Processing Applications Research Articles

Truncated amplitude flow with coded diffraction patterns

Abstract The phase retrieval problem, which involves Fourier transforms with random masks, has a wide range of applications in signal and image processing, such as diffraction imaging. In recent years, various first-order methods have been explored, including truncated amplitude flow (TAF). These methods are considered to be simple and effective for solving the phase retrieval problem. However, existing convergence results for TAF mainly focus on random Gaussian measurements, which is very far from practical scenarios. In this paper, we analyze the convergence of a revised TAF (RTAF) for the phase retrieval problem. We focus on coded diffraction patterns composed of Fourier transforms and random masks. Then we justify the linear convergence behavior of such algorithm when near the true solution. By utilizing the local initializer found by a non-truncated spectral method, we establish the exact convergence rates in both the noiseless case and the bounded noise case. Further, we have shown that a general signal x ∈ C^n can be estimated
 on an optimal sampling order (specifically, O(nlogn)). Moreover, our demonstrations indicate that sharp stability can be maintained even when the observations are contaminated by bounded noise. Our experiments have revealed that the RTAF algorithm can reliably estimate the original signal using fewer measurements, aligning with our theoretical analysis. Additionally, numerical experiments have highlighted that the RTAF algorithm outperforms the truncated Wirtinger flow (TWF) and truncated Riemannian gradient descent (TRGrad) algorithms in terms of efficiency and stability, showcasing its superior performance over state-of-the-art algorithms.

The Design of a Good Data Processing App Applying QR Code

Designing a good data processing application using QR Codes aims to increase efficiency and accuracy in data management. QR codes are a fast and easy technology for storing and scanning data, allowing users to quickly access information without entering data manually. This application is designed for various purposes, such as inventory, time and attendance management, and document tracking. The application design process includes user needs analysis, interface design, back-end development, and integration with QR code technology. The prototype method is used during the system development stage. The programming language used is a hypertext preprocessor with a MySQL database, and a framework is used to ensure that the application behaves as expected and can provide effective solutions in data processing. The results of this design are expected to significantly contribute to operational efficiency, reduce human error in data entry, and increase the speed of accessing and managing information. Apart from that, implementing QR codes in data processing applications is hoped to be widely used in various industries and business fields.

Avoiding decoherence with giant atoms in a two-dimensional structured environment

Giant atoms are quantum emitters that can couple to light at multiple discrete points. Such atoms have been shown to interact without decohering via a one-dimensional waveguide. Here, we study how giant atoms behave when coupled to a two-dimensional square lattice of coupled cavities, an environment characterized by a finite-energy band and band gaps. In particular, we describe the role that bound states in the continuum (BICs) play in how giant atoms avoid decoherence. By developing numerical methods, we are able to investigate the dynamics of the system and show the appearance of interfering BICs within a single giant atom, as well as oscillating BICs between many giant atoms. In this way, we find the geometric arrangements of atomic coupling points that yield protection from decoherence in the two-dimensional lattice. These results on engineering the interaction between light and matter may find applications in quantum simulation and quantum information processing. Published by the American Physical Society 2024

Tunable Optical Filter Based on Thin Film Lithium Niobate Photonic Crystals

In this paper, we propose a tunable optical filter with a high Q factor based on photonic crystal technology, achieving a Q factor of 442.85 using thin film lithium niobate technology. By proportionally adjusting the dimensions of the unit structure, this filter enables coarse tuning across the 1520–1570 nm range (C band). Furthermore, by utilizing the thermo-optic effect of lithium niobate, we can achieve fine-tuning of the optical filter within a temperature range of 300 K to 600 K, allowing for a center wavelength tuning capability of up to 3.7 nm. This design not only enhances the filter’s performance but also broadens its potential applications in optical communication and optical signal processing.

Advancements and gaps in natural language processing and machine learning applications in healthcare: a comprehensive review of electronic medical records and medical imaging

This article presents a thorough examination of the progress and limitations in the application of Natural Language Processing (NLP) and Machine Learning (ML), particularly Deep Learning (DL), in the healthcare industry. This paper examines the progress and limitations in the utilisation of Natural Language Processing (NLP) and Machine Learning (ML) in the healthcare field, specifically in relation to Electronic Medical Records (EMRs). The review also examines the incorporation of Natural Language Processing (NLP) and Machine Learning (ML) in medical imaging as a supplementary field, emphasising the transformative impact of these technologies on the analysis of healthcare data and patient care. This review attempts to analyse both fields in order to offer insights into the current state of research and suggest potential chances for future advancements. The focus is on the use of these technologies in Electronic Medical Records (EMRs) and medical imaging. The review methodically detects, chooses, and assesses literature published between 2015 and 2023, utilizing keywords pertaining to natural language processing (NLP) and healthcare in databases such as SCOPUS. After applying precise inclusion criteria, 100 papers were thoroughly examined. The paper emphasizes notable progress in utilizing NLP and ML methodologies to improve healthcare decision-making, extract information from unorganized data, and evaluate medical pictures. The key findings highlight the successful combination of natural language processing (NLP) and image processing to enhance the accuracy of diagnoses and improve patient care. The study also demonstrates the effectiveness of deep learning-based NLP pipelines in extracting valuable information from electronic medical records (EMRs). Additionally, the research suggests that NLP has the potential to optimize the allocation of medical imaging resources. The identified gaps encompass the necessity for scalable and practical implementations, improved interdisciplinary collaboration, the consideration of ethical factors, the analysis of longitudinal patient data, and the customization of approaches for specific medical situations. Subsequent investigations should focus on these deficiencies in order to fully exploit the capabilities of natural language processing (NLP) and machine learning (ML) in the healthcare sector, consequently enhancing patient outcomes and the delivery of healthcare services.

Project Based Learning: Development of Monitoring Wind Direction and Speed with Solar Power as Learning Media

Learning in the classroom needs appropriate methods and packaging to get good results for students. This study aims to determine the differences in learning methods using project-based practical learning and regular learning. The study was carried out using quantitative methods by conducting evaluations and interviews with students. Data processing uses the Manova test statistical data processing application. The sample from this research was students in the 3rd semester of the Airport Engineering Technology Study Program. From the results of data processing, it can be seen that there are differences in terms of understanding, collaboration and learning outcomes between students who use project-based learning methods and those who use regular learning systems. Project-based learning methods have fostered better understanding and collaboration. We hope that with the results of this research, educators will use project-based learning methods more often to foster understanding and collaboration between students in the campus environment.

Investigation of a fast gate driver based on a half-turn planar transformer.

This paper presents a magnetically isolated gate driver for the fast switching of IGBT (Insulated Gate Bipolar Transistor) in compact pulsed power sources with sharp rising edges and flat-top pulses for the application of electromagnetic launch and food processing. The proposed gate driver is implemented based on a planar transformer with a half-turn winding arrangement to increase the amplitude of the driving voltage pulse. With the half-turn winding arrangement, the leakage inductance of transformers decreases by 31.1% compared to the interleaving structure. This decrease enables a fast rise time of the driving voltage pulse. Furthermore, the gate drivers are used to drive the IGBT switching a Blumlein pulse forming network. The results show that the di/dt of the applied commercially available Si IGBT is about 10.10 A/ns with a gate voltage of 50V and a gate capacitance charging time of about 88ns, proving the effectiveness of the gate driver and providing a high-performance driving scheme for the fast switching of Si IGBT.

Unsupervised hindi word sense disambiguation using graph based centrality measures

The task of word sense disambiguation (WSD) plays a key role in multiple applications of natural language processing. In this paper, we propose a novel unsupervised method for targeted Hindi WSD task. First, we create a weighted graph where the nodes correspond to various synsets of the target word and the neighboring context words. The edges in the graph represent the semantic relations between these synsets in the Hindi WordNet hierarchy. A path-based similarity measure, namely Leacock-Chodorow similarity measure, is used to assign weights to edges. An unsupervised weighted graph-based centrality algorithm is used to identify the correct sense of a target word in a given context. The performance of the proposed algorithm is measured on 20 ambiguous Hindi nouns using four different graph-based centrality measures. We observed a maximum accuracy of 66.92% using PageRank centrality measure which is significantly better than earlier reported graph-based Hindi WSD algorithmsevaluated on the same dataset.

Enhanced catalytic performance of methanol-tolerant Rhizopus stolonifera immobilized on polyurethane foam with hydrophobic coating for biodiesel production

The limited methanol tolerance and glycerol adsorption tendency of whole-cell catalysts are the main factors contributing to the extended catalytic cycle and limited catalyst reusability in biodiesel preparation. In this study, a 7.5 % methanol-tolerant Rhizopus stolonifera mutant (MTS2) was developed through the atmospheric and room temperature plasma (ARTP), exhibiting a 1.5-fold increase in methanol tolerance compared with the wild type strain (WT-RS), along with a 93.5 % increase in lipases activity. Polyurethane foam PF3 with an average pore size of 0.15 mm and 90 A rigidity is optimal for MTS2 immobilization (MTS2 @PF3), enabling its application in continuous processing for industrial applications. Utilizing soybean oil as a substrate, the catalyst MTS2 @PF3 produced 95.5 % of fatty acid methyl esters (FAME) in 36 hours under optimal conditions (30°C, 180 r/min, catalyst dosage of 15 % g/g oil, 30 % water content, and methanol addition in Scheme 3), achieving over a 50 % reduction in reaction time compared with WT-RS@PF3. To address limitations in catalyst reusability due to glycerol adsorption, various modifiers, including rosin-based modifier (RM), amino silicone oil (ASO), graphene (GRA), betaine and Tween 80 were evaluated. The glycerol adsorption rates experienced substantial reductions (39.3 % with RM and 85.6 % with betaine), and the catalysts maintained above 60 % FAME yield even after 8 reaction cycles. The combination of enhanced methanol tolerance, lipase activity, and reduced glycerol adsorption contribute to enhanced catalytic efficiency and reusability, which can reduce production costs for biodiesel manufacturing. The composite of these attributes makes MTS2 @RM1-PF3 and Betaine-MTS2 @PF3 as promising candidate for industrial biodiesel production, addressing previous limitations in catalyst performance and sustainability. The developed immobilized whole-cell catalysts exhibit promising potential for more sustainable and cost-effective processes in biodiesel manufacturing.

Interfacial structure modification and enhanced emulsification stability of microalgae protein through interaction with anionic polysaccharides

Microalgae protein (MP) have emerged as a focal point of research within food processing due to its nutritional value and foaming properties. However, its isoelectric point around pH 4 leads to it susceptible to collision, binding, and precipitation. Additionally, MP has poor emulsification properties and only shows stability under strongly alkaline conditions. This study investigated the effects of food-grade anionic polysaccharides (guar gum (GG), gum arabic (GA), low acyl gellan gum (LG), and pectin (PT)) on the molecular structure and emulsification properties of MP. Results indicated that these anionic polysaccharides enhanced the UV absorption of MP near 620 nm, especially at 14 % content, while fluorescence intensity decreased due to amino acid residues masking without structural changes. The addition of polysaccharides resulted in bimodal or multimodal particle size distributions, with LG and PT showing larger particle sizes. At pH 4, negatively charged polysaccharides formed stable complexes with near-neutral MP, improving solution stability via electrostatic repulsion and diminishing turbidity. The droplet distribution analysis indicated that higher anionic polysaccharide ratios (1:4, 1:2, and 1:1) correlated with smaller droplet sizes and increased emulsion stability. Zeta-potential measurements revealed negative charges for emulsions, with LG-MP and PT-MP complexes displaying higher absolute values (15.0 to 20.7 mV) compared to GG-MP and GA-MP complexes, indicating superior stability. Storage stability analysis showed that LG-MP and PT-MP complexes stabilized emulsions had minimal delamination over two weeks. Rheological assessments showed that increasing GG and GA contents from 14 % to 50 % had negligible effects on apparent viscosity, while LG-MP (1:1) complexes stabilized emulsion displayed higher viscosity compared to PT-MP emulsions. Frequency sweep results showed that GG-MP, GA-MP, and LG-MP emulsions had greater elastic moduli (G') than viscous moduli (G"), indicating elastic behavior, whereas PT-MP emulsions transitioned from liquid-like to solid-like behavior as frequency increased. This study illustrates the advantages of high LG and PT content in preventing particle aggregation and enhancing emulsion stability, providing a theoretical and practical foundation for MP applications in food processing.

Featuring the aspects with temperature dependent viscosity of inclined MHD Williamson fluid along with heat source/sink, Soret and Dufour effects: A predictor-corrector approach

The boundary layer flow analysis of Williamson fluid moving on the stretched cylinder has applications in the polymer processing, drug delivery system, cooling systems, rheology of food products, dyeing, and finishing. The numerical solutions of governing Navier-Stokes equations at different scenarios of thermal and solutal aspects have lots of practical implications, including biomedical engineering, cooling and heating processes, chemical reactors, aerodynamics, environmental science, and thermal storage systems. From these practical applications, the authors were motivated to conduct a study in which the numerical behavior is examined by applying the Adams-Bashforth predictor and corrector approach of numerical analysis for the Williamson fluid model in the presence of varying viscosity, natural convection, and the inclined magnetic field. Furthermore, Joule heating, thermal radiation, and heat source/sink effects are included in the thermal aspects because these are the important concepts in thermal management and engineering and have applications in solar thermal energy systems, combustion engines, heat exchangers, nuclear reactors, thermal imaging, and safety. Apart from thermal aspects, the analysis of mass transfer focuses on the influence of chemical reactions and Soret-Dufour effects. The similarity transformations are adopted to convert partial differential equations into ordinary differential equations. Specifically, the well-known predictor and corrector numerical method named the Adams-Bashforth method in combination with the Runge-Kutta 4th order scheme is used to achieve the numerical solutions. The temperature, concentration, and velocity regions are displayed on the graphical and numerical conducts. A comparison is also made between the results obtained by the shooting method and the numerical findings of skin friction, Nusselt number, and Sherwood number determined by the Adams-Bashforth method. The determining results concluded that the restrictions in the flow of Williamson fluid are caused by variations in the inclination magnetic field, the surface curvature, and the Williamson parameter, whereas the motion of the fluid increases due to convection, which occurs due to thermal phenomena. The determined results indicate that the temperature distribution is enhanced due to the addition of thermal radiation, Eckert number, and magnetic field. The surface curvature, Soret number, and reaction coefficient marks the decrease impact on the concentration region. The temperature-dependent viscosity increases the drag force and friction coefficient. The Nusselt number is amplified by applying thermal radiation to the surface of the cylinder. The chemical reaction is the source of the increment in the Sherwood number.

Digital Twin Methodology in Food Processing: Basic Concepts and Applications.

The goal of this article is to present a concise review about digital twin (DT) methodology and its application in food processing. We aim to identify the building blocks, current state and bottlenecks, and to discuss future developments of this approach. DT methodology appears as a powerful approach for digital transformation of food production, via integration of modelling and simulation tools, sensors, actuators and communication platforms. This methodology allows developing virtual environments for real-time monitoring and controlling of processes, as well as providing actionable metrics for decision-making, which are not possible to obtain by physical sensors. So far, main applications were focused on refrigerated transport and storage of fresh produces, and thermal processes like cooking and drying. DT methodology can provide useful solutions to food industry towards productivity and sustainability, but requires of multidisciplinary efforts. Wide and effective implementation of this approach will largely depend on developing high-fidelity digital models with real-time simulation capability.

Simulation and non-similar analysis of magnetized SWCNT-MWCNT hybrid nanofluid flow in porous media using Darcy-Forchheimer-Brinkman model

The field of hybrid nanofluid transport through porous media holds immense promise for optimizing thermal processing and various thermodynamic applications. This study investigates the flow dynamics of a hybrid nanofluid, comprising water and a synergistic combination of single-walled and multi-walled carbon nanotubes (SWCNT-MWCNT), as it traverses a vertically stretched porous surface. The mathematical modeling of this flow scenario considers the influential factors of magnetohydrodynamics (MHD), viscous dissipation, heat sources, and ohmic heating. The Darcy-Forchheimer-Brinkman model is employed to capture the transport of fluid through the porous medium. Through the application of Local Non-Similarity (LNS) technique, the governing equations are converted into a dimensionless system and solved numerically using the robust bvp4c function in MATLAB. Interestingly, higher values of heat source parameter leads to a rising trend in the temperature profile, highlighting the intricate interplay between the thermal and fluid dynamic aspects of the system. This work provides valuable insights into the tailored design of hybrid nanofluids and porous media configurations to harness their enhanced thermal transport capabilities, with potential applications in diverse fields such as energy storage systems, heat exchangers, and thermal management devices. The findings contribute to the broader understanding of hybrid nanofluid transport in porous media and pave the way for the development of innovative thermal management solutions in a wide range of industrial and technological domains.

Revolutionizing Knowledge Management in Transportation Agencies: The Role of Generative AI and Scalable Frameworks

The increased interconnectedness of the globe has led to a fast-paced expansion of transportation systems and, consequently, to the creation of effective Knowledge Management Strategies. By simply put, the ways of decision making, operations management, and solving problems related to the complex infrastructure systems have all improved. Knowledge management is made easier by the use of Generative Artificial Intelligence (AI) which enables all the processes of data generation, data synthesis and even gives room for a real-time insight within a transport department. There is also the availability of Structure and Generative Theory which allows agencies to cope with needs that keep changing without losing the quality and the speed of the flow of the knowledge. In this sense, the present contribution focuses on the perspective of generative AI and structural frameworks in transportation agencies understanding knowledge management and its future prospects in terms of technology usefulness (Chen & Li, 2021). In these external environments, transportation agencies manage vast and complex datasets that change rapidly and are often dispersed. For instance, Generative AI has applications in natural language processing, predictive analytics and intelligent reporting, which assist agencies in solving hurdles posed by conventional knowledge management. When these AI systems are given, scalable frameworks guarantee that the knowledge management systems will address the challenges that come with increased complexity in operations. This research examines how generative artificial intelligence can be applied in various knowledge management situations, with particular focus on how it can be used to deal with mundane processes, enhance responses and help to mitigate problems in the field of transportation (Nguyen et al., 2023). The results underscore the possible benefits of using generative AI within efficient KM systems, such as lower costs, better accuracy, and improved accessibility to the users. On the other hand, the concerns such as data quality, costs of implementation, and other factors like the presence of easy to use interface for non-technical personnel, are also brought up by the authors. These perspectives will be useful for transportation agencies that seek to transform their KM activities. Overcoming these obstacles, generative AI and scalable frameworks can promote radical transformation and development of dynamic and sustainable knowledge systems that meet the needs of transport management in the case of Rahman and Smith, 2023.

Emerging Trends in Real-time Recommendation Systems: A Deep Dive into Multi-behavior Streaming Processing and Recommendation for E-commerce Platforms

This paper aims to assess the effectiveness of recommendation systems, focusing on multi-behavior streaming processing to enhance accuracy. It studies recommendation system performance using AI and data science, focusing on user behaviour, algorithm refinement, and satisfaction. E-commerce, streaming, and social media datasets with 93 anonymous participants were employed in experiments. These files from 93 anonymized users show typical internet use. Data anonymization and strict data management ensured anonymity. These databases track clicks, views, purchases, and ratings for empirical research and model evaluation. Collaborative filtering, matrix factorization, and TensorFlow/Keras train and assess application-specific recommendation models. Multi-behavior streaming processing and recommendation accounts assess each method's pros and downsides, system correctness, efficacy, and user involvement. The outcome compares domain-wide recommendation system precision, recall, NDCG, and conversion rate. Multi-behavior streaming processing adapts to user preferences and interactions to improve model accuracy and adaptability. Multi-behavior streaming processing improved model accuracy and flexibility by reacting to user inputs and choices. With error margins for all significant metrics, statistical significance was confirmed. The findings suggest that recommendation systems with real-time adaptation and multi-behavior streaming processing can improve user satisfaction and engagement in the changing digital landscape. It encourages algorithm advancement, model interpretation, user-centric evaluation, and ethics to improve information retrieval and personalisation. The study concluded that algorithm refinement, transparent model interpretation, user-centric evaluation, and ethical problems including data protection and bias mitigation increase information retrieval and personalisation. For durable and flexible recommendation systems, research should improve multi-behavior processing and sectoral applications.

Design of Power Optimised Truncated Approximate Booth Multiplier

Abstract: In digital signal processing and arithmetic circuits, Booth multipliers are widely used for efficient multiplication of signed numbers. However, conventional Booth multipliers consume significant power due to their full precision operation. This paper explores efficient approximation techniques for Booth multipliers enhanced with flip-flop clock gating to reduce power consumption. Booth multipliers traditionally consume significant power due to their full precision operations. In this approach it mainly focuses on truncating less significant bits in the Booth encoding to lower computational demands without compromising performance. Additionally, flip-flop clock gating dynamically disables clock signals to idle flip-flops during non-active computation phases, further reducing power dissipation. Simulation results demonstrate substantial power savings compared to conventional Booth multipliers while maintaining acceptable accuracy for digital signal processing applications. These findings contribute to advancing energy-efficient arithmetic units, particularly beneficial for battery-powered devices and embedded systems where power efficiency is crucial

Advancements in Computer Vision: A Comprehensive Survey of Image Processing and Interdisciplinary Applications

Computer vision and image processing are rapidly evolving fields with broad applications across numerous domains, including healthcare, autonomous driving, surveillance, and entertainment. These fields have transformed from simple data recording techniques into sophisticated systems that incorporate digital image processing, pattern recognition, machine learning, and computer graphics. This evolution has prompted interdisciplinary interest, pushed the technology’s boundaries and expanded its practical uses. This paper offers a comprehensive survey of recent advancements in computer vision, focusing on image processing and its applications across various fields. It delves into the theoretical foundations and technologies that make computer vision a valuable tool for interpreting images and videos, extracting relevant information, recognizing patterns, and understanding events. The ability of computer vision to analyze large datasets across multiple application domains makes it instrumental in tasks such as object identification, facial recognition, scene understanding, and even real-time action prediction. This versatility has established computer vision as a key driver of data-driven insights in both scientific and commercial sectors. The study categorizes computer vision into four main areas: image processing, object recognition, machine learning, and computer graphics. Each of these categories is essential to the functionality of modern computer vision systems. Image processing involves techniques for enhancing image quality and extracting important features. Object recognition and machine learning enable the identification of specific elements within images and allow systems to learn from large datasets, enhancing accuracy over time. Computer graphics, on the other hand, aid in visualizing and interpreting processed data. By offering insights into the latest techniques and evaluating their performance, this survey highlights the current state of computer vision while shedding light on future trends. Computer vision’s expanding utility across various fields underscores its critical role in driving interdisciplinary innovation and addressing complex challenges.

A Bibliometric Review of Natural Language Processing Applications in Psychology from 1991 to 2023

Natural language processing (NLP) has emerged as a promising approach in psychological research. However, prior reviews often focused on specific subject areas and reported varying findings regarding the use of NLP methods. To address this gap in the literature, we conducted a comprehensive review of NLP applications in psychological research. Our study includes (1) a large-scale bibliometric review of 4,909 papers (1991–2023) and (2) a focused methodological review of the 100 most-cited articles. Results revealed exponential growth in NLP applications since 2012, with Health, Education, and Marketing as dominant topics. Sentiment analysis was the most common technique, deep learning—particularly pre-trained models—gained popularity, and automated text analysis tools were widely used due to their ease of implementation.

Application of Badminton Sequence Image Processing Based on Feature Extraction Algorithm

Application of Badminton Sequence Image Processing Based on Feature Extraction Algorithm

Recent Advances in Nanomaterial-Based Biosignal Sensors.

Recent research for medical fields, robotics, and wearable electronics aims to utilize biosignal sensors to gather bio-originated information and generate new values such as evaluating user well-being, predicting behavioral patterns, and supporting disease diagnosis and prevention. Notably, most biosignal sensors are designed for body placement to directly acquire signals, and the incorporation of nanomaterials such as metal-based nanoparticles or nanowires, carbon-based or polymer-based nanomaterials-offering stretchability, high surface-to-volume ratio, and tunability for various properties-enhances their adaptability for such applications. This review categorizes nanomaterial-based biosignal sensors into three types and analyzes them: 1) biophysical sensors that detect deformation such as folding, stretching, and even pulse, 2) bioelectric sensors that capture electric signal originating from human body such as heart and nerves, and 3) biochemical sensors that catch signals from bio-originated fluids such as sweat, saliva and blood. Then, limitations and improvements to nanomaterial-based biosignal sensors is depicted. Lastly, it is highlighted on deep learning-based signal processing and human-machine interface applications, which can enhance the potential of biosignal sensors. Through thispaper, it is aim to provide an understanding of nanomaterial-based biosignal sensors, outline the current state of the technology, discuss the challenges that be addressed, and suggest directions for development.