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Indoor cultivation of Cannabis sativa is essential for precise environmental control, maximizing cannabinoid yield, ensuring year-round product availability, and allowing genetic enhancement, making it indispensable for commercial producers and cannabis consumers alike. SmartGrow DataControl presents a cutting-edge computational tool designed specifically for monitoring, collecting, and controlling microclimatic factors in indoor crop cultivation. This system utilizes Model-View-Controller (MVC) architecture patterns and layered architectural patterns to seamlessly combine wireless devices with open-source databases. It focuses on energy efficiency by implementing standardized communication protocols like as TCP/IP, MQTT, HTTP, and Web Socket. The platform intends to optimize data collection for agricultural physiology studies, specifically in crops like Cannabis sativa, by utilizing advanced optical and Internet of Things (IoT) technologies. It is designed with a user-friendly interface to facilitate ease of use. Experimental validation verifies the effectiveness of SmartGrow DataControl, enabling farmers to effectively oversee crops and increase productivity through gradual technical improvements.

When several hydroacoustic modems operate simultaneously in an area of mutual coverage, collisions of data packets received from several sources may occur, which lead to information loss. With an increase in the number of simultaneously operating hydroacoustic modems, physical layer algorithms do not provide stable data transmission and the likelihood of collisions increases, which makes the operation of modems ineffective. To ensure effective operation in a hydroacoustic signal propagation environment and to reduce collisions when exchanging data between two modems that do not have the ability to operate synchronously and to reduce the access time to the signal propagation environment, methods of the medium access control layer using link layer protocols are required. Typically, this problem is solved using code separation of hydroacoustic channels. If you need to transfer over a network, this option will not work, since network transfer involves working on the basis of “broadcast” messages, particularly between data source and data sink that remain too far from each other, outside of their mutual audibility. In practical use, it is convenient to place these protocols into a software environment for developing specific user applications for solving network communication problems. This software framework allows for custom modification of existing network algorithms, as well as the inclusion of new network hydroacoustic communication algorithms. To build a predictive model, the DACAP, T-Lohi, Flooding, and ICRP protocols were used in this work. The implementation is performed in Erlang. The paper presents algorithms for implementing these protocols. A comparative analysis of network operation with and without protocols is provided. Efficiency of operation, i.e., data rates and probabilities of data delivery, was assessed.

Blockchain applications in healthcare have grown rapidly and it includes 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 biggest disruptive technologies today. However, Porru, et. al (2017) have reported 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. Secure and Sustainable Software Engineering Framework for Healthcare Blockchain Applications (S3EF-HBCA) significant contribution includes requirements engineering for healthcare, business process modelling for healthcare, domain modelling for healthcare, a reference architecture for healthcare, and validation by case study on Electronic Healthcare Record Management System (EHR), and simulation with Business Process Modelling Notation (BPMN) tools. The simulation shows it has taken 10.45 minutes to process 100 instances of real-time data and service requests. The overall result shows encouragement in terms of process, tools, standards, and testing.

OptiGUI DataCollector is a Python 3.8-based graphical user interface that facilitates automated data collection in optics and photonics research and development equipment. It provides an intuitive and easy-to-use platform for controlling a wide range of optical instruments, including spectrometers and lasers. OptiGUI DataCollector is a flexible and modular framework that enables simple integration with different types of devices. It simplifies experimental workflow and reduces human error by automating parameter control, data acquisition, and analysis. OptiGUI DataCollector is currently focused on optical mode conversion utilizing fiber optic technologies but can be expanded to other R&D processes.

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.

Existing software tools for specifying and executing multidrone missions are limited to route planning or tightly coupled to specific drone hardware. We introduce EAMOS (Execution of Aerial Multidrone Missions and Operations Specification Framework), which allows us to specify missions intuitively, text-based, and provides a mission compiler, a mission middle layer, and a distributed drone execution environment. The middle layer wraps the control of individual drone-specific capabilities, such as launch, fly to position, or perform a maneuver, into a public API that transparently utilizes the capabilities of numerous drone platforms. We exploit the Go programming language to implement critical components of the framework and provide an interface for ROS-based drone platforms. EAMOS automates the mission execution on real, virtual, and even hybrid robotic setups involving real and virtual drones. We demonstrate the successful deployment of EAMOS with four missions executed on Pixhawk/PX4-equipped quadcopters and virtual drones simulated with Airsim. We assess the performance of our proposed approach by analyzing the number of nodes and arcs of the mission graphs, which are an essential artifact of our mission compilation, the utilization of ROS service calls during mission execution, and the duration of compilation, deployment, and mission execution. Overall, our experiments showed that our drones correctly behaved during mission execution as expected and specified by their mission, the generated mission artifacts were efficiently manageable, and processing times allowed for a fluent workflow.

In this article, we present a framework, called Rehab-Immersive (RI), for the development of virtual reality clinical applications as a complement to the rehabilitation of patients with spinal cord injuries. RI addresses the interaction of patients with virtual worlds, considering upper limb motor impairments. A preconfiguration allows customization for each patient’s specific needs. RI also stores kinematics data, providing clinical staff with a valuable tool to evaluate progress and patient exercise performance. As an example, a virtual version of the Box & Block test is presented.

Abstract 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.

The demand for deep learning frameworks capable of running in edge computing environments is rapidly increasing due to the exponential growth of data volume and the need for real-time processing. However, edge computing environments often have limited resources, necessitating the distribution of deep learning models. Distributing deep learning models can be challenging as it requires specifying the resource type for each process and ensuring that the models are lightweight without performance degradation. To address this issue, we propose the Microservice Deep-learning Edge Detection (MDED) framework, designed for easy deployment and distributed processing in edge computing environments. The MDED framework leverages Docker-based containers and Kubernetes orchestration to obtain a pedestrian-detection deep learning model with a speed of up to 19 FPS, satisfying the semi-real-time condition. The framework employs an ensemble of high-level feature-specific networks (HFN) and low-level feature-specific networks (LFN) trained on the MOT17Det dataset, achieving an accuracy improvement of up to AP50 and AP0.18 on MOT20Det data.

To address the new functional requirements brought by the introduction of new weapons and new combat modes, a comprehensive survey of the research progress in the area of combat simulation software is performed from the perspective of software engineering. First, the top-level specification, simulation engine, and simulation framework of combat simulation software are reviewed. Then, several typical combat simulation software systems are demonstrated, and the relevant software frameworks are analyzed. Finally, combining the application prospect of artificial intelligence, metaverse, and other new technologies in combat simulation, the development trends of combat simulation software are presented, namely intellectualization, adaptation to an LVC (live, virtual, and constructive) system, and a more game-based experience. Based on a comprehensive comparison between the mentioned simulation frameworks, we believe that the AFSIM (Advanced framework for simulation, integration, and modeling) and the E-CARGO (Environments—classes, agents, roles, groups, and objects) are appropriate candidates for developing distributed combat simulation software.

Abstract Recent advances in artificial intelligence (AI), driven mainly by deep neural networks, have yielded remarkable progress in fields, such as computer vision, natural language processing, and reinforcement learning. Despite these successes, the inability to predict how AI systems will behave “in the wild” impacts almost all stages of planning and deployment, including research and development, verification and validation, and user trust and acceptance. The field of explainable artificial intelligence (XAI) seeks to develop techniques enabling AI algorithms to generate explanations of their results; generally these are human‐interpretable representations or visualizations that are meant to “explain” how the system produced its outputs. We introduce the Explainable AI Toolkit (XAITK), a DARPA‐sponsored effort that builds on results from the 4‐year DARPA XAI program. The XAITK has two goals: (a) to consolidate research results from DARPA XAI into a single publicly accessible repository; and (b) to identify operationally relevant capabilities developed on DARPA XAI and assist in their transition to interested partners. We first describe the XAITK website and associated capabilities. These place the research results from DARPA XAI in the wider context of general research in the field of XAI, and include performer contributions of code, data, publications, and reports. We then describe the XAITK analytics and autonomy software frameworks. These are Python‐based frameworks focused on particular XAI domains, and designed to provide a single integration endpoint for multiple algorithm implementations from across DARPA XAI. Each framework generalizes APIs for system‐level data and control while providing a plugin interface for existing and future algorithm implementations. The XAITK project can be followed at: https://xaitk.org.

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.

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.

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").

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").

The report presents an analysis of Big Data storage solutions in different directions. The purpose of this paper is to introduce the technology of Big Data storage, prospects of storage technologies, for example, the software DIRAC. The DIRAC is a software framework for distributed computing. The report considers popular storage technologies and lists their limitations. The main problems are the storage of large data, the lack of quality in the processing, scalability, the lack of rapid availability, the lack of implementation of intelligent data retrieval. Experimental computing tasks demand a wide range of requirements in terms of CPU usage, data access or memory consumption and unstable profile of resource use for a certain period. The DIRAC Data Management System (DMS), together with the DIRAC Storage Management System (SMS) provides the necessary functionality to execute and control all the activities related with data.

The goal of this paper is to give a specification of the software framework that evaluates the efficiency of different mechanisms for concurrent processes, notably process synchronization mechanisms. The paper discusses the concept of this framework, the potential users of it and some necessary considerations, including assumptions. Further, it defines general requirements for the framework and presents its desired conceptual design. The conclusions and possible directions for future work end the paper.

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.

This paper presents a platform for airborne sensor applications using low-cost, open-source components carried by an easy-to-fly unmanned aircraft vehicle (UAV). The system, available in open-source , is designed for researchers, students and makers for a broad range of exploration and data-collection needs. The main contribution is the extensible architecture for modularized airborne sensor deployment and real-time data visualisation. Our open-source Android application provides data collection, flight path definition and map tools. Total cost of the system is below 800 dollars. The flexibility of the system is illustrated by mapping the location of Bluetooth beacons (iBeacons) on a ground field and by measuring water temperature in a lake.

Programmable Logic Controllers (PLCs) are widely used for control and automation of machines and processes, so the quality of PLC software has a direct impact on production efficiency. This paper describes a PLC program for a food production line that was completely rewritten using a different software architecture and framework. The PLC hardware and the production line equipment were not changed, so this project provides an opportunity to quantify the impact of different PLC software architecture on production efficiency. The average number of cases of products produced per production hour during the first ten months with the new program was 6.1% higher than the average during the previous ten months with the old program. PLC software, unlike most other software, is often used by the end-user for troubleshooting. In this case, a relatively simple architecture and framework that favors the end-user significantly improved production efficiency over a more sophisticated architecture and framework that favors the software developer. Suggestions for further research on software architecture and framework are given.

Imaging technology is highly important in today’s medical environments. It provides information upon which the accuracy of the diagnosis and consequently the wellbeing of the patient rely. Increasing the quality and significance of medical image data is therefore one the aims of scientific research and development. We introduce an integrated hardware and software framework for real time image processing in medical environments, which we call RealTimeFrame. Our project is designed to offer flexibility, easy expandability and high performance. We use standard personal computer hardware to run our multithreaded software. A frame grabber card is used to capture video signals from medical imaging systems. A modular, user-defined process chain performs arbitrary manipulations on the image data. The graphical user interface offers configuration options and displays the processed image in either window or full screen mode. Image source and processing routines are encapsulated in dynamic library modules for easy functionality extension without recompilation of the entire software framework. Documented template modules for sources and processing steps are part of the software’s source code.

Abstract Background The amount of web-based resources (databases, tools etc.) in biomedicine has increased, but the integrated usage of those resources is complex due to differences in access protocols and data formats. However, distributed data processing is becoming inevitable in several domains, in particular in biomedicine, where researchers face rapidly increasing data sizes. This big data is difficult to process locally because of the large processing, memory and storage capacity required. Results This manuscript describes a framework, called MAPI, which provides a uniform representation of resources available over the Internet, in particular for Web Services. The framework enhances their interoperability and collaborative use by enabling a uniform and remote access. The framework functionality is organized in modules that can be combined and configured in different ways to fulfil concrete development requirements. Conclusions The framework has been tested in the biomedical application domain where it has been a base for developing several clients that are able to integrate different web resources. The MAPI binaries and documentation are freely available at http://www.bitlab-es.com/mapi under the Creative Commons Attribution-No Derivative Works 2.5 Spain License. The MAPI source code is available by request (GPL v3 license).