Scalability Of System Research Articles

Detailed analysis of Sustainable Infrastructure Design and Benefits for urban Cities

Addressing the issues of urbanization, climate change, and resource scarcity now centers on the junction of infrastructure development and sustainability. This review study looks at how new ideas and technologies are developing sustainable infrastructure solutions. It assesses research and development in important domains including smart cities, green infrastructure, renewable energy, circular economy, resilience, and social equality critically. The notion of green infrastructure is covered at the outset of the article, along with how it can be used to manage environmental issues including stormwater runoff, air quality, and urban heat islands. It examines the most recent developments in renewable energy infrastructure, evaluating the scalability, efficiency, and integration of solar, wind, hydropower, and geothermal systems into the current energy infrastructures. The analysis also looks at how smart cities and infrastructure have developed, with an emphasis on how IoT, AI, and data analytics are used to improve quality of life, mobility, and sustainability. It goes over case studies of prosperous smart city projects and how they've improved public services, strengthened urban infrastructure resilience, and decreased greenhouse gas emissions. The study concludes with a discussion of new developments and technologies, including digital twins, self-driving cars, decentralized energy systems, and green building materials, that will influence sustainable infrastructure in the future. It highlights the compensations and difficulties of numerous technologies and suggests directions for further study and advancement in the area.

Evaluation of a Blockchain-Based Prescription System and Data Source for National Research and Development

In the Philippines, healthcare providers, government agencies, and research institutions use data from patient prescriptions to generate reports for health planning and decision-making. However, current e-prescription systems have vulnerabilities, including erroneous information, hacking attempts, a single point of failure, and medical fraud. In addition to affecting the quality of data reporting, these issues violate a patient’s rights to data privacy. One promising solution is a blockchain-based prescription system. Blockchain’s immutable ledger accurately traces medical fraud and erroneous information, while its decentralized nature reduces the impact of failures. Performance is an important consideration, as healthcare systems need to be scalable and time-sensitive. This study aims to understand the performance and security of blockchain-based prescription systems. It focuses on evaluating system performance and scalability when using different encryption algorithms. The study found that using the most secure technology had only a small performance impact for all prototype features except key generation and report viewing. In addition, the results suggest that the proposed system’s scalability is sufficient to service the entire Philippines. This knowledge will help improve prescription systems to protect patients’ rights and improve report reliability.

In-Storage Computing for Hadoop MapReduce Framework: Challenges and Possibilities

Solid State Drives (SSDs) were initially developed as faster storage devices intended to replace conventional magnetic Hard Disk Drives (HDDs). However, high computational capabilities enable SSDs to be computing nodes, not just faster storage devices. Such capability is generally called ”In-Storage Computing (ISC)”. Today’s Hadoop MapReduce framework has become a de facto standard for big data processing. This paper explores In-Storage Computing challenges and opportunities for the Hadoop MapReduce framework. For this, we integrate a Hadoop MapReduce system with ISC SSD devices that implement the Hadoop Mapper inside real SSD firmware. This offloads Map tasks from the host MapReduce system to the ISC SSDs. We additionally optimize the host Hadoop system to make the best use of our proposed ISC Hadoop system. Experimental results demonstrate our ISC Hadoop MapReduce system achieves a remarkable performance gain (2.3 faster) as well as significant energy savings (11.5 lower) compared to a typical Hadoop MapReduce system. Further, the experiment suggests such ISC augmented systems can provide a very promising computing model in terms of a system scalability.

IoT-Based Health Monitoring System: Design, Implementation, and Performance Evaluation

The Internet of Things (IoT) technology's rapid improvements have opened the door for creative solutions across a range of industries, including healthcare. An IoT-based health monitoring system that aims to revolutionise patient care and healthcare administration is described in this study along with its design, implementation, and performance evaluation. To continuously gather and send health-related data, our system makes use of a network of wearable sensors and gadgets that are seamlessly incorporated into a patient's daily life. Vital indicators like heart rate, blood pressure, temperature, and activity levels are tracked by these devices. To enable real-time analysis and storage, the data is safely sent to a centralised server. Both patients and healthcare professionals can access this information through a user-friendly smartphone application, enabling proactive healthcare decision-making. An effective and scalable architecture is used in the implementation of this system to guarantee the confidentiality, accuracy, and reliability of the data. The data is analysed using machine learning algorithms, which enables the early identification of abnormalities and trends that could portend serious health problems. The system can also produce warnings and notifications, ensuring prompt intervention when it's necessary. Our IoT-based health monitoring system's performance review indicates how well it performs in terms of enhancing healthcare outcomes. The solution gives healthcare professionals immediate access to crucial health information, allowing them to personalise treatment regimens, offer remote consultations, and make educated judgements. Continuous monitoring benefits patients by allowing for early intervention, fewer hospital stays, and an improvement in general health. Additionally, the system's scalability and versatility make it appropriate for a variety of healthcare settings, including small-scale home care and extensive hospital networks.

Innovative security solutions: context-aware facial recognition system using VB.NET

A sophisticated VB.NET intelligent security system is presented in this paper to meet the needs of comprehensive security surveillance in varied contexts. The system's context-aware architecture lets it adapt to environmental changes and assess user activity for better security. The system uses real-time data to reduce false alarms and ensure fast security measures using reactive alerts and reaction processes. The system's novel security features, built on VB.NET, provide flexibility and quick reactions to emerging threats. This research shows that intelligent security monitoring technologies have achieved a peak, with excellent performance, and are ready for implementation in controlled-access public spaces. The system reacts to the surroundings and detects the subject behaviour. The system may now access new conceptual words for security issues that demand more focused solutions. A system with capabilities encourages security protocol innovation and maintains secure public spaces. This innovative technique increases security, scalability, and future flexibility for security systems. In the hunt for creative, secure areas with intelligent monitoring and danger detection, the example of an intelligent security system is impressive.

Challenges and Solutions in Cross-Platform ML Systems Integration

This paper explores the complexities of cross-platform integration within the realms of Data Engineering and Machine Learning Operations (MLOps), crucial for enhancing the lifecycle management of machine learning (ML) models. It delves into the challenges and strategies associated with integrating diverse technologies and practices to improve the scalability, reliability, and maintainability of ML systems. The paper highlights the significance of addressing technical, organizational, and cultural barriers to foster a cohesive, agile environment conducive to the iterative development and operationalization of ML models. Through a detailed examination of the multifaceted nature of these challenges, including technical interoperability, organizational alignment, and fostering a collaborative culture, the paper provides insights into navigating the complexities of integrating disparate technologies and methodologies. It also underscores the importance of strategic organizational changes, technological innovation, and a commitment to data governance and collaborative culture in achieving successful cross-platform integration. A case study of a financial firm’s integration of ML within its call center operations exemplifies the transformative potential and practical implications of effectively addressing these challenges. The paper concludes by arguing that the seamless integration of platforms and tools extends beyond technical endeavors to encompass strategic, organizational, and cultural shifts, illuminating the path toward optimizing the efficiency and effectiveness of ML model lifecycles through MLOps.

Design and construction of an Arduino-based load-shedding management system for improving electricity consumption in a medium-sized creative office building

Standalone energy generation is revolutionizing electricity production and consumption by offering decentralized and sustainable options, benefiting communities, enhancing resilience, and reducing dependence on traditional power grids. Nevertheless, standalone electricity systems can face overloading issues when demand varies or exceeds capacity, necessitating careful control and adaptable solutions. The scalability of standalone electricity systems is limited by infrastructure, technology, and cost, hindering easy capacity expansion to accommodate higher loads. This paper explores the feasibility of managing load shedding in an office building with a standalone energy source, assessing its impact on optimizing electricity consumption and contributing to operational efficiency, sustainability, and resilience. A hardware prototype has been developed for a programmable interface designed to automatically manage various loads from the distribution board in a controlled manner. This includes controlling lighting, heating, cooling, security devices, and essential office gadgets. The system is implemented and validated through experiments utilizing the Arduino Mega control board. Operators can input load-shedding times flexibly using a 4x4 matrix keypad synchronized with the real-time clock (RTC). The microcontroller then instructs the relay to shed the specified load, displaying the shedding time on the LCD. The results indicate that the programmable interface exhibits a high dynamic response speed, highlighting its effectiveness and flexibility in load-shedding management. Comparing the cost of building the device with the long-term cost of overloading a standalone electricity generator, considering accelerated wear and tear, increased maintenance expenses, reduced operational lifespan, potential premature equipment failure, and overall decreased reliability, underscores the high-cost efficiency of adopting this device in office buildings. Additionally, implementing the system in an office building with an energy consumption of 23 kWh resulted in a discernible annual energy saving of 2.52 kWh by shedding off unnecessary loads at estimated timings.

Robot-Mediated Nudges for Workplace Health: Not a One-Size-Fits-All Modeling Problem

Prolonged sedentary behavior in the vast population of office and remote workers leads to increased cardiovascular and musculoskeletal health challenges, and existing solutions for encouraging breaks are either costly health coaches or notification systems that are easily ignored. A socially assistive robot (SAR) for promoting healthy workplace practices could provide the physical presence of a health coach along with the scalability of a notification system. To investigate the impact of such a system, we implemented a SAR as an alternative break-taking support solution and examined its impact on individual users’ break-taking habits over relatively long-term deployments. We conducted an initial two-month-long study (N = 7) to begin to understand the robot’s influence beyond the point of novelty, and we followed up with a week-long data collection (N = 14) to augment the dataset size. The resulting data was used to inform a robot behavior model and formulate possible methods of personalizing robot behaviors. We found that uninterrupted sitting time tended to decrease with our SAR intervention. During model formulation, we found participant responsiveness to the break-taking prompts could be classified into three archetypes and that archetype-specific adjustments to the general model led to improved system success. These results indicate that break-taking prompts are not a one-size-fits-all problem, and that even a small dataset can support model personalization for improving the success of assistive robotic systems.

Scalable Quantum Spin Networks from Unitary Construction

AbstractSpin network (SN) systems can be used to achieve quantum state transfer with high fidelity and to generate entanglement. A new approach to design spin‐chain‐based spin network systems, for short‐range quantum information processing and phase‐sensing, has been proposed recently in Advanced Quantum Technologies. In this paper, the scalability of such systems is investigated, by designing larger SN systems that can be used for longer‐range quantum information tasks, such as connecting together quantum processors. Furthermore, more complex SN designs, which can produce different types of entangled states, are presented. Simulations of disorder effects show that even such larger SN systems are robust against realistic levels of disorder.

Securing Iot Data Transmission: A Comprehensive Approach Integrating Two-Fish Encryption With Wireless Smart Energy Systems And Iot Cloud Services

This work addresses the challenges posed by the interconnected nature of the Internet of Things (IoT). This paradigm, facilitating the seamless connectivity of diverse devices, gives rise to issues such as addressability, confidentiality, and data management. The proposed solution advocates for the integration of the robust Twofish encryption algorithm into wireless smart energy systems, creating a holistic approach that capitalizes on the capabilities of IoT cloud services. Twofish, recognized for its strong encryption capabilities, becomes an integral part of the IoT framework to ensure the security of communication and data confidentiality. Through the amalgamation of Twofish encryption with wireless smart energy systems and IoT cloud services, the approach provides a comprehensive solution for the secure transmission and management of data. This integration enhances system scalability, flexibility, and accessibility, thereby safeguarding the confidentiality of information exchanged over the internet. The paper establishes the superiority of Twofish over lightweight cryptography for low-power devices within IoT systems through a thorough analysis of critical factors. These factors encompass the robust security strength of Twofish, its effective encryption capabilities guaranteeing data confidentiality, the algorithm's maturity validated by extensive scrutiny from the cryptographic community, compatibility and interoperability with diverse platforms, resource efficiency in maintaining a balance between security and device capabilities, and the crucial long-term security assurance required for the extended operational lifetimes of IoT devices. The research findings affirm that the incorporation of Twofish into wireless smart energy systems and IoT cloud services offers a secure and efficient solution for safeguarding sensitive information within IoT environments. This consolidated approach contributes significantly to advancing secure communication and data management in the continually evolving landscape of the Internet of Things.

Achieving COVID-19 and Routine Immunization Data Systems Integration on the Electronic Management of Immunization Data System in Nigeria.

In 2021, Nigeria developed a novel Electronic Management of Immunization Data (EMID) system to address COVID-19 data management challenges and ensure the successful implementation of its COVID-19 vaccine deployment plan. The EMID system was envisioned to be interoperable with the DHIS2 national data management system and serve as a gateway into the integration of other primary health care (PHC) service data management. However, the EMID system faced challenges, including inability to filter reports, missing or loss of data, and difficulties with data synchronization, which curtailed its potential to meet the country's needs for COVID-19 data management and negatively impacted system scalability to enable integration with other PHC data systems. Multilayered stakeholder interviews were conducted to determine the optimal functionality requirements for the EMID system. Based on these findings, an optimization plan was designed and implemented to address identified gaps and create a more stable and scalable system to enable further system integrations. Following optimization, a routine immunization module was developed and integrated with the EMID system as a first step to achieving an integrated data management system for PHC services in Nigeria. The integrated system currently provides an opportunity to address data fragmentation and strengthen PHC service delivery in Nigeria. By allowing 1 health care worker to deliver both vaccinations, there is also potential for reduction in cost and redundancies, informing redistribution of the health workforce and overall system strengthening. The journey from the initial challenges faced by the EMID system to the development of an integrated system for PHC services in Nigeria has been a transformative one. Through a thorough optimization process, training and capacity-building, stakeholder-driven improvements, and an elicitation exercise, the EMID system has evolved into a powerful tool for addressing data fragmentation and enhancing public health service delivery in the country.

Towards Efficient Distributed Systems: An In-depth Analysis through Simulation and Modeling

In the dynamic landscape of distributed systems, achieving optimal efficiency remains a paramount challenge. This paper presents a groundbreaking exploration into the intricacies of distributed systems through a comprehensive simulation and modeling approach. The conceptual framework integrates discrete-event simulation, agent-based modeling, and network simulation, providing a nuanced understanding of the multifaceted dynamics within distributed environments. Methodologically, realistic scenarios are meticulously crafted and subjected to rigorous evaluation, offering profound insights into system performance, scalability, and fault tolerance. The results reveal a detailed panorama of distributed system behaviors, facilitating a comprehensive discussion on the implications of various factors. By shedding light on strengths, weaknesses, and real-world applicability, this research contributes significantly to advancing the discourse on efficient distributed systems. The paper concludes with recommendations for further exploration, continuous refinement of simulation models, and collaborative endeavors to propel the evolution of resilient and high-performing distributed systems

Secure decision tree classification with decentralized authorization and access control

Outsourcing decision tree classification services to the cloud is highly beneficial, yet raises critical privacy problems. In order to preserve data privacy, data owners may choose to upload encrypted data rather than raw data to the classification services. However, these solutions adopted today for encrypted data classification not only fall short in system flexibility and scalability, but also face the single point of failure problem. In this paper, we design, implement, and evaluate a secure decision tree classification scheme that allows decentralized authorization and access control service (SDTC-DAAC). Firstly, we propose a new framework that decouples data encryption and data computation logic to achieve the separation of data storage and computation, which significantly improves upon the flexibility and effectiveness, thus achieving cross-system compatibility requirements. Secondly, we present an end-to-end encrypted access control mechanism which enables authorized users from different parties to participate in calculations together. Finally, we further devise a scheme which serves decentralized storage service of data access control policies and access authorization without trusted intermediaries. Extensive property and performance analysis shows that SDTC-DAAC is effectiveness, as well as satisfying the security requirements for data privacy in an outsourcing environment.

A Comprehensive Study of the Transition from Monolithic to Micro services-Based Software Architectures

Purpose: This study examines the transition from monolithic to microservices architectures, analyzing this transformative shift's underlying motives, advantages, and obstacles.
 Methodology: This study incorporates literature reviews, empirical case studies, and interviews with professionals in the field to offer a deep comprehension of the topic utilizing a comprehensive methodology.
 Findings: The key findings of this study underscore a wide range of incentives behind the adoption of microservices, encompassing scalability and fault tolerance. Additionally, the study identifies the problems associated with implementing microservices and adapting organizational structures to accommodate this architectural approach.
 Unique contribution to theory, policy and practice: The research makes a distinct contribution to both theoretical understanding and practical application by presenting a refined conceptual framework and providing actionable insights for organizations undergoing this architectural transition. From a policy perspective, it promotes the implementation of regulatory frameworks that are adaptable and conducive to fostering innovation. This piece presents microservices as a crucial paradigm that contributes to improving scalability, flexibility, and resilience in software systems.

Sambat Online Program's Pursuit of Citizen-Centric Information Services Empowerment

The rise of digital technology has revived engagement between governments and citizens, advancing e-governance with improved communication, transparency, and flexibility. The Sambat Online Program promotes a citizen-centric approach to information services in Malang City which strategies for advancing e-governance in the digital age is considered important. This study aims to analyze challenges, strategies, and propose an actionable framework to improve Malang City's e-governance, with a focus on the Sambat Online Program’s pursuit of citizen-centric information excellence. This study used a qualitative case study approach to examine the program. The results show that the Sambat Online application in Malang City faces organizational challenges and social issues such as lack of leadership, resistance to change, digital divide, digital literacy, and social inclusion needs. Technological challenges occur in terms of cybersecurity, updates, and system scalability. It also found that the Malang City Government needs a user-oriented e-government approach to ensure data security and privacy. In other words, the platform makes it easy for users to submit complaints, increasing citizen involvement in improving public services, however continuous program assessment and improvement are essential. Areas for improvement include improved complaint management, digital training, and strengthened cybersecurity, with a focus on complaint management as the most effective solution. In conclusion, the program enhances communication and comprehension between governing bodies and the public, highlighting its significance in contemporary governance.

The impact and value of information system audit enterprises risk management influencing quality assurance and system optimization—a touch from supply chain domains

The industries significantly rely on information technology for effective operations; however, there are hazards like data breaches and system breakdowns. Information system audits are essential to reducing these risks, ensuring quality control, and optimizing the system. These audits evaluate gaps in controls, non-compliance concerns, and vulnerabilities, allowing for the installation of efficient risk mitigation measures. Information system audits are essential for locating and reducing hazards related to an organization's information systems.Vulnerabilities, threats, and control flaws can be found through routine audits, which then allows for the deployment of the necessary controls and risk-reduction measures. Additionally, audits support quality assurance by assessing data reliability, accuracy, and completeness, as well as system performance, regulatory compliance, and industry best practices.The deployment of remedial steps to improve data integrity and system reliability is made possible by their assistance in assisting organizations in identifying gaps and flaws. Information system audits also aid system optimization by revealing details about the functionality and performance of the system.  An extensive evaluation of the literature and case study analysis was used to perform the research.For the analysis of this research, a sequential mixed technique (qualitative and quantitative) study was conducted. Delphi questionnaires were used twice to conduct the study. It encompasses organizations and entities involved in various aspects including but not limited to logistics companies, freight carriers, shipping and distribution firms, public transportation agencies, and supply chain service providers. Data from the semi-structured interviews for the qualitative study were collected via a Delphi survey, and 14 nodes and six themes were then generated.Audits assist organizations in identifying areas for improvement and informing decisions about system upgrades or alterations by analyzing system utilization, response times, resource allocation, and scalability. The results of this study highlight the importance of information system audits in assuring the prosperity, effectiveness, and resilience of businesses engaged in supply chain operations. Organizations can attain a greater level of enterprise risk management, quality assurance, and system optimization, which will improve supply chain operations in general, by managing risks, improving data quality, and optimizing system performance.

A Scalable Deep-Learning-Based Active User Detection Approach for SEU-Assisted Cell-Free Massive MIMO Systems

Massive ultra-reliable and low-latency communications (mURLLC) is an emerging and dominate traffic service in 6G. To reduce the signaling overhead and access delay, grant-free random access (GFRA) is widely used in mURLLC. As the first step in GFRA, active user detection (AUD) is aimed to identify the set of active users accurately and timely. Conventional AUD schemes relying on iterative computations over massive users bring redundant computing overload and processing delay, which seriously affect the system scalability in the mURLLC scenario. Considering the near-real-time requirement of mURLLC, we propose a scalable deep learning-based AUD approach utilizing similar channel sparsity in cell-free (CF) massive multiple input multiple-output (mMIMO) systems. By exploiting the distributed computing unit, i.e., space expansion unit (SEU), we design a SEU-assisted CF mMIMO to improve the scalability of the traditional centralized CF computing architecture. In the proposed system, all access points (APs) are divided into several clusters, and the SEU in each cluster provides a reliable distributed AUD scheme through a one-dimensional convolutional network (1D CNN). In addition, a transfer learning-based ensemble model is established at the CPU to achieve a better global detection decision. Simulation results demonstrate the superiority of our scalable deep learning-based approach, and reveal that through the transfer learning-based model fusion at the CPU, our proposed scalable SEU-assisted approach can obtain success probability close to that of the centralized CF computing scheme with less access delay. In addition, our scheme requires fewer pilots than other CS-based schemes.

An Efficient Secure Group Authenticated Key Agreement Protocol for Wireless Sensor Networks in IoT Environment

Internet of Things(IoT) consist of interconnected devices for transmitting and receiving the data over the network. Key management is important for data confidentiality while transmitting in an open network. Even though several key management techniques are feasible to use, still obtaining a key management technique is a challenge with respect to energy and computational cost. The main intention of this work is to discover and overcome the design issues of the existing system and implement a lightweight and secure solution for that issue. The existing system has a fatal security flaw that leads to the unavailability of a complete system which is considered a huge problem in Internet of things. To overcome this issue, an authenticated key management protocol is proposed which deals with the problem of single point of failure and maintains the security properties of the existing system. An authenticated scheme is provided using elliptic curve and hash functions. This scheme also provides client addition, deletion and key freshness. Security analysis and computation complexity has been also discussed. We experimented proposed algorithm and tested with Scyther verification tool. The design overcomes the issues of an existing system by utilizing our scheme in peer to peer network. This network resolves the issue of a single point of failure (SPOF) by distributing the resources and services to the multiple nodes in the network. It will dissolve the problem of SPOF and will increase the reliability and scalability of the IoT system.

Optimizing Cardiovascular Disease Prediction: A Synergistic Approach of Grey Wolf Levenberg Model and Neural Networks

Background: One of the latest issues in predicting cardiovascular disease is the limited performance of current risk prediction models. Although several models have been developed, they often fail to identify a significant proportion of individuals who go on to develop the disease. This highlights the need for more accurate and personalized prediction models. Objective: This study aims to investigate the effectiveness of the Grey Wolf Levenberg Model and Neural Networks in predicting cardiovascular diseases. The objective is to identify a synergistic approach that can improve the accuracy of predictions. Through this research, the authors seek to contribute to the development of better tools for early detection and prevention of cardiovascular diseases. Methods: The study used a quantitative approach to develop and validate the GWLM_NARX model for predicting cardiovascular disease risk. The approach involved collecting and analyzing a large dataset of clinical and demographic variables. The performance of the model was then evaluated using various metrics such as accuracy, sensitivity, and specificity. Results: the study found that the GWLM_NARX model has shown promising results in predicting cardiovascular disease. The model was found to outperform other conventional methods, with an accuracy of over 90%. The synergistic approach of Grey Wolf Levenberg Model and Neural Networks has proved to be effective in predicting cardiovascular disease with high accuracy. Conclusion: The use of the Grey Wolf Levenberg-Marquardt Neural Network Autoregressive model (GWLM-NARX) in conjunction with traditional learning algorithms, as well as advanced machine learning tools, resulted in a more accurate and effective prediction model for cardiovascular disease. The study demonstrates the potential of machine learning techniques to improve diagnosis and treatment of heart disorders. However, further research is needed to improve the scalability and accuracy of these prediction systems, given the complexity of the data associated with cardiac illness. Keywords: Cardiovascular data, Clinical data., Decision tree, GWLM-NARX, Linear model functions

Efficient Super-Resolution System With Block-Wise Hybridization and Quantized Winograd on FPGA

Super-resolution (SR) techniques aim to restore a high-resolution image from low-resolution images, which are often used to assist the enhancement of image/video quality under the rapid development of high-resolution and high-framerate media. Recently, neural network (NN)-based methods perform much better image reconstruction quality than classical approaches. However, the unacceptable computation complexity as well as the huge memory footprints of NNs limit the throughputs and scalability of these SR systems. In this work, we analyze several key issues in the design of NN-based SR systems first. Then, we propose a three-level systematic optimization methodology for SR systems to reduce computation overhead and keep image quality. At the algorithm level, we introduce image blocking to SR tasks and develop a block-wise SR algorithm based on the hybrid of NN and interpolation with a consistent image block evaluation metric. The configurable hybrid parameters help the SR algorithm to achieve a flexible trade-off between the computation overhead and image quality. At the operator level, we focus on the transpose convolution operators commonly used for upsampling in SR neural networks. We propose an efficient Winograd-based transposed convolution acceleration method. Through the efficient sub-convolutions conversion and the Winograd specialization, this methods enables unified Winograd transformations and simplified data access patterns. At the data level, we propose a novel quantization method for Winograd-aware SR neural networks to get better quantized accuracy. Comprehensive evaluations demonstrate the effectiveness of these optimizations. Our SR system reduces a large number of multiplications with great scalability and supports 4K@120fps and 8K@30fps outputs with acceptable image quality degradation.