Real-time Communication Research Articles

Embedded Cyber-Physical System for Physiological Control of Ventricular Assist Devices

Ventricular Assist Devices (VADs) play a crucial role in both bridging to transplantation and serving as destination therapy for congestive heart failure (CHF) management. This study aims to address the limitations of existing control strategies for VADs, specifically their inability to adapt automatically to hemodynamic changes. It proposes a novel embedded cyber-physical system (CPS) based on real-time data processing, reconfigurable architecture, and communication protocols aligned with Health 4.0 concepts to enhance physiological control over VADs (PC-VAD). The research employs a multi-objective PC-VAD approach within a hybrid cardiovascular simulator. An embedded CPS is introduced to overcome challenges related to differences in controller characteristics between computers and embedded systems. The study assesses the performance of the embedded CPS by comparing it with a computer-based control system. The embedded CPS demonstrates outcomes comparable to the computer-based control system, maintaining mean arterial pressure and cardiac output at physiological levels. Even in the face of variations in ejection fraction, the embedded CPS dynamically adjusts the pump's rotational speed based on simulated clinical conditions. Notably, there is no aortic reflux to the ventricle through the VAD during testing. These findings affirm the satisfactory control performance of the embedded CPS in regulating VADs. The study concludes that the embedded CPS effectively addresses the limitations of current VAD control strategies, exhibiting control performance comparable to computer-based systems. However, further experimentation and in vivo studies are necessary to validate and ensure its applicability in real-world scenarios.

A New Regression Model Applied for Packet Loss Recovery Technique in Real-Time Communications

A New Regression Model Applied for Packet Loss Recovery Technique in Real-Time Communications

Terahertz Radiation – the Dawn of a New Information Era

This mini-review article explores the evolution of wireless communication technologies, emphasizing the crucial role played by advancements in information transfer over long distances with minimal errors. Tracing developments from submergible cables to the current 5G technology, the review discusses the potential of 6G, offering insights into the transformative applications it could enable, such as holographic real-time communication and autonomous transport infrastructure. Exploring the applications of Terahertz (THz) frequencies, the article presents the limitations of current wireless speeds and proposes solutions, including the use of spintronic terahertz emitters (STEs) and antiferromagnetic materials, concluding on the challenges and prospects of integrating these technologies into practical applications.

The importance of social networks in neurosurgery training in low/middle income countries.

Neurosurgery is evolving with new techniques and technologies, relies heavily on high-quality education and training. Social networks like Twitter, Facebook, Instagram and LinkedIn have become integral to this training. These platforms enable sharing of surgical experiences, fostering global knowledge-sharing and collaboration among neurosurgeons. Virtual conferences and courses are accessible, enhancing learning regardless of location. While these networks offer real-time communication and collaborative opportunities, they also pose challenges like the spread of misinformation and potential distractions. According to the PICO format, the target population (P) for the purpose of this paper are medical students, neurosurgical residents and consultants on the role of social media (I) in neurosurgery among Low-Middle income countries (C) with the main outcome to understand the collaborative domain of learning. This cross-sectional survey, conducted in June-July 2023, involved 210 medical students, neurosurgery residents, fellows, and practicing neurosurgeons from low and middle-income countries. A structured questionnaire assessed social network usage for neurosurgery training, covering demographic details, usage frequency, and purposes like education, collaboration, and communication. Participants rated these platforms' effectiveness in training on a 1-5 scale. Data collection employed emails, social media groups, and direct messaging, assuring respondent anonymity. The survey aimed to understand and improve social networks' use in neurosurgery, focusing on professional development, challenges, and future potential in training. In a survey of 210 participants from low and middle-income countries, 85.5% were male, 14.5% female, with diverse roles: 42.9% neurosurgery residents, 40% practicing neurosurgeons, 14.6% medical students, and 2.4% other healthcare professionals. Experience ranged from 0 to 35 years, with Mexico, Nigeria, and Kenya being the top participating countries. Most respondents rated neurosurgery training resources in their countries as poor or very poor. 88.7% used social media professionally, predominantly WhatsApp and YouTube. Content focused on surgical videos, research papers, and webinars. Concerns included information quality and data privacy. Interactive case discussions, webinars, and lectures were preferred resources, and most see a future role for social media in neurosurgery training. Our study underscores the crucial role of social media in neurosurgery training and practice in low and middle-income countries (LMICs). Key resources include surgical videos, research papers, and webinars. While social media offers a cost-effective, global knowledge-sharing platform, challenges like limited internet access, digital literacy, and misinformation risks remain significant in these regions.

A Method for 5G–ICN Seamless Mobility Support Based on Router Buffered Data

The 5G core network adopts a Control and User Plane Separation (CUPS) architecture to meet the challenges of low-latency business requirements. In this architecture, a balance between management costs and User Experience (UE) is achieved by moving User Plane Function (UPF) to the edge of the network. However, cross-UPF handover during communication between the UE and the remote server will cause TCP/IP session interruption and affect continuity of delay-sensitive real-time communication continuity. Information-Centric Networks (ICNs) separate identity and location, and their ability to route based on identity can effectively handle mobility. Therefore, based on the 5G-ICN architecture, we propose a seamless mobility support method based on router buffered data (BDMM), making full use of the ICN’s identity-based routing capabilities to solve the problem of UE cross-UPF handover affecting business continuity. BDMM also uses the ICN router data buffering capabilities to reduce packet loss during handovers. We design a dynamic buffer resource allocation strategy (DBRAS) that can adjust the buffer resource allocation results in time according to network traffic changes and business types to solve the problem of unreasonable buffer resource allocation. Finally, experimental results show that our method outperforms other methods in terms of average packet delay, weighted average packet loss rate, and network overhead. In addition, our method also has good performance in average handover delay.

Advantages, Challenges and Prospects of ChatGPT in Oral English Teaching

With the development of artificial intelligence, ChatGPT has set off a hot wave in various fields, and brings opportunities and challenges to education. The positive and negative effects of ChatGPT on education have aroused heated discussion in academic circles. Based on its principle and development process, and the continuous optimization of language model, ChatGPT can play a unique advantage in oral English teaching, such as: providing personalized oral practice, real-time error correction and feedback, real dialogue simulation, safe and comfortable dialogue environment. At the same time, the challenge of ChatGPT in oral teaching is also the direction of its future technological improvement, such as: real-time communication of spoken language, recognition of voice tone and intonation, integration with VR technology, autonomous assessment of spoken language level, etc., Those can become the application prospects of ChatGPT in oral teaching. Artificial intelligence assisted oral teaching is a new mode of oral teaching.

Gleeok: A Family of Low-Latency PRFs and its Applications to Authenticated Encryption

In this paper, we propose a new family of low-latency pseudorandom functions (PRFs), dubbed Gleeok.Gleeok utilizes three 128-bit branches to achieve a 256-bit key size while maintaining low latency. The first two branches are specifically designed to defend against statistical attacks, especially for differential attacks, while the third branch provides resilience against algebraic attacks. This unique design enables Gleeok to offer ultralow latency while supporting 256-bit keys, setting it apart from existing ciphers dedicated to low-latency requirements. In addition, we propose wide-block variants having three 256-bit branches. We also present an application of Gleeok to short-input authenticated encryption which is crucial for memory encryption and various realtime communication applications. Furthermore, we present comprehensive hardware implementation results that establish the capabilities of Gleeok and demonstrate its competitiveness against related schemes in the literature. In particular, Gleeok achieves a minimum latency of roughly 360 ps with the NanGate 15 nm cell library and is thus on par with related low-latency schemes that only feature 128-bit keys while maintaining minimal overhead when equipped in an authenticated mode of operation.

3R-Focused Web Application

3R-focused web application is an effective waste reduction and waste management which provides solution for reusing and reducing waste items in 3 effective ways: Reduce, Reuse, Recycle. With the base support of 3Rs (Reduce, Reuse and Recycle) strategy which acts as a sustainable and socioeconomic option for environment as well as people. The main objective of the system will remain to reduce, reuse, and recycle the items which are easily thrown away by people without any thought. Followed by the main objectives next comes various facilities like donating the reusable waste items, availability of various recycling ideas, giving the items which are not in reusable condition to the recycling centers, lending (renting) out the items to people. This web application will target all the three categories of waste reductions; reduce, reuse, and recycle by suggesting alternatives to reusing items and recycling them as well as the option of lending them out or simply donating them. Implementing these 3 approaches in one application will target all the netizens by providing a platform for easy access and real time communication between users for instructiveness and bigger support. It will solve the problem of having to make more than one connection and more than one action to reach the desired result by giving access to all the solutions needed in just one platform.

Real-time chaotic video encryption based on multi-threaded parallel confusion and diffusion

Due to the strong correlation among adjacent pixels, image encryption schemes typically perform multiple rounds of confusion and diffusion to protect images against various attacks. This is time-consuming and cannot meet the real-time requirements for video encryption. Existing works, therefore, realize video encryption by simplifying encryption process or selectively encrypting specific pixels, resulting in lower security compared to image encryption. This paper proposes a real-time chaotic video encryption strategy based on parallel computing. It splits video frame into sub-frames, creates a dedicated set of threads to concurrently perform confusion and diffusion operations on their respective sub-frames, and efficiently outputs encrypted frames. To assess its performance, two cryptosystems are implemented using different chaotic systems. Encryption speed evaluation demonstrates a significant acceleration in byte generation, confusion, and diffusion phases, enabling real-time encryption and decryption on different X86 platforms. The average encryption time is less than 42 ms, despite performing five rounds of confusion and diffusion operations on each frame. Statistical and security analysis prove that the deployed cryptosystems exhibit exceptional statistical properties and provide resistance to different attacks. Moreover, our method is adopted to implement a multi-user, real-time mobile video secure communication system using embedded systems, validating the feasibility of the proposed strategy for various application scenarios.

IoT Control and Visualization System with Digital Twins and Augmented Reality in a Digital Transformation Space

IoT Control and Visualization System with Digital Twins and Augmented Reality in a Digital Transformation Space

Augmented Reality-Based Real-Time Visualization for Structural Modal Identification.

In the era of aging civil infrastructure and growing concerns about rapid structural deterioration due to climate change, the demand for real-time structural health monitoring (SHM) techniques has been predominant worldwide. Traditional SHM methods face challenges, including delays in processing acquired data from large structures, time-intensive dense instrumentation, and visualization of real-time structural information. To address these issues, this paper develops a novel real-time visualization method using Augmented Reality (AR) to enhance vibration-based onsite structural inspections. The proposed approach presents a visualization system designed for real-time fieldwork, enabling detailed multi-sensor analyses within the immersive environment of AR. Leveraging the remote connectivity of the AR device, real-time communication is established with an external database and Python library through a web server, expanding the analytical capabilities of data acquisition, and data processing, such as modal identification, and the resulting visualization of SHM information. The proposed system allows live visualization of time-domain, frequency-domain, and system identification information through AR. This paper provides an overview of the proposed technology and presents the results of a lab-scale experimental model. It is concluded that the proposed approach yields accurate processing of real-time data and visualization of system identification information by highlighting its potential to enhance efficiency and safety in SHM by integrating AR technology with real-world fieldwork.

Web-Based Realtime Course Platform With Integrated Live Coding Interface

<p>This paper explores an innovative technological<br />approach to a programming education platform. The platform<br />aims to enhance the learning experience by integrating web-<br />based resources with real-time video communication and live<br />coding. Utilizing a peer-to-peer connection framework, this<br />method facilitates efficient and direct communication between<br />instructors and learners, optimizing performance and reducing<br />latency. The primary goal is to create a dynamic and<br />collaborative learning environment. Through the integration of<br />peer-to-peer video chat and an Integrated Development<br />Environment (IDE), the platform aims to improve real-time<br />communication, code reviews, and interactive problem-solving,<br />providing learners with a profound programming learning<br />experience.</p>

Long-Term and Real-Time High-Speed Underwater Wireless Optical Communications in Deep Sea

Long-Term and Real-Time High-Speed Underwater Wireless Optical Communications in Deep Sea

PARSECS_RT: A real-time PARSECS-based communication protocol stack for critical sensing applications

The real-time characteristics of modular systems have been a long sought-after goal in many industries including automotive, aeronautics or mobile robotics. Yet scientists and practitioners are still struggling to find widespread implementation solutions that may accommodate diverse inter-modular real-time communication requirements. In an attempt to cover this research gap, the current paper proposes a real-time version of the PARSECS protocol for low-end devices. Our new protocol, coined as PARSECS_RT, was designed according to Open Systems Interconnection Reference Model. It offers a full communication stack from the physical layer to the application layer on top of the SPI interface in order to provide a stable, hard real-time communication platform. PARSECS_RT was evaluated in real and simulated environments providing promising results.

Empowering Agricultural Ecosystems: Leveraging 5G IoT for Enhanced Product Integrity and Sustainable Ecological Environments

The study explores the change-making potential of 5G IoT technology in improving the security of agro-products and contributing to conservation of environments in the ecological field. Through the process of a series conducted in expression using the real agricultural settings, the study gives a visible proof that food traceability, quality assurance, resource management and environment conservation all gain the significant improvements. By relying on IoT sensors utilizing the latest 5G connectivity, stakeholders can have real-time data analytics and communication to take well-informed decisions, optimize resource usage, and manage the environmental risks. The analytics showed a noticeable boost in product traceability with real-time tracking, temperature monitoring during transportation, and automatic data logging rendering success rates above 95%. Resource management benefits are demonstrated in H2O and fertilizer reduction, reaching 30%, and up to 20% higher yields of crops than is traditional. Ecological conservation results exhibit the improved ecosystem health and the possibility to detect environmental pollution in real-time allows operational conservation measures. The research emphasizes on the prospect of 5G IoT whose benefits might transform agricultural practices and guarantee the authenticity and sustainability of food in the face of fast-changing environmental conditions.

Analysis and Performance Comparison of IoT Message Transfer Protocols Applying in Real Photovoltaic System

AbstractThe adoption of reliable and real-time communication technology is an absolute necessity for the advancement of Internet of Things (IoT) applications. Messaging protocols such as MQTT, AMQP, and HTTP are frequently used for communication with resource-constrained IoT devices. However, choosing a suitable and effective messaging protocol presents a daunting challenge for organizations, as it depends on the specific characteristics and messaging requirements of the IoT system. Therefore, it is crucial to have a comprehensive understanding of three established messaging protocols, such as the Hypertext Transfer Protocol (HTTP), the Message Queuing Telemetry Transport (MQTT), and the Advanced Message Queuing Protocol (AMQP), to appropriately apply them in practical projects. In this paper, information technology solutions are provided for a chain of solar farms to improve harvest productivity, facilitate warning notifications, and enable remote control. Subsequently, a detailed comparative analysis is performed, considering various interconnected criteria, to gain valuable insight into the strengths and limitations of these protocols. The results show that MQTT and AMQP play a role in enhancing overall efficiency and speed within the framework of our suggested photovoltaic system.

Enhancing Public Transit System Through AI and IoT

The paper investigates the synergy between AI and IoT in revolutionizing public transit systems, emphasizing their role in addressing existing challenges and improving overall efficiency. It explores the application of IoT devices in creating a smart infrastructure that enables real-time data collection, monitoring, and management of transit operations. The research delves into the realm of predictive maintenance, showcasing how AI algorithms, powered by IoT data, can anticipate potential issues in transit vehicles. By analyzing sensor data, transit authorities can proactively address maintenance needs, minimizing downtime, reducing repair costs, and ensuring a more reliable and sustainable public transit service. Route optimization emerges as another crucial aspect of the study, highlighting how AI algorithms leverage historical and real-time data to recommend the most efficient transit routes. Factors such as traffic patterns, weather conditions, and passenger demand are considered to enhance overall system efficiency and reduce travel time for passengers. The paper introduces the concept of dynamic scheduling, illustrating how AI-driven algorithms adapt transit schedules in real-time based on changing passenger needs and external factors. This dynamic approach aims to provide more responsive services, ultimately reducing wait times and improving overall user satisfaction. Passenger information systems are explored as a pivotal component, illustrating how AI and IoT technologies enhance the passenger experience. Real-time communication through mobile apps, digital displays, and other channels ensures that passengers have accurate and timely information about arrival times, delays, and alternative routes, empowering them to make informed decisions. The researchers also delve into fare optimization, examining how AI algorithms analyze data on passenger demographics, travel patterns, and economic factors to create fair and affordable fare structures. This approach aims to encourage ridership, increase revenue, and improve the financial sustainability of public transit systems. The abstract presents a comprehensive overview of how the integration of AI and IoT technologies in public transit systems transforms urban mobility. The findings suggest that leveraging real-time data, predictive analytics, and dynamic solutions can significantly enhance the reliability, accessibility, and sustainability of public transit. As cities continue to explore innovative solutions, the abstract serves as a roadmap for developing smarter, user-friendly, and efficient urban transportation networks, ultimately contributing to improved quality of life for residents.

Automatic bioacoustics noise reduction method based on a deep feature loss network

Acoustic sensors that collect acoustic data over extended periods and broad ranges are widely used in bioacoustics monitoring. However, in open environments, acoustic data collected using acoustic sensors can be subject to interference from various real-world noises, thereby influencing the subsequent analysis and processing of bioacoustic data. Existing bioacoustic noise reduction methods are limited in their application because of their low efficiency, unsuitability for non-stationary noise, generally unimproved signal-to-noise ratio (SNR) efficacy, and considerable amounts of residual noise. These limitations hinder the effective processing of recorded signals for which extraneous noise overlaps with bird vocalizations. In this study, we propose a bioacoustic noise reduction method based on a deep feature loss network for bird sounds. The method has a rapid denoising speed and can more effectively remove background noise from field recording signals without distorting the bird acoustic spectrum. The denoising effects of the proposed method were compared with those of a speech enhancement generative adversarial network, web real-time communications denoising, and other noise reduction methods. The denoising ability of these methods for different noises was evaluated using spectrograms and objective evaluation measures such as the SNR and perceptual evaluation of speech quality (PESQ). The experimental results revealed that our proposed noise reduction method can obtain higher SNRs and PESQ scores than other noise reduction methods, with the SNR increasing by up to 35.83 dB following denoising.

E-Gyan Portal

- The "E-Gyan Portal" project aims to revolutionize distance learning through the design and development of an automated system tailored for managing the distance learning process. It includes modules for student registration, access to library resources, and self-assessment tools. This project addresses the shortcomings of conventional distance education methods by introducing a digital platform focused on easy access to learning materials and seamless communication between students and faculty. The system eliminates geographical and time constraints, allowing students to pursue courses at their convenience. By leveraging modern technology, the E-Gyan Portal enhances the educational experience by providing centralized course management, interactive learning modules, real-time communication, and progress tracking. The project utilizes a robust hardware and software infrastructure, including Python with Django Framework for programming, SQLite for the database, and a suite of web development tools for user interface design. Drawing from extensive literature review, the project envisions future prospects for e-learning, emphasizing trends such as artificial intelligence integration and virtual reality. The E-Gyan Portal represents a significant advancement in distance education, promising a more accessible, interactive, and personalized learning experience for students worldwide.

Dual Data Streaming on Tropospheric Communication Links Based on the Determination of Beam Pointing Dynamics Using a Modified Ray-Based Channel Model

Tropospheric systems are widely used by military forces as they provide long-distance, real-time communication. Slow-fading propagation loss reduces link availability and limits its data-carrying capacity. Beam pointing dynamics provides knowledge of favorable heights at different times of the day in different seasons and a useful steering range. Beam steering, based on the beam pointing dynamics of the link, can overcome slow fading. The main contributions of this paper are the derivation of a realistic and accurate tropospheric channel model obtained by making important modifications to Dinc’s ray-based model. This paper also presents a method for determining beam pointing dynamics using the modified model. Beam pointing dynamics for two different links located in India have been determined in this paper using real-world data obtained from the Indian Meteorological Department. Another significant contribution of the paper is presenting the prospect of dual data streaming on tropospheric links using a fixed beam and a dynamically steered beam, based on the examination of beam pointing dynamics obtained for the two links. The main result presented in this paper is the comparison of powers received from the most favorable heights in a steerable beam system with the powers received in conventional fixed-beam systems for different days of the year. It has been shown that a higher received power can be achieved with beam steering. Another important result shown is the comparison of the achievable data rates for a single fixed-beam system and a dual-beam (one fixed beam and one dynamically steered beam) system. It has been shown that almost double the data rate is achievable in a dual-beam system. The method for the determination of beam pointing dynamics and the possibility of dual data streaming presented in this paper can significantly enhance the availability and capacity of tropospheric links.