Communication Module Research Articles

Oceanographic Parameter Measurement System Based on LoRA Communication Module

Measurement of oceanographic parameters is needed to determine the condition of the ecosystem in the water area so that it can be the basis for carrying out conservation activities in marine waters. Measurement of physical and chemical parameters used manually which causes the data obtained is still susceptible to observational errors. In this study, a system was designed that can measure oceanographic parameters periodically. The designed system consists of a temperature sensor, turbidity sensor, pH sensor and TDS sensor integrated with a microcontroller device. The system consists of two main parts, a sensor node that has a function as a transmitter and a gateway that has a function as a receiver. Initial testing is done by calibrating and validating data for each sensor used. The next test was carried out by testing the transmission of data from the measurement of oceanographic parameters in the form of water pH, salinity, water temperature and water turbidity from the sensor node to the gateway using the LoRA Ebyte E220 communication module. The test results show that the sensor node measurement data can be sent to the gateway by setting the distance between devices as far as 500 meters. The resulting data transmission delay is in the range of 2-10 seconds

Experts’ consensus on the design of a technical communication module for vocational college students in the manufacturing sector

In the twenty-first century one of the most significant challenges is preparing professionals to adequately communicate technical information when writing and speaking. A number of studies have been published on various aspects of technical communication (TC), but few studies in China have investigated the curriculum design for TC. In this study, expert consensus was sought to explore elements required for a TC module for vocational college students in the manufacturing sector using the fuzzy Delphi method (FDM). The FDM was employed to determine consensus among a panel of 12 experts. Two rounds of data collection were conducted to garner opinions from the experts. The first round was semi-structured interviews among five experts to design the items for the FDM questionnaire with a five-point linguistic scale. The designed FDM questionnaire was distributed to 12 experts to gather their opinions on the elements required for the TC module. The data analysis determined the consensus on the elements of the TC module in four categories: objectives, contents, learning activities, and assessment methods. Their levels of agreement were assessed to determine elements that needed to be highlighted for implementation in the classroom. These findings could be used to develop a more relevant instructional module for TC in the manufacturing sector.

Probing an Easy-to-Deploy Multi-Agent Manufacturing System Based on Agent Computing Node: Architecture, Implementation, and Case Study

Abstract Due to the widespread adoption of personalized customization services, the application contexts within discrete manufacturing workshops have become increasingly intricate, necessitating the modern industry to evolve toward a more adaptable production trajectory. The pre-established production rules in a traditional centralized control manufacturing system present difficulties in accommodating dynamic situations. Although a multi-agent manufacturing system (MAMS) yields natural advantages in handling dynamic emergencies, the current research is limited to the computer simulation level and lacks integration with the underlying physical devices. In order to mitigate said challenges, the standardization modeling approach for the agent computing node (ACN) to facilitate the implementation of a readily deployable MAMS was proposed in the present study. Initially, adapters encompassing communication, decision, and control modules were developed within the industrial personnel computer-based computing node to accommodate the heterogeneous interface protocols of diverse machines. These adapters enable communication and interaction among machines while laying the computational foundation for the ACN. Accordingly, the models of the machine agent, the part agent, and the monitoring agent were constructed based on ACNs and could perceive the dynamic production information and support the enabling application. Subsequently, to guide ACNs in making scheduling decisions beneficial to global performance, an improved negotiation mechanism in MAMS was achieved in real-time. Finally, the proposed MAMS based on the ACN was deployed in an actual flexible machining workshop. Comparative experiments were implemented and, as exhibited from the experimental results, the proposed ACNs possessed the capabilities of achieving optimal global decision-making and facilitating straightforward deployment.

Research based on open space collaborative intelligent fire system

The open space collaborative intelligent fire protection system studied in this article uses STM32F407 as the main control chip of the drone, and the STM32F103 is used as the main control chip of the fire truck. ZigBee wireless communication and other modules. The main innovation of this article is to use OpenMV's ground black line recognition positioning, the attitude fusion algorithm of the main controller module, and the PID control algorithm for real -time positioning of drones and fire trucks in the testing area of the test. After actual testing and verification, this system can complete the full coverage of the patrol area in different environments, fire identification in the patrol area, and fire extinguishing tasks. Through the ZigBee wireless communication module Essence.

The Effectiveness of a Technical Communication Module for Automobile Manufacturing Students at Vocational Colleges

This study employs a one-group pretest and posttest design to assess the written and oral technical communication (TC) skills of students in vocational colleges in China. Fifty-nine 1st-year students in automotive engineering participated in a 3-week implementation of a TC module. To measure learner differences, students took oral and written tests before and after the intervention, respectively. Results showed that the module effectively improved students’ written and oral TC skills. In addition, findings from interviews with participants and English teachers indicated that the TC module is suitable from a pedagogical perspective.

A new intelligent cross-domain routing method in SDN based on a proposed multiagent reinforcement learning algorithm

PurposeA cross-domain intelligent software-defined network (SDN) routing method based on a proposed multiagent deep reinforcement learning (MDRL) method is developed.Design/methodology/approachFirst, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between the root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to acquire global network state information in real time. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a network traffic state prediction mechanism is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time.FindingsExperimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and open shortest path first (OSPF) routing methods.Originality/valueMessage transmission and message synchronization for multicontroller interdomain routing in SDN have long adaptation times and slow convergence speeds, coupled with the shortcomings of traditional interdomain routing methods, such as cumbersome configuration and inflexible acquisition of network state information. These drawbacks make it difficult to obtain global state information about the network, and the optimal routing decision cannot be made in real time, affecting network performance. This paper proposes a cross-domain intelligent SDN routing method based on a proposed MDRL method. First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to realize the real-time acquisition of global network state information. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a prediction mechanism for the network traffic state is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time. Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and OSPF routing methods.

Design And Development of a Compact Carbon Gas Emission Monitoring Device for Private Vehicles

Poor air quality is a major concern in urban areas, with a large percentage of the population exposed to high levels of pollutants. Using sensor and monitoring technology, devices can automatically and periodically monitor indoor air quality. Containment measures should be taken when dangerous airborne chemicals are detected. Air pollution, both indoors and outdoors, causes health problems. The Philippines faces challenges in air quality reporting due to the high cost of monitoring equipment. A project is underway to collect real-time data using sensors and an Arduino Uno-based monitoring system. The power management design's PCB layouts were completed using two software applications: Eagle CAD for the nano power boost charger circuit and Ultiboard for the boost converter circuit. The layout design adhered to the IPC2221A standards, ensuring accurate component footprints and copper trace width, which were determined based on the current flow through the trace in the PCB design. The circuit boards were carefully sized to be smaller than 4“x4” to enable smooth integration into an air quality monitoring device. The integrated design functioned as a wireless sensing application, effectively monitoring air quality. The carbon gas monitoring system had the capability to detect and collect various gases, including Carbon Dioxide, Nitrogen Monoxide, Carbon Monoxide, and Dust Density. Additionally, it was equipped to measure temperature and humidity. Achieving this required using the Arduino Uno application for programming and incorporating an additional sensor, resulting in a cost-effective design solution. The prototype successfully integrated air quality sensors, microcontrollers, and communication modules. The system's performance was evaluated against existing testing centers, and the results were presented.

DWDM-based Effective Group Velocity Dispersion Module for Long-distance Quantum Communication

DWDM-based Effective Group Velocity Dispersion Module for Long-distance Quantum Communication

Learnings and Reflections on Transition to New Competency-based Medical Education, MBBS curriculum in Microbiology: A Cross-sectional Observational Study

Introduction: Conventional teaching in microbiology revolved around the study of microorganisms without clinical relevance. Therefore, a New Competency-based Undergraduate (UG) Medical curriculum (New CBME) was rolled out in the academic year 2019 to make Indian Medical Graduates more clinically competent yet globally relevant. Aim: To assess the impact of the change in curriculum on students’ performance and desired educational outcomes before and after the implementation of the new curriculum. Likewise, the study aimed to analyse the experiences and reflections of medical teachers towards these amendments. Materials and Methods: A cross-sectional observational study was conducted on 82 students from the second year Bachelor of Medicine, Bachelor of Surgery (MBBS) with a traditional curriculum and 74 with the new Competency-based Medical Education (CBME) curriculum at the Department of Microbiology, Smt. B.K. Shah Medical Institute and Research Centre, Piparia, Vadodara, Gujarat, India, between February 2020 and November 2021. In both batches, topics from core as well as non core areas of various systems were included along with the Attitude, Ethics and Communication (AETCOM) modules. A comparison of both groups was done, and Chisquare values and p-values were calculated. Results: The batch with the new CBME curriculum was able to provide a provisional diagnosis for a case-based question better than the Traditional batch {n=60/74 (81%) vs n=44/82 (54%), p-value=0.0005, significant}. However, the correct aetiology, pathogenesis, and laboratory diagnosis were written better in the traditional batch compared to the new one, n=44/82 (54%) vs n=24/74 (32%). Regarding the AETCOM skills, the new batch had a better understanding of proper communication and ethics, i.e., 73/74 (99%), p-value<0.00001, Significant, while 66/82 students (80%) of the traditional batch and 67/74 (90%) of the new batch were aware of confidentiality issues. In viva voce, the new batch showed a lot of confusion regarding the names, types of micro-organisms, and the laboratory diagnosis of diseases caused by them compared to the traditional batch (p-value <0.00156, significant). Conclusion: After implementing the new CBME curriculum to undergraduate students and analysing the feedback of teachers and the performance of students, it was felt that an appraisal of the new curriculum is required for a satisfactory outcome in microbiology so that microbiology content can be incorporated with clinical relevance.

Design and Implementation of Smart Trolley System

We are now using the rapidly growing artificial intelligence in our day-to- This project aims to develop a Smart Trolley System using Bluetooth and Arduino technology to enhance shopping experiences in retail environments. Traditional shopping carts lack interactive features to assist shoppers during their shopping journey. Our proposed Smart Trolley System addresses this gap by integrating Bluetooth communication with Arduino microcontrollers to create an intelligent and interactive shopping companion. The system consists of two main components: the Smart Trolley Unit and the Shopper Application. The Smart Trolley Unit is equipped with sensors for detecting items placed within the cart, an Arduino microcontroller for processing data, and Bluetooth modules for communication. The Shopper Application is a mobile app installed on the shopper's smart phone, serving as the interface for interacting with the Smart Trolley System.

Enhancing Collaboration in Heterogeneous Multiagent Systems Through Communication Complementary Graph.

Heterogeneous multiagent systems are characterized by diverse task distributions, which are prevalent in practical scenarios, such as distributed decision making and robotic collaboration. A significant challenge in these systems is the constraint of limited observations, where each agent has access only to partial information. Many studies facilitate information exchange by employing shared parameters among agents. However, this approach is generally more effective for homogeneous systems where agents have similar observation or action spaces. In heterogeneous systems, indiscriminate parameter sharing can significantly increase the exploration cost required for effective adaptation. To address this challenge, we propose a novel communication complementary graph model (CCGM) for enhancing collaboration in heterogeneous multiagent systems. Our approach builds upon the training framework of heterogeneous agent reinforcement learning (HARL) with trust region learning and nonparameter sharing. This model utilizes advantage function decomposition and sequential updates to promote policy convergence. Within this framework, we introduce a novel communication method inspired by signaling games, where agents acting as receivers, process messages from other agents alongside their own observations. CCGM aligns the messages with observations in a graph-based communication module, which establishes communication relationships and supplements observational information. Subsequently, agents generate self-interested information, which they then share with others as senders. We evaluate our algorithm across various environments, including multiagent particle environments (MPE) and multiagent MuJoCo (MAMuJoCo) robot experiments. The results demonstrate the effectiveness of CCGM in enhancing HARL-based algorithms.

Detection of Apparent Defects in HPLC/Dual Mode Portable Sorting Device Based on Deep Learning and Image Processing

Abstract As the growth of automated detection technology, traditional manual detection has gradually been replaced. To improve the effectiveness of defect detection, a HPLC/dual mode portable sorting device with deep learning and image processing is raised for apparent defect detection. The product image is segmented using flood filled mean shift method, and defect detection is achieved using Yolo v3 algorithm. An LME2918 chip is the main component of the communication module in the portable device. Based on experimental data, image segmentation accuracy can range from 80% to 100%, and image matching accuracy can range from 85% to 95%. Under the power line carrier mode, the average success rate of apparent defect detection in portable sorting devices can reach 85%, and the change in success rate is not significant, indicating that the detection is relatively stable; Under wireless communication mode, the average success rate can reach 83%, and the change in success rate is relatively obvious, because wireless communication is easily affected by the external environment. The experimental data shows that the defect detection effect of the HPLC/dual mode portable sorting device based on deep learning and image processing meets the design requirements.

Enhancing Residential Security: Implementation of an IoT-based Anti-Theft Flooring System

The Anti-Theft Flooring System (ATFS) is Internet of Things (IoT) solution developed for enhancing residential safety and security. The system employs pressure sensors that are integrated into the flooring or into the floor mat, this system easily identifies intruder presence upon stepping, initiating a secure One-Time Password (OTP) generation process. The OTP is dispatched to pre-registered mobile numbers, serving as a safe and secure verification mechanism. The OTP when entered correctly within time triggers the system to unlock the door automatically. In case, OTP is entered wrongly or remains unentered for a 60-second timeframe, a Global System for Mobile (GSM) communication module triggers an alarm to promptly inform the user of potential security threats. This is how the system seamlessly combines IoT technology for real-time communication, offering an effective and proactive approach to residential safety at lower costs.

Development of a Low-Cost Arduino-Driven Drone for Rapid Search and Rescue in Philippine Terrain

Search and rescue (SAR) operations in the Philippines are often hampered by challenging terrains and the high costs associated with advanced technology. To address these issues, this study developed, tested, and validated a low-cost Arduino-driven drone optimized for SAR missions. The drone integrates an Arduino Mega 2560 microcontroller with an ArduPilot flight controller, GPS module, infrared sensors, ultrasonic sensors, and a thermal camera, alongside communication modules using Bluetooth Low Energy (BLE) and GSM/GPS. The design aimed to create a reliable, efficient tool capable of navigating complex environments, detecting human presence, avoiding obstacles, and maintaining stable communication with ground control. Extensive field testing demonstrated the drone's navigation stability, human detection accuracy of 95%, and obstacle avoidance success rate of 98%. Iterative debugging and optimization further enhanced performance, extending flight time to 30 minutes and improving sensor accuracy and communication reliability. Final validation confirmed the drone's readiness for real-world deployment, proving it to be a cost-effective solution for SAR operations in disasterprone regions. This research highlights the potential of using affordable, readily available components to significantly improve SAR efficiency and effectiveness in the Philippines.

Beyond Silicon: The Advent of Biomolecular Computing

ABSTRACT: Bio computing is an emerging interdisciplinary field that harnesses the information processing capabilities of biological substrates like DNA, proteins and cells to perform computational tasks. Rather than relying solely on conventional silicon-based computers, bio computing leverages the innate computational properties of biomolecules to encode, store, process and transmit information in unconventional ways. Core approaches include DNA computing, which uses DNA biochemistry to solve problems in a massively parallel fashion. Protein computing utilizes protein conformational dynamics to implement logic gates and communication modules for molecular information processing. Cellular computing focuses on engineering gene circuits and synthetic biology tools to program computational behaviours in living cells. Neural computing builds artificial neural networks inspired by biological brains. Key application areas include biomedicine, smart drug delivery systems, biosensing, hybrid organic-inorganic electronics, and biomolecular manufacturing. While still facing challenges around biocompatibility, programming complexity and ethical concerns, bio computing has achieved major technical milestones demonstrating its promise. Continued progress at the interface of biology and computing could enable future technologies like bio processors, in-vivo biocomputers, living materials and bio-intelligent systems. With responsible development, bio-inspired computation may catalyse the next revolution in human technological capabilities. This emerging field thus warrants enthusiastic attention as computation further converges with the living world.

Implementation of Wearable IoT Devices for Continuous Physiological Monitoring and Analysis

The development of wearable devices for ongoing physiological monitoring and analysis has been made possible by the emergence of the Internet of Things (IoT), which has completely changed the healthcare industry. This study discusses the use of such wearable IoT devices and their potential to improve healthcare delivery. Our research focuses on the development, manufacture, and implementation of wearable sensors capable of real-time monitoring of vital signs like heart rate, body temperature, blood pressure, and activity levels. These devices leverage the Internet of Things to transmit data wirelessly to a cloud service for further analysis. The core components of these gadgets are miniature sensors, low-power microcontrollers, and wireless communication modules. These developments permit the invisible and continuous collection of data, which aids in the early detection of irregularities and permits urgent medical treatments. Data collected by these devices is processed by sophisticated analytics and machine learning algorithms before being transmitted securely to a cloud-based server. As a result, doctors and nurses are able to anticipate health issues, monitor their patients' physical conditions in real time, and provide individualised care. Los patients have more control over their own medical decisions since they have access to their own data. Frequent use of Internet-connected devices for monitoring and analysing physical phenomena may lead to better medical care, lower hospitalisation costs, and higher quality of life. Additionally, these devices have the potential to revolutionise clinical research by providing extensive real-world data for medical research. These wearable IoT devices have the potential to enhance the management of chronic illnesses, encourage early treatments, and raise general wellbeing. Wearable physiological monitoring technology has a lot of potential for the future of healthcare thanks to the advancements of the internet of things.

Fabrication of Smart Meter for Accurate Use in Home and Industry

This study addresses the challenges posed by conventional energy meters, which rely on manual readings, leading to human errors and inefficiencies. In response to this, a battery-powered smart meter was developed utilizing an STM32 microcontroller, ADE7758 and STPM32 metering integrated circuits (ICs), SIM and ESP32 communication modules, along with a MYSQL database. Real-time energy data from both single and three-phase appliances were collected, and their energy consumption, errors, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) were quantified. The model demonstrated an acceptable accuracy level, with an estimated MAE of approximately 2.912 units and an estimated RMSE of around 4.048 units, particularly in predicting motor power consumption. Additionally, ARIMA forecasting was applied to a three-phase asynchronous motor, revealing an average active motor power of 250.95 watts, indicating precise results over time.

Enhancing IoT Network Reliability: Evaluating LoRa Module Susceptibility to Interference

The Internet of Things (IoT) is gaining popularity, leading to the widespread use of remote communication modules (LoRa), known for their energy efficiency and wide coverage range. However, as the number of LoRa modules used in IoT networks grows, the possibility of interference from third-party devices operating at the same frequency becomes a concern. This study aimed to examine the vulnerability of LoRa modules to electromagnetic interference (EMI) when transmitting text messages and images. Radiation emission conditions were measured in the test area for evaluating LoRa module performance, and susceptibility to interference was assessed under non-line-of-sight (NLOS) conditions. The study's outcomes reveal that interference with LoRa transmitters has no noticeable effect on the range within a distance of up to 50 meters. In contrast, the interference power required to disrupt the LoRa receiver decreases with increasing distance. Additionally, interference from frequencies outside the designated LoRa working frequency (915 MHz) has no discernible impact on module performance. Introducing a delivery delay check demonstrates consistent performance even in interference. These findings deepen our understanding of the susceptibility of LoRa modules to tampering, emphasizing the importance of implementing effective disruption management strategies in IoT deployments. By considering the potential impact of electromagnetic interference (EMI) on LoRa modules, developers can design more robust IoT networks, ensuring reliable communication and improved system performance. Overall, the research focuses on the interference characteristics of LoRa modules, providing insights for developing resilient and interference-resistant IoT solutions. It underscores the necessity of addressing interference issues to ensure the reliable operation of IoT devices across diverse environments.

Laboratory Reactor Monitoring System Based on IoT

In response to the limitations of existing laboratory reaction vessels, such as the lack of remote monitoring and low level of intelligence, a laboratory reaction vessel monitoring system based on the Internet of Things (IoT) is designed. This monitoring system utilizes a controller with the STM32F407 chip as its core to collect and control the temperature and motor parameters of the reaction vessel. Data exchange with the OneNET cloud platform is facilitated through a WiFi communication module. The remote monitoring terminal establishes a WiFi connection with the OneNET cloud platform, enabling real-time data and command transmission with the STM32F407 controller. Experimental results demonstrate that the system can effectively achieve real-time remote monitoring of the reaction vessel’s operational status, meeting the expected objectives.

Narracja filmowa – funkcja terapeutyczna. Kilka słów o Amatorze (1979) Krzysztofa Kieślowskiego

The strategy of the (auto)therapist in film art is a process of dynamic interaction between the transmitting and receiving roles, i.e. the director – the protagonist of his work and the projected viewers. The film is a space enabling the creator to analyze the problems of a modern man in order to increase the skills of self-understanding, self-distancing from emotions and developing reflection. The latter is conducive to the coherence of narratives about oneself and the construction of one’s identity. Results and conclusions: Projection and identification, which is part of the emotional and cognitive responses available to the creator and recipient of the work, are processes of therapeutic importance. Creation (and contact with its effect – a finished work) is then a tool helping to regulate: modulation of attention, emotions (modulation of emotion), fosters cognition (modulation of cognition), understanding behavior (modulation of behavior) and communication (modulation of communication).