Communication Interface Research Articles

Low-frequency Motor Cortex EEG Predicts Four Rates of Force Development.

The movement-related cortical potential (MRCP) is a low-frequency component of the electroencephalography (EEG) signal that originates from the motor cortex and surrounding cortical regions. As the MRCP reflects both the intention and execution of motor control, it has the potential to serve as a communication interface between patients and neurorehabilitation robots. In this study, we investigated the EEG signal recorded centered at the Cz electrode with the aim of decoding four rates of force development (RFD) during isometric contractions of the tibialis anterior muscle. The four levels of RFD were defined with respect to the maximum voluntary contraction (MVC) of the muscle as follows: Slow (20% MVC/s), Medium (30% MVC/s), Fast (60% MVC/s), and Ballistic (120% MVC/s). Three feature sets were assessed for describing the EEG traces in the classification process. These included: (i) MRCP Morphological Characteristics in the δ-band, such as timing and amplitude; (ii) MRCP Statistical Characteristics in the δ-band, such as standard deviation, mean, and kurtosis; and (iii) Wideband Time-frequency Features in the 0.1-90 Hz range. The four levels of RFD were accurately classified using a support vector machine. When utilizing the Wideband Time-frequency Features, the accuracy was 83% ± 9% (mean ± SD). Meanwhile, when using the MRCP Statistical Characteristics, the accuracy was 78% ± 12% (mean ± SD). The analysis of the MRCP waveform revealed that it contains highly informative data on the planning, execution, completion, and duration of the isometric dorsiflexion task. The temporal analysis emphasized the importance of the δ-band in translating to motor command, and this has promising implications for the field of neural engineering systems.

Upgrade of ATLAS hadronic Tile Calorimeter for the High Luminosity LHC

The Tile Calorimeter (TileCal) is the hadronic calorimeter covering the central region of the ATLAS experiment. The High-Luminosity phase of LHC, delivering five times the LHC nominal instantaneous luminosity, is expected to start in 2029. TileCal will require new electronics to meet the requirements of a 1 MHz trigger, higher ambient radiation, and to ensure better performance under high pile-up conditions. Both the on- and off-detector TileCal electronics will be replaced during the shutdown of 2026–2028. Approximately 10% of the PMTs, those reading out the most exposed cells, will be replaced. PMT signals from every TileCal cell will be digitized and sent directly to the back-end electronics, where the signals are reconstructed, stored, and sent to the first level of trigger at a rate of 40 MHz. This will provide better precision of the calorimeter signals used by the trigger system and will allow the development of more complex trigger algorithms. The modular front-end electronics feature radiation-tolerant components and redundant design to minimise single points of failure. The timing, control and communication interface with the off-detector electronics is implemented with modern Field Programmable Gate Arrays (FPGAs) and high speed fiber optic links running up to 9.6 Gb/s. The TileCal upgrade program has included extensive R&D and test beam studies. A Demonstrator module equipped with the new electronics but with reverse compatibility with the existing readout system was inserted in ATLAS in 2019 for testing in actual detector conditions. The status of the various components and the results of test-beam campaigns with the electronics prototypes will be discussed.

CRaTeBo: a high-speed, radiation-tolerant and versatile testing platform for FPGA radiation qualification for high-energy particle accelerator applications

The CHARM Radiation Tolerant FPGA Tester Board (CRaTeBo) is a FPGA testing platform for the CERN High-energy Accelerator (CHARM) irradiation facility. It is meant to ease the radiation testing of FPGA-based systems by providing users with a radiation-tolerant carrier card featuring an FPGA interface, a high-speed communication interface, a flexible power supply, and an HPC-FMC connector for additional front-end electronics. It is foreseen to be a permanent installation in the CHARM facility at CERN, giving users the possibility to carry out radiation tests of their system with minimum effort on the test setup development.

A Review of Motor Brain-Computer Interfaces Using Intracranial Electroencephalography Based on Surface Electrodes and Depth Electrodes.

Brain-computer interfaces (BCIs) provide a communication interface between the brain and external devices and have the potential to restore communication and control in patients with neurological injury or disease. For the invasive BCIs, most studies recruited participants from hospitals requiring invasive device implantation. Three widely used clinical invasive devices that have the potential for BCIs applications include surface electrodes used in electrocorticography (ECoG) and depth electrodes used in Stereo-electroencephalography (SEEG) and deep brain stimulation (DBS). This review focused on BCIs research using surface (ECoG) and depth electrodes (including SEEG, and DBS electrodes) for movement decoding on human subjects. Unlike previous reviews, the findings presented here are from the perspective of the decoding target or task. In detail, five tasks will be considered, consisting of the kinematic decoding, kinetic decoding,identification of body parts, dexterous hand decoding, and motion intention decoding. The typical studies are surveyed and analyzed. The reviewed literature demonstrated a distributed motor-related network that spanned multiple brain regions. Comparison between surface and depth studies demonstrated that richer information can be obtained using surface electrodes. With regard to the decoding algorithms, deep learning exhibited superior performance using raw signals than traditional machine learning algorithms. Despite the promising achievement made by the open-loop BCIs, closed-loop BCIs with sensory feedback are still in their early stage, and the chronic implantation of both ECoG surface and depth electrodes has not been thoroughly evaluated.

FPGA Implementation of SPI Protocol

Field Programmable Gate Array (FPGA) are versatile devices that can be programmed to implement various digital circuits, including communication protocols like Serial Peripheral Interface (SPI). The Serial peripheral interface protocol is a synchronous serial communication interface that allows for full duplex communication between a master device and a multiple slave MOSI, MISO, SCLK, and SS. MOSI and MISO are data lines, while SCLK is the clock signal generated by the master to synchronize transfers. SS is used by the master to select individual slave devices. By implementing the SPI protocol in FPGA, to gain flexibility and customization, as FPGAs can be reprogrammed for different applications without the need for dedicated hardware changes. The FPGA implementation of the Serial Peripheral Interface (SPI) protocol involves a structured methodology. With the help of pipeline buffer and DAM technique power dissipation is performed effectively. Finally, integrate the FPGA SPI module into the larger system, ensuring seamless communication with other components or microcontrollers. This systematic approach ensures a reliable and tailored FPGA based SPI implementation.

Remote-Controlled Affordable Solenoid Valve Design and a Web-Based Approach to Its Implementation

Irrigating agricultural lands remotely using automated irrigation systems can mitigate water and energy wastage, reducing it to a minimum. This study aims to design an agricultural irrigation system that operates by remotely opening and closing solenoid valves. The envisioned solenoid valve comprises a wireless microcontroller, a power circuit, and a basic valve mechanism. The remote and automated irrigation system can monitor and receive commands for the connected solenoid valves through web services and a web interface. After every operation executed by the system, interfaces displaying the real-time status or historical operational states of the controlled solenoid valves are presented to the users. This system is orchestrated using a web server operating on a cloud server, serving as the system control center. Solenoid valves positioned at ten different points are integrated, facilitating real-time and programmable use based on user requests. The system uses a secured website, accessed via user passwords, as a communication interface to receive irrigation control requests. It offers the capability for multiple users to control the system simultaneously through the same interface. Ultimately, this research seeks to establish a secure, remote, and automated irrigation system based on the control and scheduling of low-cost solenoid valves via a web page.

Three-Dimensional Structure Reconstruction System of Nasal Cavity Based on a Short-Source Acoustic Tube

The nasal cavity three-dimensional structure reconstruction is significant for the diagnosis and treatment of nasal and nasopharynx diseases. Currently, most systems for nasal cavity three-dimensional structure reconstruction are based on a long-source acoustic tube. However, the long-source acoustic tube has the weaknesses of low portability and high cost. To address these issues, a nasal cavity three-dimensional structure reconstruction system based on a short-source acoustic tube was designed in this research. The designed system comprises the lower computer of a nasal acoustic signal acquisition device and the upper computer of nasal acoustic signal analysis software. The lower computer of a nasal acoustic signal acquisition device consists of a signal processing unit, a detection unit, a microcontroller, and a USB communication interface. It is used to collect the original acoustic signal in the nasal cavity. The upper computer of nasal acoustic signal analysis software consists of the nasal cavity acoustic signal preprocessing method and the nasal cavity three-dimensional structure hierarchical reconstruction method. The nasal cavity acoustic signal preprocessing method is used to eliminate the DC offset component and suppress the high-frequency noise. Then, the nasal cavity three-dimensional structure hierarchical reconstruction method is used to establish the relationship between the cross-sectional area of the nasal cavity and the depth of the nasal cavity. In order to verify the effectiveness of the system designed in this research, the straight tube model and the simulated nasal cavity model were selected as test subjects, and the depth–cross-sectional area was selected as the evaluation indicator of the accuracy of the nasal cavity three-dimensional structure reconstruction result. The experimental results show that the root mean squared error and the mean relative error of the depth–cross-sectional area were reduced from 0.6284 cm2 to 0.0201 cm2 and 47.6467% to 1.8248%, respectively, under the straight tube model by using the nasal cavity acoustic signal preprocessing method. The root mean squared error and the mean relative error of the depth–cross-sectional area were reduced from 4.7730 cm2 to 0.1510 cm2 and 107.8128% to 3.3096%, respectively, under the simulated nasal cavity model. Meanwhile, this system can increase the effective measurement depth of the nasal cavity from 7 cm to 13 cm based on the preprocessing method. The results demonstrated that the designed system can not only reconstruct and visualize a nasal cavity three-dimensional structure with high precision but also generate comprehensive test reports. Therefore, this system can provide a basis for further auxiliary diagnosis of the disease and facilitate doctors in explaining the physiological structure of the nasal cavity to patients.

Beyond Words: An Intelligent Human-Machine Dialogue System with Multimodal Generation and Emotional Comprehension

Intelligent service robots have become an indispensable aspect of modern-day society, playing a crucial role in various domains ranging from healthcare to hospitality. Among these robotic systems, human-machine dialogue systems are particularly noteworthy as they deliver both auditory and visual services to users, effectively bridging the communication gap between humans and machines. Despite their utility, the majority of existing approaches to these systems primarily concentrate on augmenting the logical coherence of the system’s responses, inadvertently neglecting the significance of user emotions in shaping a comprehensive communication experience. To tackle this shortcoming, we propose the development of an innovative human-machine dialogue system that is both intelligent and emotionally sensitive, employing multimodal generation techniques. This system is architecturally comprised of three components: (1) data collection and processing, responsible for gathering and preparing relevant information, (2) a dialogue engine, which generates contextually appropriate responses, and (3) an interaction module, responsible for facilitating the communication interface between users and the system. To validate our proposed approach, we have constructed a prototype system and conducted an evaluation of the performance of the core dialogue engine by utilizing an open dataset. The results of our study indicate that our system demonstrates a remarkable level of multimodal generation response, ultimately offering a more human-like dialogue experience.

Subway Automatic Ticket Booking System using Verilog

Using Verilog HDL language to research a subway automatic ticket booking system. The design of this subway ticketing system takes convenience, quickness and simplicity as the core, and takes saving time for passengers. It completes the main process of buying subway tickets for passengers. Firstly, this project studies the development of subway ticketing system at home and abroad, and then studies the basic components of subway ticketing system. The paper also simulates the ticket selection module, change processing module and display interface module on Xilinx Vivado. In the Verilog design, finite state machines (FSMs) are employed to model the various states of the ticket booking process. These FSMs ensure smooth transitions between states, enabling the system to handle scenarios such as ticket availability checks, payment processing, and generating tickets. Additionally, the communication interface is responsible for securely transmitting booking information to the subway network, ensuring seamless integration with the overall subway infrastructure.

Editorial: Designing for contemporary relevance and authenticity—Identity as a lens for reimagining science activities at the interface of science education and communication

Editorial: Designing for contemporary relevance and authenticity—Identity as a lens for reimagining science activities at the interface of science education and communication

Retracted: The Development of Data Acquisition System of Formula SAE Race Car Based on CAN Bus Communication Interface and Closed-Loop Design of Racing Car

Retracted: The Development of Data Acquisition System of Formula SAE Race Car Based on CAN Bus Communication Interface and Closed-Loop Design of Racing Car

Real-Time Remote Patient Monitoring and Alarming System for Noncommunicable Lifestyle Diseases.

Telemedicine and remote patient monitoring (RPM) systems have been gaining interest and received adaptation in healthcare sectors since the COVID-19 pandemic due to their efficiency and capability to deliver timely healthcare services while containing COVID-19 transmission. These systems were developed using the latest technology in wireless sensors, medical devices, cloud computing, mobile computing, telecommunications, and machine learning technologies. In this article, a real-time remote patient monitoring system is proposed with an accessible, compact, accurate, and low-cost design. The implemented system is designed to an end-to-end communication interface between medical practitioners and patients. The objective of this study is to provide remote healthcare services to patients who need ongoing care or those who have been discharged from the hospital without affecting their daily routines. The developed monitoring system was then evaluated on 1177 records from MIMIC-III clinical dataset (aged between 19 and 99 years). The performance analysis of the proposed system achieved 88.7% accuracy in generating alerts with logistic regression classification algorithm. This result reflects positively on the quality and robustness of the proposed study. Since the processing time of the proposed system is less than 2 minutes, it can be stated that the system has a high computational speed and is convenient to use in real-time monitoring. Furthermore, the proposed system will fulfil to cover the lower doctor-to-patient ratio by monitoring patients from remote locations and aged people who reside in their residences.

Stochastic Gradient Descent for matrix completion: Hybrid parallelization on shared- and distributed-memory systems

The purpose of this study is to investigate the hybrid parallelization of the Stochastic Gradient Descent (SGD) algorithm for solving the matrix completion problem on a high-performance computing platform. We propose a hybrid parallel decentralized SGD framework with asynchronous inter-process communication and a novel flexible partitioning scheme to attain scalability up to hundreds of processors. We utilize Message Passing Interface (MPI) for inter-node communication and POSIX threads for intra-node parallelism. We tested our method by using different real-world benchmark datasets. Experimental results on a hybrid parallel architecture showed that, compared to the state-of-the-art, the proposed algorithm achieves 6× higher throughput on sparse datasets, while it achieves comparable throughput on relatively dense datasets.

Local Weather Station Design and Development for Cost-Effective Environmental Monitoring and Real-Time Data Sharing.

Current weather monitoring systems often remain out of reach for small-scale users and local communities due to their high costs and complexity. This paper addresses this significant issue by introducing a cost-effective, easy-to-use local weather station. Utilizing low-cost sensors, this weather station is a pivotal tool in making environmental monitoring more accessible and user-friendly, particularly for those with limited resources. It offers efficient in-site measurements of various environmental parameters, such as temperature, relative humidity, atmospheric pressure, carbon dioxide concentration, and particulate matter, including PM 1, PM 2.5, and PM 10. The findings demonstrate the station's capability to monitor these variables remotely and provide forecasts with a high degree of accuracy, displaying an error margin of just 0.67%. Furthermore, the station's use of the Autoregressive Integrated Moving Average (ARIMA) model enables short-term, reliable forecasts crucial for applications in agriculture, transportation, and air quality monitoring. Furthermore, the weather station's open-source nature significantly enhances environmental monitoring accessibility for smaller users and encourages broader public data sharing. With this approach, crucial in addressing climate change challenges, the station empowers communities to make informed decisions based on real-time data. In designing and developing this low-cost, efficient monitoring system, this work provides a valuable blueprint for future advancements in environmental technologies, emphasizing sustainability. The proposed automatic weather station not only offers an economical solution for environmental monitoring but also features a user-friendly interface for seamless data communication between the sensor platform and end users. This system ensures the transmission of data through various web-based platforms, catering to users with diverse technical backgrounds. Furthermore, by leveraging historical data through the ARIMA model, the station enhances its utility in providing short-term forecasts and supporting critical decision-making processes across different sectors.

Comparison of Advanced Control Strategies Applied to a Multiple-Degrees-of-Freedom Wave Energy Converter: Nonlinear Model Predictive Controller versus Reinforcement Learning

Achieving energy maximizing control of a Wave Energy Converter (WEC) not only needs a comprehensive dynamic model of the system—including nonlinear hydrodynamic effects and nonlinear characteristics of Power Take-Off (PTO)—but to treat the entire system using an integrated approach, i.e., as a cyber–physical system considering the WEC dynamics, control strategy, and communication interface. The resulting energy-maximizing optimization formulation leads to a non-quadratic and nonstandard cost function. This article compares the (1) Nonlinear Model Predictive Controller (NMPC) and (2) Reinforcement Learning (RL) techniques as applied to a class of multiple-degrees-of-freedom nonlinear WEC–PTO systems subjected to linear as well as nonlinear hydrodynamic conditions in simulation, using the WEC-Sim™ toolbox. The results show that with an optimal choice of RL agent and hyperparameters, as well as suitable training conditions, the RL algorithm is more robust under more stringent operating requirements, for which the NMPC algorithm fails to converge. Further, RL agents are computationally efficient on real-time target machines with a significantly reduced Task Execution Time (TET).

Shared Task Representation for Human–Robot Collaborative Navigation: The Collaborative Search Case

Recent research in Human Robot Collaboration (HRC) has spread and specialised in many sub-fields. Many show considerable advances, but the human–robot collaborative navigation (HRCN) field seems to be stuck focusing on implicit collaboration settings, on hypothetical or simulated task allocation problems, on shared autonomy or on having the human as a manager. This work takes a step forward by presenting an end-to-end system capable of handling real-world human–robot collaborative navigation tasks. This system makes use of the Social Reward Sources model (SRS), a knowledge representation to simultaneously tackle task allocation and path planning, proposes a multi-agent Monte Carlo Tree Search (MCTS) planner for human–robot teams, presents the collaborative search as a testbed for HRCN and studies the usage of smartphones for communication in this setting. The detailed experiments prove the viability of the approach, explore collaboration roles adopted by the human–robot team and test the acceptability and utility of different communication interface designs.

Characteristics of physical reality, virtual reality, and interface in interactive digital communication experience

ABSTRACT Communication methods in fashion design have changed with the emergence of various devices and media in the digital environment, and combining fashion design with AI, IoT, and AR has increased in the wake of the 4th Industrial Revolution. This causes an expansion of fashion design, and interactive digital communication fashion design extends bodily experience from physical reality to virtual space. This study aimed to identify the experiential characteristics of physical reality, virtual reality, and interface for interactive digital communication and develop a measurement scale for each space. It used a Delphi method collecting opinions from industry, academic, and government experts through a questionnaire. The research findings offer a tool for measuring the characteristics of interactive digital communication fashion design experiences that allow a better understanding of the significance of the communication type in each space.

Augmented reality-enabled human-robot collaboration to balance construction waste sorting efficiency and occupational safety and health

Construction waste sorting (CWS) is highly recommended as a key step for construction waste management. However, current CWS involves humans’ manual hand-picking, which poses significant threats to their occupational safety and health (OSH). Robotic sorting promises to change the situation by adopting modern artificial intelligence and automation technologies. However, in practice, it is usually challenging for robots to do an efficient job (e.g., measured by quickness and accuracy) owing to the difficulties in precisely recognizing compositions of the mixed and heterogeneous waste stream. Leveraging augmented reality (AR) as a communication interface, this research aims to develop a human-robot collaboration (HRC) approach to address the dilemmatic balance between CWS efficiency and OSH. Firstly, a model for human-robot collaborative sorting using AR is established. Then, a prototype for the AR-enable collaborative sorting system is developed and evaluated. The experimental results demonstrate that the proposed AR-enabled HRC method can improve the accuracy rate of CWS by 10% and 15% for sorting isolated waste and obscured waste, respectively, when compared to the method without human involvement. Interview results indicate a significant improvement in OSH, especially the reduction of contamination risks and machinery risks. The research lays out a human-robot collaborative paradigm for productive and safe CWS via an immersive and interactive interface like AR.

Design of smart farming communication and web interface using MQTT and Node.js

Abstract The sustainable development goals (SDGs) are a UN agenda that has been approved by all UN member states. The SDGs have 17 targets, one of which is to eliminate hunger. In 2050, the world’s population is expected to reach 9.7 billion people. Improved soil and water management, according to the World Resources Institute, is one of the options for feeding 10 billion people sustainably by 2050. In comparison to conventional farming, smart and precision farming produces higher productivity at a lower cost. Based on the search for literature studies related to the development of agricultural technology, it was found that communication methods and online interfaces still require further improvement. The steps for developing the system are designing the architecture and end-to-end communication flow, designing use case diagrams, designing entity-relationship diagrams, designing user flow diagrams, implementing the system through code development, and finally testing the system. Planned communication and web design for precision smart agriculture are implemented effectively. The MQTT is used to communicate with the Node.js server worker. Data from numeric image feeds and images are directly processed by the system. The server will store all received data, including numeric data and live feeds, for future use. The back end of the website has many functions such as dataset management, device management, user administration, firmware management, control management, and live image feed management are some of the capabilities available. When 100 users access the system simultaneously, the RAM usage on the server is 167 MB. RAM utilization reaches 389 MB when 400 users access the system simultaneously. The limit for simultaneous user connections to the web interface is 400 users. The maximum number of devices that can be connected simultaneously via MQTT communication is 900.

Dissecting the Permeability of the Escherichia coli Cell Envelope to a Small Molecule Using Tailored Intensiometric Fluorescent Protein Sensors.

Membranes provide a highly selective barrier that defines the boundaries of any cell while providing an interface for communication and nutrient uptake. However, despite their central physiological role, our capacity to study or even engineer the permeation of distinct solutes across biological membranes remains rudimentary. This especially applies to Gram-negative bacteria, where the outer and inner membrane impose two permeation barriers. Addressing this analytical challenge, we exemplify how the permeability of the Escherichia coli cell envelope can be dissected using a small-molecule-responsive fluorescent protein sensor. The approach is exemplified for the biotechnologically relevant macrolide rapamycin, for which we first construct an intensiometric rapamycin detector (iRapTor) while comprehensively probing key design principles in the iRapTor scaffold. Specifically, this includes the scope of minimal copolymeric linkers as a function of topology and the concomitant need for gate post residues. In a subsequent step, we apply iRapTors to assess the permeability of the E. coli cell envelope to rapamycin. Despite its lipophilic character, rapamycin does not readily diffuse across the E. coli envelope but can be enhanced by recombinantly expressing a nanopore in the outer membrane. Our study thus provides a blueprint for studying and actuating the permeation of small molecules across the prokaryotic cell envelope.