Software Components Research Articles

Safety-enhanced control for a MuscleDrive waist-assistive exoskeleton

This study introduces the MuscleDrive Powered Waist-Assistive Exoskeleton (PWE), which is specifically designed with comprehensive safety features in both its hardware and software components. The hardware of the MuscleDrive PWE incorporates fluidic muscle actuators (FMAs) to improve mechanical compliance, taking advantage of their lightweight and elastic properties. The software component features an innovative control system that utilizes the PSTC-RLESO controller. This controller ensures precise and reliable torque trajectory tracking during normal operation and facilitates smooth, safe recovery from significant torque errors during abnormal interactions. Using the Lyapunov stability theorem, this study demonstrates that the closed-loop states of the MuscleDrive PWE with PSTC-RLESO are globally uniformly ultimately bounded (UUB). Experimental comparisons between PSTC-RLESO and a PID controller show that PSTC-RLESO significantly enhances safety during MuscleDrive PWE operation while maintaining efficient tracking performance. In the experiments, safety performance is assessed through damping injection by PSTC-RLESO. Additionally, tests conducted on seven healthy subjects indicate that the MuscleDrive PWE provides adequate torque assistance and ensures user safety, as evidenced by surface electromyography (sEMG) da ta and questionnaire responses.

Research on the Bill of Quantities Pricing Model for Prefabricated Buildings Based on BIM

In recent years, with continuous advancements in prefabricated construction technology and management practices, the accuracy and quality of prefabricated components have improved. As a result, the practice of pre-manufacturing components off-site and assembling them on-site has become an inevitable construction method for the development of the building industry. This paper discusses the current issues faced by prefabricated buildings and analyzes the feasibility of integrating BIM (Building Information Modeling) technology with cost estimation for prefabricated construction.The paper proposes a cost estimation process for prefabricated buildings, which involves adapting BIM’s measurement rules to suit local requirements for concrete quantity calculations. By creating precast (PC) components in BIM software and adding cost-related information, a BIM-based quantity take-off list can be generated.This study provides valuable insights into the application of BIM in the cost management of prefabricated buildings and the further research on quantity take-offs for such projects. It also broadens the scope of BIM applications, offering significant potential for the development and widespread adoption of BIM technology.

Portable real-time polarimeter for partially and fully polarized light.

We present a portable polarimeter capable of real-time visualization of partial and fully polarized light over a broad band of wavelengths. Our system utilizes a Raspberry Pi computer with a low-cost data acquisition "HAT" and an integrated photodetection circuit. Wide bandwidth operation is achieved through digital calibration of an arbitrary retardance waveplate presented herein. All mechanical, electrical, and software components are open source and available on a GitHub repository. This completely integrated approach provides an efficient tool for modern optics research laboratories and is well-suited for educational demonstrations.

Ventricular Fibrillation Prediction and Detection: A Comprehensive Review of Modern Techniques

This review offers a detailed examination of modern ECG signal processing techniques employed in the prediction and detection of ventricular fibrillation (VF). It contains a thorough analysis of recent advancements in the field, exploring the strengths, limitations, and real-world applications of these techniques. By evaluating the current state of research, the review seeks to identify the most effective approaches and highlight key areas where further investigation is needed, ultimately guiding future research efforts toward improving VF prediction and detection. Overall, AI has shown significant potential in a range of VF-related tasks. However, real-world implementation encounters several challenges, including difficulties in accurately interpreting ECG signals, the variability in individual physiological responses, and the infrequency of ventricular fibrillation events. Additionally, there are issues related to the critical timing required for detecting VF, the presence of similar arrhythmias, the need for adaptation to new ECG devices, energy consumption concerns, and the complex process of obtaining regulatory and legislative approvals for integrating software components into medical equipment. We consider that the present work might be useful in approaching the above challenges.

Processing content query requests for CSAF documents using a grapHQL-BASED API

Security advisories serve as an essential resource for individuals tasked with ensuring safety, as they offer information about vulnerabilities and the impacted IT systems and software components. These advisories are released by manufacturers, IT security experts, or coordinating organizations to assist users in comprehending vulnerabilities and taking steps to either eliminate or reduce them. The lack of a standardized format across different manufacturers has made working with security advisories more complex for IT security professionals. The Common Security Advisory Framework (CSAF) was introduced to address this issue, providing a standardized format for security advisories. Consequently, certain advisories are now offered in the CSAF format. The increase in the number of CSAF documents affects the processing time of these documents by IT security specialists. To improve the efficiency of processing these queries, this paper proposes to focus on documents containing specific content, rather than processing all documents. To do this, it proposes to use GraphQL, an open source data query language that allows for efficient formalization of CSAF document queries. This paper aims to implement a GraphQL-based API to improve the efficiency of processing CSAF document queries.

An IoT Based Smart Dustbin System

All human being are throws waste in dustbin or some other different places. The waste are plastics, degradable and non-degradable. All people are trying to put the waste in dustbin or garbage bin only. In cities, there are many public places where we see that dustbin or garbage bin are placed but there are overflowing. This create unhygienic condition in the surrounding. And it also create some serious diseases. At a same time, an odor extends throughout the city, and degrading the environment. Recycling bin is really a waste management processing, but they are limited space in a garbage bin, it does not require extra waste. Waste disposal is an efficient method of eliminating garbage disposed in commercial settings such as businesses, classrooms, colleges, shopping centers, and other public areas. We have to design the project, where the dustbin is fulled or not and the waste level of the trash bin is measured. The NodeMCU and the ultrasonic sensor is a hardware component for measuring the garbage bin. The software component is an IFTTT Webhook, and it is used to receiving a notification.The main concept of this project is when more than 70 percent of the garbage bin is filled, the IFTTT Webhook sends the notification and we receive the email.

Automatically Injecting Robustness Statements into Distributed Applications

When developing a distributed application, several issues need to be handled, and software components should include some mechanisms to make their execution resilient when network faults, delays, or tampering occur. For example, synchronous calls represent a too-tight connection between a client requesting a service and the service itself, whereby potential network delays or temporary server overloads would keep the client side hanging, exposing it to a domino effect. The proposed approach assists developers in dealing with such issues by providing an automatic tool that enhances a distributed application using simple blocking calls and makes it robust in the face of adverse events. The proposed devised solution consists in automatically identifying the parts of the application that connect to remote services using simple synchronous calls and substituting them with a generated customized snippet of code that handles potential network delays or faults. To accurately perform the proposed transformation, the devised tool finds application code statements that are data-dependent on the results of the original synchronous calls. Then, for the dependent statements, a solution involving guarding code, proper synchronization, and timeouts is injected. We experimented with the analysis and transformation of several applications and report a meaningful example, together with the analysis of the results achieved.

The VNF Cybersecurity Dataset for Research (VNFCYBERDATA)

Virtualisation has received widespread adoption and deployment across a wide range of enterprises and industries throughout the years. Network Function Virtualisation (NFV) is a technical concept that presents a method for dynamically delivering virtualised network functions as virtualised or software components. Virtualised Network Function (VNF) has distinct advantages, but it also faces serious security challenges. Cyberattacks such as Denial of Service (DoS), malware/rootkit injection, port scanning, and so on can target VNF appliances just like any other network infrastructure. To create exceptional training exercises for machine or deep learning (ML/DL) models to combat cyberattacks in VNF, a suitable dataset (VNFCYBERDATA) exhibiting an actual reflection, or one that is reasonably close to an actual reflection, of the problem that the ML/DL model could address is required. This article describes a real VNF dataset that contains over seven million data points and twenty-five cyberattacks generated from five VNF appliances. To facilitate a realistic examination of VNF traffic, the dataset includes both benign and malicious traffic.

DREAM: Debugging and Repairing AutoML Pipelines

Deep Learning models have become an integrated component of modern software systems. In response to the challenge of model design, researchers proposed Automated Machine Learning (AutoML) systems, which automatically search for model architecture and hyperparameters for a given task. Like other software systems, existing AutoML systems have shortcomings in their design. We identify two common and severe shortcomings in AutoML, performance issue (i.e., searching for the desired model takes an unreasonably long time) and ineffective search issue (i.e., AutoML systems are not able to find an accurate enough model). After analyzing the workflow of AutoML, we observe that existing AutoML systems overlook potential opportunities in search space, search method, and search feedback, which results in performance and ineffective search issues. Based on our analysis, we design and implement DREAM, an automatic and general-purpose tool to alleviate and repair the shortcomings of AutoML pipelines and conduct effective model searches for diverse tasks. It monitors the process of AutoML to collect detailed feedback and automatically repairs shortcomings by expanding search space and leveraging a feedback-driven search strategy. Our evaluation results show that DREAM can be applied on two state-of-the-art AutoML pipelines and effectively and efficiently repair their shortcomings.

Modular shadowgraph imaging for zooplankton ecological studies in diverse field and mesocosm settings

AbstractVarious iterations of shadowgraph imaging have been used to quantify zooplankton in situ with high spatial resolution. Because these systems can image relatively large volumes of water, they are especially useful for resolving less common meso‐ or macrozooplankton taxa (< 50 ind. m−3), such as larval fishes and gelatinous animals. However, larger volume imagers are typically integrated with heavy towed vehicles and deployed from research vessels, which introduces high costs and limits sampling approaches. Here we demonstrate that versatile configurations of shadowgraph imaging, including modular benchtop, handheld, and towed, compact vehicle systems (along with customizable software), allow for tailoring sampling to a variety of marine and freshwater settings (including mesocosms). These systems encompass a suite of possible architectures, designed for adapting the imaging depth of field, acquisition rates, sensor configuration, and deployment method to fit a wide range of sampling protocols, with high vertical resolution (~ 5 cm) and adequate taxonomic capabilities for > 0.5 mm organisms. The benchtop system facilitates an interactive approach to observe and quantify zooplankton behaviors and optical properties. Video footage from the benchtop system generates thousands of regions of interest min−1 for target organisms with variable orientations and swimming postures. When used in conjunction with in situ imaging, the benchtop system can build large machine learning training libraries targeted toward rare or morphologically diverse zooplankton, which often includes the larval stages of economically valuable taxa. These modular hardware and software components increase affordability and versatility while broadening the scope of scientific questions addressed by plankton imaging systems.

Optimization of Software Component Allocation for Autonomous Driving in Cloud–Vehicular Edge

Optimization of Software Component Allocation for Autonomous Driving in Cloud–Vehicular Edge

Utility-Driven Approach to Onboard Scheduling and Execution for an Autonomous Europa Lander Mission

Jupiter’s icy moon Europa is among the most promising locations to explore for signs of extraterrestrial life in our solar system. However, searching for biosignatures on the surface of Europa presents an unprecedented set of challenges: immense uncertainty, limited energy, and few opportunities for operator feedback. Effectively carrying out a robotic science campaign under these conditions will require a system with a greater degree of autonomy than any planetary exploration mission to date. In particular, onboard scheduling and execution are required for robustness to uncertainty in surface conditions, variation in lander performance, and science discoveries to maximize the quantity and quality of science data returned to Earth during a fixed, limited lander lifetime. Here we represent a proposed Europa Lander surface mission as a utility-driven hierarchical task network and establish through analysis that onboard autonomy using automated mission planning with execution feedback and predictive task models results in mission execution that is more consistently productive compared to traditional static approaches. We design a simulated onboard autonomy framework built by integrating two software components—Multi-mission EXECutive and Europa Lander Autonomy Prototype—to properly simulate the Europa Lander domain and demonstrate empirically that the proposed planning and execution system is capable of commanding a set of realistic surface scenarios as part of a larger Europa Lander surface autonomy software prototype. We expect that an approach to scheduling and execution grounded in decision theory will be an enabling technology for future tightly constrained planetary surface missions.

Mobile-Controlled River Cleaning Robot: A Review

This project focuses more on "Remote Controlled Unmanned River Cleaning Bot". In India water pollution is increasing day by day, Water pollution is a significant environmental issue, with rivers often acting as the primary carriers of waste materials such as plastics, debris, and other pollutants. Current manual cleaning methods are inefficient, costly, and other pollutants. Current manual cleaning methods are inefficient, costly, and unable to access certain areas of the water. This paper presents the development of a mobile-controlled river cleaning robot designed to tackle these challenges. The robot can be remotely operated via a mobile application, allowing for precise control of its navigation and cleaning operations. It is equipped with waste collection mechanisms and sensors to detect and remove debris from the river’s surface. The study covers the design, hardware and software components, and testing of the robot in real-world conditions. Results indicate that the mobile-controlled robot offers an effective, cost-effcient, and environmentally sustainable solution for maintaining cleaner waterways. This innovation has the potential to revolutionize river cleaning processes and significantly reduce water pollution.

BehaviorMate: An Intranet of Things Approach for Adaptable Control of Behavioral and Navigation-Based Experiments.

Investigators conducting behavioral experiments often need precise control over the timing of the delivery of stimuli to subjects and to collect the precise times of the subsequent behavioral responses. Furthermore, investigators want fine-tuned control over how various multi-modal cues are presented. behaviorMate takes an "Intranet of Things" approach, using a networked system of hardware and software components for achieving these goals. The system outputs a file with integrated timestamp-event pairs that investigators can then format and process using their own analysis pipelines. We present an overview of the electronic components and GUI application that make up behaviorMate as well as mechanical designs for compatible experimental rigs to provide the reader with the ability to set up their own system. A wide variety of paradigms are supported, including goal-oriented learning, random foraging, and context switching. We demonstrate behaviorMate's utility and reliability with a range of use cases from several published studies and benchmark tests. Finally, we present experimental validation demonstrating different modalities of hippocampal place field studies. Both treadmill with burlap belt and virtual reality with running wheel paradigms were performed to confirm the efficacy and flexibility of the approach. Previous solutions rely on proprietary systems that may have large upfront costs or present frameworks that require customized software to be developed. behaviorMate uses open-source software and a flexible configuration system to mitigate both concerns. behaviorMate has a proven record for head-fixed imaging experiments and could be easily adopted for task control in a variety of experimental situations.

Assessment of digital literacy and user experience in patients with cochlear implants

Abstract Introduction The cochlear implant (CI) is the treatment of choice for people affected by deafness or profound hearing loss. Patients with cochlear implants (PwCIs) can enhance their hearing outcomes using digital hearing training programs (dHTPs). Current technological trends leans towards personalized, AI-driven dHTPs, aiming to optimize user experience (UX) and adherence. This study aims to investigate the digital literacy skills and UX of PwCI before and after testing a prototype of a dHTP with an AI-driven design (TraiCI). Methods Two research-specific questionnaires developed by the Hearing Center Düsseldorf (QDL and QUX) were used to assess digital literacy skills and user experience in TraiCI. Additionally, the User Experience Questionnaire (UEQ) was employed to evaluate general user experience. The UEQ assesses software quality across dimensions such as transparency and stimulation using paired opposites. Results 22 outpatient PwCIs the Hearing Center Düsseldorf with unilateral or bilateral CI (mean age 54 years; SD = 15) were included. In QDL, PmCI demonstrated high levels of digital literacy skills. The majority of PmCI (80.6%) reported prior experience with dHTP. Usage of these programs occurred daily to multiple times per week within the first six months after CI treatment, with session durations predominantly ranging from 10 to 30 minutes. QUX data indicated a smooth initiation process (70.8%) and seamless login experience (95.5%). The UEQ addressed various aspects of user experience, with ‘transparency’ receiving the highest scores (MW = 1.9; SD = 0.6), while ‘stimulation’ received the least positive ratings (MW = 0.0; SD = 2.1). Conclusions The results show a high degree of digital literacy among PwCIs. They encountered minimal challenges in operating hardware and software components. Further refinement is necessary to align with PwCI’s expectations and maximize benefits. Early and iterative involvement of PwCIs in future projects could optimize outcomes. Key messages • The study highlights the proficient digital literacy skills of cochlear implant patients (PwCIs). • Further refinement of dHTPs is needed to better align with PwCIs’ expectations and maximize benefits, emphasizing the importance of iterative involvement for optimized outcomes.

Portfolio Evolution Analysis for SPL Scoping: Unveiling the dynamics with dedicated time-series dashboards

Software Product Line Engineering (SPLE) is a recognized methodology for systematically developing reusable software components and tailored software products for specific market segments. A critical activity within domain engineering is SPL Scoping, aimed at profiling the SPL boundaries and fitting this market segment. Yet, this market is a moving target, which requires SPLs to be proactive in anticipating and addressing future challenges and opportunities. In this setting, rather than all the possible products potentially derived from the SPL’s Feature Model (i.e., the potential portfolio), the subset of products actually deployed is a better indicator of market adoption and customer preferences, and ultimately, of the SPL’s fitness to the market. This study characterizes the phenomenon of ‘deployed portfolio evolution’ in terms of the size and frequency of portfolio changes. We then elaborate on the advantages of utilizing this data source and the necessity for dedicated tools for Portfolio Evolution Analysis integrated within variability managers. To achieve this, we introduce the functional requirements, implementation, and architecture of PortfolioScan, a dashboard for Portfolio Evolution Analysis built on top of pure::variants. The dashboard enables the evaluation of feature usage through time-series charts such as temporal heat maps, line charts, and also a scoping-criteria comparison matrix. Evaluation results (n=13) indicate a positive perception. Practitioners also recognize the benefits of Portfolio Evolution Analysis for Testing Resource Optimization and Configuration Assistance. The ultimate goal of the study is to establish a basis for more responsive and market-oriented decision-making in SPL tasks.

Componentizing autonomous underwater vehicles by physical-running algorithms

Autonomous underwater vehicles (AUV) constitute a specific type of cyber-physical system that utilize electronic, mechanical, and software components. A component-based approach can address the development complexities of these systems through composable and reusable components and their integration, simplifying the development process and contributing to a more systematic, disciplined, and measurable engineering approach. In this article, we propose an architecture to design and describe the optimal performance of components for an AUV engineering process. The architecture involves a computing approach that carries out the automatic control of a testbed using genetic algorithms, where components undergo a ‘physical-running’ evaluation. The procedure, defined from a method engineering perspective, complements the proposed architecture by demonstrating its application. We conducted an experiment to determine the optimal operating modes of an AUV thruster with a flexible propeller using the proposed method. The results indicate that it is feasible to design and assess physical components directly using genetic algorithms in real-world settings, dispensing with the corresponding computational model and associated engineering stages for obtaining an optimized and tested operational scope. Furthermore, we have developed a cost-based model to illustrate that designing an AUV from a physical-running perspective encompasses extensive feasibility zones, where it proves to be more cost-effective than an approach based on simulation.

METHODS AND PRACTICES OF TRAINING AND APPLICATION OF CONVOLUTIONAL NEURAL NETWORKS FOR DETECTING VARIOUS DEFECTS ON THE SURFACES OF BUILDING STRUCTURES

The subject of research in this article is the practice of implementing and applying types of artificial intelligence – convolutional neural networks and data science - to detect various defects on the surfaces of building structures. The purpose of the study is to identify the advantages and disadvantages of implementing and using convolutional neural network technologies together with other software and hardware components to automate traditional methods of monitoring the technical condition of the external surfaces of buildings and structures. The article solves the following tasks: to substantiate the effectiveness of implementation of convolutional neural network technologies and data science methods; to apply them in practice with software and hardware technologies to automate traditional methods of performing work on detecting various defects on the surfaces of building structures in the construction and operation of buildings and structures; to show the problems and shortcomings of these methods and technologies. To solve the tasks, an integrated approach was used with the use of general scientific and special research methods (analysis, explanation, generalisation, comparison). The following results have been obtained: the features that affect the accuracy of the analysis of the collected data used by neural convolutional network technologies to detect various defects on the surfaces of building structures have been identified; practices and methods for more efficient and accurate application of this technology have been reflected. Conclusions: the study allowed to identify the practical opportunities and problems that exist in this technology; recommendations for the effective use of this technology were developed; factors influencing the more efficient use of this technology in industry were identified.

Multi-Sensor Soil Probe and Machine Learning Modeling for Predicting Soil Properties.

We present a data-driven, in situ proximal multi-sensor digital soil mapping approach to develop digital twins for multiple agricultural fields. A novel Digital Soil CoreTM (DSC) Probe was engineered that contains seven sensors, each of a distinct modality, including sleeve friction, tip force, dielectric permittivity, electrical resistivity, soil imagery, acoustics, and visible and near-infrared spectroscopy. The DSC System integrates the DSC Probe, DSC software (v2023.10), and deployment equipment components to sense soil characteristics at a high vertical spatial resolution (mm scale) along in situ soil profiles up to a depth of 120 cm in about 60 s. The DSC Probe in situ proximal data are harmonized into a data cube providing vertical high-density knowledge associated with physical-chemical-biological soil conditions. In contrast, conventional ex situ soil samples derived from soil cores, soil pits, or surface samples analyzed using laboratory and other methods are bound by a substantially coarser spatial resolution and multiple compounding errors. Our objective was to investigate the effects of the mismatched scale between high-resolution in situ proximal sensor data and coarser-resolution ex situ soil laboratory measurements to develop soil prediction models. Our study was conducted in central California soil in almond orchards. We collected DSC sensor data and spatially co-located soil cores that were sliced into narrow layers for laboratory-based soil measurements. Partial Least Squares Regression (PLSR) cross-validation was used to compare the results of testing four data integration methods. Method A reduced the high-resolution sensor data to discrete values paired with layer-based soil laboratory measurements. Method B used stochastic distributions of sensor data paired with layer-based soil laboratory measurements. Method C allocated the same soil analytical data to each one of the high-resolution multi-sensor data within a soil layer. Method D linked the high-density multi-sensor soil data directly to crop responses (crop performance and behavior metrics), bypassing costly laboratory soil analysis. Overall, the soil models derived from Method C outperformed Methods A and B. Soil predictions derived using Method D were the most cost-effective for directly assessing soil-crop relationships, making this method well suited for industrial-scale precision agriculture applications.

Tackling the multitude of uncertainties in energy systems analysis by model coupling and high-performance computing

This paper identifies and addresses three key challenges in energy systems analysis—varying assumptions, computational limitations, and coverage of a few indicators only. First, results depend strongly on assumptions, i.e., varying input data. Hence, comparisons and robust results are hard to achieve. To address this, we use a broad range of possible inputs through an extensive literature review by scenario experts. Second, we overcome computational limitations using high-performance computing (HPC) and an automated workflow. Third, by coupling models and developing 13 indicators to evaluate the overall quality of energy systems in Germany for 2030, we include many aspects of security of supply, market impact, life cycle analysis and cost optimization. A cluster analysis of scenarios by indicators reveals three recognizable clusters, separating systems with a high share of renewables clearly from more conventional sets. Additionally, scenarios can be identified which perform very positive for many of the 13 indicators. We conclude that an automated, coupled workflow on supercomputers based on a broad parameter space is able to produce robust results for many important aspects of future energy systems. Since all models and software components are released as open-source, all components of a multi-perspective model-chain are now available to the energy system modeling community.