Communication Interface Research Articles

Decentralized Real-Time Anomaly Detection in Cyber-Physical Production Systems under Industry Constraints.

Anomaly detection is essential for realizing modern and secure cyber-physical production systems. By detecting anomalies, there is the possibility to recognize, react early, and in the best case, fix the anomaly to prevent the rise or the carryover of a failure throughout the entire manufacture. While current centralized methods demonstrate good detection abilities, they do not consider the limitations of industrial setups. To address all these constraints, in this study, we introduce an unsupervised, decentralized, and real-time process anomaly detection concept for cyber-physical production systems. We employ several 1D convolutional autoencoders in a sliding window approach to achieve adequate prediction performance and fulfill real-time requirements. To increase the flexibility and meet communication interface and processing constraints in typical cyber-physical production systems, we decentralize the execution of the anomaly detection into each separate cyber-physical system. The installation is fully automated, and no expert knowledge is needed to tackle data-driven limitations. The concept is evaluated in a real industrial cyber-physical production system. The test result confirms that the presented concept can be successfully applied to detect anomalies in all separate processes of each cyber-physical system. Therefore, the concept is promising for decentralized anomaly detection in cyber-physical production systems.

Endocrine Regulation of Microvascular Receptor-Mediated Transcytosis and Its Therapeutic Opportunities: Insights by PCSK9-Mediated Regulation.

Currently, many neurological disorders lack effective treatment options due to biological barriers that effectively separate the central nervous system (CNS) from the periphery. CNS homeostasis is maintained by a highly selective exchange of molecules, with tightly controlled ligand-specific transport systems at the blood-brain barrier (BBB) playing a key role. Exploiting or modifying these endogenous transport systems could provide a valuable tool for targeting insufficient drug delivery into the CNS or pathological changes in the microvasculature. However, little is known about how BBB transcytosis is continuously regulated to respond to temporal or chronic changes in the environment. The aim of this mini-review is to draw attention to the sensitivity of the BBB to circulating molecules derived from peripheral tissues, which may indicate a fundamental endocrine-operating regulatory system of receptor-mediated transcytosis at the BBB. We present our thoughts in the context of the recent observation that low-density lipoprotein receptor-related protein 1 (LRP1)-mediated clearance of brain amyloid-β (Aβ) across the BBB is negatively regulated by peripheral proprotein convertase subtilisin/kexin type 9 (PCSK9). We hope that our conclusions will inspire future investigations of the BBB as dynamic communication interface between the CNS and periphery, whose peripheral regulatory mechanisms could be easily exploited for therapeutic purposes.

(How) Can Machine Learning Support Teaching Staff in a Virtual Collaborative Learning Environment?

Purpose: Supervising roles within virtual collaborative learning (VCL) environments face many different challenges and are often having difficulties keeping an eye on everything and comprehending every participant’s or group’s workflow. To help supervisors with problems like that we developed a software prototype to support their daily workflow and overcome the mentioned challenges. Study design/methodology/approach: This study used a design science research approach to investigate how learning analytics might be able to support teaching staff in VCL settings. Previous studies demonstrated that this approach is very well suited to derive design guidelines for such software artifacts. Initially, a qualitative interview series with four experienced tutors was carried out to get an in-depth understanding of the challenges and tasks teaching staff typically faces in VCL environments. The interview material was systematically analysed to acquire the underlying software requirements. These were then combined with existing knowledge about support software of similar use cases to create a first prototype, primarily based on a supervised machine learning model that classifies online messages sent within the teams. Findings: The software tool we have developed has shown that machine learning processes can indeed be used to support teaching staff in VCL environments. The tool achieves satisfactory classification performance in categorizing chat messages. It could therefore be demonstrated that it is possible to classify chat messages using a software tool. The interviews conducted revealed a particular interest in the statistical evaluations of group activities in Microsoft Teams. This was said to save a lot of time in the subsequent evaluation of the groups. The interviewed eTutors also expressed an interest in receiving private information on the development of possible conflicts within groups. The interest of the interviewed supervisors was very high and additional interviews could lead to further possible feature ideas. In general, the evaluative interviews resulted in positive feedback regarding how our prototype supported eTutors during their work. Originality/value: In current times, spatially distributed and asynchronous education plays a bigger role than ever before. VCL environments are useful tools to overcome these challenges of time and space. However, unlike research on conversational agents or automated feedback systems, the focus of this work is not on the communication interface between software and users or the exclusively quantitative analysis of messages. The goal is to evaluate the content of sent messages within VCL environments using the six defined categories to gain a better qualitative understanding of the teams’ collaboration and its workflow.

Detection of anomalies of a non-deterministic software-defined networking control

Software Defined Networking (SDN) is a network architecture within the control is centralized through a software-based controller. Being a single point of attack makes the controller the preferred target in the SDN architecture. Multi-controller architecture has been introduced to reinforce the control plane. However it requires a communication interface between the controllers which is a security threat. In this objective, a dual controller architecture is introduced and it consists of one nominal controller in charge of the data plane computation plus a second one in charge of the detection of anomalies in the decisions taken by the main controller. In the case of non-determinist algorithm, the detection logic aims at determining a likelihood score of the decisions taken by the controller. A multi-criterion detection approach is proposed by considering both the performance of the decisions and the structure of the decisions taken by the controller. Such computations are probabilistic and attention has been paid to machine learning algorithms to determine this likelihood. More precisely, three formalisms are compared: Probabilistic Finite Automaton, Hidden Markov Model and Recurrent Neural Network. The impact of the control variance in the detection accuracy depending on the formalism used is discussed on a case study.

A Scheme for Distribution Automation Terminal Design Based on ESIM

To realize the operation monitoring and control of distribution network, improve the reliability of power supply and reduce the intensity of operation and maintenance, a design scheme of distribution automation terminal based on eSIM card is proposed. Based on the analysis of eSIM technology and its characteristics, the basic workflow of eSIM card is provided in combination with the remote configuration requirements of power system. Then the main control function and communication module design of the distribution network system are analyzed and explained in detail, with emphasis on the communication interface circuit and software design. The system also integrates AngularJS and SpringBoot to make the platform have the technical characteristics of separation of front and back stations and strong reliability. At the same time, the dial-up and positioning information are integrated into one module to realize real-time monitoring and control of the remote master station. The experimental analysis results show that the scheme has certain advantages in the reliability of distribution automation terminals and the simplification of operation and maintenance management, which increases the integration and functional diversity of power equipment.

A Cluster-Based UE-Scheduling Scheme for NR-V2X

The New Radio Vehicle-to-Everything (NR-V2X) technology investigated by the Third Generation Partnership Project (3GPP) explores the high reliability requirement to support message dissemination by vehicle-to-vehicle (V2V) distributed communications for safety applications via a direct communication interface. However, the natural commonality of distributed resource selection may lead to severe resource collisions in a dense high mobility environment. In this paper, we propose a UE-scheduling resource allocation scheme to reduce distributed resource contentions, where vehicles are clustered autonomously based on orthogonal resource sets detection and the cluster size is determined by the cluster-joining threshold. In addition, the algorithms of clustering maintenance are designed to improve the stability of clusters by periodically checking the status of the resource sets and the movement of vehicles. Furthermore, a detailed frame structure design is described, and theoretical performance analysis models of the proposed scheme in different traffic scenarios are investigated. Finally, simulations and numerical results demonstrate that the proposed scheme can reduce packet collisions and increase the packet delivery ratio compared to the conventional distributed scheme, and outperforms some existing schemes in cluster stability.

Speculative Exploration on Future Sustainable Human-Machine Interface Design in Automated Shuttle Buses

Automated Shuttle buses (ASB) are considered an essential and sustainable direction for the future application of autonomous driving technology in public transportation. As the driver’s role gradually decreases and disappears, the Human–Machine Interface (HMI) for information exchange and communication between users and ASB takes a more prominent role and progressively becomes a hotspot in research. However, the unpredictability and complexity of autonomous driving, an exceptionally fast-growing technology, have hindered its future study. This work first reviewed related literature in three categories: internal, external, and station of the ASB. Secondly, the importance of systemic and speculative design is affirmed by exploring existing HMI designs for ASB. Thirdly, the concepts for ASB resulting from three parallel workshops were analyzed. Finally, online questionnaires and interviews completed the critical reflection and discussion. The results show that the introduction of tools and methods related to systemic and speculative design into the design process of the HMI for ASB may help designers to think critically about the future uncertainty of ASB and to deal with the complexity of the system.

Digital Twins for the Future Power System: An Overview and a Future Perspective

The inevitable transition of the power system toward a sustainable and renewable-energy centered power system is accompanied by huge versatility and significant challenges. A corresponding shift in operation strategies, embracing more intelligence and digitization, e.g., a Cyber-Physical System (CPS), is needed to achieve an optimal, reliable and secure operation across all system levels (components, units, plants, grids) and by the use of big data. Digital twins (DTs) are a promising approach to realize CPS. In this paper, their applications in power systems are reviewed comprehensively. The review reveals that there exists a gap between available DT definitions and the requirements for DTs utilized in future power systems. Therefore, by adapting the current definitions to these requirements, a generic definition of a “Digital Twin System (DTS)” is introduced which finally allows proposing a multi-level and arbitrarily extendable “System of Digital Twin Systems (SDTSs)” idea. The SDTSs can be realized with an open-source framework that serves as a central data and communication interface between different DTSs which can interact by “Reporting Modules” and are regulated by “Control Modules” (CMs). Exemplary application scenarios involving multiple system levels are discussed to illustrate the capabilities of the proposed SDTS concept.

Upscaling Fog Computing in Oceans for Underwater Pervasive Data Science Using Low-Cost Micro-Clouds

Underwater environments are emerging as a new frontier for data science thanks to an increase in deployments of underwater sensor technology. Challenges in operating computing underwater combined with a lack of high-speed communication technology covering most aquatic areas means that there is a significant delay between the collection and analysis of data. This in turn limits the scale and complexity of the applications that can operate based on these data. In this article, we develop underwater fog computing support using low-cost micro-clouds and demonstrate how they can be used to deliver cost-effective support for data-heavy underwater applications. We develop a proof-of-concept micro-cloud prototype and use it to perform extensive benchmarks that evaluate the suitability of underwater micro-clouds for diverse underwater data science scenarios. We conduct rigorous tests in both controlled and field deployments, using river and sea waters. We also address technical challenges in enabling underwater fogs, evaluating the performance of different communication interfaces and demonstrating how accelerometers can be used to detect the likelihood of communication failures and determine which communication interface to use. Our work offers a cost-effective way to increase the scale and complexity of underwater data science applications, and demonstrates how off-the-shelf devices can be adopted for this purpose.

Evaluation of 6 DOF Robotic Arm Using Leap Motion Sensor

Due to their increased functionality, robotic arms provide a well-organized method for developing assistive devices. By 2035, statistics indicate that half of Germany’s population will be over the age of fifty, and every third person will be over the age of sixty. These aging societies face numerous obstacles when it comes to performing basic activities of daily living, or ""ADLs."" A growing body of research is focusing on Ambient Assisted Living, or ""AAL,"" as a novel approach to addressing the needs of elderly people. A critical objective of AAL is to improve the quality of life for the elderly and disabled and to assist them in maintaining an independent lifestyle. Robotics and technology-enabled environments will be critical in enabling elderly and physically disabled people to maintain a self-determined, independent lifestyle in their familiar surroundings. The purpose of this article is to propose the implementation of a novel intuitive and adaptive manipulation scheme by creating a human-machine communication interface between the Leap Motion controller and the 6-DOF Jaco robotic arm. An algorithm is developed to optimize the mapping between the user’s hand movement and the Jaco arm, as tracked by the Leap Motion controller. By constantly adapting to the user’s hand tremor or shake, the system should enable a more natural human-computer interaction and smooth manipulation of the robotic arm. The implementation would significantly improve people’s quality of life, particularly those with upper limb problems, by assisting them in performing several essential Activities of Daily Living ""ADLs."" The applications of this human-robot interaction will be discussed in relation to Ambient Assisted Living, with the introduction of several use case scenarios.

Low-Cost Communication Interface between a Smart Meter and a Smart Inverter

The need for a low-cost interface between the grid and small (<250 kW) renewable distributed energy resources (DERs) is growing in importance as the number of small DERs continues to grow. In this study, a system architecture was proposed to investigate paths to an affordable interconnection for small renewable DERs.Then, a low-cost communication interface between a smart meter and smart inverter was installed using a commercially available bridge device. The interface device was selected based on an assessment concluding that it would be able to support the emerging advanced metering infrastructure (AMI) network. Next, messages were passed across the experimental end-to-end communication interface to test their speed and reliability. Success was based on whether the key functions defined in the standard IEEE 2030.5 were executed or not, which include set points, disconnect/reconnect, and Volt-VAr optimization. The results of the testing provided detailed insights into the benefits and limitations of the proposed architecture. Intermittency of weather-dependent DERs (e.g., solar and wind) adversely impacts the power quality of a DER, making hourly day-ahead prediction nearly impossible. With this in mind, the investigation also considered the potential of using smart inverter functions to reduce DER’s intermittency.

IoT-Enabled Motion Control: Architectural Design Challenges and Solutions

Ever-increasing demands for highly-efficient customized manufacturing are driving the development of Industry 4.0. Reconfigurable manufacturing systems based on modular, convertible, and interoperable equipment present a key enabler of the fourth industrial revolution. Besides suitable mechanical design, control of these smart manufacturing resources should facilitate Internet of Things (IoT) integration and reconfigurability. Existing numerical control kernels (NCK)—the major control component for motion control—hinder rapid reconfiguration due to the complexity of their monolithic centralized structure. On the other hand, reconfigurability is naturally promoted by the distributed control paradigm, as proposed by the industrial IoT (IIoT) concept; hence, in this article, we investigate design challenges in distributing the conventional centralized NCK designs used for control of computerized numerical control systems. We introduce an architecture where each axis module is augmented with a networked, IIoT-enabled low-level controller (LLC) that performs local control and exposes a network interface for communication with other LLCs toward executing the desired process. These smart manufacturing resources communicate with an edge-based high-level controller (HLC) that provides the trajectory specification over the network and schedules manufacturing tasks. We investigate real-time and network bandwidth requirements of different mappings of the NCK layers to the LLCs and the HLC, providing design-time tradeoffs for implementing IoT-ready, distributed motion control. We demonstrate feasibility of our approach using industry-grade single-axis robots and low-cost IoT microcontrollers, and show that minimal accuracy impairment is introduced compared to the centralized setup based on ISO 230 and ISO 10791-7 standards.

Wet-Adhesive Multifunctional Hydrogel with Anti-swelling and a Skin-Seamless Interface for Underwater Electrophysiological Monitoring and Communication.

A stable and seamless adhesion between the human skin and the hydrogel-based electronic skin is necessary for accurate sensing and human health monitoring in aquatic environments. Despite achieving significant progress in this field, it remains a great challenge to design skin-interfaced conductive hydrogels with high electrical conductivity, stablility, and seamless underwater adhesion to skin. Herein, a skin-inspired conductive multifunctional hydrogel is proposed, which has a wet-adhesive/hydrophilic and a non-adhesive/hydrophobic bilayer structure. The hydrogel shows high stretchability (∼2400%) and an ultra-low modulus (4.5 kPa), which facilitate the conformal and seamless attachment of the hydrogel to the skin with reduced motion artifacts. Owing to synergistic physical and chemical interactions, this hydrogel can achieve reliable underwater adhesion and display remarkable underwater adhesion strength (388.1 kPa) to porcine skin. In addition, MXene has been employed to obtain high electrical conductivity, create a route for stable electron transport, and reinforce mechanical properties. The hydrogel also possesses self-healing ability, a low swelling ratio (∼3.8%), biocompatibility, and specific adhesion to biological tissues in water. Facilitated with these advantages, the hydrogel-based electrodes achieve reliable electrophysiological signal detection in both air and wet conditions and demonstrate a higher signal-to-noise ratio (28.3 dB) than that of commercial Ag/AgCl gel electrodes (18.5 dB). Also, the hydrogel can be utilized as a strain sensor with high sensitivity for underwater communication. This multifunctional hydrogel improves the stability of the skin-hydrogel interface in aquatic environments and is expected to be promising for the next-generation bio-integrated electronics.

Upgrade of ATLAS Hadronic Tile Calorimeter for the High Luminosity LHC

The Tile Calorimeter (TileCal) is a sampling hadron calorimeter covering the central region of the ATLAS experiment, with steel as absorber and plastic scintillators as active medium. The High Luminosity phase of the Large Hadron Collider (LHC) is expected to start in 2029 and five times the present instantaneous luminosity will be delivered by the machine. TileCal will require new electronics to meet the requirements of a 1 MHz trigger, to be robust against higher radiation level, 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. 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 commercial off-the-shelf 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 and high speed fibre optic links running up to 9.6 Gb/s. The TileCal upgrade program has included extensive research and development studies, and test beam studies. A Demonstrator module with reverse compatibility with the existing system was inserted in ATLAS in August 2019 for testing in actual detector conditions. The ongoing developments for on- and off-detector systems, together with expected performance characteristics and results of test-beam campaigns with the electronics prototypes are discussed.

Mpi4py.futures: MPI-Based Asynchronous Task Execution for Python

We present mpi4py.futures, a lightweight, asynchronous task execution framework targeting the Python programming language and using the Message Passing Interface (MPI) for interprocess communication. mpi4py.futures follows the interface of the concurrent.futures package from the Python standard library and can be used as its drop-in replacement, while allowing applications to scale over multiple compute nodes. We discuss the design, implementation, and feature set of mpi4py.futures and compare its performance to other solutions on both shared and distributed memory architectures. On a shared-memory system, we show mpi4py.futures to consistently outperform Python's concurrent.futures with speedup ratios between 1.4X and 3.7X in throughput (tasks per second) and between 1.9X and 2.9X in bandwidth. On a Cray XC40 system, we compare mpi4py.futures to Dask – a well-known Python parallel computing package. Although we note more varied results, we show mpi4py.futures to outperform Dask in most scenarios.

MEMs Based Low-Cost Urban Noise Monitoring: Tests and Case Study

Noise pollution has been one of the main causes of citizens' discomfort in the urban centres in Brazil, an issue enhanced by the Covid pandemic that resulted in an increase of noise complaints, especially those related to noise from construction sites. This context triggered the construction industry to pursue solutions to understand the acoustic reality and minimise the impacts through regulations that require long-term noise measurements. Due to the necessity of a comprehensive evaluation in several locations, class 1 Sound Level Meters measurement systems can hardly be considered because of their high costs. This paper discusses the practical implementation of MEMs in a low-cost monitoring system for urban noise, focusing on construction sites. The prototype, based on a Raspberry Pi (a single-board computer model widely used in IoT projects) and a MEMs microphone with I²S interface for high-fidelity digital audio communication, was compared in a controlled environment to a Sound Level Meter of Class 1 through validation tests, such as calibration, frequency response, and dynamic range. Field measurements were also carried out in typical urban noise-generating sound environments.

Evaluation of GBT-FPGA for timing and fast control in CBM experiment

Timing and Fast Control (TFC) system for the Compressed Baryonic Matter (CBM) experiment is being developed with focus on low and deterministic data transmission latency. This helps to minimize data corruption in the free-streaming Data Acquisition (DAQ) system during occasional data bursts caused by the expected beam intensity fluctuations. Proven in latency-optimized experimental data transport applications, the GBT-FPGA core is expected to positively contribute to the TFC system performance. In this work, the core has been integrated as the primary communication interface and its effect on transmission latency and quality of time distribution has been evaluated.

The Analysis of the Evolution of Parking Garage Façade as a Communicative Interface

Compared to the technological innovation and morphological evolution of cars, parking buildings have become a symbol of the desolation of modern cities and an object hindering the aesthetics of the city. The façade of the parking building has few restrictions depending on the composition of the internal space and is a simple skin facing the road or street and is an object of free expression. This paper aims to interpret the façade of a parking building as an interface that is formed directly or indirectly with the surrounding environment and users and to provide essential data for parking space design by understanding the design expression characteristics. As a methodology, a literature review was done to define the interface concept in architectural space. The change of the times of the parking building façade design was investigated. From the perspective of the interface, cases of parking buildings were selected and analyzed. The result showed that the parking building could be a landmark at the entrance to the city and the façade design has an essential meaning in the urban landscape. For architects, designers, and artists, the façade of the parking building can become an interface for free expression and creativity. It was proven that the characteristics of design expression were considered by matching the concepts of interface, communication, and interaction to the idea of the screen, communication, and mobile on the façade of the parking building. This study will serve as primary data to improve the environment of the parking space as a public space and the design quality of the parking building.

Electromyogram-Based Lip-Reading via Unobtrusive Dry Electrodes and Machine Learning Methods.

Lip-reading provides an effective speech communication interface for people with voice disorders and for intuitive human-machine interactions. Existing systems are generally challenged by bulkiness, obtrusiveness, and poor robustness against environmental interferences. The lack of a truly natural and unobtrusive system for converting lip movements to speech precludes the continuous use and wide-scale deployment of such devices. Here, the design of a hardware-software architecture to capture, analyze, and interpret lip movements associated with either normal or silent speech is presented. The system can recognize different and similar visemes. It is robust in a noisy or dark environment. Self-adhesive, skin-conformable, and semi-transparent dry electrodes are developed to track high-fidelity speech-relevant electromyogram signals without impeding daily activities. The resulting skin-like sensors can form seamless contact with the curvilinear and dynamic surfaces of the skin, which is crucial for a high signal-to-noise ratio and minimal interference. Machine learning algorithms are employed to decode electromyogram signals and convert them to spoken words. Finally, the applications of the developed lip-reading system in augmented reality and medical service are demonstrated, which illustrate the great potential in immersive interaction and healthcare applications.

Smart IoT lysimetry system by weighing with automatic cloud data storage

Water is an essential resource for human life and for agricultural food production, climate change and the increased demand for water resources, has already impacted the availability of this resource in the world, in view of future forecasts of scarcity its rational use will be increasingly demanded. Thus, more efficient irrigation systems that optimize water use and reduce water loss will be important tools to help agriculture in the future. This work aims to develop and calibrate an intelligent and autonomous system for determining reference evapotranspiration (ETo), and precise irrigation control optimized for IoT operation, with data storage in a cloud database. Intelligent system integrates: mechanical weighing lysimeter; sensors and actuators commanded by a digital controller based on a microcontroller platform, connected to the internet through a Wi-Fi communication interface to cloud computing services models: platform as a service (PaaS) and software as a service (SaaS). The operation test results reveal that the system is capable of accurately measuring ET O values, consistent with the values estimated by the FAO-56 standard method, with high sensitivity and precision for determining mass variations, with an R² Correlation coefficient = 0.999321, and coefficient of determination of adjusted R² of 0.999321. Being a viable option for application in precision agriculture.