to achieve data information security and privacy protection.

A constructive delay-aware model for opportunistic routing protocol in MANET

Mobile ad-hoc Networks (MANET) with mobile sinks are measured as a practical substitute to the complex deployment of conventional wireless sensing environments. This research concentrates on the high-throughput opportunistic routing with lower delay during the transmission service by the mobile nodes with an arbitrary number of mobile sinks and common network topology. Here a novel opportunistic routing (OR) model known as Delay-Aware Estimated Transmission Rate (DA-ETR) is proposed to evaluate the delay caused due to the intermittent mobile nodes and packet re-transmission process. With the execution of DA-ETR, the work demonstrates the functionality of DA-ETR seamlessly over the ETR. It specifies that the available ETR-based routing protocols for dynamic MANET are extended easily to MANET with minimum modification using the anticipated DA-ETR model. Also, the anticipated DA-ETR model with efficiency measure provides superior throughput and enhances the routing stability of OR. The novelty of the work relies on enhancing the routing performance. Thus, the proposed DA-ETR model seems lightweight and achieves easier deployment with no constant mobility prediction. The DA-ETR model for OR outperforms various existing approaches and shows better performance by achieving scalability, reliability, reduced routing and communication overhead and transmission delay. The proposed model shows a queue length of 6.5 packets, 6.3 packets/node/sec in transmission and reception rate, the delay is 50 ms, storage overhead is 80 packets, communication overhead is 20 packets and 2.5 ms packet loss. The simulation is done in the MATLAB 2020a environment.

Detection of phase modulation disorder when processing a sequence of ultra-wideband quasi-radio signals against a background of white Gaussian noise

In conditions of a complex electromagnetic environment in communication and data transmission systems, a change in the type of modulation occurs when one or more signal parameters change abruptly, therefore the task of determining the type of signal modulation can be reduced to the task of detecting a disorder, that is, detecting the fact of a change in the type of modulation of the signal and estimation of the disorder time. The purpose of the work is to synthesize an algorithm for detecting phase modulation disorder when processing a sequence of ultra-wideband quasi-radio signals with unknown amplitude and initial phase against a background of white Gaussian noise and to analyze the effectiveness of the resulting algorithm based on the results of statistical modeling. The work synthesizes an algorithm for detecting a phase modulation disorder of a received sequence of ultra-wideband quasi-radio signals against a background of white Gaussian noise. To analyze the effectiveness of the resulting algorithm, statistical modeling of algorithm for detecting a phase modulation disorder is carried out. The dependences of the probability of detection of a disorder on the signal-to-noise ratio and the length of the sequence of received signals at a fixed moment of disorder are obtained. A comparison of the detection characteristics of phase modulation disorder of ultra-wideband quasi-radio signals and narrowband radio signals is made. It is shown that when a low-order phase modulation disorder is detected, the required values of the probability of detection are achieved at low signal-to-noise ratios, while with an increase in the modulation order and the use of its modifications, the efficiency indicators of the algorithm will deteriorate. The introduction of algorithms for detecting a disorder in radio engineering applications will allow optimizing synchronization algorithms and adaptation to interference conditions with a concomitant change in the type of modulation, as well as reducing the amount of transmitted service information in telecommunication systems.

Unique Properties of Synaptosomes and Prospects for Their Use for the Treatment of Alzheimer's Disease.

Alzheimer's disease (AD) is a severe neurodegenerative condition affecting millions worldwide. Prevalence of AD correlates with increased life expectancy and aging population in the developed countries. Considering that AD is a multifactorial disease involving various pathological processes such as synaptic dysfunction, neuroinflammation, oxidative stress, and improper protein folding, a comprehensive approach targeting multiple pathways may prove effective in slowing the disease progression. Cellular therapy and its further development in the form of cell vesicle and particularly mitochondrial transplantation represent promising approaches for treating neurodegeneration. The use of synaptosomes, due to uniqueness of their contents, could mark a new stage in the development of comprehensive therapies for neurodegenerative diseases, particularly AD. Synaptosomes contain unique memory mitochondria, which differ not only in size but also in functionality compared to the mitochondria in the neuronal soma. These synaptosomal mitochondria actively participate in cellular communication and signal transmission within synapses. Synaptosomes also contain other elements such as their own protein synthesis machinery, synaptic vesicles with neurotransmitters, synaptic adhesion molecules, and microRNAs- all crucial for synaptic transmission and, consequently, cognitive processes. Complex molecular ensemble ensures maintenance of the synaptic autonomy of mitochondria. Additionally, synaptosomes, with their affinity for neurons, can serve as an optimal platform for targeted drug delivery to nerve cells. This review discusses unique composition of synaptosomes, their capabilities and advantages, as well as limitations of their suggested use astherapeutic agents for treating neurodegenerative pathologies, particularly AD.

Extracting Retrospective Information through the Public Sharing of Female Golfers' Contributions in a Digital Environment: An Analytical Perspective of Their Impact on Shaping Golf Communities

Abstract Social media are an integrated part of everyday life. They are creating a new paradigm of information consumption. The era of popularity of searching for information in large texts is irreversibly gone. The current model of communication and information distribution favours visual content. One of the major social media platforms that specializes in expressive visual content is Instagram. The essence of a communication idea is effectively rendered in a unique image. Thus, it becomes a key tool for the transmission of communication. In the above technical paper, we present empirical data that investigate the impact of Instagram posts of three professional women golfers during the calendar year 2022. The first one is the most followed golfer on social networks, the second one ranks among the current best professional golfers and the third one holds the position of the president of the Slovak Ladies Golf Association. These prominent figures in golf create a significant impact on the social opinion of the sport. The central aim of the present study is to comprehensively understand how certain types of visual content, also created through AI, elicit maximum audience engagement on social media. We present the results in the context of correlating the influence of different factors on the level of viewer interaction. The value is expressed through the number of likes awarded and the size of the membership base of the affected accounts.

1 × 4 Integrated Microlenses High-Rate Photodetector Array for Optical Communication Transmission

1 × 4 Integrated Microlenses High-Rate Photodetector Array for Optical Communication Transmission

Research on multi-layer network topology optimization strategy for railway internet of things based on game theory benefits

In the railway system environment, the interconnection of a vast array of intelligent sensing devices has brought about revolutionary changes in the management and monitoring of railway transportation. However, this also poses challenges to the communication service quality within the railway Internet of Things (IoT). Through collective intelligence and collaboration, the nodes within the railway IoT can not only share data and information but also work synergistically to enhance the overall intelligence level and improve decision-making quality of the network. Therefore, this paper proposes a reconnection mechanism based on the computation of node game-theoretic benefits and optimizes this process with the concept of swarm intelligence collaboration. Initially, the game-theoretic benefit values of the nodes in the railway IoT network are calculated. Subsequently, based on the weight priority of the edges, the two edges with the larger weights are selected, and connections are established between nodes with similar game-theoretic benefit values to enhance the network’s robustness. This approach enables rapid networking and efficient communication transmission within the railway IoT, providing robust assurance for the safe and stable operation of the railway.

Design of L+Band Bismuth-doped Fiber Laser

At present, the development of new communication bands to meet the needs of large communication transmission capacity in the current era is one of the key research topics. The conventional erbium-doped laser is limited by its structure which has the problem of short luminous bandwidth and can not cover a large communication band. Therefore, there is growing interest in designing bismuth-doped glass and fiber with ultra-wide gain bandwidth for fiber laser applications. By utilizing its ultra-wide gain bandwidth characteristics, important communication devices such as broadband fiber amplifiers and broadband tunable fiber lasers can be designed to operate in the L+band, thereby solving the problem that the ultra-wide bandwidth of the optical communication window cannot be achieved in the future high-speed communication systems. In this study, the 1550nm pump source is used to excite the bismuth-doped fiber, and the MATLAB numerical simulation modeling is used to design a bismuth-doped fiber laser with a central wavelength of 1650nm and a threshold power of 10W. To a certain extent, this design addresses the research gap in L+ band bismuth-doped fiber lasers.

Energy efficient data dissemination in wireless sensor network enabled IoT using domain‐adaptive message passing graph neural network

SummaryIn the past few years, restricted wireless sensor networks (WSNs) enabled the Internet of Things (IoT) have attracted significant attention and expansion to enhance service delivery and resource efficiency. Dissemination is a service offered by WSN that uses radio transmission and over‐the‐air programming for updating the deployed sensor nodes through online. The centralized data dissemination methods are replaced by the distributed approaches because they affect the drawbacks of a single point of failure, no scalability, and insecurity. Therefore, an Energy Efficient Protocol for Data Dissemination in Wireless Sensor network‐enabled IoT using Domain‐Adaptive Message Passing Graph Neural Network (EEP‐WSN‐IoT‐DMPGNN) is proposed in this paper. The nodes are formed as clusters utilizing the Deep Fuzzy Curriculum Clustering (DFCC) technique that rewards nodes belonging to a given cluster. By using the Crayfish Optimization Algorithm (COA), the Cluster Head (CH) selection optimally chose the ideal CH and satisfies the multiple objective functions, such as energy, delay, traffic density, and distance. Afterward, domain‐adaptive Message Passing Graph Neural Network (DMPGNN) based routing protocol is developed, the input given to the routing protocol includes a sink, action history, future node, and maximum‐distance node, which attains enhanced data transfer in the chosen path. The proposed technique attains a lower no. of dead nodes, lower energy consumption, and higher Network Lifetime while analyzed with existing techniques, such as routing technique depending on deep learning for effectual data transmission in 5G WSN communication (DL‐RPDT‐WSN), Reinforcement‐Learning base energy effectual optimized routing protocol in WSN (RL‐EERP‐WSN), and Energy‐efficient intellectual routing method for IoT‐enabled WSN (EIR‐IoT‐WSN), respectively.

A compact tri-notched flexible UWB antenna based on an inkjet-printable and plasma-activated silver nano ink

The rapid development of ultrawideband (UWB) communication systems has resulted in increasing performance requirements for the antenna system. In addition to a wide bandwidth, fast propagation rates and compact dimensions, flexibility, wearability or portability are also desirable for UWB antennas, as are excellent notch characteristics. Although progress has been made in the development of flexible/wearable antennas desired notch properties are still rather limited. Moreover, most presently available flexible UWB antennas are fabricated using environmentally not attractive subtractive etching-based processes. The usage of facile additive sustainably inkjet printing processes also utilizing low temperature plasma-activated conductive inks is rarely reported. In addition, the currently used tri-notched flexible UWB antenna designs have a relatively large footprint, which poses difficulties when integrated into miniaturized and compact communication devices. In this work, a silver nano ink is used to fabricate the antenna via inkjet printing and an efficient plasma sintering procedure. For the targeted UWB applications miniaturized tri-notched flexible antenna is realized on a flexible polyethylene terephthalate (PET) substrate with a compact size of 17.6 mm × 16 mm × 0.12 mm. The antenna operates in the UWB frequency band (2.9–10.61 GHz), and can shield interferences from WiMAX (3.3–3.6 GHz), WLAN (5.150–5.825 GHz) and X-uplink (7.9–8.4 GHz) bands, as well as exhibits a certain of bendability. Three nested "C" slots of different sizes were adopted to achieve notch features. The simulation and test results demonstrate that the proposed antenna can generate signal radiation in the desired UWB frequency band while retaining the desired notch properties and having acceptable SAR values on-body, making it a viable candidate for usage in flexible or wearable communication transmission devices. The research provides a facile and highly efficient method for fabricating flexible/wearable UWB antennas, that is, the effective combination of inkjet printing processing, flexible substrates, low temperature-activated conductive ink and antenna structure design.

Rate-Distortion-Perception Optimized Neural Speech Transmission System for High-Fidelity Semantic Communications.

We consider the problem of learned speech transmission. Existing methods have exploited joint source-channel coding (JSCC) to encode speech directly to transmitted symbols to improve the robustness over noisy channels. However, the fundamental limit of these methods is the failure of identification of content diversity across speech frames, leading to inefficient transmission. In this paper, we propose a novel neural speech transmission framework named NST. It can be optimized for superior rate-distortion-perception (RDP) performance toward the goal of high-fidelity semantic communication. Particularly, a learned entropy model assesses latent speech features to quantify the semantic content complexity, which facilitates the adaptive transmission rate allocation. NST enables a seamless integration of the source content with channel state information through variable-length joint source-channel coding, which maximizes the coding gain. Furthermore, we present a streaming variant of NST, which adopts causal coding based on sliding windows. Experimental results verify that NST outperforms existing speech transmission methods including separation-based and JSCC solutions in terms of RDP performance. Streaming NST achieves low-latency transmission with a slight quality degradation, which is tailored for real-time speech communication.