Aug 2026· Automation· Vol 7, pp. 124· 0 citations· 29 references
TL;DR
Presented demonstrator examples and test results show that SMILE provides a lightweight and accessible approach for physics laboratory automation, conceptually inspired by distributed control systems such as TANGO and EPICS, while remaining focused on small-scale experiments and rapid prototyping.
Abstract
Scalable Modular Instrumentation for Laboratory Experiments (SMILE) is a lightweight framework for the rapid development and networking of laboratory instrumentation demonstrated with a low-cost Arduino micro-controller. The approach extends the fast-prototyping paradigm of Arduino by enabling a seamless transition from standalone devices to distributed, network-accessible systems without requiring complex control infrastructures. The system architecture follows a simplistic and intuitive development workflow: devices are first implemented and defined through a human-readable serial interface, which is then reused without modification by a Python-based driver. The driver can be directly accessed or enabled as a network service via ZeroRPC, allowing transparent remote access to instrument’s functionality. A key feature of SMILE is the automatic mapping of serial commands to Python functions, which facilitates immediate integration of newly defined device commands into higher-level control and automation workflows. SMILE design allows heterogeneous system integration with both custom-built instruments and laboratory equipment with standard interfaces (e.g., GPIB, RS232/485, USB, Ethernet) to be incorporated into a unified distributed system through lightweight software layers. Presented demonstrator examples and test results show that SMILE provides a lightweight and accessible approach for physics laboratory automation, conceptually inspired by distributed control systems such as TANGO and EPICS, while remaining focused on small-scale experiments and rapid prototyping.
Remote experimental platforms enable local simulation models or physical devices to be accessed over a network, but conventional Web front ends typically require a separate page, control layout, and data communication logic for each experiment, which increases development costs. This work validates an established workflow for automatically generating a Web user interface (UI) from LabVIEW virtual instruments (VIs) using the remote interoperability protocol (RIP) and provides a reproducible protocol for implementing it. The workflow constructs LabVIEW VIs that define input controls and output indicators on the Front Panel, registers each VI in RIP Server Configuration, reads the resulting variable metadata, and generates the corresponding Web controls and output displays. Caddy is used as a reverse proxy to unify the front-end static-file path and RIP application programming interface (API) request path. The workflow is evaluated with two distinct systems: a fan speed model and a direct current (DC) motor proportional-integral-derivative (PID) position-control model. In both cases, the Web page identifies the exposed variables, writes user inputs to the LabVIEW back end, reads model outputs, and generates the interface from RIP metadata. These results validate the same automatic UI-generation process across two different dynamic systems and document the steps required to reproduce it.
Zimo Zhou, Zhongcheng Lei, Luis de la Torre et al.· Journal of Visualized Experi...· 0 citations
The accelerating complexity of wireless hardware systems, driven by the proliferation of multistandard radios, multi-band front-ends, and highly integrated system-on-chip platforms, has made
manual RF test methodologies increasingly inadequate for the validation workloads of modern
hardware engineering laboratories. Test automation, the systematic use of scripting frameworks
and programmable instrument control interfaces to execute RF measurement sequences without
manual intervention, has emerged as a foundational engineering discipline that determines the
throughput, repeatability, and traceability of wireless hardware characterization workflows. This
paper presents a comprehensive review of test automation architectures, scripting frameworks,
and instrument control strategies applicable to wireless hardware engineering. The review
examines the evolution of automation approaches from early GPIB-based sequential scripting to
modern Python-based asynchronous measurement orchestration, covering standard
communication protocols including SCPI, LXI, and VISA, scripting environments including
Python, MATLAB, and LabVIEW, specialized RF test frameworks including Keysight PathWave
and National Instruments TestStand, and emerging approaches based on cloud-connected
measurement architectures and machine learning-assisted measurement optimization. The paper
synthesizes design principles for constructing automation frameworks that maximize measurement
throughput while maintaining calibration traceability, and identifies the key challenges of timing
synchronization, instrument state management, error recovery, and data provenance that
differentiate professionally engineered automation systems from ad hoc scripts. A structured
taxonomy of automation approaches is presented according to test complexity, measurement speed
requirement, and deployment context. The review concludes by identifying the most significant
open challenges in wireless hardware test automation, including automated calibration
verification, uncertainty-aware measurement pipelines, and the integration of AI-based anomaly
detection into production test workflows.
Robert Quainoo· International Journal of Eng...· 0 citations
This study proposes a microservices-based software architecture for implementing digital twins (DT) in real-time industrial process control. Unlike monolithic approaches, the proposed architecture decouples acquisition, modeling, prediction, and actuation into independent containerized services orchestrated by Kubernetes. Communication is handled via MQTT for lightweight telemetry and gRPC for low-latency control calls, achieving highly predictable, bounded synchronization latencies below 10 ms under nominal edge conditions. A discrete-time Luenberger observer ensures bidirectional state fidelity between the physical asset and its virtual replica. Experimental validation on an edge cluster with TSN compatibility demonstrates elastic scalability, fault isolation, and 100% operational uptime during a continuous 24 h stress test, validating the architectural mechanisms designed to achieve high availability in production environments. The architecture bridges the gap between passive DT monitoring and active closed-loop control, enabling predictive interventions and real-time adaptability.
Samuel Benjamín Lascano Rivera, Mary Lascano Rivera, Omar Quimbita· Processes· 0 citations
Modern endpoint security systems can easily detect and block non-standard USB Human Interface Devices (HIDs), limiting the effectiveness of hardware-based penetration testing tools. This paper presents ControlPuc v0, a stealth-oriented hardware-software framework designed to evaluate endpoint resilience against advanced HID emulation attacks. Built on the RP2350 microcontroller with a custom micro-runtime [7], the system uses a physical pin-voltage interlock to switch between maintenance and stealth modes. To evade forensic detection, low-level firmware modifications disable Mass Storage Class descriptors, preventing automatic drive enumeration by Data Loss Prevention (DLP) systems. An anthropomorphic input engine introduces deterministic 5 ms delays between USB transactions to mimic human interaction and evade behavioral analysis. Additionally, an asynchronous outbound HTTP pull-based communication model enables reliable remote orchestration while bypassing stateful network restrictions. The framework highlights critical weaknesses in current heuristic endpoint defense mechanisms.
Anindya Das, Uttalak Mitra· International Journal of Res...· 0 citations
SINETStream is a messaging library designed for wide-area IoT data collection. While its standard Python and Java implementations have been publicly available and actively used since 2019, primarily targeting personal computers and single-board computers, directly deploying these libraries onto microcontroller units (MCUs) is impractical due to severe memory and storage constraints. In this paper, we propose a MicroPython-based implementation of SINETStream optimized for resource-constrained edge devices. To minimize the memory footprint, we have developed a prototype focusing on MQTT. We addressed strict hardware limitations by replacing highoverhead dependencies with micro-libraries and transitioning configuration parsing from YAML to JSON, thereby reducing both operational overhead and the library's footprint. To maintain essential functional compatibility with the broader SINETStream ecosystem, this prototype retains the original modular architecture, supporting both Publisher and Subscriber roles. Furthermore, it ensures payload interoperability with existing data by incorporating Apache Avro serialization and gzip compression. Experimental results using a Raspberry Pi Pico 2 W demonstrate that the compiled library occupies only about 330 KB of flash memory. Even during continuous data transmission over Wi-Fi, total RAM consumption-including the MicroPython runtime environment-remained at approximately 200 KB. This efficiency reliably leaves more than half of the system memory available for user-defined sensing, data filtering, and actuator control logic. Ultimately, this implementation successfully extends the SINETStream ecosystem to the extreme edge using lowcost hardware. Given the widespread accessibility of inexpensive MCUs, we anticipate this lightweight adaptation will encourage broader utilization, including in educational applications.
Takeshi Sakurada, Atsuko Takefusa, Kumiko Kobayashi et al.· Annual International Compute...· 0 citations
A persistent gap separates research-grade Artificial Intelligence (AI) tools from operational accelerator control systems—one that is defined by integration engineering, not model performance. Differences between major control system frameworks—Experimental Physics and Industrial Control System (EPICS), TANGO Controls (TANGO), OPC Unified Architecture (OPC-UA), Distributed Object-Oriented Control System (DOOCS), and Front-End Software Architecture (FESA)—combined with network security constraints, data quality requirements, and operator trust, creates challenges absent from research environments. Cosylab, as an industrial partner within the ARTificial Intelligence For Accelerators, user Communities and associated Technologies (ARTIFACT) network, investigate the possibilities to address this. We propose a framework-agnostic abstraction layer that connects AI services to facility control systems without necessarily modifying operational infrastructure. The architecture enforces safety by default: read-only access, scope-restricted writes, Human-in-the-Loop (HITL) approval, watchdog mechanisms, and audit logging. We also explore the possibility of using Large Language Model (LLM) applications as operator copilots—alarm interpretation, procedure assistance, knowledge retrieval—and actuating agents, each with distinct safety requirements. This integration layer forms the basis of Cosylab’s contribution to TwinRise, an EU-funded Digital Twin Engine project.
J. Varlec, Jan Jug, Tilen Žagar· EPJ Research Infrastructures· 0 citations