Processing System Research Articles

Range Safety Considerations for Processing and Launching Reactor-Based Space Nuclear Systems

Space nuclear systems (SNSs) present a robust source of power and/or propulsion technology that will fundamentally enhance and expand deep-space mission capability. In this era of space nuclear renaissance, there are several active U.S. SNS programs, one of which is seeking the first space reactor launch approval of the modern era. SNSs present complex systems with unique and novel challenges for established launch safety procedures, particularly for prelaunch and initial launch operations as prescribed by the U.S. government’s federal ranges. This paper examines high-level range safety considerations for a model expander cycle nuclear thermal propulsion SNS during ground processing and the pad and launch operational phases.

Adaptive prescribed-time control for time-varying full-state-constrained nonlinear stochastic systems with actuator failures

In this paper, the problem of prescribed-time adaptive fuzzy tracking control for a class of time-varying full-state-constrained nonlinear stochastic systems with actuator failures is considered. Using a unified barrier function in place of the traditional barrier Lyapunov function (BLF) allows for the transformation of nonlinear stochastic systems with time-varying full-state constraints into equivalent ‘unconstrained’ systems for processing, which can eliminate the feasibility condition required by the virtual controllers generated using the BLF approach. The problem of unknown control gain caused by actuator failures is solved via devising reasonable adaptive laws. At the same time, a prescribed-time tracking controller is devised by using the approximation ability of fuzzy logic systems, which can adapt to the uncertainty of the system and deal with the actuator failures. In addition, the control scheme proposed in this paper can guarantee the performance of the system within prescribed-time and all state variables do not violate the constraint boundaries despite suffering from actuator failures. Finally, the effectiveness of the investigated control strategy is verified through two simulation examples.

A Survey of Grapheme-to-Phoneme Conversion Methods

Grapheme-to-phoneme conversion (G2P) is the task of converting letters (grapheme sequences) into their pronunciations (phoneme sequences). It plays a crucial role in natural language processing, text-to-speech synthesis, and automatic speech recognition systems. This paper provides a systematical overview of the G2P conversion from different perspectives. The conversion methods are first presented in the paper; detailed discussions are conducted on methods based on deep learning technology. For each method, the key ideas, advantages, disadvantages, and representative models are summarized. This paper then mentioned the learning strategies and multilingual G2P conversions. Finally, this paper summarized the commonly used monolingual and multilingual datasets, including Mandarin, Japanese, Arabic, etc. Two tables illustrated the performance of various methods with relative datasets. After making a general overall of G2P conversion, this paper concluded with the current issues and the future directions of deep learning-based G2P conversion.

Semantic Analysis of Natural Language Texts: Ontological Approach

The development of information (intelligent) learning systems, electronic document management systems, web-oriented systems working with text information in natural language has led to an increase in the volume of educational content and/or arrays of processed full-text documents. All this requires new means of organizing access to information, many of which should be classified as intelligent systems for knowledge processing. One of the effective approaches to identifying and processing the meaning of educational content (and/or text documents) is the use of ontologies. The purpose of the article is research, analysis of various approaches to determining the semantic content of texts in natural language, consideration of existing concepts of text analysis and prospects for using the proposed ontological approach to semantic analysis of texts in natural language. Research methods are methods of semantic analysis of the main concepts of the analyzed subject area (semantic analysis of texts in natural language). The article considers an approach to the linguistic analysis of texts based on ontological modeling. The novelty of the research is the application of the proposed ontological approach to the semantic analysis of texts in natural language to determine the meaning (semantics) of text information, which is used in intelligent systems of various classes. The conclusion of the research carried out in the article is as follows: an ontological approach to the semantic analysis of natural language text, its tasks and methods is proposed. The use of the proposed approach to text analysis leads to the understanding of semantic analysis as a single triad: <Ontology – Text – Meaning>. For effective and correct extraction of knowledge, it is suggested to use a multi-level ontology. The result of the interaction of a specific natural language text with an ontology is an ontological meaning – a set of interconnected subgraphs of the ontograph. The ontological content is extracted from the ontograph using a semantic analyzer. The dialogue processor examines the syntactic tree of sentence parsing (a connected element of the natural language text) and, based on the given question, finds a fragment in the text that is the answer to the question. Computer understanding (in information or intellectual systems) of natural language text is achieved, in particular, by: immersion of the text in a single environment of knowledge – ontology; formal presentation of meaning (semantics) in the knowledge base of the corresponding system; the possibility of operations on the ontological content. The proposed approach can be used to create intelligent information repositories that work in a single knowledge environment. The proposed approach to the semantic analysis of texts in natural language is focused on the automatic extraction of metadata from texts of various nature (for example, a text document, Internet content, educational content of relevant online courses, description of computer and board games). With further development of the proposed approach, it can be used in systems of automatic referencing of scientific publications, meaningful interpretation of multimedia content, training and testing (including elements of visual display of information and elements of gamification).

Experimental Data-Driven Estimation of Impulse Response in Audio Systems Using Parametric and Non-Parametric Methods

The impulse response is a fundamental tool for characterizing linear time-invariant (LTI) systems, enabling the derivation of a mathematical model that accurately describes system dynamics under arbitrary input conditions. This study used experimental data to estimate the impulse response of an audio system—comprising an amplifier, a speaker, a room, and a microphone. Four methods were employed: two parametric and two non-parametric approaches, applied in both the time and frequency domains. The methods were evaluated quantitatively using the Root Mean Square Error (RMSE) metric and qualitatively through a perceptual analysis with six participants. The parametric frequency-domain method achieved the best perceptual results, with 75% of participants rating the output as good. While this method exhibited slightly higher RMSE compared to other techniques, its low filter order (8) resulted in superior computational efficiency. The findings highlight that perceptual alignment often diverges from purely mathematical error minimization. Real-time implementation of the selected impulse response further demonstrated its practical application in audio processing systems. This research bridges quantitative metrics and human auditory perception, emphasizing the need for balanced decision-making in audio system modeling. The results contribute to advancing data-driven methodologies in acoustics, offering insights into both experimental design and computational efficiency

Classification of real and bogus transients using active learning and semi-supervised learning

The mounting data stream of large time-domain surveys renders the visual inspections of a huge set of transient candidates impractical. Techniques based on deep learning-based are popular solutions for minimizing human intervention in the time domain community. The classification of real and bogus transients is a fundamental component in real-time data processing systems and is critical to enabling rapid follow-up observations. Most existing methods (supervised learning) require sufficiently large training samples with corresponding labels, which involve costly human labeling and are challenging in the early stages of a time-domain survey. One method that can make use of training samples with access to only a limited amount of labels is highly desirable for future large time-domain surveys. These include the forthcoming 2.5-meter Wide-Field Survey Telescope (WFST) six-year survey and the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). Deep-learning-based methods have been favored in astrophysics owing to their adaptability and remarkable performance. They have been applied to the task of the classification of real and bogus transients. Unlike most existing approaches, which necessitate massive and expensive annotated data, we aim to leverage training samples with only 1000 labels and discover real sources that vary in brightness over time in the early stages of the WFST six-year survey. We present a novel deep learning method that combines active learning and semi-supervised learning to construct a competitive real-bogus classifier. Our method incorporates an active learning stage, where we actively select the most informative or uncertain samples for annotation. This stage aims to achieve higher model performance by leveraging fewer labeled samples, thus reducing annotation costs and improving the overall learning process efficiency. Furthermore, our approach involves a semi-supervised learning stage that exploits the unlabeled data to enhance the model's performance and achieve superior results, compared to using only the limited labeled data. Our proposed methodology capitalizes on the potential of active learning and semi-supervised learning. To demonstrate the efficacy of our approach, we constructed three newly compiled datasets from the Zwicky Transient Facility (ZTF), achieving average accuracies of 98.8<!PCT!>, 98.8<!PCT!>, and 98.6<!PCT!> across these three datasets. It is important to note that our newly compiled datasets only work in terms of testing our deep learning methodology and there may be a potential bias between our datasets and the complete data stream. Therefore, the observed performance on these datasets cannot be assumed to directly translate to the general alert stream for general transient detection in actual scenarios. The algorithm will be integrated into the WFST pipeline, enabling an efficient and effective classification of transients in the early period of a time-domain survey.

The Influence of Social Networks on Tourism Support Behaviors Among Ethnic Village Residents

In the realm of tourism intervention and the reconfiguration of social networks among residents in ethnic villages, research on tourism support behavior is pivotal for comprehending the motivations behind resident participation and fostering sustainable tourism development. This study carried out a survey of 440 residents from Huangluo Yao Village and Jinkeng Dazhai Village in Longsheng County, Guilin City, Guangxi Zhuang Autonomous Region, China, using a questionnaire and explored the influencing mechanism of social networks on tourism support behavior based on the cognitive–affective processing system (CAPS) theory using structural equation modeling (SEM). The findings reveal that both strong and weak ties exert direct impacts on tourism support behavior while also indirectly influencing it via cultural identity. Furthermore, government governance capacity serves as a positive moderating factor in the relationships between ethnic village residents’ social networks—both strong and weak—and cultural identity, as well as between weak ties and tourism support behavior. The conclusions drawn from this study not only enhance the understanding of the formation mechanisms underlying tourism support behavior but also offer valuable insights for local governments aiming to implement effective strategies to promote tourism development.

Variational Autoencoders-Based Algorithm for Multi-Criteria Recommendation Systems

In recent years, recommender systems have become a crucial tool, assisting users in discovering and engaging with valuable information and services. Multi-criteria recommender systems have demonstrated significant value in assisting users to identify the most relevant items by considering various aspects of user experiences. Deep learning (DL) models demonstrated outstanding performance across different domains: computer vision, natural language processing, image analysis, pattern recognition, and recommender systems. In this study, we introduce a deep learning model using VAE to improve multi-criteria recommendation systems. Specifically, we propose a variational autoencoder-based model for multi-criteria recommendation systems (VAE-MCRS). The VAE-MCRS model is sequentially trained across multiple criteria to uncover patterns that allow for better representation of user–item interactions. The VAE-MCRS model utilizes the latent features generated by the VAE in conjunction with user–item interactions to enhance recommendation accuracy and predict ratings for unrated items. Experiments carried out using the Yahoo! Movies multi-criteria dataset demonstrate that the proposed model surpasses other state-of-the-art recommendation algorithms, achieving a Mean Absolute Error (MAE) of 0.6038 and a Root Mean Squared Error (RMSE) of 0.7085, demonstrating its superior performance in providing more precise recommendations for multi-criteria recommendation tasks.

Blockchain Framework for Examination Result Processing System for a Kenyan University

Blockchain Framework for Examination Result Processing System for a Kenyan University

Design and FPGA Implementation of Relative Dynamics Measurement Techniques for Satellite Communication Systems

Performance of a satellite communication receiver is greatly affected by the relative dynamics scenario. Relative dynamics is related to the variable motion between communication source and destination systems. It encompasses relative acceleration and relative jerk. The paper presents the phase error-based measurement techniques for relative dynamics. Binary Phase Shift Keying (BPSK) demodulator is considered for development and verification of the techniques. Paper describes the requirement and design of 3rd order BPSK demodulator for measurement and handling relative jerk. System level simulation is carried out for different scenario. Design is coded in VHDL and realized on the Xilinx platform. Application of developed system in adaptive signal processing based BPSK demodulator is also dealt in detail.

Augmented Reality Enhanced Image Processing Systems for Interactive Worker Training in Industrial Automation

This paper explores the integration of augmented reality (AR) with image processing systems to enhance interactive worker training in industrial automation environments. The proposed system leverages AR technology to overlay real-time visual information, such as instructional graphics and step-by-step guidance, onto physical workspaces, improving the learning experience for workers. By incorporating advanced image processing techniques, the system ensures precise recognition of industrial tools, components and machinery, facilitating context aware training that adapts to the real-time actions of the trainee. This approach aims to accelerate the onboarding process, reduce human error, and increase efficiency in manufacturing settings, providing workers with immersive, hands-on learning opportunities while enhancing safety and operational effectiveness. The paper discusses the design, implementation and potential benefits of AR – enhanced image processing systems in industrial training programs, with a focus on scalability and practical deployment in diverse industrial environments. Keywords— Augmented Reality (AR), Image Processing, algorithms, Interactive Training, Industrial automation.

Computer vision: applications in Biomedical Engineering

The focus of this study is the presentation of two systems, which use computer vision techniques. In the first, the authors applied these techniques to the acquisition and processing system of ovitrap images. However, the second system is a physiotherapeutic evaluation platform for dynamic monitoring of body balance. By using a depth image of the patient (based on a 3D camera), the algorithm can predict the exact positions of the joints of an individual, without temporal information. The two studies described presented showed promising results, which demonstrated that computer vision techniques are of great importance. The experiments with the Aedes Aegypti mosquito egg has allowed accurate detection and counting of them. The experiments with body balance analysis has allowed the tracking of the center of mass of an individual during physiotherapeutic exercises. Software has detected the movement of the center of mass, satisfying a physiotherapeutic need. The techniques developed for computer vision have many possible applications in different areas and different populations, not only in the areas presented in this study.

Implementation and evaluation of digital twin framework for Internet of Things based healthcare systems

AbstractThe integration of digital twins (DTs) in healthcare is critical but remains limited in real‐time patient monitoring due to challenges in achieving low‐latency telemetry transmission and efficient resource management. This paper addresses these limitations by presenting a novel cloud‐based DT framework that optimises real‐time healthcare monitoring, providing a timely solution for critical healthcare needs. The framework incorporates a Pyomo‐based dynamic optimisation model, which reduces telemetry latency by 32% and improves response time by 52%, surpassing existing systems. Leveraging low‐cost, low‐latency multimodal sensors, the system continuously monitors critical physiological parameters, including SpO2, heart rate, and body temperature, enabling proactive health interventions. A DT definition language (Digital Twin Definition Language)‐based time series analysis and twin graph platform further enhance sensor connectivity and scalability. Additionally, the integration of machine learning (ML) strengthens predictive accuracy, achieving 98% real‐time accuracy and 99.58% under cross‐validation (cv = 20) using the XGBoost algorithm. Empirical results demonstrate substantial improvements in processing time, system stability, and learning capacity, with real‐time predictions completed in 17 ms. This framework represents a significant advancement in healthcare monitoring, offering a responsive and scalable solution to latency and resource constraints in real‐time applications. Future research could explore incorporating additional sensors and advanced ML models to further expand its impact in healthcare applications.

The MASLIS: A five-year review of Costa Rica's near real-time seismic intensity processing system

The MASLIS is an online system that displays the earthquake’s seismic intensity in near-real time. The system analyzes the data from Costa Rica’s strong motion network. When the intensity exceeds a specified intensity threshold at a number of sites, the system sends several warning messages. When it was first developed in 2019, the system was tested using scenario earthquakes. After five years of operation, this article briefly discusses its performance using actual earthquake data.

Image Contrast Enhancement Using General Histogram Equalization and Homomorphic Filtering

The realm of image processing is characterized by the judicious application of mathematical operations to facilitate image transformation and refinement. By synergizing signal processing techniques, image processing can culminate in an enhanced image or the extraction of salient parameters. This research concentrates on ameliorating the contrast of images beset by low contrast, concomitantly extracting relevant image parameters. Images with subpar contrast can engender flawed outcomes in myriad disciplines, highlighting the necessity of contrast enhancement. This study introduces an innovative image processing system, conducting a comparative analysis of General Histogram Equalization and Homomorphic Filtering. The results unequivocally demonstrate the superiority of Homomorphic Filtering. The system's output manifests pronounced efficiency in elevating image contrast, heralding far-reaching implications.

Impact of structural changes of Ge–Sb–Se chalcogenide glasses on their acousto‐optic properties

AbstractAcousto‐optic (AO) devices are known for their high parallel processing capabilities and efficient transduction, essential for real‐time processing systems. As a vital AO medium, chalcogenide glasses exhibit high AO figures of merit (M2), yet high M2 is often coupled with high acoustic attenuation (α). Thus, to obtain the trade‐off between M2 and α, a series of xGeSe2−(100−x)Sb2Se3 (40 ≤ x ≤ 85) is investigated for their AO properties. When GeSe2 content reaches 50%, a stable network of [GeSe4] and [Se2Sb–Se–SbSe2] units forms, minimizing energy loss and α while maintaining a high M2. Compared with conventional AO materials, this composition achieves an exceptional equilibrium between high M2 and reduced α, offering a distinct advantage in optimizing performance. This unique composition balance provides valuable insights for developing advanced chalcogenide glasses in AO applications.

Fiducial Reference Measurements for Air Quality Monitoring Using Ground-Based MAX-DOAS Instruments (FRM4DOAS)

The UV–Visible Working Group of the Network for the Detection of Atmospheric Composition Changes (NDACC) focuses on the monitoring of air-quality-related stratospheric and tropospheric trace gases in support of trend analysis, satellite validation and model studies. Tropospheric measurements are based on MAX-DOAS-type instruments that progressively emerged in the years 2010 onward. In the interest of improving the overall consistency of the NDACC MAX-DOAS network and facilitating its further extension to the benefit of satellite validation, the ESA initiated, in late 2016, the FRM4DOAS project, which aimed to set up the first centralised data processing system for MAX-DOAS-type instruments. Developed by a consortium of European scientists with proven expertise in measurements, data extraction algorithms and software design specialities, the system has now reached pre-operational status and has demonstrated its ability to deliver a set of quality-controlled atmospheric composition data products with a latency of one day. The processing system has been designed using a highly modular approach, making it easy to integrate new tools or processing updates. It incorporates advanced algorithms selected by community consensus for the retrieval of total ozone, lower tropospheric and stratospheric NO2 vertical profiles and formaldehyde profiles. The ozone and NO2 products are currently generated from a total of 22 stations and delivered daily to the NDACC rapid delivery (RD) repository, with an additional mirroring to the ESA Validation Data Centre (EVDC). Although it is still operated in a pre-operational/demonstrational mode, FRM4DOAS was already used for several validation and science studies, and it was also deployed in support of field campaigns for the validation of the TROPOMI and GEMS satellite missions. It recently went through a CEOS-FRM self-assessment process aiming at assessing the level of maturity of the service in terms of instrumentation, operations, data sampling, metrology and verification. Based on this evaluation, it falls under class C, which is a good rating but also implies that further improvements are needed to reach full compliance with FRM standards, i.e., class A.

SEAM Update: SEAM CO2 project: Evaluating geophysical methods for characterization of CO2 injections

An essential need for carbon sequestration is to have benchmarks that can be used to measure the geophysical responses to CO2 injections to evaluate different monitoring approaches. The primary purpose of the SEG Advanced Modeling Corporation (SEAM) CO2 project is to build realistic geologic models and to conduct simulations that provide data that can be used to test geophysical tools without field experimentation. A necessary requirement to meet our goals is to test the efficiency and accuracy of the various needed simulation and processing systems (geology, reservoir, geomechanics, geophysics) and the sharing of information among them. The approach of the SEAM CO2 project has been to develop realistic geologic models representing locations where CO2 sequestration may take place, perform reservoir and combined reservoir/geomechanical simulation of CO2 injection into the model to infer the response of the model to injection, define geophysical properties of a baseline and one or more monitor models based on state-of-the-art rock physics relations, simulate the geophysical response of the model, and finally process the geophysical data to evaluate the value of information of various methods for characterization of the CO2 locations and volumes within the subsurface.

Thickness monitoring of threshing mixture on the oscillating plate of corn grain harvester

Cleaning is a critical operation in the corn grain harvesting process, and the operation is seriously affected by the uneven distribution of the threshing mixture on the oscillating plate. To monitor the thickness of the threshing mixture on the oscillating plate of the corn grain harvester, in this study, we adopted the lever principle to design a corn threshing mixture thickness monitoring device based on the piezoresistive effect and theoretically analyzed the lever impact mechanics. The factors affecting the force on the sensor of the monitoring device are the vibration frequency of the oscillating plate, the thickness of the threshing mixture, and grain moisture content, which were obtained through theoretical analysis. The operational performance of the corn threshing mixture thickness monitoring device was assessed through a set of single-factor experiments, revealing the influence laws of three factors on the impact voltage: vibration frequency, thickness of the threshing mixture, and grain moisture content. A back-propagation (BP)11Back-propagation (BP) neural network prediction model was established using an orthogonal experiment with impact voltage, vibration frequency, and grain moisture content as input layers and the thickness of the corn threshing mixture as the output layer. An STM32 microcontroller was used as the primary control unit to build a thickness monitoring data acquisition and processing system that could process and operate the impact voltage collected by the pressure sensor and the vibration frequency signal collected by a Hall sensor. The data were wirelessly transmitted to a PC to predict the thickness of the threshing mixture by using the trained BP neural network. The experimental results of monitoring the variation in corn threshing mixture thickness demonstrated that the device could effectively monitor changes in thickness. The monitoring accuracy of the monitoring device was found to be over 90%, and the system can be used for monitoring the threshing mixture thickness on the oscillating plate of the corn grain harvester.

Review on velocity slip with thermal features of irregular heat transport enhancement of hybrid nanofluids

In engineering and industrial processes, hybrid nanofluids, an advanced form of nanofluid, are used to improve thermal efficiency. Hybrid nanofluids have various uses in refrigeration, tumor treatments, power stations, cooling systems, food processing, biomedical devices, and solar thermal systems. Thus, the major purpose of this article is to examine the heat transport features of a hybrid nanofluid on the dynamics of Zn-Ti6Al4V/H2O. The flow phenomenon of such fluid is produced by a shrinking disk when momentum and the thermal phenomenon are maintained. The classical transformation is employed to alter the flow model of a hybrid nanofluid in the form of non-linear ODEs. The produced model is tackled numerically through the MATLAB solver bvp4c function. The aspects of different emerging physical constraints on the flow field, fluid temperature, drag friction, and Nusselt number are exemplified thoroughly. The study finding reveals that the addition of a hybrid nanofluid in a flow system shows a promising increase in thermal transportation rate. The friction drags coefficient and thermal transportation rate show an upsurge in both solution branches for an increment in the suction parameter. Furthermore, higher values of the thermal conductivity parameter show an enhancement in fluid temperature for both branches.