Data Transmission Module Research Articles

Real-world effectiveness of remote monitoring of the international normalized ratio in patients undergoing heart valve replacement using the CoaguChek® INRange system with a remote data transmission module

Background. Systems for self-determination of international normalized ratio (INR) may increase adherence to regular monitoring of hemostasis parameters in patients taking vitamin K antagonists. However, evidence of the effectiveness of self-determination of INR in real-world clinical practice in Russia is limited. Aim. To compare the effectiveness of remote monitoring of INR using the CoaguChek® INRange coagulometer in patients who have undergone mechanical heart valve implantation with traditional INR control by measuring it in the outpatient department laboratory. Materials and methods. A prospective study enrolled 101 patients receiving warfarin after a mitral or aortic mechanical heart valve prosthesis implantation. In Group 1 (n=51), conventional laboratory INR control was used; in Group 2 (n=50), patients monitored INR by themselves using a CoaguChek® INRange coagulometer. The follow-up duration was 1 year. Results. Patients in Groups 1 and 2 did not differ in INR: 2.62 [2.45; 2.85] vs 2.6 [2.5; 2.8], respectively (data are presented as the median [1st to 3rd quartile]), and the time of INR in the therapeutic range: 66.7% [50; 78.6] vs 67.7% [53.2; 80.6]. The intervals between INR determinations in Group 1 were significantly longer: 30.5 [29.5; 31] days vs 20 [13; 25] days. Conclusion. The CoaguChek® INRange system enables remote patient monitoring during continuous warfarin therapy and achievement of the target INR with a time in the therapeutic range comparable to that for patients with INR measurements in outpatient laboratories.

Sub-1 GHz RF-based Energy-efficient Sensor Node for Secure Communication in Low-power IoT and Embedded Applications

Background: The Internet of Things (IoT) devices consist of a microcontroller unit for data processing, a low-power wireless radio module for data transmission, and various sensors for data collection. The sensor nodes and processing devices used in the Internet of Things are resource-constrained, with power consumption and security being the two most critical parameters. Objective: This paper addresses the challenges of power consumption and security in IoT scenarios. It presents a low-power and secure heterogeneous multicore sensing architecture designed for low-power IoT and wireless sensor networks. The architecture comprises a sensing and control subsystem, an information processing unit, and a wireless communication module. Methods: The architecture uses a microcontroller unit based on ARM Cortex M4, a low-power sub-1 GHz RF-compliant communication radio, and a few sensors. The proposed architecture has been implemented and tested using the Contiki Operating System. Results: The implemented sensor node architecture demonstrated performance efficiency, lower energy consumption, and higher security. Conclusion: By leveraging efficient power management, data transmission strategies, and cryptographic security, the architecture contributes to developing energy-efficient and secure IoT devices.

EVM-Aware relay path decision with hierarchical modulation for reliable data transmission in cooperative relay networks

This paper presents a novel approach of integrating an error vector magnitude with hierarchical modulation in which HM dynamically adjusts modulation schemes to optimize spectral efficiency under varying channel conditions. Relay nodes are strategically selected based on Signal-to-Noise Ratio (SNR), with a focus on prioritizing high-SNR relays to ensure reliable data forwarding. To further enhance the reliability of the communication system, an EVM-aware relay path decision is proposed which selects the most suitable path based on EVM, considering the quality of the transmitted signal. By considering both SNR-based relay selection and EVM-aware path selection, the proposed system aims to minimize errors and optimize the overall performance of the network. The effectiveness of the proposed system is evaluated through extensive simulations in MATLAB, demonstrating that the proposed adaptive modulation scheme significantly outperforms fixed schemes, particularly under fluctuating channel conditions, ensuring robust and reliable communication across different SNR levels. This shows the potential of the proposed protocol to enhance performance in dynamic, making it particularly relevant for applications where reliable data transfer is critical.

High-performance machine-learning-based calibration of low-cost nitrogen dioxide sensor using environmental parameter differentials and global data scaling

Accurate tracking of harmful gas concentrations is essential to swiftly and effectively execute measures that mitigate the risks linked to air pollution, specifically in reducing its impact on living conditions, the environment, and the economy. One such prevalent pollutant in urban settings is nitrogen dioxide (NO2), generated from the combustion of fossil fuels in car engines, commercial manufacturing, and food processing. Its elevated levels have adverse effects on the human respiratory system, exacerbating asthma and potentially causing various lung diseases. However, precise monitoring of NO2 requires intricate and costly equipment, prompting the need for more affordable yet dependable alternatives. This paper introduces a new method for reliably calibrating cost-effective NO2 sensors by integrating machine learning with neural network surrogates, global data scaling, and an expanded set of correction model inputs. These inputs encompass differentials of environmental parameters (such as temperature, humidity, atmospheric pressure), as well as readings from both primary and supplementary low-cost NO2 detectors. The methodology was showcased using a purpose-built platform housing NO2 and environmental sensors, electronic control units, drivers, and a wireless communication module for data transmission. Comparative experiments utilized NO2 data acquired during a five-month measurement campaign in Gdansk, Poland, from three independent high-precision reference stations, and low-cost sensor data gathered by the portable measurement platforms at the same locations. The numerical experiments have been carried out using several calibration scenarios using various sets of calibration input, as well as enabling/disabling the use of differentials, global data scaling, and NO2 readings from the primary sensor. The results validate the remarkable correction quality, exhibiting a correlation coefficient exceeding 0.9 concerning reference data, with a root mean squared error below 3.2 µg/m3. This level of performance positions the calibrated sensor as a dependable and cost-effective alternative to expensive stationary equipment for NO2 monitoring.

Research on the Progress of Modulation Technology and Data Transmission Technology in Underwater Wireless Optical Communication

Recently, as an efficient data transmission method, underwater wireless optical communication has become one of the key research topics in the fields of military communication and underwater surveillance. Modulation technology and technology in the transmission process play a key role in the UWOC system, which directly affects the performance, accuracy and safety of the system. The advanced modulation techniques from Intensity Modulation (IM), Pulse Modulation, Quadrature Amplitude Modulation (QAM), and Quadrature Phase Shift Keying (QPSK) to the combination of multiple modulation techniques, as well as the development process of modulation technology in optical encryption, are summarized in this paper. In terms of data transmission technology, the technological progress process from simplex to full-duplex communication is summarized. Finally, a reference solution is proposed to solve the challenges in realizing the underwater communication goals of long-distance, high-speed, low-bit error rate, and large-capacity underwater communication at the same time, and future development is prospected.

Rancang Bangun Sistem Monitoring Kebocoran ydrofluorocarbon (HFC) pada Cold Storage System Berbasis IoT di Kapal AHTS

This research aims to design and developing a hydrofluorocarbon (HFC) leak monitoring system in the cold storage cooling system on the Anchor Handling Tug Supply (AHTS) ship by utilizing Internet of Things (IoT) technology. HFC leaks in cooling systems can cause environmental hazards and operational inefficiencies. The proposed system integrates sensors and IoT devices to continuously monitor HFC levels, detect leaks in real-time, and provide instant alerts to crew and management. The system architecture includes sensors for HFC detection, a microcontroller for data processing, and a communication module for data transmission to a central monitoring platform. Real-time data can be accessed through a user-friendly interface, enabling timely intervention and maintenance. Initial testing shows that the system is effective in identifying and reporting HFC leaks, potentially reducing environmental impacts and improving the safety and efficiency of cold storage operations on AHTS vessels. Further development and extensive field testing are planned to refine the system and validate its performance across a wide range of operational conditions.

Advanced design of target-driven self-powered sensor assisted by cascade catalytic strategy

In this work, a self-powered microsensor platform based on enzyme biofuel cells (EBFCs) was developed for intelligent monitoring of disease markers miRNA-451. The cascade catalysis system constructed by using the strategy of enzyme-like ZIF-8 nanocapsule incorporation with biological enzymes, which could simultaneously take into account the specificity of biological enzymes and the high activity of nano-enzymes, significantly promoted the electron transfer between glucose and the bio-anode surface, and improved the sensitivity and stability of the sensing system. Meanwhile, the target-triggered hybridization chain reaction (HCR) amplification strategy to achieve exponential signal amplification based on accurate recognition, and jointly improve the detection sensitivity. As expected, the micro-sensor platform has a wide linear range of 0.5–1.0 fmol/L with a low limit of detection (LOD) of 0.13 fmol/L (S/N = 3) and exhibits excellent selectivity, reproducibility and stability in interference assays under optimal detection conditions. The designed self-powered system is simple to construct, easy to transport and the data transmission mode is intelligent and controllable, which is expected to be used in basic biochemical research, clinical diagnosis and environmental monitoring.

LUOJIA Explorer: An Auto-UAV for Unexposed Space Exploration

Abstract. With the advancement of technology, unexposed spaces have emerged as a new type of strategic area, attracting significant attention from researchers. These spaces often present complex environments, such as extreme lighting variations, irregular geometries, and the lack of external positioning signals, making human entry hazardous. Fortunately, advancements in robotics and artificial intelligence have enabled unmanned systems to mitigate these challenges, allowing for safer exploration of unexposed spaces. UAVs, as vital unmanned system platforms, are extensively used across various industrial scenarios and are particularly effective for detecting unexposed spaces. To facilitate automated exploration of these areas, we propose the autonomous UAV system, LUOJIA Explorer. Equipped with a LiDAR sensor, data transmission module, and external anti-collision devices to protect the aircraft, LUOJIA Explorer is designed for effective unexposed space detection. We have integrated a laser point cloud positioning and mapping subsystem, along with a planning and control subsystem, into the LUOJIA Explorer. Through both simulation and actual experiments, the Luojia Explorer has demonstrated the capability to achieve stable flight based on self-positioning in unexposed spaces, with an exploration efficiency of 88.83 m3/s.

A smart WSNs node with sensor computing and unsupervised One-Class SVM classifier for machine fault detection

This paper proposed a novel machine fault detection method based on a smart WSNs node with sensor computing and unsupervised learning. Firstly, sensor computing mode, which achieves machine fault detection on the WSNs node, has been employed to address the limitations of raw data transmission mode, such as huge payload transmission data and node energy consumption. Secondly, a fault classifier based on unsupervised one-class support vector machine (OC-SVM) is designed to overcome the drawbacks of supervised learning, like the requirement of myriad labeled samples for model training. Finally, a smart WSNs node with sensor computing and an unsupervised OC-SVM classifier is developed and fabricated as test beds. A set of experiments has been conducted to verify the proposed method and smart node. The results show that they can reduce 99% of payload transmission data and about 5% of node energy consumption while maintaining acceptable detection accuracy (above 99.2%).

Sticker-Type Remote Monitoring System for Early Risk Detection of Catheter Associated Urinary Tract Infections.

A substantial number of critically ill patients in intensive care units (ICUs) rely on indwelling urinary catheters (IDCs), demanding regular monitoring of urine bags. This process increases the workload for healthcare providers and elevates the risk of exposure to contagious diseases. Moreover, IDCs are a primary cause of catheter-associated urinary tract infections (UTIs) in ICU patients whose delayed detection can have life-threatening complications. To address this, we have developed a Sticker Type Antenna for Remote Sensing (STARS) system capable of measuring urine flow rate and conductivity as early-risk markers for UTIs, alongside tracking patients' urine bag status to facilitate medical automation for healthcare providers. STARS comprises a simple, low-cost, disposable antenna module for contactless measurements of urine volume and conductivity, and a reusable wireless module for real-time data transmission. Systematic studies on STARS revealed its stable performance within physiologically relevant ranges of urine volume (0 to 2000 ml) and conductivity (5 to 40 mS/cm) in urine bags. As a proof-of-concept, STARS was tested in artificially created healthy and infected urine specimens to validate its non-contact sensing performance in detecting the onset of UTIs in catheterized patients within a hospital-like environment. STARS represents the first application of a real-time, contactless, wireless monitoring platform for simultaneous urine bag management and early risk detection of UTIs.

REMOTE VEHICLE DIAGNOSTIC SYSTEM DEVELOPMENT BASED ON THE INTERNET OF THINGS TECHNOLOGY

Background. Advanced artificial intelligence and IoT gateways are working together in the automotive industry to predict potential vehicle problems by analysing sensor data and optimizing quality control processes. Manufacturers can detect anomalies, improve product reliability, and eliminate manufacturing defects or malfunctions in advance. Predictive analytics also lead to improved fuel efficiency, performance and overall vehicle reliability. Objective. The purpose of this work is to develop a model for remote diagnosis of vehicle faults using a Raspberry Pi model B microcomputer and a SIM7600G-H GSM module. Configure data modules, install the necessary software and configure it, demonstrate step-by-step actions, and perform diagnostics and testing of this module for data transmission. Methods. A prototype was created on the basis of Raspberry Pi 4. and provides monitoring of machine operation in remote mode using the SIM7600E-H LTE Cat-4 4G/3G module. The design has small dimensions, easy installation, requires only initial adjustment and has a wide range of improvements. Results. This prototype uses a diagnostic OBD-II car scanner ELM327 with Bluetooth connection support, a Raspberry PI 4 model B microcomputer with 8 GB of RAM, 4 USB connectors (2 ports type USB3 and 2 ports type USB2), a Gigabit Ethernet port, a USB-C power supply port, and two micro HDMI 4K display connectors. On top of the module there are 48 pins (contacts) with which you can connect modules of different types and directions. The SIM7600G-H communication module is connected to these pins. The last element of the prototype is the SIM card of one of the telephone service providers and the micro SD card, which will act as the main memory element on which the operating system will be written and data will be stored. Conclusions. The article proposes the development of a device model using Internet of Things technologies, which is capable of providing remote diagnosis of car malfunctions. This model is based on the use of the SIM7600G-H module, which provides data transmission through the mobile network. The developed model allows you to read data from various car sensors, as well as transfer this data to a remote device for further analysis. This makes it possible to quickly detect malfunctions and make timely decisions on their correction.

Cloud platform-based design of automatic monitoring and alarm system for integrated meteorological observation

This study designs an automatic monitoring and alarm system for integrated meteorological observation based on the cloud platform to improve the automatic monitoring ability for integrated meteorological observation and the operation and maintenance management level of meteorological departments. A meteorological data collector was used to collect comprehensive meteorological observation data, transmit the collected comprehensive meteorological observation data to the main control processing module, preprocess the comprehensive meteorological observation data, and finally transmit the pre-processed comprehensive meteorological observation data to the cloud service module through the data transmission module. In the cloud service module, cloud computing and an improved K-means algorithm were used to analyze the comprehensive meteorological observation data, mine abnormal comprehensive meteorological observation data, and send alarm information to the application module. Users can view the comprehensive meteorological observation data in the cloud server on the client in real-time, thus realizing automatic monitoring and alarm for comprehensive meteorological observation. Results show that the average transmission rate of various meteorological comprehensive observation data transmitted by the system is as high as 99.48%, thereby achieving clustering of abnormal meteorological comprehensive observation data. Upon detecting abnormal meteorological data, the system sends alarm information to the mobile phone client to complete the monitoring of abnormal meteorological information.

A Low-Cost Energy Monitoring System with Universal Compatibility and Real-Time Visualization for Enhanced Accessibility and Power Savings

Energy management is important for both consumers and utility providers. Utility providers are concerned with identifying and reducing energy wastage and thefts. Consumers are interested in reducing their energy consumption and bills. In Pakistan, residential and industrial estates account for nearly 31,000 MW of the maximum total demand, while the transmission and distribution capacity has stalled at about 22,000 MW. This 9000 MW gap in demand and supply, as reported in 2022, has led to frequent load shedding. Although the country now has an excess generation capacity of about 45,000 MW, the aging transmission and distribution network cannot deliver the requisite power at all times. Hence, electricity-related problems are likely to continue for the next few years in the country and the same is true for other low- and middle-income countries (LMICs). Several energy monitoring systems (EnMS) have been proposed, but they face limitations in terms of cost, ease of application, lack of universal installation capability, customization, and data security. The research below focused on the development of an economical, secure, and customizable real-time EnMS. The proposed EnMS comprises low-cost hardware for gathering energy data with universal compatibility, a secured communication module for real-time data transmission, and a dashboard application for visualization of real-time energy consumption in a user-preferred manner, making the information easily accessible and actionable. The experimental results and analysis revealed that approximately 40% cost savings in EnMS development could be achieved compared to other commercially available EnMSs. The performance of the EnMS hardware was evaluated and validated through rigorous on-site experiments. The front-end of the EnMS was assessed through surveys and was found to be interactive and user-friendly for the target clients. The developed EnMS architecture was found to be an economical end-product and an appropriate approach for small and medium clients such as residential, institutional, commercial, and industrial consumers, all on one platform.

Smart Temperature and Humidity Tracker

The HC-05 Bluetooth module for data transmission, the things of network (IoT) smart temperature and humidity tracker presents an economical and effective Wireless Bluetooth Temperature and Humidity Monitoring System. Real-time temperature is offered by the system in a variety of situations, including homes, workplaces, warehouses, and agricultural settings. A dependable communication link between the monitoring devices and a central monitoring station—usually a computer or smartphone—is supplied by the HC-05 Bluetooth communication. The particulars for monitoring system's creation and implementation are covered in detail, with an emphasis on how the HC-05 Bluetooth modules are integrated with temperature and humidity sensors. The system design makes sure that data is acquired, processed, and transmitted in order for users to obtain accurate and timely environmental data from a distance. Important aspects of the system. The Wireless Bluetooth Temperature and moisture sensing system, in conjunction with Network of Things(IOT)-based smart temperature and humidity tracker, essentially ushers in a new era of environmental monitoring. By combining hardware and software components in a seamless manner and adding strong security measures, the system gives users practical information about temperature dynamics, which in turn promotes a more sustainable and healthy living and working environment. Keywords—HC-05 Bluetooth, NodeMCU ESP8266, DHT11 sensor, Temperature monitoring, Thingspeak.

FATE: A Flexible FPGA-Based Automatic Test Equipment for Digital ICs

The limits of chip technology are constantly being pushed with the continuous development of integrated circuit manufacturing processes and equipment. Currently, chips contain several billion, and even tens of billions, of transistors, making chip testing increasingly challenging. The verification of very large-scale integrated circuits (VLSI) requires testing on specialized automatic test equipment (ATE), but their cost and size significantly limit their applicability. The current FPGA-based ATE is limited in its scalability and support for few test channels and short test vector lengths. As a result, it is only suitable for testing specific chips in small-scale circuits and cannot be used to test VLSI. This paper proposes a low-cost hardware and software solution for testing digital integrated circuits based on design for testability (DFT) on chips, which enables the functional and performance test of the chip. The solution proposed can effectively use the resources within the FPGA to provide additional test channels. Furthermore, the round-robin data transmission mode can also support test vectors of any length and it can satisfy different types of chip test projects through the dynamic configuration of each test channel. The experiment successfully tested a digital signal processor (DSP) chip with 72 scan test pins (theoretically supporting 160 test pins). Compared to our previous work, the work in this paper increases the number of test channels by four times while reducing resource utilization per channel by 37.5%, demonstrating good scalability and versatility.

Development and machine learning-based calibration of low-cost multiparametric stations for the measurement of CO2 and CH4 in air

The pressing issue of atmospheric pollution has prompted the exploration of affordable methods for measuring and monitoring air contaminants as complementary techniques to standard methods, able to produce high-density data in time and space. The main challenge of this low-cost approach regards the in-field accuracy and reliability of the sensors. This study presents the development of low-cost stations for high-time resolution measurements of CO2 and CH4 concentrations calibrated via an in-field machine learning-based method. The calibration models were built based on measurements parallelly performed with the low-cost sensors and a CRDS analyzer for CO2 and CH4 as reference instrument, accounting for air temperature and relative humidity as external variables.To ensure versatility across locations, diversified datasets were collected, consisting of measurements performed in various environments and seasons. The calibration models, trained with 70 % for modeling, 15 % for validation, and 15 % for testing, demonstrated robustness with CO2 and CH4 predictions achieving R2 values from 0.8781 to 0.9827 and 0.7312 to 0.9410, and mean absolute errors ranging from 3.76 to 1.95 ppm and 0.03 to 0.01 ppm, for CO2 and CH4, respectively. These promising results pave the way for extending these stations to monitor additional air contaminants, like PM, NOx, and CO through the same calibration process, integrating them with remote data transmission modules to facilitate real-time access, control, and processing for end-users.

Research on the influence of solar radiation fuzzy adaptive system on the wet and hot environment in greenhouse

Chinese solar greenhouse (CSG) is an artificially constructed agricultural facility that provides a suitable environment for crop growth. However, the imbalanced temperature and humidity phenomenon occurring in greenhouse during summer greatly affects crop yield. This article proposes the application of solar radiation fuzzy adaptive system in greenhouse to address this issue. The design process includes hardware and algorithm design. The hardware part involves designing the travel distance of the curtain machine and the film winding machine, laying out the temperature and humidity sensors, and designing the control cabinet. The algorithm is optimized based on fuzzy control theory, data acquisition module consists of temperature and humidity sensors, executive components include the curtain machine and the film winding machine, and the data transmission, storage and feedback module consists of the control cabinet and the cloud-based database. These form an adaptive system, which will be subjected to a 10-day comparative experiment. The experimental results showed that in region 3, the peak value of temperature reduction in the third layer during the daytime was 26.2 °C, and the humidity increased by a maximum of 31.5 %. In region 2, the temperature increased by a maximum of 4.2 °C during the nighttime in the second layer, and the humidity decreased by 4.1 °C. Introduction of adaptive systems can play an important role in maintaining microclimate temperature and humidity in summer.

A novel bidirectionally operated chirped quantum-dot based semiconductor optical amplifier using a dual ground state spectrum

Bi-directionally operated amplifiers enabling efficient utilization of transmission wavelengths are promising candidates in densely integrated photonic circuits for future cost-effective, power-efficient optical networks. Here, we demonstrate, for the first time, a broadband semiconductor optical amplifier (SOA) based on a novel chirped multilayered quantum dot (QD) structure, which is suitable for bi-directional amplification via the dual ground state (GS) emission spectrum. The fabricated QD SOA has achieved a maximum 3-dB gain bandwidth of 50 nm while retaining on-chip gain above 20 dB at both GS wavelengths. Under an optimum pumping current of 280 mA, the bi-directionally operated QD SOA has shown around 10 dB receiver sensitivity improvement in ultra-high-speed 100 Gbaud non-return-to-zero and 53.125 Gbaud four-level pulse amplitude modulation data transmission systems.

Smart spirits: an integrated IoT-based liquor and health monitoring system

This project entails the development of a comprehensive health monitoring system utilizing Arduino Uno, a range of sensors (including alcohol, pulse, and DHT11), an LCD display, and a GSM module for data transmission to the Thingspeak application. Its objective is to monitor vital health parameters such as temperature, humidity, pulse rate, and alcohol levels in real-time, accessible via the Thingspeak mobile app. Key components include an Arduino Uno microcontroller, DHT11 sensor for temperature and humidity, pulse sensor for heart rate, alcohol sensor for detecting alcohol levels, LCD display for immediate feedback, and a GSM module for remote data transmission. The user-friendly Thingspeak app serves as an interface for seamless health metric monitoring. The system operates by collecting sensor data, displaying it locally on the LCD screen, and simultaneously transmitting it to the Thingspeak app via GSM communication. Users can then access the app to visualize and monitor their health metrics in real-time, allowing for prompt interventions or medical assistance if needed. The code implementation integrates sensor readings, LCD display control, and Thingspeak functionality. Leveraging Thingspeak's cloud-based platform enhances usability and accessibility, making remote health monitoring feasible. This system offers a versatile solution for individuals looking to efficiently track and manage their well-being, thanks to the integration of Arduino Uno, sensors, Thingspeak features, and GSM communication, making it suitable for both local and remote health monitoring applications.

Enhancing IoT Network Security Through Digital Object Architecture-Based Approaches

The Internet of Things (IoT) encompasses a network of different devices, both stationary and mobile, that may interact with the physical world. Ensuring the security of the Internet of Things (IoT) is of utmost importance in a world where devices are interconnected at various levels, including wearables, home automation, smart cities, industrial sectors, and more. Ensuring the security of this interconnected network of "things" and devices is imperative, leaving no space for mistakes or inadequacies. The purpose of this article is to present an approach that utilizes Digital Object Architecture (DOA) to identify Internet of Things (IoT) devices and applications within communication networks. This study investigates various methodologies for incorporating the DOA identifier into Internet of Things (IoT) devices that are equipped with a range of wireless data transmission modules. Furthermore, this study presents a security model that aims to strengthen the resolution system based on data transmission security. The objective is to reduce the number of service messages exchanged between internetwork parts and decrease network latency. The essay finishes by examining some strategies for modernizing DOA in order to improve the quality of service.