Solution For Data Transmission Research Articles

Signal theory based encryption of faster-than-Nyquist signals for fiber and wireless transmission

New applications such as the Internet of Things, autonomous driving, Industry X.0 and many more will transmit sensitive information via fibers and over the air with envisioned data rates beyond terabits per second. Therefore, the encryption has to be simple, fast and spectrally efficient, so that the power consumption and latency are low and the scarce bandwidth is not wasted. Various encryption schemes, based on mathematical algorithms, quantum theory, chaos communication or spectral spreading below the noise level have been explored. Besides power, spectral efficiency and latency, most of these approaches face additional challenges such as limited data rates, compatibility issues with communication standards and integration. Here, we propose a signal theory based method that enables the encryption of super-signals with bandwidths of hundreds of gigahertz without any additional bandwidth. In proof-of-concept experiments we demonstrate the encryption of a 270 GBd faster than Nyquist super-signal in a 252.4 GHz bandwidth. The encryption is simple, fast and power efficient, and offers a solution for secure data transmission in existing and future communication networks.

TxCocket: an innovative solution for efficient cross-node data transmission enabled by CXL-based shared memory

TxCocket: an innovative solution for efficient cross-node data transmission enabled by CXL-based shared memory

Optimizing conventional machining process parameters for A713 aluminum alloy using Taguchi method and passive optical network for data transmission

Abstract This study focuses on optimizing machining parameters for A713 aluminum alloy, renowned for its high strength-to-weight ratio but challenging to machine with precision. Using the Taguchi method, key parameters – cutting speed, feed rate, and depth of cut – are optimized to improve surface finish, tool life, and dimensional accuracy. An L9 orthogonal array is applied to enhance experimental efficiency. Furthermore, the integration of Passive Optical Network (PON) technology facilitates real-time data monitoring and transmission, enabling continuous feedback and timely adjustments. This hybrid approach of Taguchi optimization and PON technology achieves superior machining performance, boosting surface quality and operational efficiency. The research highlights how modern data transmission solutions like PON can enhance traditional optimization methods, providing a scalable framework for advanced machining processes in industrial applications.

HfO2 Memristor-Based Flexible Radio Frequency Switches.

Flexible and wearable electronics are experiencing rapid growth due to the increasing demand for multifunctional, lightweight, and portable devices. However, the growing demands of interactive applications driven by the rise of AI reveal the inadequate connectivity of current connection technologies. In this work, we successfully leverage memristive technology to develop a flexible radio frequency (RF) switch, optimized for 6G-compatible communication systems and adaptable to flexible applications. The flexible RF switch demonstrates a low insertion loss (2 dB) and a cutoff frequency exceeding 840 GHz, and performance metrics are maintained after 106 switching cycles and 2500 mechanical bending cycles, showing excellent reliability and robustness. Furthermore, the RF switch is fully integrable with a photolithography-processable polyimide (PSPI) substrate, enabling efficient 2.5D integration with other RF components, such as RF antennas and interconnects. This technology holds significant promise to advance 6G communications in flexible electronics, offering a scalable solution for high-speed data transmission in next-generation wearable devices.

HPDH-MI: A High Payload Data Hiding Technique for Medical Images Based on AMBTC

In the realm of electronic health (eHealth) services powered by the Internet of Things (IoT), vast quantities of medical images and visualized electronic health records collected by IoT devices must be transmitted daily. Given the sensitive nature of medical information, ensuring the security of transmitted health data is paramount. To address this critical concern, this paper introduces a novel data hiding algorithm tailored for Absolute Moment Block Truncation Coding (AMBTC) in medical images, named HPDH-MI (High Payload Data Hiding for Medical Images). The proposed method embeds secret data into the AMBTC compression code inconspicuously to avoid detection by malicious users. It achieves this by first classifying AMBTC compressed blocks into four categories—flat, smooth, complex I, and complex II—using three predetermined thresholds. A 1-bit indicator, based on the proposed grouping strategy, facilitates efficient and effective block classification. A data embedding strategy is applied to each block type, focusing on block texture and taking into account the symmetric features of the pixels within the block. This approach achieves a balance between data hiding capacity, image quality, and embedding efficiency. Experimental evaluations highlight the superior performance of HPDH-MI. When tested on medical images from the Osirix database, the method achieves an average image quality of 31.22 dB, a payload capacity of 225,911 bits, and an embedding efficiency of 41.78%. These results demonstrate that the HPDH-MI method not only significantly increases the payload for concealing secret data in AMBTC compressed medical images but also maintains high image quality and embedding efficiency. This makes it a promising solution for secure data transmission in telemedicine, addressing the challenges of limited bandwidth while enhancing steganographic capabilities in eHealth applications.

Low-Power Design of Fully Digital BPSK Modulator and Demodulator Utilizing Nanoscale FinFET for Smart Implants

This study aims to create a high-speed, low-power data transmission solution for implantable medical devices based on cutting-edge FinFET technology. The work examines the application of Binary Phase Shift Keying (BPSK) modulation through a transmission gate design, which provides an optimal blend of low resistance, high-speed performance, and minimal power consumption. Additionally, the work includes the design of a sine-to-square wave converter and a modulating signal generator. FinFET is employed owing to its high switching speed, low power consumption, low leakage current, and excellent tolerance of short channel effects. The design exhibits a steady electric field at the source end, a high electrostatic potential, and an improved ON current at low work function values using Sentaurus TCAD simulations of a 20nm FinFET, allowing high-speed data modulation in smart implants. A nonoverlapping phase generator, a low-power, current-starved gated ring oscillator, a frequency divider utilizing a True Single Phase Clock D-Flip-flop, and an XOR gate serving as a pulse counter are all featured in the design of the BPSK demodulator. This work is significant for its ability to drastically reduce power consumption to 1.75μW while retaining high data transmission speeds, making it perfect for next-generation implantable medical devices. With a 0.9 V power supply, this FinFET-based BPSK modulator and demodulator achieve a far lower power consumption than conventional CMOS-based designs, which increases device longevity and efficiency in settings with limited resources.

I-WASP—Intelligent and Wireless Technologies for Automatically Generated Shift Protocols in Conventional Tunnelling

The research project i‑WASP, a collaboration of specialized German and Austrian companies and research institutions, looks at the question of how complex processes on a conventional tunneling construction site can be automatically recorded, analyzed, and classified through the development of innovative solutions for data acquisition, transmission, and processing. A trained artificial intelligence will be able to automatically produce accurate minute diagrams based on accelerometer and positional data gathered from the construction machines.

Mitigating impulsive noise in airborne PLC: Introducing the S-SAMP-PV algorithm for MIMO OFDM systems

Power Line Communication (PLC) offers an efficient solution for data transmission over electrical power lines, presenting a promising avenue for in-flight communication in More Electrical Aircraft (MEA). A significant challenge in airborne PLC is Impulsive Noise (IN), which hampers transmission reliability. Existing noise mitigation methods, while valuable, face limitations in airborne settings due to computational intensiveness and sub-optimal sparse recovery performance. This paper introduces the Structured Sparsity Adaptive Matching Pursuit with Preliminary partial support estimation and Variable step-size (S-SAMP-PV) algorithm, devised for Multiple-Input-Multiple-Output (MIMO) systems. It uniquely pre-estimates partial support of sparse IN signals, enabling adaptive convergence without prior sparsity knowledge. This methodology substantially reduces computational demands, satisfying stringent real-time requirements of airborne applications. In simulation, the S-SAMP-PV algorithm exhibits marked advantages over traditional algorithms such as Orthogonal Matching Pursuit (OMP). Specifically, it realizes an approximate 81.3% reduction in Normalized Mean Square Error (NMSE) and demonstrates around 37% improvement in computational efficiency relative to OMP. Moreover, its Bit Error Rate (BER) performance at high Signal to Noise Ratio (SNR) approaches the ideal scenario where IN is assumed to be perfectly eliminated. These results emphasize the promise of S-SAMP-PV in elevating the performance of airborne PLC systems by efficient IN mitigation.

Connection-Aware Digital Twin for Mobile Adhoc Networks in the 5G Era

5G Mobile Adhoc Networks (5G-MANETs) are a popular and agile solution for data transmission in local contexts while maintaining communication with remote entities via 5G. These characteristics have established 5G-MANETs as versatile communication infrastructures for deploying contextual applications, leveraging physical proximity while exploiting the possibilities of the Internet. As a result, there is growing interest in exploring the potential of these networks and their performance in real-world scenarios. However, the management and monitoring of 5G-MANETs are challenging due to their inherent characteristics, such as highly variable topology, unstable connections, energy consumption of individual devices, message routing, and occasional inability to connect to 5G. Considering these challenges, the proposed work aims to address real-time monitoring of 5G-MANETs using a connection-aware Digital Twin (DT). The approach provides two main functions: offering a live virtual representation of the network, even in scenarios where multiple nodes lack 5G connectivity, and estimating the performance of the infrastructure, enabling the specification of customized conditions. To achieve this, a communication architecture is proposed, analyzing its components and defining the involved processes. The DT is implemented and evaluated in a laboratory setting, assessing its accuracy in representing the physical network under varying conditions of topology and Internet availability. The results show 100% accuracy for the DT in fully connected topologies, with ultra-low latency averaging under 80 ms, and suitable performance in partially connected contexts, with latency averages below 3000 ms.

Digital Phenotyping of Mental and Physical Conditions: Remote Monitoring of Patients Through RADAR-Base Platform.

The use of digital biomarkers through remote patient monitoring offers valuable and timely insights into a patient's condition, including aspects such as disease progression and treatment response. This serves as a complementary resource to traditional health care settings leveraging mobile technology to improve scale and lower latency, cost, and burden. Smartphones with embedded and connected sensors have immense potential for improving health care through various apps and mobile health (mHealth) platforms. This capability could enable the development of reliable digital biomarkers from long-term longitudinal data collected remotely from patients. We built an open-source platform, RADAR-base, to support large-scale data collection in remote monitoring studies. RADAR-base is a modern remote data collection platform built around Confluent's Apache Kafka to support scalability, extensibility, security, privacy, and quality of data. It provides support for study design and setup and active (eg, patient-reported outcome measures) and passive (eg, phone sensors, wearable devices, and Internet of Things) remote data collection capabilities with feature generation (eg, behavioral, environmental, and physiological markers). The back end enables secure data transmission and scalable solutions for data storage, management, and data access. The platform has been used to successfully collect longitudinal data for various cohorts in a number of disease areas including multiple sclerosis, depression, epilepsy, attention-deficit/hyperactivity disorder, Alzheimer disease, autism, and lung diseases. Digital biomarkers developed through collected data are providing useful insights into different diseases. RADAR-base offers a contemporary, open-source solution driven by the community for remotely monitoring, collecting data, and digitally characterizing both physical and mental health conditions. Clinicians have the ability to enhance their insight through the use of digital biomarkers, enabling improved prevention, personalization, and early intervention in the context of disease management.

A Case Study on the Integration of Powerline Communications and Visible Light Communications from a Power Electronics Perspective.

This paper presents a dual-purpose LED driver system that functions as both a lighting source and a Visible Light Communication (VLC) transmitter integrated with a Powerline Communication (PLC) network under the PRIME G3 standard. The system decodes PLC messages from the powerline grid and transmits the information via LED light to an optical receiver under a binary phase shift keying (BPSK) modulation. The load design targets a light flux of 800 lumens, suitable for LED light bulb applications up to 10 watts, ensuring practicality and energy efficiency. The Universal Asynchronous Receiver-Transmitter (UART) module enables communication between the PLC and VLC systems, allowing for an LED driver with dynamic control and real-time operation. Key signal processing stages are commented and developed, including a hybrid buck converter with modulation capabilities and a nonlinear optical receiver to regenerate the BPSK reference signal for VLC. Results show a successful prototype working under a laboratory environment. Experimental validation shows successful transmission of bit streams from the PLC grid to the VLC setup. A design guideline is presented in order to dictate the design of the electronic devices involved in the experiment. Finally, this research highlights the feasibility of integrating PLC and VLC technologies, offering an efficient and cost-effective solution for data transmission over existing infrastructure.

Analysis of Ultra-Dense Wavelength Division Multiplexing (UDWDM) in a Passive Optical Network (PON)

Passive Optical Networks (PONs) are essential in optical communications to meet the increasing demand for network capacity and connected users, ensuring reliable and adaptable connections for data transmissions. These networks also simplify infrastructures and efficiently utilize energy by eliminating the need for active devices. In this context, Ultra- Dense Wavelength Division Multiplexing (UDWDM) is one of the most prominent solutions for data transmission. This technology uses the narrow separation between channels from 25GHz and even as small as 6.25GHz to increase the transmission capacity. This document analyzes the performance of UDWDM considering the transmission of three simultaneous channels in a PON network for which three different scenarios have been considered for the analysis with the following parameters: i) transmission speed from 10 Gbps to 17 Gbps, ii) the distance from 10 km to 20 km and iii) the separation between channels of 15GHz, 20GHz, and 25GHz. The performance metrics for the analyzed scenario are the bit error rate and the eye diagram. To ensure the reception of the transmitted channel, an analysis of raised cosine and Gauss filters is also discussed. Including these filters allows for verifying whether their use enhances the performance of the channels compared to transmission without a filter. This is crucial for understanding how filters can optimize the quality and reliability of data transmission in a PON, which is of great importance in environments where high efficiency and connection quality are required.

Rancang Bangun Double Hull Unmanned Surface Vehicle dengan Pengiriman Data Melalui LoRa

In the era of maritime technology, Unmanned Surface Vehicles (USVs) are becoming the main vehicles on the water surface, controlled from land and capable of transmitting live data. USVs have a wide range of survey and exploration applications, relying on hydrographic knowledge for accurate mapping. USV development has adopted a double hull design, improving stability and wave resistance. Survey data transmission requires advanced technology, with Long Range (LoRa) technology being the solution for long distance data transmission with low power consumption. LoRa utilisation is expected to increase the efficiency of USVs in hydrographic surveys. The research and development (R&D) research method is used as the main approach in this research with the aim of creating and developing USVs that can increase efficiency in hydrographic surveys. The USV is equipped with an RPM sensor and flowmeter used to monitor the movement control of the USV in the waters. In addition, a depth detector is used to monitor the topography of the seabed. In order for this USV to run using a remote control that is controlled from land. The USV drive consists of a BLDC motor connected to the propeller and a servo motor connected to the ship's rudder. Furthermore, the data taken from the sensor is sent via LoRa to be delivered to the ground station. From the USV test results, it was found that the data transmission range with LoRa Ra-02 under Line of sight (LOS) conditions was 340 metres while under Non Light of sight (NLOS) conditions it was around 200 metres. The average speed travelled by USV is 0.616 km/hour in operational survey conditions and can be faster in non-operational survey conditions. The error value of the RPM sensor is obtained at 1.604% with a reading accuracy of 98.936%.

Efficient Data Transfer and Multi-Bit Multiplier Design in Processing in Memory

Processing in Memory based on memristors is considered the most effective solution to overcome the Von Neumann bottleneck issue and has become a hot research topic. The execution efficiency of logical computation and in-memory data transmission is crucial for Processing in Memory. This paper presents a design scheme for data transmission and multi-bit multipliers within MAT (a data storage set in MPU) based on the memristive alternating crossbar array structure. Firstly, to improve the data transfer efficiency, we reserve the edge row and column of the array as assistant cells for OR AND (OA) and AND data transmission logic operations to reduce the data transfer steps. Furthermore, we convert the multipliers into multi-bit addition operations via Multiple Input Multiple Output (MIMO) logical operations, which effectively improves the execution efficiency of multipliers. PSpice simulation shows that the proposed data transmission and multi-bit multiplier solution has lower latency and power consumption and higher efficiency and flexibility.

Enhancing Data Security through Machine Learning-based Key Generation and Encryption

In an era marked by growing concerns about data security and privacy, the need for robust encryption techniques has become a matter of paramount importance. The primary goal of this study is to protect sensitive information during transmission while ensuring efficient and reliable decryption at the receiver's side. To generate robust and unique cryptographic keys, the proposed approach trains an autoencoder neural network based on hashing and optionally generated prime numbers in the MNIST dataset. The key serves as the foundation for secure communication. An additional security layer to the cryptographic algorithm passing through the first ciphertext, was employed utilizing the XORed and Blum-Blum-Shub (BBS) generators to make the system resistant to various types of attacks. This approach offers a robust and innovative solution for secure data transmission, combining the strengths of autoencoder-based key generation and cryptographic encryption. Its effectiveness is demonstrated through testing and simulations.

Video Steganography with DNA and Complex Frames

A modern approach to video steganography combines DNA embedding of encrypted data with intelligent frame selection, enhancing data security during transmission. By creating fake DNA for frame selection and embedding it into strategically chosen frames, termed complex frames this method ensures robust encryption and imperceptibility, offering a promising solution for secure internet data transmission. KeyWords: Complex frames, DNA, embedding, encryption, stego video.

Adjustable random linear network coding (ARLNC): a solution for data transmission in dynamic IoT computational environments

In mobile computing environments, most IoT devices connected to networks experience variable error rates and possess limited bandwidth. The conventional method of retransmitting lost information during transmission, commonly used in data transmission protocols, increases transmission delay and consumes excessive bandwidth. To overcome this issue, forward error correction techniques, e.g., Random Linear Network Coding (RLNC) can be used in data transmission. The primary challenge in RLNC-based methodologies is sustaining a consistent coding ratio during data transmission, leading to notable bandwidth usage and transmission delay in dynamic network conditions. Therefore, this study proposes a new block-based RLNC strategy known as Adjustable RLNC (ARLNC), which dynamically adjusts the coding ratio and transmission window during runtime based on the estimated network error rate calculated via receiver feedback. The calculations in this approach are performed using a Galois field with the order of 256. Furthermore, we assessed ARLNC's performance by subjecting it to various error models such as Gilbert Elliott, exponential, and constant rates and compared it with the standard RLNC. The results show that dynamically adjusting the coding ratio and transmission window size based on network conditions significantly enhances network throughput and reduces total transmission delay in most scenarios. In contrast to the conventional RLNC method employing a fixed coding ratio, the presented approach has demonstrated significant enhancements, resulting in a 73% decrease in transmission delay and a 4 times augmentation in throughput. However, in dynamic computational environments, ARLNC generally incurs higher computational costs than the standard RLNC but excels in high-performance networks.

Phase-separated porous nanocomposite with ultralow percolation threshold for wireless bioelectronics.

Realizing the full potential of stretchable bioelectronics in wearables, biomedical implants and soft robotics necessitates conductive elastic composites that are intrinsically soft, highly conductive and strain resilient. However, existing composites usually compromise electrical durability and performance due to disrupted conductive paths under strain and rely heavily on a high content of conductive filler. Here we present an in situ phase-separation method that facilitates microscale silver nanowire assembly and creates self-organized percolation networks on pore surfaces. The resultant nanocomposites are highly conductive, strain insensitive and fatigue tolerant, while minimizing filler usage. Their resilience is rooted in multiscale porous polymer matrices that dissipate stress and rigid conductive fillers adapting to strain-induced geometry changes. Notably, the presence of porous microstructures reduces the percolation threshold (Vc = 0.00062) by 48-fold and suppresses electrical degradation even under strains exceeding 600%. Theoretical calculations yield results that are quantitatively consistent with experimental findings. By pairing these nanocomposites with near-field communication technologies, we have demonstrated stretchable wireless power and data transmission solutions that are ideal for both skin-interfaced and implanted bioelectronics. The systems enable battery-free wireless powering and sensing of a range of sweat biomarkers-with less than 10% performance variation even at 50% strain. Ultimately, our strategy offers expansive material options for diverse applications.

Wireless strain-field monitoring system for motion recognition via direct-ink-writing sensor-array

Large-area strain-monitoring is highly desired in various engineering applications. Despite the fact that the commercial strain gauges have been successfully used for strain measurement, there is still big challenge for this manually-pasting based solution especially for large-area monitoring. First, the wires of multiple commercial strain gauges for large-area monitoring will exhibit remarkably abundant and complex, and eventually reduce the reliability and convenience of the measurement system. Second, the commercial terminal instrument is usually function-redundant with complicated electronic configuration to match the low-sensitive metal-foil strain gauge. Meanwhile, the wire-based signal acquisition and processing system substantially limit the compatibility for moving objects. Here we take the advantages of direct-ink-writing (DIW) 3D printed sensor array, thereby propose a wireless-based solution for large-area strain field measurement, data transmission and processing. The sensitivity of the printed sensing unit is one order of magnitude higher than that of traditional strain gauges, while the sensor array layout is optimized for compatibility to the strain orientation and detecting domain. A high-frequency signal acquisitor is designed and assembled with a wireless transmitter for rapid multi-point strain detection and real-time data transmission. A remote data receiver and processing terminal are developed and integrated for twin-visualization of the detected strain field. In particular, the detected strain data are processed to construct a cascaded support-vector-machine (SVM) classifier, that successfully identify motion states of moving vehicles in high precision. This study provides insight into exploring the advantages of DIW printed sensor arrays combined with specifically-designed wireless system and strain-based dynamic recognition.

Embedded Highway Health Maintenance System Based on Digital Twin Superposition Model

INTRODUCTION: The highway monitoring data acquisition technology develops quickly. Based on the traditional form of continuous monitoring, intelligent management system focuses on digital and wireless transmission. In the operation of highway maintenance system, each system is independent of each other, lacking of effective connection. Moreover, the level of continuous monitoring is obviously backward, which restricts the development of highway health monitoring. It is necessary to further study the level of integration to achieve the real-time tracking and the monitoring of highway’s healthy development.
 OBJECTIVES: This paper presents a highway health maintenance system based on digital twin technology, which intends to provide a solution for efficient, stable and automatic data transmission of the highway operation and maintenance management.
 METHODS: The output of the algorithm after the noise reduction effect is compared with the data containing the generated noise. The average number of nodes is set before running the algorithm to determine the actual length of the vertical position of the embedded sensor (calculating the position of two sensor nodes). The vertical length can be referred to the combined noise level formed and the combined test to determine the position. With the help of the overall data, it can be seen that the Kalman low-pass filtering algorithm can well describe the trend of the received signal and retain the key information in the received signal.
 RESULTS: It proves that the algorithm in this paper has fast calculation speed and high efficiency, and the basic working principle is simple. Thus, it is a good data denoising solution.
 CONCLUSION: The output in the paper ensures the data exchange and the update of the whole life cycle of highway, defines the digital twin entity model, and provides a reference for the establishment of information and data network.