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IoT technology applied to agriculture has produced a number of contributions in the recent years. Such solutions are, most of the time, fully tailored to a particular functional target and focus extensively on sensor-hardware development and customization. As a result, software-centered solutions for IoT system development are infrequent. This is not suitable, as the software is the bottleneck in modern computer systems, being the main source of performance loss, errors, and even cyber attacks. This paper takes a software-centric perspective to model and design IoT systems in a flexible manner. We contribute a software framework that supports the design of the IoT systems’ software based on software services in a client–server model with REST interactions; and it is exemplified on the domain of efficient irrigation in agriculture. We decompose the services’ design into the set of constituent functions and operations both at client and server sides. As a result, we provide a simple and novel view on the design of IoT systems in agriculture from a sofware perspective: we contribute simple design structure based on the identification of the front-end software services, their internal software functions and operations, and their interconnections as software services. We have implemented the software framework on an IoT irrigation use case that monitors the conditions of the field and processes the sampled data, detecting alarms when needed. We demonstrate that the temporal overhead of our solution is bounded and suitable for the target domain, reaching a response time of roughly 11 s for bursts of 3000 requests.

Machine vision systems are an important part of modern intelligent manufacturing systems, but due to their complexity, current vision systems are often customized and inefficiently developed. Generic closed-source machine vision development software is often poorly targeted. To meet the extensive needs of product appearance quality inspection in industrial production and to improve the development efficiency and reliability of such systems, this paper designs and implements a general machine vision software framework. This framework is easy to adapt to different hardware devices for secondary development, reducing the workload in generic functional modules and program architecture design, which allows developers to focus on the design and implementation of image-processing algorithms. Based on the MVP software design principles, the framework abstracts and implements the modules common to machine vision-based product appearance quality inspection systems, such as user management, inspection configuration, task management, image acquisition, database configuration, GUI, multi-threaded architecture, IO communication, etc. Using this framework and adding the secondary development of image-processing algorithms, we successfully apply the framework to the quality inspection of the surface defects of bolts.

Model Predictive Control (MPC) can be used in the context of building automation to improve energy efficiency and occupant comfort. Ideally, the MPC algorithm should consider current- and planned usage of the controlled environment along with initial state and weather forecast to plan for optimal comfort and energy efficiency. This implies the need for an MPC application which 1) considers multiple objectives, 2) can draw on multiple data sources, and 3) provides an approach to effectively integrate against heterogeneous building automation systems to make the approach reusable across different installations. To this end, this paper presents a design and implementation of a framework for digital twins for buildings in which the controlled environments are represented as digital entities. In this framework, digital twins constitute parametrized models which are integrated into a generic control algorithm that uses data on weather forecasts, current- and planned occupancy as well as the current state of the controlled environment to perform MPC. This data is accessed through a generic data layer to enable uniform data access. This enables the framework to switch seamlessly between simulation and real-life applications and reduces the barrier towards reusing the application in a different control environment. We demonstrate an application of the digital twin framework on a case study at the University of Southern Denmark where a digital twin has been used to control heating and ventilation. From the case study, we observe that we can switch from rule-based control to model predictive control with no immediate adverse effects towards comfort or energy consumption. We also identify the potential for an increase in energy efficiency, as well as introduce the possibility of planning energy consumption against local electricity production or market conditions, while maintaining occupant comfort. [ABSTRACT FROM AUTHOR]

The gigabit optical serial interface protocol (GOSIP) provides communication via optical fibers between multiple kinds of front-end electronics and the KINPEX PCIe receiver board located in the readout host PC. In recent years, a stack of device driver software has been developed to utilize this hardware for several scenarios of data acquisition (DAQ). On top of this driver foundation, several graphical user interfaces (GUIs) have been created. These GUIs are based on the Qt graphics libraries and are designed in a modular way: All common functionalities, like generic I/O with the front-ends, handling of configuration files, and window settings are treated by a framework class GosipGUI. In the Qt workspace of such GosipGUI frame, specific subclasses may implement additional windows dedicated to operating different GOSIP front-end modules. For each kind of front-end, the GUIs allow to monitor specific register contents, set up the working configuration, and interactively change parameters like sampling thresholds during DAQ. The latter is extremely useful when qualifying and tuning the front-ends in the electronics lab or detector cave. Moreover, some of these GosipGUI implementations have been equipped with features for mostly automatic testing of ASICs in a prototype mass production. [ABSTRACT FROM AUTHOR]

The popularity of mesoscopic whole-brain imaging techniques has increased dramatically, but these techniques generate teravoxel-sized volumetric image data. Visualizing or interacting with these massive data is both necessary and essential in the bioimage analysis pipeline; however, due to their size, researchers have difficulty using typical computers to process them. The existing solutions do not consider applying web visualization and three-dimensional (3D) volume rendering methods simultaneously to reduce the number of data copy operations and provide a better way to visualize 3D structures in bioimage data. Here, we propose webTDat, an open-source, web-based, real-time 3D visualization framework for mesoscopic-scale whole-brain imaging datasets. webTDat uses an advanced rendering visualization method designed with an innovative data storage format and parallel rendering algorithms. webTDat loads the primary information in the image first and then decides whether it needs to load the secondary information in the image. By performing validation on TB-scale whole-brain datasets, webTDat achieves real-time performance during web visualization. The webTDat framework also provides a rich interface for annotation, making it a useful tool for visualizing mesoscopic whole-brain imaging data.

The popularity of mesoscopic whole-brain imaging techniques has increased dramatically, but these techniques generate teravoxel-sized volumetric image data. Visualizing or interacting with these massive data is both necessary and essential in the bioimage analysis pipeline; however, due to their size, researchers have difficulty using typical computers to process them. The existing solutions do not consider applying web visualization and three-dimensional (3D) volume rendering methods simultaneously to reduce the number of data copy operations and provide a better way to visualize 3D structures in bioimage data. Here, we propose webTDat, an open-source, web-based, real-time 3D visualization framework for mesoscopic-scale whole-brain imaging datasets. webTDat uses an advanced rendering visualization method designed with an innovative data storage format and parallel rendering algorithms. webTDat loads the primary information in the image first and then decides whether it needs to load the secondary information in the image. By performing validation on TB-scale whole-brain datasets, webTDat achieves real-time performance during web visualization. The webTDat framework also provides a rich interface for annotation, making it a useful tool for visualizing mesoscopic whole-brain imaging data. [ABSTRACT FROM AUTHOR]

The high demand of artificial intelligence services at the edges that also preserve data privacy has pushed the research on novel machine learning paradigms that fit these requirements. Federated learning has the ambition to protect data privacy through distributed learning methods that keep the data in its storage silos. Likewise, differential privacy attains to improve the protection of data privacy by measuring the privacy loss in the communication among the elements of federated learning. The prospective matching of federated learning and differential privacy to the challenges of data privacy protection has caused the release of several software tools that support their functionalities, but they lack a unified vision of these techniques, and a methodological workflow that supports their usage. Hence, we present the Sherpa.ai Federated Learning framework that is built upon a holistic view of federated learning and differential privacy. It results from both the study of how to adapt the machine learning paradigm to federated learning, and the definition of methodological guidelines for developing artificial intelligence services based on federated learning and differential privacy. We show how to follow the methodological guidelines with the Sherpa.ai Federated Learning framework by means of a classification and a regression use cases. • We analyse the most recent federated learning and differential privacy frameworks. • We present Sherpa.ai FL, a unified framework for federated learning and differential privacy. • We discuss the adaptation of machine learning models to federated learning. • We define the guidelines for privacy-preserving artificial intelligence services. [ABSTRACT FROM AUTHOR]

Complex experiments such as the stellarator Wendelstein 7-X demand efficient experimental planning, operation, and data evaluation. To assist the process organization at W7-X, a metadata framework has been implemented. It annotates a physics program with tags describing its intention, main parameters, boundary conditions, and evaluation results. The approach enables relevant metadata to be collected exactly where information is available and to forward it from experimental planning and execution to the related entry in the central W7-X Logbook. In consequence, these metadata offer possibilities for classifying, searching, filtering, and so on, both in the database of the prepared programs and in the logbook of the executed programs. Wherever possible, metadata are generated automatically. Starting from the parameters of the program, the framework allows typical physics or technical quantities or any programmatically extractable information describing the programs intention to be extracted already while editing. During discharge preparation, metadata are retrieved from the related session planning documents and from the actual session environment. Supplementary tags can be added manually from an extendible tag catalog to categorize experimental programs or to provide metadata for not fully integrated diagnostics. All metadata are logged together with status and progress information during execution and are available within the program’s log—where the metadata can be finally completed, for example, to manually qualify the success of the program or by adding programmatically produced metadata using the logbook’s programming interface. [ABSTRACT FROM AUTHOR]

Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Edge computing is the best approach for meeting the exponential demand and the real-time requirements of many video analytics applications. Since most of the recent advances regarding the extraction of information from images and video rely on computation heavy deep learning algorithms, there is a growing need for solutions that allow the deployment and use of new models on scalable and flexible edge architectures. In this work, we present Deep-Framework, a novel open source framework for developing edge-oriented real-time video analytics applications based on deep learning. Deep-Framework has a scalable multi-stream architecture based on Docker and abstracts away from the user the complexity of cluster configuration, orchestration of services, and GPU resources allocation. It provides Python interfaces for integrating deep learning models developed with the most popular frameworks and also provides high-level APIs based on standard HTTP and WebRTC interfaces for consuming the extracted video data on clients running on browsers or any other web-based platform.

As the complexity of automated driving systemss (ADSs) with automation levels above level 3 is rising, virtual testing for such systems is inevitable and necessary. The complexity of testing these levels lies in the modeling and calculation demands for the virtual environment, which consists of roads, traffic, static and dynamic objects, as well as the modeling of the car itself. An essential part of the safety and performance analysis of ADSs is the modeling and consideration of dynamic road traffic participants. There are multiple forms of traffic flow simulation software (TFSS), which are used to reproduce realistic traffic behavior and are integrated directly or over interfaces with vehicle simulation software environments. In this paper we focus on the TFSS from PTV Vissim in a co-simulation framework which combines Vissim and CarMaker. As it is a commonly used software in industry and research, it also provides complex driver models and interfaces to manipulate and develop customized traffic participants. Using the driver model DLL interface (DMDI) from Vissim it is possible to manipulate traffic participants or adjust driver models in a defined manner. Based on the DMDI, we extended the code and developed a framework for the manipulation and testing of ADSs in the traffic environment of Vissim. The efficiency and performance of the developed software framework are evaluated using the co-simulation framework for the testing of ADSs, which is based on Vissim and CarMaker.

In previous works the author has proposed a universal software framework that allows implementing the known algorithms of group method of data handling, model bases, training methods and model selection criteria. This paper introduces the solution of a topical problem of increasing the performance of the framework. Based on the review of existing computing architectures for parallel data processing and software systems for inductive modeling supporting parallel computations the author has worked out the requirements for the subsystems of parallel computing and memory management of the software framework. Using the methodology of object-oriented analysis and design the author developed the object-oriented structure of these subsystems and introduced the specifics of their operation on each of the mentioned computing architectures. The performance of the parallel implementation of the combinatorial group method of data handling algorithm on basis of the software framework was evaluated experimentally for multi-core processors.

The relevance of the work is caused by necessity of increasing efficiency of automatic data mining systems based on meta-learning. The main aim of the study is to design an automatic meta-learning system supporting selection of optimal algorithm for problem solving and calculation of optimal parameters of its functioning. The methods used in the study: inductive modeling, methods of statistical analysis of results. Results: The known meta-learning systems were integrated based on produced classification features taking into account internal structure of systems. The author has stated the requirements for implementation of the automatic meta-learning system and has offered the way to build a meta-learning system satisfying all stated requirements and accumulating meta-knowledge, building meta-models on its basis, selecting optimal algorithm from a set of available ones and calculating optimal parameters of its functioning. The object-oriented architecture of a software framework for implementation of any meta-learning system presented in the systematization was developed. The efficiency of the implemented automatic meta-learning system using algorithms of group method of data handling was experimentally examined being applied to solution of problems related to the short-term time series forecasting (1428 time series from the testing set known as "M3 Competition").

Blockchain applications in healthcare have grown rapidly. They include record-keeping, clinical trials, medical supply chains, patient monitoring, etc., where blockchain characteristics are needed to improve safety, privacy, and security. Blockchain technology is one of the most significant disruptive technologies today. However, Porru et al. 1 reported that it lacks processes, tools, and techniques. Therefore, this paper provides a systematic framework for a secure and sustainable software engineering framework for healthcare blockchain applications (S 3 EF-HBCA). S 3 EF-HBCA is a significant contribution that includes requirements engineering for healthcare, business process modeling for healthcare, domain modeling for healthcare, a reference architecture for healthcare, and validation by a case study on electronic healthcare record management system (EHR), and simulation with business process modeling notation (BPMN) tools. The simulation shows it has taken 10.45 min to process 100 instances of real-time data and service requests. The overall result shows encouragement regarding process, tools, standards, and testing., (© 2023 The Authors.)

Real-time analysis of large multimedia streams is nowadays made efficient by the existence of several Big Data streaming platforms, like Apache Flink and Samza. However, the use of such platforms is difficult due to the fact that facilities they offer are often too raw to be effectively exploited by analysts. We describe the evolution of RAM3S, a software infrastructure for the integration of Big Data stream processing platforms, to SPAF, an abstraction framework able to provide programmers with a simple but powerful API to ease the development of stream processing applications. By using SPAF, the programmer can easily implement real-time complex analyses of massive streams on top of a distributed computing infrastructure, able to manage the volume and velocity of Big Data streams, thus effectively transforming data into value., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Bartolini and Patella.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Summary: Software design and component reuse for heuristic algorithms have gained in relevance; however, further innovation is needed. In this context, hMod is presented as a software framework suited for implementing heuristic algorithms, with a focus on intensive reuse of highly cohesive operator and data components within algorithmic structures, making it possible to dynamically (re)configure and manage such a structure. Rather than a fast‐prototyping tool, hMod supports heuristic implementation in the long term, whereby complexity can escalate from simple operators to major hyperheuristic architectures. In its core resides a novel object‐oriented representation of algorithms through a pattern‐like implementation, namely, algorithm assembling (AA). Additionally, it incorporates component integration features, such as dependency injection mechanisms. hMod has been mentioned in previous research, in which hyperheuristic methods were implemented and evaluated from an optimization perspective. In this work, a description of the framework is presented from the software design perspective, including the AA model, its architecture, and a detailed presentation of the main features of the framework. Previous hMod applications have demonstrated that it supports not only the software design requirements of heuristic algorithms but performance standards as well. Available sources of the framework can be found in http://gitlab.com/eurra/hmod. [ABSTRACT FROM AUTHOR]

In this paper, we present an intelligent and flexible framework for autonomous pick-and-place tasks in previously unknown scenarios. It includes modules for object recognition, environment modeling, motion planning and collision avoidance, as well as sophisticated error handling and a task supervisor. The framework combines state-of-the-art algorithms and was validated during the first phase of the European Robotics Challenge in which it obtained first place in a field of 39 international contestants. We discuss our results and the potential application of our framework to real industrial tasks. Furthermore, we validate our approach with an application on a real harvesting manipulator. To inspire other teams participating in the challenge and as a tool for new researchers in the field, we release it as open source. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) is typically composed of spatially distributed miniature sensors that help collect large amounts of real-time environmental data from buildings. These environmental data (e.g., temperature, humidity, CO2 concentration) can assist a series of heating, ventilation, and air conditioning (HVAC) equipment to increase the building energy efficiency. From a system design perspective, heterogeneous wireless sensor networks need to address two challenges. First, sensor data acquisition, conversion, fusion, and packaging involve a series of software processing. Since each type of environmental sensor typically has unique processing requirements, it is difficult to develop an efficient software framework to combine the processing of multiple heterogeneous sensors. Second, during normal operation of a heterogeneous wireless sensor network, if users insert or remove some environmental sensors, the entire WSN system should operate normally. In this work, in order to solve the above two system design challenges, we have developed a low-power, low-cost, small form-factor WSN development platform, and its software framework can perform efficient data acquisition, conversion, fusion, and packaging for multiple heterogeneous sensors. Our proposed software framework enables easy and rapid WSN system deployment without affecting the overall functionality of each node. The proposed design differs from existing WSN platforms in that it emphasizes advanced high-level usability and reduces time to market without sacrificing low-level features. The proposed WSN system has been implemented and tested in an office building for indoor fire hazard detection. The experimental results show that our software framework can successfully complete data acquisition, conversion, fusion, and packaging tasks for three heterogeneous environmental sensors. In addition, we have verified that our software framework supports robust system operation when inserting or removing sensors from an existing heterogeneous WSN system. [ABSTRACT FROM AUTHOR]

A lack of consistent data, reliable analysis techniques, and user-friendly tools is inhibiting decision makers' abilities to assess and evaluate the sustainability performance of manufacturing processes and systems. Recent integration of information technology with advanced manufacturing technologies has led to a new paradigm, termed smart manufacturing or Industry 4.0. To leverage this paradigm shift for the advancement of sustainable manufacturing, efforts have been made to characterize unit manufacturing processes (UMPs) for sustainability assessment and to develop frameworks, methodologies, and standards utilizing information modeling approaches. This paper proposes a grey box object-oriented software framework for sustainability assessment of complex manufacturing systems, utilizing the multiple inheritance principle for composition of UMP models. Information modeling guidelines based on the framework are provided for UMP model developers and UMP performance analysts to construct and analyze system models. To demonstrate the usefulness of the framework and guidelines, a prototype information model is developed for a hybrid manufacturing system composed of fused deposition modeling (an additive process) and CNC milling (a subtractive process). [ABSTRACT FROM AUTHOR]