Prototyping Platform Research Articles

IoT Based Drone for Pesticides Spraying

The drones will carry pesticides to spray all over the farm, reducing the work of farmers and completing their tasks faster. Pesticides and fertilizer spraying are most important process in the agriculture field for good production of crop. This application provides the user-friendly interface for farmers. Drone is a pesticide spraying quadcopter for agriculture purpose which reduce the amount of work required by the farmer by allowing him to spray pesticides evenly over all the land. Using an Android app, the farmer can operate the drone, and he can connect to the app using a Wi-Fi module in the drone. Regardless of the shape of the field or type of crop, the drone will spray pesticide it will precisely route the area of that farmer's land. This project uses the Nodemcu board, which is an open source electronics prototype platform with an inbuilt Wi-Fi module. Using the accelerometer and gyro (MPU6050), we have balanced the directions and orientations.

A d-axis based current differential protection scheme for an active distribution network

The emergence of distributed generators has changed the operational mode and fault characteristics of the distribution network, in a way which can severely influence protection. This paper proposes a d-axis-based current differential protection scheme. The d-axis current characteristics of inverter-interfaced distributed generators and synchronous generators are analyzed. The differential protection criterion using sampling values of the d-axis current component is then constructed. Compared to conventional phase-based current differential protection, the proposed protection reduces the number of required communication channels, and is suitable for distribution networks with inverter-interfaced distributed generators with complex fault characteristics. Finally, a 10 kV active distribution network model is built in the PSCAD platform and protection prototypes are developed in RTDS. Superior sensitivity and fast speed are verified by simulation and RTDS-based tests.

Lossy Mode Resonance Based Microfluidic Platform Developed on Planar Waveguide for Biosensing Applications.

The development of resonance phenomena-based optical biosensors has gained relevance in recent years due to the excellent optical fiber properties and progress in the research on materials and techniques that allow resonance generation. However, for lossy mode resonance (LMR)-based sensors, the optical fiber presents disadvantages, such as the need for splicing the sensor head and the complex polarization control. To avoid these issues, planar waveguides such as coverslips are easier to handle, cost-effective, and more robust structures. In this work, a microfluidic LMR-based planar waveguide platform was proposed, and its use for biosensing applications was evaluated by detecting anti-immunoglobulin G (anti-IgG). In order to generate the wavelength resonance, the sensor surface was coated with a titanium dioxide (TiO2) thin-film. IgG antibodies were immobilized by covalent binding, and the detection assay was carried out by injecting anti-IgG in PBS buffer solutions from 5 to 20 μg/mL. The LMR wavelength shifted to higher values when increasing the analyte concentration, which means that the proposed system was able to detect the IgG/anti-IgG binding. The calibration curve was built from the experimental data obtained in three repetitions of the assay. In this way, a prototype of an LMR-based biosensing microfluidic platform developed on planar substrates was obtained for the first time.

Developing a One-Stop Platform Transportation Planning Service to Help Older Adults Move Around in Their Community Where, When, and How They Wish: Protocol for a Living Lab Study.

Background Multiple mobility-related challenges frequently appear with aging. As a result, many older adults have difficulty getting around, to go, for example, to doctors’ appointments or leisure activities. Although various means of transportation are currently available, older adults do not necessarily use them, partly because they do not know which ones are adapted to their needs and preferences. To foster older adults’ autonomy and freedom in their decision-making about transportation, it is crucial to help them make informed decisions about the means that suit them best.ObjectiveOur aim is to develop Mobilainés, a one-stop platform transportation planning service combining different transport modes and services to help older adults move around in their community where, when, and how they wish. More specifically, we aim to (1) define older adults’ mobility needs and preferences in order to conceptualize a one-stop platform; (2) cocreate a prototype of the one-stop platform; and (3) test the prototype with users in a real-life context.MethodsThis ongoing study uses a “Living Lab” co-design approach. This approach differs from traditional research on aging by facilitating intersectoral knowledge sharing and innovative solutions by and with older adults themselves. A steering committee of 8 stakeholders from the public, scientific, and private sectors, as well as older citizens, will meet quarterly throughout the study. The design comprises three phases, each with several iterative subphases. Phase 1 is exploration: through co-design workshops and literature reviews, members of the intersectoral committee will define older adults’ mobility needs and preferences to support the conceptualization of the one-stop platform. Phase 2 is experimentation: 4 personas will be produced that reflect the different needs and preferences of typical older adult end users of the platform; for development of a prototype, scenarios and mockups (static designs of the web application) will be created through co-design sessions with older adults (N=12) embodying these personas. Phase 3 is evaluation: we will test the usability of the prototype and document changes in mobility, such as the ability to move around satisfactorily and to participate in meaningful activities, by and with older adults (N=30) who use the prototype. The steering committee will identify ways to support the adoption, implementation, and scaling up of Mobilainés to ensure its sustainability. Qualitative and quantitative data will be triangulated according to each subphase objective.Results The first phase began in September 2019. The study is scheduled for completion by mid-2023.Conclusions This innovative transportation planning service will merge existing transportation options in one place. By meeting a wide variety of older adults’ needs and preferences, Mobilainés will help them feel comfortable and safe when moving around, which should increase their participation in meaningful activities and reduce the risk of social isolation.International Registered Report Identifier (IRRID)DERR1-10.2196/33894

MeVisLab-OpenVR prototyping platform for virtual reality medical applications

Virtual reality (VR) can provide an added value for diagnosis and/or intervention planning. Several VR software implementations have been proposed but they are often application dependent. Previous attempts for a more generic solution incorporating VR in medical prototyping software (MeVisLab) were still lacking functionality precluding easy and flexible development. We propose an alternative solution that uses rendering to a graphical processing unit (GPU) texture to enable rendering arbitrary Open Inventor scenes in a VR context. It facilitates flexible development of user interaction and rendering of more complex scenes involving multiple objects. We tested the platform in planning a transcatheter cardiac stent placement procedure. This approach proved to enable development of a particular implementation that facilitates planning of percutaneous treatment of a sinus venosus atrial septal defect. The implementation showed it is intuitive to plan and verify the procedure using VR. An alternative implementation for linking OpenVR with MeVisLab is provided that offers more flexible development of VR prototypes which can facilitate further clinical validation of this technology in various medical disciplines.

REIP: A Reconfigurable Environmental Intelligence Platform and Software Framework for Fast Sensor Network Prototyping.

Sensor networks have dynamically expanded our ability to monitor and study the world. Their presence and need keep increasing, and new hardware configurations expand the range of physical stimuli that can be accurately recorded. Sensors are also no longer simply recording the data, they process it and transform into something useful before uploading to the cloud. However, building sensor networks is costly and very time consuming. It is difficult to build upon other people’s work and there are only a few open-source solutions for integrating different devices and sensing modalities. We introduce REIP, a Reconfigurable Environmental Intelligence Platform for fast sensor network prototyping. REIP’s first and most central tool, implemented in this work, is an open-source software framework, an SDK, with a flexible modular API for data collection and analysis using multiple sensing modalities. REIP is developed with the aim of being user-friendly, device-agnostic, and easily extensible, allowing for fast prototyping of heterogeneous sensor networks. Furthermore, our software framework is implemented in Python to reduce the entrance barrier for future contributions. We demonstrate the potential and versatility of REIP in real world applications, along with performance studies and benchmark REIP SDK against similar systems.

Citizens in Search for a Place in the Digital Health Data Space: A Case Study.

Digital health solutions have been omnipresent in policy agendas. However, we still need to better understand how citizens experience these developments and, more specifically, how citizens would ideally want such solutions to look like. We explore the needs and concerns citizens expressed in different phases of the co-creation process for a prototype of a citizen-centred health data platform within a large-scale European project: Smart4Health. We follow a qualitative approach in our analysis of 9 discussion groups in addition to a diverse set of 49 qualitative interviews with citizens and health care professionals. We show how citizens identify the positive potential of health data infrastructures and how they relate digital health to wider developments in contemporary societies. We then outline citizens' concerns that potentially prevent them from becoming users and thus destabilize the policy vision of digital health. Four preconditions need to be met for citizens to find their place within a digital health data environment: transparency/trust, infrastructural literacy, digital justice, and a careful consideration of the distribution of responsibilities.

High-Throughput Regulatory Part Prototyping and Analysis by Cell-Free Protein Synthesis and Droplet Microfluidics.

Engineering regulatory parts for improved performance in genetic programs has played a pivotal role in the development of the synthetic biology cell programming toolbox. Here, we report the development of a novel high-throughput platform for regulatory part prototyping and analysis that leverages the advantages of engineered DNA libraries, cell-free protein synthesis (CFPS), high-throughput emulsion droplet microfluidics, standard flow sorting adapted to screen droplet reactions, and next-generation sequencing (NGS). With this integrated platform, we screened the activity of millions of genetic parts within hours, followed by NGS retrieval of the improved designs. This in vitro platform is particularly valuable for engineering regulatory parts of nonmodel organisms, where in vivo high-throughput screening methods are not readily available. The platform can be extended to multipart screening of complete genetic programs to optimize yield and stability.

Combining Precision Viticulture Technologies and Economic Indices to Sustainable Water Use Management

The scarcity of water due to climate change is endangering worldwide the production, quality, and economic viability of growing wine grapes. One of the main mitigation measures to be adopted in the viticulture sector will be an adequate irrigation strategy. Irrigation involves an increasing demand for water, a natural limited resource with increasing availability problems for the foreseeable future. Therefore, the development of a precision irrigation system, which is able to manage the efficient use of water and to monitor the crop water stress, is an important research topic for viticulture. This paper, through the analysis of a case study, aims to describe the prototype of a software platform that integrates data coming from different innovative remote and proximal sensors to monitor the hydric stress status of the vineyard. In addition, by using a cost analysis of grape cultivation and implementing economic indices, this study examines the conditions by which irrigation strategies may be economically justified, helping the decision-making process. By combining different sensors, the platform makes it possible to assess the spatial and temporal variability of water in vineyards. In addition, the output data of the platforming, matched with the economic indices, support the decision-making process for winemakers to optimize and schedule water use under water-scarce conditions.

A Platform to Develop and Apply Digital Methods for Empirical Bioethics Research: Mixed Methods Design and Development Study.

BackgroundThe rise of digital methods and computational tools has opened up the possibility of collecting and analyzing data from novel sources, such as discussions on social media. At the same time, these methods and tools introduce a dependence on technology, often resulting in a need for technical skills and expertise. Researchers from various disciplines engage in empirical bioethics research, and software development and similar skills are not usually part of their background. Therefore, researchers often depend on technical experts to develop and apply digital methods, which can create a bottleneck and hinder the broad use of digital methods in empirical bioethics research.ObjectiveThis study aimed to develop a research platform that would offer researchers the means to better leverage implemented digital methods, and that would simplify the process of developing new methods.MethodsThis study used a mixed methods approach to design and develop a research platform prototype. I combined established methods from user-centered design, rapid prototyping, and agile software development to iteratively develop the platform prototype. In collaboration with two other researchers, I tested and extended the platform prototype in situ by carrying out a study using the prototype.ResultsThe resulting research platform prototype provides three digital methods, which are composed of functional components. This modular concept allows researchers to use existing methods for their own experiments and combine implemented components into new methods.ConclusionsThe platform prototype illustrates the potential of the modular concept and empowers researchers without advanced technical skills to carry out experiments using digital methods and develop new methods. However, more work is needed to bring the prototype to a production-ready state.

Two-Dimensional Quaternary Transition Metal Sulfide CrMoA2S6 (A = C, Si, or Ge): A Bipolar Antiferromagnetic Semiconductor with a High Néel Temperature.

Bipolar antiferromagnetic semiconductors (BAFSs) make up a class of spintronic materials, holding great promise for the manipulation of spin-polarized currents simply upon application of a voltage gate, but only a few two-dimensional (2D) BAFSs with a high Néel temperature (TN) have been reported. Here, we report a family of magnetic quaternary MM'A2S6 (M = V, Cr, Mn, or Fe; M' = Nb, Mo, Tc, or Ru; A = C, Si, Ge, or Sn) nanosheets by isovalent alloying layered transitional metal trisulfides (MAS3) based on first-principles calculations. Our results show that 2D CrMoA2S6 (A = C, Si, or Ge) nanosheets are BAFSs with band gaps ranging from 1.89 to 2.23 eV. Among them, 2D CrMoC2S6 has the highest TN of 556 K with robust magnetism against carrier doping and external in-plane strain due to a strong delocalization superexchange interaction between the Cr3+ and Mo3+ cations. This study establishes that CrMoC2S6 is an ideal prototype platform for realizing electric control of spin polarization in 2D materials.

An IoT-Enabled Platform for the Assessment of Physical and Mental Activities Utilizing Augmented Reality Exergaming.

Augmented reality (AR) and Internet of Things (IoT) are among the core technological elements of modern information systems and applications in which advanced features for user interactivity and monitoring are required. These technologies are continuously improving and are available nowadays in all popular programming environments and platforms, allowing for their wide adoption in many different business and research applications. In the fields of healthcare and assisted living, AR is extensively applied in the development of exergames, facilitating the implementation of innovative gamification techniques, while IoT can effectively support the users’ health monitoring aspects. In this work, we present a prototype platform for exergames that combines AR and IoT on commodity mobile devices for the development of serious games in the healthcare domain. The main objective of the solution was to promote the utilization of gamification techniques to boost the users’ physical activities and to assist the regular assessment of their health and cognitive statuses through challenges and quests in the virtual and real world. With the integration of sensors and wearable devices by design, the platform has the capability of real-time monitoring the users’ biosignals and activities during the game, collecting data for each session, which can be analyzed afterwards by healthcare professionals. The solution was validated in real world scenarios and the results were analyzed in order to further improve the performance and usability of the prototype.

Lightweight Hardware Architecture for Object Detection in Driver Assistance Systems

Object detection on hardware platforms plays a very significant role in developing driver assistance systems (DASs) with limited computational resources. Object detection for DAS is a multiclass detection problem that involves detecting various objects like cars, auto, traffic lights, bicycles, pedestrians, etc. DAS also requires accuracy, speed, and sensitivity for detecting these objects in various challenging conditions. The lighting and weather conditions pose a serious challenge for accurate object detection for DAS. This paper proposes a speed-efficient and lightweight fully convolutional neural network (CNN) architecture for object detection in adverse rainy conditions. The proposed architecture uses a CNN-based deraining network with a custom SSIM loss function in the object detection pipeline, which can give an accurate performance using limited computational and memory resources. The object detection architecture contains some architectural modifications to the existing single shot multibox detector (SSD) architecture to make it more hardware efficient and improve accuracy on small objects. It uses a trainable color transformation module using [Formula: see text] convolutions for handling the adverse lighting conditions encountered in DAS. The architecture uses feature fusion and the dilated convolution approach to enhance the accuracy of the proposed architecture on small objects. The datasets available for object detection in DAS are very imbalanced with cars as a predominant object. The class weight penalization technique is used to improve the performance of the architecture on scarcely present objects. The performance of the architecture is evaluated on well-known datasets like Kitti, Udacity, Indian Driving Dataset (IDD), and DAWN. The architecture achieves satisfactory performance in terms of mean average precision (mAP) and detection time on all these datasets. It requires three times fewer hardware resources compared to existing architectures. The lightweight nature of the proposed architecture and modification of CNN architecture with TensorRT allow the efficient implementation on the jetson nanohardware platform for prototyping, which can be integrated with other intelligent transportation systems.

Experimental towards User Experience and User Interface in Smart Land Use Informatic Platform: Design and Development

Abstract Data and information become vital in urban management and analysis activities such as planning, city operation, and evaluation. Each platform has in own objective function and trying to create an information platform for visualizing urban situation, and trends through a variation of dataset. There are three objectives in this study are (1) to figure out the common ground of feature in urban informatic platform, (2) to create a prototype of urban informatic platform in accordance with availability of the dataset, and (3) to gather feedback from target clients towards platform usage. This study applied research and development design technique to develop urban informative platform called UD-TU (Urban Data - Thammasat University) by creating prototype platform and explain the availability of data, then a round table consultation had applied to clients towards the preferences and tasks of using urban data. Then the update version of urban informatic platform had been test again by the same group of clients again to recheck their feedback. The result shown that each type of clients requires to use the data for their objectives and tasks, while the possibility of data generation in urban area are unlimited. the availability of data.

3D Printing in the Context of Cloud Manufacturing

3D printing has drawn tremendous attention in Industry 4.0. Nowadays, with the ever-increasing consumers’ requests for 3D printing services, there lies a big challenge to eliminate the unbalanced demands and supplies of 3D printing resources in a geographically distributed environment. Cloud manufacturing, as a newly emerged service-oriented manufacturing paradigm, provides a promising way to overcome the challenge by integrating 3D printing into the cloud-based context, which may flourish the development of smart networks of virtual 3D printing cloud and also promote a novel 3D printing service business model to achieve mass customization. This paper presents a comprehensive study of 3D printing in cloud manufacturing environment. Two typical models of 3D printing, i.e. primary 3D printing cloud model and advanced 3D printing cloud model are presented and compared. The system architecture design of a 3D printing cloud platform is proposed to support the latter model. Furthermore, a series of key enabling technologies are presented based on the proposed system architecture. Finally, a prototype of 3D printing cloud platform is deployed with a case study of an aircraft engine part. The research will provide valuable theoretical and practical references to the future development of 3D printing clouds.

Addressing Clinical Documentation Challenges: The Role of AIPowered Digital Scribes

In the realm of healthcare, the burden of clinical documentation has long been a challenge for healthcare professionals. This paper presents a comprehensive study on the development and evaluation of a unified digital scribe system aimed at automating the process of recording, transcribing, and annotating clinical narratives in Spanish. The proposed system integrates cutting-edge technologies, including speech recognition and Named Entity Recognition (NER), to streamline the documentation workflow. The study evaluates the performance of the system in a simulated environment, encompassing transcription accuracy and entity recognition across medical and dental domains. Notably, the average Word Error Rates (WER) for different domains reveal promising results, showcasing the system's potential to accurately transcribe spoken clinical narratives. Furthermore, the NER model demonstrates its capability to identify key medical entities such as diseases, body parts, and medications, with F1 scores indicating strong performance. The paper also outlines the development of a prototype platform that unifies both the transcription and NER modules. This platform offers healthcare professionals a seamless solution for capturing, transcribing, and annotating clinical information, ultimately aiming to reduce documentation burden and improve clinical workflow efficiency. In conclusion, this research presents a robust foundation for the development of a digital scribe system in the Spanish healthcare system, offering promising solutions to alleviate clinical documentation challenges, reduce professional burnout, and enhance patient care through streamlined documentation practices.

Reshaping the Smart Home Research and Development in the Pandemic Era: Considerations around Scalable and Easy-to-Install Design

Smart home research has traditionally included visiting participants' homes to build testbed environments and evaluate their experience. However, in-person home deployment poses limitations around scalability and is not a feasible method in the context of the COVID-19 pandemic. The smart home research community is now facing the need to reshape and innovate research methods and design approaches. This study introduces a scalable smart home platform prototype that demonstrates possible solutions to address issues and limitations posed by the pandemic, such as improving package design, enabling user-driven installation, and facilitating remote evaluation and maintenance. The prototype uses off-the-shelf products with specially designed packaging to ensure interoperability as well as ease of shipping and installation. In this study, the prototype kits were shipped to participants' homes to understand and evaluate user perceptions and experiences around installation and initial use. Responses to a post-installation questionnaire and remote monitoring of system status showed that the participants easily completed their self-installation of the prototype without any on-site support. The study also showed potential for a scenario-based evaluation of the prototype using a remote, contactless research procedure.

Robust constraint-following control for permanent magnet linear motor with optimal design: A fuzzy approach

In this paper, robust constraint-following control (RCFC) with the optimal design is developed to handle the trajectory tracking control issues for permanent magnet linear motor (PMLM), which control performance is deteriorated mainly by friction, ripple force, and external disturbance. Specifically, a fuzzy description for the main nonlinearity of the PMLM system and its fuzzy dynamic model is formulated. Then, the tracking specification is modeled as a performance constraint, the RCFC algorithm following the Udwadia-Kalaba theory is designed to comply with this constraint, and possess strong robustness to uncertainties simultaneously. The resulting controller is demonstrated to be uniform bounded and uniform ultimate bounded with Lyapunov analysis. Furthermore, the optimal design issue via a fuzzy approach is investigated to achieve the optimal tradeoff between control effort and system performance. Finally, the rapid control prototype platform CSPACE is employed to implement the real-time control while avoiding time-consuming repetitive programming and debugging. Simulation and experimental results illustrate the actual effectiveness of the proposed algorithm.

Personalized Smart Clothing Design Based on Multimodal Visual Data Detection.

In the traditional clothing customization system, only the designer participates in the clothing design, and the style is single. In the face of numerous styles, the user just repeatedly arranges and combines the styles, but does not realize the user's innovative design. In this paper, we propose a novel multitask deep convolutional neural network training method for task-by-task transfer learning, and learn deep image features for image retrieval tasks on noisy user click data. Image retrieval model based on image-text multimodal correlation features: this paper uses image-text multimodal correlation features to calculate the correlation between query keywords and images, and calculates the correlation between images and images. In this paper, the method of automatic generation of clothing style is researched, and parameterized coding is designed for it. Taking the typical style of suits as the initial population, through the human-computer interaction interface, the scoring value is assigned to the fitness value to carry out the evolution process. The binary string of the suit style generated by the genetic algorithm is decoded through the decoding algorithm and rules, decoded into a visual style diagram of the style of the suit style, and the style diagram of the suit style is automatically drawn. From the perspective of clothing design, this paper summarizes the general design methods of outdoor sports smart clothing, follows the integration of people-clothing-environment, and proposes a wearer-centered design concept to deeply explore the rationality of outdoor sports smart clothing design methods. This paper further solves the key problems in the design process, and takes the fashion of clothing as the core principle to design the structure of clothing modeling. The fabric selection of suits is based on the principle of clothing comfort. The key is to realize the outdoor sports monitoring function of suits through sensing technology. The design uses Arduino as an electronic prototype platform, so as to detect the heart rate of the human body during exercise and the microclimate temperature under the clothes. This kind of suit with monitoring function is ultimately a combination of sensing device and clothing. It not only has monitoring function, but also has the aesthetic concept of clothing design and conforms to the performance of human body structure, which will provide reference and reference for the design of outdoor sports smart clothing.

Engineering Method and Tool for the Complete Virtual Commissioning of Robotic Cells

Intelligent robotic manufacturing cells must adapt to ever-varying operating conditions, developing autonomously optimal manufacturing strategies to achieve the best quality and overall productivity. Intelligent and cognitive behaviors are realized by using distributed controllers, in which complex control logics must interact and process a wide variety of input/output signals. In particular, programmable logic controllers (PLCs) and robot controllers must be coordinated and integrated. Then, there is the need to simulate the robotic cells’ behavior for performance verification and optimization by evaluating the effects of both PLC and robot control codes. In this context, this work proposes a method, and its implementation into an integrated tool, to exploit the potential of ABB RobotStudio software as a virtual prototyping platform for robotic cells, in which real robots control codes are executed on a virtual controller and integrated with Beckhoff PLC environment. For this purpose, a PLC Smart Component was conceived as an extension of RobotStudio functionalities to exchange signals with a TwinCAT instance. The new module allows the virtual commissioning of a complete robotic cell to be performed, assessing the control logics effects on the overall productivity. The solution is demonstrated on a robotic assembly cell, showing its feasibility and effectiveness in optimizing the final performance.