Processing Resources Research Articles

Higher-contrast images are better remembered during naturalistic encoding

It is unclear whether memory for images of poorer visibility (as low contrast or small size) will be lower due to weak signals elicited in early visual processing stages, or perhaps better since their processing may entail top-down processes (as effort and attention) associated with deeper encoding. We have recently shown that during naturalistic encoding (free viewing without task-related modulations), for image sizes between 3°–24°, bigger images stimulating more visual system processing resources at early processing stages are better remembered. Similar to size, higher contrast leads to higher activity in early visual processing. Therefore, here we hypothesized that during naturalistic encoding, at critical visibility ranges, higher contrast images will lead to higher signal-to-noise ratio and better signal quality flowing downstream and will thus be better remembered. Indeed, we found that during naturalistic encoding higher contrast images were remembered better than lower contrast ones (~ 15% higher accuracy, ~ 1.58 times better) for images at 7.5–60 RMS contrast range. Although image contrast and size modulate early visual processing very differently, our results further substantiate that at poor visibility ranges, during naturalistic non-instructed visual behavior, physical image dimensions (contributing to image visibility) impact image memory.

A Clustering and PL/SQL-Based Method for Assessing MLP-Kmeans Modeling

With new high-performance server technology in data centers and bunkers, optimizing search engines to process time and resource consumption efficiently is necessary. The database query system, upheld by the standard SQL language, has maintained the same functional design since the advent of PL/SQL. This situation is caused by recent research focused on computer resource management, encryption, and security rather than improving data mining based on AI tools, machine learning (ML), and artificial neural networks (ANNs). This work presents a projected methodology integrating a multilayer perceptron (MLP) with Kmeans. This methodology is compared with traditional PL/SQL tools and aims to improve the database response time while outlining future advantages for ML and Kmeans in data processing. We propose a new corollary: hk→H=SSE(C),wherek>0and∃X, executed on application software querying data collections with more than 306 thousand records. This study produced a comparative table between PL/SQL and MLP-Kmeans based on three hypotheses: line query, group query, and total query. The results show that line query increased to 9 ms, group query increased from 88 to 2460 ms, and total query from 13 to 279 ms. Testing one methodology against the other not only shows the incremental fatigue and time consumption that training brings to database query but also that the complexity of the use of a neural network is capable of producing more precision results than the simple use of PL/SQL instructions, and this will be more important in the future for domain-specific problems.

Optimal qubit mapping search for encoding classical data into matrix product state representation with minimal loss

Matrix product state (MPS) offers a framework for encoding classical data into quantum states, enabling the efficient utilization of quantum resources for data representation and processing. This research paper investigates techniques to enhance the efficiency and accuracy of MPS representations specifically designed for encoding classical data. Based on the observations that MPS truncation error depends on the pattern of the classical data, we devised an algorithm that finds optimal qubit mapping for given classical data, thereby improving the efficiency and fidelity of the MPS representation. Furthermore, we evaluate the impact of the optimized MPS in the context of quantum classifiers, demonstrating their enhanced performance compared to the conventional mapping. This improvement confirms the efficacy of the proposed techniques for encoding classical data into quantum states. MPS representation combined with optimal qubit mapping can pave a new way for more efficient and accurate quantum data representation and processing.

Increasing quantum communication rates using hyperentangled photonic states

Quantum communication is based on the generation of quantum states and exploitation of quantum resources for communication protocols. Currently, photons are considered as the optimal carriers of information, because they enable long-distance transition with resilience to decoherence and they are relatively easy to create and detect. Entanglement is a fundamental resource for quantum communication and information processing, and it is of particular importance for quantum repeaters. Hyperentanglement, a state where parties are entangled with two or more degrees of freedom (DoFs) simultaneously, provides an important additional resource because it increases data rates and enhances error resilience. However, in photonics, the channel capacity, i.e., the ultimate throughput, is fundamentally limited when dealing with linear elements. We propose a technique for achieving higher transmission rates for quantum communication by using hyperentangled states, based on multiplexing multiple DoFs on a single photon, transmitting the photon, and eventually demultiplexing the DoFs to different photons at the destination, using Bell state measurements. Following our scheme, one can generate two entangled qubit pairs by sending only a single photon. The proposed transmission scheme lays the groundwork for novel quantum communication protocols with higher transmission rates and refined control over scalable quantum technologies.

Tailoring stability in oil-in-water high internal phase Pickering emulsions (HIPPEs) through surface modification of beeswax-based solid lipid particles (SLPs) with various surfactants

As high-performance Pickering stabilizers, the exploration of solid lipid particles (SLPs) for oil-in-water high internal phase emulsions (HIPPEs) has garnered significant interest in the colloid domain, primarily owing to their promising applications in the food industry. Herein, as an underutilized and cost-effective food processing resource, beeswax (BW) was probed, and BW-SLPs was tuned by controlling the different type (Na-Cas or T80)/concentration (0.5–2.5 wt%) of surfactant with a view to stabilizing the HIPPEs (φ = 0.80) more effectively. At concentrations of 0.5–2.0 wt%, a more stable semi-solid HIPPEs could be formed by Na-Cas-BW-SLPs, while phase separation occurred at 2.5 wt%. However, T80-BW-SLPs could only form the fluid dynamic emulsions. The correlation between the interfacial characteristics of SLPs and the physical properties of SLPs-stabilized HIPPEs was thoroughly investigated. Microscopic images showed that Na-Cas-BW-SLPs formed a closely packed three-dimensional network structure around the oil droplets but aggregated in the aqueous phase at the concentration of 2.5 wt%. And they exhibited the capacity to improve the viscoelasticity of the interfacial film, which also endowed the HIPPEs with excellent rheological properties. While, T80-BW-SLPs were loosely arranged, formed a viscous film on the interface, and G′ of the interfacial film was about 2 orders of magnitude lower than Na-Cas-HIPPEs at the same concentration. Besides, transmission electron microscopy (TEM) analysis revealed that the Na-Cas-BW-SLPs (∼370 nm), unlike the out of shape T80-BW-SLPs (∼230 nm), exhibited a cross-linked structure with a rough surface. All SLPs demonstrated enhanced wettability and a remarkable ability to reduce interfacial tension (2 mN/m). Additionally, it further identified the inability to form stable HIPPEs up to φ = 0.84 at the optimal Na-Cas-SLPs concentration. Ultimately, the HIPPE stabilized by 3.0 wt% BW and 2.0 wt% Na-Cas, with oil fraction up to 83 vol%, exhibited the highest elastic modulus (3871.0 Pa) and demonstrated exceptional stability over a 30-day storage period.

Dynamics of self-control during choice and post-choice consumption quantity.

Observed choices between options representing a relative vice and a relative virtue have commonly been used as a measure of eating self-control in the literature. However, even though self-control operations may manifest across the post-choice consumption stage, either similarly or in different ways from the choice stage, most prior research has ignored consumption quantity of the chosen option. While the behavior of choosing a virtue instead of a vice does manifest self-control, we examine how this plays out in post-choice consumption. Specifically, we find that when processing resources are limited, after having chosen a virtue food, unrestrained eaters ironically consumed greater quantities and therefore more calories than restrained eaters (Study 1). This reflects more persistent self-control in the post-choice consumption stage among restrained eaters than unrestrained eaters, and occurs because choosing a virtue lowers accessibility of the self-control goal among unrestrained eaters relative to restrained eaters (Study 2), thereby increasing intake of the virtuous food. In contrast, subsequent to having chosen a vice, unrestrained eaters and restrained eaters did not show any such difference in intake (Study 1) or goal accessibility (Study 2). Together, these results reveal that persistence of self-control in the post-choice consumption stage depends on individuals' dietary restraint and their initial exercise of self-control in the choice decision. The mere act of choosing a virtue satisfies unrestrained eaters' self-control goal and leads to increased food intake, whereas the same act keeps the same goal activated among restrained eaters who reduce intake of the chosen virtue. Put differently, persistent self-control across choice and quantity decisions is observed only when those with a dietary goal show successful self-control enactment in the choice stage. We therefore highlight that the operation of self-control can be dynamic within a consumption episode, and thus, choice and post-choice quantity are both informative of self-control.

Mapping the landscape of spatial literacy research: Bibliometric analysis

The study aims to provide a comprehensive overview of spatial literacy research through a bibliometric analysis, focusing on its development, essential themes, key contributors, and collaboration patterns. Using statistical text-mining and citation link-based clustering techniques, 690 spatial literacy publications from the Scopus database were analyzed, with data extracted on February 18, 2024. The analysis revealed a steady increase in spatial literacy publications, peaking in 2009, and identified key peaks in cited papers in 1996, 2003 and 2012, with a subsequent decline post-2012. Document profiles primarily consisted of articles (60.14%) and sourced from journals (67.97%), with The Journal of Geography being the most active title. Notably, Newcombe, N.S., emerged as the most productive author, while the work of Bednarz and Kemp received the highest number of citations. ‘The Separability of Working Memory Resources for Spatial Thinking and Language Processing: An Individual Differences Approach’ by Shah and Miyake was identified as the most influential document, garnered a minimum of 100 citations per written document. Additionally, the United States stood out with both the largest number of publications and the highest citation impact in spatial literacy research. This bibliometric analysis provides valuable insight into the spatial literacy research landscape, guiding for future research directions and fostering collaborations in this field.

An Arsenic Removal Technology and Its Application in Arsenic-Containing Copper

The usage of copper (Cu) ores containing low or no arsenic (As) has reduced, and Cu ores containing high levels of As have emerged as vital mineral resources for Cu extraction and processing. The quality of the Cu ores has decreased from 1.6% to approximately 1.0%. The proportion of As to Cu in 15% of Cu resources currently reaches 1:5. However, during the extraction and processing of Cu ores, As presents significant environmental harm. Hence, safely and effectively removing As is paramount in Cu smelting and processing, holding substantial importance in fostering environmentally sustainable practices within the Cu extraction and processing industry. This article consolidates the resource distribution of As-containing Cu (ACC) ores, comprehensively and systematically evaluates the present advancements in extracting techniques for these minerals, and identifies the challenges inherent in pyrometallurgical and wet processes for treating ACC deposits. Pyrometallurgy is a simple primary roasting technique and has widespread applicability in the treatment of various ACC minerals. Its disadvantages are the emission of exhaust gas and the high treatment costs associated with it. The wet arsenic removal method boasts advantages including minimal air pollution and a high resource recovery rate, significantly aiding in Cu concentrate recovery; its major drawback is the production of As-containing wastewater. The hydrometallurgical removal of As from ACC mines involves extracting As through leaching. Recently, biometallurgy has presented innovative solutions using specialized microorganisms to bioleach or bioabsorb As, but large-scale industrial applications still lack specific practical implementation. This review explores the underlying causes of the challenges encountered in processing ACC minerals. Additionally, it highlights pyrometallurgical roasting coupled with high-temperature filtration as a pivotal advancement in the extraction and processing of ACC ores.

Automatic generation of nominal phrases for Portuguese and Galician

Abstract This paper presents XeraWord, an innovative tool for automatically generating nominal phrases. XeraWord can be used for different tasks, ranging from teaching languages to the creation of examples in lexicography, or even for the development of resources for natural language processing. In this area, Xera was the first experiment, allowing the automatic generation of nominal phrases in three languages: German, French and Spanish. This tool was extended to support other languages, namely, Portuguese and Galician. We start by presenting the theory behind the development of Xera and its new version, XeraWord, namely, the applied base methodology, and the natural language processing resources used to support it. Then, TraduWord, a tool specifically developed to construct resources for new languages, is presented. This tool allows the semi-automatic translation of the data required for the nominal phrase generation. For this, we discuss its advantages and disadvantages, analysing the quality of the translated resources, as well as the amount of manual work required to validate and correct these resources.

Ensemble of Neural Networks Utilizing Seismic Attributes for Rock‐Property Inversion With Uncertainty Estimation

AbstractSeismic inversion holds significant importance across various domains of geoscience and engineering, including the characterization of energy resource reservoirs, the assessment of polluted sites, and CO2 storage. It is a process of estimating rock properties from seismic data that is inherently uncertain, nonlinear, non‐unique, and highly challenging. Using multiple seismic attributes increases the size of the data, requiring considerable processing resources and time. However, deep learning can accurately fit quantities of nonlinear variables, making it an excellent method for predicting spatially distributed subsurface properties. We trained some multi‐output regression neural networks to carry out porosity inversion from seismic data. We initially computed a series of seismic attributes and generated the corresponding porosity using interpreted horizons, well logs, and seismic data. Subsequently, we proposed a technique to identify the most relevant seismic attributes for porosity inversion. Because our networks work as stochastic modeling entities, we created a weight‐averaging ensemble approach to build a strong model with the highest level of accuracy. We combined realizations from baseline entities, considering their respective performance levels. Using the statistics between these realizations and the robust model, we determined the degree of uncertainty associated with the outcome. We found an R2 of 0.993 and an MAE of 0.00112 in the F3 block offshore the Netherlands, proving the method's effectiveness. The mean porosity was 0.175193, compared to 0.175626 from a reference model, and the mean uncertainty was ±0.0008998.

POSSIBILITIES OF USING DIGITAL EDUCATIONAL RESOURCES IN THE TEACHING PROCESS OF PRIMARY SCHOOL

Today, one of the urgent problems of primary school is the digitization of the teachingprocess. It is important to form themselves as competent specialists for primary school teachers from a professional point of view, adapting schoolchildren to the changing information society, effectively organizing digital educational resources in accordance with the requirements of modern society. Digital educational resources serve as ready-made multimedia interactive products aimed at solving didactic goals and specific learning tasks for teachers. In this direction, the aim of the research is to propose a model of training for future primary school teachers by analyzing the possibilities of using digital educational resources in primary school teaching process. A teaching model designed to make the educational process interesting and meaningful by creatively using digital educational resources was presented. In the article, the meanings of the terms «digital educational resources», «readiness of the future primary school teacher to use digital educational resources» have been analyzed from scientific and methodological points of view. Practical experiment work, directions for the special course program «Methods of using digital educational resources in primary schools» was offered aimed at mastering digital educational resources as a means of self-professional development of future specialists. The research used methods of theoretical literatureanalysis and a survey of digital educational resources as an empirical method. There is an opportunity of using the research results in the methodology of effective use of digital educational resources in the training of future primary school teachers of the country.

A short review of the ADAS progress in the last decade and the potential concept of Human-Machine Symbiosis (HMS)

Abstract This short review highlights the main sensor structures that are used in the ADAS (Advanced Driver Assisted System) field, in order to outline the progress at this moment (2023). Starting from this achieved level, we have proposed another approach to the ADAS problematics, regarding the prospects for development after the current moment, a different perspective than the one that has AV (Autonomus Vehicle) as its endpoint. Computer Assisted Driving represents a necessary and sufficient solution for increasing traffic safety in the near future. The integrated driver concept in the assisted environment implies a symbiotic human-machine collaboration. This collaboration requires a psychological acceptance of the ADAS system on behalf of humans, as an essential part of the current and future automobile. The progress highlighted by the documentation undertaken allows us to imagine a concept of a symbiotic human-machine system, based on the communication between ADAS and an external computing resource that assists the driver, based on the long analysis of recorded car events. Future automobiles must include an ADAS system that provides a subtle human-machine synapse, based on a series of computerized warnings to the driver generated in a timely manner, resulting from the high predictive capacity of AI-DLM algorithms. The computing speed of the local ADAS algorithms will be supplemented by the external server processing resource, accessed at the right time, to resolve an unexpected deadlock in the car roll.

Quantum-centric supercomputing for materials science: A perspective on challenges and future directions

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Computationally hard tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their resources for simulation, analysis, and data processing. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions.

Factors Affecting Employee Performance in the Business Recovery Process at Listed Banks in Vietnam

Purpose: In recent years, many disruptions have occurred in the Vietnamese economy, which has caused listed banks to suffer many consequences, so listed banks must recognize the factors that promote recovery. Although studies on this issue have been conducted, the amount is quite limited and there is little focus on human resource factors. This empirical study was conducted to demonstrate the factors that affect employee performance and its impact on the recovery of business operations of listed banks. Design/methodology/approach: In this study, the author used questionnaires to collect data from 850 listed bank employees between 15th August 2023 and 26th December 2023. This study developed a questionnaire that used measures from many previous empirical studies to build a model to measure the impact of human resources on a company's ability to recover performance, using a five-point Likert-style scale (from 1 level equals to totally disagree; to 5 level equals to totally agree). After collecting data, this study used the SPL Smart application, to find the relationship between the role of human resources and the ability to recover business performance. From that analysis, the author will provide an objective perspective on the role of human resources in promoting the recovery performance of listed banks in Vietnam. Findings: Research results show that performance appraisal variables positively impact employee performance. On the other hand, career development and training and development do not affect employee performance. Finally, the analysis shows that there is a positive relationship between employee performance and business recovery at listed banks. Research limitations/implications: This study shows that human resources play a very important role in the recov-ery process of Vietnamese listed banks. Learning about the factors that influence employee performance will help banks align their HR strategies with organizational goals, ensuring that the workforce has the right skills and com-petencies necessary force. This process will help listed banks in Vietnam increase their ability to recover from disruptions and enhance their competitiveness. However, this study still has limitations including sample size and number of independent variables not large enough to show the importance of human resources in the recovery process of listed banks in Vietnam. Originality/value: This study contributes to emphasizing that human resources play an important key in the recov-ery process for Vietnamese listed banks. From an objective perspective, it provides insights into the role of human resources and factors to improve employee performance in listed banks.

Non-Consumptive Effects of Harmonia axyridis on the Reproduction and Metabolism of Spodoptera frugiperda.

An increasing body of research has underscored the significant impact of non-consumptive effects on the dynamics of prey pests, encompassing growth, development, reproduction, and metabolism across various vertebrate and invertebrate taxa, rivaling the influence of consumption effects. In our investigation, we delved into the non-consumptive effects exerted by the natural predatory enemy Harmonia axyridis on the reproductive capacity and metabolism of Spodoptera frugiperda adults. Our findings revealed a substantial decrease in the reproductive ability of S. frugiperda adults when exposed to the non-consumptive effects of H. axyridis. Concurrently, we observed an elevation in hydrogen peroxide (H2O2) content and the activities of antioxidant enzymes such as superoxide dismutases (SODs), catalases (CATs), and peroxidases (PODs). Furthermore, notable alterations were detected in energy metabolism, characterized by heightened triglyceride levels and diminished glycogen and trehalose concentrations. These outcomes underscored the adaptive response of the pest aimed at mitigating non-consumptive adverse effects by augmenting antioxidant enzyme activity to counteract oxidative stress and minimize cellular damage. Nonetheless, this defensive mechanism entails a significant expenditure of energy resources, resulting in shifts in energy utilization. Elevated triglyceride levels and reduced glycogen and trehalose concentrations diminish available resources for reproductive processes, such as egg laying, ultimately culminating in decreased fecundity. This study contributes novel insights into the non-consumptive effects observed in insects, while also furnishing valuable insights into the mechanisms underlying insect stress responses.

Advances in Radar Signal Processing: Integrating Deep Learning Approaches

In the military, in daily life, and in scientific research, radar technology is widely used. Radar signal processing has long been an essential component in target detection and imaging. The development of deep learning technology in recent years has given radar signal processing new approaches and resources. With its exceptional feature extraction and pattern recognition skills, deep learning has made amazing strides and has been applied to radar signal processing to enhance tasks like target detection, tracking, and recognition. Traditional radar signal processing is based on models. It mainly uses the prior information of the model and related signal processing criteria to design signal processing methods. It uses Gaussian, linear, and stationary assumptions. The deep learning method is a data-based method that does not require prior knowledge of the model and can spontaneously find the relationship between the input of the algorithm and the expected output. This article will review traditional methods and deep learning methods in radar signal processing, focusing on the application and future development direction of deep learning methods in radar signal processing, briefly sorting out the research progress in recent years, and analyze some existing problems or shortcomings of existing methods.

Mist–fog-assisted real-time emotion recognition using deep transfer learning framework for smart city 4.0

Face emotion recognition (FER) has found several applications across numerous industries. One such application area lies in the field of human–computer interaction, where it can improve user experiences by allowing systems to adjust based on users’ emotional states by leveraging the capabilities of the Internet of Things (IoT) to facilitate more responsive interactions. The increasing demand for precise and immediate emotion analysis across various applications drives the need for FER utilizing deep transfer learning (DTL). In this study, a novel Mist-Fog framework for real-time FER is introduced, which employs a DTL algorithm called Mobile-Net for quick and easy image classification on mobile and embedded devices. It addresses the challenges of operating deep neural networks (DNN) under limits on power and processing resources. By utilizing depth-wise separable convolutions, the MobileNet algorithm reduces computational complexity while maintaining reasonable accuracy on the FER-2013 dataset. A Fog-based architecture, in which a mist layer comprising edge devices like Raspberry-Pi (RPi), an ARM-based single-board computer, was considered for a smart city scenario. Further, the M/M/1/K queuing system was considered, which follows the First-Come-First-Serve (FCFS) scheduling criteria and a Markov Modulated Poisson Process (MMPP) arrival rate to evaluate the system’s performance. The effectiveness of this novel approach towards industrial computer vision paradigms is assessed pertaining to different performance criteria.

Optimzing Android Program Malware Classification Using GridSearchCV Optimized Random Forest

The growing number of smartphones, particularly Android powered ones, has increased public awareness of the security concerns posed by malware and viruses. While machine learning models have been studied for malware prediction in this field, methods for precise identification and classification still require improvement for the perfect detection of malwares and minimizing the cracks on machine learning based classification. Detection accuracy that ranges from 93% to 95% has been observed in prior research, indicates room for improvement. In order to maximize the hyperparameters, this paper suggests improving the Random Forest method by introducing the grid search algorithm which isn’t present in previous studies. A significant increase in classification accuracy is the main aim of the research. We exhibit an outstanding 99% accuracy rate in detecting malware contaminated programs, demonstrating the significance of our technique. The proposed method can be seen as a huge improvement over existing models, achieving near perfection in detection, in contrast to which typically obtained by previous models with the accuracy rate of 95% max on the same dataset. Our approach achieves such high accuracy and provides a novel remedy for the limits of the Android based platforms, particularly when program processing resources are limited. This study confirms the effectiveness of our improved Random Forest algorithm, points to a paradigm shift in malware detection, and heightened cybersecurity measures for the rapidly growing smartphone market.

Research on power quality improvement system based on dynamic voltage restorer

AbstractPower quality is a critical issue in power systems that can affect the performance and reliability of sensitive equipment. The dynamic voltage restorer (DVR) is a widely used technology for improving power quality, but it mainly focuses on mitigating voltage sags and often neglects the issue of harmonic distortion. This paper proposes a DVR voltage compensation strategy based on the discrete Fourier transform (DFT). This strategy utilizes the DFT to analyze the voltage waveform, separating the fundamental and desired harmonic components. With this strategy, the DVR can effectively compensate for voltage sag and achieve selective harmonic compensation, thereby improving the power quality of the power system. Additionally, a method utilizing hardware circuitry and an LED as a timestamp is proposed to dynamically estimate digital siginal processor (DSP) resource occupancy rate in real‐time, ensuring effective compensation while minimizing resource occupancy. A 3 kW DVR experimental prototype was constructed and tested to evaluate the practicality of the proposed strategy. The simulation and experimental results show that the method proposed in this paper successfully realizes the compensation of voltage sag and harmonics in the power system, and improves the stability and reliability of the power system.

A Novel Approach to Managing System-on-Chip Sub-Blocks Using a 16-Bit Real-Time Operating System

Embedded computers are ubiquitous in products across various industries, including the automotive and medical industries, and in consumer goods such as appliances and entertainment devices. These specialized computing systems utilize Systems on Chips (SoCs), devices that are made up of one or more main microprocessor cores. SoCs are augmented with sub-blocks that perform dedicated tasks to support the system. Sub-blocks contain custom logic or small-footprint microprocessors, depending upon their complexity, and perform support functions such as clock generation, device testing, phase-locked loop synchronization and peripheral management for interfaces such as a Universal Serial Bus (USB) or Serial Peripheral Interface (SPI). SoC designers have traditionally obtained sub-blocks from commercial vendors. While these sub-blocks have well-defined interfaces, their internal implementations are opaque. Without visibility of the specifics of the implementation, SoC designers are limited to the degree to which they can optimize these off-the-shelf sub-blocks. The result is that power and area constraints are dictated by the design of a third-party vendor. This work introduces a novel idea: using an open-source, small, multitasking, real-time operating system inside an SoC sub-block to manage multiple processes, thereby conserving code space. This OS is TurbOS, a new operating system whose primary goal is to provide the highest performance using the least amount of space. It is written in the assembly language of a new pipelined 16-bit microprocessor developed at the University of Florida, the Turbo9. TurbOS is derived from and incorporates the design benefits of an existing operating system called NitrOS-9, and reduces the code size from its progenitor by nearly 20%. Furthermore, it is over 80% smaller than the popular FreeRTOS operating system. TurbOS delivers a rich feature set for managing memory and process resources that are useful in SoC sub-block applications in an extremely small footprint of only 3 kilobytes.