Data Communication Research Articles

Cayley–Purser secured communication and jackknife correlative classification for COVID patient data analysis

Internet of Medical Things (IoMT) is a group of medical devices that connect the healthcare information technology to minimize the redundant hospital visit and healthcare system troubles. IoMT connect the patients to the doctor and transmit the medical data over the network. The spread of corona virus has put the people at high risk. Due to increasing number of cases and its stress on health professionals, IoMT technology is used in many healthcare centers. But, the security level and data classification accuracy was not improved by existing methods during the data communication. In order to solve these issues, Cayley–Purser Cryptographic Secured Communication based Jackknife Correlative Data Classification (CPCSC-JCDC) method is designed. The key objective of CPCSC-JCDC method is to collect the patient information through IoMT devices and send to the doctor in more secured manner. Initially in CPCSC-JCDC method, the patient data is collected. After the data collection process, the data gets encrypted with help of public key of the patient by using cayley–purser cryptosystem. After the encryption process, the data is sent to the doctor. The doctor receives and decrypts the patient data by using their private key. After decryption process, the doctor analyses the patient data and classifies the data as emergency case or normal case by using jackknife correlation function. This helps to minimize the patient readmission rate and increase the patient satisfaction level. Experimental evaluation is carried out by Novel Corona Virus 2019 dataset using different metrics like data classification accuracy, data classification time and security level. The evaluation result shows that CPCSC-JCDC method improves the security level as well as accuracy and minimizes the time consumption during data communication than existing works.

Partitioned neural network training via synthetic intermediate labels

Abstract The proliferation of extensive neural network architectures, particularly deep learning models, presents a challenge in terms of resource-intensive training. GPU memory constraints have become a notable bottleneck in training such sizable models. Existing strategies, including data parallelism, model parallelism, pipeline parallelism, and fully sharded data parallelism, offer partial solutions. Model parallelism, in particular, enables the distribution of the entire model across multiple GPUs, yet the ensuing data communication between these partitions slows down training. Instead of using the entire model for training, this study advocates partitioning the model across GPUs and generating synthetic intermediate labels to train individual segments. These labels, produced through a random process, mitigate memory overhead and computational load. This approach results in a more efficient training process that minimizes data communication while maintaining model accuracy. The method is validated using 6-layer fully-connected networks, via the extended MNIST, CIFAR10 and CIFAR100 datasets. It is shown that the computational improvement to reach 90% of the cross-yield accuracy can be as high as 66%. Additionally, the improvement in training bandwidth compared to standard model parallelism is quantitatively demonstrated through an example scenario. This work contributes to mitigating the resource-intensive nature of training large neural networks, paving the way for more efficient deep learning model development.

Delphi Method Consensus on Statistical Analysis and Reporting Recommendations for Single-Arm Pregnancy Medication Safety Studies Investigating Pregnancy, Birth and Neonatal Health Outcomes: A Contribution from IMI-ConcePTION.

Standardised procedures for performing and reporting safety monitoring studies investigating medications use in pregnancy may help improve data quality and the speed of data generation. The objective of this study was to provide recommendations on the statistical analysis and reporting of single-arm pregnancy medication safety studies using primary source datasets. A Delphi consensus-setting protocol was used to acquire agreement on recommendations from experts with extensive knowledge and experience in conducting studies investigating medication safety in pregnancy. A series of recommendations, along with their scientific justifications and examples of how to calculate and describe exposure and outcome incidences, were critiqued and improved through a series of online Delphi review rounds. Agreement to inclusion scoring was assessed using a five-point Likert scale. Recommendations with a median Likert-scale score of at least 4, where ≥ 80% of the expert panel scored the recommendation at level 4 or higher, was used as the threshold for inclusion. The Delphi consensus methodology produced a set of 30 recommendations spread over five themes. These included descriptions of (1) study sample, (2) medication exposure, (3) maternal outcomes, (4) pregnancy and birth outcomes, and (5) fetal and neonatal outcomes. Of the 30 recommendations, 19 were strongly advised while 11 were included for consideration where their implementation may be beneficial for supplementing data communication. Use of the finalised set of recommendations should be encouraged to help standardise published evidence around medication use in pregnancy.

Design and synthesis of millimeter-wave FSS with trapped-mode control for application in intelligent transportation systems

This research looked into a frequency selective surface (FSS) with a triple square spiral pattern for the metallization of its unit cell. It was set up on a magnetized ferrite substrate. The FSS demonstrates high-quality factor (Q-factor) resonance for short-range radar (SRR) systems applications, specifically at 24.5 GHz, 26.5 GHz, and 79 GHz frequencies. The research problem addressed in this study involves designing an FSS filter to shield the SRR from data communications standardized by IEEE 802.15.3c, thereby reducing the number and severity of road accidents involving new autonomous vehicles. Computational simulations for analyzing the electromagnetic properties of the proposed device were performed using CST® Microwave Studio, which applies the finite integration technique (FIT) for full-wave analysis.

Approach to joint wireless powering and communication with electronic implantable medical devices based on near-infrared light

This paper proposes what we believe to be a novel approach using near-infrared (NIR) light to jointly provide wireless communication and energy transfer to electronic implantable medical devices (IMDs). The proof-of-concept was implemented on a test bed constructed with commercially available components, including a single-beam 810 nm NIR LED, a monocrystalline photovoltaic cell (PV), a power management integrated circuit (PMIC), a photodetector amplifier (PDA), and a supercapacitor for energy storage. Three different experiments were conducted using a 1 cm thickness optical phantom mimicking the human soft tissue. First, we demonstrated optical power transfer capability. Second, we demonstrated data transmission capability utilizing Gaussian minimum shift keying (GMSK) modulation to carry the data. Third, we demonstrated joint data and power to IMDs. In terms of wireless energy transfer, the result shows that the supercapacitor achieves a full charge in approximately five hours with a constant NIR light exposure of 200 mW/cm2 across the optical phantom. Concerning wireless data transfer, data rates of approximately 100 kb/s are achieved. We show that combining wireless power transfer and data communication through a single beam NIR LED is feasible. Our approach aims to significantly enhance clinical applications and patient care in the field of biomedical engineering, especially in the context of providing secure and safe wireless communication links and power transfer techniques for IMDs.

Performance prediction and optimization of a high-efficiency tessellated diamond fractal MIMO antenna for terahertz 6G communication using machine learning approaches.

This paper introduces the design and exploration of a compact, high-performance multiple-input multiple-output (MIMO) antenna for 6G applications operating in the terahertz (THz) frequency range. Leveraging a meta learner-based stacked generalization ensemble strategy, this study integrates classical machine learning techniques with an optimized multi-feature stacked ensemble to predict antenna properties with greater accuracy. Specifically, a neural network is applied as a base learner for predicting antenna parameters, resulting in increased predictive performance, achieving R², EVS, MSE, RMSE, and MAE values of 0.96, 0.998, 0.00842, 0.00453, and 0.00999, respectively. Utilizing regression-based machine learning, antenna parameters are optimized to attain dual-band resonance with bandwidths of 3.34 THz and 1 THz across two bands, ensuring robust data throughput and communication stability. The antenna, designed with dimensions of 70 × 280μm², demonstrates a maximum gain of 15.82 dB, excellent isolation exceeding - 32.9 dB, and remarkable efficiency of 99.8%, underscoring its suitability for high-density, high-speed 6G environments. The design methodology integrates CST simulations and an RLC equivalent circuit model, substantiated by ADS simulations, with comparable reflection coefficients validating the accuracy of the models. With its compact footprint, broad bandwidth, and optimized isolation and efficiency, the proposed MIMO antenna is positioned as an ideal candidate for future 6G communication applications.

Photonic Nanomaterials for Wearable Health Solutions.

This review underscores the transformative potential of photonic nanomaterials in wearable health technologies, driven by increasing demands for personalized health monitoring. Their unique optical and physical properties enable rapid, precise, and sensitive real-time monitoring, outperforming conventional electrical-based sensors. Integrated into ultra-thin, flexible, and stretchable formats, these materials enhance compatibility with the human body, enabling prolonged wear, improved efficiency, and reduced power consumption. A comprehensive exploration is provided of the integration of photonic nanomaterials into wearable devices, addressing material selection, light-matter interaction principles, and device assembly strategies. The review highlights critical elements such as device form factors, sensing modalities, and power and data communication, with representative examples in skin patches and contact lenses. These devices enable precise monitoring and management of biomarkers of diseases or biological responses. Furthermore, advancements in materials and integration approaches have paved the way for continuum of care systems combining multifunctional sensors with therapeutic drug delivery mechanisms. To overcome existing barriers, this review outlines strategies of material design, device engineering, system integration, and machine learning to inspire innovation and accelerate the adoption of photonic nanomaterials for next-generation of wearable health, showcasing their versatility and transformative potential for digital health applications.

Adaptive Congestion Control Protocol based on Meta-Reinforcement Learning for Data Communication Networks

Adaptive Congestion Control Protocol based on Meta-Reinforcement Learning for Data Communication Networks

Applications of multi-GPU computing in large-scale fully kinetic particle-in-cell simulations of space plasma

The rapid development of an emerging computing device, the graphical processing unit (GPU), has significantly enhanced our ability to conduct full kinetic particle-in-cell (PIC) simulations in space physics. In this paper, we propose an approach that leverages multiple GPUs to facilitate large-scale PIC simulations. This method can effectively reduce data transmission frequency and latency during the computing process. The data communication between GPU devices is optimized through a combination of Message Passing Interface (MPI)-NVIDIA Collective Communications Library (NCCL) running pattern. Our implementation surpasses the expected linear acceleration, achieving superior computing performance and operational efficiency. The instances of large-scale PIC simulations are presented based on physical models of magnetic reconnection, plasma turbulence, and quasi-perpendicular shock. The importance of large-scale simulations is demonstrated in terms of grid resolution, macroparticles used per cell, and the mass ratio between ions and electrons. The multi-GPU enabled fully kinetic PIC simulation demonstrates its capability to efficiently handle large-scale PIC simulations as a crucial requirement for the study of space plasma physics.

Enhancing Security in Software Design Patterns and Antipatterns: A Framework for LLM-Based Detection

The detection of security vulnerabilities in software design patterns and antipatterns is crucial for maintaining robust and maintainable systems, particularly in dynamic Continuous Integration/Continuous Deployment (CI/CD) environments. Traditional static analysis tools, while effective for identifying isolated issues, often lack contextual awareness, leading to missed vulnerabilities and high rates of false positives. This paper introduces a novel framework leveraging Large Language Models (LLMs) to detect and mitigate security risks in design patterns and antipatterns. By analyzing relationships and behavioral dynamics in code, LLMs provide a nuanced, context-aware approach to identifying issues such as unauthorized state changes, insecure communication, and improper data handling. The proposed framework integrates key security heuristics—such as the principles of least privilege and input validation—to enhance LLM performance. An evaluation of the framework demonstrates its potential to outperform traditional tools in terms of accuracy and efficiency, enabling the proactive detection and remediation of vulnerabilities in real time. This study contributes to the field of software engineering by offering an innovative methodology for securing software systems using LLMs, promoting both academic research and practical application in industry settings.

Adaptive Event‐Triggered Finite‐Time Control of Affine Cyber‐Physical Systems Under Denial‐of‐Service (DoS) and Deception Attacks

ABSTRACTAiming at sustainable development to meet the requirements of Industry 5.0/6.0 revolutions, integrating a resilient, decentralized cyber‐tolerant control framework for large‐scale affine systems is indispensable. This work introduces an intelligent finite‐time cyber‐resilient control framework for an affine system under sensor/actuator deception and denial‐of‐service attacks. First, an adaptive disturbance observer employing a radial basis function neural network (RBF‐NN) has been formulated for smooth and fast estimations of unknown aggregated mismatched uncertainties, which include exogenous disturbance, sensor deception attack, and parametric perturbation. Later, an adaptive finite‐time control law integrated with RBF‐NN is formulated under event‐triggered action for the investigated cyber‐physical affine system. A suitable triggering condition for data communication through the sensor‐to‐controller channel has been derived using the Lyapunov approach, and input‐to‐state stability has been guaranteed under the designed control framework. Numerical simulations with in‐depth qualitative analysis have been showcased to validate the effectiveness and feasibility of the applied control methodology. The suggested control methodology ensures optimal communication medium utilization and is superior in mitigating cyber intrusions.

A Prototype "Smart" 3-Dimensionally Printed Model Showcasing Interactivity: Implementing Voice Command for the Ventricular and Cisternal Systems.

The next step in the evolution of static 3-dimensionally (3D) printed models may be the creation of "smart" models, where subcomponents can be seamlessly interacted with through a feedback mechanism, with potential applications in trainee education and patient counseling. Considering the complexity of the ventricular and cisternal systems, they were chosen for segmentation, using Materialize InPrint with outward hollowing using 2.5-mm wall thickness. After 3D printing, dedicated holes were drilled for placement of wired light emitting diodes (LEDs) in anatomical landmarks and connected to an Arduino Uno microcontroller. This was coupled to a Bluetooth transceiver for communication via an Android cellular device. C++ was used to match each LED to a particular pin number on the Arduino board. When the user verbalizes a structure, the Bluetooth sends a command to the Arduino, where the code looks for the "trigger word," subsequently sending a signal to illuminate the corresponding LED. The system requires wireless/cellular data for communication with the voice recognition engine on the Google server. The described method may serve as a prototype for when 3D printers are capable of simultaneously printing conductive material or wiring along with the main material within a model to allow for integration of feedback devices.

Securing Vehicle-to-Digital Twin Communications in the Internet of Vehicles

The current landscape of data-centric Internet of Vehicles (IoVs) encompasses a fusion of Human-driven Vehicles (HVs), Autonomous Vehicles (AVs), Road-Side Units (RSUs), and edge-based devices engaged in periodic communication. Given the stringent latency requirements inherent in vehicular communications, the emergence of edge-based vehicular Digital Twins (DTs) plays a pivotal role in problem-solving, ensuring rapid response, regulatory compliance, and seamless availability. While these communications serve as the backbone of IoV, they also create an opportune environment for cybercriminals to exploit. Vulnerabilities at the network layer facilitate intrusions, resulting in a surge of data falsification attacks in recent years. Addressing this challenge demands resilient and intelligent threat detection schemes capable of adapting to the dynamic nature of IoV. This study conducts a comprehensive examination of the vulnerabilities in Vehicle-to-Digital twin (V2DT) data communication through the lens of an attacker utilizing False Data Injection Attack (FDIA). It utilizes cutting-edge Blockchain-based decentralized storage and buffering mechanisms for vehicle dynamics data en route to edge-based DTs. Further, deep learning-powered sensor data analysis serves as an additional layer of security. Evaluation of the proposed threat detection and mitigation model demonstrates 100% tamper detection in V2DT communication, coupled with a 96% accurate classification of anomalous driving behaviors, including aggressive driving or FDIAs.

Controllable frequency tunability in constriction-based spin Hall nano-oscillators for neuromorphic computing

Abstract The capability to precisely tune auto-oscillation frequency in spin Hall nano-oscillators (SHNOs) holds great promise across diverse applications, from data communication to neuromorphic computing. This study systematically investigates the controllable frequency tunability of nano-constriction-based SHNOs across distinct operational regimes. Through the independent tuning of critical magnetodynamical parameters-effective magnetization ( μ 0 M eff ) and magnetic damping (α)-we unveil varying impacts on frequency tunability with changes in out-of-plane (OOP) field strengths and constriction widths. At large OOP fields, μ 0 M eff predominantly governs frequency tunability, yielding a current tunability of 1 GHz/mA-four times greater than observed at the lowest μ 0 M eff . At low OOP fields, although we observe a remarkably high frequency-tunability of 4 GHz mA−1, μ 0 M eff alters the onset of transition from a linear-like mode to a spin-wave bullet mode. In contrast, α primarily affects the threshold current with less impact on frequency tunability. Moreover, we demonstrate that M eff-driven frequency tuning enables control over the mutual synchronization of SHNOs, mimicking synaptic-like interactions between artificial neurons. Our findings greatly extend the versatility of SHNOs for oscillator-based neuromorphic computing and provide key insights into the intricate auto-oscillation dynamics under varying OOP fields.

Programmable photonic latch memory.

Significant advancements in integrated photonics have enabled high-speed and energy efficient systems for various applications, from data communications and high-performance computing to medical diagnosis, sensing, and ranging. However, data storage in these systems has been dominated by electronic memories that in addition to signal conversion between optical and electrical domains, necessitates conversion between analog to digital domains and electrical data movement between processor and memory that reduce the speed and energy efficiency. To date, scalable optical memory with optical control has remained an open problem. Here, we report an integrated photonic set-reset latch as a fundamental optical static memory unit based on universal optical logic gates. As a proof of concept, experimental implementation of the universal logic gates and realistic simulation of the latch are demonstrated on a programmable silicon photonic platform. Optical set, reset, and complementary outputs, scalability to a large number of memory units via the independent latch supply light, and compatibility with wavelength division multiplexing scheme and different photonic platforms enable more efficient and lower latency optical processing systems.

Generation of CW mid-infrared radiation in the mW power range and tuneable over 400 nm

Miniaturization of mid-infrared (MIR) spectroscopy sources has progressed significantly during the past two decades, but a solution able to provide full integration, high optical power and wide tuneability in the so-called atmospheric window (2.5 - 5 µm) is still missing. In this context, we investigated a broadband frequency-tuneable source relying on difference frequency generation (DFG) in a periodically poled lithium niobate (PPLN) ridge waveguide. By employing tuneable lasers for the pump and signal wavelengths emitting at around 1 µm and 1.55 µm, respectively, we were able to fully cover the ≈ 3 - 3.5 µm spectrum, thus translating the technological maturity of data communication photonic sources to the MIR wavelength band. Moreover, the use of a relatively large cross-section for the here-proposed PPLN ridge waveguide compared to commonly employed thin-film lithium niobate (TFLN) waveguides has allowed us to achieve low propagation and coupling losses together with high damage threshold, thereby allowing us to reach mW-level power in the MIR wavelength band.

Research on power plant security issues monitoring and fault detection using attention based LSTM model

OverviewFor Photo Voltaic (PV) arrays and Wind systems to operate as efficiently and effectively as possible, fault detection is essential. It is possible to improve the safety of renewable energy systems and guarantee that the service will continue uninterrupted if problem detection and diagnostics are performed in a timely and accurate manner. In general, wind power is one of the three major renewable energy sources, along with solar power and hydropower. Wind power is well distributed around the world, making it suitable to be exploited in human activities for the general welfare of society.ObjectivesA prototype security situational awareness system applicable to the power data communication network service and traffic model should be developed. This will help to successfully enhance the security and service quality of the power data communication network, effectively cope with network security threats in the new environment, and ensure the security of the power plant network access. The traffic of the main network of the existing data communication network will be combined and analyzed, and NQA traffic management algorithms will be studied and proposed. These actions will improve the SLA hierarchical service capability, the service quality of the core services carried by the backbone network, and strengthen the security capability of the new energy power plant communication network access system.MethodologyFor the purpose of this investigation, an attention-based long short-term memory (Att-LSTM) model was used for the categorization of time series actual data. The approach that has been developed is able to identify defects in photovoltaic arrays and inverters, which offers a dependable option for improving the efficiency and dependability of solar energy systems. For the purpose of evaluating the proposed method, a real-world solar energy dataset is used.ResultsThe findings acquired from this evaluation are compared to the results received from existing detection approaches such as Cryptography, Intrusion Detection System (IDS) methods, and Network Defense Schemes. The results obtained demonstrate that the suggested method surpasses current fault detection techniques, providing greater accuracy and better performance.

Predefined-time control for spacecraft rendezvous maneuver with quantized input

PurposeThe purpose of this paper is to solve the problem of adaptive predefined-time control for spacecraft rendezvous maneuver with input quantization and unknown parameters.Design/methodology/approachA significant error-shifting function is established to ensure that the limitation of the initial condition can be ignored. Then, a combination of neural networks method and minimum-learning-parameter method is used to mitigate the adverse effects caused by nonlinear system dynamics. An input quantization method is applied to improved efficiency of data communication between controller and actuator in rendezvous maneuver control system.FindingsThe simulation results verify the correctness and effectiveness of the designed control strategy which can effectively overcome the disadvantages caused by coupling nonlinear system dynamics.Originality/valueThis paper designs an adaptive predefined-time control method for spacecraft rendezvous maneuver control with input quantization based on backstepping control method.

Above Room‐Temperature Reversible Phase Transition and Nonlinear Optical Material PO(CH2CH2CH2CF3)3 with Hirshfeld Surface Analyses

The multifunctional materials with dielectric and nonlinear optical properties have attracted the attention of many scientists with potential applications in data communication, photoelectric devices, information processing, etc. Herein, one compound PO(CH2CH2CH2CF3)3 1, which underwent an obvious reversible structural phase transition, is reported. In the single‐crystal structure data, it is shown that space group of 1 changes from R3c to R3m, from one polar space group to another polar space group, and the dielectric constant has frequency dependence. The second harmonic generation (SHG) value is 0.50 at room‐temperature phase. The SHG value changes abruptly at around 349 K. The SHG value is about 1.09 at high‐temperature phase. Hirshfeld surface analyses reveal that H···F/F···H interactions make major contribution to the total forces, and the forces of the two kinds of hydrogen bonds are different at different phases.

Towards a new approach to ensure end-to-end reliability of aeronautical data communications

Abstract The aeronautical telecommunication network (ATN) aims to provide reliable end-to-end communications even for those including the air-to-ground segment and in particular for data link applications. The existing ATN, known as ATN/OSI, is based on OSI protocols since its first deployment. The OSI model implementation in ATN communicating entities causes great complexity in network management, particularly in terms of Internet network interoperability. Therefore, since 2010, the International Civil Aviation Organization (ICAO) proposed a migration to ATN over Internet protocol suite (IPS), called ATN/IPS. Thus, this research work focuses on specifying the reliability mechanisms required for air ground data link applications in future ATN/IPS. To achieve this, the transport protocols performance is assessed based on simulations using an ATN model developed considering the ICAO standards. The modeled legacy application enables to generate traffic based on real controller-pilot data link communications (CPDLC) log files from French area control centre (ACC). The air-to-ground subnetworks are characterised using time series delay induced from previously modeled VDL Mode 2 data link analysis. As proof-of-concept, CPDLC messages exchange from aircraft to controller and future applications that transmits heavier files from ground-to-board are simulated. Transport protocols performance are evaluated with respect to the most constraining requirements. The simulation results highlighted the limitations of both connection-oriented transport protocol class 4 (COTP4) and TCP. This enabled to provide a preliminary overview of a new QUIC-like reliable protocol that should meet the heterogeneous requirements of the legacy and the eventual future ATN/IPS applications.